Repository: lllyasviel/Fooocus Branch: main Commit: ae05379cc97b Files: 331 Total size: 8.0 MB Directory structure: gitextract____cmvti/ ├── .dockerignore ├── .gitattributes ├── .github/ │ ├── CODEOWNERS │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.yml │ │ ├── config.yml │ │ └── feature_request.yml │ ├── dependabot.yml │ └── workflows/ │ └── build_container.yml ├── .gitignore ├── Dockerfile ├── LICENSE ├── args_manager.py ├── auth-example.json ├── build_launcher.py ├── css/ │ └── style.css ├── development.md ├── docker-compose.yml ├── docker.md ├── entry_with_update.py ├── entrypoint.sh ├── environment.yaml ├── experiments_expansion.py ├── experiments_face.py ├── experiments_interrogate.py ├── experiments_mask_generation.py ├── extras/ │ ├── BLIP/ │ │ ├── configs/ │ │ │ ├── bert_config.json │ │ │ ├── caption_coco.yaml │ │ │ ├── med_config.json │ │ │ ├── nlvr.yaml │ │ │ ├── nocaps.yaml │ │ │ ├── pretrain.yaml │ │ │ ├── retrieval_coco.yaml │ │ │ ├── retrieval_flickr.yaml │ │ │ ├── retrieval_msrvtt.yaml │ │ │ └── vqa.yaml │ │ └── models/ │ │ ├── bert_tokenizer/ │ │ │ ├── config.json │ │ │ ├── tokenizer.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.txt │ │ ├── blip.py │ │ ├── blip_itm.py │ │ ├── blip_nlvr.py │ │ ├── blip_pretrain.py │ │ ├── blip_retrieval.py │ │ ├── blip_vqa.py │ │ ├── med.py │ │ ├── nlvr_encoder.py │ │ └── vit.py │ ├── GroundingDINO/ │ │ ├── config/ │ │ │ └── GroundingDINO_SwinT_OGC.py │ │ └── util/ │ │ └── inference.py │ ├── censor.py │ ├── expansion.py │ ├── face_crop.py │ ├── facexlib/ │ │ ├── detection/ │ │ │ ├── __init__.py │ │ │ ├── align_trans.py │ │ │ ├── matlab_cp2tform.py │ │ │ ├── retinaface.py │ │ │ ├── retinaface_net.py │ │ │ └── retinaface_utils.py │ │ ├── parsing/ │ │ │ ├── __init__.py │ │ │ ├── bisenet.py │ │ │ ├── parsenet.py │ │ │ └── resnet.py │ │ └── utils/ │ │ ├── __init__.py │ │ ├── face_restoration_helper.py │ │ ├── face_utils.py │ │ └── misc.py │ ├── inpaint_mask.py │ ├── interrogate.py │ ├── ip_adapter.py │ ├── preprocessors.py │ ├── resampler.py │ ├── safety_checker/ │ │ ├── configs/ │ │ │ ├── config.json │ │ │ └── preprocessor_config.json │ │ └── models/ │ │ └── safety_checker.py │ ├── sam/ │ │ └── predictor.py │ ├── vae_interpose.py │ └── wd14tagger.py ├── fooocus_colab.ipynb ├── fooocus_version.py ├── javascript/ │ ├── contextMenus.js │ ├── edit-attention.js │ ├── imageviewer.js │ ├── localization.js │ ├── script.js │ ├── viewer.js │ └── zoom.js ├── language/ │ ├── en.json │ └── example.json ├── launch.py ├── ldm_patched/ │ ├── contrib/ │ │ ├── external.py │ │ ├── external_align_your_steps.py │ │ ├── external_canny.py │ │ ├── external_clip_sdxl.py │ │ ├── external_compositing.py │ │ ├── external_custom_sampler.py │ │ ├── external_freelunch.py │ │ ├── external_hypernetwork.py │ │ ├── external_hypertile.py │ │ ├── external_images.py │ │ ├── external_latent.py │ │ ├── external_mask.py │ │ ├── external_model_advanced.py │ │ ├── external_model_downscale.py │ │ ├── external_model_merging.py │ │ ├── external_perpneg.py │ │ ├── external_photomaker.py │ │ ├── external_post_processing.py │ │ ├── external_rebatch.py │ │ ├── external_sag.py │ │ ├── external_sdupscale.py │ │ ├── external_stable3d.py │ │ ├── external_tomesd.py │ │ ├── external_upscale_model.py │ │ └── external_video_model.py │ ├── controlnet/ │ │ └── cldm.py │ ├── k_diffusion/ │ │ ├── sampling.py │ │ └── utils.py │ ├── ldm/ │ │ ├── models/ │ │ │ └── autoencoder.py │ │ ├── modules/ │ │ │ ├── attention.py │ │ │ ├── diffusionmodules/ │ │ │ │ ├── __init__.py │ │ │ │ ├── model.py │ │ │ │ ├── openaimodel.py │ │ │ │ ├── upscaling.py │ │ │ │ └── util.py │ │ │ ├── distributions/ │ │ │ │ ├── __init__.py │ │ │ │ └── distributions.py │ │ │ ├── ema.py │ │ │ ├── encoders/ │ │ │ │ ├── __init__.py │ │ │ │ └── noise_aug_modules.py │ │ │ ├── sub_quadratic_attention.py │ │ │ └── temporal_ae.py │ │ └── util.py │ ├── licenses-3rd/ │ │ ├── chainer │ │ ├── comfyui │ │ ├── diffusers │ │ ├── kdiffusion │ │ ├── ldm │ │ ├── taesd │ │ └── transformers │ ├── modules/ │ │ ├── args_parser.py │ │ ├── checkpoint_pickle.py │ │ ├── clip_config_bigg.json │ │ ├── clip_model.py │ │ ├── clip_vision.py │ │ ├── clip_vision_config_g.json │ │ ├── clip_vision_config_h.json │ │ ├── clip_vision_config_vitl.json │ │ ├── conds.py │ │ ├── controlnet.py │ │ ├── diffusers_convert.py │ │ ├── diffusers_load.py │ │ ├── gligen.py │ │ ├── latent_formats.py │ │ ├── lora.py │ │ ├── model_base.py │ │ ├── model_detection.py │ │ ├── model_management.py │ │ ├── model_patcher.py │ │ ├── model_sampling.py │ │ ├── ops.py │ │ ├── options.py │ │ ├── sample.py │ │ ├── samplers.py │ │ ├── sd.py │ │ ├── sd1_clip.py │ │ ├── sd1_clip_config.json │ │ ├── sd1_tokenizer/ │ │ │ ├── merges.txt │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.json │ │ ├── sd2_clip.py │ │ ├── sd2_clip_config.json │ │ ├── sdxl_clip.py │ │ ├── supported_models.py │ │ ├── supported_models_base.py │ │ └── utils.py │ ├── pfn/ │ │ ├── __init__.py │ │ ├── architecture/ │ │ │ ├── DAT.py │ │ │ ├── HAT.py │ │ │ ├── LICENSE-DAT │ │ │ ├── LICENSE-ESRGAN │ │ │ ├── LICENSE-HAT │ │ │ ├── LICENSE-RealESRGAN │ │ │ ├── LICENSE-SCUNet │ │ │ ├── LICENSE-SPSR │ │ │ ├── LICENSE-SwiftSRGAN │ │ │ ├── LICENSE-Swin2SR │ │ │ ├── LICENSE-SwinIR │ │ │ ├── LICENSE-lama │ │ │ ├── LaMa.py │ │ │ ├── OmniSR/ │ │ │ │ ├── ChannelAttention.py │ │ │ │ ├── LICENSE │ │ │ │ ├── OSA.py │ │ │ │ ├── OSAG.py │ │ │ │ ├── OmniSR.py │ │ │ │ ├── esa.py │ │ │ │ ├── layernorm.py │ │ │ │ └── pixelshuffle.py │ │ │ ├── RRDB.py │ │ │ ├── SCUNet.py │ │ │ ├── SPSR.py │ │ │ ├── SRVGG.py │ │ │ ├── SwiftSRGAN.py │ │ │ ├── Swin2SR.py │ │ │ ├── SwinIR.py │ │ │ ├── __init__.py │ │ │ ├── block.py │ │ │ ├── face/ │ │ │ │ ├── LICENSE-GFPGAN │ │ │ │ ├── LICENSE-RestoreFormer │ │ │ │ ├── LICENSE-codeformer │ │ │ │ ├── arcface_arch.py │ │ │ │ ├── codeformer.py │ │ │ │ ├── fused_act.py │ │ │ │ ├── gfpgan_bilinear_arch.py │ │ │ │ ├── gfpganv1_arch.py │ │ │ │ ├── gfpganv1_clean_arch.py │ │ │ │ ├── restoreformer_arch.py │ │ │ │ ├── stylegan2_arch.py │ │ │ │ ├── stylegan2_bilinear_arch.py │ │ │ │ ├── stylegan2_clean_arch.py │ │ │ │ └── upfirdn2d.py │ │ │ └── timm/ │ │ │ ├── LICENSE │ │ │ ├── drop.py │ │ │ ├── helpers.py │ │ │ └── weight_init.py │ │ ├── model_loading.py │ │ └── types.py │ ├── t2ia/ │ │ └── adapter.py │ ├── taesd/ │ │ └── taesd.py │ ├── unipc/ │ │ └── uni_pc.py │ └── utils/ │ ├── latent_visualization.py │ └── path_utils.py ├── models/ │ ├── checkpoints/ │ │ └── put_checkpoints_here │ ├── clip/ │ │ └── put_clip_or_text_encoder_models_here │ ├── clip_vision/ │ │ ├── put_clip_vision_models_here │ │ └── wd-v1-4-moat-tagger-v2.csv │ ├── configs/ │ │ ├── anything_v3.yaml │ │ ├── v1-inference.yaml │ │ ├── v1-inference_clip_skip_2.yaml │ │ ├── v1-inference_clip_skip_2_fp16.yaml │ │ ├── v1-inference_fp16.yaml │ │ ├── v1-inpainting-inference.yaml │ │ ├── v2-inference-v.yaml │ │ ├── v2-inference-v_fp32.yaml │ │ ├── v2-inference.yaml │ │ ├── v2-inference_fp32.yaml │ │ └── v2-inpainting-inference.yaml │ ├── controlnet/ │ │ └── put_controlnets_and_t2i_here │ ├── diffusers/ │ │ └── put_diffusers_models_here │ ├── embeddings/ │ │ └── put_embeddings_or_textual_inversion_concepts_here │ ├── gligen/ │ │ └── put_gligen_models_here │ ├── hypernetworks/ │ │ └── put_hypernetworks_here │ ├── inpaint/ │ │ └── put_inpaint_here │ ├── loras/ │ │ └── put_loras_here │ ├── prompt_expansion/ │ │ ├── fooocus_expansion/ │ │ │ ├── config.json │ │ │ ├── merges.txt │ │ │ ├── positive.txt │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer.json │ │ │ ├── tokenizer_config.json │ │ │ └── vocab.json │ │ └── put_prompt_expansion_here │ ├── safety_checker/ │ │ └── put_safety_checker_models_here │ ├── style_models/ │ │ └── put_t2i_style_model_here │ ├── unet/ │ │ └── put_unet_files_here │ ├── upscale_models/ │ │ └── put_esrgan_and_other_upscale_models_here │ ├── vae/ │ │ └── put_vae_here │ └── vae_approx/ │ └── put_taesd_encoder_pth_and_taesd_decoder_pth_here ├── modules/ │ ├── __init__.py │ ├── anisotropic.py │ ├── async_worker.py │ ├── auth.py │ ├── config.py │ ├── constants.py │ ├── core.py │ ├── default_pipeline.py │ ├── extra_utils.py │ ├── flags.py │ ├── gradio_hijack.py │ ├── hash_cache.py │ ├── html.py │ ├── inpaint_worker.py │ ├── launch_util.py │ ├── localization.py │ ├── lora.py │ ├── meta_parser.py │ ├── model_loader.py │ ├── ops.py │ ├── patch.py │ ├── patch_clip.py │ ├── patch_precision.py │ ├── private_logger.py │ ├── sample_hijack.py │ ├── sdxl_styles.py │ ├── style_sorter.py │ ├── ui_gradio_extensions.py │ ├── upscaler.py │ └── util.py ├── presets/ │ ├── .gitignore │ ├── anime.json │ ├── default.json │ ├── lcm.json │ ├── playground_v2.5.json │ ├── pony_v6.json │ ├── realistic.json │ └── sai.json ├── readme.md ├── requirements_docker.txt ├── requirements_versions.txt ├── sdxl_styles/ │ ├── sdxl_styles_diva.json │ ├── sdxl_styles_fooocus.json │ ├── sdxl_styles_marc_k3nt3l.json │ ├── sdxl_styles_mre.json │ ├── sdxl_styles_sai.json │ └── sdxl_styles_twri.json ├── shared.py ├── tests/ │ ├── __init__.py │ ├── test_extra_utils.py │ └── test_utils.py ├── troubleshoot.md ├── update_log.md ├── webui.py └── wildcards/ ├── .gitignore ├── animal.txt ├── artist.txt ├── color.txt ├── color_flower.txt ├── extended-color.txt ├── flower.txt └── nationality.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .dockerignore ================================================ __pycache__ *.ckpt *.safetensors *.pth *.pt *.bin *.patch *.backup *.corrupted *.partial *.onnx sorted_styles.json /input /cache /language/default.json /test_imgs config.txt config_modification_tutorial.txt user_path_config.txt user_path_config-deprecated.txt /modules/*.png /repositories /fooocus_env /venv /tmp /ui-config.json /outputs /config.json /log /webui.settings.bat /embeddings /styles.csv /params.txt /styles.csv.bak /webui-user.bat /webui-user.sh /interrogate /user.css /.idea /notification.ogg /notification.mp3 /SwinIR /textual_inversion .vscode /extensions /test/stdout.txt /test/stderr.txt /cache.json* /config_states/ /node_modules /package-lock.json /.coverage* /auth.json .DS_Store ================================================ FILE: .gitattributes ================================================ # Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker * text=auto *.sh text eol=lf ================================================ FILE: .github/CODEOWNERS ================================================ * @lllyasviel ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report.yml ================================================ name: Bug Report description: You think something is broken in Fooocus title: "[Bug]: " labels: ["bug", "triage"] body: - type: markdown attributes: value: | > The title of the bug report should be short and descriptive. > Use relevant keywords for searchability. > Do not leave it blank, but also do not put an entire error log in it. - type: checkboxes attributes: label: Checklist description: | Please perform basic debugging to see if your configuration is the cause of the issue. Basic debug procedure 　1. Update Fooocus - sometimes things just need to be updated 　2. Backup and remove your config.txt - check if the issue is caused by bad configuration 　3. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue Before making a issue report please, check that the issue hasn't been reported recently. options: - label: The issue has not been resolved by following the [troubleshooting guide](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md) - label: The issue exists on a clean installation of Fooocus - label: The issue exists in the current version of Fooocus - label: The issue has not been reported before recently - label: The issue has been reported before but has not been fixed yet - type: markdown attributes: value: | > Please fill this form with as much information as possible. Don't forget to add information about "What browsers" and provide screenshots if possible - type: textarea id: what-did attributes: label: What happened? description: Tell us what happened in a very clear and simple way placeholder: | image generation is not working as intended. validations: required: true - type: textarea id: steps attributes: label: Steps to reproduce the problem description: Please provide us with precise step by step instructions on how to reproduce the bug placeholder: | 1. Go to ... 2. Press ... 3. ... validations: required: true - type: textarea id: what-should attributes: label: What should have happened? description: Tell us what you think the normal behavior should be placeholder: | Fooocus should ... validations: required: true - type: dropdown id: browsers attributes: label: What browsers do you use to access Fooocus? multiple: true options: - Mozilla Firefox - Google Chrome - Brave - Apple Safari - Microsoft Edge - Android - iOS - Other - type: dropdown id: hosting attributes: label: Where are you running Fooocus? multiple: false options: - Locally - Locally with virtualization (e.g. Docker) - Cloud (Google Colab) - Cloud (other) - type: input id: operating-system attributes: label: What operating system are you using? placeholder: | Windows 10 - type: textarea id: logs attributes: label: Console logs description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service. render: Shell validations: required: true - type: textarea id: misc attributes: label: Additional information description: | Please provide us with any relevant additional info or context. Examples: 　I have updated my GPU driver recently. ================================================ FILE: .github/ISSUE_TEMPLATE/config.yml ================================================ blank_issues_enabled: false contact_links: - name: Ask a question url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a about: Ask the community for help ================================================ FILE: .github/ISSUE_TEMPLATE/feature_request.yml ================================================ name: Feature request description: Suggest an idea for this project title: "[Feature Request]: " labels: ["enhancement", "triage"] body: - type: checkboxes attributes: label: Is there an existing issue for this? description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. options: - label: I have searched the existing issues and checked the recent builds/commits required: true - type: markdown attributes: value: | *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* - type: textarea id: feature attributes: label: What would your feature do? description: Tell us about your feature in a very clear and simple way, and what problem it would solve validations: required: true - type: textarea id: workflow attributes: label: Proposed workflow description: Please provide us with step by step information on how you'd like the feature to be accessed and used value: | 1. Go to .... 2. Press .... 3. ... validations: required: true - type: textarea id: misc attributes: label: Additional information description: Add any other context or screenshots about the feature request here. ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "monthly" ================================================ FILE: .github/workflows/build_container.yml ================================================ name: Docker image build on: push: branches: - main tags: - v* jobs: build-and-push-image: runs-on: ubuntu-latest permissions: contents: read packages: write steps: - name: Checkout repository uses: actions/checkout@v5 - name: Log in to the Container registry uses: docker/login-action@v3 with: registry: ghcr.io username: ${{ github.repository_owner }} password: ${{ secrets.GITHUB_TOKEN }} - name: Extract metadata (tags, labels) for Docker id: meta uses: docker/metadata-action@v5 with: images: ghcr.io/${{ github.repository_owner }}/${{ github.event.repository.name }} tags: | type=semver,pattern={{version}} type=semver,pattern={{major}}.{{minor}} type=semver,pattern={{major}} type=edge,branch=main - name: Build and push Docker image uses: docker/build-push-action@v6 with: context: . file: ./Dockerfile push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} ================================================ FILE: .gitignore ================================================ __pycache__ *.ckpt *.safetensors *.pth *.pt *.bin *.patch *.backup *.corrupted *.partial *.onnx sorted_styles.json hash_cache.txt /input /cache /language/default.json /test_imgs config.txt config_modification_tutorial.txt user_path_config.txt user_path_config-deprecated.txt /modules/*.png /repositories /fooocus_env /venv /tmp /ui-config.json /outputs /config.json /log /webui.settings.bat /embeddings /styles.csv /params.txt /styles.csv.bak /webui-user.bat /webui-user.sh /interrogate /user.css /.idea /notification.ogg /notification.mp3 /SwinIR /textual_inversion .vscode /extensions /test/stdout.txt /test/stderr.txt /cache.json* /config_states/ /node_modules /package-lock.json /.coverage* /auth.json .DS_Store ================================================ FILE: Dockerfile ================================================ FROM nvidia/cuda:12.4.1-base-ubuntu22.04 ENV DEBIAN_FRONTEND noninteractive ENV CMDARGS --listen RUN apt-get update -y && \ apt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* COPY requirements_docker.txt requirements_versions.txt /tmp/ RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \ rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt RUN pip install --no-cache-dir xformers==0.0.23 --no-dependencies RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \ chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 RUN adduser --disabled-password --gecos '' user && \ mkdir -p /content/app /content/data COPY entrypoint.sh /content/ RUN chown -R user:user /content WORKDIR /content USER user COPY --chown=user:user . /content/app RUN mv /content/app/models /content/app/models.org CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ] ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: args_manager.py ================================================ import ldm_patched.modules.args_parser as args_parser args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.") args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.") args_parser.parser.add_argument("--disable-preset-selection", action='store_true', help="Disables preset selection in Gradio.") args_parser.parser.add_argument("--language", type=str, default='default', help="Translate UI using json files in [language] folder. " "For example, [--language example] will use [language/example.json] for translation.") # For example, https://github.com/lllyasviel/Fooocus/issues/849 args_parser.parser.add_argument("--disable-offload-from-vram", action="store_true", help="Force loading models to vram when the unload can be avoided. " "Some Mac users may need this.") args_parser.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) args_parser.parser.add_argument("--disable-image-log", action='store_true', help="Prevent writing images and logs to the outputs folder.") args_parser.parser.add_argument("--disable-analytics", action='store_true', help="Disables analytics for Gradio.") args_parser.parser.add_argument("--disable-metadata", action='store_true', help="Disables saving metadata to images.") args_parser.parser.add_argument("--disable-preset-download", action='store_true', help="Disables downloading models for presets", default=False) args_parser.parser.add_argument("--disable-enhance-output-sorting", action='store_true', help="Disables enhance output sorting for final image gallery.") args_parser.parser.add_argument("--enable-auto-describe-image", action='store_true', help="Enables automatic description of uov and enhance image when prompt is empty", default=False) args_parser.parser.add_argument("--always-download-new-model", action='store_true', help="Always download newer models", default=False) args_parser.parser.add_argument("--rebuild-hash-cache", help="Generates missing model and LoRA hashes.", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1) args_parser.parser.set_defaults( disable_cuda_malloc=True, in_browser=True, port=None ) args_parser.args = args_parser.parser.parse_args() # (Disable by default because of issues like https://github.com/lllyasviel/Fooocus/issues/724) args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload_from_vram if args_parser.args.disable_analytics: import os os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" if args_parser.args.disable_in_browser: args_parser.args.in_browser = False args = args_parser.args ================================================ FILE: auth-example.json ================================================ [ { "user": "sitting-duck-1", "pass": "very-bad-publicly-known-password-change-it" } ] ================================================ FILE: build_launcher.py ================================================ import os win32_root = os.path.dirname(os.path.dirname(__file__)) python_embeded_path = os.path.join(win32_root, 'python_embeded') is_win32_standalone_build = os.path.exists(python_embeded_path) and os.path.isdir(python_embeded_path) win32_cmd = ''' .\python_embeded\python.exe -s Fooocus\entry_with_update.py {cmds} %* pause ''' def build_launcher(): if not is_win32_standalone_build: return presets = [None, 'anime', 'realistic'] for preset in presets: win32_cmd_preset = win32_cmd.replace('{cmds}', '' if preset is None else f'--preset {preset}') bat_path = os.path.join(win32_root, 'run.bat' if preset is None else f'run_{preset}.bat') if not os.path.exists(bat_path): with open(bat_path, "w", encoding="utf-8") as f: f.write(win32_cmd_preset) return ================================================ FILE: css/style.css ================================================ /* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */ .loader-container { display: flex; /* Use flex to align items horizontally */ align-items: center; /* Center items vertically within the container */ white-space: nowrap; /* Prevent line breaks within the container */ } .loader { border: 8px solid #f3f3f3; /* Light grey */ border-top: 8px solid #3498db; /* Blue */ border-radius: 50%; width: 30px; height: 30px; animation: spin 2s linear infinite; } @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } } /* Style the progress bar */ progress { appearance: none; /* Remove default styling */ height: 20px; /* Set the height of the progress bar */ border-radius: 5px; /* Round the corners of the progress bar */ background-color: #f3f3f3; /* Light grey background */ width: 100%; vertical-align: middle !important; } /* Style the progress bar container */ .progress-container { margin-left: 20px; margin-right: 20px; flex-grow: 1; /* Allow the progress container to take up remaining space */ } /* Set the color of the progress bar fill */ progress::-webkit-progress-value { background-color: #3498db; /* Blue color for the fill */ } progress::-moz-progress-bar { background-color: #3498db; /* Blue color for the fill in Firefox */ } /* Style the text on the progress bar */ progress::after { content: attr(value '%'); /* Display the progress value followed by '%' */ position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); color: white; /* Set text color */ font-size: 14px; /* Set font size */ } /* Style other texts */ .loader-container > span { margin-left: 5px; /* Add spacing between the progress bar and the text */ } .progress-bar > .generating { display: none !important; } .progress-bar{ height: 30px !important; } .progress-bar span { text-align: right; width: 215px; } div:has(> #positive_prompt) { border: none; } #positive_prompt { padding: 1px; background: var(--background-fill-primary); } .type_row { height: 84px !important; } .type_row_half { height: 34px !important; } .refresh_button { border: none !important; background: none !important; font-size: none !important; box-shadow: none !important; } .advanced_check_row { width: 330px !important; } .min_check { min-width: min(1px, 100%) !important; } .resizable_area { resize: vertical; overflow: auto !important; } .performance_selection label { width: 140px !important; } .aspect_ratios label { flex: calc(50% - 5px) !important; } .aspect_ratios label span { white-space: nowrap !important; } .aspect_ratios label input { margin-left: -5px !important; } .lora_enable label { height: 100%; } .lora_enable label input { margin: auto; } .lora_enable label span { display: none; } @-moz-document url-prefix() { .lora_weight input[type=number] { width: 80px; } } #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; } .canvas-tooltip-info { position: absolute; top: 28px; left: 2px; cursor: help; background-color: rgba(0, 0, 0, 0.3); width: 20px; height: 20px; border-radius: 50%; display: flex; align-items: center; justify-content: center; flex-direction: column; z-index: 100; } .canvas-tooltip-info::after { content: ''; display: block; width: 2px; height: 7px; background-color: white; margin-top: 2px; } .canvas-tooltip-info::before { content: ''; display: block; width: 2px; height: 2px; background-color: white; } .canvas-tooltip-content { display: none; background-color: #f9f9f9; color: #333; border: 1px solid #ddd; padding: 15px; position: absolute; top: 40px; left: 10px; width: 250px; font-size: 16px; opacity: 0; border-radius: 8px; box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); z-index: 100; } .canvas-tooltip:hover .canvas-tooltip-content { display: block; animation: fadeIn 0.5s; opacity: 1; } @keyframes fadeIn { from {opacity: 0;} to {opacity: 1;} } .styler { overflow:inherit !important; } .gradio-container{ overflow: visible; } /* fullpage image viewer */ #lightboxModal{ display: none; position: fixed; z-index: 1001; 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; flex-direction: column; } .modalControls { display: flex; position: absolute; right: 0px; left: 0px; gap: 1em; padding: 1em; background-color:rgba(0,0,0,0); z-index: 1; transition: 0.2s ease background-color; } .modalControls:hover { background-color:rgba(0,0,0,0.9); } .modalClose { margin-left: auto; } .modalControls span{ color: white; text-shadow: 0px 0px 0.25em black; font-size: 35px; font-weight: bold; cursor: pointer; width: 1em; } .modalControls span:hover, .modalControls span:focus{ color: #999; text-decoration: none; } #lightboxModal > img { display: block; margin: auto; width: auto; } #lightboxModal > img.modalImageFullscreen{ object-fit: contain; height: 100%; width: 100%; min-height: 0; } .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; } #stylePreviewOverlay { opacity: 0; pointer-events: none; width: 128px; height: 128px; position: fixed; top: 0px; left: 0px; border: solid 1px lightgrey; transform: translate(-140px, 20px); background-size: cover; background-position: center; background-color: rgba(0, 0, 0, 0.3); border-radius: 5px; z-index: 100; transition: transform 0.1s ease, opacity 0.3s ease; } #stylePreviewOverlay.lower-half { transform: translate(-140px, -140px); } /* scrollable box for style selections */ .contain .tabs { height: 100%; } .contain .tabs .tabitem.style_selections_tab { height: 100%; } .contain .tabs .tabitem.style_selections_tab > div:first-child { height: 100%; } .contain .tabs .tabitem.style_selections_tab .style_selections { min-height: 200px; height: 100%; } .contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] { position: absolute; /* remove this to disable scrolling within the checkbox-group */ overflow: auto; padding-right: 2px; max-height: 100%; } .contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label { /* max-width: calc(35% - 15px) !important; */ /* add this to enable 3 columns layout */ flex: calc(50% - 5px) !important; } .contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label span { /* white-space:nowrap; */ /* add this to disable text wrapping (better choice for 3 columns layout) */ overflow: hidden; text-overflow: ellipsis; } /* styles preview tooltip */ .preview-tooltip { background-color: #fff8; font-family: monospace; text-align: center; border-radius: 5px 5px 0px 0px; display: none; /* remove this to enable tooltip in preview image */ } #inpaint_canvas .canvas-tooltip-info { top: 2px; } #inpaint_brush_color input[type=color]{ background: none; } ================================================ FILE: development.md ================================================ ## Running unit tests Native python: ``` python -m unittest tests/ ``` Embedded python (Windows zip file installation method): ``` ..\python_embeded\python.exe -m unittest ``` ================================================ FILE: docker-compose.yml ================================================ volumes: fooocus-data: services: app: build: . image: ghcr.io/lllyasviel/fooocus ports: - "7865:7865" environment: - CMDARGS=--listen # Arguments for launch.py. - DATADIR=/content/data # Directory which stores models, outputs dir - config_path=/content/data/config.txt - config_example_path=/content/data/config_modification_tutorial.txt - path_checkpoints=/content/data/models/checkpoints/ - path_loras=/content/data/models/loras/ - path_embeddings=/content/data/models/embeddings/ - path_vae_approx=/content/data/models/vae_approx/ - path_upscale_models=/content/data/models/upscale_models/ - path_inpaint=/content/data/models/inpaint/ - path_controlnet=/content/data/models/controlnet/ - path_clip_vision=/content/data/models/clip_vision/ - path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ - path_outputs=/content/app/outputs/ # Warning: If it is not located under '/content/app', you can't see history log! volumes: - fooocus-data:/content/data #- ./models:/import/models # Once you import files, you don't need to mount again. #- ./outputs:/import/outputs # Once you import files, you don't need to mount again. tty: true deploy: resources: reservations: devices: - driver: nvidia device_ids: ['0'] capabilities: [compute, utility] ================================================ FILE: docker.md ================================================ # Fooocus on Docker The docker image is based on NVIDIA CUDA 12.4 and PyTorch 2.1, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details. ## Requirements - A computer with specs good enough to run Fooocus, and proprietary Nvidia drivers - Docker, Docker Compose, or Podman ## Quick start **More information in the [notes](#notes).** ### Running with Docker Compose 1. Clone this repository 2. Run the docker container with `docker compose up`. ### Running with Docker ```sh docker run -p 7865:7865 -v fooocus-data:/content/data -it \ --gpus all \ -e CMDARGS=--listen \ -e DATADIR=/content/data \ -e config_path=/content/data/config.txt \ -e config_example_path=/content/data/config_modification_tutorial.txt \ -e path_checkpoints=/content/data/models/checkpoints/ \ -e path_loras=/content/data/models/loras/ \ -e path_embeddings=/content/data/models/embeddings/ \ -e path_vae_approx=/content/data/models/vae_approx/ \ -e path_upscale_models=/content/data/models/upscale_models/ \ -e path_inpaint=/content/data/models/inpaint/ \ -e path_controlnet=/content/data/models/controlnet/ \ -e path_clip_vision=/content/data/models/clip_vision/ \ -e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \ -e path_outputs=/content/app/outputs/ \ ghcr.io/lllyasviel/fooocus ``` ### Running with Podman ```sh podman run -p 7865:7865 -v fooocus-data:/content/data -it \ --security-opt=no-new-privileges --cap-drop=ALL --security-opt label=type:nvidia_container_t --device=nvidia.com/gpu=all \ -e CMDARGS=--listen \ -e DATADIR=/content/data \ -e config_path=/content/data/config.txt \ -e config_example_path=/content/data/config_modification_tutorial.txt \ -e path_checkpoints=/content/data/models/checkpoints/ \ -e path_loras=/content/data/models/loras/ \ -e path_embeddings=/content/data/models/embeddings/ \ -e path_vae_approx=/content/data/models/vae_approx/ \ -e path_upscale_models=/content/data/models/upscale_models/ \ -e path_inpaint=/content/data/models/inpaint/ \ -e path_controlnet=/content/data/models/controlnet/ \ -e path_clip_vision=/content/data/models/clip_vision/ \ -e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \ -e path_outputs=/content/app/outputs/ \ ghcr.io/lllyasviel/fooocus ``` When you see the message `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser. Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes/` (or `~/.local/share/containers/storage/volumes/` when using `podman`). ## Building the container locally Clone the repository first, and open a terminal in the folder. Build with `docker`: ```sh docker build . -t fooocus ``` Build with `podman`: ```sh podman build . -t fooocus ``` ## Details ### Update the container manually (`docker compose`) When you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically. Run `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version. You can then start it with `docker compose up` ### Import models, outputs If you want to import files from models or the outputs folder, you can add the following bind mounts in the [docker-compose.yml](docker-compose.yml) or your preferred method of running the container: ``` #- ./models:/import/models # Once you import files, you don't need to mount again. #- ./outputs:/import/outputs # Once you import files, you don't need to mount again. ``` After running the container, your files will be copied into `/content/data/models` and `/content/data/outputs` Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run the container without the above mounts. ### Paths inside the container |Path|Details| |-|-| |/content/app|The application stored folder| |/content/app/models.org|Original 'models' folder.
Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) | |/content/data|Persistent volume mount point| |/content/data/models|The folder is symlinked to '/content/app/models'| |/content/data/outputs|The folder is symlinked to '/content/app/outputs'| ### Environments You can change `config.txt` parameters by using environment variables. **The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`** Docker specified environments are there. They are used by 'entrypoint.sh' |Environment|Details| |-|-| |DATADIR|'/content/data' location.| |CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)| |config_path|'config.txt' location| |config_example_path|'config_modification_tutorial.txt' location| |HF_MIRROR| huggingface mirror site domain| You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments. See examples in the [docker-compose.yml](docker-compose.yml) ## Notes - Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log. - Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using "bind mount". - The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224 - You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running. ================================================ FILE: entry_with_update.py ================================================ import os import sys root = os.path.dirname(os.path.abspath(__file__)) sys.path.append(root) os.chdir(root) try: import pygit2 pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0) repo = pygit2.Repository(os.path.abspath(os.path.dirname(__file__))) branch_name = repo.head.shorthand remote_name = 'origin' remote = repo.remotes[remote_name] remote.fetch() local_branch_ref = f'refs/heads/{branch_name}' local_branch = repo.lookup_reference(local_branch_ref) remote_reference = f'refs/remotes/{remote_name}/{branch_name}' remote_commit = repo.revparse_single(remote_reference) merge_result, _ = repo.merge_analysis(remote_commit.id) if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE: print("Already up-to-date") elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD: local_branch.set_target(remote_commit.id) repo.head.set_target(remote_commit.id) repo.checkout_tree(repo.get(remote_commit.id)) repo.reset(local_branch.target, pygit2.GIT_RESET_HARD) print("Fast-forward merge") elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL: print("Update failed - Did you modify any file?") except Exception as e: print('Update failed.') print(str(e)) print('Update succeeded.') from launch import * ================================================ FILE: entrypoint.sh ================================================ #!/bin/bash ORIGINALDIR=/content/app # Use predefined DATADIR if it is defined [[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data # Make persistent dir from original dir function mklink () { mkdir -p $DATADIR/$1 ln -s $DATADIR/$1 $ORIGINALDIR } # Copy old files from import dir function import () { (test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/) } cd $ORIGINALDIR # models mklink models # Copy original files (cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/) # Import old files import models # outputs mklink outputs # Import old files import outputs # Start application python launch.py $* ================================================ FILE: environment.yaml ================================================ name: fooocus channels: - defaults dependencies: - python=3.10 - pip=23.0 - packaging ================================================ FILE: experiments_expansion.py ================================================ from modules.expansion import FooocusExpansion expansion = FooocusExpansion() text = 'a handsome man' for i in range(64): print(expansion(text, seed=i)) ================================================ FILE: experiments_face.py ================================================ import cv2 import extras.face_crop as cropper img = cv2.imread('lena.png') result = cropper.crop_image(img) cv2.imwrite('lena_result.png', result) ================================================ FILE: experiments_interrogate.py ================================================ import cv2 from extras.interrogate import default_interrogator as default_interrogator_photo from extras.wd14tagger import default_interrogator as default_interrogator_anime img = cv2.imread('./test_imgs/red_box.jpg')[:, :, ::-1].copy() print(default_interrogator_photo(img)) img = cv2.imread('./test_imgs/miku.jpg')[:, :, ::-1].copy() print(default_interrogator_anime(img)) ================================================ FILE: experiments_mask_generation.py ================================================ # https://github.com/sail-sg/EditAnything/blob/main/sam2groundingdino_edit.py import numpy as np from PIL import Image from extras.inpaint_mask import SAMOptions, generate_mask_from_image original_image = Image.open('cat.webp') image = np.array(original_image, dtype=np.uint8) sam_options = SAMOptions( dino_prompt='eye', dino_box_threshold=0.3, dino_text_threshold=0.25, dino_erode_or_dilate=0, dino_debug=False, max_detections=2, model_type='vit_b' ) mask_image, _, _, _ = generate_mask_from_image(image, sam_options=sam_options) merged_masks_img = Image.fromarray(mask_image) merged_masks_img.show() ================================================ FILE: extras/BLIP/configs/bert_config.json ================================================ { "architectures": [ "BertModel" ], "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522, "encoder_width": 768, "add_cross_attention": true } ================================================ FILE: extras/BLIP/configs/caption_coco.yaml ================================================ image_root: '/export/share/datasets/vision/coco/images/' ann_root: 'annotation' coco_gt_root: 'annotation/coco_gt' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' # size of vit model; base or large vit: 'base' vit_grad_ckpt: False vit_ckpt_layer: 0 batch_size: 32 init_lr: 1e-5 # vit: 'large' # vit_grad_ckpt: True # vit_ckpt_layer: 5 # batch_size: 16 # init_lr: 2e-6 image_size: 384 # generation configs max_length: 20 min_length: 5 num_beams: 3 prompt: 'a picture of ' # optimizer weight_decay: 0.05 min_lr: 0 max_epoch: 5 ================================================ FILE: extras/BLIP/configs/med_config.json ================================================ { "architectures": [ "BertModel" ], "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30524, "encoder_width": 768, "add_cross_attention": true } ================================================ FILE: extras/BLIP/configs/nlvr.yaml ================================================ image_root: '/export/share/datasets/vision/NLVR2/' ann_root: 'annotation' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth' #size of vit model; base or large vit: 'base' batch_size_train: 16 batch_size_test: 64 vit_grad_ckpt: False vit_ckpt_layer: 0 max_epoch: 15 image_size: 384 # optimizer weight_decay: 0.05 init_lr: 3e-5 min_lr: 0 ================================================ FILE: extras/BLIP/configs/nocaps.yaml ================================================ image_root: '/export/share/datasets/vision/nocaps/' ann_root: 'annotation' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' vit: 'base' batch_size: 32 image_size: 384 max_length: 20 min_length: 5 num_beams: 3 prompt: 'a picture of ' ================================================ FILE: extras/BLIP/configs/pretrain.yaml ================================================ train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json', '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json', ] laion_path: '' # size of vit model; base or large vit: 'base' vit_grad_ckpt: False vit_ckpt_layer: 0 image_size: 224 batch_size: 75 queue_size: 57600 alpha: 0.4 # optimizer weight_decay: 0.05 init_lr: 3e-4 min_lr: 1e-6 warmup_lr: 1e-6 lr_decay_rate: 0.9 max_epoch: 20 warmup_steps: 3000 ================================================ FILE: extras/BLIP/configs/retrieval_coco.yaml ================================================ image_root: '/export/share/datasets/vision/coco/images/' ann_root: 'annotation' dataset: 'coco' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' # size of vit model; base or large vit: 'base' batch_size_train: 32 batch_size_test: 64 vit_grad_ckpt: True vit_ckpt_layer: 4 init_lr: 1e-5 # vit: 'large' # batch_size_train: 16 # batch_size_test: 32 # vit_grad_ckpt: True # vit_ckpt_layer: 12 # init_lr: 5e-6 image_size: 384 queue_size: 57600 alpha: 0.4 k_test: 256 negative_all_rank: True # optimizer weight_decay: 0.05 min_lr: 0 max_epoch: 6 ================================================ FILE: extras/BLIP/configs/retrieval_flickr.yaml ================================================ image_root: '/export/share/datasets/vision/flickr30k/' ann_root: 'annotation' dataset: 'flickr' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth' # size of vit model; base or large vit: 'base' batch_size_train: 32 batch_size_test: 64 vit_grad_ckpt: True vit_ckpt_layer: 4 init_lr: 1e-5 # vit: 'large' # batch_size_train: 16 # batch_size_test: 32 # vit_grad_ckpt: True # vit_ckpt_layer: 10 # init_lr: 5e-6 image_size: 384 queue_size: 57600 alpha: 0.4 k_test: 128 negative_all_rank: False # optimizer weight_decay: 0.05 min_lr: 0 max_epoch: 6 ================================================ FILE: extras/BLIP/configs/retrieval_msrvtt.yaml ================================================ video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos' ann_root: 'annotation' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' # size of vit model; base or large vit: 'base' batch_size: 64 k_test: 128 image_size: 384 num_frm_test: 8 ================================================ FILE: extras/BLIP/configs/vqa.yaml ================================================ vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/ vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/ train_files: ['vqa_train','vqa_val','vg_qa'] ann_root: 'annotation' # set pretrained as a file path or an url pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' # size of vit model; base or large vit: 'base' batch_size_train: 16 batch_size_test: 32 vit_grad_ckpt: False vit_ckpt_layer: 0 init_lr: 2e-5 image_size: 480 k_test: 128 inference: 'rank' # optimizer weight_decay: 0.05 min_lr: 0 max_epoch: 10 ================================================ FILE: extras/BLIP/models/bert_tokenizer/config.json ================================================ { "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "gradient_checkpointing": false, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "transformers_version": "4.6.0.dev0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 30522 } ================================================ FILE: extras/BLIP/models/bert_tokenizer/tokenizer.json ================================================ 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awards kingdom mary spent fine doesn reported ##ling jack fall raised itself stay true studio 1988 sports replaced paris systems saint leader theatre whose market capital parents spanish canadian earth ##ity cut degree writing bay christian awarded natural higher bill ##as coast provided previous senior ft valley organization stopped onto countries parts conference queen security interest saying allowed master earlier phone matter smith winning try happened moving campaign los ##ley breath nearly mid 1987 certain girls date italian african standing fell artist ##ted shows deal mine industry 1986 ##ng everyone republic provide collection library student ##ville primary owned older via heavy 1st makes ##able attention anyone africa ##ri stated length ended fingers command staff skin foreign opening governor okay medal kill sun cover job 1985 introduced chest hell feeling ##ies success meet reason standard meeting novel 1984 trade source buildings ##land rose guy goal ##ur chapter native husband previously unit limited entered weeks producer operations mountain takes covered forced related roman complete successful key texas cold ##ya channel 1980 traditional films dance clear approximately 500 nine van prince question active tracks ireland regional silver author personal sense operation ##ine economic 1983 holding twenty isbn additional speed hour edition regular historic places whom shook movie km² secretary prior report chicago read foundation view engine scored 1982 units ask airport property ready immediately lady month listed contract ##de manager themselves lines ##ki navy writer meant ##ts runs ##ro practice championships singer glass commission required forest starting culture generally giving access attended test couple stand catholic martin caught executive ##less eye ##ey thinking chair quite shoulder 1979 hope decision plays defeated municipality whether structure offered slowly pain ice direction ##ion paper mission 1981 mostly 200 noted individual managed nature lives plant ##ha helped except studied computer figure relationship issue significant loss die smiled gun ago highest 1972 ##am male bring goals mexico problem distance commercial completely location annual famous drive 1976 neck 1978 surface caused italy understand greek highway wrong hotel comes appearance joseph double issues musical companies castle income review assembly bass initially parliament artists experience 1974 particular walk foot engineering talking window dropped ##ter miss baby boys break 1975 stars edge remember policy carried train stadium bar sex angeles evidence ##ge becoming assistant soviet 1977 upper step wing 1970 youth financial reach ##ll actor numerous ##se ##st nodded arrived ##ation minute ##nt believed sorry complex beautiful victory associated temple 1968 1973 chance perhaps metal ##son 1945 bishop ##et lee launched particularly tree le retired subject prize contains yeah theory empire ##ce suddenly waiting trust recording ##to happy terms camp champion 1971 religious pass zealand names 2nd port ancient tom corner represented watch legal anti justice cause watched brothers 45 material changes simply response louis fast ##ting answer 60 historical 1969 stories straight create feature increased rate administration virginia el activities cultural overall winner programs basketball legs guard beyond cast doctor mm flight results remains cost effect winter ##ble larger islands problems chairman grew commander isn 1967 pay failed selected hurt fort box regiment majority journal 35 edward plans ##ke ##ni shown pretty irish characters directly scene likely operated allow spring ##j junior matches looks mike houses fellow ##tion beach marriage ##ham ##ive rules oil 65 florida expected nearby congress sam peace recent iii wait subsequently cell ##do variety serving agreed please poor joe pacific attempt wood democratic piece prime ##ca rural mile touch appears township 1964 1966 soldiers ##men ##ized 1965 pennsylvania closer fighting claimed score jones physical editor ##ous filled genus specific sitting super mom ##va therefore supported status fear cases store meaning wales minor spain tower focus vice frank follow parish separate golden horse fifth remaining branch 32 presented stared ##id uses secret forms ##co baseball exactly ##ck choice note discovered travel composed truth russia ball color kiss dad wind continue ring referred numbers digital greater ##ns metres slightly direct increase 1960 responsible crew rule trees troops ##no broke goes individuals hundred weight creek sleep memory defense provides ordered code value jewish windows 1944 safe judge whatever corps realized growing pre ##ga cities alexander gaze lies spread scott letter showed situation mayor transport watching workers extended ##li expression normal ##ment chart multiple border ##ba host ##ner daily mrs walls piano ##ko heat cannot ##ate earned products drama era authority seasons join grade ##io sign difficult machine 1963 territory mainly ##wood stations squadron 1962 stepped iron 19th ##led serve appear sky speak broken charge knowledge kilometres removed ships article campus simple ##ty pushed britain ##ve leaves recently cd soft boston latter easy acquired poland ##sa quality officers presence planned nations mass broadcast jean share image influence wild offer emperor electric reading headed ability promoted yellow ministry 1942 throat smaller politician ##by latin spoke cars williams males lack pop 80 ##ier acting seeing consists ##ti estate 1961 pressure johnson newspaper jr chris olympics online conditions beat elements walking vote ##field needs carolina text featuring global block shirt levels francisco purpose females et dutch duke ahead gas twice safety serious turning highly lieutenant firm maria amount mixed daniel proposed perfect agreement affairs 3rd seconds contemporary paid 1943 prison save kitchen label administrative intended constructed academic nice teacher races 1956 formerly corporation ben nation issued shut 1958 drums housing victoria seems opera 1959 graduated function von mentioned picked build recognized shortly protection picture notable exchange elections 1980s loved percent racing fish elizabeth garden volume hockey 1941 beside settled ##ford 1940 competed replied drew 1948 actress marine scotland steel glanced farm steve 1957 risk tonight positive magic singles effects gray screen dog ##ja residents bus sides none secondary literature polish destroyed flying founder households 1939 lay reserve usa gallery ##ler 1946 industrial younger approach appearances urban ones 1950 finish avenue powerful fully growth page honor jersey projects advanced revealed basic 90 infantry pair equipment visit 33 evening search grant effort solo treatment buried republican primarily bottom owner 1970s israel gives jim dream bob remain spot 70 notes produce champions contact ed soul accepted ways del ##ally losing split price capacity basis trial questions ##ina 1955 20th guess officially memorial naval initial ##ization whispered median engineer ##ful sydney ##go columbia strength 300 1952 tears senate 00 card asian agent 1947 software 44 draw warm supposed com pro ##il transferred leaned ##at candidate escape mountains asia potential activity entertainment seem traffic jackson murder 36 slow product orchestra haven agency bbc taught website comedy unable storm planning albums rugby environment scientific grabbed protect ##hi boat typically 1954 1953 damage principal divided dedicated mount ohio ##berg pick fought driver ##der empty shoulders sort thank berlin prominent account freedom necessary efforts alex headquarters follows alongside des simon andrew suggested operating learning steps 1949 sweet technical begin easily 34 teeth speaking settlement scale ##sh renamed ray max enemy semi joint compared ##rd scottish leadership analysis offers georgia pieces captured animal deputy guest organized ##lin tony combined method challenge 1960s huge wants battalion sons rise crime types facilities telling path 1951 platform sit 1990s ##lo tells assigned rich pull ##ot commonly alive ##za letters concept conducted wearing happen bought becomes holy gets ocean defeat languages purchased coffee occurred titled ##q declared applied sciences concert sounds jazz brain ##me painting fleet tax nick ##ius michigan count animals leaders episodes ##line content ##den birth ##it clubs 64 palace critical refused fair leg laughed returning surrounding participated formation lifted pointed connected rome medicine laid taylor santa powers adam tall shared focused knowing yards entrance falls ##wa calling ##ad sources chosen beneath resources yard ##ite nominated silence zone defined ##que gained thirty 38 bodies moon ##ard adopted christmas widely register apart iran premier serves du unknown parties ##les generation ##ff continues quick fields brigade quiet teaching clothes impact weapons partner flat theater supreme 1938 37 relations ##tor plants suffered 1936 wilson kids begins ##age 1918 seats armed internet models worth laws 400 communities classes background knows thanks quarter reaching humans carry killing format kong hong setting 75 architecture disease railroad inc possibly wish arthur thoughts harry doors density ##di crowd illinois stomach tone unique reports anyway ##ir liberal der vehicle thick dry drug faced largely facility theme holds creation strange colonel ##mi revolution bell politics turns silent rail relief independence combat shape write determined sales learned 4th finger oxford providing 1937 heritage fiction situated designated allowing distribution hosted ##est sight interview estimated reduced ##ria toronto footballer keeping guys damn claim motion sport sixth stayed ##ze en rear receive handed twelve dress audience granted brazil ##well spirit ##ated noticed etc olympic representative eric tight trouble reviews drink vampire missing roles ranked newly household finals wave critics ##ee phase massachusetts pilot unlike philadelphia bright guns crown organizations roof 42 respectively clearly tongue marked circle fox korea bronze brian expanded sexual supply yourself inspired labour fc ##ah reference vision draft connection brand reasons 1935 classic driving trip jesus cells entry 1920 neither trail claims atlantic orders labor nose afraid identified intelligence calls cancer attacked passing stephen positions imperial grey jason 39 sunday 48 swedish avoid extra uncle message covers allows surprise materials fame hunter ##ji 1930 citizens figures davis environmental confirmed shit titles di performing difference acts attacks ##ov existing votes opportunity nor shop entirely trains opposite pakistan ##pa develop resulted representatives actions reality pressed ##ish barely wine conversation faculty northwest ends documentary nuclear stock grace sets eat alternative ##ps bag resulting creating surprised cemetery 1919 drop finding sarah cricket streets tradition ride 1933 exhibition target ear explained rain composer injury apartment municipal educational occupied netherlands clean billion constitution learn 1914 maximum classical francis lose opposition jose ontario bear core hills rolled ending drawn permanent fun ##tes ##lla lewis sites chamber ryan ##way scoring height 1934 ##house lyrics staring 55 officials 1917 snow oldest ##tic orange ##ger qualified interior apparently succeeded thousand dinner lights existence fans heavily 41 greatest conservative send bowl plus enter catch ##un economy duty 1929 speech authorities princess performances versions shall graduate pictures effective remembered poetry desk crossed starring starts passenger sharp ##ant acres ass weather falling rank fund supporting check adult publishing heads cm southeast lane ##burg application bc ##ura les condition transfer prevent display ex regions earl federation cool relatively answered besides 1928 obtained portion ##town mix ##ding reaction liked dean express peak 1932 ##tte counter religion chain rare miller convention aid lie vehicles mobile perform squad wonder lying crazy sword ##ping attempted centuries weren philosophy category ##ize anna interested 47 sweden wolf frequently abandoned kg literary alliance task entitled ##ay threw promotion factory tiny soccer visited matt fm achieved 52 defence internal persian 43 methods ##ging arrested otherwise cambridge programming villages elementary districts rooms criminal conflict worry trained 1931 attempts waited signal bird truck subsequent programme ##ol ad 49 communist details faith sector patrick carrying laugh ##ss controlled korean showing origin fuel evil 1927 ##ent brief identity darkness address pool missed publication web planet ian anne wings invited ##tt briefly standards kissed ##be ideas climate causing walter worse albert articles winners desire aged northeast dangerous gate doubt 1922 wooden multi ##ky poet rising funding 46 communications communication violence copies prepared ford investigation skills 1924 pulling electronic ##ak ##ial ##han containing ultimately offices singing understanding restaurant tomorrow fashion christ ward da pope stands 5th flow studios aired commissioned contained exist fresh americans ##per wrestling approved kid employed respect suit 1925 angel asking increasing frame angry selling 1950s thin finds ##nd temperature statement ali explain inhabitants towns extensive narrow 51 jane flowers images promise somewhere object fly closely ##ls 1912 bureau cape 1926 weekly presidential legislative 1921 ##ai ##au launch founding ##ny 978 ##ring artillery strike un institutions roll writers landing chose kevin anymore pp ##ut attorney fit dan billboard receiving agricultural breaking sought dave admitted lands mexican ##bury charlie specifically hole iv howard credit moscow roads accident 1923 proved wear struck hey guards stuff slid expansion 1915 cat anthony ##kin melbourne opposed sub southwest architect failure plane 1916 ##ron map camera tank listen regarding wet introduction metropolitan link ep fighter inch grown gene anger fixed buy dvd khan domestic worldwide chapel mill functions examples ##head developing 1910 turkey hits pocket antonio papers grow unless circuit 18th concerned attached journalist selection journey converted provincial painted hearing aren bands negative aside wondered knight lap survey ma ##ow noise billy ##ium shooting guide bedroom priest resistance motor homes sounded giant ##mer 150 scenes equal comic patients hidden solid actual bringing afternoon touched funds wedding consisted marie canal sr kim treaty turkish recognition residence cathedral broad knees incident shaped fired norwegian handle cheek contest represent ##pe representing beauty ##sen birds advantage emergency wrapped drawing notice pink broadcasting ##ong somehow bachelor seventh collected registered establishment alan assumed chemical personnel roger retirement jeff portuguese wore tied device threat progress advance ##ised banks hired manchester nfl teachers structures forever ##bo tennis helping saturday sale applications junction hip incorporated neighborhood dressed ceremony ##ds influenced hers visual stairs decades inner kansas hung hoped gain scheduled downtown engaged austria clock norway certainly pale protected 1913 victor employees plate putting surrounded ##ists finishing blues tropical ##ries minnesota consider philippines accept 54 retrieved 1900 concern anderson properties institution gordon successfully vietnam ##dy backing outstanding muslim crossing folk producing usual demand occurs observed lawyer educated ##ana kelly string pleasure budget items quietly colorado philip typical ##worth derived 600 survived asks mental ##ide 56 jake jews distinguished ltd 1911 sri extremely 53 athletic loud thousands worried shadow transportation horses weapon arena importance users tim objects contributed dragon douglas aware senator johnny jordan sisters engines flag investment samuel shock capable clark row wheel refers session familiar biggest wins hate maintained drove hamilton request expressed injured underground churches walker wars tunnel passes stupid agriculture softly cabinet regarded joining indiana ##ea ##ms push dates spend behavior woods protein gently chase morgan mention burning wake combination occur mirror leads jimmy indeed impossible singapore paintings covering ##nes soldier locations attendance sell historian wisconsin invasion argued painter diego changing egypt ##don experienced inches ##ku missouri vol grounds spoken switzerland ##gan reform rolling ha forget massive resigned burned allen tennessee locked values improved ##mo wounded universe sick dating facing pack purchase user ##pur moments ##ul merged anniversary 1908 coal brick understood causes dynasty queensland establish stores crisis promote hoping views cards referee extension ##si raise arizona improve colonial formal charged ##rt palm lucky hide rescue faces 95 feelings candidates juan ##ell goods 6th courses weekend 59 luke cash fallen ##om delivered affected installed carefully tries swiss hollywood costs lincoln responsibility ##he shore file proper normally maryland assistance jump constant offering friendly waters persons realize contain trophy 800 partnership factor 58 musicians cry bound oregon indicated hero houston medium ##ure consisting somewhat ##ara 57 cycle ##che beer moore frederick gotten eleven worst weak approached arranged chin loan universal bond fifteen pattern disappeared ##ney translated ##zed lip arab capture interests insurance ##chi shifted cave prix warning sections courts coat plot smell feed golf favorite maintain knife vs voted degrees finance quebec opinion translation manner ruled operate productions choose musician discovery confused tired separated stream techniques committed attend ranking kings throw passengers measure horror fan mining sand danger salt calm decade dam require runner ##ik rush associate greece ##ker rivers consecutive matthew ##ski sighed sq documents steam edited closing tie accused 1905 ##ini islamic distributed directors organisation bruce 7th breathing mad lit arrival concrete taste 08 composition shaking faster amateur adjacent stating 1906 twin flew ##ran tokyo publications ##tone obviously ridge storage 1907 carl pages concluded desert driven universities ages terminal sequence borough 250 constituency creative cousin economics dreams margaret notably reduce montreal mode 17th ears saved jan vocal ##ica 1909 andy ##jo riding roughly threatened ##ise meters meanwhile landed compete repeated grass czech regularly charges tea sudden appeal ##ung solution describes pierre classification glad parking ##ning belt physics 99 rachel add hungarian participate expedition damaged gift childhood 85 fifty ##red mathematics jumped letting defensive mph ##ux ##gh testing ##hip hundreds shoot owners matters smoke israeli kentucky dancing mounted grandfather emma designs profit argentina ##gs truly li lawrence cole begun detroit willing branches smiling decide miami enjoyed recordings ##dale poverty ethnic gay ##bi gary arabic 09 accompanied ##one ##ons fishing determine residential acid ##ary alice returns starred mail ##ang jonathan strategy ##ue net forty cook businesses equivalent commonwealth distinct ill ##cy seriously ##ors ##ped shift harris replace rio imagine formula ensure ##ber additionally scheme conservation occasionally purposes feels favor ##and ##ore 1930s contrast hanging hunt movies 1904 instruments victims danish christopher busy demon sugar earliest colony studying balance duties ##ks belgium slipped carter 05 visible stages iraq fifa ##im commune forming zero 07 continuing talked counties legend bathroom option tail clay daughters afterwards severe jaw visitors ##ded devices aviation russell kate ##vi entering subjects ##ino temporary swimming forth smooth ghost audio bush operates rocks movements signs eddie ##tz ann voices honorary 06 memories dallas pure measures racial promised 66 harvard ceo 16th parliamentary indicate benefit flesh dublin louisiana 1902 1901 patient sleeping 1903 membership coastal medieval wanting element scholars rice 62 limit survive makeup rating definitely collaboration obvious ##tan boss ms baron birthday linked soil diocese ##lan ncaa ##mann offensive shell shouldn waist ##tus plain ross organ resolution manufacturing adding relative kennedy 98 whilst moth marketing gardens crash 72 heading partners credited carlos moves cable ##zi marshall ##out depending bottle represents rejected responded existed 04 jobs denmark lock ##ating treated graham routes talent commissioner drugs secure tests reign restored photography ##gi contributions oklahoma designer disc grin seattle robin paused atlanta unusual ##gate praised las laughing satellite hungary visiting ##sky interesting factors deck poems norman ##water stuck speaker rifle domain premiered ##her dc comics actors 01 reputation eliminated 8th ceiling prisoners script ##nce leather austin mississippi rapidly admiral parallel charlotte guilty tools gender divisions fruit ##bs laboratory nelson fantasy marry rapid aunt tribe requirements aspects suicide amongst adams bone ukraine abc kick sees edinburgh clothing column rough gods hunting broadway gathered concerns ##ek spending ty 12th snapped requires solar bones cavalry ##tta iowa drinking waste index franklin charity thompson stewart tip flash landscape friday enjoy singh poem listening ##back eighth fred differences adapted bomb ukrainian surgery corporate masters anywhere ##more waves odd sean portugal orleans dick debate kent eating puerto cleared 96 expect cinema 97 guitarist blocks electrical agree involving depth dying panel struggle ##ged peninsula adults novels emerged vienna metro debuted shoes tamil songwriter meets prove beating instance heaven scared sending marks artistic passage superior 03 significantly shopping ##tive retained ##izing malaysia technique cheeks ##ola warren maintenance destroy extreme allied 120 appearing ##yn fill advice alabama qualifying policies cleveland hat battery smart authors 10th soundtrack acted dated lb glance equipped coalition funny outer ambassador roy possibility couples campbell dna loose ethan supplies 1898 gonna 88 monster ##res shake agents frequency springs dogs practices 61 gang plastic easier suggests gulf blade exposed colors industries markets pan nervous electoral charts legislation ownership ##idae mac appointment shield copy assault socialist abbey monument license throne employment jay 93 replacement charter cloud powered suffering accounts oak connecticut strongly wright colour crystal 13th context welsh networks voiced gabriel jerry ##cing forehead mp ##ens manage schedule totally remix ##ii forests occupation print nicholas brazilian strategic vampires engineers 76 roots seek correct instrumental und alfred backed hop ##des stanley robinson traveled wayne welcome austrian achieve 67 exit rates 1899 strip whereas ##cs sing deeply adventure bobby rick jamie careful components cap useful personality knee ##shi pushing hosts 02 protest ca ottoman symphony ##sis 63 boundary 1890 processes considering considerable tons ##work ##ft ##nia cooper trading dear conduct 91 illegal apple revolutionary holiday definition harder ##van jacob circumstances destruction ##lle popularity grip classified liverpool donald baltimore flows seeking honour approval 92 mechanical till happening statue critic increasingly immediate describe commerce stare ##ster indonesia meat rounds boats baker orthodox depression formally worn naked claire muttered sentence 11th emily document 77 criticism wished vessel spiritual bent virgin parker minimum murray lunch danny printed compilation keyboards false blow belonged 68 raising 78 cutting ##board pittsburgh ##up 9th shadows 81 hated indigenous jon 15th barry scholar ah ##zer oliver ##gy stick susan meetings attracted spell romantic ##ver ye 1895 photo demanded customers ##ac 1896 logan revival keys modified commanded jeans ##ious upset raw phil detective hiding resident vincent ##bly experiences diamond defeating coverage lucas external parks franchise helen bible successor percussion celebrated il lift profile clan romania ##ied mills ##su nobody achievement shrugged fault 1897 rhythm initiative breakfast carbon 700 69 lasted violent 74 wound ken killer gradually filmed °c dollars processing 94 remove criticized guests sang chemistry ##vin legislature disney ##bridge uniform escaped integrated proposal purple denied liquid karl influential morris nights stones intense experimental twisted 71 84 ##ld pace nazi mitchell ny blind reporter newspapers 14th centers burn basin forgotten surviving filed collections monastery losses manual couch description appropriate merely tag missions sebastian restoration replacing triple 73 elder julia warriors benjamin julian convinced stronger amazing declined versus merchant happens output finland bare barbara absence ignored dawn injuries ##port producers ##ram 82 luis ##ities kw admit expensive electricity nba exception symbol ##ving ladies shower sheriff characteristics ##je aimed button ratio effectively summit angle jury bears foster vessels pants executed evans dozen advertising kicked patrol 1889 competitions lifetime principles athletics ##logy birmingham sponsored 89 rob nomination 1893 acoustic ##sm creature longest ##tra credits harbor dust josh ##so territories milk infrastructure completion thailand indians leon archbishop ##sy assist pitch blake arrangement girlfriend serbian operational hence sad scent fur dj sessions hp refer rarely ##ora exists 1892 ##ten scientists dirty penalty burst portrait seed 79 pole limits rival 1894 stable alpha grave constitutional alcohol arrest flower mystery devil architectural relationships greatly habitat ##istic larry progressive remote cotton ##ics ##ok preserved reaches ##ming cited 86 vast scholarship decisions cbs joy teach 1885 editions knocked eve searching partly participation gap animated fate excellent ##ett na 87 alternate saints youngest ##ily climbed ##ita ##tors suggest ##ct discussion staying choir lakes jacket revenue nevertheless peaked instrument wondering annually managing neil 1891 signing terry ##ice apply clinical brooklyn aim catherine fuck farmers figured ninth pride hugh evolution ordinary involvement comfortable shouted tech encouraged taiwan representation sharing ##lia ##em panic exact cargo competing fat cried 83 1920s occasions pa cabin borders utah marcus ##isation badly muscles ##ance victorian transition warner bet permission ##rin slave terrible similarly shares seth uefa possession medals benefits colleges lowered perfectly mall transit ##ye ##kar publisher ##ened harrison deaths elevation ##ae asleep machines sigh ash hardly argument occasion parent leo decline 1888 contribution ##ua concentration 1000 opportunities hispanic guardian extent emotions hips mason volumes bloody controversy diameter steady mistake phoenix identify violin ##sk departure richmond spin funeral enemies 1864 gear literally connor random sergeant grab confusion 1865 transmission informed op leaning sacred suspended thinks gates portland luck agencies yours hull expert muscle layer practical sculpture jerusalem latest lloyd statistics deeper recommended warrior arkansas mess supports greg eagle 1880 recovered rated concerts rushed ##ano stops eggs files premiere keith ##vo delhi turner pit affair belief paint ##zing mate ##ach ##ev victim ##ology withdrew bonus styles fled ##ud glasgow technologies funded nbc adaptation ##ata portrayed cooperation supporters judges bernard justin hallway ralph ##ick graduating controversial distant continental spider bite ##ho recognize intention mixing ##ese egyptian bow tourism suppose claiming tiger dominated participants vi ##ru nurse partially tape ##rum psychology ##rn essential touring duo voting civilian emotional channels ##king apparent hebrew 1887 tommy carrier intersection beast hudson ##gar ##zo lab nova bench discuss costa ##ered detailed behalf drivers unfortunately obtain ##lis rocky ##dae siege friendship honey ##rian 1861 amy hang posted governments collins respond wildlife preferred operator ##po laura pregnant videos dennis suspected boots instantly weird automatic businessman alleged placing throwing ph mood 1862 perry venue jet remainder ##lli ##ci passion biological boyfriend 1863 dirt buffalo ron segment fa abuse ##era genre thrown stroke colored stress exercise displayed ##gen struggled ##tti abroad dramatic wonderful thereafter madrid component widespread ##sed tale citizen todd monday 1886 vancouver overseas forcing crying descent ##ris discussed substantial ranks regime 1870 provinces switch drum zane ted tribes proof lp cream researchers volunteer manor silk milan donated allies venture principle delivery enterprise ##ves ##ans bars traditionally witch reminded copper ##uk pete inter links colin grinned elsewhere competitive frequent ##oy scream ##hu tension texts submarine finnish defending defend pat detail 1884 affiliated stuart themes villa periods tool belgian ruling crimes answers folded licensed resort demolished hans lucy 1881 lion traded photographs writes craig ##fa trials generated beth noble debt percentage yorkshire erected ss viewed grades confidence ceased islam telephone retail ##ible chile m² roberts sixteen ##ich commented hampshire innocent dual pounds checked regulations afghanistan sung rico liberty assets bigger options angels relegated tribute wells attending leaf ##yan butler romanian forum monthly lisa patterns gmina ##tory madison hurricane rev ##ians bristol ##ula elite valuable disaster democracy awareness germans freyja ##ins loop absolutely paying populations maine sole prayer spencer releases doorway bull ##ani lover midnight conclusion ##sson thirteen lily mediterranean ##lt nhl proud sample ##hill drummer guinea ##ova murphy climb ##ston instant attributed horn ain railways steven ##ao autumn ferry opponent root traveling secured corridor stretched tales sheet trinity cattle helps indicates manhattan murdered fitted 1882 gentle grandmother mines shocked vegas produces ##light caribbean ##ou belong continuous desperate drunk historically trio waved raf dealing nathan bat murmured interrupted residing scientist pioneer harold aaron ##net delta attempting minority mini believes chorus tend lots eyed indoor load shots updated jail ##llo concerning connecting wealth ##ved slaves arrive rangers sufficient rebuilt ##wick cardinal flood muhammad whenever relation runners moral repair viewers arriving revenge punk assisted bath fairly breathe lists innings illustrated whisper nearest voters clinton ties ultimate screamed beijing lions andre fictional gathering comfort radar suitable dismissed hms ban pine wrist atmosphere voivodeship bid timber ##ned ##nan giants ##ane cameron recovery uss identical categories switched serbia laughter noah ensemble therapy peoples touching ##off locally pearl platforms everywhere ballet tables lanka herbert outdoor toured derek 1883 spaces contested swept 1878 exclusive slight connections ##dra winds prisoner collective bangladesh tube publicly wealthy thai ##ys isolated select ##ric insisted pen fortune ticket spotted reportedly animation enforcement tanks 110 decides wider lowest owen ##time nod hitting ##hn gregory furthermore magazines fighters solutions ##ery pointing requested peru reed chancellor knights mask worker eldest flames reduction 1860 volunteers ##tis reporting ##hl wire advisory endemic origins settlers pursue knock consumer 1876 eu compound creatures mansion sentenced ivan deployed guitars frowned involves mechanism kilometers perspective shops maps terminus duncan alien fist bridges ##pers heroes fed derby swallowed ##ros patent sara illness characterized adventures slide hawaii jurisdiction ##op organised ##side adelaide walks biology se ##ties rogers swing tightly boundaries ##rie prepare implementation stolen ##sha certified colombia edwards garage ##mm recalled ##ball rage harm nigeria breast ##ren furniture pupils settle ##lus cuba balls client alaska 21st linear thrust celebration latino genetic terror ##cia ##ening lightning fee witness lodge establishing skull ##ique earning hood ##ei rebellion wang sporting warned missile devoted activist porch worship fourteen package 1871 decorated ##shire housed ##ock chess sailed doctors oscar joan treat garcia harbour jeremy ##ire traditions dominant jacques ##gon ##wan relocated 1879 amendment sized companion simultaneously volleyball spun acre increases stopping loves belongs affect drafted tossed scout battles 1875 filming shoved munich tenure vertical romance pc ##cher argue ##ical craft ranging www opens honest tyler yesterday virtual ##let muslims reveal snake immigrants radical screaming speakers firing saving belonging ease lighting prefecture blame farmer hungry grows rubbed beam sur subsidiary ##cha armenian sao dropping conventional ##fer microsoft reply qualify spots 1867 sweat festivals ##ken immigration physician discover exposure sandy explanation isaac implemented ##fish hart initiated connect stakes presents heights householder pleased tourist regardless slip closest ##ction surely sultan brings riley preparation aboard slammed baptist experiment ongoing interstate organic playoffs ##ika 1877 130 ##tar hindu error tours tier plenty arrangements talks trapped excited sank ho athens 1872 denver welfare suburb athletes trick diverse belly exclusively yelled 1868 ##med conversion ##ette 1874 internationally computers conductor abilities sensitive hello dispute measured globe rocket prices amsterdam flights tigers inn municipalities emotion references 3d ##mus explains airlines manufactured pm archaeological 1873 interpretation devon comment ##ites settlements kissing absolute improvement suite impressed barcelona sullivan jefferson towers jesse julie ##tin ##lu grandson hi gauge regard rings interviews trace raymond thumb departments burns serial bulgarian scores demonstrated ##ix 1866 kyle alberta underneath romanized ##ward relieved acquisition phrase cliff reveals han cuts merger custom ##dar nee gilbert graduation ##nts assessment cafe difficulty demands swung democrat jennifer commons 1940s grove ##yo completing focuses sum substitute bearing stretch reception ##py reflected essentially destination pairs ##ched survival resource ##bach promoting doubles messages tear ##down ##fully parade florence harvey incumbent partial framework 900 pedro frozen procedure olivia controls ##mic shelter personally temperatures ##od brisbane tested sits marble comprehensive oxygen leonard ##kov inaugural iranian referring quarters attitude ##ivity mainstream lined mars dakota norfolk unsuccessful ##° explosion helicopter congressional ##sing inspector bitch seal departed divine ##ters coaching examination punishment manufacturer sink columns 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variation aids civic graphics professionals realm autonomous receiver delayed workshop militia chairs trump canyon ##point harsh extending lovely happiness ##jan stake eyebrows embassy wellington hannah ##ella sony corners bishops swear cloth contents xi namely commenced 1854 stanford nashville courage graphic commitment garrison ##bin hamlet clearing rebels attraction literacy cooking ruins temples jenny humanity celebrate hasn freight sixty rebel bastard ##art newton ##ada deer ##ges ##ching smiles delaware singers ##ets approaching assists flame ##ph boulevard barrel planted ##ome pursuit ##sia consequences posts shallow invitation rode depot ernest kane rod concepts preston topic chambers striking blast arrives descendants montgomery ranges worlds ##lay ##ari span chaos praise ##ag fewer 1855 sanctuary mud fbi ##ions programmes maintaining unity harper bore handsome closure tournaments thunder nebraska linda facade puts satisfied argentine dale cork dome panama ##yl 1858 tasks 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##eo questioned brass lifting 1852 math authored ##ual doug dimensional dynamic ##san 1851 pronounced grateful quest uncomfortable boom presidency stevens relating politicians chen barrier quinn diana mosque tribal cheese palmer portions sometime chester treasure wu bend download millions reforms registration ##osa consequently monitoring ate preliminary brandon invented ps eaten exterior intervention ports documented log displays lecture sally favourite ##itz vermont lo invisible isle breed ##ator journalists relay speaks backward explore midfielder actively stefan procedures cannon blond kenneth centered servants chains libraries malcolm essex henri slavery ##hal facts fairy coached cassie cats washed cop ##fi announcement item 2000s vinyl activated marco frontier growled curriculum ##das loyal accomplished leslie ritual kenny ##00 vii napoleon hollow hybrid jungle stationed friedrich counted ##ulated platinum theatrical seated col rubber glen 1840 diversity healing extends id 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georgian export solomon rivals swift seventeen rodriguez princeton independently sox 1847 arguing entity casting hank criteria oakland geographic milwaukee reflection expanding conquest dubbed ##tv halt brave brunswick doi arched curtis divorced predominantly somerset streams ugly zoo horrible curved buenos fierce dictionary vector theological unions handful stability chan punjab segments ##lly altar ignoring gesture monsters pastor ##stone thighs unexpected operators abruptly coin compiled associates improving migration pin ##ose compact collegiate reserved ##urs quarterfinals roster restore assembled hurry oval ##cies 1846 flags martha ##del victories sharply ##rated argues deadly neo drawings symbols performer ##iel griffin restrictions editing andrews java journals arabia compositions dee pierce removing hindi casino runway civilians minds nasa hotels ##zation refuge rent retain potentially conferences suburban conducting ##tto ##tions ##tle descended massacre ##cal ammunition 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optional chronic discharge ##rc suck compatible laurel stella shi fails wage dodge 128 informal sorts levi buddha villagers ##aka chronicles heavier summoned gateway 3000 eleventh jewelry translations accordingly seas ##ency fiber pyramid cubic dragging ##ista caring ##ops android contacted lunar ##dt kai lisbon patted 1826 sacramento theft madagascar subtropical disputes ta holidays piper willow mare cane itunes newfoundland benny companions dong raj observe roar charming plaque tibetan fossils enacted manning bubble tina tanzania ##eda ##hir funk swamp deputies cloak ufc scenario par scratch metals anthem guru engaging specially ##boat dialects nineteen cecil duet disability messenger unofficial ##lies defunct eds moonlight drainage surname puzzle honda switching conservatives mammals knox broadcaster sidewalk cope ##ried benson princes peterson ##sal bedford sharks eli wreck alberto gasp archaeology lgbt teaches securities madness compromise waving coordination davidson visions leased possibilities eighty jun fernandez enthusiasm assassin sponsorship reviewer kingdoms estonian laboratories ##fy ##nal applies verb celebrations ##zzo rowing lightweight sadness submit mvp balanced dude ##vas explicitly metric magnificent mound brett mohammad mistakes irregular ##hing ##ass sanders betrayed shipped surge ##enburg reporters termed georg pity verbal bulls abbreviated enabling appealed ##are ##atic sicily sting heel sweetheart bart spacecraft brutal monarchy ##tter aberdeen cameo diane ##ub survivor clyde ##aries complaint ##makers clarinet delicious chilean karnataka coordinates 1818 panties ##rst pretending ar dramatically kiev bella tends distances 113 catalog launching instances telecommunications portable lindsay vatican ##eim angles aliens marker stint screens bolton ##rne judy wool benedict plasma europa spark imaging filmmaker swiftly ##een contributor ##nor opted stamps apologize financing butter gideon sophisticated alignment avery chemicals yearly 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##mu dissolution dedication shin meals saddle elvis reds chaired taller appreciation functioning niece favored advocacy robbie criminals suffolk yugoslav passport constable congressman hastings vera ##rov consecrated sparks ecclesiastical confined ##ovich muller floyd nora 1822 paved 1827 cumberland ned saga spiral ##flow appreciated yi collaborative treating similarities feminine finishes ##ib jade import ##nse ##hot champagne mice securing celebrities helsinki attributes ##gos cousins phases ache lucia gandhi submission vicar spear shine tasmania biting detention constitute tighter seasonal ##gus terrestrial matthews ##oka effectiveness parody philharmonic ##onic 1816 strangers encoded consortium guaranteed regards shifts tortured collision supervisor inform broader insight theaters armour emeritus blink incorporates mapping ##50 ##ein handball flexible ##nta substantially generous thief ##own carr loses 1793 prose ucla romeo generic metallic realization damages mk commissioners zach 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clip 1817 depend nervously disco owe defenders shiva notorious disbelief shiny worcester ##gation ##yr trailing undertook islander belarus limitations watershed fuller overlooking utilized raphael 1819 synthetic breakdown klein ##nate moaned memoir lamb practicing ##erly cellular arrows exotic ##graphy witches 117 charted rey hut hierarchy subdivision freshwater giuseppe aloud reyes qatar marty sideways utterly sexually jude prayers mccarthy softball blend damien ##gging ##metric wholly erupted lebanese negro revenues tasted comparative teamed transaction labeled maori sovereignty parkway trauma gran malay 121 advancement descendant 2020 buzz salvation inventory symbolic ##making antarctica mps ##gas ##bro mohammed myanmar holt submarines tones ##lman locker patriarch bangkok emerson remarks predators kin afghan confession norwich rental emerge advantages ##zel rca ##hold shortened storms aidan ##matic autonomy compliance ##quet dudley atp ##osis 1803 motto documentation summary 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participant ##tro shire spit freeze necessity ##cos inmates nielsen councillors loaned uncommon omar peasants botanical offspring daniels formations jokes 1794 pioneers sigma licensing ##sus wheelchair polite 1807 liquor pratt trustee ##uta forewings balloon ##zz kilometre camping explicit casually shawn foolish teammates nm hassan carrie judged satisfy vanessa knives selective cnn flowed ##lice eclipse stressed eliza mathematician cease cultivated ##roy commissions browns ##ania destroyers sheridan meadow ##rius minerals ##cial downstream clash gram memoirs ventures baha seymour archie midlands edith fare flynn invite canceled tiles stabbed boulder incorporate amended camden facial mollusk unreleased descriptions yoga grabs 550 raises ramp shiver ##rose coined pioneering tunes qing warwick tops 119 melanie giles ##rous wandered ##inal annexed nov 30th unnamed ##ished organizational airplane normandy stoke whistle blessing violations chased holders shotgun ##ctic outlet reactor ##vik 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mortar structured selfish exports ##jon cds ##him unfinished ##rre mortgage destinations ##nagar canoe solitary buchanan delays magistrate fk ##pling motivation ##lier ##vier recruiting assess ##mouth malik antique 1791 pius rahman reich tub zhou smashed airs galway xii conditioning honduras discharged dexter ##pf lionel 129 debates lemon tiffany volunteered dom dioxide procession devi sic tremendous advertisements colts transferring verdict hanover decommissioned utter relate pac racism ##top beacon limp similarity terra occurrence ant ##how becky capt updates armament richie pal ##graph halloween mayo ##ssen ##bone cara serena fcc dolls obligations ##dling violated lafayette jakarta exploitation ##ime infamous iconic ##lah ##park kitty moody reginald dread spill crystals olivier modeled bluff equilibrium separating notices ordnance extinction onset cosmic attachment sammy expose privy anchored ##bil abbott admits bending baritone emmanuel policeman vaughan winged climax dresses denny 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stimulus impressions reins revolves ##oud ##gned giro honeymoon ##swell criterion ##sms ##uil libyan prefers ##osition 211 preview sucks accusation bursts metaphor diffusion tolerate faye betting cinematographer liturgical specials bitterly humboldt ##ckle flux rattled ##itzer archaeologists odor authorised marshes discretion ##ов alarmed archaic inverse ##leton explorers ##pine drummond tsunami woodlands ##minate ##tland booklet insanity owning insert crafted calculus ##tore receivers ##bt stung ##eca ##nched prevailing travellers eyeing lila graphs ##borne 178 julien ##won morale adaptive therapist erica cw libertarian bowman pitches vita ##ional crook ##ads ##entation caledonia mutiny ##sible 1840s automation ##ß flock ##pia ironic pathology ##imus remarried ##22 joker withstand energies ##att shropshire hostages madeleine tentatively conflicting mateo recipes euros ol mercenaries nico ##ndon albuquerque augmented mythical bel freud ##child cough ##lica 365 freddy lillian genetically nuremberg calder 209 bonn outdoors paste suns urgency vin restraint tyson ##cera ##selle barrage bethlehem kahn ##par mounts nippon barony happier ryu makeshift sheldon blushed castillo barking listener taped bethel fluent headlines pornography rum disclosure sighing mace doubling gunther manly ##plex rt interventions physiological forwards emerges ##tooth ##gny compliment rib recession visibly barge faults connector exquisite prefect ##rlin patio ##cured elevators brandt italics pena 173 wasp satin ea botswana graceful respectable ##jima ##rter ##oic franciscan generates ##dl alfredo disgusting ##olate ##iously sherwood warns cod promo cheryl sino ##ة ##escu twitch ##zhi brownish thom ortiz ##dron densely ##beat carmel reinforce ##bana 187 anastasia downhill vertex contaminated remembrance harmonic homework ##sol fiancee gears olds angelica loft ramsay quiz colliery sevens ##cape autism ##hil walkway ##boats ruben abnormal ounce khmer ##bbe zachary bedside morphology 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prosecutors 186 hem 520 ##xing relaxing remnant romney sorted slalom stefano ulrich ##active exemption folder pauses foliage hitchcock epithet 204 criticisms ##aca ballistic brody hinduism chaotic youths equals ##pala pts thicker analogous capitalist improvised overseeing sinatra ascended beverage ##tl straightforward ##kon curran ##west bois 325 induce surveying emperors sax unpopular ##kk cartoonist fused ##mble unto ##yuki localities ##cko ##ln darlington slain academie lobbying sediment puzzles ##grass defiance dickens manifest tongues alumnus arbor coincide 184 appalachian mustafa examiner cabaret traumatic yves bracelet draining heroin magnum baths odessa consonants mitsubishi ##gua kellan vaudeville ##fr joked null straps probation ##ław ceded interfaces ##pas ##zawa blinding viet 224 rothschild museo 640 huddersfield ##vr tactic ##storm brackets dazed incorrectly ##vu reg glazed fearful manifold benefited irony ##sun stumbling ##rte willingness balkans mei wraps ##aba injected ##lea gu syed harmless ##hammer bray takeoff poppy timor cardboard astronaut purdue weeping southbound cursing stalls diagonal ##neer lamar bryce comte weekdays harrington ##uba negatively ##see lays grouping ##cken ##henko affirmed halle modernist ##lai hodges smelling aristocratic baptized dismiss justification oilers ##now coupling qin snack healer ##qing gardener layla battled formulated stephenson gravitational ##gill ##jun 1768 granny coordinating suites ##cd ##ioned monarchs ##cote ##hips sep blended apr barrister deposition fia mina policemen paranoid ##pressed churchyard covert crumpled creep abandoning tr transmit conceal barr understands readiness spire ##cology ##enia ##erry 610 startling unlock vida bowled slots ##nat ##islav spaced trusting admire rig ##ink slack ##70 mv 207 casualty ##wei classmates ##odes ##rar ##rked amherst furnished evolve foundry menace mead ##lein flu wesleyan ##kled monterey webber ##vos wil ##mith ##на bartholomew justices restrained ##cke amenities 191 mediated sewage trenches ml mainz ##thus 1800s ##cula ##inski caine bonding 213 converts spheres superseded marianne crypt sweaty ensign historia ##br spruce ##post ##ask forks thoughtfully yukon pamphlet ames ##uter karma ##yya bryn negotiation sighs incapable ##mbre ##ntial actresses taft ##mill luce prevailed ##amine 1773 motionless envoy testify investing sculpted instructors provence kali cullen horseback ##while goodwin ##jos gaa norte ##ldon modify wavelength abd 214 skinned sprinter forecast scheduling marries squared tentative ##chman boer ##isch bolts swap fisherman assyrian impatiently guthrie martins murdoch 194 tanya nicely dolly lacy med ##45 syn decks fashionable millionaire ##ust surfing ##ml ##ision heaved tammy consulate attendees routinely 197 fuse saxophonist backseat malaya ##lord scowl tau ##ishly 193 sighted steaming ##rks 303 911 ##holes ##hong ching ##wife bless conserved jurassic stacey unix zion chunk rigorous blaine 198 peabody slayer dismay brewers nz ##jer det ##glia glover postwar int penetration sylvester imitation vertically airlift heiress knoxville viva ##uin 390 macon ##rim ##fighter ##gonal janice ##orescence ##wari marius belongings leicestershire 196 blanco inverted preseason sanity sobbing ##due ##elt ##dled collingwood regeneration flickering shortest ##mount ##osi feminism ##lat sherlock cabinets fumbled northbound precedent snaps ##mme researching ##akes guillaume insights manipulated vapor neighbour sap gangster frey f1 stalking scarcely callie barnett tendencies audi doomed assessing slung panchayat ambiguous bartlett ##etto distributing violating wolverhampton ##hetic swami histoire ##urus liable pounder groin hussain larsen popping surprises ##atter vie curt ##station mute relocate musicals authorization richter ##sef immortality tna bombings ##press deteriorated yiddish ##acious robbed colchester cs pmid ao verified balancing apostle swayed recognizable oxfordshire retention nottinghamshire contender 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proliferation sato bela analyzing parting ##gor awakened ##isman huddled secrecy ##kling hush gentry 540 dungeons ##ego coasts ##utz sacrificed ##chule landowner mutually prevalence programmer adolescent disrupted seaside gee trusts vamp georgie ##nesian ##iol schedules sindh ##market etched hm sparse bey beaux scratching gliding unidentified 216 collaborating gems jesuits oro accumulation shaping mbe anal ##xin 231 enthusiasts newscast ##egan janata dewey parkinson 179 ankara biennial towering dd inconsistent 950 ##chet thriving terminate cabins furiously eats advocating donkey marley muster phyllis leiden ##user grassland glittering iucn loneliness 217 memorandum armenians ##ddle popularized rhodesia 60s lame ##illon sans bikini header orbits ##xx ##finger ##ulator sharif spines biotechnology strolled naughty yates ##wire fremantle milo ##mour abducted removes ##atin humming wonderland ##chrome ##ester hume pivotal ##rates armand grams believers elector rte apron bis scraped ##yria 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greedy paradox yokohama dowager trunks ventured ##gement gupta vilnius olaf ##thest crimean hopper ##ej progressively arturo mouthed arrondissement ##fusion rubin simulcast oceania ##orum ##stra ##rred busiest intensely navigator cary ##vine ##hini ##bies fife rowe rowland posing insurgents shafts lawsuits activate conor inward culturally garlic 265 ##eering eclectic ##hui ##kee ##nl furrowed vargas meteorological rendezvous ##aus culinary commencement ##dition quota ##notes mommy salaries overlapping mule ##iology ##mology sums wentworth ##isk ##zione mainline subgroup ##illy hack plaintiff verdi bulb differentiation engagements multinational supplemented bertrand caller regis ##naire ##sler ##arts ##imated blossom propagation kilometer viaduct vineyards ##uate beckett optimization golfer songwriters seminal semitic thud volatile evolving ridley ##wley trivial distributions scandinavia jiang ##ject wrestled insistence ##dio emphasizes napkin ##ods adjunct rhyme ##ricted ##eti hopeless surrounds tremble 32nd smoky ##ntly oils medicinal padded steer wilkes 219 255 concessions hue uniquely blinded landon yahoo ##lane hendrix commemorating dex specify chicks ##ggio intercity 1400 morley ##torm highlighting ##oting pang oblique stalled ##liner flirting newborn 1769 bishopric shaved 232 currie ##ush dharma spartan ##ooped favorites smug novella sirens abusive creations espana ##lage paradigm semiconductor sheen ##rdo ##yen ##zak nrl renew ##pose ##tur adjutant marches norma ##enity ineffective weimar grunt ##gat lordship plotting expenditure infringement lbs refrain av mimi mistakenly postmaster 1771 ##bara ras motorsports tito 199 subjective ##zza bully stew ##kaya prescott 1a ##raphic ##zam bids styling paranormal reeve sneaking exploding katz akbar migrant syllables indefinitely ##ogical destroys replaces applause ##phine pest ##fide 218 articulated bertie ##thing ##cars ##ptic courtroom crowley aesthetics cummings tehsil hormones titanic dangerously ##ibe stadion 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238 weakening blackish ding mcgee quo ##rling northernmost xx manpower greed sampson clicking ##ange ##horpe ##inations ##roving torre ##eptive ##moral symbolism 38th asshole meritorious outfits splashed biographies sprung astros ##tale 302 737 filly raoul nw tokugawa linden clubhouse ##apa tracts romano ##pio putin tags ##note chained dickson gunshot moe gunn rashid ##tails zipper ##bas ##nea contrasted ##ply ##udes plum pharaoh ##pile aw comedies ingrid sandwiches subdivisions 1100 mariana nokia kamen hz delaney veto herring ##words possessive outlines ##roup siemens stairwell rc gallantry messiah palais yells 233 zeppelin ##dm bolivar ##cede smackdown mckinley ##mora ##yt muted geologic finely unitary avatar hamas maynard rees bog contrasting ##rut liv chico disposition pixel ##erate becca dmitry yeshiva narratives ##lva ##ulton mercenary sharpe tempered navigate stealth amassed keynes ##lini untouched ##rrie havoc lithium ##fighting abyss graf southward wolverine balloons implements ngos transitions ##icum ambushed concacaf dormant economists ##dim costing csi rana universite boulders verity ##llon collin mellon misses cypress fluorescent lifeless spence ##ulla crewe shepard pak revelations ##م jolly gibbons paw ##dro ##quel freeing ##test shack fries palatine ##51 ##hiko accompaniment cruising recycled ##aver erwin sorting synthesizers dyke realities sg strides enslaved wetland ##ghan competence gunpowder grassy maroon reactors objection ##oms carlson gearbox macintosh radios shelton ##sho clergyman prakash 254 mongols trophies oricon 228 stimuli twenty20 cantonese cortes mirrored ##saurus bhp cristina melancholy ##lating enjoyable nuevo ##wny downfall schumacher ##ind banging lausanne rumbled paramilitary reflex ax amplitude migratory ##gall ##ups midi barnard lastly sherry ##hp ##nall keystone ##kra carleton slippery ##53 coloring foe socket otter ##rgos mats ##tose consultants bafta bison topping ##km 490 primal abandonment transplant atoll hideous mort pained reproduced tae howling ##turn unlawful billionaire hotter poised lansing ##chang dinamo retro messing nfc domesday ##mina blitz timed ##athing ##kley ascending gesturing ##izations signaled tis chinatown mermaid savanna jameson ##aint catalina ##pet ##hers cochrane cy chatting ##kus alerted computation mused noelle majestic mohawk campo octagonal ##sant ##hend 241 aspiring ##mart comprehend iona paralyzed shimmering swindon rhone ##eley reputed configurations pitchfork agitation francais gillian lipstick ##ilo outsiders pontifical resisting bitterness sewer rockies ##edd ##ucher misleading 1756 exiting galloway ##nging risked ##heart 246 commemoration schultz ##rka integrating ##rsa poses shrieked ##weiler guineas gladys jerking owls goldsmith nightly penetrating ##unced lia ##33 ignited betsy ##aring ##thorpe follower vigorously ##rave coded kiran knit zoology tbilisi ##28 ##bered repository govt deciduous dino growling ##bba enhancement unleashed chanting pussy biochemistry ##eric kettle repression toxicity nrhp ##arth ##kko ##bush ernesto commended outspoken 242 mca parchment sms kristen ##aton bisexual raked glamour navajo a2 conditioned showcased ##hma spacious youthful ##esa usl appliances junta brest layne conglomerate enchanted chao loosened picasso circulating inspect montevideo ##centric ##kti piazza spurred ##aith bari freedoms poultry stamford lieu ##ect indigo sarcastic bahia stump attach dvds frankenstein lille approx scriptures pollen ##script nmi overseen ##ivism tides proponent newmarket inherit milling ##erland centralized ##rou distributors credentials drawers abbreviation ##lco ##xon downing uncomfortably ripe ##oes erase franchises ##ever populace ##bery ##khar decomposition pleas ##tet daryl sabah ##stle ##wide fearless genie lesions annette ##ogist oboe appendix nair dripped petitioned maclean mosquito parrot rpg hampered 1648 operatic reservoirs ##tham irrelevant jolt summarized ##fp medallion ##taff ##− clawed harlow 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================================================ FILE: extras/BLIP/models/blip.py ================================================ ''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li ''' import warnings warnings.filterwarnings("ignore") from extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel from transformers import BertTokenizer import torch from torch import nn import torch.nn.functional as F import os from urllib.parse import urlparse from timm.models.hub import download_cached_file class BLIP_Base(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 224, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) self.tokenizer = init_tokenizer() med_config = BertConfig.from_json_file(med_config) med_config.encoder_width = vision_width self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) def forward(self, image, caption, mode): assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" text = self.tokenizer(caption, return_tensors="pt").to(image.device) if mode=='image': # return image features image_embeds = self.visual_encoder(image) return image_embeds elif mode=='text': # return text features text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') return text_output.last_hidden_state elif mode=='multimodal': # return multimodel features image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) text.input_ids[:,0] = self.tokenizer.enc_token_id output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True, ) return output.last_hidden_state class BLIP_Decoder(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 384, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, prompt = 'a picture of ', ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) self.tokenizer = init_tokenizer() med_config = BertConfig.from_json_file(med_config) med_config.encoder_width = vision_width self.text_decoder = BertLMHeadModel(config=med_config) self.prompt = prompt self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 def forward(self, image, caption): image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) text.input_ids[:,0] = self.tokenizer.bos_token_id decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) decoder_targets[:,:self.prompt_length] = -100 decoder_output = self.text_decoder(text.input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, labels = decoder_targets, return_dict = True, ) loss_lm = decoder_output.loss return loss_lm def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): image_embeds = self.visual_encoder(image) if not sample: image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} prompt = [self.prompt] * image.size(0) input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) input_ids[:,0] = self.tokenizer.bos_token_id input_ids = input_ids[:, :-1] if sample: #nucleus sampling outputs = self.text_decoder.generate(input_ids=input_ids, max_length=max_length, min_length=min_length, do_sample=True, top_p=top_p, num_return_sequences=1, eos_token_id=self.tokenizer.sep_token_id, pad_token_id=self.tokenizer.pad_token_id, repetition_penalty=1.1, **model_kwargs) else: #beam search outputs = self.text_decoder.generate(input_ids=input_ids, max_length=max_length, min_length=min_length, num_beams=num_beams, eos_token_id=self.tokenizer.sep_token_id, pad_token_id=self.tokenizer.pad_token_id, repetition_penalty=repetition_penalty, **model_kwargs) captions = [] for output in outputs: caption = self.tokenizer.decode(output, skip_special_tokens=True) captions.append(caption[len(self.prompt):]) return captions def blip_decoder(pretrained='',**kwargs): model = BLIP_Decoder(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) assert(len(msg.missing_keys)==0) return model def blip_feature_extractor(pretrained='',**kwargs): model = BLIP_Base(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) assert(len(msg.missing_keys)==0) return model def init_tokenizer(): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "bert_tokenizer") tokenizer = BertTokenizer.from_pretrained(tokenizer_path) tokenizer.add_special_tokens({'bos_token':'[DEC]'}) tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] return tokenizer def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): assert vit in ['base', 'large'], "vit parameter must be base or large" if vit=='base': vision_width = 768 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0 or drop_path_rate ) elif vit=='large': vision_width = 1024 visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, drop_path_rate=0.1 or drop_path_rate ) return visual_encoder, vision_width def is_url(url_or_filename): parsed = urlparse(url_or_filename) return parsed.scheme in ("http", "https") def load_checkpoint(model,url_or_filename): if is_url(url_or_filename): cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) elif os.path.isfile(url_or_filename): checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) else: raise RuntimeError('checkpoint url or path is invalid') state_dict = checkpoint['model'] state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m) for key in model.state_dict().keys(): if key in state_dict.keys(): if state_dict[key].shape!=model.state_dict()[key].shape: del state_dict[key] msg = model.load_state_dict(state_dict,strict=False) print('load checkpoint from %s'%url_or_filename) return model,msg ================================================ FILE: extras/BLIP/models/blip_itm.py ================================================ from extras.BLIP.models.med import BertConfig, BertModel from transformers import BertTokenizer import torch from torch import nn import torch.nn.functional as F from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint class BLIP_ITM(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 384, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, embed_dim = 256, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) self.tokenizer = init_tokenizer() med_config = BertConfig.from_json_file(med_config) med_config.encoder_width = vision_width self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) text_width = self.text_encoder.config.hidden_size self.vision_proj = nn.Linear(vision_width, embed_dim) self.text_proj = nn.Linear(text_width, embed_dim) self.itm_head = nn.Linear(text_width, 2) def forward(self, image, caption, match_head='itm'): image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(image.device) if match_head=='itm': output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True, ) itm_output = self.itm_head(output.last_hidden_state[:,0,:]) return itm_output elif match_head=='itc': text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) sim = image_feat @ text_feat.t() return sim def blip_itm(pretrained='',**kwargs): model = BLIP_ITM(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) assert(len(msg.missing_keys)==0) return model ================================================ FILE: extras/BLIP/models/blip_nlvr.py ================================================ from extras.BLIP.models.med import BertConfig from extras.BLIP.models.nlvr_encoder import BertModel from extras.BLIP.models.vit import interpolate_pos_embed from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url from timm.models.hub import download_cached_file import torch from torch import nn import torch.nn.functional as F from transformers import BertTokenizer import numpy as np import os class BLIP_NLVR(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 480, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) self.tokenizer = init_tokenizer() med_config = BertConfig.from_json_file(med_config) med_config.encoder_width = vision_width self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) self.cls_head = nn.Sequential( nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size), nn.ReLU(), nn.Linear(self.text_encoder.config.hidden_size, 2) ) def forward(self, image, text, targets, train=True): image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0)) text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) text.input_ids[:,0] = self.tokenizer.enc_token_id output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, encoder_hidden_states = [image0_embeds,image1_embeds], encoder_attention_mask = [image_atts[:image0_embeds.size(0)], image_atts[image0_embeds.size(0):]], return_dict = True, ) hidden_state = output.last_hidden_state[:,0,:] prediction = self.cls_head(hidden_state) if train: loss = F.cross_entropy(prediction, targets) return loss else: return prediction def blip_nlvr(pretrained='',**kwargs): model = BLIP_NLVR(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) print("missing keys:") print(msg.missing_keys) return model def load_checkpoint(model,url_or_filename): if is_url(url_or_filename): cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) elif os.path.isfile(url_or_filename): checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) else: raise RuntimeError('checkpoint url or path is invalid') state_dict = checkpoint['model'] state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) for key in list(state_dict.keys()): if 'crossattention.self.' in key: new_key0 = key.replace('self','self0') new_key1 = key.replace('self','self1') state_dict[new_key0] = state_dict[key] state_dict[new_key1] = state_dict[key] elif 'crossattention.output.dense.' in key: new_key0 = key.replace('dense','dense0') new_key1 = key.replace('dense','dense1') state_dict[new_key0] = state_dict[key] state_dict[new_key1] = state_dict[key] msg = model.load_state_dict(state_dict,strict=False) print('load checkpoint from %s'%url_or_filename) return model,msg ================================================ FILE: extras/BLIP/models/blip_pretrain.py ================================================ ''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li ''' from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel from transformers import BertTokenizer import transformers transformers.logging.set_verbosity_error() import torch from torch import nn import torch.nn.functional as F from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint class BLIP_Pretrain(nn.Module): def __init__(self, med_config = 'configs/bert_config.json', image_size = 224, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, embed_dim = 256, queue_size = 57600, momentum = 0.995, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) if vit=='base': checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", map_location="cpu", check_hash=True) state_dict = checkpoint["model"] msg = self.visual_encoder.load_state_dict(state_dict,strict=False) elif vit=='large': from timm.models.helpers import load_custom_pretrained from timm.models.vision_transformer import default_cfgs load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k']) self.tokenizer = init_tokenizer() encoder_config = BertConfig.from_json_file(med_config) encoder_config.encoder_width = vision_width self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False) self.text_encoder.resize_token_embeddings(len(self.tokenizer)) text_width = self.text_encoder.config.hidden_size self.vision_proj = nn.Linear(vision_width, embed_dim) self.text_proj = nn.Linear(text_width, embed_dim) self.itm_head = nn.Linear(text_width, 2) # create momentum encoders self.visual_encoder_m, vision_width = create_vit(vit,image_size) self.vision_proj_m = nn.Linear(vision_width, embed_dim) self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False) self.text_proj_m = nn.Linear(text_width, embed_dim) self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], [self.vision_proj,self.vision_proj_m], [self.text_encoder,self.text_encoder_m], [self.text_proj,self.text_proj_m], ] self.copy_params() # create the queue self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) self.image_queue = nn.functional.normalize(self.image_queue, dim=0) self.text_queue = nn.functional.normalize(self.text_queue, dim=0) self.queue_size = queue_size self.momentum = momentum self.temp = nn.Parameter(0.07*torch.ones([])) # create the decoder decoder_config = BertConfig.from_json_file(med_config) decoder_config.encoder_width = vision_width self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config) self.text_decoder.resize_token_embeddings(len(self.tokenizer)) tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention') def forward(self, image, caption, alpha): with torch.no_grad(): self.temp.clamp_(0.001,0.5) image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, return_tensors="pt").to(image.device) text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) # get momentum features with torch.no_grad(): self._momentum_update() image_embeds_m = self.visual_encoder_m(image) image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) sim_i2t_m = image_feat_m @ text_feat_all / self.temp sim_t2i_m = text_feat_m @ image_feat_all / self.temp sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device) sim_targets.fill_diagonal_(1) sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets sim_i2t = image_feat @ text_feat_all / self.temp sim_t2i = text_feat @ image_feat_all / self.temp loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() loss_ita = (loss_i2t+loss_t2i)/2 self._dequeue_and_enqueue(image_feat_m, text_feat_m) ###============== Image-text Matching ===================### encoder_input_ids = text.input_ids.clone() encoder_input_ids[:,0] = self.tokenizer.enc_token_id # forward the positve image-text pair bs = image.size(0) output_pos = self.text_encoder(encoder_input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True, ) with torch.no_grad(): weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 weights_t2i.fill_diagonal_(0) weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4 weights_i2t.fill_diagonal_(0) # select a negative image for each text image_embeds_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_t2i[b], 1).item() image_embeds_neg.append(image_embeds[neg_idx]) image_embeds_neg = torch.stack(image_embeds_neg,dim=0) # select a negative text for each image text_ids_neg = [] text_atts_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_i2t[b], 1).item() text_ids_neg.append(encoder_input_ids[neg_idx]) text_atts_neg.append(text.attention_mask[neg_idx]) text_ids_neg = torch.stack(text_ids_neg,dim=0) text_atts_neg = torch.stack(text_atts_neg,dim=0) text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) image_atts_all = torch.cat([image_atts,image_atts],dim=0) output_neg = self.text_encoder(text_ids_all, attention_mask = text_atts_all, encoder_hidden_states = image_embeds_all, encoder_attention_mask = image_atts_all, return_dict = True, ) vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) vl_output = self.itm_head(vl_embeddings) itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], dim=0).to(image.device) loss_itm = F.cross_entropy(vl_output, itm_labels) ##================= LM ========================## decoder_input_ids = text.input_ids.clone() decoder_input_ids[:,0] = self.tokenizer.bos_token_id decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) decoder_output = self.text_decoder(decoder_input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, labels = decoder_targets, return_dict = True, ) loss_lm = decoder_output.loss return loss_ita, loss_itm, loss_lm @torch.no_grad() def copy_params(self): for model_pair in self.model_pairs: for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): param_m.data.copy_(param.data) # initialize param_m.requires_grad = False # not update by gradient @torch.no_grad() def _momentum_update(self): for model_pair in self.model_pairs: for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) @torch.no_grad() def _dequeue_and_enqueue(self, image_feat, text_feat): # gather keys before updating queue image_feats = concat_all_gather(image_feat) text_feats = concat_all_gather(text_feat) batch_size = image_feats.shape[0] ptr = int(self.queue_ptr) assert self.queue_size % batch_size == 0 # for simplicity # replace the keys at ptr (dequeue and enqueue) self.image_queue[:, ptr:ptr + batch_size] = image_feats.T self.text_queue[:, ptr:ptr + batch_size] = text_feats.T ptr = (ptr + batch_size) % self.queue_size # move pointer self.queue_ptr[0] = ptr def blip_pretrain(**kwargs): model = BLIP_Pretrain(**kwargs) return model @torch.no_grad() def concat_all_gather(tensor): """ Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. """ tensors_gather = [torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())] torch.distributed.all_gather(tensors_gather, tensor, async_op=False) output = torch.cat(tensors_gather, dim=0) return output from typing import List def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str): uninitialized_encoder_weights: List[str] = [] if decoder.__class__ != encoder.__class__: print( f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." ) def tie_encoder_to_decoder_recursively( decoder_pointer: nn.Module, encoder_pointer: nn.Module, module_name: str, uninitialized_encoder_weights: List[str], skip_key: str, depth=0, ): assert isinstance(decoder_pointer, nn.Module) and isinstance( encoder_pointer, nn.Module ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" if hasattr(decoder_pointer, "weight") and skip_key not in module_name: assert hasattr(encoder_pointer, "weight") encoder_pointer.weight = decoder_pointer.weight if hasattr(decoder_pointer, "bias"): assert hasattr(encoder_pointer, "bias") encoder_pointer.bias = decoder_pointer.bias print(module_name+' is tied') return encoder_modules = encoder_pointer._modules decoder_modules = decoder_pointer._modules if len(decoder_modules) > 0: assert ( len(encoder_modules) > 0 ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()]) encoder_layer_pos = 0 for name, module in decoder_modules.items(): if name.isdigit(): encoder_name = str(int(name) + encoder_layer_pos) decoder_name = name if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len( encoder_modules ) != len(decoder_modules): # this can happen if the name corresponds to the position in a list module list of layers # in this case the decoder has added a cross-attention that the encoder does not have # thus skip this step and subtract one layer pos from encoder encoder_layer_pos -= 1 continue elif name not in encoder_modules: continue elif depth > 500: raise ValueError( "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." ) else: decoder_name = encoder_name = name tie_encoder_to_decoder_recursively( decoder_modules[decoder_name], encoder_modules[encoder_name], module_name + "/" + name, uninitialized_encoder_weights, skip_key, depth=depth + 1, ) all_encoder_weights.remove(module_name + "/" + encoder_name) uninitialized_encoder_weights += list(all_encoder_weights) # tie weights recursively tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key) ================================================ FILE: extras/BLIP/models/blip_retrieval.py ================================================ from extras.BLIP.models.med import BertConfig, BertModel from transformers import BertTokenizer import torch from torch import nn import torch.nn.functional as F from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint class BLIP_Retrieval(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 384, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, embed_dim = 256, queue_size = 57600, momentum = 0.995, negative_all_rank = False, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) self.tokenizer = init_tokenizer() med_config = BertConfig.from_json_file(med_config) med_config.encoder_width = vision_width self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) text_width = self.text_encoder.config.hidden_size self.vision_proj = nn.Linear(vision_width, embed_dim) self.text_proj = nn.Linear(text_width, embed_dim) self.itm_head = nn.Linear(text_width, 2) # create momentum encoders self.visual_encoder_m, vision_width = create_vit(vit,image_size) self.vision_proj_m = nn.Linear(vision_width, embed_dim) self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False) self.text_proj_m = nn.Linear(text_width, embed_dim) self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], [self.vision_proj,self.vision_proj_m], [self.text_encoder,self.text_encoder_m], [self.text_proj,self.text_proj_m], ] self.copy_params() # create the queue self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) self.register_buffer("idx_queue", torch.full((1,queue_size),-100)) self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long)) self.image_queue = nn.functional.normalize(self.image_queue, dim=0) self.text_queue = nn.functional.normalize(self.text_queue, dim=0) self.queue_size = queue_size self.momentum = momentum self.temp = nn.Parameter(0.07*torch.ones([])) self.negative_all_rank = negative_all_rank def forward(self, image, caption, alpha, idx): with torch.no_grad(): self.temp.clamp_(0.001,0.5) image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(image.device) text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) ###============== Image-text Contrastive Learning ===================### idx = idx.view(-1,1) idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1) pos_idx = torch.eq(idx, idx_all).float() sim_targets = pos_idx / pos_idx.sum(1,keepdim=True) # get momentum features with torch.no_grad(): self._momentum_update() image_embeds_m = self.visual_encoder_m(image) image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, return_dict = True, mode = 'text') text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets sim_i2t = image_feat @ text_feat_m_all / self.temp sim_t2i = text_feat @ image_feat_m_all / self.temp loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() loss_ita = (loss_i2t+loss_t2i)/2 idxs = concat_all_gather(idx) self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs) ###============== Image-text Matching ===================### encoder_input_ids = text.input_ids.clone() encoder_input_ids[:,0] = self.tokenizer.enc_token_id # forward the positve image-text pair bs = image.size(0) output_pos = self.text_encoder(encoder_input_ids, attention_mask = text.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True, ) if self.negative_all_rank: # compute sample similarity with torch.no_grad(): mask = torch.eq(idx, idxs.t()) image_feat_world = concat_all_gather(image_feat) text_feat_world = concat_all_gather(text_feat) sim_i2t = image_feat @ text_feat_world.t() / self.temp sim_t2i = text_feat @ image_feat_world.t() / self.temp weights_i2t = F.softmax(sim_i2t,dim=1) weights_i2t.masked_fill_(mask, 0) weights_t2i = F.softmax(sim_t2i,dim=1) weights_t2i.masked_fill_(mask, 0) image_embeds_world = all_gather_with_grad(image_embeds) # select a negative image (from all ranks) for each text image_embeds_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_t2i[b], 1).item() image_embeds_neg.append(image_embeds_world[neg_idx]) image_embeds_neg = torch.stack(image_embeds_neg,dim=0) # select a negative text (from all ranks) for each image input_ids_world = concat_all_gather(encoder_input_ids) att_mask_world = concat_all_gather(text.attention_mask) text_ids_neg = [] text_atts_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_i2t[b], 1).item() text_ids_neg.append(input_ids_world[neg_idx]) text_atts_neg.append(att_mask_world[neg_idx]) else: with torch.no_grad(): mask = torch.eq(idx, idx.t()) sim_i2t = image_feat @ text_feat.t() / self.temp sim_t2i = text_feat @ image_feat.t() / self.temp weights_i2t = F.softmax(sim_i2t,dim=1) weights_i2t.masked_fill_(mask, 0) weights_t2i = F.softmax(sim_t2i,dim=1) weights_t2i.masked_fill_(mask, 0) # select a negative image (from same rank) for each text image_embeds_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_t2i[b], 1).item() image_embeds_neg.append(image_embeds[neg_idx]) image_embeds_neg = torch.stack(image_embeds_neg,dim=0) # select a negative text (from same rank) for each image text_ids_neg = [] text_atts_neg = [] for b in range(bs): neg_idx = torch.multinomial(weights_i2t[b], 1).item() text_ids_neg.append(encoder_input_ids[neg_idx]) text_atts_neg.append(text.attention_mask[neg_idx]) text_ids_neg = torch.stack(text_ids_neg,dim=0) text_atts_neg = torch.stack(text_atts_neg,dim=0) text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) image_atts_all = torch.cat([image_atts,image_atts],dim=0) output_neg = self.text_encoder(text_ids_all, attention_mask = text_atts_all, encoder_hidden_states = image_embeds_all, encoder_attention_mask = image_atts_all, return_dict = True, ) vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) vl_output = self.itm_head(vl_embeddings) itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], dim=0).to(image.device) loss_itm = F.cross_entropy(vl_output, itm_labels) return loss_ita, loss_itm @torch.no_grad() def copy_params(self): for model_pair in self.model_pairs: for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): param_m.data.copy_(param.data) # initialize param_m.requires_grad = False # not update by gradient @torch.no_grad() def _momentum_update(self): for model_pair in self.model_pairs: for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) @torch.no_grad() def _dequeue_and_enqueue(self, image_feat, text_feat, idxs): # gather keys before updating queue image_feats = concat_all_gather(image_feat) text_feats = concat_all_gather(text_feat) batch_size = image_feats.shape[0] ptr = int(self.ptr_queue) assert self.queue_size % batch_size == 0 # for simplicity # replace the keys at ptr (dequeue and enqueue) self.image_queue[:, ptr:ptr + batch_size] = image_feats.T self.text_queue[:, ptr:ptr + batch_size] = text_feats.T self.idx_queue[:, ptr:ptr + batch_size] = idxs.T ptr = (ptr + batch_size) % self.queue_size # move pointer self.ptr_queue[0] = ptr def blip_retrieval(pretrained='',**kwargs): model = BLIP_Retrieval(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) print("missing keys:") print(msg.missing_keys) return model @torch.no_grad() def concat_all_gather(tensor): """ Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. """ tensors_gather = [torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())] torch.distributed.all_gather(tensors_gather, tensor, async_op=False) output = torch.cat(tensors_gather, dim=0) return output class GatherLayer(torch.autograd.Function): """ Gather tensors from all workers with support for backward propagation: This implementation does not cut the gradients as torch.distributed.all_gather does. """ @staticmethod def forward(ctx, x): output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())] torch.distributed.all_gather(output, x) return tuple(output) @staticmethod def backward(ctx, *grads): all_gradients = torch.stack(grads) torch.distributed.all_reduce(all_gradients) return all_gradients[torch.distributed.get_rank()] def all_gather_with_grad(tensors): """ Performs all_gather operation on the provided tensors. Graph remains connected for backward grad computation. """ # Queue the gathered tensors world_size = torch.distributed.get_world_size() # There is no need for reduction in the single-proc case if world_size == 1: return tensors tensor_all = GatherLayer.apply(tensors) return torch.cat(tensor_all, dim=0) ================================================ FILE: extras/BLIP/models/blip_vqa.py ================================================ from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint import torch from torch import nn import torch.nn.functional as F from transformers import BertTokenizer import numpy as np class BLIP_VQA(nn.Module): def __init__(self, med_config = 'configs/med_config.json', image_size = 480, vit = 'base', vit_grad_ckpt = False, vit_ckpt_layer = 0, ): """ Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input image size vit (str): model size of vision transformer """ super().__init__() self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) self.tokenizer = init_tokenizer() encoder_config = BertConfig.from_json_file(med_config) encoder_config.encoder_width = vision_width self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) decoder_config = BertConfig.from_json_file(med_config) self.text_decoder = BertLMHeadModel(config=decoder_config) def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): image_embeds = self.visual_encoder(image) image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, return_tensors="pt").to(image.device) question.input_ids[:,0] = self.tokenizer.enc_token_id if train: ''' n: number of answers for each question weights: weight for each answer ''' answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) answer.input_ids[:,0] = self.tokenizer.bos_token_id answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) question_output = self.text_encoder(question.input_ids, attention_mask = question.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True) question_states = [] question_atts = [] for b, n in enumerate(n): question_states += [question_output.last_hidden_state[b]]*n question_atts += [question.attention_mask[b]]*n question_states = torch.stack(question_states,0) question_atts = torch.stack(question_atts,0) answer_output = self.text_decoder(answer.input_ids, attention_mask = answer.attention_mask, encoder_hidden_states = question_states, encoder_attention_mask = question_atts, labels = answer_targets, return_dict = True, reduction = 'none', ) loss = weights * answer_output.loss loss = loss.sum()/image.size(0) return loss else: question_output = self.text_encoder(question.input_ids, attention_mask = question.attention_mask, encoder_hidden_states = image_embeds, encoder_attention_mask = image_atts, return_dict = True) if inference=='generate': num_beams = 3 question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) outputs = self.text_decoder.generate(input_ids=bos_ids, max_length=10, min_length=1, num_beams=num_beams, eos_token_id=self.tokenizer.sep_token_id, pad_token_id=self.tokenizer.pad_token_id, **model_kwargs) answers = [] for output in outputs: answer = self.tokenizer.decode(output, skip_special_tokens=True) answers.append(answer) return answers elif inference=='rank': max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, answer.input_ids, answer.attention_mask, k_test) return max_ids def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): num_ques = question_states.size(0) start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token start_output = self.text_decoder(start_ids, encoder_hidden_states = question_states, encoder_attention_mask = question_atts, return_dict = True, reduction = 'none') logits = start_output.logits[:,0,:] # first token's logit # topk_probs: top-k probability # topk_ids: [num_question, k] answer_first_token = answer_ids[:,1] prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) topk_probs, topk_ids = prob_first_token.topk(k,dim=1) # answer input: [num_question*k, answer_len] input_ids = [] input_atts = [] for b, topk_id in enumerate(topk_ids): input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) input_ids = torch.cat(input_ids,dim=0) input_atts = torch.cat(input_atts,dim=0) targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) # repeat encoder's output for top-k answers question_states = tile(question_states, 0, k) question_atts = tile(question_atts, 0, k) output = self.text_decoder(input_ids, attention_mask = input_atts, encoder_hidden_states = question_states, encoder_attention_mask = question_atts, labels = targets_ids, return_dict = True, reduction = 'none') log_probs_sum = -output.loss log_probs_sum = log_probs_sum.view(num_ques,k) max_topk_ids = log_probs_sum.argmax(dim=1) max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] return max_ids def blip_vqa(pretrained='',**kwargs): model = BLIP_VQA(**kwargs) if pretrained: model,msg = load_checkpoint(model,pretrained) # assert(len(msg.missing_keys)==0) return model def tile(x, dim, n_tile): init_dim = x.size(dim) repeat_idx = [1] * x.dim() repeat_idx[dim] = n_tile x = x.repeat(*(repeat_idx)) order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) return torch.index_select(x, dim, order_index.to(x.device)) ================================================ FILE: extras/BLIP/models/med.py ================================================ ''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li * Based on huggingface code base * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert ''' import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple import torch from torch import Tensor, device, dtype, nn import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss import torch.nn.functional as F from transformers.activations import ACT2FN from transformers.file_utils import ( ModelOutput, ) from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.modeling_utils import ( PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.utils import logging from transformers.models.bert.configuration_bert import BertConfig logger = logging.get_logger(__name__) class BertEmbeddings(nn.Module): """Construct the embeddings from word and position embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.config = config def forward( self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) embeddings = inputs_embeds if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config, is_cross_attention): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads) ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) if is_cross_attention: self.key = nn.Linear(config.encoder_width, self.all_head_size) self.value = nn.Linear(config.encoder_width, self.all_head_size) else: self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.save_attention = False def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = hidden_states.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) if is_cross_attention and self.save_attention: self.save_attention_map(attention_probs) attention_probs.register_hook(self.save_attn_gradients) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs_dropped = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs_dropped = attention_probs_dropped * head_mask context_layer = torch.matmul(attention_probs_dropped, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) outputs = outputs + (past_key_value,) return outputs class BertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertAttention(nn.Module): def __init__(self, config, is_cross_attention=False): super().__init__() self.self = BertSelfAttention(config, is_cross_attention) self.output = BertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class BertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config, layer_num): super().__init__() self.config = config self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = BertAttention(config) self.layer_num = layer_num if self.config.add_cross_attention: self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, mode=None, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] if mode=='multimodal': assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class BertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, mode='multimodal', ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i in range(self.config.num_hidden_layers): layer_module = self.layer[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warn( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, mode=mode, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, mode=mode, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class BertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = BertLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class BertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BertConfig base_model_prefix = "bert" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() class BertModel(BertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) if add_pooling_layer else None self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: """ Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: attention_mask (:obj:`torch.Tensor`): Mask with ones indicating tokens to attend to, zeros for tokens to ignore. input_shape (:obj:`Tuple[int]`): The shape of the input to the model. device: (:obj:`torch.device`): The device of the input to the model. Returns: :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. """ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] elif attention_mask.dim() == 2: # Provided a padding mask of dimensions [batch_size, seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] if is_decoder: batch_size, seq_length = input_shape seq_ids = torch.arange(seq_length, device=device) causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] # in case past_key_values are used we need to add a prefix ones mask to the causal mask # causal and attention masks must have same type with pytorch version < 1.3 causal_mask = causal_mask.to(attention_mask.dtype) if causal_mask.shape[1] < attention_mask.shape[1]: prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] causal_mask = torch.cat( [ torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), causal_mask, ], axis=-1, ) extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] else: extended_attention_mask = attention_mask[:, None, None, :] else: raise ValueError( "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( input_shape, attention_mask.shape ) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, is_decoder=False, mode='multimodal', ): r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape device = input_ids.device elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape device = inputs_embeds.device elif encoder_embeds is not None: input_shape = encoder_embeds.size()[:-1] batch_size, seq_length = input_shape device = encoder_embeds.device else: raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device, is_decoder) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: if type(encoder_hidden_states) == list: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if type(encoder_attention_mask) == list: encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) if encoder_embeds is None: embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) else: embedding_output = encoder_embeds encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, mode=mode, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class BertLMHeadModel(BertPreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.bert = BertModel(config, add_pooling_layer=False) self.cls = BertOnlyMLMHead(config) self.init_weights() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, return_logits=False, is_decoder=True, reduction='mean', mode='multimodal', ): r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). Returns: Example:: >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig >>> import torch >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') >>> config = BertConfig.from_pretrained("bert-base-cased") >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.bert( input_ids, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, is_decoder=is_decoder, mode=mode, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) if return_logits: return prediction_scores[:, :-1, :].contiguous() lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if reduction=='none': lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past is not None: input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past, "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), "is_decoder": True, } def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past ================================================ FILE: extras/BLIP/models/nlvr_encoder.py ================================================ import math import os import warnings from dataclasses import dataclass from typing import Optional, Tuple import torch from torch import Tensor, device, dtype, nn import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss import torch.nn.functional as F from transformers.activations import ACT2FN from transformers.file_utils import ( ModelOutput, ) from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.modeling_utils import ( PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.utils import logging from transformers.models.bert.configuration_bert import BertConfig logger = logging.get_logger(__name__) class BertEmbeddings(nn.Module): """Construct the embeddings from word and position embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.config = config def forward( self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) embeddings = inputs_embeds if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class BertSelfAttention(nn.Module): def __init__(self, config, is_cross_attention): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads) ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) if is_cross_attention: self.key = nn.Linear(config.encoder_width, self.all_head_size) self.value = nn.Linear(config.encoder_width, self.all_head_size) else: self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.save_attention = False def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = hidden_states.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.Softmax(dim=-1)(attention_scores) if is_cross_attention and self.save_attention: self.save_attention_map(attention_probs) attention_probs.register_hook(self.save_attn_gradients) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs_dropped = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs_dropped = attention_probs_dropped * head_mask context_layer = torch.matmul(attention_probs_dropped, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) outputs = outputs + (past_key_value,) return outputs class BertSelfOutput(nn.Module): def __init__(self, config, twin=False, merge=False): super().__init__() self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) if twin: self.dense0 = nn.Linear(config.hidden_size, config.hidden_size) self.dense1 = nn.Linear(config.hidden_size, config.hidden_size) else: self.dense = nn.Linear(config.hidden_size, config.hidden_size) if merge: self.act = ACT2FN[config.hidden_act] self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size) self.merge = True else: self.merge = False def forward(self, hidden_states, input_tensor): if type(hidden_states) == list: hidden_states0 = self.dense0(hidden_states[0]) hidden_states1 = self.dense1(hidden_states[1]) if self.merge: #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1))) hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1)) else: hidden_states = (hidden_states0+hidden_states1)/2 else: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertAttention(nn.Module): def __init__(self, config, is_cross_attention=False, layer_num=-1): super().__init__() if is_cross_attention: self.self0 = BertSelfAttention(config, is_cross_attention) self.self1 = BertSelfAttention(config, is_cross_attention) else: self.self = BertSelfAttention(config, is_cross_attention) self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6)) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): if type(encoder_hidden_states)==list: self_outputs0 = self.self0( hidden_states, attention_mask, head_mask, encoder_hidden_states[0], encoder_attention_mask[0], past_key_value, output_attentions, ) self_outputs1 = self.self1( hidden_states, attention_mask, head_mask, encoder_hidden_states[1], encoder_attention_mask[1], past_key_value, output_attentions, ) attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states) outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them else: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class BertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class BertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class BertLayer(nn.Module): def __init__(self, config, layer_num): super().__init__() self.config = config self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = BertAttention(config) self.layer_num = layer_num if self.config.add_cross_attention: self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, mode=None, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] if mode=='multimodal': assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions=output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class BertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, mode='multimodal', ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i in range(self.config.num_hidden_layers): layer_module = self.layer[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: if use_cache: logger.warn( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, mode=mode, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, mode=mode, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class BertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class BertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class BertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class BertOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = BertLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class BertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BertConfig base_model_prefix = "bert" _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() class BertModel(BertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) if add_pooling_layer else None self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: """ Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: attention_mask (:obj:`torch.Tensor`): Mask with ones indicating tokens to attend to, zeros for tokens to ignore. input_shape (:obj:`Tuple[int]`): The shape of the input to the model. device: (:obj:`torch.device`): The device of the input to the model. Returns: :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. """ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. if attention_mask.dim() == 3: extended_attention_mask = attention_mask[:, None, :, :] elif attention_mask.dim() == 2: # Provided a padding mask of dimensions [batch_size, seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] if is_decoder: batch_size, seq_length = input_shape seq_ids = torch.arange(seq_length, device=device) causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] # in case past_key_values are used we need to add a prefix ones mask to the causal mask # causal and attention masks must have same type with pytorch version < 1.3 causal_mask = causal_mask.to(attention_mask.dtype) if causal_mask.shape[1] < attention_mask.shape[1]: prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] causal_mask = torch.cat( [ torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), causal_mask, ], axis=-1, ) extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] else: extended_attention_mask = attention_mask[:, None, None, :] else: raise ValueError( "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( input_shape, attention_mask.shape ) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def forward( self, input_ids=None, attention_mask=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, is_decoder=False, mode='multimodal', ): r""" encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. use_cache (:obj:`bool`, `optional`): If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up decoding (see :obj:`past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() batch_size, seq_length = input_shape device = input_ids.device elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape device = inputs_embeds.device elif encoder_embeds is not None: input_shape = encoder_embeds.size()[:-1] batch_size, seq_length = input_shape device = encoder_embeds.device else: raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device, is_decoder) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_hidden_states is not None: if type(encoder_hidden_states) == list: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() else: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if type(encoder_attention_mask) == list: encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] elif encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) if encoder_embeds is None: embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) else: embedding_output = encoder_embeds encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, mode=mode, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) ================================================ FILE: extras/BLIP/models/vit.py ================================================ ''' * Copyright (c) 2022, salesforce.com, inc. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause * By Junnan Li * Based on timm code base * https://github.com/rwightman/pytorch-image-models/tree/master/timm ''' import torch import torch.nn as nn import torch.nn.functional as F from functools import partial from timm.models.vision_transformer import _cfg, PatchEmbed from timm.models.registry import register_model from timm.models.layers import trunc_normal_, DropPath from timm.models.helpers import named_apply, adapt_input_conv def checkpoint_wrapper(x): return x class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights self.scale = qk_scale or head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.attn_gradients = None self.attention_map = None def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients def save_attention_map(self, attention_map): self.attention_map = attention_map def get_attention_map(self): return self.attention_map def forward(self, x, register_hook=False): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) if register_hook: self.save_attention_map(attn) attn.register_hook(self.save_attn_gradients) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) if use_grad_checkpointing: self.attn = checkpoint_wrapper(self.attn) self.mlp = checkpoint_wrapper(self.mlp) def forward(self, x, register_hook=False): x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class VisionTransformer(nn.Module): """ Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, use_grad_checkpointing=False, ckpt_layer=0): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True qk_scale (float): override default qk scale of head_dim ** -0.5 if set representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate norm_layer: (nn.Module): normalization layer """ super().__init__() self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) ) for i in range(depth)]) self.norm = norm_layer(embed_dim) trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def forward(self, x, register_blk=-1): B = x.shape[0] x = self.patch_embed(x) cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed[:,:x.size(1),:] x = self.pos_drop(x) for i,blk in enumerate(self.blocks): x = blk(x, register_blk==i) x = self.norm(x) return x @torch.jit.ignore() def load_pretrained(self, checkpoint_path, prefix=''): _load_weights(self, checkpoint_path, prefix) @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation """ import numpy as np def _n2p(w, t=True): if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: w = w.flatten() if t: if w.ndim == 4: w = w.transpose([3, 2, 0, 1]) elif w.ndim == 3: w = w.transpose([2, 0, 1]) elif w.ndim == 2: w = w.transpose([1, 0]) return torch.from_numpy(w) w = np.load(checkpoint_path) if not prefix and 'opt/target/embedding/kernel' in w: prefix = 'opt/target/' if hasattr(model.patch_embed, 'backbone'): # hybrid backbone = model.patch_embed.backbone stem_only = not hasattr(backbone, 'stem') stem = backbone if stem_only else backbone.stem stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) if not stem_only: for i, stage in enumerate(backbone.stages): for j, block in enumerate(stage.blocks): bp = f'{prefix}block{i + 1}/unit{j + 1}/' for r in range(3): getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) if block.downsample is not None: block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) # if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: # model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) # model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: # model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) # model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) block.attn.qkv.bias.copy_(torch.cat([ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): # interpolate position embedding embedding_size = pos_embed_checkpoint.shape[-1] num_patches = visual_encoder.patch_embed.num_patches num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches ** 0.5) if orig_size!=new_size: # class_token and dist_token are kept unchanged extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) return new_pos_embed else: return pos_embed_checkpoint ================================================ FILE: extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py ================================================ batch_size = 1 modelname = "groundingdino" backbone = "swin_T_224_1k" position_embedding = "sine" pe_temperatureH = 20 pe_temperatureW = 20 return_interm_indices = [1, 2, 3] backbone_freeze_keywords = None enc_layers = 6 dec_layers = 6 pre_norm = False dim_feedforward = 2048 hidden_dim = 256 dropout = 0.0 nheads = 8 num_queries = 900 query_dim = 4 num_patterns = 0 num_feature_levels = 4 enc_n_points = 4 dec_n_points = 4 two_stage_type = "standard" two_stage_bbox_embed_share = False two_stage_class_embed_share = False transformer_activation = "relu" dec_pred_bbox_embed_share = True dn_box_noise_scale = 1.0 dn_label_noise_ratio = 0.5 dn_label_coef = 1.0 dn_bbox_coef = 1.0 embed_init_tgt = True dn_labelbook_size = 2000 max_text_len = 256 text_encoder_type = "bert-base-uncased" use_text_enhancer = True use_fusion_layer = True use_checkpoint = True use_transformer_ckpt = True use_text_cross_attention = True text_dropout = 0.0 fusion_dropout = 0.0 fusion_droppath = 0.1 sub_sentence_present = True ================================================ FILE: extras/GroundingDINO/util/inference.py ================================================ from typing import Tuple, List import ldm_patched.modules.model_management as model_management from ldm_patched.modules.model_patcher import ModelPatcher from modules.config import path_inpaint from modules.model_loader import load_file_from_url import numpy as np import supervision as sv import torch from groundingdino.util.inference import Model from groundingdino.util.inference import load_model, preprocess_caption, get_phrases_from_posmap class GroundingDinoModel(Model): def __init__(self): self.config_file = 'extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py' self.model = None self.load_device = torch.device('cpu') self.offload_device = torch.device('cpu') @torch.no_grad() @torch.inference_mode() def predict_with_caption( self, image: np.ndarray, caption: str, box_threshold: float = 0.35, text_threshold: float = 0.25 ) -> Tuple[sv.Detections, torch.Tensor, torch.Tensor, List[str]]: if self.model is None: filename = load_file_from_url( url="https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth", file_name='groundingdino_swint_ogc.pth', model_dir=path_inpaint) model = load_model(model_config_path=self.config_file, model_checkpoint_path=filename) self.load_device = model_management.text_encoder_device() self.offload_device = model_management.text_encoder_offload_device() model.to(self.offload_device) self.model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) model_management.load_model_gpu(self.model) processed_image = GroundingDinoModel.preprocess_image(image_bgr=image).to(self.load_device) boxes, logits, phrases = predict( model=self.model, image=processed_image, caption=caption, box_threshold=box_threshold, text_threshold=text_threshold, device=self.load_device) source_h, source_w, _ = image.shape detections = GroundingDinoModel.post_process_result( source_h=source_h, source_w=source_w, boxes=boxes, logits=logits) return detections, boxes, logits, phrases def predict( model, image: torch.Tensor, caption: str, box_threshold: float, text_threshold: float, device: str = "cuda" ) -> Tuple[torch.Tensor, torch.Tensor, List[str]]: caption = preprocess_caption(caption=caption) # override to use model wrapped by patcher model = model.model.to(device) image = image.to(device) with torch.no_grad(): outputs = model(image[None], captions=[caption]) prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256) prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4) mask = prediction_logits.max(dim=1)[0] > box_threshold logits = prediction_logits[mask] # logits.shape = (n, 256) boxes = prediction_boxes[mask] # boxes.shape = (n, 4) tokenizer = model.tokenizer tokenized = tokenizer(caption) phrases = [ get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '') for logit in logits ] return boxes, logits.max(dim=1)[0], phrases default_groundingdino = GroundingDinoModel().predict_with_caption ================================================ FILE: extras/censor.py ================================================ import os import numpy as np import torch from transformers import CLIPConfig, CLIPImageProcessor import ldm_patched.modules.model_management as model_management import modules.config from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker from ldm_patched.modules.model_patcher import ModelPatcher safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker') config_path = os.path.join(safety_checker_repo_root, "configs", "config.json") preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json") class Censor: def __init__(self): self.safety_checker_model: ModelPatcher | None = None self.clip_image_processor: CLIPImageProcessor | None = None self.load_device = torch.device('cpu') self.offload_device = torch.device('cpu') def init(self): if self.safety_checker_model is None and self.clip_image_processor is None: safety_checker_model = modules.config.downloading_safety_checker_model() self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path) clip_config = CLIPConfig.from_json_file(config_path) model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config) model.eval() self.load_device = model_management.text_encoder_device() self.offload_device = model_management.text_encoder_offload_device() model.to(self.offload_device) self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) def censor(self, images: list | np.ndarray) -> list | np.ndarray: self.init() model_management.load_model_gpu(self.safety_checker_model) single = False if not isinstance(images, (list, np.ndarray)): images = [images] single = True safety_checker_input = self.clip_image_processor(images, return_tensors="pt") safety_checker_input.to(device=self.load_device) checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images, clip_input=safety_checker_input.pixel_values) checked_images = [image.astype(np.uint8) for image in checked_images] if single: checked_images = checked_images[0] return checked_images default_censor = Censor().censor ================================================ FILE: extras/expansion.py ================================================ # Fooocus GPT2 Expansion # Algorithm created by Lvmin Zhang at 2023, Stanford # If used inside Fooocus, any use is permitted. # If used outside Fooocus, only non-commercial use is permitted (CC-By NC 4.0). # This applies to the word list, vocab, model, and algorithm. import os import torch import math import ldm_patched.modules.model_management as model_management from transformers.generation.logits_process import LogitsProcessorList from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed from modules.config import path_fooocus_expansion from ldm_patched.modules.model_patcher import ModelPatcher # limitation of np.random.seed(), called from transformers.set_seed() SEED_LIMIT_NUMPY = 2**32 neg_inf = - 8192.0 def safe_str(x): x = str(x) for _ in range(16): x = x.replace(' ', ' ') return x.strip(",. \r\n") def remove_pattern(x, pattern): for p in pattern: x = x.replace(p, '') return x class FooocusExpansion: def __init__(self): self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion) positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'), encoding='utf-8').read().splitlines() positive_words = ['Ġ' + x.lower() for x in positive_words if x != ''] self.logits_bias = torch.zeros((1, len(self.tokenizer.vocab)), dtype=torch.float32) + neg_inf debug_list = [] for k, v in self.tokenizer.vocab.items(): if k in positive_words: self.logits_bias[0, v] = 0 debug_list.append(k[1:]) print(f'Fooocus V2 Expansion: Vocab with {len(debug_list)} words.') # debug_list = '\n'.join(sorted(debug_list)) # print(debug_list) # t11 = self.tokenizer(',', return_tensors="np") # t198 = self.tokenizer('\n', return_tensors="np") # eos = self.tokenizer.eos_token_id self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion) self.model.eval() load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() # MPS hack if model_management.is_device_mps(load_device): load_device = torch.device('cpu') offload_device = torch.device('cpu') use_fp16 = model_management.should_use_fp16(device=load_device) if use_fp16: self.model.half() self.patcher = ModelPatcher(self.model, load_device=load_device, offload_device=offload_device) print(f'Fooocus Expansion engine loaded for {load_device}, use_fp16 = {use_fp16}.') @torch.no_grad() @torch.inference_mode() def logits_processor(self, input_ids, scores): assert scores.ndim == 2 and scores.shape[0] == 1 self.logits_bias = self.logits_bias.to(scores) bias = self.logits_bias.clone() bias[0, input_ids[0].to(bias.device).long()] = neg_inf bias[0, 11] = 0 return scores + bias @torch.no_grad() @torch.inference_mode() def __call__(self, prompt, seed): if prompt == '': return '' if self.patcher.current_device != self.patcher.load_device: print('Fooocus Expansion loaded by itself.') model_management.load_model_gpu(self.patcher) seed = int(seed) % SEED_LIMIT_NUMPY set_seed(seed) prompt = safe_str(prompt) + ',' tokenized_kwargs = self.tokenizer(prompt, return_tensors="pt") tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device) tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device) current_token_length = int(tokenized_kwargs.data['input_ids'].shape[1]) max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0)) max_new_tokens = max_token_length - current_token_length if max_new_tokens == 0: return prompt[:-1] # https://huggingface.co/blog/introducing-csearch # https://huggingface.co/docs/transformers/generation_strategies features = self.model.generate(**tokenized_kwargs, top_k=100, max_new_tokens=max_new_tokens, do_sample=True, logits_processor=LogitsProcessorList([self.logits_processor])) response = self.tokenizer.batch_decode(features, skip_special_tokens=True) result = safe_str(response[0]) return result ================================================ FILE: extras/face_crop.py ================================================ import cv2 import numpy as np import modules.config faceRestoreHelper = None def align_warp_face(self, landmark, border_mode='constant'): affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] self.affine_matrices.append(affine_matrix) if border_mode == 'constant': border_mode = cv2.BORDER_CONSTANT elif border_mode == 'reflect101': border_mode = cv2.BORDER_REFLECT101 elif border_mode == 'reflect': border_mode = cv2.BORDER_REFLECT input_img = self.input_img cropped_face = cv2.warpAffine(input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) return cropped_face def crop_image(img_rgb): global faceRestoreHelper if faceRestoreHelper is None: from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper faceRestoreHelper = FaceRestoreHelper( upscale_factor=1, model_rootpath=modules.config.path_controlnet, device='cpu' # use cpu is safer since we are out of memory management ) faceRestoreHelper.clean_all() faceRestoreHelper.read_image(np.ascontiguousarray(img_rgb[:, :, ::-1].copy())) faceRestoreHelper.get_face_landmarks_5() landmarks = faceRestoreHelper.all_landmarks_5 # landmarks are already sorted with confidence. if len(landmarks) == 0: print('No face detected') return img_rgb else: print(f'Detected {len(landmarks)} faces') result = align_warp_face(faceRestoreHelper, landmarks[0]) return np.ascontiguousarray(result[:, :, ::-1].copy()) ================================================ FILE: extras/facexlib/detection/__init__.py ================================================ import torch from copy import deepcopy from extras.facexlib.utils import load_file_from_url from .retinaface import RetinaFace def init_detection_model(model_name, half=False, device='cuda', model_rootpath=None): if model_name == 'retinaface_resnet50': model = RetinaFace(network_name='resnet50', half=half, device=device) model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth' elif model_name == 'retinaface_mobile0.25': model = RetinaFace(network_name='mobile0.25', half=half, device=device) model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth' else: raise NotImplementedError(f'{model_name} is not implemented.') model_path = load_file_from_url( url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) # TODO: clean pretrained model load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True) # remove unnecessary 'module.' for k, v in deepcopy(load_net).items(): if k.startswith('module.'): load_net[k[7:]] = v load_net.pop(k) model.load_state_dict(load_net, strict=True) model.eval() model = model.to(device) return model ================================================ FILE: extras/facexlib/detection/align_trans.py ================================================ import cv2 import numpy as np from .matlab_cp2tform import get_similarity_transform_for_cv2 # reference facial points, a list of coordinates (x,y) REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278], [33.54930115, 92.3655014], [62.72990036, 92.20410156]] DEFAULT_CROP_SIZE = (96, 112) class FaceWarpException(Exception): def __str__(self): return 'In File {}:{}'.format(__file__, super.__str__(self)) def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False): """ Function: ---------- get reference 5 key points according to crop settings: 0. Set default crop_size: if default_square: crop_size = (112, 112) else: crop_size = (96, 112) 1. Pad the crop_size by inner_padding_factor in each side; 2. Resize crop_size into (output_size - outer_padding*2), pad into output_size with outer_padding; 3. Output reference_5point; Parameters: ---------- @output_size: (w, h) or None size of aligned face image @inner_padding_factor: (w_factor, h_factor) padding factor for inner (w, h) @outer_padding: (w_pad, h_pad) each row is a pair of coordinates (x, y) @default_square: True or False if True: default crop_size = (112, 112) else: default crop_size = (96, 112); !!! make sure, if output_size is not None: (output_size - outer_padding) = some_scale * (default crop_size * (1.0 + inner_padding_factor)) Returns: ---------- @reference_5point: 5x2 np.array each row is a pair of transformed coordinates (x, y) """ tmp_5pts = np.array(REFERENCE_FACIAL_POINTS) tmp_crop_size = np.array(DEFAULT_CROP_SIZE) # 0) make the inner region a square if default_square: size_diff = max(tmp_crop_size) - tmp_crop_size tmp_5pts += size_diff / 2 tmp_crop_size += size_diff if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]): return tmp_5pts if (inner_padding_factor == 0 and outer_padding == (0, 0)): if output_size is None: return tmp_5pts else: raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size)) # check output size if not (0 <= inner_padding_factor <= 1.0): raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)') if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None): output_size = tmp_crop_size * \ (1 + inner_padding_factor * 2).astype(np.int32) output_size += np.array(outer_padding) if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]): raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])') # 1) pad the inner region according inner_padding_factor if inner_padding_factor > 0: size_diff = tmp_crop_size * inner_padding_factor * 2 tmp_5pts += size_diff / 2 tmp_crop_size += np.round(size_diff).astype(np.int32) # 2) resize the padded inner region size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]: raise FaceWarpException('Must have (output_size - outer_padding)' '= some_scale * (crop_size * (1.0 + inner_padding_factor)') scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0] tmp_5pts = tmp_5pts * scale_factor # size_diff = tmp_crop_size * (scale_factor - min(scale_factor)) # tmp_5pts = tmp_5pts + size_diff / 2 tmp_crop_size = size_bf_outer_pad # 3) add outer_padding to make output_size reference_5point = tmp_5pts + np.array(outer_padding) tmp_crop_size = output_size return reference_5point def get_affine_transform_matrix(src_pts, dst_pts): """ Function: ---------- get affine transform matrix 'tfm' from src_pts to dst_pts Parameters: ---------- @src_pts: Kx2 np.array source points matrix, each row is a pair of coordinates (x, y) @dst_pts: Kx2 np.array destination points matrix, each row is a pair of coordinates (x, y) Returns: ---------- @tfm: 2x3 np.array transform matrix from src_pts to dst_pts """ tfm = np.float32([[1, 0, 0], [0, 1, 0]]) n_pts = src_pts.shape[0] ones = np.ones((n_pts, 1), src_pts.dtype) src_pts_ = np.hstack([src_pts, ones]) dst_pts_ = np.hstack([dst_pts, ones]) A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_) if rank == 3: tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]]) elif rank == 2: tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]]) return tfm def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'): """ Function: ---------- apply affine transform 'trans' to uv Parameters: ---------- @src_img: 3x3 np.array input image @facial_pts: could be 1)a list of K coordinates (x,y) or 2) Kx2 or 2xK np.array each row or col is a pair of coordinates (x, y) @reference_pts: could be 1) a list of K coordinates (x,y) or 2) Kx2 or 2xK np.array each row or col is a pair of coordinates (x, y) or 3) None if None, use default reference facial points @crop_size: (w, h) output face image size @align_type: transform type, could be one of 1) 'similarity': use similarity transform 2) 'cv2_affine': use the first 3 points to do affine transform, by calling cv2.getAffineTransform() 3) 'affine': use all points to do affine transform Returns: ---------- @face_img: output face image with size (w, h) = @crop_size """ if reference_pts is None: if crop_size[0] == 96 and crop_size[1] == 112: reference_pts = REFERENCE_FACIAL_POINTS else: default_square = False inner_padding_factor = 0 outer_padding = (0, 0) output_size = crop_size reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding, default_square) ref_pts = np.float32(reference_pts) ref_pts_shp = ref_pts.shape if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2: raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2') if ref_pts_shp[0] == 2: ref_pts = ref_pts.T src_pts = np.float32(facial_pts) src_pts_shp = src_pts.shape if max(src_pts_shp) < 3 or min(src_pts_shp) != 2: raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2') if src_pts_shp[0] == 2: src_pts = src_pts.T if src_pts.shape != ref_pts.shape: raise FaceWarpException('facial_pts and reference_pts must have the same shape') if align_type == 'cv2_affine': tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) elif align_type == 'affine': tfm = get_affine_transform_matrix(src_pts, ref_pts) else: tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) return face_img ================================================ FILE: extras/facexlib/detection/matlab_cp2tform.py ================================================ import numpy as np from numpy.linalg import inv, lstsq from numpy.linalg import matrix_rank as rank from numpy.linalg import norm class MatlabCp2tormException(Exception): def __str__(self): return 'In File {}:{}'.format(__file__, super.__str__(self)) def tformfwd(trans, uv): """ Function: ---------- apply affine transform 'trans' to uv Parameters: ---------- @trans: 3x3 np.array transform matrix @uv: Kx2 np.array each row is a pair of coordinates (x, y) Returns: ---------- @xy: Kx2 np.array each row is a pair of transformed coordinates (x, y) """ uv = np.hstack((uv, np.ones((uv.shape[0], 1)))) xy = np.dot(uv, trans) xy = xy[:, 0:-1] return xy def tforminv(trans, uv): """ Function: ---------- apply the inverse of affine transform 'trans' to uv Parameters: ---------- @trans: 3x3 np.array transform matrix @uv: Kx2 np.array each row is a pair of coordinates (x, y) Returns: ---------- @xy: Kx2 np.array each row is a pair of inverse-transformed coordinates (x, y) """ Tinv = inv(trans) xy = tformfwd(Tinv, uv) return xy def findNonreflectiveSimilarity(uv, xy, options=None): options = {'K': 2} K = options['K'] M = xy.shape[0] x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1)))) tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1)))) X = np.vstack((tmp1, tmp2)) u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector U = np.vstack((u, v)) # We know that X * r = U if rank(X) >= 2 * K: r, _, _, _ = lstsq(X, U, rcond=-1) r = np.squeeze(r) else: raise Exception('cp2tform:twoUniquePointsReq') sc = r[0] ss = r[1] tx = r[2] ty = r[3] Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]]) T = inv(Tinv) T[:, 2] = np.array([0, 0, 1]) return T, Tinv def findSimilarity(uv, xy, options=None): options = {'K': 2} # uv = np.array(uv) # xy = np.array(xy) # Solve for trans1 trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options) # Solve for trans2 # manually reflect the xy data across the Y-axis xyR = xy xyR[:, 0] = -1 * xyR[:, 0] trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options) # manually reflect the tform to undo the reflection done on xyR TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]]) trans2 = np.dot(trans2r, TreflectY) # Figure out if trans1 or trans2 is better xy1 = tformfwd(trans1, uv) norm1 = norm(xy1 - xy) xy2 = tformfwd(trans2, uv) norm2 = norm(xy2 - xy) if norm1 <= norm2: return trans1, trans1_inv else: trans2_inv = inv(trans2) return trans2, trans2_inv def get_similarity_transform(src_pts, dst_pts, reflective=True): """ Function: ---------- Find Similarity Transform Matrix 'trans': u = src_pts[:, 0] v = src_pts[:, 1] x = dst_pts[:, 0] y = dst_pts[:, 1] [x, y, 1] = [u, v, 1] * trans Parameters: ---------- @src_pts: Kx2 np.array source points, each row is a pair of coordinates (x, y) @dst_pts: Kx2 np.array destination points, each row is a pair of transformed coordinates (x, y) @reflective: True or False if True: use reflective similarity transform else: use non-reflective similarity transform Returns: ---------- @trans: 3x3 np.array transform matrix from uv to xy trans_inv: 3x3 np.array inverse of trans, transform matrix from xy to uv """ if reflective: trans, trans_inv = findSimilarity(src_pts, dst_pts) else: trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts) return trans, trans_inv def cvt_tform_mat_for_cv2(trans): """ Function: ---------- Convert Transform Matrix 'trans' into 'cv2_trans' which could be directly used by cv2.warpAffine(): u = src_pts[:, 0] v = src_pts[:, 1] x = dst_pts[:, 0] y = dst_pts[:, 1] [x, y].T = cv_trans * [u, v, 1].T Parameters: ---------- @trans: 3x3 np.array transform matrix from uv to xy Returns: ---------- @cv2_trans: 2x3 np.array transform matrix from src_pts to dst_pts, could be directly used for cv2.warpAffine() """ cv2_trans = trans[:, 0:2].T return cv2_trans def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True): """ Function: ---------- Find Similarity Transform Matrix 'cv2_trans' which could be directly used by cv2.warpAffine(): u = src_pts[:, 0] v = src_pts[:, 1] x = dst_pts[:, 0] y = dst_pts[:, 1] [x, y].T = cv_trans * [u, v, 1].T Parameters: ---------- @src_pts: Kx2 np.array source points, each row is a pair of coordinates (x, y) @dst_pts: Kx2 np.array destination points, each row is a pair of transformed coordinates (x, y) reflective: True or False if True: use reflective similarity transform else: use non-reflective similarity transform Returns: ---------- @cv2_trans: 2x3 np.array transform matrix from src_pts to dst_pts, could be directly used for cv2.warpAffine() """ trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective) cv2_trans = cvt_tform_mat_for_cv2(trans) return cv2_trans if __name__ == '__main__': """ u = [0, 6, -2] v = [0, 3, 5] x = [-1, 0, 4] y = [-1, -10, 4] # In Matlab, run: # # uv = [u'; v']; # xy = [x'; y']; # tform_sim=cp2tform(uv,xy,'similarity'); # # trans = tform_sim.tdata.T # ans = # -0.0764 -1.6190 0 # 1.6190 -0.0764 0 # -3.2156 0.0290 1.0000 # trans_inv = tform_sim.tdata.Tinv # ans = # # -0.0291 0.6163 0 # -0.6163 -0.0291 0 # -0.0756 1.9826 1.0000 # xy_m=tformfwd(tform_sim, u,v) # # xy_m = # # -3.2156 0.0290 # 1.1833 -9.9143 # 5.0323 2.8853 # uv_m=tforminv(tform_sim, x,y) # # uv_m = # # 0.5698 1.3953 # 6.0872 2.2733 # -2.6570 4.3314 """ u = [0, 6, -2] v = [0, 3, 5] x = [-1, 0, 4] y = [-1, -10, 4] uv = np.array((u, v)).T xy = np.array((x, y)).T print('\n--->uv:') print(uv) print('\n--->xy:') print(xy) trans, trans_inv = get_similarity_transform(uv, xy) print('\n--->trans matrix:') print(trans) print('\n--->trans_inv matrix:') print(trans_inv) print('\n---> apply transform to uv') print('\nxy_m = uv_augmented * trans') uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1)))) xy_m = np.dot(uv_aug, trans) print(xy_m) print('\nxy_m = tformfwd(trans, uv)') xy_m = tformfwd(trans, uv) print(xy_m) print('\n---> apply inverse transform to xy') print('\nuv_m = xy_augmented * trans_inv') xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1)))) uv_m = np.dot(xy_aug, trans_inv) print(uv_m) print('\nuv_m = tformfwd(trans_inv, xy)') uv_m = tformfwd(trans_inv, xy) print(uv_m) uv_m = tforminv(trans, xy) print('\nuv_m = tforminv(trans, xy)') print(uv_m) ================================================ FILE: extras/facexlib/detection/retinaface.py ================================================ import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from PIL import Image from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter from extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face from extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head from extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm, py_cpu_nms) def generate_config(network_name): cfg_mnet = { 'name': 'mobilenet0.25', 'min_sizes': [[16, 32], [64, 128], [256, 512]], 'steps': [8, 16, 32], 'variance': [0.1, 0.2], 'clip': False, 'loc_weight': 2.0, 'gpu_train': True, 'batch_size': 32, 'ngpu': 1, 'epoch': 250, 'decay1': 190, 'decay2': 220, 'image_size': 640, 'return_layers': { 'stage1': 1, 'stage2': 2, 'stage3': 3 }, 'in_channel': 32, 'out_channel': 64 } cfg_re50 = { 'name': 'Resnet50', 'min_sizes': [[16, 32], [64, 128], [256, 512]], 'steps': [8, 16, 32], 'variance': [0.1, 0.2], 'clip': False, 'loc_weight': 2.0, 'gpu_train': True, 'batch_size': 24, 'ngpu': 4, 'epoch': 100, 'decay1': 70, 'decay2': 90, 'image_size': 840, 'return_layers': { 'layer2': 1, 'layer3': 2, 'layer4': 3 }, 'in_channel': 256, 'out_channel': 256 } if network_name == 'mobile0.25': return cfg_mnet elif network_name == 'resnet50': return cfg_re50 else: raise NotImplementedError(f'network_name={network_name}') class RetinaFace(nn.Module): def __init__(self, network_name='resnet50', half=False, phase='test', device=None): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device super(RetinaFace, self).__init__() self.half_inference = half cfg = generate_config(network_name) self.backbone = cfg['name'] self.model_name = f'retinaface_{network_name}' self.cfg = cfg self.phase = phase self.target_size, self.max_size = 1600, 2150 self.resize, self.scale, self.scale1 = 1., None, None self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device) self.reference = get_reference_facial_points(default_square=True) # Build network. backbone = None if cfg['name'] == 'mobilenet0.25': backbone = MobileNetV1() self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) elif cfg['name'] == 'Resnet50': import torchvision.models as models backbone = models.resnet50(weights=None) self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) in_channels_stage2 = cfg['in_channel'] in_channels_list = [ in_channels_stage2 * 2, in_channels_stage2 * 4, in_channels_stage2 * 8, ] out_channels = cfg['out_channel'] self.fpn = FPN(in_channels_list, out_channels) self.ssh1 = SSH(out_channels, out_channels) self.ssh2 = SSH(out_channels, out_channels) self.ssh3 = SSH(out_channels, out_channels) self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel']) self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel']) self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel']) self.to(self.device) self.eval() if self.half_inference: self.half() def forward(self, inputs): out = self.body(inputs) if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50': out = list(out.values()) # FPN fpn = self.fpn(out) # SSH feature1 = self.ssh1(fpn[0]) feature2 = self.ssh2(fpn[1]) feature3 = self.ssh3(fpn[2]) features = [feature1, feature2, feature3] bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1) classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1) tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)] ldm_regressions = (torch.cat(tmp, dim=1)) if self.phase == 'train': output = (bbox_regressions, classifications, ldm_regressions) else: output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions) return output def __detect_faces(self, inputs): # get scale height, width = inputs.shape[2:] self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device) tmp = [width, height, width, height, width, height, width, height, width, height] self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device) # forawrd inputs = inputs.to(self.device) if self.half_inference: inputs = inputs.half() loc, conf, landmarks = self(inputs) # get priorbox priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:]) priors = priorbox.forward().to(self.device) return loc, conf, landmarks, priors # single image detection def transform(self, image, use_origin_size): # convert to opencv format if isinstance(image, Image.Image): image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) image = image.astype(np.float32) # testing scale im_size_min = np.min(image.shape[0:2]) im_size_max = np.max(image.shape[0:2]) resize = float(self.target_size) / float(im_size_min) # prevent bigger axis from being more than max_size if np.round(resize * im_size_max) > self.max_size: resize = float(self.max_size) / float(im_size_max) resize = 1 if use_origin_size else resize # resize if resize != 1: image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) # convert to torch.tensor format # image -= (104, 117, 123) image = image.transpose(2, 0, 1) image = torch.from_numpy(image).unsqueeze(0) return image, resize def detect_faces( self, image, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True, ): image, self.resize = self.transform(image, use_origin_size) image = image.to(self.device) if self.half_inference: image = image.half() image = image - self.mean_tensor loc, conf, landmarks, priors = self.__detect_faces(image) boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance']) boxes = boxes * self.scale / self.resize boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance']) landmarks = landmarks * self.scale1 / self.resize landmarks = landmarks.cpu().numpy() # ignore low scores inds = np.where(scores > conf_threshold)[0] boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds] # sort order = scores.argsort()[::-1] boxes, landmarks, scores = boxes[order], landmarks[order], scores[order] # do NMS bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) keep = py_cpu_nms(bounding_boxes, nms_threshold) bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep] # self.t['forward_pass'].toc() # print(self.t['forward_pass'].average_time) # import sys # sys.stdout.flush() return np.concatenate((bounding_boxes, landmarks), axis=1) def __align_multi(self, image, boxes, landmarks, limit=None): if len(boxes) < 1: return [], [] if limit: boxes = boxes[:limit] landmarks = landmarks[:limit] faces = [] for landmark in landmarks: facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)] warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112)) faces.append(warped_face) return np.concatenate((boxes, landmarks), axis=1), faces def align_multi(self, img, conf_threshold=0.8, limit=None): rlt = self.detect_faces(img, conf_threshold=conf_threshold) boxes, landmarks = rlt[:, 0:5], rlt[:, 5:] return self.__align_multi(img, boxes, landmarks, limit) # batched detection def batched_transform(self, frames, use_origin_size): """ Arguments: frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c], type=np.float32, BGR format). use_origin_size: whether to use origin size. """ from_PIL = True if isinstance(frames[0], Image.Image) else False # convert to opencv format if from_PIL: frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames] frames = np.asarray(frames, dtype=np.float32) # testing scale im_size_min = np.min(frames[0].shape[0:2]) im_size_max = np.max(frames[0].shape[0:2]) resize = float(self.target_size) / float(im_size_min) # prevent bigger axis from being more than max_size if np.round(resize * im_size_max) > self.max_size: resize = float(self.max_size) / float(im_size_max) resize = 1 if use_origin_size else resize # resize if resize != 1: if not from_PIL: frames = F.interpolate(frames, scale_factor=resize) else: frames = [ cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) for frame in frames ] # convert to torch.tensor format if not from_PIL: frames = frames.transpose(1, 2).transpose(1, 3).contiguous() else: frames = frames.transpose((0, 3, 1, 2)) frames = torch.from_numpy(frames) return frames, resize def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True): """ Arguments: frames: a list of PIL.Image, or np.array(shape=[n, h, w, c], type=np.uint8, BGR format). conf_threshold: confidence threshold. nms_threshold: nms threshold. use_origin_size: whether to use origin size. Returns: final_bounding_boxes: list of np.array ([n_boxes, 5], type=np.float32). final_landmarks: list of np.array ([n_boxes, 10], type=np.float32). """ # self.t['forward_pass'].tic() frames, self.resize = self.batched_transform(frames, use_origin_size) frames = frames.to(self.device) frames = frames - self.mean_tensor b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames) final_bounding_boxes, final_landmarks = [], [] # decode priors = priors.unsqueeze(0) b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize b_conf = b_conf[:, :, 1] # index for selection b_indice = b_conf > conf_threshold # concat b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float() for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice): # ignore low scores pred, landm = pred[inds, :], landm[inds, :] if pred.shape[0] == 0: final_bounding_boxes.append(np.array([], dtype=np.float32)) final_landmarks.append(np.array([], dtype=np.float32)) continue # sort # order = score.argsort(descending=True) # box, landm, score = box[order], landm[order], score[order] # to CPU bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy() # NMS keep = py_cpu_nms(bounding_boxes, nms_threshold) bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep] # append final_bounding_boxes.append(bounding_boxes) final_landmarks.append(landmarks) # self.t['forward_pass'].toc(average=True) # self.batch_time += self.t['forward_pass'].diff # self.total_frame += len(frames) # print(self.batch_time / self.total_frame) return final_bounding_boxes, final_landmarks ================================================ FILE: extras/facexlib/detection/retinaface_net.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F def conv_bn(inp, oup, stride=1, leaky=0): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.LeakyReLU(negative_slope=leaky, inplace=True)) def conv_bn_no_relu(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), ) def conv_bn1X1(inp, oup, stride, leaky=0): return nn.Sequential( nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup), nn.LeakyReLU(negative_slope=leaky, inplace=True)) def conv_dw(inp, oup, stride, leaky=0.1): return nn.Sequential( nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.LeakyReLU(negative_slope=leaky, inplace=True), nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.LeakyReLU(negative_slope=leaky, inplace=True), ) class SSH(nn.Module): def __init__(self, in_channel, out_channel): super(SSH, self).__init__() assert out_channel % 4 == 0 leaky = 0 if (out_channel <= 64): leaky = 0.1 self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1) self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky) self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky) self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) def forward(self, input): conv3X3 = self.conv3X3(input) conv5X5_1 = self.conv5X5_1(input) conv5X5 = self.conv5X5_2(conv5X5_1) conv7X7_2 = self.conv7X7_2(conv5X5_1) conv7X7 = self.conv7x7_3(conv7X7_2) out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1) out = F.relu(out) return out class FPN(nn.Module): def __init__(self, in_channels_list, out_channels): super(FPN, self).__init__() leaky = 0 if (out_channels <= 64): leaky = 0.1 self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky) self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky) self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky) self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky) self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky) def forward(self, input): # names = list(input.keys()) # input = list(input.values()) output1 = self.output1(input[0]) output2 = self.output2(input[1]) output3 = self.output3(input[2]) up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest') output2 = output2 + up3 output2 = self.merge2(output2) up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest') output1 = output1 + up2 output1 = self.merge1(output1) out = [output1, output2, output3] return out class MobileNetV1(nn.Module): def __init__(self): super(MobileNetV1, self).__init__() self.stage1 = nn.Sequential( conv_bn(3, 8, 2, leaky=0.1), # 3 conv_dw(8, 16, 1), # 7 conv_dw(16, 32, 2), # 11 conv_dw(32, 32, 1), # 19 conv_dw(32, 64, 2), # 27 conv_dw(64, 64, 1), # 43 ) self.stage2 = nn.Sequential( conv_dw(64, 128, 2), # 43 + 16 = 59 conv_dw(128, 128, 1), # 59 + 32 = 91 conv_dw(128, 128, 1), # 91 + 32 = 123 conv_dw(128, 128, 1), # 123 + 32 = 155 conv_dw(128, 128, 1), # 155 + 32 = 187 conv_dw(128, 128, 1), # 187 + 32 = 219 ) self.stage3 = nn.Sequential( conv_dw(128, 256, 2), # 219 +3 2 = 241 conv_dw(256, 256, 1), # 241 + 64 = 301 ) self.avg = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(256, 1000) def forward(self, x): x = self.stage1(x) x = self.stage2(x) x = self.stage3(x) x = self.avg(x) # x = self.model(x) x = x.view(-1, 256) x = self.fc(x) return x class ClassHead(nn.Module): def __init__(self, inchannels=512, num_anchors=3): super(ClassHead, self).__init__() self.num_anchors = num_anchors self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0) def forward(self, x): out = self.conv1x1(x) out = out.permute(0, 2, 3, 1).contiguous() return out.view(out.shape[0], -1, 2) class BboxHead(nn.Module): def __init__(self, inchannels=512, num_anchors=3): super(BboxHead, self).__init__() self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0) def forward(self, x): out = self.conv1x1(x) out = out.permute(0, 2, 3, 1).contiguous() return out.view(out.shape[0], -1, 4) class LandmarkHead(nn.Module): def __init__(self, inchannels=512, num_anchors=3): super(LandmarkHead, self).__init__() self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0) def forward(self, x): out = self.conv1x1(x) out = out.permute(0, 2, 3, 1).contiguous() return out.view(out.shape[0], -1, 10) def make_class_head(fpn_num=3, inchannels=64, anchor_num=2): classhead = nn.ModuleList() for i in range(fpn_num): classhead.append(ClassHead(inchannels, anchor_num)) return classhead def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2): bboxhead = nn.ModuleList() for i in range(fpn_num): bboxhead.append(BboxHead(inchannels, anchor_num)) return bboxhead def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2): landmarkhead = nn.ModuleList() for i in range(fpn_num): landmarkhead.append(LandmarkHead(inchannels, anchor_num)) return landmarkhead ================================================ FILE: extras/facexlib/detection/retinaface_utils.py ================================================ import numpy as np import torch import torchvision from itertools import product as product from math import ceil class PriorBox(object): def __init__(self, cfg, image_size=None, phase='train'): super(PriorBox, self).__init__() self.min_sizes = cfg['min_sizes'] self.steps = cfg['steps'] self.clip = cfg['clip'] self.image_size = image_size self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps] self.name = 's' def forward(self): anchors = [] for k, f in enumerate(self.feature_maps): min_sizes = self.min_sizes[k] for i, j in product(range(f[0]), range(f[1])): for min_size in min_sizes: s_kx = min_size / self.image_size[1] s_ky = min_size / self.image_size[0] dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]] dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]] for cy, cx in product(dense_cy, dense_cx): anchors += [cx, cy, s_kx, s_ky] # back to torch land output = torch.Tensor(anchors).view(-1, 4) if self.clip: output.clamp_(max=1, min=0) return output def py_cpu_nms(dets, thresh): """Pure Python NMS baseline.""" keep = torchvision.ops.nms( boxes=torch.Tensor(dets[:, :4]), scores=torch.Tensor(dets[:, 4]), iou_threshold=thresh, ) return list(keep) def point_form(boxes): """ Convert prior_boxes to (xmin, ymin, xmax, ymax) representation for comparison to point form ground truth data. Args: boxes: (tensor) center-size default boxes from priorbox layers. Return: boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. """ return torch.cat( ( boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin boxes[:, :2] + boxes[:, 2:] / 2), 1) # xmax, ymax def center_size(boxes): """ Convert prior_boxes to (cx, cy, w, h) representation for comparison to center-size form ground truth data. Args: boxes: (tensor) point_form boxes Return: boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. """ return torch.cat( (boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy boxes[:, 2:] - boxes[:, :2], 1) # w, h def intersect(box_a, box_b): """ We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then we compute the area of intersect between box_a and box_b. Args: box_a: (tensor) bounding boxes, Shape: [A,4]. box_b: (tensor) bounding boxes, Shape: [B,4]. Return: (tensor) intersection area, Shape: [A,B]. """ A = box_a.size(0) B = box_b.size(0) max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2)) inter = torch.clamp((max_xy - min_xy), min=0) return inter[:, :, 0] * inter[:, :, 1] def jaccard(box_a, box_b): """Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. Here we operate on ground truth boxes and default boxes. E.g.: A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) Args: box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] Return: jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] """ inter = intersect(box_a, box_b) area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] union = area_a + area_b - inter return inter / union # [A,B] def matrix_iou(a, b): """ return iou of a and b, numpy version for data augenmentation """ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) return area_i / (area_a[:, np.newaxis] + area_b - area_i) def matrix_iof(a, b): """ return iof of a and b, numpy version for data augenmentation """ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) return area_i / np.maximum(area_a[:, np.newaxis], 1) def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx): """Match each prior box with the ground truth box of the highest jaccard overlap, encode the bounding boxes, then return the matched indices corresponding to both confidence and location preds. Args: threshold: (float) The overlap threshold used when matching boxes. truths: (tensor) Ground truth boxes, Shape: [num_obj, 4]. priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. variances: (tensor) Variances corresponding to each prior coord, Shape: [num_priors, 4]. labels: (tensor) All the class labels for the image, Shape: [num_obj]. landms: (tensor) Ground truth landms, Shape [num_obj, 10]. loc_t: (tensor) Tensor to be filled w/ encoded location targets. conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. landm_t: (tensor) Tensor to be filled w/ encoded landm targets. idx: (int) current batch index Return: The matched indices corresponding to 1)location 2)confidence 3)landm preds. """ # jaccard index overlaps = jaccard(truths, point_form(priors)) # (Bipartite Matching) # [1,num_objects] best prior for each ground truth best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True) # ignore hard gt valid_gt_idx = best_prior_overlap[:, 0] >= 0.2 best_prior_idx_filter = best_prior_idx[valid_gt_idx, :] if best_prior_idx_filter.shape[0] <= 0: loc_t[idx] = 0 conf_t[idx] = 0 return # [1,num_priors] best ground truth for each prior best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True) best_truth_idx.squeeze_(0) best_truth_overlap.squeeze_(0) best_prior_idx.squeeze_(1) best_prior_idx_filter.squeeze_(1) best_prior_overlap.squeeze_(1) best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior # TODO refactor: index best_prior_idx with long tensor # ensure every gt matches with its prior of max overlap for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes best_truth_idx[best_prior_idx[j]] = j matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来 conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来 conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本 loc = encode(matches, priors, variances) matches_landm = landms[best_truth_idx] landm = encode_landm(matches_landm, priors, variances) loc_t[idx] = loc # [num_priors,4] encoded offsets to learn conf_t[idx] = conf # [num_priors] top class label for each prior landm_t[idx] = landm def encode(matched, priors, variances): """Encode the variances from the priorbox layers into the ground truth boxes we have matched (based on jaccard overlap) with the prior boxes. Args: matched: (tensor) Coords of ground truth for each prior in point-form Shape: [num_priors, 4]. priors: (tensor) Prior boxes in center-offset form Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: encoded boxes (tensor), Shape: [num_priors, 4] """ # dist b/t match center and prior's center g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] # encode variance g_cxcy /= (variances[0] * priors[:, 2:]) # match wh / prior wh g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] g_wh = torch.log(g_wh) / variances[1] # return target for smooth_l1_loss return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] def encode_landm(matched, priors, variances): """Encode the variances from the priorbox layers into the ground truth boxes we have matched (based on jaccard overlap) with the prior boxes. Args: matched: (tensor) Coords of ground truth for each prior in point-form Shape: [num_priors, 10]. priors: (tensor) Prior boxes in center-offset form Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: encoded landm (tensor), Shape: [num_priors, 10] """ # dist b/t match center and prior's center matched = torch.reshape(matched, (matched.size(0), 5, 2)) priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2) g_cxcy = matched[:, :, :2] - priors[:, :, :2] # encode variance g_cxcy /= (variances[0] * priors[:, :, 2:]) # g_cxcy /= priors[:, :, 2:] g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1) # return target for smooth_l1_loss return g_cxcy # Adapted from https://github.com/Hakuyume/chainer-ssd def decode(loc, priors, variances): """Decode locations from predictions using priors to undo the encoding we did for offset regression at train time. Args: loc (tensor): location predictions for loc layers, Shape: [num_priors,4] priors (tensor): Prior boxes in center-offset form. Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: decoded bounding box predictions """ boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) boxes[:, :2] -= boxes[:, 2:] / 2 boxes[:, 2:] += boxes[:, :2] return boxes def decode_landm(pre, priors, variances): """Decode landm from predictions using priors to undo the encoding we did for offset regression at train time. Args: pre (tensor): landm predictions for loc layers, Shape: [num_priors,10] priors (tensor): Prior boxes in center-offset form. Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: decoded landm predictions """ tmp = ( priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:], ) landms = torch.cat(tmp, dim=1) return landms def batched_decode(b_loc, priors, variances): """Decode locations from predictions using priors to undo the encoding we did for offset regression at train time. Args: b_loc (tensor): location predictions for loc layers, Shape: [num_batches,num_priors,4] priors (tensor): Prior boxes in center-offset form. Shape: [1,num_priors,4]. variances: (list[float]) Variances of priorboxes Return: decoded bounding box predictions """ boxes = ( priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:], priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]), ) boxes = torch.cat(boxes, dim=2) boxes[:, :, :2] -= boxes[:, :, 2:] / 2 boxes[:, :, 2:] += boxes[:, :, :2] return boxes def batched_decode_landm(pre, priors, variances): """Decode landm from predictions using priors to undo the encoding we did for offset regression at train time. Args: pre (tensor): landm predictions for loc layers, Shape: [num_batches,num_priors,10] priors (tensor): Prior boxes in center-offset form. Shape: [1,num_priors,4]. variances: (list[float]) Variances of priorboxes Return: decoded landm predictions """ landms = ( priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:], priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:], priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:], priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:], priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:], ) landms = torch.cat(landms, dim=2) return landms def log_sum_exp(x): """Utility function for computing log_sum_exp while determining This will be used to determine unaveraged confidence loss across all examples in a batch. Args: x (Variable(tensor)): conf_preds from conf layers """ x_max = x.data.max() return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max # Original author: Francisco Massa: # https://github.com/fmassa/object-detection.torch # Ported to PyTorch by Max deGroot (02/01/2017) def nms(boxes, scores, overlap=0.5, top_k=200): """Apply non-maximum suppression at test time to avoid detecting too many overlapping bounding boxes for a given object. Args: boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. scores: (tensor) The class predscores for the img, Shape:[num_priors]. overlap: (float) The overlap thresh for suppressing unnecessary boxes. top_k: (int) The Maximum number of box preds to consider. Return: The indices of the kept boxes with respect to num_priors. """ keep = torch.Tensor(scores.size(0)).fill_(0).long() if boxes.numel() == 0: return keep x1 = boxes[:, 0] y1 = boxes[:, 1] x2 = boxes[:, 2] y2 = boxes[:, 3] area = torch.mul(x2 - x1, y2 - y1) v, idx = scores.sort(0) # sort in ascending order # I = I[v >= 0.01] idx = idx[-top_k:] # indices of the top-k largest vals xx1 = boxes.new() yy1 = boxes.new() xx2 = boxes.new() yy2 = boxes.new() w = boxes.new() h = boxes.new() # keep = torch.Tensor() count = 0 while idx.numel() > 0: i = idx[-1] # index of current largest val # keep.append(i) keep[count] = i count += 1 if idx.size(0) == 1: break idx = idx[:-1] # remove kept element from view # load bboxes of next highest vals torch.index_select(x1, 0, idx, out=xx1) torch.index_select(y1, 0, idx, out=yy1) torch.index_select(x2, 0, idx, out=xx2) torch.index_select(y2, 0, idx, out=yy2) # store element-wise max with next highest score xx1 = torch.clamp(xx1, min=x1[i]) yy1 = torch.clamp(yy1, min=y1[i]) xx2 = torch.clamp(xx2, max=x2[i]) yy2 = torch.clamp(yy2, max=y2[i]) w.resize_as_(xx2) h.resize_as_(yy2) w = xx2 - xx1 h = yy2 - yy1 # check sizes of xx1 and xx2.. after each iteration w = torch.clamp(w, min=0.0) h = torch.clamp(h, min=0.0) inter = w * h # IoU = i / (area(a) + area(b) - i) rem_areas = torch.index_select(area, 0, idx) # load remaining areas) union = (rem_areas - inter) + area[i] IoU = inter / union # store result in iou # keep only elements with an IoU <= overlap idx = idx[IoU.le(overlap)] return keep, count ================================================ FILE: extras/facexlib/parsing/__init__.py ================================================ import torch from extras.facexlib.utils import load_file_from_url from .bisenet import BiSeNet from .parsenet import ParseNet def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None): if model_name == 'bisenet': model = BiSeNet(num_class=19) model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth' elif model_name == 'parsenet': model = ParseNet(in_size=512, out_size=512, parsing_ch=19) model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth' else: raise NotImplementedError(f'{model_name} is not implemented.') model_path = load_file_from_url( url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True) model.load_state_dict(load_net, strict=True) model.eval() model = model.to(device) return model ================================================ FILE: extras/facexlib/parsing/bisenet.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from .resnet import ResNet18 class ConvBNReLU(nn.Module): def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1): super(ConvBNReLU, self).__init__() self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False) self.bn = nn.BatchNorm2d(out_chan) def forward(self, x): x = self.conv(x) x = F.relu(self.bn(x)) return x class BiSeNetOutput(nn.Module): def __init__(self, in_chan, mid_chan, num_class): super(BiSeNetOutput, self).__init__() self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False) def forward(self, x): feat = self.conv(x) out = self.conv_out(feat) return out, feat class AttentionRefinementModule(nn.Module): def __init__(self, in_chan, out_chan): super(AttentionRefinementModule, self).__init__() self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False) self.bn_atten = nn.BatchNorm2d(out_chan) self.sigmoid_atten = nn.Sigmoid() def forward(self, x): feat = self.conv(x) atten = F.avg_pool2d(feat, feat.size()[2:]) atten = self.conv_atten(atten) atten = self.bn_atten(atten) atten = self.sigmoid_atten(atten) out = torch.mul(feat, atten) return out class ContextPath(nn.Module): def __init__(self): super(ContextPath, self).__init__() self.resnet = ResNet18() self.arm16 = AttentionRefinementModule(256, 128) self.arm32 = AttentionRefinementModule(512, 128) self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) def forward(self, x): feat8, feat16, feat32 = self.resnet(x) h8, w8 = feat8.size()[2:] h16, w16 = feat16.size()[2:] h32, w32 = feat32.size()[2:] avg = F.avg_pool2d(feat32, feat32.size()[2:]) avg = self.conv_avg(avg) avg_up = F.interpolate(avg, (h32, w32), mode='nearest') feat32_arm = self.arm32(feat32) feat32_sum = feat32_arm + avg_up feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest') feat32_up = self.conv_head32(feat32_up) feat16_arm = self.arm16(feat16) feat16_sum = feat16_arm + feat32_up feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest') feat16_up = self.conv_head16(feat16_up) return feat8, feat16_up, feat32_up # x8, x8, x16 class FeatureFusionModule(nn.Module): def __init__(self, in_chan, out_chan): super(FeatureFusionModule, self).__init__() self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False) self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False) self.relu = nn.ReLU(inplace=True) self.sigmoid = nn.Sigmoid() def forward(self, fsp, fcp): fcat = torch.cat([fsp, fcp], dim=1) feat = self.convblk(fcat) atten = F.avg_pool2d(feat, feat.size()[2:]) atten = self.conv1(atten) atten = self.relu(atten) atten = self.conv2(atten) atten = self.sigmoid(atten) feat_atten = torch.mul(feat, atten) feat_out = feat_atten + feat return feat_out class BiSeNet(nn.Module): def __init__(self, num_class): super(BiSeNet, self).__init__() self.cp = ContextPath() self.ffm = FeatureFusionModule(256, 256) self.conv_out = BiSeNetOutput(256, 256, num_class) self.conv_out16 = BiSeNetOutput(128, 64, num_class) self.conv_out32 = BiSeNetOutput(128, 64, num_class) def forward(self, x, return_feat=False): h, w = x.size()[2:] feat_res8, feat_cp8, feat_cp16 = self.cp(x) # return res3b1 feature feat_sp = feat_res8 # replace spatial path feature with res3b1 feature feat_fuse = self.ffm(feat_sp, feat_cp8) out, feat = self.conv_out(feat_fuse) out16, feat16 = self.conv_out16(feat_cp8) out32, feat32 = self.conv_out32(feat_cp16) out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True) out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True) out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True) if return_feat: feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True) feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True) feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True) return out, out16, out32, feat, feat16, feat32 else: return out, out16, out32 ================================================ FILE: extras/facexlib/parsing/parsenet.py ================================================ """Modified from https://github.com/chaofengc/PSFRGAN """ import numpy as np import torch.nn as nn from torch.nn import functional as F class NormLayer(nn.Module): """Normalization Layers. Args: channels: input channels, for batch norm and instance norm. input_size: input shape without batch size, for layer norm. """ def __init__(self, channels, normalize_shape=None, norm_type='bn'): super(NormLayer, self).__init__() norm_type = norm_type.lower() self.norm_type = norm_type if norm_type == 'bn': self.norm = nn.BatchNorm2d(channels, affine=True) elif norm_type == 'in': self.norm = nn.InstanceNorm2d(channels, affine=False) elif norm_type == 'gn': self.norm = nn.GroupNorm(32, channels, affine=True) elif norm_type == 'pixel': self.norm = lambda x: F.normalize(x, p=2, dim=1) elif norm_type == 'layer': self.norm = nn.LayerNorm(normalize_shape) elif norm_type == 'none': self.norm = lambda x: x * 1.0 else: assert 1 == 0, f'Norm type {norm_type} not support.' def forward(self, x, ref=None): if self.norm_type == 'spade': return self.norm(x, ref) else: return self.norm(x) class ReluLayer(nn.Module): """Relu Layer. Args: relu type: type of relu layer, candidates are - ReLU - LeakyReLU: default relu slope 0.2 - PRelu - SELU - none: direct pass """ def __init__(self, channels, relu_type='relu'): super(ReluLayer, self).__init__() relu_type = relu_type.lower() if relu_type == 'relu': self.func = nn.ReLU(True) elif relu_type == 'leakyrelu': self.func = nn.LeakyReLU(0.2, inplace=True) elif relu_type == 'prelu': self.func = nn.PReLU(channels) elif relu_type == 'selu': self.func = nn.SELU(True) elif relu_type == 'none': self.func = lambda x: x * 1.0 else: assert 1 == 0, f'Relu type {relu_type} not support.' def forward(self, x): return self.func(x) class ConvLayer(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, scale='none', norm_type='none', relu_type='none', use_pad=True, bias=True): super(ConvLayer, self).__init__() self.use_pad = use_pad self.norm_type = norm_type if norm_type in ['bn']: bias = False stride = 2 if scale == 'down' else 1 self.scale_func = lambda x: x if scale == 'up': self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2))) self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) self.relu = ReluLayer(out_channels, relu_type) self.norm = NormLayer(out_channels, norm_type=norm_type) def forward(self, x): out = self.scale_func(x) if self.use_pad: out = self.reflection_pad(out) out = self.conv2d(out) out = self.norm(out) out = self.relu(out) return out class ResidualBlock(nn.Module): """ Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html """ def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): super(ResidualBlock, self).__init__() if scale == 'none' and c_in == c_out: self.shortcut_func = lambda x: x else: self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} scale_conf = scale_config_dict[scale] self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') def forward(self, x): identity = self.shortcut_func(x) res = self.conv1(x) res = self.conv2(res) return identity + res class ParseNet(nn.Module): def __init__(self, in_size=128, out_size=128, min_feat_size=32, base_ch=64, parsing_ch=19, res_depth=10, relu_type='LeakyReLU', norm_type='bn', ch_range=[32, 256]): super().__init__() self.res_depth = res_depth act_args = {'norm_type': norm_type, 'relu_type': relu_type} min_ch, max_ch = ch_range ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731 min_feat_size = min(in_size, min_feat_size) down_steps = int(np.log2(in_size // min_feat_size)) up_steps = int(np.log2(out_size // min_feat_size)) # =============== define encoder-body-decoder ==================== self.encoder = [] self.encoder.append(ConvLayer(3, base_ch, 3, 1)) head_ch = base_ch for i in range(down_steps): cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) head_ch = head_ch * 2 self.body = [] for i in range(res_depth): self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) self.decoder = [] for i in range(up_steps): cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) head_ch = head_ch // 2 self.encoder = nn.Sequential(*self.encoder) self.body = nn.Sequential(*self.body) self.decoder = nn.Sequential(*self.decoder) self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) def forward(self, x): feat = self.encoder(x) x = feat + self.body(feat) x = self.decoder(x) out_img = self.out_img_conv(x) out_mask = self.out_mask_conv(x) return out_mask, out_img ================================================ FILE: extras/facexlib/parsing/resnet.py ================================================ import torch.nn as nn import torch.nn.functional as F def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, in_chan, out_chan, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_chan, out_chan, stride) self.bn1 = nn.BatchNorm2d(out_chan) self.conv2 = conv3x3(out_chan, out_chan) self.bn2 = nn.BatchNorm2d(out_chan) self.relu = nn.ReLU(inplace=True) self.downsample = None if in_chan != out_chan or stride != 1: self.downsample = nn.Sequential( nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_chan), ) def forward(self, x): residual = self.conv1(x) residual = F.relu(self.bn1(residual)) residual = self.conv2(residual) residual = self.bn2(residual) shortcut = x if self.downsample is not None: shortcut = self.downsample(x) out = shortcut + residual out = self.relu(out) return out def create_layer_basic(in_chan, out_chan, bnum, stride=1): layers = [BasicBlock(in_chan, out_chan, stride=stride)] for i in range(bnum - 1): layers.append(BasicBlock(out_chan, out_chan, stride=1)) return nn.Sequential(*layers) class ResNet18(nn.Module): def __init__(self): super(ResNet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) def forward(self, x): x = self.conv1(x) x = F.relu(self.bn1(x)) x = self.maxpool(x) x = self.layer1(x) feat8 = self.layer2(x) # 1/8 feat16 = self.layer3(feat8) # 1/16 feat32 = self.layer4(feat16) # 1/32 return feat8, feat16, feat32 ================================================ FILE: extras/facexlib/utils/__init__.py ================================================ from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back from .misc import img2tensor, load_file_from_url, scandir __all__ = [ 'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 'paste_face_back', 'img2tensor', 'scandir' ] ================================================ FILE: extras/facexlib/utils/face_restoration_helper.py ================================================ import cv2 import numpy as np import os import torch from torchvision.transforms.functional import normalize from extras.facexlib.detection import init_detection_model from extras.facexlib.parsing import init_parsing_model from extras.facexlib.utils.misc import img2tensor, imwrite def get_largest_face(det_faces, h, w): def get_location(val, length): if val < 0: return 0 elif val > length: return length else: return val face_areas = [] for det_face in det_faces: left = get_location(det_face[0], w) right = get_location(det_face[2], w) top = get_location(det_face[1], h) bottom = get_location(det_face[3], h) face_area = (right - left) * (bottom - top) face_areas.append(face_area) largest_idx = face_areas.index(max(face_areas)) return det_faces[largest_idx], largest_idx def get_center_face(det_faces, h=0, w=0, center=None): if center is not None: center = np.array(center) else: center = np.array([w / 2, h / 2]) center_dist = [] for det_face in det_faces: face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) dist = np.linalg.norm(face_center - center) center_dist.append(dist) center_idx = center_dist.index(min(center_dist)) return det_faces[center_idx], center_idx class FaceRestoreHelper(object): """Helper for the face restoration pipeline (base class).""" def __init__(self, upscale_factor, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', template_3points=False, pad_blur=False, use_parse=False, device=None, model_rootpath=None): self.template_3points = template_3points # improve robustness self.upscale_factor = upscale_factor # the cropped face ratio based on the square face self.crop_ratio = crop_ratio # (h, w) assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) if self.template_3points: self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) else: # standard 5 landmarks for FFHQ faces with 512 x 512 self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], [201.26117, 371.41043], [313.08905, 371.15118]]) self.face_template = self.face_template * (face_size / 512.0) if self.crop_ratio[0] > 1: self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 if self.crop_ratio[1] > 1: self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 self.save_ext = save_ext self.pad_blur = pad_blur if self.pad_blur is True: self.template_3points = False self.all_landmarks_5 = [] self.det_faces = [] self.affine_matrices = [] self.inverse_affine_matrices = [] self.cropped_faces = [] self.restored_faces = [] self.pad_input_imgs = [] if device is None: self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') else: self.device = device # init face detection model self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath) # init face parsing model self.use_parse = use_parse self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath) def set_upscale_factor(self, upscale_factor): self.upscale_factor = upscale_factor def read_image(self, img): """img can be image path or cv2 loaded image.""" # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255] if isinstance(img, str): img = cv2.imread(img) if np.max(img) > 256: # 16-bit image img = img / 65535 * 255 if len(img.shape) == 2: # gray image img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) elif img.shape[2] == 4: # RGBA image with alpha channel img = img[:, :, 0:3] self.input_img = img def get_face_landmarks_5(self, only_keep_largest=False, only_center_face=False, resize=None, blur_ratio=0.01, eye_dist_threshold=None): if resize is None: scale = 1 input_img = self.input_img else: h, w = self.input_img.shape[0:2] scale = min(h, w) / resize h, w = int(h / scale), int(w / scale) input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4) with torch.no_grad(): bboxes = self.face_det.detect_faces(input_img, 0.97) * scale for bbox in bboxes: # remove faces with too small eye distance: side faces or too small faces eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]]) if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): continue if self.template_3points: landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) else: landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) self.all_landmarks_5.append(landmark) self.det_faces.append(bbox[0:5]) if len(self.det_faces) == 0: return 0 if only_keep_largest: h, w, _ = self.input_img.shape self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] elif only_center_face: h, w, _ = self.input_img.shape self.det_faces, center_idx = get_center_face(self.det_faces, h, w) self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] # pad blurry images if self.pad_blur: self.pad_input_imgs = [] for landmarks in self.all_landmarks_5: # get landmarks eye_left = landmarks[0, :] eye_right = landmarks[1, :] eye_avg = (eye_left + eye_right) * 0.5 mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 eye_to_eye = eye_right - eye_left eye_to_mouth = mouth_avg - eye_avg # Get the oriented crop rectangle # x: half width of the oriented crop rectangle x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise # norm with the hypotenuse: get the direction x /= np.hypot(*x) # get the hypotenuse of a right triangle rect_scale = 1.5 x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) # y: half height of the oriented crop rectangle y = np.flipud(x) * [-1, 1] # c: center c = eye_avg + eye_to_mouth * 0.1 # quad: (left_top, left_bottom, right_bottom, right_top) quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) # qsize: side length of the square qsize = np.hypot(*x) * 2 border = max(int(np.rint(qsize * 0.1)), 3) # get pad # pad: (width_left, height_top, width_right, height_bottom) pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) pad = [ max(-pad[0] + border, 1), max(-pad[1] + border, 1), max(pad[2] - self.input_img.shape[0] + border, 1), max(pad[3] - self.input_img.shape[1] + border, 1) ] if max(pad) > 1: # pad image pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') # modify landmark coords landmarks[:, 0] += pad[0] landmarks[:, 1] += pad[1] # blur pad images h, w, _ = pad_img.shape y, x, _ = np.ogrid[:h, :w, :1] mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) blur = int(qsize * blur_ratio) if blur % 2 == 0: blur += 1 blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0) pad_img = pad_img.astype('float32') pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255] self.pad_input_imgs.append(pad_img) else: self.pad_input_imgs.append(np.copy(self.input_img)) return len(self.all_landmarks_5) def align_warp_face(self, save_cropped_path=None, border_mode='constant'): """Align and warp faces with face template. """ if self.pad_blur: assert len(self.pad_input_imgs) == len( self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' for idx, landmark in enumerate(self.all_landmarks_5): # use 5 landmarks to get affine matrix # use cv2.LMEDS method for the equivalence to skimage transform # ref: https://blog.csdn.net/yichxi/article/details/115827338 affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] self.affine_matrices.append(affine_matrix) # warp and crop faces if border_mode == 'constant': border_mode = cv2.BORDER_CONSTANT elif border_mode == 'reflect101': border_mode = cv2.BORDER_REFLECT101 elif border_mode == 'reflect': border_mode = cv2.BORDER_REFLECT if self.pad_blur: input_img = self.pad_input_imgs[idx] else: input_img = self.input_img cropped_face = cv2.warpAffine( input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray self.cropped_faces.append(cropped_face) # save the cropped face if save_cropped_path is not None: path = os.path.splitext(save_cropped_path)[0] save_path = f'{path}_{idx:02d}.{self.save_ext}' imwrite(cropped_face, save_path) def get_inverse_affine(self, save_inverse_affine_path=None): """Get inverse affine matrix.""" for idx, affine_matrix in enumerate(self.affine_matrices): inverse_affine = cv2.invertAffineTransform(affine_matrix) inverse_affine *= self.upscale_factor self.inverse_affine_matrices.append(inverse_affine) # save inverse affine matrices if save_inverse_affine_path is not None: path, _ = os.path.splitext(save_inverse_affine_path) save_path = f'{path}_{idx:02d}.pth' torch.save(inverse_affine, save_path) def add_restored_face(self, face): self.restored_faces.append(face) def paste_faces_to_input_image(self, save_path=None, upsample_img=None): h, w, _ = self.input_img.shape h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) if upsample_img is None: # simply resize the background upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) else: upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) assert len(self.restored_faces) == len( self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): # Add an offset to inverse affine matrix, for more precise back alignment if self.upscale_factor > 1: extra_offset = 0.5 * self.upscale_factor else: extra_offset = 0 inverse_affine[:, 2] += extra_offset inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) if self.use_parse: # inference face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) face_input = torch.unsqueeze(face_input, 0).to(self.device) with torch.no_grad(): out = self.face_parse(face_input)[0] out = out.argmax(dim=1).squeeze().cpu().numpy() mask = np.zeros(out.shape) MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] for idx, color in enumerate(MASK_COLORMAP): mask[out == idx] = color # blur the mask mask = cv2.GaussianBlur(mask, (101, 101), 11) mask = cv2.GaussianBlur(mask, (101, 101), 11) # remove the black borders thres = 10 mask[:thres, :] = 0 mask[-thres:, :] = 0 mask[:, :thres] = 0 mask[:, -thres:] = 0 mask = mask / 255. mask = cv2.resize(mask, restored_face.shape[:2]) mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3) inv_soft_mask = mask[:, :, None] pasted_face = inv_restored else: # use square parse maps mask = np.ones(self.face_size, dtype=np.float32) inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) # remove the black borders inv_mask_erosion = cv2.erode( inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) pasted_face = inv_mask_erosion[:, :, None] * inv_restored total_face_area = np.sum(inv_mask_erosion) # // 3 # compute the fusion edge based on the area of face w_edge = int(total_face_area**0.5) // 20 erosion_radius = w_edge * 2 inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) blur_size = w_edge * 2 inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) if len(upsample_img.shape) == 2: # upsample_img is gray image upsample_img = upsample_img[:, :, None] inv_soft_mask = inv_soft_mask[:, :, None] if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel alpha = upsample_img[:, :, 3:] upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] upsample_img = np.concatenate((upsample_img, alpha), axis=2) else: upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img if np.max(upsample_img) > 256: # 16-bit image upsample_img = upsample_img.astype(np.uint16) else: upsample_img = upsample_img.astype(np.uint8) if save_path is not None: path = os.path.splitext(save_path)[0] save_path = f'{path}.{self.save_ext}' imwrite(upsample_img, save_path) return upsample_img def clean_all(self): self.all_landmarks_5 = [] self.restored_faces = [] self.affine_matrices = [] self.cropped_faces = [] self.inverse_affine_matrices = [] self.det_faces = [] self.pad_input_imgs = [] ================================================ FILE: extras/facexlib/utils/face_utils.py ================================================ import cv2 import numpy as np import torch def compute_increased_bbox(bbox, increase_area, preserve_aspect=True): left, top, right, bot = bbox width = right - left height = bot - top if preserve_aspect: width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) else: width_increase = height_increase = increase_area left = int(left - width_increase * width) top = int(top - height_increase * height) right = int(right + width_increase * width) bot = int(bot + height_increase * height) return (left, top, right, bot) def get_valid_bboxes(bboxes, h, w): left = max(bboxes[0], 0) top = max(bboxes[1], 0) right = min(bboxes[2], w) bottom = min(bboxes[3], h) return (left, top, right, bottom) def align_crop_face_landmarks(img, landmarks, output_size, transform_size=None, enable_padding=True, return_inverse_affine=False, shrink_ratio=(1, 1)): """Align and crop face with landmarks. The output_size and transform_size are based on width. The height is adjusted based on shrink_ratio_h/shring_ration_w. Modified from: https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py Args: img (Numpy array): Input image. landmarks (Numpy array): 5 or 68 or 98 landmarks. output_size (int): Output face size. transform_size (ing): Transform size. Usually the four time of output_size. enable_padding (float): Default: True. shrink_ratio (float | tuple[float] | list[float]): Shring the whole face for height and width (crop larger area). Default: (1, 1). Returns: (Numpy array): Cropped face. """ lm_type = 'retinaface_5' # Options: dlib_5, retinaface_5 if isinstance(shrink_ratio, (float, int)): shrink_ratio = (shrink_ratio, shrink_ratio) if transform_size is None: transform_size = output_size * 4 # Parse landmarks lm = np.array(landmarks) if lm.shape[0] == 5 and lm_type == 'retinaface_5': eye_left = lm[0] eye_right = lm[1] mouth_avg = (lm[3] + lm[4]) * 0.5 elif lm.shape[0] == 5 and lm_type == 'dlib_5': lm_eye_left = lm[2:4] lm_eye_right = lm[0:2] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = lm[4] elif lm.shape[0] == 68: lm_eye_left = lm[36:42] lm_eye_right = lm[42:48] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = (lm[48] + lm[54]) * 0.5 elif lm.shape[0] == 98: lm_eye_left = lm[60:68] lm_eye_right = lm[68:76] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = (lm[76] + lm[82]) * 0.5 eye_avg = (eye_left + eye_right) * 0.5 eye_to_eye = eye_right - eye_left eye_to_mouth = mouth_avg - eye_avg # Get the oriented crop rectangle # x: half width of the oriented crop rectangle x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise # norm with the hypotenuse: get the direction x /= np.hypot(*x) # get the hypotenuse of a right triangle rect_scale = 1 # TODO: you can edit it to get larger rect x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) # y: half height of the oriented crop rectangle y = np.flipud(x) * [-1, 1] x *= shrink_ratio[1] # width y *= shrink_ratio[0] # height # c: center c = eye_avg + eye_to_mouth * 0.1 # quad: (left_top, left_bottom, right_bottom, right_top) quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) # qsize: side length of the square qsize = np.hypot(*x) * 2 quad_ori = np.copy(quad) # Shrink, for large face # TODO: do we really need shrink shrink = int(np.floor(qsize / output_size * 0.5)) if shrink > 1: h, w = img.shape[0:2] rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink))) img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA) quad /= shrink qsize /= shrink # Crop h, w = img.shape[0:2] border = max(int(np.rint(qsize * 0.1)), 3) crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h)) if crop[2] - crop[0] < w or crop[3] - crop[1] < h: img = img[crop[1]:crop[3], crop[0]:crop[2], :] quad -= crop[0:2] # Pad # pad: (width_left, height_top, width_right, height_bottom) h, w = img.shape[0:2] pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0)) if enable_padding and max(pad) > border - 4: pad = np.maximum(pad, int(np.rint(qsize * 0.3))) img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') h, w = img.shape[0:2] y, x, _ = np.ogrid[:h, :w, :1] mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) blur = int(qsize * 0.02) if blur % 2 == 0: blur += 1 blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur)) img = img.astype('float32') img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) img = np.clip(img, 0, 255) # float32, [0, 255] quad += pad[:2] # Transform use cv2 h_ratio = shrink_ratio[0] / shrink_ratio[1] dst_h, dst_w = int(transform_size * h_ratio), transform_size template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) # use cv2.LMEDS method for the equivalence to skimage transform # ref: https://blog.csdn.net/yichxi/article/details/115827338 affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0] cropped_face = cv2.warpAffine( img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) # gray if output_size < transform_size: cropped_face = cv2.resize( cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR) if return_inverse_affine: dst_h, dst_w = int(output_size * h_ratio), output_size template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) # use cv2.LMEDS method for the equivalence to skimage transform # ref: https://blog.csdn.net/yichxi/article/details/115827338 affine_matrix = cv2.estimateAffinePartial2D( quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0] inverse_affine = cv2.invertAffineTransform(affine_matrix) else: inverse_affine = None return cropped_face, inverse_affine def paste_face_back(img, face, inverse_affine): h, w = img.shape[0:2] face_h, face_w = face.shape[0:2] inv_restored = cv2.warpAffine(face, inverse_affine, (w, h)) mask = np.ones((face_h, face_w, 3), dtype=np.float32) inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h)) # remove the black borders inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8)) inv_restored_remove_border = inv_mask_erosion * inv_restored total_face_area = np.sum(inv_mask_erosion) // 3 # compute the fusion edge based on the area of face w_edge = int(total_face_area**0.5) // 20 erosion_radius = w_edge * 2 inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) blur_size = w_edge * 2 inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img # float32, [0, 255] return img if __name__ == '__main__': import os from extras.facexlib.detection import init_detection_model from extras.facexlib.utils.face_restoration_helper import get_largest_face from extras.facexlib.visualization import visualize_detection img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png' img_name = os.splitext(os.path.basename(img_path))[0] # initialize model det_net = init_detection_model('retinaface_resnet50', half=False) img_ori = cv2.imread(img_path) h, w = img_ori.shape[0:2] # if larger than 800, scale it scale = max(h / 800, w / 800) if scale > 1: img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR) with torch.no_grad(): bboxes = det_net.detect_faces(img, 0.97) if scale > 1: bboxes *= scale # the score is incorrect bboxes = get_largest_face(bboxes, h, w)[0] visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png') landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)]) cropped_face, inverse_affine = align_crop_face_landmarks( img_ori, landmarks, output_size=512, transform_size=None, enable_padding=True, return_inverse_affine=True, shrink_ratio=(1, 1)) cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face) img = paste_face_back(img_ori, cropped_face, inverse_affine) cv2.imwrite(f'tmp/{img_name}_back.png', img) ================================================ FILE: extras/facexlib/utils/misc.py ================================================ import cv2 import os import os.path as osp import torch from torch.hub import download_url_to_file, get_dir from urllib.parse import urlparse ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) def imwrite(img, file_path, params=None, auto_mkdir=True): """Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. """ if auto_mkdir: dir_name = os.path.abspath(os.path.dirname(file_path)) os.makedirs(dir_name, exist_ok=True) return cv2.imwrite(file_path, img, params) def img2tensor(imgs, bgr2rgb=True, float32=True): """Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. """ def _totensor(img, bgr2rgb, float32): if img.shape[2] == 3 and bgr2rgb: if img.dtype == 'float64': img = img.astype('float32') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = torch.from_numpy(img.transpose(2, 0, 1)) if float32: img = img.float() return img if isinstance(imgs, list): return [_totensor(img, bgr2rgb, float32) for img in imgs] else: return _totensor(imgs, bgr2rgb, float32) def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None): """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py """ if model_dir is None: hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') if save_dir is None: save_dir = os.path.join(ROOT_DIR, model_dir) os.makedirs(save_dir, exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(save_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file def scandir(dir_path, suffix=None, recursive=False, full_path=False): """Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. full_path (bool, optional): If set to True, include the dir_path. Default: False. Returns: A generator for all the interested files with relative paths. """ if (suffix is not None) and not isinstance(suffix, (str, tuple)): raise TypeError('"suffix" must be a string or tuple of strings') root = dir_path def _scandir(dir_path, suffix, recursive): for entry in os.scandir(dir_path): if not entry.name.startswith('.') and entry.is_file(): if full_path: return_path = entry.path else: return_path = osp.relpath(entry.path, root) if suffix is None: yield return_path elif return_path.endswith(suffix): yield return_path else: if recursive: yield from _scandir(entry.path, suffix=suffix, recursive=recursive) else: continue return _scandir(dir_path, suffix=suffix, recursive=recursive) ================================================ FILE: extras/inpaint_mask.py ================================================ import sys import modules.config import numpy as np import torch from extras.GroundingDINO.util.inference import default_groundingdino from extras.sam.predictor import SamPredictor from rembg import remove, new_session from segment_anything import sam_model_registry from segment_anything.utils.amg import remove_small_regions class SAMOptions: def __init__(self, # GroundingDINO dino_prompt: str = '', dino_box_threshold=0.3, dino_text_threshold=0.25, dino_erode_or_dilate=0, dino_debug=False, # SAM max_detections=2, model_type='vit_b' ): self.dino_prompt = dino_prompt self.dino_box_threshold = dino_box_threshold self.dino_text_threshold = dino_text_threshold self.dino_erode_or_dilate = dino_erode_or_dilate self.dino_debug = dino_debug self.max_detections = max_detections self.model_type = model_type def optimize_masks(masks: torch.Tensor) -> torch.Tensor: """ removes small disconnected regions and holes """ fine_masks = [] for mask in masks.to('cpu').numpy(): # masks: [num_masks, 1, h, w] fine_masks.append(remove_small_regions(mask[0], 400, mode="holes")[0]) masks = np.stack(fine_masks, axis=0)[:, np.newaxis] return torch.from_numpy(masks) def generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=None, sam_options: SAMOptions | None = SAMOptions) -> tuple[np.ndarray | None, int | None, int | None, int | None]: dino_detection_count = 0 sam_detection_count = 0 sam_detection_on_mask_count = 0 if image is None: return None, dino_detection_count, sam_detection_count, sam_detection_on_mask_count if extras is None: extras = {} if 'image' in image: image = image['image'] if mask_model != 'sam' or sam_options is None: result = remove( image, session=new_session(mask_model, **extras), only_mask=True, **extras ) return result, dino_detection_count, sam_detection_count, sam_detection_on_mask_count detections, boxes, logits, phrases = default_groundingdino( image=image, caption=sam_options.dino_prompt, box_threshold=sam_options.dino_box_threshold, text_threshold=sam_options.dino_text_threshold ) H, W = image.shape[0], image.shape[1] boxes = boxes * torch.Tensor([W, H, W, H]) boxes[:, :2] = boxes[:, :2] - boxes[:, 2:] / 2 boxes[:, 2:] = boxes[:, 2:] + boxes[:, :2] sam_checkpoint = modules.config.download_sam_model(sam_options.model_type) sam = sam_model_registry[sam_options.model_type](checkpoint=sam_checkpoint) sam_predictor = SamPredictor(sam) final_mask_tensor = torch.zeros((image.shape[0], image.shape[1])) dino_detection_count = boxes.size(0) if dino_detection_count > 0: sam_predictor.set_image(image) if sam_options.dino_erode_or_dilate != 0: for index in range(boxes.size(0)): assert boxes.size(1) == 4 boxes[index][0] -= sam_options.dino_erode_or_dilate boxes[index][1] -= sam_options.dino_erode_or_dilate boxes[index][2] += sam_options.dino_erode_or_dilate boxes[index][3] += sam_options.dino_erode_or_dilate if sam_options.dino_debug: from PIL import ImageDraw, Image debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black") draw = ImageDraw.Draw(debug_dino_image) for box in boxes.numpy(): draw.rectangle(box.tolist(), fill="white") return np.array(debug_dino_image), dino_detection_count, sam_detection_count, sam_detection_on_mask_count transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2]) masks, _, _ = sam_predictor.predict_torch( point_coords=None, point_labels=None, boxes=transformed_boxes, multimask_output=False, ) masks = optimize_masks(masks) sam_detection_count = len(masks) if sam_options.max_detections == 0: sam_options.max_detections = sys.maxsize sam_objects = min(len(logits), sam_options.max_detections) for obj_ind in range(sam_objects): mask_tensor = masks[obj_ind][0] final_mask_tensor += mask_tensor sam_detection_on_mask_count += 1 final_mask_tensor = (final_mask_tensor > 0).to('cpu').numpy() mask_image = np.dstack((final_mask_tensor, final_mask_tensor, final_mask_tensor)) * 255 mask_image = np.array(mask_image, dtype=np.uint8) return mask_image, dino_detection_count, sam_detection_count, sam_detection_on_mask_count ================================================ FILE: extras/interrogate.py ================================================ import os import torch import ldm_patched.modules.model_management as model_management from torchvision import transforms from torchvision.transforms.functional import InterpolationMode from modules.model_loader import load_file_from_url from modules.config import path_clip_vision from ldm_patched.modules.model_patcher import ModelPatcher from extras.BLIP.models.blip import blip_decoder blip_image_eval_size = 384 blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP') class Interrogator: def __init__(self): self.blip_model = None self.load_device = torch.device('cpu') self.offload_device = torch.device('cpu') self.dtype = torch.float32 @torch.no_grad() @torch.inference_mode() def interrogate(self, img_rgb): if self.blip_model is None: filename = load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth', model_dir=path_clip_vision, file_name='model_base_caption_capfilt_large.pth', ) model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(blip_repo_root, "configs", "med_config.json")) model.eval() self.load_device = model_management.text_encoder_device() self.offload_device = model_management.text_encoder_offload_device() self.dtype = torch.float32 model.to(self.offload_device) if model_management.should_use_fp16(device=self.load_device): model.half() self.dtype = torch.float16 self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) model_management.load_model_gpu(self.blip_model) gpu_image = transforms.Compose([ transforms.ToTensor(), transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype) caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0] return caption default_interrogator = Interrogator().interrogate ================================================ FILE: extras/ip_adapter.py ================================================ import torch import ldm_patched.modules.clip_vision import safetensors.torch as sf import ldm_patched.modules.model_management as model_management import ldm_patched.ldm.modules.attention as attention from extras.resampler import Resampler from ldm_patched.modules.model_patcher import ModelPatcher from modules.core import numpy_to_pytorch from modules.ops import use_patched_ops from ldm_patched.modules.ops import manual_cast SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2 SD_XL_CHANNELS = [640] * 8 + [1280] * 40 + [1280] * 60 + [640] * 12 + [1280] * 20 def sdp(q, k, v, extra_options): return attention.optimized_attention(q, k, v, heads=extra_options["n_heads"], mask=None) class ImageProjModel(torch.nn.Module): def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): super().__init__() self.cross_attention_dim = cross_attention_dim self.clip_extra_context_tokens = clip_extra_context_tokens self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) self.norm = torch.nn.LayerNorm(cross_attention_dim) def forward(self, image_embeds): embeds = image_embeds clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) clip_extra_context_tokens = self.norm(clip_extra_context_tokens) return clip_extra_context_tokens class To_KV(torch.nn.Module): def __init__(self, cross_attention_dim): super().__init__() channels = SD_XL_CHANNELS if cross_attention_dim == 2048 else SD_V12_CHANNELS self.to_kvs = torch.nn.ModuleList( [torch.nn.Linear(cross_attention_dim, channel, bias=False) for channel in channels]) def load_state_dict_ordered(self, sd): state_dict = [] for i in range(4096): for k in ['k', 'v']: key = f'{i}.to_{k}_ip.weight' if key in sd: state_dict.append(sd[key]) for i, v in enumerate(state_dict): self.to_kvs[i].weight = torch.nn.Parameter(v, requires_grad=False) class IPAdapterModel(torch.nn.Module): def __init__(self, state_dict, plus, cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4, sdxl_plus=False): super().__init__() self.plus = plus if self.plus: self.image_proj_model = Resampler( dim=1280 if sdxl_plus else cross_attention_dim, depth=4, dim_head=64, heads=20 if sdxl_plus else 12, num_queries=clip_extra_context_tokens, embedding_dim=clip_embeddings_dim, output_dim=cross_attention_dim, ff_mult=4 ) else: self.image_proj_model = ImageProjModel( cross_attention_dim=cross_attention_dim, clip_embeddings_dim=clip_embeddings_dim, clip_extra_context_tokens=clip_extra_context_tokens ) self.image_proj_model.load_state_dict(state_dict["image_proj"]) self.ip_layers = To_KV(cross_attention_dim) self.ip_layers.load_state_dict_ordered(state_dict["ip_adapter"]) clip_vision: ldm_patched.modules.clip_vision.ClipVisionModel = None ip_negative: torch.Tensor = None ip_adapters: dict = {} def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path): global clip_vision, ip_negative, ip_adapters if clip_vision is None and isinstance(clip_vision_path, str): clip_vision = ldm_patched.modules.clip_vision.load(clip_vision_path) if ip_negative is None and isinstance(ip_negative_path, str): ip_negative = sf.load_file(ip_negative_path)['data'] if not isinstance(ip_adapter_path, str) or ip_adapter_path in ip_adapters: return load_device = model_management.get_torch_device() offload_device = torch.device('cpu') use_fp16 = model_management.should_use_fp16(device=load_device) ip_state_dict = torch.load(ip_adapter_path, map_location="cpu", weights_only=True) plus = "latents" in ip_state_dict["image_proj"] cross_attention_dim = ip_state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[1] sdxl = cross_attention_dim == 2048 sdxl_plus = sdxl and plus if plus: clip_extra_context_tokens = ip_state_dict["image_proj"]["latents"].shape[1] clip_embeddings_dim = ip_state_dict["image_proj"]["latents"].shape[2] else: clip_extra_context_tokens = ip_state_dict["image_proj"]["proj.weight"].shape[0] // cross_attention_dim clip_embeddings_dim = None with use_patched_ops(manual_cast): ip_adapter = IPAdapterModel( ip_state_dict, plus=plus, cross_attention_dim=cross_attention_dim, clip_embeddings_dim=clip_embeddings_dim, clip_extra_context_tokens=clip_extra_context_tokens, sdxl_plus=sdxl_plus ) ip_adapter.sdxl = sdxl ip_adapter.load_device = load_device ip_adapter.offload_device = offload_device ip_adapter.dtype = torch.float16 if use_fp16 else torch.float32 ip_adapter.to(offload_device, dtype=ip_adapter.dtype) image_proj_model = ModelPatcher(model=ip_adapter.image_proj_model, load_device=load_device, offload_device=offload_device) ip_layers = ModelPatcher(model=ip_adapter.ip_layers, load_device=load_device, offload_device=offload_device) ip_adapters[ip_adapter_path] = dict( ip_adapter=ip_adapter, image_proj_model=image_proj_model, ip_layers=ip_layers, ip_unconds=None ) return @torch.no_grad() @torch.inference_mode() def clip_preprocess(image): mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=image.device, dtype=image.dtype).view([1, 3, 1, 1]) std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=image.device, dtype=image.dtype).view([1, 3, 1, 1]) image = image.movedim(-1, 1) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 B, C, H, W = image.shape assert H == 224 and W == 224 return (image - mean) / std @torch.no_grad() @torch.inference_mode() def preprocess(img, ip_adapter_path): global ip_adapters entry = ip_adapters[ip_adapter_path] ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher) pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device)) outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True) ip_adapter = entry['ip_adapter'] ip_layers = entry['ip_layers'] image_proj_model = entry['image_proj_model'] ip_unconds = entry['ip_unconds'] if ip_adapter.plus: cond = outputs.hidden_states[-2] else: cond = outputs.image_embeds cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) ldm_patched.modules.model_management.load_model_gpu(image_proj_model) cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) ldm_patched.modules.model_management.load_model_gpu(ip_layers) if ip_unconds is None: uncond = ip_negative.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) ip_unconds = [m(uncond).cpu() for m in ip_layers.model.to_kvs] entry['ip_unconds'] = ip_unconds ip_conds = [m(cond).cpu() for m in ip_layers.model.to_kvs] return ip_conds, ip_unconds @torch.no_grad() @torch.inference_mode() def patch_model(model, tasks): new_model = model.clone() def make_attn_patcher(ip_index): def patcher(n, context_attn2, value_attn2, extra_options): org_dtype = n.dtype current_step = float(model.model.diffusion_model.current_step.detach().cpu().numpy()[0]) cond_or_uncond = extra_options['cond_or_uncond'] q = n k = [context_attn2] v = [value_attn2] b, _, _ = q.shape for (cs, ucs), cn_stop, cn_weight in tasks: if current_step < cn_stop: ip_k_c = cs[ip_index * 2].to(q) ip_v_c = cs[ip_index * 2 + 1].to(q) ip_k_uc = ucs[ip_index * 2].to(q) ip_v_uc = ucs[ip_index * 2 + 1].to(q) ip_k = torch.cat([(ip_k_c, ip_k_uc)[i] for i in cond_or_uncond], dim=0) ip_v = torch.cat([(ip_v_c, ip_v_uc)[i] for i in cond_or_uncond], dim=0) # Midjourney's attention formulation of image prompt (non-official reimplementation) # Written by Lvmin Zhang at Stanford University, 2023 Dec # For non-commercial use only - if you use this in commercial project then # probably it has some intellectual property issues. # Contact lvminzhang@acm.org if you are not sure. # Below is the sensitive part with potential intellectual property issues. ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True) ip_v_offset = ip_v - ip_v_mean B, F, C = ip_k.shape channel_penalty = float(C) / 1280.0 weight = cn_weight * channel_penalty ip_k = ip_k * weight ip_v = ip_v_offset + ip_v_mean * weight k.append(ip_k) v.append(ip_v) k = torch.cat(k, dim=1) v = torch.cat(v, dim=1) out = sdp(q, k, v, extra_options) return out.to(dtype=org_dtype) return patcher def set_model_patch_replace(model, number, key): to = model.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if "attn2" not in to["patches_replace"]: to["patches_replace"]["attn2"] = {} if key not in to["patches_replace"]["attn2"]: to["patches_replace"]["attn2"][key] = make_attn_patcher(number) number = 0 for id in [4, 5, 7, 8]: block_indices = range(2) if id in [4, 5] else range(10) for index in block_indices: set_model_patch_replace(new_model, number, ("input", id, index)) number += 1 for id in range(6): block_indices = range(2) if id in [3, 4, 5] else range(10) for index in block_indices: set_model_patch_replace(new_model, number, ("output", id, index)) number += 1 for index in range(10): set_model_patch_replace(new_model, number, ("middle", 0, index)) number += 1 return new_model ================================================ FILE: extras/preprocessors.py ================================================ import cv2 import numpy as np def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 2 and x.dtype == np.uint8 y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold)) y = y.astype(np.float32) / 255.0 return y def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 3 and x.shape[2] == 3 result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)] result = np.stack(result, axis=2) return result def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): assert isinstance(x, np.ndarray) assert x.ndim == 3 and x.shape[2] == 3 H, W, C = x.shape acc_edge = None for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: Hs, Ws = int(H * k), int(W * k) small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA) edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold) if acc_edge is None: acc_edge = edge else: acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR) acc_edge = acc_edge * 0.75 + edge * 0.25 return acc_edge def norm255(x, low=4, high=96): assert isinstance(x, np.ndarray) assert x.ndim == 2 and x.dtype == np.float32 v_min = np.percentile(x, low) v_max = np.percentile(x, high) x -= v_min x /= v_max - v_min return x * 255.0 def canny_pyramid(x, canny_low_threshold, canny_high_threshold): # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions. # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions. color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold) result = np.sum(color_canny, axis=2) return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8) def cpds(x): # cv2.decolor is not "decolor", it is Cewu Lu's method # See http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html # See https://docs.opencv.org/3.0-beta/modules/photo/doc/decolor.html raw = cv2.GaussianBlur(x, (0, 0), 0.8) density, boost = cv2.decolor(raw) raw = raw.astype(np.float32) density = density.astype(np.float32) boost = boost.astype(np.float32) offset = np.sum((raw - boost) ** 2.0, axis=2) ** 0.5 result = density + offset return norm255(result, low=4, high=96).clip(0, 255).astype(np.uint8) ================================================ FILE: extras/resampler.py ================================================ # modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py import math import torch import torch.nn as nn # FFN def FeedForward(dim, mult=4): inner_dim = int(dim * mult) return nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, inner_dim, bias=False), nn.GELU(), nn.Linear(inner_dim, dim, bias=False), ) def reshape_tensor(x, heads): bs, length, width = x.shape #(bs, length, width) --> (bs, length, n_heads, dim_per_head) x = x.view(bs, length, heads, -1) # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) x = x.transpose(1, 2) # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) x = x.reshape(bs, heads, length, -1) return x class PerceiverAttention(nn.Module): def __init__(self, *, dim, dim_head=64, heads=8): super().__init__() self.scale = dim_head**-0.5 self.dim_head = dim_head self.heads = heads inner_dim = dim_head * heads self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) self.to_out = nn.Linear(inner_dim, dim, bias=False) def forward(self, x, latents): """ Args: x (torch.Tensor): image features shape (b, n1, D) latent (torch.Tensor): latent features shape (b, n2, D) """ x = self.norm1(x) latents = self.norm2(latents) b, l, _ = latents.shape q = self.to_q(latents) kv_input = torch.cat((x, latents), dim=-2) k, v = self.to_kv(kv_input).chunk(2, dim=-1) q = reshape_tensor(q, self.heads) k = reshape_tensor(k, self.heads) v = reshape_tensor(v, self.heads) # attention scale = 1 / math.sqrt(math.sqrt(self.dim_head)) weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) out = weight @ v out = out.permute(0, 2, 1, 3).reshape(b, l, -1) return self.to_out(out) class Resampler(nn.Module): def __init__( self, dim=1024, depth=8, dim_head=64, heads=16, num_queries=8, embedding_dim=768, output_dim=1024, ff_mult=4, ): super().__init__() self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) self.proj_in = nn.Linear(embedding_dim, dim) self.proj_out = nn.Linear(dim, output_dim) self.norm_out = nn.LayerNorm(output_dim) self.layers = nn.ModuleList([]) for _ in range(depth): self.layers.append( nn.ModuleList( [ PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), FeedForward(dim=dim, mult=ff_mult), ] ) ) def forward(self, x): latents = self.latents.repeat(x.size(0), 1, 1).to(x) x = self.proj_in(x) for attn, ff in self.layers: latents = attn(x, latents) + latents latents = ff(latents) + latents latents = self.proj_out(latents) return self.norm_out(latents) ================================================ FILE: extras/safety_checker/configs/config.json ================================================ { "_name_or_path": "clip-vit-large-patch14/", "architectures": [ "SafetyChecker" ], "initializer_factor": 1.0, "logit_scale_init_value": 2.6592, "model_type": "clip", "projection_dim": 768, "text_config": { "_name_or_path": "", "add_cross_attention": false, "architectures": null, "attention_dropout": 0.0, "bad_words_ids": null, "bos_token_id": 0, "chunk_size_feed_forward": 0, "cross_attention_hidden_size": null, "decoder_start_token_id": null, "diversity_penalty": 0.0, "do_sample": false, "dropout": 0.0, "early_stopping": false, "encoder_no_repeat_ngram_size": 0, "eos_token_id": 2, "exponential_decay_length_penalty": null, "finetuning_task": null, "forced_bos_token_id": null, "forced_eos_token_id": null, "hidden_act": "quick_gelu", "hidden_size": 768, "id2label": { "0": "LABEL_0", "1": "LABEL_1" }, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "is_decoder": false, "is_encoder_decoder": false, "label2id": { "LABEL_0": 0, "LABEL_1": 1 }, "layer_norm_eps": 1e-05, "length_penalty": 1.0, "max_length": 20, "max_position_embeddings": 77, "min_length": 0, "model_type": "clip_text_model", "no_repeat_ngram_size": 0, "num_attention_heads": 12, "num_beam_groups": 1, "num_beams": 1, "num_hidden_layers": 12, "num_return_sequences": 1, "output_attentions": false, "output_hidden_states": false, "output_scores": false, "pad_token_id": 1, "prefix": null, "problem_type": null, "pruned_heads": {}, "remove_invalid_values": false, "repetition_penalty": 1.0, "return_dict": true, "return_dict_in_generate": false, "sep_token_id": null, "task_specific_params": null, "temperature": 1.0, "tie_encoder_decoder": false, "tie_word_embeddings": true, "tokenizer_class": null, "top_k": 50, "top_p": 1.0, "torch_dtype": null, "torchscript": false, "transformers_version": "4.21.0.dev0", "typical_p": 1.0, "use_bfloat16": false, "vocab_size": 49408 }, "text_config_dict": { "hidden_size": 768, "intermediate_size": 3072, "num_attention_heads": 12, "num_hidden_layers": 12 }, "torch_dtype": "float32", "transformers_version": null, "vision_config": { "_name_or_path": "", "add_cross_attention": false, "architectures": null, "attention_dropout": 0.0, "bad_words_ids": null, "bos_token_id": null, "chunk_size_feed_forward": 0, "cross_attention_hidden_size": null, "decoder_start_token_id": null, "diversity_penalty": 0.0, "do_sample": false, "dropout": 0.0, "early_stopping": false, "encoder_no_repeat_ngram_size": 0, "eos_token_id": null, "exponential_decay_length_penalty": null, "finetuning_task": null, "forced_bos_token_id": null, "forced_eos_token_id": null, "hidden_act": "quick_gelu", "hidden_size": 1024, "id2label": { "0": "LABEL_0", "1": "LABEL_1" }, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "is_decoder": false, "is_encoder_decoder": false, "label2id": { "LABEL_0": 0, "LABEL_1": 1 }, "layer_norm_eps": 1e-05, "length_penalty": 1.0, "max_length": 20, "min_length": 0, "model_type": "clip_vision_model", "no_repeat_ngram_size": 0, "num_attention_heads": 16, "num_beam_groups": 1, "num_beams": 1, "num_hidden_layers": 24, "num_return_sequences": 1, "output_attentions": false, "output_hidden_states": false, "output_scores": false, "pad_token_id": null, "patch_size": 14, "prefix": null, "problem_type": null, "pruned_heads": {}, "remove_invalid_values": false, "repetition_penalty": 1.0, "return_dict": true, "return_dict_in_generate": false, "sep_token_id": null, "task_specific_params": null, "temperature": 1.0, "tie_encoder_decoder": false, "tie_word_embeddings": true, "tokenizer_class": null, "top_k": 50, "top_p": 1.0, "torch_dtype": null, "torchscript": false, "transformers_version": "4.21.0.dev0", "typical_p": 1.0, "use_bfloat16": false }, "vision_config_dict": { "hidden_size": 1024, "intermediate_size": 4096, "num_attention_heads": 16, "num_hidden_layers": 24, "patch_size": 14 } } ================================================ FILE: extras/safety_checker/configs/preprocessor_config.json ================================================ { "crop_size": 224, "do_center_crop": true, "do_convert_rgb": true, "do_normalize": true, "do_resize": true, "feature_extractor_type": "CLIPFeatureExtractor", "image_mean": [ 0.48145466, 0.4578275, 0.40821073 ], "image_std": [ 0.26862954, 0.26130258, 0.27577711 ], "resample": 3, "size": 224 } ================================================ FILE: extras/safety_checker/models/safety_checker.py ================================================ # from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from transformers.utils import logging logger = logging.get_logger(__name__) def cosine_distance(image_embeds, text_embeds): normalized_image_embeds = nn.functional.normalize(image_embeds) normalized_text_embeds = nn.functional.normalize(text_embeds) return torch.mm(normalized_image_embeds, normalized_text_embeds.t()) class StableDiffusionSafetyChecker(PreTrainedModel): config_class = CLIPConfig main_input_name = "clip_input" _no_split_modules = ["CLIPEncoderLayer"] def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModel(config.vision_config) self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False) self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False) self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False) self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False) self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False) @torch.no_grad() def forward(self, clip_input, images): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy() cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy() result = [] batch_size = image_embeds.shape[0] for i in range(batch_size): result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 for concept_idx in range(len(special_cos_dist[0])): concept_cos = special_cos_dist[i][concept_idx] concept_threshold = self.special_care_embeds_weights[concept_idx].item() result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["special_scores"][concept_idx] > 0: result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]}) adjustment = 0.01 for concept_idx in range(len(cos_dist[0])): concept_cos = cos_dist[i][concept_idx] concept_threshold = self.concept_embeds_weights[concept_idx].item() result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["concept_scores"][concept_idx] > 0: result_img["bad_concepts"].append(concept_idx) result.append(result_img) has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result] for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): if has_nsfw_concept: if torch.is_tensor(images) or torch.is_tensor(images[0]): images[idx] = torch.zeros_like(images[idx]) # black image else: images[idx] = np.zeros(images[idx].shape) # black image if any(has_nsfw_concepts): logger.warning( "Potential NSFW content was detected in one or more images. A black image will be returned instead." " Try again with a different prompt and/or seed." ) return images, has_nsfw_concepts @torch.no_grad() def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds) cos_dist = cosine_distance(image_embeds, self.concept_embeds) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) special_care = torch.any(special_scores > 0, dim=1) special_adjustment = special_care * 0.01 special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1]) concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) has_nsfw_concepts = torch.any(concept_scores > 0, dim=1) images[has_nsfw_concepts] = 0.0 # black image return images, has_nsfw_concepts ================================================ FILE: extras/sam/predictor.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import numpy as np import torch from ldm_patched.modules import model_management from ldm_patched.modules.model_patcher import ModelPatcher from segment_anything.modeling import Sam from typing import Optional, Tuple from segment_anything.utils.transforms import ResizeLongestSide class SamPredictor: def __init__( self, model: Sam, load_device=model_management.text_encoder_device(), offload_device=model_management.text_encoder_offload_device() ) -> None: """ Uses SAM to calculate the image embedding for an image, and then allow repeated, efficient mask prediction given prompts. Arguments: model (Sam): The model to use for mask prediction. """ super().__init__() self.load_device = load_device self.offload_device = offload_device # can't use model.half() here as slow_conv2d_cpu is not implemented for half model.to(self.offload_device) self.patcher = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) self.transform = ResizeLongestSide(model.image_encoder.img_size) self.reset_image() def set_image( self, image: np.ndarray, image_format: str = "RGB", ) -> None: """ Calculates the image embeddings for the provided image, allowing masks to be predicted with the 'predict' method. Arguments: image (np.ndarray): The image for calculating masks. Expects an image in HWC uint8 format, with pixel values in [0, 255]. image_format (str): The color format of the image, in ['RGB', 'BGR']. """ assert image_format in [ "RGB", "BGR", ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." if image_format != self.patcher.model.image_format: image = image[..., ::-1] # Transform the image to the form expected by the model input_image = self.transform.apply_image(image) input_image_torch = torch.as_tensor(input_image, device=self.load_device) input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :] self.set_torch_image(input_image_torch, image.shape[:2]) @torch.no_grad() def set_torch_image( self, transformed_image: torch.Tensor, original_image_size: Tuple[int, ...], ) -> None: """ Calculates the image embeddings for the provided image, allowing masks to be predicted with the 'predict' method. Expects the input image to be already transformed to the format expected by the model. Arguments: transformed_image (torch.Tensor): The input image, with shape 1x3xHxW, which has been transformed with ResizeLongestSide. original_image_size (tuple(int, int)): The size of the image before transformation, in (H, W) format. """ assert ( len(transformed_image.shape) == 4 and transformed_image.shape[1] == 3 and max(*transformed_image.shape[2:]) == self.patcher.model.image_encoder.img_size ), f"set_torch_image input must be BCHW with long side {self.patcher.model.image_encoder.img_size}." self.reset_image() self.original_size = original_image_size self.input_size = tuple(transformed_image.shape[-2:]) model_management.load_model_gpu(self.patcher) input_image = self.patcher.model.preprocess(transformed_image.to(self.load_device)) self.features = self.patcher.model.image_encoder(input_image) self.is_image_set = True def predict( self, point_coords: Optional[np.ndarray] = None, point_labels: Optional[np.ndarray] = None, box: Optional[np.ndarray] = None, mask_input: Optional[np.ndarray] = None, multimask_output: bool = True, return_logits: bool = False, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None): A Nx2 array of point prompts to the model. Each point is in (X,Y) in pixels. point_labels (np.ndarray or None): A length N array of labels for the point prompts. 1 indicates a foreground point and 0 indicates a background point. box (np.ndarray or None): A length 4 array given a box prompt to the model, in XYXY format. mask_input (np.ndarray): A low resolution mask input to the model, typically coming from a previous prediction iteration. Has form 1xHxW, where for SAM, H=W=256. multimask_output (bool): If true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction. If only a single mask is needed, the model's predicted quality score can be used to select the best mask. For non-ambiguous prompts, such as multiple input prompts, multimask_output=False can give better results. return_logits (bool): If true, returns un-thresholded masks logits instead of a binary mask. Returns: (np.ndarray): The output masks in CxHxW format, where C is the number of masks, and (H, W) is the original image size. (np.ndarray): An array of length C containing the model's predictions for the quality of each mask. (np.ndarray): An array of shape CxHxW, where C is the number of masks and H=W=256. These low resolution logits can be passed to a subsequent iteration as mask input. """ if not self.is_image_set: raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") # Transform input prompts coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None if point_coords is not None: assert ( point_labels is not None ), "point_labels must be supplied if point_coords is supplied." point_coords = self.transform.apply_coords(point_coords, self.original_size) coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.load_device) labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.load_device) coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] if box is not None: box = self.transform.apply_boxes(box, self.original_size) box_torch = torch.as_tensor(box, dtype=torch.float, device=self.load_device) box_torch = box_torch[None, :] if mask_input is not None: mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.load_device) mask_input_torch = mask_input_torch[None, :, :, :] masks, iou_predictions, low_res_masks = self.predict_torch( coords_torch, labels_torch, box_torch, mask_input_torch, multimask_output, return_logits=return_logits, ) masks = masks[0].detach().cpu().numpy() iou_predictions = iou_predictions[0].detach().cpu().numpy() low_res_masks = low_res_masks[0].detach().cpu().numpy() return masks, iou_predictions, low_res_masks @torch.no_grad() def predict_torch( self, point_coords: Optional[torch.Tensor], point_labels: Optional[torch.Tensor], boxes: Optional[torch.Tensor] = None, mask_input: Optional[torch.Tensor] = None, multimask_output: bool = True, return_logits: bool = False, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Predict masks for the given input prompts, using the currently set image. Input prompts are batched torch tensors and are expected to already be transformed to the input frame using ResizeLongestSide. Arguments: point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the model. Each point is in (X,Y) in pixels. point_labels (torch.Tensor or None): A BxN array of labels for the point prompts. 1 indicates a foreground point and 0 indicates a background point. box (np.ndarray or None): A Bx4 array given a box prompt to the model, in XYXY format. mask_input (np.ndarray): A low resolution mask input to the model, typically coming from a previous prediction iteration. Has form Bx1xHxW, where for SAM, H=W=256. Masks returned by a previous iteration of the predict method do not need further transformation. multimask_output (bool): If true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction. If only a single mask is needed, the model's predicted quality score can be used to select the best mask. For non-ambiguous prompts, such as multiple input prompts, multimask_output=False can give better results. return_logits (bool): If true, returns un-thresholded masks logits instead of a binary mask. Returns: (torch.Tensor): The output masks in BxCxHxW format, where C is the number of masks, and (H, W) is the original image size. (torch.Tensor): An array of shape BxC containing the model's predictions for the quality of each mask. (torch.Tensor): An array of shape BxCxHxW, where C is the number of masks and H=W=256. These low res logits can be passed to a subsequent iteration as mask input. """ if not self.is_image_set: raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") if point_coords is not None: points = (point_coords.to(self.load_device), point_labels.to(self.load_device)) else: points = None # load if boxes is not None: boxes = boxes.to(self.load_device) if mask_input is not None: mask_input = mask_input.to(self.load_device) model_management.load_model_gpu(self.patcher) # Embed prompts sparse_embeddings, dense_embeddings = self.patcher.model.prompt_encoder( points=points, boxes=boxes, masks=mask_input, ) # Predict masks low_res_masks, iou_predictions = self.patcher.model.mask_decoder( image_embeddings=self.features, image_pe=self.patcher.model.prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, ) # Upscale the masks to the original image resolution masks = self.patcher.model.postprocess_masks(low_res_masks, self.input_size, self.original_size) if not return_logits: masks = masks > self.patcher.model.mask_threshold return masks, iou_predictions, low_res_masks def get_image_embedding(self) -> torch.Tensor: """ Returns the image embeddings for the currently set image, with shape 1xCxHxW, where C is the embedding dimension and (H,W) are the embedding spatial dimension of SAM (typically C=256, H=W=64). """ if not self.is_image_set: raise RuntimeError( "An image must be set with .set_image(...) to generate an embedding." ) assert self.features is not None, "Features must exist if an image has been set." return self.features @property def device(self) -> torch.device: return self.patcher.model.device def reset_image(self) -> None: """Resets the currently set image.""" self.is_image_set = False self.features = None self.orig_h = None self.orig_w = None self.input_h = None self.input_w = None ================================================ FILE: extras/vae_interpose.py ================================================ # https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py import os import safetensors.torch as sf import torch import torch.nn as nn import ldm_patched.modules.model_management from ldm_patched.modules.model_patcher import ModelPatcher from modules.config import path_vae_approx class ResBlock(nn.Module): """Block with residuals""" def __init__(self, ch): super().__init__() self.join = nn.ReLU() self.norm = nn.BatchNorm2d(ch) self.long = nn.Sequential( nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1), nn.Dropout(0.1) ) def forward(self, x): x = self.norm(x) return self.join(self.long(x) + x) class ExtractBlock(nn.Module): """Increase no. of channels by [out/in]""" def __init__(self, ch_in, ch_out): super().__init__() self.join = nn.ReLU() self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1) self.long = nn.Sequential( nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1), nn.SiLU(), nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1), nn.Dropout(0.1) ) def forward(self, x): return self.join(self.long(x) + self.short(x)) class InterposerModel(nn.Module): """Main neural network""" def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12): super().__init__() self.ch_in = ch_in self.ch_out = ch_out self.ch_mid = ch_mid self.blocks = blocks self.scale = scale self.head = ExtractBlock(self.ch_in, self.ch_mid) self.core = nn.Sequential( nn.Upsample(scale_factor=self.scale, mode="nearest"), *[ResBlock(self.ch_mid) for _ in range(blocks)], nn.BatchNorm2d(self.ch_mid), nn.SiLU(), ) self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1) def forward(self, x): y = self.head(x) z = self.core(y) return self.tail(z) vae_approx_model = None vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors') def parse(x): global vae_approx_model x_origin = x.clone() if vae_approx_model is None: model = InterposerModel() model.eval() sd = sf.load_file(vae_approx_filename) model.load_state_dict(sd) fp16 = ldm_patched.modules.model_management.should_use_fp16() if fp16: model = model.half() vae_approx_model = ModelPatcher( model=model, load_device=ldm_patched.modules.model_management.get_torch_device(), offload_device=torch.device('cpu') ) vae_approx_model.dtype = torch.float16 if fp16 else torch.float32 ldm_patched.modules.model_management.load_model_gpu(vae_approx_model) x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype) x = vae_approx_model.model(x).to(x_origin) return x ================================================ FILE: extras/wd14tagger.py ================================================ # https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags # https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py # { # "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2", # "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2", # "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2", # "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger", # "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2" # } import numpy as np import csv import onnxruntime as ort from PIL import Image from onnxruntime import InferenceSession from modules.config import path_clip_vision from modules.model_loader import load_file_from_url global_model = None global_csv = None def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""): global global_model, global_csv model_name = "wd-v1-4-moat-tagger-v2" model_onnx_filename = load_file_from_url( url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx', model_dir=path_clip_vision, file_name=f'{model_name}.onnx', ) model_csv_filename = load_file_from_url( url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv', model_dir=path_clip_vision, file_name=f'{model_name}.csv', ) if global_model is not None: model = global_model else: model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers()) global_model = model input = model.get_inputs()[0] height = input.shape[1] image = Image.fromarray(image_rgb) # RGB ratio = float(height)/max(image.size) new_size = tuple([int(x*ratio) for x in image.size]) image = image.resize(new_size, Image.LANCZOS) square = Image.new("RGB", (height, height), (255, 255, 255)) square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2)) image = np.array(square).astype(np.float32) image = image[:, :, ::-1] # RGB -> BGR image = np.expand_dims(image, 0) if global_csv is not None: csv_lines = global_csv else: csv_lines = [] with open(model_csv_filename) as f: reader = csv.reader(f) next(reader) for row in reader: csv_lines.append(row) global_csv = csv_lines tags = [] general_index = None character_index = None for line_num, row in enumerate(csv_lines): if general_index is None and row[2] == "0": general_index = line_num elif character_index is None and row[2] == "4": character_index = line_num tags.append(row[1]) label_name = model.get_outputs()[0].name probs = model.run([label_name], {input.name: image})[0] result = list(zip(tags, probs[0])) general = [item for item in result[general_index:character_index] if item[1] > threshold] character = [item for item in result[character_index:] if item[1] > character_threshold] all = character + general remove = [s.strip() for s in exclude_tags.lower().split(",")] all = [tag for tag in all if tag[0] not in remove] res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ') return res ================================================ FILE: fooocus_colab.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "VjYy0F2gZIPR" }, "outputs": [], "source": [ "!pip install pygit2==1.15.1\n", "%cd /content\n", "!git clone https://github.com/lllyasviel/Fooocus.git\n", "%cd /content/Fooocus\n", "!python entry_with_update.py --share --always-high-vram\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: fooocus_version.py ================================================ version = '2.5.5' ================================================ FILE: javascript/contextMenus.js ================================================ // based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/javascript/contextMenus.js var contextMenuInit = function() { let eventListenerApplied = false; let menuSpecs = new Map(); const uid = function() { return Date.now().toString(36) + Math.random().toString(36).substring(2); }; function showContextMenu(event, element, menuEntries) { let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } let baseStyle = window.getComputedStyle(gradioApp().querySelector('button.selected')); const contextMenu = document.createElement('nav'); contextMenu.id = "context-menu"; contextMenu.style.background = baseStyle.background; contextMenu.style.color = baseStyle.color; contextMenu.style.fontFamily = baseStyle.fontFamily; contextMenu.style.top = posy + 'px'; contextMenu.style.left = posx + 'px'; const contextMenuList = document.createElement('ul'); contextMenuList.className = 'context-menu-items'; contextMenu.append(contextMenuList); menuEntries.forEach(function(entry) { let contextMenuEntry = document.createElement('a'); contextMenuEntry.innerHTML = entry['name']; contextMenuEntry.addEventListener("click", function() { entry['func'](); }); contextMenuList.append(contextMenuEntry); }); gradioApp().appendChild(contextMenu); let menuWidth = contextMenu.offsetWidth + 4; let menuHeight = contextMenu.offsetHeight + 4; let windowWidth = window.innerWidth; let windowHeight = window.innerHeight; if ((windowWidth - posx) < menuWidth) { contextMenu.style.left = windowWidth - menuWidth + "px"; } if ((windowHeight - posy) < menuHeight) { contextMenu.style.top = windowHeight - menuHeight + "px"; } } function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { var currentItems = menuSpecs.get(targetElementSelector); if (!currentItems) { currentItems = []; menuSpecs.set(targetElementSelector, currentItems); } let newItem = { id: targetElementSelector + '_' + uid(), name: entryName, func: entryFunction, isNew: true }; currentItems.push(newItem); return newItem['id']; } function removeContextMenuOption(uid) { menuSpecs.forEach(function(v) { let index = -1; v.forEach(function(e, ei) { if (e['id'] == uid) { index = ei; } }); if (index >= 0) { v.splice(index, 1); } }); } function addContextMenuEventListener() { if (eventListenerApplied) { return; } gradioApp().addEventListener("click", function(e) { if (!e.isTrusted) { return; } let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } }); gradioApp().addEventListener("contextmenu", function(e) { let oldMenu = gradioApp().querySelector('#context-menu'); if (oldMenu) { oldMenu.remove(); } menuSpecs.forEach(function(v, k) { if (e.composedPath()[0].matches(k)) { showContextMenu(e, e.composedPath()[0], v); e.preventDefault(); } }); }); eventListenerApplied = true; } return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; }; var initResponse = contextMenuInit(); var appendContextMenuOption = initResponse[0]; var removeContextMenuOption = initResponse[1]; var addContextMenuEventListener = initResponse[2]; let cancelGenerateForever = function() { clearInterval(window.generateOnRepeatInterval); }; (function() { //Start example Context Menu Items let generateOnRepeat = function(genbuttonid, interruptbuttonid) { let genbutton = gradioApp().querySelector(genbuttonid); let interruptbutton = gradioApp().querySelector(interruptbuttonid); if (!interruptbutton.offsetParent) { genbutton.click(); } clearInterval(window.generateOnRepeatInterval); window.generateOnRepeatInterval = setInterval(function() { if (!interruptbutton.offsetParent) { genbutton.click(); } }, 500); }; let generateOnRepeatForButtons = function() { generateOnRepeat('#generate_button', '#stop_button'); }; appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons); })(); //End example Context Menu Items document.onreadystatechange = function () { if (document.readyState == "complete") { addContextMenuEventListener(); } }; ================================================ FILE: javascript/edit-attention.js ================================================ function updateInput(target) { let e = new Event("input", {bubbles: true}); Object.defineProperty(e, "target", {value: target}); target.dispatchEvent(e); } function keyupEditAttention(event) { let target = event.originalTarget || event.composedPath()[0]; if (!target.matches("*:is([id*='_prompt'], .prompt) textarea")) return; if (!(event.metaKey || event.ctrlKey)) return; let isPlus = event.key == "ArrowUp"; let isMinus = event.key == "ArrowDown"; if (!isPlus && !isMinus) return; let selectionStart = target.selectionStart; let selectionEnd = target.selectionEnd; let text = target.value; function selectCurrentParenthesisBlock(OPEN, CLOSE) { if (selectionStart !== selectionEnd) return false; // Find opening parenthesis around current cursor const before = text.substring(0, selectionStart); let beforeParen = before.lastIndexOf(OPEN); if (beforeParen == -1) return false; let beforeParenClose = before.lastIndexOf(CLOSE); while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); } // Find closing parenthesis around current cursor const after = text.substring(selectionStart); let afterParen = after.indexOf(CLOSE); if (afterParen == -1) return false; let afterParenOpen = after.indexOf(OPEN); while (afterParenOpen !== -1 && afterParen > afterParenOpen) { afterParen = after.indexOf(CLOSE, afterParen + 1); afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); } if (beforeParen === -1 || afterParen === -1) return false; // Set the selection to the text between the parenthesis const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); const lastColon = parenContent.lastIndexOf(":"); selectionStart = beforeParen + 1; selectionEnd = selectionStart + lastColon; target.setSelectionRange(selectionStart, selectionEnd); return true; } function selectCurrentWord() { if (selectionStart !== selectionEnd) return false; const delimiters = ".,\\/!?%^*;:{}=`~() \r\n\t"; // seek backward until to find beggining while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { selectionStart--; } // seek forward to find end while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { selectionEnd++; } target.setSelectionRange(selectionStart, selectionEnd); return true; } // If the user hasn't selected anything, let's select their current parenthesis block or word if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { selectCurrentWord(); } event.preventDefault(); var closeCharacter = ')'; var delta = 0.1; if (selectionStart > 0 && text[selectionStart - 1] == '<') { closeCharacter = '>'; delta = 0.05; } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { // do not include spaces at the end while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { selectionEnd -= 1; } if (selectionStart == selectionEnd) { return; } text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); selectionStart += 1; selectionEnd += 1; } var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end)); if (isNaN(weight)) return; weight += isPlus ? delta : -delta; weight = parseFloat(weight.toPrecision(12)); if (String(weight).length == 1) weight += ".0"; if (closeCharacter == ')' && weight == 1) { var endParenPos = text.substring(selectionEnd).indexOf(')'); text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); selectionStart--; selectionEnd--; } else { text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end); } target.focus(); target.value = text; target.selectionStart = selectionStart; target.selectionEnd = selectionEnd; updateInput(target); } addEventListener('keydown', (event) => { keyupEditAttention(event); }); ================================================ FILE: javascript/imageviewer.js ================================================ // From A1111 function closeModal() { gradioApp().getElementById("lightboxModal").style.display = "none"; } function showModal(event) { const source = event.target || event.srcElement; const modalImage = gradioApp().getElementById("modalImage"); const lb = gradioApp().getElementById("lightboxModal"); modalImage.src = source.src; if (modalImage.style.display === 'none') { lb.style.setProperty('background-image', 'url(' + source.src + ')'); } lb.style.display = "flex"; lb.focus(); event.stopPropagation(); } function negmod(n, m) { return ((n % m) + m) % m; } function updateOnBackgroundChange() { const modalImage = gradioApp().getElementById("modalImage"); if (modalImage && modalImage.offsetParent) { let currentButton = selected_gallery_button(); if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; if (modalImage.style.display === 'none') { const modal = gradioApp().getElementById("lightboxModal"); modal.style.setProperty('background-image', `url(${modalImage.src})`); } } } } function all_gallery_buttons() { var allGalleryButtons = gradioApp().querySelectorAll('.image_gallery .thumbnails > .thumbnail-item.thumbnail-small'); var visibleGalleryButtons = []; allGalleryButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { visibleGalleryButtons.push(elem); } }); return visibleGalleryButtons; } function selected_gallery_button() { return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null; } function selected_gallery_index() { return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected')); } function modalImageSwitch(offset) { var galleryButtons = all_gallery_buttons(); if (galleryButtons.length > 1) { var currentButton = selected_gallery_button(); var result = -1; galleryButtons.forEach(function(v, i) { if (v == currentButton) { result = i; } }); if (result != -1) { var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; nextButton.click(); const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); modalImage.src = nextButton.children[0].src; if (modalImage.style.display === 'none') { modal.style.setProperty('background-image', `url(${modalImage.src})`); } setTimeout(function() { modal.focus(); }, 10); } } } function saveImage() { } function modalSaveImage(event) { event.stopPropagation(); } function modalNextImage(event) { modalImageSwitch(1); event.stopPropagation(); } function modalPrevImage(event) { modalImageSwitch(-1); event.stopPropagation(); } function modalKeyHandler(event) { switch (event.key) { case "s": saveImage(); break; case "ArrowLeft": modalPrevImage(event); break; case "ArrowRight": modalNextImage(event); break; case "Escape": closeModal(); break; } } function setupImageForLightbox(e) { if (e.dataset.modded) { return; } e.dataset.modded = true; e.style.cursor = 'pointer'; e.style.userSelect = 'none'; var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; // For Firefox, listening on click first switched to next image then shows the lightbox. // If you know how to fix this without switching to mousedown event, please. // For other browsers the event is click to make it possiblr to drag picture. var event = isFirefox ? 'mousedown' : 'click'; e.addEventListener(event, function(evt) { if (evt.button == 1) { open(evt.target.src); evt.preventDefault(); return; } if (evt.button != 0) return; modalZoomSet(gradioApp().getElementById('modalImage'), true); evt.preventDefault(); showModal(evt); }, true); } function modalZoomSet(modalImage, enable) { if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable); } function modalZoomToggle(event) { var modalImage = gradioApp().getElementById("modalImage"); modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')); event.stopPropagation(); } function modalTileImageToggle(event) { const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); const isTiling = modalImage.style.display === 'none'; if (isTiling) { modalImage.style.display = 'block'; modal.style.setProperty('background-image', 'none'); } else { modalImage.style.display = 'none'; modal.style.setProperty('background-image', `url(${modalImage.src})`); } event.stopPropagation(); } onAfterUiUpdate(function() { var fullImg_preview = gradioApp().querySelectorAll('.image_gallery > div > img'); if (fullImg_preview != null) { fullImg_preview.forEach(setupImageForLightbox); } updateOnBackgroundChange(); }); document.addEventListener("DOMContentLoaded", function() { //const modalFragment = document.createDocumentFragment(); const modal = document.createElement('div'); modal.onclick = closeModal; modal.id = "lightboxModal"; modal.tabIndex = 0; modal.addEventListener('keydown', modalKeyHandler, true); const modalControls = document.createElement('div'); modalControls.className = 'modalControls gradio-container'; modal.append(modalControls); const modalZoom = document.createElement('span'); modalZoom.className = 'modalZoom cursor'; modalZoom.innerHTML = '⤡'; modalZoom.addEventListener('click', modalZoomToggle, true); modalZoom.title = "Toggle zoomed view"; modalControls.appendChild(modalZoom); // const modalTileImage = document.createElement('span'); // modalTileImage.className = 'modalTileImage cursor'; // modalTileImage.innerHTML = '⊞'; // modalTileImage.addEventListener('click', modalTileImageToggle, true); // modalTileImage.title = "Preview tiling"; // modalControls.appendChild(modalTileImage); // // const modalSave = document.createElement("span"); // modalSave.className = "modalSave cursor"; // modalSave.id = "modal_save"; // modalSave.innerHTML = "🖫"; // modalSave.addEventListener("click", modalSaveImage, true); // modalSave.title = "Save Image(s)"; // modalControls.appendChild(modalSave); const modalClose = document.createElement('span'); modalClose.className = 'modalClose cursor'; modalClose.innerHTML = '×'; modalClose.onclick = closeModal; modalClose.title = "Close image viewer"; modalControls.appendChild(modalClose); const modalImage = document.createElement('img'); modalImage.id = 'modalImage'; modalImage.onclick = closeModal; modalImage.tabIndex = 0; modalImage.addEventListener('keydown', modalKeyHandler, true); modal.appendChild(modalImage); const modalPrev = document.createElement('a'); modalPrev.className = 'modalPrev'; modalPrev.innerHTML = '❮'; modalPrev.tabIndex = 0; modalPrev.addEventListener('click', modalPrevImage, true); modalPrev.addEventListener('keydown', modalKeyHandler, true); modal.appendChild(modalPrev); const modalNext = document.createElement('a'); modalNext.className = 'modalNext'; modalNext.innerHTML = '❯'; modalNext.tabIndex = 0; modalNext.addEventListener('click', modalNextImage, true); modalNext.addEventListener('keydown', modalKeyHandler, true); modal.appendChild(modalNext); try { gradioApp().appendChild(modal); } catch (e) { gradioApp().body.appendChild(modal); } document.body.appendChild(modal); }); ================================================ FILE: javascript/localization.js ================================================ var re_num = /^[.\d]+$/; var original_lines = {}; var translated_lines = {}; function hasLocalization() { return window.localization && Object.keys(window.localization).length > 0; } function textNodesUnder(el) { var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false); while ((n = walk.nextNode())) a.push(n); return a; } function canBeTranslated(node, text) { if (!text) return false; if (!node.parentElement) return false; var parentType = node.parentElement.nodeName; if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false; if (re_num.test(text)) return false; return true; } function getTranslation(text) { if (!text) return undefined; if (translated_lines[text] === undefined) { original_lines[text] = 1; } var tl = localization[text]; if (tl !== undefined) { translated_lines[tl] = 1; } return tl; } function processTextNode(node) { var text = node.textContent.trim(); if (!canBeTranslated(node, text)) return; var tl = getTranslation(text); if (tl !== undefined) { node.textContent = tl; if (text && node.parentElement) { node.parentElement.setAttribute("data-original-text", text); } } } function processNode(node) { if (node.nodeType == 3) { processTextNode(node); return; } if (node.title) { let tl = getTranslation(node.title); if (tl !== undefined) { node.title = tl; } } if (node.placeholder) { let tl = getTranslation(node.placeholder); if (tl !== undefined) { node.placeholder = tl; } } textNodesUnder(node).forEach(function(node) { processTextNode(node); }); } function refresh_style_localization() { processNode(document.querySelector('.style_selections')); } function refresh_aspect_ratios_label(value) { label = document.querySelector('#aspect_ratios_accordion div span'); translation = getTranslation("Aspect Ratios"); if (typeof translation == "undefined") { translation = "Aspect Ratios"; } label.textContent = translation + " " + htmlDecode(value); } function localizeWholePage() { processNode(gradioApp()); function elem(comp) { var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id; return gradioApp().getElementById(elem_id); } for (var comp of window.gradio_config.components) { if (comp.props.webui_tooltip) { let e = elem(comp); let tl = e ? getTranslation(e.title) : undefined; if (tl !== undefined) { e.title = tl; } } if (comp.props.placeholder) { let e = elem(comp); let textbox = e ? e.querySelector('[placeholder]') : null; let tl = textbox ? getTranslation(textbox.placeholder) : undefined; if (tl !== undefined) { textbox.placeholder = tl; } } } } document.addEventListener("DOMContentLoaded", function() { if (!hasLocalization()) { return; } onUiUpdate(function(m) { m.forEach(function(mutation) { mutation.addedNodes.forEach(function(node) { processNode(node); }); }); }); localizeWholePage(); if (localization.rtl) { // if the language is from right to left, (new MutationObserver((mutations, observer) => { // wait for the style to load mutations.forEach(mutation => { mutation.addedNodes.forEach(node => { if (node.tagName === 'STYLE') { observer.disconnect(); for (const x of node.sheet.rules) { // find all rtl media rules if (Array.from(x.media || []).includes('rtl')) { x.media.appendMedium('all'); // enable them } } } }); }); })).observe(gradioApp(), {childList: true}); } }); ================================================ FILE: javascript/script.js ================================================ // based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/script.js function gradioApp() { const elems = document.getElementsByTagName('gradio-app'); const elem = elems.length == 0 ? document : elems[0]; if (elem !== document) { elem.getElementById = function(id) { return document.getElementById(id); }; } return elem.shadowRoot ? elem.shadowRoot : elem; } /** * Get the currently selected top-level UI tab button (e.g. the button that says "Extras"). */ function get_uiCurrentTab() { return gradioApp().querySelector('#tabs > .tab-nav > button.selected'); } /** * Get the first currently visible top-level UI tab content (e.g. the div hosting the "txt2img" UI). */ function get_uiCurrentTabContent() { return gradioApp().querySelector('#tabs > .tabitem[id^=tab_]:not([style*="display: none"])'); } var uiUpdateCallbacks = []; var uiAfterUpdateCallbacks = []; var uiLoadedCallbacks = []; var uiTabChangeCallbacks = []; var optionsChangedCallbacks = []; var uiAfterUpdateTimeout = null; var uiCurrentTab = null; /** * Register callback to be called at each UI update. * The callback receives an array of MutationRecords as an argument. */ function onUiUpdate(callback) { uiUpdateCallbacks.push(callback); } /** * Register callback to be called soon after UI updates. * The callback receives no arguments. * * This is preferred over `onUiUpdate` if you don't need * access to the MutationRecords, as your function will * not be called quite as often. */ function onAfterUiUpdate(callback) { uiAfterUpdateCallbacks.push(callback); } /** * Register callback to be called when the UI is loaded. * The callback receives no arguments. */ function onUiLoaded(callback) { uiLoadedCallbacks.push(callback); } /** * Register callback to be called when the UI tab is changed. * The callback receives no arguments. */ function onUiTabChange(callback) { uiTabChangeCallbacks.push(callback); } /** * Register callback to be called when the options are changed. * The callback receives no arguments. * @param callback */ function onOptionsChanged(callback) { optionsChangedCallbacks.push(callback); } function executeCallbacks(queue, arg) { for (const callback of queue) { try { callback(arg); } catch (e) { console.error("error running callback", callback, ":", e); } } } /** * Schedule the execution of the callbacks registered with onAfterUiUpdate. * The callbacks are executed after a short while, unless another call to this function * is made before that time. IOW, the callbacks are executed only once, even * when there are multiple mutations observed. */ function scheduleAfterUiUpdateCallbacks() { clearTimeout(uiAfterUpdateTimeout); uiAfterUpdateTimeout = setTimeout(function() { executeCallbacks(uiAfterUpdateCallbacks); }, 200); } var executedOnLoaded = false; document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m) { if (!executedOnLoaded && gradioApp().querySelector('#generate_button')) { executedOnLoaded = true; executeCallbacks(uiLoadedCallbacks); } executeCallbacks(uiUpdateCallbacks, m); scheduleAfterUiUpdateCallbacks(); const newTab = get_uiCurrentTab(); if (newTab && (newTab !== uiCurrentTab)) { uiCurrentTab = newTab; executeCallbacks(uiTabChangeCallbacks); } }); mutationObserver.observe(gradioApp(), {childList: true, subtree: true}); initStylePreviewOverlay(); }); var onAppend = function(elem, f) { var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { if (m.addedNodes.length) { f(m.addedNodes); } }); }); observer.observe(elem, {childList: true}); } function addObserverIfDesiredNodeAvailable(querySelector, callback) { var elem = document.querySelector(querySelector); if (!elem) { window.setTimeout(() => addObserverIfDesiredNodeAvailable(querySelector, callback), 1000); return; } onAppend(elem, callback); } /** * Show reset button on toast "Connection errored out." */ addObserverIfDesiredNodeAvailable(".toast-wrap", function(added) { added.forEach(function(element) { if (element.innerText.includes("Connection errored out.")) { window.setTimeout(function() { document.getElementById("reset_button").classList.remove("hidden"); document.getElementById("generate_button").classList.add("hidden"); document.getElementById("skip_button").classList.add("hidden"); document.getElementById("stop_button").classList.add("hidden"); }); } }); }); /** * Add a ctrl+enter as a shortcut to start a generation */ document.addEventListener('keydown', function(e) { const isModifierKey = (e.metaKey || e.ctrlKey || e.altKey); const isEnterKey = (e.key == "Enter" || e.keyCode == 13); if(isModifierKey && isEnterKey) { const generateButton = gradioApp().querySelector('button:not(.hidden)[id=generate_button]'); if (generateButton) { generateButton.click(); e.preventDefault(); return; } const stopButton = gradioApp().querySelector('button:not(.hidden)[id=stop_button]') if(stopButton) { stopButton.click(); e.preventDefault(); return; } } }); function initStylePreviewOverlay() { let overlayVisible = false; const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content") const overlay = document.createElement('div'); const tooltip = document.createElement('div'); tooltip.className = 'preview-tooltip'; overlay.appendChild(tooltip); overlay.id = 'stylePreviewOverlay'; document.body.appendChild(overlay); document.addEventListener('mouseover', function (e) { const label = e.target.closest('.style_selections label'); if (!label) return; label.removeEventListener("mouseout", onMouseLeave); label.addEventListener("mouseout", onMouseLeave); overlayVisible = true; overlay.style.opacity = "1"; const originalText = label.querySelector("span").getAttribute("data-original-text"); const name = originalText || label.querySelector("span").textContent; overlay.style.backgroundImage = `url("${samplesPath.replace( "fooocus_v2", name.toLowerCase().replaceAll(" ", "_") ).replaceAll("\\", "\\\\")}")`; tooltip.textContent = name; function onMouseLeave() { overlayVisible = false; overlay.style.opacity = "0"; overlay.style.backgroundImage = ""; label.removeEventListener("mouseout", onMouseLeave); } }); document.addEventListener('mousemove', function (e) { if (!overlayVisible) return; overlay.style.left = `${e.clientX}px`; overlay.style.top = `${e.clientY}px`; overlay.className = e.clientY > window.innerHeight / 2 ? "lower-half" : "upper-half"; }); } /** * checks that a UI element is not in another hidden element or tab content */ function uiElementIsVisible(el) { if (el === document) { return true; } const computedStyle = getComputedStyle(el); const isVisible = computedStyle.display !== 'none'; if (!isVisible) return false; return uiElementIsVisible(el.parentNode); } function uiElementInSight(el) { const clRect = el.getBoundingClientRect(); const windowHeight = window.innerHeight; const isOnScreen = clRect.bottom > 0 && clRect.top < windowHeight; return isOnScreen; } function playNotification() { gradioApp().querySelector('#audio_notification audio')?.play(); } function set_theme(theme) { var gradioURL = window.location.href; if (!gradioURL.includes('?__theme=')) { window.location.replace(gradioURL + '?__theme=' + theme); } } function htmlDecode(input) { var doc = new DOMParser().parseFromString(input, "text/html"); return doc.documentElement.textContent; } ================================================ FILE: javascript/viewer.js ================================================ window.main_viewer_height = 512; function refresh_grid() { let gridContainer = document.querySelector('#final_gallery .grid-container'); let final_gallery = document.getElementById('final_gallery'); if (gridContainer) if (final_gallery) { let rect = final_gallery.getBoundingClientRect(); let cols = Math.ceil((rect.width - 16.0) / rect.height); if (cols < 2) cols = 2; gridContainer.style.setProperty('--grid-cols', cols); } } function refresh_grid_delayed() { refresh_grid(); setTimeout(refresh_grid, 100); setTimeout(refresh_grid, 500); setTimeout(refresh_grid, 1000); } function resized() { let windowHeight = window.innerHeight - 260; let elements = document.getElementsByClassName('main_view'); if (windowHeight > 745) windowHeight = 745; for (let i = 0; i < elements.length; i++) { elements[i].style.height = windowHeight + 'px'; } window.main_viewer_height = windowHeight; refresh_grid(); } function viewer_to_top(delay = 100) { setTimeout(() => window.scrollTo({top: 0, behavior: 'smooth'}), delay); } function viewer_to_bottom(delay = 100) { let element = document.getElementById('positive_prompt'); let yPos = window.main_viewer_height; if (element) { yPos = element.getBoundingClientRect().top + window.scrollY; } setTimeout(() => window.scrollTo({top: yPos - 8, behavior: 'smooth'}), delay); } window.addEventListener('resize', (e) => { resized(); }); onUiLoaded(async () => { resized(); }); function on_style_selection_blur() { let target = document.querySelector("#gradio_receiver_style_selections textarea"); target.value = "on_style_selection_blur " + Math.random(); let e = new Event("input", {bubbles: true}) Object.defineProperty(e, "target", {value: target}) target.dispatchEvent(e); } onUiLoaded(async () => { let spans = document.querySelectorAll('.aspect_ratios span'); spans.forEach(function (span) { span.innerHTML = span.innerHTML.replace(/</g, '<').replace(/>/g, '>'); }); document.querySelector('.style_selections').addEventListener('focusout', function (event) { setTimeout(() => { if (!this.contains(document.activeElement)) { on_style_selection_blur(); } }, 200); }); let inputs = document.querySelectorAll('.lora_weight input[type="range"]'); inputs.forEach(function (input) { input.style.marginTop = '12px'; }); }); ================================================ FILE: javascript/zoom.js ================================================ onUiLoaded(async() => { // Helper functions // Detect whether the element has a horizontal scroll bar function hasHorizontalScrollbar(element) { return element.scrollWidth > element.clientWidth; } // Function for defining the "Ctrl", "Shift" and "Alt" keys function isModifierKey(event, key) { switch (key) { case "Ctrl": return event.ctrlKey; case "Shift": return event.shiftKey; case "Alt": return event.altKey; default: return false; } } // Create hotkey configuration with the provided options function createHotkeyConfig(defaultHotkeysConfig) { const result = {}; // Resulting hotkey configuration for (const key in defaultHotkeysConfig) { result[key] = defaultHotkeysConfig[key]; } return result; } // Default config const defaultHotkeysConfig = { canvas_hotkey_zoom: "Shift", canvas_hotkey_adjust: "Ctrl", canvas_zoom_undo_extra_key: "Ctrl", canvas_zoom_hotkey_undo: "KeyZ", canvas_hotkey_reset: "KeyR", canvas_hotkey_fullscreen: "KeyS", canvas_hotkey_move: "KeyF", canvas_show_tooltip: true, canvas_auto_expand: true, canvas_blur_prompt: true, }; // Loading the configuration from opts const hotkeysConfig = createHotkeyConfig( defaultHotkeysConfig ); let isMoving = false; let activeElement; const elemData = {}; function applyZoomAndPan(elemId) { const targetElement = gradioApp().querySelector(elemId); if (!targetElement) { console.log("Element not found"); return; } targetElement.style.transformOrigin = "0 0"; elemData[elemId] = { zoom: 1, panX: 0, panY: 0 }; let fullScreenMode = false; // Create tooltip function createTooltip() { const toolTipElemnt = targetElement.querySelector(".image-container"); const tooltip = document.createElement("div"); tooltip.className = "canvas-tooltip"; // Creating an item of information const info = document.createElement("i"); info.className = "canvas-tooltip-info"; info.textContent = ""; // Create a container for the contents of the tooltip const tooltipContent = document.createElement("div"); tooltipContent.className = "canvas-tooltip-content"; // Define an array with hotkey information and their actions const hotkeysInfo = [ { configKey: "canvas_hotkey_zoom", action: "Zoom canvas", keySuffix: " + wheel" }, { configKey: "canvas_hotkey_adjust", action: "Adjust brush size", keySuffix: " + wheel" }, {configKey: "canvas_zoom_hotkey_undo", action: "Undo last action", keyPrefix: `${hotkeysConfig.canvas_zoom_undo_extra_key} + ` }, {configKey: "canvas_hotkey_reset", action: "Reset zoom"}, { configKey: "canvas_hotkey_fullscreen", action: "Fullscreen mode" }, {configKey: "canvas_hotkey_move", action: "Move canvas"} ]; // Create hotkeys array based on the config values const hotkeys = hotkeysInfo.map((info) => { const configValue = hotkeysConfig[info.configKey]; let key = configValue.slice(-1); if (info.keySuffix) { key = `${configValue}${info.keySuffix}`; } if (info.keyPrefix && info.keyPrefix !== "None + ") { key = `${info.keyPrefix}${configValue[3]}`; } return { key, action: info.action, }; }); hotkeys .forEach(hotkey => { const p = document.createElement("p"); p.innerHTML = `${hotkey.key} - ${hotkey.action}`; tooltipContent.appendChild(p); }); tooltip.append(info, tooltipContent); // Add a hint element to the target element toolTipElemnt.appendChild(tooltip); } //Show tool tip if setting enable if (hotkeysConfig.canvas_show_tooltip) { createTooltip(); } // Reset the zoom level and pan position of the target element to their initial values function resetZoom() { elemData[elemId] = { zoomLevel: 1, panX: 0, panY: 0 }; targetElement.style.overflow = "hidden"; targetElement.isZoomed = false; targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`; const canvas = gradioApp().querySelector( `${elemId} canvas[key="interface"]` ); toggleOverlap("off"); fullScreenMode = false; const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']"); if (closeBtn) { closeBtn.addEventListener("click", resetZoom); } if (canvas) { const parentElement = targetElement.closest('[id^="component-"]'); if ( canvas && parseFloat(canvas.style.width) > parentElement.offsetWidth && parseFloat(targetElement.style.width) > parentElement.offsetWidth ) { fitToElement(); return; } } targetElement.style.width = ""; } // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements function toggleOverlap(forced = "") { const zIndex1 = "0"; const zIndex2 = "998"; targetElement.style.zIndex = targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1; if (forced === "off") { targetElement.style.zIndex = zIndex1; } else if (forced === "on") { targetElement.style.zIndex = zIndex2; } } // Adjust the brush size based on the deltaY value from a mouse wheel event function adjustBrushSize( elemId, deltaY, withoutValue = false, percentage = 5 ) { const input = gradioApp().querySelector( `${elemId} input[aria-label='Brush radius']` ) || gradioApp().querySelector( `${elemId} button[aria-label="Use brush"]` ); if (input) { input.click(); if (!withoutValue) { const maxValue = parseFloat(input.getAttribute("max")) || 100; const changeAmount = maxValue * (percentage / 100); const newValue = parseFloat(input.value) + (deltaY > 0 ? -changeAmount : changeAmount); input.value = Math.min(Math.max(newValue, 0), maxValue); input.dispatchEvent(new Event("change")); } } } // Reset zoom when uploading a new image const fileInput = gradioApp().querySelector( `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv` ); fileInput.addEventListener("click", resetZoom); // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables function updateZoom(newZoomLevel, mouseX, mouseY) { newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15)); elemData[elemId].panX += mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel; elemData[elemId].panY += mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel; targetElement.style.transformOrigin = "0 0"; targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`; targetElement.style.overflow = "visible"; toggleOverlap("on"); return newZoomLevel; } // Change the zoom level based on user interaction function changeZoomLevel(operation, e) { if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) { e.preventDefault(); let zoomPosX, zoomPosY; let delta = 0.2; if (elemData[elemId].zoomLevel > 7) { delta = 0.9; } else if (elemData[elemId].zoomLevel > 2) { delta = 0.6; } zoomPosX = e.clientX; zoomPosY = e.clientY; fullScreenMode = false; elemData[elemId].zoomLevel = updateZoom( elemData[elemId].zoomLevel + (operation === "+" ? delta : -delta), zoomPosX - targetElement.getBoundingClientRect().left, zoomPosY - targetElement.getBoundingClientRect().top ); targetElement.isZoomed = true; } } /** * This function fits the target element to the screen by calculating * the required scale and offsets. It also updates the global variables * zoomLevel, panX, and panY to reflect the new state. */ function fitToElement() { //Reset Zoom targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; let parentElement; parentElement = targetElement.closest('[id^="component-"]'); // Get element and screen dimensions const elementWidth = targetElement.offsetWidth; const elementHeight = targetElement.offsetHeight; const screenWidth = parentElement.clientWidth - 24; const screenHeight = parentElement.clientHeight; // Calculate scale and offsets const scaleX = screenWidth / elementWidth; const scaleY = screenHeight / elementHeight; const scale = Math.min(scaleX, scaleY); const offsetX =0; const offsetY =0; // Apply scale and offsets to the element targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; // Update global variables elemData[elemId].zoomLevel = scale; elemData[elemId].panX = offsetX; elemData[elemId].panY = offsetY; fullScreenMode = false; toggleOverlap("off"); } // Undo last action function undoLastAction(e) { let isCtrlPressed = isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key) const isAuxButton = e.button >= 3; if (isAuxButton) { isCtrlPressed = true } else { if (!isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)) return; } // Move undoBtn query outside the if statement to avoid unnecessary queries const undoBtn = document.querySelector(`${activeElement} button[aria-label="Undo"]`); if ((isCtrlPressed) && undoBtn ) { e.preventDefault(); undoBtn.click(); } } /** * This function fits the target element to the screen by calculating * the required scale and offsets. It also updates the global variables * zoomLevel, panX, and panY to reflect the new state. */ // Fullscreen mode function fitToScreen() { const canvas = gradioApp().querySelector( `${elemId} canvas[key="interface"]` ); if (!canvas) return; targetElement.style.width = (canvas.offsetWidth + 2) + "px"; targetElement.style.overflow = "visible"; if (fullScreenMode) { resetZoom(); fullScreenMode = false; return; } //Reset Zoom targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; // Get scrollbar width to right-align the image const scrollbarWidth = window.innerWidth - document.documentElement.clientWidth; // Get element and screen dimensions const elementWidth = targetElement.offsetWidth; const elementHeight = targetElement.offsetHeight; const screenWidth = window.innerWidth - scrollbarWidth; const screenHeight = window.innerHeight; // Get element's coordinates relative to the page const elementRect = targetElement.getBoundingClientRect(); const elementY = elementRect.y; const elementX = elementRect.x; // Calculate scale and offsets const scaleX = screenWidth / elementWidth; const scaleY = screenHeight / elementHeight; const scale = Math.min(scaleX, scaleY); // Get the current transformOrigin const computedStyle = window.getComputedStyle(targetElement); const transformOrigin = computedStyle.transformOrigin; const [originX, originY] = transformOrigin.split(" "); const originXValue = parseFloat(originX); const originYValue = parseFloat(originY); // Calculate offsets with respect to the transformOrigin const offsetX = (screenWidth - elementWidth * scale) / 2 - elementX - originXValue * (1 - scale); const offsetY = (screenHeight - elementHeight * scale) / 2 - elementY - originYValue * (1 - scale); // Apply scale and offsets to the element targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; // Update global variables elemData[elemId].zoomLevel = scale; elemData[elemId].panX = offsetX; elemData[elemId].panY = offsetY; fullScreenMode = true; toggleOverlap("on"); } // Handle keydown events function handleKeyDown(event) { // Disable key locks to make pasting from the buffer work correctly if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { return; } // before activating shortcut, ensure user is not actively typing in an input field if (!hotkeysConfig.canvas_blur_prompt) { if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { return; } } const hotkeyActions = { [hotkeysConfig.canvas_hotkey_reset]: resetZoom, [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen, [hotkeysConfig.canvas_zoom_hotkey_undo]: undoLastAction, }; const action = hotkeyActions[event.code]; if (action) { event.preventDefault(); action(event); } if ( isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) || isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust) ) { event.preventDefault(); } } // Get Mouse position function getMousePosition(e) { mouseX = e.offsetX; mouseY = e.offsetY; } // Simulation of the function to put a long image into the screen. // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element. // We hide the image and show it to the user when it is ready. targetElement.isExpanded = false; function autoExpand() { const canvas = document.querySelector(`${elemId} canvas[key="interface"]`); if (canvas) { if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) { targetElement.style.visibility = "hidden"; setTimeout(() => { fitToScreen(); resetZoom(); targetElement.style.visibility = "visible"; targetElement.isExpanded = true; }, 10); } } } targetElement.addEventListener("mousemove", getMousePosition); targetElement.addEventListener("auxclick", undoLastAction); //observers // Creating an observer with a callback function to handle DOM changes const observer = new MutationObserver((mutationsList, observer) => { for (let mutation of mutationsList) { // If the style attribute of the canvas has changed, by observation it happens only when the picture changes if (mutation.type === 'attributes' && mutation.attributeName === 'style' && mutation.target.tagName.toLowerCase() === 'canvas') { targetElement.isExpanded = false; setTimeout(resetZoom, 10); } } }); // Apply auto expand if enabled if (hotkeysConfig.canvas_auto_expand) { targetElement.addEventListener("mousemove", autoExpand); // Set up an observer to track attribute changes observer.observe(targetElement, { attributes: true, childList: true, subtree: true }); } // Handle events only inside the targetElement let isKeyDownHandlerAttached = false; function handleMouseMove() { if (!isKeyDownHandlerAttached) { document.addEventListener("keydown", handleKeyDown); isKeyDownHandlerAttached = true; activeElement = elemId; } } function handleMouseLeave() { if (isKeyDownHandlerAttached) { document.removeEventListener("keydown", handleKeyDown); isKeyDownHandlerAttached = false; activeElement = null; } } // Add mouse event handlers targetElement.addEventListener("mousemove", handleMouseMove); targetElement.addEventListener("mouseleave", handleMouseLeave); targetElement.addEventListener("wheel", e => { // change zoom level const operation = e.deltaY > 0 ? "-" : "+"; changeZoomLevel(operation, e); // Handle brush size adjustment with ctrl key pressed if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) { e.preventDefault(); // Increase or decrease brush size based on scroll direction adjustBrushSize(elemId, e.deltaY); } }); // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. function handleMoveKeyDown(e) { // Disable key locks to make pasting from the buffer work correctly if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && e.code === 'KeyC') || e.code === "F5") { return; } // before activating shortcut, ensure user is not actively typing in an input field if (!hotkeysConfig.canvas_blur_prompt) { if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { return; } } if (e.code === hotkeysConfig.canvas_hotkey_move) { if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { e.preventDefault(); document.activeElement.blur(); isMoving = true; } } } function handleMoveKeyUp(e) { if (e.code === hotkeysConfig.canvas_hotkey_move) { isMoving = false; } } document.addEventListener("keydown", handleMoveKeyDown); document.addEventListener("keyup", handleMoveKeyUp); // Detect zoom level and update the pan speed. function updatePanPosition(movementX, movementY) { let panSpeed = 2; if (elemData[elemId].zoomLevel > 8) { panSpeed = 3.5; } elemData[elemId].panX += movementX * panSpeed; elemData[elemId].panY += movementY * panSpeed; // Delayed redraw of an element requestAnimationFrame(() => { targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`; toggleOverlap("on"); }); } function handleMoveByKey(e) { if (isMoving && elemId === activeElement) { updatePanPosition(e.movementX, e.movementY); targetElement.style.pointerEvents = "none"; targetElement.style.overflow = "visible"; } else { targetElement.style.pointerEvents = "auto"; } } // Prevents sticking to the mouse window.onblur = function() { isMoving = false; }; // Checks for extension function checkForOutBox() { const parentElement = targetElement.closest('[id^="component-"]'); if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) { resetZoom(); targetElement.isExpanded = true; } if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) { resetZoom(); } if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) { resetZoom(); } } targetElement.addEventListener("mousemove", checkForOutBox); window.addEventListener('resize', (e) => { resetZoom(); targetElement.isExpanded = false; targetElement.isZoomed = false; }); gradioApp().addEventListener("mousemove", handleMoveByKey); } applyZoomAndPan("#inpaint_canvas"); applyZoomAndPan("#inpaint_mask_canvas"); }); ================================================ FILE: language/en.json ================================================ { "Preview": "Preview", "Gallery": "Gallery", "Generate": "Generate", "Skip": "Skip", "Stop": "Stop", "Reconnect": "Reconnect", "Input Image": "Input Image", "Advanced": "Advanced", "Upscale or Variation": "Upscale or Variation", "Image Prompt": "Image Prompt", "Inpaint or Outpaint": "Inpaint or Outpaint", "Outpaint Direction": "Outpaint Direction", "Enable Advanced Masking Features": "Enable Advanced Masking Features", "Method": "Method", "Describe": "Describe", "Content Type": "Content Type", "Photograph": "Photograph", "Art/Anime": "Art/Anime", "Apply Styles": "Apply Styles", "Describe this Image into Prompt": "Describe this Image into Prompt", "Image Size and Recommended Size": "Image Size and Recommended Size", "Upscale or Variation:": "Upscale or Variation:", "Disabled": "Disabled", "Vary (Subtle)": "Vary (Subtle)", "Vary (Strong)": "Vary (Strong)", "Upscale (1.5x)": "Upscale (1.5x)", "Upscale (2x)": "Upscale (2x)", "Upscale (Fast 2x)": "Upscale (Fast 2x)", "\ud83d\udcd4 Documentation": "\uD83D\uDCD4 Documentation", "Image": "Image", "Stop At": "Stop At", "Weight": "Weight", "Type": "Type", "PyraCanny": "PyraCanny", "CPDS": "CPDS", "* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1).": "* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1).", "The scaler multiplied to positive ADM (use 1.0 to disable).": "The scaler multiplied to positive ADM (use 1.0 to disable).", "The scaler multiplied to negative ADM (use 1.0 to disable).": "The scaler multiplied to negative ADM (use 1.0 to disable).", "When to end the guidance from positive/negative ADM.": "When to end the guidance from positive/negative ADM.", "Similar to the Control Mode in A1111 (use 0.0 to disable).": "Similar to the Control Mode in A1111 (use 0.0 to disable).", "Outpaint Expansion (": "Outpaint Expansion (", "Outpaint": "Outpaint", "Left": "Left", "Right": "Right", "Top": "Top", "Bottom": "Bottom", "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)", "Advanced options": "Advanced options", "Generate mask from image": "Generate mask from image", "Settings": "Settings", "Style": "Style", "Styles": "Styles", "Preset": "Preset", "Performance": "Performance", "Speed": "Speed", "Quality": "Quality", "Extreme Speed": "Extreme Speed", "Lightning": "Lightning", "Aspect Ratios": "Aspect Ratios", "width \u00d7 height": "width \u00d7 height", "Image Number": "Image Number", "Negative Prompt": "Negative Prompt", "Describing what you do not want to see.": "Describing what you do not want to see.", "Random": "Random", "Seed": "Seed", "Disable seed increment": "Disable seed increment", "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.", "Read wildcards in order": "Read wildcards in order", "Black Out NSFW": "Black Out NSFW", "Use black image if NSFW is detected.": "Use black image if NSFW is detected.", "Save only final enhanced image": "Save only final enhanced image", "Save Metadata to Images": "Save Metadata to Images", "Adds parameters to generated images allowing manual regeneration.": "Adds parameters to generated images allowing manual regeneration.", "\ud83d\udcda History Log": "\uD83D\uDCDA History Log", "Image Style": "Image Style", "Fooocus V2": "Fooocus V2", "Random Style": "Random Style", "Default (Slightly Cinematic)": "Default (Slightly Cinematic)", "Fooocus Masterpiece": "Fooocus Masterpiece", "Fooocus Photograph": "Fooocus Photograph", "Fooocus Negative": "Fooocus Negative", "SAI 3D Model": "SAI 3D Model", "SAI Analog Film": "SAI Analog Film", "SAI Anime": "SAI Anime", "SAI Cinematic": "SAI Cinematic", "SAI Comic Book": "SAI Comic Book", "SAI Craft Clay": "SAI Craft Clay", "SAI Digital Art": "SAI Digital Art", "SAI Enhance": "SAI Enhance", "SAI Fantasy Art": "SAI Fantasy Art", "SAI Isometric": "SAI Isometric", "SAI Line Art": "SAI Line Art", "SAI Lowpoly": "SAI Lowpoly", "SAI Neonpunk": "SAI Neonpunk", "SAI Origami": "SAI Origami", "SAI Photographic": "SAI Photographic", "SAI Pixel Art": "SAI Pixel Art", "SAI Texture": "SAI Texture", "MRE Cinematic Dynamic": "MRE Cinematic Dynamic", "MRE Spontaneous Picture": "MRE Spontaneous Picture", "MRE Artistic Vision": "MRE Artistic Vision", "MRE Dark Dream": "MRE Dark Dream", "MRE Gloomy Art": "MRE Gloomy Art", "MRE Bad Dream": "MRE Bad Dream", "MRE Underground": "MRE Underground", "MRE Surreal Painting": "MRE Surreal Painting", "MRE Dynamic Illustration": "MRE Dynamic Illustration", "MRE Undead Art": "MRE Undead Art", "MRE Elemental Art": "MRE Elemental Art", "MRE Space Art": "MRE Space Art", "MRE Ancient Illustration": "MRE Ancient Illustration", "MRE Brave Art": "MRE Brave Art", "MRE Heroic Fantasy": "MRE Heroic Fantasy", "MRE Dark Cyberpunk": "MRE Dark Cyberpunk", "MRE Lyrical Geometry": "MRE Lyrical Geometry", "MRE Sumi E Symbolic": "MRE Sumi E Symbolic", "MRE Sumi E Detailed": "MRE Sumi E Detailed", "MRE Manga": "MRE Manga", "MRE Anime": "MRE Anime", "MRE Comic": "MRE Comic", "Ads Advertising": "Ads Advertising", "Ads Automotive": "Ads Automotive", "Ads Corporate": "Ads Corporate", "Ads Fashion Editorial": "Ads Fashion Editorial", "Ads Food Photography": "Ads Food Photography", "Ads Gourmet Food Photography": "Ads Gourmet Food Photography", "Ads Luxury": "Ads Luxury", "Ads Real Estate": "Ads Real Estate", "Ads Retail": "Ads Retail", "Artstyle Abstract": "Artstyle Abstract", "Artstyle Abstract Expressionism": "Artstyle Abstract Expressionism", "Artstyle Art Deco": "Artstyle Art Deco", "Artstyle Art Nouveau": "Artstyle Art Nouveau", "Artstyle Constructivist": "Artstyle Constructivist", "Artstyle Cubist": "Artstyle Cubist", "Artstyle Expressionist": "Artstyle Expressionist", "Artstyle Graffiti": "Artstyle Graffiti", "Artstyle Hyperrealism": "Artstyle Hyperrealism", "Artstyle Impressionist": "Artstyle Impressionist", "Artstyle Pointillism": "Artstyle Pointillism", "Artstyle Pop Art": "Artstyle Pop Art", "Artstyle Psychedelic": "Artstyle Psychedelic", "Artstyle Renaissance": "Artstyle Renaissance", "Artstyle Steampunk": "Artstyle Steampunk", "Artstyle Surrealist": "Artstyle Surrealist", "Artstyle Typography": "Artstyle Typography", "Artstyle Watercolor": "Artstyle Watercolor", "Futuristic Biomechanical": "Futuristic Biomechanical", "Futuristic Biomechanical Cyberpunk": "Futuristic Biomechanical Cyberpunk", "Futuristic Cybernetic": "Futuristic Cybernetic", "Futuristic Cybernetic Robot": "Futuristic Cybernetic Robot", "Futuristic Cyberpunk Cityscape": "Futuristic Cyberpunk Cityscape", "Futuristic Futuristic": "Futuristic Futuristic", "Futuristic Retro Cyberpunk": "Futuristic Retro Cyberpunk", "Futuristic Retro Futurism": "Futuristic Retro Futurism", "Futuristic Sci Fi": "Futuristic Sci Fi", "Futuristic Vaporwave": "Futuristic Vaporwave", "Game Bubble Bobble": "Game Bubble Bobble", "Game Cyberpunk Game": "Game Cyberpunk Game", "Game Fighting Game": "Game Fighting Game", "Game Gta": "Game Gta", "Game Mario": "Game Mario", "Game Minecraft": "Game Minecraft", "Game Pokemon": "Game Pokemon", "Game Retro Arcade": "Game Retro Arcade", "Game Retro Game": "Game Retro Game", "Game Rpg Fantasy Game": "Game Rpg Fantasy Game", "Game Strategy Game": "Game Strategy Game", "Game Streetfighter": "Game Streetfighter", "Game Zelda": "Game Zelda", "Misc Architectural": "Misc Architectural", "Misc Disco": "Misc Disco", "Misc Dreamscape": "Misc Dreamscape", "Misc Dystopian": "Misc Dystopian", "Misc Fairy Tale": "Misc Fairy Tale", "Misc Gothic": "Misc Gothic", "Misc Grunge": "Misc Grunge", "Misc Horror": "Misc Horror", "Misc Kawaii": "Misc Kawaii", "Misc Lovecraftian": "Misc Lovecraftian", "Misc Macabre": "Misc Macabre", "Misc Manga": "Misc Manga", "Misc Metropolis": "Misc Metropolis", "Misc Minimalist": "Misc Minimalist", "Misc Monochrome": "Misc Monochrome", "Misc Nautical": "Misc Nautical", "Misc Space": "Misc Space", "Misc Stained Glass": "Misc Stained Glass", "Misc Techwear Fashion": "Misc Techwear Fashion", "Misc Tribal": "Misc Tribal", "Misc Zentangle": "Misc Zentangle", "Papercraft Collage": "Papercraft Collage", "Papercraft Flat Papercut": "Papercraft Flat Papercut", "Papercraft Kirigami": "Papercraft Kirigami", "Papercraft Paper Mache": "Papercraft Paper Mache", "Papercraft Paper Quilling": "Papercraft Paper Quilling", "Papercraft Papercut Collage": "Papercraft Papercut Collage", "Papercraft Papercut Shadow Box": "Papercraft Papercut Shadow Box", "Papercraft Stacked Papercut": "Papercraft Stacked Papercut", "Papercraft Thick Layered Papercut": "Papercraft Thick Layered Papercut", "Photo Alien": "Photo Alien", "Photo Film Noir": "Photo Film Noir", "Photo Glamour": "Photo Glamour", "Photo Hdr": "Photo Hdr", "Photo Iphone Photographic": "Photo Iphone Photographic", "Photo Long Exposure": "Photo Long Exposure", "Photo Neon Noir": "Photo Neon Noir", "Photo Silhouette": "Photo Silhouette", "Photo Tilt Shift": "Photo Tilt Shift", "Cinematic Diva": "Cinematic Diva", "Abstract Expressionism": "Abstract Expressionism", "Academia": "Academia", "Action Figure": "Action Figure", "Adorable 3D Character": "Adorable 3D Character", "Adorable Kawaii": "Adorable Kawaii", "Art Deco": "Art Deco", "Art Nouveau": "Art Nouveau", "Astral Aura": "Astral Aura", "Avant Garde": "Avant Garde", "Baroque": "Baroque", "Bauhaus Style Poster": "Bauhaus Style Poster", "Blueprint Schematic Drawing": "Blueprint Schematic Drawing", "Caricature": "Caricature", "Cel Shaded Art": "Cel Shaded Art", "Character Design Sheet": "Character Design Sheet", "Classicism Art": "Classicism Art", "Color Field Painting": "Color Field Painting", "Colored Pencil Art": "Colored Pencil Art", "Conceptual Art": "Conceptual Art", "Constructivism": "Constructivism", "Cubism": "Cubism", "Dadaism": "Dadaism", "Dark Fantasy": "Dark Fantasy", "Dark Moody Atmosphere": "Dark Moody Atmosphere", "Dmt Art Style": "Dmt Art Style", "Doodle Art": "Doodle Art", "Double Exposure": "Double Exposure", "Dripping Paint Splatter Art": "Dripping Paint Splatter Art", "Expressionism": "Expressionism", "Faded Polaroid Photo": "Faded Polaroid Photo", "Fauvism": "Fauvism", "Flat 2d Art": "Flat 2d Art", "Fortnite Art Style": "Fortnite Art Style", "Futurism": "Futurism", "Glitchcore": "Glitchcore", "Glo Fi": "Glo Fi", "Googie Art Style": "Googie Art Style", "Graffiti Art": "Graffiti Art", "Harlem Renaissance Art": "Harlem Renaissance Art", "High Fashion": "High Fashion", "Idyllic": "Idyllic", "Impressionism": "Impressionism", "Infographic Drawing": "Infographic Drawing", "Ink Dripping Drawing": "Ink Dripping Drawing", "Japanese Ink Drawing": "Japanese Ink Drawing", "Knolling Photography": "Knolling Photography", "Light Cheery Atmosphere": "Light Cheery Atmosphere", "Logo Design": "Logo Design", "Luxurious Elegance": "Luxurious Elegance", "Macro Photography": "Macro Photography", "Mandola Art": "Mandola Art", "Marker Drawing": "Marker Drawing", "Medievalism": "Medievalism", "Minimalism": "Minimalism", "Neo Baroque": "Neo Baroque", "Neo Byzantine": "Neo Byzantine", "Neo Futurism": "Neo Futurism", "Neo Impressionism": "Neo Impressionism", "Neo Rococo": "Neo Rococo", "Neoclassicism": "Neoclassicism", "Op Art": "Op Art", "Ornate And Intricate": "Ornate And Intricate", "Pencil Sketch Drawing": "Pencil Sketch Drawing", "Pop Art 2": "Pop Art 2", "Rococo": "Rococo", "Silhouette Art": "Silhouette Art", "Simple Vector Art": "Simple Vector Art", "Sketchup": "Sketchup", "Steampunk 2": "Steampunk 2", "Surrealism": "Surrealism", "Suprematism": "Suprematism", "Terragen": "Terragen", "Tranquil Relaxing Atmosphere": "Tranquil Relaxing Atmosphere", "Sticker Designs": "Sticker Designs", "Vibrant Rim Light": "Vibrant Rim Light", "Volumetric Lighting": "Volumetric Lighting", "Watercolor 2": "Watercolor 2", "Whimsical And Playful": "Whimsical And Playful", "Models": "Models", "Base Model (SDXL only)": "Base Model (SDXL only)", "sd_xl_base_1.0_0.9vae.safetensors": "sd_xl_base_1.0_0.9vae.safetensors", "bluePencilXL_v009.safetensors": "bluePencilXL_v009.safetensors", "bluePencilXL_v050.safetensors": "bluePencilXL_v050.safetensors", "DreamShaper_8_pruned.safetensors": "DreamShaper_8_pruned.safetensors", "realisticStockPhoto_v10.safetensors": "realisticStockPhoto_v10.safetensors", "realisticVisionV51_v51VAE.safetensors": "realisticVisionV51_v51VAE.safetensors", "sd_xl_refiner_1.0_0.9vae.safetensors": "sd_xl_refiner_1.0_0.9vae.safetensors", "Refiner (SDXL or SD 1.5)": "Refiner (SDXL or SD 1.5)", "None": "None", "LoRAs": "LoRAs", "SDXL LoRA 1": "SDXL LoRA 1", "sd_xl_offset_example-lora_1.0.safetensors": "sd_xl_offset_example-lora_1.0.safetensors", "3d_render_style_xl.safetensors": "3d_render_style_xl.safetensors", "Bloodstained-XL-V1.safetensors": "Bloodstained-XL-V1.safetensors", "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors": "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors", "SDXL LoRA 2": "SDXL LoRA 2", "SDXL LoRA 3": "SDXL LoRA 3", "SDXL LoRA 4": "SDXL LoRA 4", "SDXL LoRA 5": "SDXL LoRA 5", "Refresh": "Refresh", "\ud83d\udd04 Refresh All Files": "\ud83d\udd04 Refresh All Files", "Sampling Sharpness": "Sampling Sharpness", "Higher value means image and texture are sharper.": "Higher value means image and texture are sharper.", "Guidance Scale": "Guidance Scale", "Higher value means style is cleaner, vivider, and more artistic.": "Higher value means style is cleaner, vivider, and more artistic.", "Developer Debug Mode": "Developer Debug Mode", "Developer Debug Tools": "Developer Debug Tools", "Positive ADM Guidance Scaler": "Positive ADM Guidance Scaler", "The scaler multiplied to positive ADM (use 1.0 to disable). ": "The scaler multiplied to positive ADM (use 1.0 to disable). ", "Negative ADM Guidance Scaler": "Negative ADM Guidance Scaler", "The scaler multiplied to negative ADM (use 1.0 to disable). ": "The scaler multiplied to negative ADM (use 1.0 to disable). ", "ADM Guidance End At Step": "ADM Guidance End At Step", "When to end the guidance from positive/negative ADM. ": "When to end the guidance from positive/negative ADM. ", "Refiner swap method": "Refiner swap method", "joint": "joint", "separate": "separate", "vae": "vae", "CFG Mimicking from TSNR": "CFG Mimicking from TSNR", "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).": "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).", "CLIP Skip": "CLIP Skip", "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).": "Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).", "Sampler": "Sampler", "dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu", "Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.", "euler": "euler", "euler_ancestral": "euler_ancestral", "heun": "heun", "dpm_2": "dpm_2", "dpm_2_ancestral": "dpm_2_ancestral", "lms": "lms", "dpm_fast": "dpm_fast", "dpm_adaptive": "dpm_adaptive", "dpmpp_2s_ancestral": "dpmpp_2s_ancestral", "dpmpp_sde": "dpmpp_sde", "dpmpp_sde_gpu": "dpmpp_sde_gpu", "dpmpp_2m": "dpmpp_2m", "dpmpp_2m_sde": "dpmpp_2m_sde", "dpmpp_3m_sde": "dpmpp_3m_sde", "dpmpp_3m_sde_gpu": "dpmpp_3m_sde_gpu", "ddpm": "ddpm", "ddim": "ddim", "uni_pc": "uni_pc", "uni_pc_bh2": "uni_pc_bh2", "Scheduler": "Scheduler", "karras": "karras", "Scheduler of Sampler.": "Scheduler of Sampler.", "normal": "normal", "exponential": "exponential", "sgm_uniform": "sgm_uniform", "simple": "simple", "ddim_uniform": "ddim_uniform", "VAE": "VAE", "Default (model)": "Default (model)", "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step", "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.", "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step", "Forced Overwrite of Generating Width": "Forced Overwrite of Generating Width", "Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.": "Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.", "Forced Overwrite of Generating Height": "Forced Overwrite of Generating Height", "Forced Overwrite of Denoising Strength of \"Vary\"": "Forced Overwrite of Denoising Strength of \"Vary\"", "Set as negative number to disable. For developer debugging.": "Set as negative number to disable. For developer debugging.", "Forced Overwrite of Denoising Strength of \"Upscale\"": "Forced Overwrite of Denoising Strength of \"Upscale\"", "Disable Preview": "Disable Preview", "Disable preview during generation.": "Disable preview during generation.", "Disable Intermediate Results": "Disable Intermediate Results", "Disable intermediate results during generation, only show final gallery.": "Disable intermediate results during generation, only show final gallery.", "Debug Inpaint Preprocessing": "Debug Inpaint Preprocessing", "Debug GroundingDINO": "Debug GroundingDINO", "Used for SAM object detection and box generation": "Used for SAM object detection and box generation", "GroundingDINO Box Erode or Dilate": "GroundingDINO Box Erode or Dilate", "Inpaint Engine": "Inpaint Engine", "v1": "v1", "v2.5": "v2.5", "v2.6": "v2.6", "Control Debug": "Control Debug", "Debug Preprocessors": "Debug Preprocessors", "Mixing Image Prompt and Vary/Upscale": "Mixing Image Prompt and Vary/Upscale", "Mixing Image Prompt and Inpaint": "Mixing Image Prompt and Inpaint", "Softness of ControlNet": "Softness of ControlNet", "Similar to the Control Mode in A1111 (use 0.0 to disable). ": "Similar to the Control Mode in A1111 (use 0.0 to disable). ", "Canny": "Canny", "Canny Low Threshold": "Canny Low Threshold", "Canny High Threshold": "Canny High Threshold", "FreeU": "FreeU", "Enabled": "Enabled", "B1": "B1", "B2": "B2", "S1": "S1", "S2": "S2", "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...", "Type prompt here.": "Type prompt here.", "Outpaint Expansion Direction:": "Outpaint Expansion Direction:", "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)", "Fooocus Enhance": "Fooocus Enhance", "Fooocus Cinematic": "Fooocus Cinematic", "Fooocus Sharp": "Fooocus Sharp", "For images created by Fooocus": "For images created by Fooocus", "Metadata": "Metadata", "Apply Metadata": "Apply Metadata", "Metadata Scheme": "Metadata Scheme", "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.", "fooocus (json)": "fooocus (json)", "a1111 (plain text)": "a1111 (plain text)", "Unsupported image type in input": "Unsupported image type in input", "Enhance": "Enhance", "Detection prompt": "Detection prompt", "Detection Prompt Quick List": "Detection Prompt Quick List", "Maximum number of detections": "Maximum number of detections", "Use with Enhance, skips image generation": "Use with Enhance, skips image generation", "Order of Processing": "Order of Processing", "Use before to enhance small details and after to enhance large areas.": "Use before to enhance small details and after to enhance large areas.", "Before First Enhancement": "Before First Enhancement", "After Last Enhancement": "After Last Enhancement", "Prompt Type": "Prompt Type", "Choose which prompt to use for Upscale or Variation.": "Choose which prompt to use for Upscale or Variation.", "Original Prompts": "Original Prompts", "Last Filled Enhancement Prompts": "Last Filled Enhancement Prompts", "Enable": "Enable", "Describe what you want to detect.": "Describe what you want to detect.", "Enhancement positive prompt": "Enhancement positive prompt", "Uses original prompt instead if empty.": "Uses original prompt instead if empty.", "Enhancement negative prompt": "Enhancement negative prompt", "Uses original negative prompt instead if empty.": "Uses original negative prompt instead if empty.", "Detection": "Detection", "u2net": "u2net", "u2netp": "u2netp", "u2net_human_seg": "u2net_human_seg", "u2net_cloth_seg": "u2net_cloth_seg", "silueta": "silueta", "isnet-general-use": "isnet-general-use", "isnet-anime": "isnet-anime", "sam": "sam", "Mask generation model": "Mask generation model", "Cloth category": "Cloth category", "Use singular whenever possible": "Use singular whenever possible", "full": "full", "upper": "upper", "lower": "lower", "SAM Options": "SAM Options", "SAM model": "SAM model", "vit_b": "vit_b", "vit_l": "vit_l", "vit_h": "vit_h", "Box Threshold": "Box Threshold", "Text Threshold": "Text Threshold", "Set to 0 to detect all": "Set to 0 to detect all", "Inpaint": "Inpaint", "Inpaint or Outpaint (default)": "Inpaint or Outpaint (default)", "Improve Detail (face, hand, eyes, etc.)": "Improve Detail (face, hand, eyes, etc.)", "Modify Content (add objects, change background, etc.)": "Modify Content (add objects, change background, etc.)", "Disable initial latent in inpaint": "Disable initial latent in inpaint", "Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.": "Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.", "Inpaint Denoising Strength": "Inpaint Denoising Strength", "Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)": "Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)", "Inpaint Respective Field": "Inpaint Respective Field", "The area to inpaint. Value 0 is same as \"Only Masked\" in A1111. Value 1 is same as \"Whole Image\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)": "The area to inpaint. Value 0 is same as \"Only Masked\" in A1111. Value 1 is same as \"Whole Image\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)", "Mask Erode or Dilate": "Mask Erode or Dilate", "Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)": "Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)", "Invert Mask When Generating": "Invert Mask When Generating", "Debug Enhance Masks": "Debug Enhance Masks", "Show enhance masks in preview and final results": "Show enhance masks in preview and final results", "Use GroundingDINO boxes instead of more detailed SAM masks": "Use GroundingDINO boxes instead of more detailed SAM masks", "highly detailed face": "highly detailed face", "detailed girl face": "detailed girl face", "detailed man face": "detailed man face", "detailed hand": "detailed hand", "beautiful eyes": "beautiful eyes", "face": "face", "eye": "eye", "mouth": "mouth", "hair": "hair", "hand": "hand", "body": "body" } ================================================ FILE: language/example.json ================================================ { "Generate": "生成", "Input Image": "入力画像", "Advanced": "고급", "SAI 3D Model": "SAI 3D Modèle" } ================================================ FILE: launch.py ================================================ import os import ssl import sys print('[System ARGV] ' + str(sys.argv)) root = os.path.dirname(os.path.abspath(__file__)) sys.path.append(root) os.chdir(root) os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" if "GRADIO_SERVER_PORT" not in os.environ: os.environ["GRADIO_SERVER_PORT"] = "7865" ssl._create_default_https_context = ssl._create_unverified_context import platform import fooocus_version from build_launcher import build_launcher from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content from modules.model_loader import load_file_from_url REINSTALL_ALL = False TRY_INSTALL_XFORMERS = False def prepare_environment(): torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121") torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}") requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") print(f"Python {sys.version}") print(f"Fooocus version: {fooocus_version.version}") if REINSTALL_ALL 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 TRY_INSTALL_XFORMERS: if REINSTALL_ALL or not is_installed("xformers"): xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23') if platform.system() == "Windows": if platform.python_version().startswith("3.10"): run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True) 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 -U -I --no-deps {xformers_package}", "xformers") if REINSTALL_ALL or not requirements_met(requirements_file): run_pip(f"install -r \"{requirements_file}\"", "requirements") return vae_approx_filenames = [ ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'), ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'), ('xl-to-v1_interposer-v4.0.safetensors', 'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors') ] def ini_args(): from args_manager import args return args prepare_environment() build_launcher() args = ini_args() if args.gpu_device_id is not None: os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id) print("Set device to:", args.gpu_device_id) if args.hf_mirror is not None: os.environ['HF_MIRROR'] = str(args.hf_mirror) print("Set hf_mirror to:", args.hf_mirror) from modules import config from modules.hash_cache import init_cache os.environ["U2NET_HOME"] = config.path_inpaint os.environ['GRADIO_TEMP_DIR'] = config.temp_path if config.temp_path_cleanup_on_launch: print(f'[Cleanup] Attempting to delete content of temp dir {config.temp_path}') result = delete_folder_content(config.temp_path, '[Cleanup] ') if result: print("[Cleanup] Cleanup successful") else: print(f"[Cleanup] Failed to delete content of temp dir.") def download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads, vae_downloads): from modules.util import get_file_from_folder_list for file_name, url in vae_approx_filenames: load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name) load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin', model_dir=config.path_fooocus_expansion, file_name='pytorch_model.bin' ) if args.disable_preset_download: print('Skipped model download.') return default_model, checkpoint_downloads if not args.always_download_new_model: if not os.path.isfile(get_file_from_folder_list(default_model, config.paths_checkpoints)): for alternative_model_name in previous_default_models: if os.path.isfile(get_file_from_folder_list(alternative_model_name, config.paths_checkpoints)): print(f'You do not have [{default_model}] but you have [{alternative_model_name}].') print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, ' f'but you are not using the latest models.') print('Use --always-download-new-model to avoid fallback and always get new models.') checkpoint_downloads = {} default_model = alternative_model_name break for file_name, url in checkpoint_downloads.items(): model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_checkpoints)) load_file_from_url(url=url, model_dir=model_dir, file_name=file_name) for file_name, url in embeddings_downloads.items(): load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name) for file_name, url in lora_downloads.items(): model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_loras)) load_file_from_url(url=url, model_dir=model_dir, file_name=file_name) for file_name, url in vae_downloads.items(): load_file_from_url(url=url, model_dir=config.path_vae, file_name=file_name) return default_model, checkpoint_downloads config.default_base_model_name, config.checkpoint_downloads = download_models( config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads, config.embeddings_downloads, config.lora_downloads, config.vae_downloads) config.update_files() init_cache(config.model_filenames, config.paths_checkpoints, config.lora_filenames, config.paths_loras) from webui import * ================================================ FILE: ldm_patched/contrib/external.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import os import sys import json import hashlib import traceback import math import time import random from PIL import Image, ImageOps, ImageSequence from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch pass # sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched")) import ldm_patched.modules.diffusers_load import ldm_patched.modules.samplers import ldm_patched.modules.sample import ldm_patched.modules.sd import ldm_patched.modules.utils import ldm_patched.modules.controlnet import ldm_patched.modules.clip_vision import ldm_patched.modules.model_management from ldm_patched.modules.args_parser import args import importlib import ldm_patched.utils.path_utils import ldm_patched.utils.latent_visualization def before_node_execution(): ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() def interrupt_processing(value=True): ldm_patched.modules.model_management.interrupt_current_processing(value) MAX_RESOLUTION=8192 class CLIPTextEncode: @classmethod def INPUT_TYPES(s): return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "conditioning" def encode(self, clip, text): tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled}]], ) class ConditioningCombine: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "combine" CATEGORY = "conditioning" def combine(self, conditioning_1, conditioning_2): return (conditioning_1 + conditioning_2, ) class ConditioningAverage : @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "addWeighted" CATEGORY = "conditioning" def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): out = [] if len(conditioning_from) > 1: print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] pooled_output_from = conditioning_from[0][1].get("pooled_output", None) for i in range(len(conditioning_to)): t1 = conditioning_to[i][0] pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) t0 = cond_from[:,:t1.shape[1]] if t0.shape[1] < t1.shape[1]: t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) t_to = conditioning_to[i][1].copy() if pooled_output_from is not None and pooled_output_to is not None: t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) elif pooled_output_from is not None: t_to["pooled_output"] = pooled_output_from n = [tw, t_to] out.append(n) return (out, ) class ConditioningConcat: @classmethod def INPUT_TYPES(s): return {"required": { "conditioning_to": ("CONDITIONING",), "conditioning_from": ("CONDITIONING",), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "concat" CATEGORY = "conditioning" def concat(self, conditioning_to, conditioning_from): out = [] if len(conditioning_from) > 1: print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] for i in range(len(conditioning_to)): t1 = conditioning_to[i][0] tw = torch.cat((t1, cond_from),1) n = [tw, conditioning_to[i][1].copy()] out.append(n) return (out, ) class ConditioningSetArea: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): c = [] for t in conditioning: n = [t[0], t[1].copy()] n[1]['area'] = (height // 8, width // 8, y // 8, x // 8) n[1]['strength'] = strength n[1]['set_area_to_bounds'] = False c.append(n) return (c, ) class ConditioningSetAreaPercentage: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, width, height, x, y, strength): c = [] for t in conditioning: n = [t[0], t[1].copy()] n[1]['area'] = ("percentage", height, width, y, x) n[1]['strength'] = strength n[1]['set_area_to_bounds'] = False c.append(n) return (c, ) class ConditioningSetMask: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "mask": ("MASK", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning" def append(self, conditioning, mask, set_cond_area, strength): c = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask.shape) < 3: mask = mask.unsqueeze(0) for t in conditioning: n = [t[0], t[1].copy()] _, h, w = mask.shape n[1]['mask'] = mask n[1]['set_area_to_bounds'] = set_area_to_bounds n[1]['mask_strength'] = strength c.append(n) return (c, ) class ConditioningZeroOut: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", )}} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "zero_out" CATEGORY = "advanced/conditioning" def zero_out(self, conditioning): c = [] for t in conditioning: d = t[1].copy() if "pooled_output" in d: d["pooled_output"] = torch.zeros_like(d["pooled_output"]) n = [torch.zeros_like(t[0]), d] c.append(n) return (c, ) class ConditioningSetTimestepRange: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_range" CATEGORY = "advanced/conditioning" def set_range(self, conditioning, start, end): c = [] for t in conditioning: d = t[1].copy() d['start_percent'] = start d['end_percent'] = end n = [t[0], d] c.append(n) return (c, ) class VAEDecode: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" CATEGORY = "latent" def decode(self, vae, samples): return (vae.decode(samples["samples"]), ) class VAEDecodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "decode" CATEGORY = "_for_testing" def decode(self, vae, samples, tile_size): return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), ) class VAEEncode: @classmethod def INPUT_TYPES(s): return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "latent" @staticmethod def vae_encode_crop_pixels(pixels): x = (pixels.shape[1] // 8) * 8 y = (pixels.shape[2] // 8) * 8 if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) // 2 y_offset = (pixels.shape[2] % 8) // 2 pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] return pixels def encode(self, vae, pixels): pixels = self.vae_encode_crop_pixels(pixels) t = vae.encode(pixels[:,:,:,:3]) return ({"samples":t}, ) class VAEEncodeTiled: @classmethod def INPUT_TYPES(s): return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) }} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "_for_testing" def encode(self, vae, pixels, tile_size): pixels = VAEEncode.vae_encode_crop_pixels(pixels) t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) return ({"samples":t}, ) class VAEEncodeForInpaint: @classmethod def INPUT_TYPES(s): return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "encode" CATEGORY = "latent/inpaint" def encode(self, vae, pixels, mask, grow_mask_by=6): x = (pixels.shape[1] // 8) * 8 y = (pixels.shape[2] // 8) * 8 mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") pixels = pixels.clone() if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) // 2 y_offset = (pixels.shape[2] % 8) // 2 pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] #grow mask by a few pixels to keep things seamless in latent space if grow_mask_by == 0: mask_erosion = mask else: kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) padding = math.ceil((grow_mask_by - 1) / 2) mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) m = (1.0 - mask.round()).squeeze(1) for i in range(3): pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] *= m pixels[:,:,:,i] += 0.5 t = vae.encode(pixels) return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) class InpaintModelConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "vae": ("VAE", ), "pixels": ("IMAGE", ), "mask": ("MASK", ), }} RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/inpaint" def encode(self, positive, negative, pixels, vae, mask): x = (pixels.shape[1] // 8) * 8 y = (pixels.shape[2] // 8) * 8 mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") orig_pixels = pixels pixels = orig_pixels.clone() if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) // 2 y_offset = (pixels.shape[2] % 8) // 2 pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] m = (1.0 - mask.round()).squeeze(1) for i in range(3): pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] *= m pixels[:,:,:,i] += 0.5 concat_latent = vae.encode(pixels) orig_latent = vae.encode(orig_pixels) out_latent = {} out_latent["samples"] = orig_latent out_latent["noise_mask"] = mask out = [] for conditioning in [positive, negative]: c = [] for t in conditioning: d = t[1].copy() d["concat_latent_image"] = concat_latent d["concat_mask"] = mask n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1], out_latent) class SaveLatent: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT", ), "filename_prefix": ("STRING", {"default": "latents/ldm_patched"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "_for_testing" def save(self, samples, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) # support save metadata for latent sharing prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = None if not args.disable_server_info: metadata = {"prompt": prompt_info} if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) file = f"{filename}_{counter:05}_.latent" results = list() results.append({ "filename": file, "subfolder": subfolder, "type": "output" }) file = os.path.join(full_output_folder, file) output = {} output["latent_tensor"] = samples["samples"] output["latent_format_version_0"] = torch.tensor([]) ldm_patched.modules.utils.save_torch_file(output, file, metadata=metadata) return { "ui": { "latents": results } } class LoadLatent: @classmethod def INPUT_TYPES(s): input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] return {"required": {"latent": [sorted(files), ]}, } CATEGORY = "_for_testing" RETURN_TYPES = ("LATENT", ) FUNCTION = "load" def load(self, latent): latent_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) latent = safetensors.torch.load_file(latent_path, device="cpu") multiplier = 1.0 if "latent_format_version_0" not in latent: multiplier = 1.0 / 0.18215 samples = {"samples": latent["latent_tensor"].float() * multiplier} return (samples, ) @classmethod def IS_CHANGED(s, latent): image_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, latent): if not ldm_patched.utils.path_utils.exists_annotated_filepath(latent): return "Invalid latent file: {}".format(latent) return True class CheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "config_name": (ldm_patched.utils.path_utils.get_filename_list("configs"), ), "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), )}} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "advanced/loaders" def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): config_path = ldm_patched.utils.path_utils.get_full_path("configs", config_name) ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) return ldm_patched.modules.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) class CheckpointLoaderSimple: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "loaders" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return out[:3] class DiffusersLoader: @classmethod def INPUT_TYPES(cls): paths = [] for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): if os.path.exists(search_path): for root, subdir, files in os.walk(search_path, followlinks=True): if "model_index.json" in files: paths.append(os.path.relpath(root, start=search_path)) return {"required": {"model_path": (paths,), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "advanced/loaders/deprecated" def load_checkpoint(self, model_path, output_vae=True, output_clip=True): for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): if os.path.exists(search_path): path = os.path.join(search_path, model_path) if os.path.exists(path): model_path = path break return ldm_patched.modules.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) class unCLIPCheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") FUNCTION = "load_checkpoint" CATEGORY = "loaders" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return out class CLIPSetLastLayer: @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP", ), "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "set_last_layer" CATEGORY = "conditioning" def set_last_layer(self, clip, stop_at_clip_layer): clip = clip.clone() clip.clip_layer(stop_at_clip_layer) return (clip,) class LoraLoader: def __init__(self): self.loaded_lora = None @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP", ), "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL", "CLIP") FUNCTION = "load_lora" CATEGORY = "loaders" def load_lora(self, model, clip, lora_name, strength_model, strength_clip): if strength_model == 0 and strength_clip == 0: return (model, clip) lora_path = ldm_patched.utils.path_utils.get_full_path("loras", lora_name) lora = None if self.loaded_lora is not None: if self.loaded_lora[0] == lora_path: lora = self.loaded_lora[1] else: temp = self.loaded_lora self.loaded_lora = None del temp if lora is None: lora = ldm_patched.modules.utils.load_torch_file(lora_path, safe_load=True) self.loaded_lora = (lora_path, lora) model_lora, clip_lora = ldm_patched.modules.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) return (model_lora, clip_lora) class LoraLoaderModelOnly(LoraLoader): @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_lora_model_only" def load_lora_model_only(self, model, lora_name, strength_model): return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) class VAELoader: @staticmethod def vae_list(): vaes = ldm_patched.utils.path_utils.get_filename_list("vae") approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") sdxl_taesd_enc = False sdxl_taesd_dec = False sd1_taesd_enc = False sd1_taesd_dec = False for v in approx_vaes: if v.startswith("taesd_decoder."): sd1_taesd_dec = True elif v.startswith("taesd_encoder."): sd1_taesd_enc = True elif v.startswith("taesdxl_decoder."): sdxl_taesd_dec = True elif v.startswith("taesdxl_encoder."): sdxl_taesd_enc = True if sd1_taesd_dec and sd1_taesd_enc: vaes.append("taesd") if sdxl_taesd_dec and sdxl_taesd_enc: vaes.append("taesdxl") return vaes @staticmethod def load_taesd(name): sd = {} approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) enc = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", encoder)) for k in enc: sd["taesd_encoder.{}".format(k)] = enc[k] dec = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", decoder)) for k in dec: sd["taesd_decoder.{}".format(k)] = dec[k] if name == "taesd": sd["vae_scale"] = torch.tensor(0.18215) elif name == "taesdxl": sd["vae_scale"] = torch.tensor(0.13025) return sd @classmethod def INPUT_TYPES(s): return {"required": { "vae_name": (s.vae_list(), )}} RETURN_TYPES = ("VAE",) FUNCTION = "load_vae" CATEGORY = "loaders" #TODO: scale factor? def load_vae(self, vae_name): if vae_name in ["taesd", "taesdxl"]: sd = self.load_taesd(vae_name) else: vae_path = ldm_patched.utils.path_utils.get_full_path("vae", vae_name) sd = ldm_patched.modules.utils.load_torch_file(vae_path) vae = ldm_patched.modules.sd.VAE(sd=sd) return (vae,) class ControlNetLoader: @classmethod def INPUT_TYPES(s): return {"required": { "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" CATEGORY = "loaders" def load_controlnet(self, control_net_name): controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path) return (controlnet,) class DiffControlNetLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} RETURN_TYPES = ("CONTROL_NET",) FUNCTION = "load_controlnet" CATEGORY = "loaders" def load_controlnet(self, model, control_net_name): controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path, model) return (controlnet,) class ControlNetApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_controlnet" CATEGORY = "conditioning" def apply_controlnet(self, conditioning, control_net, image, strength): if strength == 0: return (conditioning, ) c = [] control_hint = image.movedim(-1,1) for t in conditioning: n = [t[0], t[1].copy()] c_net = control_net.copy().set_cond_hint(control_hint, strength) if 'control' in t[1]: c_net.set_previous_controlnet(t[1]['control']) n[1]['control'] = c_net n[1]['control_apply_to_uncond'] = True c.append(n) return (c, ) class ControlNetApplyAdvanced: @classmethod def INPUT_TYPES(s): return {"required": {"positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }} RETURN_TYPES = ("CONDITIONING","CONDITIONING") RETURN_NAMES = ("positive", "negative") FUNCTION = "apply_controlnet" CATEGORY = "conditioning" def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): if strength == 0: return (positive, negative) control_hint = image.movedim(-1,1) cnets = {} out = [] for conditioning in [positive, negative]: c = [] for t in conditioning: d = t[1].copy() prev_cnet = d.get('control', None) if prev_cnet in cnets: c_net = cnets[prev_cnet] else: c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) c_net.set_previous_controlnet(prev_cnet) cnets[prev_cnet] = c_net d['control'] = c_net d['control_apply_to_uncond'] = False n = [t[0], d] c.append(n) out.append(c) return (out[0], out[1]) class UNETLoader: @classmethod def INPUT_TYPES(s): return {"required": { "unet_name": (ldm_patched.utils.path_utils.get_filename_list("unet"), ), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" CATEGORY = "advanced/loaders" def load_unet(self, unet_name): unet_path = ldm_patched.utils.path_utils.get_full_path("unet", unet_name) model = ldm_patched.modules.sd.load_unet(unet_path) return (model,) class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name): clip_path = ldm_patched.utils.path_utils.get_full_path("clip", clip_name) clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return (clip,) class DualCLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name1": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), "clip_name2": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "advanced/loaders" def load_clip(self, clip_name1, clip_name2): clip_path1 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name1) clip_path2 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name2) clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return (clip,) class CLIPVisionLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip_vision"), ), }} RETURN_TYPES = ("CLIP_VISION",) FUNCTION = "load_clip" CATEGORY = "loaders" def load_clip(self, clip_name): clip_path = ldm_patched.utils.path_utils.get_full_path("clip_vision", clip_name) clip_vision = ldm_patched.modules.clip_vision.load(clip_path) return (clip_vision,) class CLIPVisionEncode: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "image": ("IMAGE",) }} RETURN_TYPES = ("CLIP_VISION_OUTPUT",) FUNCTION = "encode" CATEGORY = "conditioning" def encode(self, clip_vision, image): output = clip_vision.encode_image(image) return (output,) class StyleModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "style_model_name": (ldm_patched.utils.path_utils.get_filename_list("style_models"), )}} RETURN_TYPES = ("STYLE_MODEL",) FUNCTION = "load_style_model" CATEGORY = "loaders" def load_style_model(self, style_model_name): style_model_path = ldm_patched.utils.path_utils.get_full_path("style_models", style_model_name) style_model = ldm_patched.modules.sd.load_style_model(style_model_path) return (style_model,) class StyleModelApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "style_model": ("STYLE_MODEL", ), "clip_vision_output": ("CLIP_VISION_OUTPUT", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_stylemodel" CATEGORY = "conditioning/style_model" def apply_stylemodel(self, clip_vision_output, style_model, conditioning): cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) c = [] for t in conditioning: n = [torch.cat((t[0], cond), dim=1), t[1].copy()] c.append(n) return (c, ) class unCLIPConditioning: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning": ("CONDITIONING", ), "clip_vision_output": ("CLIP_VISION_OUTPUT", ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_adm" CATEGORY = "conditioning" def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): if strength == 0: return (conditioning, ) c = [] for t in conditioning: o = t[1].copy() x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} if "unclip_conditioning" in o: o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] else: o["unclip_conditioning"] = [x] n = [t[0], o] c.append(n) return (c, ) class GLIGENLoader: @classmethod def INPUT_TYPES(s): return {"required": { "gligen_name": (ldm_patched.utils.path_utils.get_filename_list("gligen"), )}} RETURN_TYPES = ("GLIGEN",) FUNCTION = "load_gligen" CATEGORY = "loaders" def load_gligen(self, gligen_name): gligen_path = ldm_patched.utils.path_utils.get_full_path("gligen", gligen_name) gligen = ldm_patched.modules.sd.load_gligen(gligen_path) return (gligen,) class GLIGENTextBoxApply: @classmethod def INPUT_TYPES(s): return {"required": {"conditioning_to": ("CONDITIONING", ), "clip": ("CLIP", ), "gligen_textbox_model": ("GLIGEN", ), "text": ("STRING", {"multiline": True}), "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" CATEGORY = "conditioning/gligen" def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): c = [] cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) for t in conditioning_to: n = [t[0], t[1].copy()] position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] prev = [] if "gligen" in n[1]: prev = n[1]['gligen'][2] n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) c.append(n) return (c, ) class EmptyLatentImage: def __init__(self): self.device = ldm_patched.modules.model_management.intermediate_device() @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} RETURN_TYPES = ("LATENT",) FUNCTION = "generate" CATEGORY = "latent" def generate(self, width, height, batch_size=1): latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) return ({"samples":latent}, ) class LatentFromBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), "length": ("INT", {"default": 1, "min": 1, "max": 64}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "frombatch" CATEGORY = "latent/batch" def frombatch(self, samples, batch_index, length): s = samples.copy() s_in = samples["samples"] batch_index = min(s_in.shape[0] - 1, batch_index) length = min(s_in.shape[0] - batch_index, length) s["samples"] = s_in[batch_index:batch_index + length].clone() if "noise_mask" in samples: masks = samples["noise_mask"] if masks.shape[0] == 1: s["noise_mask"] = masks.clone() else: if masks.shape[0] < s_in.shape[0]: masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] s["noise_mask"] = masks[batch_index:batch_index + length].clone() if "batch_index" not in s: s["batch_index"] = [x for x in range(batch_index, batch_index+length)] else: s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] return (s,) class RepeatLatentBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "amount": ("INT", {"default": 1, "min": 1, "max": 64}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "repeat" CATEGORY = "latent/batch" def repeat(self, samples, amount): s = samples.copy() s_in = samples["samples"] s["samples"] = s_in.repeat((amount, 1,1,1)) if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: masks = samples["noise_mask"] if masks.shape[0] < s_in.shape[0]: masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) if "batch_index" in s: offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] return (s,) class LatentUpscale: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "crop": (s.crop_methods,)}} RETURN_TYPES = ("LATENT",) FUNCTION = "upscale" CATEGORY = "latent" def upscale(self, samples, upscale_method, width, height, crop): if width == 0 and height == 0: s = samples else: s = samples.copy() if width == 0: height = max(64, height) width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2])) elif height == 0: width = max(64, width) height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3])) else: width = max(64, width) height = max(64, height) s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) return (s,) class LatentUpscaleBy: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} RETURN_TYPES = ("LATENT",) FUNCTION = "upscale" CATEGORY = "latent" def upscale(self, samples, upscale_method, scale_by): s = samples.copy() width = round(samples["samples"].shape[3] * scale_by) height = round(samples["samples"].shape[2] * scale_by) s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") return (s,) class LatentRotate: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), }} RETURN_TYPES = ("LATENT",) FUNCTION = "rotate" CATEGORY = "latent/transform" def rotate(self, samples, rotation): s = samples.copy() rotate_by = 0 if rotation.startswith("90"): rotate_by = 1 elif rotation.startswith("180"): rotate_by = 2 elif rotation.startswith("270"): rotate_by = 3 s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) return (s,) class LatentFlip: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), }} RETURN_TYPES = ("LATENT",) FUNCTION = "flip" CATEGORY = "latent/transform" def flip(self, samples, flip_method): s = samples.copy() if flip_method.startswith("x"): s["samples"] = torch.flip(samples["samples"], dims=[2]) elif flip_method.startswith("y"): s["samples"] = torch.flip(samples["samples"], dims=[3]) return (s,) class LatentComposite: @classmethod def INPUT_TYPES(s): return {"required": { "samples_to": ("LATENT",), "samples_from": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "composite" CATEGORY = "latent" def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): x = x // 8 y = y // 8 feather = feather // 8 samples_out = samples_to.copy() s = samples_to["samples"].clone() samples_to = samples_to["samples"] samples_from = samples_from["samples"] if feather == 0: s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] else: samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] mask = torch.ones_like(samples_from) for t in range(feather): if y != 0: mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) if y + samples_from.shape[2] < samples_to.shape[2]: mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) if x != 0: mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) if x + samples_from.shape[3] < samples_to.shape[3]: mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) rev_mask = torch.ones_like(mask) - mask s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask samples_out["samples"] = s return (samples_out,) class LatentBlend: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "blend_factor": ("FLOAT", { "default": 0.5, "min": 0, "max": 1, "step": 0.01 }), }} RETURN_TYPES = ("LATENT",) FUNCTION = "blend" CATEGORY = "_for_testing" def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): samples_out = samples1.copy() samples1 = samples1["samples"] samples2 = samples2["samples"] if samples1.shape != samples2.shape: samples2.permute(0, 3, 1, 2) samples2 = ldm_patched.modules.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') samples2.permute(0, 2, 3, 1) samples_blended = self.blend_mode(samples1, samples2, blend_mode) samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) samples_out["samples"] = samples_blended return (samples_out,) def blend_mode(self, img1, img2, mode): if mode == "normal": return img2 else: raise ValueError(f"Unsupported blend mode: {mode}") class LatentCrop: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "crop" CATEGORY = "latent/transform" def crop(self, samples, width, height, x, y): s = samples.copy() samples = samples['samples'] x = x // 8 y = y // 8 #enfonce minimum size of 64 if x > (samples.shape[3] - 8): x = samples.shape[3] - 8 if y > (samples.shape[2] - 8): y = samples.shape[2] - 8 new_height = height // 8 new_width = width // 8 to_x = new_width + x to_y = new_height + y s['samples'] = samples[:,:,y:to_y, x:to_x] return (s,) class SetLatentNoiseMask: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }} RETURN_TYPES = ("LATENT",) FUNCTION = "set_mask" CATEGORY = "latent/inpaint" def set_mask(self, samples, mask): s = samples.copy() s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) return (s,) def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): latent_image = latent["samples"] if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] callback = ldm_patched.utils.latent_visualization.prepare_callback(model, steps) disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) out = latent.copy() out["samples"] = samples return (out, ) class KSampler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) class KSamplerAdvanced: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "add_noise": (["enable", "disable"], ), "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), "return_with_leftover_noise": (["disable", "enable"], ), } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): force_full_denoise = True if return_with_leftover_noise == "enable": force_full_denoise = False disable_noise = False if add_noise == "disable": disable_noise = True return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) class SaveImage: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() self.type = "output" self.prefix_append = "" self.compress_level = 4 @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ldm_patched"})}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image" def save_images(self, images, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) metadata = None if not args.disable_server_info: metadata = PngInfo() if prompt is not None: metadata.add_text("prompt", json.dumps(prompt)) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add_text(x, json.dumps(extra_pnginfo[x])) file = f"{filename}_{counter:05}_.png" img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 return { "ui": { "images": results } } class PreviewImage(SaveImage): def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_temp_directory() self.type = "temp" self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) self.compress_level = 1 @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } class LoadImage: @classmethod def INPUT_TYPES(s): input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True})}, } CATEGORY = "image" RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "load_image" def load_image(self, image): image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) img = Image.open(image_path) output_images = [] output_masks = [] for i in ImageSequence.Iterator(img): i = ImageOps.exif_transpose(i) if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) image = i.convert("RGB") image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] if 'A' in i.getbands(): mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 mask = 1. - torch.from_numpy(mask) else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") output_images.append(image) output_masks.append(mask.unsqueeze(0)) if len(output_images) > 1: output_image = torch.cat(output_images, dim=0) output_mask = torch.cat(output_masks, dim=0) else: output_image = output_images[0] output_mask = output_masks[0] return (output_image, output_mask) @classmethod def IS_CHANGED(s, image): image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, image): if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True class LoadImageMask: _color_channels = ["alpha", "red", "green", "blue"] @classmethod def INPUT_TYPES(s): input_dir = ldm_patched.utils.path_utils.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] return {"required": {"image": (sorted(files), {"image_upload": True}), "channel": (s._color_channels, ), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "load_image" def load_image(self, image, channel): image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) i = Image.open(image_path) i = ImageOps.exif_transpose(i) if i.getbands() != ("R", "G", "B", "A"): if i.mode == 'I': i = i.point(lambda i: i * (1 / 255)) i = i.convert("RGBA") mask = None c = channel[0].upper() if c in i.getbands(): mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 mask = torch.from_numpy(mask) if c == 'A': mask = 1. - mask else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") return (mask.unsqueeze(0),) @classmethod def IS_CHANGED(s, image, channel): image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) m = hashlib.sha256() with open(image_path, 'rb') as f: m.update(f.read()) return m.digest().hex() @classmethod def VALIDATE_INPUTS(s, image): if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): return "Invalid image file: {}".format(image) return True class ImageScale: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "crop": (s.crop_methods,)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, width, height, crop): if width == 0 and height == 0: s = image else: samples = image.movedim(-1,1) if width == 0: width = max(1, round(samples.shape[3] * height / samples.shape[2])) elif height == 0: height = max(1, round(samples.shape[2] * width / samples.shape[3])) s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, crop) s = s.movedim(1,-1) return (s,) class ImageScaleBy: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, scale_by): samples = image.movedim(-1,1) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) class ImageInvert: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "invert" CATEGORY = "image" def invert(self, image): s = 1.0 - image return (s,) class ImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} RETURN_TYPES = ("IMAGE",) FUNCTION = "batch" CATEGORY = "image" def batch(self, image1, image2): if image1.shape[1:] != image2.shape[1:]: image2 = ldm_patched.modules.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) s = torch.cat((image1, image2), dim=0) return (s,) class EmptyImage: def __init__(self, device="cpu"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "generate" CATEGORY = "image" def generate(self, width, height, batch_size=1, color=0): r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) return (torch.cat((r, g, b), dim=-1), ) class ImagePadForOutpaint: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), } } RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "expand_image" CATEGORY = "image" def expand_image(self, image, left, top, right, bottom, feathering): d1, d2, d3, d4 = image.size() new_image = torch.ones( (d1, d2 + top + bottom, d3 + left + right, d4), dtype=torch.float32, ) * 0.5 new_image[:, top:top + d2, left:left + d3, :] = image mask = torch.ones( (d2 + top + bottom, d3 + left + right), dtype=torch.float32, ) t = torch.zeros( (d2, d3), dtype=torch.float32 ) if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: for i in range(d2): for j in range(d3): dt = i if top != 0 else d2 db = d2 - i if bottom != 0 else d2 dl = j if left != 0 else d3 dr = d3 - j if right != 0 else d3 d = min(dt, db, dl, dr) if d >= feathering: continue v = (feathering - d) / feathering t[i, j] = v * v mask[top:top + d2, left:left + d3] = t return (new_image, mask) NODE_CLASS_MAPPINGS = { "KSampler": KSampler, "CheckpointLoaderSimple": CheckpointLoaderSimple, "CLIPTextEncode": CLIPTextEncode, "CLIPSetLastLayer": CLIPSetLastLayer, "VAEDecode": VAEDecode, "VAEEncode": VAEEncode, "VAEEncodeForInpaint": VAEEncodeForInpaint, "VAELoader": VAELoader, "EmptyLatentImage": EmptyLatentImage, "LatentUpscale": LatentUpscale, "LatentUpscaleBy": LatentUpscaleBy, "LatentFromBatch": LatentFromBatch, "RepeatLatentBatch": RepeatLatentBatch, "SaveImage": SaveImage, "PreviewImage": PreviewImage, "LoadImage": LoadImage, "LoadImageMask": LoadImageMask, "ImageScale": ImageScale, "ImageScaleBy": ImageScaleBy, "ImageInvert": ImageInvert, "ImageBatch": ImageBatch, "ImagePadForOutpaint": ImagePadForOutpaint, "EmptyImage": EmptyImage, "ConditioningAverage": ConditioningAverage , "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, "ConditioningSetArea": ConditioningSetArea, "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, "ConditioningSetMask": ConditioningSetMask, "KSamplerAdvanced": KSamplerAdvanced, "SetLatentNoiseMask": SetLatentNoiseMask, "LatentComposite": LatentComposite, "LatentBlend": LatentBlend, "LatentRotate": LatentRotate, "LatentFlip": LatentFlip, "LatentCrop": LatentCrop, "LoraLoader": LoraLoader, "CLIPLoader": CLIPLoader, "UNETLoader": UNETLoader, "DualCLIPLoader": DualCLIPLoader, "CLIPVisionEncode": CLIPVisionEncode, "StyleModelApply": StyleModelApply, "unCLIPConditioning": unCLIPConditioning, "ControlNetApply": ControlNetApply, "ControlNetApplyAdvanced": ControlNetApplyAdvanced, "ControlNetLoader": ControlNetLoader, "DiffControlNetLoader": DiffControlNetLoader, "StyleModelLoader": StyleModelLoader, "CLIPVisionLoader": CLIPVisionLoader, "VAEDecodeTiled": VAEDecodeTiled, "VAEEncodeTiled": VAEEncodeTiled, "unCLIPCheckpointLoader": unCLIPCheckpointLoader, "GLIGENLoader": GLIGENLoader, "GLIGENTextBoxApply": GLIGENTextBoxApply, "InpaintModelConditioning": InpaintModelConditioning, "CheckpointLoader": CheckpointLoader, "DiffusersLoader": DiffusersLoader, "LoadLatent": LoadLatent, "SaveLatent": SaveLatent, "ConditioningZeroOut": ConditioningZeroOut, "ConditioningSetTimestepRange": ConditioningSetTimestepRange, "LoraLoaderModelOnly": LoraLoaderModelOnly, } NODE_DISPLAY_NAME_MAPPINGS = { # Sampling "KSampler": "KSampler", "KSamplerAdvanced": "KSampler (Advanced)", # Loaders "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", "CheckpointLoaderSimple": "Load Checkpoint", "VAELoader": "Load VAE", "LoraLoader": "Load LoRA", "CLIPLoader": "Load CLIP", "ControlNetLoader": "Load ControlNet Model", "DiffControlNetLoader": "Load ControlNet Model (diff)", "StyleModelLoader": "Load Style Model", "CLIPVisionLoader": "Load CLIP Vision", "UpscaleModelLoader": "Load Upscale Model", # Conditioning "CLIPVisionEncode": "CLIP Vision Encode", "StyleModelApply": "Apply Style Model", "CLIPTextEncode": "CLIP Text Encode (Prompt)", "CLIPSetLastLayer": "CLIP Set Last Layer", "ConditioningCombine": "Conditioning (Combine)", "ConditioningAverage ": "Conditioning (Average)", "ConditioningConcat": "Conditioning (Concat)", "ConditioningSetArea": "Conditioning (Set Area)", "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", "ConditioningSetMask": "Conditioning (Set Mask)", "ControlNetApply": "Apply ControlNet", "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", # Latent "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", "SetLatentNoiseMask": "Set Latent Noise Mask", "VAEDecode": "VAE Decode", "VAEEncode": "VAE Encode", "LatentRotate": "Rotate Latent", "LatentFlip": "Flip Latent", "LatentCrop": "Crop Latent", "EmptyLatentImage": "Empty Latent Image", "LatentUpscale": "Upscale Latent", "LatentUpscaleBy": "Upscale Latent By", "LatentComposite": "Latent Composite", "LatentBlend": "Latent Blend", "LatentFromBatch" : "Latent From Batch", "RepeatLatentBatch": "Repeat Latent Batch", # Image "SaveImage": "Save Image", "PreviewImage": "Preview Image", "LoadImage": "Load Image", "LoadImageMask": "Load Image (as Mask)", "ImageScale": "Upscale Image", "ImageScaleBy": "Upscale Image By", "ImageUpscaleWithModel": "Upscale Image (using Model)", "ImageInvert": "Invert Image", "ImagePadForOutpaint": "Pad Image for Outpainting", "ImageBatch": "Batch Images", # _for_testing "VAEDecodeTiled": "VAE Decode (Tiled)", "VAEEncodeTiled": "VAE Encode (Tiled)", } EXTENSION_WEB_DIRS = {} def load_custom_node(module_path, ignore=set()): module_name = os.path.basename(module_path) if os.path.isfile(module_path): sp = os.path.splitext(module_path) module_name = sp[0] try: if os.path.isfile(module_path): module_spec = importlib.util.spec_from_file_location(module_name, module_path) module_dir = os.path.split(module_path)[0] else: module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) module_dir = module_path module = importlib.util.module_from_spec(module_spec) sys.modules[module_name] = module module_spec.loader.exec_module(module) if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) if os.path.isdir(web_dir): EXTENSION_WEB_DIRS[module_name] = web_dir if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: for name in module.NODE_CLASS_MAPPINGS: if name not in ignore: NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name] if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) return True else: print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") return False except Exception as e: print(traceback.format_exc()) print(f"Cannot import {module_path} module for custom nodes:", e) return False def load_custom_nodes(): base_node_names = set(NODE_CLASS_MAPPINGS.keys()) node_paths = ldm_patched.utils.path_utils.get_folder_paths("custom_nodes") node_import_times = [] for custom_node_path in node_paths: possible_modules = os.listdir(os.path.realpath(custom_node_path)) if "__pycache__" in possible_modules: possible_modules.remove("__pycache__") for possible_module in possible_modules: module_path = os.path.join(custom_node_path, possible_module) if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue if module_path.endswith(".disabled"): continue time_before = time.perf_counter() success = load_custom_node(module_path, base_node_names) node_import_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_import_times) > 0: print("\nImport times for custom nodes:") for n in sorted(node_import_times): if n[2]: import_message = "" else: import_message = " (IMPORT FAILED)" print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) print() def init_custom_nodes(): extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched_extras") extras_files = [ "nodes_latent.py", "nodes_hypernetwork.py", "nodes_upscale_model.py", "nodes_post_processing.py", "nodes_mask.py", "nodes_compositing.py", "nodes_rebatch.py", "nodes_model_merging.py", "nodes_tomesd.py", "nodes_clip_sdxl.py", "nodes_canny.py", "nodes_freelunch.py", "nodes_custom_sampler.py", "nodes_hypertile.py", "nodes_model_advanced.py", "nodes_model_downscale.py", "nodes_images.py", "nodes_video_model.py", "nodes_sag.py", "nodes_perpneg.py", "nodes_stable3d.py", "nodes_sdupscale.py", "nodes_photomaker.py", ] for node_file in extras_files: load_custom_node(os.path.join(extras_dir, node_file)) load_custom_nodes() ================================================ FILE: ldm_patched/contrib/external_align_your_steps.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py #from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html import numpy as np import torch def loglinear_interp(t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ xs = np.linspace(0, 1, len(t_steps)) ys = np.log(t_steps[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() return interped_ys NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} class AlignYourStepsScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model_type": (["SD1", "SDXL", "SVD"], ), "steps": ("INT", {"default": 10, "min": 10, "max": 10000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model_type, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) sigmas = NOISE_LEVELS[model_type][:] if (steps + 1) != len(sigmas): sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), ) NODE_CLASS_MAPPINGS = { "AlignYourStepsScheduler": AlignYourStepsScheduler, } ================================================ FILE: ldm_patched/contrib/external_canny.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py #From https://github.com/kornia/kornia import math import torch import torch.nn.functional as F import ldm_patched.modules.model_management def get_canny_nms_kernel(device=None, dtype=None): """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" return torch.tensor( [ [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]], [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]], [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]], [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]], [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], ], device=device, dtype=dtype, ) def get_hysteresis_kernel(device=None, dtype=None): """Utility function that returns the 3x3 kernels for the Canny hysteresis.""" return torch.tensor( [ [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]], [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]], [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]], [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], ], device=device, dtype=dtype, ) def gaussian_blur_2d(img, kernel_size, sigma): ksize_half = (kernel_size - 1) * 0.5 x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) pdf = torch.exp(-0.5 * (x / sigma).pow(2)) x_kernel = pdf / pdf.sum() x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] img = torch.nn.functional.pad(img, padding, mode="reflect") img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3]) return img def get_sobel_kernel2d(device=None, dtype=None): kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype) kernel_y = kernel_x.transpose(0, 1) return torch.stack([kernel_x, kernel_y]) def spatial_gradient(input, normalized: bool = True): r"""Compute the first order image derivative in both x and y using a Sobel operator. .. image:: _static/img/spatial_gradient.png Args: input: input image tensor with shape :math:`(B, C, H, W)`. mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. normalized: whether the output is normalized. Return: the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. .. note:: See a working example `here `__. Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = spatial_gradient(input) # 1x3x2x4x4 >>> output.shape torch.Size([1, 3, 2, 4, 4]) """ # KORNIA_CHECK_IS_TENSOR(input) # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) # allocate kernel kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype) if normalized: kernel = normalize_kernel2d(kernel) # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel[:, None, ...] # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] out_channels: int = 2 padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate') out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1) return out.reshape(b, c, out_channels, h, w) def rgb_to_grayscale(image, rgb_weights = None): r"""Convert a RGB image to grayscale version of image. .. image:: _static/img/rgb_to_grayscale.png The image data is assumed to be in the range of (0, 1). Args: image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`. rgb_weights: Weights that will be applied on each channel (RGB). The sum of the weights should add up to one. Returns: grayscale version of the image with shape :math:`(*,1,H,W)`. .. note:: See a working example `here `__. Example: >>> input = torch.rand(2, 3, 4, 5) >>> gray = rgb_to_grayscale(input) # 2x1x4x5 """ if len(image.shape) < 3 or image.shape[-3] != 3: raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") if rgb_weights is None: # 8 bit images if image.dtype == torch.uint8: rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8) # floating point images elif image.dtype in (torch.float16, torch.float32, torch.float64): rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype) else: raise TypeError(f"Unknown data type: {image.dtype}") else: # is tensor that we make sure is in the same device/dtype rgb_weights = rgb_weights.to(image) # unpack the color image channels with RGB order r: Tensor = image[..., 0:1, :, :] g: Tensor = image[..., 1:2, :, :] b: Tensor = image[..., 2:3, :, :] w_r, w_g, w_b = rgb_weights.unbind() return w_r * r + w_g * g + w_b * b def canny( input, low_threshold = 0.1, high_threshold = 0.2, kernel_size = 5, sigma = 1, hysteresis = True, eps = 1e-6, ): r"""Find edges of the input image and filters them using the Canny algorithm. .. image:: _static/img/canny.png Args: input: input image tensor with shape :math:`(B,C,H,W)`. low_threshold: lower threshold for the hysteresis procedure. high_threshold: upper threshold for the hysteresis procedure. kernel_size: the size of the kernel for the gaussian blur. sigma: the standard deviation of the kernel for the gaussian blur. hysteresis: if True, applies the hysteresis edge tracking. Otherwise, the edges are divided between weak (0.5) and strong (1) edges. eps: regularization number to avoid NaN during backprop. Returns: - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. .. note:: See a working example `here `__. Example: >>> input = torch.rand(5, 3, 4, 4) >>> magnitude, edges = canny(input) # 5x3x4x4 >>> magnitude.shape torch.Size([5, 1, 4, 4]) >>> edges.shape torch.Size([5, 1, 4, 4]) """ # KORNIA_CHECK_IS_TENSOR(input) # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) # KORNIA_CHECK( # low_threshold <= high_threshold, # "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: " # f"{low_threshold}>{high_threshold}", # ) # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}') # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}') device = input.device dtype = input.dtype # To Grayscale if input.shape[1] == 3: input = rgb_to_grayscale(input) # Gaussian filter blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma) # Compute the gradients gradients: Tensor = spatial_gradient(blurred, normalized=False) # Unpack the edges gx: Tensor = gradients[:, :, 0] gy: Tensor = gradients[:, :, 1] # Compute gradient magnitude and angle magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps) angle: Tensor = torch.atan2(gy, gx) # Radians to Degrees angle = 180.0 * angle / math.pi # Round angle to the nearest 45 degree angle = torch.round(angle / 45) * 45 # Non-maximal suppression nms_kernels: Tensor = get_canny_nms_kernel(device, dtype) nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2) # Get the indices for both directions positive_idx: Tensor = (angle / 45) % 8 positive_idx = positive_idx.long() negative_idx: Tensor = ((angle / 45) + 4) % 8 negative_idx = negative_idx.long() # Apply the non-maximum suppression to the different directions channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx) channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx) channel_select_filtered: Tensor = torch.stack( [channel_select_filtered_positive, channel_select_filtered_negative], 1 ) is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0 magnitude = magnitude * is_max # Threshold edges: Tensor = F.threshold(magnitude, low_threshold, 0.0) low: Tensor = magnitude > low_threshold high: Tensor = magnitude > high_threshold edges = low * 0.5 + high * 0.5 edges = edges.to(dtype) # Hysteresis if hysteresis: edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype) hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype) while ((edges_old - edges).abs() != 0).any(): weak: Tensor = (edges == 0.5).float() strong: Tensor = (edges == 1).float() hysteresis_magnitude: Tensor = F.conv2d( edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2 ) hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype) hysteresis_magnitude = hysteresis_magnitude * weak + strong edges_old = edges.clone() edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5 edges = hysteresis_magnitude return magnitude, edges class Canny: @classmethod def INPUT_TYPES(s): return {"required": {"image": ("IMAGE",), "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) }} RETURN_TYPES = ("IMAGE",) FUNCTION = "detect_edge" CATEGORY = "image/preprocessors" def detect_edge(self, image, low_threshold, high_threshold): output = canny(image.to(ldm_patched.modules.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) img_out = output[1].to(ldm_patched.modules.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) return (img_out,) NODE_CLASS_MAPPINGS = { "Canny": Canny, } ================================================ FILE: ldm_patched/contrib/external_clip_sdxl.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch from ldm_patched.contrib.external import MAX_RESOLUTION class CLIPTextEncodeSDXLRefiner: @classmethod def INPUT_TYPES(s): return {"required": { "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, ascore, width, height, text): tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], ) class CLIPTextEncodeSDXL: @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), "text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}), "clip": ("CLIP", ), "text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}), "clip": ("CLIP", ), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "encode" CATEGORY = "advanced/conditioning" def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): tokens = clip.tokenize(text_g) tokens["l"] = clip.tokenize(text_l)["l"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) NODE_CLASS_MAPPINGS = { "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, } ================================================ FILE: ldm_patched/contrib/external_compositing.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import numpy as np import torch import ldm_patched.modules.utils from enum import Enum def resize_mask(mask, shape): return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) class PorterDuffMode(Enum): ADD = 0 CLEAR = 1 DARKEN = 2 DST = 3 DST_ATOP = 4 DST_IN = 5 DST_OUT = 6 DST_OVER = 7 LIGHTEN = 8 MULTIPLY = 9 OVERLAY = 10 SCREEN = 11 SRC = 12 SRC_ATOP = 13 SRC_IN = 14 SRC_OUT = 15 SRC_OVER = 16 XOR = 17 def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): if mode == PorterDuffMode.ADD: out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) out_image = torch.clamp(src_image + dst_image, 0, 1) elif mode == PorterDuffMode.CLEAR: out_alpha = torch.zeros_like(dst_alpha) out_image = torch.zeros_like(dst_image) elif mode == PorterDuffMode.DARKEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) elif mode == PorterDuffMode.DST: out_alpha = dst_alpha out_image = dst_image elif mode == PorterDuffMode.DST_ATOP: out_alpha = src_alpha out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image elif mode == PorterDuffMode.DST_IN: out_alpha = src_alpha * dst_alpha out_image = dst_image * src_alpha elif mode == PorterDuffMode.DST_OUT: out_alpha = (1 - src_alpha) * dst_alpha out_image = (1 - src_alpha) * dst_image elif mode == PorterDuffMode.DST_OVER: out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha out_image = dst_image + (1 - dst_alpha) * src_image elif mode == PorterDuffMode.LIGHTEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) elif mode == PorterDuffMode.MULTIPLY: out_alpha = src_alpha * dst_alpha out_image = src_image * dst_image elif mode == PorterDuffMode.OVERLAY: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) elif mode == PorterDuffMode.SCREEN: out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha out_image = src_image + dst_image - src_image * dst_image elif mode == PorterDuffMode.SRC: out_alpha = src_alpha out_image = src_image elif mode == PorterDuffMode.SRC_ATOP: out_alpha = dst_alpha out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image elif mode == PorterDuffMode.SRC_IN: out_alpha = src_alpha * dst_alpha out_image = src_image * dst_alpha elif mode == PorterDuffMode.SRC_OUT: out_alpha = (1 - dst_alpha) * src_alpha out_image = (1 - dst_alpha) * src_image elif mode == PorterDuffMode.SRC_OVER: out_alpha = src_alpha + (1 - src_alpha) * dst_alpha out_image = src_image + (1 - src_alpha) * dst_image elif mode == PorterDuffMode.XOR: out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image else: out_alpha = None out_image = None return out_image, out_alpha class PorterDuffImageComposite: @classmethod def INPUT_TYPES(s): return { "required": { "source": ("IMAGE",), "source_alpha": ("MASK",), "destination": ("IMAGE",), "destination_alpha": ("MASK",), "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), }, } RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "composite" CATEGORY = "mask/compositing" def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) out_images = [] out_alphas = [] for i in range(batch_size): src_image = source[i] dst_image = destination[i] assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels src_alpha = source_alpha[i].unsqueeze(2) dst_alpha = destination_alpha[i].unsqueeze(2) if dst_alpha.shape[:2] != dst_image.shape[:2]: upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_image.shape != dst_image.shape: upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) if src_alpha.shape != dst_alpha.shape: upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) out_images.append(out_image) out_alphas.append(out_alpha.squeeze(2)) result = (torch.stack(out_images), torch.stack(out_alphas)) return result class SplitImageWithAlpha: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), } } CATEGORY = "mask/compositing" RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "split_image_with_alpha" def split_image_with_alpha(self, image: torch.Tensor): out_images = [i[:,:,:3] for i in image] out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) return result class JoinImageWithAlpha: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "alpha": ("MASK",), } } CATEGORY = "mask/compositing" RETURN_TYPES = ("IMAGE",) FUNCTION = "join_image_with_alpha" def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): batch_size = min(len(image), len(alpha)) out_images = [] alpha = 1.0 - resize_mask(alpha, image.shape[1:]) for i in range(batch_size): out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) result = (torch.stack(out_images),) return result NODE_CLASS_MAPPINGS = { "PorterDuffImageComposite": PorterDuffImageComposite, "SplitImageWithAlpha": SplitImageWithAlpha, "JoinImageWithAlpha": JoinImageWithAlpha, } NODE_DISPLAY_NAME_MAPPINGS = { "PorterDuffImageComposite": "Porter-Duff Image Composite", "SplitImageWithAlpha": "Split Image with Alpha", "JoinImageWithAlpha": "Join Image with Alpha", } ================================================ FILE: ldm_patched/contrib/external_custom_sampler.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.modules.samplers import ldm_patched.modules.sample from ldm_patched.k_diffusion import sampling as k_diffusion_sampling import ldm_patched.utils.latent_visualization import torch import ldm_patched.modules.utils class BasicScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model, scheduler, steps, denoise): total_steps = steps if denoise < 1.0: total_steps = int(steps/denoise) ldm_patched.modules.model_management.load_models_gpu([model]) sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu() sigmas = sigmas[-(steps + 1):] return (sigmas, ) class KarrasScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min, rho): sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) class ExponentialScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min): sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) return (sigmas, ) class PolyexponentialScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, sigma_max, sigma_min, rho): sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) return (sigmas, ) class SDTurboScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "steps": ("INT", {"default": 1, "min": 1, "max": 10}), "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, model, steps, denoise): start_step = 10 - int(10 * denoise) timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] sigmas = model.model_sampling.sigma(timesteps) sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) return (sigmas, ) class VPScheduler: @classmethod def INPUT_TYPES(s): return {"required": {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/schedulers" FUNCTION = "get_sigmas" def get_sigmas(self, steps, beta_d, beta_min, eps_s): sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) return (sigmas, ) class SplitSigmas: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), "step": ("INT", {"default": 0, "min": 0, "max": 10000}), } } RETURN_TYPES = ("SIGMAS","SIGMAS") CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" def get_sigmas(self, sigmas, step): sigmas1 = sigmas[:step + 1] sigmas2 = sigmas[step:] return (sigmas1, sigmas2) class FlipSigmas: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), } } RETURN_TYPES = ("SIGMAS",) CATEGORY = "sampling/custom_sampling/sigmas" FUNCTION = "get_sigmas" def get_sigmas(self, sigmas): sigmas = sigmas.flip(0) if sigmas[0] == 0: sigmas[0] = 0.0001 return (sigmas,) class KSamplerSelect: @classmethod def INPUT_TYPES(s): return {"required": {"sampler_name": (ldm_patched.modules.samplers.SAMPLER_NAMES, ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, sampler_name): sampler = ldm_patched.modules.samplers.sampler_object(sampler_name) return (sampler, ) class SamplerDPMPP_2M_SDE: @classmethod def INPUT_TYPES(s): return {"required": {"solver_type": (['midpoint', 'heun'], ), "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "noise_device": (['gpu', 'cpu'], ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, solver_type, eta, s_noise, noise_device): if noise_device == 'cpu': sampler_name = "dpmpp_2m_sde" else: sampler_name = "dpmpp_2m_sde_gpu" sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) return (sampler, ) class SamplerDPMPP_SDE: @classmethod def INPUT_TYPES(s): return {"required": {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), "noise_device": (['gpu', 'cpu'], ), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, eta, s_noise, r, noise_device): if noise_device == 'cpu': sampler_name = "dpmpp_sde" else: sampler_name = "dpmpp_sde_gpu" sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) return (sampler, ) class SamplerTCD: @classmethod def INPUT_TYPES(s): return { "required": { "eta": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), } } RETURN_TYPES = ("SAMPLER",) CATEGORY = "sampling/custom_sampling/samplers" FUNCTION = "get_sampler" def get_sampler(self, eta=0.3): sampler = ldm_patched.modules.samplers.ksampler("tcd", {"eta": eta}) return (sampler, ) class SamplerCustom: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "add_noise": ("BOOLEAN", {"default": True}), "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "sampler": ("SAMPLER", ), "sigmas": ("SIGMAS", ), "latent_image": ("LATENT", ), } } RETURN_TYPES = ("LATENT","LATENT") RETURN_NAMES = ("output", "denoised_output") FUNCTION = "sample" CATEGORY = "sampling/custom_sampling" def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): latent = latent_image latent_image = latent["samples"] if not add_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = ldm_patched.modules.sample.prepare_noise(latent_image, noise_seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] x0_output = {} callback = ldm_patched.utils.latent_visualization.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED samples = ldm_patched.modules.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) out = latent.copy() out["samples"] = samples if "x0" in x0_output: out_denoised = latent.copy() out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out return (out, out_denoised) NODE_CLASS_MAPPINGS = { "SamplerCustom": SamplerCustom, "BasicScheduler": BasicScheduler, "KarrasScheduler": KarrasScheduler, "ExponentialScheduler": ExponentialScheduler, "PolyexponentialScheduler": PolyexponentialScheduler, "VPScheduler": VPScheduler, "SDTurboScheduler": SDTurboScheduler, "KSamplerSelect": KSamplerSelect, "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SamplerTCD": SamplerTCD, "SplitSigmas": SplitSigmas, "FlipSigmas": FlipSigmas, } ================================================ FILE: ldm_patched/contrib/external_freelunch.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) import torch def Fourier_filter(x, threshold, scale): # FFT x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) B, C, H, W = x_freq.shape mask = torch.ones((B, C, H, W), device=x.device) crow, ccol = H // 2, W //2 mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale x_freq = x_freq * mask # IFFT x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real return x_filtered.to(x.dtype) class FreeU: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(h.shape[1], None) if scale is not None: h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) class FreeU_V2: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, b1, b2, s1, s2): model_channels = model.model.model_config.unet_config["model_channels"] scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} on_cpu_devices = {} def output_block_patch(h, hsp, transformer_options): scale = scale_dict.get(h.shape[1], None) if scale is not None: hidden_mean = h.mean(1).unsqueeze(1) B = hidden_mean.shape[0] hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) if hsp.device not in on_cpu_devices: try: hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) except: print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") on_cpu_devices[hsp.device] = True hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) else: hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) return h, hsp m = model.clone() m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "FreeU": FreeU, "FreeU_V2": FreeU_V2, } ================================================ FILE: ldm_patched/contrib/external_hypernetwork.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.modules.utils import ldm_patched.utils.path_utils import torch def load_hypernetwork_patch(path, strength): sd = ldm_patched.modules.utils.load_torch_file(path, safe_load=True) activation_func = sd.get('activation_func', 'linear') is_layer_norm = sd.get('is_layer_norm', False) use_dropout = sd.get('use_dropout', False) activate_output = sd.get('activate_output', False) last_layer_dropout = sd.get('last_layer_dropout', False) valid_activation = { "linear": torch.nn.Identity, "relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, "swish": torch.nn.Hardswish, "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, "softsign": torch.nn.Softsign, "mish": torch.nn.Mish, } if activation_func not in valid_activation: print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout) return None out = {} for d in sd: try: dim = int(d) except: continue output = [] for index in [0, 1]: attn_weights = sd[dim][index] keys = attn_weights.keys() linears = filter(lambda a: a.endswith(".weight"), keys) linears = list(map(lambda a: a[:-len(".weight")], linears)) layers = [] i = 0 while i < len(linears): lin_name = linears[i] last_layer = (i == (len(linears) - 1)) penultimate_layer = (i == (len(linears) - 2)) lin_weight = attn_weights['{}.weight'.format(lin_name)] lin_bias = attn_weights['{}.bias'.format(lin_name)] layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) layers.append(layer) if activation_func != "linear": if (not last_layer) or (activate_output): layers.append(valid_activation[activation_func]()) if is_layer_norm: i += 1 ln_name = linears[i] ln_weight = attn_weights['{}.weight'.format(ln_name)] ln_bias = attn_weights['{}.bias'.format(ln_name)] ln = torch.nn.LayerNorm(ln_weight.shape[0]) ln.load_state_dict({"weight": ln_weight, "bias": ln_bias}) layers.append(ln) if use_dropout: if (not last_layer) and (not penultimate_layer or last_layer_dropout): layers.append(torch.nn.Dropout(p=0.3)) i += 1 output.append(torch.nn.Sequential(*layers)) out[dim] = torch.nn.ModuleList(output) class hypernetwork_patch: def __init__(self, hypernet, strength): self.hypernet = hypernet self.strength = strength def __call__(self, q, k, v, extra_options): dim = k.shape[-1] if dim in self.hypernet: hn = self.hypernet[dim] k = k + hn[0](k) * self.strength v = v + hn[1](v) * self.strength return q, k, v def to(self, device): for d in self.hypernet.keys(): self.hypernet[d] = self.hypernet[d].to(device) return self return hypernetwork_patch(out, strength) class HypernetworkLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "hypernetwork_name": (ldm_patched.utils.path_utils.get_filename_list("hypernetworks"), ), "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "load_hypernetwork" CATEGORY = "loaders" def load_hypernetwork(self, model, hypernetwork_name, strength): hypernetwork_path = ldm_patched.utils.path_utils.get_full_path("hypernetworks", hypernetwork_name) model_hypernetwork = model.clone() patch = load_hypernetwork_patch(hypernetwork_path, strength) if patch is not None: model_hypernetwork.set_model_attn1_patch(patch) model_hypernetwork.set_model_attn2_patch(patch) return (model_hypernetwork,) NODE_CLASS_MAPPINGS = { "HypernetworkLoader": HypernetworkLoader } ================================================ FILE: ldm_patched/contrib/external_hypertile.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py #Taken from: https://github.com/tfernd/HyperTile/ import math from einops import rearrange # Use torch rng for consistency across generations from torch import randint def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: min_value = min(min_value, value) # All big divisors of value (inclusive) divisors = [i for i in range(min_value, value + 1) if value % i == 0] ns = [value // i for i in divisors[:max_options]] # has at least 1 element if len(ns) - 1 > 0: idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() else: idx = 0 return ns[idx] class HyperTile: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), "scale_depth": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "model_patches" def patch(self, model, tile_size, swap_size, max_depth, scale_depth): model_channels = model.model.model_config.unet_config["model_channels"] latent_tile_size = max(32, tile_size) // 8 self.temp = None def hypertile_in(q, k, v, extra_options): model_chans = q.shape[-2] orig_shape = extra_options['original_shape'] apply_to = [] for i in range(max_depth + 1): apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) if model_chans in apply_to: shape = extra_options["original_shape"] aspect_ratio = shape[-1] / shape[-2] hw = q.size(1) h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 nh = random_divisor(h, latent_tile_size * factor, swap_size) nw = random_divisor(w, latent_tile_size * factor, swap_size) if nh * nw > 1: q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) self.temp = (nh, nw, h, w) return q, k, v return q, k, v def hypertile_out(out, extra_options): if self.temp is not None: nh, nw, h, w = self.temp self.temp = None out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) return out m = model.clone() m.set_model_attn1_patch(hypertile_in) m.set_model_attn1_output_patch(hypertile_out) return (m, ) NODE_CLASS_MAPPINGS = { "HyperTile": HyperTile, } ================================================ FILE: ldm_patched/contrib/external_images.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.contrib.external import ldm_patched.utils.path_utils from ldm_patched.modules.args_parser import args from PIL import Image from PIL.PngImagePlugin import PngInfo import numpy as np import json import os MAX_RESOLUTION = ldm_patched.contrib.external.MAX_RESOLUTION class ImageCrop: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "crop" CATEGORY = "image/transform" def crop(self, image, width, height, x, y): x = min(x, image.shape[2] - 1) y = min(y, image.shape[1] - 1) to_x = width + x to_y = height + y img = image[:,y:to_y, x:to_x, :] return (img,) class RepeatImageBatch: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "amount": ("INT", {"default": 1, "min": 1, "max": 64}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "repeat" CATEGORY = "image/batch" def repeat(self, image, amount): s = image.repeat((amount, 1,1,1)) return (s,) class SaveAnimatedWEBP: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() self.type = "output" self.prefix_append = "" methods = {"default": 4, "fastest": 0, "slowest": 6} @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ldm_patched"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "lossless": ("BOOLEAN", {"default": True}), "quality": ("INT", {"default": 80, "min": 0, "max": 100}), "method": (list(s.methods.keys()),), # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): method = self.methods.get(method) filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = pil_images[0].getexif() if not args.disable_server_info: if prompt is not None: metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) if extra_pnginfo is not None: inital_exif = 0x010f for x in extra_pnginfo: metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) inital_exif -= 1 if num_frames == 0: num_frames = len(pil_images) c = len(pil_images) for i in range(0, c, num_frames): file = f"{filename}_{counter:05}_.webp" pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) counter += 1 animated = num_frames != 1 return { "ui": { "images": results, "animated": (animated,) } } class SaveAnimatedPNG: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() self.type = "output" self.prefix_append = "" @classmethod def INPUT_TYPES(s): return {"required": {"images": ("IMAGE", ), "filename_prefix": ("STRING", {"default": "ldm_patched"}), "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () FUNCTION = "save_images" OUTPUT_NODE = True CATEGORY = "image/animation" def save_images(self, images, fps, compress_level, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) results = list() pil_images = [] for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) pil_images.append(img) metadata = None if not args.disable_server_info: metadata = PngInfo() if prompt is not None: metadata.add(b"ldm_patched", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add(b"ldm_patched", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) file = f"{filename}_{counter:05}_.png" pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) results.append({ "filename": file, "subfolder": subfolder, "type": self.type }) return { "ui": { "images": results, "animated": (True,)} } NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, } ================================================ FILE: ldm_patched/contrib/external_latent.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.modules.utils import torch def reshape_latent_to(target_shape, latent): if latent.shape[1:] != target_shape[1:]: latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0]) class LatentAdd: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 + s2 return (samples_out,) class LatentSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 - s2 return (samples_out,) class LatentMultiply: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, multiplier): samples_out = samples.copy() s1 = samples["samples"] samples_out["samples"] = s1 * multiplier return (samples_out,) class LatentInterpolate: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples1, samples2, ratio): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] s2 = reshape_latent_to(s1.shape, s2) m1 = torch.linalg.vector_norm(s1, dim=(1)) m2 = torch.linalg.vector_norm(s2, dim=(1)) s1 = torch.nan_to_num(s1 / m1) s2 = torch.nan_to_num(s2 / m2) t = (s1 * ratio + s2 * (1.0 - ratio)) mt = torch.linalg.vector_norm(t, dim=(1)) st = torch.nan_to_num(t / mt) samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) return (samples_out,) class LatentBatch: @classmethod def INPUT_TYPES(s): return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} RETURN_TYPES = ("LATENT",) FUNCTION = "batch" CATEGORY = "latent/batch" def batch(self, samples1, samples2): samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] if s1.shape[1:] != s2.shape[1:]: s2 = ldm_patched.modules.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") s = torch.cat((s1, s2), dim=0) samples_out["samples"] = s samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) return (samples_out,) class LatentBatchSeedBehavior: @classmethod def INPUT_TYPES(s): return {"required": { "samples": ("LATENT",), "seed_behavior": (["random", "fixed"],),}} RETURN_TYPES = ("LATENT",) FUNCTION = "op" CATEGORY = "latent/advanced" def op(self, samples, seed_behavior): samples_out = samples.copy() latent = samples["samples"] if seed_behavior == "random": if 'batch_index' in samples_out: samples_out.pop('batch_index') elif seed_behavior == "fixed": batch_number = samples_out.get("batch_index", [0])[0] samples_out["batch_index"] = [batch_number] * latent.shape[0] return (samples_out,) NODE_CLASS_MAPPINGS = { "LatentAdd": LatentAdd, "LatentSubtract": LatentSubtract, "LatentMultiply": LatentMultiply, "LatentInterpolate": LatentInterpolate, "LatentBatch": LatentBatch, "LatentBatchSeedBehavior": LatentBatchSeedBehavior, } ================================================ FILE: ldm_patched/contrib/external_mask.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import numpy as np import scipy.ndimage import torch import ldm_patched.modules.utils from ldm_patched.contrib.external import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): source = source.to(destination.device) if resize_source: source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") source = ldm_patched.modules.utils.repeat_to_batch_size(source, destination.shape[0]) x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) left, top = (x // multiplier, y // multiplier) right, bottom = (left + source.shape[3], top + source.shape[2],) if mask is None: mask = torch.ones_like(source) else: mask = mask.to(destination.device, copy=True) mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0]) # calculate the bounds of the source that will be overlapping the destination # this prevents the source trying to overwrite latent pixels that are out of bounds # of the destination visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) mask = mask[:, :, :visible_height, :visible_width] inverse_mask = torch.ones_like(mask) - mask source_portion = mask * source[:, :, :visible_height, :visible_width] destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] destination[:, :, top:bottom, left:right] = source_portion + destination_portion return destination class LatentCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("LATENT",), "source": ("LATENT",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("LATENT",) FUNCTION = "composite" CATEGORY = "latent" def composite(self, destination, source, x, y, resize_source, mask = None): output = destination.copy() destination = destination["samples"].clone() source = source["samples"] output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) return (output,) class ImageCompositeMasked: @classmethod def INPUT_TYPES(s): return { "required": { "destination": ("IMAGE",), "source": ("IMAGE",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "resize_source": ("BOOLEAN", {"default": False}), }, "optional": { "mask": ("MASK",), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "composite" CATEGORY = "image" def composite(self, destination, source, x, y, resize_source, mask = None): destination = destination.clone().movedim(-1, 1) output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) return (output,) class MaskToImage: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("IMAGE",) FUNCTION = "mask_to_image" def mask_to_image(self, mask): result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) return (result,) class ImageToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "channel": (["red", "green", "blue", "alpha"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, channel): channels = ["red", "green", "blue", "alpha"] mask = image[:, :, :, channels.index(channel)] return (mask,) class ImageColorToMask: @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "image_to_mask" def image_to_mask(self, image, color): temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] mask = torch.where(temp == color, 255, 0).float() return (mask,) class SolidMask: @classmethod def INPUT_TYPES(cls): return { "required": { "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "solid" def solid(self, value, width, height): out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") return (out,) class InvertMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "invert" def invert(self, mask): out = 1.0 - mask return (out,) class CropMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "crop" def crop(self, mask, x, y, width, height): mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = mask[:, y:y + height, x:x + width] return (out,) class MaskComposite: @classmethod def INPUT_TYPES(cls): return { "required": { "destination": ("MASK",), "source": ("MASK",), "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "combine" def combine(self, destination, source, x, y, operation): output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() source = source.reshape((-1, source.shape[-2], source.shape[-1])) left, top = (x, y,) right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) visible_width, visible_height = (right - left, bottom - top,) source_portion = source[:, :visible_height, :visible_width] destination_portion = destination[:, top:bottom, left:right] if operation == "multiply": output[:, top:bottom, left:right] = destination_portion * source_portion elif operation == "add": output[:, top:bottom, left:right] = destination_portion + source_portion elif operation == "subtract": output[:, top:bottom, left:right] = destination_portion - source_portion elif operation == "and": output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "or": output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() elif operation == "xor": output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() output = torch.clamp(output, 0.0, 1.0) return (output,) class FeatherMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), } } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "feather" def feather(self, mask, left, top, right, bottom): output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() left = min(left, output.shape[-1]) right = min(right, output.shape[-1]) top = min(top, output.shape[-2]) bottom = min(bottom, output.shape[-2]) for x in range(left): feather_rate = (x + 1.0) / left output[:, :, x] *= feather_rate for x in range(right): feather_rate = (x + 1) / right output[:, :, -x] *= feather_rate for y in range(top): feather_rate = (y + 1) / top output[:, y, :] *= feather_rate for y in range(bottom): feather_rate = (y + 1) / bottom output[:, -y, :] *= feather_rate return (output,) class GrowMask: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), "tapered_corners": ("BOOLEAN", {"default": True}), }, } CATEGORY = "mask" RETURN_TYPES = ("MASK",) FUNCTION = "expand_mask" def expand_mask(self, mask, expand, tapered_corners): c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = [] for m in mask: output = m.numpy() for _ in range(abs(expand)): if expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) output = torch.from_numpy(output) out.append(output) return (torch.stack(out, dim=0),) NODE_CLASS_MAPPINGS = { "LatentCompositeMasked": LatentCompositeMasked, "ImageCompositeMasked": ImageCompositeMasked, "MaskToImage": MaskToImage, "ImageToMask": ImageToMask, "ImageColorToMask": ImageColorToMask, "SolidMask": SolidMask, "InvertMask": InvertMask, "CropMask": CropMask, "MaskComposite": MaskComposite, "FeatherMask": FeatherMask, "GrowMask": GrowMask, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageToMask": "Convert Image to Mask", "MaskToImage": "Convert Mask to Image", } ================================================ FILE: ldm_patched/contrib/external_model_advanced.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.utils.path_utils import ldm_patched.modules.sd import ldm_patched.modules.model_sampling import torch class LCM(ldm_patched.modules.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) x0 = model_input - model_output * sigma sigma_data = 0.5 scaled_timestep = timestep * 10.0 #timestep_scaling c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 return c_out * x0 + c_skip * model_input class ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete): original_timesteps = 50 def __init__(self, model_config=None): super().__init__(model_config) self.skip_steps = self.num_timesteps // self.original_timesteps sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) for x in range(self.original_timesteps): sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] self.set_sigmas(sigmas_valid) def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) def sigma(self, timestep): t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def rescale_zero_terminal_snr_sigmas(sigmas): alphas_cumprod = 1 / ((sigmas * sigmas) + 1) alphas_bar_sqrt = alphas_cumprod.sqrt() # Store old values. alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() # Shift so the last timestep is zero. alphas_bar_sqrt -= (alphas_bar_sqrt_T) # Scale so the first timestep is back to the old value. alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) # Convert alphas_bar_sqrt to betas alphas_bar = alphas_bar_sqrt**2 # Revert sqrt alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["eps", "v_prediction", "lcm", "tcd"]), "zsnr": ("BOOLEAN", {"default": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, zsnr): m = model.clone() sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete if sampling == "eps": sampling_type = ldm_patched.modules.model_sampling.EPS elif sampling == "v_prediction": sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION elif sampling == "lcm": sampling_type = LCM sampling_base = ModelSamplingDiscreteDistilled elif sampling == "tcd": sampling_type = ldm_patched.modules.model_sampling.EPS sampling_base = ModelSamplingDiscreteDistilled class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) if zsnr: model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) m.add_object_patch("model_sampling", model_sampling) return (m, ) class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() latent_format = None sigma_data = 1.0 if sampling == "eps": sampling_type = ldm_patched.modules.model_sampling.EPS elif sampling == "v_prediction": sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION elif sampling == "edm_playground_v2.5": sampling_type = ldm_patched.modules.model_sampling.EDM sigma_data = 0.5 latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5() class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) m.add_object_patch("model_sampling", model_sampling) if latent_format is not None: m.add_object_patch("latent_format", latent_format) return (m, ) class RescaleCFG: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "advanced/model" def patch(self, model, multiplier): def rescale_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] sigma = args["sigma"] sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) x_orig = args["input"] #rescale cfg has to be done on v-pred model output x = x_orig / (sigma * sigma + 1.0) cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) #rescalecfg x_cfg = uncond + cond_scale * (cond - uncond) ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) x_rescaled = x_cfg * (ro_pos / ro_cfg) x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) m = model.clone() m.set_model_sampler_cfg_function(rescale_cfg) return (m, ) NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, "RescaleCFG": RescaleCFG, } ================================================ FILE: ldm_patched/contrib/external_model_downscale.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import ldm_patched.modules.utils class PatchModelAddDownscale: upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), "downscale_after_skip": ("BOOLEAN", {"default": True}), "downscale_method": (s.upscale_methods,), "upscale_method": (s.upscale_methods,), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): sigma_start = model.model.model_sampling.percent_to_sigma(start_percent) sigma_end = model.model.model_sampling.percent_to_sigma(end_percent) def input_block_patch(h, transformer_options): if transformer_options["block"][1] == block_number: sigma = transformer_options["sigmas"][0].item() if sigma <= sigma_start and sigma >= sigma_end: h = ldm_patched.modules.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") return h def output_block_patch(h, hsp, transformer_options): if h.shape[2] != hsp.shape[2]: h = ldm_patched.modules.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") return h, hsp m = model.clone() if downscale_after_skip: m.set_model_input_block_patch_after_skip(input_block_patch) else: m.set_model_input_block_patch(input_block_patch) m.set_model_output_block_patch(output_block_patch) return (m, ) NODE_CLASS_MAPPINGS = { "PatchModelAddDownscale": PatchModelAddDownscale, } NODE_DISPLAY_NAME_MAPPINGS = { # Sampling "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", } ================================================ FILE: ldm_patched/contrib/external_model_merging.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.modules.sd import ldm_patched.modules.utils import ldm_patched.modules.model_base import ldm_patched.modules.model_management import ldm_patched.utils.path_utils import json import os from ldm_patched.modules.args_parser import args class ModelMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, ratio): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class ModelSubtract: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, multiplier): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, - multiplier, multiplier) return (m, ) class ModelAdd: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") for k in kp: m.add_patches({k: kp[k]}, 1.0, 1.0) return (m, ) class CLIPMergeSimple: @classmethod def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",), "clip2": ("CLIP",), "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, clip1, clip2, ratio): m = clip1.clone() kp = clip2.get_key_patches() for k in kp: if k.endswith(".position_ids") or k.endswith(".logit_scale"): continue m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) class ModelMergeBlocks: @classmethod def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",), "model2": ("MODEL",), "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) }} RETURN_TYPES = ("MODEL",) FUNCTION = "merge" CATEGORY = "advanced/model_merging" def merge(self, model1, model2, **kwargs): m = model1.clone() kp = model2.get_key_patches("diffusion_model.") default_ratio = next(iter(kwargs.values())) for k in kp: ratio = default_ratio k_unet = k[len("diffusion_model."):] last_arg_size = 0 for arg in kwargs: if k_unet.startswith(arg) and last_arg_size < len(arg): ratio = kwargs[arg] last_arg_size = len(arg) m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) return (m, ) def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} enable_modelspec = True if isinstance(model.model, ldm_patched.modules.model_base.SDXL): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner): metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" else: enable_modelspec = False if enable_modelspec: metadata["modelspec.sai_model_spec"] = "1.0.0" metadata["modelspec.implementation"] = "sgm" metadata["modelspec.title"] = "{} {}".format(filename, counter) #TODO: # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", # "v2-inpainting" if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS: metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata) class CheckpointSave: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip": ("CLIP",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} class CLIPSave: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",), "filename_prefix": ("STRING", {"default": "clip/ldm_patched"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) ldm_patched.modules.model_management.load_models_gpu([clip.load_model()]) clip_sd = clip.get_sd() for prefix in ["clip_l.", "clip_g.", ""]: k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) current_clip_sd = {} for x in k: current_clip_sd[x] = clip_sd.pop(x) if len(current_clip_sd) == 0: continue p = prefix[:-1] replace_prefix = {} filename_prefix_ = filename_prefix if len(p) > 0: filename_prefix_ = "{}_{}".format(filename_prefix_, p) replace_prefix[prefix] = "" replace_prefix["transformer."] = "" full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) return {} class VAESave: def __init__(self): self.output_dir = ldm_patched.utils.path_utils.get_output_directory() @classmethod def INPUT_TYPES(s): return {"required": { "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "vae/ldm_patched_vae"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} RETURN_TYPES = () FUNCTION = "save" OUTPUT_NODE = True CATEGORY = "advanced/model_merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) prompt_info = "" if prompt is not None: prompt_info = json.dumps(prompt) metadata = {} if not args.disable_server_info: metadata["prompt"] = prompt_info if extra_pnginfo is not None: for x in extra_pnginfo: metadata[x] = json.dumps(extra_pnginfo[x]) output_checkpoint = f"{filename}_{counter:05}_.safetensors" output_checkpoint = os.path.join(full_output_folder, output_checkpoint) ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) return {} NODE_CLASS_MAPPINGS = { "ModelMergeSimple": ModelMergeSimple, "ModelMergeBlocks": ModelMergeBlocks, "ModelMergeSubtract": ModelSubtract, "ModelMergeAdd": ModelAdd, "CheckpointSave": CheckpointSave, "CLIPMergeSimple": CLIPMergeSimple, "CLIPSave": CLIPSave, "VAESave": VAESave, } ================================================ FILE: ldm_patched/contrib/external_perpneg.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import ldm_patched.modules.model_management import ldm_patched.modules.sample import ldm_patched.modules.samplers import ldm_patched.modules.utils class PerpNeg: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL", ), "empty_conditioning": ("CONDITIONING", ), "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, empty_conditioning, neg_scale): m = model.clone() nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning) def cfg_function(args): model = args["model"] noise_pred_pos = args["cond_denoised"] noise_pred_neg = args["uncond_denoised"] cond_scale = args["cond_scale"] x = args["input"] sigma = args["sigma"] model_options = args["model_options"] nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") (noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options) pos = noise_pred_pos - noise_pred_nocond neg = noise_pred_neg - noise_pred_nocond perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg perp_neg = perp * neg_scale cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) cfg_result = x - cfg_result return cfg_result m.set_model_sampler_cfg_function(cfg_function) return (m, ) NODE_CLASS_MAPPINGS = { "PerpNeg": PerpNeg, } NODE_DISPLAY_NAME_MAPPINGS = { "PerpNeg": "Perp-Neg", } ================================================ FILE: ldm_patched/contrib/external_photomaker.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import torch.nn as nn import ldm_patched.utils.path_utils import ldm_patched.modules.clip_model import ldm_patched.modules.clip_vision import ldm_patched.modules.ops # code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 VISION_CONFIG_DICT = { "hidden_size": 1024, "image_size": 224, "intermediate_size": 4096, "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "hidden_act": "quick_gelu", } class MLP(nn.Module): def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=ldm_patched.modules.ops): super().__init__() if use_residual: assert in_dim == out_dim self.layernorm = operations.LayerNorm(in_dim) self.fc1 = operations.Linear(in_dim, hidden_dim) self.fc2 = operations.Linear(hidden_dim, out_dim) self.use_residual = use_residual self.act_fn = nn.GELU() def forward(self, x): residual = x x = self.layernorm(x) x = self.fc1(x) x = self.act_fn(x) x = self.fc2(x) if self.use_residual: x = x + residual return x class FuseModule(nn.Module): def __init__(self, embed_dim, operations): super().__init__() self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) self.layer_norm = operations.LayerNorm(embed_dim) def fuse_fn(self, prompt_embeds, id_embeds): stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds stacked_id_embeds = self.mlp2(stacked_id_embeds) stacked_id_embeds = self.layer_norm(stacked_id_embeds) return stacked_id_embeds def forward( self, prompt_embeds, id_embeds, class_tokens_mask, ) -> torch.Tensor: # id_embeds shape: [b, max_num_inputs, 1, 2048] id_embeds = id_embeds.to(prompt_embeds.dtype) num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case batch_size, max_num_inputs = id_embeds.shape[:2] # seq_length: 77 seq_length = prompt_embeds.shape[1] # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] flat_id_embeds = id_embeds.view( -1, id_embeds.shape[-2], id_embeds.shape[-1] ) # valid_id_mask [b*max_num_inputs] valid_id_mask = ( torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] < num_inputs[:, None] ) valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) class_tokens_mask = class_tokens_mask.view(-1) valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) # slice out the image token embeddings image_token_embeds = prompt_embeds[class_tokens_mask] stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) return updated_prompt_embeds class PhotoMakerIDEncoder(ldm_patched.modules.clip_model.CLIPVisionModelProjection): def __init__(self): self.load_device = ldm_patched.modules.model_management.text_encoder_device() offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device) super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast) self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False) self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast) def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): b, num_inputs, c, h, w = id_pixel_values.shape id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) shared_id_embeds = self.vision_model(id_pixel_values)[2] id_embeds = self.visual_projection(shared_id_embeds) id_embeds_2 = self.visual_projection_2(shared_id_embeds) id_embeds = id_embeds.view(b, num_inputs, 1, -1) id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) return updated_prompt_embeds class PhotoMakerLoader: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker_model_name": (ldm_patched.utils.path_utils.get_filename_list("photomaker"), )}} RETURN_TYPES = ("PHOTOMAKER",) FUNCTION = "load_photomaker_model" CATEGORY = "_for_testing/photomaker" def load_photomaker_model(self, photomaker_model_name): photomaker_model_path = ldm_patched.utils.path_utils.get_full_path("photomaker", photomaker_model_name) photomaker_model = PhotoMakerIDEncoder() data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True) if "id_encoder" in data: data = data["id_encoder"] photomaker_model.load_state_dict(data) return (photomaker_model,) class PhotoMakerEncode: @classmethod def INPUT_TYPES(s): return {"required": { "photomaker": ("PHOTOMAKER",), "image": ("IMAGE",), "clip": ("CLIP", ), "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}), }} RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_photomaker" CATEGORY = "_for_testing/photomaker" def apply_photomaker(self, photomaker, image, clip, text): special_token = "photomaker" pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() try: index = text.split(" ").index(special_token) + 1 except ValueError: index = -1 tokens = clip.tokenize(text, return_word_ids=True) out_tokens = {} for k in tokens: out_tokens[k] = [] for t in tokens[k]: f = list(filter(lambda x: x[2] != index, t)) while len(f) < len(t): f.append(t[-1]) out_tokens[k].append(f) cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) if index > 0: token_index = index - 1 num_id_images = 1 class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) else: out = cond return ([[out, {"pooled_output": pooled}]], ) NODE_CLASS_MAPPINGS = { "PhotoMakerLoader": PhotoMakerLoader, "PhotoMakerEncode": PhotoMakerEncode, } ================================================ FILE: ldm_patched/contrib/external_post_processing.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import numpy as np import torch import torch.nn.functional as F from PIL import Image import math import ldm_patched.modules.utils class Blend: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image1": ("IMAGE",), "image2": ("IMAGE",), "blend_factor": ("FLOAT", { "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01 }), "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blend_images" CATEGORY = "image/postprocessing" def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): image2 = image2.to(image1.device) if image1.shape != image2.shape: image2 = image2.permute(0, 3, 1, 2) image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = image2.permute(0, 2, 3, 1) blended_image = self.blend_mode(image1, image2, blend_mode) blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor blended_image = torch.clamp(blended_image, 0, 1) return (blended_image,) def blend_mode(self, img1, img2, mode): if mode == "normal": return img2 elif mode == "multiply": return img1 * img2 elif mode == "screen": return 1 - (1 - img1) * (1 - img2) elif mode == "overlay": return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) elif mode == "soft_light": return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) elif mode == "difference": return img1 - img2 else: raise ValueError(f"Unsupported blend mode: {mode}") def g(self, x): return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) def gaussian_kernel(kernel_size: int, sigma: float, device=None): x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") d = torch.sqrt(x * x + y * y) g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) return g / g.sum() class Blur: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "blur_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "blur" CATEGORY = "image/postprocessing" def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): if blur_radius == 0: return (image,) batch_size, height, width, channels = image.shape kernel_size = blur_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] blurred = blurred.permute(0, 2, 3, 1) return (blurred,) class Quantize: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "colors": ("INT", { "default": 256, "min": 1, "max": 256, "step": 1 }), "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "quantize" CATEGORY = "image/postprocessing" def bayer(im, pal_im, order): def normalized_bayer_matrix(n): if n == 0: return np.zeros((1,1), "float32") else: q = 4 ** n m = q * normalized_bayer_matrix(n - 1) return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q num_colors = len(pal_im.getpalette()) // 3 spread = 2 * 256 / num_colors bayer_n = int(math.log2(order)) bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) result = torch.from_numpy(np.array(im).astype(np.float32)) tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) result = result.to(dtype=torch.uint8) im = Image.fromarray(result.cpu().numpy()) im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) return im def quantize(self, image: torch.Tensor, colors: int, dither: str): batch_size, height, width, _ = image.shape result = torch.zeros_like(image) for b in range(batch_size): im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 if dither == "none": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) elif dither == "floyd-steinberg": quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) elif dither.startswith("bayer"): order = int(dither.split('-')[-1]) quantized_image = Quantize.bayer(im, pal_im, order) quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 result[b] = quantized_array return (result,) class Sharpen: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "image": ("IMAGE",), "sharpen_radius": ("INT", { "default": 1, "min": 1, "max": 31, "step": 1 }), "sigma": ("FLOAT", { "default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1 }), "alpha": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1 }), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "sharpen" CATEGORY = "image/postprocessing" def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): if sharpen_radius == 0: return (image,) batch_size, height, width, channels = image.shape kernel_size = sharpen_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) center = kernel_size // 2 kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] sharpened = sharpened.permute(0, 2, 3, 1) result = torch.clamp(sharpened, 0, 1) return (result,) class ImageScaleToTotalPixels: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, image, upscale_method, megapixels): samples = image.movedim(-1,1) total = int(megapixels * 1024 * 1024) scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) return (s,) NODE_CLASS_MAPPINGS = { "ImageBlend": Blend, "ImageBlur": Blur, "ImageQuantize": Quantize, "ImageSharpen": Sharpen, "ImageScaleToTotalPixels": ImageScaleToTotalPixels, } ================================================ FILE: ldm_patched/contrib/external_rebatch.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch class LatentRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "latents": ("LATENT",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("LATENT",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "latent/batch" @staticmethod def get_batch(latents, list_ind, offset): '''prepare a batch out of the list of latents''' samples = latents[list_ind]['samples'] shape = samples.shape mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") if mask.shape[0] < samples.shape[0]: mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] if 'batch_index' in latents[list_ind]: batch_inds = latents[list_ind]['batch_index'] else: batch_inds = [x+offset for x in range(shape[0])] return samples, mask, batch_inds @staticmethod def get_slices(indexable, num, batch_size): '''divides an indexable object into num slices of length batch_size, and a remainder''' slices = [] for i in range(num): slices.append(indexable[i*batch_size:(i+1)*batch_size]) if num * batch_size < len(indexable): return slices, indexable[num * batch_size:] else: return slices, None @staticmethod def slice_batch(batch, num, batch_size): result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] return list(zip(*result)) @staticmethod def cat_batch(batch1, batch2): if batch1[0] is None: return batch2 result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] return result def rebatch(self, latents, batch_size): batch_size = batch_size[0] output_list = [] current_batch = (None, None, None) processed = 0 for i in range(len(latents)): # fetch new entry of list #samples, masks, indices = self.get_batch(latents, i) next_batch = self.get_batch(latents, i, processed) processed += len(next_batch[2]) # set to current if current is None if current_batch[0] is None: current_batch = next_batch # add previous to list if dimensions do not match elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) current_batch = next_batch # cat if everything checks out else: current_batch = self.cat_batch(current_batch, next_batch) # add to list if dimensions gone above target batch size if current_batch[0].shape[0] > batch_size: num = current_batch[0].shape[0] // batch_size sliced, remainder = self.slice_batch(current_batch, num, batch_size) for i in range(num): output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) current_batch = remainder #add remainder if current_batch[0] is not None: sliced, _ = self.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) #get rid of empty masks for s in output_list: if s['noise_mask'].mean() == 1.0: del s['noise_mask'] return (output_list,) class ImageRebatch: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), }} RETURN_TYPES = ("IMAGE",) INPUT_IS_LIST = True OUTPUT_IS_LIST = (True, ) FUNCTION = "rebatch" CATEGORY = "image/batch" def rebatch(self, images, batch_size): batch_size = batch_size[0] output_list = [] all_images = [] for img in images: for i in range(img.shape[0]): all_images.append(img[i:i+1]) for i in range(0, len(all_images), batch_size): output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) return (output_list,) NODE_CLASS_MAPPINGS = { "RebatchLatents": LatentRebatch, "RebatchImages": ImageRebatch, } NODE_DISPLAY_NAME_MAPPINGS = { "RebatchLatents": "Rebatch Latents", "RebatchImages": "Rebatch Images", } ================================================ FILE: ldm_patched/contrib/external_sag.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch from torch import einsum import torch.nn.functional as F import math from einops import rearrange, repeat import os from ldm_patched.ldm.modules.attention import optimized_attention, _ATTN_PRECISION import ldm_patched.modules.samplers # from ldm_patched.modules/ldm/modules/attention.py # but modified to return attention scores as well as output def attention_basic_with_sim(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 h = heads q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) # force cast to fp32 to avoid overflowing if _ATTN_PRECISION =="fp32": sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if mask is not None: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) # attention, what we cannot get enough of sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return (out, sim) def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): # reshape and GAP the attention map _, hw1, hw2 = attn.shape b, _, lh, lw = x0.shape attn = attn.reshape(b, -1, hw1, hw2) # Global Average Pool mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length() mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)] # Reshape mask = ( mask.reshape(b, *mid_shape) .unsqueeze(1) .type(attn.dtype) ) # Upsample mask = F.interpolate(mask, (lh, lw)) blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) blurred = blurred * mask + x0 * (1 - mask) return blurred def gaussian_blur_2d(img, kernel_size, sigma): ksize_half = (kernel_size - 1) * 0.5 x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) pdf = torch.exp(-0.5 * (x / sigma).pow(2)) x_kernel = pdf / pdf.sum() x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] img = F.pad(img, padding, mode="reflect") img = F.conv2d(img, kernel2d, groups=img.shape[-3]) return img class SelfAttentionGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}), "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, scale, blur_sigma): m = model.clone() attn_scores = None # TODO: make this work properly with chunked batches # currently, we can only save the attn from one UNet call def attn_and_record(q, k, v, extra_options): nonlocal attn_scores # if uncond, save the attention scores heads = extra_options["n_heads"] cond_or_uncond = extra_options["cond_or_uncond"] b = q.shape[0] // len(cond_or_uncond) if 1 in cond_or_uncond: uncond_index = cond_or_uncond.index(1) # do the entire attention operation, but save the attention scores to attn_scores (out, sim) = attention_basic_with_sim(q, k, v, heads=heads) # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] n_slices = heads * b attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] return out else: return optimized_attention(q, k, v, heads=heads) def post_cfg_function(args): nonlocal attn_scores uncond_attn = attn_scores sag_scale = scale sag_sigma = blur_sigma sag_threshold = 1.0 model = args["model"] uncond_pred = args["uncond_denoised"] uncond = args["uncond"] cfg_result = args["denoised"] sigma = args["sigma"] model_options = args["model_options"] x = args["input"] if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding return cfg_result # create the adversarially blurred image degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) degraded_noised = degraded + x - uncond_pred # call into the UNet (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options) return cfg_result + (degraded - sag) * sag_scale m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) # from diffusers: # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) return (m, ) NODE_CLASS_MAPPINGS = { "SelfAttentionGuidance": SelfAttentionGuidance, } NODE_DISPLAY_NAME_MAPPINGS = { "SelfAttentionGuidance": "Self-Attention Guidance", } ================================================ FILE: ldm_patched/contrib/external_sdupscale.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import ldm_patched.contrib.external import ldm_patched.modules.utils class SD_4XUpscale_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "positive": ("CONDITIONING",), "negative": ("CONDITIONING",), "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/upscale_diffusion" def encode(self, images, positive, negative, scale_ratio, noise_augmentation): width = max(1, round(images.shape[-2] * scale_ratio)) height = max(1, round(images.shape[-3] * scale_ratio)) pixels = ldm_patched.modules.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center") out_cp = [] out_cn = [] for t in positive: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cp.append(n) for t in negative: n = [t[0], t[1].copy()] n[1]['concat_image'] = pixels n[1]['noise_augmentation'] = noise_augmentation out_cn.append(n) latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) return (out_cp, out_cn, {"samples":latent}) NODE_CLASS_MAPPINGS = { "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, } ================================================ FILE: ldm_patched/contrib/external_stable3d.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import torch import ldm_patched.contrib.external import ldm_patched.modules.utils def camera_embeddings(elevation, azimuth): elevation = torch.as_tensor([elevation]) azimuth = torch.as_tensor([azimuth]) embeddings = torch.stack( [ torch.deg2rad( (90 - elevation) - (90) ), # Zero123 polar is 90-elevation torch.sin(torch.deg2rad(azimuth)), torch.cos(torch.deg2rad(azimuth)), torch.deg2rad( 90 - torch.full_like(elevation, 0) ), ], dim=-1).unsqueeze(1) return embeddings class StableZero123_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = camera_embeddings(elevation, azimuth) cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) return (positive, negative, {"samples":latent}) class StableZero123_Conditioning_Batched: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/3d_models" def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) cam_embeds = [] for i in range(batch_size): cam_embeds.append(camera_embeddings(elevation, azimuth)) elevation += elevation_batch_increment azimuth += azimuth_batch_increment cam_embeds = torch.cat(cam_embeds, dim=0) cond = torch.cat([ldm_patched.modules.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) NODE_CLASS_MAPPINGS = { "StableZero123_Conditioning": StableZero123_Conditioning, "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, } ================================================ FILE: ldm_patched/contrib/external_tomesd.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py #Taken from: https://github.com/dbolya/tomesd import torch from typing import Tuple, Callable import math def do_nothing(x: torch.Tensor, mode:str=None): return x def mps_gather_workaround(input, dim, index): if input.shape[-1] == 1: return torch.gather( input.unsqueeze(-1), dim - 1 if dim < 0 else dim, index.unsqueeze(-1) ).squeeze(-1) else: return torch.gather(input, dim, index) def bipartite_soft_matching_random2d(metric: torch.Tensor, w: int, h: int, sx: int, sy: int, r: int, no_rand: bool = False) -> Tuple[Callable, Callable]: """ Partitions the tokens into src and dst and merges r tokens from src to dst. Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. Args: - metric [B, N, C]: metric to use for similarity - w: image width in tokens - h: image height in tokens - sx: stride in the x dimension for dst, must divide w - sy: stride in the y dimension for dst, must divide h - r: number of tokens to remove (by merging) - no_rand: if true, disable randomness (use top left corner only) """ B, N, _ = metric.shape if r <= 0 or w == 1 or h == 1: return do_nothing, do_nothing gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather with torch.no_grad(): hsy, wsx = h // sy, w // sx # For each sy by sx kernel, randomly assign one token to be dst and the rest src if no_rand: rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) else: rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) # Image is not divisible by sx or sy so we need to move it into a new buffer if (hsy * sy) < h or (wsx * sx) < w: idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view else: idx_buffer = idx_buffer_view # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) # We're finished with these del idx_buffer, idx_buffer_view # rand_idx is currently dst|src, so split them num_dst = hsy * wsx a_idx = rand_idx[:, num_dst:, :] # src b_idx = rand_idx[:, :num_dst, :] # dst def split(x): C = x.shape[-1] src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) return src, dst # Cosine similarity between A and B metric = metric / metric.norm(dim=-1, keepdim=True) a, b = split(metric) scores = a @ b.transpose(-1, -2) # Can't reduce more than the # tokens in src r = min(a.shape[1], r) # Find the most similar greedily node_max, node_idx = scores.max(dim=-1) edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] unm_idx = edge_idx[..., r:, :] # Unmerged Tokens src_idx = edge_idx[..., :r, :] # Merged Tokens dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: src, dst = split(x) n, t1, c = src.shape unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) return torch.cat([unm, dst], dim=1) def unmerge(x: torch.Tensor) -> torch.Tensor: unm_len = unm_idx.shape[1] unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] _, _, c = unm.shape src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) # Combine back to the original shape out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) return out return merge, unmerge def get_functions(x, ratio, original_shape): b, c, original_h, original_w = original_shape original_tokens = original_h * original_w downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) stride_x = 2 stride_y = 2 max_downsample = 1 if downsample <= max_downsample: w = int(math.ceil(original_w / downsample)) h = int(math.ceil(original_h / downsample)) r = int(x.shape[1] * ratio) no_rand = False m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) return m, u nothing = lambda y: y return nothing, nothing class TomePatchModel: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "_for_testing" def patch(self, model, ratio): self.u = None def tomesd_m(q, k, v, extra_options): #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q #however from my basic testing it seems that using q instead gives better results m, self.u = get_functions(q, ratio, extra_options["original_shape"]) return m(q), k, v def tomesd_u(n, extra_options): return self.u(n) m = model.clone() m.set_model_attn1_patch(tomesd_m) m.set_model_attn1_output_patch(tomesd_u) return (m, ) NODE_CLASS_MAPPINGS = { "TomePatchModel": TomePatchModel, } ================================================ FILE: ldm_patched/contrib/external_upscale_model.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import os from ldm_patched.pfn import model_loading from ldm_patched.modules import model_management import torch import ldm_patched.modules.utils import ldm_patched.utils.path_utils class UpscaleModelLoader: @classmethod def INPUT_TYPES(s): return {"required": { "model_name": (ldm_patched.utils.path_utils.get_filename_list("upscale_models"), ), }} RETURN_TYPES = ("UPSCALE_MODEL",) FUNCTION = "load_model" CATEGORY = "loaders" def load_model(self, model_name): model_path = ldm_patched.utils.path_utils.get_full_path("upscale_models", model_name) sd = ldm_patched.modules.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"module.":""}) out = model_loading.load_state_dict(sd).eval() return (out, ) class ImageUpscaleWithModel: @classmethod def INPUT_TYPES(s): return {"required": { "upscale_model": ("UPSCALE_MODEL",), "image": ("IMAGE",), }} RETURN_TYPES = ("IMAGE",) FUNCTION = "upscale" CATEGORY = "image/upscaling" def upscale(self, upscale_model, image): device = model_management.get_torch_device() upscale_model.to(device) in_img = image.movedim(-1,-3).to(device) free_memory = model_management.get_free_memory(device) tile = 512 overlap = 32 oom = True while oom: try: steps = in_img.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = ldm_patched.modules.utils.ProgressBar(steps) s = ldm_patched.modules.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) oom = False except model_management.OOM_EXCEPTION as e: tile //= 2 if tile < 128: raise e upscale_model.cpu() s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) return (s,) NODE_CLASS_MAPPINGS = { "UpscaleModelLoader": UpscaleModelLoader, "ImageUpscaleWithModel": ImageUpscaleWithModel } ================================================ FILE: ldm_patched/contrib/external_video_model.py ================================================ # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py import ldm_patched.contrib.external import torch import ldm_patched.modules.utils import ldm_patched.modules.sd import ldm_patched.utils.path_utils import ldm_patched.contrib.external_model_merging class ImageOnlyCheckpointLoader: @classmethod def INPUT_TYPES(s): return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), }} RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") FUNCTION = "load_checkpoint" CATEGORY = "loaders/video_models" def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) return (out[0], out[3], out[2]) class SVD_img2vid_Conditioning: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 1024, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "conditioning/video_models" def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] if augmentation_level > 0: encode_pixels += torch.randn_like(pixels) * augmentation_level t = vae.encode(encode_pixels) positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([video_frames, 4, height // 8, width // 8]) return (positive, negative, {"samples":latent}) class VideoLinearCFGGuidance: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), }} RETURN_TYPES = ("MODEL",) FUNCTION = "patch" CATEGORY = "sampling/video_models" def patch(self, model, min_cfg): def linear_cfg(args): cond = args["cond"] uncond = args["uncond"] cond_scale = args["cond_scale"] scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) return uncond + scale * (cond - uncond) m = model.clone() m.set_model_sampler_cfg_function(linear_cfg) return (m, ) class ImageOnlyCheckpointSave(ldm_patched.contrib.external_model_merging.CheckpointSave): CATEGORY = "_for_testing" @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), "clip_vision": ("CLIP_VISION",), "vae": ("VAE",), "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),}, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): ldm_patched.contrib.external_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} NODE_CLASS_MAPPINGS = { "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, "VideoLinearCFGGuidance": VideoLinearCFGGuidance, "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, } NODE_DISPLAY_NAME_MAPPINGS = { "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", } ================================================ FILE: ldm_patched/controlnet/cldm.py ================================================ #taken from: https://github.com/lllyasviel/ControlNet #and modified import torch import torch as th import torch.nn as nn from ldm_patched.ldm.modules.diffusionmodules.util import ( zero_module, timestep_embedding, ) from ldm_patched.ldm.modules.attention import SpatialTransformer from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample from ldm_patched.ldm.util import exists import ldm_patched.modules.ops class ControlledUnetModel(UNetModel): #implemented in the ldm unet pass class ControlNet(nn.Module): def __init__( self, image_size, in_channels, model_channels, hint_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=torch.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, device=None, operations=ldm_patched.modules.ops.disable_weight_init, **kwargs, ): super().__init__() assert use_spatial_transformer == True, "use_spatial_transformer has to be true" if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' # from omegaconf.listconfig import ListConfig # if type(context_dim) == ListConfig: # context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.dims = dims self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) transformer_depth = transformer_depth[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim) elif self.num_classes == "continuous": print("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) self.input_hint_block = TimestepEmbedSequential( operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), nn.SiLU(), operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels mid_block = [ ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] if transformer_depth_middle >= 0: mid_block += [SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) self._feature_size += ch def make_zero_conv(self, channels, operations=None, dtype=None, device=None): return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) def forward(self, x, hint, timesteps, context, y=None, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) guided_hint = self.input_hint_block(hint, emb, context) outs = [] hs = [] if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) h += guided_hint guided_hint = None else: h = module(h, emb, context) outs.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) outs.append(self.middle_block_out(h, emb, context)) return outs ================================================ FILE: ldm_patched/k_diffusion/sampling.py ================================================ import math from scipy import integrate import torch from torch import nn import torchsde from tqdm.auto import trange, tqdm from . import utils def append_zero(x): return torch.cat([x, x.new_zeros([1])]) def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): """Constructs the noise schedule of Karras et al. (2022).""" ramp = torch.linspace(0, 1, n, device=device) min_inv_rho = sigma_min ** (1 / rho) max_inv_rho = sigma_max ** (1 / rho) sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho return append_zero(sigmas).to(device) def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): """Constructs an exponential noise schedule.""" sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() return append_zero(sigmas) def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): """Constructs an polynomial in log sigma noise schedule.""" ramp = torch.linspace(1, 0, n, device=device) ** rho sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) return append_zero(sigmas) def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): """Constructs a continuous VP noise schedule.""" t = torch.linspace(1, eps_s, n, device=device) sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) return append_zero(sigmas) def to_d(x, sigma, denoised): """Converts a denoiser output to a Karras ODE derivative.""" return (x - denoised) / utils.append_dims(sigma, x.ndim) def get_ancestral_step(sigma_from, sigma_to, eta=1.): """Calculates the noise level (sigma_down) to step down to and the amount of noise to add (sigma_up) when doing an ancestral sampling step.""" if not eta: return sigma_to, 0. sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 return sigma_down, sigma_up def default_noise_sampler(x): return lambda sigma, sigma_next: torch.randn_like(x) class BatchedBrownianTree: """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" def __init__(self, x, t0, t1, seed=None, **kwargs): self.cpu_tree = True if "cpu" in kwargs: self.cpu_tree = kwargs.pop("cpu") t0, t1, self.sign = self.sort(t0, t1) w0 = kwargs.get('w0', torch.zeros_like(x)) if seed is None: seed = torch.randint(0, 2 ** 63 - 1, []).item() self.batched = True try: assert len(seed) == x.shape[0] w0 = w0[0] except TypeError: seed = [seed] self.batched = False if self.cpu_tree: self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] else: self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] @staticmethod def sort(a, b): return (a, b, 1) if a < b else (b, a, -1) def __call__(self, t0, t1): t0, t1, sign = self.sort(t0, t1) if self.cpu_tree: w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) else: w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) return w if self.batched else w[0] class BrownianTreeNoiseSampler: """A noise sampler backed by a torchsde.BrownianTree. Args: x (Tensor): The tensor whose shape, device and dtype to use to generate random samples. sigma_min (float): The low end of the valid interval. sigma_max (float): The high end of the valid interval. seed (int or List[int]): The random seed. If a list of seeds is supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each with its own seed. transform (callable): A function that maps sigma to the sampler's internal timestep. """ def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): self.transform = transform t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) def __call__(self, sigma, sigma_next): t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) return self.tree(t0, t1) / (t1 - t0).abs().sqrt() @torch.no_grad() def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat # Euler method x = x + d * dt return x @torch.no_grad() def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) # Euler method dt = sigma_down - sigmas[i] x = x + d * dt if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == 0: # Euler method x = x + d * dt else: # Heun's method x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) d_prime = (d + d_2) / 2 x = x + d_prime * dt return x @torch.no_grad() def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: eps = torch.randn_like(x) * s_noise x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) if sigmas[i + 1] == 0: # Euler method dt = sigmas[i + 1] - sigma_hat x = x + d * dt else: # DPM-Solver-2 sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() dt_1 = sigma_mid - sigma_hat dt_2 = sigmas[i + 1] - sigma_hat x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 return x @torch.no_grad() def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver second-order steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) d = to_d(x, sigmas[i], denoised) if sigma_down == 0: # Euler method dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver-2 sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() dt_1 = sigma_mid - sigmas[i] dt_2 = sigma_down - sigmas[i] x_2 = x + d * dt_1 denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) d_2 = to_d(x_2, sigma_mid, denoised_2) x = x + d_2 * dt_2 x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x def linear_multistep_coeff(order, t, i, j): if order - 1 > i: raise ValueError(f'Order {order} too high for step {i}') def fn(tau): prod = 1. for k in range(order): if j == k: continue prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) return prod return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] @torch.no_grad() def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigmas_cpu = sigmas.detach().cpu().numpy() ds = [] for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) d = to_d(x, sigmas[i], denoised) ds.append(d) if len(ds) > order: ds.pop(0) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) cur_order = min(i + 1, order) coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) return x class PIDStepSizeController: """A PID controller for ODE adaptive step size control.""" def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): self.h = h self.b1 = (pcoeff + icoeff + dcoeff) / order self.b2 = -(pcoeff + 2 * dcoeff) / order self.b3 = dcoeff / order self.accept_safety = accept_safety self.eps = eps self.errs = [] def limiter(self, x): return 1 + math.atan(x - 1) def propose_step(self, error): inv_error = 1 / (float(error) + self.eps) if not self.errs: self.errs = [inv_error, inv_error, inv_error] self.errs[0] = inv_error factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 factor = self.limiter(factor) accept = factor >= self.accept_safety if accept: self.errs[2] = self.errs[1] self.errs[1] = self.errs[0] self.h *= factor return accept class DPMSolver(nn.Module): """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): super().__init__() self.model = model self.extra_args = {} if extra_args is None else extra_args self.eps_callback = eps_callback self.info_callback = info_callback def t(self, sigma): return -sigma.log() def sigma(self, t): return t.neg().exp() def eps(self, eps_cache, key, x, t, *args, **kwargs): if key in eps_cache: return eps_cache[key], eps_cache sigma = self.sigma(t) * x.new_ones([x.shape[0]]) eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) if self.eps_callback is not None: self.eps_callback() return eps, {key: eps, **eps_cache} def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) x_1 = x - self.sigma(t_next) * h.expm1() * eps return x_1, eps_cache def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) return x_2, eps_cache def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): eps_cache = {} if eps_cache is None else eps_cache h = t_next - t eps, eps_cache = self.eps(eps_cache, 'eps', x, t) s1 = t + r1 * h s2 = t + r2 * h u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) return x_3, eps_cache def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler if not t_end > t_start and eta: raise ValueError('eta must be 0 for reverse sampling') m = math.floor(nfe / 3) + 1 ts = torch.linspace(t_start, t_end, m + 1, device=x.device) if nfe % 3 == 0: orders = [3] * (m - 2) + [2, 1] else: orders = [3] * (m - 1) + [nfe % 3] for i in range(len(orders)): eps_cache = {} t, t_next = ts[i], ts[i + 1] if eta: sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) t_next_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 else: t_next_, su = t_next, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, t) denoised = x - self.sigma(t) * eps if self.info_callback is not None: self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) if orders[i] == 1: x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) elif orders[i] == 2: x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) else: x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) return x def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler if order not in {2, 3}: raise ValueError('order should be 2 or 3') forward = t_end > t_start if not forward and eta: raise ValueError('eta must be 0 for reverse sampling') h_init = abs(h_init) * (1 if forward else -1) atol = torch.tensor(atol) rtol = torch.tensor(rtol) s = t_start x_prev = x accept = True pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} while s < t_end - 1e-5 if forward else s > t_end + 1e-5: eps_cache = {} t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) if eta: sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) t_ = torch.minimum(t_end, self.t(sd)) su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 else: t_, su = t, 0. eps, eps_cache = self.eps(eps_cache, 'eps', x, s) denoised = x - self.sigma(s) * eps if order == 2: x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) else: x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 accept = pid.propose_step(error) if accept: x_prev = x_low x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) s = t info['n_accept'] += 1 else: info['n_reject'] += 1 info['nfe'] += order info['steps'] += 1 if self.info_callback is not None: self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) return x, info @torch.no_grad() def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(total=n, disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) @torch.no_grad() def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" if sigma_min <= 0 or sigma_max <= 0: raise ValueError('sigma_min and sigma_max must not be 0') with tqdm(disable=disable) as pbar: dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) if callback is not None: dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) if return_info: return x, info return x @torch.no_grad() def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigma_down == 0: # Euler method d = to_d(x, sigmas[i], denoised) dt = sigma_down - sigmas[i] x = x + d * dt else: # DPM-Solver++(2S) t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) r = 1 / 2 h = t_next - t s = t + r * h x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 # Noise addition if sigmas[i + 1] > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x @torch.no_grad() def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): """DPM-Solver++ (stochastic).""" sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() seed = extra_args.get("seed", None) noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Euler method d = to_d(x, sigmas[i], denoised) dt = sigmas[i + 1] - sigmas[i] x = x + d * dt else: # DPM-Solver++ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t s = t + h * r fac = 1 / (2 * r) # Step 1 sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) s_ = t_fn(sd) x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) # Step 2 sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) t_next_ = t_fn(sd) denoised_d = (1 - fac) * denoised + fac * denoised_2 x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su return x @torch.no_grad() def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): """DPM-Solver++(2M).""" extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) sigma_fn = lambda t: t.neg().exp() t_fn = lambda sigma: sigma.log().neg() old_denoised = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) h = t_next - t if old_denoised is None or sigmas[i + 1] == 0: x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised else: h_last = t - t_fn(sigmas[i - 1]) r = h_last / h denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d old_denoised = denoised return x @torch.no_grad() def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): """DPM-Solver++(2M) SDE.""" if solver_type not in {'heun', 'midpoint'}: raise ValueError('solver_type must be \'heun\' or \'midpoint\'') seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) old_denoised = None h_last = None h = None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: # DPM-Solver++(2M) SDE t, s = -sigmas[i].log(), -sigmas[i + 1].log() h = s - t eta_h = eta * h x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised if old_denoised is not None: r = h_last / h if solver_type == 'heun': x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) elif solver_type == 'midpoint': x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) if eta: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise old_denoised = denoised h_last = h return x @torch.no_grad() def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """DPM-Solver++(3M) SDE.""" seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) denoised_1, denoised_2 = None, None h, h_1, h_2 = None, None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: # Denoising step x = denoised else: t, s = -sigmas[i].log(), -sigmas[i + 1].log() h = s - t h_eta = h * (eta + 1) x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised if h_2 is not None: r0 = h_1 / h r1 = h_2 / h d1_0 = (denoised - denoised_1) / r0 d1_1 = (denoised_1 - denoised_2) / r1 d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) d2 = (d1_0 - d1_1) / (r0 + r1) phi_2 = h_eta.neg().expm1() / h_eta + 1 phi_3 = phi_2 / h_eta - 0.5 x = x + phi_2 * d1 - phi_3 * d2 elif h_1 is not None: r = h_1 / h d = (denoised - denoised_1) / r phi_2 = h_eta.neg().expm1() / h_eta + 1 x = x + phi_2 * d if eta: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise denoised_1, denoised_2 = denoised, denoised_1 h_1, h_2 = h, h_1 return x @torch.no_grad() def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) @torch.no_grad() def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) @torch.no_grad() def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): alpha_cumprod = 1 / ((sigma * sigma) + 1) alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) alpha = (alpha_cumprod / alpha_cumprod_prev) mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) if sigma_prev > 0: mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) return mu def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) if sigmas[i + 1] != 0: x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) return x @torch.no_grad() def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) @torch.no_grad() def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) x = denoised if sigmas[i + 1] > 0: x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1]) return x @torch.no_grad() def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) s_end = sigmas[-1] for i in trange(len(sigmas) - 1, disable=disable): gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. eps = torch.randn_like(x) * s_noise sigma_hat = sigmas[i] * (gamma + 1) if gamma > 0: x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 denoised = model(x, sigma_hat * s_in, **extra_args) d = to_d(x, sigma_hat, denoised) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) dt = sigmas[i + 1] - sigma_hat if sigmas[i + 1] == s_end: # Euler method x = x + d * dt elif sigmas[i + 2] == s_end: # Heun's method x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) w = 2 * sigmas[0] w2 = sigmas[i+1]/w w1 = 1 - w2 d_prime = d * w1 + d_2 * w2 x = x + d_prime * dt else: # Heun++ x_2 = x + d * dt denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) d_2 = to_d(x_2, sigmas[i + 1], denoised_2) dt_2 = sigmas[i + 2] - sigmas[i + 1] x_3 = x_2 + d_2 * dt_2 denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) d_3 = to_d(x_3, sigmas[i + 2], denoised_3) w = 3 * sigmas[0] w2 = sigmas[i + 1] / w w3 = sigmas[i + 2] / w w1 = 1 - w2 - w3 d_prime = w1 * d + w2 * d_2 + w3 * d_3 x = x + d_prime * dt return x @torch.no_grad() def sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, eta=0.3): extra_args = {} if extra_args is None else extra_args noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) model_sampling = model.inner_model.inner_model.model_sampling timesteps_s = torch.floor((1 - eta) * model_sampling.timestep(sigmas)).to(dtype=torch.long).detach().cpu() timesteps_s[-1] = 0 alpha_prod_s = model_sampling.alphas_cumprod[timesteps_s] beta_prod_s = 1 - alpha_prod_s for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) # predicted_original_sample eps = (x - denoised) / sigmas[i] denoised = alpha_prod_s[i + 1].sqrt() * denoised + beta_prod_s[i + 1].sqrt() * eps if callback is not None: callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) x = denoised if eta > 0 and sigmas[i + 1] > 0: noise = noise_sampler(sigmas[i], sigmas[i + 1]) x = x / alpha_prod_s[i+1].sqrt() + noise * (sigmas[i+1]**2 + 1 - 1/alpha_prod_s[i+1]).sqrt() else: x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2) return x @torch.no_grad() def sample_restart(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None): """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023) Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]} If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list """ extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) step_id = 0 def heun_step(x, old_sigma, new_sigma, second_order=True): nonlocal step_id denoised = model(x, old_sigma * s_in, **extra_args) d = to_d(x, old_sigma, denoised) if callback is not None: callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised}) dt = new_sigma - old_sigma if new_sigma == 0 or not second_order: # Euler method x = x + d * dt else: # Heun's method x_2 = x + d * dt denoised_2 = model(x_2, new_sigma * s_in, **extra_args) d_2 = to_d(x_2, new_sigma, denoised_2) d_prime = (d + d_2) / 2 x = x + d_prime * dt step_id += 1 return x steps = sigmas.shape[0] - 1 if restart_list is None: if steps >= 20: restart_steps = 9 restart_times = 1 if steps >= 36: restart_steps = steps // 4 restart_times = 2 sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device) restart_list = {0.1: [restart_steps + 1, restart_times, 2]} else: restart_list = {} restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()} step_list = [] for i in range(len(sigmas) - 1): step_list.append((sigmas[i], sigmas[i + 1])) if i + 1 in restart_list: restart_steps, restart_times, restart_max = restart_list[i + 1] min_idx = i + 1 max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0)) if max_idx < min_idx: sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] while restart_times > 0: restart_times -= 1 step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:])) last_sigma = None for old_sigma, new_sigma in tqdm(step_list, disable=disable): if last_sigma is None: last_sigma = old_sigma elif last_sigma < old_sigma: x = x + torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5 x = heun_step(x, old_sigma, new_sigma) last_sigma = new_sigma return x ================================================ FILE: ldm_patched/k_diffusion/utils.py ================================================ from contextlib import contextmanager import hashlib import math from pathlib import Path import shutil import urllib import warnings from PIL import Image import torch from torch import nn, optim from torch.utils import data def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): """Apply passed in transforms for HuggingFace Datasets.""" images = [transform(image.convert(mode)) for image in examples[image_key]] return {image_key: images} def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') expanded = x[(...,) + (None,) * dims_to_append] # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. # https://github.com/pytorch/pytorch/issues/84364 return expanded.detach().clone() if expanded.device.type == 'mps' else expanded def n_params(module): """Returns the number of trainable parameters in a module.""" return sum(p.numel() for p in module.parameters()) def download_file(path, url, digest=None): """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) if not path.exists(): with urllib.request.urlopen(url) as response, open(path, 'wb') as f: shutil.copyfileobj(response, f) if digest is not None: file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() if digest != file_digest: raise OSError(f'hash of {path} (url: {url}) failed to validate') return path @contextmanager def train_mode(model, mode=True): """A context manager that places a model into training mode and restores the previous mode on exit.""" modes = [module.training for module in model.modules()] try: yield model.train(mode) finally: for i, module in enumerate(model.modules()): module.training = modes[i] def eval_mode(model): """A context manager that places a model into evaluation mode and restores the previous mode on exit.""" return train_mode(model, False) @torch.no_grad() def ema_update(model, averaged_model, decay): """Incorporates updated model parameters into an exponential moving averaged version of a model. It should be called after each optimizer step.""" model_params = dict(model.named_parameters()) averaged_params = dict(averaged_model.named_parameters()) assert model_params.keys() == averaged_params.keys() for name, param in model_params.items(): averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) model_buffers = dict(model.named_buffers()) averaged_buffers = dict(averaged_model.named_buffers()) assert model_buffers.keys() == averaged_buffers.keys() for name, buf in model_buffers.items(): averaged_buffers[name].copy_(buf) class EMAWarmup: """Implements an EMA warmup using an inverse decay schedule. If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are good values for models you plan to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at 215.4k steps). Args: inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. power (float): Exponential factor of EMA warmup. Default: 1. min_value (float): The minimum EMA decay rate. Default: 0. max_value (float): The maximum EMA decay rate. Default: 1. start_at (int): The epoch to start averaging at. Default: 0. last_epoch (int): The index of last epoch. Default: 0. """ def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, last_epoch=0): self.inv_gamma = inv_gamma self.power = power self.min_value = min_value self.max_value = max_value self.start_at = start_at self.last_epoch = last_epoch def state_dict(self): """Returns the state of the class as a :class:`dict`.""" return dict(self.__dict__.items()) def load_state_dict(self, state_dict): """Loads the class's state. Args: state_dict (dict): scaler state. Should be an object returned from a call to :meth:`state_dict`. """ self.__dict__.update(state_dict) def get_value(self): """Gets the current EMA decay rate.""" epoch = max(0, self.last_epoch - self.start_at) value = 1 - (1 + epoch / self.inv_gamma) ** -self.power return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) def step(self): """Updates the step count.""" self.last_epoch += 1 class InverseLR(optim.lr_scheduler._LRScheduler): """Implements an inverse decay learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. inv_gamma is the number of steps/epochs required for the learning rate to decay to (1 / 2)**power of its original value. Args: optimizer (Optimizer): Wrapped optimizer. inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. power (float): Exponential factor of learning rate decay. Default: 1. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.inv_gamma = inv_gamma self.power = power if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] class ExponentialLR(optim.lr_scheduler._LRScheduler): """Implements an exponential learning rate schedule with an optional exponential warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate continuously by decay (default 0.5) every num_steps steps. Args: optimizer (Optimizer): Wrapped optimizer. num_steps (float): The number of steps to decay the learning rate by decay in. decay (float): The factor by which to decay the learning rate every num_steps steps. Default: 0.5. warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) Default: 0. min_lr (float): The minimum learning rate. Default: 0. last_epoch (int): The index of last epoch. Default: -1. verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False``. """ def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., last_epoch=-1, verbose=False): self.num_steps = num_steps self.decay = decay if not 0. <= warmup < 1: raise ValueError('Invalid value for warmup') self.warmup = warmup self.min_lr = min_lr super().__init__(optimizer, last_epoch, verbose) def get_lr(self): if not self._get_lr_called_within_step: warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.") return self._get_closed_form_lr() def _get_closed_form_lr(self): warmup = 1 - self.warmup ** (self.last_epoch + 1) lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch return [warmup * max(self.min_lr, base_lr * lr_mult) for base_lr in self.base_lrs] def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): """Draws samples from an lognormal distribution.""" return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from an optionally truncated log-logistic distribution.""" min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) min_cdf = min_value.log().sub(loc).div(scale).sigmoid() max_cdf = max_value.log().sub(loc).div(scale).sigmoid() u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf return u.logit().mul(scale).add(loc).exp().to(dtype) def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): """Draws samples from an log-uniform distribution.""" min_value = math.log(min_value) max_value = math.log(max_value) return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): """Draws samples from a truncated v-diffusion training timestep distribution.""" min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf return torch.tan(u * math.pi / 2) * sigma_data def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): """Draws samples from a split lognormal distribution.""" n = torch.randn(shape, device=device, dtype=dtype).abs() u = torch.rand(shape, device=device, dtype=dtype) n_left = n * -scale_1 + loc n_right = n * scale_2 + loc ratio = scale_1 / (scale_1 + scale_2) return torch.where(u < ratio, n_left, n_right).exp() class FolderOfImages(data.Dataset): """Recursively finds all images in a directory. It does not support classes/targets.""" IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} def __init__(self, root, transform=None): super().__init__() self.root = Path(root) self.transform = nn.Identity() if transform is None else transform self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) def __repr__(self): return f'FolderOfImages(root="{self.root}", len: {len(self)})' def __len__(self): return len(self.paths) def __getitem__(self, key): path = self.paths[key] with open(path, 'rb') as f: image = Image.open(f).convert('RGB') image = self.transform(image) return image, class CSVLogger: def __init__(self, filename, columns): self.filename = Path(filename) self.columns = columns if self.filename.exists(): self.file = open(self.filename, 'a') else: self.file = open(self.filename, 'w') self.write(*self.columns) def write(self, *args): print(*args, sep=',', file=self.file, flush=True) @contextmanager def tf32_mode(cudnn=None, matmul=None): """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" cudnn_old = torch.backends.cudnn.allow_tf32 matmul_old = torch.backends.cuda.matmul.allow_tf32 try: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul yield finally: if cudnn is not None: torch.backends.cudnn.allow_tf32 = cudnn_old if matmul is not None: torch.backends.cuda.matmul.allow_tf32 = matmul_old ================================================ FILE: ldm_patched/ldm/models/autoencoder.py ================================================ import torch # import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from typing import Any, Dict, List, Optional, Tuple, Union from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution from ldm_patched.ldm.util import instantiate_from_config from ldm_patched.ldm.modules.ema import LitEma import ldm_patched.modules.ops class DiagonalGaussianRegularizer(torch.nn.Module): def __init__(self, sample: bool = True): super().__init__() self.sample = sample def get_trainable_parameters(self) -> Any: yield from () def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: log = dict() posterior = DiagonalGaussianDistribution(z) if self.sample: z = posterior.sample() else: z = posterior.mode() kl_loss = posterior.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] log["kl_loss"] = kl_loss return z, log class AbstractAutoencoder(torch.nn.Module): """ This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, unCLIP models, etc. Hence, it is fairly general, and specific features (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. """ def __init__( self, ema_decay: Union[None, float] = None, monitor: Union[None, str] = None, input_key: str = "jpg", **kwargs, ): super().__init__() self.input_key = input_key self.use_ema = ema_decay is not None if monitor is not None: self.monitor = monitor if self.use_ema: self.model_ema = LitEma(self, decay=ema_decay) logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") def get_input(self, batch) -> Any: raise NotImplementedError() def on_train_batch_end(self, *args, **kwargs): # for EMA computation if self.use_ema: self.model_ema(self) @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: logpy.info(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: logpy.info(f"{context}: Restored training weights") def encode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("encode()-method of abstract base class called") def decode(self, *args, **kwargs) -> torch.Tensor: raise NotImplementedError("decode()-method of abstract base class called") def instantiate_optimizer_from_config(self, params, lr, cfg): logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config") return get_obj_from_str(cfg["target"])( params, lr=lr, **cfg.get("params", dict()) ) def configure_optimizers(self) -> Any: raise NotImplementedError() class AutoencodingEngine(AbstractAutoencoder): """ Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL (we also restore them explicitly as special cases for legacy reasons). Regularizations such as KL or VQ are moved to the regularizer class. """ def __init__( self, *args, encoder_config: Dict, decoder_config: Dict, regularizer_config: Dict, **kwargs, ): super().__init__(*args, **kwargs) self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) self.regularization: AbstractRegularizer = instantiate_from_config( regularizer_config ) def get_last_layer(self): return self.decoder.get_last_layer() def encode( self, x: torch.Tensor, return_reg_log: bool = False, unregularized: bool = False, ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: z = self.encoder(x) if unregularized: return z, dict() z, reg_log = self.regularization(z) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: x = self.decoder(z, **kwargs) return x def forward( self, x: torch.Tensor, **additional_decode_kwargs ) -> Tuple[torch.Tensor, torch.Tensor, dict]: z, reg_log = self.encode(x, return_reg_log=True) dec = self.decode(z, **additional_decode_kwargs) return z, dec, reg_log class AutoencodingEngineLegacy(AutoencodingEngine): def __init__(self, embed_dim: int, **kwargs): self.max_batch_size = kwargs.pop("max_batch_size", None) ddconfig = kwargs.pop("ddconfig") super().__init__( encoder_config={ "target": "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", "params": ddconfig, }, decoder_config={ "target": "ldm_patched.ldm.modules.diffusionmodules.model.Decoder", "params": ddconfig, }, **kwargs, ) self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d( (1 + ddconfig["double_z"]) * ddconfig["z_channels"], (1 + ddconfig["double_z"]) * embed_dim, 1, ) self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim def get_autoencoder_params(self) -> list: params = super().get_autoencoder_params() return params def encode( self, x: torch.Tensor, return_reg_log: bool = False ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: if self.max_batch_size is None: z = self.encoder(x) z = self.quant_conv(z) else: N = x.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) z = list() for i_batch in range(n_batches): z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) z_batch = self.quant_conv(z_batch) z.append(z_batch) z = torch.cat(z, 0) z, reg_log = self.regularization(z) if return_reg_log: return z, reg_log return z def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: if self.max_batch_size is None: dec = self.post_quant_conv(z) dec = self.decoder(dec, **decoder_kwargs) else: N = z.shape[0] bs = self.max_batch_size n_batches = int(math.ceil(N / bs)) dec = list() for i_batch in range(n_batches): dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) dec_batch = self.decoder(dec_batch, **decoder_kwargs) dec.append(dec_batch) dec = torch.cat(dec, 0) return dec class AutoencoderKL(AutoencodingEngineLegacy): def __init__(self, **kwargs): if "lossconfig" in kwargs: kwargs["loss_config"] = kwargs.pop("lossconfig") super().__init__( regularizer_config={ "target": ( "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer" ) }, **kwargs, ) ================================================ FILE: ldm_patched/ldm/modules/attention.py ================================================ import math import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from typing import Optional, Any from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention from ldm_patched.modules import model_management if model_management.xformers_enabled(): import xformers import xformers.ops from ldm_patched.modules.args_parser import args import ldm_patched.modules.ops ops = ldm_patched.modules.ops.disable_weight_init # CrossAttn precision handling if args.disable_attention_upcast: print("disabling upcasting of attention") _ATTN_PRECISION = "fp16" else: _ATTN_PRECISION = "fp32" def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): super().__init__() self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( operations.Linear(dim, inner_dim, dtype=dtype, device=device), nn.GELU() ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) self.net = nn.Sequential( project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) ) def forward(self, x): return self.net(x) def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) def attention_basic(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 h = heads q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) # force cast to fp32 to avoid overflowing if _ATTN_PRECISION =="fp32": sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale else: sim = einsum('b i d, b j d -> b i j', q, k) * scale del q, k if exists(mask): if mask.dtype == torch.bool: mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) else: sim += mask # attention, what we cannot get enough of sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return out def attention_sub_quad(query, key, value, heads, mask=None): b, _, dim_head = query.shape dim_head //= heads scale = dim_head ** -0.5 query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) dtype = query.dtype upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32 if upcast_attention: bytes_per_token = torch.finfo(torch.float32).bits//8 else: bytes_per_token = torch.finfo(query.dtype).bits//8 batch_x_heads, q_tokens, _ = query.shape _, _, k_tokens = key.shape qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True) kv_chunk_size_min = None kv_chunk_size = None query_chunk_size = None for x in [4096, 2048, 1024, 512, 256]: count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) if count >= k_tokens: kv_chunk_size = k_tokens query_chunk_size = x break if query_chunk_size is None: query_chunk_size = 512 hidden_states = efficient_dot_product_attention( query, key, value, query_chunk_size=query_chunk_size, kv_chunk_size=kv_chunk_size, kv_chunk_size_min=kv_chunk_size_min, use_checkpoint=False, upcast_attention=upcast_attention, mask=mask, ) hidden_states = hidden_states.to(dtype) hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states def attention_split(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads scale = dim_head ** -0.5 h = heads q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) mem_free_total = model_management.get_free_memory(q.device) if _ATTN_PRECISION =="fp32": element_size = 4 else: element_size = q.element_size() gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size modifier = 3 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") if steps > 64: max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) first_op_done = False cleared_cache = False while True: try: slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size if _ATTN_PRECISION =="fp32": with torch.autocast(enabled=False, device_type = 'cuda'): s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale else: s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale if mask is not None: if len(mask.shape) == 2: s1 += mask[i:end] else: s1 += mask[:, i:end] s2 = s1.softmax(dim=-1).to(v.dtype) del s1 first_op_done = True r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) del s2 break except model_management.OOM_EXCEPTION as e: if first_op_done == False: model_management.soft_empty_cache(True) if cleared_cache == False: cleared_cache = True print("out of memory error, emptying cache and trying again") continue steps *= 2 if steps > 64: raise e print("out of memory error, increasing steps and trying again", steps) else: raise e del q, k, v r1 = ( r1.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return r1 BROKEN_XFORMERS = False try: x_vers = xformers.__version__ #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error) BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23") except: pass def attention_xformers(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads if BROKEN_XFORMERS: if b * heads > 65535: return attention_pytorch(q, k, v, heads, mask) q, k, v = map( lambda t: t.unsqueeze(3) .reshape(b, -1, heads, dim_head) .permute(0, 2, 1, 3) .reshape(b * heads, -1, dim_head) .contiguous(), (q, k, v), ) if mask is not None: pad = 8 - q.shape[1] % 8 mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device) mask_out[:, :, :mask.shape[-1]] = mask mask = mask_out[:, :, :mask.shape[-1]] out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) out = ( out.unsqueeze(0) .reshape(b, heads, -1, dim_head) .permute(0, 2, 1, 3) .reshape(b, -1, heads * dim_head) ) return out def attention_pytorch(q, k, v, heads, mask=None): b, _, dim_head = q.shape dim_head //= heads q, k, v = map( lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v), ) out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) return out optimized_attention = attention_basic if model_management.xformers_enabled(): print("Using xformers cross attention") optimized_attention = attention_xformers elif model_management.pytorch_attention_enabled(): print("Using pytorch cross attention") optimized_attention = attention_pytorch else: if args.attention_split: print("Using split optimization for cross attention") optimized_attention = attention_split else: print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --attention-split") optimized_attention = attention_sub_quad optimized_attention_masked = optimized_attention def optimized_attention_for_device(device, mask=False, small_input=False): if small_input: if model_management.pytorch_attention_enabled(): return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases else: return attention_basic if device == torch.device("cpu"): return attention_sub_quad if mask: return optimized_attention_masked return optimized_attention class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.heads = heads self.dim_head = dim_head self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) def forward(self, x, context=None, value=None, mask=None): q = self.to_q(x) context = default(context, x) k = self.to_k(context) if value is not None: v = self.to_v(value) del value else: v = self.to_v(context) if mask is None: out = optimized_attention(q, k, v, self.heads) else: out = optimized_attention_masked(q, k, v, self.heads, mask) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops): super().__init__() self.ff_in = ff_in or inner_dim is not None if inner_dim is None: inner_dim = dim self.is_res = inner_dim == dim if self.ff_in: self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) if disable_temporal_crossattention: if switch_temporal_ca_to_sa: raise ValueError else: self.attn2 = None else: context_dim_attn2 = None if not switch_temporal_ca_to_sa: context_dim_attn2 = context_dim self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) self.checkpoint = checkpoint self.n_heads = n_heads self.d_head = d_head self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa def forward(self, x, context=None, transformer_options={}): return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint) def _forward(self, x, context=None, transformer_options={}): extra_options = {} block = transformer_options.get("block", None) block_index = transformer_options.get("block_index", 0) transformer_patches = {} transformer_patches_replace = {} for k in transformer_options: if k == "patches": transformer_patches = transformer_options[k] elif k == "patches_replace": transformer_patches_replace = transformer_options[k] else: extra_options[k] = transformer_options[k] extra_options["n_heads"] = self.n_heads extra_options["dim_head"] = self.d_head if self.ff_in: x_skip = x x = self.ff_in(self.norm_in(x)) if self.is_res: x += x_skip n = self.norm1(x) if self.disable_self_attn: context_attn1 = context else: context_attn1 = None value_attn1 = None if "attn1_patch" in transformer_patches: patch = transformer_patches["attn1_patch"] if context_attn1 is None: context_attn1 = n value_attn1 = context_attn1 for p in patch: n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) if block is not None: transformer_block = (block[0], block[1], block_index) else: transformer_block = None attn1_replace_patch = transformer_patches_replace.get("attn1", {}) block_attn1 = transformer_block if block_attn1 not in attn1_replace_patch: block_attn1 = block if block_attn1 in attn1_replace_patch: if context_attn1 is None: context_attn1 = n value_attn1 = n n = self.attn1.to_q(n) context_attn1 = self.attn1.to_k(context_attn1) value_attn1 = self.attn1.to_v(value_attn1) n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) n = self.attn1.to_out(n) else: n = self.attn1(n, context=context_attn1, value=value_attn1) if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: n = p(n, extra_options) x += n if "middle_patch" in transformer_patches: patch = transformer_patches["middle_patch"] for p in patch: x = p(x, extra_options) if self.attn2 is not None: n = self.norm2(x) if self.switch_temporal_ca_to_sa: context_attn2 = n else: context_attn2 = context value_attn2 = None if "attn2_patch" in transformer_patches: patch = transformer_patches["attn2_patch"] value_attn2 = context_attn2 for p in patch: n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) attn2_replace_patch = transformer_patches_replace.get("attn2", {}) block_attn2 = transformer_block if block_attn2 not in attn2_replace_patch: block_attn2 = block if block_attn2 in attn2_replace_patch: if value_attn2 is None: value_attn2 = context_attn2 n = self.attn2.to_q(n) context_attn2 = self.attn2.to_k(context_attn2) value_attn2 = self.attn2.to_v(value_attn2) n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) n = self.attn2.to_out(n) else: n = self.attn2(n, context=context_attn2, value=value_attn2) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] for p in patch: n = p(n, extra_options) x += n if self.is_res: x_skip = x x = self.ff(self.norm3(x)) if self.is_res: x += x_skip return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image NEW: use_linear for more efficiency instead of the 1x1 convs """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False, use_linear=False, use_checkpoint=True, dtype=None, device=None, operations=ops): super().__init__() if exists(context_dim) and not isinstance(context_dim, list): context_dim = [context_dim] * depth self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) if not use_linear: self.proj_in = operations.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations) for d in range(depth)] ) if not use_linear: self.proj_out = operations.Conv2d(inner_dim,in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device) else: self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) self.use_linear = use_linear def forward(self, x, context=None, transformer_options={}): # note: if no context is given, cross-attention defaults to self-attention if not isinstance(context, list): context = [context] * len(self.transformer_blocks) b, c, h, w = x.shape x_in = x x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = rearrange(x, 'b c h w -> b (h w) c').contiguous() if self.use_linear: x = self.proj_in(x) for i, block in enumerate(self.transformer_blocks): transformer_options["block_index"] = i x = block(x, context=context[i], transformer_options=transformer_options) if self.use_linear: x = self.proj_out(x) x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() if not self.use_linear: x = self.proj_out(x) return x + x_in class SpatialVideoTransformer(SpatialTransformer): def __init__( self, in_channels, n_heads, d_head, depth=1, dropout=0.0, use_linear=False, context_dim=None, use_spatial_context=False, timesteps=None, merge_strategy: str = "fixed", merge_factor: float = 0.5, time_context_dim=None, ff_in=False, checkpoint=False, time_depth=1, disable_self_attn=False, disable_temporal_crossattention=False, max_time_embed_period: int = 10000, dtype=None, device=None, operations=ops ): super().__init__( in_channels, n_heads, d_head, depth=depth, dropout=dropout, use_checkpoint=checkpoint, context_dim=context_dim, use_linear=use_linear, disable_self_attn=disable_self_attn, dtype=dtype, device=device, operations=operations ) self.time_depth = time_depth self.depth = depth self.max_time_embed_period = max_time_embed_period time_mix_d_head = d_head n_time_mix_heads = n_heads time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) inner_dim = n_heads * d_head if use_spatial_context: time_context_dim = context_dim self.time_stack = nn.ModuleList( [ BasicTransformerBlock( inner_dim, n_time_mix_heads, time_mix_d_head, dropout=dropout, context_dim=time_context_dim, # timesteps=timesteps, checkpoint=checkpoint, ff_in=ff_in, inner_dim=time_mix_inner_dim, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, dtype=dtype, device=device, operations=operations ) for _ in range(self.depth) ] ) assert len(self.time_stack) == len(self.transformer_blocks) self.use_spatial_context = use_spatial_context self.in_channels = in_channels time_embed_dim = self.in_channels * 4 self.time_pos_embed = nn.Sequential( operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy ) def forward( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, time_context: Optional[torch.Tensor] = None, timesteps: Optional[int] = None, image_only_indicator: Optional[torch.Tensor] = None, transformer_options={} ) -> torch.Tensor: _, _, h, w = x.shape x_in = x spatial_context = None if exists(context): spatial_context = context if self.use_spatial_context: assert ( context.ndim == 3 ), f"n dims of spatial context should be 3 but are {context.ndim}" if time_context is None: time_context = context time_context_first_timestep = time_context[::timesteps] time_context = repeat( time_context_first_timestep, "b ... -> (b n) ...", n=h * w ) elif time_context is not None and not self.use_spatial_context: time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) if time_context.ndim == 2: time_context = rearrange(time_context, "b c -> b 1 c") x = self.norm(x) if not self.use_linear: x = self.proj_in(x) x = rearrange(x, "b c h w -> b (h w) c") if self.use_linear: x = self.proj_in(x) num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) emb = self.time_pos_embed(t_emb) emb = emb[:, None, :] for it_, (block, mix_block) in enumerate( zip(self.transformer_blocks, self.time_stack) ): transformer_options["block_index"] = it_ x = block( x, context=spatial_context, transformer_options=transformer_options, ) x_mix = x x_mix = x_mix + emb B, S, C = x_mix.shape x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options x_mix = rearrange( x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps ) x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) if self.use_linear: x = self.proj_out(x) x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) if not self.use_linear: x = self.proj_out(x) out = x + x_in return out ================================================ FILE: ldm_patched/ldm/modules/diffusionmodules/__init__.py ================================================ ================================================ FILE: ldm_patched/ldm/modules/diffusionmodules/model.py ================================================ # pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from typing import Optional, Any from ldm_patched.modules import model_management import ldm_patched.modules.ops ops = ldm_patched.modules.ops.disable_weight_init if model_management.xformers_enabled_vae(): import xformers import xformers.ops def get_timestep_embedding(timesteps, embedding_dim): """ This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ assert len(timesteps.shape) == 1 half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) emb = emb.to(device=timesteps.device) emb = timesteps.float()[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0,1,0,0)) return emb def nonlinearity(x): # swish return x*torch.sigmoid(x) def Normalize(in_channels, num_groups=32): return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): try: x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") except: #operation not implemented for bf16 b, c, h, w = x.shape out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) split = 8 l = out.shape[1] // split for i in range(0, out.shape[1], l): out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) del x x = out if self.with_conv: x = self.conv(x) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0,1,0,1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.swish = torch.nn.SiLU(inplace=True) self.norm1 = Normalize(in_channels) self.conv1 = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) self.conv2 = ops.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = ops.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = self.swish(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(self.swish(temb))[:,:,None,None] h = self.norm2(h) h = self.swish(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x+h def slice_attention(q, k, v): r1 = torch.zeros_like(k, device=q.device) scale = (int(q.shape[-1])**(-0.5)) mem_free_total = model_management.get_free_memory(q.device) gb = 1024 ** 3 tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() modifier = 3 if q.element_size() == 2 else 2.5 mem_required = tensor_size * modifier steps = 1 if mem_required > mem_free_total: steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) while True: try: slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] for i in range(0, q.shape[1], slice_size): end = i + slice_size s1 = torch.bmm(q[:, i:end], k) * scale s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) del s1 r1[:, :, i:end] = torch.bmm(v, s2) del s2 break except model_management.OOM_EXCEPTION as e: model_management.soft_empty_cache(True) steps *= 2 if steps > 128: raise e print("out of memory error, increasing steps and trying again", steps) return r1 def normal_attention(q, k, v): # compute attention b,c,h,w = q.shape q = q.reshape(b,c,h*w) q = q.permute(0,2,1) # b,hw,c k = k.reshape(b,c,h*w) # b,c,hw v = v.reshape(b,c,h*w) r1 = slice_attention(q, k, v) h_ = r1.reshape(b,c,h,w) del r1 return h_ def xformers_attention(q, k, v): # compute attention B, C, H, W = q.shape q, k, v = map( lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), (q, k, v), ) try: out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) out = out.transpose(1, 2).reshape(B, C, H, W) except NotImplementedError as e: out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) return out def pytorch_attention(q, k, v): # compute attention B, C, H, W = q.shape q, k, v = map( lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), (q, k, v), ) try: out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = out.transpose(2, 3).reshape(B, C, H, W) except model_management.OOM_EXCEPTION as e: print("scaled_dot_product_attention OOMed: switched to slice attention") out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) return out class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) if model_management.xformers_enabled_vae(): print("Using xformers attention in VAE") self.optimized_attention = xformers_attention elif model_management.pytorch_attention_enabled(): print("Using pytorch attention in VAE") self.optimized_attention = pytorch_attention else: print("Using split attention in VAE") self.optimized_attention = normal_attention def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) h_ = self.optimized_attention(q, k, v) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): return AttnBlock(in_channels) class Model(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = self.ch*4 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.use_timestep = use_timestep if self.use_timestep: # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ ops.Linear(self.ch, self.temb_ch), ops.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] skip_in = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): if i_block == self.num_res_blocks: skip_in = ch*in_ch_mult[i_level] block.append(ResnetBlock(in_channels=block_in+skip_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = ops.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t=None, context=None): #assert x.shape[2] == x.shape[3] == self.resolution if context is not None: # assume aligned context, cat along channel axis x = torch.cat((x, context), dim=1) if self.use_timestep: # timestep embedding assert t is not None temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) else: temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block]( torch.cat([h, hs.pop()], dim=1), temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h def get_last_layer(self): return self.conv_out.weight class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", **ignore_kwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels # downsampling self.conv_in = ops.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # end self.norm_out = Normalize(block_in) self.conv_out = ops.Conv2d(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # timestep embedding temb = None # downsampling h = self.conv_in(x) for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h, temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions-1: h = self.down[i_level].downsample(h) # middle h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, conv_out_op=ops.Conv2d, resnet_op=ResnetBlock, attn_op=AttnBlock, **ignorekwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.tanh_out = tanh_out # compute in_ch_mult, block_in and curr_res at lowest res in_ch_mult = (1,)+tuple(ch_mult) block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) print("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = ops.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) # middle self.mid = nn.Module() self.mid.block_1 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = attn_op(block_in) self.mid.block_2 = resnet_op(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): block.append(resnet_op(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(attn_op(block_in)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = conv_out_op(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z, **kwargs): #assert z.shape[1:] == self.z_shape[1:] self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle h = self.mid.block_1(h, temb, **kwargs) h = self.mid.attn_1(h, **kwargs) h = self.mid.block_2(h, temb, **kwargs) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block](h, temb, **kwargs) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h, **kwargs) if i_level != 0: h = self.up[i_level].upsample(h) # end if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h, **kwargs) if self.tanh_out: h = torch.tanh(h) return h ================================================ FILE: ldm_patched/ldm/modules/diffusionmodules/openaimodel.py ================================================ from abc import abstractmethod import torch as th import torch.nn as nn import torch.nn.functional as F from einops import rearrange from .util import ( checkpoint, avg_pool_nd, zero_module, timestep_embedding, AlphaBlender, ) from ..attention import SpatialTransformer, SpatialVideoTransformer, default from ldm_patched.ldm.util import exists import ldm_patched.modules.ops ops = ldm_patched.modules.ops.disable_weight_init class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ #This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): for layer in ts: if isinstance(layer, VideoResBlock): x = layer(x, emb, num_video_frames, image_only_indicator) elif isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialVideoTransformer): x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, SpatialTransformer): x = layer(x, context, transformer_options) if "transformer_index" in transformer_options: transformer_options["transformer_index"] += 1 elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: x = layer(x) return x class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, *args, **kwargs): return forward_timestep_embed(self, *args, **kwargs) class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) def forward(self, x, output_shape=None): assert x.shape[1] == self.channels if self.dims == 3: shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] if output_shape is not None: shape[1] = output_shape[3] shape[2] = output_shape[4] else: shape = [x.shape[2] * 2, x.shape[3] * 2] if output_shape is not None: shape[0] = output_shape[2] shape[1] = output_shape[3] x = F.interpolate(x, size=shape, mode="nearest") if self.use_conv: x = self.conv(x) return x class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = operations.conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, kernel_size=3, exchange_temb_dims=False, skip_t_emb=False, dtype=None, device=None, operations=ops ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.exchange_temb_dims = exchange_temb_dims if isinstance(kernel_size, list): padding = [k // 2 for k in kernel_size] else: padding = kernel_size // 2 self.in_layers = nn.Sequential( operations.GroupNorm(32, channels, dtype=dtype, device=device), nn.SiLU(), operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) elif down: self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) else: self.h_upd = self.x_upd = nn.Identity() self.skip_t_emb = skip_t_emb if self.skip_t_emb: self.emb_layers = None self.exchange_temb_dims = False else: self.emb_layers = nn.Sequential( nn.SiLU(), operations.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device ), ) self.out_layers = nn.Sequential( operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), nn.SiLU(), nn.Dropout(p=dropout), operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) , ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = operations.conv_nd( dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device ) else: self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = None if not self.skip_t_emb: emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] h = out_norm(h) if emb_out is not None: scale, shift = th.chunk(emb_out, 2, dim=1) h *= (1 + scale) h += shift h = out_rest(h) else: if emb_out is not None: if self.exchange_temb_dims: emb_out = rearrange(emb_out, "b t c ... -> b c t ...") h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class VideoResBlock(ResBlock): def __init__( self, channels: int, emb_channels: int, dropout: float, video_kernel_size=3, merge_strategy: str = "fixed", merge_factor: float = 0.5, out_channels=None, use_conv: bool = False, use_scale_shift_norm: bool = False, dims: int = 2, use_checkpoint: bool = False, up: bool = False, down: bool = False, dtype=None, device=None, operations=ops ): super().__init__( channels, emb_channels, dropout, out_channels=out_channels, use_conv=use_conv, use_scale_shift_norm=use_scale_shift_norm, dims=dims, use_checkpoint=use_checkpoint, up=up, down=down, dtype=dtype, device=device, operations=operations ) self.time_stack = ResBlock( default(out_channels, channels), emb_channels, dropout=dropout, dims=3, out_channels=default(out_channels, channels), use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=use_checkpoint, exchange_temb_dims=True, dtype=dtype, device=device, operations=operations ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy, rearrange_pattern="b t -> b 1 t 1 1", ) def forward( self, x: th.Tensor, emb: th.Tensor, num_video_frames: int, image_only_indicator = None, ) -> th.Tensor: x = super().forward(x, emb) x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = self.time_stack( x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) ) x = self.time_mixer( x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator ) x = rearrange(x, "b c t h w -> (b t) c h w") return x class Timestep(nn.Module): def __init__(self, dim): super().__init__() self.dim = dim def forward(self, t): return timestep_embedding(t, self.dim) def apply_control(h, control, name): if control is not None and name in control and len(control[name]) > 0: ctrl = control[name].pop() if ctrl is not None: try: h += ctrl except: print("warning control could not be applied", h.shape, ctrl.shape) return h class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, dtype=th.float32, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, adm_in_channels=None, transformer_depth_middle=None, transformer_depth_output=None, use_temporal_resblock=False, use_temporal_attention=False, time_context_dim=None, extra_ff_mix_layer=False, use_spatial_context=False, merge_strategy=None, merge_factor=0.0, video_kernel_size=None, disable_temporal_crossattention=False, max_ddpm_temb_period=10000, device=None, operations=ops, ): super().__init__() if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' # from omegaconf.listconfig import ListConfig # if type(context_dim) == ListConfig: # context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) transformer_depth = transformer_depth[:] transformer_depth_output = transformer_depth_output[:] self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = dtype self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.use_temporal_resblocks = use_temporal_resblock self.predict_codebook_ids = n_embed is not None self.default_num_video_frames = None self.default_image_only_indicator = None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) elif self.num_classes == "continuous": print("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), nn.SiLU(), operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 def get_attention_layer( ch, num_heads, dim_head, depth=1, context_dim=None, use_checkpoint=False, disable_self_attn=False, ): if use_temporal_attention: return SpatialVideoTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, time_context_dim=time_context_dim, dropout=dropout, ff_in=extra_ff_mix_layer, use_spatial_context=use_spatial_context, merge_strategy=merge_strategy, merge_factor=merge_factor, checkpoint=use_checkpoint, use_linear=use_linear_in_transformer, disable_self_attn=disable_self_attn, disable_temporal_crossattention=disable_temporal_crossattention, max_time_embed_period=max_ddpm_temb_period, dtype=self.dtype, device=device, operations=operations ) else: return SpatialTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations ) def get_resblock( merge_factor, merge_strategy, video_kernel_size, ch, time_embed_dim, dropout, out_channels, dims, use_checkpoint, use_scale_shift_norm, down=False, up=False, dtype=None, device=None, operations=ops ): if self.use_temporal_resblocks: return VideoResBlock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) else: return ResBlock( channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_channels, use_checkpoint=use_checkpoint, dims=dims, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, dtype=dtype, device=device, operations=operations ) for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations, ) ] ch = mult * model_channels num_transformers = transformer_depth.pop(0) if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append(get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels mid_block = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] if transformer_depth_middle >= 0: mid_block += [get_attention_layer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint ), get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=None, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations )] self.middle_block = TimestepEmbedSequential(*mid_block) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch + ich, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, dtype=self.dtype, device=device, operations=operations ) ] ch = model_channels * mult num_transformers = transformer_depth_output.pop() if num_transformers > 0: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( get_attention_layer( ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, dtype=self.dtype, device=device, operations=operations ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), nn.SiLU(), zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( operations.GroupNorm(32, ch, dtype=self.dtype, device=device), operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ transformer_options["original_shape"] = list(x.shape) transformer_options["transformer_index"] = 0 transformer_patches = transformer_options.get("patches", {}) num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) time_context = kwargs.get("time_context", None) assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] for p in patch: h = p(h, transformer_options) hs.append(h) if "input_block_patch_after_skip" in transformer_patches: patch = transformer_patches["input_block_patch_after_skip"] for p in patch: h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() hsp = apply_control(hsp, control, 'output') if "output_block_patch" in transformer_patches: patch = transformer_patches["output_block_patch"] for p in patch: h, hsp = p(h, hsp, transformer_options) h = th.cat([h, hsp], dim=1) del hsp if len(hs) > 0: output_shape = hs[-1].shape else: output_shape = None h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) ================================================ FILE: ldm_patched/ldm/modules/diffusionmodules/upscaling.py ================================================ import torch import torch.nn as nn import numpy as np from functools import partial from .util import extract_into_tensor, make_beta_schedule from ldm_patched.ldm.util import default class AbstractLowScaleModel(nn.Module): # for concatenating a downsampled image to the latent representation def __init__(self, noise_schedule_config=None): super(AbstractLowScaleModel, self).__init__() if noise_schedule_config is not None: self.register_schedule(**noise_schedule_config) def register_schedule(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) def q_sample(self, x_start, t, noise=None, seed=None): if noise is None: if seed is None: noise = torch.randn_like(x_start) else: noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) def forward(self, x): return x, None def decode(self, x): return x class SimpleImageConcat(AbstractLowScaleModel): # no noise level conditioning def __init__(self): super(SimpleImageConcat, self).__init__(noise_schedule_config=None) self.max_noise_level = 0 def forward(self, x): # fix to constant noise level return x, torch.zeros(x.shape[0], device=x.device).long() class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): super().__init__(noise_schedule_config=noise_schedule_config) self.max_noise_level = max_noise_level def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) z = self.q_sample(x, noise_level, seed=seed) return z, noise_level ================================================ FILE: ldm_patched/ldm/modules/diffusionmodules/util.py ================================================ # adopted from # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py # and # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py # and # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py # # thanks! import os import math import torch import torch.nn as nn import numpy as np from einops import repeat, rearrange from ldm_patched.ldm.util import instantiate_from_config class AlphaBlender(nn.Module): strategies = ["learned", "fixed", "learned_with_images"] def __init__( self, alpha: float, merge_strategy: str = "learned_with_images", rearrange_pattern: str = "b t -> (b t) 1 1", ): super().__init__() self.merge_strategy = merge_strategy self.rearrange_pattern = rearrange_pattern assert ( merge_strategy in self.strategies ), f"merge_strategy needs to be in {self.strategies}" if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif ( self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images" ): self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor: # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) if self.merge_strategy == "fixed": # make shape compatible # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) alpha = self.mix_factor.to(image_only_indicator.device) elif self.merge_strategy == "learned": alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device)) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) elif self.merge_strategy == "learned_with_images": assert image_only_indicator is not None, "need image_only_indicator ..." alpha = torch.where( image_only_indicator.bool(), torch.ones(1, 1, device=image_only_indicator.device), rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), ) alpha = rearrange(alpha, self.rearrange_pattern) # make shape compatible # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) else: raise NotImplementedError() return alpha def forward( self, x_spatial, x_temporal, image_only_indicator=None, ) -> torch.Tensor: alpha = self.get_alpha(image_only_indicator) x = ( alpha.to(x_spatial.dtype) * x_spatial + (1.0 - alpha).to(x_spatial.dtype) * x_temporal ) return x def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 ) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = np.clip(betas, a_min=0, a_max=0.999) elif schedule == "squaredcos_cap_v2": # used for karlo prior # return early return betas_for_alpha_bar( n_timestep, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, ) elif schedule == "sqrt_linear": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) elif schedule == "sqrt": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas.numpy() def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') # assert ddim_timesteps.shape[0] == num_ddim_timesteps # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: print(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # select alphas for computing the variance schedule alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def checkpoint(func, inputs, params, flag): """ Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. """ if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) else: return func(*inputs) class CheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function ctx.input_tensors = list(args[:length]) ctx.input_params = list(args[length:]) ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), "dtype": torch.get_autocast_gpu_dtype(), "cache_enabled": torch.is_autocast_cache_enabled()} with torch.no_grad(): output_tensors = ctx.run_function(*ctx.input_tensors) return output_tensors @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] with torch.enable_grad(), \ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): # Fixes a bug where the first op in run_function modifies the # Tensor storage in place, which is not allowed for detach()'d # Tensors. shallow_copies = [x.view_as(x) for x in ctx.input_tensors] output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, ) del ctx.input_tensors del ctx.input_params del output_tensors return (None, None) + input_grads def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half ) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = repeat(timesteps, 'b -> b d', d=dim) return embedding def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def scale_module(module, scale): """ Scale the parameters of a module and return it. """ for p in module.parameters(): p.detach().mul_(scale) return module def mean_flat(tensor): """ Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class HybridConditioner(nn.Module): def __init__(self, c_concat_config, c_crossattn_config): super().__init__() self.concat_conditioner = instantiate_from_config(c_concat_config) self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) def forward(self, c_concat, c_crossattn): c_concat = self.concat_conditioner(c_concat) c_crossattn = self.crossattn_conditioner(c_crossattn) return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() ================================================ FILE: ldm_patched/ldm/modules/distributions/__init__.py ================================================ ================================================ FILE: ldm_patched/ldm/modules/distributions/distributions.py ================================================ import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" # Force variances to be Tensors. Broadcasting helps convert scalars to # Tensors, but it does not work for torch.exp(). logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) ) ================================================ FILE: ldm_patched/ldm/modules/ema.py ================================================ import torch from torch import nn class LitEma(nn.Module): def __init__(self, model, decay=0.9999, use_num_upates=True): super().__init__() if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.m_name2s_name = {} self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates else torch.tensor(-1, dtype=torch.int)) for name, p in model.named_parameters(): if p.requires_grad: # remove as '.'-character is not allowed in buffers s_name = name.replace('.', '') self.m_name2s_name.update({name: s_name}) self.register_buffer(s_name, p.clone().detach().data) self.collected_params = [] def reset_num_updates(self): del self.num_updates self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) def forward(self, model): decay = self.decay if self.num_updates >= 0: self.num_updates += 1 decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: sname = self.m_name2s_name[key] shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) else: assert not key in self.m_name2s_name def copy_to(self, model): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) else: assert not key in self.m_name2s_name def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data) ================================================ FILE: ldm_patched/ldm/modules/encoders/__init__.py ================================================ ================================================ FILE: ldm_patched/ldm/modules/encoders/noise_aug_modules.py ================================================ from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation from ..diffusionmodules.openaimodel import Timestep import torch class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): super().__init__(*args, **kwargs) if clip_stats_path is None: clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) else: clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu", weights_only=True) self.register_buffer("data_mean", clip_mean[None, :], persistent=False) self.register_buffer("data_std", clip_std[None, :], persistent=False) self.time_embed = Timestep(timestep_dim) def scale(self, x): # re-normalize to centered mean and unit variance x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) return x def unscale(self, x): # back to original data stats x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) return x def forward(self, x, noise_level=None, seed=None): if noise_level is None: noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() else: assert isinstance(noise_level, torch.Tensor) x = self.scale(x) z = self.q_sample(x, noise_level, seed=seed) z = self.unscale(z) noise_level = self.time_embed(noise_level) return z, noise_level ================================================ FILE: ldm_patched/ldm/modules/sub_quadratic_attention.py ================================================ # original source: # https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py # license: # MIT # credit: # Amin Rezaei (original author) # Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) # implementation of: # Self-attention Does Not Need O(n2) Memory": # https://arxiv.org/abs/2112.05682v2 from functools import partial import torch from torch import Tensor from torch.utils.checkpoint import checkpoint import math try: from typing import Optional, NamedTuple, List, Protocol except ImportError: from typing import Optional, NamedTuple, List from typing_extensions import Protocol from torch import Tensor from typing import List from ldm_patched.modules import model_management def dynamic_slice( x: Tensor, starts: List[int], sizes: List[int], ) -> Tensor: slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] return x[slicing] class AttnChunk(NamedTuple): exp_values: Tensor exp_weights_sum: Tensor max_score: Tensor class SummarizeChunk(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> AttnChunk: ... class ComputeQueryChunkAttn(Protocol): @staticmethod def __call__( query: Tensor, key_t: Tensor, value: Tensor, ) -> Tensor: ... def _summarize_chunk( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> AttnChunk: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_weights = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) max_score, _ = torch.max(attn_weights, -1, keepdim=True) max_score = max_score.detach() attn_weights -= max_score if mask is not None: attn_weights += mask torch.exp(attn_weights, out=attn_weights) exp_weights = attn_weights.to(value.dtype) exp_values = torch.bmm(exp_weights, value) max_score = max_score.squeeze(-1) return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) def _query_chunk_attention( query: Tensor, key_t: Tensor, value: Tensor, summarize_chunk: SummarizeChunk, kv_chunk_size: int, mask, ) -> Tensor: batch_x_heads, k_channels_per_head, k_tokens = key_t.shape _, _, v_channels_per_head = value.shape def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: key_chunk = dynamic_slice( key_t, (0, 0, chunk_idx), (batch_x_heads, k_channels_per_head, kv_chunk_size) ) value_chunk = dynamic_slice( value, (0, chunk_idx, 0), (batch_x_heads, kv_chunk_size, v_channels_per_head) ) if mask is not None: mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] return summarize_chunk(query, key_chunk, value_chunk, mask=mask) chunks: List[AttnChunk] = [ chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) ] acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) chunk_values, chunk_weights, chunk_max = acc_chunk global_max, _ = torch.max(chunk_max, 0, keepdim=True) max_diffs = torch.exp(chunk_max - global_max) chunk_values *= torch.unsqueeze(max_diffs, -1) chunk_weights *= max_diffs all_values = chunk_values.sum(dim=0) all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) return all_values / all_weights # TODO: refactor CrossAttention#get_attention_scores to share code with this def _get_attention_scores_no_kv_chunking( query: Tensor, key_t: Tensor, value: Tensor, scale: float, upcast_attention: bool, mask, ) -> Tensor: if upcast_attention: with torch.autocast(enabled=False, device_type = 'cuda'): query = query.float() key_t = key_t.float() attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) else: attn_scores = torch.baddbmm( torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), query, key_t, alpha=scale, beta=0, ) if mask is not None: attn_scores += mask try: attn_probs = attn_scores.softmax(dim=-1) del attn_scores except model_management.OOM_EXCEPTION: print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") attn_scores -= attn_scores.max(dim=-1, keepdim=True).values torch.exp(attn_scores, out=attn_scores) summed = torch.sum(attn_scores, dim=-1, keepdim=True) attn_scores /= summed attn_probs = attn_scores hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) return hidden_states_slice class ScannedChunk(NamedTuple): chunk_idx: int attn_chunk: AttnChunk def efficient_dot_product_attention( query: Tensor, key_t: Tensor, value: Tensor, query_chunk_size=1024, kv_chunk_size: Optional[int] = None, kv_chunk_size_min: Optional[int] = None, use_checkpoint=True, upcast_attention=False, mask = None, ): """Computes efficient dot-product attention given query, transposed key, and value. This is efficient version of attention presented in https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. Args: query: queries for calculating attention with shape of `[batch * num_heads, tokens, channels_per_head]`. key_t: keys for calculating attention with shape of `[batch * num_heads, channels_per_head, tokens]`. value: values to be used in attention with shape of `[batch * num_heads, tokens, channels_per_head]`. query_chunk_size: int: query chunks size kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) Returns: Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. """ batch_x_heads, q_tokens, q_channels_per_head = query.shape _, _, k_tokens = key_t.shape scale = q_channels_per_head ** -0.5 kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) if kv_chunk_size_min is not None: kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) if mask is not None and len(mask.shape) == 2: mask = mask.unsqueeze(0) def get_query_chunk(chunk_idx: int) -> Tensor: return dynamic_slice( query, (0, chunk_idx, 0), (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) ) def get_mask_chunk(chunk_idx: int) -> Tensor: if mask is None: return None chunk = min(query_chunk_size, q_tokens) return mask[:,chunk_idx:chunk_idx + chunk] summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk compute_query_chunk_attn: ComputeQueryChunkAttn = partial( _get_attention_scores_no_kv_chunking, scale=scale, upcast_attention=upcast_attention ) if k_tokens <= kv_chunk_size else ( # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) partial( _query_chunk_attention, kv_chunk_size=kv_chunk_size, summarize_chunk=summarize_chunk, ) ) if q_tokens <= query_chunk_size: # fast-path for when there's just 1 query chunk return compute_query_chunk_attn( query=query, key_t=key_t, value=value, mask=mask, ) # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, # and pass slices to be mutated, instead of torch.cat()ing the returned slices res = torch.cat([ compute_query_chunk_attn( query=get_query_chunk(i * query_chunk_size), key_t=key_t, value=value, mask=get_mask_chunk(i * query_chunk_size) ) for i in range(math.ceil(q_tokens / query_chunk_size)) ], dim=1) return res ================================================ FILE: ldm_patched/ldm/modules/temporal_ae.py ================================================ import functools from typing import Callable, Iterable, Union import torch from einops import rearrange, repeat import ldm_patched.modules.ops ops = ldm_patched.modules.ops.disable_weight_init from .diffusionmodules.model import ( AttnBlock, Decoder, ResnetBlock, ) from .diffusionmodules.openaimodel import ResBlock, timestep_embedding from .attention import BasicTransformerBlock def partialclass(cls, *args, **kwargs): class NewCls(cls): __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) return NewCls class VideoResBlock(ResnetBlock): def __init__( self, out_channels, *args, dropout=0.0, video_kernel_size=3, alpha=0.0, merge_strategy="learned", **kwargs, ): super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) if video_kernel_size is None: video_kernel_size = [3, 1, 1] self.time_stack = ResBlock( channels=out_channels, emb_channels=0, dropout=dropout, dims=3, use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=False, skip_t_emb=True, ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def get_alpha(self, bs): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError() def forward(self, x, temb, skip_video=False, timesteps=None): b, c, h, w = x.shape if timesteps is None: timesteps = b x = super().forward(x, temb) if not skip_video: x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_stack(x, temb) alpha = self.get_alpha(bs=b // timesteps).to(x.device) x = alpha * x + (1.0 - alpha) * x_mix x = rearrange(x, "b c t h w -> (b t) c h w") return x class AE3DConv(ops.Conv2d): def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): super().__init__(in_channels, out_channels, *args, **kwargs) if isinstance(video_kernel_size, Iterable): padding = [int(k // 2) for k in video_kernel_size] else: padding = int(video_kernel_size // 2) self.time_mix_conv = ops.Conv3d( in_channels=out_channels, out_channels=out_channels, kernel_size=video_kernel_size, padding=padding, ) def forward(self, input, timesteps=None, skip_video=False): if timesteps is None: timesteps = input.shape[0] x = super().forward(input) if skip_video: return x x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) x = self.time_mix_conv(x) return rearrange(x, "b c t h w -> (b t) c h w") class AttnVideoBlock(AttnBlock): def __init__( self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" ): super().__init__(in_channels) # no context, single headed, as in base class self.time_mix_block = BasicTransformerBlock( dim=in_channels, n_heads=1, d_head=in_channels, checkpoint=False, ff_in=True, ) time_embed_dim = self.in_channels * 4 self.video_time_embed = torch.nn.Sequential( ops.Linear(self.in_channels, time_embed_dim), torch.nn.SiLU(), ops.Linear(time_embed_dim, self.in_channels), ) self.merge_strategy = merge_strategy if self.merge_strategy == "fixed": self.register_buffer("mix_factor", torch.Tensor([alpha])) elif self.merge_strategy == "learned": self.register_parameter( "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) ) else: raise ValueError(f"unknown merge strategy {self.merge_strategy}") def forward(self, x, timesteps=None, skip_time_block=False): if skip_time_block: return super().forward(x) if timesteps is None: timesteps = x.shape[0] x_in = x x = self.attention(x) h, w = x.shape[2:] x = rearrange(x, "b c h w -> b (h w) c") x_mix = x num_frames = torch.arange(timesteps, device=x.device) num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) num_frames = rearrange(num_frames, "b t -> (b t)") t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) emb = self.video_time_embed(t_emb) # b, n_channels emb = emb[:, None, :] x_mix = x_mix + emb alpha = self.get_alpha().to(x.device) x_mix = self.time_mix_block(x_mix, timesteps=timesteps) x = alpha * x + (1.0 - alpha) * x_mix # alpha merge x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) x = self.proj_out(x) return x_in + x def get_alpha( self, ): if self.merge_strategy == "fixed": return self.mix_factor elif self.merge_strategy == "learned": return torch.sigmoid(self.mix_factor) else: raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") def make_time_attn( in_channels, attn_type="vanilla", attn_kwargs=None, alpha: float = 0, merge_strategy: str = "learned", ): return partialclass( AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy ) class Conv2DWrapper(torch.nn.Conv2d): def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: return super().forward(input) class VideoDecoder(Decoder): available_time_modes = ["all", "conv-only", "attn-only"] def __init__( self, *args, video_kernel_size: Union[int, list] = 3, alpha: float = 0.0, merge_strategy: str = "learned", time_mode: str = "conv-only", **kwargs, ): self.video_kernel_size = video_kernel_size self.alpha = alpha self.merge_strategy = merge_strategy self.time_mode = time_mode assert ( self.time_mode in self.available_time_modes ), f"time_mode parameter has to be in {self.available_time_modes}" if self.time_mode != "attn-only": kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) if self.time_mode not in ["conv-only", "only-last-conv"]: kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) if self.time_mode not in ["attn-only", "only-last-conv"]: kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) super().__init__(*args, **kwargs) def get_last_layer(self, skip_time_mix=False, **kwargs): if self.time_mode == "attn-only": raise NotImplementedError("TODO") else: return ( self.conv_out.time_mix_conv.weight if not skip_time_mix else self.conv_out.weight ) ================================================ FILE: ldm_patched/ldm/util.py ================================================ import importlib import torch from torch import optim import numpy as np from inspect import isfunction from PIL import Image, ImageDraw, ImageFont def log_txt_as_img(wh, xc, size=10): # wh a tuple of (width, height) # xc a list of captions to plot b = len(xc) txts = list() for bi in range(b): txt = Image.new("RGB", wh, color="white") draw = ImageDraw.Draw(txt) font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) nc = int(40 * (wh[0] / 256)) lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: print("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) txts = np.stack(txts) txts = torch.tensor(txts) return txts def ismap(x): if not isinstance(x, torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] > 3) def isimage(x): if not isinstance(x,torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def mean_flat(tensor): """ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") return total_params def instantiate_from_config(config): if not "target" in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) class AdamWwithEMAandWings(optim.Optimizer): # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code ema_power=1., param_names=()): """AdamW that saves EMA versions of the parameters.""" if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= ema_decay <= 1.0: raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, ema_power=ema_power, param_names=param_names) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] ema_params_with_grad = [] state_sums = [] max_exp_avg_sqs = [] state_steps = [] amsgrad = group['amsgrad'] beta1, beta2 = group['betas'] ema_decay = group['ema_decay'] ema_power = group['ema_power'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('AdamW does not support sparse gradients') grads.append(p.grad) state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of parameter values state['param_exp_avg'] = p.detach().float().clone() exp_avgs.append(state['exp_avg']) exp_avg_sqs.append(state['exp_avg_sq']) ema_params_with_grad.append(state['param_exp_avg']) if amsgrad: max_exp_avg_sqs.append(state['max_exp_avg_sq']) # update the steps for each param group update state['step'] += 1 # record the step after step update state_steps.append(state['step']) optim._functional.adamw(params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=amsgrad, beta1=beta1, beta2=beta2, lr=group['lr'], weight_decay=group['weight_decay'], eps=group['eps'], maximize=False) cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) for param, ema_param in zip(params_with_grad, ema_params_with_grad): ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) return loss ================================================ FILE: ldm_patched/licenses-3rd/chainer ================================================ Copyright (c) 2015 Preferred Infrastructure, Inc. Copyright (c) 2015 Preferred Networks, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/licenses-3rd/kdiffusion ================================================ Copyright (c) 2022 Katherine Crowson Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/licenses-3rd/ldm ================================================ MIT License Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/licenses-3rd/taesd ================================================ MIT License Copyright (c) 2023 Ollin Boer Bohan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/licenses-3rd/transformers ================================================ Copyright 2018- The Hugging Face team. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/modules/args_parser.py ================================================ import argparse import enum import ldm_patched.modules.options class EnumAction(argparse.Action): """ Argparse action for handling Enums """ def __init__(self, **kwargs): # Pop off the type value enum_type = kwargs.pop("type", None) # Ensure an Enum subclass is provided if enum_type is None: raise ValueError("type must be assigned an Enum when using EnumAction") if not issubclass(enum_type, enum.Enum): raise TypeError("type must be an Enum when using EnumAction") # Generate choices from the Enum choices = tuple(e.value for e in enum_type) kwargs.setdefault("choices", choices) kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") super(EnumAction, self).__init__(**kwargs) self._enum = enum_type def __call__(self, parser, namespace, values, option_string=None): # Convert value back into an Enum value = self._enum(values) setattr(namespace, self.dest, value) parser = argparse.ArgumentParser() parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0") parser.add_argument("--port", type=int, default=8188) parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*") parser.add_argument("--web-upload-size", type=float, default=100) parser.add_argument("--hf-mirror", type=str, default=None) parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append') parser.add_argument("--output-path", type=str, default=None) parser.add_argument("--temp-path", type=str, default=None) parser.add_argument("--cache-path", type=str, default=None) parser.add_argument("--in-browser", action="store_true") parser.add_argument("--disable-in-browser", action="store_true") parser.add_argument("--gpu-device-id", type=int, default=None, metavar="DEVICE_ID") cm_group = parser.add_mutually_exclusive_group() cm_group.add_argument("--async-cuda-allocation", action="store_true") cm_group.add_argument("--disable-async-cuda-allocation", action="store_true") parser.add_argument("--disable-attention-upcast", action="store_true") fp_group = parser.add_mutually_exclusive_group() fp_group.add_argument("--all-in-fp32", action="store_true") fp_group.add_argument("--all-in-fp16", action="store_true") fpunet_group = parser.add_mutually_exclusive_group() fpunet_group.add_argument("--unet-in-bf16", action="store_true") fpunet_group.add_argument("--unet-in-fp16", action="store_true") fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true") fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--vae-in-fp16", action="store_true") fpvae_group.add_argument("--vae-in-fp32", action="store_true") fpvae_group.add_argument("--vae-in-bf16", action="store_true") parser.add_argument("--vae-in-cpu", action="store_true") fpte_group = parser.add_mutually_exclusive_group() fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true") fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true") fpte_group.add_argument("--clip-in-fp16", action="store_true") fpte_group.add_argument("--clip-in-fp32", action="store_true") parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1) parser.add_argument("--disable-ipex-hijack", action="store_true") class LatentPreviewMethod(enum.Enum): NoPreviews = "none" Auto = "auto" Latent2RGB = "fast" TAESD = "taesd" parser.add_argument("--preview-option", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, action=EnumAction) attn_group = parser.add_mutually_exclusive_group() attn_group.add_argument("--attention-split", action="store_true") attn_group.add_argument("--attention-quad", action="store_true") attn_group.add_argument("--attention-pytorch", action="store_true") parser.add_argument("--disable-xformers", action="store_true") vram_group = parser.add_mutually_exclusive_group() vram_group.add_argument("--always-gpu", action="store_true") vram_group.add_argument("--always-high-vram", action="store_true") vram_group.add_argument("--always-normal-vram", action="store_true") vram_group.add_argument("--always-low-vram", action="store_true") vram_group.add_argument("--always-no-vram", action="store_true") vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1) parser.add_argument("--always-offload-from-vram", action="store_true") parser.add_argument("--pytorch-deterministic", action="store_true") parser.add_argument("--disable-server-log", action="store_true") parser.add_argument("--debug-mode", action="store_true") parser.add_argument("--is-windows-embedded-python", action="store_true") parser.add_argument("--disable-server-info", action="store_true") parser.add_argument("--multi-user", action="store_true") if ldm_patched.modules.options.args_parsing: args = parser.parse_args([]) else: args = parser.parse_args([]) if args.is_windows_embedded_python: args.in_browser = True if args.disable_in_browser: args.in_browser = False ================================================ FILE: ldm_patched/modules/checkpoint_pickle.py ================================================ import pickle load = pickle.load class Empty: pass class Unpickler(pickle.Unpickler): def find_class(self, module, name): #TODO: safe unpickle if module.startswith("pytorch_lightning"): return Empty return super().find_class(module, name) ================================================ FILE: ldm_patched/modules/clip_config_bigg.json ================================================ { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "gelu", "hidden_size": 1280, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 20, "num_hidden_layers": 32, "pad_token_id": 1, "projection_dim": 1280, "torch_dtype": "float32", "vocab_size": 49408 } ================================================ FILE: ldm_patched/modules/clip_model.py ================================================ import torch from ldm_patched.ldm.modules.attention import optimized_attention_for_device class CLIPAttention(torch.nn.Module): def __init__(self, embed_dim, heads, dtype, device, operations): super().__init__() self.heads = heads self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x, mask=None, optimized_attention=None): q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) out = optimized_attention(q, k, v, self.heads, mask) return self.out_proj(out) ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), "gelu": torch.nn.functional.gelu, } class CLIPMLP(torch.nn.Module): def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): super().__init__() self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) self.activation = ACTIVATIONS[activation] self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) def forward(self, x): x = self.fc1(x) x = self.activation(x) x = self.fc2(x) return x class CLIPLayer(torch.nn.Module): def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) def forward(self, x, mask=None, optimized_attention=None): x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) x += self.mlp(self.layer_norm2(x)) return x class CLIPEncoder(torch.nn.Module): def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): super().__init__() self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) if intermediate_output is not None: if intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output intermediate = None for i, l in enumerate(self.layers): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() return x, intermediate class CLIPEmbeddings(torch.nn.Module): def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): super().__init__() self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, input_tokens): return self.token_embedding(input_tokens) + self.position_embedding.weight class CLIPTextModel_(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] super().__init__() self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): x = self.embeddings(input_tokens) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) if mask is not None: mask += causal_mask else: mask = causal_mask x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) x = self.final_layer_norm(x) if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.num_layers = config_dict["num_hidden_layers"] self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) self.dtype = dtype def get_input_embeddings(self): return self.text_model.embeddings.token_embedding def set_input_embeddings(self, embeddings): self.text_model.embeddings.token_embedding = embeddings def forward(self, *args, **kwargs): return self.text_model(*args, **kwargs) class CLIPVisionEmbeddings(torch.nn.Module): def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): super().__init__() self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) self.patch_embedding = operations.Conv2d( in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, dtype=dtype, device=device ) num_patches = (image_size // patch_size) ** 2 num_positions = num_patches + 1 self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) def forward(self, pixel_values): embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device) class CLIPVision(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() num_layers = config_dict["num_hidden_layers"] embed_dim = config_dict["hidden_size"] heads = config_dict["num_attention_heads"] intermediate_size = config_dict["intermediate_size"] intermediate_activation = config_dict["hidden_act"] self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations) self.pre_layrnorm = operations.LayerNorm(embed_dim) self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.post_layernorm = operations.LayerNorm(embed_dim) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): x = self.embeddings(pixel_values) x = self.pre_layrnorm(x) #TODO: attention_mask? x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() self.vision_model = CLIPVision(config_dict, dtype, device, operations) self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) return (x[0], x[1], out) ================================================ FILE: ldm_patched/modules/clip_vision.py ================================================ from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace import os import torch import json import ldm_patched.modules.ops import ldm_patched.modules.model_patcher import ldm_patched.modules.model_management import ldm_patched.modules.utils import ldm_patched.modules.clip_model class Output: def __getitem__(self, key): return getattr(self, key) def __setitem__(self, key, item): setattr(self, key, item) def clip_preprocess(image, size=224): mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) if not (image.shape[2] == size and image.shape[3] == size): scale = (size / min(image.shape[2], image.shape[3])) image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True) h = (image.shape[2] - size)//2 w = (image.shape[3] - size)//2 image = image[:,:,h:h+size,w:w+size] image = torch.clip((255. * image), 0, 255).round() / 255.0 return (image - mean.view([3,1,1])) / std.view([3,1,1]) class ClipVisionModel(): def __init__(self, json_config): with open(json_config) as f: config = json.load(f) self.load_device = ldm_patched.modules.model_management.text_encoder_device() offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() self.dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device) self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.manual_cast) self.model.eval() self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) def load_sd(self, sd): return self.model.load_state_dict(sd, strict=False) def get_sd(self): return self.model.state_dict() def encode_image(self, image): ldm_patched.modules.model_management.load_model_gpu(self.patcher) pixel_values = clip_preprocess(image.to(self.load_device)).float() out = self.model(pixel_values=pixel_values, intermediate_output=-2) outputs = Output() outputs["last_hidden_state"] = out[0].to(ldm_patched.modules.model_management.intermediate_device()) outputs["image_embeds"] = out[2].to(ldm_patched.modules.model_management.intermediate_device()) outputs["penultimate_hidden_states"] = out[1].to(ldm_patched.modules.model_management.intermediate_device()) return outputs def convert_to_transformers(sd, prefix): sd_k = sd.keys() if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: keys_to_replace = { "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", } for x in keys_to_replace: if x in sd_k: sd[keys_to_replace[x]] = sd.pop(x) if "{}proj".format(prefix) in sd_k: sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) sd = transformers_convert(sd, prefix, "vision_model.", 48) else: replace_prefix = {prefix: ""} sd = state_dict_prefix_replace(sd, replace_prefix) return sd def load_clipvision_from_sd(sd, prefix="", convert_keys=False): if convert_keys: sd = convert_to_transformers(sd, prefix) if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") else: return None clip = ClipVisionModel(json_config) m, u = clip.load_sd(sd) if len(m) > 0: print("extra clip vision:", m) u = set(u) keys = list(sd.keys()) for k in keys: if k not in u: t = sd.pop(k) del t return clip def load(ckpt_path): sd = load_torch_file(ckpt_path) if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) else: return load_clipvision_from_sd(sd) ================================================ FILE: ldm_patched/modules/clip_vision_config_g.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1664, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 8192, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 48, "patch_size": 14, "projection_dim": 1280, "torch_dtype": "float32" } ================================================ FILE: ldm_patched/modules/clip_vision_config_h.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "gelu", "hidden_size": 1280, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 5120, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 32, "patch_size": 14, "projection_dim": 1024, "torch_dtype": "float32" } ================================================ FILE: ldm_patched/modules/clip_vision_config_vitl.json ================================================ { "attention_dropout": 0.0, "dropout": 0.0, "hidden_act": "quick_gelu", "hidden_size": 1024, "image_size": 224, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_channels": 3, "num_hidden_layers": 24, "patch_size": 14, "projection_dim": 768, "torch_dtype": "float32" } ================================================ FILE: ldm_patched/modules/conds.py ================================================ import torch import math import ldm_patched.modules.utils class CONDRegular: def __init__(self, cond): self.cond = cond def _copy_with(self, cond): return self.__class__(cond) def process_cond(self, batch_size, device, **kwargs): return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) def can_concat(self, other): if self.cond.shape != other.cond.shape: return False return True def concat(self, others): conds = [self.cond] for x in others: conds.append(x.cond) return torch.cat(conds) class CONDNoiseShape(CONDRegular): def process_cond(self, batch_size, device, area, **kwargs): data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(data, batch_size).to(device)) class CONDCrossAttn(CONDRegular): def can_concat(self, other): s1 = self.cond.shape s2 = other.cond.shape if s1 != s2: if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen return False mult_min = math.lcm(s1[1], s2[1]) diff = mult_min // min(s1[1], s2[1]) if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much return False return True def concat(self, others): conds = [self.cond] crossattn_max_len = self.cond.shape[1] for x in others: c = x.cond crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) conds.append(c) out = [] for c in conds: if c.shape[1] < crossattn_max_len: c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result out.append(c) return torch.cat(out) class CONDConstant(CONDRegular): def __init__(self, cond): self.cond = cond def process_cond(self, batch_size, device, **kwargs): return self._copy_with(self.cond) def can_concat(self, other): if self.cond != other.cond: return False return True def concat(self, others): return self.cond ================================================ FILE: ldm_patched/modules/controlnet.py ================================================ import torch import math import os import ldm_patched.modules.utils import ldm_patched.modules.model_management import ldm_patched.modules.model_detection import ldm_patched.modules.model_patcher import ldm_patched.modules.ops import ldm_patched.controlnet.cldm import ldm_patched.t2ia.adapter def broadcast_image_to(tensor, target_batch_size, batched_number): current_batch_size = tensor.shape[0] #print(current_batch_size, target_batch_size) if current_batch_size == 1: return tensor per_batch = target_batch_size // batched_number tensor = tensor[:per_batch] if per_batch > tensor.shape[0]: tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) current_batch_size = tensor.shape[0] if current_batch_size == target_batch_size: return tensor else: return torch.cat([tensor] * batched_number, dim=0) class ControlBase: def __init__(self, device=None): self.cond_hint_original = None self.cond_hint = None self.strength = 1.0 self.timestep_percent_range = (0.0, 1.0) self.global_average_pooling = False self.timestep_range = None if device is None: device = ldm_patched.modules.model_management.get_torch_device() self.device = device self.previous_controlnet = None def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)): self.cond_hint_original = cond_hint self.strength = strength self.timestep_percent_range = timestep_percent_range return self def pre_run(self, model, percent_to_timestep_function): self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) if self.previous_controlnet is not None: self.previous_controlnet.pre_run(model, percent_to_timestep_function) def set_previous_controlnet(self, controlnet): self.previous_controlnet = controlnet return self def cleanup(self): if self.previous_controlnet is not None: self.previous_controlnet.cleanup() if self.cond_hint is not None: del self.cond_hint self.cond_hint = None self.timestep_range = None def get_models(self): out = [] if self.previous_controlnet is not None: out += self.previous_controlnet.get_models() return out def copy_to(self, c): c.cond_hint_original = self.cond_hint_original c.strength = self.strength c.timestep_percent_range = self.timestep_percent_range c.global_average_pooling = self.global_average_pooling def inference_memory_requirements(self, dtype): if self.previous_controlnet is not None: return self.previous_controlnet.inference_memory_requirements(dtype) return 0 def control_merge(self, control_input, control_output, control_prev, output_dtype): out = {'input':[], 'middle':[], 'output': []} if control_input is not None: for i in range(len(control_input)): key = 'input' x = control_input[i] if x is not None: x *= self.strength if x.dtype != output_dtype: x = x.to(output_dtype) out[key].insert(0, x) if control_output is not None: for i in range(len(control_output)): if i == (len(control_output) - 1): key = 'middle' index = 0 else: key = 'output' index = i x = control_output[i] if x is not None: if self.global_average_pooling: x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) x *= self.strength if x.dtype != output_dtype: x = x.to(output_dtype) out[key].append(x) if control_prev is not None: for x in ['input', 'middle', 'output']: o = out[x] for i in range(len(control_prev[x])): prev_val = control_prev[x][i] if i >= len(o): o.append(prev_val) elif prev_val is not None: if o[i] is None: o[i] = prev_val else: if o[i].shape[0] < prev_val.shape[0]: o[i] = prev_val + o[i] else: o[i] += prev_val return out class ControlNet(ControlBase): def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): super().__init__(device) self.control_model = control_model self.load_device = load_device self.control_model_wrapped = ldm_patched.modules.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=ldm_patched.modules.model_management.unet_offload_device()) self.global_average_pooling = global_average_pooling self.model_sampling_current = None self.manual_cast_dtype = manual_cast_dtype def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None dtype = self.control_model.dtype if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype output_dtype = x_noisy.dtype if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.cond_hint = None self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) context = cond['c_crossattn'] y = cond.get('y', None) if y is not None: y = y.to(dtype) timestep = self.model_sampling_current.timestep(t) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y) return self.control_merge(None, control, control_prev, output_dtype) def copy(self): c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) self.copy_to(c) return c def get_models(self): out = super().get_models() out.append(self.control_model_wrapped) return out def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) self.model_sampling_current = model.model_sampling def cleanup(self): self.model_sampling_current = None super().cleanup() class ControlLoraOps: class Linear(torch.nn.Module): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} super().__init__() self.in_features = in_features self.out_features = out_features self.weight = None self.up = None self.down = None self.bias = None def forward(self, input): weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) if self.up is not None: return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) else: return torch.nn.functional.linear(input, weight, bias) class Conv2d(torch.nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.transposed = False self.output_padding = 0 self.groups = groups self.padding_mode = padding_mode self.weight = None self.bias = None self.up = None self.down = None def forward(self, input): weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) if self.up is not None: return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) else: return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) class ControlLora(ControlNet): def __init__(self, control_weights, global_average_pooling=False, device=None): ControlBase.__init__(self, device) self.control_weights = control_weights self.global_average_pooling = global_average_pooling def pre_run(self, model, percent_to_timestep_function): super().pre_run(model, percent_to_timestep_function) controlnet_config = model.model_config.unet_config.copy() controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] self.manual_cast_dtype = model.manual_cast_dtype dtype = model.get_dtype() if self.manual_cast_dtype is None: class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.disable_weight_init): pass else: class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.manual_cast): pass dtype = self.manual_cast_dtype controlnet_config["operations"] = control_lora_ops controlnet_config["dtype"] = dtype self.control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) self.control_model.to(ldm_patched.modules.model_management.get_torch_device()) diffusion_model = model.diffusion_model sd = diffusion_model.state_dict() cm = self.control_model.state_dict() for k in sd: weight = sd[k] try: ldm_patched.modules.utils.set_attr(self.control_model, k, weight) except: pass for k in self.control_weights: if k not in {"lora_controlnet"}: ldm_patched.modules.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(ldm_patched.modules.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) self.copy_to(c) return c def cleanup(self): del self.control_model self.control_model = None super().cleanup() def get_models(self): out = ControlBase.get_models(self) return out def inference_memory_requirements(self, dtype): return ldm_patched.modules.utils.calculate_parameters(self.control_weights) * ldm_patched.modules.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) def load_controlnet(ckpt_path, model=None): controlnet_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) if "lora_controlnet" in controlnet_data: return ControlLora(controlnet_data) controlnet_config = None if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format unet_dtype = ldm_patched.modules.model_management.unet_dtype() controlnet_config = ldm_patched.modules.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype) diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(controlnet_config) diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: k_in = "controlnet_down_blocks.{}{}".format(count, s) k_out = "zero_convs.{}.0{}".format(count, s) if k_in not in controlnet_data: loop = False break diffusers_keys[k_in] = k_out count += 1 count = 0 loop = True while loop: suffix = [".weight", ".bias"] for s in suffix: if count == 0: k_in = "controlnet_cond_embedding.conv_in{}".format(s) else: k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) k_out = "input_hint_block.{}{}".format(count * 2, s) if k_in not in controlnet_data: k_in = "controlnet_cond_embedding.conv_out{}".format(s) loop = False diffusers_keys[k_in] = k_out count += 1 new_sd = {} for k in diffusers_keys: if k in controlnet_data: new_sd[diffusers_keys[k]] = controlnet_data.pop(k) leftover_keys = controlnet_data.keys() if len(leftover_keys) > 0: print("leftover keys:", leftover_keys) controlnet_data = new_sd pth_key = 'control_model.zero_convs.0.0.weight' pth = False key = 'zero_convs.0.0.weight' if pth_key in controlnet_data: pth = True key = pth_key prefix = "control_model." elif key in controlnet_data: prefix = "" else: net = load_t2i_adapter(controlnet_data) if net is None: print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) return net if controlnet_config is None: unet_dtype = ldm_patched.modules.model_management.unet_dtype() controlnet_config = ldm_patched.modules.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config load_device = ldm_patched.modules.model_management.get_torch_device() manual_cast_dtype = ldm_patched.modules.model_management.unet_manual_cast(unet_dtype, load_device) if manual_cast_dtype is not None: controlnet_config["operations"] = ldm_patched.modules.ops.manual_cast controlnet_config.pop("out_channels") controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) if pth: if 'difference' in controlnet_data: if model is not None: ldm_patched.modules.model_management.load_models_gpu([model]) model_sd = model.model_state_dict() for x in controlnet_data: c_m = "control_model." if x.startswith(c_m): sd_key = "diffusion_model.{}".format(x[len(c_m):]) if sd_key in model_sd: cd = controlnet_data[x] cd += model_sd[sd_key].type(cd.dtype).to(cd.device) else: print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.control_model = control_model missing, unexpected = w.load_state_dict(controlnet_data, strict=False) else: missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) print(missing, unexpected) global_average_pooling = False filename = os.path.splitext(ckpt_path)[0] if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling global_average_pooling = True control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) return control class T2IAdapter(ControlBase): def __init__(self, t2i_model, channels_in, device=None): super().__init__(device) self.t2i_model = t2i_model self.channels_in = channels_in self.control_input = None def scale_image_to(self, width, height): unshuffle_amount = self.t2i_model.unshuffle_amount width = math.ceil(width / unshuffle_amount) * unshuffle_amount height = math.ceil(height / unshuffle_amount) * unshuffle_amount return width, height def get_control(self, x_noisy, t, cond, batched_number): control_prev = None if self.previous_controlnet is not None: control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) if self.timestep_range is not None: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if control_prev is not None: return control_prev else: return None if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: if self.cond_hint is not None: del self.cond_hint self.control_input = None self.cond_hint = None width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) if self.channels_in == 1 and self.cond_hint.shape[1] > 1: self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) if x_noisy.shape[0] != self.cond_hint.shape[0]: self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) if self.control_input is None: self.t2i_model.to(x_noisy.dtype) self.t2i_model.to(self.device) self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) self.t2i_model.cpu() control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) mid = None if self.t2i_model.xl == True: mid = control_input[-1:] control_input = control_input[:-1] return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) def copy(self): c = T2IAdapter(self.t2i_model, self.channels_in) self.copy_to(c) return c def load_t2i_adapter(t2i_data): if 'adapter' in t2i_data: t2i_data = t2i_data['adapter'] if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format prefix_replace = {} for i in range(4): for j in range(2): prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) prefix_replace["adapter."] = "" t2i_data = ldm_patched.modules.utils.state_dict_prefix_replace(t2i_data, prefix_replace) keys = t2i_data.keys() if "body.0.in_conv.weight" in keys: cin = t2i_data['body.0.in_conv.weight'].shape[1] model_ad = ldm_patched.t2ia.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) elif 'conv_in.weight' in keys: cin = t2i_data['conv_in.weight'].shape[1] channel = t2i_data['conv_in.weight'].shape[0] ksize = t2i_data['body.0.block2.weight'].shape[2] use_conv = False down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) if len(down_opts) > 0: use_conv = True xl = False if cin == 256 or cin == 768: xl = True model_ad = ldm_patched.t2ia.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) else: return None missing, unexpected = model_ad.load_state_dict(t2i_data) if len(missing) > 0: print("t2i missing", missing) if len(unexpected) > 0: print("t2i unexpected", unexpected) return T2IAdapter(model_ad, model_ad.input_channels) ================================================ FILE: ldm_patched/modules/diffusers_convert.py ================================================ import re import torch # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py # =================# # UNet Conversion # # =================# unet_conversion_map = [ # (stable-diffusion, HF Diffusers) ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ] unet_conversion_map_resnet = [ # (stable-diffusion, HF Diffusers) ("in_layers.0", "norm1"), ("in_layers.2", "conv1"), ("out_layers.0", "norm2"), ("out_layers.3", "conv2"), ("emb_layers.1", "time_emb_proj"), ("skip_connection", "conv_shortcut"), ] unet_conversion_map_layer = [] # hardcoded number of downblocks and resnets/attentions... # would need smarter logic for other networks. for i in range(4): # loop over downblocks/upblocks for j in range(2): # loop over resnets/attentions for downblocks hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) if i < 3: # no attention layers in down_blocks.3 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) for j in range(3): # loop over resnets/attentions for upblocks hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) if i > 0: # no attention layers in up_blocks.0 hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) if i < 3: # no downsample in down_blocks.3 hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) # no upsample in up_blocks.3 hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) hf_mid_atn_prefix = "mid_block.attentions.0." sd_mid_atn_prefix = "middle_block.1." unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) for j in range(2): hf_mid_res_prefix = f"mid_block.resnets.{j}." sd_mid_res_prefix = f"middle_block.{2 * j}." unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) def convert_unet_state_dict(unet_state_dict): # buyer beware: this is a *brittle* function, # and correct output requires that all of these pieces interact in # the exact order in which I have arranged them. mapping = {k: k for k in unet_state_dict.keys()} for sd_name, hf_name in unet_conversion_map: mapping[hf_name] = sd_name for k, v in mapping.items(): if "resnets" in k: for sd_part, hf_part in unet_conversion_map_resnet: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): for sd_part, hf_part in unet_conversion_map_layer: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} return new_state_dict # ================# # VAE Conversion # # ================# vae_conversion_map = [ # (stable-diffusion, HF Diffusers) ("nin_shortcut", "conv_shortcut"), ("norm_out", "conv_norm_out"), ("mid.attn_1.", "mid_block.attentions.0."), ] for i in range(4): # down_blocks have two resnets for j in range(2): hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." sd_down_prefix = f"encoder.down.{i}.block.{j}." vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) if i < 3: hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." sd_downsample_prefix = f"down.{i}.downsample." vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"up.{3 - i}.upsample." vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) # up_blocks have three resnets # also, up blocks in hf are numbered in reverse from sd for j in range(3): hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) # this part accounts for mid blocks in both the encoder and the decoder for i in range(2): hf_mid_res_prefix = f"mid_block.resnets.{i}." sd_mid_res_prefix = f"mid.block_{i + 1}." vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) vae_conversion_map_attn = [ # (stable-diffusion, HF Diffusers) ("norm.", "group_norm."), ("q.", "query."), ("k.", "key."), ("v.", "value."), ("q.", "to_q."), ("k.", "to_k."), ("v.", "to_v."), ("proj_out.", "to_out.0."), ("proj_out.", "proj_attn."), ] def reshape_weight_for_sd(w): # convert HF linear weights to SD conv2d weights return w.reshape(*w.shape, 1, 1) def convert_vae_state_dict(vae_state_dict): mapping = {k: k for k in vae_state_dict.keys()} for k, v in mapping.items(): for sd_part, hf_part in vae_conversion_map: v = v.replace(hf_part, sd_part) mapping[k] = v for k, v in mapping.items(): if "attentions" in k: for sd_part, hf_part in vae_conversion_map_attn: v = v.replace(hf_part, sd_part) mapping[k] = v new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} weights_to_convert = ["q", "k", "v", "proj_out"] for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: print(f"Reshaping {k} for SD format") new_state_dict[k] = reshape_weight_for_sd(v) return new_state_dict # =========================# # Text Encoder Conversion # # =========================# textenc_conversion_lst = [ # (stable-diffusion, HF Diffusers) ("resblocks.", "text_model.encoder.layers."), ("ln_1", "layer_norm1"), ("ln_2", "layer_norm2"), (".c_fc.", ".fc1."), (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} textenc_pattern = re.compile("|".join(protected.keys())) # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp code2idx = {"q": 0, "k": 1, "v": 2} def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): new_state_dict = {} capture_qkv_weight = {} capture_qkv_bias = {} for k, v in text_enc_dict.items(): if not k.startswith(prefix): continue if ( k.endswith(".self_attn.q_proj.weight") or k.endswith(".self_attn.k_proj.weight") or k.endswith(".self_attn.v_proj.weight") ): k_pre = k[: -len(".q_proj.weight")] k_code = k[-len("q_proj.weight")] if k_pre not in capture_qkv_weight: capture_qkv_weight[k_pre] = [None, None, None] capture_qkv_weight[k_pre][code2idx[k_code]] = v continue if ( k.endswith(".self_attn.q_proj.bias") or k.endswith(".self_attn.k_proj.bias") or k.endswith(".self_attn.v_proj.bias") ): k_pre = k[: -len(".q_proj.bias")] k_code = k[-len("q_proj.bias")] if k_pre not in capture_qkv_bias: capture_qkv_bias[k_pre] = [None, None, None] capture_qkv_bias[k_pre][code2idx[k_code]] = v continue relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) new_state_dict[relabelled_key] = v for k_pre, tensors in capture_qkv_weight.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) for k_pre, tensors in capture_qkv_bias.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) return new_state_dict def convert_text_enc_state_dict(text_enc_dict): return text_enc_dict ================================================ FILE: ldm_patched/modules/diffusers_load.py ================================================ import os import ldm_patched.modules.sd def first_file(path, filenames): for f in filenames: p = os.path.join(path, f) if os.path.exists(p): return p return None def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) text_encoder_paths = [text_encoder1_path] if text_encoder2_path is not None: text_encoder_paths.append(text_encoder2_path) unet = ldm_patched.modules.sd.load_unet(unet_path) clip = None if output_clip: clip = ldm_patched.modules.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) vae = None if output_vae: sd = ldm_patched.modules.utils.load_torch_file(vae_path) vae = ldm_patched.modules.sd.VAE(sd=sd) return (unet, clip, vae) ================================================ FILE: ldm_patched/modules/gligen.py ================================================ import torch from torch import nn from ldm_patched.ldm.modules.attention import CrossAttention from inspect import isfunction def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * torch.nn.functional.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) class GatedCrossAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() self.attn = CrossAttention( query_dim=query_dim, context_dim=context_dim, heads=n_heads, dim_head=d_head) self.ff = FeedForward(query_dim, glu=True) self.norm1 = nn.LayerNorm(query_dim) self.norm2 = nn.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): x = x + self.scale * \ torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() # we need a linear projection since we need cat visual feature and obj # feature self.linear = nn.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, heads=n_heads, dim_head=d_head) self.ff = FeedForward(query_dim, glu=True) self.norm1 = nn.LayerNorm(query_dim) self.norm2 = nn.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): N_visual = x.shape[1] objs = self.linear(objs) x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class GatedSelfAttentionDense2(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): super().__init__() # we need a linear projection since we need cat visual feature and obj # feature self.linear = nn.Linear(context_dim, query_dim) self.attn = CrossAttention( query_dim=query_dim, context_dim=query_dim, dim_head=d_head) self.ff = FeedForward(query_dim, glu=True) self.norm1 = nn.LayerNorm(query_dim) self.norm2 = nn.LayerNorm(query_dim) self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) # this can be useful: we can externally change magnitude of tanh(alpha) # for example, when it is set to 0, then the entire model is same as # original one self.scale = 1 def forward(self, x, objs): B, N_visual, _ = x.shape B, N_ground, _ = objs.shape objs = self.linear(objs) # sanity check size_v = math.sqrt(N_visual) size_g = math.sqrt(N_ground) assert int(size_v) == size_v, "Visual tokens must be square rootable" assert int(size_g) == size_g, "Grounding tokens must be square rootable" size_v = int(size_v) size_g = int(size_g) # select grounding token and resize it to visual token size as residual out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ :, N_visual:, :] out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) out = torch.nn.functional.interpolate( out, (size_v, size_v), mode='bicubic') residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) # add residual to visual feature x = x + self.scale * torch.tanh(self.alpha_attn) * residual x = x + self.scale * \ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) return x class FourierEmbedder(): def __init__(self, num_freqs=64, temperature=100): self.num_freqs = num_freqs self.temperature = temperature self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) @torch.no_grad() def __call__(self, x, cat_dim=-1): "x: arbitrary shape of tensor. dim: cat dim" out = [] for freq in self.freq_bands: out.append(torch.sin(freq * x)) out.append(torch.cos(freq * x)) return torch.cat(out, cat_dim) class PositionNet(nn.Module): def __init__(self, in_dim, out_dim, fourier_freqs=8): super().__init__() self.in_dim = in_dim self.out_dim = out_dim self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy self.linears = nn.Sequential( nn.Linear(self.in_dim + self.position_dim, 512), nn.SiLU(), nn.Linear(512, 512), nn.SiLU(), nn.Linear(512, out_dim), ) self.null_positive_feature = torch.nn.Parameter( torch.zeros([self.in_dim])) self.null_position_feature = torch.nn.Parameter( torch.zeros([self.position_dim])) def forward(self, boxes, masks, positive_embeddings): B, N, _ = boxes.shape dtype = self.linears[0].weight.dtype masks = masks.unsqueeze(-1).to(dtype) positive_embeddings = positive_embeddings.to(dtype) # embedding position (it may includes padding as placeholder) xyxy_embedding = self.fourier_embedder(boxes.to(dtype)) # B*N*4 --> B*N*C # learnable null embedding positive_null = self.null_positive_feature.view(1, 1, -1) xyxy_null = self.null_position_feature.view(1, 1, -1) # replace padding with learnable null embedding positive_embeddings = positive_embeddings * \ masks + (1 - masks) * positive_null xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null objs = self.linears( torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) assert objs.shape == torch.Size([B, N, self.out_dim]) return objs class Gligen(nn.Module): def __init__(self, modules, position_net, key_dim): super().__init__() self.module_list = nn.ModuleList(modules) self.position_net = position_net self.key_dim = key_dim self.max_objs = 30 self.current_device = torch.device("cpu") def _set_position(self, boxes, masks, positive_embeddings): objs = self.position_net(boxes, masks, positive_embeddings) def func(x, extra_options): key = extra_options["transformer_index"] module = self.module_list[key] return module(x, objs) return func def set_position(self, latent_image_shape, position_params, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu") boxes = [] positive_embeddings = [] for p in position_params: x1 = (p[4]) / w y1 = (p[3]) / h x2 = (p[4] + p[2]) / w y2 = (p[3] + p[1]) / h masks[len(boxes)] = 1.0 boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] positive_embeddings += [p[0]] append_boxes = [] append_conds = [] if len(boxes) < self.max_objs: append_boxes = [torch.zeros( [self.max_objs - len(boxes), 4], device="cpu")] append_conds = [torch.zeros( [self.max_objs - len(boxes), self.key_dim], device="cpu")] box_out = torch.cat( boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) masks = masks.unsqueeze(0).repeat(batch, 1) conds = torch.cat(positive_embeddings + append_conds).unsqueeze(0).repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def set_empty(self, latent_image_shape, device): batch, c, h, w = latent_image_shape masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) box_out = torch.zeros([self.max_objs, 4], device="cpu").repeat(batch, 1, 1) conds = torch.zeros([self.max_objs, self.key_dim], device="cpu").repeat(batch, 1, 1) return self._set_position( box_out.to(device), masks.to(device), conds.to(device)) def load_gligen(sd): sd_k = sd.keys() output_list = [] key_dim = 768 for a in ["input_blocks", "middle_block", "output_blocks"]: for b in range(20): k_temp = filter(lambda k: "{}.{}.".format(a, b) in k and ".fuser." in k, sd_k) k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) n_sd = {} for k in k_temp: n_sd[k[1]] = sd[k[0]] if len(n_sd) > 0: query_dim = n_sd["linear.weight"].shape[0] key_dim = n_sd["linear.weight"].shape[1] if key_dim == 768: # SD1.x n_heads = 8 d_head = query_dim // n_heads else: d_head = 64 n_heads = query_dim // d_head gated = GatedSelfAttentionDense( query_dim, key_dim, n_heads, d_head) gated.load_state_dict(n_sd, strict=False) output_list.append(gated) if "position_net.null_positive_feature" in sd_k: in_dim = sd["position_net.null_positive_feature"].shape[0] out_dim = sd["position_net.linears.4.weight"].shape[0] class WeightsLoader(torch.nn.Module): pass w = WeightsLoader() w.position_net = PositionNet(in_dim, out_dim) w.load_state_dict(sd, strict=False) gligen = Gligen(output_list, w.position_net, key_dim) return gligen ================================================ FILE: ldm_patched/modules/latent_formats.py ================================================ import torch class LatentFormat: scale_factor = 1.0 latent_rgb_factors = None taesd_decoder_name = None def process_in(self, latent): return latent * self.scale_factor def process_out(self, latent): return latent / self.scale_factor class SD15(LatentFormat): def __init__(self, scale_factor=0.18215): self.scale_factor = scale_factor self.latent_rgb_factors = [ # R G B [ 0.3512, 0.2297, 0.3227], [ 0.3250, 0.4974, 0.2350], [-0.2829, 0.1762, 0.2721], [-0.2120, -0.2616, -0.7177] ] self.taesd_decoder_name = "taesd_decoder" class SDXL(LatentFormat): def __init__(self): self.scale_factor = 0.13025 self.latent_rgb_factors = [ # R G B [ 0.3920, 0.4054, 0.4549], [-0.2634, -0.0196, 0.0653], [ 0.0568, 0.1687, -0.0755], [-0.3112, -0.2359, -0.2076] ] self.taesd_decoder_name = "taesdxl_decoder" class SDXL_Playground_2_5(LatentFormat): def __init__(self): self.scale_factor = 0.5 self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) self.latent_rgb_factors = [ # R G B [ 0.3920, 0.4054, 0.4549], [-0.2634, -0.0196, 0.0653], [ 0.0568, 0.1687, -0.0755], [-0.3112, -0.2359, -0.2076] ] self.taesd_decoder_name = "taesdxl_decoder" def process_in(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return (latent - latents_mean) * self.scale_factor / latents_std def process_out(self, latent): latents_mean = self.latents_mean.to(latent.device, latent.dtype) latents_std = self.latents_std.to(latent.device, latent.dtype) return latent * latents_std / self.scale_factor + latents_mean class SD_X4(LatentFormat): def __init__(self): self.scale_factor = 0.08333 self.latent_rgb_factors = [ [-0.2340, -0.3863, -0.3257], [ 0.0994, 0.0885, -0.0908], [-0.2833, -0.2349, -0.3741], [ 0.2523, -0.0055, -0.1651] ] class SC_Prior(LatentFormat): def __init__(self): self.scale_factor = 1.0 self.latent_rgb_factors = [ [-0.0326, -0.0204, -0.0127], [-0.1592, -0.0427, 0.0216], [ 0.0873, 0.0638, -0.0020], [-0.0602, 0.0442, 0.1304], [ 0.0800, -0.0313, -0.1796], [-0.0810, -0.0638, -0.1581], [ 0.1791, 0.1180, 0.0967], [ 0.0740, 0.1416, 0.0432], [-0.1745, -0.1888, -0.1373], [ 0.2412, 0.1577, 0.0928], [ 0.1908, 0.0998, 0.0682], [ 0.0209, 0.0365, -0.0092], [ 0.0448, -0.0650, -0.1728], [-0.1658, -0.1045, -0.1308], [ 0.0542, 0.1545, 0.1325], [-0.0352, -0.1672, -0.2541] ] class SC_B(LatentFormat): def __init__(self): self.scale_factor = 1.0 / 0.43 self.latent_rgb_factors = [ [ 0.1121, 0.2006, 0.1023], [-0.2093, -0.0222, -0.0195], [-0.3087, -0.1535, 0.0366], [ 0.0290, -0.1574, -0.4078] ] ================================================ FILE: ldm_patched/modules/lora.py ================================================ import ldm_patched.modules.utils LORA_CLIP_MAP = { "mlp.fc1": "mlp_fc1", "mlp.fc2": "mlp_fc2", "self_attn.k_proj": "self_attn_k_proj", "self_attn.q_proj": "self_attn_q_proj", "self_attn.v_proj": "self_attn_v_proj", "self_attn.out_proj": "self_attn_out_proj", } def load_lora(lora, to_load): patch_dict = {} loaded_keys = set() for x in to_load: alpha_name = "{}.alpha".format(x) alpha = None if alpha_name in lora.keys(): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) A_name = None if regular_lora in lora.keys(): A_name = regular_lora B_name = "{}.lora_down.weight".format(x) mid_name = "{}.lora_mid.weight".format(x) elif diffusers_lora in lora.keys(): A_name = diffusers_lora B_name = "{}_lora.down.weight".format(x) mid_name = None elif transformers_lora in lora.keys(): A_name = transformers_lora B_name ="{}.lora_linear_layer.down.weight".format(x) mid_name = None if A_name is not None: mid = None if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) loaded_keys.add(A_name) loaded_keys.add(B_name) ######## loha hada_w1_a_name = "{}.hada_w1_a".format(x) hada_w1_b_name = "{}.hada_w1_b".format(x) hada_w2_a_name = "{}.hada_w2_a".format(x) hada_w2_b_name = "{}.hada_w2_b".format(x) hada_t1_name = "{}.hada_t1".format(x) hada_t2_name = "{}.hada_t2".format(x) if hada_w1_a_name in lora.keys(): hada_t1 = None hada_t2 = None if hada_t1_name in lora.keys(): hada_t1 = lora[hada_t1_name] hada_t2 = lora[hada_t2_name] loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) loaded_keys.add(hada_w2_b_name) ######## lokr lokr_w1_name = "{}.lokr_w1".format(x) lokr_w2_name = "{}.lokr_w2".format(x) lokr_w1_a_name = "{}.lokr_w1_a".format(x) lokr_w1_b_name = "{}.lokr_w1_b".format(x) lokr_t2_name = "{}.lokr_t2".format(x) lokr_w2_a_name = "{}.lokr_w2_a".format(x) lokr_w2_b_name = "{}.lokr_w2_b".format(x) lokr_w1 = None if lokr_w1_name in lora.keys(): lokr_w1 = lora[lokr_w1_name] loaded_keys.add(lokr_w1_name) lokr_w2 = None if lokr_w2_name in lora.keys(): lokr_w2 = lora[lokr_w2_name] loaded_keys.add(lokr_w2_name) lokr_w1_a = None if lokr_w1_a_name in lora.keys(): lokr_w1_a = lora[lokr_w1_a_name] loaded_keys.add(lokr_w1_a_name) lokr_w1_b = None if lokr_w1_b_name in lora.keys(): lokr_w1_b = lora[lokr_w1_b_name] loaded_keys.add(lokr_w1_b_name) lokr_w2_a = None if lokr_w2_a_name in lora.keys(): lokr_w2_a = lora[lokr_w2_a_name] loaded_keys.add(lokr_w2_a_name) lokr_w2_b = None if lokr_w2_b_name in lora.keys(): lokr_w2_b = lora[lokr_w2_b_name] loaded_keys.add(lokr_w2_b_name) lokr_t2 = None if lokr_t2_name in lora.keys(): lokr_t2 = lora[lokr_t2_name] loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) #glora a1_name = "{}.a1.weight".format(x) a2_name = "{}.a2.weight".format(x) b1_name = "{}.b1.weight".format(x) b2_name = "{}.b2.weight".format(x) if a1_name in lora: patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) loaded_keys.add(a1_name) loaded_keys.add(a2_name) loaded_keys.add(b1_name) loaded_keys.add(b2_name) w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) w_norm = lora.get(w_norm_name, None) b_norm = lora.get(b_norm_name, None) if w_norm is not None: loaded_keys.add(w_norm_name) patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) for x in lora.keys(): if x not in loaded_keys: print("lora key not loaded", x) return patch_dict def model_lora_keys_clip(model, key_map={}): sdk = model.state_dict().keys() text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" clip_l_present = False for b in range(32): #TODO: clean up for c in LORA_CLIP_MAP: k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) key_map[lora_key] = k lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base key_map[lora_key] = k clip_l_present = True lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) if k in sdk: if clip_l_present: lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base key_map[lora_key] = k lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k else: lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner key_map[lora_key] = k lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora key_map[lora_key] = k return key_map def model_lora_keys_unet(model, key_map={}): sdk = model.state_dict().keys() for k in sdk: if k.startswith("diffusion_model.") and k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model.model_config.unet_config) for k in diffusers_keys: if k.endswith(".weight"): unet_key = "diffusion_model.{}".format(diffusers_keys[k]) key_lora = k[:-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = unet_key diffusers_lora_prefix = ["", "unet."] for p in diffusers_lora_prefix: diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) if diffusers_lora_key.endswith(".to_out.0"): diffusers_lora_key = diffusers_lora_key[:-2] key_map[diffusers_lora_key] = unet_key return key_map ================================================ FILE: ldm_patched/modules/model_base.py ================================================ import torch from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation import ldm_patched.modules.model_management import ldm_patched.modules.conds import ldm_patched.modules.ops from enum import Enum from . import utils class ModelType(Enum): EPS = 1 V_PREDICTION = 2 V_PREDICTION_EDM = 3 from ldm_patched.modules.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM def model_sampling(model_config, model_type): s = ModelSamplingDiscrete if model_type == ModelType.EPS: c = EPS elif model_type == ModelType.V_PREDICTION: c = V_PREDICTION elif model_type == ModelType.V_PREDICTION_EDM: c = V_PREDICTION s = ModelSamplingContinuousEDM class ModelSampling(s, c): pass return ModelSampling(model_config) class BaseModel(torch.nn.Module): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__() unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config self.manual_cast_dtype = model_config.manual_cast_dtype if not unet_config.get("disable_unet_model_creation", False): if self.manual_cast_dtype is not None: operations = ldm_patched.modules.ops.manual_cast else: operations = ldm_patched.modules.ops.disable_weight_init self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations) self.model_type = model_type self.model_sampling = model_sampling(model_config, model_type) self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: self.adm_channels = 0 self.inpaint_model = False print("model_type", model_type.name) print("UNet ADM Dimension", self.adm_channels) def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t xc = self.model_sampling.calculate_input(sigma, x) if c_concat is not None: xc = torch.cat([xc] + [c_concat], dim=1) context = c_crossattn dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype xc = xc.to(dtype) t = self.model_sampling.timestep(t).float() context = context.to(dtype) extra_conds = {} for o in kwargs: extra = kwargs[o] if hasattr(extra, "dtype"): if extra.dtype != torch.int and extra.dtype != torch.long: extra = extra.to(dtype) extra_conds[o] = extra model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() return self.model_sampling.calculate_denoised(sigma, model_output, x) def get_dtype(self): return self.diffusion_model.dtype def is_adm(self): return self.adm_channels > 0 def encode_adm(self, **kwargs): return None def extra_conds(self, **kwargs): out = {} if self.inpaint_model: concat_keys = ("mask", "masked_image") cond_concat = [] denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) concat_latent_image = kwargs.get("concat_latent_image", None) if concat_latent_image is None: concat_latent_image = kwargs.get("latent_image", None) else: concat_latent_image = self.process_latent_in(concat_latent_image) noise = kwargs.get("noise", None) device = kwargs["device"] if concat_latent_image.shape[1:] != noise.shape[1:]: concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) if len(denoise_mask.shape) == len(noise.shape): denoise_mask = denoise_mask[:,:1] denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) if denoise_mask.shape[-2:] != noise.shape[-2:]: denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) def blank_inpaint_image_like(latent_image): blank_image = torch.ones_like(latent_image) # these are the values for "zero" in pixel space translated to latent space blank_image[:,0] *= 0.8223 blank_image[:,1] *= -0.6876 blank_image[:,2] *= 0.6364 blank_image[:,3] *= 0.1380 return blank_image for ck in concat_keys: if denoise_mask is not None: if ck == "mask": cond_concat.append(denoise_mask.to(device)) elif ck == "masked_image": cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space else: if ck == "mask": cond_concat.append(torch.ones_like(noise)[:,:1]) elif ck == "masked_image": cond_concat.append(blank_inpaint_image_like(noise)) data = torch.cat(cond_concat, dim=1) out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data) adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = ldm_patched.modules.conds.CONDRegular(adm) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) return out def load_model_weights(self, sd, unet_prefix=""): to_load = {} keys = list(sd.keys()) for k in keys: if k.startswith(unet_prefix): to_load[k[len(unet_prefix):]] = sd.pop(k) to_load = self.model_config.process_unet_state_dict(to_load) m, u = self.diffusion_model.load_state_dict(to_load, strict=False) if len(m) > 0: print("unet missing:", m) if len(u) > 0: print("unet unexpected:", u) del to_load return self def process_latent_in(self, latent): return self.latent_format.process_in(latent) def process_latent_out(self, latent): return self.latent_format.process_out(latent) def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): extra_sds = [] if clip_state_dict is not None: extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) if vae_state_dict is not None: extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) if clip_vision_state_dict is not None: extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) unet_state_dict = self.diffusion_model.state_dict() unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) if self.get_dtype() == torch.float16: extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds) if self.model_type == ModelType.V_PREDICTION: unet_state_dict["v_pred"] = torch.tensor([]) for sd in extra_sds: unet_state_dict.update(sd) return unet_state_dict def set_inpaint(self): self.inpaint_model = True def memory_required(self, input_shape): if ldm_patched.modules.model_management.xformers_enabled() or ldm_patched.modules.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype #TODO: this needs to be tweaked area = input_shape[0] * input_shape[2] * input_shape[3] return (area * ldm_patched.modules.model_management.dtype_size(dtype) / 50) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. area = input_shape[0] * input_shape[2] * input_shape[3] return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): adm_inputs = [] weights = [] noise_aug = [] for unclip_cond in unclip_conditioning: for adm_cond in unclip_cond["clip_vision_output"].image_embeds: weight = unclip_cond["strength"] noise_augment = unclip_cond["noise_augmentation"] noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight weights.append(weight) noise_aug.append(noise_augment) adm_inputs.append(adm_out) if len(noise_aug) > 1: adm_out = torch.stack(adm_inputs).sum(0) noise_augment = noise_augment_merge noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) adm_out = torch.cat((c_adm, noise_level_emb), 1) return adm_out class SD21UNCLIP(BaseModel): def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) def encode_adm(self, **kwargs): unclip_conditioning = kwargs.get("unclip_conditioning", None) device = kwargs["device"] if unclip_conditioning is None: return torch.zeros((1, self.adm_channels)) else: return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) def sdxl_pooled(args, noise_augmentor): if "unclip_conditioning" in args: return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] else: return args["pooled_output"] class SDXLRefiner(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) if kwargs.get("prompt_type", "") == "negative": aesthetic_score = kwargs.get("aesthetic_score", 2.5) else: aesthetic_score = kwargs.get("aesthetic_score", 6) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([aesthetic_score]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SDXL(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) def encode_adm(self, **kwargs): clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 768) height = kwargs.get("height", 768) crop_w = kwargs.get("crop_w", 0) crop_h = kwargs.get("crop_h", 0) target_width = kwargs.get("target_width", width) target_height = kwargs.get("target_height", height) out = [] out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([target_height]))) out.append(self.embedder(torch.Tensor([target_width]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SVD_img2vid(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): super().__init__(model_config, model_type, device=device) self.embedder = Timestep(256) def encode_adm(self, **kwargs): fps_id = kwargs.get("fps", 6) - 1 motion_bucket_id = kwargs.get("motion_bucket_id", 127) augmentation = kwargs.get("augmentation_level", 0) out = [] out.append(self.embedder(torch.Tensor([fps_id]))) out.append(self.embedder(torch.Tensor([motion_bucket_id]))) out.append(self.embedder(torch.Tensor([augmentation]))) flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) return flat def extra_conds(self, **kwargs): out = {} adm = self.encode_adm(**kwargs) if adm is not None: out['y'] = ldm_patched.modules.conds.CONDRegular(adm) latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) device = kwargs["device"] if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) if "time_conditioning" in kwargs: out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"]) out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device)) out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0]) return out class Stable_Zero123(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): super().__init__(model_config, model_type, device=device) self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) self.cc_projection.weight.copy_(cc_projection_weight) self.cc_projection.bias.copy_(cc_projection_bias) def extra_conds(self, **kwargs): out = {} latent_image = kwargs.get("concat_latent_image", None) noise = kwargs.get("noise", None) if latent_image is None: latent_image = torch.zeros_like(noise) if latent_image.shape[1:] != noise.shape[1:]: latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: if cross_attn.shape[-1] != 768: cross_attn = self.cc_projection(cross_attn) out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) return out class SD_X4Upscaler(BaseModel): def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): super().__init__(model_config, model_type, device=device) self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) def extra_conds(self, **kwargs): out = {} image = kwargs.get("concat_image", None) noise = kwargs.get("noise", None) noise_augment = kwargs.get("noise_augmentation", 0.0) device = kwargs["device"] seed = kwargs["seed"] - 10 noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) if image is None: image = torch.zeros_like(noise)[:,:3] if image.shape[1:] != noise.shape[1:]: image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") noise_level = torch.tensor([noise_level], device=device) if noise_augment > 0: image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) image = utils.resize_to_batch_size(image, noise.shape[0]) out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(image) out['y'] = ldm_patched.modules.conds.CONDRegular(noise_level) return out ================================================ FILE: ldm_patched/modules/model_detection.py ================================================ import ldm_patched.modules.supported_models import ldm_patched.modules.supported_models_base def count_blocks(state_dict_keys, prefix_string): count = 0 while True: c = False for k in state_dict_keys: if k.startswith(prefix_string.format(count)): c = True break if c == False: break count += 1 return count def calculate_transformer_depth(prefix, state_dict_keys, state_dict): context_dim = None use_linear_in_transformer = False transformer_prefix = prefix + "1.transformer_blocks." transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) if len(transformer_keys) > 0: last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack return None def detect_unet_config(state_dict, key_prefix, dtype): state_dict_keys = list(state_dict.keys()) unet_config = { "use_checkpoint": False, "image_size": 32, "use_spatial_transformer": True, "legacy": False } y_input = '{}label_emb.0.0.weight'.format(key_prefix) if y_input in state_dict_keys: unet_config["num_classes"] = "sequential" unet_config["adm_in_channels"] = state_dict[y_input].shape[1] else: unet_config["adm_in_channels"] = None unet_config["dtype"] = dtype model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] out_key = '{}out.2.weight'.format(key_prefix) if out_key in state_dict: out_channels = state_dict[out_key].shape[0] else: out_channels = 4 num_res_blocks = [] channel_mult = [] attention_resolutions = [] transformer_depth = [] transformer_depth_output = [] context_dim = None use_linear_in_transformer = False video_model = False current_res = 1 count = 0 last_res_blocks = 0 last_channel_mult = 0 input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') for count in range(input_block_count): prefix = '{}input_blocks.{}.'.format(key_prefix, count) prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) if len(block_keys) == 0: break block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) if "{}0.op.weight".format(prefix) in block_keys: #new layer num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) current_res *= 2 last_res_blocks = 0 last_channel_mult = 0 out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) else: res_block_prefix = "{}0.in_layers.0.weight".format(prefix) if res_block_prefix in block_keys: last_res_blocks += 1 last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) if out is not None: transformer_depth.append(out[0]) if context_dim is None: context_dim = out[1] use_linear_in_transformer = out[2] video_model = out[3] else: transformer_depth.append(0) res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) if res_block_prefix in block_keys_output: out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) if out is not None: transformer_depth_output.append(out[0]) else: transformer_depth_output.append(0) num_res_blocks.append(last_res_blocks) channel_mult.append(last_channel_mult) if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') else: transformer_depth_middle = -1 unet_config["in_channels"] = in_channels unet_config["out_channels"] = out_channels unet_config["model_channels"] = model_channels unet_config["num_res_blocks"] = num_res_blocks unet_config["transformer_depth"] = transformer_depth unet_config["transformer_depth_output"] = transformer_depth_output unet_config["channel_mult"] = channel_mult unet_config["transformer_depth_middle"] = transformer_depth_middle unet_config['use_linear_in_transformer'] = use_linear_in_transformer unet_config["context_dim"] = context_dim if video_model: unet_config["extra_ff_mix_layer"] = True unet_config["use_spatial_context"] = True unet_config["merge_strategy"] = "learned_with_images" unet_config["merge_factor"] = 0.0 unet_config["video_kernel_size"] = [3, 1, 1] unet_config["use_temporal_resblock"] = True unet_config["use_temporal_attention"] = True else: unet_config["use_temporal_resblock"] = False unet_config["use_temporal_attention"] = False return unet_config def model_config_from_unet_config(unet_config): for model_config in ldm_patched.modules.supported_models.models: if model_config.matches(unet_config): return model_config(unet_config) print("no match", unet_config) return None def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False): unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype) model_config = model_config_from_unet_config(unet_config) if model_config is None and use_base_if_no_match: return ldm_patched.modules.supported_models_base.BASE(unet_config) else: return model_config def convert_config(unet_config): new_config = unet_config.copy() num_res_blocks = new_config.get("num_res_blocks", None) channel_mult = new_config.get("channel_mult", None) if isinstance(num_res_blocks, int): num_res_blocks = len(channel_mult) * [num_res_blocks] if "attention_resolutions" in new_config: attention_resolutions = new_config.pop("attention_resolutions") transformer_depth = new_config.get("transformer_depth", None) transformer_depth_middle = new_config.get("transformer_depth_middle", None) if isinstance(transformer_depth, int): transformer_depth = len(channel_mult) * [transformer_depth] if transformer_depth_middle is None: transformer_depth_middle = transformer_depth[-1] t_in = [] t_out = [] s = 1 for i in range(len(num_res_blocks)): res = num_res_blocks[i] d = 0 if s in attention_resolutions: d = transformer_depth[i] t_in += [d] * res t_out += [d] * (res + 1) s *= 2 transformer_depth = t_in transformer_depth_output = t_out new_config["transformer_depth"] = t_in new_config["transformer_depth_output"] = t_out new_config["transformer_depth_middle"] = transformer_depth_middle new_config["num_res_blocks"] = num_res_blocks return new_config def unet_config_from_diffusers_unet(state_dict, dtype): match = {} transformer_depth = [] attn_res = 1 down_blocks = count_blocks(state_dict, "down_blocks.{}") for i in range(down_blocks): attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') for ab in range(attn_blocks): transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') transformer_depth.append(transformer_count) if transformer_count > 0: match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] attn_res *= 2 if attn_blocks == 0: transformer_depth.append(0) transformer_depth.append(0) match["transformer_depth"] = transformer_depth match["model_channels"] = state_dict["conv_in.weight"].shape[0] match["in_channels"] = state_dict["conv_in.weight"].shape[1] match["adm_in_channels"] = None if "class_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] elif "add_embedding.linear_1.weight" in state_dict: match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], 'use_temporal_attention': False, 'use_temporal_resblock': False} SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'use_temporal_attention': False, 'use_temporal_resblock': False} supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega] for unet_config in supported_models: matches = True for k in match: if match[k] != unet_config[k]: matches = False break if matches: return convert_config(unet_config) return None def model_config_from_diffusers_unet(state_dict, dtype): unet_config = unet_config_from_diffusers_unet(state_dict, dtype) if unet_config is not None: return model_config_from_unet_config(unet_config) return None ================================================ FILE: ldm_patched/modules/model_management.py ================================================ import psutil from enum import Enum from ldm_patched.modules.args_parser import args import ldm_patched.modules.utils import torch import sys class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram NO_VRAM = 1 #Very low vram: enable all the options to save vram LOW_VRAM = 2 NORMAL_VRAM = 3 HIGH_VRAM = 4 SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. class CPUState(Enum): GPU = 0 CPU = 1 MPS = 2 # Determine VRAM State vram_state = VRAMState.NORMAL_VRAM set_vram_to = VRAMState.NORMAL_VRAM cpu_state = CPUState.GPU total_vram = 0 lowvram_available = True xpu_available = False if args.pytorch_deterministic: print("Using deterministic algorithms for pytorch") torch.use_deterministic_algorithms(True, warn_only=True) directml_enabled = False if args.directml is not None: import torch_directml directml_enabled = True device_index = args.directml if device_index < 0: directml_device = torch_directml.device() else: directml_device = torch_directml.device(device_index) print("Using directml with device:", torch_directml.device_name(device_index)) # torch_directml.disable_tiled_resources(True) lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. try: import intel_extension_for_pytorch as ipex if torch.xpu.is_available(): xpu_available = True except: pass try: if torch.backends.mps.is_available(): cpu_state = CPUState.MPS import torch.mps except: pass if args.always_cpu: if args.always_cpu > 0: torch.set_num_threads(args.always_cpu) print(f"Running on {torch.get_num_threads()} CPU threads") cpu_state = CPUState.CPU def is_intel_xpu(): global cpu_state global xpu_available if cpu_state == CPUState.GPU: if xpu_available: return True return False def get_torch_device(): global directml_enabled global cpu_state if directml_enabled: global directml_device return directml_device if cpu_state == CPUState.MPS: return torch.device("mps") if cpu_state == CPUState.CPU: return torch.device("cpu") else: if is_intel_xpu(): return torch.device("xpu") else: return torch.device(torch.cuda.current_device()) def get_total_memory(dev=None, torch_total_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_total = psutil.virtual_memory().total mem_total_torch = mem_total else: if directml_enabled: mem_total = 1024 * 1024 * 1024 #TODO mem_total_torch = mem_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] mem_total = torch.xpu.get_device_properties(dev).total_memory mem_total_torch = mem_reserved else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_cuda = torch.cuda.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_cuda if torch_total_too: return (mem_total, mem_total_torch) else: return mem_total total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) if not args.always_normal_vram and not args.always_cpu: if lowvram_available and total_vram <= 4096: print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --always-normal-vram") set_vram_to = VRAMState.LOW_VRAM try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError except: OOM_EXCEPTION = Exception XFORMERS_VERSION = "" XFORMERS_ENABLED_VAE = True if args.disable_xformers: XFORMERS_IS_AVAILABLE = False else: try: import xformers import xformers.ops XFORMERS_IS_AVAILABLE = True try: XFORMERS_IS_AVAILABLE = xformers._has_cpp_library except: pass try: XFORMERS_VERSION = xformers.version.__version__ print("xformers version:", XFORMERS_VERSION) if XFORMERS_VERSION.startswith("0.0.18"): print() print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") print("Please downgrade or upgrade xformers to a different version.") print() XFORMERS_ENABLED_VAE = False except: pass except: XFORMERS_IS_AVAILABLE = False def is_nvidia(): global cpu_state if cpu_state == CPUState.GPU: if torch.version.cuda: return True return False ENABLE_PYTORCH_ATTENTION = False if args.attention_pytorch: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False VAE_DTYPE = torch.float32 try: if is_nvidia(): torch_version = torch.version.__version__ if int(torch_version[0]) >= 2: if ENABLE_PYTORCH_ATTENTION == False and args.attention_split == False and args.attention_quad == False: ENABLE_PYTORCH_ATTENTION = True if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: VAE_DTYPE = torch.bfloat16 if is_intel_xpu(): if args.attention_split == False and args.attention_quad == False: ENABLE_PYTORCH_ATTENTION = True except: pass if is_intel_xpu(): VAE_DTYPE = torch.bfloat16 if args.vae_in_cpu: VAE_DTYPE = torch.float32 if args.vae_in_fp16: VAE_DTYPE = torch.float16 elif args.vae_in_bf16: VAE_DTYPE = torch.bfloat16 elif args.vae_in_fp32: VAE_DTYPE = torch.float32 if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_math_sdp(True) torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) if args.always_low_vram: set_vram_to = VRAMState.LOW_VRAM lowvram_available = True elif args.always_no_vram: set_vram_to = VRAMState.NO_VRAM elif args.always_high_vram or args.always_gpu: vram_state = VRAMState.HIGH_VRAM FORCE_FP32 = False FORCE_FP16 = False if args.all_in_fp32: print("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if args.all_in_fp16: print("Forcing FP16.") FORCE_FP16 = True if lowvram_available: if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): vram_state = set_vram_to if cpu_state != CPUState.GPU: vram_state = VRAMState.DISABLED if cpu_state == CPUState.MPS: vram_state = VRAMState.SHARED print(f"Set vram state to: {vram_state.name}") ALWAYS_VRAM_OFFLOAD = args.always_offload_from_vram if ALWAYS_VRAM_OFFLOAD: print("Always offload VRAM") def get_torch_device_name(device): if hasattr(device, 'type'): if device.type == "cuda": try: allocator_backend = torch.cuda.get_allocator_backend() except: allocator_backend = "" return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) else: return "{}".format(device.type) elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.get_device_name(device)) else: return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) try: print("Device:", get_torch_device_name(get_torch_device())) except: print("Could not pick default device.") print("VAE dtype:", VAE_DTYPE) current_loaded_models = [] def module_size(module): module_mem = 0 sd = module.state_dict() for k in sd: t = sd[k] module_mem += t.nelement() * t.element_size() return module_mem class LoadedModel: def __init__(self, model): self.model = model self.model_accelerated = False self.device = model.load_device def model_memory(self): return self.model.model_size() def model_memory_required(self, device): if device == self.model.current_device: return 0 else: return self.model_memory() def model_load(self, lowvram_model_memory=0): patch_model_to = None if lowvram_model_memory == 0: patch_model_to = self.device self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) try: self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU except Exception as e: self.model.unpatch_model(self.model.offload_device) self.model_unload() raise e if lowvram_model_memory > 0: print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) mem_counter = 0 for m in self.real_model.modules(): if hasattr(m, "ldm_patched_cast_weights"): m.prev_ldm_patched_cast_weights = m.ldm_patched_cast_weights m.ldm_patched_cast_weights = True module_mem = module_size(m) if mem_counter + module_mem < lowvram_model_memory: m.to(self.device) mem_counter += module_mem elif hasattr(m, "weight"): #only modules with ldm_patched_cast_weights can be set to lowvram mode m.to(self.device) mem_counter += module_size(m) print("lowvram: loaded module regularly", m) self.model_accelerated = True if is_intel_xpu() and not args.disable_ipex_hijack: self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) return self.real_model def model_unload(self): if self.model_accelerated: for m in self.real_model.modules(): if hasattr(m, "prev_ldm_patched_cast_weights"): m.ldm_patched_cast_weights = m.prev_ldm_patched_cast_weights del m.prev_ldm_patched_cast_weights self.model_accelerated = False self.model.unpatch_model(self.model.offload_device) self.model.model_patches_to(self.model.offload_device) def __eq__(self, other): return self.model is other.model def minimum_inference_memory(): return (1024 * 1024 * 1024) def unload_model_clones(model): to_unload = [] for i in range(len(current_loaded_models)): if model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload for i in to_unload: print("unload clone", i) current_loaded_models.pop(i).model_unload() def free_memory(memory_required, device, keep_loaded=[]): unloaded_model = False for i in range(len(current_loaded_models) -1, -1, -1): if not ALWAYS_VRAM_OFFLOAD: if get_free_memory(device) > memory_required: break shift_model = current_loaded_models[i] if shift_model.device == device: if shift_model not in keep_loaded: m = current_loaded_models.pop(i) m.model_unload() del m unloaded_model = True if unloaded_model: soft_empty_cache() else: if vram_state != VRAMState.HIGH_VRAM: mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) if mem_free_torch > mem_free_total * 0.25: soft_empty_cache() def load_models_gpu(models, memory_required=0): global vram_state inference_memory = minimum_inference_memory() extra_mem = max(inference_memory, memory_required) models_to_load = [] models_already_loaded = [] for x in models: loaded_model = LoadedModel(x) if loaded_model in current_loaded_models: index = current_loaded_models.index(loaded_model) current_loaded_models.insert(0, current_loaded_models.pop(index)) models_already_loaded.append(loaded_model) else: if hasattr(x, "model"): print(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) if len(models_to_load) == 0: devs = set(map(lambda a: a.device, models_already_loaded)) for d in devs: if d != torch.device("cpu"): free_memory(extra_mem, d, models_already_loaded) return print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") total_memory_required = {} for loaded_model in models_to_load: unload_model_clones(loaded_model.model) total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) for device in total_memory_required: if device != torch.device("cpu"): free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) for loaded_model in models_to_load: model = loaded_model.model torch_dev = model.load_device if is_device_cpu(torch_dev): vram_set_state = VRAMState.DISABLED else: vram_set_state = vram_state lowvram_model_memory = 0 if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): model_size = loaded_model.model_memory_required(torch_dev) current_free_mem = get_free_memory(torch_dev) lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary vram_set_state = VRAMState.LOW_VRAM else: lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 64 * 1024 * 1024 cur_loaded_model = loaded_model.model_load(lowvram_model_memory) current_loaded_models.insert(0, loaded_model) return def load_model_gpu(model): return load_models_gpu([model]) def cleanup_models(): to_delete = [] for i in range(len(current_loaded_models)): if sys.getrefcount(current_loaded_models[i].model) <= 2: to_delete = [i] + to_delete for i in to_delete: x = current_loaded_models.pop(i) x.model_unload() del x def dtype_size(dtype): dtype_size = 4 if dtype == torch.float16 or dtype == torch.bfloat16: dtype_size = 2 elif dtype == torch.float32: dtype_size = 4 else: try: dtype_size = dtype.itemsize except: #Old pytorch doesn't have .itemsize pass return dtype_size def unet_offload_device(): if vram_state == VRAMState.HIGH_VRAM: return get_torch_device() else: return torch.device("cpu") def unet_inital_load_device(parameters, dtype): torch_dev = get_torch_device() if vram_state == VRAMState.HIGH_VRAM: return torch_dev cpu_dev = torch.device("cpu") if ALWAYS_VRAM_OFFLOAD: return cpu_dev model_size = dtype_size(dtype) * parameters mem_dev = get_free_memory(torch_dev) mem_cpu = get_free_memory(cpu_dev) if mem_dev > mem_cpu and model_size < mem_dev: return torch_dev else: return cpu_dev def unet_dtype(device=None, model_params=0): if args.unet_in_bf16: return torch.bfloat16 if args.unet_in_fp16: return torch.float16 if args.unet_in_fp8_e4m3fn: return torch.float8_e4m3fn if args.unet_in_fp8_e5m2: return torch.float8_e5m2 if should_use_fp16(device=device, model_params=model_params): return torch.float16 return torch.float32 # None means no manual cast def unet_manual_cast(weight_dtype, inference_device): if weight_dtype == torch.float32: return None fp16_supported = ldm_patched.modules.model_management.should_use_fp16(inference_device, prioritize_performance=False) if fp16_supported and weight_dtype == torch.float16: return None if fp16_supported: return torch.float16 else: return torch.float32 def text_encoder_offload_device(): if args.always_gpu: return get_torch_device() else: return torch.device("cpu") def text_encoder_device(): if args.always_gpu: return get_torch_device() elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: if is_intel_xpu(): return torch.device("cpu") if should_use_fp16(prioritize_performance=False): return get_torch_device() else: return torch.device("cpu") else: return torch.device("cpu") def text_encoder_dtype(device=None): if args.clip_in_fp8_e4m3fn: return torch.float8_e4m3fn elif args.clip_in_fp8_e5m2: return torch.float8_e5m2 elif args.clip_in_fp16: return torch.float16 elif args.clip_in_fp32: return torch.float32 if is_device_cpu(device): return torch.float16 if should_use_fp16(device, prioritize_performance=False): return torch.float16 else: return torch.float32 def intermediate_device(): if args.always_gpu: return get_torch_device() else: return torch.device("cpu") def vae_device(): if args.vae_in_cpu: return torch.device("cpu") return get_torch_device() def vae_offload_device(): if args.always_gpu: return get_torch_device() else: return torch.device("cpu") def vae_dtype(): global VAE_DTYPE return VAE_DTYPE def get_autocast_device(dev): if hasattr(dev, 'type'): return dev.type return "cuda" def supports_dtype(device, dtype): #TODO if dtype == torch.float32: return True if is_device_cpu(device): return False if dtype == torch.float16: return True if dtype == torch.bfloat16: return True return False def device_supports_non_blocking(device): if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking return True def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: device_supports_cast = True elif tensor.dtype == torch.bfloat16: if hasattr(device, 'type') and device.type.startswith("cuda"): device_supports_cast = True elif is_intel_xpu(): device_supports_cast = True non_blocking = device_supports_non_blocking(device) if device_supports_cast: if copy: if tensor.device == device: return tensor.to(dtype, copy=copy, non_blocking=non_blocking) return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) def xformers_enabled(): global directml_enabled global cpu_state if cpu_state != CPUState.GPU: return False if is_intel_xpu(): return False if directml_enabled: return False return XFORMERS_IS_AVAILABLE def xformers_enabled_vae(): enabled = xformers_enabled() if not enabled: return False return XFORMERS_ENABLED_VAE def pytorch_attention_enabled(): global ENABLE_PYTORCH_ATTENTION return ENABLE_PYTORCH_ATTENTION def pytorch_attention_flash_attention(): global ENABLE_PYTORCH_ATTENTION if ENABLE_PYTORCH_ATTENTION: #TODO: more reliable way of checking for flash attention? if is_nvidia(): #pytorch flash attention only works on Nvidia return True return False def get_free_memory(dev=None, torch_free_too=False): global directml_enabled if dev is None: dev = get_torch_device() if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): mem_free_total = psutil.virtual_memory().available mem_free_torch = mem_free_total else: if directml_enabled: mem_free_total = 1024 * 1024 * 1024 #TODO mem_free_torch = mem_free_total elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_allocated = stats['allocated_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_torch = mem_reserved - mem_active mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated else: stats = torch.cuda.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] mem_free_cuda, _ = torch.cuda.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_cuda + mem_free_torch if torch_free_too: return (mem_free_total, mem_free_torch) else: return mem_free_total def cpu_mode(): global cpu_state return cpu_state == CPUState.CPU def mps_mode(): global cpu_state return cpu_state == CPUState.MPS def is_device_cpu(device): if hasattr(device, 'type'): if (device.type == 'cpu'): return True return False def is_device_mps(device): if hasattr(device, 'type'): if (device.type == 'mps'): return True return False def should_use_fp16(device=None, model_params=0, prioritize_performance=True): global directml_enabled if device is not None: if is_device_cpu(device): return False if FORCE_FP16: return True if device is not None: #TODO if is_device_mps(device): return False if FORCE_FP32: return False if directml_enabled: return False if cpu_mode() or mps_mode(): return False #TODO ? if is_intel_xpu(): return True if torch.cuda.is_bf16_supported(): return True props = torch.cuda.get_device_properties("cuda") if props.major < 6: return False fp16_works = False #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled #when the model doesn't actually fit on the card #TODO: actually test if GP106 and others have the same type of behavior nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"] for x in nvidia_10_series: if x in props.name.lower(): fp16_works = True if fp16_works: free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True if props.major < 7: return False #FP16 is just broken on these cards nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] for x in nvidia_16_series: if x in props.name: return False return True def soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): torch.xpu.empty_cache() elif torch.cuda.is_available(): if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda torch.cuda.empty_cache() torch.cuda.ipc_collect() def unload_all_models(): free_memory(1e30, get_torch_device()) def resolve_lowvram_weight(weight, model, key): #TODO: remove return weight #TODO: might be cleaner to put this somewhere else import threading class InterruptProcessingException(Exception): pass interrupt_processing_mutex = threading.RLock() interrupt_processing = False def interrupt_current_processing(value=True): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: interrupt_processing = value def processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: return interrupt_processing def throw_exception_if_processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: if interrupt_processing: interrupt_processing = False raise InterruptProcessingException() ================================================ FILE: ldm_patched/modules/model_patcher.py ================================================ import torch import copy import inspect import ldm_patched.modules.utils import ldm_patched.modules.model_management class ModelPatcher: def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): self.size = size self.model = model self.patches = {} self.backup = {} self.object_patches = {} self.object_patches_backup = {} self.model_options = {"transformer_options":{}} self.model_size() self.load_device = load_device self.offload_device = offload_device if current_device is None: self.current_device = self.offload_device else: self.current_device = current_device self.weight_inplace_update = weight_inplace_update def model_size(self): if self.size > 0: return self.size model_sd = self.model.state_dict() self.size = ldm_patched.modules.model_management.module_size(self.model) self.model_keys = set(model_sd.keys()) return self.size def clone(self): n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) n.patches = {} for k in self.patches: n.patches[k] = self.patches[k][:] n.object_patches = self.object_patches.copy() n.model_options = copy.deepcopy(self.model_options) n.model_keys = self.model_keys return n def is_clone(self, other): if hasattr(other, 'model') and self.model is other.model: return True return False def memory_required(self, input_shape): return self.model.memory_required(input_shape=input_shape) def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): if len(inspect.signature(sampler_cfg_function).parameters) == 3: self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way else: self.model_options["sampler_cfg_function"] = sampler_cfg_function if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] if disable_cfg1_optimization: self.model_options["disable_cfg1_optimization"] = True def set_model_unet_function_wrapper(self, unet_wrapper_function): self.model_options["model_function_wrapper"] = unet_wrapper_function def set_model_patch(self, patch, name): to = self.model_options["transformer_options"] if "patches" not in to: to["patches"] = {} to["patches"][name] = to["patches"].get(name, []) + [patch] def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): to = self.model_options["transformer_options"] if "patches_replace" not in to: to["patches_replace"] = {} if name not in to["patches_replace"]: to["patches_replace"][name] = {} if transformer_index is not None: block = (block_name, number, transformer_index) else: block = (block_name, number) to["patches_replace"][name][block] = patch def set_model_attn1_patch(self, patch): self.set_model_patch(patch, "attn1_patch") def set_model_attn2_patch(self, patch): self.set_model_patch(patch, "attn2_patch") def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) def set_model_attn1_output_patch(self, patch): self.set_model_patch(patch, "attn1_output_patch") def set_model_attn2_output_patch(self, patch): self.set_model_patch(patch, "attn2_output_patch") def set_model_input_block_patch(self, patch): self.set_model_patch(patch, "input_block_patch") def set_model_input_block_patch_after_skip(self, patch): self.set_model_patch(patch, "input_block_patch_after_skip") def set_model_output_block_patch(self, patch): self.set_model_patch(patch, "output_block_patch") def add_object_patch(self, name, obj): self.object_patches[name] = obj def model_patches_to(self, device): to = self.model_options["transformer_options"] if "patches" in to: patches = to["patches"] for name in patches: patch_list = patches[name] for i in range(len(patch_list)): if hasattr(patch_list[i], "to"): patch_list[i] = patch_list[i].to(device) if "patches_replace" in to: patches = to["patches_replace"] for name in patches: patch_list = patches[name] for k in patch_list: if hasattr(patch_list[k], "to"): patch_list[k] = patch_list[k].to(device) if "model_function_wrapper" in self.model_options: wrap_func = self.model_options["model_function_wrapper"] if hasattr(wrap_func, "to"): self.model_options["model_function_wrapper"] = wrap_func.to(device) def model_dtype(self): if hasattr(self.model, "get_dtype"): return self.model.get_dtype() def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): p = set() for k in patches: if k in self.model_keys: p.add(k) current_patches = self.patches.get(k, []) current_patches.append((strength_patch, patches[k], strength_model)) self.patches[k] = current_patches return list(p) def get_key_patches(self, filter_prefix=None): ldm_patched.modules.model_management.unload_model_clones(self) model_sd = self.model_state_dict() p = {} for k in model_sd: if filter_prefix is not None: if not k.startswith(filter_prefix): continue if k in self.patches: p[k] = [model_sd[k]] + self.patches[k] else: p[k] = (model_sd[k],) return p def model_state_dict(self, filter_prefix=None): sd = self.model.state_dict() keys = list(sd.keys()) if filter_prefix is not None: for k in keys: if not k.startswith(filter_prefix): sd.pop(k) return sd def patch_model(self, device_to=None, patch_weights=True): for k in self.object_patches: old = getattr(self.model, k) if k not in self.object_patches_backup: self.object_patches_backup[k] = old setattr(self.model, k, self.object_patches[k]) if patch_weights: model_sd = self.model_state_dict() for key in self.patches: if key not in model_sd: print("could not patch. key doesn't exist in model:", key) continue weight = model_sd[key] inplace_update = self.weight_inplace_update if key not in self.backup: self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) if device_to is not None: temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) else: temp_weight = weight.to(torch.float32, copy=True) out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) if inplace_update: ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight) else: ldm_patched.modules.utils.set_attr(self.model, key, out_weight) del temp_weight if device_to is not None: self.model.to(device_to) self.current_device = device_to return self.model def calculate_weight(self, patches, weight, key): for p in patches: alpha = p[0] v = p[1] strength_model = p[2] if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (self.calculate_weight(v[1:], v[0].clone(), key), ) if len(v) == 1: patch_type = "diff" elif len(v) == 2: patch_type = v[0] v = v[1] if patch_type == "diff": w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lora": #lora/locon mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: #locon mid weights, hopefully the math is fine because I didn't properly test it mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha *= v[2] / dim try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: alpha *= v[2] / w1b.shape[0] w2a = v[3] w2b = v[4] if v[5] is not None: #cp decomposition t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "glora": if v[4] is not None: alpha *= v[4] / v[0].shape[0] a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) else: print("patch type not recognized", patch_type, key) return weight def unpatch_model(self, device_to=None): keys = list(self.backup.keys()) if self.weight_inplace_update: for k in keys: ldm_patched.modules.utils.copy_to_param(self.model, k, self.backup[k]) else: for k in keys: ldm_patched.modules.utils.set_attr(self.model, k, self.backup[k]) self.backup = {} if device_to is not None: self.model.to(device_to) self.current_device = device_to keys = list(self.object_patches_backup.keys()) for k in keys: setattr(self.model, k, self.object_patches_backup[k]) self.object_patches_backup = {} ================================================ FILE: ldm_patched/modules/model_sampling.py ================================================ import torch from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule import math import numpy as np class EPS: def calculate_input(self, sigma, noise): sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): if max_denoise: noise = noise * torch.sqrt(1.0 + sigma ** 2.0) else: noise = noise * sigma noise += latent_image return noise def inverse_noise_scaling(self, sigma, latent): return latent class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class EDM(V_PREDICTION): def calculate_denoised(self, sigma, model_output, model_input): sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} beta_schedule = sampling_settings.get("beta_schedule", "linear") linear_start = sampling_settings.get("linear_start", 0.00085) linear_end = sampling_settings.get("linear_end", 0.012) self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3) self.sigma_data = 1.0 def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if given_betas is not None: betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = torch.cumprod(alphas, dim=0) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32) self.set_sigmas(sigmas) self.set_alphas_cumprod(alphas_cumprod.float()) def set_sigmas(self, sigmas): self.register_buffer('sigmas', sigmas.float()) self.register_buffer('log_sigmas', sigmas.log().float()) def set_alphas_cumprod(self, alphas_cumprod): self.register_buffer("alphas_cumprod", alphas_cumprod.float()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): log_sigma = sigma.log() dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) def sigma(self, timestep): t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) low_idx = t.floor().long() high_idx = t.ceil().long() w = t.frac() log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] return log_sigma.exp().to(timestep.device) def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent * 999.0)).item() class ModelSamplingContinuousEDM(torch.nn.Module): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} sigma_min = sampling_settings.get("sigma_min", 0.002) sigma_max = sampling_settings.get("sigma_max", 120.0) sigma_data = sampling_settings.get("sigma_data", 1.0) self.set_parameters(sigma_min, sigma_max, sigma_data) def set_parameters(self, sigma_min, sigma_max, sigma_data): self.sigma_data = sigma_data sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers self.register_buffer('log_sigmas', sigmas.log()) @property def sigma_min(self): return self.sigmas[0] @property def sigma_max(self): return self.sigmas[-1] def timestep(self, sigma): return 0.25 * sigma.log() def sigma(self, timestep): return (timestep / 0.25).exp() def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent log_sigma_min = math.log(self.sigma_min) return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) class StableCascadeSampling(ModelSamplingDiscrete): def __init__(self, model_config=None): super().__init__() if model_config is not None: sampling_settings = model_config.sampling_settings else: sampling_settings = {} self.set_parameters(sampling_settings.get("shift", 1.0)) def set_parameters(self, shift=1.0, cosine_s=8e-3): self.shift = shift self.cosine_s = torch.tensor(cosine_s) self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 #This part is just for compatibility with some schedulers in the codebase self.num_timesteps = 10000 sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) for x in range(self.num_timesteps): t = (x + 1) / self.num_timesteps sigmas[x] = self.sigma(t) self.set_sigmas(sigmas) def sigma(self, timestep): alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) if self.shift != 1.0: var = alpha_cumprod logSNR = (var/(1-var)).log() logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) alpha_cumprod = logSNR.sigmoid() alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 def timestep(self, sigma): var = 1 / ((sigma * sigma) + 1) var = var.clamp(0, 1.0) s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s return t def percent_to_sigma(self, percent): if percent <= 0.0: return 999999999.9 if percent >= 1.0: return 0.0 percent = 1.0 - percent return self.sigma(torch.tensor(percent)) ================================================ FILE: ldm_patched/modules/ops.py ================================================ import torch import ldm_patched.modules.model_management def cast_bias_weight(s, input): bias = None non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device) if s.bias is not None: bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) return weight, bias class disable_weight_init: class Linear(torch.nn.Linear): ldm_patched_cast_weights = False def reset_parameters(self): return None def forward_ldm_patched_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return torch.nn.functional.linear(input, weight, bias) def forward(self, *args, **kwargs): if self.ldm_patched_cast_weights: return self.forward_ldm_patched_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv2d(torch.nn.Conv2d): ldm_patched_cast_weights = False def reset_parameters(self): return None def forward_ldm_patched_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): if self.ldm_patched_cast_weights: return self.forward_ldm_patched_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class Conv3d(torch.nn.Conv3d): ldm_patched_cast_weights = False def reset_parameters(self): return None def forward_ldm_patched_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): if self.ldm_patched_cast_weights: return self.forward_ldm_patched_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class GroupNorm(torch.nn.GroupNorm): ldm_patched_cast_weights = False def reset_parameters(self): return None def forward_ldm_patched_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) def forward(self, *args, **kwargs): if self.ldm_patched_cast_weights: return self.forward_ldm_patched_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) class LayerNorm(torch.nn.LayerNorm): ldm_patched_cast_weights = False def reset_parameters(self): return None def forward_ldm_patched_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) def forward(self, *args, **kwargs): if self.ldm_patched_cast_weights: return self.forward_ldm_patched_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @classmethod def conv_nd(s, dims, *args, **kwargs): if dims == 2: return s.Conv2d(*args, **kwargs) elif dims == 3: return s.Conv3d(*args, **kwargs) else: raise ValueError(f"unsupported dimensions: {dims}") class manual_cast(disable_weight_init): class Linear(disable_weight_init.Linear): ldm_patched_cast_weights = True class Conv2d(disable_weight_init.Conv2d): ldm_patched_cast_weights = True class Conv3d(disable_weight_init.Conv3d): ldm_patched_cast_weights = True class GroupNorm(disable_weight_init.GroupNorm): ldm_patched_cast_weights = True class LayerNorm(disable_weight_init.LayerNorm): ldm_patched_cast_weights = True ================================================ FILE: ldm_patched/modules/options.py ================================================ args_parsing = False def enable_args_parsing(enable=True): global args_parsing args_parsing = enable ================================================ FILE: ldm_patched/modules/sample.py ================================================ import torch import ldm_patched.modules.model_management import ldm_patched.modules.samplers import ldm_patched.modules.conds import ldm_patched.modules.utils import math import numpy as np def prepare_noise(latent_image, seed, noise_inds=None): """ creates random noise given a latent image and a seed. optional arg skip can be used to skip and discard x number of noise generations for a given seed """ generator = torch.manual_seed(seed) if noise_inds is None: return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1]+1): noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] noises = torch.cat(noises, axis=0) return noises def prepare_mask(noise_mask, shape, device): """ensures noise mask is of proper dimensions""" noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") noise_mask = torch.cat([noise_mask] * shape[1], dim=1) noise_mask = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0]) noise_mask = noise_mask.to(device) return noise_mask def get_models_from_cond(cond, model_type): models = [] for c in cond: if model_type in c: models += [c[model_type]] return models def convert_cond(cond): out = [] for c in cond: temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove temp["cross_attn"] = c[0] temp["model_conds"] = model_conds out.append(temp) return out def get_additional_models(positive, negative, dtype): """loads additional models in positive and negative conditioning""" control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")) inference_memory = 0 control_models = [] for m in control_nets: control_models += m.get_models() inference_memory += m.inference_memory_requirements(dtype) gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen") gligen = [x[1] for x in gligen] models = control_models + gligen return models, inference_memory def cleanup_additional_models(models): """cleanup additional models that were loaded""" for m in models: if hasattr(m, 'cleanup'): m.cleanup() def prepare_sampling(model, noise_shape, positive, negative, noise_mask): device = model.load_device positive = convert_cond(positive) negative = convert_cond(negative) if noise_mask is not None: noise_mask = prepare_mask(noise_mask, noise_shape, device) real_model = None models, inference_memory = get_additional_models(positive, negative, model.model_dtype()) ldm_patched.modules.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) real_model = model.model return real_model, positive, negative, noise_mask, models def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) sampler = ldm_patched.modules.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) sigmas = sigmas.to(model.load_device) samples = ldm_patched.modules.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples ================================================ FILE: ldm_patched/modules/samplers.py ================================================ from ldm_patched.k_diffusion import sampling as k_diffusion_sampling from ldm_patched.unipc import uni_pc import torch import collections from ldm_patched.modules import model_management import math def get_area_and_mult(conds, x_in, timestep_in): area = (x_in.shape[2], x_in.shape[3], 0, 0) strength = 1.0 if 'timestep_start' in conds: timestep_start = conds['timestep_start'] if timestep_in[0] > timestep_start: return None if 'timestep_end' in conds: timestep_end = conds['timestep_end'] if timestep_in[0] < timestep_end: return None if 'area' in conds: area = conds['area'] if 'strength' in conds: strength = conds['strength'] input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] if 'mask' in conds: # Scale the mask to the size of the input # The mask should have been resized as we began the sampling process mask_strength = 1.0 if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] assert(mask.shape[1] == x_in.shape[2]) assert(mask.shape[2] == x_in.shape[3]) mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) else: mask = torch.ones_like(input_x) mult = mask * strength if 'mask' not in conds: rr = 8 if area[2] != 0: for t in range(rr): mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) if (area[0] + area[2]) < x_in.shape[2]: for t in range(rr): mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) if area[3] != 0: for t in range(rr): mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) if (area[1] + area[3]) < x_in.shape[3]: for t in range(rr): mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] for c in model_conds: conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) control = conds.get('control', None) patches = None if 'gligen' in conds: gligen = conds['gligen'] patches = {} gligen_type = gligen[0] gligen_model = gligen[1] if gligen_type == "position": gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) else: gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) patches['middle_patch'] = [gligen_patch] cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches']) return cond_obj(input_x, mult, conditioning, area, control, patches) def cond_equal_size(c1, c2): if c1 is c2: return True if c1.keys() != c2.keys(): return False for k in c1: if not c1[k].can_concat(c2[k]): return False return True def can_concat_cond(c1, c2): if c1.input_x.shape != c2.input_x.shape: return False def objects_concatable(obj1, obj2): if (obj1 is None) != (obj2 is None): return False if obj1 is not None: if obj1 is not obj2: return False return True if not objects_concatable(c1.control, c2.control): return False if not objects_concatable(c1.patches, c2.patches): return False return cond_equal_size(c1.conditioning, c2.conditioning) def cond_cat(c_list): c_crossattn = [] c_concat = [] c_adm = [] crossattn_max_len = 0 temp = {} for x in c_list: for k in x: cur = temp.get(k, []) cur.append(x[k]) temp[k] = cur out = {} for k in temp: conds = temp[k] out[k] = conds[0].concat(conds[1:]) return out def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): out_cond = torch.zeros_like(x_in) out_count = torch.ones_like(x_in) * 1e-37 out_uncond = torch.zeros_like(x_in) out_uncond_count = torch.ones_like(x_in) * 1e-37 COND = 0 UNCOND = 1 to_run = [] for x in cond: p = get_area_and_mult(x, x_in, timestep) if p is None: continue to_run += [(p, COND)] if uncond is not None: for x in uncond: p = get_area_and_mult(x, x_in, timestep) if p is None: continue to_run += [(p, UNCOND)] while len(to_run) > 0: first = to_run[0] first_shape = first[0][0].shape to_batch_temp = [] for x in range(len(to_run)): if can_concat_cond(to_run[x][0], first[0]): to_batch_temp += [x] to_batch_temp.reverse() to_batch = to_batch_temp[:1] free_memory = model_management.get_free_memory(x_in.device) for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp)//i] input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] if model.memory_required(input_shape) < free_memory: to_batch = batch_amount break input_x = [] mult = [] c = [] cond_or_uncond = [] area = [] control = None patches = None for x in to_batch: o = to_run.pop(x) p = o[0] input_x.append(p.input_x) mult.append(p.mult) c.append(p.conditioning) area.append(p.area) cond_or_uncond.append(o[1]) control = p.control patches = p.patches batch_chunks = len(cond_or_uncond) input_x = torch.cat(input_x) c = cond_cat(c) timestep_ = torch.cat([timestep] * batch_chunks) if control is not None: c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) transformer_options = {} if 'transformer_options' in model_options: transformer_options = model_options['transformer_options'].copy() if patches is not None: if "patches" in transformer_options: cur_patches = transformer_options["patches"].copy() for p in patches: if p in cur_patches: cur_patches[p] = cur_patches[p] + patches[p] else: cur_patches[p] = patches[p] else: transformer_options["patches"] = patches transformer_options["cond_or_uncond"] = cond_or_uncond[:] transformer_options["sigmas"] = timestep c['transformer_options'] = transformer_options if 'model_function_wrapper' in model_options: output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) else: output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) del input_x for o in range(batch_chunks): if cond_or_uncond[o] == COND: out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] else: out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] del mult out_cond /= out_count del out_count out_uncond /= out_uncond_count del out_uncond_count return out_cond, out_uncond #The main sampling function shared by all the samplers #Returns denoised def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: uncond_ = None else: uncond_ = uncond cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options) if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} cfg_result = x - model_options["sampler_cfg_function"](args) else: cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale for fn in model_options.get("sampler_post_cfg_function", []): args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, "sigma": timestep, "model_options": model_options, "input": x} cfg_result = fn(args) return cfg_result class CFGNoisePredictor(torch.nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None): out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed) return out def forward(self, *args, **kwargs): return self.apply_model(*args, **kwargs) class KSamplerX0Inpaint(torch.nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if denoise_mask is not None: latent_mask = 1. - denoise_mask x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask return out def simple_scheduler(model, steps): s = model.model_sampling sigs = [] ss = len(s.sigmas) / steps for x in range(steps): sigs += [float(s.sigmas[-(1 + int(x * ss))])] sigs += [0.0] return torch.FloatTensor(sigs) def ddim_scheduler(model, steps): s = model.model_sampling sigs = [] ss = len(s.sigmas) // steps x = 1 while x < len(s.sigmas): sigs += [float(s.sigmas[x])] x += ss sigs = sigs[::-1] sigs += [0.0] return torch.FloatTensor(sigs) def normal_scheduler(model, steps, sgm=False, floor=False): s = model.model_sampling start = s.timestep(s.sigma_max) end = s.timestep(s.sigma_min) if sgm: timesteps = torch.linspace(start, end, steps + 1)[:-1] else: timesteps = torch.linspace(start, end, steps) sigs = [] for x in range(len(timesteps)): ts = timesteps[x] sigs.append(s.sigma(ts)) sigs += [0.0] return torch.FloatTensor(sigs) def get_mask_aabb(masks): if masks.numel() == 0: return torch.zeros((0, 4), device=masks.device, dtype=torch.int) b = masks.shape[0] bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) for i in range(b): mask = masks[i] if mask.numel() == 0: continue if torch.max(mask != 0) == False: is_empty[i] = True continue y, x = torch.where(mask) bounding_boxes[i, 0] = torch.min(x) bounding_boxes[i, 1] = torch.min(y) bounding_boxes[i, 2] = torch.max(x) bounding_boxes[i, 3] = torch.max(y) return bounding_boxes, is_empty def resolve_areas_and_cond_masks(conditions, h, w, device): # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. # While we're doing this, we can also resolve the mask device and scaling for performance reasons for i in range(len(conditions)): c = conditions[i] if 'area' in c: area = c['area'] if area[0] == "percentage": modified = c.copy() area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) modified['area'] = area c = modified conditions[i] = c if 'mask' in c: mask = c['mask'] mask = mask.to(device=device) modified = c.copy() if len(mask.shape) == 2: mask = mask.unsqueeze(0) if mask.shape[1] != h or mask.shape[2] != w: mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1) if modified.get("set_area_to_bounds", False): bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) boxes, is_empty = get_mask_aabb(bounds) if is_empty[0]: # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) modified['area'] = (8, 8, 0, 0) else: box = boxes[0] H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) H = max(8, H) W = max(8, W) area = (int(H), int(W), int(Y), int(X)) modified['area'] = area modified['mask'] = mask conditions[i] = modified def create_cond_with_same_area_if_none(conds, c): if 'area' not in c: return c_area = c['area'] smallest = None for x in conds: if 'area' in x: a = x['area'] if c_area[2] >= a[2] and c_area[3] >= a[3]: if a[0] + a[2] >= c_area[0] + c_area[2]: if a[1] + a[3] >= c_area[1] + c_area[3]: if smallest is None: smallest = x elif 'area' not in smallest: smallest = x else: if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: smallest = x else: if smallest is None: smallest = x if smallest is None: return if 'area' in smallest: if smallest['area'] == c_area: return out = c.copy() out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? conds += [out] def calculate_start_end_timesteps(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None if 'start_percent' in x: timestep_start = s.percent_to_sigma(x['start_percent']) if 'end_percent' in x: timestep_end = s.percent_to_sigma(x['end_percent']) if (timestep_start is not None) or (timestep_end is not None): n = x.copy() if (timestep_start is not None): n['timestep_start'] = timestep_start if (timestep_end is not None): n['timestep_end'] = timestep_end conds[t] = n def pre_run_control(model, conds): s = model.model_sampling for t in range(len(conds)): x = conds[t] timestep_start = None timestep_end = None percent_to_timestep_function = lambda a: s.percent_to_sigma(a) if 'control' in x: x['control'].pre_run(model, percent_to_timestep_function) def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): cond_cnets = [] cond_other = [] uncond_cnets = [] uncond_other = [] for t in range(len(conds)): x = conds[t] if 'area' not in x: if name in x and x[name] is not None: cond_cnets.append(x[name]) else: cond_other.append((x, t)) for t in range(len(uncond)): x = uncond[t] if 'area' not in x: if name in x and x[name] is not None: uncond_cnets.append(x[name]) else: uncond_other.append((x, t)) if len(uncond_cnets) > 0: return for x in range(len(cond_cnets)): temp = uncond_other[x % len(uncond_other)] o = temp[0] if name in o and o[name] is not None: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond += [n] else: n = o.copy() n[name] = uncond_fill_func(cond_cnets, x) uncond[temp[1]] = n def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): for t in range(len(conds)): x = conds[t] params = x.copy() params["device"] = device params["noise"] = noise params["width"] = params.get("width", noise.shape[3] * 8) params["height"] = params.get("height", noise.shape[2] * 8) params["prompt_type"] = params.get("prompt_type", prompt_type) for k in kwargs: if k not in params: params[k] = kwargs[k] out = model_function(**params) x = x.copy() model_conds = x['model_conds'].copy() for k in out: model_conds[k] = out[k] x['model_conds'] = model_conds conds[t] = x return conds class Sampler: def sample(self): pass def max_denoise(self, model_wrap, sigmas): max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) sigma = float(sigmas[0]) return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma class UNIPC(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar) class UNIPCBH2(Sampler): def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar) KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "tcd", "edm_playground_v2.5", "restart"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): self.sampler_function = sampler_function self.extra_options = extra_options self.inpaint_options = inpaint_options def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): extra_args["denoise_mask"] = denoise_mask model_k = KSamplerX0Inpaint(model_wrap) model_k.latent_image = latent_image if self.inpaint_options.get("random", False): #TODO: Should this be the default? generator = torch.manual_seed(extra_args.get("seed", 41) + 1) model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) else: model_k.noise = noise if self.max_denoise(model_wrap, sigmas): noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0) else: noise = noise * sigmas[0] k_callback = None total_steps = len(sigmas) - 1 if callback is not None: k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) if latent_image is not None: noise += latent_image samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) return samples def ksampler(sampler_name, extra_options={}, inpaint_options={}): if sampler_name == "dpm_fast": def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] total_steps = len(sigmas) - 1 return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_fast_function elif sampler_name == "dpm_adaptive": def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable): sigma_min = sigmas[-1] if sigma_min == 0: sigma_min = sigmas[-2] return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable) sampler_function = dpm_adaptive_function else: sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) return KSAMPLER(sampler_function, extra_options, inpaint_options) def wrap_model(model): model_denoise = CFGNoisePredictor(model) return model_denoise def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): positive = positive[:] negative = negative[:] resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) model_wrap = wrap_model(model) calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) if latent_image is not None: latent_image = model.process_latent_in(latent_image) if hasattr(model, 'extra_conds'): positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) #make sure each cond area has an opposite one with the same area for c in positive: create_cond_with_same_area_if_none(negative, c) for c in negative: create_cond_with_same_area_if_none(positive, c) pre_run_control(model, negative + positive) apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return model.process_latent_out(samples.to(torch.float32)) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] def calculate_sigmas_scheduler(model, scheduler_name, steps): if scheduler_name == "karras": sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "exponential": sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "normal": sigmas = normal_scheduler(model, steps) elif scheduler_name == "simple": sigmas = simple_scheduler(model, steps) elif scheduler_name == "ddim_uniform": sigmas = ddim_scheduler(model, steps) elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model, steps, sgm=True) else: print("error invalid scheduler", scheduler_name) return sigmas def sampler_object(name): if name == "uni_pc": sampler = UNIPC() elif name == "uni_pc_bh2": sampler = UNIPCBH2() elif name == "ddim": sampler = ksampler("euler", inpaint_options={"random": True}) else: sampler = ksampler(name) return sampler class KSampler: SCHEDULERS = SCHEDULER_NAMES SAMPLERS = SAMPLER_NAMES def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): self.model = model self.device = device if scheduler not in self.SCHEDULERS: scheduler = self.SCHEDULERS[0] if sampler not in self.SAMPLERS: sampler = self.SAMPLERS[0] self.scheduler = scheduler self.sampler = sampler self.set_steps(steps, denoise) self.denoise = denoise self.model_options = model_options def calculate_sigmas(self, steps): sigmas = None discard_penultimate_sigma = False if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']: steps += 1 discard_penultimate_sigma = True sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas def set_steps(self, steps, denoise=None): self.steps = steps if denoise is None or denoise > 0.9999: self.sigmas = self.calculate_sigmas(steps).to(self.device) else: new_steps = int(steps/denoise) sigmas = self.calculate_sigmas(new_steps).to(self.device) self.sigmas = sigmas[-(steps + 1):] def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): if sigmas is None: sigmas = self.sigmas if last_step is not None and last_step < (len(sigmas) - 1): sigmas = sigmas[:last_step + 1] if force_full_denoise: sigmas[-1] = 0 if start_step is not None: if start_step < (len(sigmas) - 1): sigmas = sigmas[start_step:] else: if latent_image is not None: return latent_image else: return torch.zeros_like(noise) sampler = sampler_object(self.sampler) return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) ================================================ FILE: ldm_patched/modules/sd.py ================================================ import torch from ldm_patched.modules import model_management from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine import yaml import ldm_patched.modules.utils from . import clip_vision from . import gligen from . import diffusers_convert from . import model_base from . import model_detection from . import sd1_clip from . import sd2_clip from . import sdxl_clip import ldm_patched.modules.model_patcher import ldm_patched.modules.lora import ldm_patched.t2ia.adapter import ldm_patched.modules.supported_models_base import ldm_patched.taesd.taesd def load_model_weights(model, sd): m, u = model.load_state_dict(sd, strict=False) m = set(m) unexpected_keys = set(u) k = list(sd.keys()) for x in k: if x not in unexpected_keys: w = sd.pop(x) del w if len(m) > 0: print("extra", m) return model def load_clip_weights(model, sd): k = list(sd.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") sd[y] = sd.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd: ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() sd = ldm_patched.modules.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) return load_model_weights(model, sd) def load_lora_for_models(model, clip, lora, strength_model, strength_clip): key_map = {} if model is not None: key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) loaded = ldm_patched.modules.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) else: k = () new_modelpatcher = None if clip is not None: new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) else: k1 = () new_clip = None k = set(k) k1 = set(k1) for x in loaded: if (x not in k) and (x not in k1): print("NOT LOADED", x) return (new_modelpatcher, new_clip) class CLIP: def __init__(self, target=None, embedding_directory=None, no_init=False): if no_init: return params = target.params.copy() clip = target.clip tokenizer = target.tokenizer load_device = model_management.text_encoder_device() offload_device = model_management.text_encoder_offload_device() params['device'] = offload_device params['dtype'] = model_management.text_encoder_dtype(load_device) self.cond_stage_model = clip(**(params)) self.tokenizer = tokenizer(embedding_directory=embedding_directory) self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) self.layer_idx = None def clone(self): n = CLIP(no_init=True) n.patcher = self.patcher.clone() n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx return n def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) def clip_layer(self, layer_idx): self.layer_idx = layer_idx def tokenize(self, text, return_word_ids=False): return self.tokenizer.tokenize_with_weights(text, return_word_ids) def encode_from_tokens(self, tokens, return_pooled=False): if self.layer_idx is not None: self.cond_stage_model.clip_layer(self.layer_idx) else: self.cond_stage_model.reset_clip_layer() self.load_model() cond, pooled = self.cond_stage_model.encode_token_weights(tokens) if return_pooled: return cond, pooled return cond def encode(self, text): tokens = self.tokenize(text) return self.encode_from_tokens(tokens) def load_sd(self, sd): return self.cond_stage_model.load_sd(sd) def get_sd(self): return self.cond_stage_model.state_dict() def load_model(self): model_management.load_model_gpu(self.patcher) return self.patcher def get_key_patches(self): return self.patcher.get_key_patches() class VAE: def __init__(self, sd=None, device=None, config=None, dtype=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) self.downscale_ratio = 8 self.latent_channels = 4 if config is None: if "decoder.mid.block_1.mix_factor" in sd: encoder_config = {'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} decoder_config = encoder_config.copy() decoder_config["video_kernel_size"] = [3, 1, 1] decoder_config["alpha"] = 0.0 self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: self.first_stage_model = ldm_patched.taesd.taesd.TAESD() else: #default SD1.x/SD2.x VAE parameters 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} if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE ddconfig['ch_mult'] = [1, 2, 4] self.downscale_ratio = 4 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() m, u = self.first_stage_model.load_state_dict(sd, strict=False) if len(m) > 0: print("Missing VAE keys", m) if len(u) > 0: print("Leftover VAE keys", u) if device is None: device = model_management.vae_device() self.device = device offload_device = model_management.vae_offload_device() if dtype is None: dtype = model_management.vae_dtype() self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) self.output_device = model_management.intermediate_device() self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = ldm_patched.modules.utils.ProgressBar(steps) decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() output = torch.clamp(( (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar)) / 3.0) / 2.0, min=0.0, max=1.0) return output def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): steps = pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = ldm_patched.modules.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) samples /= 3.0 return samples def decode(self, samples_in): try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") pixel_samples = self.decode_tiled_(samples_in) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): model_management.load_model_gpu(self.patcher) output = self.decode_tiled_(samples, tile_x, tile_y, overlap) return output.movedim(1,-1) def encode(self, pixel_samples): pixel_samples = pixel_samples.movedim(-1,1) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device) for x in range(0, pixel_samples.shape[0], batch_number): pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() except model_management.OOM_EXCEPTION as e: print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") samples = self.encode_tiled_(pixel_samples) return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): model_management.load_model_gpu(self.patcher) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) return samples def get_sd(self): return self.first_stage_model.state_dict() class StyleModel: def __init__(self, model, device="cpu"): self.model = model def get_cond(self, input): return self.model(input.last_hidden_state) def load_style_model(ckpt_path): model_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: model = ldm_patched.t2ia.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) else: raise Exception("invalid style model {}".format(ckpt_path)) model.load_state_dict(model_data) return StyleModel(model) def load_clip(ckpt_paths, embedding_directory=None): clip_data = [] for p in ckpt_paths: clip_data.append(ldm_patched.modules.utils.load_torch_file(p, safe_load=True)) class EmptyClass: pass for i in range(len(clip_data)): if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: clip_data[i] = ldm_patched.modules.utils.transformers_convert(clip_data[i], "", "text_model.", 32) clip_target = EmptyClass() clip_target.params = {} if len(clip_data) == 1: if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sdxl_clip.SDXLRefinerClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) for c in clip_data: m, u = clip.load_sd(c) if len(m) > 0: print("clip missing:", m) if len(u) > 0: print("clip unexpected:", u) return clip def load_gligen(ckpt_path): data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): #TODO: this function is a mess and should be removed eventually if config is None: with open(config_path, 'r') as stream: config = yaml.safe_load(stream) model_config_params = config['model']['params'] clip_config = model_config_params['cond_stage_config'] scale_factor = model_config_params['scale_factor'] vae_config = model_config_params['first_stage_config'] fp16 = False if "unet_config" in model_config_params: if "params" in model_config_params["unet_config"]: unet_config = model_config_params["unet_config"]["params"] if "use_fp16" in unet_config: fp16 = unet_config.pop("use_fp16") if fp16: unet_config["dtype"] = torch.float16 noise_aug_config = None if "noise_aug_config" in model_config_params: noise_aug_config = model_config_params["noise_aug_config"] model_type = model_base.ModelType.EPS if "parameterization" in model_config_params: if model_config_params["parameterization"] == "v": model_type = model_base.ModelType.V_PREDICTION clip = None vae = None class WeightsLoader(torch.nn.Module): pass if state_dict is None: state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path) class EmptyClass: pass model_config = ldm_patched.modules.supported_models_base.BASE({}) from . import latent_formats model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) model_config.unet_config = model_detection.convert_config(unet_config) if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) else: model = model_base.BaseModel(model_config, model_type=model_type) if config['model']["target"].endswith("LatentInpaintDiffusion"): model.set_inpaint() if fp16: model = model.half() offload_device = model_management.unet_offload_device() model = model.to(offload_device) model.load_model_weights(state_dict, "model.diffusion_model.") if output_vae: vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) vae = VAE(sd=vae_sd, config=vae_config) if output_clip: w = WeightsLoader() clip_target = EmptyClass() clip_target.params = clip_config.get("params", {}) if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): clip_target.clip = sd2_clip.SD2ClipModel clip_target.tokenizer = sd2_clip.SD2Tokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model.clip_h elif clip_config["target"].endswith("FrozenCLIPEmbedder"): clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model.clip_l load_clip_weights(w, state_dict) return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None): sd = ldm_patched.modules.utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None vae = None vae_filename = None model = None model_patcher = None clip_target = None parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.") unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) class WeightsLoader(torch.nn.Module): pass model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) model_config.set_manual_cast(manual_cast_dtype) if model_config is None: raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) if model_config.clip_vision_prefix is not None: if output_clipvision: clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) if output_model: inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) offload_device = model_management.unet_offload_device() model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device) model.load_model_weights(sd, "model.diffusion_model.") if output_vae: if vae_filename_param is None: vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) else: vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param) vae_filename = vae_filename_param vae = VAE(sd=vae_sd) if output_clip: w = WeightsLoader() clip_target = model_config.clip_target() if clip_target is not None: clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model sd = model_config.process_clip_state_dict(sd) load_model_weights(w, sd) left_over = sd.keys() if len(left_over) > 0: print("left over keys:", left_over) if output_model: model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) if inital_load_device != torch.device("cpu"): print("loaded straight to GPU") model_management.load_model_gpu(model_patcher) return model_patcher, clip, vae, vae_filename, clipvision def load_unet_state_dict(sd): #load unet in diffusers format parameters = ldm_patched.modules.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) if "input_blocks.0.0.weight" in sd: #ldm model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) if model_config is None: return None new_sd = sd else: #diffusers model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) if model_config is None: return None diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.unet_config) new_sd = {} for k in diffusers_keys: if k in sd: new_sd[diffusers_keys[k]] = sd.pop(k) else: print(diffusers_keys[k], k) offload_device = model_management.unet_offload_device() model_config.set_manual_cast(manual_cast_dtype) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: print("left over keys in unet:", left_over) return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) def load_unet(unet_path): sd = ldm_patched.modules.utils.load_torch_file(unet_path) model = load_unet_state_dict(sd) if model is None: print("ERROR UNSUPPORTED UNET", unet_path) raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) return model def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None): clip_sd = None load_models = [model] if clip is not None: load_models.append(clip.load_model()) clip_sd = clip.get_sd() model_management.load_models_gpu(load_models) clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata) ================================================ FILE: ldm_patched/modules/sd1_clip.py ================================================ import os from transformers import CLIPTokenizer import ldm_patched.modules.ops import torch import traceback import zipfile from . import model_management import ldm_patched.modules.clip_model import json def gen_empty_tokens(special_tokens, length): start_token = special_tokens.get("start", None) end_token = special_tokens.get("end", None) pad_token = special_tokens.get("pad") output = [] if start_token is not None: output.append(start_token) if end_token is not None: output.append(end_token) output += [pad_token] * (length - len(output)) return output class ClipTokenWeightEncoder: def encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 has_weights = False for x in token_weight_pairs: tokens = list(map(lambda a: a[0], x)) max_token_len = max(len(tokens), max_token_len) has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) to_encode.append(tokens) sections = len(to_encode) if has_weights or sections == 0: to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) out, pooled = self.encode(to_encode) if pooled is not None: first_pooled = pooled[0:1].to(model_management.intermediate_device()) else: first_pooled = pooled output = [] for k in range(0, sections): z = out[k:k+1] if has_weights: z_empty = out[-1] for i in range(len(z)): for j in range(len(z[i])): weight = token_weight_pairs[k][j][1] if weight != 1.0: z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] output.append(z) if (len(output) == 0): return out[-1:].to(model_management.intermediate_device()), first_pooled return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): """Uses the CLIP transformer encoder for text (from huggingface)""" LAYERS = [ "last", "pooled", "hidden" ] def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=ldm_patched.modules.clip_model.CLIPTextModel, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True): # clip-vit-base-patch32 super().__init__() assert layer in self.LAYERS if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") with open(textmodel_json_config) as f: config = json.load(f) self.transformer = model_class(config, dtype, device, ldm_patched.modules.ops.manual_cast) self.num_layers = self.transformer.num_layers self.max_length = max_length if freeze: self.freeze() self.layer = layer self.layer_idx = None self.special_tokens = special_tokens self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = False self.layer_norm_hidden_state = layer_norm_hidden_state if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers self.clip_layer(layer_idx) self.layer_default = (self.layer, self.layer_idx) def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def clip_layer(self, layer_idx): if abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" self.layer_idx = layer_idx def reset_clip_layer(self): self.layer = self.layer_default[0] self.layer_idx = self.layer_default[1] def set_up_textual_embeddings(self, tokens, current_embeds): out_tokens = [] next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1 embedding_weights = [] for x in tokens: tokens_temp = [] for y in x: if isinstance(y, int): if y == token_dict_size: #EOS token y = -1 tokens_temp += [y] else: if y.shape[0] == current_embeds.weight.shape[1]: embedding_weights += [y] tokens_temp += [next_new_token] next_new_token += 1 else: print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1]) while len(tokens_temp) < len(x): tokens_temp += [self.special_tokens["pad"]] out_tokens += [tokens_temp] n = token_dict_size if len(embedding_weights) > 0: new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1] for x in embedding_weights: new_embedding.weight[n] = x n += 1 new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding self.transformer.set_input_embeddings(new_embedding) processed_tokens = [] for x in out_tokens: processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one return processed_tokens def forward(self, tokens): backup_embeds = self.transformer.get_input_embeddings() device = backup_embeds.weight.device tokens = self.set_up_textual_embeddings(tokens, backup_embeds) tokens = torch.LongTensor(tokens).to(device) attention_mask = None if self.enable_attention_masks: attention_mask = torch.zeros_like(tokens) max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 for x in range(attention_mask.shape[0]): for y in range(attention_mask.shape[1]): attention_mask[x, y] = 1 if tokens[x, y] == max_token: break outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": z = outputs[0] else: z = outputs[1] if outputs[2] is not None: pooled_output = outputs[2].float() else: pooled_output = None if self.text_projection is not None and pooled_output is not None: pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() return z.float(), pooled_output def encode(self, tokens): return self(tokens) def load_sd(self, sd): if "text_projection" in sd: self.text_projection[:] = sd.pop("text_projection") if "text_projection.weight" in sd: self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) return self.transformer.load_state_dict(sd, strict=False) def parse_parentheses(string): result = [] current_item = "" nesting_level = 0 for char in string: if char == "(": if nesting_level == 0: if current_item: result.append(current_item) current_item = "(" else: current_item = "(" else: current_item += char nesting_level += 1 elif char == ")": nesting_level -= 1 if nesting_level == 0: result.append(current_item + ")") current_item = "" else: current_item += char else: current_item += char if current_item: result.append(current_item) return result def token_weights(string, current_weight): a = parse_parentheses(string) out = [] for x in a: weight = current_weight if len(x) >= 2 and x[-1] == ')' and x[0] == '(': x = x[1:-1] xx = x.rfind(":") weight *= 1.1 if xx > 0: try: weight = float(x[xx+1:]) x = x[:xx] except: pass out += token_weights(x, weight) else: out += [(x, current_weight)] return out def escape_important(text): text = text.replace("\\)", "\0\1") text = text.replace("\\(", "\0\2") return text def unescape_important(text): text = text.replace("\0\1", ")") text = text.replace("\0\2", "(") return text def safe_load_embed_zip(embed_path): with zipfile.ZipFile(embed_path) as myzip: names = list(filter(lambda a: "data/" in a, myzip.namelist())) names.reverse() for n in names: with myzip.open(n) as myfile: data = myfile.read() number = len(data) // 4 length_embed = 1024 #sd2.x if number < 768: continue if number % 768 == 0: length_embed = 768 #sd1.x num_embeds = number // length_embed embed = torch.frombuffer(data, dtype=torch.float) out = embed.reshape((num_embeds, length_embed)).clone() del embed return out def expand_directory_list(directories): dirs = set() for x in directories: dirs.add(x) for root, subdir, file in os.walk(x, followlinks=True): dirs.add(root) return list(dirs) def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): if isinstance(embedding_directory, str): embedding_directory = [embedding_directory] embedding_directory = expand_directory_list(embedding_directory) valid_file = None for embed_dir in embedding_directory: embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) embed_dir = os.path.abspath(embed_dir) try: if os.path.commonpath((embed_dir, embed_path)) != embed_dir: continue except: continue if not os.path.isfile(embed_path): extensions = ['.safetensors', '.pt', '.bin'] for x in extensions: t = embed_path + x if os.path.isfile(t): valid_file = t break else: valid_file = embed_path if valid_file is not None: break if valid_file is None: return None embed_path = valid_file embed_out = None try: if embed_path.lower().endswith(".safetensors"): import safetensors.torch embed = safetensors.torch.load_file(embed_path, device="cpu") else: if 'weights_only' in torch.load.__code__.co_varnames: try: embed = torch.load(embed_path, weights_only=True, map_location="cpu") except: embed_out = safe_load_embed_zip(embed_path) else: embed = torch.load(embed_path, map_location="cpu", weights_only=True) except Exception as e: print(traceback.format_exc()) print() print("error loading embedding, skipping loading:", embedding_name) return None if embed_out is None: if 'string_to_param' in embed: values = embed['string_to_param'].values() embed_out = next(iter(values)) elif isinstance(embed, list): out_list = [] for x in range(len(embed)): for k in embed[x]: t = embed[x][k] if t.shape[-1] != embedding_size: continue out_list.append(t.reshape(-1, t.shape[-1])) embed_out = torch.cat(out_list, dim=0) elif embed_key is not None and embed_key in embed: embed_out = embed[embed_key] else: values = embed.values() embed_out = next(iter(values)) return embed_out class SDTokenizer: def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) self.max_length = max_length empty = self.tokenizer('')["input_ids"] if has_start_token: self.tokens_start = 1 self.start_token = empty[0] self.end_token = empty[1] else: self.tokens_start = 0 self.start_token = None self.end_token = empty[0] self.pad_with_end = pad_with_end self.pad_to_max_length = pad_to_max_length vocab = self.tokenizer.get_vocab() self.inv_vocab = {v: k for k, v in vocab.items()} self.embedding_directory = embedding_directory self.max_word_length = 8 self.embedding_identifier = "embedding:" self.embedding_size = embedding_size self.embedding_key = embedding_key def _try_get_embedding(self, embedding_name:str): ''' Takes a potential embedding name and tries to retrieve it. Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. ''' embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) if embed is None: stripped = embedding_name.strip(',') if len(stripped) < len(embedding_name): embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) return (embed, embedding_name[len(stripped):]) return (embed, "") def tokenize_with_weights(self, text:str, return_word_ids=False): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' if self.pad_with_end: pad_token = self.end_token else: pad_token = 0 text = escape_important(text) parsed_weights = token_weights(text, 1.0) #tokenize words tokens = [] for weighted_segment, weight in parsed_weights: to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') to_tokenize = [x for x in to_tokenize if x != ""] for word in to_tokenize: #if we find an embedding, deal with the embedding if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: embedding_name = word[len(self.embedding_identifier):].strip('\n') embed, leftover = self._try_get_embedding(embedding_name) if embed is None: print(f"warning, embedding:{embedding_name} does not exist, ignoring") else: if len(embed.shape) == 1: tokens.append([(embed, weight)]) else: tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) #if we accidentally have leftover text, continue parsing using leftover, else move on to next word if leftover != "": word = leftover else: continue #parse word tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]]) #reshape token array to CLIP input size batched_tokens = [] batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) for i, t_group in enumerate(tokens): #determine if we're going to try and keep the tokens in a single batch is_large = len(t_group) >= self.max_word_length while len(t_group) > 0: if len(t_group) + len(batch) > self.max_length - 1: remaining_length = self.max_length - len(batch) - 1 #break word in two and add end token if is_large: batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) batch.append((self.end_token, 1.0, 0)) t_group = t_group[remaining_length:] #add end token and pad else: batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) #start new batch batch = [] if self.start_token is not None: batch.append((self.start_token, 1.0, 0)) batched_tokens.append(batch) else: batch.extend([(t,w,i+1) for t,w in t_group]) t_group = [] #fill last batch batch.append((self.end_token, 1.0, 0)) if self.pad_to_max_length: batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] return batched_tokens def untokenize(self, token_weight_pair): return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) class SD1Tokenizer: def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer): self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory)) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return getattr(self, self.clip).untokenize(token_weight_pair) class SD1ClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs): super().__init__() self.clip_name = clip_name self.clip = "clip_{}".format(self.clip_name) setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) def clip_layer(self, layer_idx): getattr(self, self.clip).clip_layer(layer_idx) def reset_clip_layer(self): getattr(self, self.clip).reset_clip_layer() def encode_token_weights(self, token_weight_pairs): token_weight_pairs = token_weight_pairs[self.clip_name] out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs) return out, pooled def load_sd(self, sd): return getattr(self, self.clip).load_sd(sd) ================================================ FILE: ldm_patched/modules/sd1_clip_config.json ================================================ { "_name_or_path": "openai/clip-vit-large-patch14", "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "quick_gelu", "hidden_size": 768, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "projection_dim": 768, "torch_dtype": "float32", "transformers_version": "4.24.0", "vocab_size": 49408 } ================================================ FILE: ldm_patched/modules/sd1_tokenizer/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n 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ay int elli desp ite che e sar ah be an reco gni ar sen tal ented pas sion ic h ab c lead s dise ase v is se c pre senting m illi hol e sho ts de part surger y gov t b in du al e vi lon ger ev ol scre en portra it et c lo se ch at p en p i om a s ick er c compan ies en try plan e gr y ven e liver pool premi ere sha red a red fil ms ir a holi days cric ket ici an v ing . ) ul timate di vision con duc se pt for ces mon t s mart disa pp sun shine in d b less ma de col ors fran k ir on bott le s go m ood j ason er ic bir th te en respon se tar get state ment fe ar th el al um ar ab bl in direc tion ste ps er ial wor ked at l ðŁĴ ķ fel t pol i scen es hom es b ell e at ate ful t in l ace fol ks p se an n wis dom fa v but ter s r are as sm oo bi z dg es app o mo re the m effe ct windo ws sun ny cap ital tot ally c ities gr ant mb ers s low au tu il ities w ro ri sing st ics viol ence i gh qu ot h it t c herit age bu ff ne s z ar den tial ex ac ed ge de ep aren a be came benef its mar ks mb 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36218, "ĭãĤ": 45737, "Į": 234, "Į": 490, "ĮãĤĬãģ": 45969, "ĮãĤĬãģŁãģĦ": 47021, "Įë": 17003, "į": 235, "į": 491, "İ": 236, "İ": 492, "ı": 237, "ı": 493, "IJ": 238, "IJ": 494, "ij": 239, "ij": 495, "Ĵ": 240, "Ĵ": 496, "ĵ": 241, "ĵ": 497, "Ķ": 242, "Ķ": 498, "Ķë": 37978, "Ķï¸ı": 24395, "Ķï¸ı": 7443, "ķ": 243, "ķ": 499, "ķãĤ": 26609, "ķï¸ı": 44853, "ĸ": 244, "ĸ": 500, "ĸï¸ı": 28877, "Ĺ": 245, "Ĺ": 501, "ĺ": 246, "ĺ": 502, "Ļ": 247, "Ļ": 503, "ļ": 248, "ļ": 504, "Ľ": 249, "Ľ": 505, "ľ": 250, "ľ": 506, "ľë": 39810, "Ŀ": 251, "Ŀ": 507, "ŀ": 252, "ŀ": 508, "Ł": 253, "Ł": 509, "ŁãģĦ": 46023, "ł": 254, "ł": 510, "łï¸ı": 27899, "łï¸ı": 12715, "łĪ": 43364, "Ń": 255, "Ń": 511 } ================================================ FILE: ldm_patched/modules/sd2_clip.py ================================================ from ldm_patched.modules import sd1_clip import torch import os class SD2ClipHModel(sd1_clip.SDClipModel): def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}) class SD2ClipHTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) class SD2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None): super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer) class SD2ClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None, **kwargs): super().__init__(device=device, dtype=dtype, clip_name="h", clip_model=SD2ClipHModel, **kwargs) ================================================ FILE: ldm_patched/modules/sd2_clip_config.json ================================================ { "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "gelu", "hidden_size": 1024, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 1, "projection_dim": 1024, "torch_dtype": "float32", "vocab_size": 49408 } ================================================ FILE: ldm_patched/modules/sdxl_clip.py ================================================ from ldm_patched.modules import sd1_clip import torch import os class SDXLClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): if layer == "penultimate": layer="hidden" layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) def load_sd(self, sd): return super().load_sd(sd) class SDXLClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None): super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') class SDXLTokenizer: def __init__(self, embedding_directory=None): self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) def tokenize_with_weights(self, text:str, return_word_ids=False): out = {} out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) return out def untokenize(self, token_weight_pair): return self.clip_g.untokenize(token_weight_pair) class SDXLClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None): super().__init__() self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) self.clip_g = SDXLClipG(device=device, dtype=dtype) def clip_layer(self, layer_idx): self.clip_l.clip_layer(layer_idx) self.clip_g.clip_layer(layer_idx) def reset_clip_layer(self): self.clip_g.reset_clip_layer() self.clip_l.reset_clip_layer() def encode_token_weights(self, token_weight_pairs): token_weight_pairs_g = token_weight_pairs["g"] token_weight_pairs_l = token_weight_pairs["l"] g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) return torch.cat([l_out, g_out], dim=-1), g_pooled def load_sd(self, sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: return self.clip_g.load_sd(sd) else: return self.clip_l.load_sd(sd) class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): def __init__(self, device="cpu", dtype=None): super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG) ================================================ FILE: ldm_patched/modules/supported_models.py ================================================ import torch from . import model_base from . import utils from . import sd1_clip from . import sd2_clip from . import sdxl_clip from . import supported_models_base from . import latent_formats from . import diffusers_convert class SD15(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD15 def process_clip_state_dict(self, state_dict): k = list(state_dict.keys()) for x in k: if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") state_dict[y] = state_dict.pop(x) if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] if ids.dtype == torch.float32: state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() replace_prefix = {} replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l." state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {"clip_l.": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def clip_target(self): return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) class SD20(supported_models_base.BASE): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } latent_format = latent_formats.SD15 def model_type(self, state_dict, prefix=""): if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) out = state_dict[k] if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. return model_base.ModelType.V_PREDICTION return model_base.ModelType.EPS def process_clip_state_dict(self, state_dict): replace_prefix = {} replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} replace_prefix["clip_h"] = "cond_stage_model.model" state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) return state_dict def clip_target(self): return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) class SD21UnclipL(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 1536, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} class SD21UnclipH(SD20): unet_config = { "context_dim": 1024, "model_channels": 320, "use_linear_in_transformer": True, "adm_in_channels": 2048, "use_temporal_attention": False, } clip_vision_prefix = "embedder.model.visual." noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} class SDXLRefiner(supported_models_base.BASE): unet_config = { "model_channels": 384, "use_linear_in_transformer": True, "context_dim": 1280, "adm_in_channels": 2560, "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], "use_temporal_attention": False, } latent_format = latent_formats.SDXL def get_model(self, state_dict, prefix="", device=None): return model_base.SDXLRefiner(self, device=device) def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") replace_prefix["clip_g"] = "conditioner.embedders.0.model" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) class SDXL(supported_models_base.BASE): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 10, 10], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } latent_format = latent_formats.SDXL def model_type(self, state_dict, prefix=""): if "v_pred" in state_dict: return model_base.ModelType.V_PREDICTION else: return model_base.ModelType.EPS def get_model(self, state_dict, prefix="", device=None): out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): keys_to_replace = {} replace_prefix = {} replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model" state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection" keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {} keys_to_replace = {} state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") for k in state_dict: if k.startswith("clip_l"): state_dict_g[k] = state_dict[k] replace_prefix["clip_g"] = "conditioner.embedders.1.model" replace_prefix["clip_l"] = "conditioner.embedders.0" state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) return state_dict_g def clip_target(self): return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) class SSD1B(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 2, 2, 4, 4], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class Segmind_Vega(SDXL): unet_config = { "model_channels": 320, "use_linear_in_transformer": True, "transformer_depth": [0, 0, 1, 1, 2, 2], "context_dim": 2048, "adm_in_channels": 2816, "use_temporal_attention": False, } class SVD_img2vid(supported_models_base.BASE): unet_config = { "model_channels": 320, "in_channels": 8, "use_linear_in_transformer": True, "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], "context_dim": 1024, "adm_in_channels": 768, "use_temporal_attention": True, "use_temporal_resblock": True } clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." latent_format = latent_formats.SD15 sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} def get_model(self, state_dict, prefix="", device=None): out = model_base.SVD_img2vid(self, device=device) return out def clip_target(self): return None class Stable_Zero123(supported_models_base.BASE): unet_config = { "context_dim": 768, "model_channels": 320, "use_linear_in_transformer": False, "adm_in_channels": None, "use_temporal_attention": False, "in_channels": 8, } unet_extra_config = { "num_heads": 8, "num_head_channels": -1, } clip_vision_prefix = "cond_stage_model.model.visual." latent_format = latent_formats.SD15 def get_model(self, state_dict, prefix="", device=None): out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) return out def clip_target(self): return None class SD_X4Upscaler(SD20): unet_config = { "context_dim": 1024, "model_channels": 256, 'in_channels': 7, "use_linear_in_transformer": True, "adm_in_channels": None, "use_temporal_attention": False, } unet_extra_config = { "disable_self_attentions": [True, True, True, False], "num_classes": 1000, "num_heads": 8, "num_head_channels": -1, } latent_format = latent_formats.SD_X4 sampling_settings = { "linear_start": 0.0001, "linear_end": 0.02, } def get_model(self, state_dict, prefix="", device=None): out = model_base.SD_X4Upscaler(self, device=device) return out models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler] models += [SVD_img2vid] ================================================ FILE: ldm_patched/modules/supported_models_base.py ================================================ import torch from . import model_base from . import utils from . import latent_formats class ClipTarget: def __init__(self, tokenizer, clip): self.clip = clip self.tokenizer = tokenizer self.params = {} class BASE: unet_config = {} unet_extra_config = { "num_heads": -1, "num_head_channels": 64, } clip_prefix = [] clip_vision_prefix = None noise_aug_config = None sampling_settings = {} latent_format = latent_formats.LatentFormat manual_cast_dtype = None @classmethod def matches(s, unet_config): for k in s.unet_config: if s.unet_config[k] != unet_config[k]: return False return True def model_type(self, state_dict, prefix=""): return model_base.ModelType.EPS def inpaint_model(self): return self.unet_config["in_channels"] > 4 def __init__(self, unet_config): self.unet_config = unet_config self.latent_format = self.latent_format() for x in self.unet_extra_config: self.unet_config[x] = self.unet_extra_config[x] def get_model(self, state_dict, prefix="", device=None): if self.noise_aug_config is not None: out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) else: out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) if self.inpaint_model(): out.set_inpaint() return out def process_clip_state_dict(self, state_dict): return state_dict def process_unet_state_dict(self, state_dict): return state_dict def process_vae_state_dict(self, state_dict): return state_dict def process_clip_state_dict_for_saving(self, state_dict): replace_prefix = {"": "cond_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_clip_vision_state_dict_for_saving(self, state_dict): replace_prefix = {} if self.clip_vision_prefix is not None: replace_prefix[""] = self.clip_vision_prefix return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_unet_state_dict_for_saving(self, state_dict): replace_prefix = {"": "model.diffusion_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def process_vae_state_dict_for_saving(self, state_dict): replace_prefix = {"": "first_stage_model."} return utils.state_dict_prefix_replace(state_dict, replace_prefix) def set_manual_cast(self, manual_cast_dtype): self.manual_cast_dtype = manual_cast_dtype ================================================ FILE: ldm_patched/modules/utils.py ================================================ import torch import math import struct import ldm_patched.modules.checkpoint_pickle import safetensors.torch import numpy as np from PIL import Image def load_torch_file(ckpt, safe_load=False, device=None): if device is None: device = torch.device("cpu") if ckpt.lower().endswith(".safetensors"): sd = safetensors.torch.load_file(ckpt, device=device.type) else: if safe_load: if not 'weights_only' in torch.load.__code__.co_varnames: print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") safe_load = False if safe_load: pl_sd = torch.load(ckpt, map_location=device, weights_only=True) else: pl_sd = torch.load(ckpt, map_location=device, pickle_module=ldm_patched.modules.checkpoint_pickle) if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd return sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: safetensors.torch.save_file(sd, ckpt, metadata=metadata) else: safetensors.torch.save_file(sd, ckpt) def calculate_parameters(sd, prefix=""): params = 0 for k in sd.keys(): if k.startswith(prefix): params += sd[k].nelement() return params def state_dict_key_replace(state_dict, keys_to_replace): for x in keys_to_replace: if x in state_dict: state_dict[keys_to_replace[x]] = state_dict.pop(x) return state_dict def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): if filter_keys: out = {} else: out = state_dict for rp in replace_prefix: replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) for x in replace: w = state_dict.pop(x[0]) out[x[1]] = w return out def transformers_convert(sd, prefix_from, prefix_to, number): keys_to_replace = { "{}positional_embedding": "{}embeddings.position_embedding.weight", "{}token_embedding.weight": "{}embeddings.token_embedding.weight", "{}ln_final.weight": "{}final_layer_norm.weight", "{}ln_final.bias": "{}final_layer_norm.bias", } for k in keys_to_replace: x = k.format(prefix_from) if x in sd: sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) resblock_to_replace = { "ln_1": "layer_norm1", "ln_2": "layer_norm2", "mlp.c_fc": "mlp.fc1", "mlp.c_proj": "mlp.fc2", "attn.out_proj": "self_attn.out_proj", } for resblock in range(number): for x in resblock_to_replace: for y in ["weight", "bias"]: k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) if k in sd: sd[k_to] = sd.pop(k) for y in ["weight", "bias"]: k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) if k_from in sd: weights = sd.pop(k_from) shape_from = weights.shape[0] // 3 for x in range(3): p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] return sd UNET_MAP_ATTENTIONS = { "proj_in.weight", "proj_in.bias", "proj_out.weight", "proj_out.bias", "norm.weight", "norm.bias", } TRANSFORMER_BLOCKS = { "norm1.weight", "norm1.bias", "norm2.weight", "norm2.bias", "norm3.weight", "norm3.bias", "attn1.to_q.weight", "attn1.to_k.weight", "attn1.to_v.weight", "attn1.to_out.0.weight", "attn1.to_out.0.bias", "attn2.to_q.weight", "attn2.to_k.weight", "attn2.to_v.weight", "attn2.to_out.0.weight", "attn2.to_out.0.bias", "ff.net.0.proj.weight", "ff.net.0.proj.bias", "ff.net.2.weight", "ff.net.2.bias", } UNET_MAP_RESNET = { "in_layers.2.weight": "conv1.weight", "in_layers.2.bias": "conv1.bias", "emb_layers.1.weight": "time_emb_proj.weight", "emb_layers.1.bias": "time_emb_proj.bias", "out_layers.3.weight": "conv2.weight", "out_layers.3.bias": "conv2.bias", "skip_connection.weight": "conv_shortcut.weight", "skip_connection.bias": "conv_shortcut.bias", "in_layers.0.weight": "norm1.weight", "in_layers.0.bias": "norm1.bias", "out_layers.0.weight": "norm2.weight", "out_layers.0.bias": "norm2.bias", } UNET_MAP_BASIC = { ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias") } def unet_to_diffusers(unet_config): num_res_blocks = unet_config["num_res_blocks"] channel_mult = unet_config["channel_mult"] transformer_depth = unet_config["transformer_depth"][:] transformer_depth_output = unet_config["transformer_depth_output"][:] num_blocks = len(channel_mult) transformers_mid = unet_config.get("transformer_depth_middle", None) diffusers_unet_map = {} for x in range(num_blocks): n = 1 + (num_res_blocks[x] + 1) * x for i in range(num_res_blocks[x]): for b in UNET_MAP_RESNET: diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) num_transformers = transformer_depth.pop(0) if num_transformers > 0: for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) n += 1 for k in ["weight", "bias"]: diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) i = 0 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) for t in range(transformers_mid): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) for i, n in enumerate([0, 2]): for b in UNET_MAP_RESNET: diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) num_res_blocks = list(reversed(num_res_blocks)) for x in range(num_blocks): n = (num_res_blocks[x] + 1) * x l = num_res_blocks[x] + 1 for i in range(l): c = 0 for b in UNET_MAP_RESNET: diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) c += 1 num_transformers = transformer_depth_output.pop() if num_transformers > 0: c += 1 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) for t in range(num_transformers): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) if i == l - 1: for k in ["weight", "bias"]: diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) n += 1 for k in UNET_MAP_BASIC: diffusers_unet_map[k[1]] = k[0] return diffusers_unet_map def repeat_to_batch_size(tensor, batch_size): if tensor.shape[0] > batch_size: return tensor[:batch_size] elif tensor.shape[0] < batch_size: return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] return tensor def resize_to_batch_size(tensor, batch_size): in_batch_size = tensor.shape[0] if in_batch_size == batch_size: return tensor if batch_size <= 1: return tensor[:batch_size] output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) if batch_size < in_batch_size: scale = (in_batch_size - 1) / (batch_size - 1) for i in range(batch_size): output[i] = tensor[min(round(i * scale), in_batch_size - 1)] else: scale = in_batch_size / batch_size for i in range(batch_size): output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] return output def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: state_dict[k] = state_dict[k].to(dtype) return state_dict def safetensors_header(safetensors_path, max_size=100*1024*1024): with open(safetensors_path, "rb") as f: header = f.read(8) length_of_header = struct.unpack(' max_size: return None return f.read(length_of_header) def set_attr(obj, attr, value): attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False)) del prev def copy_to_param(obj, attr, value): # inplace update tensor instead of replacing it attrs = attr.split(".") for name in attrs[:-1]: obj = getattr(obj, name) prev = getattr(obj, attrs[-1]) prev.data.copy_(value) def get_attr(obj, attr): attrs = attr.split(".") for name in attrs: obj = getattr(obj, name) return obj def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' c = b1.shape[-1] #norms b1_norms = torch.norm(b1, dim=-1, keepdim=True) b2_norms = torch.norm(b2, dim=-1, keepdim=True) #normalize b1_normalized = b1 / b1_norms b2_normalized = b2 / b2_norms #zero when norms are zero b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 #slerp dot = (b1_normalized*b2_normalized).sum(1) omega = torch.acos(dot) so = torch.sin(omega) #technically not mathematically correct, but more pleasing? res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) #edge cases for same or polar opposites res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] return res def generate_bilinear_data(length_old, length_new, device): coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") ratios = coords_1 - coords_1.floor() coords_1 = coords_1.to(torch.int64) coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 coords_2[:,:,:,-1] -= 1 coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") coords_2 = coords_2.to(torch.int64) return ratios, coords_1, coords_2 orig_dtype = samples.dtype samples = samples.float() n,c,h,w = samples.shape h_new, w_new = (height, width) #linear w ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) coords_1 = coords_1.expand((n, c, h, -1)) coords_2 = coords_2.expand((n, c, h, -1)) ratios = ratios.expand((n, 1, h, -1)) pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h, w_new, c).movedim(-1, 1) #linear h ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) return result.to(orig_dtype) def lanczos(samples, width, height): images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] result = torch.stack(images) return result.to(samples.device, samples.dtype) def common_upscale(samples, width, height, upscale_method, crop): if crop == "center": old_width = samples.shape[3] old_height = samples.shape[2] old_aspect = old_width / old_height new_aspect = width / height x = 0 y = 0 if old_aspect > new_aspect: x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) elif old_aspect < new_aspect: y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) s = samples[:,:,y:old_height-y,x:old_width-x] else: s = samples if upscale_method == "bislerp": return bislerp(s, width, height) elif upscale_method == "lanczos": return lanczos(s, width, height) else: return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) @torch.inference_mode() def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) for b in range(samples.shape[0]): s = samples[b:b+1] out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) for y in range(0, s.shape[2], tile_y - overlap): for x in range(0, s.shape[3], tile_x - overlap): s_in = s[:,:,y:y+tile_y,x:x+tile_x] ps = function(s_in).to(output_device) mask = torch.ones_like(ps) feather = round(overlap * upscale_amount) for t in range(feather): mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask if pbar is not None: pbar.update(1) output[b:b+1] = out/out_div return output PROGRESS_BAR_ENABLED = True def set_progress_bar_enabled(enabled): global PROGRESS_BAR_ENABLED PROGRESS_BAR_ENABLED = enabled PROGRESS_BAR_HOOK = None def set_progress_bar_global_hook(function): global PROGRESS_BAR_HOOK PROGRESS_BAR_HOOK = function class ProgressBar: def __init__(self, total): global PROGRESS_BAR_HOOK self.total = total self.current = 0 self.hook = PROGRESS_BAR_HOOK def update_absolute(self, value, total=None, preview=None): if total is not None: self.total = total if value > self.total: value = self.total self.current = value if self.hook is not None: self.hook(self.current, self.total, preview) def update(self, value): self.update_absolute(self.current + value) ================================================ FILE: ldm_patched/pfn/__init__.py ================================================ ================================================ FILE: ldm_patched/pfn/architecture/DAT.py ================================================ # pylint: skip-file import math import re import numpy as np import torch import torch.nn as nn import torch.utils.checkpoint as checkpoint from einops import rearrange from einops.layers.torch import Rearrange from torch import Tensor from torch.nn import functional as F from .timm.drop import DropPath from .timm.weight_init import trunc_normal_ def img2windows(img, H_sp, W_sp): """ Input: Image (B, C, H, W) Output: Window Partition (B', N, C) """ B, C, H, W = img.shape img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) img_perm = ( img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) ) return img_perm def windows2img(img_splits_hw, H_sp, W_sp, H, W): """ Input: Window Partition (B', N, C) Output: Image (B, H, W, C) """ B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return img class SpatialGate(nn.Module): """Spatial-Gate. Args: dim (int): Half of input channels. """ def __init__(self, dim): super().__init__() self.norm = nn.LayerNorm(dim) self.conv = nn.Conv2d( dim, dim, kernel_size=3, stride=1, padding=1, groups=dim ) # DW Conv def forward(self, x, H, W): # Split x1, x2 = x.chunk(2, dim=-1) B, N, C = x.shape x2 = ( self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) .flatten(2) .transpose(-1, -2) .contiguous() ) return x1 * x2 class SGFN(nn.Module): """Spatial-Gate Feed-Forward Network. Args: in_features (int): Number of input channels. hidden_features (int | None): Number of hidden channels. Default: None out_features (int | None): Number of output channels. Default: None act_layer (nn.Module): Activation layer. Default: nn.GELU drop (float): Dropout rate. Default: 0.0 """ def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.sg = SpatialGate(hidden_features // 2) self.fc2 = nn.Linear(hidden_features // 2, out_features) self.drop = nn.Dropout(drop) def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.sg(x, H, W) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class DynamicPosBias(nn.Module): # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py """Dynamic Relative Position Bias. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. residual (bool): If True, use residual strage to connect conv. """ def __init__(self, dim, num_heads, residual): super().__init__() self.residual = residual self.num_heads = num_heads self.pos_dim = dim // 4 self.pos_proj = nn.Linear(2, self.pos_dim) self.pos1 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.pos_dim), ) self.pos2 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.pos_dim), ) self.pos3 = nn.Sequential( nn.LayerNorm(self.pos_dim), nn.ReLU(inplace=True), nn.Linear(self.pos_dim, self.num_heads), ) def forward(self, biases): if self.residual: pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads pos = pos + self.pos1(pos) pos = pos + self.pos2(pos) pos = self.pos3(pos) else: pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) return pos class Spatial_Attention(nn.Module): """Spatial Window Self-Attention. It supports rectangle window (containing square window). Args: dim (int): Number of input channels. idx (int): The indentix of window. (0/1) split_size (tuple(int)): Height and Width of spatial window. dim_out (int | None): The dimension of the attention output. Default: None num_heads (int): Number of attention heads. Default: 6 attn_drop (float): Dropout ratio of attention weight. Default: 0.0 proj_drop (float): Dropout ratio of output. Default: 0.0 qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set position_bias (bool): The dynamic relative position bias. Default: True """ def __init__( self, dim, idx, split_size=[8, 8], dim_out=None, num_heads=6, attn_drop=0.0, proj_drop=0.0, qk_scale=None, position_bias=True, ): super().__init__() self.dim = dim self.dim_out = dim_out or dim self.split_size = split_size self.num_heads = num_heads self.idx = idx self.position_bias = position_bias head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 if idx == 0: H_sp, W_sp = self.split_size[0], self.split_size[1] elif idx == 1: W_sp, H_sp = self.split_size[0], self.split_size[1] else: print("ERROR MODE", idx) exit(0) self.H_sp = H_sp self.W_sp = W_sp if self.position_bias: self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) # generate mother-set position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) biases = biases.flatten(1).transpose(0, 1).contiguous().float() self.register_buffer("rpe_biases", biases) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.H_sp) coords_w = torch.arange(self.W_sp) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_flatten = torch.flatten(coords, 1) relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += self.H_sp - 1 relative_coords[:, :, 1] += self.W_sp - 1 relative_coords[:, :, 0] *= 2 * self.W_sp - 1 relative_position_index = relative_coords.sum(-1) self.register_buffer("relative_position_index", relative_position_index) self.attn_drop = nn.Dropout(attn_drop) def im2win(self, x, H, W): B, N, C = x.shape x = x.transpose(-2, -1).contiguous().view(B, C, H, W) x = img2windows(x, self.H_sp, self.W_sp) x = ( x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) .permute(0, 2, 1, 3) .contiguous() ) return x def forward(self, qkv, H, W, mask=None): """ Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size Output: x (B, H, W, C) """ q, k, v = qkv[0], qkv[1], qkv[2] B, L, C = q.shape assert L == H * W, "flatten img_tokens has wrong size" # partition the q,k,v, image to window q = self.im2win(q, H, W) k = self.im2win(k, H, W) v = self.im2win(v, H, W) q = q * self.scale attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N # calculate drpe if self.position_bias: pos = self.pos(self.rpe_biases) # select position bias relative_position_bias = pos[self.relative_position_index.view(-1)].view( self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 ) relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() attn = attn + relative_position_bias.unsqueeze(0) N = attn.shape[3] # use mask for shift window if mask is not None: nW = mask.shape[0] attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( 0 ) attn = attn.view(-1, self.num_heads, N, N) attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape( -1, self.H_sp * self.W_sp, C ) # B head N N @ B head N C # merge the window, window to image x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C return x class Adaptive_Spatial_Attention(nn.Module): # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT """Adaptive Spatial Self-Attention Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 6 split_size (tuple(int)): Height and Width of spatial window. shift_size (tuple(int)): Shift size for spatial window. qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. drop (float): Dropout rate. Default: 0.0 attn_drop (float): Attention dropout rate. Default: 0.0 rg_idx (int): The indentix of Residual Group (RG) b_idx (int): The indentix of Block in each RG """ def __init__( self, dim, num_heads, reso=64, split_size=[8, 8], shift_size=[1, 2], qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, rg_idx=0, b_idx=0, ): super().__init__() self.dim = dim self.num_heads = num_heads self.split_size = split_size self.shift_size = shift_size self.b_idx = b_idx self.rg_idx = rg_idx self.patches_resolution = reso self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) assert ( 0 <= self.shift_size[0] < self.split_size[0] ), "shift_size must in 0-split_size0" assert ( 0 <= self.shift_size[1] < self.split_size[1] ), "shift_size must in 0-split_size1" self.branch_num = 2 self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(drop) self.attns = nn.ModuleList( [ Spatial_Attention( dim // 2, idx=i, split_size=split_size, num_heads=num_heads // 2, dim_out=dim // 2, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, position_bias=True, ) for i in range(self.branch_num) ] ) if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 ): attn_mask = self.calculate_mask( self.patches_resolution, self.patches_resolution ) self.register_buffer("attn_mask_0", attn_mask[0]) self.register_buffer("attn_mask_1", attn_mask[1]) else: attn_mask = None self.register_buffer("attn_mask_0", None) self.register_buffer("attn_mask_1", None) self.dwconv = nn.Sequential( nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), nn.BatchNorm2d(dim), nn.GELU(), ) self.channel_interaction = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(dim, dim // 8, kernel_size=1), nn.BatchNorm2d(dim // 8), nn.GELU(), nn.Conv2d(dim // 8, dim, kernel_size=1), ) self.spatial_interaction = nn.Sequential( nn.Conv2d(dim, dim // 16, kernel_size=1), nn.BatchNorm2d(dim // 16), nn.GELU(), nn.Conv2d(dim // 16, 1, kernel_size=1), ) def calculate_mask(self, H, W): # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py # calculate attention mask for shift window img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 h_slices_0 = ( slice(0, -self.split_size[0]), slice(-self.split_size[0], -self.shift_size[0]), slice(-self.shift_size[0], None), ) w_slices_0 = ( slice(0, -self.split_size[1]), slice(-self.split_size[1], -self.shift_size[1]), slice(-self.shift_size[1], None), ) h_slices_1 = ( slice(0, -self.split_size[1]), slice(-self.split_size[1], -self.shift_size[1]), slice(-self.shift_size[1], None), ) w_slices_1 = ( slice(0, -self.split_size[0]), slice(-self.split_size[0], -self.shift_size[0]), slice(-self.shift_size[0], None), ) cnt = 0 for h in h_slices_0: for w in w_slices_0: img_mask_0[:, h, w, :] = cnt cnt += 1 cnt = 0 for h in h_slices_1: for w in w_slices_1: img_mask_1[:, h, w, :] = cnt cnt += 1 # calculate mask for window-0 img_mask_0 = img_mask_0.view( 1, H // self.split_size[0], self.split_size[0], W // self.split_size[1], self.split_size[1], 1, ) img_mask_0 = ( img_mask_0.permute(0, 1, 3, 2, 4, 5) .contiguous() .view(-1, self.split_size[0], self.split_size[1], 1) ) # nW, sw[0], sw[1], 1 mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) attn_mask_0 = attn_mask_0.masked_fill( attn_mask_0 != 0, float(-100.0) ).masked_fill(attn_mask_0 == 0, float(0.0)) # calculate mask for window-1 img_mask_1 = img_mask_1.view( 1, H // self.split_size[1], self.split_size[1], W // self.split_size[0], self.split_size[0], 1, ) img_mask_1 = ( img_mask_1.permute(0, 1, 3, 2, 4, 5) .contiguous() .view(-1, self.split_size[1], self.split_size[0], 1) ) # nW, sw[1], sw[0], 1 mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) attn_mask_1 = attn_mask_1.masked_fill( attn_mask_1 != 0, float(-100.0) ).masked_fill(attn_mask_1 == 0, float(0.0)) return attn_mask_0, attn_mask_1 def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ B, L, C = x.shape assert L == H * W, "flatten img_tokens has wrong size" qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C # V without partition v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) # image padding max_split_size = max(self.split_size[0], self.split_size[1]) pad_l = pad_t = 0 pad_r = (max_split_size - W % max_split_size) % max_split_size pad_b = (max_split_size - H % max_split_size) % max_split_size qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W qkv = ( F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) .reshape(3, B, C, -1) .transpose(-2, -1) ) # l r t b _H = pad_b + H _W = pad_r + W _L = _H * _W # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 ): qkv = qkv.view(3, B, _H, _W, C) qkv_0 = torch.roll( qkv[:, :, :, :, : C // 2], shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(2, 3), ) qkv_0 = qkv_0.view(3, B, _L, C // 2) qkv_1 = torch.roll( qkv[:, :, :, :, C // 2 :], shifts=(-self.shift_size[1], -self.shift_size[0]), dims=(2, 3), ) qkv_1 = qkv_1.view(3, B, _L, C // 2) if self.patches_resolution != _H or self.patches_resolution != _W: mask_tmp = self.calculate_mask(_H, _W) x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) else: x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) x1 = torch.roll( x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) ) x2 = torch.roll( x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) ) x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) # attention output attened_x = torch.cat([x1, x2], dim=2) else: x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( B, L, C // 2 ) x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( B, L, C // 2 ) # attention output attened_x = torch.cat([x1, x2], dim=2) # convolution output conv_x = self.dwconv(v) # Adaptive Interaction Module (AIM) # C-Map (before sigmoid) channel_map = ( self.channel_interaction(conv_x) .permute(0, 2, 3, 1) .contiguous() .view(B, 1, C) ) # S-Map (before sigmoid) attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) spatial_map = self.spatial_interaction(attention_reshape) # C-I attened_x = attened_x * torch.sigmoid(channel_map) # S-I conv_x = torch.sigmoid(spatial_map) * conv_x conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) x = attened_x + conv_x x = self.proj(x) x = self.proj_drop(x) return x class Adaptive_Channel_Attention(nn.Module): # The implementation builds on XCiT code https://github.com/facebookresearch/xcit """Adaptive Channel Self-Attention Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. Default: 6 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. attn_drop (float): Attention dropout rate. Default: 0.0 drop_path (float): Stochastic depth rate. Default: 0.0 """ def __init__( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, ): super().__init__() self.num_heads = num_heads self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.dwconv = nn.Sequential( nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), nn.BatchNorm2d(dim), nn.GELU(), ) self.channel_interaction = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(dim, dim // 8, kernel_size=1), nn.BatchNorm2d(dim // 8), nn.GELU(), nn.Conv2d(dim // 8, dim, kernel_size=1), ) self.spatial_interaction = nn.Sequential( nn.Conv2d(dim, dim // 16, kernel_size=1), nn.BatchNorm2d(dim // 16), nn.GELU(), nn.Conv2d(dim // 16, 1, kernel_size=1), ) def forward(self, x, H, W): """ Input: x: (B, H*W, C), H, W Output: x: (B, H*W, C) """ B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) qkv = qkv.permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] q = q.transpose(-2, -1) k = k.transpose(-2, -1) v = v.transpose(-2, -1) v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) q = torch.nn.functional.normalize(q, dim=-1) k = torch.nn.functional.normalize(k, dim=-1) attn = (q @ k.transpose(-2, -1)) * self.temperature attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) # attention output attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) # convolution output conv_x = self.dwconv(v_) # Adaptive Interaction Module (AIM) # C-Map (before sigmoid) attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) channel_map = self.channel_interaction(attention_reshape) # S-Map (before sigmoid) spatial_map = ( self.spatial_interaction(conv_x) .permute(0, 2, 3, 1) .contiguous() .view(B, N, 1) ) # S-I attened_x = attened_x * torch.sigmoid(spatial_map) # C-I conv_x = conv_x * torch.sigmoid(channel_map) conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) x = attened_x + conv_x x = self.proj(x) x = self.proj_drop(x) return x class DATB(nn.Module): def __init__( self, dim, num_heads, reso=64, split_size=[2, 4], shift_size=[1, 2], expansion_factor=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, rg_idx=0, b_idx=0, ): super().__init__() self.norm1 = norm_layer(dim) if b_idx % 2 == 0: # DSTB self.attn = Adaptive_Spatial_Attention( dim, num_heads=num_heads, reso=reso, split_size=split_size, shift_size=shift_size, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, rg_idx=rg_idx, b_idx=b_idx, ) else: # DCTB self.attn = Adaptive_Channel_Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() ffn_hidden_dim = int(dim * expansion_factor) self.ffn = SGFN( in_features=dim, hidden_features=ffn_hidden_dim, out_features=dim, act_layer=act_layer, ) self.norm2 = norm_layer(dim) def forward(self, x, x_size): """ Input: x: (B, H*W, C), x_size: (H, W) Output: x: (B, H*W, C) """ H, W = x_size x = x + self.drop_path(self.attn(self.norm1(x), H, W)) x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) return x class ResidualGroup(nn.Module): """ResidualGroup Args: dim (int): Number of input channels. reso (int): Input resolution. num_heads (int): Number of attention heads. split_size (tuple(int)): Height and Width of spatial window. expansion_factor (float): Ratio of ffn hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None drop (float): Dropout rate. Default: 0 attn_drop(float): Attention dropout rate. Default: 0 drop_paths (float | None): Stochastic depth rate. act_layer (nn.Module): Activation layer. Default: nn.GELU norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm depth (int): Number of dual aggregation Transformer blocks in residual group. use_chk (bool): Whether to use checkpointing to save memory. resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__( self, dim, reso, num_heads, split_size=[2, 4], expansion_factor=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_paths=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, depth=2, use_chk=False, resi_connection="1conv", rg_idx=0, ): super().__init__() self.use_chk = use_chk self.reso = reso self.blocks = nn.ModuleList( [ DATB( dim=dim, num_heads=num_heads, reso=reso, split_size=split_size, shift_size=[split_size[0] // 2, split_size[1] // 2], expansion_factor=expansion_factor, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_paths[i], act_layer=act_layer, norm_layer=norm_layer, rg_idx=rg_idx, b_idx=i, ) for i in range(depth) ] ) if resi_connection == "1conv": self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == "3conv": self.conv = nn.Sequential( nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim, 3, 1, 1), ) def forward(self, x, x_size): """ Input: x: (B, H*W, C), x_size: (H, W) Output: x: (B, H*W, C) """ H, W = x_size res = x for blk in self.blocks: if self.use_chk: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) x = self.conv(x) x = rearrange(x, "b c h w -> b (h w) c") x = res + x return x class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError( f"scale {scale} is not supported. " "Supported scales: 2^n and 3." ) super(Upsample, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): h, w = self.input_resolution flops = h * w * self.num_feat * 3 * 9 return flops class DAT(nn.Module): """Dual Aggregation Transformer Args: img_size (int): Input image size. Default: 64 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 180 depths (tuple(int)): Depth of each residual group (number of DATB in each RG). split_size (tuple(int)): Height and Width of spatial window. num_heads (tuple(int)): Number of attention heads in different residual groups. expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 act_layer (nn.Module): Activation layer. Default: nn.GELU norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm use_chk (bool): Whether to use checkpointing to save memory. upscale: Upscale factor. 2/3/4 for image SR img_range: Image range. 1. or 255. resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__(self, state_dict): super().__init__() # defaults img_size = 64 in_chans = 3 embed_dim = 180 split_size = [2, 4] depth = [2, 2, 2, 2] num_heads = [2, 2, 2, 2] expansion_factor = 4.0 qkv_bias = True qk_scale = None drop_rate = 0.0 attn_drop_rate = 0.0 drop_path_rate = 0.1 act_layer = nn.GELU norm_layer = nn.LayerNorm use_chk = False upscale = 2 img_range = 1.0 resi_connection = "1conv" upsampler = "pixelshuffle" self.model_arch = "DAT" self.sub_type = "SR" self.state = state_dict state_keys = state_dict.keys() if "conv_before_upsample.0.weight" in state_keys: if "conv_up1.weight" in state_keys: upsampler = "nearest+conv" else: upsampler = "pixelshuffle" supports_fp16 = False elif "upsample.0.weight" in state_keys: upsampler = "pixelshuffledirect" else: upsampler = "" num_feat = ( state_dict.get("conv_before_upsample.0.weight", None).shape[1] if state_dict.get("conv_before_upsample.weight", None) else 64 ) num_in_ch = state_dict["conv_first.weight"].shape[1] in_chans = num_in_ch if "conv_last.weight" in state_keys: num_out_ch = state_dict["conv_last.weight"].shape[0] else: num_out_ch = num_in_ch upscale = 1 if upsampler == "nearest+conv": upsample_keys = [ x for x in state_keys if "conv_up" in x and "bias" not in x ] for upsample_key in upsample_keys: upscale *= 2 elif upsampler == "pixelshuffle": upsample_keys = [ x for x in state_keys if "upsample" in x and "conv" not in x and "bias" not in x ] for upsample_key in upsample_keys: shape = state_dict[upsample_key].shape[0] upscale *= math.sqrt(shape // num_feat) upscale = int(upscale) elif upsampler == "pixelshuffledirect": upscale = int( math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) ) max_layer_num = 0 max_block_num = 0 for key in state_keys: result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) if result: layer_num, block_num = result.groups() max_layer_num = max(max_layer_num, int(layer_num)) max_block_num = max(max_block_num, int(block_num)) depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] if "layers.0.blocks.1.attn.temperature" in state_keys: num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] num_heads = [num_heads_num for _ in range(max_layer_num + 1)] else: num_heads = depth embed_dim = state_dict["conv_first.weight"].shape[0] expansion_factor = float( state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim ) # TODO: could actually count the layers, but this should do if "layers.0.conv.4.weight" in state_keys: resi_connection = "3conv" else: resi_connection = "1conv" if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ "layers.0.blocks.2.attn.attn_mask_0" ].shape img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: split_sizes = ( state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 ) split_size = [int(x) for x in split_sizes] self.in_nc = num_in_ch self.out_nc = num_out_ch self.num_feat = num_feat self.embed_dim = embed_dim self.num_heads = num_heads self.depth = depth self.scale = upscale self.upsampler = upsampler self.img_size = img_size self.img_range = img_range self.expansion_factor = expansion_factor self.resi_connection = resi_connection self.split_size = split_size self.supports_fp16 = False # Too much weirdness to support this at the moment self.supports_bfp16 = True self.min_size_restriction = 16 num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler # ------------------------- 1, Shallow Feature Extraction ------------------------- # self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) # ------------------------- 2, Deep Feature Extraction ------------------------- # self.num_layers = len(depth) self.use_chk = use_chk self.num_features = ( self.embed_dim ) = embed_dim # num_features for consistency with other models heads = num_heads self.before_RG = nn.Sequential( Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) ) curr_dim = embed_dim dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) ] # stochastic depth decay rule self.layers = nn.ModuleList() for i in range(self.num_layers): layer = ResidualGroup( dim=embed_dim, num_heads=heads[i], reso=img_size, split_size=split_size, expansion_factor=expansion_factor, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], act_layer=act_layer, norm_layer=norm_layer, depth=depth[i], use_chk=use_chk, resi_connection=resi_connection, rg_idx=i, ) self.layers.append(layer) self.norm = norm_layer(curr_dim) # build the last conv layer in deep feature extraction if resi_connection == "1conv": self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == "3conv": # to save parameters and memory self.conv_after_body = nn.Sequential( nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), ) # ------------------------- 3, Reconstruction ------------------------- # if self.upsampler == "pixelshuffle": # for classical SR self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == "pixelshuffledirect": # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep( upscale, embed_dim, num_out_ch, (img_size, img_size) ) self.apply(self._init_weights) self.load_state_dict(state_dict, strict=True) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance( m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) ): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def forward_features(self, x): _, _, H, W = x.shape x_size = [H, W] x = self.before_RG(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) return x def forward(self, x): """ Input: x: (B, C, H, W) """ self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == "pixelshuffle": # for image SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) elif self.upsampler == "pixelshuffledirect": # for lightweight SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.upsample(x) x = x / self.img_range + self.mean return x ================================================ FILE: ldm_patched/pfn/architecture/HAT.py ================================================ # pylint: skip-file # HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py import math import re import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from .timm.helpers import to_2tuple from .timm.weight_init import trunc_normal_ def drop_path(x, drop_prob: float = 0.0, training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py """ if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * ( x.ndim - 1 ) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) # type: ignore class ChannelAttention(nn.Module): """Channel attention used in RCAN. Args: num_feat (int): Channel number of intermediate features. squeeze_factor (int): Channel squeeze factor. Default: 16. """ def __init__(self, num_feat, squeeze_factor=16): super(ChannelAttention, self).__init__() self.attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid(), ) def forward(self, x): y = self.attention(x) return x * y class CAB(nn.Module): def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): super(CAB, self).__init__() self.cab = nn.Sequential( nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), nn.GELU(), nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), ChannelAttention(num_feat, squeeze_factor), ) def forward(self, x): return self.cab(x) class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (b, h, w, c) window_size (int): window size Returns: windows: (num_windows*b, window_size, window_size, c) """ b, h, w, c = x.shape x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) windows = ( x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) ) return windows def window_reverse(windows, window_size, h, w): """ Args: windows: (num_windows*b, window_size, window_size, c) window_size (int): Window size h (int): Height of image w (int): Width of image Returns: x: (b, h, w, c) """ b = int(windows.shape[0] / (h * w / window_size / window_size)) x = windows.view( b, h // window_size, w // window_size, window_size, window_size, -1 ) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) return x class WindowAttention(nn.Module): r"""Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 """ def __init__( self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, ): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( # type: ignore torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) ) # 2*Wh-1 * 2*Ww-1, nH self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) trunc_normal_(self.relative_position_bias_table, std=0.02) self.softmax = nn.Softmax(dim=-1) def forward(self, x, rpi, mask=None): """ Args: x: input features with shape of (num_windows*b, n, c) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ b_, n, c = x.shape qkv = ( self.qkv(x) .reshape(b_, n, 3, self.num_heads, c // self.num_heads) .permute(2, 0, 3, 1, 4) ) q, k, v = ( qkv[0], qkv[1], qkv[2], ) # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = q @ k.transpose(-2, -1) relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1, ) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nw = mask.shape[0] attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze( 1 ).unsqueeze(0) attn = attn.view(-1, self.num_heads, n, n) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(b_, n, c) x = self.proj(x) x = self.proj_drop(x) return x class HAB(nn.Module): r"""Hybrid Attention Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__( self, dim, input_resolution, num_heads, window_size=7, shift_size=0, compress_ratio=3, squeeze_factor=30, conv_scale=0.01, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert ( 0 <= self.shift_size < self.window_size ), "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, ) self.conv_scale = conv_scale self.conv_block = CAB( num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, ) def forward(self, x, x_size, rpi_sa, attn_mask): h, w = x_size b, _, c = x.shape # assert seq_len == h * w, "input feature has wrong size" shortcut = x x = self.norm1(x) x = x.view(b, h, w, c) # Conv_X conv_x = self.conv_block(x.permute(0, 3, 1, 2)) conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll( x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) ) attn_mask = attn_mask else: shifted_x = x attn_mask = None # partition windows x_windows = window_partition( shifted_x, self.window_size ) # nw*b, window_size, window_size, c x_windows = x_windows.view( -1, self.window_size * self.window_size, c ) # nw*b, window_size*window_size, c # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c # reverse cyclic shift if self.shift_size > 0: attn_x = torch.roll( shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) ) else: attn_x = shifted_x attn_x = attn_x.view(b, h * w, c) # FFN x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale x = x + self.drop_path(self.mlp(self.norm2(x))) return x class PatchMerging(nn.Module): r"""Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def forward(self, x): """ x: b, h*w, c """ h, w = self.input_resolution b, seq_len, c = x.shape assert seq_len == h * w, "input feature has wrong size" assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." x = x.view(b, h, w, c) x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c x = self.norm(x) x = self.reduction(x) return x class OCAB(nn.Module): # overlapping cross-attention block def __init__( self, dim, input_resolution, window_size, overlap_ratio, num_heads, qkv_bias=True, qk_scale=None, mlp_ratio=2, norm_layer=nn.LayerNorm, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.window_size = window_size self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 self.overlap_win_size = int(window_size * overlap_ratio) + window_size self.norm1 = norm_layer(dim) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.unfold = nn.Unfold( kernel_size=(self.overlap_win_size, self.overlap_win_size), stride=window_size, padding=(self.overlap_win_size - window_size) // 2, ) # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( # type: ignore torch.zeros( (window_size + self.overlap_win_size - 1) * (window_size + self.overlap_win_size - 1), num_heads, ) ) # 2*Wh-1 * 2*Ww-1, nH trunc_normal_(self.relative_position_bias_table, std=0.02) self.softmax = nn.Softmax(dim=-1) self.proj = nn.Linear(dim, dim) self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU ) def forward(self, x, x_size, rpi): h, w = x_size b, _, c = x.shape shortcut = x x = self.norm1(x) x = x.view(b, h, w, c) qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2) # 3, b, c, h, w q = qkv[0].permute(0, 2, 3, 1) # b, h, w, c kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w # partition windows q_windows = window_partition( q, self.window_size ) # nw*b, window_size, window_size, c q_windows = q_windows.view( -1, self.window_size * self.window_size, c ) # nw*b, window_size*window_size, c kv_windows = self.unfold(kv) # b, c*w*w, nw kv_windows = rearrange( kv_windows, "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch", nc=2, ch=c, owh=self.overlap_win_size, oww=self.overlap_win_size, ).contiguous() # 2, nw*b, ow*ow, c # Do the above rearrangement without the rearrange function # kv_windows = kv_windows.view( # 2, b, self.overlap_win_size, self.overlap_win_size, c, -1 # ) # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous() # kv_windows = kv_windows.view( # 2, -1, self.overlap_win_size * self.overlap_win_size, c # ) k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c b_, nq, _ = q_windows.shape _, n, _ = k_windows.shape d = self.dim // self.num_heads q = q_windows.reshape(b_, nq, self.num_heads, d).permute( 0, 2, 1, 3 ) # nw*b, nH, nq, d k = k_windows.reshape(b_, n, self.num_heads, d).permute( 0, 2, 1, 3 ) # nw*b, nH, n, d v = v_windows.reshape(b_, n, self.num_heads, d).permute( 0, 2, 1, 3 ) # nw*b, nH, n, d q = q * self.scale attn = q @ k.transpose(-2, -1) relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( self.window_size * self.window_size, self.overlap_win_size * self.overlap_win_size, -1, ) # ws*ws, wse*wse, nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, ws*ws, wse*wse attn = attn + relative_position_bias.unsqueeze(0) attn = self.softmax(attn) attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim) # merge windows attn_windows = attn_windows.view( -1, self.window_size, self.window_size, self.dim ) x = window_reverse(attn_windows, self.window_size, h, w) # b h w c x = x.view(b, h * w, self.dim) x = self.proj(x) + shortcut x = x + self.mlp(self.norm2(x)) return x class AttenBlocks(nn.Module): """A series of attention blocks for one RHAG. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, compress_ratio, squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList( [ HAB( dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, ) for i in range(depth) ] ) # OCAB self.overlap_attn = OCAB( dim=dim, input_resolution=input_resolution, window_size=window_size, overlap_ratio=overlap_ratio, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, mlp_ratio=mlp_ratio, # type: ignore norm_layer=norm_layer, ) # patch merging layer if downsample is not None: self.downsample = downsample( input_resolution, dim=dim, norm_layer=norm_layer ) else: self.downsample = None def forward(self, x, x_size, params): for blk in self.blocks: x = blk(x, x_size, params["rpi_sa"], params["attn_mask"]) x = self.overlap_attn(x, x_size, params["rpi_oca"]) if self.downsample is not None: x = self.downsample(x) return x class RHAG(nn.Module): """Residual Hybrid Attention Group (RHAG). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, compress_ratio, squeeze_factor, conv_scale, overlap_ratio, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection="1conv", ): super(RHAG, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = AttenBlocks( dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint, ) if resi_connection == "1conv": self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == "identity": self.conv = nn.Identity() self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None, ) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None, ) def forward(self, x, x_size, params): return ( self.patch_embed( self.conv( self.patch_unembed(self.residual_group(x, x_size, params), x_size) ) ) + x ) class PatchEmbed(nn.Module): r"""Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [ img_size[0] // patch_size[0], # type: ignore img_size[1] // patch_size[1], # type: ignore ] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): x = x.flatten(2).transpose(1, 2) # b Ph*Pw c if self.norm is not None: x = self.norm(x) return x class PatchUnEmbed(nn.Module): r"""Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [ img_size[0] // patch_size[0], # type: ignore img_size[1] // patch_size[1], # type: ignore ] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim def forward(self, x, x_size): x = ( x.transpose(1, 2) .contiguous() .view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) ) # b Ph*Pw c return x class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError( f"scale {scale} is not supported. " "Supported scales: 2^n and 3." ) super(Upsample, self).__init__(*m) class HAT(nn.Module): r"""Hybrid Attention Transformer A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`. Some codes are based on SwinIR. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__( self, state_dict, **kwargs, ): super(HAT, self).__init__() # Defaults img_size = 64 patch_size = 1 in_chans = 3 embed_dim = 96 depths = (6, 6, 6, 6) num_heads = (6, 6, 6, 6) window_size = 7 compress_ratio = 3 squeeze_factor = 30 conv_scale = 0.01 overlap_ratio = 0.5 mlp_ratio = 4.0 qkv_bias = True qk_scale = None drop_rate = 0.0 attn_drop_rate = 0.0 drop_path_rate = 0.1 norm_layer = nn.LayerNorm ape = False patch_norm = True use_checkpoint = False upscale = 2 img_range = 1.0 upsampler = "" resi_connection = "1conv" self.state = state_dict self.model_arch = "HAT" self.sub_type = "SR" self.supports_fp16 = False self.support_bf16 = True self.min_size_restriction = 16 state_keys = list(state_dict.keys()) num_feat = state_dict["conv_last.weight"].shape[1] in_chans = state_dict["conv_first.weight"].shape[1] num_out_ch = state_dict["conv_last.weight"].shape[0] embed_dim = state_dict["conv_first.weight"].shape[0] if "conv_before_upsample.0.weight" in state_keys: if "conv_up1.weight" in state_keys: upsampler = "nearest+conv" else: upsampler = "pixelshuffle" supports_fp16 = False elif "upsample.0.weight" in state_keys: upsampler = "pixelshuffledirect" else: upsampler = "" upscale = 1 if upsampler == "nearest+conv": upsample_keys = [ x for x in state_keys if "conv_up" in x and "bias" not in x ] for upsample_key in upsample_keys: upscale *= 2 elif upsampler == "pixelshuffle": upsample_keys = [ x for x in state_keys if "upsample" in x and "conv" not in x and "bias" not in x ] for upsample_key in upsample_keys: shape = self.state[upsample_key].shape[0] upscale *= math.sqrt(shape // num_feat) upscale = int(upscale) elif upsampler == "pixelshuffledirect": upscale = int( math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) ) max_layer_num = 0 max_block_num = 0 for key in state_keys: result = re.match( r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key ) if result: layer_num, block_num = result.groups() max_layer_num = max(max_layer_num, int(layer_num)) max_block_num = max(max_block_num, int(block_num)) depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] if ( "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" in state_keys ): num_heads_num = self.state[ "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" ].shape[-1] num_heads = [num_heads_num for _ in range(max_layer_num + 1)] else: num_heads = depths mlp_ratio = float( self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] / embed_dim ) # TODO: could actually count the layers, but this should do if "layers.0.conv.4.weight" in state_keys: resi_connection = "3conv" else: resi_connection = "1conv" window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0])) # Not sure if this is needed or used at all anywhere in HAT's config if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: img_size = int( math.sqrt( self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] ) * window_size ) self.window_size = window_size self.shift_size = window_size // 2 self.overlap_ratio = overlap_ratio self.in_nc = in_chans self.out_nc = num_out_ch self.num_feat = num_feat self.embed_dim = embed_dim self.num_heads = num_heads self.depths = depths self.window_size = window_size self.mlp_ratio = mlp_ratio self.scale = upscale self.upsampler = upsampler self.img_size = img_size self.img_range = img_range self.resi_connection = resi_connection num_in_ch = in_chans # num_out_ch = in_chans # num_feat = 64 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler # relative position index relative_position_index_SA = self.calculate_rpi_sa() relative_position_index_OCA = self.calculate_rpi_oca() self.register_buffer("relative_position_index_SA", relative_position_index_SA) self.register_buffer("relative_position_index_OCA", relative_position_index_OCA) # ------------------------- 1, shallow feature extraction ------------------------- # self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) # ------------------------- 2, deep feature extraction ------------------------- # self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter( # type: ignore[arg-type] torch.zeros(1, num_patches, embed_dim) ) trunc_normal_(self.absolute_pos_embed, std=0.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) ] # stochastic depth decay rule # build Residual Hybrid Attention Groups (RHAG) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RHAG( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor, conv_scale=conv_scale, overlap_ratio=overlap_ratio, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[ sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore ], # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection, ) self.layers.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == "1conv": self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == "identity": self.conv_after_body = nn.Identity() # ------------------------- 3, high quality image reconstruction ------------------------- # if self.upsampler == "pixelshuffle": # for classical SR self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.apply(self._init_weights) self.load_state_dict(self.state, strict=False) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def calculate_rpi_sa(self): # calculate relative position index for SA coords_h = torch.arange(self.window_size) coords_w = torch.arange(self.window_size) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size - 1 relative_coords[:, :, 0] *= 2 * self.window_size - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww return relative_position_index def calculate_rpi_oca(self): # calculate relative position index for OCA window_size_ori = self.window_size window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) coords_h = torch.arange(window_size_ori) coords_w = torch.arange(window_size_ori) coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws coords_h = torch.arange(window_size_ext) coords_w = torch.arange(window_size_ext) coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse relative_coords = ( coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] ) # 2, ws*ws, wse*wse relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # ws*ws, wse*wse, 2 relative_coords[:, :, 0] += ( window_size_ori - window_size_ext + 1 ) # shift to start from 0 relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 relative_position_index = relative_coords.sum(-1) return relative_position_index def calculate_mask(self, x_size): # calculate attention mask for SW-MSA h, w = x_size img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 h_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) w_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition( img_mask, self.window_size ) # nw, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( attn_mask == 0, float(0.0) ) return attn_mask @torch.jit.ignore # type: ignore def no_weight_decay(self): return {"absolute_pos_embed"} @torch.jit.ignore # type: ignore def no_weight_decay_keywords(self): return {"relative_position_bias_table"} def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") return x def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) # Calculate attention mask and relative position index in advance to speed up inference. # The original code is very time-cosuming for large window size. attn_mask = self.calculate_mask(x_size).to(x.device) params = { "attn_mask": attn_mask, "rpi_sa": self.relative_position_index_SA, "rpi_oca": self.relative_position_index_OCA, } x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size, params) x = self.norm(x) # b seq_len c x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range x = self.check_image_size(x) if self.upsampler == "pixelshuffle": # for classical SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) x = x / self.img_range + self.mean return x[:, :, : H * self.upscale, : W * self.upscale] ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-DAT ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-ESRGAN ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-HAT ================================================ MIT License Copyright (c) 2022 Xiangyu Chen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-RealESRGAN ================================================ BSD 3-Clause License Copyright (c) 2021, Xintao Wang All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-SCUNet ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-SPSR ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2018-2022 BasicSR Authors Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-SwiftSRGAN ================================================ Creative Commons Legal Code CC0 1.0 Universal CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER. Statement of Purpose The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an "owner") of an original work of authorship and/or a database (each, a "Work"). Certain owners wish to permanently relinquish those rights to a Work for the purpose of contributing to a commons of creative, cultural and scientific works ("Commons") that the public can reliably and without fear of later claims of infringement build upon, modify, incorporate in other works, reuse and redistribute as freely as possible in any form whatsoever and for any purposes, including without limitation commercial purposes. These owners may contribute to the Commons to promote the ideal of a free culture and the further production of creative, cultural and scientific works, or to gain reputation or greater distribution for their Work in part through the use and efforts of others. For these and/or other purposes and motivations, and without any expectation of additional consideration or compensation, the person associating CC0 with a Work (the "Affirmer"), to the extent that he or she is an owner of Copyright and Related Rights in the Work, voluntarily elects to apply CC0 to the Work and publicly distribute the Work under its terms, with knowledge of his or her Copyright and Related Rights in the Work and the meaning and intended legal effect of CC0 on those rights. 1. Copyright and Related Rights. A Work made available under CC0 may be protected by copyright and related or neighboring rights ("Copyright and Related Rights"). Copyright and Related Rights include, but are not limited to, the following: i. the right to reproduce, adapt, distribute, perform, display, communicate, and translate a Work; ii. moral rights retained by the original author(s) and/or performer(s); iii. publicity and privacy rights pertaining to a person's image or likeness depicted in a Work; iv. rights protecting against unfair competition in regards to a Work, subject to the limitations in paragraph 4(a), below; v. rights protecting the extraction, dissemination, use and reuse of data in a Work; vi. database rights (such as those arising under Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, and under any national implementation thereof, including any amended or successor version of such directive); and vii. other similar, equivalent or corresponding rights throughout the world based on applicable law or treaty, and any national implementations thereof. 2. Waiver. To the greatest extent permitted by, but not in contravention of, applicable law, Affirmer hereby overtly, fully, permanently, irrevocably and unconditionally waives, abandons, and surrenders all of Affirmer's Copyright and Related Rights and associated claims and causes of action, whether now known or unknown (including existing as well as future claims and causes of action), in the Work (i) in all territories worldwide, (ii) for the maximum duration provided by applicable law or treaty (including future time extensions), (iii) in any current or future medium and for any number of copies, and (iv) for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes (the "Waiver"). Affirmer makes the Waiver for the benefit of each member of the public at large and to the detriment of Affirmer's heirs and successors, fully intending that such Waiver shall not be subject to revocation, rescission, cancellation, termination, or any other legal or equitable action to disrupt the quiet enjoyment of the Work by the public as contemplated by Affirmer's express Statement of Purpose. 3. Public License Fallback. Should any part of the Waiver for any reason be judged legally invalid or ineffective under applicable law, then the Waiver shall be preserved to the maximum extent permitted taking into account Affirmer's express Statement of Purpose. In addition, to the extent the Waiver is so judged Affirmer hereby grants to each affected person a royalty-free, non transferable, non sublicensable, non exclusive, irrevocable and unconditional license to exercise Affirmer's Copyright and Related Rights in the Work (i) in all territories worldwide, (ii) for the maximum duration provided by applicable law or treaty (including future time extensions), (iii) in any current or future medium and for any number of copies, and (iv) for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes (the "License"). The License shall be deemed effective as of the date CC0 was applied by Affirmer to the Work. Should any part of the License for any reason be judged legally invalid or ineffective under applicable law, such partial invalidity or ineffectiveness shall not invalidate the remainder of the License, and in such case Affirmer hereby affirms that he or she will not (i) exercise any of his or her remaining Copyright and Related Rights in the Work or (ii) assert any associated claims and causes of action with respect to the Work, in either case contrary to Affirmer's express Statement of Purpose. 4. Limitations and Disclaimers. a. No trademark or patent rights held by Affirmer are waived, abandoned, surrendered, licensed or otherwise affected by this document. b. Affirmer offers the Work as-is and makes no representations or warranties of any kind concerning the Work, express, implied, statutory or otherwise, including without limitation warranties of title, merchantability, fitness for a particular purpose, non infringement, or the absence of latent or other defects, accuracy, or the present or absence of errors, whether or not discoverable, all to the greatest extent permissible under applicable law. c. Affirmer disclaims responsibility for clearing rights of other persons that may apply to the Work or any use thereof, including without limitation any person's Copyright and Related Rights in the Work. Further, Affirmer disclaims responsibility for obtaining any necessary consents, permissions or other rights required for any use of the Work. d. Affirmer understands and acknowledges that Creative Commons is not a party to this document and has no duty or obligation with respect to this CC0 or use of the Work. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-Swin2SR ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2021] [SwinIR Authors] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-SwinIR ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2021] [SwinIR Authors] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LICENSE-lama ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [2021] Samsung Research Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/LaMa.py ================================================ # pylint: skip-file """ Model adapted from advimman's lama project: https://github.com/advimman/lama """ # Fast Fourier Convolution NeurIPS 2020 # original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py # paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf from typing import List import torch import torch.nn as nn import torch.nn.functional as F from torchvision.transforms.functional import InterpolationMode, rotate class LearnableSpatialTransformWrapper(nn.Module): def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): super().__init__() self.impl = impl self.angle = torch.rand(1) * angle_init_range if train_angle: self.angle = nn.Parameter(self.angle, requires_grad=True) self.pad_coef = pad_coef def forward(self, x): if torch.is_tensor(x): return self.inverse_transform(self.impl(self.transform(x)), x) elif isinstance(x, tuple): x_trans = tuple(self.transform(elem) for elem in x) y_trans = self.impl(x_trans) return tuple( self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x) ) else: raise ValueError(f"Unexpected input type {type(x)}") def transform(self, x): height, width = x.shape[2:] pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect") x_padded_rotated = rotate( x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0 ) return x_padded_rotated def inverse_transform(self, y_padded_rotated, orig_x): height, width = orig_x.shape[2:] pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) y_padded = rotate( y_padded_rotated, -self.angle.to(y_padded_rotated), InterpolationMode.BILINEAR, fill=0, ) y_height, y_width = y_padded.shape[2:] y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] return y class SELayer(nn.Module): def __init__(self, channel, reduction=16): super(SELayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction, channel, bias=False), nn.Sigmoid(), ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) res = x * y.expand_as(x) return res class FourierUnit(nn.Module): def __init__( self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode="bilinear", spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm="ortho", ): # bn_layer not used super(FourierUnit, self).__init__() self.groups = groups self.conv_layer = torch.nn.Conv2d( in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), out_channels=out_channels * 2, kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False, ) self.bn = torch.nn.BatchNorm2d(out_channels * 2) self.relu = torch.nn.ReLU(inplace=True) # squeeze and excitation block self.use_se = use_se if use_se: if se_kwargs is None: se_kwargs = {} self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) self.spatial_scale_factor = spatial_scale_factor self.spatial_scale_mode = spatial_scale_mode self.spectral_pos_encoding = spectral_pos_encoding self.ffc3d = ffc3d self.fft_norm = fft_norm def forward(self, x): half_check = False if x.type() == "torch.cuda.HalfTensor": # half only works on gpu anyway half_check = True batch = x.shape[0] if self.spatial_scale_factor is not None: orig_size = x.shape[-2:] x = F.interpolate( x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False, ) # (batch, c, h, w/2+1, 2) fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) if half_check == True: ffted = torch.fft.rfftn( x.float(), dim=fft_dim, norm=self.fft_norm ) # .type(torch.cuda.HalfTensor) else: ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) ffted = torch.stack((ffted.real, ffted.imag), dim=-1) ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) ffted = ffted.view( ( batch, -1, ) + ffted.size()[3:] ) if self.spectral_pos_encoding: height, width = ffted.shape[-2:] coords_vert = ( torch.linspace(0, 1, height)[None, None, :, None] .expand(batch, 1, height, width) .to(ffted) ) coords_hor = ( torch.linspace(0, 1, width)[None, None, None, :] .expand(batch, 1, height, width) .to(ffted) ) ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) if self.use_se: ffted = self.se(ffted) if half_check == True: ffted = self.conv_layer(ffted.half()) # (batch, c*2, h, w/2+1) else: ffted = self.conv_layer( ffted ) # .type(torch.cuda.FloatTensor) # (batch, c*2, h, w/2+1) ffted = self.relu(self.bn(ffted)) # forcing to be always float ffted = ffted.float() ffted = ( ffted.view( ( batch, -1, 2, ) + ffted.size()[2:] ) .permute(0, 1, 3, 4, 2) .contiguous() ) # (batch,c, t, h, w/2+1, 2) ffted = torch.complex(ffted[..., 0], ffted[..., 1]) ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] output = torch.fft.irfftn( ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm ) if half_check == True: output = output.half() if self.spatial_scale_factor is not None: output = F.interpolate( output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False, ) return output class SpectralTransform(nn.Module): def __init__( self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs, ): # bn_layer not used super(SpectralTransform, self).__init__() self.enable_lfu = enable_lfu if stride == 2: self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) else: self.downsample = nn.Identity() self.stride = stride self.conv1 = nn.Sequential( nn.Conv2d( in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False ), nn.BatchNorm2d(out_channels // 2), nn.ReLU(inplace=True), ) fu_class = FourierUnit self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs) if self.enable_lfu: self.lfu = fu_class(out_channels // 2, out_channels // 2, groups) self.conv2 = torch.nn.Conv2d( out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False ) def forward(self, x): x = self.downsample(x) x = self.conv1(x) output = self.fu(x) if self.enable_lfu: _, c, h, _ = x.shape split_no = 2 split_s = h // split_no xs = torch.cat( torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 ).contiguous() xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() xs = self.lfu(xs) xs = xs.repeat(1, 1, split_no, split_no).contiguous() else: xs = 0 output = self.conv2(x + output + xs) return output class FFC(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride=1, padding=0, dilation=1, groups=1, bias=False, enable_lfu=True, padding_type="reflect", gated=False, **spectral_kwargs, ): super(FFC, self).__init__() assert stride == 1 or stride == 2, "Stride should be 1 or 2." self.stride = stride in_cg = int(in_channels * ratio_gin) in_cl = in_channels - in_cg out_cg = int(out_channels * ratio_gout) out_cl = out_channels - out_cg # groups_g = 1 if groups == 1 else int(groups * ratio_gout) # groups_l = 1 if groups == 1 else groups - groups_g self.ratio_gin = ratio_gin self.ratio_gout = ratio_gout self.global_in_num = in_cg module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d self.convl2l = module( in_cl, out_cl, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d self.convl2g = module( in_cl, out_cg, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d self.convg2l = module( in_cg, out_cl, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform self.convg2g = module( in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs, ) self.gated = gated module = ( nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d ) self.gate = module(in_channels, 2, 1) def forward(self, x): x_l, x_g = x if type(x) is tuple else (x, 0) out_xl, out_xg = 0, 0 if self.gated: total_input_parts = [x_l] if torch.is_tensor(x_g): total_input_parts.append(x_g) total_input = torch.cat(total_input_parts, dim=1) gates = torch.sigmoid(self.gate(total_input)) g2l_gate, l2g_gate = gates.chunk(2, dim=1) else: g2l_gate, l2g_gate = 1, 1 if self.ratio_gout != 1: out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate if self.ratio_gout != 0: out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) return out_xl, out_xg class FFC_BN_ACT(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride=1, padding=0, dilation=1, groups=1, bias=False, norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity, padding_type="reflect", enable_lfu=True, **kwargs, ): super(FFC_BN_ACT, self).__init__() self.ffc = FFC( in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride, padding, dilation, groups, bias, enable_lfu, padding_type=padding_type, **kwargs, ) lnorm = nn.Identity if ratio_gout == 1 else norm_layer gnorm = nn.Identity if ratio_gout == 0 else norm_layer global_channels = int(out_channels * ratio_gout) self.bn_l = lnorm(out_channels - global_channels) self.bn_g = gnorm(global_channels) lact = nn.Identity if ratio_gout == 1 else activation_layer gact = nn.Identity if ratio_gout == 0 else activation_layer self.act_l = lact(inplace=True) self.act_g = gact(inplace=True) def forward(self, x): x_l, x_g = self.ffc(x) x_l = self.act_l(self.bn_l(x_l)) x_g = self.act_g(self.bn_g(x_g)) return x_l, x_g class FFCResnetBlock(nn.Module): def __init__( self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, spatial_transform_kwargs=None, inline=False, **conv_kwargs, ): super().__init__() self.conv1 = FFC_BN_ACT( dim, dim, kernel_size=3, padding=dilation, dilation=dilation, norm_layer=norm_layer, activation_layer=activation_layer, padding_type=padding_type, **conv_kwargs, ) self.conv2 = FFC_BN_ACT( dim, dim, kernel_size=3, padding=dilation, dilation=dilation, norm_layer=norm_layer, activation_layer=activation_layer, padding_type=padding_type, **conv_kwargs, ) if spatial_transform_kwargs is not None: self.conv1 = LearnableSpatialTransformWrapper( self.conv1, **spatial_transform_kwargs ) self.conv2 = LearnableSpatialTransformWrapper( self.conv2, **spatial_transform_kwargs ) self.inline = inline def forward(self, x): if self.inline: x_l, x_g = ( x[:, : -self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num :], ) else: x_l, x_g = x if type(x) is tuple else (x, 0) id_l, id_g = x_l, x_g x_l, x_g = self.conv1((x_l, x_g)) x_l, x_g = self.conv2((x_l, x_g)) x_l, x_g = id_l + x_l, id_g + x_g out = x_l, x_g if self.inline: out = torch.cat(out, dim=1) return out class ConcatTupleLayer(nn.Module): def forward(self, x): assert isinstance(x, tuple) x_l, x_g = x assert torch.is_tensor(x_l) or torch.is_tensor(x_g) if not torch.is_tensor(x_g): return x_l return torch.cat(x, dim=1) class FFCResNetGenerator(nn.Module): def __init__( self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=18, norm_layer=nn.BatchNorm2d, padding_type="reflect", activation_layer=nn.ReLU, up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={}, spatial_transform_layers=None, spatial_transform_kwargs={}, max_features=1024, out_ffc=False, out_ffc_kwargs={}, ): assert n_blocks >= 0 super().__init__() """ init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False} downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False} resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False} spatial_transform_kwargs = {} out_ffc_kwargs = {} """ """ print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer, padding_type, activation_layer, up_norm_layer, up_activation, spatial_transform_layers, add_out_act, max_features, out_ffc, file=sys.stderr) 4 3 64 3 18 reflect ReLU(inplace=True) None sigmoid 1024 False """ init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} resnet_conv_kwargs = { "ratio_gin": 0.75, "ratio_gout": 0.75, "enable_lfu": False, } spatial_transform_kwargs = {} out_ffc_kwargs = {} model = [ nn.ReflectionPad2d(3), FFC_BN_ACT( input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer, activation_layer=activation_layer, **init_conv_kwargs, ), ] ### downsample for i in range(n_downsampling): mult = 2**i if i == n_downsampling - 1: cur_conv_kwargs = dict(downsample_conv_kwargs) cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0) else: cur_conv_kwargs = downsample_conv_kwargs model += [ FFC_BN_ACT( min(max_features, ngf * mult), min(max_features, ngf * mult * 2), kernel_size=3, stride=2, padding=1, norm_layer=norm_layer, activation_layer=activation_layer, **cur_conv_kwargs, ) ] mult = 2**n_downsampling feats_num_bottleneck = min(max_features, ngf * mult) ### resnet blocks for i in range(n_blocks): cur_resblock = FFCResnetBlock( feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer, norm_layer=norm_layer, **resnet_conv_kwargs, ) if spatial_transform_layers is not None and i in spatial_transform_layers: cur_resblock = LearnableSpatialTransformWrapper( cur_resblock, **spatial_transform_kwargs ) model += [cur_resblock] model += [ConcatTupleLayer()] ### upsample for i in range(n_downsampling): mult = 2 ** (n_downsampling - i) model += [ nn.ConvTranspose2d( min(max_features, ngf * mult), min(max_features, int(ngf * mult / 2)), kernel_size=3, stride=2, padding=1, output_padding=1, ), up_norm_layer(min(max_features, int(ngf * mult / 2))), up_activation, ] if out_ffc: model += [ FFCResnetBlock( ngf, padding_type=padding_type, activation_layer=activation_layer, norm_layer=norm_layer, inline=True, **out_ffc_kwargs, ) ] model += [ nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), ] model.append(nn.Sigmoid()) self.model = nn.Sequential(*model) def forward(self, image, mask): return self.model(torch.cat([image, mask], dim=1)) class LaMa(nn.Module): def __init__(self, state_dict) -> None: super(LaMa, self).__init__() self.model_arch = "LaMa" self.sub_type = "Inpaint" self.in_nc = 4 self.out_nc = 3 self.scale = 1 self.min_size = None self.pad_mod = 8 self.pad_to_square = False self.model = FFCResNetGenerator(self.in_nc, self.out_nc) self.state = { k.replace("generator.model", "model.model"): v for k, v in state_dict.items() } self.supports_fp16 = False self.support_bf16 = True self.load_state_dict(self.state, strict=False) def forward(self, img, mask): masked_img = img * (1 - mask) inpainted_mask = mask * self.model.forward(masked_img, mask) result = inpainted_mask + (1 - mask) * img return result ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py ================================================ import math import torch.nn as nn class CA_layer(nn.Module): def __init__(self, channel, reduction=16): super(CA_layer, self).__init__() # global average pooling self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False), nn.GELU(), nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False), # nn.Sigmoid() ) def forward(self, x): y = self.fc(self.gap(x)) return x * y.expand_as(x) class Simple_CA_layer(nn.Module): def __init__(self, channel): super(Simple_CA_layer, self).__init__() self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Conv2d( in_channels=channel, out_channels=channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, ) def forward(self, x): return x * self.fc(self.gap(x)) class ECA_layer(nn.Module): """Constructs a ECA module. Args: channel: Number of channels of the input feature map k_size: Adaptive selection of kernel size """ def __init__(self, channel): super(ECA_layer, self).__init__() b = 1 gamma = 2 k_size = int(abs(math.log(channel, 2) + b) / gamma) k_size = k_size if k_size % 2 else k_size + 1 self.avg_pool = nn.AdaptiveAvgPool2d(1) self.conv = nn.Conv1d( 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False ) # self.sigmoid = nn.Sigmoid() def forward(self, x): # x: input features with shape [b, c, h, w] # b, c, h, w = x.size() # feature descriptor on the global spatial information y = self.avg_pool(x) # Two different branches of ECA module y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) # Multi-scale information fusion # y = self.sigmoid(y) return x * y.expand_as(x) class ECA_MaxPool_layer(nn.Module): """Constructs a ECA module. Args: channel: Number of channels of the input feature map k_size: Adaptive selection of kernel size """ def __init__(self, channel): super(ECA_MaxPool_layer, self).__init__() b = 1 gamma = 2 k_size = int(abs(math.log(channel, 2) + b) / gamma) k_size = k_size if k_size % 2 else k_size + 1 self.max_pool = nn.AdaptiveMaxPool2d(1) self.conv = nn.Conv1d( 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False ) # self.sigmoid = nn.Sigmoid() def forward(self, x): # x: input features with shape [b, c, h, w] # b, c, h, w = x.size() # feature descriptor on the global spatial information y = self.max_pool(x) # Two different branches of ECA module y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) # Multi-scale information fusion # y = self.sigmoid(y) return x * y.expand_as(x) ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/OSA.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: OSA.py # Created Date: Tuesday April 28th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Sunday, 23rd April 2023 3:07:42 pm # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# import torch import torch.nn.functional as F from einops import rearrange, repeat from einops.layers.torch import Rearrange, Reduce from torch import einsum, nn from .layernorm import LayerNorm2d # helpers def exists(val): return val is not None def default(val, d): return val if exists(val) else d def cast_tuple(val, length=1): return val if isinstance(val, tuple) else ((val,) * length) # helper classes class PreNormResidual(nn.Module): def __init__(self, dim, fn): super().__init__() self.norm = nn.LayerNorm(dim) self.fn = fn def forward(self, x): return self.fn(self.norm(x)) + x class Conv_PreNormResidual(nn.Module): def __init__(self, dim, fn): super().__init__() self.norm = LayerNorm2d(dim) self.fn = fn def forward(self, x): return self.fn(self.norm(x)) + x class FeedForward(nn.Module): def __init__(self, dim, mult=2, dropout=0.0): super().__init__() inner_dim = int(dim * mult) self.net = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(inner_dim, dim), nn.Dropout(dropout), ) def forward(self, x): return self.net(x) class Conv_FeedForward(nn.Module): def __init__(self, dim, mult=2, dropout=0.0): super().__init__() inner_dim = int(dim * mult) self.net = nn.Sequential( nn.Conv2d(dim, inner_dim, 1, 1, 0), nn.GELU(), nn.Dropout(dropout), nn.Conv2d(inner_dim, dim, 1, 1, 0), nn.Dropout(dropout), ) def forward(self, x): return self.net(x) class Gated_Conv_FeedForward(nn.Module): def __init__(self, dim, mult=1, bias=False, dropout=0.0): super().__init__() hidden_features = int(dim * mult) self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) self.dwconv = nn.Conv2d( hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1, groups=hidden_features * 2, bias=bias, ) self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) def forward(self, x): x = self.project_in(x) x1, x2 = self.dwconv(x).chunk(2, dim=1) x = F.gelu(x1) * x2 x = self.project_out(x) return x # MBConv class SqueezeExcitation(nn.Module): def __init__(self, dim, shrinkage_rate=0.25): super().__init__() hidden_dim = int(dim * shrinkage_rate) self.gate = nn.Sequential( Reduce("b c h w -> b c", "mean"), nn.Linear(dim, hidden_dim, bias=False), nn.SiLU(), nn.Linear(hidden_dim, dim, bias=False), nn.Sigmoid(), Rearrange("b c -> b c 1 1"), ) def forward(self, x): return x * self.gate(x) class MBConvResidual(nn.Module): def __init__(self, fn, dropout=0.0): super().__init__() self.fn = fn self.dropsample = Dropsample(dropout) def forward(self, x): out = self.fn(x) out = self.dropsample(out) return out + x class Dropsample(nn.Module): def __init__(self, prob=0): super().__init__() self.prob = prob def forward(self, x): device = x.device if self.prob == 0.0 or (not self.training): return x keep_mask = ( torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_() > self.prob ) return x * keep_mask / (1 - self.prob) def MBConv( dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0 ): hidden_dim = int(expansion_rate * dim_out) stride = 2 if downsample else 1 net = nn.Sequential( nn.Conv2d(dim_in, hidden_dim, 1), # nn.BatchNorm2d(hidden_dim), nn.GELU(), nn.Conv2d( hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim ), # nn.BatchNorm2d(hidden_dim), nn.GELU(), SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate), nn.Conv2d(hidden_dim, dim_out, 1), # nn.BatchNorm2d(dim_out) ) if dim_in == dim_out and not downsample: net = MBConvResidual(net, dropout=dropout) return net # attention related classes class Attention(nn.Module): def __init__( self, dim, dim_head=32, dropout=0.0, window_size=7, with_pe=True, ): super().__init__() assert ( dim % dim_head ) == 0, "dimension should be divisible by dimension per head" self.heads = dim // dim_head self.scale = dim_head**-0.5 self.with_pe = with_pe self.to_qkv = nn.Linear(dim, dim * 3, bias=False) self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) self.to_out = nn.Sequential( nn.Linear(dim, dim, bias=False), nn.Dropout(dropout) ) # relative positional bias if self.with_pe: self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads) pos = torch.arange(window_size) grid = torch.stack(torch.meshgrid(pos, pos)) grid = rearrange(grid, "c i j -> (i j) c") rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange( grid, "j ... -> 1 j ..." ) rel_pos += window_size - 1 rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum( dim=-1 ) self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False) def forward(self, x): batch, height, width, window_height, window_width, _, device, h = ( *x.shape, x.device, self.heads, ) # flatten x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d") # project for queries, keys, values q, k, v = self.to_qkv(x).chunk(3, dim=-1) # split heads q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v)) # scale q = q * self.scale # sim sim = einsum("b h i d, b h j d -> b h i j", q, k) # add positional bias if self.with_pe: bias = self.rel_pos_bias(self.rel_pos_indices) sim = sim + rearrange(bias, "i j h -> h i j") # attention attn = self.attend(sim) # aggregate out = einsum("b h i j, b h j d -> b h i d", attn, v) # merge heads out = rearrange( out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width ) # combine heads out out = self.to_out(out) return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width) class Block_Attention(nn.Module): def __init__( self, dim, dim_head=32, bias=False, dropout=0.0, window_size=7, with_pe=True, ): super().__init__() assert ( dim % dim_head ) == 0, "dimension should be divisible by dimension per head" self.heads = dim // dim_head self.ps = window_size self.scale = dim_head**-0.5 self.with_pe = with_pe self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) self.qkv_dwconv = nn.Conv2d( dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias, ) self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) def forward(self, x): # project for queries, keys, values b, c, h, w = x.shape qkv = self.qkv_dwconv(self.qkv(x)) q, k, v = qkv.chunk(3, dim=1) # split heads q, k, v = map( lambda t: rearrange( t, "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d", h=self.heads, w1=self.ps, w2=self.ps, ), (q, k, v), ) # scale q = q * self.scale # sim sim = einsum("b h i d, b h j d -> b h i j", q, k) # attention attn = self.attend(sim) # aggregate out = einsum("b h i j, b h j d -> b h i d", attn, v) # merge heads out = rearrange( out, "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)", x=h // self.ps, y=w // self.ps, head=self.heads, w1=self.ps, w2=self.ps, ) out = self.to_out(out) return out class Channel_Attention(nn.Module): def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): super(Channel_Attention, self).__init__() self.heads = heads self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) self.ps = window_size self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) self.qkv_dwconv = nn.Conv2d( dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias, ) self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) def forward(self, x): b, c, h, w = x.shape qkv = self.qkv_dwconv(self.qkv(x)) qkv = qkv.chunk(3, dim=1) q, k, v = map( lambda t: rearrange( t, "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)", ph=self.ps, pw=self.ps, head=self.heads, ), qkv, ) q = F.normalize(q, dim=-1) k = F.normalize(k, dim=-1) attn = (q @ k.transpose(-2, -1)) * self.temperature attn = attn.softmax(dim=-1) out = attn @ v out = rearrange( out, "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)", h=h // self.ps, w=w // self.ps, ph=self.ps, pw=self.ps, head=self.heads, ) out = self.project_out(out) return out class Channel_Attention_grid(nn.Module): def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): super(Channel_Attention_grid, self).__init__() self.heads = heads self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) self.ps = window_size self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) self.qkv_dwconv = nn.Conv2d( dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias, ) self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) def forward(self, x): b, c, h, w = x.shape qkv = self.qkv_dwconv(self.qkv(x)) qkv = qkv.chunk(3, dim=1) q, k, v = map( lambda t: rearrange( t, "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)", ph=self.ps, pw=self.ps, head=self.heads, ), qkv, ) q = F.normalize(q, dim=-1) k = F.normalize(k, dim=-1) attn = (q @ k.transpose(-2, -1)) * self.temperature attn = attn.softmax(dim=-1) out = attn @ v out = rearrange( out, "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)", h=h // self.ps, w=w // self.ps, ph=self.ps, pw=self.ps, head=self.heads, ) out = self.project_out(out) return out class OSA_Block(nn.Module): def __init__( self, channel_num=64, bias=True, ffn_bias=True, window_size=8, with_pe=False, dropout=0.0, ): super(OSA_Block, self).__init__() w = window_size self.layer = nn.Sequential( MBConv( channel_num, channel_num, downsample=False, expansion_rate=1, shrinkage_rate=0.25, ), Rearrange( "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w ), # block-like attention PreNormResidual( channel_num, Attention( dim=channel_num, dim_head=channel_num // 4, dropout=dropout, window_size=window_size, with_pe=with_pe, ), ), Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"), Conv_PreNormResidual( channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) ), # channel-like attention Conv_PreNormResidual( channel_num, Channel_Attention( dim=channel_num, heads=4, dropout=dropout, window_size=window_size ), ), Conv_PreNormResidual( channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) ), Rearrange( "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w ), # grid-like attention PreNormResidual( channel_num, Attention( dim=channel_num, dim_head=channel_num // 4, dropout=dropout, window_size=window_size, with_pe=with_pe, ), ), Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"), Conv_PreNormResidual( channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) ), # channel-like attention Conv_PreNormResidual( channel_num, Channel_Attention_grid( dim=channel_num, heads=4, dropout=dropout, window_size=window_size ), ), Conv_PreNormResidual( channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) ), ) def forward(self, x): out = self.layer(x) return out ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/OSAG.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: OSAG.py # Created Date: Tuesday April 28th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Sunday, 23rd April 2023 3:08:49 pm # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# import torch.nn as nn from .esa import ESA from .OSA import OSA_Block class OSAG(nn.Module): def __init__( self, channel_num=64, bias=True, block_num=4, ffn_bias=False, window_size=0, pe=False, ): super(OSAG, self).__init__() # print("window_size: %d" % (window_size)) # print("with_pe", pe) # print("ffn_bias: %d" % (ffn_bias)) # block_script_name = kwargs.get("block_script_name", "OSA") # block_class_name = kwargs.get("block_class_name", "OSA_Block") # script_name = "." + block_script_name # package = __import__(script_name, fromlist=True) block_class = OSA_Block # getattr(package, block_class_name) group_list = [] for _ in range(block_num): temp_res = block_class( channel_num, bias, ffn_bias=ffn_bias, window_size=window_size, with_pe=pe, ) group_list.append(temp_res) group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias)) self.residual_layer = nn.Sequential(*group_list) esa_channel = max(channel_num // 4, 16) self.esa = ESA(esa_channel, channel_num) def forward(self, x): out = self.residual_layer(x) out = out + x return self.esa(out) ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/OmniSR.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: OmniSR.py # Created Date: Tuesday April 28th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Sunday, 23rd April 2023 3:06:36 pm # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# import math import torch import torch.nn as nn import torch.nn.functional as F from .OSAG import OSAG from .pixelshuffle import pixelshuffle_block class OmniSR(nn.Module): def __init__( self, state_dict, **kwargs, ): super(OmniSR, self).__init__() self.state = state_dict bias = True # Fine to assume this for now block_num = 1 # Fine to assume this for now ffn_bias = True pe = True num_feat = state_dict["input.weight"].shape[0] or 64 num_in_ch = state_dict["input.weight"].shape[1] or 3 num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh pixelshuffle_shape = state_dict["up.0.weight"].shape[0] up_scale = math.sqrt(pixelshuffle_shape / num_out_ch) if up_scale - int(up_scale) > 0: print( "out_nc is probably different than in_nc, scale calculation might be wrong" ) up_scale = int(up_scale) res_num = 0 for key in state_dict.keys(): if "residual_layer" in key: temp_res_num = int(key.split(".")[1]) if temp_res_num > res_num: res_num = temp_res_num res_num = res_num + 1 # zero-indexed residual_layer = [] self.res_num = res_num if ( "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" in state_dict.keys() ): rel_pos_bias_weight = state_dict[ "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" ].shape[0] self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) else: self.window_size = 8 self.up_scale = up_scale for _ in range(res_num): temp_res = OSAG( channel_num=num_feat, bias=bias, block_num=block_num, ffn_bias=ffn_bias, window_size=self.window_size, pe=pe, ) residual_layer.append(temp_res) self.residual_layer = nn.Sequential(*residual_layer) self.input = nn.Conv2d( in_channels=num_in_ch, out_channels=num_feat, kernel_size=3, stride=1, padding=1, bias=bias, ) self.output = nn.Conv2d( in_channels=num_feat, out_channels=num_feat, kernel_size=3, stride=1, padding=1, bias=bias, ) self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias) # self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias) # for m in self.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, sqrt(2. / n)) # chaiNNer specific stuff self.model_arch = "OmniSR" self.sub_type = "SR" self.in_nc = num_in_ch self.out_nc = num_out_ch self.num_feat = num_feat self.scale = up_scale self.supports_fp16 = True # TODO: Test this self.supports_bfp16 = True self.min_size_restriction = 16 self.load_state_dict(state_dict, strict=False) def check_image_size(self, x): _, _, h, w = x.size() # import pdb; pdb.set_trace() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) residual = self.input(x) out = self.residual_layer(residual) # origin out = torch.add(self.output(out), residual) out = self.up(out) out = out[:, :, : H * self.up_scale, : W * self.up_scale] return out ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/esa.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: esa.py # Created Date: Tuesday April 28th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Thursday, 20th April 2023 9:28:06 am # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# import torch import torch.nn as nn import torch.nn.functional as F from .layernorm import LayerNorm2d def moment(x, dim=(2, 3), k=2): assert len(x.size()) == 4 mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1) mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim) return mk class ESA(nn.Module): """ Modification of Enhanced Spatial Attention (ESA), which is proposed by `Residual Feature Aggregation Network for Image Super-Resolution` Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes are deleted. """ def __init__(self, esa_channels, n_feats, conv=nn.Conv2d): super(ESA, self).__init__() f = esa_channels self.conv1 = conv(n_feats, f, kernel_size=1) self.conv_f = conv(f, f, kernel_size=1) self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) self.conv3 = conv(f, f, kernel_size=3, padding=1) self.conv4 = conv(f, n_feats, kernel_size=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x): c1_ = self.conv1(x) c1 = self.conv2(c1_) v_max = F.max_pool2d(c1, kernel_size=7, stride=3) c3 = self.conv3(v_max) c3 = F.interpolate( c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False ) cf = self.conv_f(c1_) c4 = self.conv4(c3 + cf) m = self.sigmoid(c4) return x * m class LK_ESA(nn.Module): def __init__( self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True ): super(LK_ESA, self).__init__() f = esa_channels self.conv1 = conv(n_feats, f, kernel_size=1) self.conv_f = conv(f, f, kernel_size=1) kernel_size = 17 kernel_expand = kernel_expand padding = kernel_size // 2 self.vec_conv = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(1, kernel_size), padding=(0, padding), groups=2, bias=bias, ) self.vec_conv3x1 = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(1, 3), padding=(0, 1), groups=2, bias=bias, ) self.hor_conv = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(kernel_size, 1), padding=(padding, 0), groups=2, bias=bias, ) self.hor_conv1x3 = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(3, 1), padding=(1, 0), groups=2, bias=bias, ) self.conv4 = conv(f, n_feats, kernel_size=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x): c1_ = self.conv1(x) res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) res = self.hor_conv(res) + self.hor_conv1x3(res) cf = self.conv_f(c1_) c4 = self.conv4(res + cf) m = self.sigmoid(c4) return x * m class LK_ESA_LN(nn.Module): def __init__( self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True ): super(LK_ESA_LN, self).__init__() f = esa_channels self.conv1 = conv(n_feats, f, kernel_size=1) self.conv_f = conv(f, f, kernel_size=1) kernel_size = 17 kernel_expand = kernel_expand padding = kernel_size // 2 self.norm = LayerNorm2d(n_feats) self.vec_conv = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(1, kernel_size), padding=(0, padding), groups=2, bias=bias, ) self.vec_conv3x1 = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(1, 3), padding=(0, 1), groups=2, bias=bias, ) self.hor_conv = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(kernel_size, 1), padding=(padding, 0), groups=2, bias=bias, ) self.hor_conv1x3 = nn.Conv2d( in_channels=f * kernel_expand, out_channels=f * kernel_expand, kernel_size=(3, 1), padding=(1, 0), groups=2, bias=bias, ) self.conv4 = conv(f, n_feats, kernel_size=1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) def forward(self, x): c1_ = self.norm(x) c1_ = self.conv1(c1_) res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) res = self.hor_conv(res) + self.hor_conv1x3(res) cf = self.conv_f(c1_) c4 = self.conv4(res + cf) m = self.sigmoid(c4) return x * m class AdaGuidedFilter(nn.Module): def __init__( self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True ): super(AdaGuidedFilter, self).__init__() self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Conv2d( in_channels=n_feats, out_channels=1, kernel_size=1, padding=0, stride=1, groups=1, bias=True, ) self.r = 5 def box_filter(self, x, r): channel = x.shape[1] kernel_size = 2 * r + 1 weight = 1.0 / (kernel_size**2) box_kernel = weight * torch.ones( (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device ) output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel) return output def forward(self, x): _, _, H, W = x.shape N = self.box_filter( torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r ) # epsilon = self.fc(self.gap(x)) # epsilon = torch.pow(epsilon, 2) epsilon = 1e-2 mean_x = self.box_filter(x, self.r) / N var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x A = var_x / (var_x + epsilon) b = (1 - A) * mean_x m = A * x + b # mean_A = self.box_filter(A, self.r) / N # mean_b = self.box_filter(b, self.r) / N # m = mean_A * x + mean_b return x * m class AdaConvGuidedFilter(nn.Module): def __init__( self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True ): super(AdaConvGuidedFilter, self).__init__() f = esa_channels self.conv_f = conv(f, f, kernel_size=1) kernel_size = 17 kernel_expand = kernel_expand padding = kernel_size // 2 self.vec_conv = nn.Conv2d( in_channels=f, out_channels=f, kernel_size=(1, kernel_size), padding=(0, padding), groups=f, bias=bias, ) self.hor_conv = nn.Conv2d( in_channels=f, out_channels=f, kernel_size=(kernel_size, 1), padding=(padding, 0), groups=f, bias=bias, ) self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Conv2d( in_channels=f, out_channels=f, kernel_size=1, padding=0, stride=1, groups=1, bias=True, ) def forward(self, x): y = self.vec_conv(x) y = self.hor_conv(y) sigma = torch.pow(y, 2) epsilon = self.fc(self.gap(y)) weight = sigma / (sigma + epsilon) m = weight * x + (1 - weight) return x * m ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/layernorm.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: layernorm.py # Created Date: Tuesday April 28th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Thursday, 20th April 2023 9:28:20 am # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# import torch import torch.nn as nn class LayerNormFunction(torch.autograd.Function): @staticmethod def forward(ctx, x, weight, bias, eps): ctx.eps = eps N, C, H, W = x.size() mu = x.mean(1, keepdim=True) var = (x - mu).pow(2).mean(1, keepdim=True) y = (x - mu) / (var + eps).sqrt() ctx.save_for_backward(y, var, weight) y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) return y @staticmethod def backward(ctx, grad_output): eps = ctx.eps N, C, H, W = grad_output.size() y, var, weight = ctx.saved_variables g = grad_output * weight.view(1, C, 1, 1) mean_g = g.mean(dim=1, keepdim=True) mean_gy = (g * y).mean(dim=1, keepdim=True) gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) return ( gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(dim=0), None, ) class LayerNorm2d(nn.Module): def __init__(self, channels, eps=1e-6): super(LayerNorm2d, self).__init__() self.register_parameter("weight", nn.Parameter(torch.ones(channels))) self.register_parameter("bias", nn.Parameter(torch.zeros(channels))) self.eps = eps def forward(self, x): return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) class GRN(nn.Module): """GRN (Global Response Normalization) layer""" def __init__(self, dim): super().__init__() self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) def forward(self, x): Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) return self.gamma * (x * Nx) + self.beta + x ================================================ FILE: ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py ================================================ #!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: pixelshuffle.py # Created Date: Friday July 1st 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Friday, 1st July 2022 10:18:39 am # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# import torch.nn as nn def pixelshuffle_block( in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False ): """ Upsample features according to `upscale_factor`. """ padding = kernel_size // 2 conv = nn.Conv2d( in_channels, out_channels * (upscale_factor**2), kernel_size, padding=1, bias=bias, ) pixel_shuffle = nn.PixelShuffle(upscale_factor) return nn.Sequential(*[conv, pixel_shuffle]) ================================================ FILE: ldm_patched/pfn/architecture/RRDB.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- import functools import math import re from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from . import block as B # Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/esrgan.py # Which enhanced stuff that was already here class RRDBNet(nn.Module): def __init__( self, state_dict, norm=None, act: str = "leakyrelu", upsampler: str = "upconv", mode: B.ConvMode = "CNA", ) -> None: """ ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks. By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. This is old-arch Residual in Residual Dense Block Network and is not the newest revision that's available at github.com/xinntao/ESRGAN. This is on purpose, the newest Network has severely limited the potential use of the Network with no benefits. This network supports model files from both new and old-arch. Args: norm: Normalization layer act: Activation layer upsampler: Upsample layer. upconv, pixel_shuffle mode: Convolution mode """ super(RRDBNet, self).__init__() self.model_arch = "ESRGAN" self.sub_type = "SR" self.state = state_dict self.norm = norm self.act = act self.upsampler = upsampler self.mode = mode self.state_map = { # currently supports old, new, and newer RRDBNet arch models # ESRGAN, BSRGAN/RealSR, Real-ESRGAN "model.0.weight": ("conv_first.weight",), "model.0.bias": ("conv_first.bias",), "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", ), } if "params_ema" in self.state: self.state = self.state["params_ema"] # self.model_arch = "RealESRGAN" self.num_blocks = self.get_num_blocks() self.plus = any("conv1x1" in k for k in self.state.keys()) if self.plus: self.model_arch = "ESRGAN+" self.state = self.new_to_old_arch(self.state) self.key_arr = list(self.state.keys()) self.in_nc: int = self.state[self.key_arr[0]].shape[1] self.out_nc: int = self.state[self.key_arr[-1]].shape[0] self.scale: int = self.get_scale() self.num_filters: int = self.state[self.key_arr[0]].shape[0] c2x2 = False if self.state["model.0.weight"].shape[-2] == 2: c2x2 = True self.scale = round(math.sqrt(self.scale / 4)) self.model_arch = "ESRGAN-2c2" self.supports_fp16 = True self.supports_bfp16 = True self.min_size_restriction = None # Detect if pixelunshuffle was used (Real-ESRGAN) if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in ( self.in_nc / 4, self.in_nc / 16, ): self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc)) else: self.shuffle_factor = None upsample_block = { "upconv": B.upconv_block, "pixel_shuffle": B.pixelshuffle_block, }.get(self.upsampler) if upsample_block is None: raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found") if self.scale == 3: upsample_blocks = upsample_block( in_nc=self.num_filters, out_nc=self.num_filters, upscale_factor=3, act_type=self.act, c2x2=c2x2, ) else: upsample_blocks = [ upsample_block( in_nc=self.num_filters, out_nc=self.num_filters, act_type=self.act, c2x2=c2x2, ) for _ in range(int(math.log(self.scale, 2))) ] self.model = B.sequential( # fea conv B.conv_block( in_nc=self.in_nc, out_nc=self.num_filters, kernel_size=3, norm_type=None, act_type=None, c2x2=c2x2, ), B.ShortcutBlock( B.sequential( # rrdb blocks *[ B.RRDB( nf=self.num_filters, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=self.norm, act_type=self.act, mode="CNA", plus=self.plus, c2x2=c2x2, ) for _ in range(self.num_blocks) ], # lr conv B.conv_block( in_nc=self.num_filters, out_nc=self.num_filters, kernel_size=3, norm_type=self.norm, act_type=None, mode=self.mode, c2x2=c2x2, ), ) ), *upsample_blocks, # hr_conv0 B.conv_block( in_nc=self.num_filters, out_nc=self.num_filters, kernel_size=3, norm_type=None, act_type=self.act, c2x2=c2x2, ), # hr_conv1 B.conv_block( in_nc=self.num_filters, out_nc=self.out_nc, kernel_size=3, norm_type=None, act_type=None, c2x2=c2x2, ), ) # Adjust these properties for calculations outside of the model if self.shuffle_factor: self.in_nc //= self.shuffle_factor**2 self.scale //= self.shuffle_factor self.load_state_dict(self.state, strict=False) def new_to_old_arch(self, state): """Convert a new-arch model state dictionary to an old-arch dictionary.""" if "params_ema" in state: state = state["params_ema"] if "conv_first.weight" not in state: # model is already old arch, this is a loose check, but should be sufficient return state # add nb to state keys for kind in ("weight", "bias"): self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ f"model.1.sub./NB/.{kind}" ] del self.state_map[f"model.1.sub./NB/.{kind}"] old_state = OrderedDict() for old_key, new_keys in self.state_map.items(): for new_key in new_keys: if r"\1" in old_key: for k, v in state.items(): sub = re.sub(new_key, old_key, k) if sub != k: old_state[sub] = v else: if new_key in state: old_state[old_key] = state[new_key] # upconv layers max_upconv = 0 for key in state.keys(): match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) if match is not None: _, key_num, key_type = match.groups() old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] max_upconv = max(max_upconv, int(key_num) * 3) # final layers for key in state.keys(): if key in ("HRconv.weight", "conv_hr.weight"): old_state[f"model.{max_upconv + 2}.weight"] = state[key] elif key in ("HRconv.bias", "conv_hr.bias"): old_state[f"model.{max_upconv + 2}.bias"] = state[key] elif key in ("conv_last.weight",): old_state[f"model.{max_upconv + 4}.weight"] = state[key] elif key in ("conv_last.bias",): old_state[f"model.{max_upconv + 4}.bias"] = state[key] # Sort by first numeric value of each layer def compare(item1, item2): parts1 = item1.split(".") parts2 = item2.split(".") int1 = int(parts1[1]) int2 = int(parts2[1]) return int1 - int2 sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) # Rebuild the output dict in the right order out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) return out_dict def get_scale(self, min_part: int = 6) -> int: n = 0 for part in list(self.state): parts = part.split(".")[1:] if len(parts) == 2: part_num = int(parts[0]) if part_num > min_part and parts[1] == "weight": n += 1 return 2**n def get_num_blocks(self) -> int: nbs = [] state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", ) for state_key in state_keys: for k in self.state: m = re.search(state_key, k) if m: nbs.append(int(m.group(1))) if nbs: break return max(*nbs) + 1 def forward(self, x): if self.shuffle_factor: _, _, h, w = x.size() mod_pad_h = ( self.shuffle_factor - h % self.shuffle_factor ) % self.shuffle_factor mod_pad_w = ( self.shuffle_factor - w % self.shuffle_factor ) % self.shuffle_factor x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor) x = self.model(x) return x[:, :, : h * self.scale, : w * self.scale] return self.model(x) ================================================ FILE: ldm_patched/pfn/architecture/SCUNet.py ================================================ # pylint: skip-file # ----------------------------------------------------------------------------------- # SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 # Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc # ----------------------------------------------------------------------------------- import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from einops.layers.torch import Rearrange from .timm.drop import DropPath from .timm.weight_init import trunc_normal_ # Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py class WMSA(nn.Module): """Self-attention module in Swin Transformer""" def __init__(self, input_dim, output_dim, head_dim, window_size, type): super(WMSA, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.head_dim = head_dim self.scale = self.head_dim**-0.5 self.n_heads = input_dim // head_dim self.window_size = window_size self.type = type self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) self.relative_position_params = nn.Parameter( torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) ) # TODO recover # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) self.relative_position_params = nn.Parameter( torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) ) self.linear = nn.Linear(self.input_dim, self.output_dim) trunc_normal_(self.relative_position_params, std=0.02) self.relative_position_params = torch.nn.Parameter( self.relative_position_params.view( 2 * window_size - 1, 2 * window_size - 1, self.n_heads ) .transpose(1, 2) .transpose(0, 1) ) def generate_mask(self, h, w, p, shift): """generating the mask of SW-MSA Args: shift: shift parameters in CyclicShift. Returns: attn_mask: should be (1 1 w p p), """ # supporting square. attn_mask = torch.zeros( h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device, ) if self.type == "W": return attn_mask s = p - shift attn_mask[-1, :, :s, :, s:, :] = True attn_mask[-1, :, s:, :, :s, :] = True attn_mask[:, -1, :, :s, :, s:] = True attn_mask[:, -1, :, s:, :, :s] = True attn_mask = rearrange( attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" ) return attn_mask def forward(self, x): """Forward pass of Window Multi-head Self-attention module. Args: x: input tensor with shape of [b h w c]; attn_mask: attention mask, fill -inf where the value is True; Returns: output: tensor shape [b h w c] """ if self.type != "W": x = torch.roll( x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2), ) x = rearrange( x, "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", p1=self.window_size, p2=self.window_size, ) h_windows = x.size(1) w_windows = x.size(2) # square validation # assert h_windows == w_windows x = rearrange( x, "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", p1=self.window_size, p2=self.window_size, ) qkv = self.embedding_layer(x) q, k, v = rearrange( qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim ).chunk(3, dim=0) sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale # Adding learnable relative embedding sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") # Using Attn Mask to distinguish different subwindows. if self.type != "W": attn_mask = self.generate_mask( h_windows, w_windows, self.window_size, shift=self.window_size // 2 ) sim = sim.masked_fill_(attn_mask, float("-inf")) probs = nn.functional.softmax(sim, dim=-1) output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) output = rearrange(output, "h b w p c -> b w p (h c)") output = self.linear(output) output = rearrange( output, "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", w1=h_windows, p1=self.window_size, ) if self.type != "W": output = torch.roll( output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2), ) return output def relative_embedding(self): cord = torch.tensor( np.array( [ [i, j] for i in range(self.window_size) for j in range(self.window_size) ] ) ) relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 # negative is allowed return self.relative_position_params[ :, relation[:, :, 0].long(), relation[:, :, 1].long() ] class Block(nn.Module): def __init__( self, input_dim, output_dim, head_dim, window_size, drop_path, type="W", input_resolution=None, ): """SwinTransformer Block""" super(Block, self).__init__() self.input_dim = input_dim self.output_dim = output_dim assert type in ["W", "SW"] self.type = type if input_resolution <= window_size: self.type = "W" self.ln1 = nn.LayerNorm(input_dim) self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.ln2 = nn.LayerNorm(input_dim) self.mlp = nn.Sequential( nn.Linear(input_dim, 4 * input_dim), nn.GELU(), nn.Linear(4 * input_dim, output_dim), ) def forward(self, x): x = x + self.drop_path(self.msa(self.ln1(x))) x = x + self.drop_path(self.mlp(self.ln2(x))) return x class ConvTransBlock(nn.Module): def __init__( self, conv_dim, trans_dim, head_dim, window_size, drop_path, type="W", input_resolution=None, ): """SwinTransformer and Conv Block""" super(ConvTransBlock, self).__init__() self.conv_dim = conv_dim self.trans_dim = trans_dim self.head_dim = head_dim self.window_size = window_size self.drop_path = drop_path self.type = type self.input_resolution = input_resolution assert self.type in ["W", "SW"] if self.input_resolution <= self.window_size: self.type = "W" self.trans_block = Block( self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path, self.type, self.input_resolution, ) self.conv1_1 = nn.Conv2d( self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True, ) self.conv1_2 = nn.Conv2d( self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True, ) self.conv_block = nn.Sequential( nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), nn.ReLU(True), nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), ) def forward(self, x): conv_x, trans_x = torch.split( self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 ) conv_x = self.conv_block(conv_x) + conv_x trans_x = Rearrange("b c h w -> b h w c")(trans_x) trans_x = self.trans_block(trans_x) trans_x = Rearrange("b h w c -> b c h w")(trans_x) res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) x = x + res return x class SCUNet(nn.Module): def __init__( self, state_dict, in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64, drop_path_rate=0.0, input_resolution=256, ): super(SCUNet, self).__init__() self.model_arch = "SCUNet" self.sub_type = "SR" self.num_filters: int = 0 self.state = state_dict self.config = config self.dim = dim self.head_dim = 32 self.window_size = 8 self.in_nc = in_nc self.out_nc = self.in_nc self.scale = 1 self.supports_fp16 = True # drop path rate for each layer dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] begin = 0 self.m_down1 = [ ConvTransBlock( dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution, ) for i in range(config[0]) ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] begin += config[0] self.m_down2 = [ ConvTransBlock( dim, dim, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution // 2, ) for i in range(config[1]) ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] begin += config[1] self.m_down3 = [ ConvTransBlock( 2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution // 4, ) for i in range(config[2]) ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] begin += config[2] self.m_body = [ ConvTransBlock( 4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution // 8, ) for i in range(config[3]) ] begin += config[3] self.m_up3 = [ nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + [ ConvTransBlock( 2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution // 4, ) for i in range(config[4]) ] begin += config[4] self.m_up2 = [ nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + [ ConvTransBlock( dim, dim, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution // 2, ) for i in range(config[5]) ] begin += config[5] self.m_up1 = [ nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + [ ConvTransBlock( dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], "W" if not i % 2 else "SW", input_resolution, ) for i in range(config[6]) ] self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] self.m_head = nn.Sequential(*self.m_head) self.m_down1 = nn.Sequential(*self.m_down1) self.m_down2 = nn.Sequential(*self.m_down2) self.m_down3 = nn.Sequential(*self.m_down3) self.m_body = nn.Sequential(*self.m_body) self.m_up3 = nn.Sequential(*self.m_up3) self.m_up2 = nn.Sequential(*self.m_up2) self.m_up1 = nn.Sequential(*self.m_up1) self.m_tail = nn.Sequential(*self.m_tail) # self.apply(self._init_weights) self.load_state_dict(state_dict, strict=True) def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (64 - h % 64) % 64 mod_pad_w = (64 - w % 64) % 64 x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") return x def forward(self, x0): h, w = x0.size()[-2:] x0 = self.check_image_size(x0) x1 = self.m_head(x0) x2 = self.m_down1(x1) x3 = self.m_down2(x2) x4 = self.m_down3(x3) x = self.m_body(x4) x = self.m_up3(x + x4) x = self.m_up2(x + x3) x = self.m_up1(x + x2) x = self.m_tail(x + x1) x = x[:, :, :h, :w] return x def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) ================================================ FILE: ldm_patched/pfn/architecture/SPSR.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- import math import torch import torch.nn as nn import torch.nn.functional as F from . import block as B class Get_gradient_nopadding(nn.Module): def __init__(self): super(Get_gradient_nopadding, self).__init__() kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore def forward(self, x): x_list = [] for i in range(x.shape[1]): x_i = x[:, i] x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) x_list.append(x_i) x = torch.cat(x_list, dim=1) return x class SPSRNet(nn.Module): def __init__( self, state_dict, norm=None, act: str = "leakyrelu", upsampler: str = "upconv", mode: B.ConvMode = "CNA", ): super(SPSRNet, self).__init__() self.model_arch = "SPSR" self.sub_type = "SR" self.state = state_dict self.norm = norm self.act = act self.upsampler = upsampler self.mode = mode self.num_blocks = self.get_num_blocks() self.in_nc: int = self.state["model.0.weight"].shape[1] self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] self.scale = self.get_scale(4) self.num_filters: int = self.state["model.0.weight"].shape[0] self.supports_fp16 = True self.supports_bfp16 = True self.min_size_restriction = None n_upscale = int(math.log(self.scale, 2)) if self.scale == 3: n_upscale = 1 fea_conv = B.conv_block( self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None ) rb_blocks = [ B.RRDB( self.num_filters, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) for _ in range(self.num_blocks) ] LR_conv = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=norm, act_type=None, mode=mode, ) if upsampler == "upconv": upsample_block = B.upconv_block elif upsampler == "pixelshuffle": upsample_block = B.pixelshuffle_block else: raise NotImplementedError(f"upsample mode [{upsampler}] is not found") if self.scale == 3: a_upsampler = upsample_block( self.num_filters, self.num_filters, 3, act_type=act ) else: a_upsampler = [ upsample_block(self.num_filters, self.num_filters, act_type=act) for _ in range(n_upscale) ] self.HR_conv0_new = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=act, ) self.HR_conv1_new = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.model = B.sequential( fea_conv, B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), *a_upsampler, self.HR_conv0_new, ) self.get_g_nopadding = Get_gradient_nopadding() self.b_fea_conv = B.conv_block( self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None ) self.b_concat_1 = B.conv_block( 2 * self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.b_block_1 = B.RRDB( self.num_filters * 2, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) self.b_concat_2 = B.conv_block( 2 * self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.b_block_2 = B.RRDB( self.num_filters * 2, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) self.b_concat_3 = B.conv_block( 2 * self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.b_block_3 = B.RRDB( self.num_filters * 2, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) self.b_concat_4 = B.conv_block( 2 * self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.b_block_4 = B.RRDB( self.num_filters * 2, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) self.b_LR_conv = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=norm, act_type=None, mode=mode, ) if upsampler == "upconv": upsample_block = B.upconv_block elif upsampler == "pixelshuffle": upsample_block = B.pixelshuffle_block else: raise NotImplementedError(f"upsample mode [{upsampler}] is not found") if self.scale == 3: b_upsampler = upsample_block( self.num_filters, self.num_filters, 3, act_type=act ) else: b_upsampler = [ upsample_block(self.num_filters, self.num_filters, act_type=act) for _ in range(n_upscale) ] b_HR_conv0 = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=act, ) b_HR_conv1 = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) self.conv_w = B.conv_block( self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None ) self.f_concat = B.conv_block( self.num_filters * 2, self.num_filters, kernel_size=3, norm_type=None, act_type=None, ) self.f_block = B.RRDB( self.num_filters * 2, kernel_size=3, gc=32, stride=1, bias=True, pad_type="zero", norm_type=norm, act_type=act, mode="CNA", ) self.f_HR_conv0 = B.conv_block( self.num_filters, self.num_filters, kernel_size=3, norm_type=None, act_type=act, ) self.f_HR_conv1 = B.conv_block( self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None ) self.load_state_dict(self.state, strict=False) def get_scale(self, min_part: int = 4) -> int: n = 0 for part in list(self.state): parts = part.split(".") if len(parts) == 3: part_num = int(parts[1]) if part_num > min_part and parts[0] == "model" and parts[2] == "weight": n += 1 return 2**n def get_num_blocks(self) -> int: nb = 0 for part in list(self.state): parts = part.split(".") n_parts = len(parts) if n_parts == 5 and parts[2] == "sub": nb = int(parts[3]) return nb def forward(self, x): x_grad = self.get_g_nopadding(x) x = self.model[0](x) x, block_list = self.model[1](x) x_ori = x for i in range(5): x = block_list[i](x) x_fea1 = x for i in range(5): x = block_list[i + 5](x) x_fea2 = x for i in range(5): x = block_list[i + 10](x) x_fea3 = x for i in range(5): x = block_list[i + 15](x) x_fea4 = x x = block_list[20:](x) # short cut x = x_ori + x x = self.model[2:](x) x = self.HR_conv1_new(x) x_b_fea = self.b_fea_conv(x_grad) x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) x_cat_1 = self.b_block_1(x_cat_1) x_cat_1 = self.b_concat_1(x_cat_1) x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) x_cat_2 = self.b_block_2(x_cat_2) x_cat_2 = self.b_concat_2(x_cat_2) x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) x_cat_3 = self.b_block_3(x_cat_3) x_cat_3 = self.b_concat_3(x_cat_3) x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) x_cat_4 = self.b_block_4(x_cat_4) x_cat_4 = self.b_concat_4(x_cat_4) x_cat_4 = self.b_LR_conv(x_cat_4) # short cut x_cat_4 = x_cat_4 + x_b_fea x_branch = self.b_module(x_cat_4) # x_out_branch = self.conv_w(x_branch) ######## x_branch_d = x_branch x_f_cat = torch.cat([x_branch_d, x], dim=1) x_f_cat = self.f_block(x_f_cat) x_out = self.f_concat(x_f_cat) x_out = self.f_HR_conv0(x_out) x_out = self.f_HR_conv1(x_out) ######### # return x_out_branch, x_out, x_grad return x_out ================================================ FILE: ldm_patched/pfn/architecture/SRVGG.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- import math import torch.nn as nn import torch.nn.functional as F class SRVGGNetCompact(nn.Module): """A compact VGG-style network structure for super-resolution. It is a compact network structure, which performs upsampling in the last layer and no convolution is conducted on the HR feature space. Args: num_in_ch (int): Channel number of inputs. Default: 3. num_out_ch (int): Channel number of outputs. Default: 3. num_feat (int): Channel number of intermediate features. Default: 64. num_conv (int): Number of convolution layers in the body network. Default: 16. upscale (int): Upsampling factor. Default: 4. act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. """ def __init__( self, state_dict, act_type: str = "prelu", ): super(SRVGGNetCompact, self).__init__() self.model_arch = "SRVGG (RealESRGAN)" self.sub_type = "SR" self.act_type = act_type self.state = state_dict if "params" in self.state: self.state = self.state["params"] self.key_arr = list(self.state.keys()) self.in_nc = self.get_in_nc() self.num_feat = self.get_num_feats() self.num_conv = self.get_num_conv() self.out_nc = self.in_nc # :( self.pixelshuffle_shape = None # Defined in get_scale() self.scale = self.get_scale() self.supports_fp16 = True self.supports_bfp16 = True self.min_size_restriction = None self.body = nn.ModuleList() # the first conv self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1)) # the first activation if act_type == "relu": activation = nn.ReLU(inplace=True) elif act_type == "prelu": activation = nn.PReLU(num_parameters=self.num_feat) elif act_type == "leakyrelu": activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) self.body.append(activation) # type: ignore # the body structure for _ in range(self.num_conv): self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1)) # activation if act_type == "relu": activation = nn.ReLU(inplace=True) elif act_type == "prelu": activation = nn.PReLU(num_parameters=self.num_feat) elif act_type == "leakyrelu": activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) self.body.append(activation) # type: ignore # the last conv self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1)) # type: ignore # upsample self.upsampler = nn.PixelShuffle(self.scale) self.load_state_dict(self.state, strict=False) def get_num_conv(self) -> int: return (int(self.key_arr[-1].split(".")[1]) - 2) // 2 def get_num_feats(self) -> int: return self.state[self.key_arr[0]].shape[0] def get_in_nc(self) -> int: return self.state[self.key_arr[0]].shape[1] def get_scale(self) -> int: self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0] # Assume out_nc is the same as in_nc # I cant think of a better way to do that self.out_nc = self.in_nc scale = math.sqrt(self.pixelshuffle_shape / self.out_nc) if scale - int(scale) > 0: print( "out_nc is probably different than in_nc, scale calculation might be wrong" ) scale = int(scale) return scale def forward(self, x): out = x for i in range(0, len(self.body)): out = self.body[i](out) out = self.upsampler(out) # add the nearest upsampled image, so that the network learns the residual base = F.interpolate(x, scale_factor=self.scale, mode="nearest") out += base return out ================================================ FILE: ldm_patched/pfn/architecture/SwiftSRGAN.py ================================================ # From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py import torch from torch import nn class SeperableConv2d(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True ): super(SeperableConv2d, self).__init__() self.depthwise = nn.Conv2d( in_channels, in_channels, kernel_size=kernel_size, stride=stride, groups=in_channels, bias=bias, padding=padding, ) self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) def forward(self, x): return self.pointwise(self.depthwise(x)) class ConvBlock(nn.Module): def __init__( self, in_channels, out_channels, use_act=True, use_bn=True, discriminator=False, **kwargs, ): super(ConvBlock, self).__init__() self.use_act = use_act self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn) self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity() self.act = ( nn.LeakyReLU(0.2, inplace=True) if discriminator else nn.PReLU(num_parameters=out_channels) ) def forward(self, x): return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) class UpsampleBlock(nn.Module): def __init__(self, in_channels, scale_factor): super(UpsampleBlock, self).__init__() self.conv = SeperableConv2d( in_channels, in_channels * scale_factor**2, kernel_size=3, stride=1, padding=1, ) self.ps = nn.PixelShuffle( scale_factor ) # (in_channels * 4, H, W) -> (in_channels, H*2, W*2) self.act = nn.PReLU(num_parameters=in_channels) def forward(self, x): return self.act(self.ps(self.conv(x))) class ResidualBlock(nn.Module): def __init__(self, in_channels): super(ResidualBlock, self).__init__() self.block1 = ConvBlock( in_channels, in_channels, kernel_size=3, stride=1, padding=1 ) self.block2 = ConvBlock( in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False ) def forward(self, x): out = self.block1(x) out = self.block2(out) return out + x class Generator(nn.Module): """Swift-SRGAN Generator Args: in_channels (int): number of input image channels. num_channels (int): number of hidden channels. num_blocks (int): number of residual blocks. upscale_factor (int): factor to upscale the image [2x, 4x, 8x]. Returns: torch.Tensor: super resolution image """ def __init__( self, state_dict, ): super(Generator, self).__init__() self.model_arch = "Swift-SRGAN" self.sub_type = "SR" self.state = state_dict if "model" in self.state: self.state = self.state["model"] self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0] self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0] self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0] self.num_blocks = len( set([x.split(".")[1] for x in self.state.keys() if "residual" in x]) ) self.scale: int = 2 ** len( set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x]) ) in_channels = self.in_nc num_channels = self.num_filters num_blocks = self.num_blocks upscale_factor = self.scale self.supports_fp16 = True self.supports_bfp16 = True self.min_size_restriction = None self.initial = ConvBlock( in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False ) self.residual = nn.Sequential( *[ResidualBlock(num_channels) for _ in range(num_blocks)] ) self.convblock = ConvBlock( num_channels, num_channels, kernel_size=3, stride=1, padding=1, use_act=False, ) self.upsampler = nn.Sequential( *[ UpsampleBlock(num_channels, scale_factor=2) for _ in range(upscale_factor // 2) ] ) self.final_conv = SeperableConv2d( num_channels, in_channels, kernel_size=9, stride=1, padding=4 ) self.load_state_dict(self.state, strict=False) def forward(self, x): initial = self.initial(x) x = self.residual(initial) x = self.convblock(x) + initial x = self.upsampler(x) return (torch.tanh(self.final_conv(x)) + 1) / 2 ================================================ FILE: ldm_patched/pfn/architecture/Swin2SR.py ================================================ # pylint: skip-file # ----------------------------------------------------------------------------------- # Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 # Written by Conde and Choi et al. # From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py # ----------------------------------------------------------------------------------- import math import re import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint # Originally from the timm package from .timm.drop import DropPath from .timm.helpers import to_2tuple from .timm.weight_init import trunc_normal_ class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = ( x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) ) return windows def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view( B, H // window_size, W // window_size, window_size, window_size, -1 ) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): r"""Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 pretrained_window_size (tuple[int]): The height and width of the window in pre-training. """ def __init__( self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0.0, proj_drop=0.0, pretrained_window_size=[0, 0], ): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.pretrained_window_size = pretrained_window_size self.num_heads = num_heads self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # type: ignore # mlp to generate continuous relative position bias self.cpb_mlp = nn.Sequential( nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False), ) # get relative_coords_table relative_coords_h = torch.arange( -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 ) relative_coords_w = torch.arange( -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 ) relative_coords_table = ( torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) .permute(1, 2, 0) .contiguous() .unsqueeze(0) ) # 1, 2*Wh-1, 2*Ww-1, 2 if pretrained_window_size[0] > 0: relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 else: relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 relative_coords_table *= 8 # normalize to -8, 8 relative_coords_table = ( torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / np.log2(8) ) self.register_buffer("relative_coords_table", relative_coords_table) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(dim)) # type: ignore self.v_bias = nn.Parameter(torch.zeros(dim)) # type: ignore else: self.q_bias = None self.v_bias = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv_bias = None if self.q_bias is not None: qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) # type: ignore qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = ( qkv[0], qkv[1], qkv[2], ) # make torchscript happy (cannot use tensor as tuple) # cosine attention attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) logit_scale = torch.clamp( self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), ).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( -1, self.num_heads ) relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( # type: ignore self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1, ) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, Wh*Ww, Wh*Ww relative_position_bias = 16 * torch.sigmoid(relative_position_bias) attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( 1 ).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self) -> str: return ( f"dim={self.dim}, window_size={self.window_size}, " f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" ) def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops class SwinTransformerBlock(nn.Module): r"""Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm pretrained_window_size (int): Window size in pre-training. """ def __init__( self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert ( 0 <= self.shift_size < self.window_size ), "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, pretrained_window_size=to_2tuple(pretrained_window_size), ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, ) if self.shift_size > 0: attn_mask = self.calculate_mask(self.input_resolution) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) w_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition( img_mask, self.window_size ) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( attn_mask == 0, float(0.0) ) return attn_mask def forward(self, x, x_size): H, W = x_size B, L, C = x.shape # assert L == H * W, "input feature has wrong size" shortcut = x x = x.view(B, H, W, C) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll( x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) ) else: shifted_x = x # partition windows x_windows = window_partition( shifted_x, self.window_size ) # nW*B, window_size, window_size, C x_windows = x_windows.view( -1, self.window_size * self.window_size, C ) # nW*B, window_size*window_size, C # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size if self.input_resolution == x_size: attn_windows = self.attn( x_windows, mask=self.attn_mask ) # nW*B, window_size*window_size, C else: attn_windows = self.attn( x_windows, mask=self.calculate_mask(x_size).to(x.device) ) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll( shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) ) else: x = shifted_x x = x.view(B, H * W, C) x = shortcut + self.drop_path(self.norm1(x)) # FFN x = x + self.drop_path(self.norm2(self.mlp(x))) return x def extra_repr(self) -> str: return ( f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" ) def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class PatchMerging(nn.Module): r"""Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(2 * dim) def forward(self, x): """ x: B, H*W, C """ H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.view(B, H, W, C) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.reduction(x) x = self.norm(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim flops += H * W * self.dim // 2 return flops class BasicLayer(nn.Module): """A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. pretrained_window_size (int): Local window size in pre-training. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, pretrained_window_size=0, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList( [ SwinTransformerBlock( dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, pretrained_window_size=pretrained_window_size, ) for i in range(depth) ] ) # patch merging layer if downsample is not None: self.downsample = downsample( input_resolution, dim=dim, norm_layer=norm_layer ) else: self.downsample = None def forward(self, x, x_size): for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) if self.downsample is not None: x = self.downsample(x) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() # type: ignore if self.downsample is not None: flops += self.downsample.flops() return flops def _init_respostnorm(self): for blk in self.blocks: nn.init.constant_(blk.norm1.bias, 0) # type: ignore nn.init.constant_(blk.norm1.weight, 0) # type: ignore nn.init.constant_(blk.norm2.bias, 0) # type: ignore nn.init.constant_(blk.norm2.weight, 0) # type: ignore class PatchEmbed(nn.Module): r"""Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=patch_size, stride=patch_size # type: ignore ) if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints # assert H == self.img_size[0] and W == self.img_size[1], # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): Ho, Wo = self.patches_resolution flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) # type: ignore if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection="1conv", ): super(RSTB, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = BasicLayer( dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint, ) if resi_connection == "1conv": self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == "3conv": # to save parameters and memory self.conv = nn.Sequential( nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim, 3, 1, 1), ) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, norm_layer=None, ) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim, norm_layer=None, ) def forward(self, x, x_size): return ( self.patch_embed( self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) ) + x ) def flops(self): flops = 0 flops += self.residual_group.flops() H, W = self.input_resolution flops += H * W * self.dim * self.dim * 9 flops += self.patch_embed.flops() flops += self.patch_unembed.flops() return flops class PatchUnEmbed(nn.Module): r"""Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim def forward(self, x, x_size): B, HW, C = x.shape x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C return x def flops(self): flops = 0 return flops class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError( f"scale {scale} is not supported. " "Supported scales: 2^n and 3." ) super(Upsample, self).__init__(*m) class Upsample_hf(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError( f"scale {scale} is not supported. " "Supported scales: 2^n and 3." ) super(Upsample_hf, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): H, W = self.input_resolution # type: ignore flops = H * W * self.num_feat * 3 * 9 return flops class Swin2SR(nn.Module): r"""Swin2SR A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__( self, state_dict, **kwargs, ): super(Swin2SR, self).__init__() # Defaults img_size = 128 patch_size = 1 in_chans = 3 embed_dim = 96 depths = [6, 6, 6, 6] num_heads = [6, 6, 6, 6] window_size = 7 mlp_ratio = 4.0 qkv_bias = True drop_rate = 0.0 attn_drop_rate = 0.0 drop_path_rate = 0.1 norm_layer = nn.LayerNorm ape = False patch_norm = True use_checkpoint = False upscale = 2 img_range = 1.0 upsampler = "" resi_connection = "1conv" num_in_ch = in_chans num_out_ch = in_chans num_feat = 64 self.model_arch = "Swin2SR" self.sub_type = "SR" self.state = state_dict if "params_ema" in self.state: self.state = self.state["params_ema"] elif "params" in self.state: self.state = self.state["params"] state_keys = self.state.keys() if "conv_before_upsample.0.weight" in state_keys: if "conv_aux.weight" in state_keys: upsampler = "pixelshuffle_aux" elif "conv_up1.weight" in state_keys: upsampler = "nearest+conv" else: upsampler = "pixelshuffle" supports_fp16 = False elif "upsample.0.weight" in state_keys: upsampler = "pixelshuffledirect" else: upsampler = "" num_feat = ( self.state.get("conv_before_upsample.0.weight", None).shape[1] if self.state.get("conv_before_upsample.weight", None) else 64 ) num_in_ch = self.state["conv_first.weight"].shape[1] in_chans = num_in_ch if "conv_last.weight" in state_keys: num_out_ch = self.state["conv_last.weight"].shape[0] else: num_out_ch = num_in_ch upscale = 1 if upsampler == "nearest+conv": upsample_keys = [ x for x in state_keys if "conv_up" in x and "bias" not in x ] for upsample_key in upsample_keys: upscale *= 2 elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux": upsample_keys = [ x for x in state_keys if "upsample" in x and "conv" not in x and "bias" not in x ] for upsample_key in upsample_keys: shape = self.state[upsample_key].shape[0] upscale *= math.sqrt(shape // num_feat) upscale = int(upscale) elif upsampler == "pixelshuffledirect": upscale = int( math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) ) max_layer_num = 0 max_block_num = 0 for key in state_keys: result = re.match( r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key ) if result: layer_num, block_num = result.groups() max_layer_num = max(max_layer_num, int(layer_num)) max_block_num = max(max_block_num, int(block_num)) depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] if ( "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" in state_keys ): num_heads_num = self.state[ "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" ].shape[-1] num_heads = [num_heads_num for _ in range(max_layer_num + 1)] else: num_heads = depths embed_dim = self.state["conv_first.weight"].shape[0] mlp_ratio = float( self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] / embed_dim ) # TODO: could actually count the layers, but this should do if "layers.0.conv.4.weight" in state_keys: resi_connection = "3conv" else: resi_connection = "1conv" window_size = int( math.sqrt( self.state[ "layers.0.residual_group.blocks.0.attn.relative_position_index" ].shape[0] ) ) if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: img_size = int( math.sqrt( self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] ) * window_size ) # The JPEG models are the only ones with window-size 7, and they also use this range img_range = 255.0 if window_size == 7 else 1.0 self.in_nc = num_in_ch self.out_nc = num_out_ch self.num_feat = num_feat self.embed_dim = embed_dim self.num_heads = num_heads self.depths = depths self.window_size = window_size self.mlp_ratio = mlp_ratio self.scale = upscale self.upsampler = upsampler self.img_size = img_size self.img_range = img_range self.resi_connection = resi_connection self.supports_fp16 = False # Too much weirdness to support this at the moment self.supports_bfp16 = True self.min_size_restriction = 16 ## END AUTO DETECTION if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler self.window_size = window_size ##################################################################################################### ################################### 1, shallow feature extraction ################################### self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) ##################################################################################################### ################################### 2, deep feature extraction ###################################### self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) # type: ignore trunc_normal_(self.absolute_pos_embed, std=0.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) ] # stochastic depth decay rule # build Residual Swin Transformer blocks (RSTB) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection, ) self.layers.append(layer) if self.upsampler == "pixelshuffle_hf": self.layers_hf = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results # type: ignore norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection, ) self.layers_hf.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == "1conv": self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == "3conv": # to save parameters and memory self.conv_after_body = nn.Sequential( nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), ) ##################################################################################################### ################################ 3, high quality image reconstruction ################################ if self.upsampler == "pixelshuffle": # for classical SR self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == "pixelshuffle_aux": self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_after_aux = nn.Sequential( nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == "pixelshuffle_hf": self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.upsample_hf = Upsample_hf(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.conv_first_hf = nn.Sequential( nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) self.conv_before_upsample_hf = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == "pixelshuffledirect": # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep( upscale, embed_dim, num_out_ch, (patches_resolution[0], patches_resolution[1]), ) elif self.upsampler == "nearest+conv": # for real-world SR (less artifacts) assert self.upscale == 4, "only support x4 now." self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: # for image denoising and JPEG compression artifact reduction self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) self.apply(self._init_weights) self.load_state_dict(state_dict) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore # type: ignore def no_weight_decay(self): return {"absolute_pos_embed"} @torch.jit.ignore # type: ignore def no_weight_decay_keywords(self): return {"relative_position_bias_table"} def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") return x def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward_features_hf(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers_hf: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.upsampler == "pixelshuffle": # for classical SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) elif self.upsampler == "pixelshuffle_aux": bicubic = F.interpolate( x, size=(H * self.upscale, W * self.upscale), mode="bicubic", align_corners=False, ) bicubic = self.conv_bicubic(bicubic) x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) aux = self.conv_aux(x) # b, 3, LR_H, LR_W x = self.conv_after_aux(aux) x = ( self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] + bicubic[:, :, : H * self.upscale, : W * self.upscale] ) x = self.conv_last(x) aux = aux / self.img_range + self.mean elif self.upsampler == "pixelshuffle_hf": # for classical SR with HF x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x_before = self.conv_before_upsample(x) x_out = self.conv_last(self.upsample(x_before)) x_hf = self.conv_first_hf(x_before) x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf x_hf = self.conv_before_upsample_hf(x_hf) x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) x = x_out + x_hf x_hf = x_hf / self.img_range + self.mean elif self.upsampler == "pixelshuffledirect": # for lightweight SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.upsample(x) elif self.upsampler == "nearest+conv": # for real-world SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.lrelu( self.conv_up1( torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") ) ) x = self.lrelu( self.conv_up2( torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") ) ) x = self.conv_last(self.lrelu(self.conv_hr(x))) else: # for image denoising and JPEG compression artifact reduction x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) x = x / self.img_range + self.mean if self.upsampler == "pixelshuffle_aux": # NOTE: I removed an "aux" output here. not sure what that was for return x[:, :, : H * self.upscale, : W * self.upscale] # type: ignore elif self.upsampler == "pixelshuffle_hf": x_out = x_out / self.img_range + self.mean # type: ignore return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale] # type: ignore else: return x[:, :, : H * self.upscale, : W * self.upscale] def flops(self): flops = 0 H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() # type: ignore flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() # type: ignore return flops ================================================ FILE: ldm_patched/pfn/architecture/SwinIR.py ================================================ # pylint: skip-file # ----------------------------------------------------------------------------------- # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 # Originally Written by Ze Liu, Modified by Jingyun Liang. # ----------------------------------------------------------------------------------- import math import re import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint # Originally from the timm package from .timm.drop import DropPath from .timm.helpers import to_2tuple from .timm.weight_init import trunc_normal_ class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = ( x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) ) return windows def window_reverse(windows, window_size, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ B = int(windows.shape[0] / (H * W / window_size / window_size)) x = windows.view( B, H // window_size, W // window_size, window_size, window_size, -1 ) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) return x class WindowAttention(nn.Module): r"""Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args: dim (int): Number of input channels. window_size (tuple[int]): The height and width of the window. num_heads (int): Number of attention heads. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 proj_drop (float, optional): Dropout ratio of output. Default: 0.0 """ def __init__( self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, ): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( # type: ignore torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) ) # 2*Wh-1 * 2*Ww-1, nH # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = ( coords_flatten[:, :, None] - coords_flatten[:, None, :] ) # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute( 1, 2, 0 ).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) trunc_normal_(self.relative_position_bias_table, std=0.02) self.softmax = nn.Softmax(dim=-1) def forward(self, x, mask=None): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv = ( self.qkv(x) .reshape(B_, N, 3, self.num_heads, C // self.num_heads) .permute(2, 0, 3, 1, 4) ) q, k, v = ( qkv[0], qkv[1], qkv[2], ) # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = q @ k.transpose(-2, -1) relative_position_bias = self.relative_position_bias_table[ self.relative_position_index.view(-1) # type: ignore ].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1, ) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute( 2, 0, 1 ).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( 1 ).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x def extra_repr(self) -> str: return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}" def flops(self, N): # calculate flops for 1 window with token length of N flops = 0 # qkv = self.qkv(x) flops += N * self.dim * 3 * self.dim # attn = (q @ k.transpose(-2, -1)) flops += self.num_heads * N * (self.dim // self.num_heads) * N # x = (attn @ v) flops += self.num_heads * N * N * (self.dim // self.num_heads) # x = self.proj(x) flops += N * self.dim * self.dim return flops class SwinTransformerBlock(nn.Module): r"""Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float, optional): Stochastic depth rate. Default: 0.0 act_layer (nn.Module, optional): Activation layer. Default: nn.GELU norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__( self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = 0 self.window_size = min(self.input_resolution) assert ( 0 <= self.shift_size < self.window_size ), "shift_size must in 0-window_size" self.norm1 = norm_layer(dim) self.attn = WindowAttention( dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, ) if self.shift_size > 0: attn_mask = self.calculate_mask(self.input_resolution) else: attn_mask = None self.register_buffer("attn_mask", attn_mask) def calculate_mask(self, x_size): # calculate attention mask for SW-MSA H, W = x_size img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 h_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) w_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) cnt = 0 for h in h_slices: for w in w_slices: img_mask[:, h, w, :] = cnt cnt += 1 mask_windows = window_partition( img_mask, self.window_size ) # nW, window_size, window_size, 1 mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( attn_mask == 0, float(0.0) ) return attn_mask def forward(self, x, x_size): H, W = x_size B, L, C = x.shape # assert L == H * W, "input feature has wrong size" shortcut = x x = self.norm1(x) x = x.view(B, H, W, C) # cyclic shift if self.shift_size > 0: shifted_x = torch.roll( x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) ) else: shifted_x = x # partition windows x_windows = window_partition( shifted_x, self.window_size ) # nW*B, window_size, window_size, C x_windows = x_windows.view( -1, self.window_size * self.window_size, C ) # nW*B, window_size*window_size, C # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size if self.input_resolution == x_size: attn_windows = self.attn( x_windows, mask=self.attn_mask ) # nW*B, window_size*window_size, C else: attn_windows = self.attn( x_windows, mask=self.calculate_mask(x_size).to(x.device) ) # merge windows attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C # reverse cyclic shift if self.shift_size > 0: x = torch.roll( shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) ) else: x = shifted_x x = x.view(B, H * W, C) # FFN x = shortcut + self.drop_path(x) x = x + self.drop_path(self.mlp(self.norm2(x))) return x def extra_repr(self) -> str: return ( f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" ) def flops(self): flops = 0 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class PatchMerging(nn.Module): r"""Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def forward(self, x): """ x: B, H*W, C """ H, W = self.input_resolution B, L, C = x.shape assert L == H * W, "input feature has wrong size" assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." x = x.view(B, H, W, C) x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C x = self.norm(x) x = self.reduction(x) return x def extra_repr(self) -> str: return f"input_resolution={self.input_resolution}, dim={self.dim}" def flops(self): H, W = self.input_resolution flops = H * W * self.dim flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim return flops class BasicLayer(nn.Module): """A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, ): super().__init__() self.dim = dim self.input_resolution = input_resolution self.depth = depth self.use_checkpoint = use_checkpoint # build blocks self.blocks = nn.ModuleList( [ SwinTransformerBlock( dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer, ) for i in range(depth) ] ) # patch merging layer if downsample is not None: self.downsample = downsample( input_resolution, dim=dim, norm_layer=norm_layer ) else: self.downsample = None def forward(self, x, x_size): for blk in self.blocks: if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, x_size) else: x = blk(x, x_size) if self.downsample is not None: x = self.downsample(x) return x def extra_repr(self) -> str: return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" def flops(self): flops = 0 for blk in self.blocks: flops += blk.flops() # type: ignore if self.downsample is not None: flops += self.downsample.flops() return flops class RSTB(nn.Module): """Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. """ def __init__( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection="1conv", ): super(RSTB, self).__init__() self.dim = dim self.input_resolution = input_resolution self.residual_group = BasicLayer( dim=dim, input_resolution=input_resolution, depth=depth, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop, attn_drop=attn_drop, drop_path=drop_path, norm_layer=norm_layer, downsample=downsample, use_checkpoint=use_checkpoint, ) if resi_connection == "1conv": self.conv = nn.Conv2d(dim, dim, 3, 1, 1) elif resi_connection == "3conv": # to save parameters and memory self.conv = nn.Sequential( nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(dim // 4, dim, 3, 1, 1), ) self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None, ) self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None, ) def forward(self, x, x_size): return ( self.patch_embed( self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) ) + x ) def flops(self): flops = 0 flops += self.residual_group.flops() H, W = self.input_resolution flops += H * W * self.dim * self.dim * 9 flops += self.patch_embed.flops() flops += self.patch_unembed.flops() return flops class PatchEmbed(nn.Module): r"""Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [ img_size[0] // patch_size[0], # type: ignore img_size[1] // patch_size[1], # type: ignore ] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim if norm_layer is not None: self.norm = norm_layer(embed_dim) else: self.norm = None def forward(self, x): x = x.flatten(2).transpose(1, 2) # B Ph*Pw C if self.norm is not None: x = self.norm(x) return x def flops(self): flops = 0 H, W = self.img_size if self.norm is not None: flops += H * W * self.embed_dim # type: ignore return flops class PatchUnEmbed(nn.Module): r"""Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None """ def __init__( self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None ): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) patches_resolution = [ img_size[0] // patch_size[0], # type: ignore img_size[1] // patch_size[1], # type: ignore ] self.img_size = img_size self.patch_size = patch_size self.patches_resolution = patches_resolution self.num_patches = patches_resolution[0] * patches_resolution[1] self.in_chans = in_chans self.embed_dim = embed_dim def forward(self, x, x_size): B, HW, C = x.shape x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C return x def flops(self): flops = 0 return flops class Upsample(nn.Sequential): """Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat): m = [] if (scale & (scale - 1)) == 0: # scale = 2^n for _ in range(int(math.log(scale, 2))): m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(2)) elif scale == 3: m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) m.append(nn.PixelShuffle(3)) else: raise ValueError( f"scale {scale} is not supported. " "Supported scales: 2^n and 3." ) super(Upsample, self).__init__(*m) class UpsampleOneStep(nn.Sequential): """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. """ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): self.num_feat = num_feat self.input_resolution = input_resolution m = [] m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) m.append(nn.PixelShuffle(scale)) super(UpsampleOneStep, self).__init__(*m) def flops(self): H, W = self.input_resolution # type: ignore flops = H * W * self.num_feat * 3 * 9 return flops class SwinIR(nn.Module): r"""SwinIR A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. '1conv'/'3conv' """ def __init__( self, state_dict, **kwargs, ): super(SwinIR, self).__init__() # Defaults img_size = 64 patch_size = 1 in_chans = 3 embed_dim = 96 depths = [6, 6, 6, 6] num_heads = [6, 6, 6, 6] window_size = 7 mlp_ratio = 4.0 qkv_bias = True qk_scale = None drop_rate = 0.0 attn_drop_rate = 0.0 drop_path_rate = 0.1 norm_layer = nn.LayerNorm ape = False patch_norm = True use_checkpoint = False upscale = 2 img_range = 1.0 upsampler = "" resi_connection = "1conv" num_feat = 64 num_in_ch = in_chans num_out_ch = in_chans supports_fp16 = True self.start_unshuffle = 1 self.model_arch = "SwinIR" self.sub_type = "SR" self.state = state_dict if "params_ema" in self.state: self.state = self.state["params_ema"] elif "params" in self.state: self.state = self.state["params"] state_keys = self.state.keys() if "conv_before_upsample.0.weight" in state_keys: if "conv_up1.weight" in state_keys: upsampler = "nearest+conv" else: upsampler = "pixelshuffle" supports_fp16 = False elif "upsample.0.weight" in state_keys: upsampler = "pixelshuffledirect" else: upsampler = "" num_feat = ( self.state.get("conv_before_upsample.0.weight", None).shape[1] if self.state.get("conv_before_upsample.weight", None) else 64 ) if "conv_first.1.weight" in self.state: self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight") self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias") self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3)) num_in_ch = self.state["conv_first.weight"].shape[1] in_chans = num_in_ch if "conv_last.weight" in state_keys: num_out_ch = self.state["conv_last.weight"].shape[0] else: num_out_ch = num_in_ch upscale = 1 if upsampler == "nearest+conv": upsample_keys = [ x for x in state_keys if "conv_up" in x and "bias" not in x ] for upsample_key in upsample_keys: upscale *= 2 elif upsampler == "pixelshuffle": upsample_keys = [ x for x in state_keys if "upsample" in x and "conv" not in x and "bias" not in x ] for upsample_key in upsample_keys: shape = self.state[upsample_key].shape[0] upscale *= math.sqrt(shape // num_feat) upscale = int(upscale) elif upsampler == "pixelshuffledirect": upscale = int( math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) ) max_layer_num = 0 max_block_num = 0 for key in state_keys: result = re.match( r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key ) if result: layer_num, block_num = result.groups() max_layer_num = max(max_layer_num, int(layer_num)) max_block_num = max(max_block_num, int(block_num)) depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] if ( "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" in state_keys ): num_heads_num = self.state[ "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" ].shape[-1] num_heads = [num_heads_num for _ in range(max_layer_num + 1)] else: num_heads = depths embed_dim = self.state["conv_first.weight"].shape[0] mlp_ratio = float( self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] / embed_dim ) # TODO: could actually count the layers, but this should do if "layers.0.conv.4.weight" in state_keys: resi_connection = "3conv" else: resi_connection = "1conv" window_size = int( math.sqrt( self.state[ "layers.0.residual_group.blocks.0.attn.relative_position_index" ].shape[0] ) ) if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: img_size = int( math.sqrt( self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] ) * window_size ) # The JPEG models are the only ones with window-size 7, and they also use this range img_range = 255.0 if window_size == 7 else 1.0 self.in_nc = num_in_ch self.out_nc = num_out_ch self.num_feat = num_feat self.embed_dim = embed_dim self.num_heads = num_heads self.depths = depths self.window_size = window_size self.mlp_ratio = mlp_ratio self.scale = upscale / self.start_unshuffle self.upsampler = upsampler self.img_size = img_size self.img_range = img_range self.resi_connection = resi_connection self.supports_fp16 = False # Too much weirdness to support this at the moment self.supports_bfp16 = True self.min_size_restriction = 16 self.img_range = img_range if in_chans == 3: rgb_mean = (0.4488, 0.4371, 0.4040) self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) else: self.mean = torch.zeros(1, 1, 1, 1) self.upscale = upscale self.upsampler = upsampler self.window_size = window_size ##################################################################################################### ################################### 1, shallow feature extraction ################################### self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) ##################################################################################################### ################################### 2, deep feature extraction ###################################### self.num_layers = len(depths) self.embed_dim = embed_dim self.ape = ape self.patch_norm = patch_norm self.num_features = embed_dim self.mlp_ratio = mlp_ratio # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed.patches_resolution self.patches_resolution = patches_resolution # merge non-overlapping patches into image self.patch_unembed = PatchUnEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None, ) # absolute position embedding if self.ape: self.absolute_pos_embed = nn.Parameter( # type: ignore torch.zeros(1, num_patches, embed_dim) ) trunc_normal_(self.absolute_pos_embed, std=0.02) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) ] # stochastic depth decay rule # build Residual Swin Transformer blocks (RSTB) self.layers = nn.ModuleList() for i_layer in range(self.num_layers): layer = RSTB( dim=embed_dim, input_resolution=(patches_resolution[0], patches_resolution[1]), depth=depths[i_layer], num_heads=num_heads[i_layer], window_size=window_size, mlp_ratio=self.mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[ sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore ], # no impact on SR results norm_layer=norm_layer, downsample=None, use_checkpoint=use_checkpoint, img_size=img_size, patch_size=patch_size, resi_connection=resi_connection, ) self.layers.append(layer) self.norm = norm_layer(self.num_features) # build the last conv layer in deep feature extraction if resi_connection == "1conv": self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) elif resi_connection == "3conv": # to save parameters and memory self.conv_after_body = nn.Sequential( nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), ) ##################################################################################################### ################################ 3, high quality image reconstruction ################################ if self.upsampler == "pixelshuffle": # for classical SR self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.upsample = Upsample(upscale, num_feat) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) elif self.upsampler == "pixelshuffledirect": # for lightweight SR (to save parameters) self.upsample = UpsampleOneStep( upscale, embed_dim, num_out_ch, (patches_resolution[0], patches_resolution[1]), ) elif self.upsampler == "nearest+conv": # for real-world SR (less artifacts) self.conv_before_upsample = nn.Sequential( nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) ) self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) if self.upscale == 4: self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) elif self.upscale == 8: self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) else: # for image denoising and JPEG compression artifact reduction self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) self.apply(self._init_weights) self.load_state_dict(self.state, strict=False) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore # type: ignore def no_weight_decay(self): return {"absolute_pos_embed"} @torch.jit.ignore # type: ignore def no_weight_decay_keywords(self): return {"relative_position_bias_table"} def check_image_size(self, x): _, _, h, w = x.size() mod_pad_h = (self.window_size - h % self.window_size) % self.window_size mod_pad_w = (self.window_size - w % self.window_size) % self.window_size x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") return x def forward_features(self, x): x_size = (x.shape[2], x.shape[3]) x = self.patch_embed(x) if self.ape: x = x + self.absolute_pos_embed x = self.pos_drop(x) for layer in self.layers: x = layer(x, x_size) x = self.norm(x) # B L C x = self.patch_unembed(x, x_size) return x def forward(self, x): H, W = x.shape[2:] x = self.check_image_size(x) self.mean = self.mean.type_as(x) x = (x - self.mean) * self.img_range if self.start_unshuffle > 1: x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle) if self.upsampler == "pixelshuffle": # for classical SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.conv_last(self.upsample(x)) elif self.upsampler == "pixelshuffledirect": # for lightweight SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.upsample(x) elif self.upsampler == "nearest+conv": # for real-world SR x = self.conv_first(x) x = self.conv_after_body(self.forward_features(x)) + x x = self.conv_before_upsample(x) x = self.lrelu( self.conv_up1( torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") # type: ignore ) ) if self.upscale == 4: x = self.lrelu( self.conv_up2( torch.nn.functional.interpolate( # type: ignore x, scale_factor=2, mode="nearest" ) ) ) elif self.upscale == 8: x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) x = self.conv_last(self.lrelu(self.conv_hr(x))) else: # for image denoising and JPEG compression artifact reduction x_first = self.conv_first(x) res = self.conv_after_body(self.forward_features(x_first)) + x_first x = x + self.conv_last(res) x = x / self.img_range + self.mean return x[:, :, : H * self.upscale, : W * self.upscale] def flops(self): flops = 0 H, W = self.patches_resolution flops += H * W * 3 * self.embed_dim * 9 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() # type: ignore flops += H * W * 3 * self.embed_dim * self.embed_dim flops += self.upsample.flops() # type: ignore return flops ================================================ FILE: ldm_patched/pfn/architecture/__init__.py ================================================ ================================================ FILE: ldm_patched/pfn/architecture/block.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- from __future__ import annotations from collections import OrderedDict try: from typing import Literal except ImportError: from typing_extensions import Literal import torch import torch.nn as nn #################### # Basic blocks #################### def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1): # helper selecting activation # neg_slope: for leakyrelu and init of prelu # n_prelu: for p_relu num_parameters act_type = act_type.lower() if act_type == "relu": layer = nn.ReLU(inplace) elif act_type == "leakyrelu": layer = nn.LeakyReLU(neg_slope, inplace) elif act_type == "prelu": layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) else: raise NotImplementedError( "activation layer [{:s}] is not found".format(act_type) ) return layer def norm(norm_type: str, nc: int): # helper selecting normalization layer norm_type = norm_type.lower() if norm_type == "batch": layer = nn.BatchNorm2d(nc, affine=True) elif norm_type == "instance": layer = nn.InstanceNorm2d(nc, affine=False) else: raise NotImplementedError( "normalization layer [{:s}] is not found".format(norm_type) ) return layer def pad(pad_type: str, padding): # helper selecting padding layer # if padding is 'zero', do by conv layers pad_type = pad_type.lower() if padding == 0: return None if pad_type == "reflect": layer = nn.ReflectionPad2d(padding) elif pad_type == "replicate": layer = nn.ReplicationPad2d(padding) else: raise NotImplementedError( "padding layer [{:s}] is not implemented".format(pad_type) ) return layer def get_valid_padding(kernel_size, dilation): kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) padding = (kernel_size - 1) // 2 return padding class ConcatBlock(nn.Module): # Concat the output of a submodule to its input def __init__(self, submodule): super(ConcatBlock, self).__init__() self.sub = submodule def forward(self, x): output = torch.cat((x, self.sub(x)), dim=1) return output def __repr__(self): tmpstr = "Identity .. \n|" modstr = self.sub.__repr__().replace("\n", "\n|") tmpstr = tmpstr + modstr return tmpstr class ShortcutBlock(nn.Module): # Elementwise sum the output of a submodule to its input def __init__(self, submodule): super(ShortcutBlock, self).__init__() self.sub = submodule def forward(self, x): output = x + self.sub(x) return output def __repr__(self): tmpstr = "Identity + \n|" modstr = self.sub.__repr__().replace("\n", "\n|") tmpstr = tmpstr + modstr return tmpstr class ShortcutBlockSPSR(nn.Module): # Elementwise sum the output of a submodule to its input def __init__(self, submodule): super(ShortcutBlockSPSR, self).__init__() self.sub = submodule def forward(self, x): return x, self.sub def __repr__(self): tmpstr = "Identity + \n|" modstr = self.sub.__repr__().replace("\n", "\n|") tmpstr = tmpstr + modstr return tmpstr def sequential(*args): # Flatten Sequential. It unwraps nn.Sequential. if len(args) == 1: if isinstance(args[0], OrderedDict): raise NotImplementedError("sequential does not support OrderedDict input.") return args[0] # No sequential is needed. modules = [] for module in args: if isinstance(module, nn.Sequential): for submodule in module.children(): modules.append(submodule) elif isinstance(module, nn.Module): modules.append(module) return nn.Sequential(*modules) ConvMode = Literal["CNA", "NAC", "CNAC"] # 2x2x2 Conv Block def conv_block_2c2( in_nc, out_nc, act_type="relu", ): return sequential( nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1), nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0), act(act_type) if act_type else None, ) def conv_block( in_nc: int, out_nc: int, kernel_size, stride=1, dilation=1, groups=1, bias=True, pad_type="zero", norm_type: str | None = None, act_type: str | None = "relu", mode: ConvMode = "CNA", c2x2=False, ): """ Conv layer with padding, normalization, activation mode: CNA --> Conv -> Norm -> Act NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) """ if c2x2: return conv_block_2c2(in_nc, out_nc, act_type=act_type) assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) padding = get_valid_padding(kernel_size, dilation) p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None padding = padding if pad_type == "zero" else 0 c = nn.Conv2d( in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, groups=groups, ) a = act(act_type) if act_type else None if mode in ("CNA", "CNAC"): n = norm(norm_type, out_nc) if norm_type else None return sequential(p, c, n, a) elif mode == "NAC": if norm_type is None and act_type is not None: a = act(act_type, inplace=False) # Important! # input----ReLU(inplace)----Conv--+----output # |________________________| # inplace ReLU will modify the input, therefore wrong output n = norm(norm_type, in_nc) if norm_type else None return sequential(n, a, p, c) else: assert False, f"Invalid conv mode {mode}" #################### # Useful blocks #################### class ResNetBlock(nn.Module): """ ResNet Block, 3-3 style with extra residual scaling used in EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) """ def __init__( self, in_nc, mid_nc, out_nc, kernel_size=3, stride=1, dilation=1, groups=1, bias=True, pad_type="zero", norm_type=None, act_type="relu", mode: ConvMode = "CNA", res_scale=1, ): super(ResNetBlock, self).__init__() conv0 = conv_block( in_nc, mid_nc, kernel_size, stride, dilation, groups, bias, pad_type, norm_type, act_type, mode, ) if mode == "CNA": act_type = None if mode == "CNAC": # Residual path: |-CNAC-| act_type = None norm_type = None conv1 = conv_block( mid_nc, out_nc, kernel_size, stride, dilation, groups, bias, pad_type, norm_type, act_type, mode, ) # if in_nc != out_nc: # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ # None, None) # print('Need a projecter in ResNetBlock.') # else: # self.project = lambda x:x self.res = sequential(conv0, conv1) self.res_scale = res_scale def forward(self, x): res = self.res(x).mul(self.res_scale) return x + res class RRDB(nn.Module): """ Residual in Residual Dense Block (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) """ def __init__( self, nf, kernel_size=3, gc=32, stride=1, bias: bool = True, pad_type="zero", norm_type=None, act_type="leakyrelu", mode: ConvMode = "CNA", _convtype="Conv2D", _spectral_norm=False, plus=False, c2x2=False, ): super(RRDB, self).__init__() self.RDB1 = ResidualDenseBlock_5C( nf, kernel_size, gc, stride, bias, pad_type, norm_type, act_type, mode, plus=plus, c2x2=c2x2, ) self.RDB2 = ResidualDenseBlock_5C( nf, kernel_size, gc, stride, bias, pad_type, norm_type, act_type, mode, plus=plus, c2x2=c2x2, ) self.RDB3 = ResidualDenseBlock_5C( nf, kernel_size, gc, stride, bias, pad_type, norm_type, act_type, mode, plus=plus, c2x2=c2x2, ) def forward(self, x): out = self.RDB1(x) out = self.RDB2(out) out = self.RDB3(out) return out * 0.2 + x class ResidualDenseBlock_5C(nn.Module): """ Residual Dense Block style: 5 convs The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) Modified options that can be used: - "Partial Convolution based Padding" arXiv:1811.11718 - "Spectral normalization" arXiv:1802.05957 - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. {Rakotonirina} and A. {Rasoanaivo} Args: nf (int): Channel number of intermediate features (num_feat). gc (int): Channels for each growth (num_grow_ch: growth channel, i.e. intermediate channels). convtype (str): the type of convolution to use. Default: 'Conv2D' gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new trainable parameters) plus (bool): enable the additional residual paths from ESRGAN+ (adds trainable parameters) """ def __init__( self, nf=64, kernel_size=3, gc=32, stride=1, bias: bool = True, pad_type="zero", norm_type=None, act_type="leakyrelu", mode: ConvMode = "CNA", plus=False, c2x2=False, ): super(ResidualDenseBlock_5C, self).__init__() ## + self.conv1x1 = conv1x1(nf, gc) if plus else None ## + self.conv1 = conv_block( nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=act_type, mode=mode, c2x2=c2x2, ) self.conv2 = conv_block( nf + gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=act_type, mode=mode, c2x2=c2x2, ) self.conv3 = conv_block( nf + 2 * gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=act_type, mode=mode, c2x2=c2x2, ) self.conv4 = conv_block( nf + 3 * gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=act_type, mode=mode, c2x2=c2x2, ) if mode == "CNA": last_act = None else: last_act = act_type self.conv5 = conv_block( nf + 4 * gc, nf, 3, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=last_act, mode=mode, c2x2=c2x2, ) def forward(self, x): x1 = self.conv1(x) x2 = self.conv2(torch.cat((x, x1), 1)) if self.conv1x1: # pylint: disable=not-callable x2 = x2 + self.conv1x1(x) # + x3 = self.conv3(torch.cat((x, x1, x2), 1)) x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) if self.conv1x1: x4 = x4 + x2 # + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) return x5 * 0.2 + x def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) #################### # Upsampler #################### def pixelshuffle_block( in_nc: int, out_nc: int, upscale_factor=2, kernel_size=3, stride=1, bias=True, pad_type="zero", norm_type: str | None = None, act_type="relu", ): """ Pixel shuffle layer (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, CVPR17) """ conv = conv_block( in_nc, out_nc * (upscale_factor**2), kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=None, act_type=None, ) pixel_shuffle = nn.PixelShuffle(upscale_factor) n = norm(norm_type, out_nc) if norm_type else None a = act(act_type) if act_type else None return sequential(conv, pixel_shuffle, n, a) def upconv_block( in_nc: int, out_nc: int, upscale_factor=2, kernel_size=3, stride=1, bias=True, pad_type="zero", norm_type: str | None = None, act_type="relu", mode="nearest", c2x2=False, ): # Up conv # described in https://distill.pub/2016/deconv-checkerboard/ upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) conv = conv_block( in_nc, out_nc, kernel_size, stride, bias=bias, pad_type=pad_type, norm_type=norm_type, act_type=act_type, c2x2=c2x2, ) return sequential(upsample, conv) ================================================ FILE: ldm_patched/pfn/architecture/face/LICENSE-GFPGAN ================================================ Tencent is pleased to support the open source community by making GFPGAN available. 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MIT License (MIT) ----------------- Copyright (c) 2013 noamraph Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/pfn/architecture/face/LICENSE-RestoreFormer ================================================ Tencent is pleased to support the open source community by making GFPGAN available. Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below. Terms of the Apache License Version 2.0: --------------------------------------------- Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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MIT License (MIT) ----------------- Copyright (c) 2013 noamraph Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: ldm_patched/pfn/architecture/face/LICENSE-codeformer ================================================ S-Lab License 1.0 Copyright 2022 S-Lab Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. 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In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work. ================================================ FILE: ldm_patched/pfn/architecture/face/arcface_arch.py ================================================ import torch.nn as nn def conv3x3(inplanes, outplanes, stride=1): """A simple wrapper for 3x3 convolution with padding. Args: inplanes (int): Channel number of inputs. outplanes (int): Channel number of outputs. stride (int): Stride in convolution. Default: 1. """ return nn.Conv2d( inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False ) class BasicBlock(nn.Module): """Basic residual block used in the ResNetArcFace architecture. Args: inplanes (int): Channel number of inputs. planes (int): Channel number of outputs. stride (int): Stride in convolution. Default: 1. downsample (nn.Module): The downsample module. Default: None. """ expansion = 1 # output channel expansion ratio def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class IRBlock(nn.Module): """Improved residual block (IR Block) used in the ResNetArcFace architecture. Args: inplanes (int): Channel number of inputs. planes (int): Channel number of outputs. stride (int): Stride in convolution. Default: 1. downsample (nn.Module): The downsample module. Default: None. use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. """ expansion = 1 # output channel expansion ratio def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): super(IRBlock, self).__init__() self.bn0 = nn.BatchNorm2d(inplanes) self.conv1 = conv3x3(inplanes, inplanes) self.bn1 = nn.BatchNorm2d(inplanes) self.prelu = nn.PReLU() self.conv2 = conv3x3(inplanes, planes, stride) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride self.use_se = use_se if self.use_se: self.se = SEBlock(planes) def forward(self, x): residual = x out = self.bn0(x) out = self.conv1(out) out = self.bn1(out) out = self.prelu(out) out = self.conv2(out) out = self.bn2(out) if self.use_se: out = self.se(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.prelu(out) return out class Bottleneck(nn.Module): """Bottleneck block used in the ResNetArcFace architecture. Args: inplanes (int): Channel number of inputs. planes (int): Channel number of outputs. stride (int): Stride in convolution. Default: 1. downsample (nn.Module): The downsample module. Default: None. """ expansion = 4 # output channel expansion ratio def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d( planes, planes, kernel_size=3, stride=stride, padding=1, bias=False ) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d( planes, planes * self.expansion, kernel_size=1, bias=False ) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class SEBlock(nn.Module): """The squeeze-and-excitation block (SEBlock) used in the IRBlock. Args: channel (int): Channel number of inputs. reduction (int): Channel reduction ration. Default: 16. """ def __init__(self, channel, reduction=16): super(SEBlock, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d( 1 ) # pool to 1x1 without spatial information self.fc = nn.Sequential( nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel), nn.Sigmoid(), ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y class ResNetArcFace(nn.Module): """ArcFace with ResNet architectures. Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Args: block (str): Block used in the ArcFace architecture. layers (tuple(int)): Block numbers in each layer. use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. """ def __init__(self, block, layers, use_se=True): if block == "IRBlock": block = IRBlock self.inplanes = 64 self.use_se = use_se super(ResNetArcFace, self).__init__() self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.prelu = nn.PReLU() self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.bn4 = nn.BatchNorm2d(512) self.dropout = nn.Dropout() self.fc5 = nn.Linear(512 * 8 * 8, 512) self.bn5 = nn.BatchNorm1d(512) # initialization for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.xavier_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.xavier_normal_(m.weight) nn.init.constant_(m.bias, 0) def _make_layer(self, block, planes, num_blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False, ), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append( block(self.inplanes, planes, stride, downsample, use_se=self.use_se) ) self.inplanes = planes for _ in range(1, num_blocks): layers.append(block(self.inplanes, planes, use_se=self.use_se)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.prelu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.bn4(x) x = self.dropout(x) x = x.view(x.size(0), -1) x = self.fc5(x) x = self.bn5(x) return x ================================================ FILE: ldm_patched/pfn/architecture/face/codeformer.py ================================================ """ Modified from https://github.com/sczhou/CodeFormer VQGAN code, adapted from the original created by the Unleashing Transformers authors: https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py This version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me. """ import math from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F import logging as logger from torch import Tensor class VectorQuantizer(nn.Module): def __init__(self, codebook_size, emb_dim, beta): super(VectorQuantizer, self).__init__() self.codebook_size = codebook_size # number of embeddings self.emb_dim = emb_dim # dimension of embedding self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) self.embedding.weight.data.uniform_( -1.0 / self.codebook_size, 1.0 / self.codebook_size ) def forward(self, z): # reshape z -> (batch, height, width, channel) and flatten z = z.permute(0, 2, 3, 1).contiguous() z_flattened = z.view(-1, self.emb_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z d = ( (z_flattened**2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) ) mean_distance = torch.mean(d) # find closest encodings # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) min_encoding_scores, min_encoding_indices = torch.topk( d, 1, dim=1, largest=False ) # [0-1], higher score, higher confidence min_encoding_scores = torch.exp(-min_encoding_scores / 10) min_encodings = torch.zeros( min_encoding_indices.shape[0], self.codebook_size ).to(z) min_encodings.scatter_(1, min_encoding_indices, 1) # get quantized latent vectors z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) # compute loss for embedding loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( (z_q - z.detach()) ** 2 ) # preserve gradients z_q = z + (z_q - z).detach() # perplexity e_mean = torch.mean(min_encodings, dim=0) perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return ( z_q, loss, { "perplexity": perplexity, "min_encodings": min_encodings, "min_encoding_indices": min_encoding_indices, "min_encoding_scores": min_encoding_scores, "mean_distance": mean_distance, }, ) def get_codebook_feat(self, indices, shape): # input indices: batch*token_num -> (batch*token_num)*1 # shape: batch, height, width, channel indices = indices.view(-1, 1) min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) min_encodings.scatter_(1, indices, 1) # get quantized latent vectors z_q = torch.matmul(min_encodings.float(), self.embedding.weight) if shape is not None: # reshape back to match original input shape z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() return z_q class GumbelQuantizer(nn.Module): def __init__( self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0, ): super().__init__() self.codebook_size = codebook_size # number of embeddings self.emb_dim = emb_dim # dimension of embedding self.straight_through = straight_through self.temperature = temp_init self.kl_weight = kl_weight self.proj = nn.Conv2d( num_hiddens, codebook_size, 1 ) # projects last encoder layer to quantized logits self.embed = nn.Embedding(codebook_size, emb_dim) def forward(self, z): hard = self.straight_through if self.training else True logits = self.proj(z) soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) # + kl divergence to the prior loss qy = F.softmax(logits, dim=1) diff = ( self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() ) min_encoding_indices = soft_one_hot.argmax(dim=1) return z_q, diff, {"min_encoding_indices": min_encoding_indices} class Downsample(nn.Module): def __init__(self, in_channels): super().__init__() self.conv = torch.nn.Conv2d( in_channels, in_channels, kernel_size=3, stride=2, padding=0 ) def forward(self, x): pad = (0, 1, 0, 1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) return x class Upsample(nn.Module): def __init__(self, in_channels): super().__init__() self.conv = nn.Conv2d( in_channels, in_channels, kernel_size=3, stride=1, padding=1 ) def forward(self, x): x = F.interpolate(x, scale_factor=2.0, mode="nearest") x = self.conv(x) return x class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = normalize(in_channels) self.q = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.k = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.v = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.proj_out = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b, c, h, w = q.shape q = q.reshape(b, c, h * w) q = q.permute(0, 2, 1) k = k.reshape(b, c, h * w) w_ = torch.bmm(q, k) w_ = w_ * (int(c) ** (-0.5)) w_ = F.softmax(w_, dim=2) # attend to values v = v.reshape(b, c, h * w) w_ = w_.permute(0, 2, 1) h_ = torch.bmm(v, w_) h_ = h_.reshape(b, c, h, w) h_ = self.proj_out(h_) return x + h_ class Encoder(nn.Module): def __init__( self, in_channels, nf, out_channels, ch_mult, num_res_blocks, resolution, attn_resolutions, ): super().__init__() self.nf = nf self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.attn_resolutions = attn_resolutions curr_res = self.resolution in_ch_mult = (1,) + tuple(ch_mult) blocks = [] # initial convultion blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) # residual and downsampling blocks, with attention on smaller res (16x16) for i in range(self.num_resolutions): block_in_ch = nf * in_ch_mult[i] block_out_ch = nf * ch_mult[i] for _ in range(self.num_res_blocks): blocks.append(ResBlock(block_in_ch, block_out_ch)) block_in_ch = block_out_ch if curr_res in attn_resolutions: blocks.append(AttnBlock(block_in_ch)) if i != self.num_resolutions - 1: blocks.append(Downsample(block_in_ch)) curr_res = curr_res // 2 # non-local attention block blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore blocks.append(AttnBlock(block_in_ch)) # type: ignore blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore # normalise and convert to latent size blocks.append(normalize(block_in_ch)) # type: ignore blocks.append( nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore ) self.blocks = nn.ModuleList(blocks) def forward(self, x): for block in self.blocks: x = block(x) return x class Generator(nn.Module): def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): super().__init__() self.nf = nf self.ch_mult = ch_mult self.num_resolutions = len(self.ch_mult) self.num_res_blocks = res_blocks self.resolution = img_size self.attn_resolutions = attn_resolutions self.in_channels = emb_dim self.out_channels = 3 block_in_ch = self.nf * self.ch_mult[-1] curr_res = self.resolution // 2 ** (self.num_resolutions - 1) blocks = [] # initial conv blocks.append( nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1) ) # non-local attention block blocks.append(ResBlock(block_in_ch, block_in_ch)) blocks.append(AttnBlock(block_in_ch)) blocks.append(ResBlock(block_in_ch, block_in_ch)) for i in reversed(range(self.num_resolutions)): block_out_ch = self.nf * self.ch_mult[i] for _ in range(self.num_res_blocks): blocks.append(ResBlock(block_in_ch, block_out_ch)) block_in_ch = block_out_ch if curr_res in self.attn_resolutions: blocks.append(AttnBlock(block_in_ch)) if i != 0: blocks.append(Upsample(block_in_ch)) curr_res = curr_res * 2 blocks.append(normalize(block_in_ch)) blocks.append( nn.Conv2d( block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1 ) ) self.blocks = nn.ModuleList(blocks) def forward(self, x): for block in self.blocks: x = block(x) return x class VQAutoEncoder(nn.Module): def __init__( self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None, ): super().__init__() self.in_channels = 3 self.nf = nf self.n_blocks = res_blocks self.codebook_size = codebook_size self.embed_dim = emb_dim self.ch_mult = ch_mult self.resolution = img_size self.attn_resolutions = attn_resolutions self.quantizer_type = quantizer self.encoder = Encoder( self.in_channels, self.nf, self.embed_dim, self.ch_mult, self.n_blocks, self.resolution, self.attn_resolutions, ) if self.quantizer_type == "nearest": self.beta = beta # 0.25 self.quantize = VectorQuantizer( self.codebook_size, self.embed_dim, self.beta ) elif self.quantizer_type == "gumbel": self.gumbel_num_hiddens = emb_dim self.straight_through = gumbel_straight_through self.kl_weight = gumbel_kl_weight self.quantize = GumbelQuantizer( self.codebook_size, self.embed_dim, self.gumbel_num_hiddens, self.straight_through, self.kl_weight, ) self.generator = Generator( nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim ) if model_path is not None: chkpt = torch.load(model_path, map_location="cpu", weights_only=True) if "params_ema" in chkpt: self.load_state_dict( torch.load(model_path, map_location="cpu", weights_only=True)["params_ema"] ) logger.info(f"vqgan is loaded from: {model_path} [params_ema]") elif "params" in chkpt: self.load_state_dict( torch.load(model_path, map_location="cpu", weights_only=True)["params"] ) logger.info(f"vqgan is loaded from: {model_path} [params]") else: raise ValueError("Wrong params!") def forward(self, x): x = self.encoder(x) quant, codebook_loss, quant_stats = self.quantize(x) x = self.generator(quant) return x, codebook_loss, quant_stats def calc_mean_std(feat, eps=1e-5): """Calculate mean and std for adaptive_instance_normalization. Args: feat (Tensor): 4D tensor. eps (float): A small value added to the variance to avoid divide-by-zero. Default: 1e-5. """ size = feat.size() assert len(size) == 4, "The input feature should be 4D tensor." b, c = size[:2] feat_var = feat.view(b, c, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(b, c, 1, 1) feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) return feat_mean, feat_std def adaptive_instance_normalization(content_feat, style_feat): """Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. """ size = content_feat.size() style_mean, style_std = calc_mean_std(style_feat) content_mean, content_std = calc_mean_std(content_feat) normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand( size ) return normalized_feat * style_std.expand(size) + style_mean.expand(size) class PositionEmbeddingSine(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__( self, num_pos_feats=64, temperature=10000, normalize=False, scale=None ): super().__init__() self.num_pos_feats = num_pos_feats self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, x, mask=None): if mask is None: mask = torch.zeros( (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool ) not_mask = ~mask # pylint: disable=invalid-unary-operand-type y_embed = not_mask.cumsum(1, dtype=torch.float32) x_embed = not_mask.cumsum(2, dtype=torch.float32) if self.normalize: eps = 1e-6 y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack( (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 ).flatten(3) pos_y = torch.stack( (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 ).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos def _get_activation_fn(activation): """Return an activation function given a string""" if activation == "relu": return F.relu if activation == "gelu": return F.gelu if activation == "glu": return F.glu raise RuntimeError(f"activation should be relu/gelu, not {activation}.") class TransformerSALayer(nn.Module): def __init__( self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu" ): super().__init__() self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) # Implementation of Feedforward model - MLP self.linear1 = nn.Linear(embed_dim, dim_mlp) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_mlp, embed_dim) self.norm1 = nn.LayerNorm(embed_dim) self.norm2 = nn.LayerNorm(embed_dim) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.activation = _get_activation_fn(activation) def with_pos_embed(self, tensor, pos: Optional[Tensor]): return tensor if pos is None else tensor + pos def forward( self, tgt, tgt_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, query_pos: Optional[Tensor] = None, ): # self attention tgt2 = self.norm1(tgt) q = k = self.with_pos_embed(tgt2, query_pos) tgt2 = self.self_attn( q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask )[0] tgt = tgt + self.dropout1(tgt2) # ffn tgt2 = self.norm2(tgt) tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) tgt = tgt + self.dropout2(tgt2) return tgt def normalize(in_channels): return torch.nn.GroupNorm( num_groups=32, num_channels=in_channels, eps=1e-6, affine=True ) @torch.jit.script # type: ignore def swish(x): return x * torch.sigmoid(x) class ResBlock(nn.Module): def __init__(self, in_channels, out_channels=None): super(ResBlock, self).__init__() self.in_channels = in_channels self.out_channels = in_channels if out_channels is None else out_channels self.norm1 = normalize(in_channels) self.conv1 = nn.Conv2d( in_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore ) self.norm2 = normalize(out_channels) self.conv2 = nn.Conv2d( out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore ) if self.in_channels != self.out_channels: self.conv_out = nn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore ) def forward(self, x_in): x = x_in x = self.norm1(x) x = swish(x) x = self.conv1(x) x = self.norm2(x) x = swish(x) x = self.conv2(x) if self.in_channels != self.out_channels: x_in = self.conv_out(x_in) return x + x_in class Fuse_sft_block(nn.Module): def __init__(self, in_ch, out_ch): super().__init__() self.encode_enc = ResBlock(2 * in_ch, out_ch) self.scale = nn.Sequential( nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), ) self.shift = nn.Sequential( nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), ) def forward(self, enc_feat, dec_feat, w=1): enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) scale = self.scale(enc_feat) shift = self.shift(enc_feat) residual = w * (dec_feat * scale + shift) out = dec_feat + residual return out class CodeFormer(VQAutoEncoder): def __init__(self, state_dict): dim_embd = 512 n_head = 8 n_layers = 9 codebook_size = 1024 latent_size = 256 connect_list = ["32", "64", "128", "256"] fix_modules = ["quantize", "generator"] # This is just a guess as I only have one model to look at position_emb = state_dict["position_emb"] dim_embd = position_emb.shape[1] latent_size = position_emb.shape[0] try: n_layers = len( set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x]) ) except: pass codebook_size = state_dict["quantize.embedding.weight"].shape[0] # This is also just another guess n_head_exp = ( state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd ) n_head = 2**n_head_exp in_nc = state_dict["encoder.blocks.0.weight"].shape[1] self.model_arch = "CodeFormer" self.sub_type = "Face SR" self.scale = 8 self.in_nc = in_nc self.out_nc = in_nc self.state = state_dict self.supports_fp16 = False self.supports_bf16 = True self.min_size_restriction = 16 super(CodeFormer, self).__init__( 512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size ) if fix_modules is not None: for module in fix_modules: for param in getattr(self, module).parameters(): param.requires_grad = False self.connect_list = connect_list self.n_layers = n_layers self.dim_embd = dim_embd self.dim_mlp = dim_embd * 2 self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) # type: ignore self.feat_emb = nn.Linear(256, self.dim_embd) # transformer self.ft_layers = nn.Sequential( *[ TransformerSALayer( embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0 ) for _ in range(self.n_layers) ] ) # logits_predict head self.idx_pred_layer = nn.Sequential( nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False) ) self.channels = { "16": 512, "32": 256, "64": 256, "128": 128, "256": 128, "512": 64, } # after second residual block for > 16, before attn layer for ==16 self.fuse_encoder_block = { "512": 2, "256": 5, "128": 8, "64": 11, "32": 14, "16": 18, } # after first residual block for > 16, before attn layer for ==16 self.fuse_generator_block = { "16": 6, "32": 9, "64": 12, "128": 15, "256": 18, "512": 21, } # fuse_convs_dict self.fuse_convs_dict = nn.ModuleDict() for f_size in self.connect_list: in_ch = self.channels[f_size] self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) self.load_state_dict(state_dict) def _init_weights(self, module): if isinstance(module, (nn.Linear, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=0.02) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def forward(self, x, weight=0.5, **kwargs): detach_16 = True code_only = False adain = True # ################### Encoder ##################### enc_feat_dict = {} out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.encoder.blocks): x = block(x) if i in out_list: enc_feat_dict[str(x.shape[-1])] = x.clone() lq_feat = x # ################# Transformer ################### # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) # BCHW -> BC(HW) -> (HW)BC feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) query_emb = feat_emb # Transformer encoder for layer in self.ft_layers: query_emb = layer(query_emb, query_pos=pos_emb) # output logits logits = self.idx_pred_layer(query_emb) # (hw)bn logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n if code_only: # for training stage II # logits doesn't need softmax before cross_entropy loss return logits, lq_feat # ################# Quantization ################### # if self.training: # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) # # b(hw)c -> bc(hw) -> bchw # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) # ------------ soft_one_hot = F.softmax(logits, dim=2) _, top_idx = torch.topk(soft_one_hot, 1, dim=2) quant_feat = self.quantize.get_codebook_feat( top_idx, shape=[x.shape[0], 16, 16, 256] # type: ignore ) # preserve gradients # quant_feat = lq_feat + (quant_feat - lq_feat).detach() if detach_16: quant_feat = quant_feat.detach() # for training stage III if adain: quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) # ################## Generator #################### x = quant_feat fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.generator.blocks): x = block(x) if i in fuse_list: # fuse after i-th block f_size = str(x.shape[-1]) if weight > 0: x = self.fuse_convs_dict[f_size]( enc_feat_dict[f_size].detach(), x, weight ) out = x # logits doesn't need softmax before cross_entropy loss # return out, logits, lq_feat return out, logits ================================================ FILE: ldm_patched/pfn/architecture/face/fused_act.py ================================================ # pylint: skip-file # type: ignore # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 import torch from torch import nn from torch.autograd import Function fused_act_ext = None class FusedLeakyReLUFunctionBackward(Function): @staticmethod def forward(ctx, grad_output, out, negative_slope, scale): ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale empty = grad_output.new_empty(0) grad_input = fused_act_ext.fused_bias_act( grad_output, empty, out, 3, 1, negative_slope, scale ) dim = [0] if grad_input.ndim > 2: dim += list(range(2, grad_input.ndim)) grad_bias = grad_input.sum(dim).detach() return grad_input, grad_bias @staticmethod def backward(ctx, gradgrad_input, gradgrad_bias): (out,) = ctx.saved_tensors gradgrad_out = fused_act_ext.fused_bias_act( gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale ) return gradgrad_out, None, None, None class FusedLeakyReLUFunction(Function): @staticmethod def forward(ctx, input, bias, negative_slope, scale): empty = input.new_empty(0) out = fused_act_ext.fused_bias_act( input, bias, empty, 3, 0, negative_slope, scale ) ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale return out @staticmethod def backward(ctx, grad_output): (out,) = ctx.saved_tensors grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( grad_output, out, ctx.negative_slope, ctx.scale ) return grad_input, grad_bias, None, None class FusedLeakyReLU(nn.Module): def __init__(self, channel, negative_slope=0.2, scale=2**0.5): super().__init__() self.bias = nn.Parameter(torch.zeros(channel)) self.negative_slope = negative_slope self.scale = scale def forward(self, input): return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) ================================================ FILE: ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py ================================================ # pylint: skip-file # type: ignore import math import random import torch from torch import nn from .gfpganv1_arch import ResUpBlock from .stylegan2_bilinear_arch import ( ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, StyleGAN2GeneratorBilinear, ) class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, lr_mlp=0.01, narrow=1, sft_half=False, ): super(StyleGAN2GeneratorBilinearSFT, self).__init__( out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, lr_mlp=lr_mlp, narrow=narrow, ) self.sft_half = sft_half def forward( self, styles, conditions, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2GeneratorBilinearSFT. Args: styles (list[Tensor]): Sample codes of styles. conditions (list[Tensor]): SFT conditions to generators. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): The truncation ratio. Default: 1. truncation_latent (Tensor | None): The truncation latent tensor. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latents with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) # the conditions may have fewer levels if i < len(conditions): # SFT part to combine the conditions if self.sft_half: # only apply SFT to half of the channels out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) out_sft = out_sft * conditions[i - 1] + conditions[i] out = torch.cat([out_same, out_sft], dim=1) else: # apply SFT to all the channels out = out * conditions[i - 1] + conditions[i] out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space i += 2 image = skip if return_latents: return image, latent else: return image, None class GFPGANBilinear(nn.Module): """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for deployment. It can be easily converted to the clean version: GFPGANv1Clean. Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. fix_decoder (bool): Whether to fix the decoder. Default: True. num_mlp (int): Layer number of MLP style layers. Default: 8. lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. input_is_latent (bool): Whether input is latent style. Default: False. different_w (bool): Whether to use different latent w for different layers. Default: False. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, channel_multiplier=1, decoder_load_path=None, fix_decoder=True, # for stylegan decoder num_mlp=8, lr_mlp=0.01, input_is_latent=False, different_w=False, narrow=1, sft_half=False, ): super(GFPGANBilinear, self).__init__() self.input_is_latent = input_is_latent self.different_w = different_w self.num_style_feat = num_style_feat self.min_size_restriction = 512 unet_narrow = narrow * 0.5 # by default, use a half of input channels channels = { "4": int(512 * unet_narrow), "8": int(512 * unet_narrow), "16": int(512 * unet_narrow), "32": int(512 * unet_narrow), "64": int(256 * channel_multiplier * unet_narrow), "128": int(128 * channel_multiplier * unet_narrow), "256": int(64 * channel_multiplier * unet_narrow), "512": int(32 * channel_multiplier * unet_narrow), "1024": int(16 * channel_multiplier * unet_narrow), } self.log_size = int(math.log(out_size, 2)) first_out_size = 2 ** (int(math.log(out_size, 2))) self.conv_body_first = ConvLayer( 3, channels[f"{first_out_size}"], 1, bias=True, activate=True ) # downsample in_channels = channels[f"{first_out_size}"] self.conv_body_down = nn.ModuleList() for i in range(self.log_size, 2, -1): out_channels = channels[f"{2**(i - 1)}"] self.conv_body_down.append(ResBlock(in_channels, out_channels)) in_channels = out_channels self.final_conv = ConvLayer( in_channels, channels["4"], 3, bias=True, activate=True ) # upsample in_channels = channels["4"] self.conv_body_up = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) in_channels = out_channels # to RGB self.toRGB = nn.ModuleList() for i in range(3, self.log_size + 1): self.toRGB.append( EqualConv2d( channels[f"{2**i}"], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0, ) ) if different_w: linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat else: linear_out_channel = num_style_feat self.final_linear = EqualLinear( channels["4"] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None, ) # the decoder: stylegan2 generator with SFT modulations self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( out_size=out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, lr_mlp=lr_mlp, narrow=narrow, sft_half=sft_half, ) # load pre-trained stylegan2 model if necessary if decoder_load_path: self.stylegan_decoder.load_state_dict( torch.load( decoder_load_path, map_location=lambda storage, loc: storage, weights_only=True)["params_ema"] ) # fix decoder without updating params if fix_decoder: for _, param in self.stylegan_decoder.named_parameters(): param.requires_grad = False # for SFT modulations (scale and shift) self.condition_scale = nn.ModuleList() self.condition_shift = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] if sft_half: sft_out_channels = out_channels else: sft_out_channels = out_channels * 2 self.condition_scale.append( nn.Sequential( EqualConv2d( out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ScaledLeakyReLU(0.2), EqualConv2d( out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1, ), ) ) self.condition_shift.append( nn.Sequential( EqualConv2d( out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ScaledLeakyReLU(0.2), EqualConv2d( out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ) ) def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): """Forward function for GFPGANBilinear. Args: x (Tensor): Input images. return_latents (bool): Whether to return style latents. Default: False. return_rgb (bool): Whether return intermediate rgb images. Default: True. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. """ conditions = [] unet_skips = [] out_rgbs = [] # encoder feat = self.conv_body_first(x) for i in range(self.log_size - 2): feat = self.conv_body_down[i](feat) unet_skips.insert(0, feat) feat = self.final_conv(feat) # style code style_code = self.final_linear(feat.view(feat.size(0), -1)) if self.different_w: style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) # decode for i in range(self.log_size - 2): # add unet skip feat = feat + unet_skips[i] # ResUpLayer feat = self.conv_body_up[i](feat) # generate scale and shift for SFT layers scale = self.condition_scale[i](feat) conditions.append(scale.clone()) shift = self.condition_shift[i](feat) conditions.append(shift.clone()) # generate rgb images if return_rgb: out_rgbs.append(self.toRGB[i](feat)) # decoder image, _ = self.stylegan_decoder( [style_code], conditions, return_latents=return_latents, input_is_latent=self.input_is_latent, randomize_noise=randomize_noise, ) return image, out_rgbs ================================================ FILE: ldm_patched/pfn/architecture/face/gfpganv1_arch.py ================================================ # pylint: skip-file # type: ignore import math import random import torch from torch import nn from torch.nn import functional as F from .fused_act import FusedLeakyReLU from .stylegan2_arch import ( ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU, StyleGAN2Generator, ) class StyleGAN2GeneratorSFT(StyleGAN2Generator): """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), lr_mlp=0.01, narrow=1, sft_half=False, ): super(StyleGAN2GeneratorSFT, self).__init__( out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, resample_kernel=resample_kernel, lr_mlp=lr_mlp, narrow=narrow, ) self.sft_half = sft_half def forward( self, styles, conditions, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2GeneratorSFT. Args: styles (list[Tensor]): Sample codes of styles. conditions (list[Tensor]): SFT conditions to generators. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): The truncation ratio. Default: 1. truncation_latent (Tensor | None): The truncation latent tensor. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latents with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) # the conditions may have fewer levels if i < len(conditions): # SFT part to combine the conditions if self.sft_half: # only apply SFT to half of the channels out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) out_sft = out_sft * conditions[i - 1] + conditions[i] out = torch.cat([out_same, out_sft], dim=1) else: # apply SFT to all the channels out = out * conditions[i - 1] + conditions[i] out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space i += 2 image = skip if return_latents: return image, latent else: return image, None class ConvUpLayer(nn.Module): """Convolutional upsampling layer. It uses bilinear upsampler + Conv. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. stride (int): Stride of the convolution. Default: 1 padding (int): Zero-padding added to both sides of the input. Default: 0. bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. activate (bool): Whether use activateion. Default: True. """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0, activate=True, ): super(ConvUpLayer, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding # self.scale is used to scale the convolution weights, which is related to the common initializations. self.scale = 1 / math.sqrt(in_channels * kernel_size**2) self.weight = nn.Parameter( torch.randn(out_channels, in_channels, kernel_size, kernel_size) ) if bias and not activate: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter("bias", None) # activation if activate: if bias: self.activation = FusedLeakyReLU(out_channels) else: self.activation = ScaledLeakyReLU(0.2) else: self.activation = None def forward(self, x): # bilinear upsample out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) # conv out = F.conv2d( out, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding, ) # activation if self.activation is not None: out = self.activation(out) return out class ResUpBlock(nn.Module): """Residual block with upsampling. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. """ def __init__(self, in_channels, out_channels): super(ResUpBlock, self).__init__() self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) self.conv2 = ConvUpLayer( in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True ) self.skip = ConvUpLayer( in_channels, out_channels, 1, bias=False, activate=False ) def forward(self, x): out = self.conv1(x) out = self.conv2(out) skip = self.skip(x) out = (out + skip) / math.sqrt(2) return out class GFPGANv1(nn.Module): """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. fix_decoder (bool): Whether to fix the decoder. Default: True. num_mlp (int): Layer number of MLP style layers. Default: 8. lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. input_is_latent (bool): Whether input is latent style. Default: False. different_w (bool): Whether to use different latent w for different layers. Default: False. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, channel_multiplier=1, resample_kernel=(1, 3, 3, 1), decoder_load_path=None, fix_decoder=True, # for stylegan decoder num_mlp=8, lr_mlp=0.01, input_is_latent=False, different_w=False, narrow=1, sft_half=False, ): super(GFPGANv1, self).__init__() self.input_is_latent = input_is_latent self.different_w = different_w self.num_style_feat = num_style_feat unet_narrow = narrow * 0.5 # by default, use a half of input channels channels = { "4": int(512 * unet_narrow), "8": int(512 * unet_narrow), "16": int(512 * unet_narrow), "32": int(512 * unet_narrow), "64": int(256 * channel_multiplier * unet_narrow), "128": int(128 * channel_multiplier * unet_narrow), "256": int(64 * channel_multiplier * unet_narrow), "512": int(32 * channel_multiplier * unet_narrow), "1024": int(16 * channel_multiplier * unet_narrow), } self.log_size = int(math.log(out_size, 2)) first_out_size = 2 ** (int(math.log(out_size, 2))) self.conv_body_first = ConvLayer( 3, channels[f"{first_out_size}"], 1, bias=True, activate=True ) # downsample in_channels = channels[f"{first_out_size}"] self.conv_body_down = nn.ModuleList() for i in range(self.log_size, 2, -1): out_channels = channels[f"{2**(i - 1)}"] self.conv_body_down.append( ResBlock(in_channels, out_channels, resample_kernel) ) in_channels = out_channels self.final_conv = ConvLayer( in_channels, channels["4"], 3, bias=True, activate=True ) # upsample in_channels = channels["4"] self.conv_body_up = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) in_channels = out_channels # to RGB self.toRGB = nn.ModuleList() for i in range(3, self.log_size + 1): self.toRGB.append( EqualConv2d( channels[f"{2**i}"], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0, ) ) if different_w: linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat else: linear_out_channel = num_style_feat self.final_linear = EqualLinear( channels["4"] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None, ) # the decoder: stylegan2 generator with SFT modulations self.stylegan_decoder = StyleGAN2GeneratorSFT( out_size=out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, resample_kernel=resample_kernel, lr_mlp=lr_mlp, narrow=narrow, sft_half=sft_half, ) # load pre-trained stylegan2 model if necessary if decoder_load_path: self.stylegan_decoder.load_state_dict( torch.load( decoder_load_path, map_location=lambda storage, loc: storage, weights_only=True)["params_ema"] ) # fix decoder without updating params if fix_decoder: for _, param in self.stylegan_decoder.named_parameters(): param.requires_grad = False # for SFT modulations (scale and shift) self.condition_scale = nn.ModuleList() self.condition_shift = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] if sft_half: sft_out_channels = out_channels else: sft_out_channels = out_channels * 2 self.condition_scale.append( nn.Sequential( EqualConv2d( out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ScaledLeakyReLU(0.2), EqualConv2d( out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1, ), ) ) self.condition_shift.append( nn.Sequential( EqualConv2d( out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ScaledLeakyReLU(0.2), EqualConv2d( out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0, ), ) ) def forward( self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs ): """Forward function for GFPGANv1. Args: x (Tensor): Input images. return_latents (bool): Whether to return style latents. Default: False. return_rgb (bool): Whether return intermediate rgb images. Default: True. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. """ conditions = [] unet_skips = [] out_rgbs = [] # encoder feat = self.conv_body_first(x) for i in range(self.log_size - 2): feat = self.conv_body_down[i](feat) unet_skips.insert(0, feat) feat = self.final_conv(feat) # style code style_code = self.final_linear(feat.view(feat.size(0), -1)) if self.different_w: style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) # decode for i in range(self.log_size - 2): # add unet skip feat = feat + unet_skips[i] # ResUpLayer feat = self.conv_body_up[i](feat) # generate scale and shift for SFT layers scale = self.condition_scale[i](feat) conditions.append(scale.clone()) shift = self.condition_shift[i](feat) conditions.append(shift.clone()) # generate rgb images if return_rgb: out_rgbs.append(self.toRGB[i](feat)) # decoder image, _ = self.stylegan_decoder( [style_code], conditions, return_latents=return_latents, input_is_latent=self.input_is_latent, randomize_noise=randomize_noise, ) return image, out_rgbs class FacialComponentDiscriminator(nn.Module): """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.""" def __init__(self): super(FacialComponentDiscriminator, self).__init__() # It now uses a VGG-style architectrue with fixed model size self.conv1 = ConvLayer( 3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ) self.conv2 = ConvLayer( 64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ) self.conv3 = ConvLayer( 128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ) self.conv4 = ConvLayer( 128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ) self.conv5 = ConvLayer( 256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ) self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) def forward(self, x, return_feats=False, **kwargs): """Forward function for FacialComponentDiscriminator. Args: x (Tensor): Input images. return_feats (bool): Whether to return intermediate features. Default: False. """ feat = self.conv1(x) feat = self.conv3(self.conv2(feat)) rlt_feats = [] if return_feats: rlt_feats.append(feat.clone()) feat = self.conv5(self.conv4(feat)) if return_feats: rlt_feats.append(feat.clone()) out = self.final_conv(feat) if return_feats: return out, rlt_feats else: return out, None ================================================ FILE: ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py ================================================ # pylint: skip-file # type: ignore import math import random import torch from torch import nn from torch.nn import functional as F from .stylegan2_clean_arch import StyleGAN2GeneratorClean class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). It is the clean version without custom compiled CUDA extensions used in StyleGAN2. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1, sft_half=False, ): super(StyleGAN2GeneratorCSFT, self).__init__( out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, narrow=narrow, ) self.sft_half = sft_half def forward( self, styles, conditions, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2GeneratorCSFT. Args: styles (list[Tensor]): Sample codes of styles. conditions (list[Tensor]): SFT conditions to generators. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): The truncation ratio. Default: 1. truncation_latent (Tensor | None): The truncation latent tensor. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latents with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) # the conditions may have fewer levels if i < len(conditions): # SFT part to combine the conditions if self.sft_half: # only apply SFT to half of the channels out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) out_sft = out_sft * conditions[i - 1] + conditions[i] out = torch.cat([out_same, out_sft], dim=1) else: # apply SFT to all the channels out = out * conditions[i - 1] + conditions[i] out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space i += 2 image = skip if return_latents: return image, latent else: return image, None class ResBlock(nn.Module): """Residual block with bilinear upsampling/downsampling. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. """ def __init__(self, in_channels, out_channels, mode="down"): super(ResBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) if mode == "down": self.scale_factor = 0.5 elif mode == "up": self.scale_factor = 2 def forward(self, x): out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) # upsample/downsample out = F.interpolate( out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False ) out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) # skip x = F.interpolate( x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False ) skip = self.skip(x) out = out + skip return out class GFPGANv1Clean(nn.Module): """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. It is the clean version without custom compiled CUDA extensions used in StyleGAN2. Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. fix_decoder (bool): Whether to fix the decoder. Default: True. num_mlp (int): Layer number of MLP style layers. Default: 8. input_is_latent (bool): Whether input is latent style. Default: False. different_w (bool): Whether to use different latent w for different layers. Default: False. narrow (float): The narrow ratio for channels. Default: 1. sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. """ def __init__( self, state_dict, ): super(GFPGANv1Clean, self).__init__() out_size = 512 num_style_feat = 512 channel_multiplier = 2 decoder_load_path = None fix_decoder = False num_mlp = 8 input_is_latent = True different_w = True narrow = 1 sft_half = True self.model_arch = "GFPGAN" self.sub_type = "Face SR" self.scale = 8 self.in_nc = 3 self.out_nc = 3 self.state = state_dict self.supports_fp16 = False self.supports_bf16 = True self.min_size_restriction = 512 self.input_is_latent = input_is_latent self.different_w = different_w self.num_style_feat = num_style_feat unet_narrow = narrow * 0.5 # by default, use a half of input channels channels = { "4": int(512 * unet_narrow), "8": int(512 * unet_narrow), "16": int(512 * unet_narrow), "32": int(512 * unet_narrow), "64": int(256 * channel_multiplier * unet_narrow), "128": int(128 * channel_multiplier * unet_narrow), "256": int(64 * channel_multiplier * unet_narrow), "512": int(32 * channel_multiplier * unet_narrow), "1024": int(16 * channel_multiplier * unet_narrow), } self.log_size = int(math.log(out_size, 2)) first_out_size = 2 ** (int(math.log(out_size, 2))) self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1) # downsample in_channels = channels[f"{first_out_size}"] self.conv_body_down = nn.ModuleList() for i in range(self.log_size, 2, -1): out_channels = channels[f"{2**(i - 1)}"] self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down")) in_channels = out_channels self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1) # upsample in_channels = channels["4"] self.conv_body_up = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up")) in_channels = out_channels # to RGB self.toRGB = nn.ModuleList() for i in range(3, self.log_size + 1): self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1)) if different_w: linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat else: linear_out_channel = num_style_feat self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel) # the decoder: stylegan2 generator with SFT modulations self.stylegan_decoder = StyleGAN2GeneratorCSFT( out_size=out_size, num_style_feat=num_style_feat, num_mlp=num_mlp, channel_multiplier=channel_multiplier, narrow=narrow, sft_half=sft_half, ) # load pre-trained stylegan2 model if necessary if decoder_load_path: self.stylegan_decoder.load_state_dict( torch.load( decoder_load_path, map_location=lambda storage, loc: storage, weights_only=True)["params_ema"] ) # fix decoder without updating params if fix_decoder: for _, param in self.stylegan_decoder.named_parameters(): param.requires_grad = False # for SFT modulations (scale and shift) self.condition_scale = nn.ModuleList() self.condition_shift = nn.ModuleList() for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] if sft_half: sft_out_channels = out_channels else: sft_out_channels = out_channels * 2 self.condition_scale.append( nn.Sequential( nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), ) ) self.condition_shift.append( nn.Sequential( nn.Conv2d(out_channels, out_channels, 3, 1, 1), nn.LeakyReLU(0.2, True), nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), ) ) self.load_state_dict(state_dict) def forward( self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs ): """Forward function for GFPGANv1Clean. Args: x (Tensor): Input images. return_latents (bool): Whether to return style latents. Default: False. return_rgb (bool): Whether return intermediate rgb images. Default: True. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. """ conditions = [] unet_skips = [] out_rgbs = [] # encoder feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) for i in range(self.log_size - 2): feat = self.conv_body_down[i](feat) unet_skips.insert(0, feat) feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) # style code style_code = self.final_linear(feat.view(feat.size(0), -1)) if self.different_w: style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) # decode for i in range(self.log_size - 2): # add unet skip feat = feat + unet_skips[i] # ResUpLayer feat = self.conv_body_up[i](feat) # generate scale and shift for SFT layers scale = self.condition_scale[i](feat) conditions.append(scale.clone()) shift = self.condition_shift[i](feat) conditions.append(shift.clone()) # generate rgb images if return_rgb: out_rgbs.append(self.toRGB[i](feat)) # decoder image, _ = self.stylegan_decoder( [style_code], conditions, return_latents=return_latents, input_is_latent=self.input_is_latent, randomize_noise=randomize_noise, ) return image, out_rgbs ================================================ FILE: ldm_patched/pfn/architecture/face/restoreformer_arch.py ================================================ # pylint: skip-file # type: ignore """Modified from https://github.com/wzhouxiff/RestoreFormer """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F class VectorQuantizer(nn.Module): """ see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py ____________________________________________ Discretization bottleneck part of the VQ-VAE. Inputs: - n_e : number of embeddings - e_dim : dimension of embedding - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 _____________________________________________ """ def __init__(self, n_e, e_dim, beta): super(VectorQuantizer, self).__init__() self.n_e = n_e self.e_dim = e_dim self.beta = beta self.embedding = nn.Embedding(self.n_e, self.e_dim) self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) def forward(self, z): """ Inputs the output of the encoder network z and maps it to a discrete one-hot vector that is the index of the closest embedding vector e_j z (continuous) -> z_q (discrete) z.shape = (batch, channel, height, width) quantization pipeline: 1. get encoder input (B,C,H,W) 2. flatten input to (B*H*W,C) """ # reshape z -> (batch, height, width, channel) and flatten z = z.permute(0, 2, 3, 1).contiguous() z_flattened = z.view(-1, self.e_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z d = ( torch.sum(z_flattened**2, dim=1, keepdim=True) + torch.sum(self.embedding.weight**2, dim=1) - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) ) # could possible replace this here # #\start... # find closest encodings min_value, min_encoding_indices = torch.min(d, dim=1) min_encoding_indices = min_encoding_indices.unsqueeze(1) min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) min_encodings.scatter_(1, min_encoding_indices, 1) # dtype min encodings: torch.float32 # min_encodings shape: torch.Size([2048, 512]) # min_encoding_indices.shape: torch.Size([2048, 1]) # get quantized latent vectors z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) # .........\end # with: # .........\start # min_encoding_indices = torch.argmin(d, dim=1) # z_q = self.embedding(min_encoding_indices) # ......\end......... (TODO) # compute loss for embedding loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( (z_q - z.detach()) ** 2 ) # preserve gradients z_q = z + (z_q - z).detach() # perplexity e_mean = torch.mean(min_encodings, dim=0) perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) def get_codebook_entry(self, indices, shape): # shape specifying (batch, height, width, channel) # TODO: check for more easy handling with nn.Embedding min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) min_encodings.scatter_(1, indices[:, None], 1) # get quantized latent vectors z_q = torch.matmul(min_encodings.float(), self.embedding.weight) if shape is not None: z_q = z_q.view(shape) # reshape back to match original input shape z_q = z_q.permute(0, 3, 1, 2).contiguous() return z_q # pytorch_diffusion + derived encoder decoder def nonlinearity(x): # swish return x * torch.sigmoid(x) def Normalize(in_channels): return torch.nn.GroupNorm( num_groups=32, num_channels=in_channels, eps=1e-6, affine=True ) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = torch.nn.Conv2d( in_channels, in_channels, kernel_size=3, stride=1, padding=1 ) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d( in_channels, in_channels, kernel_size=3, stride=2, padding=0 ) def forward(self, x): if self.with_conv: pad = (0, 1, 0, 1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__( self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512 ): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.norm1 = Normalize(in_channels) self.conv1 = torch.nn.Conv2d( in_channels, out_channels, kernel_size=3, stride=1, padding=1 ) if temb_channels > 0: self.temb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d( out_channels, out_channels, kernel_size=3, stride=1, padding=1 ) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = torch.nn.Conv2d( in_channels, out_channels, kernel_size=3, stride=1, padding=1 ) else: self.nin_shortcut = torch.nn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0 ) def forward(self, x, temb): h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] h = self.norm2(h) h = nonlinearity(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x + h class MultiHeadAttnBlock(nn.Module): def __init__(self, in_channels, head_size=1): super().__init__() self.in_channels = in_channels self.head_size = head_size self.att_size = in_channels // head_size assert ( in_channels % head_size == 0 ), "The size of head should be divided by the number of channels." self.norm1 = Normalize(in_channels) self.norm2 = Normalize(in_channels) self.q = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.k = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.v = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.proj_out = torch.nn.Conv2d( in_channels, in_channels, kernel_size=1, stride=1, padding=0 ) self.num = 0 def forward(self, x, y=None): h_ = x h_ = self.norm1(h_) if y is None: y = h_ else: y = self.norm2(y) q = self.q(y) k = self.k(h_) v = self.v(h_) # compute attention b, c, h, w = q.shape q = q.reshape(b, self.head_size, self.att_size, h * w) q = q.permute(0, 3, 1, 2) # b, hw, head, att k = k.reshape(b, self.head_size, self.att_size, h * w) k = k.permute(0, 3, 1, 2) v = v.reshape(b, self.head_size, self.att_size, h * w) v = v.permute(0, 3, 1, 2) q = q.transpose(1, 2) v = v.transpose(1, 2) k = k.transpose(1, 2).transpose(2, 3) scale = int(self.att_size) ** (-0.5) q.mul_(scale) w_ = torch.matmul(q, k) w_ = F.softmax(w_, dim=3) w_ = w_.matmul(v) w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] w_ = w_.view(b, h, w, -1) w_ = w_.permute(0, 3, 1, 2) w_ = self.proj_out(w_) return x + w_ class MultiHeadEncoder(nn.Module): def __init__( self, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=(16,), dropout=0.0, resamp_with_conv=True, in_channels=3, resolution=512, z_channels=256, double_z=True, enable_mid=True, head_size=1, **ignore_kwargs ): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.enable_mid = enable_mid # downsampling self.conv_in = torch.nn.Conv2d( in_channels, self.ch, kernel_size=3, stride=1, padding=1 ) curr_res = resolution in_ch_mult = (1,) + tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch * in_ch_mult[i_level] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append( ResnetBlock( in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(MultiHeadAttnBlock(block_in, head_size)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle if self.enable_mid: self.mid = nn.Module() self.mid.block_1 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) self.mid.block_2 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d( block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1, ) def forward(self, x): hs = {} # timestep embedding temb = None # downsampling h = self.conv_in(x) hs["in"] = h for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](h, temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) if i_level != self.num_resolutions - 1: # hs.append(h) hs["block_" + str(i_level)] = h h = self.down[i_level].downsample(h) # middle # h = hs[-1] if self.enable_mid: h = self.mid.block_1(h, temb) hs["block_" + str(i_level) + "_atten"] = h h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) hs["mid_atten"] = h # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) # hs.append(h) hs["out"] = h return hs class MultiHeadDecoder(nn.Module): def __init__( self, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=(16,), dropout=0.0, resamp_with_conv=True, in_channels=3, resolution=512, z_channels=256, give_pre_end=False, enable_mid=True, head_size=1, **ignorekwargs ): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.enable_mid = enable_mid # compute in_ch_mult, block_in and curr_res at lowest res block_in = ch * ch_mult[self.num_resolutions - 1] curr_res = resolution // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) print( "Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape) ) ) # z to block_in self.conv_in = torch.nn.Conv2d( z_channels, block_in, kernel_size=3, stride=1, padding=1 ) # middle if self.enable_mid: self.mid = nn.Module() self.mid.block_1 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) self.mid.block_2 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): block.append( ResnetBlock( in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(MultiHeadAttnBlock(block_in, head_size)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d( block_in, out_ch, kernel_size=3, stride=1, padding=1 ) def forward(self, z): # assert z.shape[1:] == self.z_shape[1:] self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle if self.enable_mid: h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.up[i_level].block[i_block](h, temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class MultiHeadDecoderTransformer(nn.Module): def __init__( self, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=(16,), dropout=0.0, resamp_with_conv=True, in_channels=3, resolution=512, z_channels=256, give_pre_end=False, enable_mid=True, head_size=1, **ignorekwargs ): super().__init__() self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.enable_mid = enable_mid # compute in_ch_mult, block_in and curr_res at lowest res block_in = ch * ch_mult[self.num_resolutions - 1] curr_res = resolution // 2 ** (self.num_resolutions - 1) self.z_shape = (1, z_channels, curr_res, curr_res) print( "Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape) ) ) # z to block_in self.conv_in = torch.nn.Conv2d( z_channels, block_in, kernel_size=3, stride=1, padding=1 ) # middle if self.enable_mid: self.mid = nn.Module() self.mid.block_1 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) self.mid.block_2 = ResnetBlock( in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, ) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): block.append( ResnetBlock( in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout, ) ) block_in = block_out if curr_res in attn_resolutions: attn.append(MultiHeadAttnBlock(block_in, head_size)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d( block_in, out_ch, kernel_size=3, stride=1, padding=1 ) def forward(self, z, hs): # assert z.shape[1:] == self.z_shape[1:] # self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle if self.enable_mid: h = self.mid.block_1(h, temb) h = self.mid.attn_1(h, hs["mid_atten"]) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.up[i_level].block[i_block](h, temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block]( h, hs["block_" + str(i_level) + "_atten"] ) # hfeature = h.clone() if i_level != 0: h = self.up[i_level].upsample(h) # end if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class RestoreFormer(nn.Module): def __init__( self, state_dict, ): super(RestoreFormer, self).__init__() n_embed = 1024 embed_dim = 256 ch = 64 out_ch = 3 ch_mult = (1, 2, 2, 4, 4, 8) num_res_blocks = 2 attn_resolutions = (16,) dropout = 0.0 in_channels = 3 resolution = 512 z_channels = 256 double_z = False enable_mid = True fix_decoder = False fix_codebook = True fix_encoder = False head_size = 8 self.model_arch = "RestoreFormer" self.sub_type = "Face SR" self.scale = 8 self.in_nc = 3 self.out_nc = out_ch self.state = state_dict self.supports_fp16 = False self.supports_bf16 = True self.min_size_restriction = 16 self.encoder = MultiHeadEncoder( ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, in_channels=in_channels, resolution=resolution, z_channels=z_channels, double_z=double_z, enable_mid=enable_mid, head_size=head_size, ) self.decoder = MultiHeadDecoderTransformer( ch=ch, out_ch=out_ch, ch_mult=ch_mult, num_res_blocks=num_res_blocks, attn_resolutions=attn_resolutions, dropout=dropout, in_channels=in_channels, resolution=resolution, z_channels=z_channels, enable_mid=enable_mid, head_size=head_size, ) self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) if fix_decoder: for _, param in self.decoder.named_parameters(): param.requires_grad = False for _, param in self.post_quant_conv.named_parameters(): param.requires_grad = False for _, param in self.quantize.named_parameters(): param.requires_grad = False elif fix_codebook: for _, param in self.quantize.named_parameters(): param.requires_grad = False if fix_encoder: for _, param in self.encoder.named_parameters(): param.requires_grad = False self.load_state_dict(state_dict) def encode(self, x): hs = self.encoder(x) h = self.quant_conv(hs["out"]) quant, emb_loss, info = self.quantize(h) return quant, emb_loss, info, hs def decode(self, quant, hs): quant = self.post_quant_conv(quant) dec = self.decoder(quant, hs) return dec def forward(self, input, **kwargs): quant, diff, info, hs = self.encode(input) dec = self.decode(quant, hs) return dec, None ================================================ FILE: ldm_patched/pfn/architecture/face/stylegan2_arch.py ================================================ # pylint: skip-file # type: ignore import math import random import torch from torch import nn from torch.nn import functional as F from .fused_act import FusedLeakyReLU, fused_leaky_relu from .upfirdn2d import upfirdn2d class NormStyleCode(nn.Module): def forward(self, x): """Normalize the style codes. Args: x (Tensor): Style codes with shape (b, c). Returns: Tensor: Normalized tensor. """ return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) def make_resample_kernel(k): """Make resampling kernel for UpFirDn. Args: k (list[int]): A list indicating the 1D resample kernel magnitude. Returns: Tensor: 2D resampled kernel. """ k = torch.tensor(k, dtype=torch.float32) if k.ndim == 1: k = k[None, :] * k[:, None] # to 2D kernel, outer product # normalize k /= k.sum() return k class UpFirDnUpsample(nn.Module): """Upsample, FIR filter, and downsample (upsampole version). References: 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 Args: resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. factor (int): Upsampling scale factor. Default: 2. """ def __init__(self, resample_kernel, factor=2): super(UpFirDnUpsample, self).__init__() self.kernel = make_resample_kernel(resample_kernel) * (factor**2) self.factor = factor pad = self.kernel.shape[0] - factor self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) def forward(self, x): out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) return out def __repr__(self): return f"{self.__class__.__name__}(factor={self.factor})" class UpFirDnDownsample(nn.Module): """Upsample, FIR filter, and downsample (downsampole version). Args: resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. factor (int): Downsampling scale factor. Default: 2. """ def __init__(self, resample_kernel, factor=2): super(UpFirDnDownsample, self).__init__() self.kernel = make_resample_kernel(resample_kernel) self.factor = factor pad = self.kernel.shape[0] - factor self.pad = ((pad + 1) // 2, pad // 2) def forward(self, x): out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) return out def __repr__(self): return f"{self.__class__.__name__}(factor={self.factor})" class UpFirDnSmooth(nn.Module): """Upsample, FIR filter, and downsample (smooth version). Args: resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. upsample_factor (int): Upsampling scale factor. Default: 1. downsample_factor (int): Downsampling scale factor. Default: 1. kernel_size (int): Kernel size: Default: 1. """ def __init__( self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1 ): super(UpFirDnSmooth, self).__init__() self.upsample_factor = upsample_factor self.downsample_factor = downsample_factor self.kernel = make_resample_kernel(resample_kernel) if upsample_factor > 1: self.kernel = self.kernel * (upsample_factor**2) if upsample_factor > 1: pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) elif downsample_factor > 1: pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) self.pad = ((pad + 1) // 2, pad // 2) else: raise NotImplementedError def forward(self, x): out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) return out def __repr__(self): return ( f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}" f", downsample_factor={self.downsample_factor})" ) class EqualLinear(nn.Module): """Equalized Linear as StyleGAN2. Args: in_channels (int): Size of each sample. out_channels (int): Size of each output sample. bias (bool): If set to ``False``, the layer will not learn an additive bias. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. lr_mul (float): Learning rate multiplier. Default: 1. activation (None | str): The activation after ``linear`` operation. Supported: 'fused_lrelu', None. Default: None. """ def __init__( self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None, ): super(EqualLinear, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.lr_mul = lr_mul self.activation = activation if self.activation not in ["fused_lrelu", None]: raise ValueError( f"Wrong activation value in EqualLinear: {activation}" "Supported ones are: ['fused_lrelu', None]." ) self.scale = (1 / math.sqrt(in_channels)) * lr_mul self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) if bias: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter("bias", None) def forward(self, x): if self.bias is None: bias = None else: bias = self.bias * self.lr_mul if self.activation == "fused_lrelu": out = F.linear(x, self.weight * self.scale) out = fused_leaky_relu(out, bias) else: out = F.linear(x, self.weight * self.scale, bias=bias) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, bias={self.bias is not None})" ) class ModulatedConv2d(nn.Module): """Modulated Conv2d used in StyleGAN2. There is no bias in ModulatedConv2d. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether to demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. Default: (1, 3, 3, 1). eps (float): A value added to the denominator for numerical stability. Default: 1e-8. """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, resample_kernel=(1, 3, 3, 1), eps=1e-8, ): super(ModulatedConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.demodulate = demodulate self.sample_mode = sample_mode self.eps = eps if self.sample_mode == "upsample": self.smooth = UpFirDnSmooth( resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size, ) elif self.sample_mode == "downsample": self.smooth = UpFirDnSmooth( resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size, ) elif self.sample_mode is None: pass else: raise ValueError( f"Wrong sample mode {self.sample_mode}, " "supported ones are ['upsample', 'downsample', None]." ) self.scale = 1 / math.sqrt(in_channels * kernel_size**2) # modulation inside each modulated conv self.modulation = EqualLinear( num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None, ) self.weight = nn.Parameter( torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) ) self.padding = kernel_size // 2 def forward(self, x, style): """Forward function. Args: x (Tensor): Tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). Returns: Tensor: Modulated tensor after convolution. """ b, c, h, w = x.shape # c = c_in # weight modulation style = self.modulation(style).view(b, 1, c, 1, 1) # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) if self.demodulate: demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) weight = weight * demod.view(b, self.out_channels, 1, 1, 1) weight = weight.view( b * self.out_channels, c, self.kernel_size, self.kernel_size ) if self.sample_mode == "upsample": x = x.view(1, b * c, h, w) weight = weight.view( b, self.out_channels, c, self.kernel_size, self.kernel_size ) weight = weight.transpose(1, 2).reshape( b * c, self.out_channels, self.kernel_size, self.kernel_size ) out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) out = out.view(b, self.out_channels, *out.shape[2:4]) out = self.smooth(out) elif self.sample_mode == "downsample": x = self.smooth(x) x = x.view(1, b * c, *x.shape[2:4]) out = F.conv2d(x, weight, padding=0, stride=2, groups=b) out = out.view(b, self.out_channels, *out.shape[2:4]) else: x = x.view(1, b * c, h, w) # weight: (b*c_out, c_in, k, k), groups=b out = F.conv2d(x, weight, padding=self.padding, groups=b) out = out.view(b, self.out_channels, *out.shape[2:4]) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, " f"kernel_size={self.kernel_size}, " f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" ) class StyleConv(nn.Module): """Style conv. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. Default: (1, 3, 3, 1). """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, resample_kernel=(1, 3, 3, 1), ): super(StyleConv, self).__init__() self.modulated_conv = ModulatedConv2d( in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode, resample_kernel=resample_kernel, ) self.weight = nn.Parameter(torch.zeros(1)) # for noise injection self.activate = FusedLeakyReLU(out_channels) def forward(self, x, style, noise=None): # modulate out = self.modulated_conv(x, style) # noise injection if noise is None: b, _, h, w = out.shape noise = out.new_empty(b, 1, h, w).normal_() out = out + self.weight * noise # activation (with bias) out = self.activate(out) return out class ToRGB(nn.Module): """To RGB from features. Args: in_channels (int): Channel number of input. num_style_feat (int): Channel number of style features. upsample (bool): Whether to upsample. Default: True. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. Default: (1, 3, 3, 1). """ def __init__( self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1) ): super(ToRGB, self).__init__() if upsample: self.upsample = UpFirDnUpsample(resample_kernel, factor=2) else: self.upsample = None self.modulated_conv = ModulatedConv2d( in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None, ) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, x, style, skip=None): """Forward function. Args: x (Tensor): Feature tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). skip (Tensor): Base/skip tensor. Default: None. Returns: Tensor: RGB images. """ out = self.modulated_conv(x, style) out = out + self.bias if skip is not None: if self.upsample: skip = self.upsample(skip) out = out + skip return out class ConstantInput(nn.Module): """Constant input. Args: num_channel (int): Channel number of constant input. size (int): Spatial size of constant input. """ def __init__(self, num_channel, size): super(ConstantInput, self).__init__() self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) def forward(self, batch): out = self.weight.repeat(batch, 1, 1, 1) return out class StyleGAN2Generator(nn.Module): """StyleGAN2 Generator. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. narrow (float): Narrow ratio for channels. Default: 1.0. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), lr_mlp=0.01, narrow=1, ): super(StyleGAN2Generator, self).__init__() # Style MLP layers self.num_style_feat = num_style_feat style_mlp_layers = [NormStyleCode()] for i in range(num_mlp): style_mlp_layers.append( EqualLinear( num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, activation="fused_lrelu", ) ) self.style_mlp = nn.Sequential(*style_mlp_layers) channels = { "4": int(512 * narrow), "8": int(512 * narrow), "16": int(512 * narrow), "32": int(512 * narrow), "64": int(256 * channel_multiplier * narrow), "128": int(128 * channel_multiplier * narrow), "256": int(64 * channel_multiplier * narrow), "512": int(32 * channel_multiplier * narrow), "1024": int(16 * channel_multiplier * narrow), } self.channels = channels self.constant_input = ConstantInput(channels["4"], size=4) self.style_conv1 = StyleConv( channels["4"], channels["4"], kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, resample_kernel=resample_kernel, ) self.to_rgb1 = ToRGB( channels["4"], num_style_feat, upsample=False, resample_kernel=resample_kernel, ) self.log_size = int(math.log(out_size, 2)) self.num_layers = (self.log_size - 2) * 2 + 1 self.num_latent = self.log_size * 2 - 2 self.style_convs = nn.ModuleList() self.to_rgbs = nn.ModuleList() self.noises = nn.Module() in_channels = channels["4"] # noise for layer_idx in range(self.num_layers): resolution = 2 ** ((layer_idx + 5) // 2) shape = [1, 1, resolution, resolution] self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) # style convs and to_rgbs for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.style_convs.append( StyleConv( in_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode="upsample", resample_kernel=resample_kernel, ) ) self.style_convs.append( StyleConv( out_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, resample_kernel=resample_kernel, ) ) self.to_rgbs.append( ToRGB( out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel, ) ) in_channels = out_channels def make_noise(self): """Make noise for noise injection.""" device = self.constant_input.weight.device noises = [torch.randn(1, 1, 4, 4, device=device)] for i in range(3, self.log_size + 1): for _ in range(2): noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) return noises def get_latent(self, x): return self.style_mlp(x) def mean_latent(self, num_latent): latent_in = torch.randn( num_latent, self.num_style_feat, device=self.constant_input.weight.device ) latent = self.style_mlp(latent_in).mean(0, keepdim=True) return latent def forward( self, styles, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2Generator. Args: styles (list[Tensor]): Sample codes of styles. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): TODO. Default: 1. truncation_latent (Tensor | None): TODO. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latent with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) i += 2 image = skip if return_latents: return image, latent else: return image, None class ScaledLeakyReLU(nn.Module): """Scaled LeakyReLU. Args: negative_slope (float): Negative slope. Default: 0.2. """ def __init__(self, negative_slope=0.2): super(ScaledLeakyReLU, self).__init__() self.negative_slope = negative_slope def forward(self, x): out = F.leaky_relu(x, negative_slope=self.negative_slope) return out * math.sqrt(2) class EqualConv2d(nn.Module): """Equalized Linear as StyleGAN2. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. stride (int): Stride of the convolution. Default: 1 padding (int): Zero-padding added to both sides of the input. Default: 0. bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0, ): super(EqualConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.scale = 1 / math.sqrt(in_channels * kernel_size**2) self.weight = nn.Parameter( torch.randn(out_channels, in_channels, kernel_size, kernel_size) ) if bias: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter("bias", None) def forward(self, x): out = F.conv2d( x, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding, ) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, " f"kernel_size={self.kernel_size}," f" stride={self.stride}, padding={self.padding}, " f"bias={self.bias is not None})" ) class ConvLayer(nn.Sequential): """Conv Layer used in StyleGAN2 Discriminator. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Kernel size. downsample (bool): Whether downsample by a factor of 2. Default: False. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). bias (bool): Whether with bias. Default: True. activate (bool): Whether use activateion. Default: True. """ def __init__( self, in_channels, out_channels, kernel_size, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True, ): layers = [] # downsample if downsample: layers.append( UpFirDnSmooth( resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size, ) ) stride = 2 self.padding = 0 else: stride = 1 self.padding = kernel_size // 2 # conv layers.append( EqualConv2d( in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias and not activate, ) ) # activation if activate: if bias: layers.append(FusedLeakyReLU(out_channels)) else: layers.append(ScaledLeakyReLU(0.2)) super(ConvLayer, self).__init__(*layers) class ResBlock(nn.Module): """Residual block used in StyleGAN2 Discriminator. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). """ def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): super(ResBlock, self).__init__() self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) self.conv2 = ConvLayer( in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True, ) self.skip = ConvLayer( in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False, ) def forward(self, x): out = self.conv1(x) out = self.conv2(out) skip = self.skip(x) out = (out + skip) / math.sqrt(2) return out ================================================ FILE: ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py ================================================ # pylint: skip-file # type: ignore import math import random import torch from torch import nn from torch.nn import functional as F from .fused_act import FusedLeakyReLU, fused_leaky_relu class NormStyleCode(nn.Module): def forward(self, x): """Normalize the style codes. Args: x (Tensor): Style codes with shape (b, c). Returns: Tensor: Normalized tensor. """ return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) class EqualLinear(nn.Module): """Equalized Linear as StyleGAN2. Args: in_channels (int): Size of each sample. out_channels (int): Size of each output sample. bias (bool): If set to ``False``, the layer will not learn an additive bias. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. lr_mul (float): Learning rate multiplier. Default: 1. activation (None | str): The activation after ``linear`` operation. Supported: 'fused_lrelu', None. Default: None. """ def __init__( self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None, ): super(EqualLinear, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.lr_mul = lr_mul self.activation = activation if self.activation not in ["fused_lrelu", None]: raise ValueError( f"Wrong activation value in EqualLinear: {activation}" "Supported ones are: ['fused_lrelu', None]." ) self.scale = (1 / math.sqrt(in_channels)) * lr_mul self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) if bias: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter("bias", None) def forward(self, x): if self.bias is None: bias = None else: bias = self.bias * self.lr_mul if self.activation == "fused_lrelu": out = F.linear(x, self.weight * self.scale) out = fused_leaky_relu(out, bias) else: out = F.linear(x, self.weight * self.scale, bias=bias) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, bias={self.bias is not None})" ) class ModulatedConv2d(nn.Module): """Modulated Conv2d used in StyleGAN2. There is no bias in ModulatedConv2d. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether to demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. eps (float): A value added to the denominator for numerical stability. Default: 1e-8. """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, eps=1e-8, interpolation_mode="bilinear", ): super(ModulatedConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.demodulate = demodulate self.sample_mode = sample_mode self.eps = eps self.interpolation_mode = interpolation_mode if self.interpolation_mode == "nearest": self.align_corners = None else: self.align_corners = False self.scale = 1 / math.sqrt(in_channels * kernel_size**2) # modulation inside each modulated conv self.modulation = EqualLinear( num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None, ) self.weight = nn.Parameter( torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) ) self.padding = kernel_size // 2 def forward(self, x, style): """Forward function. Args: x (Tensor): Tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). Returns: Tensor: Modulated tensor after convolution. """ b, c, h, w = x.shape # c = c_in # weight modulation style = self.modulation(style).view(b, 1, c, 1, 1) # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) if self.demodulate: demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) weight = weight * demod.view(b, self.out_channels, 1, 1, 1) weight = weight.view( b * self.out_channels, c, self.kernel_size, self.kernel_size ) if self.sample_mode == "upsample": x = F.interpolate( x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners, ) elif self.sample_mode == "downsample": x = F.interpolate( x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners, ) b, c, h, w = x.shape x = x.view(1, b * c, h, w) # weight: (b*c_out, c_in, k, k), groups=b out = F.conv2d(x, weight, padding=self.padding, groups=b) out = out.view(b, self.out_channels, *out.shape[2:4]) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, " f"kernel_size={self.kernel_size}, " f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" ) class StyleConv(nn.Module): """Style conv. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, interpolation_mode="bilinear", ): super(StyleConv, self).__init__() self.modulated_conv = ModulatedConv2d( in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode, interpolation_mode=interpolation_mode, ) self.weight = nn.Parameter(torch.zeros(1)) # for noise injection self.activate = FusedLeakyReLU(out_channels) def forward(self, x, style, noise=None): # modulate out = self.modulated_conv(x, style) # noise injection if noise is None: b, _, h, w = out.shape noise = out.new_empty(b, 1, h, w).normal_() out = out + self.weight * noise # activation (with bias) out = self.activate(out) return out class ToRGB(nn.Module): """To RGB from features. Args: in_channels (int): Channel number of input. num_style_feat (int): Channel number of style features. upsample (bool): Whether to upsample. Default: True. """ def __init__( self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear" ): super(ToRGB, self).__init__() self.upsample = upsample self.interpolation_mode = interpolation_mode if self.interpolation_mode == "nearest": self.align_corners = None else: self.align_corners = False self.modulated_conv = ModulatedConv2d( in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None, interpolation_mode=interpolation_mode, ) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, x, style, skip=None): """Forward function. Args: x (Tensor): Feature tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). skip (Tensor): Base/skip tensor. Default: None. Returns: Tensor: RGB images. """ out = self.modulated_conv(x, style) out = out + self.bias if skip is not None: if self.upsample: skip = F.interpolate( skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners, ) out = out + skip return out class ConstantInput(nn.Module): """Constant input. Args: num_channel (int): Channel number of constant input. size (int): Spatial size of constant input. """ def __init__(self, num_channel, size): super(ConstantInput, self).__init__() self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) def forward(self, batch): out = self.weight.repeat(batch, 1, 1, 1) return out class StyleGAN2GeneratorBilinear(nn.Module): """StyleGAN2 Generator. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. narrow (float): Narrow ratio for channels. Default: 1.0. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, lr_mlp=0.01, narrow=1, interpolation_mode="bilinear", ): super(StyleGAN2GeneratorBilinear, self).__init__() # Style MLP layers self.num_style_feat = num_style_feat style_mlp_layers = [NormStyleCode()] for i in range(num_mlp): style_mlp_layers.append( EqualLinear( num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, activation="fused_lrelu", ) ) self.style_mlp = nn.Sequential(*style_mlp_layers) channels = { "4": int(512 * narrow), "8": int(512 * narrow), "16": int(512 * narrow), "32": int(512 * narrow), "64": int(256 * channel_multiplier * narrow), "128": int(128 * channel_multiplier * narrow), "256": int(64 * channel_multiplier * narrow), "512": int(32 * channel_multiplier * narrow), "1024": int(16 * channel_multiplier * narrow), } self.channels = channels self.constant_input = ConstantInput(channels["4"], size=4) self.style_conv1 = StyleConv( channels["4"], channels["4"], kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, interpolation_mode=interpolation_mode, ) self.to_rgb1 = ToRGB( channels["4"], num_style_feat, upsample=False, interpolation_mode=interpolation_mode, ) self.log_size = int(math.log(out_size, 2)) self.num_layers = (self.log_size - 2) * 2 + 1 self.num_latent = self.log_size * 2 - 2 self.style_convs = nn.ModuleList() self.to_rgbs = nn.ModuleList() self.noises = nn.Module() in_channels = channels["4"] # noise for layer_idx in range(self.num_layers): resolution = 2 ** ((layer_idx + 5) // 2) shape = [1, 1, resolution, resolution] self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) # style convs and to_rgbs for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.style_convs.append( StyleConv( in_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode="upsample", interpolation_mode=interpolation_mode, ) ) self.style_convs.append( StyleConv( out_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, interpolation_mode=interpolation_mode, ) ) self.to_rgbs.append( ToRGB( out_channels, num_style_feat, upsample=True, interpolation_mode=interpolation_mode, ) ) in_channels = out_channels def make_noise(self): """Make noise for noise injection.""" device = self.constant_input.weight.device noises = [torch.randn(1, 1, 4, 4, device=device)] for i in range(3, self.log_size + 1): for _ in range(2): noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) return noises def get_latent(self, x): return self.style_mlp(x) def mean_latent(self, num_latent): latent_in = torch.randn( num_latent, self.num_style_feat, device=self.constant_input.weight.device ) latent = self.style_mlp(latent_in).mean(0, keepdim=True) return latent def forward( self, styles, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2Generator. Args: styles (list[Tensor]): Sample codes of styles. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): TODO. Default: 1. truncation_latent (Tensor | None): TODO. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latent with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) i += 2 image = skip if return_latents: return image, latent else: return image, None class ScaledLeakyReLU(nn.Module): """Scaled LeakyReLU. Args: negative_slope (float): Negative slope. Default: 0.2. """ def __init__(self, negative_slope=0.2): super(ScaledLeakyReLU, self).__init__() self.negative_slope = negative_slope def forward(self, x): out = F.leaky_relu(x, negative_slope=self.negative_slope) return out * math.sqrt(2) class EqualConv2d(nn.Module): """Equalized Linear as StyleGAN2. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. stride (int): Stride of the convolution. Default: 1 padding (int): Zero-padding added to both sides of the input. Default: 0. bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. bias_init_val (float): Bias initialized value. Default: 0. """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0, ): super(EqualConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.scale = 1 / math.sqrt(in_channels * kernel_size**2) self.weight = nn.Parameter( torch.randn(out_channels, in_channels, kernel_size, kernel_size) ) if bias: self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) else: self.register_parameter("bias", None) def forward(self, x): out = F.conv2d( x, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding, ) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, " f"out_channels={self.out_channels}, " f"kernel_size={self.kernel_size}," f" stride={self.stride}, padding={self.padding}, " f"bias={self.bias is not None})" ) class ConvLayer(nn.Sequential): """Conv Layer used in StyleGAN2 Discriminator. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Kernel size. downsample (bool): Whether downsample by a factor of 2. Default: False. bias (bool): Whether with bias. Default: True. activate (bool): Whether use activateion. Default: True. """ def __init__( self, in_channels, out_channels, kernel_size, downsample=False, bias=True, activate=True, interpolation_mode="bilinear", ): layers = [] self.interpolation_mode = interpolation_mode # downsample if downsample: if self.interpolation_mode == "nearest": self.align_corners = None else: self.align_corners = False layers.append( torch.nn.Upsample( scale_factor=0.5, mode=interpolation_mode, align_corners=self.align_corners, ) ) stride = 1 self.padding = kernel_size // 2 # conv layers.append( EqualConv2d( in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias and not activate, ) ) # activation if activate: if bias: layers.append(FusedLeakyReLU(out_channels)) else: layers.append(ScaledLeakyReLU(0.2)) super(ConvLayer, self).__init__(*layers) class ResBlock(nn.Module): """Residual block used in StyleGAN2 Discriminator. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. """ def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"): super(ResBlock, self).__init__() self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) self.conv2 = ConvLayer( in_channels, out_channels, 3, downsample=True, interpolation_mode=interpolation_mode, bias=True, activate=True, ) self.skip = ConvLayer( in_channels, out_channels, 1, downsample=True, interpolation_mode=interpolation_mode, bias=False, activate=False, ) def forward(self, x): out = self.conv1(x) out = self.conv2(out) skip = self.skip(x) out = (out + skip) / math.sqrt(2) return out ================================================ FILE: ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py ================================================ # pylint: skip-file # type: ignore import math import torch from torch import nn from torch.nn import functional as F from torch.nn import init from torch.nn.modules.batchnorm import _BatchNorm @torch.no_grad() def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): """Initialize network weights. Args: module_list (list[nn.Module] | nn.Module): Modules to be initialized. scale (float): Scale initialized weights, especially for residual blocks. Default: 1. bias_fill (float): The value to fill bias. Default: 0 kwargs (dict): Other arguments for initialization function. """ if not isinstance(module_list, list): module_list = [module_list] for module in module_list: for m in module.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, **kwargs) m.weight.data *= scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, nn.Linear): init.kaiming_normal_(m.weight, **kwargs) m.weight.data *= scale if m.bias is not None: m.bias.data.fill_(bias_fill) elif isinstance(m, _BatchNorm): init.constant_(m.weight, 1) if m.bias is not None: m.bias.data.fill_(bias_fill) class NormStyleCode(nn.Module): def forward(self, x): """Normalize the style codes. Args: x (Tensor): Style codes with shape (b, c). Returns: Tensor: Normalized tensor. """ return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) class ModulatedConv2d(nn.Module): """Modulated Conv2d used in StyleGAN2. There is no bias in ModulatedConv2d. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether to demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. eps (float): A value added to the denominator for numerical stability. Default: 1e-8. """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, eps=1e-8, ): super(ModulatedConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.demodulate = demodulate self.sample_mode = sample_mode self.eps = eps # modulation inside each modulated conv self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) # initialization default_init_weights( self.modulation, scale=1, bias_fill=1, a=0, mode="fan_in", nonlinearity="linear", ) self.weight = nn.Parameter( torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) / math.sqrt(in_channels * kernel_size**2) ) self.padding = kernel_size // 2 def forward(self, x, style): """Forward function. Args: x (Tensor): Tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). Returns: Tensor: Modulated tensor after convolution. """ b, c, h, w = x.shape # c = c_in # weight modulation style = self.modulation(style).view(b, 1, c, 1, 1) # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) weight = self.weight * style # (b, c_out, c_in, k, k) if self.demodulate: demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) weight = weight * demod.view(b, self.out_channels, 1, 1, 1) weight = weight.view( b * self.out_channels, c, self.kernel_size, self.kernel_size ) # upsample or downsample if necessary if self.sample_mode == "upsample": x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) elif self.sample_mode == "downsample": x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) b, c, h, w = x.shape x = x.view(1, b * c, h, w) # weight: (b*c_out, c_in, k, k), groups=b out = F.conv2d(x, weight, padding=self.padding, groups=b) out = out.view(b, self.out_channels, *out.shape[2:4]) return out def __repr__(self): return ( f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, " f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})" ) class StyleConv(nn.Module): """Style conv used in StyleGAN2. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number of the output. kernel_size (int): Size of the convolving kernel. num_style_feat (int): Channel number of style features. demodulate (bool): Whether demodulate in the conv layer. Default: True. sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. """ def __init__( self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None, ): super(StyleConv, self).__init__() self.modulated_conv = ModulatedConv2d( in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode, ) self.weight = nn.Parameter(torch.zeros(1)) # for noise injection self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x, style, noise=None): # modulate out = self.modulated_conv(x, style) * 2**0.5 # for conversion # noise injection if noise is None: b, _, h, w = out.shape noise = out.new_empty(b, 1, h, w).normal_() out = out + self.weight * noise # add bias out = out + self.bias # activation out = self.activate(out) return out class ToRGB(nn.Module): """To RGB (image space) from features. Args: in_channels (int): Channel number of input. num_style_feat (int): Channel number of style features. upsample (bool): Whether to upsample. Default: True. """ def __init__(self, in_channels, num_style_feat, upsample=True): super(ToRGB, self).__init__() self.upsample = upsample self.modulated_conv = ModulatedConv2d( in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None, ) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, x, style, skip=None): """Forward function. Args: x (Tensor): Feature tensor with shape (b, c, h, w). style (Tensor): Tensor with shape (b, num_style_feat). skip (Tensor): Base/skip tensor. Default: None. Returns: Tensor: RGB images. """ out = self.modulated_conv(x, style) out = out + self.bias if skip is not None: if self.upsample: skip = F.interpolate( skip, scale_factor=2, mode="bilinear", align_corners=False ) out = out + skip return out class ConstantInput(nn.Module): """Constant input. Args: num_channel (int): Channel number of constant input. size (int): Spatial size of constant input. """ def __init__(self, num_channel, size): super(ConstantInput, self).__init__() self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) def forward(self, batch): out = self.weight.repeat(batch, 1, 1, 1) return out class StyleGAN2GeneratorClean(nn.Module): """Clean version of StyleGAN2 Generator. Args: out_size (int): The spatial size of outputs. num_style_feat (int): Channel number of style features. Default: 512. num_mlp (int): Layer number of MLP style layers. Default: 8. channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. narrow (float): Narrow ratio for channels. Default: 1.0. """ def __init__( self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 ): super(StyleGAN2GeneratorClean, self).__init__() # Style MLP layers self.num_style_feat = num_style_feat style_mlp_layers = [NormStyleCode()] for i in range(num_mlp): style_mlp_layers.extend( [ nn.Linear(num_style_feat, num_style_feat, bias=True), nn.LeakyReLU(negative_slope=0.2, inplace=True), ] ) self.style_mlp = nn.Sequential(*style_mlp_layers) # initialization default_init_weights( self.style_mlp, scale=1, bias_fill=0, a=0.2, mode="fan_in", nonlinearity="leaky_relu", ) # channel list channels = { "4": int(512 * narrow), "8": int(512 * narrow), "16": int(512 * narrow), "32": int(512 * narrow), "64": int(256 * channel_multiplier * narrow), "128": int(128 * channel_multiplier * narrow), "256": int(64 * channel_multiplier * narrow), "512": int(32 * channel_multiplier * narrow), "1024": int(16 * channel_multiplier * narrow), } self.channels = channels self.constant_input = ConstantInput(channels["4"], size=4) self.style_conv1 = StyleConv( channels["4"], channels["4"], kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, ) self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False) self.log_size = int(math.log(out_size, 2)) self.num_layers = (self.log_size - 2) * 2 + 1 self.num_latent = self.log_size * 2 - 2 self.style_convs = nn.ModuleList() self.to_rgbs = nn.ModuleList() self.noises = nn.Module() in_channels = channels["4"] # noise for layer_idx in range(self.num_layers): resolution = 2 ** ((layer_idx + 5) // 2) shape = [1, 1, resolution, resolution] self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) # style convs and to_rgbs for i in range(3, self.log_size + 1): out_channels = channels[f"{2**i}"] self.style_convs.append( StyleConv( in_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode="upsample", ) ) self.style_convs.append( StyleConv( out_channels, out_channels, kernel_size=3, num_style_feat=num_style_feat, demodulate=True, sample_mode=None, ) ) self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) in_channels = out_channels def make_noise(self): """Make noise for noise injection.""" device = self.constant_input.weight.device noises = [torch.randn(1, 1, 4, 4, device=device)] for i in range(3, self.log_size + 1): for _ in range(2): noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) return noises def get_latent(self, x): return self.style_mlp(x) def mean_latent(self, num_latent): latent_in = torch.randn( num_latent, self.num_style_feat, device=self.constant_input.weight.device ) latent = self.style_mlp(latent_in).mean(0, keepdim=True) return latent def forward( self, styles, input_is_latent=False, noise=None, randomize_noise=True, truncation=1, truncation_latent=None, inject_index=None, return_latents=False, ): """Forward function for StyleGAN2GeneratorClean. Args: styles (list[Tensor]): Sample codes of styles. input_is_latent (bool): Whether input is latent style. Default: False. noise (Tensor | None): Input noise or None. Default: None. randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. truncation (float): The truncation ratio. Default: 1. truncation_latent (Tensor | None): The truncation latent tensor. Default: None. inject_index (int | None): The injection index for mixing noise. Default: None. return_latents (bool): Whether to return style latents. Default: False. """ # style codes -> latents with Style MLP layer if not input_is_latent: styles = [self.style_mlp(s) for s in styles] # noises if noise is None: if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise noise = [ getattr(self.noises, f"noise{i}") for i in range(self.num_layers) ] # style truncation if truncation < 1: style_truncation = [] for style in styles: style_truncation.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_truncation # get style latents with injection if len(styles) == 1: inject_index = self.num_latent if styles[0].ndim < 3: # repeat latent code for all the layers latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: # used for encoder with different latent code for each layer latent = styles[0] elif len(styles) == 2: # mixing noises if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = ( styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) ) latent = torch.cat([latent1, latent2], 1) # main generation out = self.constant_input(latent.shape[0]) out = self.style_conv1(out, latent[:, 0], noise=noise[0]) skip = self.to_rgb1(out, latent[:, 1]) i = 1 for conv1, conv2, noise1, noise2, to_rgb in zip( self.style_convs[::2], self.style_convs[1::2], noise[1::2], noise[2::2], self.to_rgbs, ): out = conv1(out, latent[:, i], noise=noise1) out = conv2(out, latent[:, i + 1], noise=noise2) skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space i += 2 image = skip if return_latents: return image, latent else: return image, None ================================================ FILE: ldm_patched/pfn/architecture/face/upfirdn2d.py ================================================ # pylint: skip-file # type: ignore # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 import os import torch from torch.autograd import Function from torch.nn import functional as F upfirdn2d_ext = None class UpFirDn2dBackward(Function): @staticmethod def forward( ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size ): up_x, up_y = up down_x, down_y = down g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) grad_input = upfirdn2d_ext.upfirdn2d( grad_output, grad_kernel, down_x, down_y, up_x, up_y, g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1, ) grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) ctx.save_for_backward(kernel) pad_x0, pad_x1, pad_y0, pad_y1 = pad ctx.up_x = up_x ctx.up_y = up_y ctx.down_x = down_x ctx.down_y = down_y ctx.pad_x0 = pad_x0 ctx.pad_x1 = pad_x1 ctx.pad_y0 = pad_y0 ctx.pad_y1 = pad_y1 ctx.in_size = in_size ctx.out_size = out_size return grad_input @staticmethod def backward(ctx, gradgrad_input): (kernel,) = ctx.saved_tensors gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) gradgrad_out = upfirdn2d_ext.upfirdn2d( gradgrad_input, kernel, ctx.up_x, ctx.up_y, ctx.down_x, ctx.down_y, ctx.pad_x0, ctx.pad_x1, ctx.pad_y0, ctx.pad_y1, ) # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], # ctx.out_size[1], ctx.in_size[3]) gradgrad_out = gradgrad_out.view( ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] ) return gradgrad_out, None, None, None, None, None, None, None, None class UpFirDn2d(Function): @staticmethod def forward(ctx, input, kernel, up, down, pad): up_x, up_y = up down_x, down_y = down pad_x0, pad_x1, pad_y0, pad_y1 = pad kernel_h, kernel_w = kernel.shape _, channel, in_h, in_w = input.shape ctx.in_size = input.shape input = input.reshape(-1, in_h, in_w, 1) ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 ctx.out_size = (out_h, out_w) ctx.up = (up_x, up_y) ctx.down = (down_x, down_y) ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) g_pad_x0 = kernel_w - pad_x0 - 1 g_pad_y0 = kernel_h - pad_y0 - 1 g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) out = upfirdn2d_ext.upfirdn2d( input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 ) # out = out.view(major, out_h, out_w, minor) out = out.view(-1, channel, out_h, out_w) return out @staticmethod def backward(ctx, grad_output): kernel, grad_kernel = ctx.saved_tensors grad_input = UpFirDn2dBackward.apply( grad_output, kernel, grad_kernel, ctx.up, ctx.down, ctx.pad, ctx.g_pad, ctx.in_size, ctx.out_size, ) return grad_input, None, None, None, None def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): if input.device.type == "cpu": out = upfirdn2d_native( input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] ) else: out = UpFirDn2d.apply( input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) ) return out def upfirdn2d_native( input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 ): _, channel, in_h, in_w = input.shape input = input.reshape(-1, in_h, in_w, 1) _, in_h, in_w, minor = input.shape kernel_h, kernel_w = kernel.shape out = input.view(-1, in_h, 1, in_w, 1, minor) out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad( out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] ) out = out[ :, max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), :, ] out = out.permute(0, 3, 1, 2) out = out.reshape( [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] ) w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) out = F.conv2d(out, w) out = out.reshape( -1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, ) out = out.permute(0, 2, 3, 1) out = out[:, ::down_y, ::down_x, :] out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 return out.view(-1, channel, out_h, out_w) ================================================ FILE: ldm_patched/pfn/architecture/timm/LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "{}" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2019 Ross Wightman Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: ldm_patched/pfn/architecture/timm/drop.py ================================================ """ DropBlock, DropPath PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. Papers: DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) Code: DropBlock impl inspired by two Tensorflow impl that I liked: - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py Hacked together by / Copyright 2020 Ross Wightman """ import torch import torch.nn as nn import torch.nn.functional as F def drop_block_2d( x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False, ): """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few training runs with success, but needs further validation and possibly optimization for lower runtime impact. """ _, C, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) # seed_drop_rate, the gamma parameter gamma = ( gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((W - block_size + 1) * (H - block_size + 1)) ) # Forces the block to be inside the feature map. w_i, h_i = torch.meshgrid( torch.arange(W).to(x.device), torch.arange(H).to(x.device) ) valid_block = ( (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) if batchwise: # one mask for whole batch, quite a bit faster uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) else: uniform_noise = torch.rand_like(x) block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) block_mask = -F.max_pool2d( -block_mask, kernel_size=clipped_block_size, # block_size, stride=1, padding=clipped_block_size // 2, ) if with_noise: normal_noise = ( torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) ) if inplace: x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) else: x = x * block_mask + normal_noise * (1 - block_mask) else: normalize_scale = ( block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) ).to(x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x def drop_block_fast_2d( x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, ): """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid block mask at edges. """ _, _, H, W = x.shape total_size = W * H clipped_block_size = min(block_size, min(W, H)) gamma = ( gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((W - block_size + 1) * (H - block_size + 1)) ) block_mask = torch.empty_like(x).bernoulli_(gamma) block_mask = F.max_pool2d( block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2, ) if with_noise: normal_noise = torch.empty_like(x).normal_() if inplace: x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) else: x = x * (1.0 - block_mask) + normal_noise * block_mask else: block_mask = 1 - block_mask normalize_scale = ( block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) ).to(dtype=x.dtype) if inplace: x.mul_(block_mask * normalize_scale) else: x = x * block_mask * normalize_scale return x class DropBlock2d(nn.Module): """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf""" def __init__( self, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False, fast: bool = True, ): super(DropBlock2d, self).__init__() self.drop_prob = drop_prob self.gamma_scale = gamma_scale self.block_size = block_size self.with_noise = with_noise self.inplace = inplace self.batchwise = batchwise self.fast = fast # FIXME finish comparisons of fast vs not def forward(self, x): if not self.training or not self.drop_prob: return x if self.fast: return drop_block_fast_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, ) else: return drop_block_2d( x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise, ) def drop_path( x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True ): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * ( x.ndim - 1 ) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def forward(self, x): return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) def extra_repr(self): return f"drop_prob={round(self.drop_prob,3):0.3f}" ================================================ FILE: ldm_patched/pfn/architecture/timm/helpers.py ================================================ """ Layer/Module Helpers Hacked together by / Copyright 2020 Ross Wightman """ import collections.abc from itertools import repeat # From PyTorch internals def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): min_value = min_value or divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < round_limit * v: new_v += divisor return new_v ================================================ FILE: ldm_patched/pfn/architecture/timm/weight_init.py ================================================ import math import warnings import torch from torch.nn.init import _calculate_fan_in_and_fan_out def _no_grad_trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn( "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2, ) with torch.no_grad(): # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.0)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_( tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 ) -> torch.Tensor: r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are applied while sampling the normal with mean/std applied, therefore a, b args should be adjusted to match the range of mean, std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ return _no_grad_trunc_normal_(tensor, mean, std, a, b) def trunc_normal_tf_( tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 ) -> torch.Tensor: r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 and the result is subsquently scaled and shifted by the mean and std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ _no_grad_trunc_normal_(tensor, 0, 1.0, a, b) with torch.no_grad(): tensor.mul_(std).add_(mean) return tensor def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == "fan_in": denom = fan_in elif mode == "fan_out": denom = fan_out elif mode == "fan_avg": denom = (fan_in + fan_out) / 2 variance = scale / denom # type: ignore if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) elif distribution == "normal": tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) # pylint: disable=invalid-unary-operand-type tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") ================================================ FILE: ldm_patched/pfn/model_loading.py ================================================ import logging as logger from .architecture.DAT import DAT from .architecture.face.codeformer import CodeFormer from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.HAT import HAT from .architecture.LaMa import LaMa from .architecture.OmniSR.OmniSR import OmniSR from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.Swin2SR import Swin2SR from .architecture.SwinIR import SwinIR from .types import PyTorchModel class UnsupportedModel(Exception): pass def load_state_dict(state_dict) -> PyTorchModel: logger.debug(f"Loading state dict into pytorch model arch") state_dict_keys = list(state_dict.keys()) if "params_ema" in state_dict_keys: state_dict = state_dict["params_ema"] elif "params-ema" in state_dict_keys: state_dict = state_dict["params-ema"] elif "params" in state_dict_keys: state_dict = state_dict["params"] state_dict_keys = list(state_dict.keys()) # SRVGGNet Real-ESRGAN (v2) if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: model = RealESRGANv2(state_dict) # SPSR (ESRGAN with lots of extra layers) elif "f_HR_conv1.0.weight" in state_dict: model = SPSR(state_dict) # Swift-SRGAN elif ( "model" in state_dict_keys and "initial.cnn.depthwise.weight" in state_dict["model"].keys() ): model = SwiftSRGAN(state_dict) # SwinIR, Swin2SR, HAT elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: if ( "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" in state_dict_keys ): model = HAT(state_dict) elif "patch_embed.proj.weight" in state_dict_keys: model = Swin2SR(state_dict) else: model = SwinIR(state_dict) # GFPGAN elif ( "toRGB.0.weight" in state_dict_keys and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys ): model = GFPGANv1Clean(state_dict) # RestoreFormer elif ( "encoder.conv_in.weight" in state_dict_keys and "encoder.down.0.block.0.norm1.weight" in state_dict_keys ): model = RestoreFormer(state_dict) elif ( "encoder.blocks.0.weight" in state_dict_keys and "quantize.embedding.weight" in state_dict_keys ): model = CodeFormer(state_dict) # LaMa elif ( "model.model.1.bn_l.running_mean" in state_dict_keys or "generator.model.1.bn_l.running_mean" in state_dict_keys ): model = LaMa(state_dict) # Omni-SR elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: model = OmniSR(state_dict) # SCUNet elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: model = SCUNet(state_dict) # DAT elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: model = DAT(state_dict) # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 else: try: model = ESRGAN(state_dict) except: # pylint: disable=raise-missing-from raise UnsupportedModel return model ================================================ FILE: ldm_patched/pfn/types.py ================================================ from typing import Union from .architecture.DAT import DAT from .architecture.face.codeformer import CodeFormer from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean from .architecture.face.restoreformer_arch import RestoreFormer from .architecture.HAT import HAT from .architecture.LaMa import LaMa from .architecture.OmniSR.OmniSR import OmniSR from .architecture.RRDB import RRDBNet as ESRGAN from .architecture.SCUNet import SCUNet from .architecture.SPSR import SPSRNet as SPSR from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 from .architecture.SwiftSRGAN import Generator as SwiftSRGAN from .architecture.Swin2SR import Swin2SR from .architecture.SwinIR import SwinIR PyTorchSRModels = ( RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR, SCUNet, DAT, ) PyTorchSRModel = Union[ RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR, SCUNet, DAT, ] def is_pytorch_sr_model(model: object): return isinstance(model, PyTorchSRModels) PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer) PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer] def is_pytorch_face_model(model: object): return isinstance(model, PyTorchFaceModels) PyTorchInpaintModels = (LaMa,) PyTorchInpaintModel = Union[LaMa] def is_pytorch_inpaint_model(model: object): return isinstance(model, PyTorchInpaintModels) PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels) PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel] def is_pytorch_model(model: object): return isinstance(model, PyTorchModels) ================================================ FILE: ldm_patched/t2ia/adapter.py ================================================ #taken from https://github.com/TencentARC/T2I-Adapter import torch import torch.nn as nn from collections import OrderedDict def conv_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D convolution module. """ if dims == 1: return nn.Conv1d(*args, **kwargs) elif dims == 2: return nn.Conv2d(*args, **kwargs) elif dims == 3: return nn.Conv3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels if not self.use_conv: padding = [x.shape[2] % 2, x.shape[3] % 2] self.op.padding = padding x = self.op(x) return x class ResnetBlock(nn.Module): def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): super().__init__() ps = ksize // 2 if in_c != out_c or sk == False: self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: # print('n_in') self.in_conv = None self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) if sk == False: self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) else: self.skep = None self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=use_conv) def forward(self, x): if self.down == True: x = self.down_opt(x) if self.in_conv is not None: # edit x = self.in_conv(x) h = self.block1(x) h = self.act(h) h = self.block2(h) if self.skep is not None: return h + self.skep(x) else: return h + x class Adapter(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): super(Adapter, self).__init__() self.unshuffle_amount = 8 resblock_no_downsample = [] resblock_downsample = [3, 2, 1] self.xl = xl if self.xl: self.unshuffle_amount = 16 resblock_no_downsample = [1] resblock_downsample = [2] self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.channels = channels self.nums_rb = nums_rb self.body = [] for i in range(len(channels)): for j in range(nums_rb): if (i in resblock_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) elif (i in resblock_no_downsample) and (j == 0): self.body.append( ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) else: self.body.append( ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) self.body = nn.ModuleList(self.body) self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) def forward(self, x): # unshuffle x = self.unshuffle(x) # extract features features = [] x = self.conv_in(x) for i in range(len(self.channels)): for j in range(self.nums_rb): idx = i * self.nums_rb + j x = self.body[idx](x) if self.xl: features.append(None) if i == 0: features.append(None) features.append(None) if i == 2: features.append(None) else: features.append(None) features.append(None) features.append(x) return features class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype ret = super().forward(x.type(torch.float32)) return ret.type(orig_type) class QuickGELU(nn.Module): def forward(self, x: torch.Tensor): return x * torch.sigmoid(1.702 * x) class ResidualAttentionBlock(nn.Module): def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): super().__init__() self.attn = nn.MultiheadAttention(d_model, n_head) self.ln_1 = LayerNorm(d_model) self.mlp = nn.Sequential( OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), ("c_proj", nn.Linear(d_model * 4, d_model))])) self.ln_2 = LayerNorm(d_model) self.attn_mask = attn_mask def attention(self, x: torch.Tensor): self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] def forward(self, x: torch.Tensor): x = x + self.attention(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class StyleAdapter(nn.Module): def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): super().__init__() scale = width ** -0.5 self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) self.num_token = num_token self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) self.ln_post = LayerNorm(width) self.ln_pre = LayerNorm(width) self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) def forward(self, x): # x shape [N, HW+1, C] style_embedding = self.style_embedding + torch.zeros( (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) x = torch.cat([x, style_embedding], dim=1) x = self.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.transformer_layes(x) x = x.permute(1, 0, 2) # LND -> NLD x = self.ln_post(x[:, -self.num_token:, :]) x = x @ self.proj return x class ResnetBlock_light(nn.Module): def __init__(self, in_c): super().__init__() self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) self.act = nn.ReLU() self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) def forward(self, x): h = self.block1(x) h = self.act(h) h = self.block2(h) return h + x class extractor(nn.Module): def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): super().__init__() self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) self.body = [] for _ in range(nums_rb): self.body.append(ResnetBlock_light(inter_c)) self.body = nn.Sequential(*self.body) self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) self.down = down if self.down == True: self.down_opt = Downsample(in_c, use_conv=False) def forward(self, x): if self.down == True: x = self.down_opt(x) x = self.in_conv(x) x = self.body(x) x = self.out_conv(x) return x class Adapter_light(nn.Module): def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): super(Adapter_light, self).__init__() self.unshuffle_amount = 8 self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) self.channels = channels self.nums_rb = nums_rb self.body = [] self.xl = False for i in range(len(channels)): if i == 0: self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) else: self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) self.body = nn.ModuleList(self.body) def forward(self, x): # unshuffle x = self.unshuffle(x) # extract features features = [] for i in range(len(self.channels)): x = self.body[i](x) features.append(None) features.append(None) features.append(x) return features ================================================ FILE: ldm_patched/taesd/taesd.py ================================================ #!/usr/bin/env python3 """ Tiny AutoEncoder for Stable Diffusion (DNN for encoding / decoding SD's latent space) """ import torch import torch.nn as nn import ldm_patched.modules.utils import ldm_patched.modules.ops def conv(n_in, n_out, **kwargs): return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) class Clamp(nn.Module): def forward(self, x): return torch.tanh(x / 3) * 3 class Block(nn.Module): def __init__(self, n_in, n_out): super().__init__() self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.fuse = nn.ReLU() def forward(self, x): return self.fuse(self.conv(x) + self.skip(x)) def Encoder(): return nn.Sequential( conv(3, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), conv(64, 4), ) def Decoder(): return nn.Sequential( Clamp(), conv(4, 64), nn.ReLU(), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), Block(64, 64), conv(64, 3), ) class TAESD(nn.Module): latent_magnitude = 3 latent_shift = 0.5 def __init__(self, encoder_path=None, decoder_path=None): """Initialize pretrained TAESD on the given device from the given checkpoints.""" super().__init__() self.taesd_encoder = Encoder() self.taesd_decoder = Decoder() self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) if encoder_path is not None: self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True)) if decoder_path is not None: self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True)) @staticmethod def scale_latents(x): """raw latents -> [0, 1]""" return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) @staticmethod def unscale_latents(x): """[0, 1] -> raw latents""" return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) def decode(self, x): x_sample = self.taesd_decoder(x * self.vae_scale) x_sample = x_sample.sub(0.5).mul(2) return x_sample def encode(self, x): return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale ================================================ FILE: ldm_patched/unipc/uni_pc.py ================================================ #code taken from: https://github.com/wl-zhao/UniPC and modified import torch import torch.nn.functional as F import math from tqdm.auto import trange, tqdm class NoiseScheduleVP: def __init__( self, schedule='discrete', betas=None, alphas_cumprod=None, continuous_beta_0=0.1, continuous_beta_1=20., ): """Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. *** The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: log_alpha_t = self.marginal_log_mean_coeff(t) sigma_t = self.marginal_std(t) lambda_t = self.marginal_lambda(t) Moreover, as lambda(t) is an invertible function, we also support its inverse function: t = self.inverse_lambda(lambda_t) =============================================================== We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). 1. For discrete-time DPMs: For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: t_i = (i + 1) / N e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. Args: betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. **Important**: Please pay special attention for the args for `alphas_cumprod`: The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have alpha_{t_n} = \sqrt{\hat{alpha_n}}, and log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). 2. For continuous-time DPMs: We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise schedule are the default settings in DDPM and improved-DDPM: Args: beta_min: A `float` number. The smallest beta for the linear schedule. beta_max: A `float` number. The largest beta for the linear schedule. cosine_s: A `float` number. The hyperparameter in the cosine schedule. cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. T: A `float` number. The ending time of the forward process. =============================================================== Args: schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, 'linear' or 'cosine' for continuous-time DPMs. Returns: A wrapper object of the forward SDE (VP type). =============================================================== Example: # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): >>> ns = NoiseScheduleVP('discrete', betas=betas) # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) # For continuous-time DPMs (VPSDE), linear schedule: >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) """ if schedule not in ['discrete', 'linear', 'cosine']: raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) self.schedule = schedule if schedule == 'discrete': if betas is not None: log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) else: assert alphas_cumprod is not None log_alphas = 0.5 * torch.log(alphas_cumprod) self.total_N = len(log_alphas) self.T = 1. self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) self.log_alpha_array = log_alphas.reshape((1, -1,)) else: self.total_N = 1000 self.beta_0 = continuous_beta_0 self.beta_1 = continuous_beta_1 self.cosine_s = 0.008 self.cosine_beta_max = 999. self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) self.schedule = schedule if schedule == 'cosine': # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. self.T = 0.9946 else: self.T = 1. def marginal_log_mean_coeff(self, t): """ Compute log(alpha_t) of a given continuous-time label t in [0, T]. """ if self.schedule == 'discrete': return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) elif self.schedule == 'linear': return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 elif self.schedule == 'cosine': log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 return log_alpha_t def marginal_alpha(self, t): """ Compute alpha_t of a given continuous-time label t in [0, T]. """ return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): """ Compute sigma_t of a given continuous-time label t in [0, T]. """ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def inverse_lambda(self, lamb): """ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. """ if self.schedule == 'linear': tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) Delta = self.beta_0**2 + tmp return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) elif self.schedule == 'discrete': log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) return t.reshape((-1,)) else: log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s t = t_fn(log_alpha) return t def model_wrapper( model, noise_schedule, model_type="noise", model_kwargs={}, guidance_type="uncond", condition=None, unconditional_condition=None, guidance_scale=1., classifier_fn=None, classifier_kwargs={}, ): """Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. We support four types of the diffusion model by setting `model_type`: 1. "noise": noise prediction model. (Trained by predicting noise). 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). 3. "v": velocity prediction model. (Trained by predicting the velocity). The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." arXiv preprint arXiv:2202.00512 (2022). [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." arXiv preprint arXiv:2210.02303 (2022). 4. "score": marginal score function. (Trained by denoising score matching). Note that the score function and the noise prediction model follows a simple relationship: ``` noise(x_t, t) = -sigma_t * score(x_t, t) ``` We support three types of guided sampling by DPMs by setting `guidance_type`: 1. "uncond": unconditional sampling by DPMs. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. The input `model` has the following format: `` model(x, t_input, **model_kwargs) -> noise | x_start | v | score `` The input `classifier_fn` has the following format: `` classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) `` [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. The input `model` has the following format: `` model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score `` And if cond == `unconditional_condition`, the model output is the unconditional DPM output. [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." arXiv preprint arXiv:2207.12598 (2022). The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) or continuous-time labels (i.e. epsilon to T). We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: `` def model_fn(x, t_continuous) -> noise: t_input = get_model_input_time(t_continuous) return noise_pred(model, x, t_input, **model_kwargs) `` where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. =============================================================== Args: model: A diffusion model with the corresponding format described above. noise_schedule: A noise schedule object, such as NoiseScheduleVP. model_type: A `str`. The parameterization type of the diffusion model. "noise" or "x_start" or "v" or "score". model_kwargs: A `dict`. A dict for the other inputs of the model function. guidance_type: A `str`. The type of the guidance for sampling. "uncond" or "classifier" or "classifier-free". condition: A pytorch tensor. The condition for the guided sampling. Only used for "classifier" or "classifier-free" guidance type. unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. Only used for "classifier-free" guidance type. guidance_scale: A `float`. The scale for the guided sampling. classifier_fn: A classifier function. Only used for the classifier guidance. classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. Returns: A noise prediction model that accepts the noised data and the continuous time as the inputs. """ def get_model_input_time(t_continuous): """ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. For continuous-time DPMs, we just use `t_continuous`. """ if noise_schedule.schedule == 'discrete': return (t_continuous - 1. / noise_schedule.total_N) * 1000. else: return t_continuous def noise_pred_fn(x, t_continuous, cond=None): if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) t_input = get_model_input_time(t_continuous) output = model(x, t_input, **model_kwargs) if model_type == "noise": return output elif model_type == "x_start": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) elif model_type == "v": alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) dims = x.dim() return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x elif model_type == "score": sigma_t = noise_schedule.marginal_std(t_continuous) dims = x.dim() return -expand_dims(sigma_t, dims) * output def cond_grad_fn(x, t_input): """ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). """ with torch.enable_grad(): x_in = x.detach().requires_grad_(True) log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) return torch.autograd.grad(log_prob.sum(), x_in)[0] def model_fn(x, t_continuous): """ The noise predicition model function that is used for DPM-Solver. """ if t_continuous.reshape((-1,)).shape[0] == 1: t_continuous = t_continuous.expand((x.shape[0])) if guidance_type == "uncond": return noise_pred_fn(x, t_continuous) elif guidance_type == "classifier": assert classifier_fn is not None t_input = get_model_input_time(t_continuous) cond_grad = cond_grad_fn(x, t_input) sigma_t = noise_schedule.marginal_std(t_continuous) noise = noise_pred_fn(x, t_continuous) return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad elif guidance_type == "classifier-free": if guidance_scale == 1. or unconditional_condition is None: return noise_pred_fn(x, t_continuous, cond=condition) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t_continuous] * 2) c_in = torch.cat([unconditional_condition, condition]) noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) return noise_uncond + guidance_scale * (noise - noise_uncond) assert model_type in ["noise", "x_start", "v"] assert guidance_type in ["uncond", "classifier", "classifier-free"] return model_fn class UniPC: def __init__( self, model_fn, noise_schedule, predict_x0=True, thresholding=False, max_val=1., variant='bh1', noise_mask=None, masked_image=None, noise=None, ): """Construct a UniPC. We support both data_prediction and noise_prediction. """ self.model = model_fn self.noise_schedule = noise_schedule self.variant = variant self.predict_x0 = predict_x0 self.thresholding = thresholding self.max_val = max_val self.noise_mask = noise_mask self.masked_image = masked_image self.noise = noise def dynamic_thresholding_fn(self, x0, t=None): """ The dynamic thresholding method. """ dims = x0.dim() p = self.dynamic_thresholding_ratio s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s return x0 def noise_prediction_fn(self, x, t): """ Return the noise prediction model. """ if self.noise_mask is not None: return self.model(x, t) * self.noise_mask else: return self.model(x, t) def data_prediction_fn(self, x, t): """ Return the data prediction model (with thresholding). """ noise = self.noise_prediction_fn(x, t) dims = x.dim() alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) if self.thresholding: p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) x0 = torch.clamp(x0, -s, s) / s if self.noise_mask is not None: x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image return x0 def model_fn(self, x, t): """ Convert the model to the noise prediction model or the data prediction model. """ if self.predict_x0: return self.data_prediction_fn(x, t) else: return self.noise_prediction_fn(x, t) def get_time_steps(self, skip_type, t_T, t_0, N, device): """Compute the intermediate time steps for sampling. """ if skip_type == 'logSNR': lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) return self.noise_schedule.inverse_lambda(logSNR_steps) elif skip_type == 'time_uniform': return torch.linspace(t_T, t_0, N + 1).to(device) elif skip_type == 'time_quadratic': t_order = 2 t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) return t else: raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): """ Get the order of each step for sampling by the singlestep DPM-Solver. """ if order == 3: K = steps // 3 + 1 if steps % 3 == 0: orders = [3,] * (K - 2) + [2, 1] elif steps % 3 == 1: orders = [3,] * (K - 1) + [1] else: orders = [3,] * (K - 1) + [2] elif order == 2: if steps % 2 == 0: K = steps // 2 orders = [2,] * K else: K = steps // 2 + 1 orders = [2,] * (K - 1) + [1] elif order == 1: K = steps orders = [1,] * steps else: raise ValueError("'order' must be '1' or '2' or '3'.") if skip_type == 'logSNR': # To reproduce the results in DPM-Solver paper timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) else: timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] return timesteps_outer, orders def denoise_to_zero_fn(self, x, s): """ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. """ return self.data_prediction_fn(x, s) def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): if len(t.shape) == 0: t = t.view(-1) if 'bh' in self.variant: return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) else: assert self.variant == 'vary_coeff' return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') ns = self.noise_schedule assert order <= len(model_prev_list) # first compute rks t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_t = ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = (lambda_prev_i - lambda_prev_0) / h rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) K = len(rks) # build C matrix C = [] col = torch.ones_like(rks) for k in range(1, K + 1): C.append(col) col = col * rks / (k + 1) C = torch.stack(C, dim=1) if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) C_inv_p = torch.linalg.inv(C[:-1, :-1]) A_p = C_inv_p if use_corrector: print('using corrector') C_inv = torch.linalg.inv(C) A_c = C_inv hh = -h if self.predict_x0 else h h_phi_1 = torch.expm1(hh) h_phi_ks = [] factorial_k = 1 h_phi_k = h_phi_1 for k in range(1, K + 2): h_phi_ks.append(h_phi_k) h_phi_k = h_phi_k / hh - 1 / factorial_k factorial_k *= (k + 1) model_t = None if self.predict_x0: x_t_ = ( sigma_t / sigma_prev_0 * x - alpha_t * h_phi_1 * model_prev_0 ) # now predictor x_t = x_t_ if len(D1s) > 0: # compute the residuals for predictor for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) # now corrector if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) else: log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) x_t_ = ( (torch.exp(log_alpha_t - log_alpha_prev_0)) * x - (sigma_t * h_phi_1) * model_prev_0 ) # now predictor x_t = x_t_ if len(D1s) > 0: # compute the residuals for predictor for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) # now corrector if use_corrector: model_t = self.model_fn(x_t, t) D1_t = (model_t - model_prev_0) x_t = x_t_ k = 0 for k in range(K - 1): x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) return x_t, model_t def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') ns = self.noise_schedule assert order <= len(model_prev_list) dims = x.dim() # first compute rks t_prev_0 = t_prev_list[-1] lambda_prev_0 = ns.marginal_lambda(t_prev_0) lambda_t = ns.marginal_lambda(t) model_prev_0 = model_prev_list[-1] sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) alpha_t = torch.exp(log_alpha_t) h = lambda_t - lambda_prev_0 rks = [] D1s = [] for i in range(1, order): t_prev_i = t_prev_list[-(i + 1)] model_prev_i = model_prev_list[-(i + 1)] lambda_prev_i = ns.marginal_lambda(t_prev_i) rk = ((lambda_prev_i - lambda_prev_0) / h)[0] rks.append(rk) D1s.append((model_prev_i - model_prev_0) / rk) rks.append(1.) rks = torch.tensor(rks, device=x.device) R = [] b = [] hh = -h[0] if self.predict_x0 else h[0] h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 h_phi_k = h_phi_1 / hh - 1 factorial_i = 1 if self.variant == 'bh1': B_h = hh elif self.variant == 'bh2': B_h = torch.expm1(hh) else: raise NotImplementedError() for i in range(1, order + 1): R.append(torch.pow(rks, i - 1)) b.append(h_phi_k * factorial_i / B_h) factorial_i *= (i + 1) h_phi_k = h_phi_k / hh - 1 / factorial_i R = torch.stack(R) b = torch.tensor(b, device=x.device) # now predictor use_predictor = len(D1s) > 0 and x_t is None if len(D1s) > 0: D1s = torch.stack(D1s, dim=1) # (B, K) if x_t is None: # for order 2, we use a simplified version if order == 2: rhos_p = torch.tensor([0.5], device=b.device) else: rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) else: D1s = None if use_corrector: # print('using corrector') # for order 1, we use a simplified version if order == 1: rhos_c = torch.tensor([0.5], device=b.device) else: rhos_c = torch.linalg.solve(R, b) model_t = None if self.predict_x0: x_t_ = ( expand_dims(sigma_t / sigma_prev_0, dims) * x - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) else: x_t_ = ( expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 ) if x_t is None: if use_predictor: pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) return x_t, model_t def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False ): # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end # t_T = self.noise_schedule.T if t_start is None else t_start device = x.device steps = len(timesteps) - 1 if method == 'multistep': assert steps >= order # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) assert timesteps.shape[0] - 1 == steps # with torch.no_grad(): for step_index in trange(steps, disable=disable_pbar): if self.noise_mask is not None: x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) if step_index == 0: vec_t = timesteps[0].expand((x.shape[0])) model_prev_list = [self.model_fn(x, vec_t)] t_prev_list = [vec_t] elif step_index < order: init_order = step_index # Init the first `order` values by lower order multistep DPM-Solver. # for init_order in range(1, order): vec_t = timesteps[init_order].expand(x.shape[0]) x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list.append(model_x) t_prev_list.append(vec_t) else: extra_final_step = 0 if step_index == (steps - 1): extra_final_step = 1 for step in range(step_index, step_index + 1 + extra_final_step): vec_t = timesteps[step].expand(x.shape[0]) if lower_order_final: step_order = min(order, steps + 1 - step) else: step_order = order # print('this step order:', step_order) if step == steps: # print('do not run corrector at the last step') use_corrector = False else: use_corrector = True x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) for i in range(order - 1): t_prev_list[i] = t_prev_list[i + 1] model_prev_list[i] = model_prev_list[i + 1] t_prev_list[-1] = vec_t # We do not need to evaluate the final model value. if step < steps: if model_x is None: model_x = self.model_fn(x, vec_t) model_prev_list[-1] = model_x if callback is not None: callback(step_index, model_prev_list[-1], x, steps) else: raise NotImplementedError() # if denoise_to_zero: # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) return x ############################################################# # other utility functions ############################################################# def interpolate_fn(x, xp, yp): """ A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd). The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) Args: x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. yp: PyTorch tensor with shape [C, K]. Returns: The function values f(x), with shape [N, C]. """ N, K = x.shape[0], xp.shape[1] all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) sorted_all_x, x_indices = torch.sort(all_x, dim=2) x_idx = torch.argmin(x_indices, dim=2) cand_start_idx = x_idx - 1 start_idx = torch.where( torch.eq(x_idx, 0), torch.tensor(1, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) start_idx2 = torch.where( torch.eq(x_idx, 0), torch.tensor(0, device=x.device), torch.where( torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, ), ) y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) return cand def expand_dims(v, dims): """ Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. """ return v[(...,) + (None,)*(dims - 1)] class SigmaConvert: schedule = "" def marginal_log_mean_coeff(self, sigma): return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) def marginal_alpha(self, t): return torch.exp(self.marginal_log_mean_coeff(t)) def marginal_std(self, t): return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) def marginal_lambda(self, t): """ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. """ log_mean_coeff = self.marginal_log_mean_coeff(t) log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) return log_mean_coeff - log_std def predict_eps_sigma(model, input, sigma_in, **kwargs): sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) input = input * ((sigma ** 2 + 1.0) ** 0.5) return (input - model(input, sigma_in, **kwargs)) / sigma def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'): timesteps = sigmas.clone() if sigmas[-1] == 0: timesteps = sigmas[:] timesteps[-1] = 0.001 else: timesteps = sigmas.clone() ns = SigmaConvert() if image is not None: img = image * ns.marginal_alpha(timesteps[0]) if max_denoise: noise_mult = 1.0 else: noise_mult = ns.marginal_std(timesteps[0]) img += noise * noise_mult else: img = noise model_type = "noise" model_fn = model_wrapper( lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), ns, model_type=model_type, guidance_type="uncond", model_kwargs=extra_args, ) order = min(3, len(timesteps) - 2) uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) x /= ns.marginal_alpha(timesteps[-1]) return x ================================================ FILE: ldm_patched/utils/latent_visualization.py ================================================ import torch from PIL import Image import struct import numpy as np from ldm_patched.modules.args_parser import args, LatentPreviewMethod from ldm_patched.taesd.taesd import TAESD import ldm_patched.utils.path_utils import ldm_patched.modules.utils MAX_PREVIEW_RESOLUTION = 512 class LatentPreviewer: def decode_latent_to_preview(self, x0): pass def decode_latent_to_preview_image(self, preview_format, x0): preview_image = self.decode_latent_to_preview(x0) return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) class TAESDPreviewerImpl(LatentPreviewer): def __init__(self, taesd): self.taesd = taesd def decode_latent_to_preview(self, x0): x_sample = self.taesd.decode(x0[:1])[0].detach() x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) preview_image = Image.fromarray(x_sample) return preview_image class Latent2RGBPreviewer(LatentPreviewer): def __init__(self, latent_rgb_factors): self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") def decode_latent_to_preview(self, x0): latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors latents_ubyte = (((latent_image + 1) / 2) .clamp(0, 1) # change scale from -1..1 to 0..1 .mul(0xFF) # to 0..255 .byte()).cpu() return Image.fromarray(latents_ubyte.numpy()) def get_previewer(device, latent_format): previewer = None method = args.preview_option if method != LatentPreviewMethod.NoPreviews: # TODO previewer methods taesd_decoder_path = None if latent_format.taesd_decoder_name is not None: taesd_decoder_path = next( (fn for fn in ldm_patched.utils.path_utils.get_filename_list("vae_approx") if fn.startswith(latent_format.taesd_decoder_name)), "" ) taesd_decoder_path = ldm_patched.utils.path_utils.get_full_path("vae_approx", taesd_decoder_path) if method == LatentPreviewMethod.Auto: method = LatentPreviewMethod.Latent2RGB if taesd_decoder_path: method = LatentPreviewMethod.TAESD if method == LatentPreviewMethod.TAESD: if taesd_decoder_path: taesd = TAESD(None, taesd_decoder_path).to(device) previewer = TAESDPreviewerImpl(taesd) else: print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) if previewer is None: if latent_format.latent_rgb_factors is not None: previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) return previewer def prepare_callback(model, steps, x0_output_dict=None): preview_format = "JPEG" if preview_format not in ["JPEG", "PNG"]: preview_format = "JPEG" previewer = get_previewer(model.load_device, model.model.latent_format) pbar = ldm_patched.modules.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): if x0_output_dict is not None: x0_output_dict["x0"] = x0 preview_bytes = None if previewer: preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) pbar.update_absolute(step + 1, total_steps, preview_bytes) return callback ================================================ FILE: ldm_patched/utils/path_utils.py ================================================ import os import time supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) folder_names_and_paths = {} base_path = os.getcwd() models_dir = os.path.join(base_path, "models") folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions) folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions) folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) output_directory = os.path.join(os.getcwd(), "output") temp_directory = os.path.join(os.getcwd(), "temp") input_directory = os.path.join(os.getcwd(), "input") user_directory = os.path.join(os.getcwd(), "user") filename_list_cache = {} if not os.path.exists(input_directory): try: pass # os.makedirs(input_directory) except: print("Failed to create input directory") def set_output_directory(output_dir): global output_directory output_directory = output_dir def set_temp_directory(temp_dir): global temp_directory temp_directory = temp_dir def set_input_directory(input_dir): global input_directory input_directory = input_dir def get_output_directory(): global output_directory return output_directory def get_temp_directory(): global temp_directory return temp_directory def get_input_directory(): global input_directory return input_directory #NOTE: used in http server so don't put folders that should not be accessed remotely def get_directory_by_type(type_name): if type_name == "output": return get_output_directory() if type_name == "temp": return get_temp_directory() if type_name == "input": return get_input_directory() return None # determine base_dir rely on annotation if name is 'filename.ext [annotation]' format # otherwise use default_path as base_dir def annotated_filepath(name): if name.endswith("[output]"): base_dir = get_output_directory() name = name[:-9] elif name.endswith("[input]"): base_dir = get_input_directory() name = name[:-8] elif name.endswith("[temp]"): base_dir = get_temp_directory() name = name[:-7] else: return name, None return name, base_dir def get_annotated_filepath(name, default_dir=None): name, base_dir = annotated_filepath(name) if base_dir is None: if default_dir is not None: base_dir = default_dir else: base_dir = get_input_directory() # fallback path return os.path.join(base_dir, name) def exists_annotated_filepath(name): name, base_dir = annotated_filepath(name) if base_dir is None: base_dir = get_input_directory() # fallback path filepath = os.path.join(base_dir, name) return os.path.exists(filepath) def add_model_folder_path(folder_name, full_folder_path): global folder_names_and_paths if folder_name in folder_names_and_paths: folder_names_and_paths[folder_name][0].append(full_folder_path) else: folder_names_and_paths[folder_name] = ([full_folder_path], set()) def get_folder_paths(folder_name): return folder_names_and_paths[folder_name][0][:] def recursive_search(directory, excluded_dir_names=None): if not os.path.isdir(directory): return [], {} if excluded_dir_names is None: excluded_dir_names = [] result = [] dirs = {} # Attempt to add the initial directory to dirs with error handling try: dirs[directory] = os.path.getmtime(directory) except FileNotFoundError: print(f"Warning: Unable to access {directory}. Skipping this path.") for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] for file_name in filenames: relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) result.append(relative_path) for d in subdirs: path = os.path.join(dirpath, d) try: dirs[path] = os.path.getmtime(path) except FileNotFoundError: print(f"Warning: Unable to access {path}. Skipping this path.") continue return result, dirs def filter_files_extensions(files, extensions): return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) def get_full_path(folder_name, filename): global folder_names_and_paths if folder_name not in folder_names_and_paths: return None folders = folder_names_and_paths[folder_name] filename = os.path.relpath(os.path.join("/", filename), "/") for x in folders[0]: full_path = os.path.join(x, filename) if os.path.isfile(full_path): return full_path return None def get_filename_list_(folder_name): global folder_names_and_paths output_list = set() folders = folder_names_and_paths[folder_name] output_folders = {} for x in folders[0]: files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) output_list.update(filter_files_extensions(files, folders[1])) output_folders = {**output_folders, **folders_all} return (sorted(list(output_list)), output_folders, time.perf_counter()) def cached_filename_list_(folder_name): global filename_list_cache global folder_names_and_paths if folder_name not in filename_list_cache: return None out = filename_list_cache[folder_name] for x in out[1]: time_modified = out[1][x] folder = x if os.path.getmtime(folder) != time_modified: return None folders = folder_names_and_paths[folder_name] for x in folders[0]: if os.path.isdir(x): if x not in out[1]: return None return out def get_filename_list(folder_name): out = cached_filename_list_(folder_name) if out is None: out = get_filename_list_(folder_name) global filename_list_cache filename_list_cache[folder_name] = out return list(out[0]) def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0): def map_filename(filename): prefix_len = len(os.path.basename(filename_prefix)) prefix = filename[:prefix_len + 1] try: digits = int(filename[prefix_len + 1:].split('_')[0]) except: digits = 0 return (digits, prefix) def compute_vars(input, image_width, image_height): input = input.replace("%width%", str(image_width)) input = input.replace("%height%", str(image_height)) return input filename_prefix = compute_vars(filename_prefix, image_width, image_height) subfolder = os.path.dirname(os.path.normpath(filename_prefix)) filename = os.path.basename(os.path.normpath(filename_prefix)) full_output_folder = os.path.join(output_dir, subfolder) if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: err = "**** ERROR: Saving image outside the output folder is not allowed." + \ "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ "\n output_dir: " + output_dir + \ "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) print(err) raise Exception(err) try: counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 except ValueError: counter = 1 except FileNotFoundError: os.makedirs(full_output_folder, exist_ok=True) counter = 1 return full_output_folder, filename, counter, subfolder, filename_prefix ================================================ FILE: models/checkpoints/put_checkpoints_here ================================================ ================================================ FILE: models/clip/put_clip_or_text_encoder_models_here ================================================ ================================================ FILE: models/clip_vision/put_clip_vision_models_here ================================================ ================================================ FILE: models/clip_vision/wd-v1-4-moat-tagger-v2.csv ================================================ tag_id,name,category,count 9999999,general,9,807858 9999998,sensitive,9,3771700 9999997,questionable,9,769899 9999996,explicit,9,560281 470575,1girl,0,4225150 212816,solo,0,3515897 13197,long_hair,0,2982517 8601,breasts,0,2323580 469576,looking_at_viewer,0,2089971 3389,blush,0,2040471 1815,smile,0,1903619 15080,short_hair,0,1568265 11906,open_mouth,0,1565950 16751,bangs,0,1516840 10959,blue_eyes,0,1225129 566835,multiple_girls,0,1120328 429,skirt,0,1100620 87788,blonde_hair,0,1098200 403247,large_breasts,0,1083979 412368,simple_background,0,1074818 16867,brown_hair,0,1072209 12590,shirt,0,1001030 13200,black_hair,0,981413 380350,hair_ornament,0,939495 8526,red_eyes,0,897316 1882,thighhighs,0,890813 5735,gloves,0,886283 383159,long_sleeves,0,883900 540830,1boy,0,881194 2373,hat,0,879697 515193,white_background,0,874291 2241,dress,0,838290 4563,bow,0,795194 464575,ribbon,0,793922 9294,navel,0,786293 375387,holding,0,732899 1821,2girls,0,729721 6126,animal_ears,0,722334 4607,cleavage,0,693321 658573,hair_between_eyes,0,692014 376054,bare_shoulders,0,656975 1709,twintails,0,648384 16578,brown_eyes,0,645874 16613,jewelry,0,644654 667868,medium_breasts,0,642525 12289,sitting,0,630993 417660,very_long_hair,0,622920 572080,closed_mouth,0,618062 464906,underwear,0,610036 8889,nipples,0,591774 16509,school_uniform,0,585679 10960,green_eyes,0,584783 10953,blue_hair,0,564360 15675,standing,0,551783 15654,purple_eyes,0,536623 466499,collarbone,0,520875 391,panties,0,506334 3843,jacket,0,493731 15674,tail,0,487490 1681,monochrome,0,478584 444,swimsuit,0,467619 608813,full_body,0,463008 465619,closed_eyes,0,455512 464561,hair_ribbon,0,449452 89189,yellow_eyes,0,447582 376766,white_shirt,0,435867 547463,upper_body,0,434670 2355,ponytail,0,431021 11449,weapon,0,430315 11429,pink_hair,0,427100 16442,purple_hair,0,426385 8101,ass,0,423113 4334,braid,0,417832 464559,flower,0,411874 63,comic,0,411524 3522,ahoge,0,408654 16581,white_hair,0,407226 472154,short_sleeves,0,389311 384553,:d,0,387533 622137,hetero,0,384576 374844,hair_bow,0,381335 513837,greyscale,0,377967 16580,grey_hair,0,375103 1300281,male_focus,0,371503 2750,heart,0,361897 2363,pantyhose,0,356177 484168,sidelocks,0,354421 6539,bikini,0,349809 3870,thighs,0,348316 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1400511,fur-trimmed_hood,0,2739 1205954,looking_at_penis,0,2738 413033,gaping,0,2738 25477,chemise,0,2737 548543,arm_hair,0,2736 1874313,covering_own_eyes,0,2735 484394,four-leaf_clover,0,2735 649327,wide_ponytail,0,2734 457141,lace_panties,0,2733 1344905,holding_dagger,0,2733 1215711,numbered,0,2733 610726,shared_clothes,0,2731 1714873,chest_sarashi,0,2729 406406,shoe_dangle,0,2727 652707,domino_mask,0,2727 4177,lotion,0,2724 279898,shell_casing,0,2722 409789,rubber_boots,0,2718 478568,medal,0,2716 10624,exercise,0,2713 2833,collage,0,2711 1384369,green_hairband,0,2709 8808,hallway,0,2704 666648,mitsudomoe_(shape),0,2704 378,cheese,0,2702 406895,tunic,0,2702 743282,layered_clothes,0,2701 494802,on_shoulder,0,2701 464876,bento,0,2699 602690,stuffed_cat,0,2699 1770615,red_gemstone,0,2695 1373278,black_ascot,0,2693 619493,ears_down,0,2691 395226,shoulder_carry,0,2688 1441861,orange_footwear,0,2687 1350604,cat_ear_panties,0,2687 1435434,two-tone_skirt,0,2685 1474162,gekkoukan_high_school_uniform,0,2684 503705,paw_shoes,0,2682 397148,huge_nipples,0,2680 433752,yunomi,0,2679 558702,pinching,0,2676 1383796,rabbit_hood,0,2675 15996,moonlight,0,2673 2490,pirate,0,2673 656171,hand_on_another's_chest,0,2673 405984,snot,0,2672 9002,stethoscope,0,2671 530091,corruption,0,2670 535299,red_sclera,0,2668 1316609,mole_on_thigh,0,2667 724362,single_detached_sleeve,0,2664 417408,keyhole,0,2663 619992,world_war_ii,0,2662 626084,bangs_pinned_back,0,2662 478424,striker_unit,0,2662 1322674,latin_cross,0,2660 1474402,qing_guanmao,0,2656 631299,pillarboxed,0,2655 12650,subtitled,0,2654 644051,aiming_at_viewer,0,2652 399594,urethra,0,2652 2626,volleyball,0,2652 640241,helmet_removed,0,2652 709557,torn_bodysuit,0,2652 1183480,blue_belt,0,2651 1471518,horseshoe_ornament,0,2650 5272,dice,0,2649 429439,fetal_position,0,2649 587679,vibrator_under_clothes,0,2648 11981,kyuubi,0,2644 465450,trash_can,0,2644 1242915,rose_print,0,2642 10731,chainsaw,0,2641 1312757,bra_peek,0,2641 420964,hip_bones,0,2640 3237,goldfish,0,2639 484121,leg_lock,0,2639 1390670,breast_tattoo,0,2639 505313,torn_sleeves,0,2635 470159,jealous,0,2634 1383010,purple_choker,0,2634 1291568,pink_apron,0,2634 473427,green_thighhighs,0,2632 1330014,year_of_the_tiger,0,2632 494260,purple_wings,0,2630 407041,profanity,0,2629 375792,kogal,0,2629 2848,wedding,0,2628 447824,doorway,0,2627 1449257,gem_uniform_(houseki_no_kuni),0,2625 350,ramen,0,2624 1280149,crescent_earrings,0,2624 528555,flat_ass,0,2623 397935,ladder,0,2623 378452,hand_in_panties,0,2622 650514,wet_swimsuit,0,2618 8874,pudding,0,2617 555933,sword_of_hisou,0,2617 1441888,over-rim_eyewear,0,2615 519703,alternate_universe,0,2615 413035,soccer_ball,0,2614 515648,coattails,0,2613 1261354,feather_trim,0,2613 414783,padlock,0,2612 1229660,multiple_horns,0,2612 11316,abstract,0,2611 1262166,seamed_legwear,0,2605 15201,soldier,0,2605 1468295,quarter_note,0,2603 617590,wa_maid,0,2602 449458,bullpup,0,2602 474982,white_robe,0,2602 667185,burn_scar,0,2600 1467259,diagonal_bangs,0,2598 10309,fashion,0,2597 663385,crossed_ankles,0,2597 1554926,wide_spread_legs,0,2594 605757,pant_suit,0,2593 673352,flaming_eye,0,2591 675804,nervous_smile,0,2591 8796,electric_fan,0,2586 10631,paddle,0,2584 619600,zombie_pose,0,2584 1297470,lifted_by_another,0,2583 400527,skirt_tug,0,2582 449624,bass_guitar,0,2582 12326,owl,0,2578 695265,clitoral_stimulation,0,2577 1355655,grey_coat,0,2576 1178138,snowflake_print,0,2576 1901,orgy,0,2575 682862,hand_on_another's_hip,0,2573 1434382,spade_(shape),0,2572 1444529,linea_alba,0,2572 469445,sleepwear,0,2572 534387,heart_pillow,0,2567 1438791,holding_pom_poms,0,2566 1515727,strap_between_breasts,0,2565 11243,shower_head,0,2564 1397369,year_of_the_ox,0,2563 1392007,pink_bodysuit,0,2562 547698,sunburst,0,2561 32298,duffel_bag,0,2558 540097,turtle_shell,0,2557 615041,finger_to_cheek,0,2553 619145,orange_gloves,0,2552 609122,multiple_belts,0,2552 595006,green_sweater,0,2550 1431325,mismatched_bikini,0,2548 1395493,eyepatch_bikini,0,2548 375886,sheep_girl,0,2547 1376523,weibo_username,0,2544 1161083,2018,0,2544 670773,happy_valentine,0,2543 69844,pig,0,2542 1518440,mouth_drool,0,2542 14861,chef_hat,0,2541 649248,red_pupils,0,2541 1304208,black_cardigan,0,2539 622606,anime_coloring,0,2537 1396558,frilled_hair_tubes,0,2537 10340,shark,0,2537 13958,locker_room,0,2535 525182,santa_bikini,0,2535 4031,negligee,0,2533 1344732,striped_jacket,0,2533 821841,2019,0,2532 476787,talons,0,2531 380173,seashell,0,2528 422957,come_hither,0,2527 1410787,st._gloriana's_school_uniform,0,2527 9554,g-string,0,2526 722842,frilled_kimono,0,2525 1161081,2016,0,2522 1416804,orange_scrunchie,0,2521 1283223,shibari_over_clothes,0,2520 1515354,brown_theme,0,2518 1201870,prone_bone,0,2517 428664,embers,0,2517 1582500,scar_on_arm,0,2515 578713,giving,0,2514 656916,animal_penis,0,2514 1336997,green_coat,0,2514 527684,doll_hug,0,2510 1338640,yellow_scrunchie,0,2508 10168,suspension,0,2506 475709,bandaid_on_pussy,0,2505 1314965,horizontal_pupils,0,2504 381372,cold,0,2502 602427,wrestling_outfit,0,2502 499060,ainu_clothes,0,2500 586749,mismatched_footwear,0,2499 1530508,scar_on_nose,0,2498 1515361,white_theme,0,2496 632927,alternate_headwear,0,2496 1723482,flame-tipped_tail,0,2496 550123,sitting_on_face,0,2495 11433,nipple_rings,0,2494 228096,spacesuit,0,2492 677406,multiple_earrings,0,2492 471929,cum_on_stomach,0,2492 835764,untied_panties,0,2491 640801,clothes_in_mouth,0,2490 4011,blade,0,2489 390993,sigh,0,2487 9101,cigar,0,2487 404436,ribbed_shirt,0,2487 642455,shirt_removed,0,2487 14074,raincoat,0,2487 1437532,two-tone_jacket,0,2486 551584,updo,0,2485 398742,pastry,0,2484 402204,holding_panties,0,2483 495857,arm_around_neck,0,2483 725441,black_headband,0,2482 1393295,orange_choker,0,2481 515468,head-mounted_display,0,2479 556999,two-footed_footjob,0,2477 379432,lights,0,2477 399308,walk-in,0,2476 405623,dog_boy,0,2474 633092,arm_held_back,0,2473 374959,still_life,0,2472 607667,checkered_scarf,0,2472 1393855,holding_flag,0,2471 685091,hand_on_another's_back,0,2471 388452,sweets,0,2470 376473,mallet,0,2470 393166,bloom,0,2470 10736,spandex,0,2468 1319452,hand_on_another's_arm,0,2467 612542,bow_bikini,0,2462 1538888,kitauji_high_school_uniform,0,2459 9511,spotlight,0,2458 533035,undersized_clothes,0,2458 1451957,naoetsu_high_school_uniform,0,2457 663171,sun_symbol,0,2456 495002,skirt_around_one_leg,0,2455 527607,magazine_(object),0,2455 461542,red_sash,0,2454 480444,shouji,0,2452 883683,white_feathers,0,2450 523426,hand_grab,0,2450 10412,shaved_ice,0,2449 374334,open_shorts,0,2448 540150,puckered_lips,0,2446 446993,open_collar,0,2445 692418,suspender_shorts,0,2443 472424,skull_and_crossbones,0,2442 470441,plastic_bag,0,2441 410525,typo,0,2441 473040,tombstone,0,2439 374379,hairy,0,2438 542457,2015,0,2435 11943,zero_suit,0,2433 433704,super_saiyan,0,2428 1409556,ooarai_military_uniform,0,2427 409526,thigh_sex,0,2426 816551,jackal_ears,0,2425 1515357,orange_theme,0,2424 1403936,bodysuit_under_clothes,0,2424 1827216,cube_hair_ornament,0,2422 375083,tuxedo,0,2421 2316,carpet,0,2421 701781,calendar_(medium),0,2420 637971,heart_tattoo,0,2420 468923,screaming,0,2419 13265,cum_on_boy,0,2417 11717,mohawk,0,2417 647581,look-alike,0,2416 1322421,bandaid_on_cheek,0,2416 3917,toothbrush,0,2415 486062,on_lap,0,2414 570082,swirl_lollipop,0,2414 572679,rubbing_eyes,0,2414 462454,small_penis,0,2412 1141250,black_fur,0,2412 469020,rice_bowl,0,2410 523488,flower_pot,0,2410 6339,dusk,0,2409 1274212,green_cape,0,2409 617471,female_pov,0,2408 375037,striped_socks,0,2408 1257075,number_tattoo,0,2406 1434570,hadanugi_dousa,0,2405 691977,egyptian_clothes,0,2405 7867,orc,0,2401 418007,o3o,0,2397 662701,brown_ribbon,0,2397 514768,grey_sky,0,2395 505860,tally,0,2394 390347,flask,0,2391 1161082,2017,0,2391 242,princess,0,2389 862221,club_(weapon),0,2388 404541,hexagram,0,2388 1791866,holding_smoking_pipe,0,2388 645791,patterned_background,0,2386 445826,phimosis,0,2386 601821,nape,0,2386 423600,bit_gag,0,2385 512534,squirrel_girl,0,2383 569544,condom_on_penis,0,2382 1673151,uneven_sleeves,0,2382 1835734,red_one-piece_swimsuit,0,2380 1410099,purple_sleeves,0,2380 1437301,black_bag,0,2379 375272,evening_gown,0,2371 573757,beach_towel,0,2370 643796,power_symbol,0,2368 510070,:i,0,2368 407322,smell,0,2367 587435,side_cutout,0,2366 580695,big_belly,0,2366 13691,bomber_jacket,0,2365 1304570,cat_ear_headphones,0,2364 556491,convenient_arm,0,2363 669792,futa_with_male,0,2362 453104,cameo,0,2357 400905,talisman,0,2355 623327,creator_connection,0,2354 1513962,tam_o'_shanter,0,2353 396063,asphyxiation,0,2352 459289,blue_lips,0,2352 1268355,carrot_necklace,0,2352 528325,red_pantyhose,0,2347 413782,d-pad,0,2340 297185,drumsticks,0,2339 493298,police_hat,0,2339 419192,monkey_tail,0,2339 456655,broken_glass,0,2339 440796,triangle,0,2338 479397,white_headband,0,2336 3874,sink,0,2335 474252,path,0,2335 382047,sunrise,0,2335 1484339,diagonal-striped_bow,0,2333 455175,smiley_face,0,2331 676653,mouse_(computer),0,2331 1300235,purple_pants,0,2329 513424,millipen_(medium),0,2329 613439,roman_numeral,0,2328 1227710,tail_through_clothes,0,2327 1695986,tied_up_(nonsexual),0,2326 622943,cover_image,0,2326 484578,disembodied_head,0,2324 517255,plum_blossoms,0,2324 1373030,pink-framed_eyewear,0,2319 776463,enpera,0,2318 395568,cyberpunk,0,2316 1551114,covered_abs,0,2316 390096,hill,0,2314 494832,faux_traditional_media,0,2311 480812,steam_censor,0,2309 699975,shoulder_spikes,0,2309 1441884,rectangular_eyewear,0,2307 673610,green_choker,0,2307 526974,coffee_cup,0,2304 377197,desert,0,2303 3054,curry,0,2301 1631337,shimakaze_(kancolle)_(cosplay),0,2301 12396,ice_cube,0,2299 425654,beak,0,2297 1505132,russian_text,0,2297 398959,trick_or_treat,0,2295 1407477,black_scrunchie,0,2295 380573,quill,0,2294 589610,spider_web_print,0,2294 16614,diadem,0,2294 4381,ufo,0,2291 426146,fur_coat,0,2290 1787327,shuuchiin_academy_school_uniform,0,2290 631055,tree_shade,0,2288 502177,bird_girl,0,2288 1396985,holding_sheath,0,2287 578084,black_tail,0,2287 393321,scratches,0,2287 466898,strangling,0,2286 700189,finger_in_another's_mouth,0,2286 665350,microdress,0,2285 189096,anal_fingering,0,2284 634002,matching_outfit,0,2284 488880,fourth_wall,0,2284 426384,clitoral_hood,0,2282 8371,hamster,0,2282 1343573,holding_arrow,0,2279 856710,neck_tattoo,0,2279 2909,cyclops,0,2273 498627,short_sword,0,2272 410605,shared_scarf,0,2270 581705,sitting_on_desk,0,2270 410379,praying,0,2268 13606,cervix,0,2268 506562,tri_tails,0,2264 222208,eraser,0,2262 1321704,string_of_flags,0,2262 160066,driving,0,2261 11486,jellyfish,0,2261 419919,diving_mask,0,2261 1528617,retrofit_(azur_lane),0,2261 435180,bad_hands,0,2260 1853,tribadism,0,2257 666137,aqua_bikini,0,2257 462978,??,0,2257 575698,torn_cape,0,2255 613216,multicolored_legwear,0,2254 1312065,chinese_knot,0,2254 1320347,octarian,0,2254 401614,tennis_uniform,0,2253 554120,dirty_face,0,2251 1637933,evolutionary_line,0,2251 506895,ankle_cuffs,0,2250 1392360,black_camisole,0,2248 1533601,sideless_outfit,0,2246 673694,large_tail,0,2246 1262143,phone_screen,0,2246 5871,restaurant,0,2244 539295,feather_hair,0,2244 16335,spatula,0,2243 453895,mechanical_parts,0,2243 613940,white_cat,0,2242 1793456,cooperative_fellatio,0,2241 8721,breast_bondage,0,2241 1376935,blue_ascot,0,2240 490993,colored_tongue,0,2240 1430633,two-tone_bikini,0,2240 399776,torch,0,2240 400119,melting,0,2239 381796,satchel,0,2239 1524489,looking_at_object,0,2238 534340,calendar_(object),0,2238 384086,jet,0,2234 854287,ribbed_legwear,0,2231 543351,crossed_bandaids,0,2231 548910,chain_necklace,0,2230 109323,cliff,0,2230 1252969,white_nails,0,2230 1342770,strappy_heels,0,2227 844443,red_umbrella,0,2225 564233,hand_on_hilt,0,2221 1514281,shark_hair_ornament,0,2221 539390,insignia,0,2220 410599,crotchless_panties,0,2217 423171,prayer_beads,0,2214 1286664,kabedon,0,2214 490692,shading_eyes,0,2213 434965,studded_belt,0,2213 1321301,purple_shorts,0,2212 1391834,feather-trimmed_sleeves,0,2212 1570687,starter_pokemon_trio,0,2212 640820,spoken_anger_vein,0,2211 457376,milk_bottle,0,2211 1276335,fur-trimmed_legwear,0,2210 550560,oni_mask,0,2210 703219,multicolored_wings,0,2205 615392,hair_horns,0,2205 14927,dessert,0,2204 1577661,dynamax_band,0,2204 3139,samurai,0,2203 493584,striped_pants,0,2203 379208,smelling,0,2202 466731,ear_tag,0,2200 1443349,blue_cardigan,0,2200 144057,harem_outfit,0,2200 381887,caught,0,2199 1513986,creature_and_personification,0,2198 712510,bare_hips,0,2197 1452471,hikarizaka_private_high_school_uniform,0,2196 539585,sleeping_upright,0,2196 379758,cave,0,2196 479466,fine_art_parody,0,2195 444189,arabian_clothes,0,2195 1402052,white_cloak,0,2195 1304064,black_apron,0,2195 455722,curtain_grab,0,2192 381727,open_skirt,0,2191 491230,horseback_riding,0,2190 1422860,two-tone_swimsuit,0,2190 427930,liquid,0,2189 1447394,whistle_around_neck,0,2188 1447450,d-pad_hair_ornament,0,2187 638777,ritual_baton,0,2186 87501,wire,0,2185 480182,keyboard_(instrument),0,2185 950553,yoga_pants,0,2185 687728,hands_on_own_head,0,2184 664440,lace-trimmed_dress,0,2183 543455,bolt_action,0,2182 458230,strapless_bra,0,2181 423438,racket,0,2181 458132,winged_hat,0,2180 514239,holding_condom,0,2180 482447,tailcoat,0,2180 1098217,drawing_tablet,0,2177 1399795,no_eyewear,0,2173 540767,abstract_background,0,2172 1297466,pulled_by_another,0,2171 488260,purple_cape,0,2171 501554,sweater_around_waist,0,2168 595850,hooded_cape,0,2167 513765,broken_horn,0,2164 633435,magical_musket,0,2162 1243347,mega_pokemon,0,2161 8279,church,0,2160 572264,gold_chain,0,2160 1344680,holding_doll,0,2159 379555,graffiti,0,2159 519892,naked_coat,0,2158 486210,tie_clip,0,2158 433619,dreadlocks,0,2157 1076181,flower_(symbol),0,2157 10861,town,0,2156 9032,attack,0,2155 519804,black_lips,0,2153 839099,green_lips,0,2152 482936,long_pointy_ears,0,2150 1609053,twitching_penis,0,2149 438849,pill,0,2149 478965,light_bulb,0,2148 457115,when_you_see_it,0,2147 453756,;3,0,2147 405207,arch,0,2145 644564,butterfly_print,0,2145 683211,carrying_over_shoulder,0,2141 467696,black_rose,0,2139 389161,french_fries,0,2139 427771,omelet,0,2138 637281,mechanical_legs,0,2137 1446770,instant_loss,0,2136 2281,festival,0,2135 1280102,single_hair_intake,0,2135 1411065,anzio_school_uniform,0,2135 403072,rocket_launcher,0,2131 1338775,patterned_clothing,0,2126 425236,pulling,0,2124 1315557,grey_bow,0,2123 553947,>_o,0,2122 644284,holding_sign,0,2121 530881,spilling,0,2120 721674,purple_scarf,0,2119 713394,hand_on_another's_thigh,0,2118 1448342,two-tone_gloves,0,2118 7728,tankini,0,2116 442279,vial,0,2115 1411023,st._gloriana's_military_uniform,0,2114 560724,belly_chain,0,2113 635980,impossible_leotard,0,2112 430139,single_vertical_stripe,0,2112 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417809,denim_skirt,0,2084 561087,covering_nipples,0,2083 1420004,blur_censor,0,2082 1594627,monocle_hair_ornament,0,2081 648389,spread_fingers,0,2079 448292,porkpie_hat,0,2078 1350232,orange_shorts,0,2074 667237,oversized_animal,0,2073 661191,aqua_skirt,0,2072 669534,clothes_down,0,2070 435549,veranda,0,2069 442051,dust,0,2069 1769934,finger_in_own_mouth,0,2068 541927,manga_(object),0,2068 402726,spanked,0,2067 482791,tusks,0,2067 1338653,fur_scarf,0,2066 521817,black_feathers,0,2065 700045,stray_pubic_hair,0,2065 1289190,vertical-striped_skirt,0,2063 1269637,hands_on_headwear,0,2061 488487,ankle_socks,0,2061 389909,potato_chips,0,2061 631042,pool_ladder,0,2058 1307801,feather_earrings,0,2057 460050,sleep_molestation,0,2055 473703,car_interior,0,2050 405548,tail_bell,0,2050 519626,on_table,0,2050 12284,gown,0,2049 1394292,blue_serafuku,0,2048 527651,naked_sheet,0,2048 879036,false_smile,0,2048 6267,whale,0,2048 3993,mop,0,2043 1277599,ribbon-trimmed_skirt,0,2043 491714,santa_dress,0,2042 1257581,bowl_hat,0,2042 699964,pocky_day,0,2042 448198,shako_cap,0,2041 589583,kappougi,0,2040 1370858,mechanical_horns,0,2039 1453261,low_twin_braids,0,2038 378768,dreaming,0,2037 1782786,median_furrow,0,2033 447322,korean_clothes,0,2031 381083,sketchbook,0,2031 1327736,purple_coat,0,2030 653262,back_tattoo,0,2029 439517,nose_bubble,0,2026 2130,tanuki,0,2026 541092,tegaki,0,2026 419258,child_on_child,0,2023 466441,sweet_potato,0,2023 104991,have_to_pee,0,2022 1254442,scissor_blade,0,2022 513425,nib_pen_(medium),0,2022 287085,boxing_gloves,0,2017 1389873,orange_kimono,0,2017 8235,shuriken,0,2016 1465099,black_sports_bra,0,2016 477241,birthday_cake,0,2014 615345,bunny_pose,0,2014 1349485,clover_hair_ornament,0,2012 784913,wife_and_wife,0,2012 581551,covered_face,0,2012 10884,crotch_rub,0,2010 385780,high_contrast,0,2005 1343930,black_hakama,0,2005 1391600,borrowed_garments,0,2004 711978,clothed_animal,0,2003 453979,gusset,0,2002 1282702,wooden_bucket,0,2002 460720,wind_chime,0,2002 555866,pillow_grab,0,2002 1233843,impossible_bodysuit,0,2002 686220,pink_sleeves,0,2002 1279634,mating_press,0,2000 576093,hands_on_lap,0,1999 511515,grimace,0,1998 5114,turtle,0,1997 495234,inflatable_toy,0,1997 396726,wreath,0,1997 6327,afro,0,1996 551169,transparent_umbrella,0,1993 1721304,dyed_bangs,0,1992 585,taiyaki,0,1991 661899,glowing_sword,0,1991 521722,nengajou,0,1991 118742,team_rocket,0,1991 454425,juice_box,0,1989 493037,drawer,0,1989 380358,setsubun,0,1989 1743,baseball,0,1984 450205,prehensile_hair,0,1984 580340,picture_(object),0,1983 408804,bad_proportions,0,1982 1618015,slime_(creature),0,1982 549041,vocaloid_append,0,1982 1231742,three-dimensional_maneuver_gear,0,1982 702226,holding_cat,0,1981 717296,hand_in_another's_hair,0,1981 416114,symmetry,0,1979 470701,cube,0,1978 559145,school_hat,0,1977 409034,bite_mark,0,1976 403942,earpiece,0,1974 4510,crepe,0,1973 416356,corpse,0,1973 662965,tokkuri,0,1972 461237,skewer,0,1972 379744,counter,0,1972 1468298,beamed_sixteenth_notes,0,1971 43623,scepter,0,1971 10638,boxers,0,1970 2560,wrestling,0,1970 1360724,brown_cape,0,1970 175633,comb,0,1969 1372686,shark_hood,0,1968 1427258,holding_sack,0,1967 1463294,holding_controller,0,1967 1374594,grey-framed_eyewear,0,1964 1343802,holding_basket,0,1962 516012,clothed_masturbation,0,1962 516930,hanfu,0,1959 535055,warship,0,1959 549629,ankle_lace-up,0,1955 699142,diamond-shaped_pupils,0,1954 589659,in_bucket,0,1952 1770614,green_gemstone,0,1951 1204400,playing_with_own_hair,0,1951 558703,cheek_pinching,0,1949 488,flute,0,1949 408254,tripping,0,1948 494375,shared_umbrella,0,1948 416493,red_sky,0,1948 646637,cocktail_glass,0,1948 421443,spinning,0,1947 419280,between_thighs,0,1947 1240137,swim_briefs,0,1947 558384,on_grass,0,1944 1667144,mod3_(girls'_frontline),0,1944 552818,parted_hair,0,1942 1637,failure,0,1941 473206,grey_socks,0,1940 378083,barrel,0,1940 666929,light_trail,0,1939 1392029,multicolored_bodysuit,0,1937 502382,hyur,0,1937 422427,sports_bikini,0,1935 465416,sleeveless_sweater,0,1935 1456557,hair_through_headwear,0,1931 608539,column_lineup,0,1930 574719,multicolored_swimsuit,0,1930 657424,candlestand,0,1928 383903,vending_machine,0,1927 498946,turnaround,0,1926 1330708,purple_sweater,0,1926 422821,safety_pin,0,1925 414746,spider_girl,0,1925 1334708,holding_paintbrush,0,1925 499,toast,0,1924 393832,impregnation,0,1924 374542,daisy,0,1923 477184,large_hat,0,1923 498823,burning,0,1923 550621,head_back,0,1922 600284,cheek_poking,0,1921 605455,volleyball_uniform,0,1920 1227460,hands_on_another's_head,0,1919 514943,torn_shorts,0,1919 5001,squid,0,1915 1691759,censored_nipples,0,1914 4864,lion,0,1913 1435804,bisexual_female,0,1913 616023,aqua_dress,0,1913 1442709,fewer_digits,0,1913 377200,balcony,0,1912 604468,eye_mask,0,1912 565089,caustics,0,1911 1258088,ribbon-trimmed_clothes,0,1911 1499947,looking_over_eyewear,0,1911 1314744,yellow_choker,0,1910 541224,heart_hands_duo,0,1910 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437999,full_nelson,0,1629 444634,torn,0,1629 403915,wheel,0,1628 2232,tarot,0,1627 478821,teasing,0,1627 539771,incoming_gift,0,1627 6303,naginata,0,1627 1623670,diamond_button,0,1626 1373026,green-framed_eyewear,0,1623 610198,leotard_pull,0,1622 660019,color_connection,0,1620 462556,cuddling,0,1620 1496652,gold_hairband,0,1619 599839,horned_helmet,0,1618 1434344,pencil_dress,0,1618 1402835,scene_reference,0,1617 464552,cucumber,0,1616 1530483,elite_ii_(arknights),0,1616 8989,viera,0,1612 540139,different_reflection,0,1612 1088997,bear_hair_ornament,0,1612 667586,single_pauldron,0,1612 815986,fortissimo,0,1612 1400382,barbell_piercing,0,1611 544708,fish_girl,0,1611 454517,unconscious,0,1610 574422,claw_(weapon),0,1609 379311,chandelier,0,1608 377993,petting,0,1608 450208,chocolate_bar,0,1607 713479,2014,0,1607 476495,dowsing_rod,0,1607 469396,role_reversal,0,1605 375381,hair_in_mouth,0,1602 565136,projected_inset,0,1601 664546,taut_dress,0,1599 692799,choko_(cup),0,1598 613128,embellished_costume,0,1596 1379633,fff_threesome,0,1595 457418,fighter_jet,0,1595 442863,heckler_&_koch,0,1594 393579,mechanical_pencil,0,1593 514889,polo_shirt,0,1593 508957,health_bar,0,1593 684621,puffy_detached_sleeves,0,1593 700119,cropped_vest,0,1592 421489,ushanka,0,1591 403478,suppressor,0,1590 411198,leg_hold,0,1589 1396796,green_neckerchief,0,1589 1260991,pink_pants,0,1588 669791,futa_with_futa,0,1588 723472,stiletto_heels,0,1588 1274842,two-handed,0,1587 412925,cupless_bra,0,1587 642312,handsfree_ejaculation,0,1586 1598810,nontraditional_playboy_bunny,0,1586 1403286,black_mask,0,1586 412357,lounge_chair,0,1585 664263,bikini_top_removed,0,1585 430449,spine,0,1585 442852,old_woman,0,1585 1439570,tall_female,0,1585 406711,strap-on,0,1584 1342607,holding_lantern,0,1584 512488,crotch_plate,0,1583 679286,faux_figurine,0,1582 12083,graveyard,0,1582 668887,shallow_water,0,1580 1362227,holding_stick,0,1580 469368,striped_gloves,0,1579 683983,poke_ball_print,0,1579 1515352,aqua_theme,0,1578 388093,hakurei_reimu_(cosplay),0,1578 600210,perineum,0,1578 452130,neon_lights,0,1578 492105,groin_tendon,0,1577 395436,reindeer_costume,0,1577 721109,visible_air,0,1577 592444,mars_symbol,0,1576 399999,whipped_cream,0,1575 452157,hagoita,0,1575 759492,wolf_boy,0,1575 538746,lipstick_tube,0,1574 542357,red_cross,0,1573 405026,large_insertion,0,1573 410111,bowing,0,1570 480324,staff_(music),0,1570 495020,crazy_smile,0,1570 397693,shikigami,0,1569 703456,sorcerer's_sutra_scroll,0,1569 1426454,holding_clipboard,0,1568 1505130,german_text,0,1567 391475,fireflies,0,1567 29555,office,0,1567 451262,notepad,0,1566 1440623,jacket_partially_removed,0,1566 1514129,sunburst_background,0,1565 545758,wisteria,0,1563 550778,checkerboard_cookie,0,1563 1517305,presenting_armpit,0,1563 582361,head_between_breasts,0,1562 1374895,spiked_tail,0,1561 660754,aqua_shirt,0,1560 537783,bamboo_broom,0,1559 1781164,gem_(symbol),0,1559 1296314,single_gauntlet,0,1558 15578,eagle,0,1557 1240006,bishamonten's_pagoda,0,1554 485203,video_game,0,1553 585862,reach-around,0,1553 617315,pantyhose_under_shorts,0,1552 1438860,black_sash,0,1551 699609,frilled_cuffs,0,1550 16279,air_conditioner,0,1549 657500,fish_hair_ornament,0,1548 892083,green_apron,0,1545 2812,ramune,0,1545 1314693,grey_scarf,0,1545 1505167,simplified_chinese_text,0,1544 473599,fingers,0,1543 9781,pacifier,0,1543 585009,sword_over_shoulder,0,1542 399875,beach_chair,0,1541 1296346,stationary_restraints,0,1540 501344,fallen_down,0,1539 1274924,hand_on_another's_ass,0,1539 671484,stuffed_shark,0,1539 632854,naked_hoodie,0,1539 1557024,mouth_veil,0,1539 8775,mahjong,0,1538 1464685,collared_cape,0,1537 394989,hexagon,0,1537 476602,kusazuri,0,1537 590941,santa_boots,0,1536 378038,pentacle,0,1535 693272,tan_background,0,1535 1373024,white-framed_eyewear,0,1534 1775923,circled_9,0,1534 467745,japanese_flag,0,1533 453530,power_suit,0,1532 1665195,reference_inset,0,1531 1781330,nijigasaki_academy_school_uniform,0,1531 607070,caressing_testicles,0,1529 1247633,narrowed_eyes,0,1529 1791857,painting_(action),0,1528 633053,hooded_sweater,0,1527 1835746,pink_one-piece_swimsuit,0,1526 440361,lipstick_mark,0,1526 650139,clothed_female_nude_female,0,1526 1258091,ribbon-trimmed_collar,0,1526 524796,age_progression,0,1525 1558733,tulip_hat,0,1525 3489,dinosaur,0,1523 598269,kissing_forehead,0,1523 1328687,earth_(ornament),0,1523 420294,yellow_skin,0,1522 1333530,grey_fur,0,1521 714422,sports_car,0,1520 664350,tail_censor,0,1520 9587,love_letter,0,1520 724196,thigh_pouch,0,1520 1265860,pink_blood,0,1519 482956,caution_tape,0,1518 415621,blueberry,0,1518 712801,pussy_piercing,0,1517 472861,ovum,0,1517 478228,title,0,1515 503126,ribbon_in_mouth,0,1514 374573,centaur,0,1514 10562,truck,0,1513 628220,claw_ring,0,1511 541729,rotational_symmetry,0,1510 1333955,side-tie_leotard,0,1510 1298471,blood_from_eyes,0,1509 1424925,holding_needle,0,1509 1320208,grey_necktie,0,1509 1452545,kibito_high_school_uniform,0,1509 656250,arm_between_breasts,0,1506 629486,used_tissue,0,1504 1346802,spiked_shell,0,1503 1678267,palette_(object),0,1502 485953,puffy_pants,0,1502 1446284,looking_at_animal,0,1502 615984,bendy_straw,0,1502 1560,mascot,0,1500 1424211,sakugawa_school_uniform,0,1500 16695,magnifying_glass,0,1499 1482386,tiger_boy,0,1499 1923,truth,0,1498 528496,holding_shoes,0,1498 415310,overgrown,0,1498 712381,thigh_cutout,0,1498 1298573,sanshoku_dango,0,1496 416448,yagasuri,0,1495 1672391,viewer_holding_leash,0,1495 1450243,weibo_logo,0,1494 635465,twitching,0,1493 452581,vibrator_under_panties,0,1491 414700,buttjob,0,1491 607798,arm_wrap,0,1491 1859633,a_certain_high_school_uniform,0,1490 362945,garden,0,1488 399302,drying,0,1487 593466,flame_print,0,1487 1179281,bear_girl,0,1487 1323483,fangs_out,0,1486 1396969,holding_innertube,0,1485 453719,plant_girl,0,1484 1390886,brown_kimono,0,1484 180889,morning,0,1483 16135,cotton_candy,0,1483 376147,hanging,0,1482 460511,hand_puppet,0,1481 437614,boned_meat,0,1481 714136,hand_on_another's_stomach,0,1481 1234797,foot_up,0,1478 397906,party_hat,0,1478 709214,alternate_skin_color,0,1478 520939,wiping_tears,0,1477 655903,fake_facial_hair,0,1477 1373018,striped_kimono,0,1477 10125,halberd,0,1477 461720,buzz_cut,0,1477 1637102,doughnut_hair_bun,0,1476 465754,spanking,0,1476 544204,bowl_cut,0,1476 705551,pixiv_id,0,1476 475794,column,0,1475 699449,raimon,0,1474 8918,inflation,0,1473 516011,playstation_portable,0,1472 492764,heart_tail,0,1472 14637,tight_shirt,0,1471 394771,utility_belt,0,1471 544443,penis_out,0,1470 387935,spirit,0,1470 421056,spiked_club,0,1468 466411,invisible_penis,0,1467 1568979,plaid_headwear,0,1467 617246,leather_belt,0,1467 1325039,oral_invitation,0,1467 434522,gym_storeroom,0,1465 1747766,lapels,0,1464 625484,glitch,0,1464 405444,helicopter,0,1463 533100,sitting_on_object,0,1462 1609998,after_ejaculation,0,1462 1411201,kiyosumi_school_uniform,0,1462 1305007,chest_belt,0,1461 590857,okamisty,0,1461 670994,chocolate_on_body,0,1460 1240596,mole_on_neck,0,1460 389933,harem_pants,0,1459 376486,skyline,0,1459 1495030,red-tinted_eyewear,0,1459 628115,open_window,0,1458 14860,chef,0,1458 480571,mechanization,0,1458 662800,fake_antlers,0,1458 9590,jersey,0,1458 637922,2013,0,1457 656303,shirt_in_mouth,0,1456 1441520,horns_through_headwear,0,1456 398229,people,0,1455 381954,untying,0,1454 483333,pavement,0,1454 392935,tree_stump,0,1453 410544,handkerchief,0,1452 1257314,idol_clothes,0,1452 622601,window_shade,0,1451 493565,timestamp,0,1451 418781,pet_play,0,1450 399061,camcorder,0,1449 595751,ass_cutout,0,1449 1605421,halloween_bucket,0,1449 580394,dirndl,0,1447 1227348,back-seamed_legwear,0,1445 642015,expressive_hair,0,1445 472201,baguette,0,1444 439406,shiba_inu,0,1444 1378645,constellation_print,0,1444 535,molestation,0,1442 1373313,excessive_pubic_hair,0,1442 11754,neck,0,1442 1317604,panty_straps,0,1442 1476167,holding_handheld_game_console,0,1442 556841,sitting_on_stairs,0,1441 504308,redesign,0,1440 1549885,crane_(machine),0,1439 974780,stomach_cutout,0,1439 1267456,holding_leaf,0,1438 389048,donation_box,0,1437 523847,omurice,0,1436 426749,2012,0,1435 1378370,black_buruma,0,1433 470025,open_pants,0,1432 1279772,yellow_wings,0,1432 473475,orange_thighhighs,0,1431 547320,excessive_cum,0,1431 478325,2010,0,1431 399246,stadium,0,1430 658254,holding_helmet,0,1430 1507608,h&k_hk416,0,1430 1279653,holding_brush,0,1429 451782,brushing_teeth,0,1427 461401,treasure_chest,0,1427 494781,makizushi,0,1427 1441880,hand_on_eyewear,0,1427 565996,goat_ears,0,1427 414107,gamepad,0,1426 1602480,index_fingers_together,0,1425 405498,panda_ears,0,1424 162717,triforce,0,1424 431374,curtsey,0,1423 479141,raised_fist,0,1422 526532,oversized_shirt,0,1422 1397563,fur-trimmed_skirt,0,1422 629378,ruffling_hair,0,1422 1321158,brown_necktie,0,1422 1442004,eyewear_on_headwear,0,1421 380988,goblin,0,1417 1374287,ribbed_bodysuit,0,1417 673730,palm_leaf,0,1416 1518970,pectoral_grab,0,1416 646260,pointless_condom,0,1414 421385,kanji,0,1414 381861,\o/,0,1411 1414389,pink_sailor_collar,0,1409 8234,radio,0,1408 545201,micro_panties,0,1407 587990,bodice,0,1407 500820,stone_lantern,0,1406 1422826,sangvis_ferri,0,1406 384435,onmyouji,0,1405 1285391,turtleneck_dress,0,1405 1667891,pom_pom_hair_ornament,0,1404 467946,coca-cola,0,1403 12086,globe,0,1403 1163924,blue_fur,0,1403 539536,blue_headband,0,1401 494162,monkey_ears,0,1399 1478255,dangle_earrings,0,1397 665766,carrying_under_arm,0,1396 501337,39,0,1396 594236,expressive_clothes,0,1394 662045,lotion_bottle,0,1394 534991,pillbox_hat,0,1394 379633,fertilization,0,1393 460870,spell_card,0,1392 536429,cupping_hands,0,1392 400656,red_hood,0,1392 431361,baseball_mitt,0,1391 1598686,male_playboy_bunny,0,1389 1835740,purple_one-piece_swimsuit,0,1388 3197,takoyaki,0,1387 508969,cow_boy,0,1387 380,priest,0,1386 1413926,orange_sailor_collar,0,1386 497917,thrusters,0,1386 525748,dakimakura_(object),0,1384 420884,countdown,0,1384 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1332978,short_jumpsuit,0,1366 16601,overcoat,0,1365 5323,kettle,0,1364 1624910,sidepec,0,1364 473249,print_bra,0,1363 1409636,furrification,0,1363 723185,stone_floor,0,1362 389374,cat_teaser,0,1361 395712,christmas_lights,0,1361 384969,intravenous_drip,0,1360 424974,no_testicles,0,1358 481054,grey_bra,0,1358 1454017,debt,0,1358 991822,blue_eyeshadow,0,1356 14704,nike,0,1355 419304,crosswalk,0,1355 659938,poker_chip,0,1355 494704,2011,0,1355 572582,masturbation_through_clothes,0,1354 1344701,grey_border,0,1354 649700,full-package_futanari,0,1353 8411,sheet_music,0,1353 683789,wooden_table,0,1351 1288040,incoming_food,0,1351 1249322,hauchiwa,0,1349 1282898,cloth_gag,0,1349 394997,sweatpants,0,1349 1515355,black_theme,0,1348 1455250,chest_strap,0,1348 449943,stab,0,1347 389647,clock_tower,0,1346 1665347,full-body_tattoo,0,1345 1249520,drawing_bow,0,1344 8617,potion,0,1344 680046,carrying_person,0,1344 563716,italian_flag,0,1342 374948,school_briefcase,0,1341 1373027,brown-framed_eyewear,0,1341 1268086,pink_bag,0,1341 593696,leather_gloves,0,1340 903375,flip_phone,0,1339 635016,broken_chain,0,1339 548895,frilled_socks,0,1338 594785,blowing_kiss,0,1338 405,soccer,0,1337 468165,mixing_bowl,0,1337 395851,log,0,1336 1454154,black_pubic_hair,0,1336 375527,kimono_skirt,0,1336 562826,slapping,0,1334 4577,snorkel,0,1334 587202,shoelaces,0,1333 9355,humiliation,0,1333 2839,lube,0,1333 1608327,mixed-language_text,0,1333 626479,orange_slice,0,1333 585203,veiny_breasts,0,1333 1761435,onee-loli,0,1332 500152,breathing_fire,0,1332 559601,cheering,0,1331 505317,lily_of_the_valley,0,1331 1287296,diving_mask_on_head,0,1330 473312,camellia,0,1329 3408,origami,0,1329 410102,yes,0,1328 621930,mandarin_collar,0,1328 1276269,ribbed_sleeves,0,1327 1406765,jaguar_ears,0,1327 408186,frottage,0,1326 565797,tropical_drink,0,1326 705858,partially_underwater_shot,0,1326 624870,imminent_fellatio,0,1326 1584543,pokedex_number,0,1326 600401,wooden_fence,0,1325 1317361,multicolored_bow,0,1325 1396461,brown_hairband,0,1323 1387512,orange_ascot,0,1323 413721,latex_gloves,0,1322 457946,stone_wall,0,1321 1402473,blue_sash,0,1320 1578780,diagonal-striped_necktie,0,1320 496388,._.,0,1317 1354736,green_flower,0,1316 9396,april_fools,0,1316 568512,bird_on_shoulder,0,1315 1719547,sailor_moon_redraw_challenge_(meme),0,1314 11756,cup_ramen,0,1313 573617,food-themed_clothes,0,1313 564677,playstation_controller,0,1313 61654,tail_grab,0,1313 486968,red_bandana,0,1313 639779,mechanical_tail,0,1312 393602,severed_head,0,1311 58241,platform_heels,0,1311 482510,plaid_panties,0,1309 722706,jojo_pose,0,1309 1793460,cooperative_paizuri,0,1308 525108,hands_on_feet,0,1307 8803,cleaning,0,1307 843166,yellow_hoodie,0,1307 609486,single_strap,0,1307 539990,tail_between_legs,0,1306 1365638,bandaid_on_hand,0,1306 473470,wrist_guards,0,1305 9628,scooter,0,1304 1314748,pink_collar,0,1304 1422416,twitter_logo,0,1304 1780,cow,0,1303 1409793,grey_kimono,0,1303 546044,sleeveless_hoodie,0,1302 698497,gloves_removed,0,1301 563461,full-length_zipper,0,1301 1262922,head_on_another's_shoulder,0,1301 1383769,dot_mouth,0,1300 395433,traditional_clothes,0,1300 668383,lace-trimmed_sleeves,0,1300 1259138,mole_on_ass,0,1300 1410489,shower_(place),0,1299 477993,bad_perspective,0,1298 391175,stove,0,1298 424623,unitard,0,1298 825568,shortstack,0,1298 392839,torn_panties,0,1297 661926,ankle_strap,0,1296 1574672,two-sided_cape,0,1295 16550,stream,0,1294 12727,phonograph,0,1293 10399,x,0,1293 15305,steampunk,0,1293 488193,slap_mark,0,1293 543247,eyebrow_piercing,0,1293 416904,cat_on_head,0,1292 612104,respirator,0,1292 963327,yordle,0,1292 592457,nippleless_clothes,0,1291 389021,deer,0,1290 388798,hatsune_miku_(cosplay),0,1289 1606641,black_male_underwear,0,1288 595244,hamaya,0,1286 444567,tail_wrap,0,1286 524686,hand_net,0,1285 511641,single_pantsleg,0,1285 1476395,pokemon_on_shoulder,0,1284 380213,sponge,0,1283 1504221,purple_tail,0,1283 1318470,yellow_pupils,0,1282 1601080,animal_ear_legwear,0,1282 431872,balancing,0,1281 629555,desk_lamp,0,1280 1475076,blue_horns,0,1278 686531,undone_necktie,0,1277 477355,chainmail,0,1277 695246,hakama_pants,0,1276 1414657,purple_capelet,0,1275 531186,gold_armor,0,1274 473618,clitoris_piercing,0,1274 433212,bookmark,0,1274 15307,werewolf,0,1274 374909,whispering,0,1272 518423,black_leggings,0,1271 637282,pants_rolled_up,0,1269 690442,boots_removed,0,1269 992674,emoji,0,1269 695232,mismatched_pubic_hair,0,1268 492677,bolo_tie,0,1267 534114,cocktail_dress,0,1267 1507443,sidelighting,0,1266 410474,picnic_basket,0,1265 1554055,fiery_horns,0,1265 11623,bad_end,0,1263 1238376,multicolored_shirt,0,1263 8500,eggplant,0,1263 1325030,new_school_swimsuit,0,1263 399269,tablecloth,0,1262 1400049,on_bench,0,1262 685325,shiny_legwear,0,1261 1586950,sobu_high_school_uniform,0,1261 628271,turning_head,0,1260 1392009,green_bodysuit,0,1260 457562,fake_mustache,0,1259 1565319,power_suit_(metroid),0,1258 652288,alphes_(style),0,1258 1713829,mithra_(ff11),0,1257 382610,skateboard,0,1257 1542756,turtleneck_leotard,0,1256 1411229,orange_neckerchief,0,1256 392251,fireplace,0,1256 1394090,see-through_skirt,0,1256 551591,deerstalker,0,1255 415735,sideways,0,1254 414232,cum_on_hands,0,1254 726190,breast_conscious,0,1254 613598,polka_dot_legwear,0,1254 418106,dark_penis,0,1253 4654,flustered,0,1253 666658,grey_bikini,0,1252 711344,hand_on_own_shoulder,0,1252 1237065,sunflower_hair_ornament,0,1251 1304010,pink_pupils,0,1250 702474,ribbed_leotard,0,1250 3334,kigurumi,0,1249 798294,club_(shape),0,1249 1429613,tomoeda_elementary_school_uniform,0,1249 543408,real_world_location,0,1249 1492501,shinda_sekai_sensen_uniform,0,1249 4930,doctor,0,1248 565279,german_flag,0,1248 616223,no_gloves,0,1248 1501189,stitched_face,0,1247 652468,brown_bikini,0,1246 674500,large_testicles,0,1246 1762322,greyscale_with_colored_background,0,1246 507478,aqua_panties,0,1245 726943,pink_coat,0,1245 383146,panties_on_head,0,1245 1274419,spoken_character,0,1244 519211,ryona,0,1244 447287,high_kick,0,1243 9348,wheelchair,0,1243 1413210,blue_collar,0,1243 1824446,lycoris_uniform,0,1243 1349438,animal_bag,0,1242 491661,hat_tip,0,1242 496346,impossible_swimsuit,0,1241 643516,pyrokinesis,0,1241 632853,fake_wings,0,1240 603398,lace-trimmed_skirt,0,1239 1781886,stroking_own_chin,0,1239 383835,runes,0,1239 1492898,green_sleeves,0,1239 443587,sunscreen,0,1238 427020,stepped_on,0,1238 575113,kimono_pull,0,1238 651163,kourindou_tengu_costume,0,1238 583212,fingering_through_clothes,0,1237 492714,curry_rice,0,1237 495137,tulip,0,1236 330013,pie,0,1236 488259,skull_mask,0,1236 11216,soup,0,1235 11603,paper_airplane,0,1235 557999,wiping_face,0,1235 1771225,lord_camelot_(fate),0,1235 522405,kanabou,0,1234 684234,perpendicular_paizuri,0,1234 548540,puckered_anus,0,1234 588173,sex_machine,0,1233 420441,teamwork,0,1232 439204,friends,0,1231 629461,duffel_coat,0,1231 176537,bartender,0,1231 416557,ammunition_belt,0,1230 179157,tent,0,1230 510652,flashback,0,1230 541435,cellphone_picture,0,1227 465875,age_regression,0,1227 600052,butterfly_on_hand,0,1227 518510,dust_cloud,0,1227 1372592,bath_yukata,0,1227 1283900,single_fingerless_glove,0,1226 491621,cheek_bulge,0,1225 609657,animal_hug,0,1225 1501551,sakuramon,0,1225 330372,sausage,0,1224 603173,molten_rock,0,1223 3419,shinai,0,1222 15057,nearly_naked_apron,0,1221 468159,sparkler,0,1220 1403859,3d_background,0,1220 498369,naked_bandage,0,1220 549321,cum_on_penis,0,1219 1271959,short_sidetail,0,1218 434471,angel_and_devil,0,1217 475249,large_wings,0,1217 710257,odd_one_out,0,1217 688960,holding_pillow,0,1216 1835593,ornate_ring,0,1216 509739,burnt_clothes,0,1215 452607,2009,0,1215 1377225,bruise_on_face,0,1215 1856853,asticassia_school_uniform,0,1215 1369179,orange_fur,0,1214 1461344,papakha,0,1214 164246,teruterubouzu,0,1212 408976,bat_ears,0,1212 9574,sick,0,1211 11998,open_robe,0,1211 1410988,pravda_school_uniform,0,1211 1585295,athletic_leotard,0,1209 11273,harp,0,1208 1330230,black_wristband,0,1208 13916,tempura,0,1206 577080,hand_on_lap,0,1206 1388990,two-tone_legwear,0,1205 995808,penis_size_difference,0,1205 476199,striped_sweater,0,1205 2660,lettuce,0,1204 570296,giving_up_the_ghost,0,1204 411779,ankh,0,1204 451184,holographic_interface,0,1203 547391,winged_footwear,0,1203 468977,split_screen,0,1203 660817,opening_door,0,1203 537130,arm_blade,0,1202 513421,acrylic_paint_(medium),0,1202 822829,suspended_congress,0,1201 374515,hawaiian_shirt,0,1201 578544,leg_armor,0,1201 1419859,fur-trimmed_cloak,0,1201 1281997,asymmetrical_horns,0,1201 473050,crate,0,1200 405415,milking_machine,0,1199 10246,wig,0,1199 1361800,bow_earrings,0,1198 518732,anilingus,0,1197 735235,eye_of_horus,0,1197 652086,gathers,0,1196 271102,ladybug,0,1196 5632,laser,0,1195 628425,tiered_tray,0,1195 10428,wading_pool,0,1194 494464,uvula,0,1194 700361,watermelon_bar,0,1194 656386,hands_on_another's_cheeks,0,1194 1392006,yellow_bodysuit,0,1193 685266,sandals_removed,0,1192 472922,inset,0,1191 1627136,excalibur_morgan_(fate),0,1191 389703,ema,0,1190 1342945,behind_another,0,1190 8260,ferris_wheel,0,1189 636872,lizard_tail,0,1189 251194,gym,0,1188 15187,machine,0,1186 5497,fountain,0,1186 515008,cum_on_self,0,1186 1292445,torn_scarf,0,1186 421282,pasta,0,1184 3493,voyeurism,0,1183 891632,artificial_eye,0,1183 671651,hair_ears,0,1183 1349107,candy_hair_ornament,0,1183 1090326,bird_mask,0,1182 1355967,on_vehicle,0,1181 437001,living_clothes,0,1181 664061,grey_ribbon,0,1179 426371,through_wall,0,1179 1534682,aqua_headwear,0,1179 542830,chest_of_drawers,0,1178 658981,open_belt,0,1178 629694,leopard_tail,0,1178 1853184,kamiyama_high_school_uniform_(hyouka),0,1178 6259,park,0,1177 324075,ballerina,0,1177 373823,ketchup,0,1175 1251057,ginkgo_leaf,0,1175 9706,snail,0,1174 1360843,neckwear_grab,0,1174 419773,iphone,0,1174 12011,potato,0,1173 456884,brown_panties,0,1172 511422,newhalf,0,1172 487203,overcast,0,1172 1354455,year_of_the_rat,0,1172 1561175,champion_uniform,0,1172 456589,leather_boots,0,1171 374749,heartbeat,0,1171 541262,disgust,0,1170 1514953,cropped_shoulders,0,1170 1504868,eyebrow_cut,0,1170 504744,load_bearing_vest,0,1169 1230967,rook_(chess),0,1169 433522,cheek_squash,0,1168 648546,belt_boots,0,1168 1457585,hooded_cardigan,0,1168 582353,lace-trimmed_gloves,0,1167 383854,native_american,0,1167 653049,red_eyeliner,0,1166 252960,tengu,0,1166 1494963,orange-tinted_eyewear,0,1166 379968,breast_expansion,0,1166 419074,hitting,0,1165 514538,hands_on_ass,0,1164 708149,blue_armor,0,1164 674293,gift_bag,0,1164 1241321,striped_horns,0,1164 442124,orange_panties,0,1163 1394304,honeycomb_(pattern),0,1163 662440,konohagakure_symbol,0,1163 667190,plate_armor,0,1163 1023384,white_serafuku,0,1161 1506275,riding_pokemon,0,1161 535323,art_brush,0,1160 1605653,utensil_in_mouth,0,1160 494932,hickey,0,1159 626246,crystal_hair,0,1158 574252,mismatched_sleeves,0,1157 1384370,two-tone_hairband,0,1156 660778,knees_apart_feet_together,0,1154 412641,steering_wheel,0,1154 383675,bus_stop,0,1153 1235654,gradient_clothes,0,1153 393169,torn_jeans,0,1153 721796,kesa,0,1153 16114,chalk,0,1152 554447,dark_aura,0,1152 1086828,bow_(music),0,1152 530749,orange_pantyhose,0,1151 404788,wrestling_ring,0,1151 381519,vibrator_in_thighhighs,0,1150 522500,dark_green_hair,0,1150 408474,flashlight,0,1150 456305,pink_pantyhose,0,1150 494923,futa_on_male,0,1150 703702,hooded_track_jacket,0,1150 1399177,brown_cloak,0,1150 1719544,they_had_lots_of_sex_afterwards_(meme),0,1150 553041,flower_necklace,0,1149 211997,battle_axe,0,1149 1379986,alpaca_ears,0,1149 501457,lalafell,0,1148 1405177,purple_belt,0,1146 1469769,grey_sleeves,0,1146 3533,laundry,0,1145 1845182,guiding_hand,0,1145 483079,shards,0,1145 1452744,collared_coat,0,1145 1294981,digitigrade,0,1145 1349162,holding_balloon,0,1144 68505,bikesuit,0,1143 1400652,torpedo_launcher,0,1143 493245,theft,0,1142 509961,battle_rifle,0,1142 724856,low_neckline,0,1142 11370,island,0,1142 1465616,eyewear_strap,0,1142 1682706,phoenix_crown,0,1142 419288,cum_on_feet,0,1141 469760,oven_mitts,0,1141 641957,bishop_(chess),0,1141 1589331,off-shoulder_bikini,0,1141 478077,ready_to_draw,0,1140 374636,unicorn,0,1140 781851,user_interface,0,1139 1509564,holding_game_controller,0,1139 494838,soda_bottle,0,1139 5525,chimney,0,1138 1932,ipod,0,1138 616555,uchikake,0,1138 1479378,silver_trim,0,1138 596634,gradient_legwear,0,1138 659417,mechanical_ears,0,1136 1440480,holding_water_gun,0,1136 397626,guitar_case,0,1136 1473511,petals_on_liquid,0,1136 68500,ruler,0,1134 687207,round_window,0,1134 62962,buruma_pull,0,1133 1810603,back_focus,0,1133 2370,cactus,0,1132 611089,implied_yuri,0,1131 441185,ballet_slippers,0,1131 424682,horse_penis,0,1131 599606,fuuin_no_tsue,0,1129 10326,archery,0,1129 615286,pinching_sleeves,0,1129 1306233,triangle_print,0,1129 615369,blue_sclera,0,1129 10023,toilet_use,0,1128 1259665,bow_choker,0,1128 601192,mechanical_hands,0,1128 1476659,french_text,0,1127 543093,motherly,0,1127 533771,kitchen_knife,0,1127 165653,shirt_grab,0,1127 1314077,dice_hair_ornament,0,1126 529177,ootachi,0,1126 482051,drum_set,0,1125 563204,dumbbell,0,1125 526083,brown_socks,0,1124 682286,title_parody,0,1124 632801,blue_tongue,0,1124 451182,grimoire,0,1123 393428,vaulting_horse,0,1123 1853211,single_hair_ring,0,1122 623242,light_censor,0,1121 1350077,mask_around_neck,0,1121 4861,lighter,0,1120 406035,legs_over_head,0,1120 5884,champagne,0,1120 482391,bad_food,0,1119 432965,red_oni,0,1119 484724,pouty_lips,0,1119 688664,adjusting_legwear,0,1117 1262324,shared_bathing,0,1117 1339894,fishnet_top,0,1117 1345473,ribbon-trimmed_dress,0,1117 12413,coffin,0,1117 1467809,3others,0,1116 1334628,anchor_print,0,1116 10938,reindeer,0,1115 1820926,sleeveless_turtleneck_leotard,0,1114 1277344,soap_censor,0,1113 644571,flock,0,1113 375275,climbing,0,1113 522628,upshorts,0,1113 396796,luggage,0,1113 685558,cat_day,0,1113 16355,sundae,0,1112 607136,split_ponytail,0,1112 471850,cum_in_clothes,0,1111 422468,satin,0,1111 642029,queen_(chess),0,1111 516468,fake_cover,0,1110 877384,hooded_robe,0,1110 1764814,compass_rose_halo,0,1110 705059,fish_print,0,1109 1260944,heart-shaped_lock,0,1109 398497,mouth_pull,0,1108 473421,pink_socks,0,1108 388012,legs_folded,0,1108 472508,drum_(container),0,1108 653485,torn_leotard,0,1108 842878,liquid_hair,0,1108 5563,comparison,0,1107 1399082,heart_button,0,1107 473988,cooler,0,1105 707712,consensual_tentacles,0,1105 413758,ammunition,0,1105 1346569,ass_support,0,1105 618005,pennant,0,1104 1425103,body_freckles,0,1104 652996,salaryman,0,1104 10157,honey,0,1103 723350,weight_conscious,0,1102 657530,spoken_object,0,1101 634911,photo_inset,0,1101 1835748,striped_one-piece_swimsuit,0,1100 724478,pink_wings,0,1100 550954,drawing_sword,0,1100 1262645,yellow_apron,0,1100 557859,puppet_strings,0,1100 668848,multiple_monochrome,0,1099 421120,shell_bikini,0,1099 519382,decepticon,0,1099 463031,anal_hair,0,1098 684567,looking_at_mirror,0,1098 499809,butterfly_net,0,1097 375377,boar,0,1097 1290693,looking_at_breasts,0,1096 416752,strap_lift,0,1096 536575,cheek_press,0,1095 1467811,6+others,0,1095 1607725,stuffed_winged_unicorn,0,1095 652002,no_lineart,0,1094 1409106,head_on_pillow,0,1094 399923,washing,0,1094 497039,test_plugsuit,0,1094 374998,abuse,0,1093 1411111,keizoku_military_uniform,0,1093 566734,tanzaku,0,1092 458295,rising_sun_flag,0,1092 572545,wall_of_text,0,1092 1349919,brown_bowtie,0,1092 1402459,blue_cloak,0,1092 1231611,arrow_through_heart,0,1092 1438305,holding_baseball_bat,0,1091 547746,patterned,0,1091 689483,plaid_jacket,0,1091 414165,cum_on_pussy,0,1090 575592,glowing_wings,0,1090 403486,party_popper,0,1090 661553,grey_belt,0,1089 462342,after_rape,0,1089 1247470,chocolate_on_breasts,0,1089 665720,candy_wrapper,0,1089 614276,bow_legwear,0,1088 27649,phallic_symbol,0,1088 1475838,blue_overalls,0,1088 496608,master_sword,0,1088 541640,button_eyes,0,1088 643517,purple_fire,0,1088 533033,covering_ass,0,1087 482734,bicorne,0,1087 1257558,round_image,0,1087 575714,c:,0,1087 388391,screwdriver,0,1086 419287,swim_cap,0,1085 436891,combat_boots,0,1085 15731,clothes,0,1085 476893,torn_swimsuit,0,1085 507338,fishnet_gloves,0,1085 317,bus,0,1084 598531,dropping,0,1084 499376,wrinkled_skin,0,1084 483095,birthmark,0,1084 1334561,loose_bowtie,0,1083 1193565,jimiko,0,1082 1102042,strawberry_hair_ornament,0,1081 386413,clog_sandals,0,1081 1411060,saunders_military_uniform,0,1080 4051,duster,0,1079 474653,cutting_board,0,1079 411563,forked_tongue,0,1079 1361077,mole_above_mouth,0,1078 608993,uncommon_stimulation,0,1078 1336227,red_bag,0,1078 1499492,k/da_(league_of_legends),0,1078 411498,tearing_clothes,0,1077 2795,picnic,0,1077 7702,hairjob,0,1076 13242,hanetsuki,0,1076 512928,white_tail,0,1076 485392,denim_jacket,0,1076 716813,sword_behind_back,0,1075 1353739,borrowed_design,0,1075 663596,aestus_estus,0,1075 549266,grocery_bag,0,1074 1258750,mechanical_eye,0,1074 11435,spreader_bar,0,1073 457698,comforting,0,1073 8722,detective,0,1073 634879,fume,0,1073 1271601,bunny-shaped_pupils,0,1073 701116,weighing_scale,0,1072 417929,tennis_ball,0,1072 1326209,yellow_sash,0,1072 665044,holding_own_tail,0,1072 1367101,holding_scissors,0,1072 1708252,licking_another's_face,0,1071 490582,onbashira,0,1071 1881200,ashford_academy_school_uniform,0,1070 1327857,red_apron,0,1070 597826,santa_gloves,0,1070 1483316,single_horizontal_stripe,0,1069 479250,tricorne,0,1069 699876,imminent_anal,0,1069 473049,doily,0,1068 1299138,back-print_panties,0,1068 1750472,galaxy_expedition_team_survey_corps_uniform,0,1068 555432,head_down,0,1067 1335609,cross_print,0,1067 1379921,camouflage_jacket,0,1067 1391097,grey_neckerchief,0,1067 1512007,pill_earrings,0,1067 705129,holding_jacket,0,1065 547495,morning_glory,0,1065 1270497,pearl_bracelet,0,1064 699336,sharp_toenails,0,1064 1505137,thai_text,0,1064 1255353,loungewear,0,1064 502383,elezen,0,1064 365028,stealth_sex,0,1063 1579902,nata_(tool),0,1062 547034,guided_breast_grab,0,1061 540293,vampire_costume,0,1061 602058,shoulder_strap,0,1060 659959,scarf_over_mouth,0,1058 489995,bulletproof_vest,0,1058 1274530,angora_rabbit,0,1058 374852,wakizashi,0,1057 637714,holding_legs,0,1055 1445390,aria_company_uniform,0,1055 4760,campfire,0,1055 390867,soda,0,1055 378755,beans,0,1055 1346040,pink_hakama,0,1055 1352539,squidbeak_splatoon,0,1055 204274,upshirt,0,1053 1440630,pink_capelet,0,1053 1283735,grey_nails,0,1052 10639,yarn,0,1052 378487,telescope,0,1050 1248044,tooth_necklace,0,1050 564526,vertical-striped_pantyhose,0,1050 463235,training_bra,0,1049 759865,accidental_exposure,0,1049 49867,summer_festival,0,1049 479155,necktie_grab,0,1049 598441,yin_yang_orb,0,1049 1421701,smokestack_hair_ornament,0,1049 462605,snack,0,1048 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1506800,diamond_(gemstone),0,940 1382643,flaming_weapon,0,940 1271930,nanodesu_(phrase),0,940 1406788,otter_ears,0,940 3614,stain,0,939 1350036,crystal_earrings,0,939 1349615,red_fur,0,939 1448822,brown_hoodie,0,939 468645,child_drawing,0,938 255403,cleaver,0,938 382573,akanbe,0,937 1243923,backpack_removed,0,937 405411,team_9,0,937 753278,analog_clock,0,936 603414,space_helmet,0,936 645622,sleeveless_coat,0,934 460553,no_eyebrows,0,934 507379,yellow_belt,0,934 600871,ar-15,0,934 468827,pushing,0,933 519098,yarn_ball,0,933 660791,fur_cape,0,933 9535,icing,0,932 12002,foam,0,932 605380,vertical-striped_bikini,0,932 538478,haniwa_(statue),0,932 474917,peace_symbol,0,931 396687,hourglass,0,931 540308,baggy_clothes,0,931 1201168,undressing_another,0,931 568529,barefoot_sandals,0,931 1309935,notched_ear,0,931 12456,dvd_cover,0,930 1386653,falchion_(fire_emblem),0,930 8697,porch,0,929 453294,houndstooth,0,929 1407009,japari_bun,0,929 413107,puffy_cheeks,0,928 1265662,lace-trimmed_hairband,0,928 393568,amulet,0,927 1423578,brown_collar,0,926 417078,bayonet,0,926 632074,owl_ears,0,925 548331,bamboo_steamer,0,924 394157,papers,0,924 545481,hand_on_leg,0,924 584716,camouflage_pants,0,924 1350589,bandaid_on_forehead,0,924 1478379,dress_flower,0,924 540095,bilingual,0,923 4219,henshin,0,923 1553320,two-tone_headwear,0,923 487790,studded_bracelet,0,923 1430735,black_garter_belt,0,922 1363208,blue_bag,0,922 712473,mummy_costume,0,921 1426299,tokisadame_school_uniform,0,921 1258824,print_shorts,0,921 1493305,heel_up,0,920 471824,searchlight,0,919 1509089,between_pectorals,0,919 1427498,holding_pizza,0,918 673693,kouhaku_nawa,0,918 1058783,aviator_sunglasses,0,918 1375732,snap-fit_buckle,0,918 693639,striped_hoodie,0,918 1393881,green_bag,0,917 191357,loose_shirt,0,917 617955,polka_dot_skirt,0,917 1312469,purple_hakama,0,916 409179,smoking_gun,0,915 657589,crotch_cutout,0,915 1759071,cetacean_tail,0,915 1301010,orange_sweater,0,914 408039,crystal_ball,0,914 415025,convenience_store,0,914 390528,seaweed,0,914 564002,guided_penetration,0,913 1154025,ankle_wrap,0,913 517244,anglerfish,0,913 585400,inverted_cross,0,912 15147,concert,0,912 1289607,nursing_handjob,0,912 1607304,linear_hatching,0,911 377994,playing,0,911 448692,saddle,0,911 643356,dress_removed,0,911 10014,washing_machine,0,910 239,valkyrie,0,909 1518587,striped_headwear,0,909 710509,antique_firearm,0,908 1365439,jaguar_print,0,908 652982,visor_(armor),0,907 9716,strawberry_panties,0,906 438209,checkered_flag,0,906 1378073,ears_visible_through_hair,0,905 617340,strapless_swimsuit,0,905 537085,objectification,0,905 182494,audience,0,904 1450864,head_chain,0,904 663896,hand_on_another's_neck,0,903 465140,breastfeeding,0,903 1403674,pink_camisole,0,903 670641,clothes_between_breasts,0,902 664838,green_wings,0,902 5119,pinwheel,0,902 521714,cursive,0,902 1274515,double_w,0,902 663895,hand_on_own_neck,0,901 474428,blowing,0,901 692139,penguin_hood,0,901 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539119,ultra_ball,0,886 2430,recorder,0,885 564894,united_states,0,885 1490333,yellow_sleeves,0,885 401062,x3,0,885 1456713,cross_choker,0,884 607245,cropped_arms,0,883 579303,tail_ring,0,883 4281,hole,0,882 1303240,polka_dot_scrunchie,0,882 495021,rider_belt,0,882 570043,pine_tree,0,881 661864,pink_belt,0,880 652399,araki_hirohiko_(style),0,880 1393886,multicolored_kimono,0,879 1414615,mole_on_stomach,0,879 514585,plaid_bra,0,879 581487,hishaku,0,879 9656,crazy,0,878 559663,unamused,0,878 1451576,checkered_sash,0,878 1437142,purple_hoodie,0,878 699281,oversized_food,0,877 500653,mahjong_tile,0,877 1433794,holding_saucer,0,876 585602,jeweled_branch_of_hourai,0,876 1326378,black_umbrella,0,876 406042,exhausted,0,876 430683,sling,0,876 15227,screentones,0,875 1480168,white_sports_bra,0,875 668837,ghost_costume,0,875 395011,tube_dress,0,874 463435,parka,0,874 416210,dirty_feet,0,874 497015,wringing_clothes,0,873 1582498,scar_on_leg,0,873 378541,gills,0,872 468583,melon_bread,0,872 430376,bear_costume,0,872 381280,lighthouse,0,872 716956,puff_and_slash_sleeves,0,872 607934,tentacles_on_male,0,871 1593160,unusually_open_eyes,0,871 1328259,multiple_moles,0,870 401136,kimono_lift,0,869 1292517,glowing_butterfly,0,869 388245,cum_in_nose,0,868 473178,\n/,0,868 405504,apron_lift,0,867 672588,cardigan_vest,0,867 663869,looking_through_legs,0,867 681979,double_\m/,0,867 460270,sparrow,0,865 138127,art_nouveau,0,865 1404921,pink_ascot,0,864 10268,net,0,864 564789,romper,0,864 545590,easter_egg,0,864 1881202,st._chronica_academy_school_uniform,0,864 1826631,tracen_training_uniform,0,864 607513,arms_around_waist,0,863 645655,wall_clock,0,863 376621,wa_lolita,0,863 1336108,crime_prevention_buzzer,0,863 635240,star_pasties,0,862 418811,acoustic_guitar,0,861 1786594,tokyo-3_middle_school_uniform,0,860 637908,adapted_uniform,0,860 389325,grave,0,859 388208,orange_hoodie,0,859 399680,arachne,0,859 549347,green_pantyhose,0,859 3912,p90,0,858 1874891,object_through_head,0,858 423080,hat_with_ears,0,857 546085,brown_bra,0,856 1300914,leash_pull,0,856 1470700,black_undershirt,0,855 1374880,bralines,0,855 461078,squinting,0,854 697415,storefront,0,854 389926,panty_lift,0,853 1292892,cracked_wall,0,853 16792,golf_club,0,852 799867,futasub,0,852 1504031,white_butterfly,0,852 428710,buster_sword,0,852 1371471,anchor_necklace,0,852 513996,lyrics,0,851 531189,foliage,0,851 405020,wheelbarrow,0,851 1235655,gradient_dress,0,850 1822470,wataboushi,0,849 390026,chalice,0,849 594313,shoulder_holster,0,849 1384374,grey_hairband,0,849 529750,glowing_hair,0,849 1360271,grey_cape,0,849 472653,stained_panties,0,849 10056,grill,0,848 413290,hand_under_shirt,0,848 1411208,purple_neckerchief,0,848 1270535,curtained_hair,0,848 1613886,animal_ear_headwear,0,848 164255,nudist,0,846 1243627,penis_peek,0,846 438748,breast_poke,0,845 533029,dragging,0,845 1715534,baton_(conducting),0,845 468850,tall,0,844 582333,ojou-sama_pose,0,844 664894,aqua_gloves,0,844 457370,masochism,0,844 570496,struggling,0,844 675461,rolling_suitcase,0,844 561654,animal_skull,0,844 16918,tutu,0,843 1448672,tsab_ground_military_uniform,0,843 499780,holding_breath,0,843 477179,tire,0,842 1317436,satin_panties,0,842 1398131,hooded_bodysuit,0,842 690260,vibrator_on_nipple,0,841 482315,breast_padding,0,841 1298677,armpit_cutout,0,841 1372787,multi-strapped_panties,0,841 469235,amplifier,0,840 400231,kotoyoro,0,840 1208724,string_bra,0,840 1337502,heart_lock_(kantai_collection),0,840 393062,skirt_flip,0,839 626440,season_connection,0,839 688469,spiked_choker,0,839 381750,thermos,0,838 8409,spaghetti,0,838 1344738,snowflake_background,0,838 600460,group_picture,0,837 496226,multiple_legs,0,837 8698,windowsill,0,837 1229971,shoulder_cannon,0,837 1411084,pravda_military_uniform,0,837 1411077,chi-hatan_military_uniform,0,837 439167,urethral_insertion,0,836 46728,excited,0,836 386926,polar_bear,0,836 742744,bean_bag_chair,0,835 555321,hand_gesture,0,835 1468128,training_corps_(emblem),0,835 1575514,yuigaoka_school_uniform,0,835 4517,gymnastics,0,834 594978,naked_tabard,0,834 702958,holding_ribbon,0,834 469710,energy_drink,0,834 419383,wallet,0,833 1835765,evangelion_(mecha),0,833 609535,witch_(madoka_magica),0,833 1642271,yurigaoka_girls_academy_school_uniform,0,833 1396676,dressing_another,0,832 557780,alternate_wings,0,831 1376753,crescent_print,0,831 680577,color_trace,0,831 1316299,multicolored_scarf,0,831 720999,aqua_jacket,0,831 383546,stole,0,831 1242619,fake_nails,0,830 509088,penis_in_panties,0,829 646377,kikumon,0,829 1375970,hair_flowing_over,0,829 664951,coat_removed,0,829 571951,flaming_sword,0,828 1355541,lattice,0,828 488521,canopy_bed,0,828 1328348,group_name,0,828 614256,bunny_hat,0,827 411205,pigeon,0,827 1441859,aqua_footwear,0,826 505302,bar_stool,0,826 679205,catchphrase,0,826 1545323,multicolored_headwear,0,826 1452498,grey_capelet,0,826 1518971,pectoral_press,0,826 550074,in_water,0,825 1345310,collared_vest,0,825 1407027,jaguar_tail,0,825 397136,trigram,0,824 228097,astronaut,0,824 1377986,riyo_(lyomsnpmp)_(style),0,824 1475691,hanasakigawa_school_uniform,0,824 653176,circle_skirt,0,823 1392086,grey_bodysuit,0,823 1247424,brick_floor,0,823 1529528,yellow_raincoat,0,823 403070,grenade_launcher,0,823 409820,cat_costume,0,822 620885,latex_legwear,0,822 440354,yo-yo,0,822 440482,leg_wrap,0,822 556519,electrical_outlet,0,822 562990,bath_stool,0,821 1358423,multicolored_footwear,0,821 400267,dumpling,0,821 1281944,anchor_choker,0,821 511246,blank_speech_bubble,0,820 1397788,brown_choker,0,820 472542,beam_saber,0,820 487949,arm_above_head,0,819 455483,shorts_around_one_leg,0,819 394402,wheat,0,818 15450,onion,0,818 586471,rounded_corners,0,818 1605157,colored_nipples,0,818 476906,spray_can,0,817 1501670,double_fox_shadow_puppet,0,817 533099,sitting_on_rock,0,816 507276,paper_crane,0,816 628065,eyepatch_removed,0,816 1324692,pink_fur,0,816 1232450,sparkle_print,0,816 1417853,heart_collar,0,816 1734,karaoke,0,815 5364,ganguro,0,815 1409664,floating_scarf,0,815 1406324,no_blindfold,0,815 544688,bunching_hair,0,814 11414,nipple_clamps,0,814 678345,maneki-neko,0,814 574527,surcoat,0,814 473342,imperial_japanese_army,0,814 1549436,cutout_above_navel,0,814 1281282,taimanin_suit,0,814 1327704,nejiri_hachimaki,0,813 1371202,yellow_cape,0,812 622973,chicken_(food),0,812 261984,snow_bunny,0,812 1092127,condom_belt,0,812 1281553,german_flag_bikini,0,811 121795,inline_skates,0,810 488001,tape_measure,0,810 10727,bib,0,809 504316,hands_on_hilt,0,809 330159,green_tea,0,809 1318429,pendant_watch,0,809 15619,sand_castle,0,809 839892,no_mole,0,809 14739,hammock,0,808 393203,handstand,0,808 1287846,ammunition_pouch,0,808 1453635,cartoon_bone,0,808 1161750,boy_sandwich,0,807 1251054,okobo,0,807 484561,arm_cuffs,0,806 488430,seat,0,805 535613,upturned_eyes,0,805 1827382,st._theresa's_girls_academy_school_uniform,0,805 721903,fur_cloak,0,805 642323,teacher_and_student,0,804 704182,character_signature,0,804 713277,no_eyepatch,0,804 682665,super_soaker,0,804 1474354,holding_ladle,0,803 1483518,orange_sleeves,0,803 413459,inflatable_raft,0,803 462239,chinese_new_year,0,803 483045,kinchaku,0,802 519941,finger_biting,0,802 399166,cursor,0,802 684797,hands_on_another's_hips,0,802 1617036,pokemon_move,0,802 2477,lightsaber,0,801 498775,orange_skin,0,801 9166,windmill,0,801 1390981,ribbon_braid,0,801 1296117,sideways_hat,0,801 1113314,thick_lips,0,800 553675,ehoumaki,0,800 1152233,star_tattoo,0,800 1467949,single_ear_cover,0,800 1835737,green_one-piece_swimsuit,0,799 531424,multiple_heads,0,799 695119,blood_in_hair,0,798 593299,pink_border,0,798 1319348,holding_bucket,0,797 1257023,dog_penis,0,797 509238,dialogue_box,0,797 603240,vertical-striped_panties,0,797 622330,oversized_limbs,0,797 548294,nib_pen_(object),0,796 596588,jacket_pull,0,796 558489,white_armor,0,796 394916,2008,0,796 613430,bagged_fish,0,795 662803,honeycomb_background,0,795 433742,frozen,0,795 475494,dissolving,0,795 1505371,star_guardian_(league_of_legends),0,795 714122,ball_and_chain_restraint,0,794 1322883,flag_background,0,794 659790,eldritch_abomination,0,794 1648109,hololive_idol_uniform,0,794 659364,licking_armpit,0,793 1272545,orange_leotard,0,793 1320993,print_necktie,0,793 7947,beltskirt,0,792 475951,ornament,0,792 591348,breast_cutout,0,792 614915,pink_cape,0,792 1713082,neck_tassel,0,792 549921,swinging,0,791 822043,mundane_utility,0,791 705927,frilled_leotard,0,790 1569456,pizza_slice,0,790 1299125,elbows_on_table,0,789 7700,weightlifting,0,789 410619,toe_ring,0,788 701996,nipple_bar,0,788 1728479,kousaka_kirino's_school_uniform,0,788 1393397,orange_goggles,0,788 6352,elephant,0,787 1293918,bicycle_basket,0,787 3499,kappa,0,787 5946,boxcutter,0,787 592592,personality_switch,0,787 547300,misunderstanding,0,787 1544416,clothes_between_thighs,0,787 721466,pointy_nose,0,787 1648167,x-shaped_pupils,0,787 452532,two-handed_handjob,0,786 392867,melon,0,786 702627,ass-to-ass,0,786 548516,pinstripe_shirt,0,786 741418,card_parody,0,786 504065,cephalopod_eyes,0,785 1391806,two-tone_bow,0,785 1390068,joy-con,0,785 5466,mummy,0,784 398573,cartridge,0,783 619281,blue_border,0,783 383898,bullying,0,783 472408,sneezing,0,783 4899,stats,0,783 1365868,holding_envelope,0,783 496190,kabuto_(helmet),0,783 1396770,animal_on_arm,0,782 5547,pet,0,782 1522400,tented_shirt,0,782 582033,clothes_tug,0,782 411403,tongs,0,782 1488405,mole_on_cheek,0,781 444001,legband,0,781 379013,note,0,781 1409157,flower_tattoo,0,781 553040,flower_bracelet,0,780 1577478,super_saiyan_1,0,780 1398925,speaking_tube_headset,0,780 468311,tail_hug,0,779 1495160,mismatched_eyebrows,0,779 1860041,multiple_drawing_challenge,0,779 1481466,purple_horns,0,779 1426179,luna_nova_school_uniform,0,779 573016,hands_on_shoulders,0,778 1432417,brown_tail,0,778 419647,stakes_of_purgatory,0,778 492703,long_neck,0,777 723519,chin_strap,0,777 393452,leaf_umbrella,0,777 715181,post-apocalypse,0,777 550385,linked_piercing,0,776 15836,butter,0,776 379180,zora,0,776 1376893,pink_eyeshadow,0,776 499526,green_socks,0,775 2730,massage,0,775 1464853,blue-tinted_eyewear,0,775 658977,wooden_chair,0,774 397923,group_hug,0,774 657414,panties_under_buruma,0,774 382030,beetle,0,774 529455,tight_dress,0,774 1347574,mole_on_body,0,774 284608,tea_set,0,773 453888,hand_mirror,0,773 416380,gokkun,0,773 399607,hair_dryer,0,773 643401,dragon_boy,0,773 463449,sugar_cube,0,772 501887,levitation,0,772 467444,kerchief,0,772 1427360,gold_choker,0,772 646311,polka_dot_shirt,0,771 446404,cheek_pull,0,771 1283338,american_flag_print,0,771 1411059,saunders_school_uniform,0,771 501282,lipgloss,0,770 1352247,variable_fighter,0,770 375406,clown,0,770 507447,family_crest,0,770 416825,omikuji,0,769 524801,hakurei_shrine,0,769 641133,aqua_bra,0,769 214260,keep_out,0,769 1378265,z-ring,0,769 435855,messy_room,0,768 1339258,logo_parody,0,768 1252372,sitting_on_bench,0,768 1441858,lap_pillow_invitation,0,768 474442,plume,0,768 1136370,sextuplets,0,768 1303976,print_sarong,0,767 534139,popped_button,0,767 1433477,floating_cape,0,767 1245350,implied_fellatio,0,767 168769,band_uniform,0,766 712902,pumpkin_hair_ornament,0,765 7839,beam,0,765 375496,kagami_mochi,0,765 716637,argyle_sweater,0,765 380200,2007,0,765 1571287,berry_(pokemon),0,765 7461,lizard,0,764 587390,hand_on_headphones,0,764 1680143,pegasus_knight_uniform_(fire_emblem),0,764 447159,fishing_line,0,764 605773,head_on_hand,0,763 317012,zeon,0,763 646375,sayagata,0,763 635045,solid_eyes,0,762 1340897,blue_feathers,0,762 1373241,meka_(overwatch),0,762 1594642,holomyth,0,762 6527,cart,0,761 449699,grand_piano,0,761 460701,pagoda,0,761 15902,jetpack,0,761 1008938,clothes_theft,0,760 1315295,holding_another's_hair,0,760 683907,jingasa,0,760 2145,juice,0,759 691041,yellow_sky,0,759 499998,sitting_backwards,0,758 1336500,pointing_at_another,0,758 606330,condom_box,0,758 1552258,fish_boy,0,758 960973,cardigan_around_waist,0,758 623986,green_sclera,0,758 5099,pegasus,0,757 746897,pussy_juice_drip_through_clothes,0,757 1289376,bishamonten's_spear,0,757 1539213,flower_over_eye,0,756 511500,head_on_chest,0,756 1421046,holding_cane,0,756 509654,forced_orgasm,0,755 1548224,butterfly_brooch,0,755 382343,adjusting_panties,0,754 457036,steak,0,754 1412249,green_scrunchie,0,754 5817,sauna,0,753 443238,wardrobe_error,0,752 3438,japan,0,751 5402,crowbar,0,751 1510994,sweaty_clothes,0,751 1398336,year_of_the_dog,0,751 527344,body_armor,0,751 701994,pov_across_table,0,750 14011,height_chart,0,749 460435,ivy,0,749 421692,game_boy,0,749 426382,bear_tail,0,749 778226,greek_clothes,0,749 1039471,prostration,0,748 1255173,shoujo_kitou-chuu,0,748 1386173,pixiv_username,0,748 1366556,splattershot_(splatoon),0,748 9745,hungry,0,747 385480,buruma_aside,0,747 399404,skull_necklace,0,747 467744,mount_fuji,0,747 1611404,food-themed_earrings,0,747 452085,hospital_gown,0,746 658126,cream_on_face,0,746 396015,gunblade,0,746 646003,curled_fingers,0,746 484040,winding_key,0,745 9347,puppy,0,745 1059619,kissing_penis,0,745 375353,soviet,0,745 475528,ankle_grab,0,744 8921,white_day,0,744 5102,wizard,0,744 397561,radish,0,744 471296,infinity,0,744 693309,sleeve_grab,0,743 531095,book_hug,0,742 1375741,extra_faces,0,742 3742,ferret,0,741 436459,acorn,0,741 583137,ear_protection,0,741 1239921,pink_sky,0,740 1353065,falling_feathers,0,740 1496708,hitodama_print,0,740 1257082,clock_eyes,0,740 729551,kiwi_(fruit),0,739 1193090,military_helmet,0,739 1343008,orange_vest,0,738 395496,cloth,0,738 384700,dock,0,738 541741,strapless_bottom,0,738 563180,nintendo_3ds,0,738 386212,diving,0,737 630151,bikini_shorts,0,737 1429582,dress_swimsuit,0,737 9476,minotaur,0,736 381360,wrong_feet,0,736 1469257,single_epaulette,0,736 563949,french_flag,0,735 1424344,sling_bikini_top,0,735 458787,testicle_grab,0,734 450248,sickle,0,733 429978,between_toes,0,733 120759,mat,0,733 474597,mitre,0,733 1657712,veiny_arms,0,733 576346,adjusting_necktie,0,732 1305509,box_of_chocolates,0,732 596273,behind_back,0,732 1489407,martial_arts_belt,0,732 1595590,pectoral_focus,0,732 393640,aurora,0,731 554570,jockstrap,0,731 478042,track_uniform,0,731 11347,army,0,730 404973,galaxy,0,730 1520977,print_mug,0,730 487783,leather_pants,0,729 375625,model_kit,0,729 1344687,holding_letter,0,729 1344492,on_motorcycle,0,729 1001710,shell_necklace,0,729 1303975,blue_sarong,0,728 489578,whiskey,0,728 467700,salad,0,728 548098,mast,0,728 485599,silver_dress,0,728 1377852,hair_on_horn,0,728 642272,above_clouds,0,727 1427943,yellow_tank_top,0,727 12070,battleship,0,727 611065,lace_choker,0,727 445428,guard_rail,0,726 564925,polka_dot_ribbon,0,726 1286244,rei_no_pool,0,726 490067,holding_head,0,726 630542,butterfly_sitting,0,725 1379634,mmm_threesome,0,725 1392344,white_sarong,0,724 524545,box_art,0,724 630404,pumpkin_hat,0,724 287045,hanami,0,724 1262145,hands_on_ground,0,724 1562798,rhodes_island_logo,0,724 495149,throwing_knife,0,723 1404665,aqua_neckerchief,0,723 411775,clothesline,0,723 483853,bulletin_board,0,722 665892,cross-laced_legwear,0,722 1411110,keizoku_school_uniform,0,722 441997,track_and_field,0,721 877388,long_tail,0,721 621690,no_mask,0,721 678477,shared_speech_bubble,0,720 434547,oonusa,0,720 1601728,gladiator_sandals,0,720 1325214,seal_(animal),0,719 1660195,long_earlobes,0,719 474347,affectionate,0,718 1495060,pants_tucked_in,0,718 1386603,orange_cape,0,717 401696,hook,0,717 552418,detached_hair,0,717 1282902,solid_circle_pupils,0,716 571097,crane_(animal),0,716 486022,vacuum_cleaner,0,716 409759,cleave_gag,0,716 1657724,just_the_tip,0,716 1597931,pancake_stack,0,716 1419059,blue_tank_top,0,716 82088,sewing,0,715 449709,uneven_twintails,0,715 1070070,fishnet_bodysuit,0,715 1481865,brown_corset,0,715 420279,sock_pull,0,714 648669,lowleg_skirt,0,714 12500,pendulum,0,714 1441951,gold_footwear,0,714 490118,pool_of_blood,0,713 468109,tengu_mask,0,713 523961,hair_between_breasts,0,713 562139,glove_biting,0,713 1419480,maid_day,0,713 1260902,folded_hair,0,712 1494953,pink-tinted_eyewear,0,712 4410,dragonfly,0,711 487992,inkwell,0,711 1326625,implied_yaoi,0,711 1273445,miracle_mallet,0,711 547020,fidgeting,0,710 707514,stone_stairs,0,710 1423439,livestream,0,710 1417085,double_horizontal_stripe,0,709 109159,nyan,0,708 119610,triplets,0,708 561371,qr_code,0,708 1329132,cherry_hair_ornament,0,708 500191,rabbit_costume,0,707 1247324,implied_fingering,0,707 1466798,holding_money,0,706 1324059,holding_jewelry,0,706 663412,holding_own_foot,0,706 1338731,holding_skull,0,706 533238,milky_way,0,706 1450600,very_wide_shot,0,706 607013,stitched_mouth,0,706 809507,eye_black,0,706 675626,in_palm,0,705 537098,watching_television,0,705 376977,parrot,0,705 479840,severed_limb,0,705 1416325,multicolored_hairband,0,705 576440,naked_cloak,0,704 638337,multicolored_stripes,0,704 572327,splatter,0,704 467754,foot_hold,0,703 876691,cum_in_container,0,703 54531,blood_bag,0,703 532082,knight_(chess),0,703 1398335,year_of_the_pig,0,703 512129,symbolism,0,702 1386018,ooarai_(emblem),0,702 1627128,caliburn_(fate),0,700 1076064,covering_one_breast,0,700 384589,lyre,0,700 1247797,bike_shorts_under_shorts,0,700 1532157,green_eyeshadow,0,700 1515529,shindan_maker,0,700 465272,kyuudou,0,699 378206,joystick,0,699 615409,bag_of_chips,0,699 1426900,white_bird,0,699 1375854,dream_soul,0,699 393506,shrimp_tempura,0,698 4374,meta,0,698 1505135,italian_text,0,698 971148,underbutt,0,698 605322,checkered_shirt,0,698 1353857,blood_on_arm,0,698 1258093,ribbon-trimmed_headwear,0,697 694148,candlelight,0,697 1531641,light_blue_background,0,697 580277,green_tail,0,696 470859,purple_socks,0,696 632045,no_jacket,0,696 476447,kurokote,0,695 613159,homu,0,695 1470859,black_garter_straps,0,694 502186,broken_window,0,694 478554,contrast,0,694 1258486,musical_note_print,0,694 381761,cola,0,694 1574670,two-sided_dress,0,694 649072,grimoire_of_alice,0,693 394720,afterglow,0,693 407128,crumbs,0,693 477060,hair_lift,0,693 1386162,facebook_username,0,693 411331,long_toenails,0,692 525804,self_hug,0,692 552753,perfume_bottle,0,692 1401831,purple_ascot,0,691 643172,shotgun_shell,0,691 502652,ink_(medium),0,690 624228,sky_print,0,690 535487,hand_over_eye,0,690 1631697,alternate_pectoral_size,0,689 1263595,soap_bottle,0,688 505038,duel_disk,0,688 434881,baby_bottle,0,688 1238293,grey_wings,0,687 1749113,arthropod_limbs,0,687 1470190,frilled_sailor_collar,0,687 427948,tying,0,686 397500,electric_plug,0,686 389771,syrup,0,686 391741,fingersmile,0,686 2685,kickboard,0,685 1252120,roundel,0,685 1578028,heart_on_chest,0,685 1370485,crescent_rose,0,685 10240,hardhat,0,684 433080,koi,0,684 664262,bikini_bottom_removed,0,684 662225,floating_book,0,683 3513,goat,0,683 1417605,blue_shawl,0,683 4854,cast,0,682 437278,masu,0,682 168887,age_comparison,0,682 572094,h&k_ump,0,681 14528,bathrobe,0,680 518347,yamakasa,0,680 1494956,purple-tinted_eyewear,0,680 1318748,floating_weapon,0,680 492292,behind-the-head_headphones,0,679 513429,watercolor_pencil_(medium),0,679 725442,blue_bandana,0,679 1345296,skeleton_print,0,679 1332216,propeller_hair_ornament,0,679 1370713,pink_umbrella,0,678 417910,duckling,0,678 433244,first_aid_kit,0,677 446984,gun_to_head,0,677 504867,digital_dissolve,0,677 1253889,joestar_birthmark,0,677 719987,ghost_pose,0,677 1289258,bokura_wa_ima_no_naka_de,0,676 389367,pineapple,0,675 1349482,igote,0,675 618480,sitting_on_shoulder,0,675 1230297,single_wrist_cuff,0,675 1513506,patchwork_clothes,0,675 1353604,walking_on_liquid,0,674 529805,shamoji,0,674 547795,breaking,0,674 410219,scylla,0,674 720294,weasel_ears,0,674 974485,hoodie_lift,0,674 516577,groom,0,673 1867868,motosu_school_uniform,0,673 374958,cyrillic,0,672 622480,food_art,0,671 451855,studded_collar,0,671 1556083,camouflage_headwear,0,671 510646,blue_pupils,0,670 489491,platform_boots,0,670 14645,marshmallow,0,670 403,chikan,0,669 1344657,cat_bag,0,669 473987,tooth,0,669 1253133,spade_hair_ornament,0,669 403466,medallion,0,668 1240072,hair_color_connection,0,668 381685,pig_ears,0,668 565798,crazy_straw,0,668 1574664,two-sided_skirt,0,668 616835,calligraphy_brush_(medium),0,668 1455458,brown_flower,0,668 191640,ak-47,0,667 378288,blob,0,667 542882,against_railing,0,667 1268234,alternate_hair_ornament,0,667 1274150,print_sleeves,0,667 449449,beretta_92,0,666 556112,red_tail,0,666 571640,jacket_over_swimsuit,0,666 381749,tamagoyaki,0,666 657138,cat_mask,0,666 646318,necktie_removed,0,666 413830,wakamezake,0,665 537572,teeth_hold,0,665 496893,checkered_dress,0,665 1429073,holding_beachball,0,664 1433301,two-tone_leotard,0,664 1331283,bridal_legwear,0,664 1494960,yellow-tinted_eyewear,0,664 510173,easter,0,664 690728,missile_pod,0,663 396000,bowler_hat,0,663 561682,clownfish,0,663 412041,biwa_lute,0,663 4947,groceries,0,662 1243501,gibson_les_paul,0,662 1322934,gesugao,0,662 1578455,cumulonimbus_cloud,0,662 589932,negative_space,0,661 423622,pelt,0,661 1609367,tail_around_leg,0,661 1391188,hand_tattoo,0,661 1684976,craft_essence_(fate),0,661 590202,pinstripe_suit,0,660 1453787,golden_arms,0,660 1799877,tracen_swimsuit,0,660 1401249,grey_leotard,0,659 727812,loaded_interior,0,659 3927,gao,0,659 1252918,sitting_on_table,0,659 1317359,floating_clothes,0,659 673705,hand_rest,0,659 1440820,usekh_collar,0,659 1328313,shell_hair_ornament,0,658 1325319,wrist_bow,0,658 1402775,white_mask,0,658 1391449,cropped_sweater,0,658 551086,protecting,0,657 1333942,exposed_pocket,0,657 609075,red_mask,0,656 1231690,slim_legs,0,655 1425321,hogwarts_school_uniform,0,655 457678,animal_slippers,0,655 1249968,little_red_riding_hood_(grimm)_(cosplay),0,655 1435997,eye_trail,0,655 528408,soft_serve,0,654 613600,checkered_legwear,0,654 1435395,grey_tank_top,0,653 1583008,side-tie_peek,0,653 1861772,crisis_management_form_(machimazo),0,653 647108,flying_teardrops,0,652 666041,multiple_torii,0,652 546978,two-finger_salute,0,652 529223,cellphone_charm,0,652 1297290,split_theme,0,652 1460120,fur-trimmed_footwear,0,652 1327283,cracked_floor,0,651 563545,braiding_hair,0,650 791840,blue_umbrella,0,650 1328442,green_belt,0,649 658222,stuffed_penguin,0,649 1497365,pov_doorway,0,649 728040,flat_chest_grab,0,648 524807,unfastened,0,647 172018,nail_bat,0,647 11665,seatbelt,0,646 723518,arms_between_legs,0,645 522469,centauroid,0,642 562074,plaid_ribbon,0,641 679788,wet_towel,0,641 1782264,sticker_on_face,0,641 1714871,midriff_sarashi,0,640 1684568,paint_splatter_on_face,0,640 464635,cattail,0,640 707905,object_on_breast,0,640 1329330,bunny_day,0,640 1881204,starlight_academy_school_uniform,0,639 655737,pants_under_skirt,0,636 1442516,paw_print_pattern,0,636 518157,peony_(flower),0,635 1427573,brown_sleeves,0,635 551767,pastry_bag,0,633 1012946,breasts_on_table,0,633 449872,walther,0,632 1535711,cross_tie,0,632 543243,chrysanthemum,0,631 1407354,brown_neckerchief,0,629 1468297,sixteenth_note,0,629 647474,stuffed_dog,0,628 1325576,four-leaf_clover_hair_ornament,0,628 1397372,year_of_the_rooster,0,628 549834,person_on_head,0,628 1588824,lifebuoy_ornament,0,628 492648,yellow_socks,0,626 651505,animal_on_hand,0,625 1414486,red_mittens,0,625 1291060,rabbit_on_head,0,623 1672268,qingxin_flower,0,618 385430,hatsune_miku,4,78616 12239,hakurei_reimu,4,67710 12242,kirisame_marisa,4,62327 12248,remilia_scarlet,4,46894 12249,flandre_scarlet,4,43434 12314,izayoi_sakuya,4,42465 1301082,admiral_(kancolle),4,34812 1478495,artoria_pendragon_(fate),4,33041 9123,alice_margatroid,4,32516 388200,kochiya_sanae,4,32504 12247,patchouli_knowledge,4,32120 12244,konpaku_youmu,4,31146 1720,cirno,4,30904 12246,yakumo_yukari,4,29607 427142,komeiji_koishi,4,27406 12308,shameimaru_aya,4,25978 12307,fujiwara_no_mokou,4,24550 12304,reisen_udongein_inaba,4,24375 11374,hong_meiling,4,23457 592924,akemi_homura,4,23123 427143,komeiji_satori,4,22757 592925,kaname_madoka,4,22080 12245,saigyouji_yuyuko,4,21758 1631074,kaga_(kancolle),4,20549 395218,inubashiri_momiji,4,20497 12250,yakumo_ran,4,18043 390427,kagamine_rin,4,17416 602257,konpaku_youmu_(ghost),4,17065 384842,moriya_suwako,4,17047 2082,rumia,4,16841 593109,miki_sayaka,4,16686 427145,kaenbyou_rin,4,16675 401582,kazami_yuuka,4,16551 1275729,shimakaze_(kancolle),4,16285 427184,reiuji_utsuho,4,16263 6,saber,4,15967 1275718,hibiki_(kancolle),4,15954 474,chen,4,15887 12302,kamishirasawa_keine,4,15388 473327,tatara_kogasa,4,15022 383392,kawashiro_nitori,4,14973 1414209,mash_kyrielight,4,14919 415326,hinanawi_tenshi,4,14700 589430,sakura_kyouko,4,14333 593108,tomoe_mami,4,14245 1275728,shigure_(kancolle),4,14238 12306,houraisan_kaguya,4,13824 1799,koakuma,4,13641 1350122,kongou_(kancolle),4,13106 1602203,ganyu_(genshin_impact),4,12981 12300,mystia_lorelei,4,12948 12303,inaba_tewi,4,12571 12313,ibuki_suika,4,12550 1275720,inazuma_(kancolle),4,12537 475833,souryuu_asuka_langley,4,12338 503349,hijiri_byakuren,4,12211 1630449,akagi_(kancolle),4,12210 473267,nazrin,4,11834 393597,kagamine_len,4,11428 503343,houjuu_nue,4,11360 1270870,tenryuu_(kancolle),4,11216 1473623,tamamo_(fate),4,11128 1631504,yuudachi_(kancolle),4,10901 12305,yagokoro_eirin,4,10879 384841,yasaka_kanako,4,10876 457810,megurine_luka,4,10737 1275719,ikazuchi_(kancolle),4,10591 1479739,jeanne_d'arc_alter_(fate),4,10481 413783,mizuhashi_parsee,4,10406 1471064,abigail_williams_(fate),4,10091 1275715,fubuki_(kancolle),4,10071 456150,akiyama_mio,4,9944 1275711,akatsuki_(kancolle),4,9910 1631523,zuikaku_(kancolle),4,9855 1391754,fujimaru_ritsuka_(female),4,9711 1391753,fujimaru_ritsuka_(male),4,9444 638495,toyosatomimi_no_miko,4,9247 1631219,nagato_(kancolle),4,9212 1630699,hamakaze_(kancolle),4,9178 13814,morichika_rinnosuke,4,9131 1335350,haruna_(kancolle),4,9124 9466,suzumiya_haruhi,4,8980 7441,fate_testarossa,4,8966 6313,link,4,8924 465977,hoshiguma_yuugi,4,8846 3861,pikachu,4,8817 1686237,raiden_shogun,4,8798 1433170,nero_claudius_(fate),4,8774 1351818,kashima_(kancolle),4,8665 472387,nakano_azusa,4,8571 1593594,gawr_gura,4,8530 1533309,houshou_marine,4,8514 384197,kagiyama_hina,4,8503 465646,tohsaka_rin,4,8476 599169,shanghai_doll,4,8466 9715,nagato_yuki,4,8457 700405,nishizumi_miho,4,8294 12299,wriggle_nightbug,4,8246 1333465,rem_(re:zero),4,8222 7493,daiyousei,4,8196 638499,mononobe_no_futo,4,8163 1728232,avatar_(ff14),4,8144 1631280,ryuujou_(kancolle),4,8080 455424,hirasawa_yui,4,8070 465688,shiki_eiki,4,7982 600200,kyubey,4,7934 12311,onozuka_komachi,4,7924 1380245,suzuya_(kancolle),4,7856 1452630,scathach_(fate),4,7702 12342,usami_renko,4,7645 1581467,lumine_(genshin_impact),4,7630 395918,misaka_mikoto,4,7624 391888,tifa_lockhart,4,7606 1277919,inkling,4,7466 8552,takamachi_nanoha,4,7465 1401122,yorha_no._2_type_b,4,7437 503351,toramaru_shou,4,7431 12108,illyasviel_von_einzbern,4,7414 1631004,houshou_(kancolle),4,7348 506041,maribel_hearn,4,7317 1233711,imaizumi_kagerou,4,7306 8327,ayanami_rei,4,7267 1639113,shigure_kai_ni_(kancolle),4,7221 1408636,jeanne_d'arc_(fate),4,7191 378876,joseph_joestar,4,7142 1639135,yuudachi_kai_ni_(kancolle),4,7052 1231258,matoi_ryuuko,4,7016 456151,tainaka_ritsu,4,6997 1635696,hu_tao_(genshin_impact),4,6878 452135,producer_(idolmaster),4,6859 1275723,murakumo_(kancolle),4,6824 1405000,serval_(kemono_friends),4,6806 592977,nishikino_maki,4,6768 1765420,jeanne_d'arc_alter_(avenger)_(fate),4,6725 593046,sonoda_umi,4,6721 1277794,amatsukaze_(kancolle),4,6636 544560,himekaidou_hatate,4,6634 1585358,northern_ocean_princess,4,6621 1631456,ushio_(kancolle),4,6609 662185,shibuya_rin,4,6569 615562,toujou_nozomi,4,6537 94496,princess_zelda,4,6535 1600114,zhongli_(genshin_impact),4,6477 1438816,okita_souji_(fate),4,6473 1285206,atago_(kancolle),4,6461 638538,kaku_seiga,4,6444 1275734,yukikaze_(kancolle),4,6441 1631360,shoukaku_(kancolle),4,6390 503350,murasa_minamitsu,4,6356 1511608,inkling_girl,4,6321 592974,yazawa_nico,4,6312 713730,kafuu_chino,4,6283 593043,ayase_eli,4,6236 1682530,tamamo_no_mae_(fate/extra),4,6180 1631488,yamato_(kancolle),4,6121 466373,dawn_(pokemon),4,6100 1479836,shirakami_fubuki,4,6085 413645,nagae_iku,4,6078 1523054,manjuu_(azur_lane),4,6061 1531173,usada_pekora,4,6052 487910,joseph_joestar_(young),4,6030 1275712,akebono_(kancolle),4,6011 1233709,hata_no_kokoro,4,5911 1631124,kitakami_(kancolle),4,5907 54494,kujo_jotaro,4,5902 1649146,gilgamesh_(fate),4,5822 570051,ibaraki_kasen,4,5806 1287532,megumin,4,5792 669326,kaito_(vocaloid),4,5790 1239690,rensouhou-chan,4,5717 1369674,lillie_(pokemon),4,5687 7438,matou_sakura,4,5678 1596257,mona_(genshin_impact),4,5668 1733099,yae_miko,4,5664 1435530,shuten_douji_(fate),4,5656 473328,kumoi_ichirin,4,5646 413652,kurodani_yamame,4,5643 619180,kasodani_kyouko,4,5610 638583,soga_no_tojiko,4,5592 619181,miyako_yoshika,4,5551 1427460,prinz_eugen_(kancolle),4,5516 101752,hoshii_miki,4,5496 1631257,ooyodo_(kancolle),4,5453 456152,kotobuki_tsumugi,4,5431 1600563,keqing_(genshin_impact),4,5414 1448304,astolfo_(fate),4,5413 1630648,female_admiral_(kancolle),4,5408 13108,chun-li,4,5374 1372463,darjeeling_(girls_und_panzer),4,5362 382074,hiiragi_kagami,4,5341 1361469,mutsu_(kancolle),4,5321 1631410,tatsuta_(kancolle),4,5315 1478494,cu_chulainn_(fate),4,5269 1630515,asashio_(kancolle),4,5250 466360,may_(pokemon),4,5242 1532746,byleth_(fire_emblem),4,5192 1243755,kijin_seija,4,5186 1424365,pyra_(xenoblade),4,5178 7439,kinomoto_sakura,4,5176 1593595,mori_calliope,4,5176 1328621,clownpiece,4,5157 713720,nishizumi_maho,4,5153 1593596,ninomae_ina'nis,4,5129 593045,minami_kotori,4,5119 717860,rosa_(pokemon),4,5119 1405300,kaban_(kemono_friends),4,5109 1648389,yor_briar,4,5092 10658,emiya_shirou,4,5021 1495213,minato_aqua,4,5017 713721,itsumi_erika,4,4979 466669,c.c.,4,4976 710903,meiko_(vocaloid),4,4975 11941,samus_aran,4,4963 383424,aki_minoriko,4,4963 615290,hilda_(pokemon),4,4950 1630531,bismarck_(kancolle),4,4949 1646740,archer_(fate),4,4941 1448306,mordred_(fate),4,4940 1599785,aether_(genshin_impact),4,4932 1631313,sendai_(kancolle),4,4923 1533332,marnie_(pokemon),4,4923 1464562,minamoto_no_raikou_(fate),4,4918 1631252,ooi_(kancolle),4,4907 656409,yuzuki_yukari,4,4892 382075,izumi_konata,4,4858 452021,hieda_no_akyuu,4,4837 434767,caesar_anthonio_zeppeli,4,4796 1350123,akashi_(kancolle),4,4794 1233706,sekibanki,4,4768 9071,amami_haruka,4,4760 727352,anchovy_(girls_und_panzer),4,4757 1507496,takao_(kancolle),4,4740 1293664,musashi_(kancolle),4,4740 699794,nanami_chiaki,4,4735 1328650,junko_(touhou),4,4731 510663,yoko_littner,4,4679 1590956,mythra_(xenoblade),4,4644 1682533,nero_claudius_(fate/extra),4,4616 1734279,okita_souji_(koha-ace),4,4610 1442931,amiya_(arknights),4,4608 1275730,shiranui_(kancolle),4,4605 7866,tsukino_usagi,4,4576 1339317,d.va_(overwatch),4,4559 593044,kousaka_honoka,4,4558 638500,futatsuiwa_mamizou,4,4557 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1607938,getou_suguru,4,684 376441,arle_nadja,4,683 664614,ryuzaki_kaoru,4,683 1797173,hyakumantenbara_salome,4,683 1619673,puru-see_(hoshizuki_(seigetsu)),4,682 1152270,ibuki_tsubasa,4,681 1227600,mochizuki_anna,4,681 1275005,ousaka_shizuku,4,681 1782428,bb_(swimsuit_mooncancer)_(third_ascension)_(fate),4,679 1693037,le_malin_(listless_lapin)_(azur_lane),4,679 1768854,mysterious_heroine_x_alter_(first_ascension)_(fate),4,678 1631348,shinshuu_maru_(kancolle),4,678 480982,asbel_lhant,4,677 1316228,nakagawa_natsuki,4,677 1667164,g41_(girls'_frontline),4,677 404920,sage_(dq3),4,676 1331206,kawakami_mai,4,676 1423942,eris_greyrat,4,676 1560517,utage_(arknights),4,676 1405059,sand_cat_(kemono_friends),4,675 1680295,mika_(blue_archive),4,675 664407,himekawa_yuki,4,674 1631118,kishinami_(kancolle),4,674 1285963,alena_(dq4),4,672 1645008,smart_falcon_(umamusume),4,672 497689,cheria_barnes,4,671 1631097,kasuga_maru_(kancolle),4,671 1432300,yatadera_narumi,4,669 1387122,mordred_(swimsuit_rider)_(fate),4,667 1529378,sakata_kintoki_(fate),4,666 1408392,golden_snub-nosed_monkey_(kemono_friends),4,666 1780213,hina_(swimsuit)_(blue_archive),4,666 1875916,tiki_(adult)_(fire_emblem),4,665 1386436,amami_rantarou,4,664 548087,hasegawa_kobato,4,663 1257848,hoshimiya_kate,4,663 1335159,sugimoto_saichi,4,663 596743,fukawa_touko,4,662 1476820,yozora_mel,4,662 1789595,saijo_juri,4,661 1327917,yuel_(granblue_fantasy),4,660 1460549,nekomiya_hinata,4,660 688953,igarashi_kyoko,4,658 1639090,maya_kai_ni_(kancolle),4,658 1588338,tomoe_gozen_(swimsuit_saber)_(fate),4,658 1657407,blue_poison_(shoal_beat)_(arknights),4,658 1856336,hasumi_(gym_uniform)_(blue_archive),4,656 1598223,kayoko_(blue_archive),4,655 1434774,uehara_himari,4,654 1344209,roxy_migurdia,4,653 1837736,saren_(summer)_(princess_connect!),4,653 1485459,jean_bart_(azur_lane),4,653 713347,katagiri_sanae,4,652 1440285,queen_elizabeth_(azur_lane),4,652 1667205,9a-91_(girls'_frontline),4,651 1479271,rengoku_kyoujurou,4,650 1386290,keebo,4,649 1599618,whislash_(arknights),4,646 971934,vampy,4,645 1639099,naganami_kai_ni_(kancolle),4,644 1326283,jeanne_d'arc_(granblue_fantasy),4,643 1644991,matikanefukukitaru_(umamusume),4,643 1645010,sweep_tosho_(umamusume),4,642 879608,toyokawa_fuka,4,641 1387119,kiyohime_(swimsuit_lancer)_(fate),4,641 1836895,karyl_(summer)_(princess_connect!),4,641 1596254,razor_(genshin_impact),4,640 1480768,siro_(dennou_shoujo_youtuber_siro),4,639 1491126,tsuyuri_kanao,4,637 1667169,negev_(girls'_frontline),4,636 1440310,eldridge_(azur_lane),4,636 1284942,midway_princess,4,635 1420829,matsubara_kanon,4,633 1541801,magallan_(arknights),4,633 1782915,ijichi_nijika,4,632 1411715,okusawa_misaki,4,628 1875918,tiki_(young)_(fire_emblem),4,627 1790615,spicy_nun_(diva),4,625 1067206,hayasaka_mirei,4,623 1781831,jeanne_d'arc_(swimsuit_archer)_(second_ascension)_(fate),4,623 1420746,udagawa_tomoe,4,621 1445024,shirasagi_chisato,4,621 1678878,la_pluma_(arknights),4,619 1265247,sato_shin,4,617 1491428,takamiya_rion,4,612 1623487,crypto_(apex_legends),4,612 1533772,uzuki_sayaka,4,607 1533771,kumada_masaru,4,600 ================================================ FILE: models/configs/anything_v3.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 params: layer: "hidden" layer_idx: -2 ================================================ FILE: models/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: models/configs/v1-inference_clip_skip_2.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 params: layer: "hidden" layer_idx: -2 ================================================ FILE: models/configs/v1-inference_clip_skip_2_fp16.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: use_fp16: True image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_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 params: layer: "hidden" layer_idx: -2 ================================================ FILE: models/configs/v1-inference_fp16.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: use_fp16: True image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_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: models/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: models/configs/v2-inference-v.yaml ================================================ model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False # we set this to false because this is an inference only config unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: #attn_type: "vanilla-xformers" double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" ================================================ FILE: models/configs/v2-inference-v_fp32.yaml ================================================ model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: parameterization: "v" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False # we set this to false because this is an inference only config unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: #attn_type: "vanilla-xformers" double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" ================================================ FILE: models/configs/v2-inference.yaml ================================================ model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False # we set this to false because this is an inference only config unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: True image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: #attn_type: "vanilla-xformers" double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" ================================================ FILE: models/configs/v2-inference_fp32.yaml ================================================ model: base_learning_rate: 1.0e-4 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False # we set this to false because this is an inference only config unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True use_fp16: False image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: #attn_type: "vanilla-xformers" double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" ================================================ FILE: models/configs/v2-inpainting-inference.yaml ================================================ model: base_learning_rate: 5.0e-05 target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false conditioning_key: hybrid scale_factor: 0.18215 monitor: val/loss_simple_ema finetune_keys: null use_ema: False unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: use_checkpoint: True image_size: 32 # unused in_channels: 9 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_head_channels: 64 # need to fix for flash-attn use_spatial_transformer: True use_linear_in_transformer: True transformer_depth: 1 context_dim: 1024 legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: #attn_type: "vanilla-xformers" double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [ ] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder params: freeze: True layer: "penultimate" data: target: ldm.data.laion.WebDataModuleFromConfig params: tar_base: null # for concat as in LAION-A p_unsafe_threshold: 0.1 filter_word_list: "data/filters.yaml" max_pwatermark: 0.45 batch_size: 8 num_workers: 6 multinode: True min_size: 512 train: shards: - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" shuffle: 10000 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.RandomCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 # NOTE use enough shards to avoid empty validation loops in workers validation: shards: - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " shuffle: 0 image_key: jpg image_transforms: - target: torchvision.transforms.Resize params: size: 512 interpolation: 3 - target: torchvision.transforms.CenterCrop params: size: 512 postprocess: target: ldm.data.laion.AddMask params: mode: "512train-large" p_drop: 0.25 lightning: find_unused_parameters: True modelcheckpoint: params: every_n_train_steps: 5000 callbacks: metrics_over_trainsteps_checkpoint: params: every_n_train_steps: 10000 image_logger: target: main.ImageLogger params: enable_autocast: False disabled: False batch_frequency: 1000 max_images: 4 increase_log_steps: False log_first_step: False log_images_kwargs: use_ema_scope: False inpaint: False plot_progressive_rows: False plot_diffusion_rows: False N: 4 unconditional_guidance_scale: 5.0 unconditional_guidance_label: [""] ddim_steps: 50 # todo check these out for depth2img, ddim_eta: 0.0 # todo check these out for depth2img, trainer: benchmark: True val_check_interval: 5000000 num_sanity_val_steps: 0 accumulate_grad_batches: 1 ================================================ FILE: models/controlnet/put_controlnets_and_t2i_here ================================================ ================================================ FILE: models/diffusers/put_diffusers_models_here ================================================ ================================================ FILE: models/embeddings/put_embeddings_or_textual_inversion_concepts_here ================================================ ================================================ FILE: models/gligen/put_gligen_models_here ================================================ ================================================ FILE: models/hypernetworks/put_hypernetworks_here ================================================ ================================================ FILE: models/inpaint/put_inpaint_here ================================================ ================================================ FILE: models/loras/put_loras_here ================================================ ================================================ FILE: models/prompt_expansion/fooocus_expansion/config.json ================================================ { "_name_or_path": "gpt2", "activation_function": "gelu_new", "architectures": [ "GPT2LMHeadModel" ], "attn_pdrop": 0.1, "bos_token_id": 50256, "embd_pdrop": 0.1, "eos_token_id": 50256, "pad_token_id": 50256, "initializer_range": 0.02, "layer_norm_epsilon": 1e-05, "model_type": "gpt2", "n_ctx": 1024, "n_embd": 768, "n_head": 12, "n_inner": null, "n_layer": 12, "n_positions": 1024, "reorder_and_upcast_attn": false, "resid_pdrop": 0.1, "scale_attn_by_inverse_layer_idx": false, "scale_attn_weights": true, "summary_activation": null, "summary_first_dropout": 0.1, "summary_proj_to_labels": true, "summary_type": "cls_index", "summary_use_proj": true, "task_specific_params": { "text-generation": { "do_sample": true, "max_length": 50 } }, "torch_dtype": "float32", "transformers_version": "4.23.0.dev0", "use_cache": true, "vocab_size": 50257 } ================================================ FILE: models/prompt_expansion/fooocus_expansion/merges.txt ================================================ #version: 0.2 - Trained by `huggingface/tokenizers` Ġ t Ġ a h e i n r e o n Ġt he e r Ġ s a t Ġ w Ġ o e n Ġ c i t i s a n o r e s Ġ b e d Ġ f in g Ġ p o u Ġa n a l a r Ġt o Ġ m Ġo f Ġ in Ġ d Ġ h Ġan d i c a s l e Ġt h i on o m l l en t Ġ n Ġ l s t Ġ re v e Ġ e r o l y Ġb e Ġ g Ġ T c t Ġ S i d o t Ġ I u t e t Ġ A Ġ is Ġ on i m a m o w a y a d s e Ġth at Ġ C i g Ġf or a c Ġ y v er u r Ġ u l d Ġs t Ġ M ' s Ġ he Ġ it at ion it h i r c e Ġy ou i l Ġ B Ġw h o l Ġ P Ġw ith Ġ 1 t er c h Ġa s Ġw e Ġ ( n d i ll Ġ D i f Ġ 2 a g er s k e Ġ " Ġ H e m Ġc on Ġ W Ġ R he r Ġw as Ġ r o d Ġ F u l at e Ġa t r i p p o re ĠT he Ġs e u s Ġp ro Ġh a u m Ġa re Ġd e a in an d Ġo r ig h es t is t a b r om Ġ N t h Ġc om Ġ G u n o p 0 0 Ġ L Ġn ot es s Ġe x Ġ v re s Ġ E e w it y an t Ġb y e l o s or t o c q u Ġf rom Ġha ve Ġs u i ve ou ld Ġs h Ġth is n t r a p e igh t ar t m ent Ġa l u st en d - - al l Ġ O ac k Ġc h Ġ le i es re d ar d â Ģ ou t Ġ J Ġa b e ar i v al ly ou r o st g h p t Ġp l as t Ġc an a k om e u d T he Ġh is Ġd o Ġg o Ġh as g e ' t Ġ U r ou Ġs a Ġ j Ġb ut Ġw or Ġa ll e ct Ġ k am e Ġw ill o k Ġw he Ġthe y id e 0 1 f f ic h p l t her Ġt r . . 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sincere singular situated sleek slick smart snug solemn solid soothing sophisticated sought sparkling special spectacular sped spirited spiritual splendid spread stable steady still stimulated stimulating stirred straightforward striking strong structured stunning sturdy stylish sublime successful sunny superb superior supplied supported supportive supreme sure surreal sweet symbolic symmetry synchronized systematic tailored taking targeted taught tempting tender terrific thankful theatrical thought thoughtful thrilled thrilling thriving tidy timeless touching tough trained tranquil transformed translucent transparent transported tremendous trendy tried trim true trustworthy unbelievable unconditional uncovered unified unique united universal unmatched unparalleled upheld valiant valued varied very vibrant virtuous vivid warm wealthy whole winning wished witty wonderful worshipped worthy ================================================ FILE: 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14804, "Ġbaseline": 14805, "ĠRate": 14806, "Ġislands": 14807, "Ġ((": 14808, "green": 14809, "ixels": 14810, "Ġnamely": 14811, "ĠVillage": 14812, "than": 14813, "amy": 14814, "Version": 14815, "gmail": 14816, "entials": 14817, "ĠSud": 14818, "ĠMelbourne": 14819, "Ġarriving": 14820, "Ġquantum": 14821, "eff": 14822, "ropolitan": 14823, "Tri": 14824, "Ġfuneral": 14825, "ĠIR": 14826, "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ": 14827, "ĠCob": 14828, "itably": 14829, "Ġturb": 14830, "Ġcombo": 14831, "Review": 14832, "Ġdeployment": 14833, "uity": 14834, "ĠBott": 14835, "Ġinvisible": 14836, "Ġrendering": 14837, "Ġunlocked": 14838, "Ġaqu": 14839, "ĠVladimir": 14840, "Ġpad": 14841, "ĠBrain": 14842, "ĠLegacy": 14843, "dragon": 14844, "ĠKurdish": 14845, "Ġsounded": 14846, "Ġdetained": 14847, "ĠDM": 14848, "gary": 14849, "Ġdaughters": 14850, "Ġdisturbing": 14851, "uka": 14852, "ĠParad": 14853, "Ġtast": 14854, "Ġunfortunate": 14855, "Ġul": 14856, "emin": 14857, "Ġattendance": 14858, "trl": 14859, "Ġparks": 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"UTERS": 14974, "Ġabol": 14975, "Ġrefresh": 14976, "Ġarbitrary": 14977, "pection": 14978, "Ġtroubles": 14979, "Ġ});": 14980, "tv": 14981, "Ġpilots": 14982, "Ġdistribute": 14983, "Ġaudit": 14984, "Ġpause": 14985, "original": 14986, "Ġrivals": 14987, "£": 14988, "Fig": 14989, "TL": 14990, "abil": 14991, "rying": 14992, "Lin": 14993, "ioned": 14994, "lon": 14995, "Ġfancy": 14996, "Ġcrashed": 14997, "Ġtract": 14998, "Ġshed": 14999, "Ġconsume": 15000, "Based": 15001, "download": 15002, "init": 15003, "Ġvoltage": 15004, "Introdu": 15005, "Ġcondemned": 15006, "ĠFinance": 15007, "respect": 15008, "Ġexcluded": 15009, "Ġestablishing": 15010, "heric": 15011, "Ġheritage": 15012, "Ġspectacular": 15013, "Ġunst": 15014, "ĠSnowden": 15015, "ĠLane": 15016, "San": 15017, "Ġprotections": 15018, "struction": 15019, "incinn": 15020, "Ġmacro": 15021, "Custom": 15022, "iosity": 15023, "Ġesp": 15024, "Ġfunctioning": 15025, "Ġmush": 15026, "Ġpuzzle": 15027, "Ġethical": 15028, "Mal": 15029, "Ġgoverning": 15030, "ĠFerguson": 15031, "Ġrestored": 15032, "Ġstressed": 15033, "ĠCounter": 15034, "ĠKas": 15035, "clip": 15036, "ANS": 15037, "Ġseiz": 15038, "UK": 15039, "byss": 15040, "oldown": 15041, "api": 15042, "Ġpermanently": 15043, "ounters": 15044, "West": 15045, "Through": 15046, "Light": 15047, "atoes": 15048, "Ġneat": 15049, "Ġcord": 15050, "urer": 15051, "Ġseverely": 15052, "ĠAven": 15053, "Ġinterrog": 15054, "Ġtriple": 15055, "Given": 15056, "Number": 15057, "Ġarise": 15058, "Ġsher": 15059, "plant": 15060, "Ġflower": 15061, "ĠCou": 15062, "Ġate": 15063, "Ġnewer": 15064, "bul": 15065, "Ġmeanwhile": 15066, "ĠLair": 15067, "Ġadjustment": 15068, "ĠCopyright": 15069, "Ġdivers": 15070, "iological": 15071, "Ġgamers": 15072, "oat": 15073, "Ġhistorically": 15074, "Ġanalog": 15075, "Ġlongtime": 15076, "Ġprescription": 15077, "ĠMist": 15078, "ĠHyper": 15079, "ĠMaine": 15080, "ĠDeity": 15081, "Ġmultipl": 15082, "ĠReincarn": 15083, "ĠHyd": 15084, "ĠPic": 15085, "Sil": 15086, "rants": 15087, "ĠCris": 15088, ".;": 15089, "({": 15090, "ependence": 15091, "Ġrecy": 15092, "ateur": 15093, "Ġquad": 15094, "Ġglob": 15095, "Ġconced": 15096, "team": 15097, "Ġcapitalist": 15098, "ĠLot": 15099, "Ġroyal": 15100, "ĠCyber": 15101, "Ġblacks": 15102, "metic": 15103, "riv": 15104, "ĠDanny": 15105, "Ġspo": 15106, "ĠRO": 15107, "Ġanimated": 15108, "rypted": 15109, "ĠDeputy": 15110, "Ġrendered": 15111, "FE": 15112, "Ġstreak": 15113, "Ġclouds": 15114, "ĠDoug": 15115, "~~~~~~~~": 15116, "Ġdiscour": 15117, "ĠVeh": 15118, "Ġpsychology": 15119, "ĠJourney": 15120, "Ġcrystal": 15121, "ĠFrost": 15122, "Ġsuspicion": 15123, "Ġrelate": 15124, "orus": 15125, "ĠCrypt": 15126, "ĠNVIDIA": 15127, "comed": 15128, "uting": 15129, "incinnati": 15130, "Ġvulnerability": 15131, "ostic": 15132, "Ġisolation": 15133, "Ġcooling": 15134, "ĠCoalition": 15135, "Ġ119": 15136, "Four": 15137, "ĠDeal": 15138, "Ġâī": 15139, "semble": 15140, "rament": 15141, "ĠBarcelona": 15142, "Ġ102": 15143, "Ġcocaine": 15144, "ocalypse": 15145, "Feb": 15146, "ogenic": 15147, "Ġmutation": 15148, "Ġcryptoc": 15149, "ĠKel": 15150, "ĠGit": 15151, "ais": 15152, "Ġsisters": 15153, "ANK": 15154, "Ġactivate": 15155, "Ter": 15156, "Ġdread": 15157, "ylon": 15158, "Ġpropri": 15159, "Aust": 15160, "ĠDefault": 15161, "Ġoutdoor": 15162, "Ġsheer": 15163, "ceive": 15164, "Ġgently": 15165, "о": 15166, "Program": 15167, "ĠâĨĴ": 15168, "Ġvegan": 15169, "ĠCrus": 15170, "Ġresponsibilities": 15171, "ĠHR": 15172, "OLD": 15173, "Ġprevents": 15174, "Ġstiff": 15175, "ĠWere": 15176, "Ġathletic": 15177, "ĠScore": 15178, "Ġ):": 15179, "Ġcolumns": 15180, "ĠLoc": 15181, "available": 15182, "ĠFram": 15183, "ĠSessions": 15184, "Ġcompanion": 15185, "Ġpacks": 15186, "140": 15187, "ĠKnights": 15188, "Ġfart": 15189, "Ġstreams": 15190, "Ġshore": 15191, "Ġappeals": 15192, "ĠPerformance": 15193, "haul": 15194, "ĠStra": 15195, "ĠNag": 15196, "103": 15197, "ĠTransportation": 15198, "BB": 15199, "Ev": 15200, "zan": 15201, "Public": 15202, "Ġtwin": 15203, "ulsion": 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16291, "ĠDiet": 16292, "Ġpronounced": 16293, "Ġcreators": 16294, "Ġearthquake": 16295, "attery": 16296, "geons": 16297, "Ġod": 16298, "Ġlaying": 16299, "orp": 16300, "Ult": 16301, "project": 16302, "Ġundermin": 16303, "Ġsequel": 16304, "Sam": 16305, "ĠDarkness": 16306, "Ġreception": 16307, "bull": 16308, "YS": 16309, "ĠVir": 16310, "Ġsequences": 16311, "ĠCoin": 16312, "Ġoutfit": 16313, "ĠWait": 16314, "119": 16315, "Ġdelivers": 16316, "......": 16317, "Ġblown": 16318, "ĠEsc": 16319, "ĠMath": 16320, "perm": 16321, "ĠUl": 16322, "Ġglim": 16323, "Ġfacial": 16324, "Ġgreenhouse": 16325, "Ġtokens": 16326, "/-": 16327, "ĠAnnual": 16328, "ĠONE": 16329, "Ġteenage": 16330, "ĠPhysical": 16331, "ĠLang": 16332, "ĠCelt": 16333, "Ġsued": 16334, "ividually": 16335, "Ġpatience": 16336, "chair": 16337, "regular": 16338, "Ġaug": 16339, "inv": 16340, "except": 16341, "ĠLil": 16342, "Ġnest": 16343, "fd": 16344, "sum": 16345, "ĠChase": 16346, "Russia": 16347, "ĠJennifer": 16348, "Ġoffseason": 16349, 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16463, "Ġintentionally": 16464, "Ġdemol": 16465, "ueller": 16466, "ĠSale": 16467, "Ġdebris": 16468, "spring": 16469, "Ġleap": 16470, ">>>>": 16471, "Ġcontainers": 16472, "selling": 16473, "ranean": 16474, "attering": 16475, "Ġcommented": 16476, "ĠCM": 16477, "onut": 16478, "Ġwoods": 16479, "especially": 16480, "Ġorganize": 16481, "ivic": 16482, "ĠWoods": 16483, "anga": 16484, "squ": 16485, "Ġmaj": 16486, "amon": 16487, "Ġaxis": 16488, "Ġ1974": 16489, "ĠDenmark": 16490, "Ġwarrior": 16491, "ĠPand": 16492, "Ġoutlined": 16493, "ĠBO": 16494, "insula": 16495, "zilla": 16496, "ebook": 16497, "Ġdare": 16498, "Ġsearched": 16499, "Ġnavigate": 16500, "Sn": 16501, "writing": 16502, "Ġunited": 16503, "Japan": 16504, "ĠHebrew": 16505, "Ġflame": 16506, "Ġrelies": 16507, "Ġcatching": 16508, "ĠSho": 16509, "Ġimprisonment": 16510, "Ġpockets": 16511, "Ġclosure": 16512, "ĠFam": 16513, "tim": 16514, "adequ": 16515, "Activity": 16516, "Ġrecruiting": 16517, "ĠWATCH": 16518, "ĠArgentina": 16519, "dest": 16520, "Ġapologize": 16521, "oro": 16522, "Ġlacks": 16523, "Ġtuned": 16524, "ĠGriffin": 16525, "Ġinfamous": 16526, "Ġcelebrity": 16527, "sson": 16528, "Ġ----------------------------------------------------------------": 16529, "ĠIsis": 16530, "ĠDisplay": 16531, "Ġcredibility": 16532, "Ġeconomies": 16533, "Ġheadline": 16534, "ĠCowboys": 16535, "Ġindef": 16536, "Ġlately": 16537, "Ġincentives": 16538, "button": 16539, "ĠMob": 16540, "Aut": 16541, "Ġresigned": 16542, "ĠOm": 16543, "camp": 16544, "Ġprofiles": 16545, "Ġschemes": 16546, "olphins": 16547, "ayed": 16548, "Clinton": 16549, "enh": 16550, "ĠYahoo": 16551, "Ġabst": 16552, "Ġank": 16553, "suits": 16554, "Ġwished": 16555, "ĠMarco": 16556, "udden": 16557, "Ġsphere": 16558, "ĠBishop": 16559, "Ġincorporated": 16560, "ĠPlant": 16561, "114": 16562, "Ġhated": 16563, "pic": 16564, "Ġdonate": 16565, "Ġlined": 16566, "Ġbeans": 16567, "Ġstealing": 16568, "Ġcostume": 16569, "Ġsheriff": 16570, "Ġforty": 16571, "Ġintact": 16572, "Ġadapted": 16573, "Ġtravelling": 16574, "bart": 16575, "Ġnicely": 16576, "Ġdried": 16577, "Ġscal": 16578, "osity": 16579, "NOTE": 16580, "ĠBh": 16581, "ĠBroncos": 16582, "ĠIgn": 16583, "Ġintimate": 16584, "Ġchemistry": 16585, "Ġoptimal": 16586, "Deb": 16587, "ĠGeneration": 16588, "Ġ],": 16589, "ichi": 16590, "ĠWii": 16591, "ĠYOUR": 16592, "ventions": 16593, "Write": 16594, "Ġpopul": 16595, "unning": 16596, "ĠWor": 16597, "Vol": 16598, "Ġqueen": 16599, "heads": 16600, "KK": 16601, "Ġanalyze": 16602, "opic": 16603, "earchers": 16604, "Ġdot": 16605, "legraph": 16606, "astically": 16607, "Ġupgrades": 16608, "Ġcares": 16609, "Ġextending": 16610, "Ġfreeze": 16611, "Ġinability": 16612, "Ġorgans": 16613, "Ġpretend": 16614, "Ġoutlet": 16615, "113": 16616, "olan": 16617, "ĠMall": 16618, "uling": 16619, "talk": 16620, "Ġexpressing": 16621, "ĠAlways": 16622, "ĠBegin": 16623, "files": 16624, "Ġlicenses": 16625, "%%": 16626, "ĠMitt": 16627, "Ġfilters": 16628, "ĠMilwaukee": 16629, "GN": 16630, "Ġunfold": 16631, "Mo": 16632, "Ġnutrition": 16633, "ppo": 16634, "Bo": 16635, "Ġfounding": 16636, "Ġundermine": 16637, "Ġeasiest": 16638, "ĠCzech": 16639, "ĠMack": 16640, "Ġsexuality": 16641, "ĠNixon": 16642, "Win": 16643, "ĠArn": 16644, "ĠKin": 16645, "ãĤ£": 16646, "icer": 16647, "Ġfortun": 16648, "Ġsurfaces": 16649, "aghd": 16650, "Ġcarriers": 16651, "ĠPART": 16652, "ĠTib": 16653, "Ġinterval": 16654, "Ġfrustrating": 16655, "ĠShip": 16656, "ĠArmed": 16657, "ffe": 16658, "Ġboats": 16659, "ĠAbraham": 16660, "inis": 16661, "Ġsuited": 16662, "thread": 16663, "iov": 16664, "abul": 16665, "ĠVenezuela": 16666, "Ġtom": 16667, "super": 16668, "Ġcastle": 16669, "although": 16670, "ioxide": 16671, "eches": 16672, "Ġevolutionary": 16673, "Ġnegotiate": 16674, "Ġconfronted": 16675, "Remember": 16676, "Ġ170": 16677, "Such": 16678, "Ġ911": 16679, "mult": 16680, "ĠAbyss": 16681, "urry": 16682, "kees": 16683, "spec": 16684, "ĠBarbara": 16685, "Ġbelonging": 16686, "Ġvillain": 16687, "istani": 16688, "Ġaccountable": 16689, "Ġportions": 16690, "ĠDecl": 16691, "Ur": 16692, "ĠKate": 16693, "gre": 16694, "Ġmagazines": 16695, "UCK": 16696, "Ġregulate": 16697, "omon": 16698, "ĠAlmost": 16699, "Ġoverview": 16700, "Ġscram": 16701, "Ġloot": 16702, "ĠFitz": 16703, "Ġcharacteristic": 16704, "ĠSnake": 16705, "say": 16706, "ĠRico": 16707, "Ġtrait": 16708, "ĠJoined": 16709, "aucus": 16710, "Ġadaptation": 16711, "ĠAirlines": 16712, "Ġarchae": 16713, "ĠIde": 16714, "Ġbikes": 16715, "Ġliterary": 16716, "Ġinfluences": 16717, "ĠUsed": 16718, "Creat": 16719, "Ġplea": 16720, "ĠDefence": 16721, "ĠAssass": 16722, "Ġpond": 16723, "ULT": 16724, ")\"": 16725, "Ġevaluated": 16726, "Ġobtaining": 16727, "Ġdemographic": 16728, "Ġvigil": 16729, "aley": 16730, "Ġspouse": 16731, "ĠSeahawks": 16732, "respons": 16733, "ĠBelt": 16734, "umatic": 16735, "Ġrises": 16736, "runner": 16737, "ĠMichelle": 16738, "Ġpotent": 16739, "race": 16740, "ĠPAC": 16741, "Find": 16742, "olesterol": 16743, "ISS": 16744, "ĠIntroduced": 16745, "resses": 16746, "ignment": 16747, "Os": 16748, "ĠTu": 16749, "ĠDex": 16750, "icides": 16751, "Ġsparked": 16752, "ĠLaura": 16753, "ĠBryant": 16754, "Ġsmiling": 16755, "ĠNexus": 16756, "Ġdefendants": 16757, "ĠCatal": 16758, "Ġdishes": 16759, "shaped": 16760, "Ġprolong": 16761, "mt": 16762, "($": 16763, "ãĢĤ": 16764, "Ġcalculations": 16765, "ĠSame": 16766, "Ġpiv": 16767, "HH": 16768, "Ġcancelled": 16769, "Ġgrin": 16770, "Ġterritories": 16771, "istically": 16772, "Come": 16773, "ĠParent": 16774, "Project": 16775, "Ġneglig": 16776, "ĠPrivacy": 16777, "Ġammo": 16778, "LECT": 16779, "olutely": 16780, "ĠEpic": 16781, "Ġmisunder": 16782, "wal": 16783, "April": 16784, "mos": 16785, "pathy": 16786, "ĠCarson": 16787, "Ġalbums": 16788, "ĠEasy": 16789, "Ġpistol": 16790, "<<": 16791, "Ġ\\(": 16792, "target": 16793, "help": 16794, "Ġinterpre": 16795, "conscious": 16796, "ĠHousing": 16797, "ĠJoint": 16798, "127": 16799, "Ġbeers": 16800, "science": 16801, "ĠFirefox": 16802, "effective": 16803, "ĠCabin": 16804, "ĠOkay": 16805, "ĠApplic": 16806, "Ġspacecraft": 16807, "ĠSR": 16808, "vet": 16809, "ĠStrange": 16810, "SB": 16811, "Ġcorps": 16812, "iberal": 16813, "efficient": 16814, "Ġprevalence": 16815, "Ġeconomists": 16816, "118": 16817, "Thread": 16818, "ordable": 16819, "ODE": 16820, "ĠCant": 16821, "=-=-": 16822, "ifiable": 16823, "ĠAround": 16824, "Ġpole": 16825, "Ġwillingness": 16826, "CLA": 16827, "ĠKid": 16828, "Ġcomplement": 16829, "Ġscattered": 16830, "Ġinmates": 16831, "Ġbleeding": 16832, "every": 16833, "Ġqueue": 16834, "ĠTrain": 16835, "Ġhij": 16836, "Ġmelee": 16837, "pleted": 16838, "Ġdigit": 16839, "Ġgem": 16840, "official": 16841, "Ġlifting": 16842, "е": 16843, "Requ": 16844, "itutes": 16845, "Ġpackaging": 16846, "ĠWorkers": 16847, "hran": 16848, "ĠLebanon": 16849, "olesc": 16850, "Ġpunished": 16851, "ĠJuan": 16852, "Ġjam": 16853, "ĠDocument": 16854, "Ġmapping": 16855, "icates": 16856, "Ġinevitably": 16857, "Ġvanilla": 16858, "ĠTon": 16859, "Ġwatches": 16860, "Ġleagues": 16861, 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16919, "ĠRocket": 16920, "vid": 16921, "Exception": 16922, "pent": 16923, "Ġorganizing": 16924, "Ġencounters": 16925, "ĠTOD": 16926, "Ġjewel": 16927, "Save": 16928, "ĠChristie": 16929, "Ġheating": 16930, "Ġlazy": 16931, "ĠCP": 16932, "Ġcousin": 16933, "Config": 16934, "Ġregener": 16935, "Ġnearest": 16936, "Ġachieving": 16937, "ENS": 16938, "throw": 16939, "ĠRichmond": 16940, "antle": 16941, "2002": 16942, "Ġanten": 16943, "bird": 16944, "133": 16945, "Ġnarc": 16946, "raint": 16947, "unny": 16948, "ĠHispanic": 16949, "ournaments": 16950, "Ġprophe": 16951, "ĠThailand": 16952, "ĠTi": 16953, "Ġinjection": 16954, "Ġinherit": 16955, "ravis": 16956, "Ġmedi": 16957, "Ġwhoever": 16958, "ĠDEBUG": 16959, "GP": 16960, "ĠHud": 16961, "Card": 16962, "prom": 16963, "Ġpor": 16964, "Ġoverhead": 16965, "Law": 16966, "Ġviolate": 16967, "Ġheated": 16968, "Ġdescriptions": 16969, "Ġachievements": 16970, "ĠBeer": 16971, "ĠQuant": 16972, "Was": 16973, "Ġeighth": 16974, "ĠIv": 16975, "Ġspecialized": 16976, "UPDATE": 16977, "ĠDelta": 16978, "Pop": 16979, "Jul": 16980, "ĠAsk": 16981, "ophy": 16982, "Ġnewsletters": 16983, "ĠTool": 16984, "Ġgard": 16985, "ĠConfeder": 16986, "ĠGMT": 16987, "ĠAbbott": 16988, "Ġimmunity": 16989, "ĠVM": 16990, "Islam": 16991, "Ġimplicit": 16992, "wd": 16993, "Ġ1944": 16994, "ravity": 16995, "ometric": 16996, "Ġsurviving": 16997, "urai": 16998, "ĠPrison": 16999, "Ġrust": 17000, "ĠSketch": 17001, "Ġbees": 17002, "ĠTheory": 17003, "Ġmerit": 17004, "Tex": 17005, "chat": 17006, "Ġmim": 17007, "Ġpaste": 17008, "ĠKoch": 17009, "Ġignorance": 17010, "ĠShoot": 17011, "Ġbasement": 17012, "United": 17013, "ĠAdvis": 17014, "height": 17015, "Ġfoster": 17016, "Ġdetain": 17017, "information": 17018, "Ġneural": 17019, "';": 17020, "Ġproves": 17021, "allery": 17022, "Ġinvitation": 17023, "umbers": 17024, "Ġcattle": 17025, "Ġbicycle": 17026, "zi": 17027, "Ġconsultant": 17028, "Ġapology": 17029, "ĠTiger": 17030, "Ġ123": 17031, "999": 17032, "Ġindividually": 17033, "rt": 17034, "igion": 17035, "ĠBrazilian": 17036, "Ġdisturb": 17037, "Ġentrepreneurs": 17038, "Ġforests": 17039, "cerpt": 17040, "plates": 17041, "pher": 17042, "clipse": 17043, "Ġtwitter": 17044, "Ġacids": 17045, "ographical": 17046, "hum": 17047, "ĠBald": 17048, "ifully": 17049, "Ġcompiler": 17050, "ĠDA": 17051, "Ġdonor": 17052, "asi": 17053, "Ġtribal": 17054, "lash": 17055, "ĠConfig": 17056, "Ġapplicants": 17057, "Ġsalaries": 17058, "135": 17059, "Putin": 17060, "ĠFocus": 17061, "irs": 17062, "Ġmisconduct": 17063, "ĠHaz": 17064, "Ġeaten": 17065, "Mobile": 17066, "Muslim": 17067, "ĠMarcus": 17068, "viol": 17069, "Ġfavorable": 17070, "Ġstub": 17071, "adin": 17072, "ĠHob": 17073, "Ġfaithful": 17074, "Ġelectronics": 17075, "Ġvacuum": 17076, "wait": 17077, "backed": 17078, "economic": 17079, "dist": 17080, "Ġtenure": 17081, "Ġsincere": 17082, "ĠTogether": 17083, "ĠWave": 17084, "Ġprogression": 17085, "Ġdenying": 17086, "Ġdistress": 17087, "braska": 17088, "third": 17089, "Ġmixing": 17090, "Ġcolonial": 17091, "Ġprivately": 17092, "Ġunrest": 17093, "aternity": 17094, "Ġpremises": 17095, "anti": 17096, "gregation": 17097, "Ġlicence": 17098, "ĠHind": 17099, "ĠSamuel": 17100, "Ġconvincing": 17101, "ĠAce": 17102, "ĠRust": 17103, "ĠNetanyahu": 17104, "Ġhandles": 17105, "ĠPatch": 17106, "oriented": 17107, "aho": 17108, "ĠGonz": 17109, "Ġhackers": 17110, "claimer": 17111, "Ġcustoms": 17112, "ĠGran": 17113, "fighters": 17114, "Ġluc": 17115, "Ġmanuscript": 17116, "arenthood": 17117, "Ġdevil": 17118, "Ġwarriors": 17119, "Ġoffenders": 17120, "William": 17121, "Ġholidays": 17122, "Ġnightmare": 17123, "Ġlever": 17124, "ifferent": 17125, "Stat": 17126, "Ġexhibition": 17127, "puted": 17128, "ĠPure": 17129, "Ġalpha": 17130, "Ġenthusiasm": 17131, "ĠRepresentatives": 17132, "EAR": 17133, "ĠTyp": 17134, "Ġwheat": 17135, "ĠAlf": 17136, "Ġcorrection": 17137, "Ġevangel": 17138, "ATT": 17139, "Miss": 17140, "Ġsoup": 17141, "Ġimplied": 17142, "param": 17143, "Ġsexy": 17144, "ĠLux": 17145, "Ġrepublic": 17146, "patch": 17147, "ablish": 17148, "Ġicons": 17149, "Ġfathers": 17150, "ĠGET": 17151, "ĠCarib": 17152, "Ġregulated": 17153, "ĠCohen": 17154, "ĠBobby": 17155, "Ġner": 17156, "Ġbent": 17157, "ventory": 17158, "ĠAlong": 17159, "ĠEST": 17160, "ĠWallace": 17161, "Ġmurders": 17162, "rise": 17163, "kell": 17164, "ĠCommonwealth": 17165, "Ġnasty": 17166, "eta": 17167, "ĠMIT": 17168, "Ġadministered": 17169, "Ġgenuinely": 17170, "Editor": 17171, "nick": 17172, "Ġhydro": 17173, "********************************": 17174, "ĠBle": 17175, "Ġfines": 17176, "Ġgorge": 17177, "ausible": 17178, "rh": 17179, "Ġapple": 17180, "mentioned": 17181, "Ġrope": 17182, "otyp": 17183, "HR": 17184, "Ġdisappointing": 17185, "Ġcage": 17186, "nik": 17187, "Ġdoubts": 17188, "ĠFREE": 17189, "prints": 17190, "ĠMUST": 17191, "Ġvendors": 17192, "ĠInqu": 17193, "Ġliberals": 17194, "Ġcontractor": 17195, "Ġupside": 17196, "children": 17197, "Ġtricky": 17198, "Ġregulators": 17199, "charged": 17200, "liter": 17201, "Ġ***": 17202, "Ġrebell": 17203, "lang": 17204, "Ġlocals": 17205, "Ġphysicians": 17206, "Ġhey": 17207, "arse": 17208, "tm": 17209, "ĠLex": 17210, "Ġbehavioral": 17211, "successful": 17212, "FX": 17213, "Ġbrick": 17214, "ovic": 17215, "Ġconform": 17216, "Ġreviewing": 17217, "Ġinsights": 17218, "Ġbiology": 17219, "ĠRemove": 17220, "ĠExtra": 17221, "Ġcommitting": 17222, "induced": 17223, "ignty": 17224, "igm": 17225, "Ġatomic": 17226, "Common": 17227, "ĠEM": 17228, "ĠPere": 17229, "ĠItems": 17230, "eh": 17231, "Ġpreserved": 17232, "ĠHood": 17233, "Ġprisoner": 17234, "Ġbankruptcy": 17235, "Ġgren": 17236, "ushes": 17237, "Ġexploitation": 17238, "Ġsignatures": 17239, "Ġfinan": 17240, "],\"": 17241, "ĠMR": 17242, "Ġmeg": 17243, "remlin": 17244, "Ġmusicians": 17245, "Ġselecting": 17246, "Ġexamining": 17247, "INK": 17248, "lated": 17249, "Hi": 17250, "Ġartic": 17251, "Ġpets": 17252, "Ġimpair": 17253, "ĠMAN": 17254, "Ġtablets": 17255, "include": 17256, "Range": 17257, "Ġcaut": 17258, "Ġlogs": 17259, "Ġmounting": 17260, "Ġunaware": 17261, "Ġdynamics": 17262, "ĠPalestine": 17263, "ĠQuarter": 17264, "ĠPurple": 17265, "Ġma": 17266, "ĠImport": 17267, "Ġcollections": 17268, "ciation": 17269, "Ġsuccessor": 17270, "Ġclone": 17271, "Ġaiming": 17272, "Ġpossessed": 17273, "Ġsticking": 17274, "Ġshaking": 17275, "Ġlocate": 17276, "ĠHockey": 17277, "Turn": 17278, "170": 17279, "Ġfifteen": 17280, "ĠHarrison": 17281, "Ġcontinuously": 17282, "ĠTC": 17283, "ĠValent": 17284, "ĠRescue": 17285, "Ġbypass": 17286, "amount": 17287, "Ġmast": 17288, "Ġprotects": 17289, "Ġartistic": 17290, "Ġsometime": 17291, "Ġshoe": 17292, "Ġshouted": 17293, "ificant": 17294, "etitive": 17295, "ĠRegister": 17296, "ĠJin": 17297, "Ġconcentrated": 17298, "lington": 17299, "onies": 17300, "Ġgenerator": 17301, "yrim": 17302, "ĠArmen": 17303, "Ġclearing": 17304, "ido": 17305, "ĠTW": 17306, "alph": 17307, "Ġladies": 17308, "Hard": 17309, "Ġdialog": 17310, "Ġinputs": 17311, "æľ": 17312, "Ġposes": 17313, "Ġslots": 17314, "ĠPremium": 17315, "Ġleaks": 17316, "Ġbosses": 17317, "Ġ113": 17318, "course": 17319, "Acc": 17320, "ĠNewton": 17321, "ĠAustria": 17322, "ĠMage": 17323, "Ġteaches": 17324, "abad": 17325, "Ġwears": 17326, "Ġcyl": 17327, "Ġcurse": 17328, "ĠSales": 17329, "ĠWings": 17330, "Ġpsy": 17331, "Ġgaps": 17332, "ĠIceland": 17333, "ĠPinterest": 17334, "Ġlandlord": 17335, "Ġdefinitions": 17336, "ĠKer": 17337, "Ġsufficiently": 17338, "ĠPence": 17339, "ĠArchitect": 17340, "Ġsurpass": 17341, "Ġ114": 17342, "Ġsuperhero": 17343, "ĠDisease": 17344, "Ġpriests": 17345, "ĠCulture": 17346, "Ġdefinitive": 17347, "Ġsecretly": 17348, "ĠDance": 17349, "install": 17350, "chief": 17351, "ĠJessica": 17352, "Would": 17353, "Updated": 17354, "Ġlocker": 17355, "ĠKay": 17356, "Ġmemorial": 17357, "è¦": 17358, "fat": 17359, "Ġdisgu": 17360, "Ġflavors": 17361, "ĠBaseball": 17362, "ĠResistance": 17363, "Ġkicks": 17364, "Ġenv": 17365, "Ġteenagers": 17366, "Dark": 17367, "ĠCAR": 17368, "Ġhalt": 17369, "ĠLG": 17370, "ĠGabriel": 17371, "Ġfever": 17372, "Ġsatur": 17373, "Ġmall": 17374, "Ġaffiliate": 17375, "ĠSleep": 17376, "ĠSpecific": 17377, "ĠVel": 17378, "Ġjar": 17379, "ĠSacred": 17380, "ĠEdwards": 17381, "ĠACL": 17382, "Ġretained": 17383, "ĠGiant": 17384, "Ġlimitation": 17385, "inces": 17386, "Ġrefusal": 17387, "ĠTale": 17388, "ĠButler": 17389, "Ġaccidents": 17390, "ĠCSS": 17391, "Ġimported": 17392, "ĠCopy": 17393, "α": 17394, "ERT": 17395, "zel": 17396, "Ġdivisions": 17397, "hots": 17398, "ĠAlb": 17399, "ĠDS": 17400, "Loader": 17401, "Washington": 17402, "atisf": 17403, "ĠCreative": 17404, "\\.": 17405, "ĠAutom": 17406, "redict": 17407, "Ġreceptor": 17408, "ĠCarlos": 17409, "Method": 17410, "oka": 17411, "Ġmalicious": 17412, "Ġstepping": 17413, ",[": 17414, "ĠDad": 17415, "Ġattraction": 17416, "ĠEffects": 17417, "ĠPirate": 17418, "ĠCer": 17419, "ĠIndustry": 17420, "ĠRud": 17421, "Ġcharter": 17422, "Ġdining": 17423, "Ġinsists": 17424, "Ġconfigure": 17425, "Ġ(#": 17426, "ĠSimple": 17427, "ĠScroll": 17428, "UTC": 17429, "175": 17430, "ĠKon": 17431, "Ġmarketplace": 17432, "ĠãĤ": 17433, "Ġrefres": 17434, "Ġgates": 17435, "erred": 17436, "ĠPod": 17437, "Ġbehave": 17438, "Frank": 17439, "node": 17440, "Ġendorsed": 17441, "hett": 17442, "asive": 17443, "ĠHomeland": 17444, "Ġrides": 17445, "ĠLeave": 17446, "erness": 17447, "Ġflooding": 17448, "AFP": 17449, "Ġrisen": 17450, "Ġcontinually": 17451, "Ġunanim": 17452, "ĠContract": 17453, "ĠPas": 17454, "Ġguided": 17455, "ĠChile": 17456, "bd": 17457, "Ġsucc": 17458, "ptic": 17459, "Ġcommittees": 17460, "ĠLuther": 17461, "ĠAnyone": 17462, "Ġsab": 17463, "124": 17464, "Ġpixel": 17465, "ĠBak": 17466, "ĠTag": 17467, "ĠBennett": 17468, "Enter": 17469, "small": 17470, "ĠPresidential": 17471, "Ġpul": 17472, "Ġcontrace": 17473, "archive": 17474, "Ġcoastal": 17475, "ĠKids": 17476, "192": 17477, "âĢ²": 17478, "icky": 17479, "INGTON": 17480, "Ġwolf": 17481, "ĠStalin": 17482, "Tur": 17483, "idget": 17484, "amas": 17485, "ĠUnless": 17486, "Ġsponsor": 17487, "Ġmorph": 17488, "ĠChoose": 17489, "Ġrunner": 17490, "Ġunbel": 17491, "Ġmud": 17492, "ĠMana": 17493, "Ġdubbed": 17494, "Ġgodd": 17495, "urers": 17496, "window": 17497, "Ġrelied": 17498, "Ġcelebrating": 17499, "osc": 17500, "Ġ135": 17501, "Ġlobbying": 17502, "Ġincomplete": 17503, "Ġrestriction": 17504, "Ġincap": 17505, "itus": 17506, "Ġexpectation": 17507, "ĠApollo": 17508, "Ġintens": 17509, "Ġsync": 17510, "GH": 17511, "Ġmanipulation": 17512, "BY": 17513, "Ġspear": 17514, "Ġbreasts": 17515, "Ġvolcan": 17516, "ilia": 17517, "Material": 17518, "Ġformats": 17519, "ĠBast": 17520, "Ġparliamentary": 17521, "Ġsnake": 17522, "Ġservants": 17523, "ĠTrudeau": 17524, "ĠGrim": 17525, "ĠArabic": 17526, "ĠSCP": 17527, "ĠBoys": 17528, "station": 17529, "Ġprospective": 17530, "orde": 17531, "initialized": 17532, "Ġbored": 17533, "ABLE": 17534, "Ġaccessed": 17535, "Ġtaxi": 17536, "ĠShell": 17537, "aiden": 17538, "ursed": 17539, "inates": 17540, "ĠInsurance": 17541, "ĠPete": 17542, "September": 17543, "650": 17544, "Ġadventures": 17545, "ĠCover": 17546, "Ġtribute": 17547, "Ġsketch": 17548, "Ġempower": 17549, "ĠØ": 17550, "ĠGlenn": 17551, "ĠDaw": 17552, "=\\\"": 17553, "ĠPolitics": 17554, "Ġguides": 17555, "Ġdioxide": 17556, "ĠGore": 17557, "ĠBright": 17558, "ĠSierra": 17559, "Ġvalued": 17560, "cond": 17561, "Ġpointer": 17562, "Select": 17563, "Ġrisky": 17564, "Ġabsorb": 17565, "images": 17566, "Ġrefuses": 17567, "Ġbonuses": 17568, "___": 17569, "Ġhilar": 17570, "ĠFeatures": 17571, "220": 17572, "ĠCollector": 17573, "Foot": 17574, "Ġ1964": 17575, "culus": 17576, "Ġdawn": 17577, "Ġworkout": 17578, "ĠLO": 17579, "Ġphilosophical": 17580, "ĠSandy": 17581, "ĠYouth": 17582, "Ġliable": 17583, "Af": 17584, "blue": 17585, "Ġoverturn": 17586, "lessness": 17587, "ĠTribune": 17588, "ĠIng": 17589, "Ġfactories": 17590, "Ġcatches": 17591, "Ġprone": 17592, "Ġmatrix": 17593, "Ġlogin": 17594, "Ġinacc": 17595, "Ġexert": 17596, "sys": 17597, "Ġneedle": 17598, "ĠQur": 17599, "Ġnotified": 17600, "oulder": 17601, "tx": 17602, "Ġreminds": 17603, "Ġpublishers": 17604, "Ġnort": 17605, "Ġgit": 17606, "Ġflies": 17607, "ĠEmily": 17608, "Ġflowing": 17609, "ĠAlien": 17610, "ĠStrateg": 17611, "Ġhardest": 17612, "Ġmodification": 17613, "API": 17614, "ĠMY": 17615, "Ġcrashes": 17616, "stairs": 17617, "number": 17618, "Ġurging": 17619, "channel": 17620, "ĠFalcon": 17621, "Ġinhabitants": 17622, "Ġterrifying": 17623, "Ġutilize": 17624, "Ġbanner": 17625, "Ġcigarettes": 17626, "Ġsenses": 17627, "ĠHolmes": 17628, "Ġpractition": 17629, "ĠPhillips": 17630, "otto": 17631, "Ġcompile": 17632, "Model": 17633, "ĠKo": 17634, "Ġ[]": 17635, "Americans": 17636, "ĠTerms": 17637, "Ġmedications": 17638, "ĠAna": 17639, "Ġfundamentally": 17640, "ĠNotice": 17641, "Ġweaker": 17642, "Ġ0000": 17643, "Ġgarlic": 17644, "Ġoutbreak": 17645, "Ġeconomist": 17646, "ĠBirth": 17647, "Ġobstacles": 17648, "arcer": 17649, "ĠOrthodox": 17650, "Ġplacebo": 17651, "ĠCrew": 17652, "aspberry": 17653, "ĠAngels": 17654, "Ġdischarge": 17655, "Ġdestructive": 17656, "117": 17657, "ĠRising": 17658, "Ġdairy": 17659, "late": 17660, "Ġcollision": 17661, "ĠTigers": 17662, "eanor": 17663, "ocumented": 17664, "ĠInvalid": 17665, "Ġdont": 17666, "ĠLiter": 17667, "ĠVa": 17668, "Ġhydrogen": 17669, "Ġvariants": 17670, "ĠBrowns": 17671, "Ġ1965": 17672, "Ġindigenous": 17673, "Ġtrades": 17674, "Ġremainder": 17675, "Ġswept": 17676, "ĠImpact": 17677, "Ġredist": 17678, "Ġunint": 17679, "graduate": 17680, "ãĥķ": 17681, "ĠWILL": 17682, "ãģ®ç": 17683, "ĠCritical": 17684, "Ġfisher": 17685, "Ġvicious": 17686, "Ġreversed": 17687, "Year": 17688, "ĠSox": 17689, "Ġshootings": 17690, "Ġfilming": 17691, "Ġtouchdowns": 17692, "aires": 17693, "mel": 17694, "Ġgrandfather": 17695, "Ġaffection": 17696, "ingle": 17697, "Ġoverly": 17698, "Additional": 17699, "Ġsupreme": 17700, "ĠGrad": 17701, "Ġsporting": 17702, "Ġmercy": 17703, "ĠBrooks": 17704, "ounty": 17705, "Ġperforms": 17706, "Ġtightly": 17707, "Ġdemons": 17708, "Ġkillings": 17709, "Ġfaction": 17710, "ĠNova": 17711, "auts": 17712, "Ġundoubtedly": 17713, "arin": 17714, "Ġunderway": 17715, "rak": 17716, "Ġliv": 17717, "ĠRegion": 17718, "Ġbriefing": 17719, "sers": 17720, "cloud": 17721, "ĠMik": 17722, "usp": 17723, "Ġprediction": 17724, "azor": 17725, "Ġportable": 17726, "ĠGand": 17727, "Ġpresenting": 17728, "Ġ1080": 17729, "»": 17730, "ushi": 17731, "ĠSpark": 17732, "thereum": 17733, "Ġjustification": 17734, "ĠNy": 17735, "Ġcontractors": 17736, "mingham": 17737, "ĠStyle": 17738, "åħ": 17739, "ĠChronicles": 17740, "ĠPicture": 17741, "Ġproving": 17742, "Ġwives": 17743, "sett": 17744, "Ġmolecules": 17745, "ĠFairy": 17746, "Ġconsisting": 17747, "Ġpier": 17748, "alone": 17749, "inition": 17750, "Ġnucle": 17751, "json": 17752, "Ġgotta": 17753, "Ġmobil": 17754, "Ġverbal": 17755, "arium": 17756, "Ġmonument": 17757, "ucked": 17758, "Ġ256": 17759, "Tech": 17760, "minecraft": 17761, "ĠTrack": 17762, "Ġtile": 17763, "Ġcompatibility": 17764, "asis": 17765, "Ġsadd": 17766, "Ġinstructed": 17767, "ĠMueller": 17768, "Ġlethal": 17769, "Ġhormone": 17770, "Ġorche": 17771, "else": 17772, "Ġskelet": 17773, "Ġentertaining": 17774, "Ġminimize": 17775, "again": 17776, "Ġundergo": 17777, "Ġconstraints": 17778, "Ġcigarette": 17779, "ĠIslamist": 17780, "Ġtravels": 17781, "ĠPanthers": 17782, "lings": 17783, "Care": 17784, "Ġlawsuits": 17785, "uras": 17786, "Ġcryst": 17787, "Ġlowered": 17788, "Ġaerial": 17789, "Ġcombinations": 17790, "Ġhaun": 17791, "Ġcha": 17792, "Ġvine": 17793, "Ġquantities": 17794, "Ġlinking": 17795, "bank": 17796, "Ġsoy": 17797, "Bill": 17798, "ĠAngela": 17799, "Ġrecipient": 17800, "ĠProtest": 17801, "Ġsocket": 17802, "Ġsolidarity": 17803, "ĠâĨ": 17804, "mill": 17805, "Ġvaries": 17806, "ĠPakistani": 17807, "Dragon": 17808, "Ġune": 17809, "Ġhorizon": 17810, "³³³³³³³³": 17811, "Ġprovinces": 17812, "Ġfrankly": 17813, "Ġenacted": 17814, "notes": 17815, "['": 17816, "Ġ192": 17817, "ocracy": 17818, "Ġendorsement": 17819, "Ġovertime": 17820, "True": 17821, "Lab": 17822, "licted": 17823, "ĠDNC": 17824, "Ġbeats": 17825, "ĠJamie": 17826, "152": 17827, "ĠINT": 17828, "Contact": 17829, "Ġaccounted": 17830, "hash": 17831, "ĠPackers": 17832, "pires": 17833, "Ġlesbian": 17834, "Ġamendments": 17835, "Ġhopeful": 17836, "ĠFinland": 17837, "Ġspotlight": 17838, "Ġconfigured": 17839, "Ġtroubled": 17840, "Ġgaze": 17841, "ĠCalgary": 17842, "Ġreliability": 17843, "Ġinsurg": 17844, "swer": 17845, "buy": 17846, "ĠSkin": 17847, "Ġpixels": 17848, "Ġhandgun": 17849, "Ġparas": 17850, "Ġcategor": 17851, "ĠEL": 17852, "ĠRex": 17853, "Indeed": 17854, "Ġkinda": 17855, "Ġconjunction": 17856, "ĠBryan": 17857, "ĠManufact": 17858, "yang": 17859, "Plus": 17860, "SQL": 17861, "ishment": 17862, "Ġdominate": 17863, "Ġnail": 17864, "Ġoath": 17865, "Ġerupt": 17866, "ĠFine": 17867, "itbart": 17868, "ĠChip": 17869, "ĠAbd": 17870, "ĠNam": 17871, "Ġbuyer": 17872, "Ġdissent": 17873, "Leaks": 17874, "Contin": 17875, "Ġrider": 17876, "ĠSomeone": 17877, "Ġillusion": 17878, "cin": 17879, "ĠBoeing": 17880, "Ġinadequ": 17881, "ovation": 17882, "iants": 17883, "Ġrebuild": 17884, "450": 17885, "ĠDestiny": 17886, "SW": 17887, "ĠTill": 17888, "Hit": 17889, "iaz": 17890, "ĠBangl": 17891, "achers": 17892, "ĠReform": 17893, "Ġsegments": 17894, "Ġsystematic": 17895, "dc": 17896, "ĠConservatives": 17897, "Ġportal": 17898, "hor": 17899, "ĠDragonbound": 17900, "Ġdragged": 17901, "omo": 17902, "Ġthee": 17903, "advert": 17904, "ĠReports": 17905, "ĠEt": 17906, "Ġbarrels": 17907, "August": 17908, "Ġcomparisons": 17909, "Ġhex": 17910, "Ġanthrop": 17911, "\"[": 17912, "borough": 17913, "abi": 17914, "Ġpictured": 17915, "playing": 17916, "ĠAddress": 17917, "ĠMirror": 17918, "Smith": 17919, "Ġtires": 17920, "ĠNPR": 17921, "AAAA": 17922, "Ġclassification": 17923, "ĠThan": 17924, "ĠHarm": 17925, "ĠRA": 17926, "Ġrejection": 17927, "mination": 17928, "Ġranged": 17929, "ĠFalls": 17930, "DI": 17931, "Host": 17932, "ãĤ´": 17933, "ĠExample": 17934, "listed": 17935, "thirds": 17936, "Ġsafegu": 17937, "brand": 17938, "Ġprobable": 17939, "Canada": 17940, "ITION": 17941, "ĠQaeda": 17942, "Ġchick": 17943, "Ġimports": 17944, "hit": 17945, "loc": 17946, "WW": 17947, "Ġblew": 17948, "Ġanytime": 17949, "Ġwholes": 17950, "iked": 17951, "Ġcalculation": 17952, "create": 17953, "ĠOri": 17954, "Ġupgraded": 17955, "Ġappar": 17956, "utory": 17957, "ĠMol": 17958, "Brit": 17959, "ĠJong": 17960, "INAL": 17961, "ĠStarting": 17962, "Ġdice": 17963, "urtle": 17964, "Ġrelying": 17965, "closure": 17966, "Ġprofitable": 17967, "Ġslaughter": 17968, "ĠManual": 17969, "caster": 17970, "Ġ\"$": 17971, "Ġfeather": 17972, "ĠSimply": 17973, "ieves": 17974, "Ġdeterior": 17975, "ĠPCI": 17976, "Ġstamp": 17977, "Ġflaws": 17978, "Ġshade": 17979, "hammer": 17980, "Ġpassport": 17981, "Ġconting": 17982, "amel": 17983, "Ġobservers": 17984, "Ġneglect": 17985, "ĠRB": 17986, "ĠBrotherhood": 17987, "Ġskeptical": 17988, "family": 17989, "usk": 17990, "Ġemotionally": 17991, "âĻ": 17992, "ĠBeta": 17993, "asonable": 17994, "idity": 17995, "ĠMul": 17996, "Ġkicking": 17997, "ĠCarm": 17998, "ollah": 17999, "VERTIS": 18000, "ĠAthen": 18001, "Ġladder": 18002, "ĠBullet": 18003, "å£": 18004, "0001": 18005, "ĠWildlife": 18006, "ĠMask": 18007, "ĠNan": 18008, "Rev": 18009, "Ġunacceptable": 18010, "legal": 18011, "Ġcrowded": 18012, "agi": 18013, "ĠCox": 18014, "je": 18015, "Ġmorality": 18016, "Ġfuels": 18017, "Ġcables": 18018, "Ġmankind": 18019, "ĠCaribbean": 18020, "Ġanchor": 18021, "Ġbyte": 18022, "ĠOften": 18023, "ĠOz": 18024, "Ġcrafted": 18025, "Ġhistorian": 18026, "ĠWu": 18027, "Ġtowers": 18028, "ĠCitizens": 18029, "Ġhelm": 18030, "Ġcredentials": 18031, "Ġsingular": 18032, "ĠJesse": 18033, "Ġtackles": 18034, "Ġcontempt": 18035, "Ġafore": 18036, "ĠShadows": 18037, "Ġnil": 18038, "Ġurgent": 18039, "apple": 18040, "blood": 18041, "Ġvon": 18042, "Ġoffline": 18043, "Ġbreathe": 18044, "Ġjumps": 18045, "Ġirrelevant": 18046, "oxic": 18047, "omal": 18048, "important": 18049, "Jim": 18050, "Ġgloves": 18051, "arming": 18052, "depth": 18053, "Ġtalents": 18054, "ookie": 18055, "ĠSB": 18056, "Ġpalm": 18057, "uffs": 18058, "esta": 18059, "IGH": 18060, "Ġcanon": 18061, "ĠVerizon": 18062, "ĠPle": 18063, "Ġcoupled": 18064, "velt": 18065, "Ġfundraising": 18066, "ĠGetting": 18067, "ĠDLC": 18068, "Ġmathematical": 18069, "ĠHS": 18070, "ĠCardinals": 18071, "telling": 18072, "Ġsponsors": 18073, "ĠÏ": 18074, "ĠBulls": 18075, "option": 18076, "Ġpropose": 18077, "Ġmemorable": 18078, "Ġembraced": 18079, "Ġdeclining": 18080, "Health": 18081, "eda": 18082, "Ġ};": 18083, "Ġspam": 18084, "mile": 18085, "Ġpitcher": 18086, "ĠEight": 18087, "Ġcaring": 18088, "utic": 18089, "role": 18090, "Ġairline": 18091, "ernandez": 18092, "ĠAthlet": 18093, "Ġcertification": 18094, "uxe": 18095, "riger": 18096, "Ġempir": 18097, "Ġsensation": 18098, "Ġdism": 18099, "Ġbolt": 18100, "Ġevolve": 18101, "House": 18102, "Ġconsultation": 18103, "ĠDuty": 18104, "Ġtouches": 18105, "ĠNathan": 18106, "Ġfaint": 18107, "had": 18108, "\"(": 18109, "ĠConsumer": 18110, "ĠExtreme": 18111, "Ġ127": 18112, "ĠHerm": 18113, "ĠSacrament": 18114, "izoph": 18115, "Ġanxious": 18116, "ulously": 18117, "Ġsocially": 18118, "ĠUTC": 18119, "Ġsolving": 18120, "ĠLetter": 18121, "History": 18122, "educ": 18123, "Price": 18124, "));": 18125, "Ġreload": 18126, "amic": 18127, "Ġpork": 18128, "Ġdiscourse": 18129, "Ġtournaments": 18130, "airo": 18131, "ĠKur": 18132, "ĠCosta": 18133, "Ġviolating": 18134, "Ġinterfere": 18135, "Ġrecreational": 18136, "uffle": 18137, "Ġspeeches": 18138, "Ġneeding": 18139, "Ġremembers": 18140, "Ġcredited": 18141, "nia": 18142, "focused": 18143, "amera": 18144, "Ġbru": 18145, "umbs": 18146, "ĠCuban": 18147, "Ġpreceding": 18148, "Ġnonsense": 18149, "acial": 18150, "Ġsmartphones": 18151, "ĠStories": 18152, "Sports": 18153, "ĠEmergency": 18154, "ouncing": 18155, "efined": 18156, "Ġber": 18157, "Ġconsulting": 18158, "Ġmasters": 18159, "heastern": 18160, ".\"[": 18161, "ĠRunning": 18162, "Ġsuscept": 18163, "ĠFeng": 18164, "America": 18165, "prises": 18166, "stitial": 18167, "ĠWeekly": 18168, "ĠGreater": 18169, "modules": 18170, "ifter": 18171, "Graphics": 18172, "uler": 18173, "Ġwholly": 18174, "Ġsuppress": 18175, "Ġconcealed": 18176, "Ġhappily": 18177, "Ġaccepts": 18178, "ĠEnjoy": 18179, "Ġrivers": 18180, "ĠExcept": 18181, "225": 18182, "ĠNHS": 18183, "ĠMcConnell": 18184, "Ġpussy": 18185, "ferred": 18186, "utable": 18187, "Ġattain": 18188, "Ġ>=": 18189, "Ġdeposits": 18190, "rophic": 18191, "Ġnotorious": 18192, "ĠShaw": 18193, "ilitation": 18194, "Ġepidemic": 18195, "allic": 18196, "Ġsmallest": 18197, "ovich": 18198, "Ġaccessories": 18199, "perties": 18200, "Ġsurplus": 18201, "ĠMech": 18202, "Ġambig": 18203, "ĠImmigration": 18204, "Ġchim": 18205, "eval": 18206, "Ġpracticing": 18207, "ĠMystery": 18208, "Ġdomains": 18209, "ĠSilicon": 18210, "apps": 18211, "Ġkilometers": 18212, "ea": 18213, "ĠSmash": 18214, "Ġwarranty": 18215, "Ġnost": 18216, "sil": 18217, "rev": 18218, "Jon": 18219, "ĠDublin": 18220, "Ġtastes": 18221, "Ġbout": 18222, "great": 18223, "error": 18224, "Ġswitches": 18225, "ĠBapt": 18226, "DO": 18227, "oki": 18228, "Ġsourced": 18229, "produ": 18230, "Ġattachment": 18231, "ĠIssue": 18232, "ĠQuestion": 18233, "Join": 18234, "Ġfitted": 18235, "Ġunlawful": 18236, "^^": 18237, "erek": 18238, "Ġauthentication": 18239, "Ġstole": 18240, "Ġaccountability": 18241, "label": 18242, "Search": 18243, "Ġalbeit": 18244, "atican": 18245, "funded": 18246, "ĠAdding": 18247, "ĠIQ": 18248, "Ġsubmar": 18249, "lit": 18250, "aque": 18251, "ĠLearning": 18252, "Ġinteger": 18253, "Master": 18254, "ĠChrom": 18255, "Ġpremier": 18256, "Op": 18257, "ĠLiu": 18258, "Ġblessed": 18259, "ĠGlobe": 18260, "ĠResponse": 18261, "Ġlegitim": 18262, "ĠMerkel": 18263, "Ġdisposal": 18264, "´": 18265, "Ġgauge": 18266, "peat": 18267, "Ġinduced": 18268, "Ġquestionable": 18269, "arthy": 18270, "ĠVit": 18271, "ĠFeed": 18272, "Until": 18273, "Ut": 18274, "worthy": 18275, "RY": 18276, "ĠHerald": 18277, "ĠHammer": 18278, "Ġmedal": 18279, "ĠRivers": 18280, "ĠHack": 18281, "Ġclarify": 18282, "Ġtracked": 18283, "Ġautonomous": 18284, "Ġtenant": 18285, "ĠQatar": 18286, "erie": 18287, "Ġgrim": 18288, "ĠMonitor": 18289, "Ġresistant": 18290, "ĠSpec": 18291, "ĠWells": 18292, "NAS": 18293, "148": 18294, "Ġminers": 18295, "iotics": 18296, "Ġmisses": 18297, "116": 18298, "gian": 18299, "git": 18300, "ĠEyes": 18301, "pres": 18302, "Ġgraduated": 18303, "Ġangel": 18304, "Ġsynchron": 18305, "Ġefficiently": 18306, "Ġtransmitted": 18307, "Harry": 18308, "Ġglobally": 18309, "ENCE": 18310, "ĠMontana": 18311, "raged": 18312, "ĠPrevention": 18313, "Ġpiss": 18314, "ĠLl": 18315, "Ġshelf": 18316, "ĠBJP": 18317, "ĠTestament": 18318, "ĠLate": 18319, "iker": 18320, "ĠHapp": 18321, "ĠJulian": 18322, "hall": 18323, "Ġspont": 18324, "Ġshutdown": 18325, "Ġinconsistent": 18326, "Ġsubscribers": 18327, "Ġskeleton": 18328, "ĠNebraska": 18329, "Ġinspire": 18330, "ĠVoid": 18331, "Feed": 18332, "Ġangles": 18333, "ĠSprings": 18334, "Ġbenchmark": 18335, "Ġvaccines": 18336, "izophren": 18337, "sexual": 18338, "uffed": 18339, "Ġshine": 18340, "ĠKath": 18341, "Ġgesture": 18342, "inea": 18343, "Ġrip": 18344, "Ġoppression": 18345, "Ġconscience": 18346, "bt": 18347, "ĠLum": 18348, "Ġincidence": 18349, "ĠFa": 18350, "wr": 18351, "Ġmineral": 18352, "ĠSpurs": 18353, "alky": 18354, "Ġthunder": 18355, "Ġopio": 18356, "Being": 18357, "ĠPalm": 18358, "Ġwasted": 18359, "Ġlb": 18360, "iaries": 18361, "ĠInitiative": 18362, "Ġcurric": 18363, "Ġmarker": 18364, "ĠMcL": 18365, "Ġextensions": 18366, "ĠPv": 18367, "ĠArms": 18368, "Ġofferings": 18369, "Ġdefenses": 18370, "Ġvendor": 18371, "Ġcontradict": 18372, "ĠColin": 18373, "Ġreddit": 18374, "Ġperipher": 18375, "122": 18376, "Ġsins": 18377, "Edit": 18378, "ICT": 18379, "Soft": 18380, "ĠShah": 18381, "Ġadministrator": 18382, "ĠTrip": 18383, "Ġpornography": 18384, "Ġtuition": 18385, "inence": 18386, "ĠProgress": 18387, "Ġcatalog": 18388, "Ġsuite": 18389, "Ġhike": 18390, "Ġreproductive": 18391, "engine": 18392, "Ġdrought": 18393, "ĠNoah": 18394, "Ġ230": 18395, "Ġdude": 18396, "Ġrelaxed": 18397, "Ġpartition": 18398, "Ġparticipant": 18399, "Ġtelesc": 18400, "Ġfeas": 18401, "ĠFF": 18402, "owner": 18403, "Ġsweeping": 18404, "Ġlenses": 18405, "Ġmatchup": 18406, "ĠRepl": 18407, "ournals": 18408, "Ġcredible": 18409, "Ġgrandmother": 18410, "Ġthermal": 18411, "Ġsubscribing": 18412, "Ġidentities": 18413, "colm": 18414, "UCT": 18415, "Ġreluctant": 18416, "users": 18417, "ĠCort": 18418, "Ġassisted": 18419, "OSS": 18420, "ATIONS": 18421, "ISH": 18422, "Ġpharmaceutical": 18423, "icable": 18424, "adian": 18425, "ĠSonic": 18426, "ĠFury": 18427, "ĠMong": 18428, "AH": 18429, "ĠPsychology": 18430, "Ġphosph": 18431, "Ġtreats": 18432, "ŃĶ": 18433, "Ġsteadily": 18434, "ĠHello": 18435, "Ġrelates": 18436, "Ġclue": 18437, "Expl": 18438, "auth": 18439, "Ġrevision": 18440, "Ġeld": 18441, "osion": 18442, "Ġbron": 18443, "144": 18444, "rikes": 18445, "Ġmines": 18446, "Ġblanket": 18447, "ĠFail": 18448, "eled": 18449, "ĠImagine": 18450, "ĠPlanned": 18451, "aic": 18452, "Request": 18453, "Mad": 18454, "ĠHorse": 18455, "ĠEagle": 18456, "Ġcapac": 18457, "157": 18458, "Ġling": 18459, "ĠNice": 18460, "ĠParenthood": 18461, "minster": 18462, "ogs": 18463, "ensitive": 18464, "Nothing": 18465, "Ġcarn": 18466, "Fin": 18467, "ĠPE": 18468, "Ġrifles": 18469, "ĠLP": 18470, "Sand": 18471, "ĠguiActive": 18472, "Ġtourist": 18473, "CNN": 18474, "Ġunveiled": 18475, "Ġpredecessor": 18476, "}{": 18477, "uber": 18478, "Ġoffshore": 18479, "Ġoptical": 18480, "ĠRot": 18481, "ĠPearl": 18482, "eton": 18483, "Ġstared": 18484, "Ġfarther": 18485, "atility": 18486, "contin": 18487, "ĠGy": 18488, "ĠFoster": 18489, "ĠCoc": 18490, "rients": 18491, "Ġdesigning": 18492, "ĠEconomy": 18493, "ONG": 18494, "Women": 18495, "ĠNancy": 18496, "erver": 18497, "Ġmascul": 18498, "Ġcasualties": 18499, "Ġ225": 18500, "ĠSullivan": 18501, "ĠChoice": 18502, "Ġaster": 18503, "ws": 18504, "Ġhotels": 18505, "Ġconsiderations": 18506, "Ġcouch": 18507, "ĠStrip": 18508, "ĠGn": 18509, "Ġmanipulate": 18510, "lied": 18511, "Ġsynthetic": 18512, "Ġassaulted": 18513, "Ġoffenses": 18514, "ĠDrake": 18515, "Ġimpe": 18516, "October": 18517, "ĠHeritage": 18518, "hl": 18519, "ĠBlair": 18520, "Unlike": 18521, "Ġgrief": 18522, "Ġ450": 18523, "Ġopted": 18524, "Ġresignation": 18525, "ilo": 18526, "Ġverse": 18527, "ĠTomb": 18528, "Ġupt": 18529, "Ġaired": 18530, "ĠHook": 18531, "ĠMLB": 18532, "Ġassumes": 18533, "outed": 18534, "ĠVers": 18535, "Ġinferior": 18536, "Ġbundle": 18537, "ĠDNS": 18538, "ographer": 18539, "Ġmultip": 18540, "ĠSouls": 18541, "Ġillustrated": 18542, "Ġtactic": 18543, "Ġdressing": 18544, "Ġduo": 18545, "Conf": 18546, "Ġrelent": 18547, "Ġcant": 18548, "Ġscarce": 18549, "Ġcandy": 18550, "ĠCF": 18551, "Ġaffiliated": 18552, "Ġsprint": 18553, "ylan": 18554, "ĠGarcia": 18555, "Ġjunk": 18556, "Print": 18557, "exec": 18558, "Crit": 18559, "Ġportrait": 18560, "iries": 18561, "ĠOFF": 18562, "Ġdisputes": 18563, "WR": 18564, "Love": 18565, "ãģĦ": 18566, "ĠReyn": 18567, "Ġhipp": 18568, "opath": 18569, "Ġfloors": 18570, "ĠFeel": 18571, "Ġworries": 18572, "Ġsettlements": 18573, "ĠPos": 18574, "Ġmosque": 18575, "Ġfinals": 18576, "Ġcrushed": 18577, "ĠProbably": 18578, "ĠBot": 18579, "ĠMans": 18580, "ĠPeriod": 18581, "Ġsovereignty": 18582, "Ġseller": 18583, "Ġapost": 18584, "Ġamateur": 18585, "Ġdorm": 18586, "Ġconsuming": 18587, "Ġarmour": 18588, "ĠRoose": 18589, "Ġintensive": 18590, "Ġeliminating": 18591, "ĠSunni": 18592, "ĠAleppo": 18593, "jin": 18594, "Ġadvise": 18595, "pal": 18596, "ĠHalo": 18597, "Ġdescent": 18598, "Ġsimpler": 18599, "Ġbooth": 18600, "STR": 18601, "Later": 18602, "ĠCave": 18603, "===": 18604, "Ġmol": 18605, "Ġfist": 18606, "Ġshotgun": 18607, "supp": 18608, "Ġrobbery": 18609, "Effect": 18610, "Ġobscure": 18611, "ĠProfessional": 18612, "Ġembassy": 18613, "Ġmilitant": 18614, "Ġincarcer": 18615, "Ġgenerates": 18616, "Ġlaunches": 18617, "Ġadministrators": 18618, "Ġshaft": 18619, "Ġcircular": 18620, "Ġfreshman": 18621, "ĠWes": 18622, "ĠJoel": 18623, "ĠDrew": 18624, "ĠDuncan": 18625, "ĠApparently": 18626, "sight": 18627, "ĠInternal": 18628, "ĠIndividual": 18629, "ĠFE": 18630, "Ġbore": 18631, "ĠMt": 18632, "Ġbroadly": 18633, "ĠOptions": 18634, "ountain": 18635, "ipes": 18636, "ĠVideos": 18637, "204": 18638, "Ġhills": 18639, "Ġsimulation": 18640, "Ġdisappointment": 18641, "itan": 18642, "ĠLaboratory": 18643, "Ġupward": 18644, "Ġboundary": 18645, "Ġdarker": 18646, "hart": 18647, "Ġdominance": 18648, "Cong": 18649, "ĠOracle": 18650, "ĠLords": 18651, "Ġscholarship": 18652, "ĠVincent": 18653, "ede": 18654, "ĠRah": 18655, "Ġencourages": 18656, "rov": 18657, "Ġquo": 18658, "Ġpremise": 18659, "ĠCrisis": 18660, "ĠHolocaust": 18661, "Ġrhythm": 18662, "Ġmetric": 18663, "club": 18664, "Ġtransported": 18665, "Ġnod": 18666, "ĠPist": 18667, "Ġancestors": 18668, "ĠFreder": 18669, "thumbnails": 18670, "ĠCE": 18671, "OND": 18672, "Phil": 18673, "venge": 18674, 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"ĠAj": 22028, "Ġunpleasant": 22029, "European": 22030, "expensive": 22031, "Ġscreenshot": 22032, "ĠUV": 22033, "Ġallied": 22034, "ĠPersian": 22035, "Ġmonopoly": 22036, "Ġatom": 22037, "ĠRedskins": 22038, "\"><": 22039, "Ġcancell": 22040, "Ġcinema": 22041, "131": 22042, "fair": 22043, "ĠAlfred": 22044, "Ġduck": 22045, "args": 22046, "223": 22047, "ĠISI": 22048, "Ġsignaling": 22049, "inar": 22050, "Ġlaughs": 22051, "Ġforwards": 22052, "Ġreckless": 22053, "Ġlisteners": 22054, "ativity": 22055, "Ġvastly": 22056, "nant": 22057, "Less": 22058, "ĠHunting": 22059, "ĠScientific": 22060, "ITED": 22061, "Ġknight": 22062, "ĠHTC": 22063, "usa": 22064, "tmp": 22065, "Ġrude": 22066, "ĠLegendary": 22067, "Ġarises": 22068, "Bad": 22069, "ĠClaim": 22070, "peg": 22071, "Ġrealities": 22072, "Think": 22073, "Ġ°": 22074, "Ġrode": 22075, "Ġstrive": 22076, "Ġanecd": 22077, "Ġshorts": 22078, "Ġhypothes": 22079, "Ġcoordinated": 22080, "ĠGandhi": 22081, "ĠFPS": 22082, "RED": 22083, "Ġsusceptible": 22084, 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27369, "ãģĭ": 27370, "349": 27371, "blocks": 27372, "Ġverge": 27373, "estine": 27374, "ĠPodesta": 27375, "Ġinvasive": 27376, "Ġprofoundly": 27377, "ĠAo": 27378, "each": 27379, "Ġlest": 27380, "interpret": 27381, "Ġshrinking": 27382, "Ġerrone": 27383, "Ġchees": 27384, "lys": 27385, "ĠIvy": 27386, "ĠDirectory": 27387, "Ġhinted": 27388, "VICE": 27389, "Ġcontacting": 27390, "ĠGent": 27391, "hei": 27392, "Ġlabeling": 27393, "Ġmercury": 27394, "ĠLite": 27395, "Ġexpires": 27396, "Ġdestabil": 27397, "ritis": 27398, "cu": 27399, "Ġfeathers": 27400, "Ġsteer": 27401, "Ġprogrammed": 27402, "ĠVader": 27403, "Going": 27404, "ĠElim": 27405, "Ġyo": 27406, "ĠMiche": 27407, "Ġ203": 27408, "Ġsleeves": 27409, "Ġbully": 27410, "ĠHumans": 27411, "368": 27412, "Ġcompress": 27413, "ĠBanner": 27414, "ARS": 27415, "Ġawhile": 27416, "Ġcalib": 27417, "Ġsponsorship": 27418, "ĠDifficulty": 27419, "ĠPapers": 27420, "Ġidentifier": 27421, "}.": 27422, "Ġyog": 27423, "ĠShia": 27424, "Ġcleanup": 27425, "Ġvibe": 27426, 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27763, "åŃ": 27764, "Ġtx": 27765, "ĠDiscussion": 27766, "Ġcatastrophe": 27767, "ppe": 27768, "gio": 27769, "Ġcommunism": 27770, "Ġhalted": 27771, "ĠGuant": 27772, "clean": 27773, "ĠSched": 27774, "ĠKanye": 27775, "Ġwander": 27776, "ĠSeriously": 27777, "Ġ188": 27778, "ennial": 27779, "follow": 27780, "productive": 27781, "ĠFlow": 27782, "ĠSail": 27783, "Ġcraw": 27784, "Ġsimulations": 27785, "oru": 27786, "angles": 27787, "ĠNolan": 27788, "Ġmenstru": 27789, "470": 27790, "Ġ207": 27791, "aja": 27792, "Ġcasually": 27793, "boarding": 27794, "Ġ222": 27795, "ovy": 27796, "ĠNumbers": 27797, "umat": 27798, "OE": 27799, "287": 27800, "ĠClemson": 27801, "Ġcerts": 27802, "Ġslid": 27803, "ĠTribe": 27804, "Ġtoast": 27805, "Ġfortunes": 27806, "Ġfals": 27807, "ĠCommittees": 27808, "Ġgp": 27809, "Ġfiery": 27810, "ĠNets": 27811, "ĠAnime": 27812, "Package": 27813, "ĠCompare": 27814, "laughter": 27815, "infect": 27816, "Ġatrocities": 27817, "Ġjustices": 27818, "Ġinsults": 27819, "ĠVernon": 27820, "Ġshaken": 27821, "Ġpersona": 27822, "estamp": 27823, "367": 27824, "brain": 27825, "Ġexperimenting": 27826, "Ken": 27827, "ĠElectronics": 27828, "Ġ161": 27829, "domain": 27830, "Ġgraphical": 27831, "bishop": 27832, "Ġwhopping": 27833, "ĠEvangel": 27834, "Ġadvertisers": 27835, "ĠSpear": 27836, "Ġbids": 27837, "Ġdestroys": 27838, "utz": 27839, "Ġundersc": 27840, "ĠADD": 27841, "Ġants": 27842, "ĠCum": 27843, "ipples": 27844, "ĠFill": 27845, "Ġglanced": 27846, "Ġindicted": 27847, "ĠEff": 27848, "Ġmiscon": 27849, "ĠDesktop": 27850, "Ġabide": 27851, "ãĥĢ": 27852, "ĠIo": 27853, "ĠCoul": 27854, "Ġcapsule": 27855, "ĠChrys": 27856, "MON": 27857, "Ġundes": 27858, "ĠIRA": 27859, "Ġcitation": 27860, "Ġdictate": 27861, "ĠNetworks": 27862, "ĠConflict": 27863, "ĠStuff": 27864, "xa": 27865, "isec": 27866, "ĠChemistry": 27867, "Ġquarterly": 27868, "Williams": 27869, "anan": 27870, "Opt": 27871, "ĠAlexandria": 27872, "outheastern": 27873, "ĠSpringfield": 27874, "ĠBlacks": 27875, "Ġgeography": 27876, "242": 27877, "Ġutmost": 27878, "ĠExxon": 27879, "abouts": 27880, "EVA": 27881, "ĠEnable": 27882, "ĠBarr": 27883, "Ġdisagreed": 27884, "ĠCyprus": 27885, "Ġdementia": 27886, "Ġlabs": 27887, "Ġubiquitous": 27888, "ĠLOVE": 27889, "Ġconsolidated": 27890, "sr": 27891, "Ġcreamy": 27892, "ĠTimber": 27893, "Regardless": 27894, "ĠCertificate": 27895, "Ġ\"...": 27896, "ogenous": 27897, "Captain": 27898, "Ġinsulting": 27899, "ĠSoros": 27900, "ĠInstr": 27901, "ĠBulgaria": 27902, "better": 27903, "Ġsucking": 27904, "ĠDavidson": 27905, "atz": 27906, "Ġcollateral": 27907, "gif": 27908, "Ġplagued": 27909, "ĠCancel": 27910, "ĠGardner": 27911, "RB": 27912, "Ġsixteen": 27913, "Remove": 27914, "uristic": 27915, "cook": 27916, "Rod": 27917, "Ġcomprising": 27918, "fle": 27919, ")âĢĶ": 27920, "ĠViking": 27921, "growth": 27922, "agonal": 27923, "Ġsrf": 27924, "afety": 27925, "mot": 27926, "Nearly": 27927, "stown": 27928, "ĠFactor": 27929, "Ġautomobile": 27930, "Ġprocedural": 27931, "mask": 27932, "ampires": 27933, "Ġdisappears": 27934, "jab": 27935, "315": 27936, "Ġ1951": 27937, "needed": 27938, "Ġdaring": 27939, "leader": 27940, "Ġpodium": 27941, "Ġunhealthy": 27942, "Ġmund": 27943, "Ġpyramid": 27944, "ocre": 27945, "Ġkissed": 27946, "Ġdreamed": 27947, "ĠFantastic": 27948, "ĠGly": 27949, "åĬ": 27950, "Ġgreatness": 27951, "Ġspices": 27952, "Ġmetropolitan": 27953, "Ġcompuls": 27954, "iets": 27955, "1016": 27956, "ĠSham": 27957, "ĠPyr": 27958, "flies": 27959, "ĠMidnight": 27960, "Ġswallowed": 27961, "Ġgenres": 27962, "ĠLucky": 27963, "ĠRewards": 27964, "Ġdispatch": 27965, "ĠIPA": 27966, "ĠApply": 27967, "Ġaven": 27968, "alities": 27969, "312": 27970, "things": 27971, "Ġ().": 27972, "Ġmates": 27973, "ĠSz": 27974, "ĠCOP": 27975, "olate": 27976, "OFF": 27977, "Ġrecharge": 27978, "caps": 27979, "ĠYorker": 27980, "icone": 27981, "Ġgalaxies": 27982, "ileaks": 27983, "Dave": 27984, "ĠPuzz": 27985, "ĠCeltic": 27986, "ĠAFC": 27987, "276": 27988, "ĠSons": 27989, "Ġaffirmative": 27990, "Hor": 27991, "Ġtutorials": 27992, "ĠCITY": 27993, "ĠRosa": 27994, "ĠExtension": 27995, "Series": 27996, "Ġfats": 27997, "Ġrab": 27998, "lis": 27999, "Ġunic": 28000, "Ġeve": 28001, "ĠSpin": 28002, "Ġadulthood": 28003, "typ": 28004, "Ġsectarian": 28005, "Ġcheckout": 28006, "ĠCycl": 28007, "Single": 28008, "Ġmartyr": 28009, "Ġchilling": 28010, "888": 28011, "oufl": 28012, "Ġ];": 28013, "Ġcongestion": 28014, "mk": 28015, "ĠWhereas": 28016, "Ġ1938": 28017, "urrencies": 28018, "erion": 28019, "Ġboast": 28020, "ĠPatients": 28021, "Ġchap": 28022, "ĠBD": 28023, "realDonaldTrump": 28024, "Ġexamines": 28025, "hov": 28026, "Ġstartling": 28027, "ĠBabylon": 28028, "wid": 28029, "omew": 28030, "brance": 28031, "ĠOdyssey": 28032, "wig": 28033, "Ġtorch": 28034, "ĠVox": 28035, "ĠMoz": 28036, "ĠTroll": 28037, "ĠAns": 28038, "Similarly": 28039, "ĠFul": 28040, "006": 28041, "Unless": 28042, "ĠAlone": 28043, "stead": 28044, "ĠPublisher": 28045, "rights": 28046, "tu": 28047, "ĠDoesn": 28048, "Ġprofessionally": 28049, "Ġclo": 28050, "icz": 28051, "Ġsteals": 28052, "Ġá": 28053, "1986": 28054, "Ġsturdy": 28055, "ĠJohann": 28056, "Ġmedals": 28057, "Ġfilings": 28058, "ĠFraser": 28059, "done": 28060, "Ġmultinational": 28061, "Ġfeder": 28062, "Ġworthless": 28063, "Ġpest": 28064, "Yesterday": 28065, "ankind": 28066, "Ġgays": 28067, "Ġborne": 28068, "ĠPOS": 28069, "Picture": 28070, "Ġpercentages": 28071, "251": 28072, "rame": 28073, "Ġpotions": 28074, "AMD": 28075, "ĠLebanese": 28076, "Ġrang": 28077, "ĠLSU": 28078, "ongs": 28079, "Ġpeninsula": 28080, "ĠClause": 28081, "ALK": 28082, "oha": 28083, "ĠMacBook": 28084, "Ġunanimous": 28085, "Ġlenders": 28086, "Ġhangs": 28087, "Ġfranchises": 28088, "orers": 28089, "ĠUpdates": 28090, "Ġisolate": 28091, "andro": 28092, "Soon": 28093, "Ġdisruptive": 28094, "ĠSurve": 28095, "Ġstitches": 28096, "ĠScorp": 28097, "ĠDominion": 28098, "Ġsupplying": 28099, "Arg": 28100, "Ġturret": 28101, "ĠLuk": 28102, "Ġbrackets": 28103, "*)": 28104, "ĠRevolutionary": 28105, "ĠHonest": 28106, "Ġnoticing": 28107, "ĠShannon": 28108, "Ġafforded": 28109, "Ġtha": 28110, "ĠJanet": 28111, "!--": 28112, "ĠNarendra": 28113, "ĠPlot": 28114, "Hol": 28115, "sever": 28116, "eenth": 28117, "Ġobstruction": 28118, "Ġ1024": 28119, "staff": 28120, "jas": 28121, "orget": 28122, "scenes": 28123, "laughs": 28124, "ĠFargo": 28125, "crime": 28126, "Ġorchestr": 28127, "Ġdelet": 28128, "iliary": 28129, "rieved": 28130, "Ġmilitar": 28131, "ĠGreene": 28132, "âĹı": 28133, "ãģ¦": 28134, "ĠGuards": 28135, "Ġunleashed": 28136, "ĠWeber": 28137, "Ġadjustable": 28138, "Ġcaliber": 28139, "Ġmotivations": 28140, "ĠÃł": 28141, "mAh": 28142, "ĠLanka": 28143, "handle": 28144, "Ġpent": 28145, "ĠRav": 28146, "ĠAngular": 28147, "ĠKau": 28148, "umbing": 28149, "Ġphilanthrop": 28150, "Ġdehyd": 28151, "Ġtoxicity": 28152, "eer": 28153, "ĠYORK": 28154, "witz": 28155, "å¼": 28156, "ĠIE": 28157, "community": 28158, "ĠAH": 28159, "Ġretali": 28160, "Ġmassively": 28161, "ĠDaniels": 28162, "ĠDEL": 28163, "Ġcarcin": 28164, "Url": 28165, "Ġrouting": 28166, "ĠNPCs": 28167, "ĠRAF": 28168, "ryce": 28169, "Ġwaived": 28170, "ĠGuatem": 28171, "Everybody": 28172, "Ġcovenant": 28173, "Ġ173": 28174, "Ġrelaxing": 28175, "Ġquart": 28176, "almost": 28177, "Ġguarded": 28178, "ĠSoldiers": 28179, "ĠPLAY": 28180, "Ġoutgoing": 28181, "LAND": 28182, "Ġrewrite": 28183, "ĠMOV": 28184, "ĠImper": 28185, "ĠSolution": 28186, "Ġphenomenal": 28187, "Ġlongevity": 28188, "Ġimpat": 28189, "ĠNissan": 28190, "irie": 28191, "Ġodor": 28192, "ĠZar": 28193, "oks": 28194, "Ġmilitias": 28195, "ĠSPEC": 28196, "Ġtolerated": 28197, "arser": 28198, "ĠBradford": 28199, "+,": 28200, "Ġsurreal": 28201, "sf": 28202, "Canadian": 28203, "Ġresemblance": 28204, "Ġcarbohydrate": 28205, "VIEW": 28206, "Ġaccessory": 28207, "meal": 28208, "largest": 28209, "iegel": 28210, "Someone": 28211, "Ġtoughest": 28212, "oso": 28213, "Ġfunnel": 28214, "Ġcondemnation": 28215, "luent": 28216, "Ġwired": 28217, "ĠSunset": 28218, "Jesus": 28219, "ĠPST": 28220, "ĠPages": 28221, "ĠTycoon": 28222, "ĠPF": 28223, "Ġselections": 28224, "Ġà¤": 28225, "partisan": 28226, "Ġhighs": 28227, "ĠRune": 28228, "Ġcrafts": 28229, "lead": 28230, "ĠParents": 28231, "Ġreclaim": 28232, "eker": 28233, "ĠAllied": 28234, "aeper": 28235, "Ġlooming": 28236, "Ġbeneficiaries": 28237, "ĠHull": 28238, "Students": 28239, "Jewish": 28240, "dj": 28241, "Ġpact": 28242, "template": 28243, "ĠOfficials": 28244, "ĠBaylor": 28245, "Ġhemp": 28246, "Ġyouths": 28247, "ĠLevels": 28248, "ĠXiao": 28249, "ĠChes": 28250, "Ġendeavor": 28251, "ĠRemoved": 28252, "Ġhippocamp": 28253, "Hell": 28254, "ãĤĬ": 28255, "805": 28256, "Ġdinosaur": 28257, "ĠWrath": 28258, "ĠIndonesian": 28259, "Ġcalculator": 28260, "ĠDictionary": 28261, "Ġ420": 28262, "ĠMAG": 28263, "(_": 28264, "!,": 28265, "tarians": 28266, "Ġrestricting": 28267, "racuse": 28268, "Ġweekday": 28269, "OUNT": 28270, "Ġshrugged": 28271, "leground": 28272, "Ġbald": 28273, "ĠDoctors": 28274, "Ġtouted": 28275, "ĠMaxwell": 28276, "Ġ214": 28277, "Ġdiplomat": 28278, "Ġrepression": 28279, "Ġconstituency": 28280, "vice": 28281, "ranked": 28282, "ĠNapoleon": 28283, "gang": 28284, "ĠForever": 28285, "tun": 28286, "Ġbulb": 28287, "ĠPDT": 28288, "ĠCisco": 28289, "VEN": 28290, "Ġresumed": 28291, "Steven": 28292, "ĠManitoba": 28293, "Ġfabulous": 28294, "ĠAgents": 28295, "1984": 28296, "Ġamusing": 28297, "ĠMysteries": 28298, "Ġorthodox": 28299, "floor": 28300, "Ġquestionnaire": 28301, "Ġpenetrate": 28302, "Ġfilmmakers": 28303, "ĠUnc": 28304, "Ġstamped": 28305, "Ġthirteen": 28306, "Ġoutfield": 28307, "Ġforwarded": 28308, "Ġappra": 28309, "Ġaided": 28310, "try": 28311, "Ġunfocused": 28312, "ĠLiz": 28313, "ĠWendy": 28314, "ĠScene": 28315, "Charg": 28316, "Ġrejects": 28317, "Ġleftist": 28318, "ĠProvidence": 28319, "ĠBrid": 28320, "regn": 28321, "Ġprophecy": 28322, "ĠLIVE": 28323, "499": 28324, "Ġforge": 28325, "ĠFML": 28326, "Ġintrinsic": 28327, "ĠFrog": 28328, "Ġwont": 28329, "ĠHolt": 28330, "Ġfamed": 28331, "CLUS": 28332, "aepernick": 28333, "ĠHate": 28334, "ĠCay": 28335, "Ġregistering": 28336, "ortality": 28337, "ropy": 28338, "ocalyptic": 28339, "aan": 28340, "nav": 28341, "Ġfascist": 28342, "IFIED": 28343, "Ġimplicated": 28344, "ĠResort": 28345, "ĠChandler": 28346, "ĠBrick": 28347, "Pin": 28348, "ysc": 28349, "Usage": 28350, "ĠHelm": 28351, "usra": 28352, "âĺħâĺħ": 28353, "ĠAbbas": 28354, "Ġunanimously": 28355, "Ġkeeper": 28356, "Ġaddicted": 28357, "???": 28358, "Ġhelmets": 28359, "Ġantioxid": 28360, "apsed": 28361, "808": 28362, "giene": 28363, "Ġwaits": 28364, "Ġminion": 28365, "raved": 28366, "ĠPorsche": 28367, "Ġdreaming": 28368, "Ġ171": 28369, "ĠCain": 28370, "Ġunfor": 28371, "asso": 28372, "ĠConfiguration": 28373, "kun": 28374, "hardt": 28375, "Ġnested": 28376, "ĠLDS": 28377, "LES": 28378, "Ġtying": 28379, "enos": 28380, "Ġcue": 28381, "ĠMarqu": 28382, "skirts": 28383, "Ġclicked": 28384, "Ġexpiration": 28385, "ĠAccordingly": 28386, "ĠWC": 28387, "Ġblessings": 28388, "Ġaddictive": 28389, "ĠNarr": 28390, "yx": 28391, "ĠJaguars": 28392, "Ġrents": 28393, "ĠSiber": 28394, "Ġtipped": 28395, "ousse": 28396, "ĠFitzgerald": 28397, "Ġhierarch": 28398, "outine": 28399, "Ġwavelength": 28400, ">.": 28401, "chid": 28402, "ĠProcessing": 28403, "/+": 28404, "ranking": 28405, "Easy": 28406, "ĠConstruct": 28407, "Ġtet": 28408, "insured": 28409, "HUD": 28410, "Ġquoting": 28411, "Ġcommunicated": 28412, "inx": 28413, "Ġinmate": 28414, "Ġerected": 28415, "ĠAbsolutely": 28416, "ĠSurely": 28417, "Ġunim": 28418, "ĠThrone": 28419, "heid": 28420, "Ġclaws": 28421, "Ġsuperstar": 28422, "ĠLenn": 28423, "ĠWhis": 28424, "Uk": 28425, "abol": 28426, "Ġsket": 28427, "ĠNiet": 28428, "Ġperks": 28429, "Ġaffinity": 28430, "Ġopenings": 28431, "phasis": 28432, "Ġdiscriminate": 28433, "Tip": 28434, "vc": 28435, "Ġgrinding": 28436, "ĠJenny": 28437, "Ġasthma": 28438, "holes": 28439, "ĠHomer": 28440, "Ġregisters": 28441, "ĠGlad": 28442, "Ġcreations": 28443, "Ġlithium": 28444, "Ġapplause": 28445, "until": 28446, "Justice": 28447, "ĠTurks": 28448, "Ġscandals": 28449, "Ġbake": 28450, "tank": 28451, "Mech": 28452, "ĠMeans": 28453, "ĠMaid": 28454, "Republicans": 28455, "isal": 28456, "windows": 28457, "ĠSantos": 28458, "Ġvegetation": 28459, "338": 28460, "tri": 28461, "Ġflux": 28462, "insert": 28463, "Ġclarified": 28464, "Ġmortg": 28465, "ĠChim": 28466, "ĠTort": 28467, "Ġdisclaim": 28468, "metal": 28469, "ĠAside": 28470, "Ġinduction": 28471, "Ġinfl": 28472, "Ġatheists": 28473, "amph": 28474, "Ġether": 28475, "ĠVital": 28476, "ĠBuilt": 28477, "Mind": 28478, "Ġweaponry": 28479, "SET": 28480, "Ġ186": 28481, "admin": 28482, "gam": 28483, "contract": 28484, "afa": 28485, "Ġderivatives": 28486, "Ġsnacks": 28487, "Ġchurn": 28488, "Econom": 28489, "Ġcapped": 28490, "ĠUnderstanding": 28491, "ĠHers": 28492, "ĠIz": 28493, "Ġduct": 28494, "IENT": 28495, "aughty": 28496, "ĠâľĶ": 28497, "ĠNP": 28498, "Ġsailing": 28499, "Initialized": 28500, "Ġted": 28501, "Ġreactors": 28502, "ĠLomb": 28503, "Ġchoke": 28504, "ĠWorm": 28505, "Ġadmiration": 28506, "Ġswung": 28507, "ensibly": 28508, "Ġrash": 28509, "ĠGoals": 28510, "ĠImportant": 28511, "Shot": 28512, "ĠRas": 28513, "Ġtrainers": 28514, "ĠBun": 28515, "Working": 28516, "Ġharmed": 28517, "ĠPandora": 28518, "ĠLTE": 28519, "Ġmushroom": 28520, "ĠCHAR": 28521, "ĠFee": 28522, "ĠMoy": 28523, "Born": 28524, "oliberal": 28525, "ĠMartial": 28526, "Ġgentlemen": 28527, "Ġlingering": 28528, "Official": 28529, "Ġgraffiti": 28530, "ĠNames": 28531, "Der": 28532, "Ġquint": 28533, "istrate": 28534, "azeera": 28535, "ĠNOTICE": 28536, "ĠFlorence": 28537, "Ġpayable": 28538, "Ġdepicts": 28539, "ĠSpecies": 28540, "Heart": 28541, "âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ": 28542, "Ġenclosed": 28543, "Increases": 28544, "Daily": 28545, "ĠLis": 28546, "Ġenactment": 28547, "ĠBacon": 28548, "ĠSteele": 28549, "demand": 28550, "Ġ183": 28551, "Ġmouths": 28552, "Ġstranded": 28553, "Ġenhancement": 28554, "011": 28555, "ĠWhats": 28556, "Ġhealed": 28557, "eny": 28558, "ĠRab": 28559, "Ġ340": 28560, "ĠLabyrinth": 28561, "roach": 28562, "ĠYosh": 28563, "ĠClippers": 28564, "Ġconcerts": 28565, "Internet": 28566, "355": 28567, "Ġstickers": 28568, "Ġtermed": 28569, "ĠAxe": 28570, "Ġgrandparents": 28571, "France": 28572, "ĠClim": 28573, "ĠUh": 28574, "ulic": 28575, "Ġthrill": 28576, "centric": 28577, "ĠOverview": 28578, "ĠConduct": 28579, "Ġsubstantive": 28580, "Ġ182": 28581, "mur": 28582, "Ġstray": 28583, "ĠCoff": 28584, "Ġrepetitive": 28585, "ĠForgotten": 28586, "Ġqualification": 28587, "ewitness": 28588, "ĠZimbabwe": 28589, "Ġsimulated": 28590, "ĠJD": 28591, "253": 28592, "ĠWare": 28593, "Ġunsc": 28594, "Times": 28595, "Ġsummons": 28596, "Ġdisconnected": 28597, "Ġ184": 28598, "cius": 28599, "ĠGujar": 28600, "odka": 28601, "Ġerase": 28602, "ĠTobacco": 28603, "elected": 28604, "Ġuncont": 28605, "ĠShepard": 28606, "ĠLamp": 28607, "Ġalerted": 28608, "Ġoperative": 28609, "arna": 28610, "uint": 28611, "Ġnegligence": 28612, "acements": 28613, "Ġsupra": 28614, "Ġprevail": 28615, "ĠShark": 28616, "Ġbelts": 28617, "ãģ«": 28618, "Ġtighter": 28619, "Engineers": 28620, "Ġinactive": 28621, "Ġexponent": 28622, "ĠWillie": 28623, "aples": 28624, "Ġheir": 28625, "ĠHits": 28626, "iann": 28627, "ĠSays": 28628, "Ġcurrents": 28629, "ĠBengal": 28630, "Ġarist": 28631, "Buffer": 28632, "Ġbreeze": 28633, "ĠWesley": 28634, "Cola": 28635, "Ġpronoun": 28636, "Ġdeed": 28637, "ĠKling": 28638, "Ġoft": 28639, "Ġinflict": 28640, "Ġpunishing": 28641, "Ġnm": 28642, "iku": 28643, "ODUCT": 28644, "014": 28645, "Ġsubsidy": 28646, "ĠDEA": 28647, "ĠHerbert": 28648, "ĠJal": 28649, "Bank": 28650, "Ġdeferred": 28651, "Ġshipment": 28652, "Bott": 28653, "Ġalle": 28654, "bearing": 28655, "HTML": 28656, "Offline": 28657, "Ġ213": 28658, "Ġscrolling": 28659, "Ġscanned": 28660, "ĠLibyan": 28661, "ĠTOP": 28662, "chrom": 28663, "dt": 28664, "column": 28665, "PsyNetMessage": 28666, "Zero": 28667, "Ġtorso": 28668, "050": 28669, "âķIJ": 28670, "Ġimperson": 28671, "ĠSchwartz": 28672, "udic": 28673, "Ġpissed": 28674, "ĠSapp": 28675, "257": 28676, "ĠISPs": 28677, "ogl": 28678, "Ġsupervised": 28679, "Ġadolescent": 28680, "Ġattained": 28681, "ĠDelivery": 28682, "ĠBunny": 28683, "Ġ1937": 28684, "Ġminiature": 28685, "Ġos": 28686, "Ġ370": 28687, "608": 28688, "ĠMourinho": 28689, "Ġinnate": 28690, "Ġtempo": 28691, "ĠNM": 28692, "ĠFallen": 28693, "009": 28694, "Ġprovocative": 28695, "Streamer": 28696, "ĠBenedict": 28697, "ĠBolshe": 28698, "Ġturtle": 28699, "ĠPCB": 28700, "ĠEqual": 28701, "Director": 28702, "ĠRend": 28703, "Ġfluids": 28704, "Authorities": 28705, "Ġcousins": 28706, "requency": 28707, "ĠNeighbor": 28708, "sets": 28709, "shared": 28710, "Charles": 28711, "password": 28712, "Ġgears": 28713, "Ġ211": 28714, "ĠHardware": 28715, "rika": 28716, "Ġupstream": 28717, "Hom": 28718, "Ġdisproportionately": 28719, "ivities": 28720, "Ġundefined": 28721, "Ġelectrons": 28722, "Ġcommemor": 28723, "Eventually": 28724, "Ġ><": 28725, "Ġirresponsible": 28726, "218": 28727, "ĠReleased": 28728, "ĠOVER": 28729, "ĠIGN": 28730, "ĠBread": 28731, "stellar": 28732, "ĠSage": 28733, "tted": 28734, "damage": 28735, "edition": 28736, "ĠPrec": 28737, "Ġlime": 28738, "Ġconfinement": 28739, "Ġcalorie": 28740, "weapon": 28741, "Ġdiffering": 28742, "ĠSina": 28743, "mys": 28744, "amd": 28745, "Ġintricate": 28746, "kk": 28747, "ĠPAT": 28748, "ão": 28749, "stones": 28750, "links": 28751, "Ġranch": 28752, "Semitic": 28753, "Ġdifferentiate": 28754, "ĠSinger": 28755, "occupied": 28756, "Ġfortress": 28757, "cmd": 28758, "Ġinterception": 28759, "ĠAnkara": 28760, "Ġrept": 28761, "ĠSolitaire": 28762, "Ġremake": 28763, "pred": 28764, "Ġdared": 28765, "autions": 28766, "ĠBACK": 28767, "Running": 28768, "Ġdebugging": 28769, "Ġgraphs": 28770, "399": 28771, "ĠNigel": 28772, "Ġbun": 28773, "Ġpillow": 28774, "Ġprogressed": 28775, "fashioned": 28776, "Ġobedience": 28777, "ERN": 28778, "Ġrehears": 28779, "Cell": 28780, "tl": 28781, "Sher": 28782, "Ġherald": 28783, "ĠPayment": 28784, "ĠCory": 28785, "ĠDept": 28786, "Ġrepent": 28787, "ĠWeak": 28788, "uckland": 28789, "Ġpleasing": 28790, "Ġshortages": 28791, "Ġjurors": 28792, "ĠKab": 28793, "qqa": 28794, "Anti": 28795, "Ġwow": 28796, "ĠRCMP": 28797, "Ġtsun": 28798, "ĠSic": 28799, "Ġcomprises": 28800, "Ġspies": 28801, "Ġprecinct": 28802, "nu": 28803, "Ġurges": 28804, "Ġtimed": 28805, "Ġstripes": 28806, "ĠBoots": 28807, "Ġyen": 28808, "Advanced": 28809, "Ġdiscrete": 28810, "ĠArchangel": 28811, "employment": 28812, "Diff": 28813, "Ġmonuments": 28814, "Ġ209": 28815, "worker": 28816, "Ġ196": 28817, "ĠIg": 28818, "utterstock": 28819, "TPS": 28820, "Jac": 28821, "Ġhomelessness": 28822, "Ġcommentator": 28823, "Ġracially": 28824, "fing": 28825, "seed": 28826, "Ele": 28827, "ellation": 28828, "Ġethanol": 28829, "Ġparish": 28830, "ĠDong": 28831, "ĠAwakening": 28832, "Ġdeviation": 28833, "ĠBearing": 28834, "ĠTsuk": 28835, "Ġrecess": 28836, "Ġlymph": 28837, "ĠCannabis": 28838, "åľ": 28839, "ĠNEWS": 28840, "Ġdra": 28841, "ĠStefan": 28842, "ĠWrong": 28843, 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28901, "Ġbricks": 28902, "Los": 28903, "rily": 28904, "ĠUSSR": 28905, "Ġrud": 28906, "Ġlaud": 28907, "ĠWise": 28908, "ĠEmerald": 28909, "Ġrevived": 28910, "Ġdamned": 28911, "ĠRepair": 28912, "idem": 28913, "ctica": 28914, "Ġpatriarch": 28915, "ĠNurs": 28916, "meg": 28917, "Ġcheapest": 28918, "reements": 28919, "empty": 28920, "ĠCelebr": 28921, "Ġdeprivation": 28922, "chanted": 28923, "ĠThumbnails": 28924, "Energy": 28925, "ĠEthan": 28926, "ĠQing": 28927, "Ġopposes": 28928, "WIND": 28929, "vik": 28930, "ĠMau": 28931, "ĠSUB": 28932, "667": 28933, "GRE": 28934, "ĠVolunte": 28935, "nton": 28936, "Cook": 28937, "åIJ": 28938, "esque": 28939, "Ġplummet": 28940, "Ġsuing": 28941, "Ġpronounce": 28942, "Ġresisting": 28943, "ĠFishing": 28944, "ĠTrials": 28945, "Ġyell": 28946, "Ġ310": 28947, "Ġinduct": 28948, "Ġpersonalized": 28949, "often": 28950, "Reb": 28951, "EMBER": 28952, "Ġviewpoint": 28953, "Ġexistential": 28954, "())": 28955, "remove": 28956, "MENTS": 28957, "lasses": 28958, "Ġevapor": 28959, "Ġaisle": 28960, "meta": 28961, "Ġreflective": 28962, "Ġentitlement": 28963, "Ġdevised": 28964, "music": 28965, "ascade": 28966, "Ġwinding": 28967, "offset": 28968, "Ġaccessibility": 28969, "kered": 28970, "Better": 28971, "ĠJohnston": 28972, "thinking": 28973, "Snow": 28974, "ĠCroatia": 28975, "ĠAtomic": 28976, "271": 28977, "348": 28978, "Ġtextbook": 28979, "ĠSixth": 28980, "ĠاÙĦ": 28981, "Ġslider": 28982, "ĠBurger": 28983, "bol": 28984, "Sync": 28985, "Ġgrandchildren": 28986, "Ġcerv": 28987, "+)": 28988, "Ġeternity": 28989, "Ġtweeting": 28990, "Ġspeculative": 28991, "Ġpivotal": 28992, "ĠWP": 28993, "ĠTER": 28994, "ynamic": 28995, "Ġupl": 28996, "ĠCats": 28997, "perhaps": 28998, "Ġclassmates": 28999, "Ġblatant": 29000, "'-": 29001, "Ġlakh": 29002, "antine": 29003, "ĠBorg": 29004, "iom": 29005, "/(": 29006, "ĠAthletic": 29007, "Ġsar": 29008, "OTA": 29009, "ĠHoffman": 29010, "Nevertheless": 29011, "Ġadorable": 29012, "Ġspawned": 29013, "Associated": 29014, "ĠDomestic": 29015, "Ġimplant": 29016, "ĠLuxem": 29017, "ĠKens": 29018, "Ġpumps": 29019, "ĠSAT": 29020, "Attributes": 29021, "509": 29022, "avour": 29023, "Ġcentralized": 29024, "ĠTN": 29025, "Ġfreshly": 29026, "ĠAchieve": 29027, "Ġoutsiders": 29028, "herty": 29029, "ĠRee": 29030, "ĠTowers": 29031, "ĠDart": 29032, "akable": 29033, "Ġmp": 29034, "ĠHeavenly": 29035, "Ġripe": 29036, "ĠCaroline": 29037, "ryan": 29038, "Ġclassics": 29039, "Ġretiring": 29040, "Ġ228": 29041, "Ġah": 29042, "Ġdealings": 29043, "Ġpunching": 29044, "ĠChapman": 29045, "Options": 29046, "maxwell": 29047, "volume": 29048, "Ġstal": 29049, "Ġexported": 29050, "ĠQuite": 29051, "Ġnumerical": 29052, "Burn": 29053, "Fact": 29054, "ĠKeystone": 29055, "Ġtrending": 29056, "Ġaltering": 29057, "ĠAfricans": 29058, "478": 29059, "ĠMN": 29060, "ĠKnock": 29061, "Ġtemptation": 29062, "Ġprestige": 29063, "Overview": 29064, "ĠTraditional": 29065, "ĠBahrain": 29066, "Private": 29067, "ĠHOU": 29068, "Ġbarr": 29069, "ĠTat": 29070, "Cube": 29071, "USD": 29072, "ĠGrande": 29073, "ĠGat": 29074, "ĠFlo": 29075, "Ġresides": 29076, "Ġindec": 29077, "volent": 29078, "Ġperpetual": 29079, "ubes": 29080, "Ġworldview": 29081, "ĠQuantum": 29082, "Ġfiltered": 29083, "Ġensu": 29084, "orgetown": 29085, "ERSON": 29086, "ĠMild": 29087, "379": 29088, "OTT": 29089, "Ã¥": 29090, "Ġvitamins": 29091, "Ġribbon": 29092, "Ġsincerely": 29093, "ĠHin": 29094, "Ġeighteen": 29095, "Ġcontradictory": 29096, "Ġglaring": 29097, "Ġexpectancy": 29098, "Ġconspir": 29099, "Ġmonstrous": 29100, "Ġ380": 29101, "reci": 29102, "Ġhandic": 29103, "Ġpumped": 29104, "Ġindicative": 29105, "Ġrapp": 29106, "Ġavail": 29107, "ĠLEGO": 29108, "ĠMarijuana": 29109, "1985": 29110, "erton": 29111, "Ġtwentieth": 29112, "################################": 29113, "ĠSwamp": 29114, "Ġvaluation": 29115, "Ġaffiliates": 29116, "adjusted": 29117, "ĠFacility": 29118, "262": 29119, "Ġenzymes": 29120, "itudinal": 29121, "Ġimprint": 29122, "Site": 29123, "Ġinstaller": 29124, "ĠTRA": 29125, "mology": 29126, "linear": 29127, "ĠCollective": 29128, "igating": 29129, "ĠToken": 29130, "Ġspeculated": 29131, "KN": 29132, "ĠCly": 29133, "ority": 29134, "Ġdefer": 29135, "Ġinspectors": 29136, "approved": 29137, "RM": 29138, "ĠSuns": 29139, "Ġinforming": 29140, "ĠSyracuse": 29141, "ibli": 29142, "765": 29143, "Ġglove": 29144, "Ġauthorize": 29145, "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦": 29146, "ĠCruise": 29147, "Ġcontracting": 29148, "shell": 29149, "IFE": 29150, "ĠJewel": 29151, "pract": 29152, "ĠPhotoshop": 29153, "ĠKnowing": 29154, "harm": 29155, "Ġattractions": 29156, "adan": 29157, "etus": 29158, "018": 29159, "wagen": 29160, "Alt": 29161, "Ġmultiply": 29162, "Ġequilibrium": 29163, ":{": 29164, "ĠFighters": 29165, "ĠEdgar": 29166, "Ġfourteen": 29167, "Govern": 29168, "Ġmisuse": 29169, "Ġabusing": 29170, "Ġancestry": 29171, "ramer": 29172, "644": 29173, "Ġworms": 29174, "Ġthicker": 29175, "ĠCombine": 29176, "Ġpeasants": 29177, "Ġvind": 29178, "Ġconquest": 29179, "Ġmocked": 29180, "Ġcinnamon": 29181, "ĠCald": 29182, "ĠGallup": 29183, "Ġavoidance": 29184, "Ġincarnation": 29185, "ĠStrat": 29186, "Ġtasted": 29187, "enta": 29188, "ĠNeal": 29189, "pared": 29190, "Ġterminology": 29191, "jection": 29192, "Scientists": 29193, "ĠINS": 29194, "ĠDee": 29195, "Ġdirectories": 29196, "Road": 29197, "ĠShap": 29198, "bright": 29199, "ĠDirectors": 29200, "ĠColumn": 29201, "Ġbob": 29202, "Ġpreferably": 29203, "Ġglitch": 29204, "furt": 29205, "Ġeg": 29206, "idis": 29207, "CBC": 29208, "Ġsurrendered": 29209, "Ġtestament": 29210, "336": 29211, "uggest": 29212, "ĠNil": 29213, "another": 29214, "Ġpathetic": 29215, "ĠDonna": 29216, "Ġ218": 29217, "ĠAvery": 29218, "Ġwhiskey": 29219, "Ġfixture": 29220, "ĠConquest": 29221, "Ġbets": 29222, "Occ": 29223, "ĠLeicester": 29224, "].\"": 29225, "Ġ));": 29226, "Ġflashes": 29227, "456": 29228, "Ġmasked": 29229, "gebra": 29230, "Ġcomputed": 29231, "chel": 29232, "auder": 29233, "Ġdefeats": 29234, "ĠLiberation": 29235, "ĠOsama": 29236, "ĠVive": 29237, "Changes": 29238, "Channel": 29239, "Ġtariffs": 29240, "Ġmage": 29241, "ĠSax": 29242, "Ġinadvertently": 29243, "ĠCRE": 29244, "ĠReaper": 29245, "inky": 29246, "grading": 29247, "Ġstereotyp": 29248, "Ġcurl": 29249, "ĠFANT": 29250, "Ġframeworks": 29251, "Mom": 29252, "ĠAnch": 29253, "Ġflavour": 29254, "carbon": 29255, "Ġpermitting": 29256, "letcher": 29257, "ĠMozilla": 29258, "ĠParking": 29259, "ĠChamp": 29260, "Scroll": 29261, "Ġmurderer": 29262, "Ġrested": 29263, "Ġowes": 29264, "ĠPoss": 29265, "ADD": 29266, "IFF": 29267, "resolution": 29268, "ĠMining": 29269, "Ġcomparative": 29270, "Dim": 29271, "Ġneighbouring": 29272, "ĠAST": 29273, "ĠToxic": 29274, "Ġbiases": 29275, "Ġgunfire": 29276, "urous": 29277, "ĠMoment": 29278, "1983": 29279, "Ġpervasive": 29280, "ttp": 29281, "ĠNormally": 29282, "rir": 29283, "Sarah": 29284, "ĠAlbany": 29285, "Ġunsett": 29286, "ĠSMS": 29287, "ipers": 29288, "layer": 29289, "ĠWhites": 29290, "uple": 29291, "Ġturbo": 29292, "ĠLeeds": 29293, "Ġthats": 29294, "ĠMiner": 29295, "MER": 29296, "ĠReign": 29297, "Ġperme": 29298, "ĠBlitz": 29299, "Ġ1934": 29300, "Ġintimidating": 29301, "tube": 29302, "Ġeccentric": 29303, "abolic": 29304, "boxes": 29305, "ĠAssociates": 29306, "votes": 29307, "Ġsimulate": 29308, "umbo": 29309, "astery": 29310, "Ġshipments": 29311, "FFFF": 29312, "anth": 29313, "Ġseasoned": 29314, "Ġexperimentation": 29315, "âĸł": 29316, "laws": 29317, "Meet": 29318, "iddles": 29319, "antics": 29320, "Rating": 29321, "ISIS": 29322, "hift": 29323, "Ġfronts": 29324, "buf": 29325, "017": 29326, "Ġunatt": 29327, "ĠDil": 29328, "leases": 29329, "ĠGardens": 29330, "777": 29331, "touch": 29332, "vell": 29333, "458": 29334, "Ġ=====": 29335, "saving": 29336, "Ġerosion": 29337, "ĠQuin": 29338, "Ġearns": 29339, "Ġaccomplishment": 29340, "ĠWei": 29341, "Ġ<[": 29342, "_____": 29343, "Ġirrig": 29344, "ĠTeddy": 29345, "Ġconquered": 29346, "ĠArmored": 29347, "Ġasserts": 29348, "Ġmanipulating": 29349, "ré": 29350, "Ġtranscripts": 29351, "Gallery": 29352, "Ġplotting": 29353, "Neil": 29354, "Ġbetrayal": 29355, "loader": 29356, "ĠSul": 29357, "Ġdisplacement": 29358, "Ġroyalty": 29359, "ĠWI": 29360, "heit": 29361, "ĠDevices": 29362, "allel": 29363, "Ġmunicipalities": 29364, "Ġcanal": 29365, "Stars": 29366, "ĠUAE": 29367, "Ġ\"âĢ¦": 29368, "ĠCU": 29369, "above": 29370, "Ġresonance": 29371, "ĠguiActiveUn": 29372, "added": 29373, "ĠBraves": 29374, "ĠIbn": 29375, "Ġhereby": 29376, "ĠBRE": 29377, "Ġshareholder": 29378, "ĠHir": 29379, "ĠJi": 29380, "Ġstrangely": 29381, "Ġadmired": 29382, "Ġplight": 29383, "Ġbachelor": 29384, "ĠPole": 29385, "ciplinary": 29386, "Tony": 29387, "ĠArmenian": 29388, "Ġunman": 29389, "ĠZionist": 29390, "Stage": 29391, "iscover": 29392, "Ġautomotive": 29393, "Ġsidelines": 29394, "Ġslick": 29395, "ĠRenaissance": 29396, "ĠFUN": 29397, "Images": 29398, "ĠHaj": 29399, "Ġping": 29400, "Ġshortcut": 29401, "ĠBlvd": 29402, "ĠLooks": 29403, "Ġbursts": 29404, "Ġclamp": 29405, "Ġmish": 29406, "Ġsorting": 29407, "Ġpatriot": 29408, "Ġcorrectness": 29409, "ĠScandinav": 29410, "ĠCavaliers": 29411, "python": 29412, "azar": 29413, "Ġ375": 29414, "ĠJaune": 29415, "409": 29416, "Ġdetrimental": 29417, "Ġstabbing": 29418, "Ġpoisoned": 29419, "Ġfountain": 29420, "ocent": 29421, "orst": 29422, "ĠMari": 29423, "Ġrains": 29424, "ĠOvers": 29425, "ĠInstitution": 29426, "udget": 29427, "AMY": 29428, "tale": 29429, "ĠKR": 29430, "ĠPrices": 29431, "Ġheadaches": 29432, "Ġlandsl": 29433, "ĠAura": 29434, "Bonus": 29435, "ĠZhao": 29436, "ĠHip": 29437, "Ġhops": 29438, "ĠKurdistan": 29439, "Ġexploiting": 29440, "ryn": 29441, "Ġhypocrisy": 29442, "opening": 29443, "Ġgunshot": 29444, "Ġwed": 29445, "interstitial": 29446, "Interstitial": 29447, "Ġamen": 29448, "Breaking": 29449, "Ġmarketed": 29450, "Wire": 29451, "ĠCrowd": 29452, "Continue": 29453, "ĠKnown": 29454, "ĠEffective": 29455, "orean": 29456, "izons": 29457, "Joseph": 29458, "Ġescalation": 29459, "username": 29460, "Ġcurtain": 29461, "ATES": 29462, "ĠPAR": 29463, "ĠMiy": 29464, "Ġcounterfe": 29465, "lene": 29466, "Ġcontenders": 29467, "daily": 29468, "ĠAsc": 29469, "ĠPhillip": 29470, "mostly": 29471, "Ġfilename": 29472, "hene": 29473, "Ġresembling": 29474, "Ġstaging": 29475, "ĠChloe": 29476, "Ġwiring": 29477, "Hon": 29478, "ĠRenew": 29479, "ottage": 29480, "ĠHybrid": 29481, "much": 29482, "Ġstrokes": 29483, "Ġpolicymakers": 29484, "APTER": 29485, "ĠArkham": 29486, "plot": 29487, "Ġassistants": 29488, "Ġdeport": 29489, "ĠSega": 29490, "Ġinfluenza": 29491, "ĠCursed": 29492, "ĠKobe": 29493, "Ġskinny": 29494, "Provider": 29495, "ĠRip": 29496, "Ġincremental": 29497, "products": 29498, "BF": 29499, "Ġdome": 29500, "ĠCredits": 29501, "Ġlosers": 29502, "ints": 29503, "ĠBetty": 29504, "ĠTalent": 29505, "ĠDAM": 29506, "Lv": 29507, "Ess": 29508, "Ġdens": 29509, "temp": 29510, "Judge": 29511, "odic": 29512, "Ġ'(": 29513, "URES": 29514, "etsk": 29515, "VO": 29516, "Ġretrieved": 29517, "Ġarchitects": 29518, "Ùĩ": 29519, "Ġethic": 29520, "ĠSecondary": 29521, "stocks": 29522, "adia": 29523, "Ġ325": 29524, "ĠOpinion": 29525, "Ġsimultaneous": 29526, "Ġdizz": 29527, "ulp": 29528, "Ġsmuggling": 29529, "ippery": 29530, "Random": 29531, "facing": 29532, "ĠDas": 29533, "Ġstockp": 29534, "Ġdisclosures": 29535, "pointer": 29536, "Ġcoral": 29537, "ĠSelection": 29538, "ĠPike": 29539, "ivalent": 29540, "Ġruthless": 29541, "ĠRim": 29542, "Ġensuing": 29543, "ĠExperiment": 29544, "Ġcongressman": 29545, "Ġbeliever": 29546, "Ġunspecified": 29547, "ĠMord": 29548, "Ġknowledgeable": 29549, "ĠVERY": 29550, "TX": 29551, "Ġstraps": 29552, "Ġturf": 29553, "apeshifter": 29554, "Ġmarital": 29555, "Ġflock": 29556, "ãģĨ": 29557, "263": 29558, "AMES": 29559, "ĠOpposition": 29560, "Ġtreasures": 29561, "ĠGOD": 29562, "Ġmodeled": 29563, "ĠWORLD": 29564, "Ġ([": 29565, "ĠUsage": 29566, "HF": 29567, "Ġ$(": 29568, "ussed": 29569, "Ġpioneer": 29570, "Eight": 29571, "parse": 29572, "bread": 29573, "ritz": 29574, "ĠMiranda": 29575, "ĠKant": 29576, "++)": 29577, "oren": 29578, "Ġprovoked": 29579, "Ġbreeds": 29580, "ĠIncludes": 29581, "ĠPastebin": 29582, "ĠFlip": 29583, "Java": 29584, "Ġbrink": 29585, "Ġrumored": 29586, "Ġunseen": 29587, "Ġgarnered": 29588, "ĠDefin": 29589, "alted": 29590, "Ġtattoos": 29591, "Ġhesitation": 29592, "isitions": 29593, "ĠWeaver": 29594, "ĠReporting": 29595, "Ġtherapies": 29596, "Ġconsultants": 29597, "Ġresidual": 29598, "ĠMali": 29599, "ĠRoma": 29600, "iago": 29601, "ĠResidents": 29602, "ubi": 29603, "Ġremedies": 29604, "Ġadaptive": 29605, "ĠAlive": 29606, "ĠBarcl": 29607, "Ġwallets": 29608, "crypt": 29609, "etermination": 29610, "ĠPelosi": 29611, "Ġslipping": 29612, "otonin": 29613, "Ġalliances": 29614, "patrick": 29615, "iris": 29616, "Ġorth": 29617, "ĠPerkins": 29618, "ĠDeV": 29619, "ĠGets": 29620, "Ġdrying": 29621, "gee": 29622, "forest": 29623, "ĠForget": 29624, "orem": 29625, "339": 29626, "Ġvaguely": 29627, "ĠDion": 29628, "ĠPorn": 29629, "ĠHOW": 29630, "Ġpneum": 29631, "Ġrubble": 29632, "ĠTaste": 29633, "encia": 29634, "ĠGel": 29635, "Ġdst": 29636, "Ġ245": 29637, "ĠMorocco": 29638, "inflamm": 29639, "ĠTwins": 29640, "Ġbots": 29641, "daughter": 29642, "ĠBalk": 29643, "Ġbrethren": 29644, "Ġlogos": 29645, "Ġgobl": 29646, "fps": 29647, "Ġsubdivision": 29648, "Ġpawn": 29649, "Ġsqueezed": 29650, "Ġmorale": 29651, "ĠDW": 29652, "'\"": 29653, "Ġknot": 29654, "ooky": 29655, "Ġdivisive": 29656, "Ġboosted": 29657, "chy": 29658, "ãĥIJ": 29659, "ifact": 29660, "Ġnewcomers": 29661, "ĠWrestling": 29662, "Ġscouts": 29663, "wolves": 29664, "Rat": 29665, "Ġnineteenth": 29666, "ĠOsborne": 29667, "Stats": 29668, "Ġempowered": 29669, "Ġpsychopath": 29670, "ĠOEM": 29671, "uggage": 29672, "ĠPK": 29673, "ĠMohammad": 29674, "Pak": 29675, "Ġanarchists": 29676, "ĠExtract": 29677, "esthes": 29678, "ĠStockholm": 29679, "loo": 29680, "ĠGraph": 29681, "Ġdeploying": 29682, "ĠStranger": 29683, "ĠMold": 29684, "Ġstaffer": 29685, "Ġdiscounted": 29686, "uckle": 29687, "please": 29688, "ĠLanding": 29689, "ÃŃa": 29690, "Ġ193": 29691, "Ġante": 29692, "Ġrepetition": 29693, "Ġ+/-": 29694, "Ġparody": 29695, "Ġlively": 29696, "AAA": 29697, "ĠHorus": 29698, "Ġpits": 29699, "inders": 29700, "LOC": 29701, "ĠVenice": 29702, "406": 29703, "ĠDiscover": 29704, "âĨ": 29705, "ellectual": 29706, "Ġpens": 29707, "Ġeyel": 29708, "iguous": 29709, "Impl": 29710, "Ġjoking": 29711, "Ġinval": 29712, "ĠBelfast": 29713, "Ġcreditors": 29714, "ĠSkywalker": 29715, "ovsky": 29716, "Ġceasefire": 29717, "Ġseals": 29718, "isoft": 29719, ")).": 29720, "ĠFelix": 29721, "ITS": 29722, "Ġtresp": 29723, "ĠBlockchain": 29724, "eware": 29725, "ĠSchwar": 29726, "enne": 29727, "mounted": 29728, "ĠBeacon": 29729, "lesh": 29730, "Ġimmensely": 29731, "Ġcheering": 29732, "Employ": 29733, "scene": 29734, "ishly": 29735, "atchewan": 29736, "ĠNicolas": 29737, "Ġdrained": 29738, "ĠExit": 29739, "ĠAzerb": 29740, "jun": 29741, "Ġfloated": 29742, "uania": 29743, "Deep": 29744, "Ġsuperv": 29745, "Ġmystical": 29746, "ĠDollar": 29747, "ĠApostle": 29748, "ĠREL": 29749, "ĠProvided": 29750, "ĠBucks": 29751, "ãĥ´": 29752, "cutting": 29753, "Ġenhancements": 29754, "ĠPenguins": 29755, "ĠIsaiah": 29756, "Ġjerk": 29757, "ĠWyn": 29758, "Ġstalled": 29759, "Ġcryptocurrencies": 29760, "ĠRoland": 29761, "single": 29762, "Ġlumin": 29763, "ĠFellow": 29764, "ĠCapacity": 29765, "ĠKazakh": 29766, "WN": 29767, "Ġfinanced": 29768, "389": 29769, "Ġtid": 29770, "Ġcollusion": 29771, "ĠMyr": 29772, "îĢ": 29773, "Senator": 29774, "Ġpediatric": 29775, "Ġneatly": 29776, "Ġsandwiches": 29777, "ĠArchitecture": 29778, "Ġtucked": 29779, "Ġbalcony": 29780, "Ġearthquakes": 29781, "quire": 29782, "Future": 29783, "Ġhefty": 29784, "éĹ": 29785, "Ġspecializes": 29786, "Ġstresses": 29787, "Ġsender": 29788, "Ġmisunderstanding": 29789, "Ġepile": 29790, "Ġprovoke": 29791, "ĠColors": 29792, "Ġdismay": 29793, "uko": 29794, "[_": 29795, "586": 29796, "neutral": 29797, "Ġdonating": 29798, "ĠRandall": 29799, "Multi": 29800, "Ġconveniently": 29801, "ĠSung": 29802, "ĠCoca": 29803, "Ġtents": 29804, "ĠAcceler": 29805, "Ġpartnered": 29806, "272": 29807, "irming": 29808, "ĠBAS": 29809, "sometimes": 29810, "Ġobjected": 29811, "ubric": 29812, "posed": 29813, "LCS": 29814, "grass": 29815, "Ġattributable": 29816, "VIS": 29817, "Israeli": 29818, "Ġrepeats": 29819, "ĠRM": 29820, "vag": 29821, "uta": 29822, "inous": 29823, "Ġinert": 29824, "ĠMiguel": 29825, "æŃ": 29826, "ĠHawaiian": 29827, "Board": 29828, "Ġartific": 29829, "ĠAzerbai": 29830, "asio": 29831, "ĠRent": 29832, "AIN": 29833, "Ġappliances": 29834, "Ġnationality": 29835, "Ġasshole": 29836, "ĠNeb": 29837, "Ġnotch": 29838, "hani": 29839, "ĠBride": 29840, "Availability": 29841, "Ġintercepted": 29842, "Ġcontinental": 29843, "Ġswelling": 29844, "ĠPerspect": 29845, "bies": 29846, ".<": 29847, "ithmetic": 29848, "ĠLara": 29849, "Ġtempting": 29850, "addr": 29851, "Ġoverseeing": 29852, "clad": 29853, "ĠDV": 29854, "ĠGingrich": 29855, "Ġmun": 29856, "ĠAppropri": 29857, "Ġalterations": 29858, "ĠPatreon": 29859, "Ġhavoc": 29860, "Ġdisciplines": 29861, "Ġnotoriously": 29862, "akuya": 29863, "ieri": 29864, "?).": 29865, "ĠWent": 29866, "Ġsilicon": 29867, "Ġtremb": 29868, "Container": 29869, "Known": 29870, "Ġmortar": 29871, "este": 29872, "icka": 29873, "Arthur": 29874, "ĠPreviously": 29875, "ĠMarty": 29876, "Ġsparse": 29877, "gins": 29878, "Ġinward": 29879, "ĠParticipant": 29880, "Copy": 29881, "ĠMisc": 29882, "Ġantibiotic": 29883, "ĠRetro": 29884, "Ġelusive": 29885, "Ġassail": 29886, "ĠBattalion": 29887, "ĠBought": 29888, "Ġdiminish": 29889, "ĠEuropa": 29890, "session": 29891, "ĠDangerous": 29892, "iesel": 29893, "Ġdisbelief": 29894, "Ġblasts": 29895, "extreme": 29896, "ĠBoyd": 29897, "ĠProjects": 29898, "ĠGuys": 29899, "Ġundergone": 29900, "Ġgrill": 29901, "ĠDwight": 29902, "Ġ197": 29903, "USER": 29904, "Ġfilesystem": 29905, "Ġclocks": 29906, "Taylor": 29907, "Ġwrapper": 29908, "Ġfolding": 29909, "ousand": 29910, "ĠPhilippine": 29911, "ATIONAL": 29912, "ĠPerth": 29913, "Ġashes": 29914, "Ġaccumulate": 29915, "ĠGateway": 29916, "Shop": 29917, "orkshire": 29918, "Han": 29919, "ĠBarrel": 29920, "ĠLeh": 29921, "ĠXV": 29922, "Ġwhim": 29923, "Ġrepo": 29924, "ĠCG": 29925, "ĠMam": 29926, "Ġincorporating": 29927, "Ġbailout": 29928, "Ġlinguistic": 29929, "Ġdisinteg": 29930, "CLE": 29931, "Ġcinematic": 29932, "ĠFiber": 29933, "Syn": 29934, "ilion": 29935, "ĠCompos": 29936, "chens": 29937, "Ġneoc": 29938, "Ġboiled": 29939, "FINE": 29940, "ono": 29941, "uncle": 29942, "iken": 29943, "ĠBM": 29944, "ι": 29945, "Ġreceipts": 29946, "Ġdisposed": 29947, "ĠThirty": 29948, "ĠRough": 29949, "ĠABS": 29950, "Ġnotwithstanding": 29951, "ollen": 29952, "#$": 29953, "Ġunreliable": 29954, "Ġbloom": 29955, "Ġmediocre": 29956, "Ġtram": 29957, "ĠTasman": 29958, "Ġshakes": 29959, "Ġmanifesto": 29960, "ĠMW": 29961, "Ġsatisfactory": 29962, "Ġshores": 29963, "Ġcomputation": 29964, "Ġassertions": 29965, "ormons": 29966, "arag": 29967, "abit": 29968, "Democrats": 29969, "ĠLoot": 29970, "ĠVolks": 29971, "haired": 29972, "Ġgravitational": 29973, "Sing": 29974, "ĠMiz": 29975, "Ġthrottle": 29976, "Ġtyranny": 29977, "ĠViews": 29978, "Ġrobber": 29979, "ĠMinority": 29980, "Ġshrine": 29981, "scope": 29982, "purpose": 29983, "Ġnucleus": 29984, "ourcing": 29985, "ĠUSDA": 29986, "ĠDHS": 29987, "wra": 29988, "ĠBowie": 29989, "Scale": 29990, "ĠBEL": 29991, "xi": 29992, "Iter": 29993, "Ġ(),": 29994, "wright": 29995, "Ġsailors": 29996, "oused": 29997, "NASA": 29998, "ĠProof": 29999, "ĠMineral": 30000, "token": 30001, "ĠFD": 30002, "Rew": 30003, "Ġell": 30004, "630": 30005, "Ġchancellor": 30006, "ĠGos": 30007, "Ġamounted": 30008, "ĠRecre": 30009, "omez": 30010, "ĠOptim": 30011, "ĠOlive": 30012, "Ġtracker": 30013, "owler": 30014, "ĠUnique": 30015, "Root": 30016, "Ġmaritime": 30017, "ĠQuran": 30018, "ĠAdapt": 30019, "Ġecosystems": 30020, "ĠRepeat": 30021, "ĠSoy": 30022, "ĠIMP": 30023, "Ġgraduating": 30024, "andem": 30025, "Pur": 30026, "ĠReset": 30027, "ĠTrick": 30028, "ĠPhilly": 30029, "ĠTue": 30030, "ĠMalaysian": 30031, "Ġclimax": 30032, "Ġbury": 30033, "Ġconspic": 30034, "ĠSouthampton": 30035, "ĠFlowers": 30036, "Ġescorted": 30037, "ĠEducational": 30038, "ĠIRC": 30039, "Ġbrutally": 30040, "eating": 30041, "Ġpillar": 30042, "ĠSang": 30043, "ĠJude": 30044, "arling": 30045, "ĠAmnesty": 30046, "Ġreminding": 30047, "ĠAdministrative": 30048, "hesda": 30049, "Ġflashed": 30050, "ĠPBS": 30051, "perate": 30052, "feature": 30053, "Ġswipe": 30054, "Ġgraves": 30055, "oultry": 30056, "261": 30057, "breaks": 30058, "ĠGuer": 30059, "Ġshrimp": 30060, "ĠVoting": 30061, "quist": 30062, "Ġanalytical": 30063, "Ġtablespoons": 30064, "ĠSOU": 30065, "Ġresearched": 30066, "Ġdisrupted": 30067, "Ġjour": 30068, "Ġreplica": 30069, "Ġcartoons": 30070, "bians": 30071, "})": 30072, "copy": 30073, "Got": 30074, "ouched": 30075, "PUT": 30076, "Ġswarm": 30077, "notations": 30078, "said": 30079, "Ġrebuilt": 30080, "Ġcollaborate": 30081, "Ġraging": 30082, "Ġnar": 30083, "Ġdemographics": 30084, "ĠDDR": 30085, "Ġdistrust": 30086, "ossier": 30087, "ĠKro": 30088, "Ġpumpkin": 30089, "Ġregrets": 30090, "Ġfatalities": 30091, "ĠLens": 30092, "ĠOle": 30093, "pd": 30094, "Ġpuppet": 30095, "ĠOutlook": 30096, "ĠStam": 30097, "Ol": 30098, "Fair": 30099, "UU": 30100, "Ġrewritten": 30101, "ı": 30102, "Ġfascinated": 30103, "Ġvectors": 30104, "Ġtribunal": 30105, "uay": 30106, "ĠMats": 30107, "ĠCoins": 30108, "[[": 30109, "Ġ181": 30110, "Ġrenders": 30111, "ĠKaepernick": 30112, "Ġespionage": 30113, "Ġsumm": 30114, "Ġditch": 30115, "Account": 30116, "Ġspreadsheet": 30117, "Ġmutant": 30118, "past": 30119, "407": 30120, "Ġdye": 30121, "Ġinitiation": 30122, "Ġ4000": 30123, "Ġpunishable": 30124, "Ġthinner": 30125, "ĠKhal": 30126, "Ġintermedi": 30127, "Dun": 30128, "ĠGotham": 30129, "Ġeagerly": 30130, "Ġvaginal": 30131, "powers": 30132, "VW": 30133, "ĠWATCHED": 30134, "Ġpredator": 30135, "amsung": 30136, "Ġdisparity": 30137, "Ġ[*": 30138, "Ġamph": 30139, "Ġoutskirts": 30140, "ĠSpirits": 30141, "Ġskeletal": 30142, "л": 30143, "ĠRear": 30144, "Ġissuance": 30145, "ĠLogic": 30146, "released": 30147, "ZZ": 30148, "ĠBound": 30149, "Entry": 30150, "Ġexits": 30151, "isol": 30152, "ĠFounder": 30153, "Ġwre": 30154, "ĠGreenland": 30155, "ĠMMO": 30156, "taker": 30157, "INC": 30158, "ãģ¾": 30159, "Ġhourly": 30160, "henko": 30161, "Ġfantasies": 30162, "Ġdisob": 30163, "Ġdemolition": 30164, "ãĥĭ": 30165, "Ġenlisted": 30166, "ratulations": 30167, "Ġmisguided": 30168, "Ġensured": 30169, "Ġdiscouraged": 30170, "mort": 30171, "Ġflank": 30172, "Ġcess": 30173, "Ġreacts": 30174, "ĠSere": 30175, "sensitive": 30176, "ĠSerpent": 30177, "assad": 30178, "Ġ247": 30179, "Ġcalmly": 30180, "busters": 30181, "Ġbleed": 30182, "ĠStro": 30183, "Ġamusement": 30184, "ĠAntarctica": 30185, "Ġscept": 30186, "ĠGaw": 30187, "aq": 30188, "asonic": 30189, "Ġsprawling": 30190, "native": 30191, "aturated": 30192, "ĠBattlefield": 30193, "IVERS": 30194, "EB": 30195, "ĠGems": 30196, "ĠNorthwestern": 30197, "ĠFilms": 30198, "ĠAutomatic": 30199, "Ġapprehend": 30200, "ãģ¨": 30201, "ĠguiName": 30202, "Ġbackend": 30203, "Ġevidenced": 30204, "geant": 30205, "012": 30206, "ĠSiege": 30207, "ĠexternalTo": 30208, "ĠunfocusedRange": 30209, "ĠguiActiveUnfocused": 30210, "ĠguiIcon": 30211, "ĠexternalToEVA": 30212, "ĠexternalToEVAOnly": 30213, "Fri": 30214, "chard": 30215, "enaries": 30216, "Ġchiefs": 30217, "Ġcf": 30218, "ĠHUD": 30219, "Ġcorrobor": 30220, "ĠdB": 30221, "ĠTaken": 30222, "ĠPatricia": 30223, "rail": 30224, "ĠCharm": 30225, "ĠLibertarian": 30226, "rieve": 30227, "Personal": 30228, "ĠOUR": 30229, "geries": 30230, "Ġdumping": 30231, "Ġneurological": 30232, "itimate": 30233, "ĠClintons": 30234, "rafted": 30235, "ĠMolly": 30236, "Ġterminals": 30237, "register": 30238, "Ġflare": 30239, "Ġencoded": 30240, "Ġautopsy": 30241, "pel": 30242, "machine": 30243, "Ġexemptions": 30244, "ĠRoyals": 30245, "distance": 30246, "Ġdrafts": 30247, "Ġlame": 30248, "ĠCunning": 30249, "Ġspouses": 30250, "ĠMarkets": 30251, "ĠCarrier": 30252, "Ġimplying": 30253, "ĠYak": 30254, "sid": 30255, "Ġloser": 30256, "Ġvigilant": 30257, "Ġimpeachment": 30258, "Ġaugmented": 30259, "ĠEmployees": 30260, "Ġunintended": 30261, "ternally": 30262, "ĠWatt": 30263, "Ġrecognizable": 30264, "essim": 30265, "æĿ": 30266, "Ġcoated": 30267, "rha": 30268, "Ġlieutenant": 30269, "ĠLegislation": 30270, "published": 30271, "444": 30272, "013": 30273, "Ġideally": 30274, "ĠPassword": 30275, "Ġsimplify": 30276, "ĠMeta": 30277, "ĠMRI": 30278, "Ġpleading": 30279, "organized": 30280, "handler": 30281, "Ġunravel": 30282, "correct": 30283, "Ġicy": 30284, "Ġparanoid": 30285, "Ġpasser": 30286, "Ġinspections": 30287, "ofer": 30288, "ĠHealthcare": 30289, "283": 30290, "ĠBrut": 30291, "iola": 30292, "forge": 30293, "ĠMedieval": 30294, "MSN": 30295, "ievers": 30296, "ĠProgramming": 30297, "åī": 30298, "Ġ223": 30299, "mu": 30300, "ĠCLE": 30301, "uga": 30302, "Ġshoppers": 30303, "Ġinformative": 30304, "ĠPlans": 30305, "Ġsupplementation": 30306, "ĠTests": 30307, "tyard": 30308, "ocytes": 30309, "ĠVega": 30310, "ĠGujarat": 30311, "ermanent": 30312, "Except": 30313, "ĠLOT": 30314, "alla": 30315, "ĠCumm": 30316, "ĠOsw": 30317, "Ġvenom": 30318, "ĠDebt": 30319, "ĠDOWN": 30320, "Ġreunion": 30321, "Ġmuc": 30322, "ĠRelief": 30323, "Ġgeop": 30324, "ĠðŁĺ": 30325, "alogue": 30326, "Anth": 30327, "echo": 30328, "Ġcorros": 30329, "Ġreplication": 30330, "ĠBlazing": 30331, "ĠDaughter": 30332, "Ġinflic": 30333, "ĠLindsey": 30334, "ÙĪ": 30335, "284": 30336, "Exit": 30337, "Ġgloom": 30338, "TAIN": 30339, "Ġundermining": 30340, "Ġadvising": 30341, "hidden": 30342, "Ġoverflow": 30343, "Ġgor": 30344, "urdue": 30345, "Ġechoes": 30346, "enhagen": 30347, "Ġimpuls": 30348, "drug": 30349, "cash": 30350, "Ġasync": 30351, "Ġmirac": 30352, "atts": 30353, "punk": 30354, "Ġpivot": 30355, "ĠLegislative": 30356, "Ġbloggers": 30357, "ĠClaw": 30358, "sburg": 30359, "dyl": 30360, "ĠRecommend": 30361, "Ġverte": 30362, "Ġprohibiting": 30363, "ĠPanther": 30364, "Jonathan": 30365, "Ġomin": 30366, "Ġhateful": 30367, "281": 30368, 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"jac": 30482, "Ġ194": 30483, "019": 30484, "Ġpillars": 30485, "riched": 30486, "/\"": 30487, "tk": 30488, "Ġlivelihood": 30489, "Ġroasted": 30490, "ahon": 30491, "ĠHutch": 30492, "assert": 30493, "Ġdividend": 30494, "Ġknit": 30495, "Ġdaunting": 30496, "Ġdisturbance": 30497, "Ġshale": 30498, "Ġcultivated": 30499, "Ġrefrigerator": 30500, "LB": 30501, "ĠNET": 30502, "Ġcommercials": 30503, "Ġthinkers": 30504, "455": 30505, "Ġchop": 30506, "Broad": 30507, "Ġsuspicions": 30508, "Ġtagged": 30509, "lifting": 30510, "Ġstylish": 30511, "ĠShields": 30512, "Shortly": 30513, "Ġtails": 30514, "Auth": 30515, "STE": 30516, "ĠGAME": 30517, "Ġseism": 30518, "ĠKis": 30519, "ologne": 30520, "Ġcowork": 30521, "Ġforcibly": 30522, "Ġthyroid": 30523, "ĠPB": 30524, "ANE": 30525, "married": 30526, "horse": 30527, "Ġpolymer": 30528, "ĠChal": 30529, "odor": 30530, "DEBUG": 30531, "ĠContext": 30532, "Ġbliss": 30533, "Ġpinpoint": 30534, "ĠMathemat": 30535, "legram": 30536, "ĠWeekend": 30537, "Ġlabelled": 30538, "Ġbart": 30539, "itles": 30540, "Ġestrogen": 30541, "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ": 30542, "\"'": 30543, "Ġvisibly": 30544, "Ġoutsider": 30545, "aida": 30546, "Area": 30547, "Ġdissemin": 30548, "Ġdishonest": 30549, "ĠClosed": 30550, "ĠBulletin": 30551, "ĠRamsey": 30552, "sword": 30553, "ĠXI": 30554, "ourced": 30555, "Same": 30556, "346": 30557, "ĠRepe": 30558, "ĠKou": 30559, "cake": 30560, "emis": 30561, "Cache": 30562, "ĠMeaning": 30563, "ĠEnlight": 30564, "onomy": 30565, "Ġmanifestation": 30566, "sworth": 30567, "Jay": 30568, "Ġchore": 30569, "ör": 30570, "Dream": 30571, "Ġsanctioned": 30572, "Ġculturally": 30573, "ĠAra": 30574, "Nav": 30575, "Ġtheological": 30576, "Ġstrut": 30577, "ĠVO": 30578, "ĠHandbook": 30579, "Ġconstructing": 30580, "Ġ¶": 30581, "ĠBenefits": 30582, "ĠPsychological": 30583, "sac": 30584, "å¸": 30585, "policy": 30586, "ĠMatters": 30587, "ĠReported": 30588, "ĠByte": 30589, "Ġvitro": 30590, "ĠMaiden": 30591, "Ġlam": 30592, "ĠJennings": 30593, "Ġgarment": 30594, "ĠRutgers": 30595, "ĠStafford": 30596, "ĠWellington": 30597, "Ġintermitt": 30598, "Ġnpm": 30599, "Ġordeal": 30600, "Ġplugged": 30601, "ooming": 30602, "inished": 30603, "framework": 30604, "Ġtimber": 30605, "Ġcass": 30606, "Ġ850": 30607, "iless": 30608, "ĠRedux": 30609, "768": 30610, "Stre": 30611, "Ġsurpassed": 30612, "whel": 30613, "Ġparallels": 30614, "Ġveil": 30615, "ĠGI": 30616, "ĠREST": 30617, "Ġreadiness": 30618, "sort": 30619, "Ġmodifying": 30620, "ĠSlate": 30621, "ruff": 30622, "Ġmarble": 30623, "Ġinfrared": 30624, "Ġauditor": 30625, "ĠFANTASY": 30626, "ĠPoverty": 30627, "ĠSPD": 30628, "Ġ\"(": 30629, "Ky": 30630, "RAY": 30631, "Ġexecutions": 30632, "ĠBeverly": 30633, "ĠMarxism": 30634, "ĠBurst": 30635, "ĠKali": 30636, "estones": 30637, "Clearly": 30638, "Ell": 30639, "ãģ§": 30640, "ĠProceedings": 30641, "Token": 30642, "IFIC": 30643, "ña": 30644, "Central": 30645, "ĠHaley": 30646, "ĠDrama": 30647, "Ġformations": 30648, "ORN": 30649, "Books": 30650, "Ġdominating": 30651, "ĠFlyers": 30652, "ĠCompanion": 30653, "Ġdisciplined": 30654, "ĠYugoslav": 30655, "ĠSpells": 30656, "Ġvengeance": 30657, "Ġlandlords": 30658, "Len": 30659, "ĠOgre": 30660, "anoia": 30661, "Ġpiercing": 30662, "Ġcongreg": 30663, "Ġscorer": 30664, "obia": 30665, "Ġnickel": 30666, "ĠLearns": 30667, "Ġrejo": 30668, "Ġmasterpiece": 30669, "Flash": 30670, "Ġinhabited": 30671, "ĠOpenGL": 30672, "ĠDud": 30673, "ĠICO": 30674, "Ġarter": 30675, "Ġplur": 30676, "Ġmastery": 30677, "Ġlongstanding": 30678, "sted": 30679, "Ġwines": 30680, "Ġtelevised": 30681, "ĠShrine": 30682, "ĠBayern": 30683, "Ġâĵĺ": 30684, "Ġenclosure": 30685, "john": 30686, "Ġprophets": 30687, "ĠResurrection": 30688, "ĠOrders": 30689, "Ġuneven": 30690, "rals": 30691, "Ġdwind": 30692, "ĠLah": 30693, "ĠSloven": 30694, "378": 30695, "Ġinsistence": 30696, "affle": 30697, "ĠClone": 30698, "Ġhardship": 30699, "ĠCongressman": 30700, "Ġplead": 30701, "Ġreviewers": 30702, "Ġcured": 30703, "Ġ1935": 30704, "asley": 30705, "fake": 30706, "ĠThinking": 30707, "ydia": 30708, "PART": 30709, "ĠDota": 30710, "oit": 30711, "Ġwhipped": 30712, "Ġbouncing": 30713, "ĠHispanics": 30714, "comings": 30715, "Ġcannabin": 30716, "ĠChambers": 30717, "ĠZack": 30718, "Optional": 30719, "Ġcoats": 30720, "Ġprowess": 30721, "ĠNorton": 30722, "Ġplainly": 30723, "Ġfreight": 30724, "Ġinhibition": 30725, "Ġclam": 30726, "Ġ303": 30727, "kef": 30728, "aleigh": 30729, "Luke": 30730, "Ġpsycho": 30731, "atorium": 30732, "MED": 30733, "Ġtreaties": 30734, "Ġindisc": 30735, "Ġdc": 30736, "OPS": 30737, "Ġresilient": 30738, "ĠInterstate": 30739, "Ġslack": 30740, "Ġmundane": 30741, "Ġestablishes": 30742, "359": 30743, "Ġstrained": 30744, "Ġnond": 30745, "Sus": 30746, "Ġcaste": 30747, "arate": 30748, "ieving": 30749, "Ġunfairly": 30750, "Ġparser": 30751, "onial": 30752, "ursive": 30753, "Via": 30754, "ĠOtto": 30755, "ĠAuthorities": 30756, "stroke": 30757, "KR": 30758, "ĠMercy": 30759, "Ġfurnished": 30760, "Ġoutset": 30761, "Ġmetic": 30762, "1982": 30763, "olithic": 30764, "ĠTent": 30765, "ogical": 30766, "ĠAircraft": 30767, "Ġhides": 30768, "ĠBecame": 30769, "Ġeducators": 30770, "reaching": 30771, "Ġvolatility": 30772, "Ġtoddler": 30773, "ĠNASCAR": 30774, "ĠTwelve": 30775, "ĠHighlights": 30776, "Ġgrape": 30777, "Ġsplits": 30778, "Ġpeasant": 30779, "Ġreneg": 30780, "ĠMSI": 30781, "Temp": 30782, "stars": 30783, "Ġtrek": 30784, "ĠHyde": 30785, "binding": 30786, "Ġrealism": 30787, "Ġoxide": 30788, "ĠHos": 30789, "Ġmounts": 30790, "Ġbiting": 30791, "Ġcollapsing": 30792, "Ġpostal": 30793, "Ġmuseums": 30794, "Ġdetached": 30795, "Ġrespecting": 30796, "Ġmonopol": 30797, "Ġworkflow": 30798, "ĠCake": 30799, "Template": 30800, "ĠOrganisation": 30801, "Ġpersistence": 30802, "369": 30803, "Coming": 30804, "Brad": 30805, "Ġredundant": 30806, "ĠGTA": 30807, "Ġbending": 30808, "Ġrevoked": 30809, "Ġoffending": 30810, "Ġframing": 30811, "Ġprintf": 30812, "Commun": 30813, "members": 30814, "Outside": 30815, "Ġconstrued": 30816, "Ġcoded": 30817, "FORE": 30818, "Ġchast": 30819, "Chat": 30820, "Indian": 30821, "ĠYard": 30822, "?!\"": 30823, "ĠPorts": 30824, "ĠXavier": 30825, "ĠRET": 30826, "'.\"": 30827, "ĠBoat": 30828, "ivated": 30829, "icht": 30830, "umerable": 30831, "Ds": 30832, "ĠDunn": 30833, "Ġcoffin": 30834, "Ġsecurely": 30835, "ĠRaptors": 30836, "ĠBes": 30837, "Installation": 30838, "Ġinception": 30839, "ĠHealthy": 30840, "endants": 30841, "Ġpsychologists": 30842, "ĠSheikh": 30843, "cultural": 30844, "ĠBlackBerry": 30845, "shift": 30846, "Fred": 30847, "oche": 30848, "Ġcakes": 30849, "ĠSEO": 30850, "ĠGian": 30851, "ĠAsians": 30852, "ogging": 30853, "element": 30854, "Ġpundits": 30855, "ĠVaugh": 30856, "ĠGavin": 30857, "Ġhitter": 30858, "Ġdrowned": 30859, "Ġchalk": 30860, "ĠZika": 30861, "Ġmeasles": 30862, "802": 30863, "âĢ¦..": 30864, "ĠAWS": 30865, "]\"": 30866, "Ġdistort": 30867, "ĠMast": 30868, "Ġantibodies": 30869, "ĠMash": 30870, "Memory": 30871, "ĠUganda": 30872, "ĠProb": 30873, "Ġvomiting": 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"isson": 30927, "ĠDurham": 30928, "slaught": 30929, "ĠGraduate": 30930, "Ġsubconscious": 30931, "ĠExcellent": 30932, "ĠDum": 30933, "-----": 30934, "Ġpiles": 30935, "ĠWORK": 30936, "ĠGarn": 30937, "ĠFol": 30938, "ĠATM": 30939, "Ġavoids": 30940, "ĠTul": 30941, "Ġbleak": 30942, "ELY": 30943, "ivist": 30944, "lightly": 30945, "Pers": 30946, "ĠDob": 30947, "ĠLS": 30948, "Ġinsanity": 30949, "ε": 30950, "atalie": 30951, "Enlarge": 30952, "Ġtwists": 30953, "Ġfaulty": 30954, "Ġpiracy": 30955, "Ġimpover": 30956, "Ġrugged": 30957, "ĠFashion": 30958, "Ġsands": 30959, "'?": 30960, "swick": 30961, "Ġnatives": 30962, "Ġhen": 30963, "ĠNoise": 30964, "ãĥĹ": 30965, "Ġgreens": 30966, "Ġfreezer": 30967, "Ġdynasty": 30968, "ĠFathers": 30969, "ĠNewark": 30970, "Ġarchaeological": 30971, "Ġot": 30972, "obar": 30973, "Ġblockade": 30974, "Ġallerg": 30975, "LV": 30976, "Ġdebit": 30977, "ĠRFC": 30978, "ĠMilton": 30979, "ĠPressure": 30980, "Ġwillingly": 30981, "Ġdisproportionate": 30982, "Ġoppressive": 30983, "Ġdiamonds": 30984, "Ġbelongings": 30985, "1970": 30986, "Ġbells": 30987, "Ġimperialism": 30988, "Ġ227": 30989, "Ġexploding": 30990, "ĠEclipse": 30991, "Ġ1919": 30992, "Ġrant": 30993, "Ġnominations": 30994, "347": 30995, "Ġpeacefully": 30996, "rica": 30997, "ĠFUCK": 30998, "Ġvibration": 30999, "malink": 31000, "Ġropes": 31001, "ĠIvanka": 31002, "ĠBrewery": 31003, "ĠBooker": 31004, "ĠOwens": 31005, "goers": 31006, "Services": 31007, "ĠSnape": 31008, "Ġ191": 31009, "395": 31010, "Ġ299": 31011, "justice": 31012, "Ġbri": 31013, "Ġdiscs": 31014, "Ġprominently": 31015, "Ġvulgar": 31016, "Ġskipping": 31017, "lves": 31018, "Ġtsunami": 31019, "374": 31020, "ĠUrug": 31021, "ĠEid": 31022, "recated": 31023, "phen": 31024, "Ġfaults": 31025, "ĠStarted": 31026, "950": 31027, "Ġpi": 31028, "Ġdetector": 31029, "Ġbastard": 31030, "Ġvalidated": 31031, "SpaceEngineers": 31032, "OURCE": 31033, "Ġ(~": 31034, "Ġunsur": 31035, "Ġaffirmed": 31036, "Ġfascism": 31037, "Ġresolving": 31038, "ĠChavez": 31039, "ĠCyn": 31040, "Ġdetract": 31041, "Lost": 31042, "Ġrigged": 31043, "Ġhomage": 31044, "ĠBruno": 31045, "555": 31046, "eca": 31047, "Ġpresses": 31048, "Ġhumour": 31049, "Ġspacing": 31050, "Ġ'/": 31051, "olkien": 31052, "Coun": 31053, "OPER": 31054, "Tre": 31055, "Son": 31056, "ĠCambodia": 31057, "ierre": 31058, "mong": 31059, "ozy": 31060, "Ġliquidity": 31061, "ĠSoviets": 31062, "ĠFernando": 31063, "Ġ229": 31064, "Ġslug": 31065, "ĠCatalan": 31066, "electric": 31067, "Ġscenery": 31068, "ĠHearth": 31069, "Ġconstrained": 31070, "Ġgoalie": 31071, "ĠGuidelines": 31072, "ĠAmmo": 31073, "ĠPearson": 31074, "Ġtaxed": 31075, "Ġfetus": 31076, "Response": 31077, "ĠAlexis": 31078, "thia": 31079, "Guy": 31080, "Ġreconstruct": 31081, "Ġextremes": 31082, "Ġconcluding": 31083, "ĠPeg": 31084, "ooks": 31085, "Ġdeductions": 31086, "Rose": 31087, "Ġgroundbreaking": 31088, "ĠTarg": 31089, "ãĥģ": 31090, "ĠReve": 31091, "resource": 31092, "Ġmoons": 31093, "Ġelectromagnetic": 31094, "Ġamidst": 31095, "ĠViktor": 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"520": 31211, "GY": 31212, "Ġslapped": 31213, "ĠMTV": 31214, "Ġpara": 31215, "pull": 31216, "Multiple": 31217, "asher": 31218, "Ġnour": 31219, "ĠSeg": 31220, "Spell": 31221, "vous": 31222, "ordial": 31223, "Senior": 31224, "ĠGoldberg": 31225, "ĠPlasma": 31226, "need": 31227, "Ġmessenger": 31228, "eret": 31229, "Ġteamed": 31230, "Ġliteracy": 31231, "ĠLeah": 31232, "ĠDoyle": 31233, "Ġemitted": 31234, "UX": 31235, "Ġevade": 31236, "Ġmaze": 31237, "Ġwrongly": 31238, "ĠLars": 31239, "Ġstereotype": 31240, "Ġpledges": 31241, "Ġaroma": 31242, "ĠMET": 31243, "Ġacre": 31244, "ĠOD": 31245, "Ġff": 31246, "Ġbreweries": 31247, "ĠHilton": 31248, "undle": 31249, "ĠKak": 31250, "ĠThankfully": 31251, "ĠCanucks": 31252, "inctions": 31253, "ĠAppears": 31254, "Ġcoer": 31255, "Ġundermined": 31256, "rovers": 31257, "Andre": 31258, "Ġblaze": 31259, "umers": 31260, "Ġfamine": 31261, "amphetamine": 31262, "ulkan": 31263, "Amount": 31264, "Ġdesperation": 31265, "wikipedia": 31266, "development": 31267, 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31325, "ĠUndead": 31326, "isites": 31327, "ĠFIRST": 31328, "ño": 31329, "audi": 31330, "Ġhostages": 31331, "Ġcompliant": 31332, "Ġalumni": 31333, "Seven": 31334, "Ġcybersecurity": 31335, "either": 31336, "Collect": 31337, "Ġinvariably": 31338, "ĠSoci": 31339, "Ġlawmaker": 31340, "Ġale": 31341, "ĠPersonally": 31342, "Nazi": 31343, "Ġcustomization": 31344, "ĠProc": 31345, "ĠSaskatchewan": 31346, "eaturing": 31347, "Ġspared": 31348, "Ġdiscontinued": 31349, "Ġcomputational": 31350, "ĠMotorola": 31351, "Ġsupremacist": 31352, "governmental": 31353, "Ġparadise": 31354, "ĠDowning": 31355, "ĠNikon": 31356, "Ġcatalyst": 31357, "berra": 31358, "Toronto": 31359, "875": 31360, "beta": 31361, "ĠMacron": 31362, "Ġunrealistic": 31363, "vector": 31364, "ĠVehicles": 31365, "itiveness": 31366, "ĠRV": 31367, "ĠColbert": 31368, "sin": 31369, "oji": 31370, "entin": 31371, "ĠKrish": 31372, "hello": 31373, "ffield": 31374, "oky": 31375, "ĠTate": 31376, "Ġmaple": 31377, "Ġaids": 31378, "chemical": 31379, "334": 31380, "nuts": 31381, "ĠWarp": 31382, "Ġxx": 31383, "ĠRobb": 31384, "umerous": 31385, "_-_": 31386, "ftime": 31387, "ĠVW": 31388, "Ġwinger": 31389, "ĠDome": 31390, "tools": 31391, "ĠPV": 31392, "ĠGeorgetown": 31393, "Ġgeared": 31394, "Ġjihadists": 31395, "Ġcp": 31396, "Ġsteroids": 31397, "Mother": 31398, "clerosis": 31399, "ĠDRM": 31400, "nesia": 31401, "Ġlinger": 31402, "Ġimmersive": 31403, "ĠCOUN": 31404, "Ġoutweigh": 31405, "ensual": 31406, "Band": 31407, "Ġtransforms": 31408, "matched": 31409, "psons": 31410, "ĠJudicial": 31411, "factor": 31412, "Ġreferral": 31413, "Ġoddly": 31414, "ĠWenger": 31415, "Bring": 31416, "ĠBows": 31417, "602": 31418, "ICLE": 31419, "Ġlions": 31420, "ĠAcademic": 31421, "ĠThorn": 31422, "ĠRaider": 31423, "kefeller": 31424, "Storage": 31425, "Lower": 31426, "ĠOrt": 31427, "ĠEquality": 31428, "ALT": 31429, "ĠSOC": 31430, "Types": 31431, "Ġlyn": 31432, "ĠAsset": 31433, "coat": 31434, "TPP": 31435, "CVE": 31436, "ĠPioneer": 31437, "application": 31438, "Modern": 31439, "ĠHK": 31440, "Environment": 31441, "Alright": 31442, "Rain": 31443, "IPP": 31444, "ĠShiite": 31445, "Ġmound": 31446, "ĠAbilities": 31447, "condition": 31448, "Staff": 31449, "Ġcompetence": 31450, "ĠMoor": 31451, "ĠDiablo": 31452, "Ġwithheld": 31453, "Ġostensibly": 31454, "ĠBrom": 31455, "Ġmsg": 31456, "Ġdenomin": 31457, "ĠReferences": 31458, "ĠFP": 31459, "Ġplunged": 31460, "Ġpamph": 31461, "moving": 31462, "central": 31463, "Ġdownright": 31464, "Ġfading": 31465, "Tal": 31466, "Typ": 31467, "ĠThy": 31468, "ukes": 31469, "ithe": 31470, "Ġove": 31471, "Ġbattled": 31472, "Ġseafood": 31473, "Ġfigur": 31474, "ĠRD": 31475, "crop": 31476, "Ġsquads": 31477, "{\\": 31478, "à¹": 31479, "ĠEh": 31480, "Ġinterviewing": 31481, "ĠQin": 31482, "Ġaspiring": 31483, "PLIC": 31484, "Ġclauses": 31485, "ĠGast": 31486, "ĠNir": 31487, "Ġluggage": 31488, "Ġhose": 31489, "Ġsystemd": 31490, "Ġdescending": 31491, "ĠRevised": 31492, "ĠRails": 31493, "align": 31494, "709": 31495, "337": 31496, "Ġfug": 31497, "charging": 31498, "tags": 31499, "Ġuter": 31500, "kish": 31501, "WARNING": 31502, "490": 31503, "profits": 31504, "Ġvoyage": 31505, "Ġace": 31506, "ĠVanguard": 31507, "ĠTanks": 31508, "ĠMuk": 31509, "Ġ226": 31510, "Safe": 31511, "Armor": 31512, "Ġvolcanic": 31513, "Ġwomb": 31514, "ĠMIL": 31515, "Ġbeginner": 31516, "ĠRecogn": 31517, "ĠAAP": 31518, "PLAY": 31519, ")!": 31520, "Ġdetecting": 31521, "cn": 31522, "Ġbreaches": 31523, "Basically": 31524, "ĠPag": 31525, "ĠMunicipal": 31526, "ĠIndie": 31527, "ĠLaf": 31528, "ĠDisable": 31529, "ĠOlson": 31530, "Ġrestrained": 31531, "Ġrulings": 31532, "Ġhumane": 31533, "events": 31534, "ĠCinema": 31535, "displayText": 31536, "ĠHatch": 31537, "actionDate": 31538, "onnaissance": 31539, "Ġassaulting": 31540, "ĠLug": 31541, "CHAT": 31542, "Ġvigorous": 31543, "ĠPerse": 31544, "Ġintolerance": 31545, "ĠSnapchat": 31546, "ĠSharks": 31547, "Ġdummy": 31548, "ĠDiagn": 31549, "ĠGuitar": 31550, "imeters": 31551, "403": 31552, "REG": 31553, "Ax": 31554, "Ġseparates": 31555, "ĠMahm": 31556, "Ġtv": 31557, "jah": 31558, "OOL": 31559, "Circ": 31560, "ĠWindsor": 31561, "ussian": 31562, "Ġintuition": 31563, "Ġdisdain": 31564, "ĠDonovan": 31565, "Ġ221": 31566, "Emb": 31567, "Ġcondemning": 31568, "Ġgenerosity": 31569, "zzy": 31570, "Ġpanties": 31571, "ĠPrevent": 31572, "ActionCode": 31573, "ANA": 31574, "342": 31575, "externalActionCode": 31576, "Ġspecifying": 31577, "Ġcrystall": 31578, "Jere": 31579, "Ġrupt": 31580, "ĠApprentice": 31581, "Ġprofiling": 31582, "к": 31583, "Strike": 31584, "Ġsideline": 31585, "Ġobligated": 31586, "Ġoccult": 31587, "Ġbureaucratic": 31588, "antically": 31589, "rupted": 31590, "negative": 31591, "ĠEthiopia": 31592, "ĠCivic": 31593, "Ġinsiders": 31594, "eligible": 31595, "ĠTVs": 31596, "ĠBAR": 31597, "ĠTI": 31598, "iologist": 31599, "ĠAIR": 31600, "Ġsubstituted": 31601, "Arab": 31602, "ĠSaul": 31603, "ĠYog": 31604, "prem": 31605, "Ġbuilders": 31606, "Ġstationary": 31607, "Ġdoubtful": 31608, "Ġvigorously": 31609, "Ġthrilling": 31610, "Physical": 31611, "ĠCarey": 31612, "ĠHydra": 31613, "geoning": 31614, "ĠSly": 31615, "yton": 31616, "Ġborrowers": 31617, "ĠParkinson": 31618, "Ġë": 31619, "ĠJamaica": 31620, "Ġsatir": 31621, "Ġinsurgents": 31622, "ĠFirm": 31623, "Ġisot": 31624, "ĠKarn": 31625, "ourning": 31626, "akens": 31627, "docs": 31628, "little": 31629, "ĠMonaco": 31630, "CLASS": 31631, "Turkey": 31632, "Ly": 31633, "ĠConan": 31634, "assic": 31635, "Ġstarred": 31636, "ĠPacers": 31637, "eties": 31638, "Ġtipping": 31639, "Moon": 31640, "ĠRw": 31641, "same": 31642, "Ġcavity": 31643, "Ġgoof": 31644, "ĠZo": 31645, "Shock": 31646, "ummer": 31647, "Ġemphasizes": 31648, "Ġregrett": 31649, "Ġnovelty": 31650, "Ġenvy": 31651, "ĠPassive": 31652, "rw": 31653, "505": 31654, "Ġindifferent": 31655, "ĠRica": 31656, "ĠHimself": 31657, "ĠFreddie": 31658, "Ġadip": 31659, "ä¸Ģ": 31660, "Ġbreakout": 31661, "Ġhurried": 31662, "ĠHuang": 31663, "ĠDisk": 31664, "Ġroaming": 31665, "?????-?????-": 31666, "UV": 31667, "ĠRicky": 31668, "ĠSigma": 31669, "Ġmarginalized": 31670, "Ġedits": 31671, "Ġ304": 31672, "memory": 31673, "Ġspecimen": 31674, "293": 31675, "ãģ¯": 31676, "Ġvertically": 31677, "Ġaudition": 31678, "ĠHeck": 31679, "Ġcaster": 31680, "ĠHoldings": 31681, "adal": 31682, "ĠCron": 31683, "ĠLiam": 31684, "Ġdeflect": 31685, "Pick": 31686, "ĠDebug": 31687, "REF": 31688, "Ġversatility": 31689, "othes": 31690, "classified": 31691, "ĠMahar": 31692, "ĠHort": 31693, "Counter": 31694, "stasy": 31695, "noticed": 31696, "331": 31697, "ĠShim": 31698, "fuck": 31699, "ĠBie": 31700, "Ġairing": 31701, "ĠProtein": 31702, "ĠHolding": 31703, "Ġspectators": 31704, "iliated": 31705, "ĠThatcher": 31706, "nosis": 31707, "ãĥ¼ãĥ³": 31708, "Tele": 31709, "Boston": 31710, "ĠTempl": 31711, "stay": 31712, "Ġdeclarations": 31713, "479": 31714, "Volume": 31715, "ĠDesigner": 31716, "ĠOverwatch": 31717, "idae": 31718, "Ġonwards": 31719, "Ġnets": 31720, "ĠManila": 31721, "particularly": 31722, "Ġpolitic": 31723, "oother": 31724, "Ġportraits": 31725, "Ġpavement": 31726, "cffff": 31727, "Ġsaints": 31728, "Ġbeginners": 31729, "ESPN": 31730, "Ġshortcomings": 31731, "âķIJâķIJ": 31732, "Ġcomet": 31733, "ĠOrganic": 31734, "quel": 31735, "Ġhospitalized": 31736, "Break": 31737, "Ġpeel": 31738, "dylib": 31739, "aspx": 31740, "urances": 31741, "ĠTIM": 31742, "Pg": 31743, "Ġreadable": 31744, "ĠMalik": 31745, "Ġmuzzle": 31746, "Ġbenchmarks": 31747, "dal": 31748, "ĠVacc": 31749, "ĠHicks": 31750, "609": 31751, "ĠBiblical": 31752, "heng": 31753, "Ġoverload": 31754, "ĠCivilization": 31755, "Ġimmoral": 31756, "Ġfries": 31757, "ãĤĴ": 31758, "Ġreproduced": 31759, "Ġformulation": 31760, "jug": 31761, "irez": 31762, "gear": 31763, "Ġcoached": 31764, "MpServer": 31765, "ĠSJ": 31766, "ĠKw": 31767, "Init": 31768, "deal": 31769, "ĠOro": 31770, "ĠLoki": 31771, "ĠSongs": 31772, "Ġ232": 31773, "ĠLouise": 31774, "asionally": 31775, "Ġuncond": 31776, "ollywood": 31777, "Ġprogressives": 31778, "ĠEnough": 31779, "ĠDoe": 31780, "Ġwreckage": 31781, "Ġbrushed": 31782, "ĠBaseType": 31783, "Ġzoning": 31784, "ishable": 31785, "hetically": 31786, "ĠCaucus": 31787, "ĠHue": 31788, "Ġkarma": 31789, "ĠSporting": 31790, "Ġtrader": 31791, "Ġseeming": 31792, "ĠCapture": 31793, "430": 31794, "bish": 31795, "Ġtunes": 31796, "Ġindoors": 31797, "ĠSphere": 31798, "ĠDancing": 31799, "TERN": 31800, "Ġnob": 31801, "ĠGST": 31802, "maps": 31803, "Ġpeppers": 31804, "Fit": 31805, "Ġoversees": 31806, "ĠRabbi": 31807, "ĠRuler": 31808, "vertising": 31809, "office": 31810, "xxx": 31811, "Ġraft": 31812, "Changed": 31813, "Ġtextbooks": 31814, "Links": 31815, "ĠOmn": 31816, "ãĢij": 31817, "Ġinconvenience": 31818, "ĠDonetsk": 31819, "=~": 31820, "Ġimplicitly": 31821, "Ġboosts": 31822, "ĠBones": 31823, "ĠBoom": 31824, "Courtesy": 31825, "Ġsensational": 31826, "ANY": 31827, "Ġgreedy": 31828, "eden": 31829, "Ġinexper": 31830, "ĠLer": 31831, "ĠVale": 31832, "Ġtighten": 31833, "ĠEAR": 31834, "ĠNum": 31835, "Ġancestor": 31836, "Sent": 31837, "ĠHorde": 31838, "urgical": 31839, "allah": 31840, "Ġsap": 31841, "amba": 31842, "ĠSpread": 31843, "twitch": 31844, "Ġgrandson": 31845, "Ġfracture": 31846, "Ġmoderator": 31847, "ĠSeventh": 31848, "ĠReverse": 31849, "Ġestimation": 31850, "Choose": 31851, "Ġparach": 31852, "Ġbarric": 31853, "ãĢIJ": 31854, "Ġcompass": 31855, "Ġallergic": 31856, "âĢķ": 31857, "OTHER": 31858, "errilla": 31859, "Ġwagon": 31860, "Ġzinc": 31861, "Ġrubbed": 31862, "ĠFuller": 31863, "ĠLuxembourg": 31864, "ĠHoover": 31865, "Ġliar": 31866, "ĠEvening": 31867, "ĠCobb": 31868, "esteem": 31869, "Ġselector": 31870, "ĠBrawl": 31871, "isance": 31872, "ĠEk": 31873, "Ġtroop": 31874, "Ġguts": 31875, "ĠAppeal": 31876, "ĠTibetan": 31877, "Ġroutines": 31878, "ĠMent": 31879, "Ġsummarized": 31880, "steamapps": 31881, "Ġtranqu": 31882, "Ġ1929": 31883, "oran": 31884, "ĠAuthent": 31885, "Ġgmaxwell": 31886, "Ġapprehens": 31887, "Ġpoems": 31888, "Ġsausage": 31889, "ĠWebster": 31890, "urus": 31891, "Ġthemed": 31892, "Ġlounge": 31893, "Ġcharger": 31894, "Spoiler": 31895, "Ġspilled": 31896, "hog": 31897, "ĠSunder": 31898, "ĠAin": 31899, "ĠAngry": 31900, "Ġdisqual": 31901, "ĠFrequency": 31902, "ĠEthernet": 31903, "Ġhelper": 31904, "Percent": 31905, "Ġhorrifying": 31906, "Ġail": 31907, "ĠAllan": 31908, "EEE": 31909, "ĠCrossing": 31910, "449": 31911, "Ġholog": 31912, "ĠPuzzles": 31913, "ĠGoes": 31914, "erenn": 31915, "604": 31916, "ãģı": 31917, "ĠRafael": 31918, "Ġatten": 31919, "ĠEmanuel": 31920, "Ġupro": 31921, "ĠSusp": 31922, "Psych": 31923, "ĠTrainer": 31924, "ĠNES": 31925, "ĠHunts": 31926, "becue": 31927, "Ġcounselor": 31928, "Rule": 31929, "Ġtoxins": 31930, "Ġbanners": 31931, "rifice": 31932, "Ġgreeting": 31933, "Ġfrenzy": 31934, "Ġallocate": 31935, "Ġ*)": 31936, "expr": 31937, "503": 31938, "ĠChick": 31939, "ĠTorn": 31940, "Ġconsolidation": 31941, "ĠFletcher": 31942, "switch": 31943, "frac": 31944, "clips": 31945, "ĠMcKin": 31946, "ĠLunar": 31947, "Month": 31948, "ITCH": 31949, "Ġscholarly": 31950, 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33128, "Delivery": 33129, "Ġconcession": 33130, "Ġgastro": 33131, "ĠSprite": 33132, "ÄŁ": 33133, "andel": 33134, "Ġgimm": 33135, "Ġautobi": 33136, "ĠTurtle": 33137, "Ġwonderfully": 33138, "ĠHaram": 33139, "ĠWorldwide": 33140, "ĠHandle": 33141, "Ġtheorists": 33142, "Ġsleek": 33143, "ĠZhu": 33144, "ographically": 33145, "EGA": 33146, "ĠOwners": 33147, "aths": 33148, "ĠAntarctic": 33149, "natal": 33150, "=\"\"": 33151, "flags": 33152, "````": 33153, "Ġsul": 33154, "Kh": 33155, "Ġpotassium": 33156, "Ġlineman": 33157, "Ġcereal": 33158, "ĠSeasons": 33159, "Ġ2022": 33160, "Ġmathematic": 33161, "Ġastronomers": 33162, "professional": 33163, "Ġfares": 33164, "cknowled": 33165, "Ġchi": 33166, "Ġyoungsters": 33167, "Ġmistakenly": 33168, "Ġhemisphere": 33169, "ĠDivinity": 33170, "rone": 33171, "Ġ\",": 33172, "rings": 33173, "Ġattracts": 33174, "vana": 33175, "å¹": 33176, "CAP": 33177, "Ġplaylist": 33178, "Ġporch": 33179, "ãģ£": 33180, "Ġincorporates": 33181, "Ġsoak": 33182, "Ġasserting": 33183, "ĠTerrorism": 33184, "ĠPablo": 33185, "Ja": 33186, "cester": 33187, "Ġfearing": 33188, "ĠPrayer": 33189, "Ġescalated": 33190, "GW": 33191, "Ġrobe": 33192, "ĠBrighton": 33193, "acists": 33194, "ĠSymphony": 33195, "ĠDwarf": 33196, "ĠParade": 33197, "ĠLego": 33198, "Ġinexpl": 33199, "Ġlords": 33200, "leaf": 33201, "RAG": 33202, "liber": 33203, "Ġcigars": 33204, "ĠJehovah": 33205, "606": 33206, "WINDOWS": 33207, "ĠLiberia": 33208, "ebus": 33209, "Heavy": 33210, "Ġlubric": 33211, "ĠRW": 33212, "anguages": 33213, "Ġnarrowed": 33214, "computer": 33215, "ĠEmber": 33216, "Ġmurdering": 33217, "Ġdownstream": 33218, "ĠTuls": 33219, "ĠTables": 33220, "Topic": 33221, "ĠAccuracy": 33222, "=/": 33223, "lost": 33224, "ĠRei": 33225, "Ġprogresses": 33226, "bear": 33227, "Ġestablishments": 33228, "Justin": 33229, "ĠPeach": 33230, "ĠGomez": 33231, "å¿": 33232, "ĠTriangle": 33233, "Ident": 33234, "ĠHive": 33235, "Resources": 33236, "Ġmixes": 33237, "ĠAssuming": 33238, "Mu": 33239, "Ġhypoc": 33240, "Ġsane": 33241, "ĠWan": 33242, "idious": 33243, "Success": 33244, "Ġio": 33245, "Angel": 33246, "Ġdangerously": 33247, "ĠCreature": 33248, "WORK": 33249, ":[": 33250, "ĠKatrina": 33251, "Listener": 33252, "Miller": 33253, "ĠIdlib": 33254, "hang": 33255, "Ġcircumvent": 33256, "href": 33257, "Ġcelestial": 33258, "ĠWeeks": 33259, "ĠPug": 33260, "ĠDalton": 33261, "Ġsubpoena": 33262, "uku": 33263, "Ġpersisted": 33264, "pei": 33265, "olding": 33266, "ĠDocuments": 33267, "ĠHast": 33268, "ĠCENT": 33269, "Ġprimer": 33270, "Ġsynonymous": 33271, "Ġnib": 33272, "ombs": 33273, "Ġnotation": 33274, "ĠDish": 33275, "ĠAtmosp": 33276, "Ġforbid": 33277, "ĠANG": 33278, "pattern": 33279, "los": 33280, "Ġprojectiles": 33281, "brown": 33282, ".\",": 33283, "ĠVenom": 33284, "Ġfiercely": 33285, "ublished": 33286, "ĠUran": 33287, "ĠNicarag": 33288, "410": 33289, "ĠCAL": 33290, "OTOS": 33291, "ĠMiracle": 33292, "ĠEnchant": 33293, "Ġguarding": 33294, "append": 33295, "Attach": 33296, "Ġleveled": 33297, "Ġcondoms": 33298, "ihilation": 33299, "649": 33300, "Ġnightmares": 33301, "ĠTHEY": 33302, "ĠSTART": 33303, "ĠKinn": 33304, "Ġroommate": 33305, "Ġhygiene": 33306, "opping": 33307, "Job": 33308, "Ġlvl": 33309, "ĠVER": 33310, "ĠKeeping": 33311, "abetic": 33312, "Ġformatting": 33313, "erala": 33314, "Ġrevisions": 33315, "Ġresurg": 33316, "Tel": 33317, "ĠGoodman": 33318, "353": 33319, "pod": 33320, "Ġindisp": 33321, "ĠTranslation": 33322, "Ġgown": 33323, "ĠMund": 33324, "Ġcis": 33325, "Ġbystand": 33326, "collect": 33327, "ĠPunjab": 33328, "actively": 33329, "ĠGamb": 33330, "tell": 33331, "Ġimporting": 33332, "gencies": 33333, "Ġlocom": 33334, "ĠBrill": 33335, "Holy": 33336, "ĠBerger": 33337, "Ġshowdown": 33338, "Ġresponders": 33339, "ILY": 33340, "Ġtakedown": 33341, "leted": 33342, "Ġmattered": 33343, "Ġpredictive": 33344, "Ġoverlay": 33345, "GPU": 33346, "ĠVick": 33347, "Ġconveyed": 33348, "Tab": 33349, "peer": 33350, "Scan": 33351, "Ġdefensively": 33352, "vae": 33353, "Ġapproving": 33354, "Ġtiers": 33355, 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33412, "Ġreacting": 33413, "ĠAlv": 33414, "omsday": 33415, "ĠCRA": 33416, "Ġtranslator": 33417, "Ġbogus": 33418, "atu": 33419, "Website": 33420, "olls": 33421, "Ġbaptism": 33422, "Ġsibling": 33423, "ĠAutumn": 33424, "vez": 33425, "ãģ®é": 33426, "guards": 33427, "Georg": 33428, "assadors": 33429, "ĠFreud": 33430, "Ġcontinents": 33431, "ĠRegistry": 33432, "Bernie": 33433, "ĸļ士": 33434, "Ġtolerant": 33435, "ĠUW": 33436, "Ġhorribly": 33437, "995": 33438, "ĠMIDI": 33439, "Ġimpatient": 33440, "ocado": 33441, "eri": 33442, "ĠWorst": 33443, "ĠNorris": 33444, "ĠTalking": 33445, "Ġdefends": 33446, "ensable": 33447, "Ġ2021": 33448, "Ġanatomy": 33449, "Lew": 33450, "Ġdrawer": 33451, "ĠCanberra": 33452, "Ġpatriotic": 33453, "é¾įåĸļ士": 33454, "ĠAvg": 33455, "ARM": 33456, "Ġundisclosed": 33457, "Ġfarewell": 33458, "459": 33459, "bable": 33460, "ĠAllison": 33461, "OLOG": 33462, "Ġconco": 33463, "tight": 33464, "ĠACPI": 33465, "ĠMines": 33466, "lich": 33467, "ĠâĶľ": 33468, "represented": 33469, "200000": 33470, "Ġenthusiast": 33471, "OTS": 33472, "bil": 33473, "ĠIngredients": 33474, "Ġinventor": 33475, "ĠMySQL": 33476, "³³³": 33477, "ĠABOUT": 33478, "within": 33479, "Ġmk": 33480, "Bul": 33481, "ĠFake": 33482, "Ġdraconian": 33483, "Wa": 33484, "helm": 33485, "ĠTerran": 33486, "erville": 33487, "Ġcommonplace": 33488, "SIZE": 33489, "Ġ\"<": 33490, "replace": 33491, "ographs": 33492, "ĠSELECT": 33493, "incible": 33494, "ĠMostly": 33495, "ĠSheffield": 33496, "ĠIDE": 33497, "uggle": 33498, "Ġcitations": 33499, "hurst": 33500, "ĠUnix": 33501, "Ġunleash": 33502, "ĠPiper": 33503, "ĠNano": 33504, "Ġsuccumb": 33505, "Ġreluctance": 33506, "Ġ2500": 33507, "ĠMerchant": 33508, "Ġwiret": 33509, "Ġcombos": 33510, "ĠBirthday": 33511, "Ġcharcoal": 33512, "ĠUPS": 33513, "ĠFairfax": 33514, "Ġdriveway": 33515, "ĠTek": 33516, "ĠPitch": 33517, "overe": 33518, "Ġtechnicians": 33519, "ĠActual": 33520, "flation": 33521, "ĠFiscal": 33522, "ĠEmpty": 33523, "anamo": 33524, "Ġmagnesium": 33525, "Ġslut": 33526, "Ġgrowers": 33527, "Investigators": 33528, "():": 33529, "ĠSatellite": 33530, "ĠKeynes": 33531, "missive": 33532, "lane": 33533, "Ġborough": 33534, "344": 33535, "ĠTEAM": 33536, "ĠBethesda": 33537, "CV": 33538, "hower": 33539, "ĠRAD": 33540, "Ġchant": 33541, "ĠRiy": 33542, "Ġcompositions": 33543, "Ġmildly": 33544, "Ġmeddling": 33545, "Ġagility": 33546, "aneers": 33547, "501": 33548, "Ġsynth": 33549, "linger": 33550, "291": 33551, "Ġexclaimed": 33552, "Party": 33553, "Ġcontamin": 33554, "ĠManor": 33555, "ĠRespond": 33556, "Ġpraising": 33557, "Ġmanners": 33558, "fleet": 33559, "Summer": 33560, "ĠLynd": 33561, "ĠDefinitely": 33562, "grim": 33563, "Ġbowling": 33564, "stri": 33565, "çĽ": 33566, "ynt": 33567, "Ġmandates": 33568, "DIV": 33569, "Ġreconcile": 33570, "views": 33571, "ĠDamon": 33572, "vette": 33573, "Flo": 33574, "ĠGreatest": 33575, "ilon": 33576, "icia": 33577, "Ġportrayal": 33578, "Ġcushion": 33579, "504": 33580, "1979": 33581, "ossal": 33582, "Applic": 33583, "scription": 33584, "Ġmitigation": 33585, "ATS": 33586, "pac": 33587, "Ġerased": 33588, "Ġdeficiencies": 33589, "ĠHollande": 33590, "ĠXu": 33591, "Ġbred": 33592, "Ġpregnancies": 33593, "femin": 33594, "Ġemph": 33595, "Ġplanners": 33596, "Ġoutper": 33597, "uttering": 33598, "Ġperpetrator": 33599, "Ġmotto": 33600, "ĠEllison": 33601, "ĠNEVER": 33602, "Ġadmittedly": 33603, "ARI": 33604, "ĠAzerbaijan": 33605, "Ġmillisec": 33606, "Ġcombustion": 33607, "ĠBottle": 33608, "ĠLund": 33609, "ĠPs": 33610, "ĠDress": 33611, "Ġfabricated": 33612, "Ġbattered": 33613, "Ġsidel": 33614, "ĠNotting": 33615, "Foreign": 33616, "ĠJerome": 33617, "020": 33618, "ĠArbit": 33619, "Ġknots": 33620, "ĠRIGHT": 33621, "Moving": 33622, "ãģĻ": 33623, "Ġsurgeries": 33624, "Ġcourthouse": 33625, "Ġmastered": 33626, "Ġhovering": 33627, "ĠBran": 33628, "ĠAlison": 33629, "Ġsafest": 33630, "military": 33631, "Ġbullied": 33632, "Ġbarrage": 33633, "Reader": 33634, "ESE": 33635, "ĠGeographic": 33636, "Tools": 33637, "314": 33638, "ĠGeek": 33639, "roth": 33640, "glers": 33641, "ĠFIN": 33642, "Ïģ": 33643, "ĠAston": 33644, "altern": 33645, "488": 33646, "Ġveterin": 33647, "Gamer": 33648, "Ġintel": 33649, "renches": 33650, "Shield": 33651, "Ġamnesty": 33652, "ĠBhar": 33653, "Ġpiled": 33654, "Ġhonorable": 33655, "ĠInstitutes": 33656, "Ġsoaked": 33657, "Ġcoma": 33658, "ĠEFF": 33659, "341": 33660, "bytes": 33661, "ĠGmail": 33662, "lein": 33663, "ĠCanadiens": 33664, "material": 33665, "Il": 33666, "Ġinstructors": 33667, "ĠKY": 33668, "Ġconceive": 33669, "ubb": 33670, "ĠPossible": 33671, "Ġeasing": 33672, "ĠChristina": 33673, "Ġcaric": 33674, "ĠHDR": 33675, "ROM": 33676, "Ġshovel": 33677, "delete": 33678, "Ġpuff": 33679, "ĠChanging": 33680, "Ġseamlessly": 33681, "Attribute": 33682, "Ġacquisitions": 33683, "akery": 33684, "ĠEF": 33685, "Ġautistic": 33686, "ĠTakes": 33687, "ĠPowder": 33688, "ĠStir": 33689, "510": 33690, "ĠBubble": 33691, "settings": 33692, "ĠFowler": 33693, "Ġmustard": 33694, "Ġmoreover": 33695, "Ġcopyrighted": 33696, "ĠLEDs": 33697, "1500": 33698, "æī": 33699, "ĠHIS": 33700, "enf": 33701, "Ġcustod": 33702, "ĠHuck": 33703, "Gi": 33704, "Ġimg": 33705, "Answer": 33706, "Ct": 33707, "jay": 33708, "ĠInfrastructure": 33709, "Ġfederally": 33710, "Loc": 33711, "Ġmicrobes": 33712, "Ġoverrun": 33713, "dds": 33714, "otent": 33715, "adiator": 33716, ">>>>>>>>": 33717, "Ġtornado": 33718, "Ġadjud": 33719, "Ġintrigued": 33720, "Ġsi": 33721, "ĠRevelation": 33722, "progress": 33723, "Ġburglary": 33724, "ĠSaiyan": 33725, "ĠKathy": 33726, "Ġserpent": 33727, "ĠAndreas": 33728, "Ġcompel": 33729, "essler": 33730, "ĠPlastic": 33731, "ĠAdvent": 33732, "ĠPositive": 33733, "ĠQt": 33734, "ĠHindus": 33735, "registered": 33736, "ularity": 33737, "Ġrighteousness": 33738, "Ġdemonic": 33739, "uitive": 33740, "ĠBDS": 33741, "ĠGregg": 33742, "cia": 33743, "ĠCrusade": 33744, "ĠSinai": 33745, "WARE": 33746, "+(": 33747, "Ġmell": 33748, "Ġderail": 33749, "yards": 33750, "Ast": 33751, "Ġnoticeably": 33752, "ĠOber": 33753, "Ram": 33754, "Ġunnoticed": 33755, "Ġseq": 33756, "avage": 33757, "Ts": 33758, "Ġ640": 33759, "Ġconcede": 33760, "Ġ])": 33761, "Fill": 33762, "Ġcaptivity": 33763, "ĠImprovement": 33764, "ĠCrusader": 33765, "araoh": 33766, "MAP": 33767, "æĹ": 33768, "Ġstride": 33769, "always": 33770, "Fly": 33771, "Nit": 33772, "Ġalgae": 33773, "ĠCooking": 33774, "ĠDoors": 33775, "Malley": 33776, "Ġpolicemen": 33777, "ãģį": 33778, "Ġastronaut": 33779, "accessible": 33780, "495": 33781, "ĠRAW": 33782, "cliffe": 33783, "udicrous": 33784, "Ġdepended": 33785, "alach": 33786, "Ġventures": 33787, "rake": 33788, "Ġtits": 33789, "ĠHou": 33790, "Ġcondom": 33791, "ormonal": 33792, "Ġindent": 33793, "Ġuploading": 33794, "Footnote": 33795, "Important": 33796, "Ġ271": 33797, "Ġmindful": 33798, "Ġcontends": 33799, "Cra": 33800, "Ġcalibr": 33801, "ĠOECD": 33802, "plugin": 33803, "Fat": 33804, "ĠISS": 33805, "ĠDynamics": 33806, "ansen": 33807, "686": 33808, "'),": 33809, "Ġsprite": 33810, "Ġhandheld": 33811, "ĠHipp": 33812, "=~=~": 33813, "Trust": 33814, "Ġsemantics": 33815, "ĠBundes": 33816, "ĠReno": 33817, "ĠLiterature": 33818, "sense": 33819, "Gary": 33820, "ĠAeg": 33821, "ĠTrin": 33822, "EEK": 33823, "Ġcleric": 33824, "ĠSSH": 33825, "Ġchrist": 33826, "Ġinvading": 33827, "ibu": 33828, "Ġenum": 33829, "aura": 33830, "Ġallege": 33831, "ĠIncredible": 33832, "BBC": 33833, "Ġthru": 33834, "Ġsailed": 33835, "Ġemulate": 33836, "Ġinsecurity": 33837, "Ġcrou": 33838, "Ġaccommodations": 33839, "Ġincompetent": 33840, "Ġslips": 33841, "ĠEarthqu": 33842, "sama": 33843, "ILLE": 33844, "ĠiPhones": 33845, "asaki": 33846, "Ġbye": 33847, "Ġard": 33848, "Ġextras": 33849, "Ġslaughtered": 33850, "Ġcrowdfunding": 33851, "resso": 33852, "Ġfilib": 33853, "ĠERROR": 33854, "ĠTLS": 33855, "egg": 33856, "ĠItal": 33857, "Ġenlist": 33858, "ĠCatalonia": 33859, "ĠScots": 33860, "Ġsergeant": 33861, "Ġdissolve": 33862, "NH": 33863, "Ġstandings": 33864, "rique": 33865, "IQ": 33866, "Ġbeneficiary": 33867, "Ġaquarium": 33868, "YouTube": 33869, "ĠPowerShell": 33870, "Ġbrightest": 33871, "ĠWarrant": 33872, "Sold": 33873, "Writing": 33874, "Ġbeginnings": 33875, "ĠReserved": 33876, "ĠLatinos": 33877, "heading": 33878, "Ġ440": 33879, "Ġrooftop": 33880, "ATING": 33881, "Ġ390": 33882, "VPN": 33883, "Gs": 33884, "kernel": 33885, "turned": 33886, "Ġpreferable": 33887, "Ġturnovers": 33888, "ĠHels": 33889, "Sa": 33890, "ĠShinji": 33891, "veh": 33892, "ĠMODULE": 33893, "Viol": 33894, "Ġexiting": 33895, "Ġjab": 33896, "ĠVanilla": 33897, "Ġacron": 33898, "ĠGap": 33899, "bern": 33900, "Ak": 33901, "ĠMcGu": 33902, "Ġendlessly": 33903, "ĠFarage": 33904, "ĠNoel": 33905, "Va": 33906, "MK": 33907, "Ġbrute": 33908, "ĠKru": 33909, "ĠESV": 33910, "ĠOlivia": 33911, "âĢł": 33912, "ĠKaf": 33913, "Ġtrusting": 33914, "Ġhots": 33915, "324": 33916, "Ġmalaria": 33917, "Ġjson": 33918, "Ġpounding": 33919, "ortment": 33920, "Country": 33921, "Ġpostponed": 33922, "Ġunequiv": 33923, "?),": 33924, "ĠRooney": 33925, "udding": 33926, "ĠLeap": 33927, "urrence": 33928, "shapeshifter": 33929, "ĠHAS": 33930, "osate": 33931, "Ġcavern": 33932, "Ġconservatism": 33933, "ĠBAD": 33934, "Ġmileage": 33935, "Ġarresting": 33936, "Vaults": 33937, "Ġmixer": 33938, "Democratic": 33939, "ĠBenson": 33940, "Ġauthored": 33941, "8000": 33942, "Ġproactive": 33943, "ĠSpiritual": 33944, "tre": 33945, "Ġincarcerated": 33946, "ĠSort": 33947, "Ġpeaked": 33948, "Ġwielding": 33949, "reciation": 33950, "×Ļ×": 33951, "Patch": 33952, "ĠEmmy": 33953, "Ġexqu": 33954, "tto": 33955, "ĠRatio": 33956, "ĠPicks": 33957, "ĠGry": 33958, "phant": 33959, "Ġfret": 33960, "Ġethn": 33961, "Ġarchived": 33962, "%-": 33963, "cases": 33964, "ĠBlaze": 33965, "Ġimb": 33966, "cv": 33967, "yss": 33968, "imony": 33969, "Ġcountdown": 33970, "Ġawakening": 33971, "ĠTunisia": 33972, "ĠRefer": 33973, "ĠMJ": 33974, "Ġunnatural": 33975, "ĠCarnegie": 33976, "izen": 33977, "ĠNuggets": 33978, "hess": 33979, "Ġevils": 33980, "647": 33981, "Ġintroductory": 33982, "loving": 33983, "ĠMcMahon": 33984, "Ġambiguity": 33985, "Label": 33986, "ĠAlmighty": 33987, "Ġcoloring": 33988, "ĠClaus": 33989, "setting": 33990, "NULL": 33991, "ĠFavorite": 33992, "ĠSIG": 33993, ">(": 33994, "ĠShiva": 33995, "ĠMayer": 33996, "Ġstormed": 33997, "ĠCoverage": 33998, "weapons": 33999, "igham": 34000, "Ġunanswered": 34001, "Ġleve": 34002, "Ġcoy": 34003, "cas": 34004, "bags": 34005, "asured": 34006, "Seattle": 34007, "ĠSantorum": 34008, "serious": 34009, "Ġcourageous": 34010, "ĠSoup": 34011, "Ġconfiscated": 34012, "Ġ///": 34013, "Ġunconventional": 34014, "Ġmoms": 34015, "ĠRohingya": 34016, "ĠOrchestra": 34017, "ĠPotion": 34018, "Ġdiscredit": 34019, "ĠFIL": 34020, "fixed": 34021, "ĠDeer": 34022, "doi": 34023, "ĠDimension": 34024, "Ġbureaucrats": 34025, "eteen": 34026, "ĠactionGroup": 34027, "ohm": 34028, "Ġbumps": 34029, "ĠUtility": 34030, "Ġsubmarines": 34031, "renheit": 34032, "research": 34033, "ĠShapiro": 34034, "Ġsketches": 34035, "Ġdeceptive": 34036, "ĠVil": 34037, "esame": 34038, "ĠEssentially": 34039, "Ġrampage": 34040, "isky": 34041, "Ġmuttered": 34042, "thritis": 34043, "Ġ236": 34044, "fet": 34045, "bars": 34046, "Ġpupil": 34047, "ĠThou": 34048, "oS": 34049, "song": 34050, "Ġfractured": 34051, "Ġrevert": 34052, "picture": 34053, "Ġcriterion": 34054, "usher": 34055, "Ġrepercussions": 34056, "ĠVintage": 34057, "ĠSuperintendent": 34058, "Officers": 34059, "Ġflagged": 34060, "Ġblames": 34061, "Ġinverse": 34062, "ographers": 34063, "Ġmakeshift": 34064, "Ġdevoid": 34065, "Ġfossils": 34066, "ĠAristotle": 34067, "ĠFunds": 34068, "Ġdepleted": 34069, "ĠFlu": 34070, "ĠYuan": 34071, "Ġwoes": 34072, "Ġlipid": 34073, "Ġsitu": 34074, "requisites": 34075, "Ġfurnish": 34076, "ĠSamar": 34077, "Ġshameful": 34078, "Ġadversely": 34079, "Ġadept": 34080, "Ġremorse": 34081, "Ġmurderous": 34082, "uckles": 34083, "ĠESL": 34084, "Ġ314": 34085, "sent": 34086, "Ġredef": 34087, "ĠCache": 34088, "ĠPurs": 34089, "igans": 34090, "Ġ460": 34091, "Ġprescriptions": 34092, "Ġfres": 34093, "Fuck": 34094, "ocrates": 34095, "Twenty": 34096, "ĠWeird": 34097, "ĠToggle": 34098, "ĠCalled": 34099, "itizens": 34100, "Ġpoultry": 34101, "Ġharvesting": 34102, "ãĤ¦ãĤ¹": 34103, "Bottom": 34104, "Ġcautioned": 34105, "tn": 34106, "396": 34107, "ĠNikki": 34108, "Ġevaluations": 34109, "Ġharassing": 34110, "Ġbindings": 34111, "ĠMonetary": 34112, "Ġhitters": 34113, "Ġadversary": 34114, "unts": 34115, "Ġsetback": 34116, "Ġencrypt": 34117, "ĠCait": 34118, "Ġlows": 34119, "enges": 34120, "ĠNorn": 34121, "Ġbulbs": 34122, "Ġbottled": 34123, "ĠVoyager": 34124, "317": 34125, "Ġspheres": 34126, "politics": 34127, "Ġsubtract": 34128, "Ġsensations": 34129, "Ġappalling": 34130, "Ġ316": 34131, "Ġenvironmentally": 34132, "ĠSTEM": 34133, "Ġpublishes": 34134, "560": 34135, "Ġdiligence": 34136, "484": 34137, "Ġadvises": 34138, "Ġpetrol": 34139, "Ġimagining": 34140, "Ġpatrols": 34141, "ĠInteger": 34142, "ĠAshes": 34143, "actus": 34144, "ĠRadiant": 34145, "ĠLT": 34146, "itability": 34147, "htaking": 34148, "Setting": 34149, "Ġnuanced": 34150, "ĠReef": 34151, "ĠDevelopers": 34152, "Ni": 34153, "pieces": 34154, "990": 34155, "License": 34156, "Ġlowers": 34157, "ĠOttoman": 34158, "327": 34159, "ooo": 34160, "Ġquitting": 34161, "markets": 34162, "Behind": 34163, "Ġbasin": 34164, "Ġdocs": 34165, "anie": 34166, "flash": 34167, "ctl": 34168, "Ġcivilized": 34169, "ĠFukushima": 34170, "\"],\"": 34171, "ĠKS": 34172, "ĠHonestly": 34173, "arat": 34174, "Ġconstructs": 34175, "ĠLans": 34176, "ĠDire": 34177, "ĠLIKE": 34178, "ĠTrouble": 34179, "Ġwithholding": 34180, "ĠOblivion": 34181, "Ġsanity": 34182, "anya": 34183, "Const": 34184, "Ġgrocer": 34185, "ĠCelsius": 34186, "Ġrecounted": 34187, "ĠWife": 34188, "Border": 34189, "atered": 34190, "happy": 34191, "Ġspoiler": 34192, "Ġlogically": 34193, "Hall": 34194, "Ġsucceeding": 34195, "Ġpolymorph": 34196, "Ġaxes": 34197, "ĠShotgun": 34198, "ĠSlim": 34199, "ĠPrinciples": 34200, "ĠLeth": 34201, "arta": 34202, "Ġscor": 34203, "Screenshot": 34204, "Ġrelaxation": 34205, "#$#$": 34206, "Ġdeterrent": 34207, "iddy": 34208, "Ġpowerless": 34209, "Ġlesbians": 34210, "Ġchords": 34211, "ĠEdited": 34212, "selected": 34213, "Ġseparatists": 34214, "0002": 34215, "Ġairspace": 34216, "Ġturnaround": 34217, "Ġcunning": 34218, "PATH": 34219, "Poly": 34220, "Ġbombed": 34221, "Ġtion": 34222, "xs": 34223, "Ġwithhold": 34224, "Ġwaged": 34225, "ĠLiberties": 34226, "Flag": 34227, "Ġcomforting": 34228, "454": 34229, "ĠIris": 34230, "arers": 34231, "Ġrag": 34232, "Ġrelocated": 34233, "ĠGuarant": 34234, "Ġstrategically": 34235, "Ġgamma": 34236, "uberty": 34237, "ĠLockheed": 34238, "gres": 34239, "Ġgrilled": 34240, "ĠLowe": 34241, "stats": 34242, "ĠRocks": 34243, "Ġsensing": 34244, "Ġrenting": 34245, "ĠGeological": 34246, "اØ": 34247, "otrop": 34248, "Ġsew": 34249, "Ġimproperly": 34250, "486": 34251, "Ġâĸł": 34252, "Ġstarving": 34253, "ĠBj": 34254, "Discussion": 34255, "328": 34256, "ĠCombo": 34257, "ĠFixes": 34258, "NAT": 34259, "Ġstriving": 34260, "thora": 34261, "Ġharvested": 34262, "ĠPing": 34263, "Ġplayful": 34264, "Ġavenues": 34265, "Ġoccupational": 34266, "Ġwakes": 34267, "ĠCourier": 34268, "Ġdrummer": 34269, "ĠBrowser": 34270, "ĠHouth": 34271, "itu": 34272, "Ġapparel": 34273, "paste": 34274, "Ġhunted": 34275, "ĠSecondly": 34276, "lain": 34277, "XY": 34278, "ĠPIN": 34279, "icons": 34280, "Ġcocktails": 34281, "Ġsizable": 34282, "Ġhurdles": 34283, "estinal": 34284, "ĠRecreation": 34285, "Ġeco": 34286, "648": 34287, "ĠDied": 34288, "mint": 34289, "Ġfingerprints": 34290, "Ġdispose": 34291, "ĠBosnia": 34292, "tsy": 34293, "2200": 34294, "Ġinspected": 34295, "ĠFou": 34296, "Ġfuss": 34297, "Ġambush": 34298, "ĠRak": 34299, "Ġmanifested": 34300, "Prosecut": 34301, "Ġsuffice": 34302, "rences": 34303, "Ġcompensated": 34304, "ĠCyrus": 34305, "Ġgenus": 34306, "ĠWolverine": 34307, "ĠTrends": 34308, "Ġhikes": 34309, "ĠSeen": 34310, "Ġenrol": 34311, "Cold": 34312, "Ġpolitely": 34313, "ĠSlav": 34314, "ĠRupert": 34315, "Ġeyewitness": 34316, "ĠAlto": 34317, "Ġuncomp": 34318, "Ġposterior": 34319, "Must": 34320, "ĠHerz": 34321, "Ġprogressively": 34322, "Ġ234": 34323, "Ġindifference": 34324, "ĠCunningham": 34325, "Ġacademia": 34326, "Ġsewer": 34327, "Ġastounding": 34328, "ĠAES": 34329, "rather": 34330, "Ġeldest": 34331, "Ġclimbs": 34332, "ĠAdds": 34333, "Ġoutcry": 34334, "Ġcontag": 34335, "ĠHouses": 34336, "Ġpept": 34337, "ĠMelania": 34338, "interested": 34339, "ĠUCH": 34340, "ĠRoots": 34341, "ĠHubbard": 34342, "ĠTBD": 34343, "ĠRomanian": 34344, "filename": 34345, "Stone": 34346, "ĠImpl": 34347, "Ġchromosome": 34348, "Cle": 34349, "dx": 34350, "Ġscrambled": 34351, "ĠPt": 34352, "Ġ242": 34353, "OPLE": 34354, "Ġtremendously": 34355, "Street": 34356, "Ġcraving": 34357, "Ġbundled": 34358, "ĠRG": 34359, "pipe": 34360, "Ġinjuring": 34361, "Ġarcane": 34362, "Particip": 34363, "ĠHeroic": 34364, "sty": 34365, "Ġtopping": 34366, "ĠTempest": 34367, "rentices": 34368, "bh": 34369, "Ġparanoia": 34370, "ĠUnicode": 34371, "Ġegregious": 34372, "Ġ\\'": 34373, "ĠOswald": 34374, "Ġgravel": 34375, "ĠSimpsons": 34376, "Ġbland": 34377, "ĠGuantanamo": 34378, "Writer": 34379, "liners": 34380, "ĠDice": 34381, "JC": 34382, "Ġparity": 34383, "Ġsided": 34384, "Ġ237": 34385, "ĠPyrrha": 34386, "atters": 34387, "dk": 34388, "Fine": 34389, "compan": 34390, "Ġformulated": 34391, "ĠIdol": 34392, "ilers": 34393, "hemoth": 34394, "ĠFav": 34395, "Ġintrusion": 34396, "Ġcarrots": 34397, "ĠLayer": 34398, "ĠHacker": 34399, "Ġ----------------": 34400, "Ġmoderation": 34401, "éģ": 34402, "ococ": 34403, "Ġcharacterize": 34404, "ĠTeresa": 34405, "Ġsocioeconomic": 34406, "Ġperk": 34407, "ĠParticipation": 34408, "training": 34409, "ĠPaulo": 34410, "phys": 34411, "Ġtrustworthy": 34412, "Ġembodied": 34413, "ĠMerch": 34414, "currency": 34415, "ĠPriority": 34416, "Ġteasing": 34417, "Ġabsorbing": 34418, "Ġunfinished": 34419, "ĠComparison": 34420, "Ġdisple": 34421, "writers": 34422, "Ġprofessions": 34423, "ĠPenguin": 34424, "Ġangrily": 34425, "ĠLINK": 34426, "688": 34427, "ĠCorrespond": 34428, "Ġprevailed": 34429, "Ġcartel": 34430, "lp": 34431, "asms": 34432, "ĠRedemption": 34433, "ĠIslamists": 34434, "effects": 34435, "dose": 34436, "ĠLatter": 34437, "ĠHalifax": 34438, "Ġvas": 34439, "ĠTopics": 34440, "ĠNamed": 34441, "advertising": 34442, "zza": 34443, "ICES": 34444, "Ġretarded": 34445, "achable": 34446, "ĠPuppet": 34447, "ĠItemLevel": 34448, "Ġretract": 34449, "Ġidentifiable": 34450, "Aaron": 34451, "ĠBuster": 34452, "sol": 34453, "helle": 34454, "assemb": 34455, "Hope": 34456, "ranged": 34457, "Ba": 34458, "ĠPurch": 34459, "éĢ": 34460, "ĠSiri": 34461, "Ġarrivals": 34462, "Ġ1912": 34463, "Ġshortened": 34464, "Ġ312": 34465, "Ġdiscrepancy": 34466, "ĠTemperature": 34467, "ĠWalton": 34468, "Ġkinderg": 34469, "polit": 34470, "Ġremix": 34471, "Ġconnectors": 34472, "ãĥĺãĥ©": 34473, "ĠKazakhstan": 34474, "dominated": 34475, "Ġsugars": 34476, "imble": 34477, "ĠPanic": 34478, "ĠDemand": 34479, "ĠColony": 34480, "onen": 34481, "ĠMER": 34482, "775": 34483, "uria": 34484, "azaar": 34485, "ĠDegree": 34486, "Pri": 34487, "Ġsunshine": 34488, "Ġ251": 34489, "Ġpsychedelic": 34490, "Ġdigitally": 34491, "ĠBraun": 34492, "Ġshimmer": 34493, "Ġshave": 34494, "ĠTelesc": 34495, "ĠAstral": 34496, "ĠVenezuelan": 34497, "ĠOG": 34498, "Ġcrawling": 34499, "Integ": 34500, "ĠFeather": 34501, "Ġunfolding": 34502, "Ġappropriation": 34503, "Ġè£ıè": 34504, "ĠMobility": 34505, "ĠNey": 34506, "-.": 34507, "bilt": 34508, "LIN": 34509, "ĠTube": 34510, "ĠConversely": 34511, "Ġkeyboards": 34512, "ĠCao": 34513, "Ġoverth": 34514, "Ġlaure": 34515, ">>\\": 34516, "ĠViper": 34517, "acha": 34518, "Offset": 34519, "ĠRaleigh": 34520, "ĠJae": 34521, "Jordan": 34522, "jp": 34523, "Ġtotalitarian": 34524, "Connector": 34525, "Ġobserves": 34526, "ĠSpartan": 34527, "ĠImmediately": 34528, "ĠScal": 34529, "Cool": 34530, "Ġtaps": 34531, "Ġroar": 34532, "Past": 34533, "Ġchars": 34534, "ĠBender": 34535, "ĠSheldon": 34536, "Ġpainter": 34537, "Ġbeacon": 34538, "ĠCreatures": 34539, "Ġdownturn": 34540, "Ġhinder": 34541, "ĠAndromeda": 34542, "ÃĽ": 34543, "ccoli": 34544, "ĠFitness": 34545, "etrical": 34546, "Ġutilizes": 34547, "Ġsenate": 34548, "Ġensemble": 34549, "Ġcheers": 34550, "TW": 34551, "Ġaffluent": 34552, "kil": 34553, "rylic": 34554, "ordering": 34555, "Computer": 34556, "Ġgruesome": 34557, "ostics": 34558, "ĠUbisoft": 34559, "ĠKelley": 34560, "Ġwrench": 34561, "Ġbourgeoisie": 34562, "IBLE": 34563, "ĠPreston": 34564, "worn": 34565, "arist": 34566, "reating": 34567, "Ġstained": 34568, "arine": 34569, "Ġslime": 34570, "ENN": 34571, "Ġchests": 34572, "Ġgroundwater": 34573, "annot": 34574, "ĠTray": 34575, "ĠLocke": 34576, "ĠCTR": 34577, "Ġdudes": 34578, "ĠExternal": 34579, "ĠDecoder": 34580, "Ġparamed": 34581, "ĠMedline": 34582, "809": 34583, "ĠDinner": 34584, "rupal": 34585, "gz": 34586, "ĠGum": 34587, "ĠDemo": 34588, "jee": 34589, "Ġdh": 34590, "berman": 34591, "archs": 34592, "Ġenqu": 34593, "ĠEpstein": 34594, 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34702, "μ": 34703, "iatus": 34704, "ĠErnest": 34705, "ĠDeploy": 34706, "Ġcoveted": 34707, "FML": 34708, "ĠDialogue": 34709, "Ġexited": 34710, "fruit": 34711, "Ġnerd": 34712, "\":\"\",\"": 34713, "Ġvivo": 34714, "ruly": 34715, "460": 34716, "ĠAmen": 34717, "rehensible": 34718, "Ġâĺ": 34719, "DIR": 34720, "Ġadherence": 34721, "Ġchew": 34722, "ĠCoke": 34723, "ĠSergei": 34724, "digital": 34725, "ĠNeck": 34726, "gently": 34727, "enthal": 34728, "/)": 34729, "Ġweary": 34730, "Ġguise": 34731, "ĠConcord": 34732, "ĠOnion": 34733, "atcher": 34734, "Ġbinge": 34735, "ĠDirective": 34736, "Ġmanned": 34737, "ansk": 34738, "Ġillusions": 34739, "Ġbillionaires": 34740, "383": 34741, "olyn": 34742, "odynamic": 34743, "ĠWheat": 34744, "ĠAlic": 34745, "Ġcoloured": 34746, "ĠNAFTA": 34747, "abo": 34748, "Ġmacros": 34749, "independent": 34750, "sweet": 34751, "Ġspac": 34752, "ĠKabul": 34753, "ĠÄ": 34754, "eme": 34755, "Ġdictated": 34756, "Ġshouts": 34757, "={": 34758, "Ġripping": 34759, "ĠShay": 34760, 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"ĠSurveillance": 34818, "Ġdirectives": 34819, "Ġiterations": 34820, "ĠRox": 34821, "ĠMilky": 34822, "Father": 34823, "Ġpatented": 34824, "447": 34825, "Ġprecursor": 34826, "Ġmaiden": 34827, "ĠPhen": 34828, "ĠVegan": 34829, "ĠPatent": 34830, "Kelly": 34831, "Redditor": 34832, "Ġnods": 34833, "Ġventilation": 34834, "ĠSchwarz": 34835, "Ġwizards": 34836, "Ġominous": 34837, "ĠHeads": 34838, "ĠBG": 34839, "Ġlumber": 34840, "ĠSpiel": 34841, "ĠisEnabled": 34842, "Ġancestral": 34843, "ĠShips": 34844, "Ġwrestler": 34845, "phi": 34846, "Ġyuan": 34847, "ĠRebellion": 34848, "Ġiceberg": 34849, "Ġmagically": 34850, "Ġdiversion": 34851, "arro": 34852, "ythm": 34853, "ĠRiders": 34854, "ĠRobbie": 34855, "ĠKara": 34856, "ĠMaintenance": 34857, "ĠHerb": 34858, "Ġharms": 34859, "packed": 34860, "ĠFeinstein": 34861, "Ġmarrying": 34862, "Ġblending": 34863, "ĠRates": 34864, "Ġ1880": 34865, "Ġwrink": 34866, "ĠUnch": 34867, "ĠTorch": 34868, "described": 34869, "Ġhumanoid": 34870, "ilitating": 34871, "ĠConv": 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"Ġbarring": 34928, "keye": 34929, "Ġslashed": 34930, "ĠStatistical": 34931, "éĩ": 34932, "Ġâĸº": 34933, "Allows": 34934, "Ġhumility": 34935, "Ġdrilled": 34936, "ĠFurn": 34937, "443": 34938, "Ġsewage": 34939, "Ġhomepage": 34940, "Ġcourtyard": 34941, "Ġvile": 34942, "Ġsubsidiaries": 34943, "ajo": 34944, "directory": 34945, "Ġammon": 34946, "Vers": 34947, "charges": 34948, "Ġ}}": 34949, "ĠChains": 34950, "Ġ246": 34951, "nob": 34952, "Ġpercept": 34953, "Ġgrit": 34954, "Ġfishermen": 34955, "ĠIraqis": 34956, "ĠDISTR": 34957, "ĠFULL": 34958, "ĠEvaluation": 34959, "graph": 34960, "atial": 34961, "Ġcooperating": 34962, "Ġmelan": 34963, "Ġenlightened": 34964, "Ġali": 34965, "tailed": 34966, "Ġsalute": 34967, "Ġweakest": 34968, "ĠBulldogs": 34969, "UA": 34970, "ĠAlloy": 34971, "Ġsemen": 34972, "ocene": 34973, "ĠWilliamson": 34974, "spr": 34975, ",âĢĶ": 34976, "ĠGF": 34977, "ittens": 34978, "Beat": 34979, "ĠJunk": 34980, "iphate": 34981, "ĠFarmers": 34982, "ĠBitcoins": 34983, "igers": 34984, "dh": 34985, "ĠLoyal": 34986, "payer": 34987, "Ġentertained": 34988, "Ġpenned": 34989, "Ġcoupon": 34990, "Queue": 34991, "Ġweakening": 34992, "carry": 34993, "Ġunderestimate": 34994, "Ġshootout": 34995, "Ġcharismatic": 34996, "ĠProcedure": 34997, "Ġprudent": 34998, "inances": 34999, "Ġriches": 35000, "Ġcortical": 35001, "Ġstrides": 35002, "Ġdrib": 35003, "ĠOilers": 35004, "540": 35005, "ĠPerform": 35006, "ĠBangkok": 35007, "Ġeuth": 35008, "SER": 35009, "Ġsimplistic": 35010, "tops": 35011, "campaign": 35012, "Quality": 35013, "Ġimpoverished": 35014, "ĠEisenhower": 35015, "Ġaugment": 35016, "ĠHarden": 35017, "Ġintervened": 35018, "Ġlistens": 35019, "ĠKok": 35020, "Ġsage": 35021, "Ġrubbish": 35022, "ĠDed": 35023, "Ġmull": 35024, "pelling": 35025, "Ġvideot": 35026, "Production": 35027, "DJ": 35028, "miah": 35029, "Ġadaptations": 35030, "Ġmedically": 35031, "Ġboarded": 35032, "Ġarrogance": 35033, "Ġscrapped": 35034, "Ġoppress": 35035, "FORMATION": 35036, "Ġjunction": 35037, "415": 35038, "EEEE": 35039, "Skill": 35040, "Ġsubdu": 35041, "ĠSuggest": 35042, "ĠPett": 35043, "Ġlett": 35044, "ĠManip": 35045, "ĠCaf": 35046, "ĠCooperation": 35047, "Ther": 35048, "Ġregained": 35049, "¶æ": 35050, "reflect": 35051, "Ġthugs": 35052, "ĠShelby": 35053, "Ġdictates": 35054, "ĠWeiner": 35055, "ĠHale": 35056, "Ġbattleground": 35057, "schild": 35058, "Ġcondol": 35059, "hunt": 35060, "ositories": 35061, "Ġaccuses": 35062, "Filename": 35063, "Ġshri": 35064, "Ġmotivate": 35065, "Ġreflections": 35066, "Null": 35067, "ĠLobby": 35068, "¥µ": 35069, "ĠSATA": 35070, "ĠBackup": 35071, "Ñĥ": 35072, "nin": 35073, "ĠCorrection": 35074, "Ġjuicy": 35075, "utra": 35076, "ĠPric": 35077, "Ġrestraining": 35078, "ĠAirbnb": 35079, "ĠArrest": 35080, "Ġappropriations": 35081, "Ġslopes": 35082, "Ġmanslaughter": 35083, "Ġworkings": 35084, "ĠHuss": 35085, "ĠFrey": 35086, "Leave": 35087, "ĠHarmony": 35088, "ĠFeder": 35089, "Ġ430": 35090, "Ġtrench": 35091, "Ġgladly": 35092, "Ġbullpen": 35093, "ĠGau": 35094, "bones": 35095, "Ġgroove": 35096, "Ġpretext": 35097, "ãħĭ": 35098, "Ġtransmitter": 35099, "ĠComponent": 35100, "Ġunderage": 35101, "ĠEmpires": 35102, "Tile": 35103, "Ġoy": 35104, "ĠMarvin": 35105, "ĠCAS": 35106, "Ġbloss": 35107, "Ġreplicated": 35108, "ĠMariners": 35109, "Marcus": 35110, "ĠBlocks": 35111, "Ġliberated": 35112, "Ġbutterfly": 35113, "Feel": 35114, "Ġfermentation": 35115, "Ġyoutube": 35116, "Ġoffend": 35117, "ĠTerm": 35118, "resist": 35119, "Ġcessation": 35120, "Ġinsurgency": 35121, "Ġbir": 35122, "ĠRaise": 35123, "595": 35124, "Ġhypotheses": 35125, "502": 35126, "Ġplaque": 35127, "ocrat": 35128, "Ġjackets": 35129, "ĠHuffPost": 35130, "among": 35131, "Ġconfer": 35132, "487": 35133, "ĠLilly": 35134, "Ġadapting": 35135, "ĠFay": 35136, "Ġshoved": 35137, "vec": 35138, "Ġrefine": 35139, "Ġgon": 35140, "Ġgunmen": 35141, "zai": 35142, "ĠShuttle": 35143, "ĠIzan": 35144, "Ġ1913": 35145, "Ġplethora": 35146, "··": 35147, "Ġ510": 35148, "Ġpuberty": 35149, "Ġ241": 35150, "ĠWealth": 35151, "ĠAlma": 35152, "ĠMEM": 35153, "ĠAdults": 35154, "Cas": 35155, "prison": 35156, "Race": 35157, "Ġwaterproof": 35158, "Ġathleticism": 35159, "Ġcapitalize": 35160, "ĠJuice": 35161, "Ġilluminated": 35162, "ĠPascal": 35163, "Ġirritation": 35164, "ĠWitnesses": 35165, "adle": 35166, "ĠAstro": 35167, "Ġfax": 35168, "ĠElvis": 35169, "Primary": 35170, "ĠLich": 35171, "ĠElves": 35172, "Ġresiding": 35173, "Ġstumble": 35174, "319": 35175, "ĠPKK": 35176, "Ġadversaries": 35177, "DOS": 35178, "ĠRitual": 35179, "Ġsmear": 35180, "Ġarson": 35181, "idental": 35182, "Ġscant": 35183, "Ġmonarchy": 35184, "Ġhalftime": 35185, "Ġresidue": 35186, "Ġindign": 35187, "ĠShaun": 35188, "ĠElm": 35189, "auri": 35190, "Aff": 35191, "WATCH": 35192, "ĠLyon": 35193, "helps": 35194, "361": 35195, "Ġlobbyist": 35196, "Ġdiminishing": 35197, "Ġoutbreaks": 35198, "Ġgoats": 35199, "favorite": 35200, "ĠNah": 35201, "sonian": 35202, "ĠBooster": 35203, "Ġsandbox": 35204, "ĠFare": 35205, "ĠMalta": 35206, "ĠattRot": 35207, "ĠMOR": 35208, "lde": 35209, "Ġnavigating": 35210, "Touch": 35211, "Ġuntrue": 35212, "ĠDisaster": 35213, "Ġludicrous": 35214, "Password": 35215, "ĠJFK": 35216, "blogspot": 35217, "416": 35218, "ĠUNDER": 35219, "ernal": 35220, "Ġdelaying": 35221, "TOP": 35222, "Ġimplants": 35223, "ĠAVG": 35224, "ĠHuge": 35225, "attr": 35226, "Ġjournalistic": 35227, "ĠPeyton": 35228, "ĠIA": 35229, "Rap": 35230, "goal": 35231, "ĠProgramme": 35232, "Ġsmashing": 35233, "wives": 35234, "println": 35235, "ĠPlague": 35236, "inus": 35237, "EEP": 35238, "Ġcruiser": 35239, "ĠParish": 35240, "uminium": 35241, "Ġoccupants": 35242, "ĠJihad": 35243, "mop": 35244, "Ġpint": 35245, "Ġhect": 35246, "ĠMecca": 35247, "director": 35248, "ĠFunding": 35249, "ĠMixed": 35250, "Ġstag": 35251, "Tier": 35252, "Ġgust": 35253, "Ġbrightly": 35254, "orsi": 35255, "Ġuphill": 35256, "RD": 35257, "Ġlesions": 35258, "ĠBundy": 35259, "livious": 35260, "Ġbiologist": 35261, "ĠFaculty": 35262, "ĠAuthorization": 35263, "Ġ244": 35264, "Allow": 35265, "ï¸": 35266, "ĠGiul": 35267, "Ġpertinent": 35268, "otaur": 35269, "esse": 35270, "ĠRoof": 35271, "Ġunmanned": 35272, "351": 35273, "ĠShak": 35274, "ĠOrient": 35275, "Ġendanger": 35276, "Dir": 35277, "Ġreplen": 35278, "edient": 35279, "Ġtailor": 35280, "Ġgadgets": 35281, "Ġaudible": 35282, "âĺĨ": 35283, "Nice": 35284, "Ġbombard": 35285, "ĠRape": 35286, "Ġdefiance": 35287, "ĠTWO": 35288, "ĠFilipino": 35289, "Ġunaffected": 35290, "ervatives": 35291, "Ġsoared": 35292, "ĠBolton": 35293, "Ġcompromising": 35294, "ĠBrewers": 35295, "RAL": 35296, "ĠAHL": 35297, "icycle": 35298, "Ġvampires": 35299, "Ġdipped": 35300, "oyer": 35301, "ĠXIII": 35302, "Ġsideways": 35303, "ĠWaste": 35304, "ĠDiss": 35305, "ĠâĶľâĶĢâĶĢ": 35306, "$.": 35307, "Ġhabitats": 35308, "ĠBeef": 35309, "truth": 35310, "trained": 35311, "split": 35312, "Rus": 35313, "Andy": 35314, "ĠBram": 35315, "REP": 35316, "pid": 35317, "è£ħ": 35318, "ĠMutant": 35319, "Anim": 35320, "ĠMarina": 35321, "Ġfutile": 35322, "highest": 35323, "frequency": 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35822, "rano": 35823, "ilian": 35824, "Ġknockout": 35825, "fman": 35826, "required": 35827, "ĠWikileaks": 35828, "ĠDuffy": 35829, "ONT": 35830, "Ġinsol": 35831, "ĠObjects": 35832, "Ġbou": 35833, "ĠNordic": 35834, "ĠInsert": 35835, "scan": 35836, "Ġdancers": 35837, "Ġidiots": 35838, "majority": 35839, "ĠNeville": 35840, "ĠFreeBSD": 35841, "Ġtart": 35842, "panic": 35843, "690": 35844, "Ġcocoa": 35845, "Ġsampled": 35846, "Ġlookup": 35847, "Indust": 35848, "Ġinjections": 35849, "genre": 35850, "Ġau": 35851, "Ġroadway": 35852, "Ġgenitals": 35853, "Kind": 35854, "ĠExaminer": 35855, "ĠYaz": 35856, "Fresh": 35857, "Ġparalysis": 35858, "ĠAluminum": 35859, "Ġreap": 35860, "oké": 35861, "Ġsloppy": 35862, "ĠTunnel": 35863, "posium": 35864, "nery": 35865, "enic": 35866, "Ġherbal": 35867, "ĠOuter": 35868, "ĠBuilder": 35869, "Ġincur": 35870, "Ġideologies": 35871, "Ġbackups": 35872, "consuming": 35873, "ĠDetect": 35874, "deck": 35875, "ĠKNOW": 35876, "ĠGret": 35877, "ĠMIC": 35878, "Ġtoughness": 35879, 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35994, "Ġdarkest": 35995, "Ġhitch": 35996, "Ġdosage": 35997, "Ġscaven": 35998, "ĠKeller": 35999, "ĠIllustrated": 36000, "Certainly": 36001, "ĠMavericks": 36002, "Marginal": 36003, "Ġdiarrhea": 36004, "Ġenormously": 36005, "Ġ999": 36006, "shr": 36007, "quart": 36008, "Ġadamant": 36009, "ĠMew": 36010, "Ġrenovation": 36011, "Ġcervical": 36012, "ĠPercentage": 36013, "eners": 36014, "ĠKimber": 36015, "Ġfloats": 36016, "Ġdex": 36017, "ĠWitcher": 36018, "ĠSwansea": 36019, "dm": 36020, "Ġsalty": 36021, "yellow": 36022, "Ġcape": 36023, "ĠDrain": 36024, "ĠPaula": 36025, "ĠToledo": 36026, "lesi": 36027, "Magazine": 36028, "ĠWick": 36029, "ĠMn": 36030, "ĠAck": 36031, "ĠRiding": 36032, "ASON": 36033, "Ġhomophobic": 36034, "ARP": 36035, "Ġwandered": 36036, "CPU": 36037, "oodoo": 36038, "ĠPipe": 36039, "Ġtightening": 36040, "ĠButt": 36041, "318": 36042, "Ġdeserted": 36043, "Session": 36044, "Ġfacilitating": 36045, "Jump": 36046, "Ġemergencies": 36047, "OWER": 36048, "Ġexhaustive": 36049, "ĠAFTER": 36050, "Ġheartbeat": 36051, "ĠLabel": 36052, "acky": 36053, "ĠCertified": 36054, "iltration": 36055, "Ze": 36056, "ĠUtt": 36057, "Ġ1300": 36058, "Ġpresume": 36059, "ĠDisp": 36060, "Ġsurged": 36061, "Ġdolls": 36062, "Columb": 36063, "Ġchimpan": 36064, "ĠRazor": 36065, "Ġticks": 36066, "Ġcouncillor": 36067, "Ġpilgrimage": 36068, "ĠRebels": 36069, "ĠQC": 36070, "ĠAuction": 36071, "xia": 36072, "ikk": 36073, "bred": 36074, "Ġinsertion": 36075, "Ġcoarse": 36076, "dB": 36077, "SEE": 36078, "ĠZap": 36079, "ĠFoo": 36080, "Ġcontempor": 36081, "ĠQuarterly": 36082, "otions": 36083, "ĠAlchemist": 36084, "ĠTrey": 36085, "ĠDuo": 36086, "Sweet": 36087, "804": 36088, "ĠGiov": 36089, "Ġfunn": 36090, "Nin": 36091, "hoff": 36092, "Ġramifications": 36093, "Ġ1922": 36094, "ĠExperts": 36095, "azes": 36096, "Ġgarments": 36097, "arial": 36098, "ĠNab": 36099, "Ġ257": 36100, "ĠVed": 36101, "Ġhumorous": 36102, "ĠPompe": 36103, "Ġnylon": 36104, "Ġlurking": 36105, "ĠSergey": 36106, "ĠMattis": 36107, "Ġmisogyny": 36108, "ĠComponents": 36109, "ĠWatching": 36110, "ĠFolk": 36111, "ractical": 36112, "Bush": 36113, "Ġtaped": 36114, "Ġgrouping": 36115, "Ġbeads": 36116, "Ġ2048": 36117, "Ġcondu": 36118, "querque": 36119, "Reading": 36120, "Ġgrievances": 36121, "Ultra": 36122, "Ġendpoint": 36123, "Hig": 36124, "ĠStatic": 36125, "ĠScarborough": 36126, "Lua": 36127, "ĠMessi": 36128, "aqu": 36129, "ĠPsyNet": 36130, "ĠRudd": 36131, "Ġavenue": 36132, "vp": 36133, "Jer": 36134, "Ġshady": 36135, "ĠResist": 36136, "ĠArtemis": 36137, "Ġcareless": 36138, "Ġbrokers": 36139, "Ġtemperament": 36140, "Ġ520": 36141, "Tags": 36142, "ĠTurning": 36143, "Ġuttered": 36144, "Ġpedd": 36145, "Ġimprovised": 36146, "Ġ:(": 36147, "Ġtabl": 36148, "Ġplains": 36149, "1600": 36150, "pressure": 36151, "ĠEssence": 36152, "margin": 36153, "friends": 36154, "ĠRestoration": 36155, "Ġpollut": 36156, "ĠPoker": 36157, "ĠAugustine": 36158, "ĠCIS": 36159, "ĠSEAL": 36160, "orama": 36161, "Ġthwart": 36162, "seek": 36163, "Ġpagan": 36164, "º": 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37582, "Ġbadges": 37583, "ĠBernstein": 37584, ".âĢĵ": 37585, "ĠPOP": 37586, "ĠConj": 37587, "Ġinitialization": 37588, "Ġbiodiversity": 37589, "Dub": 37590, "Ġfeudal": 37591, "Ġdisclaimer": 37592, "Ġcrow": 37593, "Ġignition": 37594, "arf": 37595, "SHA": 37596, "ĠkHz": 37597, "hazard": 37598, "ĠArtists": 37599, "oeuv": 37600, "679": 37601, "ĠRudy": 37602, "Nine": 37603, "ĠRamadan": 37604, "å½": 37605, "itto": 37606, "Ġadrenaline": 37607, "Cert": 37608, "Ġsmelled": 37609, "Ġimpunity": 37610, "Ġagendas": 37611, "ĠReborn": 37612, "ĠConcent": 37613, "ĠSeems": 37614, "Ġomega": 37615, "ĠDustin": 37616, "Ġbacker": 37617, "ĠSauce": 37618, "ĠBoyle": 37619, "WIN": 37620, "Ġspins": 37621, "Ġpauses": 37622, "upt": 37623, "Ġshredded": 37624, "Ġstrapped": 37625, "ĠCorruption": 37626, "Ġscratches": 37627, "Ġni": 37628, "Ġattire": 37629, "ĠSAF": 37630, "FactoryReloaded": 37631, "ĠIPS": 37632, "Ġ(%": 37633, "Ġseminar": 37634, "focus": 37635, "civil": 37636, "Ġ1860": 37637, "intosh": 37638, "Ġcontinual": 37639, "Ġabbrevi": 37640, "ĠSok": 37641, "ocobo": 37642, "XM": 37643, "Ġfrantic": 37644, "Ġunavoidable": 37645, "Ġartery": 37646, "Ġannotations": 37647, "bath": 37648, "Climate": 37649, "Ġdors": 37650, "ĠSlide": 37651, "coord": 37652, "ĠReload": 37653, "ĠLDL": 37654, "ĠLovecraft": 37655, "Ġunimagin": 37656, "Ġresembled": 37657, "Ġbarracks": 37658, "np": 37659, "Ġsurrogate": 37660, "Ġcategorized": 37661, "ãĤ©": 37662, "Ġvaccinated": 37663, "Ġdrainage": 37664, "Ġindist": 37665, "ĠWhatsApp": 37666, "Ġ1870": 37667, "olerance": 37668, "invoke": 37669, "amorph": 37670, "Ġreconnect": 37671, "Ġemanc": 37672, "Ġblindness": 37673, "Ġ1280": 37674, "internet": 37675, "collar": 37676, "Ġaltru": 37677, "Ġabyss": 37678, "ĠTRI": 37679, "657": 37680, "Ġinfused": 37681, "HEAD": 37682, "Ġforestry": 37683, "ĠWoody": 37684, "ĠCi": 37685, "wi": 37686, "sam": 37687, "784": 37688, "holiday": 37689, "Ġmogul": 37690, "ĠFees": 37691, "ĠDEN": 37692, "Internal": 37693, "urbed": 37694, "fusc": 37695, "atom": 37696, 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37753, "ĠVMware": 37754, "ĠFarmer": 37755, "Ġservic": 37756, "glomer": 37757, "Ġslump": 37758, "ĠFabric": 37759, "ĠDOC": 37760, "esting": 37761, "Ġreassure": 37762, "Ġphyl": 37763, "volt": 37764, "itory": 37765, "Rules": 37766, "Ġoxidation": 37767, "Ġprized": 37768, "Ġmistress": 37769, "ĠDjango": 37770, "WARN": 37771, "åij": 37772, "Ġencode": 37773, "ĠFeedback": 37774, "Ġstupidity": 37775, "Ian": 37776, "ĠYugoslavia": 37777, "ר": 37778, "acl": 37779, "UTE": 37780, "1977": 37781, "Ġqualifies": 37782, "Ġpulses": 37783, "pretty": 37784, "Ġfroze": 37785, "Ġss": 37786, "Iterator": 37787, "Ġurgently": 37788, "Ġmailed": 37789, "ĠCham": 37790, "Ġsustaining": 37791, "Ġbasil": 37792, "Ġpuppies": 37793, "ilant": 37794, "ĠPLEASE": 37795, "lap": 37796, "aceous": 37797, "Fear": 37798, "ĠMastery": 37799, "automatic": 37800, "ĠTAG": 37801, "Ġantim": 37802, "agles": 37803, "473": 37804, "frames": 37805, "Ġwhispers": 37806, "ĠWhoever": 37807, "Ġbravery": 37808, "ĠUKIP": 37809, "ractions": 37810, 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37868, "ĠBW": 37869, "Veh": 37870, "Ġharming": 37871, "Ġembraces": 37872, "Ġstrives": 37873, "Ġinland": 37874, "ĠKard": 37875, "Ġheats": 37876, "ĠGinny": 37877, "utan": 37878, "ernaut": 37879, "ylene": 37880, "ĠElev": 37881, "JD": 37882, "Ġhars": 37883, "ĠStarr": 37884, "Ġskysc": 37885, "Ġcollaborators": 37886, "Usually": 37887, "Ġrevolutions": 37888, "ĠSTATS": 37889, "Ġdismantle": 37890, "Ġconfidently": 37891, "Ġkinetic": 37892, "Ali": 37893, "Ġpercentile": 37894, "Ġextracting": 37895, "illian": 37896, "estead": 37897, "Ġphysicists": 37898, "ĠMarshal": 37899, "Ġfellowship": 37900, "Ġdashed": 37901, "ĠUR": 37902, "ĠSioux": 37903, "ĠCompact": 37904, "amide": 37905, "Python": 37906, "ĠLeigh": 37907, "ĠPharmac": 37908, "istrates": 37909, "herical": 37910, "Ġfue": 37911, "ĠEmin": 37912, "Ġ({": 37913, "ĠNeighborhood": 37914, "Ġdisrupting": 37915, "ĠDup": 37916, "Ġgland": 37917, "ĠSev": 37918, "ĠMarian": 37919, "argon": 37920, "ĠDund": 37921, "Ġ": 46904, "ĠPhilips": 46905, "ĠKafka": 46906, 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"Ġtriangular": 46963, "Ġcapit": 46964, "lik": 46965, "ĠAncients": 46966, "Ġlash": 46967, "Ġconvol": 46968, "Ġcolonel": 46969, "enemy": 46970, "Gra": 46971, "Ġpubs": 46972, "utters": 46973, "Ġassigns": 46974, "ĠPenet": 46975, "ĠMonstrous": 46976, "ĠBowen": 46977, "ilver": 46978, "Haunted": 46979, "ĠDing": 46980, "started": 46981, "plin": 46982, "Ġcontaminants": 46983, "ĠDOE": 46984, "ffen": 46985, "ĠTechnician": 46986, "Ry": 46987, "Ġrobbers": 46988, "Ġhotline": 46989, "ĠGuardiola": 46990, "ĠKaufman": 46991, "rower": 46992, "ĠDresden": 46993, "ĠAlpine": 46994, "Elf": 46995, "Ġfmt": 46996, "ĠSard": 46997, "urses": 46998, "gpu": 46999, "Unix": 47000, "Ġunequivocally": 47001, "ĠCitizenship": 47002, "quad": 47003, "mire": 47004, "ĠSweeney": 47005, "Battery": 47006, "615": 47007, "Ġpancakes": 47008, "Ġoats": 47009, "Maps": 47010, "ĠContrast": 47011, "mbudsman": 47012, "ĠEPS": 47013, "Ġsubcommittee": 47014, "Ġsourcing": 47015, "Ġsizing": 47016, "ĠBuffer": 47017, "ĠMandatory": 47018, "Ġmoderates": 47019, "ĠPatterns": 47020, "ĠChocobo": 47021, "ĠZan": 47022, "ĠSTATES": 47023, "ĠJudging": 47024, "ĠInher": 47025, "*:": 47026, "Ġbil": 47027, "ĠYen": 47028, "Ġexhilar": 47029, "ollower": 47030, "zers": 47031, "Ġsnug": 47032, "maximum": 47033, "Ġdespicable": 47034, "ĠPACK": 47035, "ĠAnnex": 47036, "Ġsarcastic": 47037, "Ġlatex": 47038, "Ġtamp": 47039, "ĠSao": 47040, "bah": 47041, "ĠReverend": 47042, "ĠChinatown": 47043, "ĠAUT": 47044, "documented": 47045, "ĠGABA": 47046, "ĠCanaan": 47047, "ĠÙħ": 47048, "Ġgoverns": 47049, "prev": 47050, "Esc": 47051, "ĠEstimates": 47052, "OSP": 47053, "Ġendeavour": 47054, "ĠClosing": 47055, "ometime": 47056, "everyone": 47057, "Ġworsen": 47058, "Ġscanners": 47059, "Ġdeviations": 47060, "ĠRobotics": 47061, "ĠCompton": 47062, "Ġsorcerer": 47063, "Ġendogenous": 47064, "Ġemulation": 47065, "ĠPiercing": 47066, "ĠAph": 47067, "ĠSocket": 47068, "Ġbould": 47069, "ĠOU": 47070, "ĠBorderlands": 47071, "Ġ1863": 47072, "Gordon": 47073, "ĠWTO": 47074, "Ġrestricts": 47075, "Ġmosaic": 47076, "Ġmelodies": 47077, "çĦ": 47078, "Tar": 47079, "Ġdisson": 47080, "ĠProvides": 47081, "Ġ......": 47082, "bek": 47083, "FIX": 47084, "Ġbroom": 47085, "anship": 47086, "Doctors": 47087, "Ġnerds": 47088, "ĠRegions": 47089, "naissance": 47090, "Ġmete": 47091, "Ġcrept": 47092, "plings": 47093, "Ġgirlfriends": 47094, "knit": 47095, "igent": 47096, "owe": 47097, "Ġushered": 47098, "ĠBaz": 47099, "Mobil": 47100, "434": 47101, "ĠPresents": 47102, "origin": 47103, "Ġinsomnia": 47104, "ĠAux": 47105, "439": 47106, "ĠChili": 47107, "irsch": 47108, "GAME": 47109, "Ġgestation": 47110, "algia": 47111, "romising": 47112, "$,": 47113, "crow": 47114, "ĠInspection": 47115, "atomic": 47116, "Relations": 47117, "JOHN": 47118, "roman": 47119, "ĠClockwork": 47120, "ĠBakr": 47121, "mone": 47122, "MET": 47123, "Ġthirsty": 47124, "Ġbc": 47125, "Ġfaculties": 47126, "Rum": 47127, "Ġnuance": 47128, "ĠDarius": 47129, "pleting": 47130, "fters": 47131, "etchup": 47132, "Registration": 47133, "ĠKE": 47134, "Rah": 47135, "Ġpreferential": 47136, "ĠLash": 47137, "ĠHH": 47138, "Valid": 47139, "ĠNAV": 47140, "Ġstarve": 47141, "ĠGong": 47142, "zynski": 47143, "ĠActress": 47144, "Ġwik": 47145, "Ġunaccompanied": 47146, "lvl": 47147, "Bride": 47148, "ADS": 47149, "ĠCommando": 47150, "ĠVaughn": 47151, "Wallet": 47152, "Ġhopping": 47153, "ĠVie": 47154, "Ġcaveats": 47155, "Ġalas": 47156, "ifled": 47157, "abuse": 47158, "661": 47159, "Ġibn": 47160, "Ġgul": 47161, "Ġrobbing": 47162, "til": 47163, "ILA": 47164, "Ġmitigating": 47165, "Ġaptly": 47166, "Ġtyrant": 47167, "Ġmidday": 47168, "ĠGilmore": 47169, "ĠDecker": 47170, "Ġ§§": 47171, "partial": 47172, "Exactly": 47173, "Ġphenotype": 47174, "Ġ[+]": 47175, "ĠPlex": 47176, "ĠIps": 47177, "versions": 47178, "Ġebook": 47179, "Ġchic": 47180, "gross": 47181, "\":\"\"},{\"": 47182, "ĠSurprisingly": 47183, "Morgan": 47184, "Ġresidues": 47185, "ĠConfederation": 47186, "infeld": 47187, "Ġlyr": 47188, "moderate": 47189, "Ġperpendicular": 47190, "VK": 47191, "Ġsynchronized": 47192, "Ġrefreshed": 47193, "Ġadore": 47194, "ĠTorment": 47195, "olina": 47196, "Ġ2600": 47197, "ItemTracker": 47198, "Ġpies": 47199, "ĠFAT": 47200, "ĠRHP": 47201, "048": 47202, "ĠRESP": 47203, "ĠBJ": 47204, "allows": 47205, "Pand": 47206, "Ġunwelcome": 47207, "ĠVoc": 47208, "ĠBastard": 47209, "ĠOW": 47210, "ĠLAR": 47211, "ĠHealer": 47212, "Environmental": 47213, "ĠKenyan": 47214, "ĠTrance": 47215, "ĠPats": 47216, "Ġaliases": 47217, "ĠGarfield": 47218, "Ġcampaigner": 47219, "Ġadvancements": 47220, "ĠOkinawa": 47221, "ĠCoh": 47222, "owsky": 47223, "Ġstarved": 47224, "Ġsizeable": 47225, "Ġ:-)": 47226, "ĠmRNA": 47227, "Ġsuspensions": 47228, "istar": 47229, "Scotland": 47230, "Prin": 47231, "------------------------------------------------": 47232, "Ġ502": 47233, "Ġteaspoons": 47234, "Ġ1050": 47235, "Ġcoercive": 47236, "ĠMasonic": 47237, "edded": 47238, "ĠPassenger": 47239, "Ġlatt": 47240, "Ġbraces": 47241, "ĠSteal": 47242, "ĠNYT": 47243, "ĠKats": 47244, "ĠCelest": 47245, "aez": 47246, "Tu": 47247, "ĠCoulter": 47248, "ðŁĺ": 47249, "Flickr": 47250, "ĠWilmington": 47251, "iths": 47252, "++;": 47253, "Ġvending": 47254, "Ġnegro": 47255, "ĠPhi": 47256, "ĠYellowstone": 47257, "Callback": 47258, "Ġshampoo": 47259, "ĠShades": 47260, "wat": 47261, "Ġsuperhuman": 47262, "Ġridiculed": 47263, "Ġholiest": 47264, "ombo": 47265, "Ġinterns": 47266, "Ġhone": 47267, "ĠParagu": 47268, "URI": 47269, "Ġdangling": 47270, "ãĤ»": 47271, "sov": 47272, "ictional": 47273, "availability": 47274, "Ġrevocation": 47275, "Ġdow": 47276, "inic": 47277, "ĠTHEIR": 47278, "Ġiso": 47279, "Ġoutings": 47280, "ĠLethal": 47281, "Ġ)))": 47282, "Ġinaccur": 47283, "Ġoutlandish": 47284, "Ġanus": 47285, "letico": 47286, "idon": 47287, "lol": 47288, "Ġunregulated": 47289, "Ġsuccumbed": 47290, "Ġcuff": 47291, "ĠWasteland": 47292, "letal": 47293, "Ġsubstr": 47294, "Ġcoffers": 47295, "Ġautomakers": 47296, "ovi": 47297, "ĠXue": 47298, "ĠDaytona": 47299, "Ġjarring": 47300, "Ġfumes": 47301, "Ġdisbanded": 47302, "zik": 47303, "itton": 47304, "Ġstrikingly": 47305, "Ġspores": 47306, "Adapter": 47307, ".):": 47308, "ĠLyndon": 47309, "ivalry": 47310, "Ġorally": 47311, "Ġtumultuous": 47312, "Ġdispleasure": 47313, "Ġcones": 47314, "orrect": 47315, "Ġappease": 47316, "Ġderby": 47317, "ĠTripoli": 47318, "ĠAless": 47319, "Ġpoked": 47320, "ĠGuilty": 47321, "vP": 47322, "Enough": 47323, "Ġoriginals": 47324, "699": 47325, "Ġrabbi": 47326, "Ġproverbial": 47327, "Ġpostpone": 47328, "elope": 47329, "ĠMisty": 47330, "Ġstaffed": 47331, "ĠUnemployment": 47332, "reditary": 47333, "Ġdiligent": 47334, "recomm": 47335, "measures": 47336, "asin": 47337, "825": 47338, "Ġponds": 47339, "Ġmmol": 47340, "ĠSAR": 47341, "ĠCARE": 47342, "Ġ371": 47343, "Ġclenched": 47344, "ĠCorsair": 47345, "Ġcaricature": 47346, "zn": 47347, "attach": 47348, "ĠSchro": 47349, "speak": 47350, "painted": 47351, "ĠSuc": 47352, "ĠENT": 47353, "Ġcellul": 47354, "ĠPaid": 47355, "diagn": 47356, "WHERE": 47357, "Ġtexted": 47358, "Barn": 47359, "Ġretracted": 47360, "ĠReferred": 47361, "Sav": 47362, "Ġupkeep": 47363, "Ġworkplaces": 47364, "ĠTokens": 47365, "Ġamplify": 47366, "clinical": 47367, "Ġmultic": 47368, "mberg": 47369, "Ġconvoluted": 47370, "Region": 47371, "565": 47372, "ĠTopic": 47373, "Ġsnail": 47374, "Ġsaline": 47375, "Ġinsurrection": 47376, "ĠPetr": 47377, "forts": 47378, "BAT": 47379, "ĠNavajo": 47380, "Ġrudimentary": 47381, "ĠLaksh": 47382, "ONDON": 47383, "Measure": 47384, "Ġtransformer": 47385, "ĠGoddard": 47386, "Ġcoincides": 47387, "irin": 47388, "Rex": 47389, "ĠBok": 47390, "quit": 47391, "Ġshotguns": 47392, "Ġproletarian": 47393, "Ġscorp": 47394, "ĠAda": 47395, "514": 47396, "Ġslander": 47397, "recorded": 47398, "Ġembell": 47399, "risome": 47400, "Ġapologizing": 47401, "ĠMulcair": 47402, "ĠGibraltar": 47403, "Cla": 47404, "Ġallot": 47405, "ĠAttention": 47406, "Ġ433": 47407, "leave": 47408, "Ġwhine": 47409, "ĠIssa": 47410, "ĠFaust": 47411, "ĠBarron": 47412, "heny": 47413, "Ġvictimized": 47414, "Jews": 47415, "Ġnurturing": 47416, "ettel": 47417, "Winged": 47418, "ĠSubtle": 47419, "Ġflavorful": 47420, "ĠReps": 47421, "enged": 47422, "callback": 47423, "Ġdirectional": 47424, "Ġclasp": 47425, "ĠDirections": 47426, "planet": 47427, "iculture": 47428, "Helper": 47429, "icion": 47430, "acia": 47431, "Ġç¥ŀ": 47432, "Ġsurges": 47433, "Ġcanoe": 47434, "ĠPremiership": 47435, "been": 47436, "Ġdefied": 47437, "ĠTrooper": 47438, "Ġtripod": 47439, "Ġgasp": 47440, "ĠEuph": 47441, "ĠAds": 47442, "vernight": 47443, "highly": 47444, "Role": 47445, "Ġentangled": 47446, "ĠZeit": 47447, "618": 47448, "ĠRusty": 47449, "Ġhavens": 47450, "ĠVaughan": 47451, "HAEL": 47452, "ĠSERVICE": 47453, "/,": 47454, "Ġstricken": 47455, "Ġdelusions": 47456, "Ġbis": 47457, "ĠHaf": 47458, "Ġgratification": 47459, "Ġenticing": 47460, "UNCH": 47461, "Adams": 47462, "ĠOLED": 47463, "ĠBeetle": 47464, "Ġ1899": 47465, "ĠSOFTWARE": 47466, "ategor": 47467, "VL": 47468, "ĠTotem": 47469, "ĠGators": 47470, "ATURES": 47471, "Ġimpedance": 47472, "Registered": 47473, "ĠCary": 47474, "ĠAerial": 47475, "onne": 47476, "enium": 47477, "Ġdred": 47478, "ĠBeg": 47479, "Ġconcurrently": 47480, "Ġsuperpower": 47481, "ĠXan": 47482, "jew": 47483, "imester": 47484, "ĠDickinson": 47485, "âĶģ": 47486, "Fla": 47487, "Ġpree": 47488, "ĠRollins": 47489, "©¶æ": 47490, "Ġdenomination": 47491, "ĠLana": 47492, "516": 47493, "Ġinciting": 47494, "scribed": 47495, "juries": 47496, "ĠWonders": 47497, "approximately": 47498, "Ġsuspending": 47499, "Ġmountainous": 47500, "ĠLaugh": 47501, "oidal": 47502, "Ns": 47503, "Detect": 47504, ")=": 47505, "ĠLuthor": 47506, "ĠSchwarzenegger": 47507, "ĠMuller": 47508, "ĠDevi": 47509, "ecycle": 47510, "Jar": 47511, "613": 47512, "ĠLongh": 47513, "Bah": 47514, "ĠSPORTS": 47515, "nw": 47516, "Ġrefinement": 47517, "Ġwaterways": 47518, "Ġdiner": 47519, "Blade": 47520, "683": 47521, "Fac": 47522, "Ġinitials": 47523, "Ġrog": 47524, "Ġparanormal": 47525, "BUT": 47526, "Ġ[(": 47527, "ĠSwanson": 47528, "ĠMesh": 47529, "âĸ¬": 47530, "Improve": 47531, "ĠRadiation": 47532, "ĠEsther": 47533, "ĠEsk": 47534, "ĠAly": 47535, "iky": 47536, "Ġirrad": 47537, "ĠBuckingham": 47538, "Ġrefill": 47539, "Ġ._": 47540, "Repe": 47541, "CONCLUS": 47542, "Ġdifferentiated": 47543, "Ġchirop": 47544, "ĠAtkins": 47545, "Pattern": 47546, "Ġexcise": 47547, "Ġcabal": 47548, "NSA": 47549, "ĠSTA": 47550, "ĠSIL": 47551, "ĠParaly": 47552, "Ġrye": 47553, "ĠHowell": 47554, "ĠCountdown": 47555, "nesses": 47556, "alysed": 47557, "Ġresize": 47558, "ãĤ½": 47559, "Ġbudgetary": 47560, "ĠStras": 47561, "wang": 47562, "Ġapiece": 47563, "Ġprecincts": 47564, "Ġpeach": 47565, "Ġskyline": 47566, "Ġ353": 47567, "popular": 47568, "Appearances": 47569, "ĠMechanics": 47570, "ĠDevOnline": 47571, "Sullivan": 47572, "Zen": 47573, "Ġpu": 47574, "opolis": 47575, "544": 47576, "Ġdeform": 47577, "Ġcounteract": 47578, "ĠLange": 47579, "Ġ417": 47580, "Console": 47581, "774": 47582, "Ġnodding": 47583, "Ġpopulism": 47584, "Ġhep": 47585, "Ġcounselling": 47586, "compliance": 47587, "UFF": 47588, "Ġundeniably": 47589, "Ġrailing": 47590, "ĠHorowitz": 47591, "ĠSimone": 47592, "ĠBungie": 47593, "Ġak": 47594, "ĠTalks": 47595, "xff": 47596, "flake": 47597, "Crash": 47598, "Ġsweaty": 47599, "Ġbanquet": 47600, "ĠOFFIC": 47601, "Ġinventive": 47602, "Ġastronomer": 47603, "ĠStamford": 47604, "ĠScare": 47605, "ĠGREEN": 47606, "olicited": 47607, "Ġrusher": 47608, "Ġcentrist": 47609, "ighting": 47610, "Ġsubclass": 47611, "Ġdisav": 47612, "Ġdefund": 47613, "ĠNanto": 47614, "ociate": 47615, "mast": 47616, "Ġpacif": 47617, "Ġmend": 47618, "eers": 47619, "immigration": 47620, "ESSION": 47621, "Ġnumbering": 47622, "Ġlaughable": 47623, "ĠEnded": 47624, "viation": 47625, "emark": 47626, "Pitt": 47627, "Ġmeticulous": 47628, "ĠLF": 47629, "Ġcongratulated": 47630, "ĠBirch": 47631, "Ġswayed": 47632, "Ġsemifinals": 47633, "Ġhumankind": 47634, "matter": 47635, "ĠEquip": 47636, "opausal": 47637, "Said": 47638, "ĠLayout": 47639, "Ġvoicing": 47640, "Ġthug": 47641, "Ġpornographic": 47642, "IPS": 47643, "Ġmoaning": 47644, "Ġgrievance": 47645, "Ġconfessions": 47646, "escal": 47647, "TEXTURE": 47648, "Authent": 47649, "osaurus": 47650, "Purchase": 47651, "Ġrelegation": 47652, "alter": 47653, "Ġ³³": 47654, "Ġriddled": 47655, "Ġogre": 47656, "ĠLowell": 47657, "Occup": 47658, "Eat": 47659, "ĠHyder": 47660, "ĠAdviser": 47661, "Commerce": 47662, "Hunt": 47663, "ĠOrth": 47664, "ĠCompetitive": 47665, "ĠCLA": 47666, "CDC": 47667, "Ġsalads": 47668, "Fle": 47669, "Ġindustrialized": 47670, "`,": 47671, "ĠOWN": 47672, "Ġbeck": 47673, "ĠParticularly": 47674, "oubt": 47675, "ĠmM": 47676, "ĠHussain": 47677, "ĠChennai": 47678, "Ġ920": 47679, "Ġappointing": 47680, "ĠCullen": 47681, ",,,,,,,,": 47682, "Ġpores": 47683, "verified": 47684, "Ġbiochemical": 47685, "emate": 47686, "Ġcowardly": 47687, "ĠHelsinki": 47688, "ĠEthiopian": 47689, "SOURCE": 47690, "ERC": 47691, "estro": 47692, "Ġbiotech": 47693, "ĠSour": 47694, "Ġbrewer": 47695, "Bloomberg": 47696, "Ġintensify": 47697, "Glass": 47698, "anco": 47699, "ĠFDR": 47700, "greSQL": 47701, "ĠFires": 47702, "©¶æ¥µ": 47703, "eco": 47704, "1001": 47705, "ĠHomeless": 47706, "Ġinstantaneous": 47707, "ĠHaste": 47708, "igel": 47709, "Diamond": 47710, "Ġpaving": 47711, "Ġlandfill": 47712, "Ġdads": 47713, "houn": 47714, ":]": 47715, "Ġincendiary": 47716, "ĠLivingston": 47717, "ĠHilbert": 47718, "ĠChecks": 47719, "styles": 47720, "inators": 47721, "ĠClive": 47722, "phrine": 47723, "Ġchimpanzees": 47724, "Ġpall": 47725, "ĠJM": 47726, "ĠAadhaar": 47727, "ðĿ": 47728, "Ġachievable": 47729, "disabled": 47730, "PET": 47731, "OOOOOOOO": 47732, "Mot": 47733, "Ġintangible": 47734, "Ġballet": 47735, "ĠWebs": 47736, "ĠEstimated": 47737, "Effects": 47738, "Ġbailed": 47739, "Joshua": 47740, "Ġturbulence": 47741, "Ġoccupant": 47742, "ĠDaylight": 47743, "Ġ361": 47744, "meet": 47745, "Ġstatically": 47746, "Ġonlook": 47747, "Ġki": 47748, "illegal": 47749, "Ġvelvet": 47750, "Ġdehydration": 47751, "Ġacquies": 47752, "ĠRez": 47753, "akura": 47754, "ĠUpton": 47755, "atro": 47756, "Ġincomprehensible": 47757, "Ġbackdoor": 47758, "ĠRhino": 47759, "727": 47760, "Ġmaths": 47761, ")+": 47762, "Ġheresy": 47763, "Ġdf": 47764, "ĠRoche": 47765, "ĠLydia": 47766, "Ġpancreat": 47767, "reply": 47768, "arrell": 47769, "Ġsolicitation": 47770, "Ġcircadian": 47771, "BIP": 47772, "Ġforay": 47773, "Ġcryptic": 47774, "izu": 47775, "imeo": 47776, "ĠTomato": 47777, "ĠHoms": 47778, "examination": 47779, "Ġquarry": 47780, "ĠValiant": 47781, "ĠJericho": 47782, "ĠINCLUD": 47783, "Ġ1840": 47784, "519": 47785, "Ġresists": 47786, "Ġsnapshots": 47787, "ĠSpur": 47788, "ĠAntiqu": 47789, "Login": 47790, "Ġbestselling": 47791, "Ġantic": 47792, "ĠSutherland": 47793, "ãĤ¢ãĥ«": 47794, "Ġ~/": 47795, "ĠParm": 47796, "èĥ": 47797, "Pages": 47798, "intensity": 47799, "Ġimmobil": 47800, "Ġ1865": 47801, "zzo": 47802, "Ġnifty": 47803, "Ġfentanyl": 47804, "ĠPreservation": 47805, "ophen": 47806, "Ġdarts": 47807, "ĠDinosaur": 47808, "pointers": 47809, "ĠRite": 47810, "suggest": 47811, "awareness": 47812, "ĠSheridan": 47813, "Ġstances": 47814, "Ġsorcery": 47815, "Ġperjury": 47816, "ĠNikola": 47817, "iever": 47818, "Ġfiance": 47819, "ĠJordanian": 47820, "ĠBalloon": 47821, "Ġnab": 47822, "Ġkb": 47823, "Ġhumanities": 47824, "ĠTanaka": 47825, "hillary": 47826, "Ġconsultancy": 47827, "ĠZub": 47828, "Ġremission": 47829, "Ġconfid": 47830, "CHQ": 47831, "ĠFug": 47832, "Ġimprovis": 47833, "Yep": 47834, "/_": 47835, "Ġunwillingness": 47836, "Ġportfolios": 47837, "055": 47838, "ĠInstructor": 47839, "aiman": 47840, "Ġclaimants": 47841, "Mbps": 47842, "ĠBye": 47843, "received": 47844, "Tweet": 47845, "Ġindemn": 47846, "riz": 47847, "amara": 47848, "Nat": 47849, "Ġevaluates": 47850, "ĠLur": 47851, "epad": 47852, "FOX": 47853, "ĠThro": 47854, "Ġrusty": 47855, "Ġbedrock": 47856, "ĠOprah": 47857, "JB": 47858, "Ġmanipulative": 47859, "Ġwillful": 47860, "Ġrelapse": 47861, "Ġextant": 47862, "Theme": 47863, "Sensor": 47864, "ĠStability": 47865, "govern": 47866, "Ġpoppy": 47867, "Ġknack": 47868, "Ġinsulated": 47869, "ĠTile": 47870, "ĠExtrem": 47871, "Ġuntold": 47872, "Ġconverge": 47873, "Ġrefuel": 47874, "igroup": 47875, "Ġdistortions": 47876, "Ġravaged": 47877, "Ġmechanically": 47878, "ĠReilly": 47879, "ĠNose": 47880, "ĠIncarnation": 47881, "ĠBecky": 47882, "abbling": 47883, "Ġtaco": 47884, "Ġrake": 47885, "Ġmelancholy": 47886, "Ġillustrious": 47887, "ĠDartmouth": 47888, "Guide": 47889, "ĠRazer": 47890, "ĠBenz": 47891, "Ultimate": 47892, "ĠSurprise": 47893, "Ġpageant": 47894, "offer": 47895, "Whoever": 47896, "Ġwiser": 47897, "Ġchemist": 47898, "ĠHELL": 47899, "ĠBulk": 47900, "Ġplutonium": 47901, "ĠCOVER": 47902, "Ö¼": 47903, "failed": 47904, "Ġtirelessly": 47905, "Ġinfertility": 47906, "ĠTrident": 47907, "ĠShowtime": 47908, "ĠCiv": 47909, "Vice": 47910, "requires": 47911, "ittance": 47912, "Ġuncontrolled": 47913, "interesting": 47914, "561": 47915, "Ġinnovate": 47916, "ategic": 47917, "Lie": 47918, "ĠSelling": 47919, "Ul": 47920, "Ġsavior": 47921, "ĠTosh": 47922, "Ġswast": 47923, "PASS": 47924, "Ġrink": 47925, "Ġcardio": 47926, "ĠIro": 47927, "udi": 47928, "Ġvantage": 47929, "Ġvans": 47930, "ĠNiño": 47931, "+=": 47932, "Ġpropagate": 47933, "": 49029, "Ġleukemia": 49030, "Ġeluc": 49031, "Ġannouncer": 49032, "ĠLithuan": 49033, "ĠArmageddon": 49034, "åĩ": 49035, "Lenin": 49036, "ĠRuk": 49037, "Ġpepp": 49038, "ĠRomantic": 49039, "ĠPIT": 49040, "ĠInterstellar": 49041, "ĠAtkinson": 49042, "Raid": 49043, "Js": 49044, "Goal": 49045, "Course": 49046, "Ġvanishing": 49047, "esley": 49048, "ĠRounds": 49049, "Elsa": 49050, "593": 49051, "Ġredundancy": 49052, "ĠSTAND": 49053, "Ġprophetic": 49054, "Ġhabitable": 49055, "ryu": 49056, "Ġfaintly": 49057, "MODE": 49058, "Ġflanked": 49059, "IRC": 49060, "Awesome": 49061, "Ġspurious": 49062, "ĠZah": 49063, "ĠMSG": 49064, "Ġshading": 49065, "Ġmotivational": 49066, "ĠSantana": 49067, "ĠSPR": 49068, "Ġexcruciating": 49069, "omial": 49070, "ĠMiko": 49071, "ĠLeopard": 49072, "Abyss": 49073, "Ġ[|": 49074, "dirty": 49075, "Ġbaths": 49076, "Ġdemoral": 49077, "andre": 49078, "PB": 49079, "Ġunification": 49080, "Ġsacrament": 49081, "Ġ[&": 49082, "Ġpriceless": 49083, "Ġgelatin": 49084, "Ġemanating": 49085, "ĠAllaah": 49086, "986": 49087, "Ġoutburst": 49088, "Ġeras": 49089, "ĠXVI": 49090, "ĠSPI": 49091, "Ott": 49092, "ĠLazarus": 49093, "PLIED": 49094, "Flying": 49095, "blogs": 49096, "Wisconsin": 49097, "Raven": 49098, "Ġrebate": 49099, "Ġcreeps": 49100, "ĠSpan": 49101, "ĠPainter": 49102, "ĠKira": 49103, "ĠAmos": 49104, "ĠCorvette": 49105, "Consumer": 49106, "ĠRecover": 49107, "cki": 49108, "Ġpesky": 49109, "ĠInvention": 49110, "Companies": 49111, "Ġchallengers": 49112, "ademic": 49113, "ĠUkrainians": 49114, "ĠNeurolog": 49115, "ĠForsaken": 49116, "Ġentrants": 49117, "Ġembattled": 49118, "Ġdefunct": 49119, "ĠGlacier": 49120, "Ġpoisons": 49121, "ĠHorses": 49122, "makes": 49123, "ĠDirt": 49124, "Ġ423": 49125, "hhh": 49126, "ĠTransformation": 49127, "QUIRE": 49128, "..................": 49129, "Ġtraveller": 49130, "ĠSexy": 49131, "ĠKern": 49132, "ipolar": 49133, "Ġransomware": 49134, "oooooooooooooooo": 49135, "Ec": 49136, "ruby": 49137, "Professional": 49138, "ĠOutbreak": 49139, "argument": 49140, "Grey": 49141, "ĠFifa": 49142, "ĠCHO": 49143, "ĠFORM": 49144, "ĠAmtrak": 49145, "-[": 49146, "Ġcradle": 49147, "Ġantioxidants": 49148, "ãģ®å®": 49149, "736": 49150, "ĠNASL": 49151, "ĠContributions": 49152, "Indiana": 49153, "ĠSTEP": 49154, "CSS": 49155, "Ġsalient": 49156, "Ġallocations": 49157, "yrights": 49158, "Ġmashed": 49159, "ĠCutter": 49160, "Sexual": 49161, "Ġpounded": 49162, "Ġfanbase": 49163, "Ġcasc": 49164, "ĠTransparency": 49165, "Ġanalytic": 49166, "ĠSummoner": 49167, "×ŀ": 49168, "ĠADC": 49169, "detail": 49170, "Ġvanquished": 49171, "Ġcrabs": 49172, "arie": 49173, "Destroy": 49174, "ĠSack": 49175, "Ġtransistor": 49176, "Alabama": 49177, "ĠKoen": 49178, "ĠFisheries": 49179, "cone": 49180, "Ġannexed": 49181, "ĠMGM": 49182, "esa": 49183, "Ġfaked": 49184, "ĠCongratulations": 49185, "Ġhindered": 49186, "Ġcorrectional": 49187, "ĠITV": 49188, "leeve": 49189, "Ġinappropriately": 49190, "licks": 49191, "Ġtrespass": 49192, "Ġpaws": 49193, "Ġnegotiator": 49194, "ĠChristensen": 49195, "limits": 49196, "ĠDianne": 49197, "Ġelegance": 49198, "ĠContracts": 49199, "anke": 49200, "Obj": 49201, "Ġvigilance": 49202, "Ġcastles": 49203, "ĠNAD": 49204, "ĠHolo": 49205, "Ġemphatically": 49206, "ĠTitus": 49207, "ĠServing": 49208, "ĠRichie": 49209, "ĠPigs": 49210, "568": 49211, "Ġanimosity": 49212, "ĠAttributes": 49213, "ĠUriel": 49214, "MQ": 49215, "myra": 49216, "ĠApplicant": 49217, "Ġpsychiatrists": 49218, "ĠVij": 49219, "ĠAbby": 49220, "agree": 49221, "Push": 49222, "ĠkWh": 49223, "hiba": 49224, "Ġincite": 49225, "ĠWeasley": 49226, "ĠTaxi": 49227, "ministic": 49228, "hyper": 49229, "ĠFarn": 49230, "Ġ601": 49231, "ĠNationwide": 49232, "Fake": 49233, "952": 49234, "Ġmaize": 49235, "Ġinteracted": 49236, "Ġtransitioned": 49237, "Ġparasitic": 49238, "Ġharmonic": 49239, "Ġdecaying": 49240, "Ġbaseless": 49241, "nsics": 49242, "Ġtranspired": 49243, "Ġabundantly": 49244, "ĠForensic": 49245, "Ġtreadmill": 49246, "ĠJav": 49247, "aband": 49248, "Ġsshd": 49249, "Ġfrontman": 49250, "ĠJakarta": 49251, "oller": 49252, "drops": 49253, "ĠSERVICES": 49254, "romptu": 49255, "ophical": 49256, "hospital": 49257, "bledon": 49258, "645": 49259, "Ġmidrange": 49260, "ĠEVENT": 49261, "culated": 49262, "rawled": 49263, "Ġperched": 49264, "Ġoverboard": 49265, "ĠPeel": 49266, "ĠPwr": 49267, "ĠCarth": 49268, "ĠCOMPLE": 49269, "coe": 49270, "shall": 49271, "Ġdeterrence": 49272, "METHOD": 49273, "ĠAbsent": 49274, "MEN": 49275, "Ġsill": 49276, "ĠLEVEL": 49277, "York": 49278, "Ġsinners": 49279, "ĠOPEC": 49280, "ĠNur": 49281, "ĠDesigns": 49282, "selection": 49283, "Ġunworthy": 49284, "CHA": 49285, "Ġstrengthens": 49286, "883": 49287, "edly": 49288, "Ġslicing": 49289, "Ġmalnutrition": 49290, "Ġfilmmaking": 49291, "ĠPolk": 49292, "urated": 49293, "Ġ421": 49294, "breakers": 49295, "!'\"": 49296, "Ġwetlands": 49297, "ĠDiscrimination": 49298, "Ġallowable": 49299, "Ġsteered": 49300, "ĠSicily": 49301, "SAM": 49302, "Ġmustache": 49303, "Ġmids": 49304, "Ġclipped": 49305, "Ġcirculate": 49306, "Ġbrittle": 49307, "ĠBuildings": 49308, "raised": 49309, "ĠRoundup": 49310, "Ġwealthier": 49311, "Ġoverwrite": 49312, "Ġoverpowered": 49313, "ĠGerrard": 49314, "sites": 49315, "PDATED": 49316, "Ġacutely": 49317, "ĠGamble": 49318, "Ġpim": 49319, "ĠKus": 49320, "Typically": 49321, "Deploy": 49322, "ĠMoroccan": 49323, "potion": 49324, "combe": 49325, "Ġvigilante": 49326, "Ġ363": 49327, "Stew": 49328, "ĠBagg": 49329, "Ġresided": 49330, "ĠSpo": 49331, "Ġremnant": 49332, "Ġemptiness": 49333, "brainer": 49334, "Ġoutpatient": 49335, "priority": 49336, "Ġleptin": 49337, "ĠPayton": 49338, "ĠGleaming": 49339, "ĠShed": 49340, "ĠPolo": 49341, "ĠMormonism": 49342, "restricted": 49343, "arlane": 49344, "wx": 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ity", "us ed", "1 3", "Ġdes ign", "Ġch ange", "Ġch ang", "Ġb o", "Ġv is", "em ber", "Ġb ook", "read y", "Ġk ill", "2 5", "pp ed", "Ġa way", "Ġab le", "Ġcount ry", "Ġcon st", "ar n", "Ġor der", "A R", "i or", "i um", "or th", "1 8", "ail able", "Ġs w", "Ġm illion", "Ġ1 3", "at ic", "t ed", "ĠG o", "Ġo per", "en g", "Ġth ing", "aj or", "con om", "ĠCom m", "Ġwh y", "u red", "ur al", "Ġs chool", "b y", "ĠM ar", "Ġa ff", "Ġd ays", "Ġan n", "us h", "an e", "I f", "e g", "Ġpro f", "Ġhe alth", "ou th", "B ut", "ion al", ". ,", "Ġs ol", "Ġal ready", "Ġ3 0", "Ġchar act", "H e", "Ġf riend", "E S", "i ans", "ic le", "' d", "ĠO n", "Ġle ast", "Ġp rom", "Ġd r", "Ġh ist", "it her", "Ġ est", "i qu", "1 7", "s on", "Ġte ll", "Ġt alk", "oh n", "o int", "le ction", "A N", "Ġunt il", "au gh", "Ġl ater", "Ġ ve", "Ġv iew", "end ing", "iv ed", "Ġwor d", "w are", "Ġc ost", "Ġen ough", "Ġg ive", "ĠUn ited", "Ġte chn", "are nt", "O R", "Ġp ar", "ĠD r", "Ġ201 6", "r ist", "er ing", "Ġ Â", "Ġl arge", "s ide", "ac y", "cc ess", "Ġw in", "Ġimport ant", "Ġ19 9", "Ġdoes n", "Ġ1 7", "Ġbus iness", "Ġcle ar", "Ġre se", "\" ,", "ur y", "Ġe qu", "as ter", "al f", "ĠAmeric an", "n ect", "Ġex pect", "ivers ity", "Ġo cc", "ĠF l", "Ġk ind", "Ġme an", "Ġp ast", "Ġde v", "Ġb as", "le t", "ra ft", "Ġor gan", "Ġde l", "Ġper form", "Ġst ory", "Ġse ason", "ĠC ol", "Ġcl aim", "Ġc ame", "Ġwith in", "Ġl ine", "Ġpro ject", "ĠA t", "Ġcontro l", "end ed", "ĠS y", "Ġa ir", "iz ation", "Ġ *", "le y", "Ġm oney", "id d", "Y ou", "f or", "Ġfam ily", "Ġm aking", "Ġb it", "Ġpol ice", "Ġhapp en", "Ġ vers", "on y", "u ff", "ĠW hen", "Ġs it", "ide o", "l f", "is on", "Ġsu re", "g in", "Ġapp ear", "Ġl ight", "Ġ es", "o f", "Ġw ater", "Ġt imes", "n ot", "Ġg row", "Ġcomp any", "ĠT e", "ow s", "Ġm ar", "our ce", "i ol", "ar m", "b r", "Ġex ample", "Ġcon c", "Ġf ore", "ĠT o", "p ro", "E N", "ri es", "Ġ2 5", "ĠC an", "ne y", "Ġact ually", "Ġe ver", "ur ity", "ak en", "ap s", "Ġt ax", "Ġm ajor", "am a", "Ġof ten", "er al", "Ġhum an", "Ġj ob", "is ter", "Ġav ailable", "oc r", "en n", "a id", "iv id", "Ġrec ord", "? \"", "Ġs ing", "ĠA m", "id ence", "Ġnew s", "st er", "Ġe conom", "Ġfollow ing", "ĠB r", "is ing", "Ġh our", "m ost", "um ent", "Ġse x", "Ġdes c", "Ġbec ome", "ĠE d", "Ġto ok", "Ġha ving", "Ġprodu ct", "a ult", "A s", "ar ing", "Ġme ans", "Ġh op", "un e", "Ġch o", "Ġc ertain", "Ġn on", "Ġde al", "2 4", "le ment", "oc i", "en e", "Ġs ide", "ĠP r", "ĠM ay", "Ġre ason", "u ed", "c hed", "ul ation", "Ġe lect", "Ġoffic ial", "Ġposs ible", "Ġh old", "and s", "ot s", "Ġc ity", "or ies", "Ġse ver", "Ġchild ren", "Ġon ce", "Ġact iv", "l er", "Ġn ight", "it ions", "ĠJ ohn", "a pe", "pl ay", "Ġd one", "Ġl im", "Ġwork ing", "ĠP res", "or ld", "e b", "ĠC o", "Ġb ody", "ail s", "ut es", "ĠM r", "Ġwhe ther", "Ġa uthor", "ro p", "Ġpro per", "Ġse en", ") ;", "Ġf ac", "ĠS u", "Ġcon d", "it ing", "Ġcour se", "Ġ }", "-------- --------", "a ign", "Ġev ent", "Ġen g", "Ġp ot", "Ġin tern", "i am", "Ġsh ort", "em pt", "ã Ĥ", "ĠG od", "il ar", "8 0", "Ġor ig", "I S", "our n", "ab ility", "it ive", "Ġd am", "Ġ1 00", "Ġp ress", "Ġdo ing", "Ġprot ect", "r ing", "Ġthough t", "Ġquest ion", "re w", "ĠW ar", "Ġsever al", "ĠSt ate", "Ġg iven", "Ġf und", "ĠT w", "Ġw ent", "an ces", "w ork", "p or", "m y", "4 0", "Ġar g", "art ment", "ust om", "Ġpol ic", "Ġme et", "Ġc reat", "2 2", "ĠSt ates", "Ġg ames", "ra w", "ut ure", "Ġunder stand", "ur s", "ĠO b", "l ish", "s y", "Ġm akes", "Ġw on", "ag on", "Ġh tt", "Ġl ove", "ent ial", "Ġcomple te", "p ar", "ĠI m", "A L", "Ġacc ount", " ł", "ore d", "ver t", "Ġ ident", "Ġ201 5", "Ġother s", "ĠM in", "i ber", "ver age", "The re", "ition al", "d d", "Ġpro b", "Ġyou ng", "Ġal ong", "Ġacc ording", "Ġy et", "Ġmem bers", "ĠWh at", "o id", "ĠM an", "A nd", "Ġam ong", "a i", "Ġem ploy", "ĠR es", "Ġ >", "Ġinv ol", "Ġl ow", "a f", "ĠC ar", "Ġh ig", "ĠO ne", "ĠS ec", "in ation", "Ġlike ly", "Ġan t", "ag ed", "ĠR uss", "Ġb en", "Ġre le", "F or", "b ack", "ĠN ot", "Ġpres ident", "b all", "Ġacc ess", "ivid ual", "ĠD em", "ĠE uro", "6 0", "Ġkn own", "ir l", "ĠG r", "Ġear ly", "u se", "iet y", "âĢ ĵ", "Ġf ight", "Ġs ent", "Ġto day", "Ġmark et", "\" .", "Ġb ased", "Ġstr ong", "ur ther", "Ġde b", "m ber", "Ġproble m", "Ġde ath", "Ġsoc ial", "im ate", "A S", "ort un", "Ġcamp aign", "er y", "C h", "Ġe y", "i ally", "Ġm us", "w h", "p os", "Ġ er", "Ġsa f", "Ġmonth s", "ir on", "Ġv iol", "Ġf ive", "Ġst re", "Ġplay ers", "in c", "al d", "y ear", "a un", "Ġsu ccess", "Ġpres ent", "ere nce", "Ġ201 4", "Ġsu gg", "Ġpartic ular", "Ġtr y", "Ġsugg est", "ĠCh rist", "on es", "Ġpri v", "2 3", "Ġc rit", "Ġl and", "Ġloc al", "if y", "2 9", "Ġa ut", "E D", "ĠG u", "Ġm ult", "Ġpolit ical", "Ġask ed", "Ġfor mer", "it ter", "ri pt", "Ġcl ose", "Ġp ract", "ĠY ork", "Ġget ting", "Ġac ross", "Ġcom b", "Ġbelie ve", "Ġ z", "Ġto get", "Ġtoget her", "ĠC ent", "ir c", "Ġind ividual", "ĠM c", "2 7", "is k", "ĠE ng", "Ġf ace", "Ġ2 4", "Ġval ue", "Ġare a", "e v", "Ġw rit", "ĠPres ident", "Ġv ot", "Ġke y", "Ġm om", "p ut", "Ġany thing", "Ġexper ience", "att le", "Ġm ind", "a ff", "om m", "Ġf uture", "g ed", "Ġc ut", "Ġto t", "it ch", "Ġv ideo", "Ġinvest ig", "Ġn et", "ĠM y", "r ict", "i en", ". )", "Ġimp ro", "th ough", "ward s", "Ġcon nect", "ĠM ed", "sel ves", "ens ive", "m b", "o ber", "at ors", "A n", "Ġ5 0", "Ġre du", "res ent", "Ġab ove", "Ġf re", "ĠEuro pe", "s w", "Ġam ount", "ĠA pp", "Ġe ither", "Ġmil it", "Ġan al", "Ġf ail", "ĠE n", "al es", "Ġspec ial", "Ġbl ack", "I T", "c her", "Ġlook ing", "Ġf ire", "y n", "Ġal most", "o on", "Ġstud y", "Ġm iss", "c hes", "ro wn", "Ġt re", "Ġcommun ity", "Ġmed ia", "Ġf ood", "Ġcom es", "ĠUn iversity", "Ġsing le", "Wh at", "u ly", "Ġh alf", "ag ue", "h od", "ĠRep ublic", "Ġstart ed", "Ġqu ick", "ot o", "b ook", "Ġiss ue", "it or", "Ġel se", "Ġcons ider", "2 6", "ro du", "Ġt aken", "2 8", "9 9", "ĠW ith", "Ġtr ue", "Ġw a", "Ġtr ad", "Ġag o", "Ġm ess", "ie f", "Ġadd ed", "o ke", "Ġb ad", "Ġf av", "3 3", "Ġsim ilar", "as k", "ĠD on", "Ġcharact er", "ort s", "ĠH ouse", "Ġreport ed", "Ġty pe", "v al", "i od", "ĠHow ever", "Ġt arg", "Ġent ire", "pp ing", "Ġhist ory", "Ġl ive", "ff ic", ".... ....", "ed eral", "Ġtr ying", "Ġdisc uss", "ĠH ar", "ac es", "l ished", "Ġse lf", "os p", "re st", "Ġro om", "el t", "Ġf all", "ol ution", "Ġe t", "Ġ x", "Ġis n", "Ġide a", "b o", "Ġs ound", "ĠD ep", "Ġsome one", "ci ally", "ull y", "Ġf oc", "Ġob ject", "if t", "ap er", "Ġplay er", "Ġr ather", "Ġserv ice", "as hing", "ĠD o", "ĠP art", "ru g", "m on", "p ly", "Ġm or", "Ġnot hing", "Ġprov ide", "I C", "un g", "Ġpart y", "Ġex ist", "Ġm ag", "7 0", "Ġr ul", "Ġh ouse", "Ġbeh ind", "Ġhow ever", "ĠW orld", "Ġs um", "Ġapp lic", "Ġ ;", "Ġfun ction", "g r", "ĠP ol", "Ġfr ont", "2 00", "Ġser ies", "Ġt em", "Ġty p", "ill s", "Ġo pt", "Ġpoint s", "Ġbel ow", "itt ed", "Ġspec ific", "Ġ201 7", "um b", "Ġr a", "Ġpre vious", "Ġpre t", "re me", "Ġc ustom", "Ġcour t", "ĠM e", "Ġre pl", "Ġwho le", "g o", "c er", "Ġt reat", "ĠA ct", "Ġprob ably", "Ġle arn", "end er", "ĠA ss", "Ġvers ion", "n ow", "Ġche ck", "ĠC al", "R E", "min ist", "O n", "our ces", "Ġben ef", "Ġd oc", "Ġdet er", "Ġen c", "Ġsu per", "Ġadd ress", "Ġv ict", "Ġ201 3", "Ġme as", "t r", "Ġf ield", "W hen", "Ġsign ific", "u ge", "Ġfe at", "Ġcomm on", "l oad", "Ġbe gin", "Ġbr ing", "Ġa ction", "er man", "Ġdesc rib", "Ġind ust", "Ġwant ed", "ri ed", "m ing", "Ġatt empt", "4 5", "f er", "Ġd ue", "ress ion", "# #", "Ġsh all", "Ġs ix", "o o", "Ġst ep", "Ġp ub", "Ġhim self", "Ġ2 3", "Ġc op", "Ġd est", "Ġst op", "A C", "ib ility", "Ġl ab", "ic ult", "Ġhour s", "Ġcre ate", "Ġf urther", "ĠAmeric a", "ĠC ity", "Ġd ou", "he ad", "S T", "ĠN orth", "c ing", "Ġn ational", "u le", "ĠIn st", "Ġt aking", "ĠQ u", "ir t", "Ġre d", "Ġrese arch", "v iron", "ĠG e", "Ġbre ak", "an a", "Ġsp ace", "ater ial", "Ġrec ent", "ĠA b", "Ġgener al", "Ġh it", "Ġper iod", "Ġevery thing", "ive ly", "Ġph ys", "Ġsay ing", "an ks", "Ġc ou", "Ġc ult", "ac ed", "e al", "u ation", "Ġc oun", "l u", "Ġinclud e", "Ġpos ition", "ĠA fter", "ĠCan ad", "ĠE m", "Ġim m", "ĠR ed", "Ġp ick", "Ġcom pl", "Ġm atter", "re g", "e xt", "ang u", "is c", "o le", "a ut", "Ġcomp et", "e ed", "f ect", "Ġ2 1", "ĠS en", "ĠThe se", "as ing", "Ġcan not", "Ġin it", "Ġrel ations", "ac hed", "Ġb ar", "Ġ4 0", "ĠT H", "Ġ201 2", "Ġv ol", "Ġg round", "Ġsec urity", "Ġup d", "il t", "3 5", "Ġconc ern", "ĠJ ust", "Ġwh ite", "Ġseem s", "ĠH er", "pe cially", "i ents", "Ġann oun", "Ġf ig", "ight s", "Ġst ri", "l ike", "id s", "Ġs us", "Ġw atch", "Ġ â", "Ġw ind", "ĠC ont", "Ġit self", "Ġm ass", "A l", "y le", "iqu e", "ĠN ational", "Ġab s", "Ġp ack", "Ġout side", "Ġan im", "Ġp ain", "et er", "Ġman ag", "du ct", "og n", "Ġ ]", "ĠSe pt", "se c", "o ff", "ĠJ an", "Ġf oot", "ad es", "Ġth ird", "Ġm ot", "Ġev idence", "int on", "Ġth reat", "a pt", "pl es", "c le", "Ġl o", "Ġde cl", "Ġit em", "med i", "Ġrep resent", "om b", "am er", "Ġsignific ant", "og raph", "s u", "Ġc al", "i res", "00 00", "I D", "A M", "Ġsim ply", "Ġlong er", "Ġf ile", "O T", "c he", "S o", "ate g", "or g", "ĠH is", "Ġen er", "Ġd om", "Ġup on", "il i", "\": \"", "Ġthem selves", "Ġcom ing", "Ġqu ite", "Ġdiff icult", "ĠB ar", "il ities", "re l", "end s", "c ial", "6 4", "Ġwom an", "ra p", "y r", "Ġne cess", "ip s", "Ġte xt", "Ġrequ ire", "Ġmilit ary", "Ġre view", "Ġresp ons", "7 5", "Ġsub ject", "Ġinst ead", "Ġiss ues", "Ġg en", "\" ,\"", "Ġmin utes", "Ġwe ap", "r ay", "am ed", "t ime", "b l", "H ow", "Ġc ode", "ĠS m", "Ġhig her", "ĠSt e", "r is", "Ġp age", "Ġstud ents", "ĠIn tern", "Ġmet hod", "ĠA ug", "ĠP er", "ĠA g", "Ġpolic y", "ĠS w", "Ġex ec", "Ġac cept", "um e", "rib ut", "Ġword s", "Ġfin al", "Ġchang es", "ĠDem ocr", "Ġfriend s", "Ġres pect", "Ġe p", "Ġcomp an", "iv il", "Ġdam age", "** **", "og le", "viron ment", "Ġne g", "ent al", "Ġa p", "Ġtot al", "iv al", "! \"", "l im", "Ġneed s", "Ġag re", "Ġdevelop ment", "Ġa ge", "ip le", "2 1", "Ġresult s", "ĠA f", "S h", "Ġg un", "ĠOb ama", "ro ll", "Ġ @", "Ġright s", "ĠB rit", "Ġrun ning", "Ġwas n", "Ġp ort", "Ġr ate", "Ġpret ty", "Ġtarg et", "Ġsa w", "Ġc irc", "Ġwor ks", "ic ro", "al t", "o ver", "ww w", "Th at", "l ier", "Ġevery one", "ud e", "Ġp ie", "idd le", "ra el", "Ġr ad", "Ġbl ock", "Ġw alk", "T o", "ã ģ", "n es", "ĠA ust", "a ul", "ro te", "ĠS outh", "ess ion", "op h", "Ġshow s", "Ġs ite", "Ġj o", "Ġr isk", "cl us", "l t", "Ġin j", "id ing", "ĠS pe", "Ġch all", "ir m", "Ġ2 2", "itt ing", "st r", "Ġh y", "L E", "ke y", "Ġbe gan", "at ur", "ashing ton", "l am", "ĠD av", "b it", "Ġs ize", "ĠP ar", "3 8", "ourn al", "f ace", "Ġdec ision", "Ġl arg", "Ġj ud", "re ct", "Ġcontin ue", "ĠO ct", "ove red", "ĠI nt", "==== ====", "Ġp arent", "ĠW ill", "Ġeas y", "Ġd rug", "ang er", "Ġs ense", "Ġd i", "id ay", "Ġener gy", "ist ic", "Ġass oci", "ar ter", "ob al", "e ks", "ĠE l", "ur ch", "Ġg irl", "o e", "it le", "Ġ2 8", "ĠC he", "Ġrequ est", "Ġso on", "Ġh ost", "k y", "Ġst ates", "om es", "Ġm aterial", "le x", "Ġmom ent", "Ġan sw", "on se", "Ġes pecially", "Ġn orm", "Ġserv ices", "p ite", "r an", "Ġro le", "4 4", ") :", "Ġc red", "C l", "____ ____", "Ġm at", "Ġl og", "ĠCl inton", "O U", "Ġoff ice", "Ġ2 6", "Ġch arg", "Ġtr ack", "m a", "Ġhe art", "Ġb all", "Ġperson al", "Ġbuild ing", "n a", "s et", "b ody", "ĠBl ack", "Ġincre ase", "itt en", "Ġneed ed", "3 6", "3 2", "= \"", "Ġl ost", "Ġbec ame", "Ġgrou ps", "ĠM us", "Ġw rote", "ĠP e", "Ġpro p", "j oy", "à ©", "ĠWh ite", "Ġde ad", ". '", "Ġhtt p", "Ġwe bs", "O S", "Ġins ide", "Ġwr ong", "Ġstat ement", "Ġ ...", "y l", "Ġfil m", "Ġmus ic", "Ġsh are", "ific ation", "Ġre lease", "Ġfor ward", "Ġst ay", "Ġcomp ut", "it te", "s er", "Ġorig inal", "Ġc ard", "Ġc and", "Ġd iv", "at ural", "Ġfav or", "O M", "Ġc ases", "us es", "Ġse ction", "Ġle ave", "g ing", "ov ed", "ĠW ashington", "3 9", "ĠG l", "Ġrequ ired", "act ion", "ap an", "o or", "it er", "ĠK ing", "Ġcount ries", "ĠG erman", "ll ing", "Ġ2 7", "3 4", "Ġquest ions", "Ġpr im", "Ġc ell", "Ġsh oot", "Ġany one", "ĠW est", "Ġaff ect", "ep end", "Ġon line", "ĠIs rael", "ĠSept ember", "Ġab ility", "Ġcont ent", "is es", "Ġre ve", "Ġl aun", "Ġind ic", "Ġfor ce", "c ast", "Ġso ld", "av ing", "f l", "Ġso ft", "Ġcompan ies", "ce ed", "Ġart icle", "Ġa ud", "Ġre v", "Ġed uc", "Ġplay ing", "0 5", "Ġhe ld", "ct or", "Ġrele ased", "Ġf ederal", "3 7", "Ġad minist", "Ġinter view", "Ġinst all", "Ġrece ived", "Ġs ource", "u k", "P h", "Ġser ious", "Ġcre ated", "Ġc ause", "Ġim medi", "Ġdef in", "u el", "ĠDep artment", "ct ions", "ĠC our", "ĠN ow", "z e", "it es", "it ution", "Ġl ate", "Ġspe ak", "n ers", "Ġleg al", "ar i", "ĠC or", "Ġwe eks", "Ġmod el", "Ġp red", "Ġex act", "B C", "ĠB y", "IN G", "os ing", "Ġt akes", "Ġreg ard", "Ġopp ortun", "Ġpr ice", "Ġ19 8", "ĠA pr", "f ully", "Ġor d", "Ġproble ms", "ru ction", "h am", "ĠC ount", "le ge", "Ġlead ers", "E T", "le v", "Ġde ep", "olog ical", "es e", "h aps", "ĠS ome", "Ġp ers", "Ġcont ract", "Ġrelations hip", "s p", "ou d", "Ġb ase", "4 8", "m it", "A d", "anc ial", "Ġcons um", "Ġpot ential", "Ġl angu", "re m", "et h", "Ġrel ig", "ress ed", "6 6", "Ġl ink", "Ġl ower", "ay er", "ĠJ une", "Ġf em", "un t", "er c", "ur d", "Ġcont act", "Ġ ill", "Ġm other", "Ġest ab", "h tt", "ĠM arch", "ĠB ro", "ĠCh ina", "Ġ2 9", "Ġs qu", "Ġprov ided", "Ġa verage", "as ons", "Ġ201 1", "Ġex am", "l in", "5 5", "n ed", "Ġper fect", "Ġt ou", "al se", "u x", "Ġbu y", "Ġsh ot", "Ġcol lect", "Ġph ot", "Ġplay ed", "Ġsur pr", "Ġofficial s", "Ġsim ple", "av y", "Ġindust ry", "Ġhand s", "g round", "Ġp ull", "Ġr ound", "Ġus er", "Ġr ange", "u ary", "Ġpriv ate", "op s", "e es", "Ġw ays", "ĠM ich", "Ġve h", "Ġex cept", "Ġter ms", "im um", "pp er", "I ON", "ore s", "ĠDr agon", "ou l", "Ġd en", "Ġperform ance", "Ġb ill", "c il", "4 7", "Ġen vironment", "Ġex c", "ad d", "Ġwor th", "Ġp ict", "Ġch ance", "Ġ201 8", "b or", "Ġspe ed", "ict ion", "Ġal leg", "ĠJ apan", "at ory", "re et", "Ġm atch", "ĠI I", "Ġst ru", "ord er", "Ġst e", "Ġl iving", "Ġst ruct", "in o", "Ġse par", "her n", "Ġresp onse", "Ġen joy", "Ġv ia", "A D", "um ents", "ace book", "Ġmem ber", "ib r", "iz ing", "Ġto ol", "ĠM on", "ĠWh ile", "h ood", "ĠA ng", "ĠD ef", "Ġoff er", "T r", "a ur", "Ġturn ed", "ĠJ uly", "d own", "an ced", "Ġrec ently", "ĠE ar", "Ġc e", "ĠSt ar", "ĠC ong", "rough t", "Ġbl ood", "Ġhop e", "Ġcom ment", "ain t", "Ġar ri", "il es", "Ġpartic ip", "ough t", "ri ption", "0 8", "4 9", "Ġg ave", "Ġse lect", "Ġkill ed", "sy ch", "Ġgo es", "i j", "Ġc oll", "Ġimp act", "at ives", "ĠS er", "0 9", "ĠAug ust", "Ġb oy", "d e", "ĠD es", "Ġf elt", "U S", "Ġexpect ed", "Ġim age", "ĠM ark", "cc ording", "o ice", "E C", "ĠM ag", "en ed", "h old", "ĠP ost", "Ġpre vent", "N o", "Ġinvol ved", "Ġey es", "Ġquick ly", "A t", "un k", "Ġbeh av", "Ġ ur", "Ġl ed", "c ome", "e y", "Ġcand id", "Ġear lier", "Ġfoc us", "et y", "P ro", "led ge", "ix ed", "ill ed", "Ġpop ular", "A P", "Ġset t", "l ight", "Ġvar ious", "in ks", "Ġlevel s", "Ġro ad", "ell ig", "ab les", "he l", "itte e", "ĠG ener", "y pe", "Ġhe ard", "ic les", "Ġm is", "Ġus ers", "ĠS an", "Ġimpro ve", "Ġf ather", "Ġse arch", "The y", "v il", "Ġprof ess", "Ġkn ew", "Ġl oss", "Ġev ents", "6 5", "Ġb illion", "0 7", "0 2", "ĠNew s", "ĠA M", "Ġco ver", "w here", "ens ion", "Ġb ott", "Ġare as", "en ces", "op e", "ĠTw itter", "a el", "Ġget s", "ĠGo ogle", "Ġs n", "i ant", "Ġv ote", "Ġnear ly", "Ġinclud ed", "Ġrec ogn", "z z", "m m", "al ed", "Ġhappen ed", "0 4", "Ġh ot", "Ġwho se", "Ġc ivil", "Ġsu ff", "o es", "it iz", "ĠSy ri", "Ġresp ond", "Ġh on", "Ġfeat ures", "Ġeconom ic", "ĠApr il", "r im", "Ġtechn ology", "Ġo ption", "ag ing", "Ġpur ch", "R e", "Ġl at", "ch ie", "is l", "Ġrec omm", "u f", "Ġtr aining", "Ġeffect s", "Ġf ast", "Ġ201 0", "Ġocc ur", "Ġwebs ite", "Ġem ail", "Ġs ens", "e ch", "Ġo il", "Ġinf lu", "Ġcurrent ly", "ĠS ch", "ĠAd d", "Ġgo al", "Ġsc ient", "Ġcon v", "1 00", "em y", "Ġdec ided", "Ġtra vel", "Ġm ention", "L L", "0 3", "Ġe lection", "Ġph one", "Ġlook s", "Ġsit uation", "Ġc y", "Ġh or", "b ed", "ĠCour t", "a ily", "av es", "Ġqu ality", "ĠCom p", "w ise", "Ġt able", "Ġst aff", "ĠW ind", "et t", "Ġtri ed", "ide red", "Ġadd ition", "Ġb ox", "Ġl ack", "ar ily", "Ġw ide", "Ġm id", "Ġbo ard", "ys is", "Ġant i", "h a", "Ġd ig", "en ing", "Ġd ro", "C on", "6 8", "Ġsl ow", "b ased", "se qu", "Ġp ath", "E x", "ak er", "Ġwork ed", "Ġp en", "Ġeng ine", "Ġlook ed", "ĠSu per", "ĠS erv", "Ġvict im", "U n", "Ġproper ty", "Ġint rodu", "Ġexec ut", "ĠP M", "L e", "Ġcol or", "ĠM ore", "Ġ6 0", "Ġnet work", "Ġd ate", "c ul", "id ge", "Ġext ra", "3 1", "Ġs le", "6 7", "Ġw ond", "Ġreport s", "j ust", "ĠAust ral", "Ġcap ital", "Ġen s", "Ġcomm and", "Ġallow ed", "Ġpre p", "Ġca pt", "h ib", "Ġnum bers", "ch an", "Ġf air", "m p", "om s", "Ġre ach", "W ith", "t ain", "Ġbro ad", "Ġcou ple", "ec ause", "ly ing", "ĠF eb", "Ġsc reen", "Ġl ives", "Ġpri or", "ĠCong ress", "A r", "Ġappro ach", "Ġe mer", "ar ies", "ĠD is", "s erv", "ĠN e", "Ġbu ilt", "c ies", "Ġre pe", "Ġrul es", "for ce", "ĠP al", "Ġfin ancial", "Ġcons idered", "ĠCh ar", "n ces", "ĠI S", "Ġb rought", "Ġb i", "i ers", "ĠS im", "O P", "Ġproduct s", "Ġvis it", "Ġdoc ument", "Ġcon duct", "Ġcomplete ly", "in ing", "ĠCal if", "ib ly", "Ġwr itten", "ĠT V", "em ents", "Ġd raw", "O ne", "Ġpub lished", "Ġsec ret", "r ain", "he t", "ĠF acebook", "ond ay", "ĠU p", "Ġsex ual", "Ġth ous", "ĠP at", "Ġ ess", "Ġstand ard", "Ġar m", "g es", "ect ion", "Ġf ell", "Ġfore ign", "an i", "ĠFr iday", "Ġreg ular", "in ary", "Ġincre ased", "Ġus ually", "Ġdem on", "Ġd ark", "Ġadd itional", "ro l", "ĠO f", "Ġprodu ction", "! !", "und red", "Ġintern ational", "id ents", "ĠF ree", "rou p", "Ġr ace", "Ġm ach", "Ġh uge", "A ll", "le ar", "ove mber", "Ġto wn", "Ġatt ention", "ĠO ff", "y ond", "ĠThe n", "f ield", "Ġter ror", "ra z", "ĠB o", "Ġmeet ing", "ĠP ark", "Ġar rest", "Ġf ear", "Ġa w", "ĠV al", "or ing", "' ,", "Ġext reme", "ar r", "Ġwork ers", "A fter", "Ġ3 1", "n et", "am ent", "Ġdirect ly", "Ġpop ulation", "ub e", "ĠOct ober", "ĠI N", "ĠJan uary", "5 9", "ĠDav id", "Ġc ross", "ce mber", "ĠF irst", "Ġmess age", "ir it", "Ġn ation", "Ġp oll", "is ions", "Ġansw er", "n y", "is ode", "Ġcar ry", "ĠRuss ia", "Ġhe ar", "eng th", "ro y", "Ġn atural", "in ally", "Ġdo g", "m itted", "Ġtr ade", "Ġsub st", "Ġmult iple", "ĠAf ric", "Ġf ans", "Ġs ort", "Ġgl obal", "ic ation", "ĠW ed", "ar a", "Ġa chie", "Ġlangu age", "ve y", "Ġt al", "Ġnecess ary", "Ġdet ails", "Ġs en", "ĠS und", "ĠRe g", "ĠR ec", "0 6", "Ġs il", "ress ive", "Ġmed ical", "un ch", "orn ia", "Ġu nd", "f ort", "oc ks", "ĠM onday", "ues day", "c raft", "7 7", "ur t", "Ġ ver", "ĠH ill", "Ġrece ive", "Ġmor ning", "es tern", "Ġb ank", "Ġs at", "ir th", "ĠH igh", "Ġdev ice", "ĠTH E", "ĠCent er", "Ġsaf e", "Ġp le", "ĠCanad a", "Ġsystem s", "Ġass ist", "Ġsur v", "Ġb attle", "ĠS oc", "vert is", "S he", "Ġp aper", "Ġgrow th", "Ġc ast", "S c", "Ġpl ans", "ll ed", "Ġpart s", "Ġw all", "Ġmove ment", "Ġpract ice", "im ately", "Ġdis play", "Ġsomet imes", "om p", "ĠP aul", "ĠY es", "k ing", "5 8", "o ly", "Ġs on", "Ġav oid", "ok es", "ĠJ ew", "Ġto wards", "as c", "Ġ //", "ĠK ore", "Ġtalk ing", "Ġcor rect", "Ġsp ent", "ic ks", "i able", "e ared", "Ġter m", "Ġwant s", "om ing", "Ġ ut", "Ġdou b", "Ġfor ces", "Ġp lease", "6 9", "ĠN ovember", "at form", "ond on", "Ġon es", "Ġimmedi ately", "ĠRuss ian", "ĠM et", "Ġde g", "Ġparent s", "C H", "ĠAmeric ans", "al y", "ĠM od", "Ġsh own", "Ġcond itions", "Ġst uff", "Ġre b", "ĠY our", "Ġinclud es", "n own", "ĠS am", "Ġexper ien", "m ission", "ĠE ven", "augh t", "Ġannoun ced", "ĠRepublic an", "Ġdeter min", "Ġdescrib ed", "ĠCount y", "( )", "Ġdo or", "Ġchang ed", "Ġne igh", "ĠH ere", "Ġcle an", "Ġp an", "ĠDe cember", "ĠEurope an", "ir ing", "ap ter", "Ġcl ub", "ĠT uesday", "Ġp aid", "ĠN et", "Ġattack s", "Ġcharact ers", "Ġal one", "Ġdirect or", "d om", "Ġ3 5", "Ġl oad", "Ġr out", "ĠCalif ornia", "Ġfin ally", "Ġr ac", "Ġcont r", "Ġexact ly", "res h", "p ri", "ĠIs lam", "Ġn ature", "Ġcare er", "Ġlat est", "Ġcon vers", "ĠS l", "p ose", "ci ent", "ĠIn c", "iv ity", "8 8", "ĠA tt", "ĠM or", "nes day", "Ġwe ight", "k en", "Ġnot e", "Ġteam s", "Ġ \\", "air s", "ĠG reen", "Ġh undred", "on ent", "Ġstre ng", "Ġcons ist", "ic ated", "Ġreg ul", "Ġl ic", "ast ic", "Ġt en", "urs day", "ellig ence", "ous ly", "ĠU K", "B I", "Ġcost s", "Ġind epend", "ĠA P", "Ġnorm al", "Ġh om", "Ġob vious", "Ġs we", "Ġst ar", "Ġread y", "ac her", "Ġimp lement", "g est", "Ġs ong", "ĠG et", "ĠL ab", "Ġinterest ing", "us ing", "Ġg iving", "ĠSund ay", "Ġet c", "Ġm iddle", "Ġrem ember", "r ight", "os ition", "ut ions", "Ġm ax", "4 6", "Ġyour self", "Ġdem and", "Ġtreat ment", "Ġd anger", "ĠC ons", "Ġgu y", "ĠBrit ish", "Ġphys ical", "Ġrel ated", "Ġrem ain", "Ġcould n", "Ġref er", "Ġc itiz", "b ox", "EN T", "bo ard", "Ġin n", "I G", "er o", "ĠSt reet", "osp ital", "ren ch", "cher s", "Ġst ra", "O L", "ag er", "ĠA N", "Ġeas ily", "I A", "en ge", "in y", "Ġcl os", "ock ed", "Ġus es", "ĠC oun", "I m", "u ild", "? ?", "m ore", "Ġan g", "Ġwr ite", "ol ute", "5 7", "Ġlead er", "Ġread ing", "< /", "Ġaut om", "est s", "4 3", "Ġleg isl", "ĠG old", "Ġdesign ed", "ĠS T", "ĠLe g", "a res", "Ġbe aut", "ĠT ex", "Ġappear s", "Ġstru gg", "ĠR om", "Ġ 00", "Ġcho ice", "Ġparticular ly", "ĠF rom", "op er", "ĠL ondon", "ann ed", "Ġallow s", "ob ile", "Ġdiffere nce", "âĢ ¢", "ĠV iew", "ĠWed nesday", "Ġal though", "Ġrel ative", "Ġapplic ation", "ate ver", "Ġare n", "Ġmy self", "Ġim ag", "Ġdis e", "Ġsoc iety", "Ġfre qu", "ĠEng lish", "Ġpo or", "ĠD ay", "Ġwrit ing", "Ġse ven", "Ġstart ing", "Ġb ud", "Ġpr int", "ĠTr ans", "uf act", "ĠSt ud", "n ew", "Ġcr im", "Ġg ives", "Ġco ol", "a e", "i ance", "ĠGener al", "Ġthink ing", "Ġsa ve", "Ġlim ited", "ĠPart y", "Ġmean ing", "p en", "ow ers", "ĠJ ack", "E M", "Ġn ice", "ru pt", "Ġg as", "Ġe ight", "Ġfe et", "Ġeff ort", "Ġ ign", "ic it", "B l", "co in", "Ġop in", "Ġbr ain", "Wh ile", "he st", "ĠTh ursday", "Ġwould n", "augh ter", "Ġtou ch", "le ments", "Ġstud ies", "Ġcent er", "c ont", "or ge", "Ġcomput er", "Ġinvestig ation", "P l", "or ks", "Ġ200 8", "Ġincre asing", "Ġst ore", "Ġcom ments", "Ġb al", "m en", "Ġdo ll", "Ġl iber", "Ġw ife", "Ġlaw s", "atur day", "it ness", "Ġmod ern", "ĠS k", "Ġadminist ration", "Ġopportun ity", "Ġs al", "Ġpower ful", "M y", "Ġclaim s", "ĠEar th", "ord s", "Ġt itle", "Ġes c", "n ame", "N ot", "om en", "Ġbe yond", "Ġc amer", "Ġse ll", "it ute", "ear ch", "Ġapp l", "im ent", "4 2", "ĠAr t", "Ġun f", "Ġviol ence", "ur g", "ĠE ast", "Ġcomp ared", "Ġopt ions", "Ġthrough out", "Ġv s", "ig r", ". [", "ac hes", "7 8", "Ġfil es", "F L", "E L", "ar ian", "ĠJ ames", "ĠA ir", "an ch", "Ġdet ail", "Ġpie ce", "P S", "Ġn amed", "Ġeduc ation", "Ġdri ve", "Ġitem s", "Ġstud ent", "ic ed", ": :", "ic o", "Ġth row", "Ġsc ene", "Ġcomple x", "Ġ200 9", "Ġpre c", "ĠB re", "7 9", "Ġcon cept", "Ġstat us", "am ing", "Ġd ied", "Ġknow ledge", "Ġbegin ning", "O D", "ru ary", "Ġcertain ly", "Ġgu ys", "Ġsl ight", "in n", "ound s", "Ġf ine", "Ġf at", "ic ations", "Ġper haps", "ĠA nt", "Ġinc ome", "Ġhtt ps", "Ġmajor ity", "port s", "st on", "Ġgreat er", "Ġfe ed", "ent ially", "Ġsaf ety", "Ġun ique", "and om", "Ġg one", "Ġshow ed", "Ġhist or", "Ġcoun ter", "i us", "id a", "Ġlead ing", "i pe", "Ġs end", "ĠDon ald", "er ve", "Ġdef ense", "ines e", "Ġy es", "ĠF ire", "ĠMus lim", "ra q", "Ġcontin ued", "os h", "Ġprov ides", "Ġpr ison", "ĠP re", "Ġhapp y", "Ġeconom y", "Ġtr ust", "ag s", "ĠG ame", "Ġweap ons", "um an", "ĠC le", "it ation", "Ġanal ysis", "ĠT imes", "Ġsc ience", "- >", "Ġfig ure", "Ġdis app", "ent y", "Ġsoft ware", "Ġu lt", "Ġoffic ers", "N ew", "I s", "Ġrem ains", "ĠInd ia", "Ġp sych", "ri ef", "Ġc at", "es c", "Ġob serv", "Ġst age", "ĠD ark", "Ġent er", "ch ange", "Ġpass ed", "Ġdes pite", "ĠO ut", "Ġmov ie", "r s", "Ġv oice", "m ine", "ĠPl ay", "Ġto ward", "ĠT er", "Ġreg ion", "Ġval ues", "or ters", "Ġm ount", "Ġoffic er", "ĠO ther", "b an", "Ġh ous", "w ood", "ro om", "I V", "ĠS un", "se e", "ĠO ver", "ro g", "9 0", "Ġl ay", "ĠT ur", "a wn", "Ġpress ure", "ĠS ub", "Ġbook s", "ed om", "ĠS and", "A A", "ag o", "Ġre asons", "f ord", "Ġactiv ity", "U T", "N ow", "ĠSen ate", "ce ll", "n ight", "Ġcall s", "in ter", "Ġlet ter", "ĠR ob", "ĠJ e", "Ġcho ose", "ĠL aw", "G et", "B e", "Ġro b", "Ġtyp es", "Ġpl atform", "Ġqu arter", "R A", "ĠT ime", "Ġmay be", "ĠC r", "9 5", "p re", "Ġmov ing", "Ġl if", "Ġgo ld", "Ġs om", "Ġpat ients", "Ġtr uth", "ĠK e", "ur ance", "ant ly", "m ar", "Ġchar ge", "ĠG reat", "Ġce le", "---------------- ----------------", "Ġro ck", "ro id", "an cy", "Ġcred it", "a ud", "B y", "ĠE very", "Ġmov ed", "ing er", "rib ution", "Ġn ames", "Ġstra ight", "ĠHe alth", "ĠW ell", "Ġfe ature", "Ġr ule", "Ġsc he", "in ated", "ĠMich ael", "ber g", "4 1", "il ed", "b and", "Ġcl ick", "ĠAng el", "on ents", " Ń", "ĠI raq", "ĠS aturday", "Ġa ware", "p art", "Ġpat tern", "O W", "ĠL et", "Ġgr ad", "ign ed", "Ġassoci ated", "Ġst yle", "n o", "i ation", "a ith", "il ies", "Ġst ories", "ur ation", "Ġindividual s", "ĠâĢ ¦", "m iss", "ĠAss oci", "ish ing", "ab y", "Ġsum mer", "ĠB en", "Ġ3 2", "Ġar ch", "ut y", "ĠTex as", "h ol", "Ġfull y", "Ġm ill", "Ġfollow ed", "ĠB ill", "ĠInd ian", "ĠSec ret", "ĠB el", "ĠFeb ruary", "Ġjob s", "Ġseem ed", "ĠGo vern", "i pped", "Ġreal ity", "Ġl ines", "Ġp ark", "Ġmeas ure", "ĠO ur", "I M", "Ġbro ther", "Ġgrow ing", "Ġb an", "Ġest im", "Ġc ry", "ĠS chool", "Ġme chan", "ĠO F", "ĠWind ows", "Ġr ates", "ĠO h", "Ġpos itive", "Ġcult ure", "ist ics", "ic a", "Ġh ar", "y a", "ite ly", "i pp", "Ġm ap", "en cies", "ĠWill iam", "I I", "ak ers", "5 6", "ĠM art", "ĠR em", "Ġal tern", "it ude", "Ġco ach", "row d", "D on", "Ġk ids", "Ġj ournal", "Ġcor por", "Ġf alse", "Ġwe b", "Ġsle ep", "Ġcont ain", "Ġst o", "Ġb ed", "iver se", "ĠR ich", "ĠCh inese", "Ġp un", "Ġme ant", "k nown", "Ġnot ice", "Ġfavor ite", "a ven", "Ġcond ition", "Ġpur pose", ") )", "Ġorgan ization", "Ġchall eng", "Ġman ufact", "Ġsus p", "ĠA c", "Ġcrit ic", "un es", "uc lear", "Ġm er", "vent ion", "Ġ8 0", "Ġm ist", "ĠU s", "ĠT or", "htt p", "ol f", "Ġlarg er", "Ġadv ant", "Ġrese ar", "Ġact ions", "m l", "Ġke pt", "Ġa im", ", '", "c ol", "Ġbenef its", "if ying", "Ġact ual", "ĠIntern ational", "Ġveh icle", "Ġch ief", "Ġeff orts", "ĠLe ague", "ĠM ost", "Ġwa it", "Ġad ult", "Ġover all", "Ġspe ech", "Ġhigh ly", "Ġfem ale", "Ġer ror", "Ġeffect ive", "5 4", "Ġenc our", "w ell", "Ġfail ed", "Ġcons erv", "Ġprogram s", "Ġt rou", "Ġa head", "5 00", "vertis ement", "I P", "ĠF ound", "p ir", "Ġ %", "Ġcr ime", "and er", "Ġloc ation", "ĠI ran", "Ġbehav ior", "az ing", "Ġr are", "Ġem b", "Ġca used", "Ġsh ip", "Ġact ive", "Ġcont ribut", "Ġg reen", "Ġac qu", "Ġref lect", "ven ue", "Ġf irm", "Ġb irth", "] .", "Ġclear ly", "Ġem ot", "Ġag ency", "ri age", "Ġmem ory", "9 8", "S A", "ĠSe e", "ac ing", "C C", "Ġbig gest", "Ġr ap", "Ġbas ic", "Ġb and", "e at", "Ġsus pect", "ĠM ac", "Ġ9 0", "m ark", "ist an", "Ġsp read", "am s", "k i", "as y", "ra v", "ĠR ober", "Ġdemon str", "r ated", "Ġabs olute", "Ġpl aces", "Ġim pl", "ibr ary", "Ġc ards", "Ġdest roy", "Ġv irt", "ve re", "Ġapp eared", "y an", "p oint", "Ġbe g", "Ġtem per", "s pe", "ant ed", "ear s", "ĠD irect", "Ġl ength", "Ġbl og", "am b", "Ġint eg", "Ġres ources", "ac c", "if ul", "Ġsp ot", "Ġfor ced", "Ġthous ands", "ĠMin ister", "Ġqu al", "ĠF rench", "at ically", "Ġgener ally", "Ġdr ink", "Ġth us", "I L", "od es", "Ġappro pri", "ĠRe ad", "Ġwh om", "Ġey e", "Ġcol lege", "Ġ4 5", "ire ction", "Ġens ure", "Ġapp arent", "id ers", "Ġrelig ious", "Ġmin or", "ol ic", "Ġt ro", "ĠWh y", "rib ute", "m et", "Ġprim ary", "Ġdevelop ed", "Ġpe ace", "Ġsk in", "st e", "av a", "Ġbl ue", "Ġfam ilies", "Ġ ir", "Ġapp ly", "Ġin form", "ĠSm ith", "C T", "i i", "Ġlim it", "Ġres ist", "........ ........", "um n", "Ġconf lic", "Ġtw e", "ud d", "ĠT om", "Ġl iter", "qu e", "b on", "Ġha ir", "Ġevent ually", "Ġp us", "Ġhelp ed", "Ġag g", "or ney", "ĠApp le", "Ġf it", "ĠS ur", "Ġpre m", "Ġs ales", "Ġsecond s", "Ġstreng th", "Ġfeel ing", "¿ ½", "Ġt our", "Ġknow s", "o om", "Ġex erc", "Ġsom ew", "ï ¿½", "> >", "Ġsp okes", "Ġide as", "Ġreg ist", "so ft", "ĠD el", "ĠP C", "Ġpro pos", "Ġlaun ch", "Ġbott om", "T H", "ĠP lease", "v est", "it z", "ĠIn ter", "Ġsc ript", "Ġr at", "ar ning", "Ġ il", "ĠJ er", "ĠA re", "Ġwh atever", "ok en", "ci ence", "Ġmod e", "Ġag ree", "Ġs ources", "Ġinit ial", "Ġrest rict", "Ġwond er", "us ion", "## ##", "ĠS il", "vil le", "Ġb urn", "t w", "as ion", "Ġ £", "Ġn or", "u ing", "Ġre ached", "Ġs un", "Ġc ateg", "ig ration", "Ġc ook", "Ġprom ot", "Ġm ale", "Ġcl imate", "Ġf ix", "Ġalleg ed", "U R", "all ed", "Ġim ages", "C ont", "ot a", "Ġschool s", "i os", "Ġd rop", "Ġst ream", "ĠM o", "Ġprevious ly", "al ing", "Ġp et", "Ġdou ble", "Ġ( @", "ann el", "Ġdef ault", "t ies", "Ġr ank", "ĠD ec", "ĠCoun cil", "Ġweap on", "Ġst ock", "Ġanal y", "ĠSt r", "Ġpict ure", "ĠPol ice", "f erence", "Ġcent ury", "Ġcitiz ens", "Ġon to", "Ġexp and", "Ġhe ro", "ĠS ol", "Ġw ild", "Ġupd ate", "Ġcustom ers", "r ont", "d ef", "Ġl ik", "Ġcrim inal", "ĠChrist ian", "S P", "7 6", "Ġle aving", "Ġother wise", "ĠD ist", "Ġbas is", "5 2", "5 3", "ic ip", "ĠB er", "Ġrecomm end", "Ġfl oor", "Ġc rowd", "ol es", "Ġ7 0", "Ġcent ral", "ĠE v", "Ġd ream", "Ġdown load", "Ġconf ir", "ĠTh om", "Ġwind ow", "Ġhapp ens", "Ġun it", "Ġt end", "Ġs pl", "Ġbec omes", "Ġfight ing", "Ġpred ict", "ĠP ress", "ĠP ower", "Ġhe avy", "ak ed", "Ġf an", "or ter", "ate gy", "B A", "iz es", "Ġsp end", "H ere", "Ġ200 7", "Ġad op", "ĠH am", "Ġfoot ball", "ĠP ort", "od ay", "5 1", "amp ions", "Ġtrans fer", "h t", "Ġ3 8", "ter m", "ac ity", "Ġb ur", "] ,", "tern al", "r ig", "b ut", "Ġthere fore", "ĠB ecause", "res p", "re y", "Ġm ission", "S ome", "Ġnot ed", "Ġass um", "Ġdise ase", "Ġed it", "Ġprog ress", "r d", "ĠB rown", "oc al", "Ġadd ing", "Ġra ised", "ĠAn y", "Ġt ick", "Ġsee ing", "ĠPe ople", "Ġagre ement", "Ġser ver", "Ġw at", "Ġdeb ate", "Ġsupp osed", "il ing", "Ġlarg est", "Ġsuccess ful", "ĠP ri", "ĠDemocr atic", "Ġj ump", "ĠSyri a", "Ġown ers", "Ġoff ers", "Ġshoot ing", "Ġeff ic", "se y", "Ġha ven", "ver se", "te red", "ĠL ight", "im al", "ĠB ig", "Ġdef end", "Ġbe at", "Ġrecord s", "% )", "Ġsc en", "Ġemploy ees", "Ġdev ices", "he m", "Ġcom mer", "ĠM ex", "Ġbenef it", "ĠPro f", "Ġil leg", "Ġsur face", "ĠAl so", "Ġh arm", "ing ly", "w ide", "ĠA lex", "Ġsh ut", "ĠC ur", "Ġl ose", "p m", "Ġchall enge", "se mb", "Ġst ation", "Ġint elligence", "Ġacc ur", "ĠFl or", "Ġrequ ires", "ĠM al", "b um", "Ġh ospital", "Ġsp irit", "Ġoff ered", "Ġprodu ce", "ĠComm un", "Ġcreat ing", "Ġcr is", "s pect", "Ġend ed", "Ġd aily", "Ġvot ers", "land s", "i as", "i h", "on a", "Ġsm art", "ĠOff ice", "ĠL ord", "ri al", "ĠIntern et", "Ġcirc um", "Ġextreme ly", "' .", "Ġopin ion", "ĠM il", "Ġg ain", "B S", "ĠF in", "y p", "Ġuse ful", "Ġbud get", "Ġcom fort", "is f", "Ġback ground", "el ine", "Ġep isode", "Ġen emy", "Ġtri al", "Ġestab lish", "d ate", "ĠC ap", "Ġcontin ues", "Ġshow ing", "ĠUn ion", "w ith", "Ġpost ed", "ĠSy stem", "Ġe at", "ri an", "Ġr ise", "ĠGerman y", "il s", "Ġsign ed", "Ġv ill", "Ġgr and", "m or", "ĠEng land", "Ġproject s", "um ber", "Ġconf erence", "z a", "Ġrespons ible", "ĠAr ab", "Ġlearn ed", "âĢĶ âĢĶ", "i pping", "ĠGe orge", "O C", "Ġreturn ed", "ĠAustral ia", "Ġb rief", "Q u", "Ġbr and", "ill ing", "ab led", "Ġhig hest", "Ġtr ain", "ĠComm ission", "wh ile", "Ġn om", "cept ion", "Ġm ut", "ĠBl ue", "Ġinc ident", "v ant", "8 6", "ĠI D", "Ġn uclear", "7 4", "ĠL ike", "ĠR E", "ĠM icro", "l i", "m ail", "Ġcharg es", "8 9", "Ġad just", "ad o", "Ġear th", "N A", "Ġpr ices", "P A", "Ġd raft", "Ġrun s", "Ġcandid ate", "ens es", "Ġmanag ement", "ĠPh il", "ĠM iss", "Ġte ach", "g ram", "Ġunderstand ing", "a it", "ic ago", "A dd", "ĠE p", "sec ut", "Ġsepar ate", "Ġinst ance", "Ġe th", "Ġun less", "**** ****", "ĠF ore", "in ate", "Ġoper ations", "S p", "Ġf aith", "g ar", "ĠCh urch", "ron ic", "Ġconf ig", "os ure", "Ġactiv ities", "Ġtrad itional", "Ġ3 6", "Ġd irection", "Ġmach ine", "Ġsur round", "Ġp ush", "un ction", "ĠE U", "Ġeas ier", "Ġarg ument", "G B", "Ġm icro", "Ġsp ending", "iz ations", "Ġthe ory", "ad ow", "Ġcall ing", "ĠL ast", "Ġd er", "Ġinflu ence", "Ġcomm it", "Ġph oto", "Ġun c", "ist ry", "g n", "ast e", "ack s", "Ġdis p", "ad y", "d o", "ĠG ood", "Ġ `", "Ġw ish", "Ġreve aled", "Âł Âł", "l ig", "Ġen force", "ĠComm ittee", "Ġche m", "Ġmil es", "Ġinterest ed", "Ġsol ution", "ic y", "in ct", "Ġ- >", "ĠD et", "Ġrem oved", "Ġcomp ar", "e ah", "Ġpl ant", "ĠS ince", "Ġachie ve", "Ġadvant age", "Ġslight ly", "b ing", "Ġpl aced", "u nder", "201 5", "ĠM ad", "Ġt im", "os es", "Ġc ru", "ĠR ock", "Ġmost ly", "Ġneg ative", "Ġset ting", "Ġprodu ced", "Ġm ur", "Ġconnect ion", "ĠM er", "Ġdri ver", "Ġexecut ive", "Ġass ault", "Ġb orn", "ĠV er", "t ained", "Ġstruct ure", "Ġredu ce", "Ġdec ades", "Ġd ed", "u ke", "ĠM any", "idd en", "Ġle ague", "S e", "Ġjo in", "Ġdis co", "Ġd ie", "c ks", "act ions", "Ġass ess", "ag n", "Ġgo als", "our s", "I R", "Ġsen ior", "ill er", "m od", "ip ment", "oc ol", "u y", "ĠQ ue", "Ġpart ies", "ir gin", "Ġle arning", "it able", "Ġstre et", "Ġcamer a", "A pp", "Ġsk ills", "b re", "c ious", "Ġcele br", "ĠFr anc", "Ġexist ing", "Ġwill ing", "l or", "Ġ id", "ĠSp ace", "Ġcrit ical", "ĠL a", "ortun ately", "Ġser ve", "Ġc old", "Ġspec ies", "T S", "Ġanim als", "ĠB ay", "Ġold er", "ĠU nder", "est ic", "ĠT re", "Ġte acher", "Ġpre fer", "v is", "Ġth read", "ĠM att", "Ġmanag er", "ãĥ »", "Ġprofess ional", "ĠV ol", "Ġnot es", "The se", "ul a", "Ġf resh", "ent ed", "u zz", "ed y", "clus ion", "ĠR el", "Ġdoub t", "E O", "Ġopen ed", "ĠB it", "Ad vertisement", "Ġgu ess", "ĠU N", "Ġse qu", "Ġexpl ain", "ott en", "Ġatt ract", "ak s", "Ġstr ing", "Ġcont ext", "oss ible", "ĠRepublic ans", "Ġsol id", "Ġc ities", "Ġask ing", "Ġr andom", "u ps", "ur ies", "ar ant", "dd en", "g l", "ĠFlor ida", "Ġdep end", "ĠSc ott", "Ġ3 3", "Ġi T", "ic on", "Ġmention ed", "Ġ2 000", "Ġclaim ed", "Ġdefin itely", "ul f", "Ġc ore", "Ġopen ing", "ĠCon st", "wh ich", "ĠT ra", "A G", "7 2", "Ġbelie ved", "ad a", "Ġ4 8", "ĠSec urity", "yr ight", "ĠP et", "ĠL ou", "Ġhold ing", "======== ========", "Ġ ice", "Ġb row", "Ġauthor ities", "h ost", "w ord", "Ġsc ore", "ĠD iv", "Ġcell s", "Ġtrans l", "Ġneigh bor", "Ġrem ove", "u ct", "Ġdist rict", "ĠA ccording", "Ġwor se", "Ġconcern s", "Ġpresident ial", "Ġpolic ies", "ĠH all", "7 3", "Ġh us", "A Y", "Ġ200 6", "ĠJ ud", "Ġindepend ent", "ĠJust ice", "ili ar", "pr int", "igh ter", "Ġprotect ion", "z en", "Ġsu dden", "h ouse", "ĠJ es", "P R", "ĠIn f", "Ġb ul", "Ġ _", "ĠServ ice", "ĠP R", "Ġstr ategy", "ff ect", "Ġgirl s", "Ġmiss ing", "oy al", "ĠTe am", "ul ated", "Ġd at", "Ġpolit ics", "ab or", "A ccording", "Ġspe ll", "Ġg raph", "ort hern", "T C", "A b", "Ġlab or", "is her", "Ġk ick", "ĠiT unes", "Ġstep s", "pos es", "Ġsmall er", "E n", "ber t", "Ġro ll", "Ġresear chers", "Ġcl osed", "Ġtrans port", "Ġlaw y", "________ ________", "ĠCh icago", "Ġas pect", "Ġn one", "Ġmar riage", "9 6", "Ġe lements", "ĠF re", "ĠS al", "Ġd ram", "F C", "t op", "e qu", "Ġhe aring", "Ġsupport ed", "Ġtest ing", "co hol", "Ġmass ive", "Ġst ick", "Ġgu ard", "is co", "ph one", "F rom", "How ever", "Ġb order", "Ġcop y", "ograph y", "l ist", "7 1", "Ġown er", "cl ass", "ru it", "r ate", "ĠO nce", "Ġdig ital", "Ġt ask", "ER S", "Ġinc red", "t es", "+ +", "ĠFr ance", "Ġb reat", "ow l", "Ġiss ued", "ĠW estern", "Ġdet ect", "Ġpart ners", "Ġsh ared", "ĠC all", "Ġcan cer", "ac he", "rib e", "Ġexpl ained", "Ġhe at", "{ \"", "Ġinvest ment", "ĠB ook", "Ġw ood", "Ġtool s", "ĠAl though", "Ġbelie f", "Ġcris is", "Ġg e", "ĠM P", "Ġoper ation", "ty pe", "~ ~", "g a", "Ġcont ains", "ant a", "Ġexp ress", "ĠG roup", "ĠJ ournal", "k a", "Ġam b", "ĠUS A", "Ġfind ing", "Ġfund ing", "h ow", "Ġestab lished", "ide os", "Ġdeg ree", "Ġdanger ous", "ang ing", "Ġfre edom", "pp ort", "out hern", "Ġch urch", "Ġc atch", "ĠTw o", "Ġpres ence", "ĠGu ard", "U p", "Ġauthor ity", "ĠPro ject", "Ġbut ton", "Ġcon sequ", "Ġval id", "Ġwe ak", "Ġstart s", "Ġref erence", "ĠM em", "\" )", "U N", "or age", "ĠO pen", "Ġcol lection", "y m", "g ency", "Ġbeaut iful", "ro s", "Ġtell s", "Ġwa iting", "n el", "Ġprov iding", "ĠDemocr ats", "Ġd aughter", "Ġm aster", "Ġpur poses", "ĠJapan ese", "Ġequ al", "Ġturn s", "Ġdoc uments", "Ġwatch ing", "R es", "Ġr an", "201 4", "Ġre ject", "ĠKore a", "Ġvictim s", "Le vel", "ere nces", "Ġw itness", "Ġ3 4", "Ġre form", "com ing", "Ġocc up", "Ġc aught", "Ġtra ffic", "ad ing", "Ġmod els", "ar io", "Ġserv ed", "Ġb atter", "u ate", "ĠSecret ary", "Ġagre ed", "Ġtr uly", "yn am", "ĠR et", "Ġun its", "ĠRes earch", "h and", "az ine", "ĠM ike", "Ġvar iety", "ot al", "Ġam azing", "Ġconfir med", "Ġentire ly", "Ġpurch ase", "Ġe lement", "Ġc ash", "Ġdeter mine", "D e", "Ġc ars", "ĠW all", "â ĸ", "Ġview s", "Ġdrug s", "Ġdep artment", "ĠSt ep", "u it", "Ġ3 9", "as ure", "ĠCl ass", "Ġc overed", "ĠB ank", "Ġme re", "u ana", "Ġmult i", "Ġm ix", "Ġun like", "lev ision", "Ġsto pped", "Ġs em", "ĠG al", "ul es", "Ġwe l", "ĠJohn son", "l a", "Ġsk ill", "Ġbec oming", "ri e", "Ġappropri ate", "f e", "ell ow", "ĠPro t", "ul ate", "oc ation", "Ġweek end", "od ies", "Ġsit es", "Ġanim al", "ĠT im", "Ġsc ale", "Ġcharg ed", "Ġinst ruct", "ill a", "Ġmethod s", "Ġc ert", "Ġjud ge", "ĠH el", "Ġdoll ars", "Ġstand ing", "ĠS qu", "Ġdeb t", "l iam", "Ġdri ving", "ĠS um", "ĠEd ition", "Ġal bum", "and on", "I F", "ĠU k", "6 3", "ad er", "Ġcommer cial", "es h", "ĠGovern ment", "Ġdisc overed", "Ġout put", "ĠHill ary", "ĠCar ol", "Ġ200 5", "Ġab use", "anc ing", "Ġsw itch", "Ġann ual", "T w", "Ġst ated", "ag ement", "in ner", "Ġdem ocr", "Ġres idents", "Ġallow ing", "Ġfact ors", "od d", "Ġf uck", "em ies", "Ġoccur red", "ot i", "Ġn orth", "ĠP ublic", "Ġinj ury", "Ġins urance", "C L", "oll y", "ã Ģ", "Ġrepe ated", "Ġar ms", "ang ed", "Ġconst ruction", "Ġf le", "P U", "ic ians", "Ġfor ms", "ĠMc C", "ant ic", "Ġm ental", "p ire", "Ġequ ipment", "Ġf ant", "Ġdiscuss ion", "Ġregard ing", "k in", "ar p", "Ġch air", "og ue", "Ġpro ceed", "ĠI d", "O ur", "Ġmur der", "M an", "Ġ4 9", "as p", "Ġsupp ly", "Ġin put", "Ġwe alth", "liam ent", "Ġpro ced", "or ial", "ĠSt at", "ĠN FL", "hen s", "ĠInst itute", "Ġput ting", "ourn ament", "et ic", "Ġloc ated", "Ġk id", "er ia", "r un", "Ġpr inc", "Ġ !", "go ing", "ĠB et", "Ġcl ot", "Ġtell ing", "Ġprop osed", "i ot", "or ry", "Ġfund s", "g ment", "ĠL ife", "Ġb aby", "ĠB ack", "Ġsp oke", "Im age", "Ġear n", "ĠA T", "g u", "Ġex change", "ĠL in", "ov ing", "Ġp air", "M ore", "az on", "Ġarrest ed", "Ġkill ing", "c an", "ĠC ard", "y d", "Ġident ified", "Ġm obile", "Ġthan ks", "ony m", "ĠF orm", "Ġhundred s", "ĠCh ris", "ĠC at", "Ġtre nd", "h at", "ĠA v", "om an", "Ġelect ric", "ĠW il", "S E", "O f", "Ġrest aur", "ot ed", "Ġtr ig", "Ġn ine", "Ġb omb", "Wh y", " ¯", "Ġco verage", "Ġapp eal", "ĠRober t", "ĠS up", "Ġfin ished", "Ġfl ow", "Ġdel iver", "Ġcal cul", "Ġphot os", "Ġph il", "Ġpie ces", "Ġapp re", "k es", "Ġr ough", "D o", "Ġpart ner", "Ġconcern ed", "Ġ3 7", "ĠG en", "C ol", "ct ors", "Ġ= >", "st ate", "Ġsuggest ed", "ĠFor ce", "C E", "Ġher self", "ĠPl an", "w orks", "o oth", "ren cy", "Ġcor ner", "Ġhus band", "Ġintern et", "ĠA ut", "em s", "os en", "ĠAt l", "g en", "Ġbal ance", "6 2", "Ġsound s", "te xt", "Ġar r", "ov es", "Ġmill ions", "Ġrad io", "Ġsat isf", "ĠD am", "M r", "G o", "S pe", "Ġcomb at", "r ant", "ĠG ree", "Ġf uel", "Ġdist ance", "Ġtest s", "Ġdec re", "ĠE r", "Ġman aged", "D S", "Ġt it", "Ġmeas ures", "ĠL iber", "Ġatt end", "as hed", "ĠJ ose", "ĠN ight", "d it", "ĠN ov", "ĠE nd", "out s", "Ġgener ation", "Ġadv oc", "y th", "Ġconvers ation", "ĠS ky", "act ive", "ce l", "ri er", "ĠFr ank", "Ġg ender", "Ġcon cent", "Ġcar ried", "and a", "ĠV irgin", "Ġarri ved", "ic ide", "ad ed", "Ġfail ure", "Ġmin imum", "le ts", "Ġwor st", "Ġkeep ing", "Ġint ended", "Ġilleg al", "Ġsub sc", "Ġdetermin ed", "Ġtri p", "Y es", "Ġra ise", "Ġ ~", "Ġfeel s", "Ġpack age", "ĠJ o", "h i", "201 6", "re al", "Ġf ra", "Ġsy mb", "M e", "uck y", "p ret", "ĠK h", "ĠEd it", "ĠWe b", "em ic", "ĠCol or", "Ġjust ice", "I nt", "Ġfar m", "ck now", "\" >", "el ess", "Ġredu ced", "Ġ5 00", "x x", "ĠR ad", "ĠW ood", "Ġcl in", "Ġhy p", "il er", "ur a", "k ins", "8 5", "6 1", "ĠThe ir", "ĠM ary", "Ġs an", "Ġno vel", "ĠWh o", "Ġcap acity", "Ġimp ossible", "Ġpl ays", "Ġmin ister", "ij uana", "ic ate", "ĠS et", "Ġf ram", "Ġ ing", "Ġcommun ities", "ĠF BI", "it a", "Ġb on", "Ġstr ateg", "Ġinterest s", "l ock", "g ers", "m as", "ĠAN D", "Ġconflic t", "Ġrequire ments", "Ġs ac", "Ġoper ating", "in i", "rel ated", "Ġcomm itted", "Ġrelative ly", "Ġs outh", "¯ ¯", "Ġaff ord", "Ġident ity", "Ġdec isions", "Ġacc used", "pl ace", "Ġvict ory", "o ch", "i at", "N ame", "C om", "t ion", "ed s", "Ġsee k", "Ġt ight", "ĠIm ages", "Ġinit i", "Ġhum ans", "Ġfam iliar", "Ġaud ience", "Ġintern al", "vent ure", "Ġs ides", "ĠT O", "Ġd im", "Ġcon clud", "Ġapp oint", "Ġenforce ment", "ĠJ im", "ĠAssoci ation", "Ġcircum st", "ĠCanad ian", "Ġjo ined", "Ġdiffere nces", "ĠL os", "Ġprot est", "Ġtw ice", "w in", "Ġgl ass", "ars h", "ĠAr my", "Ġexp ression", "Ġdec ide", "Ġplan ning", "an ia", "Ġhand le", "ĠMicro soft", "ĠN or", "Ġmax imum", "ĠRe v", "Ġse a", "Ġev al", "Ġhel ps", "re f", "Ġb ound", "Ġm outh", "Ġstand ards", "Ġcl im", "ĠC amp", "ĠF ox", "cl es", "Ġar my", "ĠTe chn", "ack ing", "x y", "S S", "Ġ4 2", "Ġbu g", "ĠUk rain", "ĠM ax", "ĠJ ones", "ĠSh ow", "l o", "Ġplan et", "Ġ7 5", "Ġwin ning", "Ġf aster", "Ġspe ct", "Ġbro ken", "T R", "Ġdef ined", "Ġhealth y", "Ġcompet ition", "htt ps", "ĠIs land", "ĠF e", "Ġannoun ce", "ĠC up", "ĠInst ead", "Ġcl ient", "Ġposs ibly", "se ction", "ock et", "l ook", "Ġfin ish", "Ġcre w", "Ġres erv", "Ġed itor", "Ġh ate", "Ġs ale", "Ġcontro vers", "Ġp ages", "w ing", "Ġnum er", "Ġopp osition", "Ġ200 4", "Ġref uge", "Ġfl ight", "Ġap art", "ĠL at", "A meric", "ĠAfric a", "Ġapplic ations", "ĠPal est", "ĠB ur", "Ġg ar", "ĠSoc ial", "Ġup gr", "Ġsh ape", "Ġspe aking", "ans ion", "a o", "ĠS n", "Ġwor ry", "ĠBrit ain", "P lease", "rou d", "Ġh un", "Ġintrodu ced", "Ġd iet", "I nd", "ĠSec ond", "Ġfun ctions", "ut s", "ĠE ach", "ĠJe ff", "Ġst ress", "Ġaccount s", "Ġgu arant", "ĠAn n", "ed ia", "Ġhon est", "Ġt ree", "ĠAfric an", "ĠB ush", "} ,", "Ġs ch", "ĠOn ly", "Ġf if", "ig an", "Ġexerc ise", "ĠEx p", "Ġscient ists", "Ġlegisl ation", "ĠW ork", "ĠS pr", "à Ĥ", "ĠH uman", "Ġ è", "Ġsur vey", "Ġr ich", "ri p", "Ġmain tain", "Ġfl o", "Ġleaders hip", "st ream", "ĠIslam ic", "Ġ 01", "ĠCol lege", "Ġmag ic", "ĠPr ime", "Ġfig ures", "201 7", "ind er", "x ual", "ĠDe ad", "Ġabsolute ly", "Ġfour th", "Ġpresent ed", "resp ond", "rib le", "Ġal cohol", "at o", "ĠD E", "por ary", "Ġgr ab", "Ġvar i", "Ġqu ant", "ĠPh oto", "Ġpl us", "r ick", "ar ks", "Ġaltern ative", "Ġp il", "Ġappro x", "th at", "Ġobject s", "ĠR o", "ĠAnd roid", "Ġsignificant ly", "ĠR oad", "k ay", "R ead", "av or", "Ġa cknow", "ĠH D", "ĠS ing", "O r", "ĠM ont", "Ġun s", "pro f", "Ġneg oti", "ĠAr ch", "ik i", "Ġte levision", "ĠJew ish", "Ġcomm ittee", "Ġmot or", "Ġappear ance", "Ġs itting", "Ġstri ke", "ĠD own", "com p", "ĠH ist", "Ġf old", "ac ement", "ĠLou is", "Ġbel ong", "ĠâĢ ¢", "Ġm ort", "Ġprep ared", "Ġ6 4", "ĠM aster", "Ġind eed", "ĠD en", "Ġre nt", "T A", "our ney", "ar c", "S u", "9 7", "Ġadv ice", "Ġchang ing", "Ġlist ed", "Ġlaun ched", "is ation", "ĠP eter", "is hes", "Ġl ived", "ĠM el", "ĠSup reme", "ĠF ederal", "Ġ) ;", "ruct ure", "Ġset s", "Ġphil os", "u ous", "Ġ ł", "Ġappl ied", "ĠN OT", "Ġhous ing", "ĠM ount", "Ġo dd", "Ġsu st", "D A", "ffic ient", "Ġ ?", "ol ved", "Ġp owers", "Ġth r", "Ġrem aining", "ĠW ater", "L C", "Ġca uses", "ãģ ®", "Ġman ner", "ad s", "Ġsuggest s", "Ġend s", "stand ing", "f ig", "ĠD un", "id th", "Ġg ay", "Ġter min", "ĠAngel es", "M S", "Ġscient ific", "Ġco al", "ap ers", "b ar", "ĠThom as", "Ġsy m", "ĠR un", "th is", "P C", "igr ants", "Ġmin ute", "ĠDist rict", "cell ent", "Ġle aves", "Ġcomple ted", "am in", "Ġfoc used", "Ġmon itor", "Ġveh icles", "M A", "ĠM ass", "ĠGr and", "Ġaffect ed", "itution al", "Ġconst ruct", "Ġfollow s", "Ġt on", "re ens", "Ġh omes", "ĠE xt", "ĠLe vel", "r ast", "ĠI r", "Ġel im", "Ġlarge ly", "ĠJ oe", "Ġvot es", "all s", "Ġbusiness es", "ĠFound ation", "ĠCent ral", "Ġy ards", "Ġmaterial s", "ul ner", "Ġgu ide", "Ġclos er", "um s", "Ġsp orts", "ed er", "J ust", "Ġtax es", "8 4", "ĠO ld", "Ġdec ade", "ol a", "Ġv ir", "Ġdro pped", "Ġdel ay", "it ect", "Ġsec ure", "ste in", "le vel", "Ġtre ated", "Ġfil ed", "ain e", "Ġv an", "Ġm ir", "Ġcol umn", "ict ed", "e per", "Ġro t", "Ġcons ult", "Ġent ry", "Ġmar ijuana", "ĠD ou", "Ġapparent ly", "ok ing", "clus ive", "Ġincre ases", "an o", "Ġspecific ally", "Ġte le", "ens ions", "Ġrelig ion", "ab ilities", "Ġfr ame", "ĠN ote", "ĠLe e", "Ġhelp ing", "Ġed ge", "ost on", "Ġorgan izations", "à ĥ", "ĠB oth", "hip s", "Ġbig ger", "Ġbo ost", "ĠSt and", "Ġro w", "ul s", "ab ase", "Ġr id", "L et", "are n", "ra ve", "Ġst ret", "P D", "Ġv ision", "Ġwe aring", "Ġappre ci", "Ġa ward", "ĠU se", "Ġfact or", "w ar", "ul ations", ") (", "Ġg od", "Ġter rit", "Ġpar am", "ast s", "8 7", "Ġen emies", "ĠG ames", "F F", "Ġacc ident", "W ell", "ĠMart in", "T ER", "Ġat h", "ĠHe ll", "Ġfor g", "Ġve ter", "ĠMed ic", "f ree", "Ġst ars", "Ġexp ensive", "Ġac ad", "ra wn", "ĠW he", "Ġl ock", "Ġform at", "Ġsold iers", "s m", "Ġag ent", "Ġrespons ibility", "or a", "ĠS cience", "Ġrap id", "Ġt ough", "ĠJes us", "Ġbelie ves", "M L", "Ġwe ar", "le te", "Ãĥ ÃĤ", "ĠD ri", "Ġcomm ission", "ĠB ob", "O h", "ap ed", "Ġwar m", "ÃĥÃĤ ÃĥÃĤ", "Ġ200 3", "ort ion", "Ġhas n", "ust er", "Ġun ivers", "ĠI ll", "Ġk ing", "olog ies", "9 4", "ĠT em", "ĠM os", "Ġpat ient", "ĠMex ico", "ce an", "ĠDe ath", "ĠSand ers", "y ou", "ĠC ast", "ĠComp any", "pt y", "Ġhappen ing", "F P", "ĠB attle", "Ġb ought", "A m", "M od", "U s", "ut ers", "ĠC re", "ĠTh ose", "Ġ4 4", "is er", "Ġs oul", "ĠT op", "ĠHar ry", "ĠA w", "Ġse at", "ff ee", "Ġrev olution", "Ġ( \"", "ĠD uring", "et te", "Ġr ing", "Ġoff ensive", "Ġreturn s", "Ġv ideos", "Ġdis cl", "Ġfam ous", "en ced", "ĠS ign", "ĠR iver", "Ġ3 00", "P M", "ĠB us", "ĠC H", "Ġcandid ates", "ard en", "Ġpercent age", "Ġvis ual", "Ġthan k", "Ġtrou ble", "ner gy", "Ġ200 1", "Ġpro ve", "ash ion", "Ġen h", "ĠL ong", "U M", "Ġconnect ed", "Ġposs ibility", "O ver", "Ġexper t", "Ġl ibrary", "art s", "ĠDirect or", "Ġfell ow", "9 2", "ir ty", "Ġd ry", "Ġsign s", "ĠL ove", "Ġqu iet", "f oot", "Ġp ure", "ĠH un", "Ġf illed", "ph as", "ĠE lect", "end ment", "ĠEx pl", "Ġun able", "n s", "m o", "Ġv ast", "ob e", "Ġident ify", "app ing", "ĠCarol ina", "g ress", "Ġpro te", "Ġf ish", "Ġcircumst ances", "raz y", "ĠPh ot", "Ġb odies", "ĠM ur", "Ġdevelop ing", "ĠA R", "Ġexperien ced", "Ġsubst ant", "ĠBo ard", "es ome", "Ġdom estic", "Ġcomb ined", "ĠP ut", "Ġchem ical", "ĠCh ild", "Ġpo ol", "ĠC y", "Ġe gg", "c ons", "st ers", "Ġh urt", "Ġmark ets", "Ġconserv ative", "Ġsupp orters", "Ġag encies", "id el", "O b", "ur b", "Ġ4 3", "ĠDef ense", "y e", "ĠA p", "du le", "Ġtemper ature", "Ġconduct ed", "ĠCh ief", "Ġpull ed", "Ġf ol", "L ast", "ont o", "os is", "V ER", "D es", "ĠP an", "F irst", "Ġadv ance", "Ġlic ense", "r ors", "ĠJ on", "Ġimag ine", "Ġhe ll", "Ġf ixed", "Ġinc or", "os ite", "ĠL og", "ick en", "] :", "Ġsurpr ise", "h ab", "Ġc raft", "ol t", "ĠJ ul", "Ġd ial", "Ġrele vant", "Ġent ered", "Ġlead s", "ĠA D", "ĠCle an", "Ġpict ures", "ess or", "Ġal t", "Ġpay ing", "P er", "ĠMark et", "Ġupd ates", "am ily", "ĠT ype", "ĠH ome", "Ġ5 5", "semb ly", "rom e", "8 3", "Ġgreat est", "Ġhe ight", "Ġhe av", "ain ts", "Ġlist en", "as er", "ĠS H", "Ġcap able", "ac le", "Ġpers pect", "in ating", "Ġoff ering", "ry pt", "ĠDe velop", "ab in", "r c", "Ġbr ight", "al ty", "ar row", "Ġsupp l", "ind ing", "ack ed", "gy pt", "ĠAn other", "p g", "ĠVirgin ia", "ĠL u", "Ġpl anned", "Ġp it", "Ġswe et", "T ype", "ĠD i", "Ġtyp ically", "ĠFranc isco", "Ġpro spect", "ĠD an", "Ġte en", "re es", "Ġsc hed", "Ġh ol", "Ġsc r", "Ġlot s", "l ife", "Ġnews p", "Ġfor get", "ĠN one", "ĠM iddle", "ĠR yan", "ed d", "Ġse vere", "Ġsu it", "ll er", "9 3", "Ġcor respond", "Ġexpl os", "u ations", "Ġfl ag", "g ame", "r id", "Ġpr in", "ĠD ata", "Ġde ploy", "ĠEn ter", "su it", "gh an", "ĠM en", "Ġthough ts", "Ġmat ters", "Ġad apt", "ĠA ri", "Ġf ill", "Ġfor th", "Ġs am", "Ġ4 1", "Ġpay ment", "ĠH or", "Ġsp ring", "du c", "Ġl osing", "Ġbring ing", "F O", "al a", "Ġdist ribution", "he red", "b our", "ĠIsrael i", "om a", "Ġcomb ination", "Ġpl enty", "V E", "C an", "ĠH aw", "Ġper man", "ĠSpe cial", "Ġto w", "Ġsee king", "Ġexam ples", "Ġclass es", "c r", "Ġbe er", "Ġmov es", "ĠI P", "ĠK n", "Ġpan el", "E ven", "Ġproper ly", "Ġr is", "Ġpl ug", "Ġestim ated", "E very", "Ġdef ensive", "ag raph", "Ġpre gn", "Ġinst it", "ĠV ict", "Ġvol ume", "Ġpos itions", "Ġl inks", "ĠPro gram", "ĠWe ek", "ag ues", "Ġtrans form", "k er", "ĠC EO", "Ġc as", "Ġopp onent", "Ġtwe et", "ĠC ode", "Ġsh op", "Ġf ly", "Ġtal ks", "Ġb ag", "Ph one", "Ġa id", "Ġpl ants", "Ġ6 5", "Ġatt orney", "ar ters", "qu est", "ĠMag ic", "Ġbeg ins", "Ġmy ster", "Ġenvironment al", "Ġst orage", "N N", "Ġm arg", "Ġs ke", "Ġmet al", "ell y", "Ġord ered", "Ġrem ained", "Ġl oved", "Ġprom pt", "Ġupd ated", "Ġexper ts", "Ġwalk ing", "Ġan cient", "Ġperform ed", "AT E", "Ġne ither", "i ency", "Ġmanufact ure", "ĠP ak", "Ġselect ed", "Ġm ine", "Ġult imately", "Ġexpl an", "Ġlab el", "ĠServ ices", "ribut ed", "Tr ump", "Ġsy n", "ĠU lt", "S C", "Ġme at", "Ġg iant", "ĠW ars", "ĠO N", "Ġad m", "Ġinter pret", "Ġeven ing", "Ġev il", "ĠB oston", "ĠW ild", "Ġ Ã", "ĠBit coin", "ĠAm azon", "D r", "ĠIn formation", "Ġobvious ly", "Ġadv anced", "Ph oto", "ol ar", "Ġwe ather", "Ġsymb ol", "Ġso le", "Ġpot entially", "ost er", "Ġorig inally", "m un", "3 00", "az e", "ess ions", "Ġde ck", "Ġst ood", "Ġyou th", "ĠB ern", "R ep", "ĠT est", "Ġbas ically", "ot ic", "Ġinvol ve", "ol it", "ly n", "S ee", "Ġair craft", "Ġconf irm", "E W", "Ġmess ages", "ĠRich ard", "Ġk it", "Ġpro hib", "Ġv ulner", "is ters", "Ġexist ence", "Ġturn ing", "ĠS P", "Ġdes ire", "Ġfl at", "Ġm ent", "se ason", "ang es", "Ġneighbor hood", "ĠL ake", "AT ION", "Ġpoint ed", "b ur", "Ġinn ov", "uc ks", "U L", "Ġprofess or", "Ġexp ressed", "A B", "ic ious", "Ġ200 2", "ĠDe v", "Ġs ession", "Ġb are", "s en", "Ġdis s", "ĠC ath", "ĠP ass", "ĠP oint", "Ġdo ctor", "or row", "ail ed", "ĠR ub", "ĠD C", "ĠChar l", "p erson", "Ġwrit er", "igh ters", "ure au", "Ġob lig", "Ġrecord ed", "Ġbro ke", "Ġord ers", "il ty", "Ġmot ion", "in ity", "l aw", "ad ium", "Ġimm igration", "Ġcontr ast", "Ġb att", "Ġex cellent", "Ġtechn ical", "am i", "Ġt un", "Ġcl oud", "ĠY ear", "ge on", "Ġcre ation", "Ġstr ange", "Ġa uth", "Ġfor t", "b orn", "Ġext ent", "ĠT oday", "ĠCl ub", "Ġr ain", "Ġs ample", "Ġaccept ed", "Ġt act", "Ġf ired", "ĠS on", "Ġstand s", "Ġb oot", "Ġ4 7", "Ġstat ements", "Ġvers ions", "Ġse lling", "ound ed", "Ġ199 0", "Ġwere n", "ĠW atch", "Ġexper iment", "P ost", "Ġret ail", "ul ed", "In st", "un te", "ãĥ ¼", "Ġdep art", "Ġb ond", "i very", "om pl", "Ġre action", "ĠSyri an", "ĠP ac", "app ed", "ani el", "D P", "Ġres olution", "Ġre act", "Ġappro ved", "on om", "m ond", "ĠO ffic", "-- -", "Ġrepl ace", "Ġt ack", "Ġsp ort", "Ġch ain", "Ġemer gency", "r ad", "ĠPalest in", "Ġ4 6", "Ġautom atically", "Ġrout e", "Ġp al", "Ġb anks", "ĠPar is", "ĠMed ia", "ro ad", "ic ing", "i xt", "ist ed", "Ġg rew", "Ġco ord", "ĠW here", "om in", "Ġsub s", "� �", "Ġ ±", "Ġcorpor ate", "Ġse lection", "n oon", "ĠRep ort", "c s", "clud ing", "ord ers", "anc he", "ĠIt s", "Ġslow ly", "ĠE gypt", "ĠA cc", "Ġcol le", "iqu es", "E X", "Ġattempt s", "ur l", "ĠC ross", "Ġfind ings", "ĠS C", "ĠO R", "Ġind ex", "ens ity", "ĠW ay", "ĠL and", "Ġsh ock", "d is", "Ġd ynam", "Ġc art", "m osp", "S ince", "i est", "ĠB oy", "Ġst orm", "ĠCont in", "201 3", "he w", "il it", "Ġess ential", "iqu id", "O ther", "ive red", "Ġreason able", "A ct", "Ġsub sequ", "ĠP ack", "ĠF ort", "Ġconsider ing", "Ġun iversity", "l og", "Ġmar ried", "Ġill ust", "ĠTr ue", "£ ı", "Ġnumer ous", "rast ructure", "Ġserious ly", "Ġrefer red", "u a", "Ġconsist ent", "on na", "ĠRe al", "ru ption", "ci ples", "Ġfact s", "9 1", "ot es", "er g", "The n", "Ġacc ompl", "N ote", "Ġre venue", "Ġpass ing", "Ġm al", "e en", "ĠY et", "Ġg ather", "ter day", "ew ork", "ĠA uthor", "P e", "Ġopt im", "Ġr ub", "Ġè £ı", "Ġun known", "st one", "Ġun ion", "ol ve", "Ġopportun ities", "Ġbrow ser", "ĠW al", "ĠC ost", "Ġreport ing", "st s", "p et", "Ġs and", "Ġsudden ly", "Ġsurpr ising", "ĠV R", "Ġsomew hat", "ĠB as", "ult ure", "iz z", "ĠC D", "Ġchalleng es", "Ġsett ings", "Ġexperien ces", "ĠF ull", "Ġcan n", "Ġrece iving", "ES T", "Ġj oint", "Ġcult ural", "Ġa st", "8 2", "as tern", "ce ived", "ĠC ru", "Ġb ull", "p ired", "am m", "Ġfac ing", "p ower", "Ġb oss", "ĠH ol", "Ġinst r", "Ġincreasing ly", "Ġsh ift", "Ġstre ets", "ĠWilliam s", "ab b", "Ġl ie", "Ġl augh", "ĠC a", "P L", "Ġadult s", "Ġcustom er", "Ġob tained", "Ġsupport ing", "ht ml", "f ire", "Ġdetail ed", "Ġpick ed", "ĠR ight", "ld er", "E E", "st ood", "ĠK im", "Ġw ire", "Ġs ight", "Ġdevelop ers", "Ġpers ons", "Ġs ad", "Ġc up", "Ġwar ning", "Ġboy s", "l ong", "Ġb ird", "f o", "Ġw al", "Ġobserv ed", "Ġz one", "iven ess", "Ġch annel", "c ript", "Ġref used", "ĠAg ain", "Ġsu c", "Ġspokes man", "ĠRe f", "r ite", "ou ston", "ãĥ ³", "ĠS her", "Ġact s", "ĠN ame", "Ġstrugg le", "ar ry", "omet imes", "Ġdisc rim", "H T", "Ġcateg ory", "Ġreal ize", "Ġemploy ee", "ĠAf ghan", "en ger", "Ġgun s", "ĠSte ve", "ĠM ot", "ĠO l", "ok ed", "Ġth ick", "Ġfair ly", "ill y", "Ġsur ve", "ĠM at", "we ight", "â Ķ", "Ġtro ops", "Ġag ents", "Ġbatter y", "Ġmot iv", "à ¡", "S ec", "d en", "o very", "L S", "Ġfl u", "Ġconf ident", "ĠO per", "Ġem pty", "Ġp hen", "Ġse ctor", "Ġexc ited", "Ġrem ote", "ap h", "o en", "Ġdestroy ed", "Ġmor al", "ĠH P", "ĠR on", "Ġd ress", "ĠB at", "Ġl it", "ĠM S", "Ġa f", "H L", "r um", "is ms", "Ġshould n", "Ġsym pt", "ĠTor onto", "het ic", "Ġcar bon", "Ġinstall ed", "Ġviol ent", "Ġsol ar", "j a", "Ġpract ices", "Ġr ide", "ĠP enn", "Ġimpro ved", "Ġaud io", "Ġbehav i", "ĠP S", "Ġe ating", "D ata", "ĠRe view", "p ass", "cl aim", "u ated", "ang ers", "c hen", "Ġproper ties", "Ġany where", "An other", "Ġbl ow", "ĠJack son", "Ġp roud", "Ġplan e", "l ines", "Ġsqu are", "Ġpro of", "ans as", "Ġtalk ed", "m akers", "Ġs ister", "Ġhold s", "Ġres ident", "Ġ= =", "Ġresist ance", "Ġspl it", "Ġpro secut", "Ġconf idence", "res ents", "Ġcut s", "Ġexcept ion", "Ġz ero", "Get ty", "Ġcop yright", "Ġtot ally", "orm al", "ific ations", "ĠAustral ian", "Ġs ick", "Ġ1 50", "Ġhouse hold", "Ġfe es", "Ġdri vers", "og en", "ĠN Y", "Ġnecess arily", "Ġregul ations", "ear ing", "s l", "Ġperspect ive", "c are", "ic ial", "H is", "Ġesc ape", "Ġsurpr ised", "ĠV an", "ur rent", "Ġv ac", "8 1", "ĠTh us", "Ġem phas", "ĠCh ampions", "ĠI ce", "Ġn arr", "Ġhead s", "Ġca using", "b el", "f ortunately", "ĠM a", "Ġtarg ets", "ci pl", "Ġafter noon", "Ġadd s", "ĠMay be", "ĠF our", "ess ed", "ple te", "Ġus ual", "ch o", "ing u", "Ġwith d", "ĠE nergy", "ĠE conom", "O O", "Ġart icles", "Ġinj ured", "Ġman age", "Ġexpl ains", "Ġdi agn", "R ec", "at ures", "Ġlink ed", "Ġdiscuss ed", "Ġexpl o", "Ġocc asion", "ath an", "Ġopp osite", "Ġfac es", "Ġden ied", "ĠK night", "Ġn ut", "Ġapprox imately", "Ġdisapp oint", "onym ous", "ĠB est", "ĠL o", "ĠH y", "ĠA ff", "Ġvot ing", "an while", "ĠII I", "Ġinstit utions", "ag ram", "ĠD aily", "Ġdr ag", "Ġnear by", "Ġgu ilty", "Ġcon ver", "P re", "s hip", "Ġre ward", "Ġphilos oph", "ĠS S", "u gh", "Ġapp s", "f riend", "Ġu pper", "Ġad vert", "Ġs now", "Ġfr ust", "Ġour selves", "F r", "ĠD ie", "amp ion", "Ġdis miss", "Ġc ere", "Ġsign al", "f rom", "Ġ ).", "Ġ5 2", "Ġcr imes", "it ors", "est ival", "use um", "Ġcoun cil", "ĠS aud", "M ay", "ĠG un", "ic ian", "et her", "Ġsu fficient", "ĠH en", "so le", "Ġhistor ical", "ĠF ar", "ĠT urn", "Ġp in", "Ġsuc ceed", "m at", "ly mp", "Ġtrad ition", "ĠO k", "Ġc ro", "Ġdesc ription", "al le", "Ġsk y", "T e", "Ġwide ly", "Ġw ave", "Ġdefin ition", "ĠJew s", "Ġcy cle", "Ġref ere", "Ġbr ings", "us al", "Ġal ive", "Ġfrequ ently", "Ġint ention", "ĠCont rol", "l v", "y stem", "Ġpriv acy", "g ent", "ren ce", "ĠQu est", "ĠChrist mas", "Ġr ail", "Ġco oper", "Ġtest ed", "ĠC apt", "as ks", "Ġcomfort able", "Ġdel ivered", "sc ape", "Ġdep th", "ĠG OP", "Ġwrit es", "Ġass ets", "Ġsa v", "im ents", "Ġtrans ition", "Ġart ist", "ĠL ook", "Ġl ob", "Ġcomp onents", "ar ity", "Ġwalk ed", "Ġro ot", "Ġparticip ants", "Ġnot iced", "Ġres c", "Ġn av", "ĠAd minist", "d a", "ut ral", "pl ate", "Ġimport ance", "Ġass ert", "ious ly", "c ription", "Ġinj uries", "ĠChe ck", "Ġregist ered", "Ġint ent", "Ġmiss ed", "ograph ic", "Ġsent ence", "oun ter", "Ġassist ance", "ev in", "Ġdat abase", "Ġbuild ings", "Ġclass ic", "Ġth inks", "ĠOh io", "P r", "ug g", "Ġfe e", "p an", "Ġeffect ively", "Ġfac ility", "Ġbe ar", "Ġch apter", "Ġdog s", "ĠCol umb", "Ġl atter", "it ial", "Ġad mitted", "T V", "ĠGe org", "Ġpost s", "\\ \\", "Ġlawy er", "Ġequ ival", "Ġm and", "Ġcontro lled", "ĠW alk", "ĠAnd rew", "Ġmen u", "am ental", "Ġprotect ed", "v a", "Ġadminist r", "or al", "Ġre in", "ĠS ar", "Ġamount s", "Ġn ative", "ĠM oon", "Ġrep resents", "Ġab andon", "Ġcarry ing", "Ġt ank", "m ary", "Ġdecl ared", "T ube", "Ġh at", "Ġpun ish", "el lect", "m es", "Ġun iverse", "ĠR od", "ph y", "Ġinf rastructure", "Ġ5 1", "Ġopp osed", "ow nt", "c a", "ĠM ake", "Ġhard ware", "Ġco ffee", "R el", "b al", "w orld", "ĠS af", "ĠSe a", "in als", "Ġown ed", "Ġh all", "ers ion", "Ġdescrib e", "ĠP ot", "Ġport ion", "Ġat mosp", "Ġgovern ments", "Ġdep ending", "Ġoff ense", "Ġtr ick", "aw a", "ĠL ine", "ĠV is", "ĠH ard", "ĠOr ig", "ĠCl ick", "Ġdes k", "ĠVal ley", "ĠS ov", "Ġmov ies", "Ġrem ark", "Ġm ail", "Ġcons cious", "Ġrul ing", "ĠR ights", "Ġmed ic", "he nt", "ĠW omen", "> <", "Ġrepl aced", "ĠP rem", "ĠTh anks", "Ġre new", "ĠB all", "if orm", "Ġsh ots", "C omm", "Ġar med", "Ġconst ant", "Ġt aste", "Ġreal ized", "Ġbu ff", "Ġm o", "Ġeffic ient", "M ost", "or ation", "if ies", "Ġcommun ication", "Ġfl ood", "Ġconsequ ences", "Ġany way", "ig g", "ĠG M", "ĠTh ank", "Ġ iron", "Ġev olution", "ĠC op", "tw itter", "Ġ9 5", "Ġrelationship s", "ad el", "ĠYou ng", "Ġpropos al", "ay ers", "uild ing", "ĠH ot", "OR E", "c os", "Ġcoll abor", "P G", "ax y", "Ġknow ing", "Ġsupport s", "ow ed", "Ġcontrol s", "Ġmere ly", "um er", "Ġath let", "Ġf ashion", "p ath", "Ġg ift", "Ġer a", "AN D", "Ġkind s", "ĠKore an", "Ġleg it", "ul ous", "Ġess entially", "Ġthe rap", "n ic", "Ġsuff ered", "Ġh ur", "Ġprom ise", "Ġex cess", "Ġover w", "Ġpr ime", "ĠH ouston", "er ry", "ĠM s", "R S", "201 2", "Ġst ores", "ĠO lymp", "Ġj ourney", "Al though", "S ub", "ĠE duc", "ĠCh apter", "Ġrequest s", "Ġconsum ers", "Ġt iny", "Ġis ol", "ĠF air", "b a", "ĠY OU", "Ġcr ash", "ce ler", "Ġemot ional", "Ġgood s", "Ġelect ed", "Ġmod er", "ĠLin ux", "Ġbl ocks", "Ġis land", "ĠSoc iety", "Ġelect ions", "Ġbroad cast", "Ġche ap", "Ġn ations", "Ġse asons", "4 00", "Ġwas te", "ĠS at", "Ġfield s", "em ploy", "Ġprof ile", "Ġauth ors", "AL L", "ĠG ra", "w est", "ĠT y", "Ġdeath s", "Ġv acc", "Ġfor med", "Ġd u", "Ġon going", "ĠMuslim s", "el f", "ig ure", "Ġass ume", "ĠUkrain e", "w ater", "Ġco ast", "Ġvot ed", "g or", "ĠA S", "ĠMich igan", "az a", "ĠAr m", "i ro", "Ġf lex", "as ters", "' '", "Ġwel come", "ar l", "Ġloc ations", "ig ation", "ĠF il", "Ġbu ying", "Ġarch itect", "Ġhard er", "ĠC ub", "Ġinter face", "Ġrestaur ant", "Ġdisco ver", "Ġex ceed", "Ġfav our", "ger y", "Ġd uty", "Ġp itch", "ad or", "ĠM ach", "b oy", "Ġrespond ed", "Ġext ended", "her s", "M any", "ra id", "if er", "ĠIn s", "S er", "Ġmed ium", "s he", "ĠS ports", "Ġmag azine", "ut ation", "Ġlim its", "ĠG all", "Ġex ternal", "raz il", "Ġyoung er", "t le", "Ġrem ind", "ĠC ON", "Ġimmedi ate", "Ġh idden", "Ġvol unte", "Ġsim pl", "od cast", "Ġph ase", "d r", "Ġpl ot", "Ġexp osure", "R I", "og rap", "v in", "an ish", "ĠAc ad", "ĠEng ine", "Ġexp ansion", "ĠP ay", "Y our", "Ġpus hed", "ĠE ll", "ĠHe ad", "Ġmarket ing", "ĠA C", "k et", "Ġh its", "Ġg ro", "ĠA ge", "ĠSc ot", "] [", "Ġst im", "Ġi Phone", "Ī Ĵ", "Ġn arrow", "ĠGet ty", "ĠTur key", "Ġperfect ly", "Ġen able", "ut ch", "Ġprec ise", "Ġreg ime", "Ġsh if", "Ġcomp ens", "g un", "d iv", "Ġch osen", "ĠK en", "An y", "Ġtre es", "Ġrecomm ended", "ĠR en", "u able", "ĠH T", "F ollow", "E G", "ĠH and", "ĠK enn", "Ġarg uments", "Ġex ists", "Ġb ike", "ĠCons erv", "Ġbre aking", "ĠG ar", "Ġc razy", "Ġvirt ual", "ay lor", "ix el", "Ġ19 80", "Ġper mission", "ĠSer ies", "Ġconsum er", "Ġclose ly", "c alled", "Ġ5 4", "Ġhop es", "Ġar ray", "ĠW in", "ĠLab our", "Ġsp ons", "ĠI re", "Ġp ow", "Ġread ers", "Ġemploy ment", "Ġcreat ure", "Ġresult ing", "Ġaccur ate", "Ġmom ents", "Ġarg ued", "Ġp ed", "D uring", "Ġ5 3", "ĠT al", "Ġs ought", "Ġsuff ering", "Ġ icon", "le e", "Ġ( $", "al ian", " °", "Ġp ra", "Ġbon us", "( \"", "k o", "Ġact ing", "D E", "f all", "Ġcompar ison", "Ġsm ooth", "ĠN AS", "u pp", "ĠJose ph", "ep ing", "ĠT ake", "ĠM id", "Ġs ending", "f ast", "ĠF all", "Ġdeal ing", "us er", "ĠOr gan", "C o", "Ġatt ached", "Ġse es", "% .", "Ġtyp ical", "AR T", "Ġfind s", "ĠAs ia", "um in", "ĠC ore", "ĠE nt", "in ent", "u ce", "ĠBl ood", "ĠN ever", "Ġem ails", "Ġhigh light", "Ġconf ront", "at us", "ut ed", "Ġun us", "Ġtop ic", "ĠAd am", "Ġb le", "at i", "Ġunder stood", "S et", "st ruct", "T P", "Ġm ob", "a a", "ĠSt art", "pect ed", "se ll", "Ġded icated", "ĠC A", "u an", "Ġsong s", "esc ription", "Ġte ch", "Ġr ape", "Ġas ide", "Ġgr ant", "Ġ5 6", "s ub", "Ġarg ue", "Ġcont aining", "Ġsche dule", "Ġliber al", "Ġpublic ly", "Ġheav ily", "ĠU t", "in er", "ĠS ection", "ĠC are", "we et", "l s", "D is", "âĶ Ģ", "ĠF ollow", "B ack", "ĠI T", "Ġb es", "j i", "ĠH it", "est ed", "Ġevery body", "ĠSw ed", "Ġfem in", "Ġfac ilities", "Ġcon ven", "C omp", "ĠO S", "c ore", "Ġan x", "Ġdiv ision", "ĠC am", "ĠSt an", "m ates", "Ġexpl ore", "pl om", "Ġsh ares", "pl oad", "an es", "Ġide al", "et ers", "ĠB ase", "Ġpl astic", "Ġdist inct", "ĠNet work", "ĠSe attle", "Ġtrad ing", "ens us", "int end", "Ġex hib", "Ġinit ially", "ĠF ood", "Ġthous and", "ĠBus iness", "act er", "Ġpar agraph", "Ġrough ly", "Ġw ww", "Ġcreat ive", "ĠCon f", "Ġconsum ption", "Ġfil ms", "ag an", "Ġob tain", "Ġt all", "Ġt or", "Ġacknow led", "Ġg rown", "al o", "K E", "Ġ4 00", "end ers", "t aining", "U G", "Ġsu icide", "Ġwat ched", "ĠL ist", "al i", "re hens", "Ġsurround ing", "Ġp ip", "Ġf lying", "ĠJ ava", "ord an", "Ġserv ing", "in ations", "p ost", "Ġsh o", "A v", "Ġj ail", "z y", "Ġ199 9", "Ġ< /", "Ġliter ally", "ĠS ir", "Ġexp osed", "Ġl ies", "st ar", "Ġb at", "Ġear ned", "ĠD ig", "Ġspec ified", "ĠSe ason", "Ġdeg rees", "Don ald", "Ġcent re", "Ġsh aring", "Ġwin ter", "ĠC O", "C he", "Ġ Î", "M P", "Ġun w", "Ġfew er", "ĠM ir", "Ġsomew here", "ĠK ey", "Ġattack ed", "ĠK ir", "Ġdom ain", "Ġstrong er", "Ġ9 9", "Ġpen alty", "I d", "Sc ript", "Ġdecl ined", "Ġne ck", "Ġfra ud", "Ġcur rency", "Ġr ising", "R C", "âĢ¦ âĢ¦", "H z", "Ġt ab", "Ġtal ent", "n am", "ĠN BA", "Ġvill age", "Ġleg s", "ĠN ext", "E d", "Ġac id", "Ġhy d", "8 00", "Ġinvol ving", "ĠIm age", "ĠBe fore", "F l", "Ġyes terday", "S ource", "Ġterror ist", "Ġsu p", "Ġsy nt", "ĠSaud i", "Ġw est", "Ġr u", "b urg", "Ġvis ible", "Ġstru ck", "r ison", "Ġaw esome", "Ġd rawn", "Ġansw ers", "ĠG irl", "ĠR am", "Ġthreat s", "Ġdef eat", "os it", "Ġv ent", "atur ally", "Americ an", "end a", "ĠH oly", "Ġr um", "% ,", "c ase", "ĠHist ory", "ĠYou Tube", "Ġsit uations", "ĠD NA", "S te", "Ġsa ved", "It em", "Ġrec ip", "olog ist", "Ġfac ed", "Ġel ig", "O nce", "ĠL i", "u h", "Ġmist ake", "ĠDiv ision", "ĠB ell", "Ġsympt oms", " ®", "Ġdom in", "Ġfall ing", "Ġend ing", "as hes", "Ġmat ches", "ĠOn line", "Ġexplan ation", "D ef", "red it", "Ġany more", "ĠT otal", "ĠF OR", "us hed", "Ġlet ters", "Ġris ks", "ĠO K", "Ġreported ly", ": \\", "Ġpl ate", "Ġsubject s", "Ġattempt ed", "if ier", "ian a", "Ġunlike ly", "ĠTh ough", "um a", "ĠIn vest", "ĠPr in", "ic an", "ĠD ar", "ĠColor ado", "au g", "Ġve get", "a os", "ri a", "Ġshe l", "Ġmark ed", "Ġ( )", "Ġsp r", "p o", "ĠL ink", "Ġdef e", "ĠJ r", "Ġthem e", "Ġpass ion", "ĠP en", "Ġinf o", "iz er", "Ġsh it", "ĠC ivil", "ap se", "c re", "Ġpo ly", "Ġcomp onent", "ĠChar les", "ĠIre land", "ĠPro v", "Ġdo ctors", "Ġgr anted", "Ġpain t", "Ġhon or", "Ġsm oke", "Ġpay ments", "Ġprim arily", "ĠKing dom", "r ich", "ate ll", "Ġde als", "Ġsched uled", "Ġfund amental", "Ġprote in", "Ġnewsp aper", "Ġcl ients", "yth on", "ĠD ate", "h us", "Ġfeed back", "Ġstret ch", "Ġc ock", "Ġhot el", "ĠQue en", "Ġsu gar", "Ġj u", "Ġmil k", "Ġappro val", "ĠL ive", "Ġequival ent", "ef ully", "Ġins ert", "z ona", "Ġext ension", "d ri", "J ohn", "Ġacc omp", "S m", "ĠF und", "Ġconst antly", "Ġ` `", "Ġgener ated", "ĠA ction", "ĠP sych", "ĠT ri", "Ġrecogn ize", "Ġv ary", "ph a", "ĠR a", "d f", "et ch", "ĠSov iet", "Tw o", "Ġpattern s", "Ġprof ession", "an ing", "T ime", "ĠL im", "Ġcol ors", "ĠA z", "ĠT R", "Ġinf ect", "Ġphen omen", "Ġshe ll", "Al so", "Ġput s", "Ġdel ivery", "Ġbro wn", "Ġprocess ing", "Ġlight s", "ess age", "ĠBro ok", "ĠA ud", "l ation", "Ġindust rial", "L ike", "ĠB razil", "rou s", "ES S", "ĠL uc", "Ġsome how", "Ġ8 5", "Ġpro port", "Ġpolit icians", "Ġindic ate", "Ġh ole", "Ġtechn iques", "Ġcompet itive", "Ġph r", "Ġv o", "ist ent", "ĠD ream", "Ġcamp us", "Ġaspect s", "Ġhelp ful", "Ġsh ield", "or se", "Ġtrig ger", "m al", "Ġ5 8", "Ġt ort", "Ġperson ally", "Ġt ag", "Ġkeep s", "ĠV ideo", "Ġben ch", "Ġg ap", "a ire", "Ġe ast", "Ġrec overy", "per ial", "Ġprof it", "ĠM ic", "Ġ5 7", "Ġcol on", "Ġstrong ly", "st yle", "Ġalleg ations", "h an", "Ġrep orters", "j o", "r ine", "arg et", "and al", "Ġ0 3", "Ġfl ash", "tr ans", "Ġstr ict", "Ġpark ing", "ĠPak istan", "Ġl i", "Ġwe ird", "ĠE ric", "Ġreg ions", "ĠJ un", "Ġint ellect", "ĠW H", "od ing", "rib utes", "up id", "ĠT it", "Ġf inger", "or ia", "Ġe lev", "ĠF ield", "Ġcon clusion", "; ;", "Ġfeel ings", "Ġext ensive", "Ġm ixed", "Ġne uro", "v y", "Ġhar ass", "ĠC irc", "ou ch", "Ġterrit ory", "Ġsuccess fully", "M ar", "Ġing red", "Ġoverw hel", "Ġl ayer", "V iew", "Ġall ies", "ill ance", "ĠTh ree", "Ġb unch", "Ġnorm ally", "Ġnet works", "Ġsac r", "ĠC IA", "b les", "Ġch ose", "Ġopp onents", "Ġregard less", "Ġfr anch", "Ġpre f", "ĠP o", "Ġbr idge", "ann a", "ĠSil ver", "Ġw age", "p age", "ri or", "Ġrad ical", "ĠL ittle", "Ġman ip", "Ġsecret ary", "Ġg ang", "D R", "F A", "Ġdec ent", "ĠSp irit", "Ġun cle", "ĠDevelop ment", "Ġinvest ors", "Ġwall s", "Ġpub lish", "Ġgener ate", "iss ions", "c ar", "Ġprom ote", "Ġcut ting", "Ġche st", "Ġdrink ing", "Ġcollect ed", "Ġ7 2", "Ġhop ing", "Ġem br", "gor ith", "Ġwar ned", "Ġinstruct ions", "O G", "ĠD id", "ĠAg ency", "Ġg ear", "Ġcritic ism", "ĠF urther", "Ġut il", "ann y", "R ed", "Ġcoun sel", "ĠAs ian", "Ġredu ction", "p ool", "Ġteach ing", "Ġdeep ly", "i y", "Ġestim ates", "Ġcho ices", "Ġperman ent", "in em", "ke l", "Ġf asc", "p se", "f ile", "ĠL ow", "ĠP erson", "Ġt ournament", "st al", "Ġm el", "U ST", "ĠR ay", "az i", "V al", "Ġcont ained", "ĠH olly", "Ġw ake", "Ġreve al", "Ġprocess es", "ĠIS IS", "Ġ0 9", "Ġbl ind", "Ġste el", "ĠB ad", "Ġcare fully", "app y", "ro it", "Ġg aming", "Ġhous es", "ĠC oll", "Ġtr uck", "er m", "Ġsc ored", "Ġocc as", "ret urn", "b ound", "v ar", "Ġsh arp", "Ġaf raid", "ĠE X", "am ber", "c ific", "Ġsche me", "N C", "ĠPol it", "Ġdecl ine", "Ġ199 8", "Ġpus hing", "Ġposs ession", "Ġpriv ile", "Ġteacher s", "Ġy ield", "H A", "ĠDav is", "it led", "#### ####", "Ġr ig", "ĠD aniel", "ac on", "Ġh ide", "ut en", "Ġcolle agues", "Ġprin ciples", "Ġl oud", "Ġs in", "ĠDem on", "Ġst one", "Ġ0 2", "Ġt aught", "Ġter rible", "Ġst uck", "ĠPol icy", "te en", "Ġimplement ation", "ĠB BC", "ĠAP I", "Ġwhe el", "all as", "Ġch ampions", "ol ars", "play er", "Ġrepeated ly", "ĠSt ill", "Ġlik es", "ast y", "es ter", "ĠCath olic", "R L", "Ġb ath", "Ġno ise", "t itle", "Ġn orthern", "P art", "Ġmag n", "Ġf ab", "ĠAs h", "Ġdis pl", "Ġtick et", "Ġm urd", "Ġalong side", "ĠMus ic", "Ġr iver", "ĠSte el", "ĠC L", "ĠPl ayer", "ĠM ult", "ow ing", "re p", "s ize", "Ġt ur", "ĠGeorg ia", "isc al", "ra ction", "Ġc able", "Ġ5 9", "Ġw ins", "Ġup coming", "Ġsurv ive", "Ġins pired", "ĠEduc ation", "Ġstat istics", "ĠF oot", "iam i", "Ġy ellow", "ĠP age", ". -", "ĠH as", "Ġur ban", "Ġa x", "es sel", "\\ \"", "Ġquarter back", "Ġreg ister", "ĠLab or", "Ġab ilities", "ĠF amily", "Ġvar iable", "ĠPr ice", "Ġcont em", "Ġth in", "ĠE qu", "d ata", "Ġg otten", "Ġconst it", "Ġas ks", "Ġt ail", "Ġexc iting", "ĠE ffect", "ĠSp anish", "Ġencour age", "ins on", "ĠA h", "Ġcommit ment", "C S", "Ġr ally", "Ġ: :", "Ġsubs id", "Ġsp in", "Ġcapt ured", "201 8", "Ġinn oc", "Ġalleged ly", "ĠC ome", "Ġart ists", "ĠN umber", "Ġelect ronic", "Ġreg ional", "ap es", "Ġw ra", "Ġmy th", "pr ise", "ĠM iller", "ĠC reat", "ĠEp isode", "b ell", "Ġdirect ed", "Ġext ract", "Ġs orry", "Ġv ice", "ag ger", "ĠSu pport", "Ġ6 6", "ĠI ron", "Ġwonder ful", "Ġg ra", "N et", "ion e", "E ng", "Ġsh ips", "ik es", "ĠK evin", "it ar", "Ġactiv ists", "tr ue", "ĠAri zona", "ent h", "ĠDes pite", "ĠS E", "Ġha bit", "ern el", "Ġin qu", "Ġab ortion", "Ġv oid", "Ġexpl icit", "Ġeng aged", "Ġang ry", "Ġr ating", "Ġfr ag", "b ro", "ick ing", "d ev", "Ġwor ried", "Ġob ser", "Ġap artment", "ĠG T", "Ġest ate", "ĠConst itution", "em on", "ĠS now", "Ġcount y", "Ġdis ag", "ĠStep hen", "Ġimm igrants", "w ind", "ĠN ations", "Ġfol ks", "O ut", "Ġg all", "Ġtarget ed", "Ġst ead", "ĠB on", "ĠL ib", "Ġinform ed", "Ġ12 0", "ch ain", "idel ines", "or ough", "Ġdri ven", "Ġregular ly", "Ġbas ket", "Ġprinc iple", "oc ument", "Ġst un", "ib ilities", "ĠRom an", "ĠAb out", "Ġal ert", "Ġdemocr acy", "Ġrepresent ed", "H S", "c ers", "p arent", "Ar t", "p ack", "Ġdi plom", "re ts", "ĠN O", "Ġcapt ure", "ĠAd v", "Ħ ¢", "Ġannounce ment", "ĠL ear", "Ġh ook", "Ġpur s", "ĠS uch", "ĠC amer", "Ġrefuge es", "ĠV e", "P ol", "Ġrecogn ized", "l ib", "Ġhad n", "A ss", "Ġpil ot", "us hing", "Ġreturn ing", "Ġtra il", "ĠSt one", "Ġrout ine", "Ġcour ts", "Ġdes per", "Ġfriend ly", "ĠIt aly", "Ġpl ed", "Ġbreat h", "Ġstud io", "N S", "Ġimp ressive", "ĠAfghan istan", "Ġf ing", "Ġd ownt", "ink ing", "ĠR og", "i ary", "col or", "se x", "ar on", "Ġf ault", "ĠN ick", "D own", "ĠR ose", "ĠS outhern", "X X", "is odes", "L ist", "6 00", "Ġout come", "er r", "Ġelse where", "Ġret ire", "Ġp ounds", "ĠGl obal", "Pe ople", "Ġcommun ications", "Ġlo an", "Ġrat io", "ĠEm pire", "Ġg onna", "Ġinv ent", "D F", "Ġ19 70", "ĠComm on", "p at", "Ġprom ised", "Ġd inner", "ĠH om", "Ġcreat es", "Ġoper ate", "ver ty", "ĠJ ordan", "et ime", "Ġsust ain", "R eg", "Ġincred ible", "im a", "Ġwar rant", "Ġm m", "A tt", "Ġlaw suit", "Ġreview s", "it ure", "ĠS ource", "l ights", "ĠF ord", "Ġ6 3", "g roup", "st ore", "Ġfeat ured", "Ġfore ver", "Ġpo verty", "ĠP op", "ĠC NN", "az z", "ab is", "ach ing", "Ġl aid", "ĠSu pp", "Ġfil ter", "en a", "ĠCommun ity", "Ġcreat ures", "u ction", "ĠR oyal", "Ġassoci ation", "ĠCon nect", "ĠBr ad", "âĸ Ī", "l ers", "the re", "ĠG i", "Ġval uable", "AC K", "ĠT aylor", "Ġl iquid", "ĠAtt orney", "ĠCar l", "ĠF inal", "ag a", "ĠWil son", "B ecause", "ĠProf essor", "ak a", "Ġincred ibly", "r ance", "! )", "R ef", "s k", "Ġsol utions", "Ġatmosp here", "Ġbl ame", "um es", "ĠN ob", "C A", "um ps", "r ical", "ĠPut in", "ĠD est", "or ic", "ĠP A", "Ġrespect ively", "w an", "Ġfif th", "â Ħ¢", "ĠC ry", "Ġgovern or", "res ident", "Ġpurch ased", "Ġh ack", "Ġint ense", "ob s", "Ġorig in", "Ġdef ine", "Ġcare ful", "** *", "Ġshould er", "Cl ick", "Ġt ied", "Ġdest ruction", "ou red", "Ġno body", "Ġh o", "ĠEx per", "Ġt ip", "\" ;", "Ġtechn ique", "Ġj ur", "ĠP ok", "b ow", "Ġleg end", "Ġacc ord", "Ġbus y", "ĠInt el", "Ġh ang", "ak i", ". ]", "âĢĶâĢĶ âĢĶâĢĶ", "Ġsur gery", "Ġrep rodu", "Ġun iform", "Ġscen es", "c ode", "Ġ6 2", "l isher", "ĠH ave", "ph ia", "Ġcry pt", "Ġrec on", "Ġsc ream", "Ġadop ted", "Ġsc ores", "N e", "ĠIt alian", "in cluding", "B O", "Ġindic ated", "Ġent ertain", "G u", "T ext", "i el", "Ġtw enty", "Ġeng age", "off s", "ĠPac ific", "Ġsm ile", "Ġperson nel", "Ġto ler", "Ġdo ors", "Ġt one", "Ġmach ines", "Ġent ering", "ten ance", "C O", "ĠJer sey", "Ġfore st", "Ġhor se", "Ġcompl aint", "ĠSpr ing", "y o", "ĠPl us", "ed ing", "ĠRet urn", "qu arters", "ial s", "c ow", "Ġacad emic", "Ġf ruit", "Ġ199 6", "og ether", "Ġw ine", "Ġpur su", "ĠSte ven", "Ġlic ens", "Wh o", "Ġclot hes", "re ction", "Ġsqu ad", "Ġst able", "Ġr aw", "z ens", "St ar", "ut ies", "anc er", "Ġke ys", "ĠM u", "Ġcompl icated", "ig er", "ĠTe xt", "Ġabs or", "Ġ6 8", "Ġfun ny", "Ġrel ief", "ĠL ew", "ĠC ook", "Ġch art", "Ġdraw ing", "G E", "Ġmod ule", "ĠB ull", "I LL", "Ġs alt", "0000 0000", "il le", "Ġres ource", "aw ay", "adel phia", "ĠB ru", "Ġ6 7", "Ġsome body", "Ġparticip ate", "Ġro se", "we red", "Ġmus cle", "Ġcons ent", "Ġcontin uing", "ĠGuard ian", "ĠOr der", "reg on", "Ġre ar", "Ġprov ision", "Ġlik ed", "ri ent", "Ġb ra", "Tr ans", "Ġmeet ings", "Ġto x", "Ġcon vent", "Ġaut o", "Ġrec ording", "ĠSo ft", "00 1", "ĠR oll", "Ġprogram ming", "Ġp ic", "Ġprov ed", "Ġst ab", "ĠA st", "Ġca ption", "ul ating", "ĠAtt ack", "Ġnew ly", "Ġ199 7", "f r", "Ġdis cipl", "ĠGree k", "Ġed ition", "ĠDo es", "ĠB ox", "if le", "ack et", "Ġpass es", "Ġgu est", "Ġac celer", "it als", "U D", "Ġaut hent", "ĠR est", "ov al", "t a", "u ine", "Ġarm or", "ĠT own", "Ġcomp at", "Ġinc hes", "Des pite", "Ġass ign", "he rent", "Ġprep are", "ĠM eg", "oc key", "Ġdep ends", "Ġtrack s", "w atch", "Ġl ists", "ĠN orthern", "Ġal ter", "re c", "ĠE astern", "Ġcond em", "Ġevery where", "? '", "Ġaff ili", "Ġf ought", "\": {\"", "Ġm ac", "it arian", "Ġsc ope", "ĠA L", "aw s", "ar ms", "Ġqu e", "Ġenjoy ed", "nes ota", "Ġagg ressive", "ĠSt ory", "ĠI V", "Ġrec ipe", "Ġrare ly", "ĠMed ical", "val ue", "ang el", "ay ing", "omet hing", "Ġsub section", "Ġs outhern", "Ġfrequ ency", "re te", "roll ed", "ult s", "ĠN ic", "Ġbeh alf", "Ġsequ ence", "ab et", "Ġcontrovers ial", "Ġcomp rom", "Ġwork er", "Ġmain ly", "Ġal gorith", "ĠM ajor", "or ce", "g ender", "Ġorgan ized", "Ġf ake", "Ġconclud ed", "ĠE D", "ĠEx ec", "r age", "Ġch ances", "ber ry", "ĠTr ad", "Ġconfig uration", "Ġwithd raw", "Ġf ro", "ud es", "ĠBro ther", "ĠB rian", "Ġtri es", "Ġsam ples", "Ġb id", "ĠGold en", "Ġphot ograph", "if est", "ĠD O", "ĠPar liament", "******** ********", "R em", "Ġcont est", "Ġsign ing", "p x", "ĠZ eal", "âĶĢ âĶĢ", "E ar", "Ġex it", "Be fore", "ĠCor por", "n ull", "mon th", "Ġrac ial", "ott ed", "ĠV eg", "ĠRe uters", "Ġsw ord", "ps on", "ĠRom ney", "a ed", "Ġt rib", "Ġin ner", "Ġprot ocol", "ĠB i", "ĠM iami", "ever al", "p ress", "Ġsh ipping", "ĠAm endment", "ĠHow ard", "con nect", "ĠD isc", "ĠJ ac", "iam ond", "ĠThere fore", "s es", "ĠPrin cess", "ĠUS B", "ĠAn th", "Ġsurve illance", "Ġap olog", "Ġ6 1", "ow a", "Ġf ulf", "j s", "Ġl uck", "ust ed", "Ġ §", "n i", "Ġant icip", "em an", "Ġwin ner", "Ġsil ver", "ll a", "ic ity", "Ġunus ual", "Ġcr ack", "Ġt ies", "e z", "Ġpract ical", "Ġprov ince", "ĠPl ace", "Ġprior ity", "IC E", "Ġdescrib es", "Ġbr anch", "F orm", "ask a", "miss ions", "b i", "Ġp orn", "ĠTur k", "Ġent hus", "Ġf ighters", "Ġ0 8", "ĠDet roit", "Ġfound ation", "av id", "A re", "Ġjud gment", "cl ing", "Ġsol ve", "ĠDes ign", "W here", "hes is", "ĠT ro", "a fter", "Ġne utral", "ĠPalestin ian", "ĠHolly wood", "Ġadv is", "ĠN on", "y es", "ol is", "Ġrep utation", "Ġsm ell", "Ġb read", "ĠB ul", "ĠBe ach", "Ġclaim ing", "Ġgen etic", "Ġtechn ologies", "Ġupgr ade", "row s", "Ġdevelop er", "ĠJ osh", "ĠDis ney", "erv ed", "ip al", "Ġun ex", "Ġbare ly", "t hen", "ĠP ub", "Ġill ness", "et ary", "ĠB al", "Ġp atch", "Ġbut t", "Ġst upid", "ĠD og", "ĠD allas", "f ront", "ie ce", "Ġprot ests", "Ġch at", "oen ix", "Ġw ing", "Ġpar liament", "Ġ7 7", "ose xual", "Ġre nder", "pt ions", "ĠCo ast", "os a", "ĠG reg", "h op", "ĠMan agement", "Ġbit coin", "Ġrec over", "Ġincor por", "or ne", "ĠUs ing", "Ġpre ced", "Ġthreat ened", "Ġspirit ual", "ĠE vent", "ĠF red", "Ġadvert ising", "Ġimprove ments", "ĠC ustom", "Ġer rors", "Ġsens itive", "ĠN avy", "Ġcre am", "L ook", "Ġex clusive", "Ġcomp rehens", "Ġde leg", "Ġcon ce", "Ġrem em", "Ġstruct ures", "Ġst ored", "N D", "Ġ1 000", "U P", "ĠB udd", "A F", "w oman", "ĠAcad emy", "ð Ł", "se a", "Ġtem porary", "Ab out", "es ters", "Ġtick ets", "Ġposs ess", "in ch", "o z", "Ġl a", "Ġcontract s", "Ġun p", "Ġc ig", "ĠK at", "ult ural", "as m", "Ġmount ain", "ĠCapt ain", "St ep", "m aking", "ĠSp ain", "Ġequ ally", "Ġl ands", "at ers", "Ġreject ed", "er a", "im m", "ri x", "C D", "Ġtrans action", "g ener", "less ly", "Ġ| |", "Ġc os", "ĠHen ry", "Ġprov isions", "Ġg ained", "Ġdirect ory", "Ġra ising", "ĠS ep", "ol en", "ond er", "Ġcon sole", "in st", "Ġb om", "Ġunc ertain", "1 50", "ock ing", "Ġmeas ured", "Ġpl ain", "Ġse ats", "Ġd ict", "S L", "af e", "Ġest imate", "iz on", "at hered", "Ġcontribut ed", "Ġep isodes", "omm od", "G r", "AN T", "Ġ6 9", "G ener", "Ġ2 50", "vious ly", "rog en", "Ġterror ism", "Ġmove ments", "ent le", "oun ce", "ĠS oul", "Ġpre v", "ĠT able", "act s", "ri ors", "t ab", "Ġsuff er", "Ġn erv", "Ġmain stream", "ĠW olf", "Ġfranch ise", "b at", "Ġdem ands", "Ġag enda", "Ġdo zen", "Ġclin ical", "iz ard", "ĠO p", "t d", "Ġvis ited", "ĠPer haps", "Ġact or", "Ġde lic", "Ġcont ribute", "Ġin ject", "ĠE s", "ac co", "Ġlist ening", "Ġcon gress", "epend ent", "Ġprem ium", "Ġ7 6", "ĠIr ish", "Ġass igned", "ĠPh ys", "Ġworld wide", "Ġnarr ative", "ot ype", "m ont", "b ase", "ĠB owl", "ĠAdminist ration", "Ġrel ation", "ĠE V", "C P", "Ġco vers", "Ġ7 8", "Ġcert ific", "Ġgr ass", "Ġ0 4", "pir acy", "ir a", "Ġengine ering", "ĠM ars", "Ġun employ", "ĠFore ign", "st ract", "Ġv en", "Ġst eal", "Ġrepl ied", "Ġult imate", "Ġtit les", "d ated", "Ġj oy", "a us", "Ġhy per", "ak u", "Ġoffic ially", "ĠPro duct", "Ġdifficult y", "per or", "Ġresult ed", "rib ed", "l ink", "wh o", "~~ ~~", "ĠSpe ed", "ĠV iet", "W ind", "ĠBar ack", "Ġrestrict ions", "ĠSh are", "Ġ199 5", "ition ally", "Ġbeaut y", "op t", "Ġm aps", "ĠC R", "ĠN ation", "ĠCru z", "W ill", "Ġelectric ity", "Ġor g", "Ġb urd", "Ġviol ation", "Ġus age", "Ġper mit", "ĠCh ron", "ĠF ant", "Ġn aturally", "Ġ0 7", "Ġth rown", "ĠAw oken", "Ġal ien", "ĠHer o", "ĠK ent", "ĠR ick", "ri ke", "Ġp ace", "}, {\"", "G L", "Ġpo ison", "ĠT ower", "Ġform al", "al ysis", "Ġgen uine", "Ġk il", "a ver", "Ġproced ure", "ĠPro p", "intend o", "ĠM ain", "as ant", "Ġtr ained", "G ame", "ĠL oad", "ĠM A", "Ġcru cial", "Ġle ts", "ĠF R", "Ġch ampion", "1 01", "ĠCon ference", "Ġwrit ers", "Ġconnect ions", "Ġo kay", "ir ms", "ĠR and", "Ġenc ounter", "ĠB uff", "Ġachie ved", "Ġche cks", "isc ons", "Ġassist ant", "Ġwhen ever", "ĠA ccess", "ĠU r", "b in", "Ġcl ock", "is p", "op her", "Ġb orrow", "Ġm ad", "Ġperson ality", "on ly", "IS T", "ab ama", "Ġg ains", "Ġcommon ly", "Ġter r", "Ġhyp ot", "Ġre ly", "Ġt iss", "iscons in", "Ġrid ic", "f unction", "ĠO regon", "Ġun com", "r ating", "el and", "ĠN C", "Ġm oon", "ann on", "Ġvulner able", "ut ive", "³³ ³³", "ĠRad io", "Ġw estern", "se ct", "ĠT ony", "Ġocc urs", "ĠO s", "ĠH on", "à Ń", "Ġv essel", "ĠScot land", "Ġdiscrim ination", "Ġsubsequ ent", "st ring", "Ġfant asy", "ĠSh adow", "Ġtest im", "W E", "it i", "r as", "Ġbo at", "Ġmar ks", "Ġord inary", "Ġre n", "Ġrepresent ative", "Ġpet ition", "Ġ7 3", "Ġad venture", "Ġign ore", "ĠPhil adelphia", "ĠS av", "V P", "Ġfact ory", "Ġt asks", "Ġdep ression", "z ed", "................ ................", "ĠSt orm", "Ġc ogn", "Ġelig ible", "Ġredu cing", "v ia", "Ġ0 5", "Ġstri king", "Ġdoll ar", "h o", "O V", "Ġinstr ument", "Ġphilosoph y", "ĠMo ore", "ĠA venue", "Ġrul ed", "ĠFr ont", "IN E", "ĠM ah", "Ġscen ario", "ĠNAS A", "Ġen orm", "Ġdeb ut", "Ġte a", "T oday", "Ġabs ence", "S im", "Ġh am", "le ep", "Ġt ables", "ĠHe art", "M I", "K e", "re qu", "V D", "m ap", "Ġchair man", "Ġp ump", "Ġrapid ly", "v i", "Ġsubstant ial", "E P", "d es", "ch ant", "ili pp", "ĠS anta", "ri ers", "anche ster", "L oad", "ĠC ase", "Ġsa ving", "Ġ7 4", "ĠA FP", "er ning", "oun ced", "ĠMin nesota", "ĠW as", "Ġrec ru", "Ġassess ment", "ĠB ron", "U E", "Ġdynam ic", "Ġf urn", "ul ator", "Ġprop ag", "h igh", "Ġacc ommod", "Ġst ack", "ĠS us", "w rit", "Ġre ven", "ĠGod d", "ĠZeal and", "ab s", "Ġbr ut", "Ġper pet", "h ot", "Ġhard ly", "ĠB urn", "ãĤ ¹", "Ġst y", "Ġtrans actions", "Ġg ate", "Ġsc reens", "Ġsub mitted", "Ġ1 01", "Ġlangu ages", "ugh t", "em en", "Ġfall s", "Ġc oc", "Ĥ ¬", "Ġstri kes", "p a", "Ġdel iber", "ĠI M", "Ġrel ax", "ann els", "ĠSen ator", "Ġext rem", "Ġ} ,", "ĠDe b", "Ġbe ll", "Ġdis order", "c ut", "Ġi OS", "Ġl ocked", "Ġem issions", "Ġshort ly", "\" ]", "ĠJud ge", "ĠS ometimes", "Ġr ival", "Ġd ust", "Ġreach ing", "F ile", "¯¯ ¯¯", "ino is", "ĠJ ason", "Ġs atell", "are t", "Ġst ations", "Ġag ric", "ĠTechn ology", "com es", "ĠUn fortunately", "ĠChild ren", "Ġappl ies", "ast ed", "Ġan ger", "ail ability", "ĠDam age", "Ġcomp are", "ĠStand ard", "Ġaim ed", "ĠB a", "angu age", "Ġreg ulation", "Ġj ury", "Ġair port", "Ġse ctions", "ĠPr ince", "em ed", "Ġmedic ine", "Ġh itting", "Ġsp ark", "ol ves", "Ġad s", "St ate", "Ġfood s", "Ġrepl acement", "Ġch icken", "Ġlow est", "Ġmind s", "Ġinvol ves", "u i", "Ġarr ang", "Ġproced ures", "ĠWh ich", "ivers ary", "Ġb ills", "Ġimprove ment", "Ġin ev", "Ġexpect ations", "Ġintellect ual", "Ġsp aces", "Ġmechan ism", "2 50", "bre ak", "ĠZ e", "ĠT enn", "ĠB alt", "Ġbar rel", "Ġstat ic", "man n", "Pol ice", "Ġt ips", "Ġhand ling", "c us", "od ed", "il ton", "ir y", "Ġjournal ists", "our se", "Ġcom ic", "Ġnom ine", "IT Y", "Ġvers us", "Ġlo op", "Ġsur f", "ĠInd ust", "ĠHun ter", "Ġbelief s", "is an", "Ġset up", "Ġbre w", "im age", "Ġcomput ers", "f ol", "} ,\"", "ĠMed al", "Ġtax p", "Ġdisplay ed", "Ġg rav", "Ġf iscal", "M on", "ĠMos cow", "ĠK ong", "ĠCent re", "Ġcamer as", "ĠMr s", "ĠH ay", "Ġa ver", "ĠK elly", "p y", "Ġrequire ment", "Ġent itled", "omb ie", "Ġsh adow", "ag ic", "ĠA k", "Ġel ite", "Ġdiv ided", "Ġhead ing", "Ġcop ies", "Ġloss es", "Ġv it", "k ed", "ĠB ry", "Ġan s", "ĠSte am", "Ġrep orter", "he im", "ĠIt em", "Ġsuper ior", "d on", "ere nt", "à ¶", "Ġtherap y", "Ġpe ak", "ĠMod el", "Ġl ying", "Ġg am", "z er", "r itten", "Ġrespons es", "Ġconsider ation", "ĠB ible", "Ġl oyal", "Ġinst ant", "Ġp m", "ĠFore st", "à ¼", "Ġext end", "Ġconv icted", "Ġfound er", "Ġconv in", "ĠO ak", "che ck", "Ġsch olars", "p ed", "Ġover se", "T op", "c ount", "ĠAr k", " ·", "Ġ0 6", "ĠL A", "m d", "ĠLat in", "im ental", "ĠC PU", "Ġsubst ance", "Ġminor ity", "Ġmanufact uring", "E r", "ocol ate", "Ġatt ended", "ĠMan ager", "r ations", "Ġappreci ate", "om y", "GB T", "id ency", "B L", "Ġguarant ee", "pos ition", "Ġo cean", "clud e", "Ġhead ed", "Ġt ape", "Ġlo ose", "Ġlog ic", "Ġpro ven", "Ġsp ir", "Ġad mit", "is a", "Ġinvestig ate", "Ġ199 4", "sy lv", "ĠL ost", "c est", "Ġ7 1", "Ġrequest ed", "Ġwind ows", "ĠPok é", "ĠWith out", "M et", "Ġbehavi our", "Ġread er", "Ġh ung", "ĠKe ep", "Ġro les", "Ġimplement ed", "Ġbl ank", "Ġserv es", "ĠJ ay", "Ġc ited", "ĠF riend", "prof it", "ap on", "Ġrep air", "it em", "arr ass", "Ġcrit ics", "ad i", "ĠF ather", "Ġsh out", "Ġf ool", "Ġ8 8", "Ġprodu cing", "Ġl ib", "Ġround s", "Ġcirc le", "Ġpre par", "Ġsub mit", "Ġn ic", "mor row", "ãĥ «", "U nder", "Ġv ital", "ater n", "Ġpass word", "Ġpublic ation", "Ġprom inent", "Ġspeak s", "Ġb ars", "Ġde eper", "ĠM ill", "port ed", "Ġw id", "Ġbut ter", "Ġsm oking", "Ġindic ates", "K ey", "rop ri", "ĠF ile", "all ing", "ast ing", "ĠR us", "Ġad j", "Ġ7 9", "av al", "Ġpres um", "bur gh", "on ic", "Ġf ur", "Ġpoll s", "ik a", "Ġsecond ary", "Ġmon ster", "ig s", "ĠCur rent", "E vent", "Ġowners hip", "end ar", "Ġarri ve", "ĠT ax", "Ġn ull", "ĠPri v", "Ġth ro", "Ġk iss", "c at", "Ġup set", "ang le", "it ches", "ect or", "olog ists", "ĠGal axy", "Ġcor ruption", "Ġh int", "ent er", "ĠH ospital", "Ġgreat ly", "Ġbeg un", "es y", "Ġso il", "ĠAnt on", "Ġmain tenance", "ãĥ ©", "Ġdo zens", "Ġhuman ity", "ĠAl abama", "Ġr om", "w orth", "ap ing", "sylv ania", "l ah", "Ġg athered", "G A", "Ġattack ing", "f ound", "ĠSqu are", "Ġar bit", "ict ions", "ĠW isconsin", "Ġd ance", "ĠS aint", "arch y", "Ġbase ball", "Ġcontribut ions", "Ġliter ature", "Ġex ha", "per ty", "t est", "Ġb ab", "Ġcontain er", "let ter", "Ġfall en", "Ġwebs ites", "Ġbott le", "ĠS ac", "Ġbre ast", "ĠP L", "Ġveter an", "Ġinterview s", "ĠA le", "Ġb anned", "eng ers", "ĠRev olution", "in th", "Ġconc erning", "IV E", "Ġexp enses", "ĠMatt hew", "ĠColumb ia", "d s", "ist ance", "Ġent ity", ".. .\"", "Ġrel iable", "Ġpar alle", "ĠChrist ians", "Ġopin ions", "Ġin du", "l ow", "Ġcompet e", "Ġth orough", "Ġemploy ed", "Ġestablish ment", "ig en", "ĠC ro", "Ġlawy ers", "ĠSt ation", "T E", "ĠL ind", "ĠP ur", "it ary", "Ġeffic iency", "âĢ IJ", "ĠL y", "Ġm ask", "Ġdis aster", "Ġag es", "ER E", "es is", "ĠH old", "Ġcas ual", "b led", "Ġen abled", "ĠEn vironment", "ĠInt elligence", "i per", "ĠM ap", "ĠB E", "Ġemer ged", "is dom", "Ġc abin", "Ġregist ration", "Ġfing ers", "Ġro ster", "Ġfram ework", "ĠDo ctor", "et ts", "Ġtransport ation", "Ġaware ness", "H er", "Ġattempt ing", "O ff", "ĠSt ore", "ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ", "ĠK now", "Ġdef ence", "Ġsc an", "ĠT en", "ĠCh air", "ĠP H", "ĠAtl anta", "Ġfuck ing", "Ġans wered", "b n", "ĠK ar", "Ġcateg ories", "Ġr ational", "Ġc ust", "Ġrob ot", "Ġcorrect ly", "Ġg if", "Ġgraph ics", "m ic", "Ġground s", "ĠO pp", "i ate", "Ġdist ributed", "Ġsan ctions", "Ġchalleng ing", "ut o", "Ġingred ients", "Ġinv ited", "Ġfound ed", "ĠRe qu", "d ed", "Ġb owl", "Ġbrother s", "ĠH a", "I O", "Ġw ages", "im ore", "oc ial", "Ġse ed", "ative ly", "Ġaddress es", "ĠI owa", "ab eth", "Ġatt itude", "is d", "ch ild", "Ġm ole", "Ġdisco very", "y ard", "B r", "Ġ8 2", "Ġsuppl ies", "ell ing", "Ġdist ingu", "C R", "Ġre cept", "Ġ vert", "Ġsw im", "b ec", "d oor", "ĠY eah", "Ġg al", "Ġinter act", "ĠE SP", "ĠC S", "amp s", "Ġconvin ced", "Ġobject ive", "Ġdis h", "ĠPhot os", "l ad", "Ġdownt own", "o il", "in ction", "Ġto morrow", "ĠC OM", "Ġsurv ival", "sh ot", "Ġsett lement", "C ons", "ĠX box", "int erest", "ĠS M", "arg o", "en ess", "Ġeth nic", "b ered", "M in", "ĠT ok", "Ġinc ent", "ĠComm and", "Ġmain tained", "Ġbreak s", "br idge", "at ar", "ag g", "ĠF inally", "un icip", "ĠO nt", "le ft", "Ġrecogn ition", "Ġ* /", "ĠP ers", "Ġwe lf", "Ġaddress ed", "ĠK ansas", "Ġvir us", "Ġwhere as", "Ġp apers", "ram s", "ĠMin istry", "Ġple asure", "Ġacqu ired", "Ġd uration", "j pg", "Ġcal m", "ĠN HL", "Ġburn ing", "Ġfold er", "ick ed", "ĠP y", "ĠIll inois", "Cl ass", "ĠGodd ess", "Ġperform ing", "Ġwelf are", "j ar", "In ter", "Ġl in", "Ġenh ance", "Ġnot ion", "f are", "yp es", "ĠAre a", "Ġcann abis", "ĠDie go", "f s", "ĠM anchester", "com m", "in ite", "Ġcover ing", "ĠS ound", "Ġ19 60", "Ġ8 4", "e lect", "z ing", "Ġcitiz en", "Ġph ones", "Ġr aid", "Ġign ored", "ĠOb ject", "Ġu pload", "c ard", "Ġmod ified", "Ġroom s", "ia h", "r ange", "he ast", "ach us", "Ġsuggest ing", "âĢ ĭ", "gr ade", "E l", "Ġclot hing", "Ġr h", "ĠH an", "un ity", "en cing", "ĠAust in", "sec ution", "t ra", "d em", "ĠQ ual", "Ġhe aven", "Ġst ages", "Ġw edd", "pl us", "ific ial", "ĠIm m", "ĠH o", "iet ies", "Ġphr ase", "Ġbr ill", "act ory", "Ġprov iders", "Ġsil ence", "Ġa er", "ĠA I", "ĠAd venture", "Ġplatform s", "Ġdemonstr ated", "Ġinter f", "ing ton", "Ġr aces", "Ġgr ade", "ult ane", "ĠTh rough", "f alse", "Ġb ow", "ĠA B", "Ġfl avor", "Ġhistor ic", "g ov", "Ġcol our", "Ġview ed", "ĠEm ail", "el come", "Ġinter vention", "Ġd iversity", "Ġperiod s", "Ġre verse", "ĠV ery", "Ġqu ote", "ĠLe ft", "th rough", "Ġsc rew", "Ġland ing", "Ġp ill", "Ġw et", "Ġprot esters", "Ġrepe at", "av ed", "er k", "Ġsal ary", "ĠPenn sylvania", "St ill", "Ġmay or", "Ġkit chen", "Ġfeat uring", "ĠM useum", "ĠT ournament", "ĠF al", "Ġser vers", "U C", "Ġany body", "im g", "ĠTr ade", "ixt ure", "the less", "Ġfin ance", "Ġcl osing", "ĠPat ri", "i ac", "ab el", "Ġ> >", "or ous", "Ġf irms", "sc reen", "un a", "Ġemb arrass", "ul se", "Ġlet ting", "Ġth rew", "ile y", "Ġch annels", "l an", "ĠVeg as", "Ġse ar", "Ġfant astic", "ar re", "uzz le", "ĠD er", "Th ose", "Ġsw ing", "Ġshe et", "ind ex", "co ver", "og an", "Ġvari ables", "ĠTe ch", "Ġsp oken", "ac hel", "ĠD a", "ĠMount ain", "Ġload ed", "Ġfoot age", "vers ion", "Ġun l", "ĠPh oenix", "Ġthrow ing", "Ġf iring", "Ġtrack ing", "Ġw idth", "Ġstrugg ling", "ro oms", "ot ion", "Ġmonth ly", "ĠSer ver", "Ġegg s", "op en", "M C", "Ġ199 3", "Ġh ired", "Ġstay ed", "ĠAll en", "Ġst ro", "Ġ9 8", "st ep", "ĠTurk ish", "Ġfab ric", "ist ing", "ĠD om", "Ġd ates", "Ġpr on", "Ġbasket ball", "Ġl ucky", "ĠArab ia", "Ġassum ed", "est y", "Ġaff airs", "Ġgl ad", "ĠInd eed", "ĠF A", "ĠW ord", "Ġjo ining", "if ice", "p read", "ir ts", "ĠSe lect", "Ġpop ulations", "aw are", "Ġn ose", "Ġcompl aints", "st art", "Ġsc oring", "Th anks", "Ġmin ing", "Ġvisit ors", "S H", "Ġdam aged", "Ġcharacter istics", "ĠP ent", "D C", "Ġ8 3", "ĠS ix", "r ates", "Ġfl ags", "ĠB rew", "d og", "M ark", "// //", "Ġexec ution", "Ġj oke", "ph ones", "Ġtestim ony", "Ġob st", "Q L", "ĠC ut", "Ġstud ied", "ĠN intendo", "ick et", "ĠN BC", "Ġl ad", "ĠB ra", "ĠM oh", "Ġk ernel", "Ġoverwhel ming", "Ġag ed", "Ġapplic able", "ĠC ond", "Ġroad s", "ĠBl ock", "m ade", "od ge", "Ġcomm ands", "Ġoff ices", "vel and", "Ġt ut", "Ġrece iver", "ĠF ro", "Ġsho pping", "Ġi P", "ĠSt re", "ĠA BC", "Ġentertain ment", "ĠB ow", "ort ed", "M c", "Ġread s", "gr ad", "ĠCol lect", "Ġâ ĪĴ", "ĠCap ital", "eder ation", "Ġemploy er", "Ġinvolve ment", "Ġanx iety", "al ia", "Ġro of", "ĠAm ong", "ĠDemocr at", "Ġstat s", "ĠV ill", "Ġconst itutional", "Ġrefer ring", "itt y", "Ġtack le", "out ube", "Ġback ed", "ĠH ong", "ĠBro ad", "Ġe le", "ĠO tt", "Ġ199 2", "h our", "achus etts", "C al", "Ġdefe ated", "Ġ8 1", "es p", "Ġseem ingly", "w as", "ĠJ enn", "ĠK urd", "Ġg ene", "Ġdisc ount", "R et", "EC T", "( );", "Ġclub s", "Ġs id", "ĠM arsh", "Che ck", "Ġp p", "ĠE ag", "ides pread", "Ġbe ings", "F T", "Ġintrodu ction", "ĠCh ange", "AR D", "Ġ1 10", "ad ows", "ier ce", "Ġme al", "a uthor", "ĠB ang", "lah oma", "Ġr anks", "201 1", "?? ??", "m ax", "Ġcoll apse", "Ġop ens", "Ġe cho", "Ġs oph", "Ġrac ist", "Ġenorm ous", "Ġw aves", "Ġt ap", "Ġcomprehens ive", ". --", "ĠR oy", "Ġfarm ers", "Rel ated", "a ired", "ron es", "ĠC rim", "Ġproport ion", "Ġdesign s", "Ġnegoti ations", "Ġvirt ually", "ĠBat man", "Ġwar n", "Ġlegit imate", "m ate", "Ġcon vention", ", ,", "net ic", "ĠS D", "Ġconsist ently", "Ġcompens ation", "Ġpunish ment", "Ġy e", "Ġt ie", "ĠB ureau", "ir lf", "ĠB u", "ĠA ren", "ĠPh ilipp", "Ġkn ife", "Ġmem ories", "ĠR oss", "Ġang le", "Ġ8 6", "ĠTh under", "Ġre nd", "ĠT our", "Ġcount s", "s ung", "ĠIm p", "Ġeduc ational", "Ġaccess ible", "C OM", "Ġd rew", "y er", "G l", "am ine", "OR T", "O B", "I B", "m aster", "Ġtri als", "og y", "h ar", "ĠTr ust", "Ġprefer red", "irlf riend", "ĠN ev", "Ġb in", "Ġc ow", "P age", "Ġsign ature", "ĠB L", "7 00", "Ġret ired", "Ġby tes", "Ġneigh b", "ĠLeg end", "Ġdev ast", "Ġsuspect ed", "is ons", "ĠPoké mon", "sc ale", "Ġcap abilities", "Ġre vel", "Ġche ese", "d y", "igr ant", "Ġfail ing", "b its", "ĠHer oes", "ĠG host", "ĠS cient", "Ġappoint ed", "ur i", "Ġinst itution", "Ġexpand ed", "g reg", "Ġmonitor ing", "Ġp odcast", "Ġcoal ition", "Ġ9 6", "J o", "Ġst olen", "ĠS ab", "Ġstop s", "Ġhol iday", "Ġint r", "C ar", "Bl ack", "ĠL GBT", "Ġwar ming", "ĠAnd erson", "Ġ8 9", "Ġprodu cer", "M ed", "Ġaccur acy", "ĠMar vel", "iz abeth", "ĠPat rick", "m ony", "Ġmin i", "ac les", "Ġover t", "the y", "Ġmembers hip", "ĠV en", "Ġex ch", "Ġrem oval", "ĠD ave", "T Y", "m ad", "ĠF ind", "Ġad equ", "Ġe c", "Ġte eth", "Ġemot ion", "Ġper m", "Ġsole ly", "d b", "Ġextra ord", "IG HT", "c al", "Ġgu idelines", "Ġd ying", "Ġsusp ended", "ĠPrem ier", "ĠAnth ony", "el ve", "Ġd ad", "ĠE th", "ĠFoot ball", "Ġabandon ed", "Ġ< <", "Ġm arch", "Ġhor ror", "âĢ¦ \"", "Ġchild hood", "Ġcampaign s", "Ġl unch", "ĠAl bert", "bl ock", "âĸĪ âĸĪ", "ound ing", "Ġb one", "or gan", "ad ers", "ĠFl ash", "ĠDri ve", "Ġton ight", "Ġw ars", "ĠF L", "Ġform ation", "con st", "New s", "Ġcom pe", "or ious", "ĠSt aff", "Ġdiscuss ions", "ĠProt ection", "ĠJ am", "Ġcrit eria", "Ġinstall ation", "Ġaccompl ish", "iz za", "Ġpub lisher", "Ġresc ue", "ĠT ry", "U LL", "ĠS om", "ĠH op", "ore t", "th s", "ord on", "Ġp ocket", "ĠIn v", "Down load", "ĠCr ime", "Ġb ene", "ĠGu ide", "ĠAs sembly", "Ġparam eters", "I E", "ĠAlex ander", "Ġconc ert", "ĠSc he", "Ġsh oes", "Ġvis iting", "Ġrec all", "Ġb ub", "Ġr ural", "Ġconc rete", "ĠR os", "N ext", "R uss", "Ġlo ans", "ĠSh ield", "Ġtre m", "hem at", "k g", "ĠHar ris", "is ition", "ĠM ove", "ĠF C", "Ġf ate", "ĠCh o", "Ġt ired", "Ġprinc ipal", "h ist", "ien ces", "ath y", "Ġse vent", "Ġm ood", "Ġstrateg ic", "Ġdise ases", "Ġfor um", "Ġtem por", "Ġhead quarters", "P ar", "ig e", "fl ix", "Ġgu itar", "Ġ9 4", "On ly", "Ġrele ases", "ro ph", "================ ================", "Ġ6 00", "ĠContin ue", "ig ate", "ĠC rit", "sy stem", "Ġdis abled", "Ġunex pected", "ith ub", "Ġuncle ar", "ĠE st", "Ġcontr ad", "Ġstrateg ies", "vent ures", "Ġpass age", "AM E", "Ġimpro ving", "Ġreve als", "Ġdecre ase", "ov a", "Ġann oy", "ĠSh ort", "ĠL ibrary", "Ġcy ber", "n ell", "ĠH ur", "ĠC B", "Ġphot ograp", "U I", "Ġs ed", "G e", "Ġ8 7", "Ġd iverse", "Ġencour aged", "Ġcons piracy", "Ġbird s", "Ġoper ator", "Ġhand ful", "Ġclass ified", "? )", "Ġdram atic", "Ġinvestig ators", "it o", "Ġw idespread", "ĠR oom", "-------------------------------- --------------------------------", "Ġcollect ive", "Ġjournal ist", "St ring", "Ġtemper atures", "il a", "Ġgu id", "Ġins pect", "Ġmiss ile", "ĠMay or", "Ġman ual", "Ġsim ultane", "Ġrat ings", "Ġsu ck", "Ġ9 7", "Ġunivers al", "Ġph arm", "Ġdis rupt", "ian o", "A V", "Ġf t", "Ġstat ist", "old s", "ĠWalk er", "ph p", "Ġunder t", "ĠL as", "ish op", "nt il", "res hold", "ĠWhe ther", "M s", "Ġden y", "ĠCl oud", "Ġprov ider", "Ġsurv iv", "ĠUp date", "h as", "Ġmist akes", "ch arge", "pl ed", "r ity", "Ġn ode", "ĠMass achusetts", "ool s", "lic ation", "Ġf ails", "em ale", "or i", "back s", "Ġsh irt", "Ġ' '", "ĠN AT", "Ġwat ers", "els on", "Ġe ase", "Ġsc ar", "Ġcont ents", "m ind", "Ġcont ribution", "Ġsh r", "Ġhand ed", "Ġst ability", "Ġtra ve", "E m", "Ġmir ror", "12 3", "Ġwe igh", "Ġf iction", "ou ver", "ist ant", "r ition", "ĠF ed", "Ġphys ically", "Ġst ake", "ĠArt icle", "ĠAr c", "ĠLew is", "ĠM ind", "Ġdemonstr ate", "Ġprof its", "v ision", "om ic", "ol id", "Ġbatt les", "Ġdri ves", "Ġeas tern", "ĠS ony", "!! !", "ar ation", "v ard", "ĠG L", "port ation", "Ġ9 2", "Ġlaw makers", "Ġprotect ing", "ĠE PA", "Ġy eah", "Ġsh ame", "ol ph", "e ven", "x it", "Ġatt ach", "Ġrepresent ing", "Ġob s", "ĠUt ah", "iff s", "ĠFre edom", "à ³", "A K", "Ġinc idents", "it age", "Ġview ers", "c d", "Ġm ouse", "Ġcl ar", "Ġaccord ance", "Ġb ot", "c or", "ĠSum mer", "he ld", "Ġinnoc ent", "Ġiniti ative", "ol s", "________________ ________________", "Ġsp ots", "p ace", "Ġconvent ional", "Ġcorpor ations", "Ġblock ed", "H D", "at tered", "Ġref ers", "Ġbu ck", "ĠDig ital", "12 0", "Ġtop ics", "T F", "Ä ģ", "br id", "re ement", "Ġunder lying", "ĠM ember", "Ġinvestig ating", "Ġpregn ancy", "Ġtouch down", "ĠB and", "ĠCall er", "Ġinst ances", "P P", "w a", "G ood", "Ġ199 1", "ĠC old", "Ġfear s", "Ġrem arks", "Ĩ Ĵ", "at al", "Ġm it", "Ġexper iments", "i pt", "Col or", "ind u", "Up date", "Ġ9 3", "A g", "Ġ å", "anc ouver", "B oth", "Ġjud ges", "Ob ject", "Ġst ere", "umb n", "Ġparticip ation", "ĠSt ars", "ĠJ ere", "Ġweek ly", "ĠB an", "Ġconvers ations", "ĠP itt", "u z", "ĠIndian a", "ĠK ick", "Ġinf ection", "Ġhero es", "Ġsett led", "Ġstri p", "Ġh al", "Ġd ump", "ĠS ci", "Ġl es", "Ġref erences", "ĠU RL", "ĠBr idge", "Ġwant ing", "For ce", "Ġex clus", "Me anwhile", "m n", "Ġg entle", "m aker", "sen al", "ĠG ro", "ou ri", "ĠR ain", "ĠAll iance", "Ġl ift", "el a", "S D", "ĠCle veland", "Ġrank ed", "Ġst adium", "Ġdead ly", "ä ¸", "Ġr iding", "ar ia", "ĠAr mor", "Ġdocument ation", "ĠGree ce", "ree k", "Ġl ens", "ĠS a", "Ġg ross", "ĠE mer", "ag ers", "ĠD ub", "ĠR h", "ĠAM D", "Ġarri val", "Ġdes ert", "Ġsupp lement", "ĠRes p", "Ġkn ee", "Ġmarg in", "f ont", "og g", "201 0", "ĠP ir", "ĠP rom", "iv als", "Ġint ake", "Ġdifferent ly", "ug s", "Ġb its", "clud ed", "Ġsearch ing", "ĠD u", "um ble", "Ġfunction al", "ĠBalt imore", "ĠC ould", "Ġdes ired", "Ġcirc uit", "ĠL yn", "ĠG O", "ĠF alse", "re pre", "' :", "alt ies", "Ġmin im", "Ġdro ve", "ĠSh ould", "Ġh ip", "Ġpro s", "Ġut ility", "ĠN ature", "ĠM ode", "P resident", "o pp", "r at", "form ance", "Ġconcent ration", "Ġf ont", "ĠB ud", "Ġam id", "Ġre vers", "ĠM L", "B ar", "Ġinter action", "Ġjur isd", "Ġspell s", "d ep", "f il", "Ġcivil ians", "ut ter", "ĠCo oper", "ĠBel ow", "Ġent rance", "Ġcon vert", "Ġcontrovers y", "ow ered", "Ġcontr ary", "Ġar c", "ĠExec utive", "ĠOffic er", "Ġpack ages", "Ġprog ressive", "w idth", "Ġreserv ed", "v ol", "ĠSam sung", "Ġprint ed", "Ġcent ers", "Ġintrodu ce", "ĠKenn edy", "Ġodd s", "Ġsure ly", "Ġindepend ence", "Ġpass engers", "repre ne", "ĠBe h", "Ġl oves", "ĠESP N", "Ġfac ilit", "Ġident ical", "Ġdo ct", "Ġpartners hip", "con f", "ĠH ide", "Ġconf used", "ĠC ow", "M en", "Ġw rest", "ĠIraq i", "Ġh oles", "ĠStud ies", "Ġpregn ant", "h ard", "Ġsign als", "I X", "Ġpull ing", "Ġgrad uate", "Ġnomine e", "D ate", "Ġper mitted", "Ġâ Ĥ¬", "ĠOk lahoma", "St art", "Ġauthor ized", "Ġal arm", "ĠC os", "v an", "Ġgener ations", "c ular", "Ġdr agon", "ĠSoft ware", "ĠEd ward", "Ġcontro ller", "S en", "ge red", "ĠV ik", "Ġappro ached", "Th ank", "Ġcan ce", "Ġform ula", "ĠSm all", "Ġweak ness", "Ġr amp", "it udes", "j ud", "Ġbrill iant", "Ġacc us", "s ource", "Ġ8 00", "ĠE vil", "S w", "Ġhom eless", "we ek", "i ens", "r ics", "ĠTh ird", "T O", "Ġorgan ic", "Ġpresent ation", "ag h", "ĠDown load", "v ation", "Ġas sembly", "or able", "hold ers", "ĠBern ie", "ĠHel p", "Ġt ong", "ĠF ight", "Ġbe ach", "B ook", "ĠL ic", "Ġr ush", "ĠR ound", "ou p", "ĠMar x", "Ġcalcul ated", "ĠDe vil", "ĠSar ah", "Ġoccasion ally", "Ġbul let", "Av ailable", "g ate", "Ġ9 1", "Ġh osp", "Ġprom ises", "ĠH IV", "ĠSt adium", "ĠSt ock", "ĠCorpor ation", "g age", "N G", "ĠC redit", "Ġs ne", "ib l", "Ġacc um", "s uch", "Ġterror ists", "Ġconscious ness", "ĠZ h", "Ġdram a", "ool a", "pir ation", "Ġlab our", "ĠN in", "Ġut ter", "Ġdemocr atic", "Ġass ass", "il ation", "Ġg est", "Ġab road", "Ġmet ab", "Ġs orts", "Ġfl av", "U B", "Ġm g", "ĠNot hing", "ĠO d", "Ġmus ical", "200 9", "Ġdro ps", "oc ated", "ater al", "0000 00", "Ġg re", "Ġequ ality", "Ġburd en", "Ġv ig", "ĠLe ader", "-------- ----", "Ġcere mony", "Ġf ighter", "Ġact ors", "Ġ æ", "am an", "F i", "Ġal ign", "put er", "Ġe lder", "ĠN SA", "Ġrepresent ation", "ĠOnt ario", "IT H", "usal em", "Ġharass ment", "itz er", "Ġsy mp", "Ġbox es", "ĠD R", "Ġman ifest", "at re", "Ġ ^", "Ġd ies", "le ton", "Ġmiss ions", "et he", "Ġres olve", "Ġfollow ers", "Ġas c", "Ġk m", "l ord", "am med", "Ġsil ent", "ĠAssoci ated", "Ġtim ing", "Ġprison ers", "ĠK ings", "ĠF ive", "Ġtow er", "Ġappro aches", "Ġprecise ly", "Ġb ureau", "ĠM other", "ĠI ss", "Ġkey board", "it ual", "Ġfund ed", "Ġstay ing", "Ġpsych ological", "Ġm ile", "ĠLe on", "ĠBar b", "w ill", "Ġw ider", "ĠAtl antic", "Ġt ill", "ĠR ome", "ro t", "Ġaccomp an", "Ġfl our", "ac o", "W orld", "ĠExp ress", "ĠY u", "C or", "Ġple ased", "part y", "Ġpoint ing", "Ġinf lation", "Ġro y", "Ġ ),", "ain er", "Ġwedd ing", "orm on", "Ġrequ iring", "Ġqual ified", "Ġse gment", "EN D", "Ġs izes", "e als", "Ġcor rupt", "ass ador", "Ġcele b", "Ġdream s", "ĠM ess", "Ġcheck ing", "ĠV ersion", "Ġprep aring", "Ġact ively", "ĠD iff", "Ġl ux", "ĠW inter", "act eria", "ĠN E", "Ġdep uty", "Ġtrans gender", "Ġsum mary", "Ġin her", "er ies", "ch ar", "ĠY an", "Ġkn ock", "ĠP ath", "Ġl ip", "roll er", "Ġimp ression", "Ġcelebr ate", "Ġsl ide", "Ġgu ests", "Ġcl ip", "F S", "Ġsav ings", "Ġcapt ain", "Ġleg acy", "ĠDen ver", "Ġw ounded", "tab oola", "AC T", "Ġpurs ue", "Ġo xy", "Ġ q", "Ġsem i", "ĠN eed", "ĠAff airs", "Ġob sc", "Ġcheck ed", "Ġd ual", "C ode", "ĠM D", "le m", "ult y", "Ġ ©", "ĠEl izabeth", "Ġcent uries", "ard ed", "s rc", "Ġev ident", "enn is", "at in", "Ġunemploy ment", "ĠMar io", "Ġint im", "Ch rist", "Ġbi ological", "Ġsold ier", "ĠAdd ed", "Ġm ath", "ĠG il", "Ġbi as", "Ġd ating", "ĠO cean", "Ġm ice", "M us", "h ire", "ĠT es", "Ser ver", "lim ited", "S ize", "Ġmet ers", "Ġrock et", "es see", "Ġcertific ate", "ĠIran ian", "AS S", "Ġgr id", "D ec", "Ġro lling", "com mun", "ĠSwed en", "b ury", "Ġtiss ue", "Ġrac ism", "ĠL ocal", "Ġmyster y", "Ġexam ine", "Ġst em", "Ġs its", "Ġhop ed", "ot ing", "Ġdial ogue", "Ġpers u", "W atch", "l ay", "M AN", "Ġch ronic", "ĠPort land", "mark et", "ĠS EC", "Ġparalle l", "Ġsc andal", "Ġcar ries", "Ġphenomen on", "h uman", "ack er", "ĠO x", "Ġretire ment", "tain ment", "ov ie", "ĠG ear", "Ġd uties", "Ġdo se", "Ġsc roll", "M B", "in f", "Ġsa uce", "Ġland scape", "red dit", "ĠChampions hip", "ĠRed dit", "al id", "Ġco in", "Ġover s", "Ġpost ing", "ab out", "Ġf el", "and y", "Ġb old", "Ġfocus ing", "e ffect", "G R", "Ġde emed", "Ġrecommend ations", "Ġste pped", "Ġvot er", "ĠDe ep", "ĠInst agram", "Ġmoder ate", "ĠMary land", "Ġrestrict ed", "ĠM B", "ĠCh all", "Ġto b", "Ġc ir", "ĠO cc", "ĠE ver", "Ġcoll aps", "IN FO", "= -", "ĠP ict", "ĠAcc ount", "n c", "Ġo ught", "Ġex port", "Ġdr unk", "( '", "Ġw ise", "ĠM ort", "ne cess", "Ġan cest", "ĠInc re", "Ġfrequ ent", "m ir", "Ġinterpret ation", "Ġdepend ent", "Ġco ins", "ĠB ol", "V ideo", "ĠJust in", "Ġfat al", "Ġcook ing", "Ġconf usion", "ip her", "Ġcust ody", "ĠMor gan", "om ach", "ĠGovern or", "Ġrestaur ants", "el ing", "Ġacknowled ged", "Ġthe r", "Ġgen es", "ch ing", "He y", "Ġtact ics", "ĠMex ican", "Ġv end", "Ġhe s", "qu er", "Ġnot ing", "ĠCamer on", "Ġtarget ing", "ro ck", "Ġcred its", "Ġemot ions", "Ġrepresent atives", "new s", "Ġlegisl ative", "Ġrem oving", "Ġtweet ed", "ĠCar ter", "ĠF ixed", "Ġfor cing", "Ġspeak er", "Ġm ales", "ĠViet nam", "l ined", "Ġconcept s", "Ġvo ices", "o ir", "ĠT rib", "W he", "ĠJer usalem", "ĠS ant", "Ġc ul", "Ġl ady", "ĠHaw ai", "Ġar ts", "ĠIn n", "ĠMach ine", "ĠEm peror", "Ġsl ot", "g ly", "ĠPro cess", "II I", "Ġathlet es", "ĠTem ple", "ĠRep resent", "Ġpres c", "Ġt ons", "Ġgold en", "Ġp unch", "ĠG R", "iver pool", "Ġen act", "Ġlob by", "Ġm os", "Ġpick ing", "Ġlif etime", "Ġcogn itive", "E ach", "z o", "Ġd ub", "Ġcons ists", "ol n", "Ġf estival", "am ous", "Ġint ellig", "w ords", "ĠSm art", "Ġde le", "Ġl apt", "Ġmag ical", "ĠS in", "b us", "ur ities", "igh th", "ĠRub y", "ĠS ure", "ol ving", "Ġj un", "O ST", "Ġimp osed", "Ġast ron", "Ġcor rel", "ĠN S", "ĠK it", "ĠF uture", "b urn", "Ġimm une", "oc us", "Ġcour ses", "ĠSt ring", "Ġle an", "Ġg host", "Ġout comes", "Ġexp ense", "Ġevery day", "Ġaccept able", "A h", "Ġequ ipped", "Ġor ange", "F R", "ĠD utch", "Th ough", "ĠR ank", "Q U", "ĠRober ts", "wh at", "re nd", "Ġdisapp ear", "Ġsp awn", "ĠL am", "o is", "Ġdes erve", "Ġmin imal", "Ġnerv ous", "ĠW ould", "Ġro ok", "ĠV ancouver", "Ġres ign", "sh ire", "ĠW orks", "ĠB uild", "Ġafford able", "ĠG ary", "ĠAren a", "Ġh anging", "Ġimpl ications", "ĠS ong", "Ġmain taining", "Ġgu ards", "C ON", "Ġder ived", "Ġexecut ed", "Ġthe ories", "Ġqu oted", "ĠAnd re", "og a", "sel ess", "in fo", "ĠBel g", "Ġt ears", "ĠSur v", "Ġbirth day", "ig ious", "im mer", "Ġspect rum", "Ġarchitect ure", "Ġrec ruit", "arm a", "T able", "Ġmon sters", "ĠG ov", "Ġdest ination", "Ġattract ive", "Ġf oss", "ĠMore over", "Ġpres ents", "TH E", "Ġrep ly", "pt on", "Ġc um", "Ġdel ight", "Ġaffect s", "Ġdon ations", "ĠT oy", "ĠH im", "M ENT", "Ġover come", "it ched", "ĠFant asy", "ĠH at", "ĠBe ast", "b ott", "Ġinvestig ations", "R un", "Ġhun ting", "d i", "f und", "Ġs essions", "est yle", "Ġport ray", "oid s", "Y eah", "Ġcommun icate", "Ġcom edy", "ĠY ang", "Ġbel t", "ĠMar ine", "Ġpredict ed", "Pl ay", "Ġimportant ly", "Ġremark able", "Ġelim inate", "D avid", "Ġb ind", "V ID", "Ġadvoc ates", "ĠG aza", "im p", "D B", "ĠN a", "ĠSim ilar", "I ES", "Ġchar ity", "v as", "m ath", "Ġâ ĸ", "ok er", "nd um", "Ġcap s", "ĠH al", "2 000", "e an", "Ġfle et", "Ġrec re", "R ight", "Ġsleep ing", "ij ing", "k ind", "Ġdesign ated", "à ¤", "Ġanim ation", "ke e", "ĠInt rodu", "Ġ/ >", "Ġdelay ed", "Ġtrem end", "Ġcur ious", "U se", "Ġle ct", "d am", "Ġinnov ation", "ĠPoint s", "Ġload ing", "Ġdisp ute", "ct ic", "ird s", "ĠB Y", "Ġn urs", "ĠVal ue", "ION S", "ĠH um", "Ġtem plate", "m ers", "Ġappear ances", "ĠEnter tainment", "Ġtransl ation", "Ġsa ke", "Ġbene ath", "Ġin hib", "Ġe uro", "abet es", "Ġstud ying", "ĠM as", "Ġper ceived", "Ġexam ined", "Ġe ager", "Ġco aches", "Ġim per", "ch i", "Ġprodu ces", "\" ).", "ĠEvery one", "Ġm unicip", "Ġg irlfriend", "Ġh ire", "ĠV ice", "Ġsu itable", "op y", "Ġin equ", "ĠD uke", "f ish", "f irst", "ĠO bs", "Ġinter ior", "ĠBru ce", "ĠR y", "Ġanal ys", "Ġconsider able", "Ġfore cast", "Ġf ert", "ors hip", "ĠD rug", "ĠA LL", ": \"", "th ur", "ĠM ail", "Ġball ot", "Ġinst antly", "ĠCh annel", "Ġp icks", "Ġ198 9", "Ġt ent", "ol i", "Ġcivil ian", "b ling", "ell o", "b u", "Ġin ch", "Ġlog o", "Ġcooper ation", "Ġwal ks", "Ġinvest ments", "Ġimp rison", "ĠF estival", "ĠK y", "Ġleg ally", "Ġg ri", "ch arg", "S l", "Ġthreat ening", "du ction", "fl ow", "Ġdismiss ed", "ibr aries", "c ap", "e le", "ĠMc G", "ĠHar vard", "ĠConserv ative", "ĠC BS", "p ng", "Ġro ots", "ĠH aving", "umb led", "ĠF un", "\\ /", "ĠS earch", "ple x", "Ġdiscuss ing", "Ġcontin u", "ĠT ai", "ĠW ik", "F ree", "f it", "Ġref use", "Ġmanag ing", "Ġsy nd", "ip edia", "w alk", "Ġprofession als", "Ġguid ance", "Ġunivers ities", "Ġas semb", "unt u", "F inally", "AS E", "ĠAut o", "ĠH ad", "Ġann iversary", "L D", "ĠD ur", "ĠUlt imate", "ih ad", "pro duct", "Ġtrans it", "Ġrest ore", "Ġexpl aining", "Ġass et", "Ġtransfer red", "Ġbur st", "ap olis", "ĠMag azine", "ĠC ra", "ĠB R", "gg ed", "ĠH E", "M ich", "b et", "ĠL ady", "yl um", "erv es", "Ġme ets", "wh ite", "L og", "Ġcorrespond ing", "Ġins isted", "G G", "Ġsurround ed", "Ġt ens", "Ġl ane", "Ġco inc", "h ome", "Ġexist ed", "ect ed", "ĠDou ble", "lam m", "Ġske pt", "ex p", "Ġper ception", "ie v", "ĠBe ing", "o ft", "Ġadop t", ". :", "] ;", "Wind ows", "Ġsatell ite", "AS H", "Ġinf ant", "d escription", "ĠMe anwhile", "c m", "oc a", "ĠT reat", "act or", "Ġtob acco", "ĠN orm", "em ption", "Ġfl esh", "Ġj e", "o op", "ĠHe aven", "Ġbe ating", "an im", "Ġgather ing", "Ġcult iv", "G O", "ab e", "ĠJon athan", "ĠSaf ety", "Ġbad ly", "pro t", "Ġcho osing", "Ġcontact ed", "Ġqu it", "Ġdist ur", "Ġst ir", "Ġto ken", "D et", "ĠP a", "Ġfunction ality", "00 3", "s ome", "Ġlimit ations", "Ġmet h", "b uild", "con fig", "N T", "re ll", "ble m", "ĠM om", "Ġveter ans", "ĠH u", "Ġtrend s", "are r", "ĠG iven", "ĠCa ption", "m ay", "AS T", "Ġwond ering", "ĠCl ark", "n ormal", "Ġsepar ated", "Ġdes p", "st ic", "b rew", "Ġrel ating", "ĠN ik", "ĠF arm", "Ġenthus i", "g ood", "d eb", "Ġactiv ist", "Ġm art", "Ġexplos ion", "ĠEconom ic", "L ink", "Ġins ight", "Ġconven ient", "Ġcounter part", "su pport", "ĠV irt", "ag en", "ĠTenn essee", "ĠSim on", "ĠA ward", "OC K", "ĠF igure", "Ġoverse as", "Ġpr ide", "ĠC as", "n ote", "m g", "C urrent", "Ġdispl ays", "cont ent", "Ġtravel ing", "Ġhosp itals", "ĠFin ancial", "ĠP ast", "Ġdefend ant", "Ġstream ing", "m ble", "ĠBer lin", "uk i", "Ġdist ribut", "Ġant ib", "Ġch ocolate", "ĠCast le", "Ġinter rupt", "ĠR ow", "Ġconvers ion", "Ġbug s", "ĠR ather", "li est", "L Y", "ĠJe an", "com mon", "ak h", "Ġ1 30", "ot ton", "ĠDe an", "Ġam endment", "Ġgame play", "ĠWar ren", "od a", "Ġhigh lights", "Ġir re", "ĠNAT O", "Ġball s", "Ġdemand ing", "U RE", "ĠL uke", "F igure", "st op", "on ia", "z one", "iz ers", "ĠW R", "Ġaward ed", "Ġregul atory", "ĠH art", "ĠS N", "pl ing", "Ġs our", "ĠP ixel", "us ive", "Ġf et", "ĠS ent", "Ġautom atic", "Ġf er", "vern ment", "ĠKh an", "T ON", "f ather", "Ġextraord inary", "th rop", "ĠP ython", "ĠG PU", "Ġsex ually", "Ġdesk top", "it ivity", "ĠAnton io", "Ġo rient", "Ġe ars", "ob by", "ous es", "vertis ements", "Ġmanufacture rs", "ic ient", "min ute", "Ġconv iction", "Ġg arden", "p ublic", "Ġsatisf ied", "f old", "O K", "Ġin hab", "ĠTh ink", "Ġprogram me", "Ġst omach", "Ġcoord in", "Ġh oly", "Ġth reshold", "Ġr het", "Ġser ial", "Ġemploy ers", "ĠEvery thing", "ra h", "Ġb other", "Ġbr ands", "Val ue", "ĠT ed", "ĠPlan et", "Ġp ink", "ĠFurther more", "s a", "P E", "re ck", "ĠUS D", "ot te", "Ġ& &", "Ġland ed", "g ets", "Ġprodu cers", "Ġhealth care", "Ġdomin ant", "Ġdest ro", "Ġam ended", "ch ron", "Ġf its", "ĠSy d", "ĠAuthor ity", "AT CH", "Ġfight s", "ĠL LC", "Ġ-- -", "ĠCor p", "Ġtox ic", "spe cific", "ĠC orn", "ĠChe l", "Ġtele phone", "ĠP ant", "Ġmyster ious", "aun ch", "od ox", "med ia", "Ġwitness es", "ag u", "Ġquestion ed", "ĠBre xit", "ĠRem ember", "ene z", "Ġend orse", "iat ric", "ĠId ent", "Ġridic ulous", "1 10", "Ġpr ayer", "Ġscient ist", "Ġ19 50", "ĠA qu", "Ġunder ground", "ĠU FC", "m are", "ĠL ater", "w ich", "Ġsubsc rib", "Ġhost s", "Ġer r", "Ġgr ants", "ant om", "Ġsum mon", "ear ly", "ĠC lear", "ĠPr im", "Ġsusp ension", "Ġguarant eed", "app er", "Ġr ice", "ĠSe an", "ĠSh in", "Ġrefere ndum", "Ġfl ed", "r ust", "Ġ3 60", "ter y", "Ġsh ocked", "B R", "ĠO il", "ĠAll ah", "Ġpart ly", "Ġign or", "Ġtrans mission", "Ġhom osexual", "ivers al", "Ġhop efully", "ãĤ ¤", "Ġless on", "L eg", "Ġ ..", "Y et", "t able", "app ropri", "re tt", "Ġbo ards", "Ġincor rect", "Ġb acteria", "ar u", "am ac", "Ġsn ap", ".' \"", "Ġpar ad", "t em", "he art", "Ġav ailability", "Ġw isdom", "Ġ( +", "Ġpri est", "ĠÂł ĠÂł", "O pen", "Ġsp an", "Ġparam eter", "Ġconv ince", "Ġ( %)", "r ac", "Ġf o", "Ġsafe ly", "Ġconver ted", "ĠOlymp ic", "Ġres erve", "Ġhe aling", "ĠM ine", "M ax", "Ġin herent", "ĠGra ham", "Ġinteg rated", "D em", "Ġpip eline", "Ġapp lying", "Ġem bed", "ĠCharl ie", "Ġc ave", "200 8", "Ġcons ensus", "Ġre wards", "P al", "ĠHT ML", "Ġpopular ity", "look ing", "ĠSw ord", "ĠAr ts", "' )", "Ġelect ron", "clus ions", "Ġinteg rity", "Ġexclus ively", "Ġgr ace", "Ġtort ure", "Ġburn ed", "tw o", "Ġ18 0", "P rodu", "Ġent reprene", "raph ics", "Ġg ym", "ric ane", "ĠT am", "Ġadministr ative", "Ġmanufacture r", "Ġ vel", "ĠN i", "Ġisol ated", "ĠMedic ine", "Ġback up", "Ġpromot ing", "Ġcommand er", "Ġfle e", "ĠRus sell", "Ġforg otten", "ĠMiss ouri", "Ġres idence", "m ons", "Ġrese mb", "Ġw and", "Ġmeaning ful", "P T", "Ġb ol", "Ġhe lic", "Ġwealth y", "Ġr ifle", "str ong", "row ing", "pl an", "as ury", "âĢ¦ .", "Ġexpand ing", "ĠHam ilton", "Ġrece ives", "S I", "eat ures", "ĠAn im", "RE E", "P ut", "Ġbrief ly", "ri ve", "Ġstim ul", "Ġ`` (", "Ġ __", "Ġch ip", "Ġha z", "Ġpri ze", "ĠTh ings", "AC E", "ul in", "d ict", "ok u", "Ġassoci ate", "ock ets", "y outube", "St ory", "ateg ory", "Ġm ild", "ail ing", "ĠY e", "O rig", "ĠK a", "or ig", "Ġpropag anda", "Ġan onymous", "Ġstrugg led", "Ġout rage", "AT ED", "ĠBe ijing", "r ary", "Ġle ather", "Ġworld s", "Ġbroad er", "12 5", "id al", "ĠBet ter", "Ġt ear", "E xt", "Ġpropos als", "Ġit er", "ĠSqu ad", "Ġvol unt", "m i", "D id", "ĠP u", "p in", "Ġspeak ers", "Ġb orders", "Ġfig ured", "= '", "Ġsimultane ously", "aed a", "Ġcharg ing", "Ġur ged", "Ġcon j", "25 6", "ĠG ordon", "mer ce", "Ġdocument ary", "Sh are", "it ol", "ON E", "ĠG arden", "h att", "ĠThom pson", "ane ous", "ap ore", "Ġt anks", "Ġless ons", "tr ack", "Ġout standing", "Ġvolunte ers", "Ġsp ray", "Ġmanag ers", "l arge", "Ġcamp s", "Ġart ificial", "ĠR u", "Ġb ags", "th al", "Ġcompat ible", "ĠBl ade", "Ġf ed", "Ġarg ues", "F I", "Ġunf air", "Ġcor n", "Ġoff set", "Ġdirect ions", "Ġdisappoint ed", "ĠCon vention", "Ġview ing", "M E", "oc ity", "Ġtown s", "Ġlay ers", "Ġro lled", "Ġjump ed", "Ġatt ribute", "Ġun necess", "inc oln", "Ġsupp ose", "ĠNet her", "ch a", "Ġbur ied", "Ġsix th", "B en", "ress ing", "OU R", "Ġw ound", "Ġcy cl", "Ġmechan isms", "Ġcongress ional", "ĠE lement", "Ġagre ements", "Ġdec or", "Ġclos est", "ĠM it", "Go ogle", "} }", "Ġm ixture", "Ġflu id", "S ign", "ĠSch olar", "Ġp ist", "ask et", "ab ling", "Ġrac ing", "he ro", "ri el", "ass y", "Ġche aper", "b en", "Ġvert ical", "amac are", "ĠRead ing", "g ments", "Ġhelic op", "Ġsacr ifice", "ay a", "p aren", "V A", "ĠL es", "ĠStud io", "Ġviol ations", "ĠAn na", "ac er", "é ¾", "ĠR at", "ĠBe ck", "ĠD ick", "ĠA CT", "Ġcomp osition", "Ġtext ure", "ĠO wn", "Ġsmart phone", "ĠN A", "Ġfor b", "im port", "Ġdef ending", "il st", "re r", "Ġo h", "ĠJere my", "Ġbank ing", "cept ions", "Ġrespect ive", "/ .", "Ġdr inks", "ĠW i", "Ġb ands", "ĠL iverpool", "Ġg rip", "ĠB uy", "Ġopen ly", "Ġreview ed", "per t", "Ġver ify", "ĠCo le", "ĠW ales", "M O", "Ġun pre", "Ġshel ter", "ĠIm perial", "Ġgu i", "ĠD ak", "Ġsuggest ions", "Ġexplicit ly", "Ġsl ave", "Ġblock chain", "Ġcompet ing", "Ġprom ising", "S ON", "Ġsoc cer", "Ġconst itution", "4 29", "Ġdist ract", "ĠU ser", "es ides", "ĠMet hod", "ĠTok yo", "Ġaccompan ied", "Cl ient", "s ur", "al og", "Ġident ification", "Ġinv asion", "as ma", "Ġindust ries", "pp ers", "Ġsub tle", "ĠUn it", "n atural", "Ġsurv ived", "Ġfl aw", "ĺ ħ", "ĠH oll", "Ġdef icit", "Ġtut orial", "ĠCh ance", "Ġarg uing", "Ġcontem porary", "Ġinteg ration", "for ward", "Ġt um", "it is", "Ġh iding", "ĠD omin", "ĠT an", "ĠB uilding", "ĠV in", "Ġspokes person", "ĠNot es", "Ġemer ging", "Ġprepar ation", "Ġpro st", "Ġsuspect s", "Ġaut onom", "D escription", "Ġdeal t", "ĠP ear", "Ġstead y", "Ġdecre ased", "Ġso vere", "ĠCl in", "Ġgrad ually", "ors es", "ĠW AR", "S erv", "ãĤ ¢", "h r", "Ġd irty", "ĠB arn", "ĠB C", "Ġd il", "Ġcal endar", "Ġcompl iance", "Ġch amber", "b b", "Ġpass enger", "ate ful", "ĠT itle", "ĠSyd ney", "ĠG ot", "Ġdark ness", "Ġdef ect", "Ġpack ed", "ass ion", "Ġgod s", "Ġh arsh", "IC K", "le ans", "Ġalgorith m", "Ġoxy gen", "Ġvis its", "Ġbl ade", "Ġkil omet", "ĠKent ucky", "Ġkill er", "P ack", "enn y", "Ġdiv ine", "Ġnom ination", "be ing", "Ġeng ines", "Ġc ats", "Ġbuff er", "ĠPh ill", "Ġtra ff", "AG E", "Ġtong ue", "Ġrad iation", "ere r", "m em", "ĠExpl icit", "é¾ į", "Ġcou ples", "Ġphys ics", "ĠMc K", "Ġpolit ically", "aw ks", "ĠBl oom", "Ġwor ship", "e ger", "ut er", "ĠF O", "Ġmat hemat", "Ġsent enced", "Ġdis k", "ĠM arg", "Ġ/ *", "P I", "Ġoption al", "Ġbab ies", "Ġse eds", "ĠScott ish", "Ġth y", "] ]", "ĠHit ler", "P H", "ng th", "Ġrec overed", "ing e", "Ġpow der", "Ġl ips", "Ġdesign er", "Ġdis orders", "Ġcour age", "Ġch aos", "\" },{\"", "Ġcar rier", "b ably", "H igh", "ĠR T", "es ity", "l en", "Ġrout es", "u ating", "F il", "N OT", "w all", "s burgh", "Ġeng aging", "ĠJava Script", "ore r", "li hood", "Ġun ions", "ĠF ederation", "ĠTes la", "Ġcomple tion", "ĠT a", "Ġprivile ge", "ĠOr ange", "Ġne ur", "paren cy", "Ġb ones", "Ġtit led", "Ġprosecut ors", "ĠM E", "Ġengine er", "ĠUn iverse", "ĠH ig", "n ie", "o ard", "Ġheart s", "ĠG re", "uss ion", "Ġmin istry", "Ġpen et", "ĠN ut", "ĠO w", "ĠX P", "in stein", "Ġbul k", "S ystem", "ic ism", "ĠMarket able", "Ġpre val", "Ġpost er", "Ġatt ending", "ur able", "Ġlicens ed", "ĠG h", "et ry", "ĠTrad able", "Ġbl ast", "à ¤", "ĠTit an", "ell ed", "d ie", "H ave", "ĠFl ame", "Ġprof ound", "Ġparticip ating", "Ġan ime", "ĠE ss", "Ġspec ify", "Ġregard ed", "ĠSpe ll", "Ġs ons", "own ed", "Ġm erc", "Ġexper imental", "land o", "h s", "ĠDun geon", "in os", "Ġcomp ly", "ĠSystem s", "ar th", "Ġse ized", "l ocal", "ĠGirl s", "ud o", "on ed", "ĠF le", "Ġconstruct ed", "Ġhost ed", "Ġsc ared", "act ic", "ĠIs lands", "ĠM ORE", "Ġbl ess", "Ġblock ing", "Ġch ips", "Ġev ac", "P s", "Ġcorpor ation", "Ġo x", "Ġlight ing", "Ġneighb ors", "ĠU b", "ar o", "Ġbe ef", "ĠU ber", "F acebook", "ar med", "it ate", "ĠR ating", "ĠQu ick", "Ġoccup ied", "Ġaim s", "ĠAdd itionally", "ĠInt erest", "Ġdram atically", "Ġhe al", "Ġpain ting", "Ġengine ers", "M M", "ĠM ust", "Ġquant ity", "P aul", "Ġearn ings", "ĠPost s", "st ra", "ãĥ¼ ãĥ", "Ġst ance", "Ġdro pping", "sc ript", "Ġd ressed", "M ake", "Ġjust ify", "ĠL td", "Ġprompt ed", "Ġscr ut", "Ġspeed s", "ĠGi ants", "om er", "ĠEd itor", "Ġdescrib ing", "ĠL ie", "ment ed", "Ġnow here", "oc aly", "Ġinst ruction", "fort able", "Ġent ities", "Ġc m", "ĠN atural", "Ġinqu iry", "Ġpress ed", "iz ont", "for ced", "Ġra ises", "ĠNet flix", "ĠS ide", "Ġout er", "Ġamong st", "im s", "ows ki", "Ġclim b", "ne ver", "Ġcomb ine", "d ing", "Ġcomp r", "Ġsignific ance", "Ġremem bered", "ĠNev ada", "ĠT el", "ĠSc ar", "ĠWar riors", "ĠJ ane", "Ġcou p", "b as", "Ġtermin al", ", -", "O H", "Ġt ension", "Ġw ings", "ĠMy ster", "�� ��", "ĠUn like", "val id", "viron ments", "ĠAl i", "Ġn aked", "book s", "ĠM un", "ĠG ulf", "Ġd ensity", "Ġdim in", "Ġdesper ate", "Ġpres idency", "Ġ198 6", "h y", "IN D", "Ġun lock", "im ens", "Ġhand led", "ĠE b", "Ġdisapp eared", "Ġgen re", "Ġ198 8", "Ġdetermin ation", "St ream", "ik o", "ap ters", "Ġacknow ledge", "J an", "Ġcapital ism", "P at", "Ġ20 20", "Ġpain ful", "Ġcur ve", "Ġbom bs", "st orm", "ĠMet al", "en cer", "ĠF ig", "ĠA aron", "anc hes", "Ġins piration", "Ġexha ust", "t ains", "ash i", "Ġdesc ript", "Ġr itual", "ĠChel sea", "Ġpromot ion", "ĠH ung", "ĠW ard", "iv a", "ĠE T", "Ġto ss", "all ow", "ĠFranc is", "D ep", "Ġhapp iness", "ĠGl ass", "Ġbet a", "Ġstreng then", "N E", "o a", "Ġbutt ons", "ĠMur ray", "Ġkick ed", "Qu est", "ĠT alk", "ĠS everal", "ĠZ ero", "Ġdr one", "ul k", "Ġc am", "ĠM obile", "Ġprevent ing", "Ġret ro", "ĠA x", "Ġcru el", "Ġflo at", ". ),", "Ġfil ing", "ĠGr ant", "ĠB or", "Ġr ib", "Ġchampions hip", "ĠM erc", "Ġsty les", "Ġc ake", "Ġbuild s", "ĠS elf", "io x", "Ġep ic", "oy d", "B el", "ĠSt ew", ". (", "ah u", "ĠBe yond", "Ġout s", "Ġsol o", "ĠT ree", "Ġpres erve", "Ġt ub", "AR E", "ro c", "ĠIm pro", "ĠW right", "Ġbu nd", "Ġtr aged", "Ġoccas ional", "b ian", "Sec ond", "r ons", "Ġinter actions", "form ed", "s ing", "Ġown s", "Ġh ockey", "Gener al", "Ġlog ical", "Ġexp end", "Ġesc al", "ĠGr iff", "ĠC rown", "ĠRes erve", "Ġsto pping", "Ġexc use", "sec ond", "Ġoper ated", "Ġre aches", "ĠMal ays", "Ġpoll ution", "ĠBrook lyn", "Ġde lete", "Ġhas h", "Bl ock", "ah a", "âĢ ³", "Ġsh orter", "p iece", "> >>", "ĠM ormon", "t or", "Ġpartic les", "ĠB art", "ry ption", "Ġad min", "Ġsqu ee", "VID IA", "Ġcreat or", "iam eter", "ic ular", "N BC", "Ġgrab bed", "Ġn odd", "Ġr ated", "Ġrot ation", "Ġgr asp", "Ġexcess ive", "ĠE C", "ĠWh it", "Ġinvent ory", "ault s", "ĠF B", "Ġe cosystem", "Ġbill ions", "Ġvent ure", "n amed", "Ġdef ender", "out e", "Inst ead", "ir able", "W ar", "Ġassum ption", "Ġb ite", "Ġearth qu", "t ail", "sp ace", "Ġgif ts", "boy s", "Ġinev itable", "Ġstruct ural", "Ġbenef icial", "Ġcompe lling", "h ole", "erv ation", "Ġco at", "o j", "inc arn", "ĠY ears", "Ġdetermin ing", "Ġrhet oric", "Ġbound aries", "Ġwh ites", "A nt", "add y", ") -", "ra ham", "eter min", "Ġhar vest", "ĠCon c", "Ġlapt op", "ĠM atch", "Ġenjoy ing", "cc a", "oll ar", "Ġtri ps", "Ġadd iction", "ĠS ak", "Ġpow ered", "Ġc ous", "ĠRuss ians", "ie re", "Ġret rie", "qu ality", "Ġdiff er", "Ġking dom", "ĠL aur", "ĠCap itol", "Ġcon clusions", "ĠAl tern", "ĠN av", "Ġtrans parent", "B ER", "G roup", "ĠCom plete", "Ġinf er", "Ġint rig", "Ġins ane", "R O", "oph ob", "is en", "qu al", "Mich ael", "Ġm useum", "ĠP ope", "Ġres et", "r ative", "f ive", "Ġagg reg", "itte es", "osit ory", "Ġcar b", "ĠRec ord", "Ġdec ides", "ĠF ix", "Ġexcept ions", "ĠCommission er", "un s", "ĠEnvironment al", "Ġlegend ary", "ist ence", "Ġtun nel", "k m", "Ġins ult", "Ġt roll", "Ġsh ake", "Ġdet ention", "qu es", "ĠCh rome", "ĠF iles", "Ġsub t", "Ġprospect s", "Ġpro l", "re nder", "pro of", "Ġperform ances", "St r", "Ġh ref", "ern ame", "Ġachieve ment", "Ġf ut", "F ull", "ĠLe ban", "go ogle", "ãĥ Ī", "amp a", "May be", "Ġproject ed", "ĠE mb", "Ġcol leg", "Ġa wards", "Ġâ Ķ", "G old", "ĠBl ake", "ĠR aj", "if ting", "Ġp ending", "Ġinst inct", "Ġdevelop ments", "Con nect", "ĠM and", "ĠW ITH", "ĠPhilipp ines", "prof ile", "Ġalt ogether", "ĠB und", "ĠT D", "oo oo", "amp ed", "ip h", "Ġste am", "Ġold est", "Ġdet ection", "ul pt", "Ġ ç", "ĠWay ne", "200 6", "f a", "Ġcir cles", "ĠF u", "Ġdon ors", "appropri ate", "ĠDak ota", "j amin", "Ġmotiv ated", "Ġpurch ases", "ĠLouis iana", "ĠS pl", "Ġgl obe", "Ġ10 5", "z ip", "c all", "Ġdepart ments", "Ġsustain able", "10 5", "ĠO P", "if iers", "Ġprevent ed", "Ġinc omp", "ĠComm ander", "Ġdom inated", "Ġ »", "Ġinvest ed", "Ġcomplex ity", "Ġin cl", "Ġens uring", "Ġreal m", "yn c", "ĠInd ependent", "r ained", "ĠJ en", "ĠFl ight", "Ġat he", "Ġspec ulation", "ĠT E", "oc ate", "t ic", "Ġpl aint", "her ry", "Ġto y", "Ġ1 11", "Ġpl ates", "st atus", "ĠIs a", "Ġdev oted", "C op", "ĠE S", "25 5", "ur rency", "M ain", "Ġsl aves", "Ġpe pper", "Ġqu otes", "Ġce iling", "ĠF ish", "Ġtrans formation", "Ġfra ction", "Ġadvant ages", "Ġto ile", "Ġstun ning", "Ġmo ist", "bre aking", "s i", "ĠL ocation", "ĠMed ium", "Ġtext s", "Ġu gly", "Ġb io", ". âĢĶ", "ĠB ased", "Ġtr ains", "ĠW ing", "ĠAn cient", "ĠRec ords", "ĠH ope", "Spe cial", "ades h", "ob i", "[ /", "Ġtempor arily", "V er", "h u", "os er", "Ġover night", "Ġm amm", "ĠTre asury", "ĠV enezuel", "ĠMeg a", "Ġt ar", "Ġexpect s", "bl ack", "or ph", "\\\\ \\\\", "Ġaccept ance", "Ġrad ar", "s is", "Ġjun ior", "Ġfram es", "Ġobserv ation", "ac ies", "P ower", "ĠAdv anced", "M ag", "olog ically", "ĠMe chan", "Ġsent ences", "Ġanaly sts", "augh ters", "force ment", "Ġv ague", "Ġcl ause", "Ġdirect ors", "Ġeval uate", "Ġcabin et", "M att", "ĠClass ic", "A ng", "Ġcl er", "ĠB uck", "Ġresear cher", "Ġ16 0", "Ġpoor ly", "Ġexperien cing", "ĠP ed", "ĠMan hattan", "Ġfre ed", "Ġthem es", "ad vant", "Ġn in", "Ġpra ise", "10 4", "ĠLib ya", "b est", "Ġtrust ed", "Ġce ase", "Ġd ign", "D irect", "Ġbomb ing", "Ġm igration", "ĠSci ences", "Ġmunicip al", "ĠA verage", "Ġgl ory", "Ġreve aling", "Ġare na", "Ġuncertain ty", "Ġbattle field", "ia o", "G od", "Ġc inem", "ra pe", "el le", "ap ons", "Ġlist ing", "Ġwa ited", "Ġsp otted", "ke ley", "ĠAud io", "e or", "ard ing", "idd ing", "ig ma", "ĠN eg", "Ġl one", "Ġ ----", "ex e", "d eg", "Ġtrans f", "Ġwas h", "Ġsl avery", "Ġexpl oring", "ĠW W", "ats on", "Ġen cl", "l ies", "ĠC reek", "Ġwood en", "Man ager", "ĠBr and", "um my", "ĠAr thur", "Ġbureau cr", "Ġbl end", "ar ians", "F urther", "Ġsupposed ly", "Ġwind s", "Ġ19 79", "Ġgrav ity", "Ġanalys es", "ĠTra vel", "ĠV eter", "Ġd umb", "Ġaltern ate", "g al", "Ġconsum ed", "Ġeffect iveness", ".' '", "Ġpath s", "ond a", "L A", "ĠStr ong", "Ġen ables", "Ġesc aped", "Ġ\" \"", "Ġ1 12", "Ġ198 3", "Ġsm iled", "Ġtend ency", "F ire", "Ġp ars", "ĠR oc", "Ġl ake", "Ġf itness", "ĠA th", "ĠH orn", "Ġh ier", "Ġimp ose", "m other", "Ġp ension", "ic ut", "bor ne", "ic iary", ". _", "ĠS U", "Ġpol ar", "is y", "eng u", "itial ized", "AT A", "w rite", "Ġexerc ises", "ĠD iamond", "ot ypes", "Ġharm ful", "on z", "Ġprint ing", "st ory", "Ġexpert ise", "ĠG er", "Ġtraged y", "ĠF ly", "Ġd ivid", "amp ire", "st ock", "M em", "Ġre ign", "Ġun ve", "Ġam end", "ĠProp het", "Ġmut ual", "ĠF ac", "Ġrepl acing", "H ar", "ĠCirc uit", "Ġthro at", "ĠSh ot", "Ġbatter ies", "Ġto ll", "Ġaddress ing", "ĠMedic aid", "Ġp upp", "ĠN ar", "ol k", "Ġequ ity", "M R", "ĠHis pan", "ĠL arge", "m id", "D ev", "Ġexp ed", "Ġdem o", "ĠMarsh all", "erg us", "Ġf iber", "Ġdiv orce", "ĠCre ate", "Ġsl ower", "ĠPark er", "ĠStud ent", "ĠTr aining", "Ret urn", "ĠT ru", "Ġc ub", "ĠRe ached", "Ġpan ic", "Ġqu arters", "Ġre ct", "Ġtreat ing", "Ġr ats", "ĠChristian ity", "ol er", "Ġsac red", "Ġdecl are", "ul ative", "et ing", "Ġdeliver ing", "est one", "Ġt el", "ĠL arry", "Ġmet a", "ac cept", "art z", "ĠRog er", "hand ed", "Ġhead er", "Ġtra pped", "ĠCent ury", "Ġkn ocked", "ĠOx ford", "Ġsurviv ors", "b ot", "Ġdemon stration", "Ġd irt", "Ġass ists", "OM E", "ĠD raft", "ortun ate", "fol io", "pe red", "ust ers", "g t", "ĠL ock", "Ġjud icial", "ver ted", "Ġsec ured", "out ing", "ĠBook s", "Ġhost ing", "Ġlif ted", "l ength", "Ġj er", "Ġwhe els", "ĠR ange", "umbn ails", "Ġdiagn osis", "te ch", "ĠStew art", "ĠP ract", "Ġnation wide", "Ġde ar", "Ġoblig ations", "Ġgrow s", "Ġmand atory", "Ġsusp icious", "! '", "A pr", "G reat", "Ġmort gage", "Ġprosecut or", "Ġeditor ial", "ĠK r", "Ġprocess ed", "ung le", "Ġflex ibility", "Ear lier", "ĠC art", "ĠS ug", "Ġfoc uses", "Ġstart up", "Ġbre ach", "ĠT ob", "cy cle", "ãĢ Į", "ro se", "Ġb izarre", "ãĢ į", "Ġveget ables", "$ $", "Ġret reat", "osh i", "ĠSh op", "ĠG round", "ĠSt op", "ĠHawai i", "ĠA y", "Per haps", "ĠBe aut", "uff er", "enn a", "Ġproduct ivity", "F ixed", "cont rol", "Ġabs ent", "ĠCamp aign", "G reen", "Ġident ifying", "Ġreg ret", "Ġpromot ed", "ĠSe ven", "Ġer u", "ne ath", "aug hed", "ĠP in", "ĠL iving", "C ost", "om atic", "me ga", "ĠN ig", "oc y", "Ġin box", "Ġem pire", "Ġhor izont", "Ġbr anches", "Ġmet aph", "Act ive", "ed i", "ĠFil m", "ĠS omething", "Ġmod s", "inc ial", "ĠOrig inal", "G en", "Ġspir its", "Ġear ning", "H ist", "Ġr iders", "Ġsacr ific", "M T", "ĠV A", "ĠS alt", "Ġoccup ation", "ĠM i", "Ġdis g", "lic t", "Ġn it", "Ġn odes", "e em", "ĠP ier", "Ġhat red", "ps y", "ãĥ ī", "Ġthe ater", "Ġsophistic ated", "Ġdef ended", "Ġbes ides", "Ġthorough ly", "ĠMedic are", "Ġbl amed", "arent ly", "Ġcry ing", "F OR", "pri v", "Ġsing ing", "ĠI l", "Ġc ute", "o ided", "olit ical", "ĠNe uro", "å ¤", "Ġdon ation", "ĠEag les", "ĠG ive", "T om", "Ġsubstant ially", "ĠLic ense", "ĠJ a", "Ġg rey", "ĠAn imal", "ĠE R", "ĠU nd", "Ġke en", "Ġconclud e", "ĠMississ ippi", "Eng ine", "ĠStud ios", "P ress", "o vers", "ll ers", "Ġ3 50", "ĠR angers", "Ġr ou", "ert o", "E p", "iss a", "iv an", "Ġse al", "ĠReg ist", "dis play", "Ġwe aken", "u um", "ĠComm ons", "ĠS ay", "Ġcult ures", "Ġl aughed", "Ġsl ip", "Ġtreat ments", "iz able", "m art", "ĠR ice", "Ġbe ast", "Ġob esity", "ĠLa ure", "ig a", "Wh ich", "hold er", "Ġelder ly", "Ġp ays", "Ġcompl ained", "Ġc rop", "Ġpro c", "Ġexplos ive", "ĠF an", "ĠAr senal", "A uthor", "ef ul", "Ġme als", "Ġ( -", "id ays", "Ġimag ination", "Ġann ually", "Ġm s", "as ures", "H ead", "ik h", "m atic", "Ġboy friend", "ĠCom puter", "Ġb ump", "Ġsur ge", "ĠCra ig", "ĠKir k", "D el", "medi ate", "Ġscen arios", "ĠM ut", "ĠSt ream", "Ġcompet itors", "Ù Ħ", "ĠStan ford", "ĠRes ources", "az ed", "b age", "Ġorgan is", "ĠRe lease", "Ġsepar ately", "Ġha bits", "Ġmeasure ments", "ĠCl ose", "Ġaccomp any", "Ġg ly", "Ġt ang", "ĠR ou", "Ġplug in", "Ġcon vey", "ĠChall enge", "oot s", "j an", "Ġcur s", "ĠRel ations", "ke eper", "Ġapproach ing", "p ing", "Spe aking", "Ġarrang ement", "ĠV I", "are ttes", "Ġaffect ing", "Ġperm its", "b ecause", "Ġu seless", "ĠH us", "!! !!", "Ġdestro ying", "Un fortunately", "Ġfasc inating", "S em", "Ġelect oral", "Ġtrans parency", "ĠCh aos", "Ġvolunte er", "Ġstatist ical", "Ġactiv ated", "ro x", "We b", "H E", "ĠHamp shire", "is ive", "M ap", "Ġtr ash", "ĠLaw rence", "st ick", "C r", "Ġr ings", "EX T", "Ġoper ational", "op es", "D oes", "ĠEv ans", "Ġwitness ed", "P ort", "Ġlaunch ing", "ec onom", "w ear", "ĠPart icip", "um m", "cul es", "ĠR AM", "ĠT un", "Ġass ured", "Ġb inary", "Ġbet ray", "Ġexpl oration", "ĠF el", "Ġad mission", "it ated", "S y", "Ġav oided", "ĠSim ulator", "Ġcelebr ated", "ĠElect ric", "¥ ŀ", "Ġcl uster", "itzer land", "he alth", "L ine", "ĠN ash", "at on", "Ġsp are", "Ġenter prise", "ĠD IS", "clud es", "Ġfl ights", "Ġreg ards", "Ġà Ĺ", "h alf", "Ġtr ucks", "Ġcontact s", "Ġunc ons", "ĠCl imate", "Ġimm ense", "N EW", "oc c", "ect ive", "Ġemb od", "Ġpat rol", "Ġbes ide", "Ġv iable", "Ġcre ep", "Ġtrig gered", "ver ning", "Ġcompar able", "q l", "Ġg aining", "ass es", "Ġ( );", "ĠG rey", "ĠM LS", "s ized", "Ġpros per", "\" ?", "Ġpoll ing", "Ġsh ar", "ĠR C", "Ġfire arm", "or ient", "Ġf ence", "Ġvari ations", "g iving", "ĠP i", "osp el", "Ġpled ge", "Ġc ure", "Ġsp y", "Ġviol ated", "Ġr ushed", "Ġstro ke", "ĠBl og", "sel s", "ĠE c", ",' '", "Ġp ale", "ĠColl ins", "ter ror", "ĠCanad ians", "Ġt une", "Ġlabor atory", "Ġn ons", "t arian", "Ġdis ability", "ĠG am", "Ġsing er", "al g", "ĠSen ior", "Ġtrad ed", "ĠWar rior", "Ġinf ring", "ĠFrank lin", "Ġstr ain", "ĠSwed ish", "Ġsevent h", "ĠB enn", "ĠT ell", "Ġsynd rome", "Ġwond ered", "id en", "++ ++", "ig o", "Ġpur ple", "Ġjournal ism", "Ġreb el", "Ġf u", "bl og", "Ġinv ite", "ren cies", "ĠCont act", "Is rael", "ĠCont ent", "Ġche er", "Ġbed room", "ĠEngine ering", "ĠQue ens", "Ġd well", "ĠPlay Station", "ĠD im", "ĠCol on", "l r", "Ġoper ates", "Ġmotiv ation", "US A", "ast ered", "C ore", "ĠTr uth", "ol o", "OS E", "ĠMem ory", "Ġpred ec", "Ġan arch", "Ġ19 20", "ĠY am", "à ¨", "b id", "Ġgr ateful", "Ġexc itement", "Ġtre asure", "Ġlong est", "ct ive", "Ġdes erves", "Ġreserv es", "Ġcop s", "ĠOtt awa", "ĠEgypt ian", "ank ed", "Ġart if", "Ġhypot hesis", ": /", "Ġpurch asing", "Ġlove ly", "H P", "Ġdiv ide", "Ġstrict ly", "Ġquestion ing", "Ġtaxp ayers", "ĠJ oy", "Ġroll s", "ĠHe avy", "Ġp orts", "Ġmag netic", "Ġinf lamm", "Ġbr ush", "t ics", "â ĪĴ", "Ġbott les", "pp y", "Ġp add", "ãĤ ¯", "m illion", "Ġdevast ating", "Ġcomp iled", "Ġmed ication", "Ġtw elve", "ĠPer ry", "Sp ace", "im b", "y our", "Ġle aked", "ĠT ar", "Ġun ity", "Ġinfect ed", "Ġtravel ed", "ID E", "ĠMc Donald", "t xt", "ĠPr inc", "Ġinter ven", "ĠTai wan", "ĠP ow", "Ġbe aring", "ĠTh read", "Ġz ones", "iz ards", "un ks", "Ch apter", "ll or", "Ġ ·", "Ġw ounds", "Ġdisc retion", "Ġsucceed ed", "ik ing", "Ġicon ic", "C all", "Ġscreen ing", "ĠM is", "ict s", "Ġmin isters", "Ġsepar ation", "Pl ayer", "Ġb ip", "Ġbel oved", "Ġcount ing", "ĠE ye", "ar ound", "ing ing", "Ġtable t", "Ġoff ence", "in ance", "h ave", "ĠInf o", "ĠNin ja", "Ġprotect ive", "ĠC ass", "M ac", "ĠQual ity", "N orth", "Ġ ic", "ĠCub a", "ĠChron icle", "ĠPro perty", "Ġfast est", "ot os", "ĠG erm", "OW N", "Ġbo om", "ĠStan ley", "ergus on", "Ġcle ver", "Ġent ers", "m ode", "ter ior", "ĠS ens", "Ġlin ear", "AR K", "Ġcomp aring", "Ġpure ly", "Ġsaf er", "ĠPot ter", "Ġc ups", "R T", "Ġgl uc", "Ġatt ributed", "Ġdu pl", "ĠP ap", "Ġprec ious", "Ġp a", "iction ary", "ĠT ig", "ĠTo o", "ol utions", "st an", "Ġrob ots", "Ġlob b", "Ġstat ute", "Ġprevent ion", "w estern", "16 0", "ĠAct ive", "ĠMar ia", "h al", "N one", "ell ar", "ĠK B", "ĠPart ners", "ĠSing le", "ĠFollow ing", "ang o", "ac ious", "Ġth ou", "Ġk g", "Ġinflu ential", "ĠFriend s", "S ur", "ain ted", "Ġfor ums", "Ġst arter", "Ġcitizens hip", "ĠE lection", "on ge", "ot ation", "os ph", ";; ;;", "ut ical", "p ur", "ere n", "Ġaccus ations", "bit ious", "ab bit", "ĠOr d", "Post ed", "ir k", "Ġsens itivity", "ic he", "ĠAm y", "ĠF ab", "Ġsum mit", "Ġped est", "Ġrub ber", "Ġagric ultural", "Ġcan cel", "A E", "Ġin aug", "Ġcont am", "Ġfirm ly", "i w", "st age", "ĠK an", "Ġt ier", "Ġinv ention", "Ġtransl ated", "ĠR ules", "B ox", "Tw itter", "ID S", "Ġp izza", "Ġdeb ug", "ĠD rop", "v s", "Ġh orses", "b ig", "Ġb oring", "Ġh ood", "ĠMcC ain", "at ched", "ĠBro s", "Ġsk ip", "Ġess ay", "st at", "ĠLeg ends", "Ġam munition", "au c", "Ġshoot er", "Ġun h", "Ġsuppl ied", "Ġgener ic", "ĠS K", "ib an", "yr ics", "Ġ25 5", "Ġclim bing", "Form er", "Ġfl ip", "Ġjump ing", "Ġfrust ration", "ĠTer ry", "Ġneighborhood s", "Ġmed ian", "be an", "Ġbr ains", "Follow ing", "Ġsh aped", "Ġdraw s", "Ġal tered", "J ack", "Ġrecip es", "Ġsk illed", "we alth", "ach i", "e lection", "Ġbehavi ors", "de als", "ĠU ntil", "F e", "Ġdecl aration", "mar ks", "ĠBet ween", "cel ona", "Ġres on", "Ġbub ble", "Am ong", "Ġim perial", "G S", "Ġfemin ist", "200 5", "ĠK yle", "Ġaccount ing", "ĠTe le", "ĠT yr", "Ġconnect ing", "Ġre hab", "ĠP red", "s im", "Ġmeant ime", "Ġphys ician", "M W", "ĠCamp bell", "ĠBr andon", "Ġcontribut ing", "ĠR ule", "ĠWe ight", "ĠN ap", "Ġinter active", "Ġv ag", "Ġhel met", "ĠCom b", "f our", "Ġsh ipped", "Ġcomple ting", "ĠP D", "PD ATE", "Ġspread ing", "Ġsc ary", "erv ing", "ĠG as", "Ġfr ank", "s chool", "Ġrom antic", "Ġstab il", "R ob", "Ġaccur ately", "Ġac ute", "ĠH ann", "Ġsymbol s", "Ġcivil ization", "ĠA W", "Ġlight ning", "Ġcons iders", "Ġven ue", "Ġ ×", "Ġo ven", "ĠS F", "h is", "Ġn u", "ĠLear n", "Ġpe oples", "Ġst d", "Ġsle e", "Ġs lic", "ĠStat istics", "Ġcor ners", "ĠB aker", "Ġ: )", "ment ation", "ol ver", "Ġlaugh ing", "ĠT odd", "ond e", "ĠH ills", "Ġn uts", "ĠW oman", "pl ane", "Ġl iver", "ĠIn side", "S orry", "Ġagre es", "Ġfund ament", "ĠF isher", "Ġa uction", "Ġthread s", "gl as", "ĠBas ic", "ĠN at", "Ġlack ing", "Ġceleb ration", "j u", "Ġs illy", "E uro", "Ġt att", "ight y", "cont rolled", "T est", "ĠSing h", "Ġr age", "Ġrh yth", "o ffic", "ĠPh antom", "Ġhead lines", "Ġrespond ing", "ĠMor ning", "Ġvit amin", "Ġboot s", "ĠS ite", "al in", "p i", "Ġvir al", "ĠU C", "D ER", "ĠSe x", "Ġst ocks", "c urrent", "Ġch urches", "ĠR are", "ĠMur phy", "Ġden ial", "ĠG aming", "Ġtou g", "Ġn ick", "Ġm akers", "ĠRon ald", "Ġgener ous", "ĠD oc", "ĠMor ris", "Ġtransform ed", "ĠN ormal", "Ġ10 4", "ĠKick starter", "ĠUp on", "On line", "ĠI RS", "Ġw rap", "Ġl oving", "Ġarri ves", "ĠD ue", "Ġhe ter", "ĠM ade", "Ġrent al", "Ġbelong s", "Ġatt orneys", "Ġcro ps", "Ġmat ched", "ul um", "ol ine", "10 9", "Ġdis par", "Ġbuy ers", "ĠCam bridge", "Ġeth ics", "rou ps", "Ġjust ified", "Ġmarg inal", "Ġrespect ed", "win ning", "Ġnodd ed", "ĠSer ge", "ĠForm er", "C raft", "######## ########", "ĠWar ner", "Ġd ash", "et e", "Ġent ert", "ĠE scape", "out heast", "Ġkn ees", "ĠB omb", "Ġr ug", "P ass", "Ġatt itudes", "go vernment", "ĠPri or", "Ġqual ities", "Ġnot ification", "ĠPh one", "l ie", "Ġanticip ated", "ĠCom bat", "ĠBar ry", "Ġ198 2", "Us ers", "on er", "Ġcomput ing", "ĠConnect icut", "Ġless er", "Ġpe ers", "ĠC u", "Ġtechn ically", "Ġsub mission", "ĠUn iversal", "Ġman ually", "our ge", "Ġrespond ents", "ĠB TC", "ĠH ost", "Ġf are", "ĠB ird", "Ġrece ipt", "al so", "Ġj ack", "Ġagric ulture", "Ġsk ull", "Ġ! =", "Ġpass ive", "ĠC I", "Ġsoc ieties", "Ġremind ed", "Ġinter ference", "B uy", "Ġâ ľ", "g on", "Ġscrut iny", "ĠW itch", "Ġconduct ing", "Ġ ãĥ", "Ġexch anges", "ĠMit chell", "Ġinhab it", "Ġtw ist", "B D", "Ġwhere ver", "group on", "Ġj okes", "ĠBen jamin", "ĠR andom", "fr ame", "ĠL ions", "Ġhighlight ed", "ĠArk ansas", "E nt", "Ġp ile", "Ġpre lim", "g s", "mind ed", "Ġfel ony", "ĠG A", "ĠL uck", "Ġpract ically", "ĠB os", "Ġact ress", "D am", "ĠB ou", "Ġvis a", "Ġembed ded", "Ġhy brid", "Ġear liest", "Ġsoon er", "s ocial", "ĠH A", "Ġste ep", "Ġdis advant", "Ġexplo it", "ĠE gg", "ĠUlt ra", "Ġnecess ity", "L ocal", "ie ge", "Ġd ated", "Ġmass es", "Ġsubsc ription", "pl ess", "Ġan onym", "Ġpresum ably", "Bl ue", "The ir", "asket ball", "ĠPhil ip", "Ġcom ed", "load ed", "r ane", "Ġref lection", "Ch ina", "Ġext ends", "Ġform ing", "Ġund ers", "200 1", "Ġgr at", "Ġconcent rations", "Ġins ulin", "Ġsec ular", "Ġwh ilst", "Ġwin ners", "Ad vertisements", "Ġdeliber ately", "ĠWork ing", "Ġs ink", "et ics", "d ale", "Ġmand ate", "Ġg ram", "Ġvac ation", "Ġwarn ings", "ri pp", "ĠTH AT", "Ġcomment ary", "Ġint u", "Ġa est", "Ġreason ing", "Ġbreak down", "ĠZ ombie", "Ġ-- >", "ĠPolit ical", "c ott", "Ġthr ust", "Ġtechn ological", "Ġdec iding", "Ġtraff icking", "L ong", "W elcome", "pr ising", "ĠCommun ications", "Ġend ors", "Ġsw ift", "Ġmetab ol", "co ins", "res a", "ĠHT TP", "Ġen roll", "ĠH appy", "us r", "int age", "Ġ[ \"", "u ably", "ĠM aterial", "Ġrepe al", "Se pt", "k h", "ĠMod i", "Ġunder neath", "ĠI L", "sh ore", "Ġdiagn osed", "ace utical", "Ġsh ower", "au x", "ĠSw itch", "ĠStre ngth", "Ġj ihad", "n ational", "Ġtra uma", "uss y", "on i", "Ġcons olid", "Ġcal ories", "ĠF lynn", "ag ged", "16 8", "ĠP ink", "Ġfulf ill", "Ġch ains", "Ġnot ably", "ĠA V", "L ife", "ĠCh uck", "m us", "ĠUr ban", "ĠH end", "Ġdep osit", "ĠS ad", "Ġaff air", "OR K", "ie val", "ĠF DA", "Ġt rop", "ĠOver all", "Ġvirt ue", "Ġsatisf action", "au nd", "Ġl un", "ĠSw itzerland", "ĠOper ation", "pro cess", "Ġsh ook", "Ġcount ies", "le ased", "ĠCharl otte", "1 12", "Ġtrans cript", "Ġre dd", "p ush", "ĠHe y", "ĠAn alysis", "[ \"", "Ġaltern atives", "ard less", "Ġele ph", "Ġpre jud", "ĠLe af", "H aving", "ĠH ub", "Ġexpress ions", "ĠVol ume", "Ġshock ing", "ĠRed s", "Ġread ily", "Ġplan ets", "ad ata", "Ġcollaps ed", "ĠMad rid", "Ġir rit", "i pper", "ĠEn c", "ĠW ire", "Ġbu zz", "ĠG P", "ash a", "Ġaccident ally", "ur u", "Ġfrust rated", "ĠS A", "Ġhung ry", "ĠH uff", "Ġlab els", "ant o", "ĠE P", "Ġbar riers", ") |", "ĠBer keley", "ĠJ ets", "Ġp airs", "ĠL an", "J ames", "ĠB ear", "Ġhum or", "ĠLiber ty", "Ġmagn itude", "Ġag ing", "ĠM ason", "Ġfriends hip", "umb ling", "Ġemer ge", "Ġnewsp apers", "Ġam bitious", "ĠRich ards", "atern al", "Ġ198 1", "Ġcook ies", "Ġsc ulpt", "Ġpur suit", "L ocation", "Ġscript s", "p c", "Ġarrang ements", "Ġd iameter", "Ġl oses", "am ation", "Ġl iqu", "ĠJ ake", "aret te", "Ġunderstand s", "ĠZ en", "v m", "Ġappro ve", "Ġw ip", "Ġult ra", "Ġint end", "ĠD I", "asc ular", "Ġst ays", "ĠK or", "ĠK l", "Ġinvest ing", "L a", "Ġbelie ving", "b ad", "m outh", "Ġtaxp ayer", "ãĥ ĥ", "ĠQue bec", "Ġl ap", "ĠSw iss", "d rop", "Ġdr ain", "ir i", "et c", "ft en", "ĠN ex", "Ġst raw", "Ġscream ing", "Ġcount ed", "Ġdam aging", "Ġamb assador", "cent ury", "Ġpro x", "Ġarrest s", "u v", "il ateral", "ĠCh arg", "Ġpresc ribed", "Ġindepend ently", "Ġf ierce", "ĠB aby", "Ġb rave", "Ġsu its", "= >", "Ġbas eline", "ĠR ate", "Ġis lands", "Ġ( (", "g reen", "ix els", "Ġname ly", "ĠVill age", "th an", "am y", "V ersion", "g mail", "ential s", "ĠS ud", "ĠMel bourne", "Ġarri ving", "Ġquant um", "e ff", "rop olitan", "T ri", "Ġfun eral", "ĠI R", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "ĠC ob", "it ably", "Ġt urb", "Ġcomb o", "Re view", "Ġdeploy ment", "u ity", "ĠB ott", "Ġinv isible", "Ġrender ing", "Ġunl ocked", "Ġa qu", "ĠVlad imir", "Ġp ad", "ĠBr ain", "ĠLeg acy", "dr agon", "ĠKurd ish", "Ġsound ed", "Ġdet ained", "ĠD M", "g ary", "Ġd aughters", "Ġdistur bing", "uk a", "ĠPar ad", "Ġt ast", "Ġunf ortunate", "Ġu l", "em in", "Ġattend ance", "tr l", "Ġpar ks", "ĠMem orial", "ĠAl ice", "oth y", "gu ard", "ĠD ise", "ĠSh an", "ĠFor um", "R ich", "Ġshif ted", "ue z", "Ġl ighter", "ĠMag n", "Ġc od", "S ch", "ham mad", "P ub", "3 50", "ĠP okemon", "Ġprot otype", "Ġun re", "B ase", "ĠStud ents", "ĠRep ly", "ĠCommun ist", "Ġg au", "ĠTy ler", "I Z", "Ġparticip ated", "Ġsup rem", "ĠDet ails", "Ġvessel s", "ro d", "Ġt ribe", "ke ep", "Ġassum ptions", "Ġp ound", "Ġcr ude", "ĠAv ailable", "Ġswim ming", "Ġin clusion", "Ġadv ances", "c ulation", "Ġconserv ation", "Ġover d", "ĠBuff alo", "Art icle", "ed ge", "Ġaw a", "ĠMad ison", "Ġsid ew", "Ġcat ast", "ĠK rist", "uc le", "ĠHigh way", "ĠTer ror", "Ġactiv ation", "Ġuncons cious", "ĠSat an", "ĠSus an", "ill ery", "Ġarr anged", "i op", "Ġrum ors", "ur ring", "th ink", "ĠKe ith", "ĠK ind", "Ġavoid ing", "by n", "n ut", "ĠSpe aker", "r us", "n ames", "Ġgu ilt", "ĠOlymp ics", "Ġsa il", "ĠM es", "lev ant", "ĠColumb us", "a ft", "C ity", "S outh", "ĠHar vey", "ĠP un", "S everal", "Ġment ally", "Ġimp ress", "m ount", "ĠUb untu", "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", "ĠSuper man", "ĠMP s", "Ġintent ions", "ĠR acing", "Ġlike lihood", "Ġ2 40", "T otal", "Ġto ys", "ĠW atson", "Ġur ge", "L ear", "ĠP aper", "Ġoccur ring", "ĠB eng", "ĠC ert", "Ġst ones", "T im", "ĠTw in", "z b", "ĠD ynam", "Ġpolit ician", "k ens", "ĠEnter prise", "UT ERS", "Ġab ol", "Ġref resh", "Ġarbit rary", "pe ction", "Ġtrou bles", "Ġ} );", "t v", "Ġpil ots", "Ġdist ribute", "Ġaud it", "Ġp ause", "orig inal", "Ġr ivals", " £", "F ig", "T L", "ab il", "ry ing", "L in", "ion ed", "l on", "Ġf ancy", "Ġcr ashed", "Ġt ract", "Ġshe d", "Ġcons ume", "B ased", "down load", "in it", "Ġvolt age", "Int rodu", "Ġcondem ned", "ĠFin ance", "res pect", "Ġex cluded", "Ġestablish ing", "her ic", "Ġher itage", "Ġspect acular", "Ġun st", "ĠSnow den", "ĠL ane", "S an", "Ġprotect ions", "st ruction", "inc inn", "Ġmac ro", "C ustom", "ios ity", "Ġes p", "Ġfunction ing", "Ġm ush", "Ġp uzzle", "Ġeth ical", "M al", "Ġgo verning", "ĠF erguson", "Ġrest ored", "Ġst ressed", "ĠCoun ter", "ĠK as", "cl ip", "AN S", "Ġse iz", "U K", "by ss", "old own", "ap i", "Ġperman ently", "oun ters", "W est", "Th rough", "L ight", "at oes", "Ġne at", "Ġc ord", "ure r", "Ġsevere ly", "ĠA ven", "Ġinter rog", "Ġtri ple", "G iven", "N umber", "Ġar ise", "Ġs her", "pl ant", "Ġfl ower", "ĠC ou", "Ġat e", "Ġnew er", "b ul", "Ġmean while", "ĠL air", "Ġadjust ment", "ĠCop yright", "Ġd ivers", "i ological", "Ġgam ers", "o at", "Ġhistor ically", "Ġanal og", "Ġlong time", "Ġpres cription", "ĠM ist", "ĠHy per", "ĠM aine", "ĠDe ity", "Ġmulti pl", "ĠRe incarn", "ĠH yd", "ĠP ic", "S il", "r ants", "ĠC ris", ". ;", "( {", "epend ence", "Ġrec y", "ate ur", "Ġqu ad", "Ġgl ob", "Ġcon ced", "te am", "Ġcapital ist", "ĠL ot", "Ġroy al", "ĠCy ber", "Ġblack s", "met ic", "ri v", "ĠD anny", "Ġsp o", "ĠR O", "Ġanim ated", "rypt ed", "ĠDep uty", "Ġrend ered", "F E", "Ġstre ak", "Ġcloud s", "ĠDou g", "~~~~ ~~~~", "Ġdisc our", "ĠVe h", "Ġpsych ology", "ĠJ ourney", "Ġcry stal", "ĠFro st", "Ġsuspic ion", "Ġrel ate", "or us", "ĠC rypt", "ĠN VIDIA", "com ed", "ut ing", "incinn ati", "Ġvulner ability", "ost ic", "Ġisol ation", "Ġcool ing", "ĠCoal ition", "Ġ1 19", "F our", "ĠDe al", "Ġâ ī", "se mble", "ram ent", "ĠBar celona", "Ġ10 2", "Ġcoc aine", "ocaly pse", "F eb", "ogen ic", "Ġmut ation", "Ġcrypt oc", "ĠK el", "ĠG it", "a is", "Ġs isters", "AN K", "Ġactiv ate", "T er", "Ġd read", "yl on", "Ġprop ri", "A ust", "ĠDef ault", "Ġout door", "Ġshe er", "ce ive", "Ġg ently", "Ð ¾", "Pro gram", "Ġâ ĨĴ", "Ġve gan", "ĠCr us", "Ġrespons ibilities", "ĠH R", "OL D", "Ġprev ents", "Ġst iff", "ĠW ere", "Ġathlet ic", "ĠSc ore", "Ġ) :", "Ġcolumn s", "ĠL oc", "av ailable", "ĠF ram", "ĠS essions", "Ġcompan ion", "Ġpack s", "14 0", "ĠKn ights", "Ġf art", "Ġstream s", "Ġsh ore", "Ġapp eals", "ĠPer formance", "h aul", "ĠSt ra", "ĠN ag", "10 3", "ĠTrans portation", "B B", "E v", "z an", "P ublic", "Ġtw in", "uls ion", "M ult", "Ġelect ro", "Ġstat ue", "ation ally", "ĠN ort", "Ġins pection", "/ *", "ig ue", "Ġcomp assion", "ĠT ales", "ĠSte in", "ĠSc reen", "ĠB ug", "ĠL ion", "g irl", "Ġwithdraw al", "Ġobject ives", "Ġblood y", "Ġprelim inary", "Ġj acket", "Ġdim ensions", "ĠC ool", "ĠOcc up", "Ġw reck", "Ġdoub led", "ank ing", "Ġ19 75", "Ġglass es", "ĠW ang", "pro v", "P ath", "connect ed", "ĠMult i", "ĠNor way", "agon ist", "Ġfe ared", "Ġtouch ing", "Ġarg uably", "¯¯¯¯ ¯¯¯¯", "ĠNC AA", "che m", "Ġsp at", "ĠW WE", "ĠC el", "ig ger", "Ġattack er", "ĠJo in", "ob ject", "ett a", "Ġelim inated", "d et", "Ġdest ruct", "ĠLuc as", "ct uary", "18 0", "ĠBr ady", "ĠBl ues", "B ay", "au kee", "Ġtim eline", "Ġdeleg ates", "w ritten", "uff icient", "Ġsh apes", "Cop yright", "ou ble", "serv ice", "Ġp ione", "Ġcolleg es", "Ġrow s", "Ġsp ite", "Ġassess ed", "3 60", "Ġle ase", "Ġconfident ial", "ck er", "ĠMan ning", "ĠV oice", "Ġse aled", "Ġcalcul ate", "N O", "ĠAss istant", "Ġteen ager", "ul ent", "ather ine", "Ġm ock", "Ġd iamond", "Ġf est", "Ġsw itched", "Ġres ume", "ĠPu erto", "Ġl anes", "ir ation", "ĠSimilar ly", "Ġro d", "ĠS el", "ĠPal ace", "ĠLim ited", "e ous", "Ġvar iant", "Ġw ard", "Ġ) )", "Sh ow", "OO K", "A lex", "ĠN ep", "br is", "ĠWik ipedia", "Ġexcept ional", "Ġman ages", "ĠD raw", "Ag ain", "Ġco pper", "ut t", "Ġex ports", "Ġport folio", "Ġelev ated", "R ated", "ĠOther wise", "ĠT act", "ĠShe l", "ĠT X", "\" âĢĶ", "Ġres ur", "ĠW a", "ven ant", "Ġmon etary", "pe ople", "E mail", "Ġfif ty", "ĠS weet", "ĠMalays ia", "Ġconf using", "ĠR io", "ud a", "uten ant", "\" );", "Ġpra ised", "Ġvol umes", "t urn", "Ġm ature", "Ġnon profit", "Ġpassion ate", "ĠPriv ate", "Ġ10 3", "Ġdesc end", "ç ¥ŀ", "uff y", "head ed", "Whe ther", "ri en", "ze ch", "be it", "Ġch rom", "ĠMc M", "Ġd ancing", "Ġe leg", "ĠNot iced", "11 5", "Ġadvoc acy", "ENT S", "amb ling", "ĠMin or", "ĠF inn", "Ġprior ities", "Ġthere of", "ĠSt age", "ĠRog ers", "Ġsubst itute", "ĠJ ar", "ĠJeff erson", "Ġlight ly", "10 2", "ĠL isa", "u its", "ys ical", "Ġshif ts", "Ġd rones", "Ġwork place", "Ġres id", "ens ed", "ah n", "Ġpref erences", "ser ver", "Ġdeb ates", "d oc", "ĠGod s", "Ġhelicop ter", "Ġhon our", "Ġconsider ably", "ed ed", "ĠF emale", "ĠAn ne", "Ġre un", "ĠF ace", "ĠHall ow", "ĠBud get", "Ġcondem n", "Ġt ender", "Pro f", "ocr atic", "ĠTurn er", "ĠAg ric", "Ġ19 76", "Ġa pt", "d isc", "ĠF ighter", "ĠA ur", "Ġgar bage", "in put", "ĠK arl", "ĠOl iver", "ĠL anguage", "k n", "N on", "ĠCl ar", "Ġtrad itions", "Ġad vertisement", "ĠS or", "Ġarch ive", "Ġvill ages", "7 50", "Ġimplement ing", "w aukee", "Ġdiet ary", "Ġswitch ing", "Rep ublic", "Ġvel ocity", "Ġc it", "ĠA wards", "Ġfin ancing", "Ġlast ed", ") ]", "Ġrem inder", "P erson", "Ġprec ision", "Ġdesign ers", "ĠF ried", "ĠB order", "Ġtr agic", "Ġw ield", "Ġiniti atives", "ĠT ank", "w er", "Ġjo ins", "R o", "in ery", "Ġar row", "Ġgener ating", "found er", "Ġsear ches", "Ġrandom ly", "A ccess", "Ġb atch", "Ġp osed", "l at", "Ġpursu ing", "as a", "Ġtest ified", "form ing", "ĠSh ar", "w iki", "ĠE ither", "S ometimes", "Ġsen ators", "ĠJohn ny", "ĠTal iban", "ĠG PS", "\":\" /", "ãģ® å", "Ġanaly zed", "ĠRub io", "ĠMove ment", "op ard", "ii i", "St and", "f ight", "Ġign oring", "i ang", "ĠG N", "so ever", "ĠST AT", "Ġref using", "Ġswe at", "Ġb ay", "P ORT", "ir med", "ak y", "Ġdis pro", "Ġlabel ed", "Ġ10 8", "H ello", "Ġple asant", "ab a", "Ġtri umph", "Ġab oard", "Ġinc om", "ĠC row", "le tt", "Ġfol k", "Ġch ase", "` `", "ĠBr us", "Ġte ens", "c ue", "Ġter rain", "h yd", "il ight", "OR Y", "Su pport", "ew s", "ll i", "rain ts", "ĠC and", "Ġab used", "ach ment", "l arg", "B as", "ĠC ancer", "Ġ19 78", "Ġsupp orter", "ac cess", "ĠTer min", "ĠT ampa", "ĠAN Y", "Ġnew est", "ĠCrim inal", "ed u", "Ġ19 30", "Ġadm its", "Ġend e", "Ġfail ures", "ur ate", "ful ness", "cy cl", "ĠSub ject", "Ġinf inite", "th ree", "W A", "p it", "ĠInst all", "R ad", "ili ation", "G M", "Ġcontin ent", "Ġaccommod ate", "ĠCl ay", "Ġp up", "ĠF unction", "Ġham mer", "ĠAlbert a", "Ġrev ised", "Ġminor ities", "Ġmeasure ment", "Con nell", "Ġdis able", "ĠM ix", "In cre", "Ġfor k", "ĠR osen", "Ġimpl ies", "umb lr", "AN G", "Ġprote ins", "Ġagg ression", "Ġfacilit ate", "S N", "Ġilleg ally", "u er", "Ġacad em", "Ġp uzz", "ĠSh ift", "p ay", "oll o", "Ġaud iences", "B uild", "Ġno ble", "Ġsynt ax", "â ĺħ", "Ġbe am", "ĠB ed", "ĠA ld", "Ġorig ins", "v ideo", "Ġ19 77", "ĠAss ault", "Ġgar age", "Te am", "Ġver dict", "Ġd war", "ĠVirt ual", "e vent", "Ke ep", "Ġsent iment", "Ġwild life", "sh irt", "Ġb urg", "Ġrecommend ation", "rep resent", "Ġgall ery", "own ers", "Ġsch olar", "Ġconven ience", "ĠSw ift", "Ġconv inc", "C ap", "Ġwar fare", "ĠVis ual", "Ġconst itute", "Ġab ort", "ĠWe ather", "ĠLook ing", "ĠH em", "Ġmart ial", "Ġinc oming", "et ition", "Ġtoler ance", "ĠCre ated", "Ġfl ows", "ĠE lder", "Ġsoul s", "Ġf oul", "ĠP ain", "ĠC AN", "Ġ2 20", "b c", "he nd", "Ġgen ius", "R eal", "ĠW r", "omet er", "p ad", "Ġlim iting", "ĠS i", "ĠL ore", "ĠAd ventures", "Ġvar ied", "D isc", "f in", "ĠPerson al", "Ch ris", "Ġinv ented", "Ġd ive", "ĠR ise", "Ġo z", "ĠCom ics", "Ġexp ose", "ĠRe b", "let ters", "s ite", "im ated", "Ġh acking", "Ġeduc ated", "ĠNob ody", "Ġdep ri", "Ġincent ive", "ãĤ ·", "Ġovers ight", "Ġtrib es", "ĠBelg ium", "Ġlicens ing", "our t", "Produ ct", "ah l", "ĠG em", "Ġspecial ist", "Ġc ra", "ann ers", "ĠCor byn", "Ġ19 73", "RE AD", "Ġsum mar", "Ġover look", "ĠApp lication", "Ġin appropriate", "Ġdownload ed", "Q ue", "ĠB ears", "Ġth umb", "ĠChar acter", "ĠReincarn ated", "ĠS id", "Ġdemonstr ates", "s ky", "ĠBloom berg", "ĠAr ray", "ĠRes ults", "ĠFour th", "ĠED T", "ĠO scar", "c end", "Ġ10 6", "ĠN ULL", "ĠH ERE", "m atch", "ĠBr un", "Ġgluc ose", "ie g", "eg u", "Ġcert ified", "Ġrel ie", "Ġhuman itarian", "Ġpr ayers", "K ing", "Ġn an", "h ou", "10 8", "ul u", "Ġrenew able", "Ġdistingu ish", "Ġd ense", "ĠV ent", "ĠPack age", "ĠB oss", "Ġedit ors", "Ġm igr", "T ra", "ĠPet ers", "ĠAr ctic", "200 4", "ĠC ape", "Ġloc ally", "Ġlast ing", "Ġhand y", ". ).", "P an", "ĠR ES", "Ind ex", "Ġt ensions", "Ġformer ly", "Ġide ological", "Ġsens ors", "Ġdeal ers", "Ġdef ines", "S k", "Ġproceed s", "Ġpro xy", "az ines", "ĠB ash", "ĠP ad", "ĠC raft", "eal ous", "Ġshe ets", "omet ry", "J une", "cl ock", "T T", "ĠThe atre", "ĠB uzz", "Ġch apters", "Ġmill enn", "Ġd ough", "ĠCongress ional", "Ġimag ined", "av ior", "Ġclin ic", "Ġ19 45", "Ġhold er", "ro ot", "oles ter", "Ġrest art", "B N", "ĠHam as", "ĠJ ob", "Ġor b", "Ġr am", "Ġdiscl ose", "Ġtransl ate", "Ġimm igrant", "Ġannoy ing", "Ġtreat y", "an ium", "ĠTe a", "ĠLeg ion", "Ġcrowd s", "ĠB ec", "ĠA er", "oh yd", "B ro", "Look ing", "Ġl bs", "Ġagg ress", "Ġse am", "Ġinter cept", "ĠM I", "mer cial", "act iv", "ĠC it", "Ġdim ension", "Ġconsist ency", "Ġr ushing", "ĠDou glas", "Ġtr im", "Inst all", "ick er", "Ġsh y", "10 6", "Ġment ions", "pe lled", "ĠT ak", "c ost", "Ġclass room", "Ġfort une", "dri ven", "Ġun le", "ĠWhe el", "Ġinvest or", "ĠM asters", "k it", "Ġassoci ations", "ĠEv olution", "op ing", "us cript", "Ġprov incial", "ĠWal ter", "av i", "S O", "Ġun limited", "Eng lish", "ĠC ards", "ĠEb ola", "ne red", "Ġreven ge", "Ġout right", "um per", "Ġf itting", "ĠSol id", "Ġform ally", "Ġproblem atic", "Ġhaz ard", "Ġenc ryption", "Ġstraight forward", "ĠA K", "Ġp se", "ĠOr b", "ĠCh amber", "ĠM ak", "Cont ents", "Ġloyal ty", "Ġl yrics", "ĠSy m", "Ġwel comed", "Ġcook ed", "Ġmon op", "Ġn urse", "Ġmis leading", "Ġe ternal", "Ġshif ting", "Ġ+ =", "V is", "Ġinst itutional", "ill ary", "Ġp ant", "VER T", "ĠA CC", "ĠEn h", "Ġinc on", "ĠRE UTERS", "Ġdon ated", "âĢ¦âĢ¦ âĢ¦âĢ¦", "In tern", "Ġexhib it", "Ġt ire", "ĠR ic", "ĠCh ampion", "ĠMu hammad", "N ING", "ĠSoc cer", "Ġmob ility", "Ġvary ing", "ĠM ovie", "Ġl ord", "o ak", "F ield", "Ġve ctor", "us ions", "Ġsc rap", "Ġen abling", "m ake", "T or", ". *", "| |", "ĠWe bsite", "ĠN PC", "Ġsocial ist", "ĠBill y", "ĠAdd itional", "Ġc argo", "Ġfar ms", "ĠSo on", "ĠPri ze", "Ġmid night", "Ġ9 00", "se en", "ĠSp ot", "Ġshe ep", "Ġspons ored", "ĠH i", "ĠJ ump", "Ġ19 67", "Micro soft", "ĠAg ent", "Ġch arts", "d ir", "Ġadj acent", "Ġtr icks", "Ġman ga", "Ġex agger", "/ >", "foot ball", "ĠF CC", "G C", "ĠT ier", "and ra", "OU ND", "% ),", "Ġfru its", "V C", "ĠA A", "R ober", "Ġmid st", "â Ĺ", "ank a", "Ġlegisl ature", "ĠNe il", "Ġtour ists", "\" \"", "ĠWar ning", "ĠNever theless", "ĠOffic ial", "ĠWh atever", "Ġm old", "Ġdraft ed", "Ġsubst ances", "Ġbre ed", "Ġt ags", "ĠT ask", "Ġver b", "Ġmanufact ured", "com ments", "ĠPol ish", "Pro v", "Ġdetermin es", "Ob ama", "k ers", "Ġutter ly", "Ġse ct", "sc he", "ĠG ates", "ĠCh ap", "Ġal uminum", "Ġz ombie", "ĠT ouch", "ĠU P", "Ġsatisf y", "Ġpred omin", "asc ript", "Ġelabor ate", "Ġ19 68", "Ġmeas uring", "ĠV ari", "any ahu", "Ġs ir", "ul ates", "id ges", "ick ets", "ĠSp encer", "T M", "oub ted", "Ġpre y", "Ġinstall ing", "ĠC ab", "re ed", "re ated", "Su pp", "Ġwr ist", "ĠK erry", "10 7", "ĠK le", "ĠR achel", "Ġc otton", "ĠA RE", "ĠE le", "Cont rol", "Ġload s", "ĠD od", "an as", "b one", "Ġclass ical", "ĠReg ional", "ĠInt eg", "V M", "Ġdes ires", "Ġaut ism", "support ed", "ĠM essage", "Ġcomp act", "writ er", "Ġ10 9", "ĠHur ricane", "c ision", "Ġcy cles", "Ġdr ill", "Ġcolle ague", "Ġm aker", "G erman", "Ġmist aken", "S un", "ĠG ay", "Ġwhat soever", "Ġsell s", "ĠA irl", "l iv", "ĠO ption", "Ġsol ved", "Ġse ctors", "Ġhorizont al", "Ġequ ation", "ĠSk ill", "ĠB io", "g ement", "ĠSn ap", "ĠLeg al", "Ġtradem ark", "Ġmake up", "Ġassemb led", "Ġsa ves", "ĠHallow een", "ĠVer mont", "ĠFR OM", "Ġfar ming", "ĠP odcast", "accept able", "ĠHig her", "Ġas leep", "ull ivan", "Ġrefere n", "ĠLe v", "Ġbul lets", "ok o", "H C", "Ġst airs", "Ġmain tains", "ĠL ower", "ĠV i", "Ġmar ine", "Ġac res", "Ġcoordin ator", "ĠJ oh", "Ġcounterpart s", "ĠBrother s", "Ġind ict", "b ra", "Ġch unk", "Ġc ents", "H ome", "ĠMon th", "Ġaccording ly", "if les", "ĠGerm ans", "ĠSy n", "H ub", "Ġey eb", "âĶĢâĶĢ âĶĢâĶĢ", "Ġr anges", "ĠHoll and", "ĠRob ot", "f c", "M ike", "Ġpl asma", "Ġsw ap", "Ġath lete", "ĠR ams", ",' \"", "Ġinfect ions", "Ġcor rid", "Ġv ib", "Ġpat ches", "Ġtradition ally", "Ġrevel ation", "Ġswe ep", "Ġgl ance", "Ġin ex", "200 3", "ĠR aw", "work ing", "os ures", "ĠD at", "ĠLyn ch", "Ġle verage", "ĠRe id", "Ġcorrel ation", "ian ces", "av ascript", "Ġrep ository", "ret ty", "Ġ19 72", "24 0", "Ġo un", "p ol", "ĠRe ed", "Ġtact ical", "is ite", "App le", "ĠQu inn", "Ġrap ed", "ill o", "Euro pe", "Ġalgorith ms", "ĠRod rig", "i u", "Ġill um", "Ġf ame", "Ġintrodu cing", "Ġdel ays", "ĠRaid ers", "Ġwh istle", "Ġnovel s", "ĠRe ally", "Ġder iv", "Ġpublic ations", "ĠNe ither", "ĠCom merce", "Ġa ston", "l anguage", "Not es", "ĠR oth", "ĠF ear", "Ġm ate", "Ġpar ade", "ĠQ B", "Ġman eu", "ĠC incinnati", "m itting", "Ġwa ist", "ĠR ew", "Ġdisc ont", "Ð °", "Ġst aring", "Ġal ias", "Ġsec urities", "Ġtoile t", "ĠJ edi", "Ġun law", "v ised", "//// ////", "] (", "ĠWe iss", "Ġpre st", "ĠComp an", "Ġmem o", "ĠGr ace", "J uly", "ĠEl ite", "cent er", "ĠSt ay", "Ġgal axy", "Ġto oth", "ĠS ettings", "Ġsubject ed", "ãĤ ¦", "Ġline back", "Ġretail ers", "ĠW ant", "Ġd angers", "A ir", "Ġvolunt ary", "ew ay", "Ġinterpret ed", "ot ine", "à §", "Ġp el", "Serv ice", "ĠEvent ually", "Ġcare ers", "Ġthreat en", "Ġmem or", "ĠBrad ley", "anc ies", "s n", "ĠUn known", "N ational", "Ġsh adows", "ail and", "ĠD ash", "Every one", "izz ard", "M arch", "= (", "Ġpull s", "Ġstr anger", "Ġback wards", "ĠBern ard", "imens ional", "Ġch ron", "Ġtheoret ical", "k top", "Ġw are", "ĠInvest ig", "ĠIn iti", "ĠOper ations", "o ven", "oc ide", "* /", "Ġfl ames", "ĠC ash", "sh it", "Ġc ab", "ĠAn aly", "ĠSe ah", "Ġdefin ing", "Ġorder ing", "Ġimm un", "Ġpers istent", "AC H", "Russ ian", "m ans", "Ġh ind", "Ġphot ography", " ©", "Ġh ug", "Ġ10 7", "ĠH ence", "i ots", "ude au", "Ġsubsid ies", "Ġroutine ly", "ĠDev ice", "it ic", "Ġdisg ust", "land er", "Ġ19 40", "Ġassign ment", "ĠB esides", "w ick", "ĠD ust", "us c", "struct ed", "11 1", "de velop", "Ġf ond", "Ġinter section", "Ġdign ity", "Ġcommission er", "With out", "re ach", "Ġcart oon", "Ġsc ales", "ãĥ Ń", "F IG", "Ġsurve ys", "ĠIndones ia", "Ġart work", "Ġun ch", "Ġcy cling", "un ct", "au er", "or ate", "ĠOb viously", "Ġcharacter ized", "fe ld", "Ġaff irm", "Ġinn ings", "Ġ é", "Ġal iens", "Ġcl oth", "et ooth", "ĠC ertain", " §", "Ġdig est", "k now", "ĠX L", "Ġpredict ions", "Ġd in", "W AR", "Ġafter math", "Ex ample", "ĠSu ccess", "ĠTh r", "IG N", "Ġmin er", "B us", "Ġcl arity", "heim er", "ĠO UT", "ĠS end", "ĠCirc le", "ĠD iet", "Ġpron ounced", "Ġcreat ors", "Ġearthqu ake", "atter y", "ge ons", "Ġo d", "Ġlay ing", "or p", "U lt", "pro ject", "Ġunder min", "Ġsequ el", "S am", "ĠDark ness", "Ġre ception", "b ull", "Y S", "ĠV ir", "Ġsequ ences", "ĠCo in", "Ġout fit", "ĠW ait", "1 19", "Ġdel ivers", ".... ..", "Ġbl own", "ĠE sc", "ĠM ath", "per m", "ĠU l", "Ġgl im", "Ġfac ial", "Ġgreen house", "Ġto kens", "/ -", "ĠAnn ual", "ĠON E", "Ġteen age", "ĠPhys ical", "ĠL ang", "ĠC elt", "Ġsu ed", "ivid ually", "Ġpat ience", "ch air", "reg ular", "Ġa ug", "in v", "ex cept", "ĠL il", "Ġn est", "f d", "s um", "ĠCh ase", "Russ ia", "ĠJenn ifer", "Ġoff season", "Over all", "F ore", "Ġr iot", "A ud", "form er", "Ġdefend ers", "ĠC T", "iot ic", "rib ly", "Ġautom ated", "Ġpen is", "Ġins ist", "Ġdi agram", "ĠS QL", "ĠG arc", "Ġw itch", "cl ient", "ier ra", "am bers", "Ġrec ount", "f ar", "V ery", "oster one", "Ġappreci ated", "ĠPer fect", "S ection", "Ġd oses", "oca ust", "Ġcost ly", "Ġg rams", "ĠSh i", "Ġwrest ling", "Ġ19 71", "Ġtro phy", "Ġn erve", "ĠK az", "ĠExper ience", "Ġpled ged", "Ġplay back", "Ġcreat ivity", "by e", "Ġattack ers", "Ġhold ers", "ĠCo ach", "ĠPh D", "Ġtransf ers", "Ġcol ored", "ĠH indu", "Ġd rown", "Ġlist ened", "ĠW A", "ias m", "P O", "Ġappeal ing", "Ġdiscl osed", "ĠCh icken", "ag ging", "Ġple aded", "Ġnav igation", "ĠReturn s", "Ġ[ [", "R OR", "E A", "Ġphotograp her", "ĠR ider", "ipp ers", "Ġsl ice", "Ġe rect", "Ġhe d", "iss ance", "ĠVik ings", "ur ious", "Ġapp et", "oubted ly", "Ch ild", "Ġauthent ic", "o os", "ĠM aking", "Ġannoun cing", "Ġb od", "Ġmet er", "ĠN ine", "ĠR ogue", "Ġwork force", "Ġrenew ed", "Ġorganis ations", "ac s", "P LE", "Sh ort", "Ġcomp ounds", "ĠVis it", "Ġen velop", "ear th", "Ġsupport ive", "gg le", "ĠBrus sels", "ĠGu ild", "Cre ate", "RE L", "Ġaver aged", "Ġ19 69", "ri ages", "Ġlength y", "Ġforg ot", "O kay", "ĠE rd", "Ġdeal er", "Ġrec ession", "D D", "Ġdesper ately", "Ġhun ger", "Ġst icks", "Ġm ph", "ĠF aith", "Ġintention ally", "Ġdem ol", "ue ller", "ĠS ale", "Ġde bris", "s pring", "Ġle ap", ">> >>", "Ġcontain ers", "se lling", "rane an", "atter ing", "Ġcomment ed", "ĠC M", "on ut", "Ġwood s", "es pecially", "Ġorgan ize", "iv ic", "ĠWood s", "ang a", "s qu", "Ġm aj", "am on", "Ġax is", "Ġ19 74", "ĠDen mark", "Ġwar rior", "ĠP and", "Ġout lined", "ĠB O", "ins ula", "z illa", "eb ook", "Ġd are", "Ġsear ched", "Ġnav igate", "S n", "writ ing", "Ġun ited", "J apan", "ĠHe brew", "Ġfl ame", "Ġrel ies", "Ġcatch ing", "ĠSh o", "Ġimprison ment", "Ġp ockets", "Ġclos ure", "ĠF am", "t im", "ade qu", "Act ivity", "Ġrecru iting", "ĠW ATCH", "ĠArgent ina", "d est", "Ġapolog ize", "or o", "Ġlack s", "Ġtun ed", "ĠGriff in", "Ġinf amous", "Ġcelebr ity", "ss on", "Ġ ----------------------------------------------------------------", "ĠIs is", "ĠDis play", "Ġcred ibility", "Ġeconom ies", "Ġhead line", "ĠCow boys", "Ġind ef", "Ġl ately", "Ġincent ives", "but ton", "ĠM ob", "A ut", "Ġres igned", "ĠO m", "c amp", "Ġprof iles", "Ġsche mes", "olph ins", "ay ed", "Cl inton", "en h", "ĠY ahoo", "Ġab st", "Ġan k", "su its", "Ġw ished", "ĠMar co", "udd en", "Ġsp here", "ĠB ishop", "Ġincorpor ated", "ĠPl ant", "11 4", "Ġh ated", "p ic", "Ġdon ate", "Ġl ined", "Ġbe ans", "Ġsteal ing", "Ġcost ume", "Ġsher iff", "Ġfor ty", "Ġint act", "Ġadapt ed", "Ġtrave lling", "b art", "Ġnice ly", "Ġdri ed", "Ġsc al", "os ity", "NOT E", "ĠB h", "ĠBron cos", "ĠI gn", "Ġint imate", "Ġchem istry", "Ġopt imal", "D eb", "ĠGener ation", "Ġ] ,", "ich i", "ĠW ii", "ĠYOU R", "vent ions", "W rite", "Ġpop ul", "un ning", "ĠW or", "V ol", "Ġqu een", "head s", "K K", "Ġanaly ze", "op ic", "ear chers", "Ġd ot", "leg raph", "ast ically", "Ġupgr ades", "Ġca res", "Ġext ending", "Ġfree ze", "Ġin ability", "Ġorg ans", "Ġpret end", "Ġout let", "11 3", "ol an", "ĠM all", "ul ing", "t alk", "Ġexpress ing", "ĠAl ways", "ĠBe gin", "f iles", "Ġlic enses", "% %", "ĠM itt", "Ġfil ters", "ĠMil waukee", "G N", "Ġunf old", "M o", "Ġnut rition", "pp o", "B o", "Ġfound ing", "Ġunder mine", "Ġeas iest", "ĠC zech", "ĠM ack", "Ġsexual ity", "ĠN ixon", "W in", "ĠAr n", "ĠK in", "ãĤ £", "ic er", "Ġfort un", "Ġsurf aces", "agh d", "Ġcar riers", "ĠP ART", "ĠT ib", "Ġinter val", "Ġfrust rating", "ĠSh ip", "ĠAr med", "ff e", "Ġbo ats", "ĠAb raham", "in is", "Ġsu ited", "th read", "i ov", "ab ul", "ĠVenezuel a", "Ġto m", "su per", "Ġcast le", "alth ough", "iox ide", "ec hes", "Ġevolution ary", "Ġnegoti ate", "Ġconfront ed", "Rem ember", "Ġ17 0", "S uch", "Ġ9 11", "m ult", "ĠA byss", "ur ry", "ke es", "spe c", "ĠBarb ara", "Ġbelong ing", "Ġvill ain", "ist ani", "Ġaccount able", "Ġport ions", "ĠDe cl", "U r", "ĠK ate", "g re", "Ġmag azines", "UC K", "Ġregul ate", "om on", "ĠAl most", "Ġover view", "Ġsc ram", "Ġl oot", "ĠF itz", "Ġcharacter istic", "ĠSn ake", "s ay", "ĠR ico", "Ġtra it", "ĠJo ined", "au cus", "Ġadapt ation", "ĠAirl ines", "Ġarch ae", "ĠI de", "Ġb ikes", "Ġliter ary", "Ġinflu ences", "ĠUs ed", "C reat", "Ġple a", "ĠDef ence", "ĠAss ass", "Ġp ond", "UL T", ") \"", "Ġeval uated", "Ġob taining", "Ġdem ographic", "Ġvig il", "ale y", "Ġsp ouse", "ĠSeah awks", "resp ons", "ĠB elt", "um atic", "Ġr ises", "run ner", "ĠMichel le", "Ġpot ent", "r ace", "ĠP AC", "F ind", "olester ol", "IS S", "ĠIntrodu ced", "ress es", "ign ment", "O s", "ĠT u", "ĠDe x", "ic ides", "Ġspark ed", "ĠLaur a", "ĠBry ant", "Ġsm iling", "ĠNex us", "Ġdefend ants", "ĠCat al", "Ġdis hes", "sh aped", "Ġpro long", "m t", "( $", "ãĢ Ĥ", "Ġcalcul ations", "ĠS ame", "Ġp iv", "H H", "Ġcance lled", "Ġgr in", "Ġterrit ories", "ist ically", "C ome", "ĠP arent", "Pro ject", "Ġneg lig", "ĠPriv acy", "Ġam mo", "LE CT", "olute ly", "ĠEp ic", "Ġmis under", "w al", "Apr il", "m os", "path y", "ĠC arson", "Ġalbum s", "ĠE asy", "Ġpist ol", "< <", "Ġ\\ (", "t arget", "hel p", "Ġinter pre", "cons cious", "ĠH ousing", "ĠJ oint", "12 7", "Ġbe ers", "s cience", "ĠFire fox", "effect ive", "ĠC abin", "ĠO kay", "ĠApp lic", "Ġspace craft", "ĠS R", "ve t", "ĠStr ange", "S B", "Ġcor ps", "iber al", "e fficient", "Ġpreval ence", "Ġeconom ists", "11 8", "Th read", "ord able", "OD E", "ĠC ant", "=- =-", "if iable", "ĠA round", "Ġpo le", "Ġwilling ness", "CL A", "ĠK id", "Ġcomple ment", "Ġsc attered", "Ġin mates", "Ġble eding", "e very", "Ġque ue", "ĠTr ain", "Ġh ij", "Ġme lee", "ple ted", "Ġdig it", "Ġg em", "offic ial", "Ġlif ting", "Ð µ", "Re qu", "it utes", "Ġpack aging", "ĠWork ers", "h ran", "ĠLeban on", "ol esc", "Ġpun ished", "ĠJ uan", "Ġj am", "ĠD ocument", "Ġm apping", "ic ates", "Ġinev itably", "Ġvan illa", "ĠT on", "Ġwat ches", "Ġle agues", "Ġiniti ated", "deg ree", "port ion", "Ġrec alls", "Ġru in", "Ġm elt", "I AN", "Ġhe m", "Ex p", "Ġb aking", "ĠCol omb", "at ible", "Ġrad ius", "pl ug", "ĠI F", "et ically", "Ġf ict", "H ER", "ĠT ap", "atin um", "Ġin k", "Ġco h", "ĠW izard", "b oth", "te x", "Ġsp ends", "ĠCurrent ly", "ĠP it", "Ġneur ons", "ig nt", "Ġr all", "Ġbus es", "b uilding", "Ġadjust ments", "Ġc ried", "ibl ical", "att ed", "ĠZ ion", "ĠM atter", "Ġmed itation", "ĠD ennis", "Ġour s", "ĠT ab", "Ġrank ings", "ort al", "Ġad vers", "Ġsur render", "ĠG ob", "ci um", "om as", "im eter", "Ġmulti player", "Ġhero in", "Ġoptim istic", "Ġindic ator", "ĠBr ig", "Ġgro cery", "Ġapplic ant", "ĠRock et", "v id", "Ex ception", "p ent", "Ġorgan izing", "Ġenc ounters", "ĠT OD", "Ġjew el", "S ave", "ĠChrist ie", "Ġhe ating", "Ġl azy", "ĠC P", "Ġcous in", "Con fig", "Ġreg ener", "Ġne arest", "Ġachie ving", "EN S", "th row", "ĠRich mond", "ant le", "200 2", "Ġan ten", "b ird", "13 3", "Ġn arc", "r aint", "un ny", "ĠHispan ic", "ourn aments", "Ġprop he", "ĠTh ailand", "ĠT i", "Ġinject ion", "Ġinher it", "rav is", "Ġmed i", "Ġwho ever", "ĠDE BUG", "G P", "ĠH ud", "C ard", "p rom", "Ġp or", "Ġover head", "L aw", "Ġviol ate", "Ġhe ated", "Ġdescript ions", "Ġachieve ments", "ĠBe er", "ĠQu ant", "W as", "Ġe ighth", "ĠI v", "Ġspecial ized", "U PDATE", "ĠD elta", "P op", "J ul", "ĠAs k", "oph y", "Ġnews letters", "ĠT ool", "Ġg ard", "ĠConf eder", "ĠGM T", "ĠAb bott", "Ġimm unity", "ĠV M", "Is lam", "Ġimpl icit", "w d", "Ġ19 44", "rav ity", "omet ric", "Ġsurv iving", "ur ai", "ĠPr ison", "Ġr ust", "ĠSk etch", "Ġbe es", "ĠThe ory", "Ġmer it", "T ex", "ch at", "Ġm im", "Ġpast e", "ĠK och", "Ġignor ance", "ĠSh oot", "Ġbas ement", "Un ited", "ĠAd vis", "he ight", "Ġf oster", "Ġdet ain", "in formation", "Ġne ural", "' ;", "Ġprov es", "all ery", "Ġinv itation", "um bers", "Ġc attle", "Ġbicy cle", "z i", "Ġconsult ant", "Ġap ology", "ĠT iger", "Ġ12 3", "99 9", "Ġind ividually", "r t", "ig ion", "ĠBrazil ian", "Ġdist urb", "Ġentreprene urs", "Ġfore sts", "cer pt", "pl ates", "p her", "clip se", "Ġtw itter", "Ġac ids", "ograph ical", "h um", "ĠB ald", "if ully", "Ġcomp iler", "ĠD A", "Ġdon or", "as i", "Ġtrib al", "l ash", "ĠCon fig", "Ġapplic ants", "Ġsal aries", "13 5", "Put in", "ĠF ocus", "ir s", "Ġmisc onduct", "ĠH az", "Ġeat en", "M obile", "Mus lim", "ĠMar cus", "v iol", "Ġfavor able", "Ġst ub", "ad in", "ĠH ob", "Ġfaith ful", "Ġelectron ics", "Ġvac uum", "w ait", "back ed", "econom ic", "d ist", "Ġten ure", "Ġsince re", "ĠT ogether", "ĠW ave", "Ġprog ression", "Ġden ying", "Ġdist ress", "br aska", "th ird", "Ġmix ing", "Ġcolon ial", "Ġpriv ately", "Ġun rest", "atern ity", "Ġprem ises", "ant i", "greg ation", "Ġlic ence", "ĠH ind", "ĠSam uel", "Ġconvinc ing", "ĠA ce", "ĠR ust", "ĠNet anyahu", "Ġhand les", "ĠP atch", "orient ed", "ah o", "ĠG onz", "Ġhack ers", "claim er", "Ġcustom s", "ĠGr an", "f ighters", "Ġl uc", "Ġman uscript", "aren thood", "Ġdev il", "Ġwar riors", "Ġoff enders", "Will iam", "Ġhol idays", "Ġnight mare", "Ġle ver", "iff erent", "St at", "Ġexhib ition", "put ed", "ĠP ure", "Ġal pha", "Ġenthus iasm", "ĠRepresent atives", "E AR", "ĠT yp", "Ġwhe at", "ĠAl f", "Ġcor rection", "Ġev angel", "AT T", "M iss", "Ġs oup", "Ġimpl ied", "par am", "Ġsex y", "ĠL ux", "Ġrep ublic", "p atch", "ab lish", "Ġic ons", "Ġfather s", "ĠG ET", "ĠCar ib", "Ġregul ated", "ĠCo hen", "ĠBob by", "Ġn er", "Ġb ent", "vent ory", "ĠAl ong", "ĠE ST", "ĠWall ace", "Ġmurd ers", "r ise", "ke ll", "ĠCommon wealth", "Ġn asty", "et a", "ĠM IT", "Ġadminist ered", "Ġgenuine ly", "Ed itor", "n ick", "Ġhyd ro", "**************** ****************", "ĠB le", "Ġfin es", "Ġg orge", "aus ible", "r h", "Ġapp le", "ment ioned", "Ġro pe", "ot yp", "H R", "Ġdisappoint ing", "Ġc age", "n ik", "Ġdoub ts", "ĠF REE", "print s", "ĠM UST", "Ġvend ors", "ĠIn qu", "Ġliber als", "Ġcontract or", "Ġup side", "child ren", "Ġtrick y", "Ġregul ators", "charg ed", "l iter", "Ġ ***", "Ġreb ell", "l ang", "Ġloc als", "Ġphys icians", "Ġhe y", "ar se", "t m", "ĠLe x", "Ġbehavior al", "success ful", "F X", "Ġbr ick", "ov ic", "Ġcon form", "Ġreview ing", "Ġins ights", "Ġbi ology", "ĠRem ove", "ĠExt ra", "Ġcomm itting", "indu ced", "ignt y", "ig m", "Ġat omic", "Comm on", "ĠE M", "ĠP ere", "ĠIt ems", "e h", "Ġpres erved", "ĠH ood", "Ġprison er", "Ġbankrupt cy", "Ġg ren", "us hes", "Ġexplo itation", "Ġsign atures", "Ġfin an", "] ,\"", "ĠM R", "Ġme g", "rem lin", "Ġmusic ians", "Ġselect ing", "Ġexam ining", "IN K", "l ated", "H i", "Ġart ic", "Ġp ets", "Ġimp air", "ĠM AN", "Ġtable ts", "in clude", "R ange", "Ġca ut", "Ġlog s", "Ġmount ing", "Ġun aware", "Ġdynam ics", "ĠPalest ine", "ĠQu arter", "ĠPur ple", "Ġm a", "ĠIm port", "Ġcollect ions", "ci ation", "Ġsuccess or", "Ġcl one", "Ġaim ing", "Ġposs essed", "Ġstick ing", "Ġsh aking", "Ġloc ate", "ĠH ockey", "T urn", "17 0", "Ġfif teen", "ĠHar rison", "Ġcontinu ously", "ĠT C", "ĠVal ent", "ĠRes cue", "Ġby pass", "am ount", "Ġm ast", "Ġprotect s", "Ġart istic", "Ġsomet ime", "Ġsh oe", "Ġshout ed", "ific ant", "et itive", "ĠReg ister", "ĠJ in", "Ġconcent rated", "ling ton", "on ies", "Ġgener ator", "yr im", "ĠAr men", "Ġclear ing", "id o", "ĠT W", "al ph", "Ġlad ies", "H ard", "Ġdial og", "Ġinput s", "æ ľ", "Ġpos es", "Ġsl ots", "ĠPrem ium", "Ġle aks", "Ġboss es", "Ġ11 3", "c ourse", "A cc", "ĠNew ton", "ĠAust ria", "ĠM age", "Ġte aches", "ab ad", "Ġwe ars", "Ġc yl", "Ġcur se", "ĠS ales", "ĠW ings", "Ġp sy", "Ġg aps", "ĠIce land", "ĠP interest", "Ġland lord", "Ġdefin itions", "ĠK er", "Ġsufficient ly", "ĠP ence", "ĠArch itect", "Ġsur pass", "Ġ11 4", "Ġsuper hero", "ĠDise ase", "Ġpri ests", "ĠC ulture", "Ġdefin itive", "Ġsecret ly", "ĠD ance", "inst all", "ch ief", "ĠJess ica", "W ould", "Up dated", "Ġlock er", "ĠK ay", "Ġmem orial", "è ¦", "f at", "Ġdis gu", "Ġflav ors", "ĠBase ball", "ĠRes istance", "Ġk icks", "Ġen v", "Ġteen agers", "D ark", "ĠC AR", "Ġh alt", "ĠL G", "ĠGab riel", "Ġfe ver", "Ġs atur", "Ġm all", "Ġaffili ate", "ĠS leep", "ĠSpe cific", "ĠV el", "Ġj ar", "ĠSac red", "ĠEd wards", "ĠA CL", "Ġret ained", "ĠG iant", "Ġlim itation", "in ces", "Ġref usal", "ĠT ale", "ĠBut ler", "Ġacc idents", "ĠC SS", "Ġimport ed", "ĠCop y", "Î ±", "ER T", "z el", "Ġdiv isions", "h ots", "ĠAl b", "ĠD S", "Load er", "W ashington", "at isf", "ĠCreat ive", "\\ .", "ĠAut om", "red ict", "Ġrecept or", "ĠCarl os", "Met hod", "ok a", "Ġmal icious", "Ġste pping", ", [", "ĠD ad", "Ġatt raction", "ĠEffect s", "ĠPir ate", "ĠC er", "ĠIndust ry", "ĠR ud", "Ġchar ter", "Ġd ining", "Ġins ists", "Ġconfig ure", "Ġ( #", "ĠSim ple", "ĠSc roll", "UT C", "17 5", "ĠK on", "Ġmarket place", "Ġ ãĤ", "Ġref res", "Ġg ates", "er red", "ĠP od", "Ġbeh ave", "Fr ank", "n ode", "Ġendors ed", "he tt", "as ive", "ĠHom eland", "Ġr ides", "ĠLe ave", "er ness", "Ġflood ing", "A FP", "Ġris en", "Ġcontin ually", "Ġun anim", "ĠCont ract", "ĠP as", "Ġgu ided", "ĠCh ile", "b d", "Ġsu cc", "pt ic", "Ġcomm ittees", "ĠL uther", "ĠAny one", "Ġs ab", "12 4", "Ġp ixel", "ĠB ak", "ĠT ag", "ĠBenn ett", "En ter", "sm all", "ĠPresident ial", "Ġp ul", "Ġcontr ace", "arch ive", "Ġcoast al", "ĠK ids", "19 2", "âĢ ²", "ick y", "ING TON", "Ġw olf", "ĠSt alin", "T ur", "id get", "am as", "ĠUn less", "Ġspons or", "Ġmor ph", "ĠCho ose", "Ġrun ner", "Ġun bel", "Ġm ud", "ĠMan a", "Ġdub bed", "Ġg odd", "ure rs", "wind ow", "Ġrel ied", "Ġcelebr ating", "os c", "Ġ13 5", "Ġlobb ying", "Ġincom plete", "Ġrestrict ion", "Ġinc ap", "it us", "Ġexpect ation", "ĠAp ollo", "Ġint ens", "Ġsyn c", "G H", "Ġmanip ulation", "B Y", "Ġspe ar", "Ġbre asts", "Ġvol can", "il ia", "M aterial", "Ġform ats", "ĠB ast", "Ġparliament ary", "Ġsn ake", "Ġserv ants", "ĠTr udeau", "ĠGr im", "ĠArab ic", "ĠSC P", "ĠBoy s", "st ation", "Ġprospect ive", "ord e", "in itialized", "Ġb ored", "AB LE", "Ġaccess ed", "Ġtax i", "ĠShe ll", "aid en", "urs ed", "in ates", "ĠIns urance", "ĠPet e", "Sept ember", "6 50", "Ġad ventures", "ĠCo ver", "Ġt ribute", "Ġsk etch", "Ġem power", "Ġ Ø", "ĠGl enn", "ĠD aw", "= \\\"", "ĠPolit ics", "Ġgu ides", "Ġd ioxide", "ĠG ore", "ĠBr ight", "ĠS ierra", "Ġval ued", "c ond", "Ġpo inter", "Se lect", "Ġrisk y", "Ġabsor b", "im ages", "Ġref uses", "Ġbon uses", "__ _", "Ġh ilar", "ĠF eatures", "2 20", "ĠCollect or", "F oot", "Ġ19 64", "cul us", "Ġd awn", "Ġwork out", "ĠL O", "Ġphilosoph ical", "ĠSand y", "ĠYou th", "Ġl iable", "A f", "bl ue", "Ġovert urn", "less ness", "ĠTrib une", "ĠIn g", "Ġfact ories", "Ġcat ches", "Ġpr one", "Ġmat rix", "Ġlog in", "Ġin acc", "Ġex ert", "s ys", "Ġneed le", "ĠQ ur", "Ġnot ified", "ould er", "t x", "Ġremind s", "Ġpublisher s", "Ġn ort", "Ġg it", "Ġfl ies", "ĠEm ily", "Ġflow ing", "ĠAl ien", "ĠStr ateg", "Ġhard est", "Ġmod ification", "AP I", "ĠM Y", "Ġcr ashes", "st airs", "n umber", "Ġur ging", "ch annel", "ĠFal con", "Ġinhabit ants", "Ġterr ifying", "Ġutil ize", "Ġban ner", "Ġcig arettes", "Ġsens es", "ĠHol mes", "Ġpract ition", "ĠPhill ips", "ott o", "Ġcomp ile", "Mod el", "ĠK o", "Ġ[ ]", "Americ ans", "ĠTer ms", "Ġmed ications", "ĠAn a", "Ġfundament ally", "ĠNot ice", "Ġwe aker", "Ġ 0000", "Ġgar lic", "Ġout break", "Ġeconom ist", "ĠB irth", "Ġobst acles", "ar cer", "ĠOr thodox", "Ġplace bo", "ĠC rew", "asp berry", "ĠAng els", "Ġdis charge", "Ġdestruct ive", "11 7", "ĠR ising", "Ġd airy", "l ate", "Ġcoll ision", "ĠTig ers", "ean or", "ocument ed", "ĠIn valid", "Ġd ont", "ĠL iter", "ĠV a", "Ġhyd rogen", "Ġvari ants", "ĠBrown s", "Ġ19 65", "Ġind igenous", "Ġtrad es", "Ġremain der", "Ġswe pt", "ĠImp act", "Ġred ist", "Ġun int", "grad uate", "ãĥ ķ", "ĠW ILL", "ãģ® ç", "ĠCrit ical", "Ġf isher", "Ġv icious", "Ġrevers ed", "Y ear", "ĠS ox", "Ġshoot ings", "Ġfil ming", "Ġtouchdown s", "ai res", "m el", "Ġgrand father", "Ġaffect ion", "ing le", "Ġover ly", "Add itional", "Ġsup reme", "ĠGr ad", "Ġsport ing", "Ġmer cy", "ĠBrook s", "ount y", "Ġperform s", "Ġtight ly", "Ġdem ons", "Ġkill ings", "Ġfact ion", "ĠNov a", "aut s", "Ġund oubtedly", "ar in", "Ġunder way", "ra k", "Ġl iv", "ĠReg ion", "Ġbrief ing", "s ers", "cl oud", "ĠM ik", "us p", "Ġpred iction", "az or", "Ġport able", "ĠG and", "Ġpresent ing", "Ġ10 80", " »", "ush i", "ĠSp ark", "there um", "Ġjust ification", "ĠN y", "Ġcontract ors", "ming ham", "ĠSt yle", "å ħ", "ĠChron icles", "ĠPict ure", "Ġprov ing", "Ġw ives", "set t", "Ġmole cules", "ĠFair y", "Ġconsist ing", "Ġp ier", "al one", "in ition", "Ġn ucle", "j son", "Ġg otta", "Ġmob il", "Ġver bal", "ar ium", "Ġmon ument", "uck ed", "Ġ25 6", "T ech", "mine craft", "ĠTr ack", "Ġt ile", "Ġcompat ibility", "as is", "Ġs add", "Ġinstruct ed", "ĠM ueller", "Ġle thal", "Ġhorm one", "Ġor che", "el se", "Ġske let", "Ġentert aining", "Ġminim ize", "ag ain", "Ġunder go", "Ġconst raints", "Ġcig arette", "ĠIslam ist", "Ġtravel s", "ĠPant hers", "l ings", "C are", "Ġlaw suits", "ur as", "Ġcry st", "Ġlow ered", "Ġaer ial", "Ġcomb inations", "Ġha un", "Ġch a", "Ġv ine", "Ġquant ities", "Ġlink ing", "b ank", "Ġso y", "B ill", "ĠAngel a", "Ġrecip ient", "ĠProt est", "Ġs ocket", "Ġsolid arity", "Ġâ Ĩ", "m ill", "Ġvar ies", "ĠPak istani", "Dr agon", "Ġun e", "Ġhor izon", "³³³³ ³³³³", "Ġprov inces", "Ġfrank ly", "Ġenact ed", "not es", "[ '", "Ġ19 2", "ocr acy", "Ġendorse ment", "Ġover time", "Tr ue", "L ab", "lic ted", "ĠD NC", "Ġbe ats", "ĠJam ie", "15 2", "ĠIN T", "Cont act", "Ġaccount ed", "h ash", "ĠPack ers", "p ires", "Ġles bian", "Ġamend ments", "Ġhop eful", "ĠFin land", "Ġspot light", "Ġconfig ured", "Ġtrou bled", "Ġg aze", "ĠCal gary", "Ġrel iability", "Ġins urg", "sw er", "b uy", "ĠSk in", "Ġp ixels", "Ġhand gun", "Ġpar as", "Ġcateg or", "ĠE L", "ĠRe x", "Ind eed", "Ġkind a", "Ġconj unction", "ĠBry an", "ĠMan ufact", "y ang", "Pl us", "S QL", "ish ment", "Ġdom inate", "Ġn ail", "Ġo ath", "Ġeru pt", "ĠF ine", "it bart", "ĠCh ip", "ĠAb d", "ĠN am", "Ġbuy er", "Ġdiss ent", "Le aks", "Cont in", "Ġr ider", "ĠSome one", "Ġill usion", "c in", "ĠBoe ing", "Ġin adequ", "ov ation", "i ants", "Ġreb uild", "4 50", "ĠDest iny", "S W", "ĠT ill", "H it", "ia z", "ĠBang l", "acher s", "ĠRe form", "Ġse gments", "Ġsystem atic", "d c", "ĠConserv atives", "Ġport al", "h or", "ĠDragon bound", "Ġdrag ged", "om o", "Ġthe e", "ad vert", "ĠRep orts", "ĠE t", "Ġbarrel s", "Aug ust", "Ġcompar isons", "Ġhe x", "Ġan throp", "\" [", "bor ough", "ab i", "Ġpict ured", "play ing", "ĠAdd ress", "ĠMir ror", "Sm ith", "Ġt ires", "ĠN PR", "AA AA", "Ġclass ification", "ĠTh an", "ĠH arm", "ĠR A", "Ġreject ion", "min ation", "Ġr anged", "ĠF alls", "D I", "H ost", "ãĤ ´", "ĠEx ample", "list ed", "th irds", "Ġsaf egu", "br and", "Ġprob able", "Can ada", "IT ION", "ĠQ aeda", "Ġch ick", "Ġimport s", "h it", "l oc", "W W", "Ġble w", "Ġany time", "Ġwh oles", "ik ed", "Ġcal culation", "cre ate", "ĠO ri", "Ġupgr aded", "Ġapp ar", "ut ory", "ĠM ol", "B rit", "ĠJ ong", "IN AL", "ĠStart ing", "Ġd ice", "urt le", "Ġre lying", "cl osure", "Ġprof itable", "Ġsl aughter", "ĠMan ual", "c aster", "Ġ\" $", "Ġfe ather", "ĠSim ply", "ie ves", "Ġdeter ior", "ĠPC I", "Ġst amp", "Ġfl aws", "Ġsh ade", "ham mer", "Ġpass port", "Ġcont ing", "am el", "Ġobser vers", "Ġneg lect", "ĠR B", "ĠBrother hood", "Ġskept ical", "f amily", "us k", "Ġemotion ally", "â Ļ", "ĠBet a", "ason able", "id ity", "ĠM ul", "Ġkick ing", "ĠC arm", "oll ah", "VERT IS", "ĠAt hen", "Ġlad der", "ĠBul let", "å £", "00 01", "ĠWild life", "ĠM ask", "ĠN an", "R ev", "Ġun acceptable", "leg al", "Ġcrowd ed", "ag i", "ĠC ox", "j e", "Ġmor ality", "Ġfu els", "Ġc ables", "Ġman kind", "ĠCarib bean", "Ġanch or", "Ġby te", "ĠO ften", "ĠO z", "Ġcraft ed", "Ġhistor ian", "ĠW u", "Ġtow ers", "ĠCitiz ens", "Ġhel m", "Ġcred entials", "Ġsing ular", "ĠJes se", "Ġtack les", "Ġcont empt", "Ġa fore", "ĠSh adows", "Ġn il", "Ġur gent", "app le", "bl ood", "Ġv on", "Ġoff line", "Ġbreat he", "Ġj umps", "Ġirre levant", "ox ic", "om al", "import ant", "J im", "Ġgl oves", "arm ing", "dep th", "Ġtal ents", "ook ie", "ĠS B", "Ġpal m", "uff s", "est a", "IG H", "Ġcan on", "ĠVer izon", "ĠP le", "Ġcou pled", "vel t", "Ġfundra ising", "ĠGet ting", "ĠD LC", "Ġmathemat ical", "ĠH S", "ĠCard inals", "te lling", "Ġspons ors", "Ġ Ï", "ĠBull s", "op tion", "Ġprop ose", "Ġmem orable", "Ġembr aced", "Ġdecl ining", "He alth", "ed a", "Ġ} ;", "Ġsp am", "m ile", "Ġpit cher", "ĠE ight", "Ġcar ing", "ut ic", "ro le", "Ġair line", "ernand ez", "ĠAth let", "Ġcert ification", "ux e", "rig er", "Ġem pir", "Ġsens ation", "Ġdis m", "Ġb olt", "Ġev olve", "H ouse", "Ġconsult ation", "ĠD uty", "Ġtou ches", "ĠN athan", "Ġf aint", "h ad", "\" (", "ĠCons umer", "ĠExt reme", "Ġ12 7", "ĠHer m", "ĠSac rament", "iz oph", "Ġanx ious", "ul ously", "Ġsoc ially", "ĠU TC", "Ġsol ving", "ĠLet ter", "Hist ory", "ed uc", "Pr ice", ") );", "Ġrel oad", "am ic", "Ġp ork", "Ġdisc ourse", "Ġt ournaments", "ai ro", "ĠK ur", "ĠCost a", "Ġviol ating", "Ġinterf ere", "Ġrecre ational", "uff le", "Ġspe eches", "Ġneed ing", "Ġremem bers", "Ġcred ited", "n ia", "f ocused", "amer a", "Ġb ru", "um bs", "ĠCub an", "Ġpreced ing", "Ġnons ense", "ac ial", "Ġsmart phones", "ĠSt ories", "S ports", "ĠEmer gency", "oun cing", "ef ined", "Ġb er", "Ġconsult ing", "Ġm asters", "he astern", ".\" [", "ĠRun ning", "Ġsus cept", "ĠF eng", "Americ a", "pr ises", "st itial", "ĠWeek ly", "ĠGreat er", "mod ules", "if ter", "G raphics", "ul er", "Ġwho lly", "Ġsupp ress", "Ġconce aled", "Ġhapp ily", "Ġaccept s", "ĠEn joy", "Ġr ivers", "ĠEx cept", "2 25", "ĠN HS", "ĠMc Connell", "Ġp ussy", "fer red", "ut able", "Ġatt ain", "Ġ> =", "Ġdepos its", "roph ic", "Ġnot orious", "ĠSh aw", "il itation", "Ġepid emic", "all ic", "Ġsmall est", "ov ich", "Ġaccess ories", "per ties", "Ġsur plus", "ĠMe ch", "Ġamb ig", "ĠImm igration", "Ġch im", "ev al", "Ġpract icing", "ĠMyster y", "Ġdom ains", "ĠSil icon", "app s", "Ġkilomet ers", "e a", "ĠSm ash", "Ġwarrant y", "Ġn ost", "s il", "re v", "J on", "ĠDub lin", "Ġtast es", "Ġb out", "g reat", "er ror", "Ġsw itches", "ĠB apt", "D O", "ok i", "Ġsour ced", "pro du", "Ġattach ment", "ĠIss ue", "ĠQuest ion", "Jo in", "Ġf itted", "Ġunlaw ful", "^ ^", "ere k", "Ġauthent ication", "Ġst ole", "Ġaccount ability", "l abel", "S earch", "Ġal beit", "atic an", "fund ed", "ĠAdd ing", "ĠI Q", "Ġsub mar", "l it", "a que", "ĠLear ning", "Ġint eger", "M aster", "ĠCh rom", "Ġprem ier", "O p", "ĠLi u", "Ġbl essed", "ĠGl obe", "ĠResp onse", "Ġlegit im", "ĠMer kel", "Ġdispos al", " ´", "Ġgau ge", "pe at", "Ġindu ced", "Ġquestion able", "arth y", "ĠV it", "ĠF eed", "U ntil", "U t", "worth y", "R Y", "ĠH erald", "ĠHam mer", "Ġmed al", "ĠR ivers", "ĠH ack", "Ġclar ify", "Ġtrack ed", "Ġautonom ous", "Ġten ant", "ĠQ atar", "er ie", "Ġgr im", "ĠMon itor", "Ġresist ant", "ĠSpe c", "ĠWell s", "N AS", "14 8", "Ġmin ers", "iot ics", "Ġmiss es", "11 6", "g ian", "g it", "ĠE yes", "p res", "Ġgrad uated", "Ġang el", "Ġsyn chron", "Ġefficient ly", "Ġtrans mitted", "H arry", "Ġglob ally", "EN CE", "ĠMont ana", "r aged", "ĠPre vention", "Ġp iss", "ĠL l", "Ġshe lf", "ĠB JP", "ĠTest ament", "ĠL ate", "ik er", "ĠH app", "ĠJul ian", "h all", "Ġsp ont", "Ġshut down", "Ġincons istent", "Ġsubscrib ers", "Ġske leton", "ĠNe braska", "Ġins pire", "ĠV oid", "F eed", "Ġang les", "ĠSpr ings", "Ġbench mark", "Ġvacc ines", "izoph ren", "se xual", "uff ed", "Ġsh ine", "ĠK ath", "Ġgest ure", "ine a", "Ġr ip", "Ġopp ression", "Ġcons cience", "b t", "ĠL um", "Ġinc idence", "ĠF a", "w r", "Ġmin eral", "ĠSp urs", "alk y", "Ġth under", "Ġop io", "Be ing", "ĠPal m", "Ġwas ted", "Ġl b", "i aries", "ĠIniti ative", "Ġcur ric", "Ġmark er", "ĠMc L", "Ġext ensions", "ĠP v", "ĠAr ms", "Ġoffer ings", "Ġdef enses", "Ġvend or", "Ġcontrad ict", "ĠCol in", "Ġredd it", "Ġper ipher", "12 2", "Ġs ins", "E dit", "IC T", "So ft", "ĠSh ah", "Ġadministr ator", "ĠT rip", "Ġporn ography", "Ġtu ition", "in ence", "ĠPro gress", "Ġcat alog", "Ġsu ite", "Ġh ike", "Ġreprodu ctive", "eng ine", "Ġd rought", "ĠNo ah", "Ġ2 30", "Ġd ude", "Ġrelax ed", "Ġpart ition", "Ġparticip ant", "Ġtel esc", "Ġfe as", "ĠF F", "own er", "Ġswe eping", "Ġl enses", "Ġmatch up", "ĠRe pl", "ourn als", "Ġcred ible", "Ġgrand mother", "Ġther mal", "Ġsubscrib ing", "Ġident ities", "col m", "U CT", "Ġreluct ant", "us ers", "ĠC ort", "Ġassist ed", "OS S", "ATION S", "IS H", "Ġpharm aceutical", "ic able", "ad ian", "ĠSon ic", "ĠF ury", "ĠM ong", "A H", "ĠPsych ology", "Ġph osph", "Ġtreat s", "Ń Ķ", "Ġstead ily", "ĠHell o", "Ġrel ates", "Ġcl ue", "Ex pl", "a uth", "Ġrev ision", "Ġe ld", "os ion", "Ġbr on", "14 4", "ri kes", "Ġmin es", "Ġblank et", "ĠF ail", "el ed", "ĠIm agine", "ĠPl anned", "a ic", "Re quest", "M ad", "ĠHor se", "ĠEag le", "Ġcap ac", "15 7", "Ġl ing", "ĠN ice", "ĠP arenthood", "min ster", "og s", "ens itive", "Not hing", "Ġcar n", "F in", "ĠP E", "Ġr ifles", "ĠL P", "S and", "Ġgui Active", "Ġtour ist", "C NN", "Ġunve iled", "Ġpredec essor", "} {", "u ber", "Ġoff shore", "Ġopt ical", "ĠR ot", "ĠPear l", "et on", "Ġst ared", "Ġfart her", "at ility", "cont in", "ĠG y", "ĠF oster", "ĠC oc", "ri ents", "Ġdesign ing", "ĠEconom y", "ON G", "W omen", "ĠN ancy", "er ver", "Ġmas cul", "Ġcasual ties", "Ġ2 25", "ĠS ullivan", "ĠCh oice", "Ġa ster", "w s", "Ġhot els", "Ġconsider ations", "Ġcou ch", "ĠSt rip", "ĠG n", "Ġmanip ulate", "l ied", "Ġsynt hetic", "Ġassault ed", "Ġoff enses", "ĠDra ke", "Ġim pe", "Oct ober", "ĠHer itage", "h l", "ĠBl air", "Un like", "Ġg rief", "Ġ4 50", "Ġopt ed", "Ġresign ation", "il o", "Ġver se", "ĠT omb", "Ġu pt", "Ġa ired", "ĠH ook", "ĠML B", "Ġassum es", "out ed", "ĠV ers", "Ġinfer ior", "Ġbund le", "ĠD NS", "ograp her", "Ġmult ip", "ĠSoul s", "Ġillust rated", "Ġtact ic", "Ġdress ing", "Ġdu o", "Con f", "Ġrel ent", "Ġc ant", "Ġscar ce", "Ġcand y", "ĠC F", "Ġaffili ated", "Ġspr int", "yl an", "ĠGarc ia", "Ġj unk", "Pr int", "ex ec", "C rit", "Ġport rait", "ir ies", "ĠOF F", "Ġdisp utes", "W R", "L ove", "ãģ Ħ", "ĠRe yn", "Ġh ipp", "op ath", "Ġflo ors", "ĠFe el", "Ġwor ries", "Ġsett lements", "ĠP os", "Ġmos que", "Ġfin als", "Ġcr ushed", "ĠPro bably", "ĠB ot", "ĠM ans", "ĠPer iod", "Ġsovere ignty", "Ġsell er", "Ġap ost", "Ġam ateur", "Ġd orm", "Ġconsum ing", "Ġarm our", "ĠRo ose", "Ġint ensive", "Ġelim inating", "ĠSun ni", "ĠAle ppo", "j in", "Ġadv ise", "p al", "ĠH alo", "Ġdes cent", "Ġsimpl er", "Ġbo oth", "ST R", "L ater", "ĠC ave", "== =", "Ġm ol", "Ġf ist", "Ġshot gun", "su pp", "Ġrob bery", "E ffect", "Ġobsc ure", "ĠProf essional", "Ġemb assy", "Ġmilit ant", "Ġinc arcer", "Ġgener ates", "Ġlaun ches", "Ġadministr ators", "Ġsh aft", "Ġcirc ular", "Ġfresh man", "ĠW es", "ĠJo el", "ĠD rew", "ĠDun can", "ĠApp arently", "s ight", "ĠIntern al", "ĠInd ividual", "ĠF E", "Ġb ore", "ĠM t", "Ġbroad ly", "ĠO ptions", "ount ain", "ip es", "ĠV ideos", "20 4", "Ġh ills", "Ġsim ulation", "Ġdisappoint ment", "it an", "ĠLabor atory", "Ġup ward", "Ġbound ary", "Ġdark er", "h art", "Ġdomin ance", "C ong", "ĠOr acle", "ĠL ords", "Ġscholars hip", "ĠVin cent", "ed e", "ĠR ah", "Ġencour ages", "ro v", "Ġqu o", "Ġprem ise", "ĠCris is", "ĠHol ocaust", "Ġrhyth m", "Ġmet ric", "cl ub", "Ġtransport ed", "Ġn od", "ĠP ist", "Ġancest ors", "ĠFred er", "th umbnails", "ĠC E", "ON D", "Ph il", "ven ge", "ĠProduct s", "cast le", "Ġqual ifying", "ĠK aren", "VERTIS EMENT", "Ġmight y", "Ġexplan ations", "Ġfix ing", "D i", "Ġdecl aring", "Ġanonym ity", "Ġju ven", "ĠN ord", "ĠDo om", "ĠAct ually", "O k", "ph is", "ĠDes ert", "Ġ11 6", "I K", "ĠF M", "Ġinc omes", "V EL", "ok ers", "Ġpe cul", "Ġlight weight", "g ue", "Ġacc ent", "Ġincre ment", "ĠCh an", "Ġcompl aining", "ĠB aghd", "Ġmidfield er", "Ġover haul", "Pro cess", "ĠH ollow", "ĠTit ans", "Sm all", "man uel", "ĠUn ity", "ĠEv ents", "S ty", "Ġdispro portion", "n esty", "en es", "ĠC od", "Ġdemonstr ations", "ĠCrim son", "ĠO H", "Ġen rolled", "Ġc el", "ĠBre tt", "Ġa ide", "Ġhe els", "Ġbroad band", "Ġmark ing", "Ġw izard", "ĠN J", "ĠChief s", "Ġingred ient", "Ġd ug", "ĠSh ut", "urch ase", "end or", "Ġfar mer", "ĠGold man", "12 9", "15 5", "Or der", "Ġl ion", "i ably", "Ġst ain", "ar ray", "ilit ary", "ĠFA Q", "Ġexpl oded", "ĠMcC arthy", "ĠT weet", "ĠG reens", "ek ing", "l n", "ens en", "Ġmotor cycle", "Ġpartic le", "Ġch olesterol", "B ron", "Ġst air", "Ġox id", "Ġdes irable", "ib les", "Ġthe or", "for cing", "Ġpromot ional", "ov o", "b oot", "ĠBon us", "raw ling", "Ġshort age", "ĠP sy", "Ġrecru ited", "Ġinf ants", "Ġtest osterone", "Ġded uct", "Ġdistinct ive", "Ġfirm ware", "bu ilt", "14 5", "Ġexpl ored", "Ġfact ions", "Ġv ide", "Ġtatt oo", "Ġfinan cially", "Ġfat igue", "Ġproceed ing", "const itutional", "Ġmis er", "Ġch airs", "gg ing", "ipp le", "Ġd ent", "Ġdis reg", "ç Ķ", "st ant", "ll o", "b ps", "aken ing", "Ġab normal", "ĠE RA", "å£ «", "ĠH BO", "ĠM AR", "Ġcon cess", "Ġserv ant", "Ġas pir", "l av", "ĠPan el", "am o", "Ġprec ip", "Ġrecord ings", "Ġproceed ed", "Ġcol ony", "ĠT ang", "ab lo", "Ġstri pped", "Le ft", "to o", "Ġpot atoes", "Ġfin est", "% ).", "Ġc rap", "ĠZ ach", "ab ases", "ĠG oth", "Ġbillion aire", "w olf", "Ġsan ction", "S K", "Ġlog ged", "P o", "ey ed", "un al", "Ġcr icket", "Ġarm ies", "Ġunc overed", "Cl oud", "ó n", "Ġreb ounds", "Ġm es", "O per", "P ac", "Ġnation ally", "Ġinsert ed", "p ict", "Ġgovern ance", "Ð ¸", "Ġprivile ges", "G ET", "Ġfavor ites", "im ity", "Ġlo ver", "the m", "em pl", "Ġgorge ous", "An n", "Ġsl ipped", "Ġve to", "B ob", "Ġsl im", "u cc", "ĠF ame", "udden ly", "Ġden ies", "ĠM aur", "Ġdist ances", "Ġw anna", "t ar", "ĠS ER", "Ġâ Ī", "Ġle mon", "at hetic", "Ġlit eral", "Ġdistingu ished", "Ġansw ering", "G I", "Ġrelig ions", "ĠPhil os", "ĠL ay", "Ġcomp os", "ire ments", "ĠK os", "ine z", "roll ing", "Ġyoung est", "and ise", "ĠB orn", "Ġalt ar", "am ina", "ĠB oot", "v oc", "Ġdig ging", "Ġpress ures", "Ġl en", "26 4", "Ġassass ination", "ĠBir mingham", "ĠMy th", "Ġsovere ign", "ĠArt ist", "ĠPhot ograph", "Ġdep icted", "Ġdisp ens", "orth y", "Ġamb ul", "int eg", "ĠC ele", "ĠTib et", "Ġhier archy", "Ġc u", "Ġpre season", "ĠPet erson", "Ġcol ours", "Ġworry ing", "Ġback ers", "ĠPal mer", "ĠÎ ¼", "Ġcontribut or", "Ġhear ings", "Ġur ine", "Ġ Ù", "ourge ois", "Sim ilar", "ĠZ immer", "s omething", "ĠUS C", "Ġstrength s", "ĠF I", "Ġlog ging", "As ked", "ĠTh ai", "in qu", "ĠW alt", "Ġcrew s", "it ism", "3 01", "Ġshar ply", "um ed", "Ġred irect", "r ators", "In f", "ĠWe apons", "Ġte asp", "19 99", "L ive", "ĠEs pecially", "ĠS ter", "ĠVeter ans", "Ġint ro", "other apy", "Ġmal ware", "Ġbre eding", "Ġmole cular", "ĠR oute", "ĠCom ment", "oc hem", "Ġa in", "Se ason", "Ġlineback er", "Ä «", "ĠEconom ics", "es ar", "ĠL ives", "ĠEm ma", "Ġk in", "ĠTer rit", "Ġpl anted", "ot on", "ĠBut ter", "ĠSp ons", "P ER", "Ġdun geon", "Ġsymb olic", "Ġfil med", "Ġdi ets", "Ġconclud es", "Ġcertain ty", "ĠForm at", "Ġstr angers", "form at", "ĠPh ase", "Ġcop ied", "Ġmet res", "ld a", "ĠUs ers", "Ġdeliber ate", "Ġwas hed", "ĠL ance", "im ation", "Ġimpro per", "ĠGen esis", "ick r", "ĠK ush", "Ġreal ise", "Ġembarrass ing", "alk ing", "b ucks", "Ġver ified", "Ġout line", "year s", "ĠIn come", "20 2", "Ġz ombies", "F inal", "ĠMill enn", "Ġmod ifications", "ĠV ision", "ĠM oses", "ver b", "iter ranean", "ĠJ et", "Ġnav al", "ĠA gg", "Ġur l", "Ġvict ories", "Ġnon etheless", "Ġinj ust", "ĠF act", "ç ļ", "Ġins ufficient", "re view", "face book", "Ġnegoti ating", "Ġguarant ees", "im en", "uten berg", "Ġg ambling", "Ġcon gr", "Load ing", "Ġnever theless", "Ġpres idents", "ĠIndust rial", "Ġ11 8", "Ġp oured", "ĠT ory", "Ġ17 5", "Ġ: =", "Sc ott", "ange red", "T ok", "Ġorgan izers", "M at", "ĠG rowth", "Ġad ul", "Ġens ures", "Ġ11 7", "é¾į å", "Ġmass acre", "Ġgr ades", "be fore", "AD VERTISEMENT", "ĠSl ow", "ĠM MA", "âĢĶ \"", "ĠV atican", "Q aeda", "Ġo we", "66 66", "ĠS orry", "ĠGr ass", "Ġbackground s", "Ġexha usted", "Ġcl an", "Ġcomprom ised", "ĠE lf", "ĠIsa ac", "ens on", "In vest", "IF A", "Ġinterrupt ed", "ãĥī ãĥ©", "Ġtw isted", "ĠDrag ons", "M ode", "ĠK remlin", "Ġfert il", "he res", "ph an", "ĠN ode", "f ed", "ĠOr c", "Ġunw illing", "C ent", "Ġprior it", "Ġgrad uates", "Ġsubject ive", "Ġiss uing", "ĠL t", "Ġview er", "Ġw oke", "Th us", "bro ok", "Ġdep ressed", "Ġbr acket", "ĠG or", "ĠFight ing", "Ġstri ker", "Rep ort", "ĠPortug al", "Ġne o", "w ed", "19 9", "Ġflee ing", "sh adow", "ident ified", "US E", "Ste am", "Ġstret ched", "Ġrevel ations", "art ed", "ĠD w", "Ġalign ment", "est on", "ĠJ ared", "S ep", "Ġblog s", "up date", "g om", "r isk", "Ġcl ash", "ĠH our", "Ġrun time", "Ġunw anted", "Ġsc am", "Ġr ack", "Ġen light", "on est", "ĠF err", "Ġconv ictions", "Ġp iano", "Ġcirc ulation", "ĠW elcome", "Ġback lash", "ĠW ade", "Ġrece ivers", "ot ive", "J eff", "Ġnetwork ing", "ĠPre p", "ĠExpl orer", "Ġlect ure", "Ġupload ed", "ĠMe at", "B LE", "ĠNaz is", "ĠSy nd", "st ud", "ro ots", "ri ans", "Ġportray ed", "Ġ ??", "ĠBudd ha", "s un", "Rober t", "ĠCom plex", "Ġover see", "Ġste alth", "T itle", "ĠJ obs", "ĠK um", "Ġappreci ation", "ĠM OD", "Ġbas ics", "Ġcl ips", "Ġnurs ing", "Ġpropos ition", "Ġreal ised", "ĠNY C", "Ġall ocated", "ri um", "ar an", "ĠPro duction", "ĠV ote", "Ġsm ugg", "Ġhun ter", "az er", "ĠCh anges", "Ġfl uct", "y on", "Ar ray", "Ġk its", "W ater", "Ġuncom mon", "Ġrest ing", "ell s", "w ould", "Ġpurs ued", "Ġassert ion", "omet own", "ĠMos ul", "ĠPl atform", "io let", "Ġshare holders", "Ġtra ils", "P ay", "ĠEn forcement", "ty pes", "ĠAn onymous", "Ġsatisf ying", "il ogy", "Ġ( '", "w ave", "c ity", "Ste ve", "Ġconfront ation", "ĠE ld", "C apt", "ah an", "ht m", "ĠC trl", "ON S", "2 30", "if a", "hold ing", "Ġdelic ate", "Ġj aw", "ĠGo ing", "or um", "S al", "Ġd ull", "ĠB eth", "Ġpr isons", "Ġe go", "ĠEl sa", "avor ite", "ĠG ang", "ĠN uclear", "Ġsp ider", "ats u", "Ġsam pling", "Ġabsor bed", "ĠPh arm", "iet h", "Ġbuck et", "ĠRec omm", "O F", "ĠF actory", "AN CE", "Ġb acter", "H as", "ĠObs erv", "12 1", "Ġprem iere", "De velop", "Ġcur rencies", "C ast", "Ġaccompany ing", "ĠNash ville", "Ġfat ty", "ĠBre nd", "Ġloc ks", "Ġcent ered", "ĠU T", "augh s", "or ie", "ĠAff ordable", "v ance", "D L", "em et", "Ġthr one", "ĠBlu etooth", "Ġn aming", "if ts", "AD E", "Ġcorrect ed", "Ġprompt ly", "ĠST R", "Ġgen ome", "Ġcop e", "Ġval ley", "Ġround ed", "ĠK end", "al ion", "p ers", "Ġtour ism", "Ġst ark", "v l", "Ġblow ing", "ĠSche dule", "st d", "Ġunh appy", "Ġlit igation", "ced es", "Ġand roid", "Ġinteg ral", "ere rs", "ud ed", "t ax", "Ġre iter", "ĠMot ors", "oci ated", "Ġwond ers", "ĠAp ost", "uck ing", "ĠRoose velt", "f ram", "Ġyield s", "Ġconstit utes", "aw k", "Int erest", "Ġinter im", "Ġbreak through", "ĠC her", "Ġpro sec", "ĠD j", "ĠM T", "Res p", "ĠP T", "Ġs perm", "ed it", "B T", "Lin ux", "count ry", "le ague", "Ġd ick", "Ġo ct", "Ġinsert ing", "Ġsc ra", "ĠBrew ing", "Ġ19 66", "Ġrun ners", "Ġpl un", "id y", "ĠD ian", "Ġdys function", "Ġex clusion", "Ġdis gr", "Ġincorpor ate", "Ġrecon c", "Ġnom inated", "ĠAr cher", "d raw", "achel or", "Ġwrit ings", "Ġshall ow", "Ġh ast", "ĠB MW", "ĠR S", "Ġth igh", "Ġ19 63", "Ġl amb", "Ġfav ored", "ag le", "Ġcool er", "ĠH ours", "ĠG U", "ĠOrig in", "Ġglim pse", "---------------- ----", "L im", "Ġche ek", "Ġj ealous", "- '", "Ġhar ness", "ĠPo ison", "Ġdis abilities", "ne apolis", "Ġout look", "Ġnot ify", "ĠIndian apolis", "Ġab rupt", "ns ic", "Ġenc rypted", "Ġfor fe", "reat h", "Ġr abb", "Ġfound ations", "Ġcompl iment", "ĠInter view", "ĠS we", "Ġad olesc", "Ġmon itors", "ĠSacrament o", "Ġtime ly", "Ġcontem pl", "Ġposition ed", "Ġpost ers", "ph ies", "iov ascular", "v oid", "ĠFif th", "Ġinvestig ative", "OU N", "Ġinteg rate", "ĠIN C", "ish a", "ibl ings", "ĠRe quest", "ĠRodrig uez", "Ġsl ides", "ĠD X", "Ġfemin ism", "Ġdat as", "Ġb end", "ir us", "ĠNig eria", "F ox", "Ch ange", "Ġair plane", "ĠLad en", "Ġpublic ity", "ixt y", "Ġcommit ments", "Ġaggreg ate", "Ġdisplay ing", "ĠAr row", "Ġ12 2", "Ġrespect s", "and roid", "s ix", "ĠSh a", "Ġrest oration", ") \\", "W S", "oy s", "Ġillust rate", "with out", "12 6", "ĠâĶ Ĥ", "Ġpick up", "n els", "Ġ ....", "f ood", "ĠF en", ") ?", "Ġphenomen a", "Ġcompan ions", "ĠW rite", "Ġsp ill", "Ġbr idges", "ĠUp dated", "ĠF o", "Ġinsect s", "ASH INGTON", "Ġsc are", "il tr", "ĠZh ang", "Ġsever ity", "Ġind ul", "14 9", "ĠCo ffee", "Ġnorm s", "Ġp ulse", "ĠF T", "Ġhorr ific", "ĠDest roy", "ĠJ SON", "Ġo live", "Ġdiscuss es", "R est", "E lect", "ĠW inn", "ĠSurv iv", "ĠH ait", "S ure", "op ed", "Ġro oted", "ĠS ke", "ĠBron ze", "Ġl ol", "Def ault", "Ġcommod ity", "red ited", "Ġliber tarian", "Ġforb idden", "Ġgr an", "à ¨", "Ġl ag", "en z", "dri ve", "Ġmathemat ics", "Ġw ires", "Ġcrit ically", "Ġcarb ohyd", "ĠChance llor", "ĠEd die", "Ġban ning", "ĠF ri", "Ġcompl ications", "et ric", "ĠBangl adesh", "Ġband width", "St op", "ĠOrig inally", "Ġhalf way", "yn asty", "sh ine", "Ġt ales", "rit ies", "av ier", "Ġspin ning", "ĠWH O", "Ġneighbour hood", "b ach", "Ġcommer ce", "ĠS le", "B U", "Ġentreprene ur", "Ġpecul iar", "ĠCom ments", "f re", "3 20", "IC S", "Ġimag ery", "ĠCan on", "ĠElect ronic", "sh ort", "( (", "D ig", "Ġcomm em", "u ced", "Ġincl ined", "ĠSum mon", "Ġcl iff", "ĠMed iterranean", "Ġpo etry", "Ġprosper ity", "ĠRe ce", "Ġp ills", "m ember", "Ġfin ale", "un c", "ĠG ig", "ä ½", "Ġl od", "Ġback ward", "- +", "ĠFor ward", "Ġth ri", "s ure", "Ġso ap", "ĠF X", "R ES", "ĠSe xual", "oul os", "Ġfool ish", "Ġright eous", "Ġco ff", "terror ism", "ust ain", "ot er", "Ġab uses", "ne xt", "Ġab usive", "Ġthere after", "Ġprohib ition", "ĠS UP", "Ġd ip", "Ġr ipped", "Ġinher ited", "Ġb ats", "st ru", "G T", "Ġflaw ed", "ph abet", "Ġf og", "do ors", "Ġim aging", "Ġdig its", "ĠHung ary", "Ġar rog", "Ġteach ings", "Ġprotocol s", "ĠB anks", "à ¸", "p ound", "ĠC urt", ".\" )", ". /", "Ġex emption", "end ix", "ĠM ull", "Ġimpro ves", "ĠG amer", "d imensional", "I con", "ĠMarg aret", "St atus", "d ates", "Ġint ends", "Ġdep ict", "Ġpark ed", "J oe", "ĠMar ines", "chn ology", "! ).", "Ġjud ged", "Ġwe ights", "R ay", "Ġapart ments", "he ster", "Ġrein force", "Ġoff ender", "occ up", "Ġs ore", "e pt", "ĠPH P", "ĠB row", "Ġauthor ization", "ĠR isk", "ĠDel aware", "ĠQ U", "Ġnot ifications", "Ġsun light", "Ġex clude", "d at", "Ġm esh", "ĠSud an", "Ġbelong ed", "Ġsub way", "Ġno on", "ĠInter ior", "ol ics", "ĠL akers", "Ġc oding", "Dis claimer", "Cal if", "O ld", "Ġdis l", "???? ?", "Ġconfir ms", "Ġrecruit ment", "Ġhom icide", "Cons ider", "ĠJeff rey", "ft y", "} ;", "Ġobject ion", "do ing", "ĠLe o", "W ant", "Ġgl ow", "ĠClar ke", "ĠNorm an", "Ġver ification", "Ġpack et", "ĠForm ula", "Ġpl ag", "es ville", "Ġshout ing", "Ġo v", "ĠR EC", "ĠB ub", "Ġn inth", "Ġener g", "Ġvalid ity", "Ġup s", "j ack", "Ġneighbor ing", "ĠN ec", "ew orks", "ĠH ab", "are z", "Ġsp ine", "Ġevent ual", "ĠLe aders", "ĠC arn", "Ġprob ation", "Ġrom ance", "ms g", "ĠMechan ical", "ER Y", "R ock", "Ġpart isan", "N ode", "ass ets", "min ent", "Ġforeign ers", "Ġtest ify", "ĠUs ually", "l ords", "ĠG ren", "ĠPow ell", "BI L", "Ġs r", "Ġadd ict", "Ġshell s", "Ġs igh", "ĠY ale", "tern ity", "Ġ7 50", "E U", "ĠR ifle", "Ġpat ron", "em a", "ĠB annon", "an ity", "Ġtrop ical", "ĠV II", "c ross", "Every thing", "ĠIS O", "Ġhum ble", "ass ing", "ĠF IG", "Ġupd ating", "ys on", "Ġcal cium", "Ġcompet ent", "Ġste ering", "Pro t", "ĠS Y", "ĠFin als", "ĠR ug", "15 9", "13 7", "ĠG olf", "Ġ12 6", "Ġaccommod ation", "ĠHug hes", "Ġaest hetic", "art isan", "ĠTw ilight", "Ġpr ince", "ĠAgric ulture", "ĠDis co", "Ġpreced ent", "Ġtyp ing", "author ized", "O ption", "ĠA ub", "l ishes", "ach t", "m ag", "P eter", "ĠU FO", "mont on", "ĠL ith", "Ġa rom", "Ġsec uring", "Ġconf ined", "priv ate", "Ġsw ords", "Ġmark ers", "Ġmetab olic", "se lect", "ĠCur se", "ĠO t", "g ressive", "Ġinc umb", "ĠS aga", "Ġpr iced", "Ġclear ance", "Cont ent", "Ġdr illing", "Ġnot ices", "Ġb ourgeois", "Ġv est", "Ġcook ie", "ĠGuard ians", "ry s", "in yl", "Ġ12 4", "Ġpl ausible", "on gh", "ĠOd in", "Ġconcept ion", "ĠY uk", "ĠBaghd ad", "ĠFl ag", "Aust ral", "ĠI BM", "Ġintern ationally", "ĠWiki Leaks", "I ED", "Ġc yn", "Ġcho oses", "ĠP ill", "Ġcomb ining", "Ġrad i", "ĠMoh ammed", "def ense", "atch ing", "Sub ject", "ic iency", "Fr ame", "Ġ{ \"", "Ġche ss", "Ġtim er", "19 0", "Ġt in", "Ġord inance", "emet ery", "Ġacc using", "Ġnotice able", "Ġcent res", "Ġl id", "ĠM ills", "img ur", "Ġz oom", "erg ic", "Ġcomp ression", "pr im", "f ind", "Ġsur g", "Ġp and", "ĠK ee", "ĠCh ad", "cell ence", "oy le", "Ġsocial ism", "ĠT ravis", "ĠM Hz", "Ġgu ild", "ALL Y", "ĠSub scribe", "ĠRel ated", "Ġoccur rence", "itch ing", "Ġfict ional", "Ġcr ush", "ĠE A", "c od", "m ix", "ĠTri ple", "Ġretrie ve", "Ġstimul us", "Ġpsych iat", "ĠDo or", "Ġhomosexual ity", "Ġelement ary", "Ġcell ular", "id ian", "ĠL aun", "Ġintrig uing", "Ġfo am", "ĠB ass", "id i", "its u", "Ġass ure", "Ġcongr at", "Ġbusiness man", "ĠBo ost", "cl ose", "Ġl ied", "Ġsc iences", "ĠO mega", "ĠG raphics", "Ġ< =", "sp oken", "Ġconnect ivity", "S aturday", "ĠAven gers", "Ġto ggle", "Ġank le", "Ġnational ist", "mod el", "ĠP ool", "ophob ia", "V ar", "ĠM ons", "ator ies", "Ġaggress ively", "C lear", "For ge", "act ers", "Ġhed ge", "Ġpip es", "Ġbl unt", "Ġs q", "Ġremote ly", "W ed", "as ers", "Ġref riger", "Ġt iles", "Ġresc ued", "Ġcompr ised", "ins ky", "Ġman if", "avan augh", "Ġprol ifer", "Ġal igned", "x ml", "Ġtri v", "Ġcoord ination", "ĠP ER", "ĠQu ote", "13 4", "b f", "ĠS aw", "Ġtermin ation", "Ġ19 0", "Ġadd itions", "Ġtri o", "Ġproject ions", "Ġpositive ly", "Ġin clusive", "Ġmem br", "19 90", "old er", "Ġpract iced", "ink le", "Ar ch", "Ġstar ters", "ari us", "Ġinter mediate", "ĠBen ef", "ĠK iller", "Ġinter ventions", "ĠK il", "ĠF lying", "In v", "Ġprem ature", "Ġpsych iatric", "Ġind ie", "Ġcoll ar", "ĠRain bow", "af i", "Ġdis ruption", "ĠFO X", "cast ing", "Ġmis dem", "c ro", "Ġw ipe", "ard on", "Ġb ast", "ĠTom my", "ĠRepresent ative", "Ġbell y", "ĠP O", "ĠBre itbart", "13 2", "Ġmess aging", "Sh ould", "Ref erences", "ĠG RE", "ist ical", "L P", "ĠC av", "ĠC razy", "Ġintu itive", "ke eping", "ĠM oss", "Ġdiscont in", "ĠMod ule", "Ġun related", "ĠPract ice", "ĠTrans port", "Ġstatist ically", "orn s", "Ġs ized", "p u", "Ġca f", "ĠWorld s", "ĠRod gers", "ĠL un", "ĠCom ic", "l iving", "Ġc ared", "Ġclim bed", ") {", "Ġconsist ed", "Ġmed ieval", "fol k", "Ġh acked", "Ġd ire", "ĠHerm ione", "Ġt ended", "ce ans", "D aniel", "w ent", "Ġlegisl ators", "Ġred es", "g ames", "Ġg n", "am iliar", "Ġ+ +", "gg y", "th reat", "Ġmag net", "Ġper ceive", "Ġz ip", "Ġindict ment", "Ġcrit ique", "g ard", "ĠSaf e", "ĠC ream", "Ġad vent", "ob a", "Ġv owed", "ous ands", "Ġsk i", "Ġabort ions", "u art", "Ġstun ned", "Ġadv ancing", "Ġlack ed", "Ġ\\ \"", "Ġsch izophren", "Ġeleg ant", "Ġconf erences", "Ġcance led", "ĠHud son", "ĠHop efully", "Ġtr ump", "Ġfrequ encies", "Ġmet eor", "ĠJun ior", "ĠFle et", "ĠMal colm", "ĠT ools", "Ġ ........", "Ġh obby", "ĠEurope ans", "Ġ15 00", "ĠInt o", "Ġs way", "ĠApp ro", "ĠCom pl", "Comm unity", "Ġt ide", "ĠSum mit", "ä »", "Ġinter vals", "ĠE ther", "Ġhabit at", "ĠSteven s", "lish ing", "ĠDom ain", "Ġtrig gers", "Ġch asing", "Ġchar m", "ĠFl ower", "it ored", "Ġbless ing", "Ġtext ures", "F ive", "Ġliqu or", "R P", "F IN", "Ġ19 62", "C AR", "Un known", "Ġres il", "ĠL ily", "Ġabund ance", "Ġpredict able", "r ar", "Ġbull shit", "le en", "che t", "M or", "M uch", "ä ¹", "Ġemphas ized", "Ġcr ust", "Ġprim itive", "Ġenjoy able", "ĠPict ures", "Ġteam mate", "pl er", "ĠT ol", "ĠK ane", "Ġsummon ed", "th y", "ram a", "ĠH onda", "Ġreal izing", "Ġquick er", "Ġconcent rate", "cle ar", "Ġ2 10", "ĠErd ogan", "ar is", "Ġrespond s", "ĠB I", "Ġelig ibility", "Ġpus hes", "ĠId aho", "Ġagg rav", "Ġru ins", "ur ations", "Ġb ans", "Ġan at", "sh are", "Ġgr ind", "h in", "um en", "Ġut ilities", "ĠYan kees", "Ġdat abases", "ĠD D", "Ġdispl aced", "Ġdepend encies", "Ġstim ulation", "h un", "h ouses", "ĠP retty", "ĠRaven s", "ĠTOD AY", "Ġassoci ates", "Ġthe rape", "cl ed", "Ġde er", "Ġrep airs", "rent ice", "Ġrecept ors", "Ġrem ed", "ĠC e", "Ġmar riages", "Ġball ots", "ĠSold ier", "Ġhilar ious", "op l", "13 8", "Ġinherent ly", "Ġignor ant", "Ġb ounce", "ĠE aster", "REL ATED", "ĠCur rency", "E V", "ãĥ ŀ", "ĠLe ad", "Ġdece ased", "B rien", "ĠMus k", "J S", "Ġmer ge", "heart ed", "c reat", "m itt", "m und", "ĠâĢ ĭ", "ĠB ag", "Ġproject ion", "Ġj ava", "ĠStand ards", "ĠLeon ard", "Ġcoc onut", "ĠPop ulation", "Ġtra ject", "Ġimp ly", "Ġcur iosity", "ĠD B", "ĠF resh", "ĠP or", "Ġheav ier", "ne ys", "gom ery", "Ġdes erved", "Ġphr ases", "ĠG C", "Ġye ast", "d esc", "De ath", "Ġreb oot", "Ġmet adata", "IC AL", "Ġrep ay", "ĠInd ependence", "Ġsubur ban", "ical s", "Ġat op", "Ġall ocation", "gener ation", "ĠG ram", "Ġmoist ure", "Ġp ine", "ĠLiber als", "Ġa ides", "Ġund erest", "ĠBer ry", "Ġcere mon", "3 70", "ast rous", "ĠPir ates", "Ġt ense", "ĠIndust ries", "ĠApp eals", "ĠN ear", "Ġè£ı ç", "Ġlo vers", "ĠC AP", "ĠC raw", "Ġg iants", "Ġeffic acy", "E lement", "ĠBeh avior", "ĠToy ota", "Ġint est", "P riv", "A I", "Ġmaneu ver", "Ġperfect ion", "Ġb ang", "p aper", "r ill", "Ge orge", "b order", "in ters", "ĠS eth", "Ġcl ues", "ĠLe vi", "ĠRe venue", "14 7", "Ġv apor", "Ġfortun ate", "Ġthreat ens", "Ġve t", "Ġdepend ency", "ers ed", "art icle", "ĠBl izzard", "Ġch lor", "Ġmin us", "ĠB ills", "Ġcryptoc urrency", "Ġmetabol ism", "ter ing", "Ġp estic", "step s", "ĠTre asure", "ract ed", "ĠConst ant", "Ġtem p", "13 9", "ĠDet ective", "ur ally", "Ġrecover ing", "Ġcort ex", "Ġ14 4", "cl osed", "Ġprejud ice", "aun ted", "Ġstorm s", "ĠN OW", "Ġmach inery", "Add ress", "Ġcompe lled", "27 0", "Ġdesp air", "b ane", "Ġveget able", "Ġbed s", "Lear n", "Ġcolor ful", "Ġsp ike", "Ġmarg ins", "Ġsymp athy", "Ġworks hop", "ĠC BC", "S at", "Ġburn s", "ĠG ender", "Ġ12 9", "ĠC able", "Ġdeb ts", "ĠThe resa", "Ġreflect ing", "Ġa irst", "Ġr im", "ram id", "Ġweakness es", "W rit", "ogg le", "t i", "ĠCh arge", "Ġwe ighed", "Ġ( .", "Ġl aughter", "Ġrou ter", "ĠDemocr acy", "D ear", "Ġhas ht", "Ġd y", "Ġhint s", "run ning", "Ġfin ishes", "ar us", "M ass", "res ult", "asc us", "Ġv intage", "Ġcon qu", "Ġwild ly", "ac ist", "Ġl ingu", "Ġprot agonist", "st rom", "te enth", "ĠSol o", "m ac", "f illed", "Ġre nown", "it ives", "Ġmot ive", "ĠAnt ar", "ĠM ann", "ĠAd just", "Ġrock ets", "Ġtrou bling", "e i", "Ġorgan isms", "ass is", "Christ ian", "Ġ14 5", "ĠH ass", "Ġsw all", "Ġw ax", "ĠSurv ival", "V S", "ĠM urd", "v d", "stand ard", "Ġdrag ons", "Ġacceler ation", "r ational", "f inal", "Ġp aired", "ĠE thereum", "Ġinterf aces", "Ġres ent", "Ġartif acts", "Å «", "are l", "Ġcompet itor", "ĠNich olas", "ĠSur face", "c pp", "ĠT ot", "Ġeconom ically", "Ġorgan ised", "Ġen forced", "in ho", "Ġvar ieties", "Ġab dom", "ĠBa iley", "id av", "ĠSal v", "p aid", "Ġalt itude", "ess ert", "ĠG utenberg", "are a", "op oulos", "Ġprofess ors", "igg s", "ĠF ate", "he y", "Ġ3 000", "D ist", "Ġtw ins", "c ill", "ĠM aps", "Ġtra ps", "Ġwe ed", "ĠK iss", "Ġy oga", "Ġrecip ients", "ĠWest minster", "Ġpool s", "ĠWal mart", "18 8", "ĠSchool s", "att ack", "ĠAR M", "par agraph", "W arning", "j l", "Ġself ish", "anche z", "ĠHe ights", "F re", "ĠS oph", "Ġ --------------------------------", "t ml", "33 3", "Ġraid s", "Ġsatell ites", "KE Y", "Ġlast s", "Ñ Ĥ", "In s", "ĠD ame", "Ġunp redict", "// /", "gh ai", "Ġart illery", "Ġcru ise", "Ġg el", "ĠCabin et", "Ġbl ows", "ĠE sp", "Ġprox imity", "ot he", "ĠSk ills", "ĠU pper", "ob o", "ĠN DP", "Ġenjoy s", "Ġrepe ating", "ĠConst ruction", "ĠQuest ions", "H illary", "Ġu int", "Ġprocess ors", "ĠGib son", "ĠMult iple", "q a", "ĠB om", "ĠM iles", "vent ional", "Ġhur ts", "s kin", "ĠA IDS", "Ġadvis ers", "ĠR oot", "Ġmethod ology", "ĠD ale", "Ġdet on", "ĠKnow ledge", "sequ ently", "Ġ12 1", "Ġconnect s", "C y", "ĠD anger", "Ġcontribut ors", "ĠB ent", "Ġbr ass", "ĠGun s", "int o", "ĠFort une", "Ġbro ker", "bal ance", "Ġlength s", "Ġv ic", "Ġaver aging", "Ġappropri ately", "ĠCamer a", "Ġsand wich", "ĠCD C", "Ġcoord inate", "Ġnav ig", "Ġgood ness", "l aim", "Ġbra ke", "Ġextrem ist", "ĠW ake", "ĠM end", "ĠT iny", "ĠC OL", "ĠR F", "ĠD ual", "ĠW ine", "C ase", "Ġref ined", "Ġl amp", "L ead", "Ġb apt", "ĠCar b", "ĠS add", "ĠMin neapolis", "PD F", "Ear ly", "ĠH idden", "I ts", "ĠT IME", "Ġp ap", "Ġcommission ed", "ĠF ew", "ĠCol ts", "ĠB ren", "Ġbot hered", "Ġlike wise", "Ex per", "ĠSch w", "c ry", "n n", "ĠM itch", "im on", "M G", "b m", "UM P", "r ays", "Ġregist ry", "Ġ2 70", "ach ine", "re lla", "ant ing", "00 000", "Ġru ined", "sp ot", "Ġt a", "Ġmaxim ize", "Ġincon ven", "D ead", "H uman", "En abled", "ĠMar ie", "Ġch ill", "ĠParad ise", "Ġstar ring", "ĠLat ino", "ĠProt ocol", "ĠE VER", "Ġsuppl iers", "m essage", "ĠBro ck", "Ġser um", "âĸĪâĸĪ âĸĪâĸĪ", "Ġen comp", "Ġamb ition", "ues e", "Ġar rows", "And rew", "Ġanten na", "Ġ19 61", "ĠB ark", "Ġb ool", "ãĤ ª", "ĠSt orage", "Ġrail way", "Ġtoug her", "ĠC ad", "Ġwas hing", "P y", "' ]", "em bed", "ĠMem phis", "ack le", "Ġfam ously", "ĠF ortunately", "ov ies", "Ġmind set", "Ġsne ak", "ĠD h", "RA W", "ĠSim pson", "Ġliv est", "Ġland mark", "Ġc ement", "L ow", "Ġthr illed", "ĠCour se", "in el", "Ġch uck", "id ate", "gl obal", "Ġwh it", "Ġ �", "ad ays", "s ki", "ĠS V", "Ġvir uses", "30 6", "ĠResp ons", "Ġthe aters", "ĠBr anch", "ĠGene va", "ĠM K", "Ġunbel iev", "Ġcommun ist", "Orig inal", "ĠRe ceived", "ĠTrans fer", "ĠAr g", "In put", "ĠStr ategy", "Ġpal ace", "the ning", "D ri", "Ġsent encing", "umbn ail", "Ġp ins", "re cy", "Ġs iblings", "Get ting", "ĠB U", "ĠNorth west", "Ġprolong ed", "ĠSak ura", "C omb", "ĠB our", "Ġinadequ ate", "ĠK ash", "Ġus ername", "ĠImpro ve", "Ġbatt ling", "ĠM AC", "Ġcurric ulum", "Ġs oda", "ĠC annon", "Ġsens ible", "sp ons", "De cember", "Ġw icked", "ĠP engu", "Ġdict ators", "ĠHe arts", "og yn", "Ġsimilar ities", "ĠSt ats", "Ġh ollow", "it ations", "\": [", "Ġh over", "ĠList en", "s ch", "S und", "Ġc ad", "ĠPar ks", "Ġl ur", "Ġhy pe", "ĠL em", "N AME", "is ure", "Fr iday", "Ġshoot s", "Ġclos es", "Ġd b", "ĠR idge", "ĠDiff erent", "Ġrepl ies", "ĠBroad way", "op ers", "Ġint oler", "ĠZe us", "akes pe", "Ġpropri etary", "Ġrequest ing", "Ġcontro llers", "ĠM IN", "im edia", "be cca", "Ġexp ans", "Ġoil s", "B ot", "ĠCh and", "Ġpr inter", "Ġto pped", "ĠP OL", "ĠEar lier", "S ocial", "av in", "Ġdecre ases", "ĠSe b", "Ġspecific ations", "ĠBl ast", "ĠK urt", "Ġfre el", "B rown", "Ġdil ig", "ro e", "ĠPro blem", "ĠQu ad", "Ġdecent ral", "ĠV ector", "an ut", "Ġplug ins", "ĠGreg ory", "Ġfuck ed", "el ines", "ĠAmb assador", "t ake", "Ġcle ans", "ong yang", "An onymous", "st ro", "\" }", "al ine", "ĠO dd", "ĠE ug", "2 16", "Ġbo il", "ĠP owers", "Ġnurs es", "Ob viously", "ĠTechn ical", "Ġexceed ed", "OR S", "Ġextrem ists", "Ġtr aces", "ex pl", "Ġcom r", "ĠS ach", ") /", "Ġm asks", "Ġsc i", "B on", "Ġreg ression", "we gian", "Ġadvis or", "it ures", "ĠV o", "ex ample", "ĠInst ruct", "Ġs iege", "Ġredu ctions", "pt r", "Ġstat utory", "Ġrem oves", "Ġp uck", "red its", "Ġbe e", "Ġsal ad", "Ġpromot ions", "ĠJosh ua", "with standing", "ET H", "ĠCh a", "im us", "Ġexpend iture", "aun ting", "Ġdelight ed", "Ġ15 5", "be h", "Ġcar pet", "ĠSp art", "Ġj ungle", "l ists", "Ġbull ying", "ĠNob el", "ĠGl en", "Ġreferen ced", "Ġintrodu ces", "se in", "Ġcho pped", "gl ass", "ĠW rest", "Ġneutral ity", "Ġâ Ļ", "Ġinvestig ator", "Ġshel ves", "Ġun constitutional", "Ġreprodu ction", "Ġmer chant", "m ia", "Ġmet rics", "Ġexplos ives", "ĠSon ia", "Ġbod ily", "Ġthick ness", "Ġpredomin antly", "ĠAb ility", "Ġmon itored", "IC H", "Ġ] .", "ĠMart inez", "Ġvis ibility", "Ġqu eries", "Ġgen ocide", "ĠWar fare", "Qu ery", "Ġstud ios", "Ġemb ry", "Ġcorrid or", "Ġclean ed", "com plete", "ĠM H", "Ġenroll ment", "ING S", "Ġimpact ed", "Ġdis astrous", "ĠY un", "ĠCl aire", "ĠBas ically", "y t", "uster ity", "Ġindirect ly", "w ik", "Ġd od", "ĠCar r", "Ġam p", "Ġprohib it", "ĠIn itial", "ĠR d", "ij i", "Ġeduc ate", "c orn", "i ott", "ĠBeaut y", "Ġdetect ive", "ĠCon n", "s ince", "Ġst agger", "Ġob ese", "Ġb ree", "olog ic", "is se", "walk er", "Ġbl ades", "Ġlaw ful", "fun c", "ĠBeh ind", "Ġappet ite", "Ġ( *", "Ġt ennis", "Ġoff spring", "Ġj ets", "Ġstruct ured", "Ġafore mentioned", "N ov", "Ġsc aling", "f ill", "Ġst ew", "Ġcur b", "ĠStep han", "ed In", "S F", "ob ic", "é ŃĶ", "ou g", "ĠM M", "Ġgen etically", "ope z", "13 6", "Ġu mb", "anc ers", "Ġcoh ort", "Ġmerch andise", "Ġimp osing", "ĠLegisl ature", "ĠArch ive", "iv ia", "ĠN aval", "Ġoff ences", "Ġmir acle", "Ġsn apped", "Ġf oes", "Ġextensive ly", "ĠR af", "Ġc ater", "ed ience", "K it", "ĠB in", "Ġrecomm ends", "ĠC ities", "Ġrig id", "ĠRE AD", "ĠNob le", "ĠT ian", "Ġcertific ates", "ant is", "o iler", "ĠBudd hist", "d id", "Ġsurvey ed", "Ġdown ward", "Ġprint s", "ĠMot ion", "ron ics", "ĠS ans", "oss ibly", "u ctions", "Ġcolon ies", "ĠDan ish", "un it", "Ġsp oil", "Ġadvis ory", "ber ries", "Pl an", "Ġspecific ation", "op hers", "ĠRes ource", "Ġsh irts", "prising ly", "commun ications", "Ġtriv ial", "Ġmention ing", "ise xual", "Ġsupp lements", "Ġsuper vision", "B P", "v or", "Ġw it", "Ġco oldown", "Ġplaint iff", "ĠReview s", "ĠS ri", "ĠM int", "ĠSug ar", "Ġafter ward", "ĠPri est", "ĠInvest ment", "og ene", "ĠT aking", "Ġstretch ing", "Ġinflamm ation", "ĠTe hran", "Ġl ining", "Ġfree zing", "ĠEnt ity", "Ġins piring", "spe cial", "pr ice", "Ġsu e", "ĠP orter", "oun ge", "ET A", "ĠD erek", "ĠLu is", "u o", "ym ph", "Ġex terior", "ih il", "ĠAsh ley", "in ator", "Ġnut rients", "ĠTh rones", "Ġfin ances", "ĠIn spect", "Ġspe cially", "ĠRequ ired", "ĠP TS", "ĠViol ence", "oint ed", "sh ots", "Ġex cerpt", "co on", "IN S", "ĠG ri", "Ġrecogn ised", "We ek", "You ng", "Ġv om", "is le", "ĠCur ry", "ĠBudd h", "Ġnot ebook", "Ġd urable", "/ ?", "ĠG ad", "ĠP upp", "Ġforg ive", "p ark", "Ġpersonal ities", "an alysis", "cl amation", "Ġelev ator", "Ġware house", "ĠR ole", "un n", "Ġillust ration", "ĠSc an", "Ġatmosp heric", "Im port", "AN C", "rict ed", "f u", "01 0", "Ġar che", "Ġreward ed", "akespe are", "Ġintern ally", "ĠR BI", "alk er", "Ġeleph ant", "ow itz", "ĠP izza", "Ġbip artisan", "é s", "Ġslow ed", "ĠSt ark", "Ġover ride", "OU S", "Ġ3 20", "undred s", "ĠDe ck", "ĠC ensus", "be e", "14 6", "ot or", "Ġ ip", "Ġu b", "oc ations", "ĠBut ton", "r ice", "Ġc ripp", "ff f", "Ġorig inated", "Ġoverwhel med", "app a", "Ġfore most", "âĢ ij", "ĠL EG", "re lease", "eat ured", "at ches", "Ġre ps", "Ġl ending", "ĠRe ference", "ĠCl ient", "16 5", "vent h", "Com plete", "ĠPat rol", "Ġsw orn", "c am", "Ġshut tle", "ĠR alph", "Ġh ometown", "- ,", "on al", "ĠB P", "å ı", "Ġpersu ade", "ĠAlex and", "Ġcomb ines", "Ġv ivid", "ĠL ag", "Ġenc oding", "Ġsal vation", "w en", "ĠRec overy", "i ya", "Un iversity", "ĠB iden", "Ġbud gets", "ĠTex ans", "f its", "Ġhon ored", "Ġp ython", "T D", "## #", "cl one", "Ġbl ink", "ĠL iquid", "Ġunemploy ed", "Ġcl ashes", "ĠCoun sel", "Ġdirect ing", "Ġpun ct", "ĠFal cons", "Ġsh ark", "ĠDam ascus", "Ġje ans", "Ġemb ark", "Ġse ize", "Ġup wards", "2 80", "ĠE z", "ĠAny thing", "Ġex otic", "l ower", "ĠCreat or", "ĠU m", "Ġsubur bs", "ber ger", "ĠW end", "Ġm int", "ĠX X", "ĠD ro", "Ġsuff ers", "Ġher b", "t ree", "Ġfrag ile", "Ġflood ed", "ĠAl cohol", "ole an", "ny der", "ĠK O", "F ram", "Ġ13 6", "Ġow ed", "ĠMe lee", "ĠH ash", "Ġwh isk", "Ġsu do", "r r", "Qu ick", "app ro", "Ġi i", "ĠEx amples", "he e", "Ġpromot es", "per ature", "k ar", "ĠHon or", "Ġs odium", "ĠL if", "ros so", "intend ent", "Ġcorrespond ent", "F ound", "sec ret", "Ġident ifies", "ag ne", "Ġl ou", "ĠP P", "Ġcoinc idence", "m ove", "Ġmilit ia", "Ġinf iltr", "ĠPrim ary", "Ġpitch ing", "ĠI b", "ĠGO OD", "ãĤ ¸", "ĠW izards", "ir al", "ĠVen us", "R R", "ĠâĢ ķ", "ĠCase y", "Ġsad ly", "Ġadm ire", "Ġembarrass ed", "c b", "M el", "Ġtub es", "Ġbeaut ifully", "ĠQueens land", "Bel ow", "re z", "qu et", "ple asant", "Ġ «", "C amp", "Ġdec isive", "19 98", "ĠL amb", "ut ton", "h n", "ĠJ agu", "au nder", "ĠC ord", "Ġcl erk", "Ġca ffe", "Ġwip ed", "Ġre im", "ĠMount ains", "Ġimprison ed", "Ġdevelop s", "ĠP ra", "Ġmodel ing", "Any one", "ance l", "ĠS it", "Ġshield s", "Ġl awn", "Ġcard iovascular", "Ġdemonstr ating", "Ġpar se", "ĠIsrael is", "Ġeuro s", "14 3", "Ġgl orious", "ins ki", "ec d", "Ġcondition ing", "Ġhel pless", "Ġmicro sc", "ĠHar bor", "Ġst akes", "Ġ2 60", "Ġun equ", "ĠFl oyd", "Ġd amp", "Ġappar atus", "ĠLaw s", "Ġcoun ters", "Ġindu ce", "at able", "ĠAh med", "Ġsl am", "N ovember", "Ġpers ist", "Ġim minent", "á n", "Ġsh red", "Ġph ases", "ĠEd monton", "ĠArm strong", "ĠMe et", "ĠK itty", "Ñ Ģ", "c irc", "ĠAd ult", "Ġa rose", "ĠX en", "D an", "g ow", "Ġsuper f", "ĠAd mir", "Ġend ure", "Ġkey word", "yr us", "Ġy arn", "Ġpath way", "ĠHop kins", "mid t", "Ġcens orship", "d ependent", "Ġinstruct or", "S ources", "Ġto e", "Ġball oon", "N ob", "Ġsw ear", "ĠCast ro", "Ġgl oss", "ĠK avanaugh", "Ġremark ably", "Ph otos", "ĠN om", "ĠS outheast", "y ers", "Ġvalid ation", "Ġcann on", "ĠVict ory", "ĠPier re", "Ġcaut ious", "Aud io", "Ġf etch", "ĠG ift", "ĠH yp", "Ġrem edy", "Z E", "Ġsc ent", "Ġbe ard", "ĠR ut", "- \"", "Ġpat ents", "H y", "Ġun just", "Ġpot ato", "Ġforth coming", "Ġche f", "ĠR ift", "aff e", "ĠR OM", "ĠL aunch", "Ġp ads", "ĠNe o", "Ġon set", "Ġsquee ze", "s afe", "Ġpref ix", "ĠT M", "ĠN early", "ĠClin ical", "ĠM ental", "ot iation", "ĠUn ic", "ant ry", "ĠC ir", "Ġep it", "à ¦", "Ġextract ed", "verse ly", "ri ad", "Ġstr ains", "Ġto ps", "Ġpo em", "ĠRand y", "ĠMap le", "TH ER", "up iter", "ĠSS D", "ļ é", "Ġun con", "per ing", "Ġsle pt", "in ers", "Ġunder water", "ĠEv idence", "g one", "20 5", "Ġhistor ians", "Ġsynt hesis", "Ġf rog", "b asketball", "Ġvibr ant", "Ġsub ord", "Ġ3 65", "ĠD ial", "Ġcooper ate", "HA HA", "Ġgreet ed", "15 8", "Ġj azz", "Ġinto x", "ĠWalk ing", "Ġsuper visor", "ĠF usion", "ĠMer cedes", "s end", "H am", "s d", "n l", "Ġtour s", "ĠF IFA", "Ġcul p", "g d", "30 4", "Ġple as", "Ġillust rates", "ĠColomb ia", "Ġhighlight ing", "ĠSum mary", "Ġexp osing", "ĠD ru", "Ġir ony", "r itional", "ĠCar roll", "ĠEll is", "P ict", "ĠR apt", "Ġad apter", "Ġun m", "Ġcor pse", "Ġceleb rities", "D en", "at um", "ĠAp ocalypse", "ĠW ag", "lin ing", "Ġhorm ones", "R ub", "ĠX i", "ĠV aults", "20 8", "alky rie", "inos aur", "Ġfeed s", "v ity", "Ġdefe ating", "W ait", "Ġemphas ize", "ĠSteel ers", "yr inth", "le ys", "ĠWhe never", "Current ly", "ĠCl ock", "Ġcollect ively", "any on", "ĠJ P", "Ġment ality", "Ġdownload s", "Ġsurround ings", "ĠBarn es", "Ġflags hip", "Ġindic ators", "Ġgra pp", "Jan uary", "ĠElement al", "ĠAthen a", "ib al", "Ġs ights", "Ġcap ita", "ĠTreat y", "Ġvo iced", "ĠG az", "let te", "Ġy a", "Ġexp ired", "Leg end", "H ot", "n ature", "Ġunst able", "Ġ2 80", "à º", "Com ment", "AL E", "Ġquest s", "Ġhand ler", "n is", "Ġvers atile", "Ġconce al", "enge ance", "ĠInter active", "Ġobs essed", "ĠDog s", "Ġcr acked", "S ound", "s v", "ĠD ylan", "ro ads", "f x", "ĠCath olics", "ĠH ag", "Ġsl ammed", "Ġgl owing", "s ale", "Ġtiss ues", "ĠCh i", "ne e", "Ġc her", "s ic", "ur rection", "Ġb acon", "ul atory", ") .\"", "Ġir regular", "FOR M", "ass ed", "Ġintention al", "Ġcompens ate", "ĠSpe aking", "ĠS ets", "15 3", "Ġconvent ions", "b ands", "em ade", "Ġe cc", "ĠWin ston", "ĠAssass in", "ĠBelg ian", "Ġdepend ence", "Ġnic he", "Ġb ark", "ĠJ azz", "Ġdisadvant age", "Ġgas oline", "Ġ16 5", "çļ Ħ", "ess a", "mod ule", "ang ular", "O Y", "ĠTreat ment", "it as", "ol ation", "ĠArn old", "Ġfe ud", "ĠN est", "Ġthe atre", "ew ater", "Ġmin ors", "olic y", "ĠH aven", "div ision", "Ġtr unk", "F ar", "ĠP ull", "Ġcapt uring", "Ġ18 00", "ĠTe en", "Ġex empl", "Ġclin ics", "ĠB urg", "Ġsubst it", "Ġpay load", "ĠL av", "ĠT roy", "ĠW itness", "Ġfrag ments", "Ġpass words", "Ġg ospel", "ĠG in", "Ġten ants", "ol ith", "S ix", "Pre vious", "ĠAg es", "ĠDar win", "Ġbl at", "Ġem pathy", "sm ith", "b ag", "ĠE cho", "ĠC amb", "ĠM add", "ĠB oo", "Ġred e", "ĠBurn ing", "Ġsmooth ly", "ĠAd rian", "ĠV ampire", "ĠMon sters", "ste am", "Sty le", "M a", "re a", "ĠD war", "aly st", "urs or", "Ġelim ination", "Ġcrypt o", "ch t", "ĠE ternal", "âĢ¦ ]", "ĠS 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"vey ard", "els h", "in arily", "ĠE y", "ĠRoll ing", "Ġev olving", "Ind ia", "Ġrecogn izes", "Ġgrad uation", "is ers", "Ġfert ility", "ĠMil an", "Comm and", "Ġbox ing", "Ġ19 43", "Ġgl uten", "ĠEm ir", "Ġid ol", "Ġcon ceived", "ĠCre ation", "Mer it", "udd y", "uss ions", "ĠLie utenant", "iet al", "Ġunch anged", "ĠSc ale", "ĠCrime a", "ball s", "ator ial", "Ġdepth s", "Ġempir ical", "Ġtrans m", "Ġuns afe", "miss ible", "com fort", "15 6", "Ġmechan ic", "00 2", "l ins", "Ġsm oked", "P os", "Ġslow ing", "Ġl av", "Tex as", "Ġche ating", "ĠMet ropolitan", "eth yl", "Ġdiscover ing", "as se", "Ġpen cil", "ĠPy ongyang", "Ġclos et", "ĠShe et", "ĠEnt ry", "ou stic", "Ġmy st", "er ate", "ari at", "Ġminer als", "Ġmusic ian", "ĠP ul", "ĠM az", "24 9", "Ġper missions", "Ġ iv", "en ary", "ick ers", "ĠB ing", "he a", "en able", "Ġgri ev", "Ġassert ed", "ĠColon el", "Ġaff idav", "w o", "Ġse ated", "ĠR ide", "Ġpaint ings", "ĠP ix", "Ġ13 7", "ish i", "umb ai", "g otten", "ĠEar l", "Ġin ning", "Ġc ensus", "Ġtrave lled", "ĠCons ult", "18 5", "b ind", "Ġsimpl icity", "Ġoverlook ed", "ĠHelp ful", "Ġmon key", "Ġoverwhelming ly", "Bl ood", "ĠFl int", "ĠJ ama", "ĠPres ent", "ĠR age", "ĠT A", "pt ive", "Ġturn out", "w ald", "ĠD olphins", "ĠV PN", "Ġon ion", "Ġcraft ing", "m ma", "ĠMerc ury", "Ġarr ange", "Ġalert s", "ĠO T", "zb ollah", "Ġg ases", "ĠRichards on", "s al", "l ar", "Ġfro st", "Ġlower ing", "Ġacc laim", "Ġstart ups", "ĠG ain", "ess ment", "Ġguard ian", "äº º", "ĠP ie", "ĠL inks", "Ġmer its", "Ġaw ake", "Ġparent al", "Ġexceed s", "Ġid le", "ĠPil ot", "Ġe Bay", "ĠAc cept", "ipe g", "C am", "ĠK ot", "Ġtrad ers", "olit ics", "unk er", "ĠP ale", "os i", "an mar", "Ġ19 47", "ĠF ell", "est ial", "it ating", "G F", "ĠS r", "if ted", "Ġconnect or", "ĠB one", "ill es", "2 60", "h ma", "Ġoverl ap", "ĠGit Hub", "Ġclean er", "ĠBapt ist", "ĠW AS", "Ġlung s", "Ñ ģ", "ĠB UT", "Ġc ite", "Ġpit ched", "reat ment", "Ġtro phies", "ĠN u", "38 6", "ĠPr ide", "Ġattend ees", "[ ]", "17 9", "Ġspat ial", "Ġpri zes", "ĠRel igion", "Ġshow case", "ĠC ategory", "vid ia", "T arget", "Pro perty", "? ,", "Ġf usion", "p ie", "ĠU CLA", "Ġsound track", "Ġprin cess", "ĠC aval", "sh ould", "Ġlim bs", "Back ground", "Ġlone ly", "Ġc ores", "ĠT ail", "she et", "Ġ13 2", "R a", "ãĤ «", "ĠB olt", "Ġbook ed", "Ġadmin ister", "Ġequ als", "w y", "Ġobserv ing", "ĠBar on", "ĠAd obe", "Ġv irgin", "ĠSocial ist", "M ove", "gh azi", "ĠLind a", "2 12", "Ġbre wing", "Ġmerch ants", "bur se", "Ġdiv or", "Ġmet als", "ĠN er", "Ġsum s", "ĠEn emy", "Ġen vision", "Ġgrant ing", "ĠH oney", "ĠSk yrim", "Ġsoc io", "gr aded", "Ġselect ive", "W ASHINGTON", "Ġ19 48", "ĠSir ius", "ĠG ross", "act ivity", "ĠI van", "Ġfur ious", "BS D", "ĠPre vious", "Ġrespons ive", "Ġchar itable", "Ġle aning", "ĠP ew", "Ġviol ates", "\\\\\\\\ \\\\\\\\", "ĠCom ing", "w ire", "Ġpo et", "Ġres olutions", "comm and", "ĠPortug uese", "Ġnick name", "Ġde af", "Feb ruary", "Ġrecogn ise", "Ġentire ty", "Ġseason al", "pl aced", "ĠTe legraph", "Ġmicro phone", "our ing", "Ġgr ains", "Ġgovern ed", "Ġpost p", "ĠW aters", "in ement", "Ġund ocumented", "ĠCom cast", "Ġf ox", "Ġassault s", "re on", "man y", "ĠJen kins", "ĠAny way", "Ġassess ments", "Ġdown s", "ĠM ouse", "Ġsuper b", "k t", "ĠD ow", "Ġtax ation", "4 01", "Ġsm iles", "Ġundert aken", "Ġex h", "Ġenthusi astic", "Ġtw ent", "Ġgovernment al", "Ġautonom y", "ĠTechn ologies", "ĠCh ain", "Ġpreval ent", "f b", "Ġnic otine", "og ram", "j ob", "Ġawa iting", "ĠMen u", "Ġdep uties", "k ov", "ish ops", "But ton", "ĠShan ghai", "Ġdies el", "ĠD uck", "R yan", "ĠPC s", "N F", "j ury", "ent e", "Ġinacc urate", "edd y", "Wh atever", "Ġshow c", "ĠN ad", "od us", "et r", "Ġplaint iffs", "ĠW OR", "ĠAss ange", "Ġpriv at", "Ġpremium s", "Ġt am", "UR L", "Ġel ites", "ĠR anger", "otten ham", "ĠH off", "ĠAt hens", "Ġdefin ite", "Ġs ighed", "Ġeven ly", "2 11", "ĠAm ber", "ak ia", "Ġmail ing", "Ġcr ashing", "ĠConfeder ate", "ru gged", "W al", "ĠDep ths", "Ġjuven ile", "Ġreact or", "Introdu ction", "ĠDel uxe", "19 95", "ĠS anchez", "ĠM ead", "iv able", ": -", "ĠPlan ning", "ĠT rap", "qu in", "ĠProt ect", "ve red", "In formation", "Ġkid ney", "inn amon", "l as", "Ġpolic ing", "Ġtoler ate", "ĠQ i", "Ġbi ased", "F ort", "ĠK i", "s ave", "Ġprivile ged", "Ġbe asts", "ĠGl as", "ĠC inem", "Ġcome back", "Sund ay", "Ġext inction", "h ops", "Ġtrans mit", "Ġdoub les", "ĠFl at", "16 7", "Ġdis puted", "Ġinjust ice", "f oo", "V ict", "role um", "ĠJul ie", "Con text", "ĠR arity", "iss ue", "Comp onent", "Ġcounsel ing", "an ne", "d ark", "Ġobject ions", "u ilt", "Ġg ast", "Ġpl ac", "Ġun used", "ãĥ ĩ", "ĠT rial", "ĠJ as", "hed ral", "ob b", "Ġtempor al", "ĠPR O", "ĠN W", "ĠAnn iversary", "L arge", "Ġther m", "Ġd avid", "Ġsystem ic", "ĠSh ir", "m ut", "ĠNe pt", "add ress", "Ġscan ning", "Ġunderstand able", "Ġcan vas", "C at", "ĠZ oo", "Ġang els", "L O", "ĠStat ement", "ĠS ig", "ov able", "ĠA way", "sh aring", "ocr ats", "st ated", "Ġweigh ing", "N or", "w ild", "B ey", "Ġaston ishing", "ĠReyn olds", "Ġop ener", "Ġtrain er", "Ġsurg ical", "p n", "Ġadjust ing", "whe el", "Ġf rown", "erv ative", "Ġsusp end", "With in", "te in", "Ġobst acle", "Ġliber ties", "ym es", "Ġur anium", "ans om", "an ol", "ub a", "ĠL oss", "Ġa rous", "ĠHend erson", "W ow", "s pl", "c ur", "Ġ Ń", "Ġtheir s", "Dam age", "Ġdownload ing", "Ġdisc ern", "ĠSt o", "ĠFl a", "Ġh ath", "ĠA j", "Ġun pleasant", "Europe an", "exp ensive", "Ġscreens hot", "ĠU V", "Ġall ied", "ĠPers ian", "Ġmonop oly", "Ġat om", "ĠReds kins", "\"> <", "Ġcan cell", "Ġcinem a", "13 1", "f air", "ĠAlf red", "Ġd uck", "arg s", "22 3", "ĠIS I", "Ġsign aling", "in ar", "Ġlaugh s", "Ġfor wards", "Ġreck less", "Ġlisten ers", "at ivity", "Ġvast ly", "n ant", "L ess", "ĠHun ting", "ĠScient ific", "IT ED", "Ġkn ight", "ĠH TC", "us a", "t mp", "Ġr ude", "ĠLegend ary", "Ġar ises", "B ad", "ĠCl aim", "pe g", "Ġreal ities", "Th ink", "Ġ °", "Ġro de", "Ġstri ve", "Ġan ecd", "Ġshort s", "Ġhypot hes", "Ġcoord inated", "ĠGand hi", "ĠF PS", "R ED", "Ġsuscept ible", "Ġshr ink", "ĠCh art", "Hel p", "Ġ ion", "de ep", "rib es", "ĠK ai", "ĠCustom er", "Sum mary", "Ġc ough", "w ife", "Ġl end", "Ġposition ing", "Ġlot tery", "ĠC anyon", "Ġf ade", "Ġbron ze", "ĠKenn y", "Ġbo asts", "ĠEnh anced", "rec ord", "Ġemer gence", "Ġa kin", "ĠB ert", "it ous", "âĸ ij", "Ġst ip", "Ġexch anged", "om ore", "als h", "Ġreserv oir", "Ġstand point", "W M", "Ġiniti ate", "Ġdec ay", "Ġbrew ery", "Ġter ribly", "Ġmort al", "lev ard", "Ġrev is", "N I", "el o", "Ġconf ess", "ĠMS NBC", "Ġsub missions", "Cont roller", "Ġ20 2", "ĠR uth", "} );", "ĠAz ure", "Ġ .\"", "20 6", "ĠMarket ing", "Ġl aund", "ien cies", "Ġrenown ed", "ĠT rou", "ĠN GO", "ble ms", "Ġterr ified", "Ġwar ns", "Ġper t", "Ġuns ure", "4 80", "ale z", "ult z", "ĠOut side", "Ġst yl", "ĠUnder ground", "Ġp anc", "Ġd ictionary", "Ġf oe", "rim inal", "ĠNor wegian", "Ġj ailed", "Ġm aternal", "é e", "ĠLu cy", "c op", "Ch o", "Ġuns igned", "ĠZe lda", "ĠIns ider", "ĠContin ued", "Ġ13 3", "ĠNar uto", "ĠMajor ity", "16 9", "ĠW o", "ãĤ ĵ", "Ġpast or", "Ġinform al", "Ð ½", "an throp", "jo in", "ãģ Ĺ", "it ational", "N P", "ĠWrit ing", "f n", "ĠB ever", "19 5", "Ġy elling", "Ġdr astically", "Ġe ject", "Ġne ut", "Ġth rive", "ĠFre qu", "ou x", "Ġpossess es", "ĠSen ators", "ĠD ES", "ĠSh akespeare", "ĠFran co", "ĠL B", "uch i", "Ġinc arn", "Ġfound ers", "F unction", "Ġbright ness", "ĠB T", "Ġwh ale", "ĠThe ater", "m ass", "ĠD oll", "S omething", "Ġecho ed", "ĠHe x", "c rit", "af ia", "Ġgodd ess", "Ġele ven", "ĠPre view", "ĠAur ora", "Ġ4 01", "uls ive", "ĠLog an", "in burgh", "ĠCent ers", "ĠON LY", "ĠA id", "Ġparad ox", "Ġh urd", "ĠL C", "D ue", "c ourt", "Ġoff ended", "Ġeval uating", "ĠMatthew s", "Ġto mb", "Ġpay roll", "Ġextra ction", "ĠH ands", "if i", "Ġsuper natural", "ĠCOM M", "] =", "dog s", "Ġ5 12", "ĠMe eting", "Rich ard", "ĠMax imum", "Ġide als", "Th ings", "m and", "ĠReg ardless", "Ġhum ili", "b uffer", "L ittle", "ĠD ani", "ĠN ak", "Ġliber ation", "ĠA be", "ĠO L", "Ġstuff ed", "ac a", "ind a", "raph ic", "Ġmos qu", "Ġcampaign ing", "Ġoccup y", "S qu", "r ina", "ĠW el", "ĠV S", "Ġphys ic", "Ġp uls", "r int", "oad ed", "ET F", "ĠArch ives", "Ġven ues", "h ner", "ĠTur bo", "Ġl ust", "Ġappeal ed", "que z", "il ib", "ĠTim othy", "Ġo mn", "d ro", "Ġobs ession", "ĠSav age", "19 96", "Gl obal", "J es", "2 14", "Ġsl iding", "Ġdisapp ro", "ĠMag ical", "Ġvolunt arily", "g b", "ane y", "Ġprop het", "ĠRe in", "ĠJul ia", "ĠW orth", "aur us", "Ġb ounds", "ie u", ")) )", "Ġcro re", "ĠCitiz en", "S ky", "Ġcolumn ist", "Ġseek ers", "ond o", "IS A", "ĠL ength", "Ġnost alg", "Ġnew com", "Ġdet rim", "ent ric", "3 75", "ĠG E", "Ġaut op", "Ġacadem ics", "App Data", "ĠS hen", "Ġid iot", "ĠTrans it", "Ġteasp oon", "W il", "K O", "ĠCom edy", "> ,", "Ġpop ulated", "W D", "Ġp igs", "ĠO culus", "Ġsymp athetic", "Ġmar athon", "19 8", "Ġseiz ure", "s ided", "Ġd op", "irt ual", "L and", "ĠFl oor", "osa urs", "... ]", "Ġl os", "Ġsubsid iary", "E Y", "ĠPart s", "ĠSt ef", "ĠJud iciary", "Ġ13 4", "Ġmir rors", "Ġk et", "t imes", "Ġneuro log", "Ġc av", "ĠGu est", "Ġtum or", "sc ill", "ĠLl oyd", "E st", "Ġcle arer", "Ġstere otypes", "Ġd ur", "not hing", "Red dit", "Ġnegoti ated", "---------------- --------", "23 5", "Ġfl own", "ĠSe oul", "ĠRes ident", "ĠS CH", "Ġdisappear ance", "ĠV ince", "g rown", "Ġgrab s", "r il", "ĠInf inite", "ĠTw enty", "Ġpedest rian", "Ġjer sey", "ĠF ur", "ĠInf inity", "ĠEll iott", "Ġment or", "Ġmor ally", "Ġob ey", "sec ure", "iff e", "Ġantib iotics", "ang led", "ĠFre eman", "ĠIntrodu ction", "J un", "Ġm arsh", "ic ans", "ĠEV ENTS", "och ond", "W all", "icult y", "Ġmisdem eanor", "Ġl y", "Th omas", "ĠRes olution", "Ġanim ations", "ĠD ry", "Ġinter course", "ĠNew castle", "ĠH og", "ĠEqu ipment", "17 7", "Ġterrit orial", "Ġarch ives", "20 3", "Fil ter", "ĠMun ich", "Ġcommand ed", "ĠW and", "Ġpit ches", "ĠCro at", "Ġrat ios", "ĠM its", "Ġaccum ulated", "ĠSpecific ally", "Ġgentle man", "acer b", "Ġp enn", "Ġa ka", "ĠF uk", "Ġinterven e", "ĠRef uge", "ĠAlz heimer", "Ġsuccess ion", "oh an", "d oes", "L ord", "Ġsepar at", "Ġcorrespond ence", "Ġsh iny", "P rior", "Ġs ulf", "Ġmiser able", "Ġded ication", "( ).", "Ġspecial ists", "Ġdefect s", "ĠC ult", "ĠX ia", "Ġje opard", "ĠO re", "Ab ility", "Ġle ar", "Ġamb itions", "ĠB MI", "ĠArab s", "Ġ19 42", "Ġpres ervation", "ific ate", "Ġash amed", "l oss", "ĠRest aur", "Ġrese mble", "Ġen rich", "ĠK N", "ĠCl an", "fl oat", "Ġplay able", "IT T", "Ġharm ony", "arr ison", "ĠWe instein", "w ere", "Ġpoison ing", "ĠCom put", "ĠWord Press", "m ajor", "ĠVal ve", "F an", "ĠTh row", "ĠRom ans", "ĠDep ression", "ad os", "Ġtort ured", "Ġbal ancing", "bott om", "Ġacqu iring", "ĠMon te", "ard i", "Ġa ura", "Ġ# #", "ĠStand ing", "ĠAtl as", "C F", "Ġintr ins", "ĠBen ghazi", "Ġcamp ing", "Ġt apped", "bl ade", "st rous", "ĠR abb", "ĠW ritten", "t ip", "ĠNe igh", "ster dam", "ĠAll ow", "ĠHe aling", "ĠR hod", "n um", "Ġcaffe ine", "ĠPer cent", "Ġbo o", "Ġapp les", "30 5", "Ġwel coming", "Ġappl aud", "Ġa usterity", " ±", "ĠRe ality", "ef e", "å ®", "Ġsu cks", "Ġtab s", "ĠPay Pal", "Ġback pack", "Ġgif ted", "abul ary", "ĠSc out", "ir teen", "Ġch in", "Ġo mitted", "Ġnegative ly", "Ġaccess ing", "ĠE arn", "Ġambul ance", "Ġhead phones", "Ġ20 5", "ĠRef resh", "p resident", "ĠKit chen", "ĠEnt ered", "ĠS nyder", "00 5", "om ical", "Ġborrow ed", "ĠN em", "Ġav iation", "Ġst all", "rim ination", "Ġuniform s", "it ime", "ĠSim mons", "ener gy", "ab lished", "y y", "qual ified", "Ġrall ies", "ĠSt uart", "fl ight", "Ġgang s", "r ag", "Ġv ault", "lu x", "ĠCom par", "Ġdesign ation", "20 9", "ĠJ os", "d ollar", "z ero", "Ġwell s", "30 3", "Ġconstitu ents", "Ġhe ck", "Ġc ows", "Ġcommand ers", "Ġdifferent ial", "ĠC atherine", "29 9", "Ġval ve", "Ġbr ace", "Ġperspect ives", "c ert", "f act", "icular ly", "ĠMc N", "pl anes", "Ġint ric", "Ġpe as", "ov an", "Ġtoss ed", "ret ch", "ĠL opez", "Ġunf amiliar", "de ath", "ĠA part", "ĠCh ang", "Ġrelie ved", "rop he", "Ġair ports", "Ġfre ak", "ut il", "M ill", "ĠCh in", "ĠOw en", "m ale", "ĠBro ken", "ĠWind s", "ro b", "r ising", "Ġfire fighters", "Ġauthor itarian", "Ġ14 8", "Bit coin", "ex ternal", "Ġbrow sers", "iche ver", "or ian", "Ġun b", "Ġpo ke", "ĠZ ot", "M id", "ĠPop ular", "Ġco vert", "Ġcont ributes", "Ġ6 50", "Ġcont ention", "G ate", "Ġcons oles", "Ġchrom os", "ĠI X", "Ġvis ually", "ĠE isen", "Ġjewel ry", "Ġdeleg ation", "Ġacceler ate", "ĠR iley", "Ġsl ope", "Ġind oor", "it ially", "Ġhuge ly", "Ġtun nels", "Ġfin ed", "Ġdirect ive", "Ġfore head", "ustom ed", "Ġsk ate", "Mus ic", "g as", "Ġrecogn izing", "am bo", "Ġover weight", "ĠGr ade", "Ù Ĭ", "Ġsound ing", "Ġlock ing", "ĠR EM", "St ore", "Ġexc av", "ĠLike wise", "ĠL ights", "Ġel bow", "ĠSupp ly", "w ic", "Ġhands ome", "19 94", "C oll", "Ġadequ ately", "ĠAssoci ate", "Ġstri ps", "Ġcrack down", "Ġmar vel", "ĠK un", "Ġpass ages", "@@ @@", "ĠT all", "Ġthought ful", "names e", "Ġprost itution", "bus iness", "Ġball istic", "person al", "c ig", "iz ational", "R ound", "ĠÂłĠÂł ĠÂłĠÂł", "ĠCole man", "Ġadm itting", "ĠPl ug", "Ġbit coins", "ĠSu z", "Ġfair ness", "Ġsupp lier", "Ġcatast rophic", "ĠHel en", "o qu", "M arc", "ĠArt icles", "g ie", "Ġend angered", "Ġdest iny", "ĠVol t", "ol ia", "ax is", "Ġche at", "Ġun ified", "IC O", "qu ote", "30 2", "ĠS ed", "Ġsupp ression", "Ġanaly zing", "Ġsqu at", "Ġfig uring", "Ġcoordin ates", "Ġch unks", "Ġ19 46", "Ġsub p", "Ġw iki", "ĠFor bes", "ĠJ upiter", "ĠE rik", "im er", "ĠCom mercial", "\\ )", "Ġlegitim acy", "Ġd ental", "ĠMe an", "Ġdefic its", "5 50", "Orig inally", "ĠHor ror", "Ġcontam ination", "ll ah", "Ġconf isc", "ĠCl are", "T B", "ĠF ailed", "an ed", "Ġrul er", "ĠCont roller", "Ġfemin ists", "F ix", "g ay", "20 7", "Ġr abbit", "Th ird", "ownt own", "Ġgl ue", "Ġvol atile", "Ġsh ining", "Ġf oll", "Ġimp aired", "Ġsup ers", "æ Ī", "Ġcl utch", "ļé ĨĴ", "Ġpro let", "Ġ( !", "Ġy elled", "ĠK iev", "ĠEr n", "ĠSh ock", "K B", "Ġsit uated", "qu ery", "ĠN as", "Ġan nex", "char acter", "ĠHol iday", "Ġautom ation", "ĠJ ill", "ĠRem astered", "Ġl inem", "Ġwild erness", "ĠHor izon", "ĠGu inea", "A Z", "Ġmain land", "Ġsec recy", "LE ASE", "Ġp unk", "ĠProv ince", "( ),", "Spe ed", "Ġhand ing", "ĠSeb ast", "S ir", "r ase", "Ġj ournals", "Ġcon gest", "ĠT ut", "ir rel", "Ġschizophren ia", "Ġmis ogyn", "health y", "I ron", "Ġreact ed", "- $", "25 2", "Ġpl ural", "Ġpl um", "Ġbarg ain", "Ġground ed", "f inder", "Ġdis se", "ĠL az", "O OD", "Ġat roc", "F actory", "Ġmin ions", "Ġo ri", "ĠB rave", "ĠP RE", "ĠMy anmar", "ĠH od", "Ġexped ition", "Ġexpl ode", "ĠCo ord", "Ġext r", "ĠB rief", "ĠAD HD", "Ġhard core", "feed ing", "Ġd ile", "ĠF ruit", "Ġvacc ination", "ĠM ao", "osp here", "Ġcont ests", "- |", "Ġf ren", "isp here", "R om", "ĠSh arp", "ĠTre nd", "Ġdis connect", "âĢ¢ âĢ¢", "Ġper secution", "Ear th", "Ġhealth ier", "38 4", "Ġc ob", "ĠTr inity", "OW S", "AN N", "Ġspecial ty", "Ġg ru", "Ġcooper ative", "wh y", "Start ing", "ĠIss ues", "st re", "ens or", "Ġ18 5", "Ad v", "! ?", "ĠRe vel", "em ia", "ĠH ulk", "Ġcelebr ations", "ĠS ou", "ra ud", "ĠKle in", "Ġun real", "con text", "Ġpartners hips", "Ġadop ting", "t ical", "Ġspl ash", "ĠHe zbollah", "c ategory", "cycl op", "xt on", "ĠD ot", "urd y", "t z", "Ġenvelop e", "ĠN L", "â ķ", "Ġwhere in", "Spe c", "18 4", "Ġte lev", "al iation", "Ġmyth s", "å °", "Ġrig orous", "Ġcommun icating", "Ġobser ver", "Ġre he", "ĠW ash", "Ġapolog ized", "ĠT in", "Ġexpend itures", "work ers", "d ocument", "Ġhes itate", "ĠLen in", "Ġunpredict able", "Ġrenew al", "cl er", "ok ia", "ĠCON T", "Ġpost season", "Tok ens", "Ġex acerb", "Ġbet ting", "Ġ14 7", "Ġelev ation", "W ood", "ĠSol omon", "19 4", "00 4", "out put", "Ġredu nd", "ĠM umbai", "Ġp H", "Ġreprodu ce", "ĠD uration", "MA X", "Ġb og", "C BS", "ĠBal ance", "ĠS gt", "ĠRec ent", "Ġc d", "Ġpo pped", "Ġincomp et", "pro p", "ay an", "g uy", "Pac ific", "Ġty r", "Ġ{ {", "ĠMy stic", "ĠD ana", "Ġmast urb", "Ġge ometry", "à ¢", "ĠCor rect", "Ġtraject ory", "Ġdistract ed", "Ġf oo", "ĠW elsh", "L uc", "m ith", "Ġrug by", "Ġrespir atory", "Ġtri angle", "Ġ2 15", "Ġunder graduate", "ĠSuper ior", "ch anging", "_ -", "Ġright ly", "Ġrefere e", "Ġluc rative", "Ġun authorized", "Ġresemb les", "ĠGN U", "ĠDer by", "Ġpath ways", "ĠL ed", "Ġend urance", "Ġst int", "Ġcollect or", "F ast", "Ġd ots", "Ġnational s", "ĠSec urities", "Ġwh ip", "Par am", "Ġlearn s", "M agic", "Ġdetail ing", "m oon", "Ġbroadcast ing", "Ġb aked", "26 5", "hol m", "ĠS ah", "ĠHus sein", "ĠCourt esy", "17 4", "Ġ14 6", "Ġge ographic", "pe ace", "Ġjud ging", "ĠS tern", "B ur", "Ġstory line", "G un", "ĠSt ick", "24 5", "30 7", "ãĤ´ ãĥ³", "ĠAdminist rator", "Ġbur nt", "Ġp ave", "ch oes", "Ex ec", "Ġcamp uses", "Res ult", "Ġmut ations", "ĠCh arter", "Ġcapt ures", "Ġcomp ares", "Ġbad ge", "S cient", "Ġer ad", "ier y", "o i", "ett es", "ĠE state", "Ġst rap", "Ġproud ly", "Ġf ried", "Ġwithd rawn", "ĠV oy", "ph ony", "It ems", "ĠP ierce", "b ard", "Ġann otation", "ant on", "ill on", "Im pro", "... )", "Ġhapp ier", "---- --", "ad just", "Ġstaff ers", "Ġactiv ism", "Ġper f", "Ġal right", "N eed", "Ġcomm ence", "Ġopio id", "ĠAm anda", "E s", "ĠP ars", "ĠK aw", "W orks", "24 8", "Ġind o", "t c", "end ant", "ĠM oto", "Ġlegal ization", "OT E", "Ġtask ed", "Ġt sp", "ĠACT IONS", "16 6", "Ġrefres hing", "ĠN R", "ĠPere z", "Ġinfring ement", "S Y", "List en", "in ning", "k u", "Ġrot ate", "pro gram", "ar ah", "Des ign", "Ġ( £", "Ġst oring", "Ġwar rants", "Ġjud gement", "ĠB rist", "us ually", "ph oto", "ĠR an", "ĠP ine", "Ġoutrage ous", "ĠValent ine", "lu ence", "ĠEvery body", "Al tern", "Ġrele vance", "Ġtermin ated", "Ġd essert", "Ġfulf illed", "Ġprosecut ed", "ĠW ords", "Ġm igrant", "Ġcultiv ation", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "idel ity", "ĠV ern", "ĠLog in", "Ġmetaph or", "ĠT ip", "Ġrecru its", "ĠP ig", "rib ing", "Ġenthusi asts", "ex per", "Ġfright ening", "ĠH air", "ans on", "str ate", "Ġh i", "He ight", "Ġown ing", "n one", "Ġdis like", "Ġkn ives", "pher d", "Ġloud ly", "ĠAP Is", "Dis play", "ĠL ac", "ĠUS S", "ab l", "ver ages", "J ew", "Ġ17 2", "ĠHist orical", "at oon", "ĠPhys ics", "in tern", "Ġwarm th", "Ġto pp", "D M", "Ġgun man", "Ġem peror", "od i", "ãĥ £", "in atory", "ĠR ib", "Ġ13 1", "ĠSat urn", "ĠSh ining", "Ġw aking", "Qu otes", "Ġcomed ian", "en berg", " ½", "Ġbelie vers", "Ġpaper work", "c ustom", "Ġle v", "Ġl ament", "Ġpour ing", "22 2", "p olitical", "ĠSupp lement", "m aid", "Ġcruel ty", "Ġt read", "ys ics", "A w", "rit es", "Ġmod ifier", "ĠP osition", "Ad am", "l b", "ub s", "Ġimper fect", "Ġcl usters", "ĠEngine er", "ĠC herry", "Ġinaug uration", "ĠS au", "Ġembod iment", "ĠUn cle", "Ġover r", "Ġexplos ions", "c ule", "ĠPrinc eton", "ĠAndre a", "Ġincorrect ly", "Ġearn est", "Ġpil gr", "ĠS print", "Ġslee ve", "Ġhe ars", "ĠAm azing", "Ġbrow sing", "ag in", "Ġhom eland", "Ġha w", "Ġd iving", "ist ered", "17 8", "Ġbarg aining", "ĠArc ade", "Ġdeleg ate", "ters on", "................................ ................................", "ĠJackson ville", "27 5", "Ġst agn", "Ġad am", "ĠSher man", "C B", "Ġsub urb", "ĠFood s", "Ġconver ting", "ĠAr ist", "Ġch ambers", "l ove", "Ġam ino", "ĠG an", "Ġmad ness", "m c", "ĠUS E", "def ined", "Ġul tr", "ind ust", "Ġw olves", "l ance", "Add itionally", "Ġcr acks", "as ia", "ĠRe ason", "ĠP ump", "Ġaccident al", "ĠL aser", "ĠR id", "Ġinitial ized", "ell i", "Ġun named", "Ġn oun", "ĠPass ed", "Ġhost age", "ĠEth iop", "sh irts", "Ġun rel", "ĠEmb assy", "Ġ19 41", "Ġat oms", "Ġpur ported", "16 4", "ĠF i", "Ġgall ons", "ĠMon ica", "Ġp g", "en ment", "Ġsort ed", "ĠG ospel", "Ġhe ights", "Ġtr aced", "Ġunder going", "She ll", "Ġs acks", "Ġproport ions", "Ġhall uc", "F ont", "ac et", "Ġwar mer", "ĠIN TER", "Ġgrab bing", "Pl ug", "Ġreal ization", "ĠBur ke", "Ġen chant", "AT ER", "ĠSe ed", "Ġabund ant", "F M", "Ġc ivic", "V s", "is i", "Ġv ow", "Ġre per", "ĠPartners hip", "Ġpenet ration", "Ġax e", "Ġsh attered", "ĠZ ombies", "Ġv inyl", "ĠAl ert", "e on", "Ġoblig ed", "ĠIll ust", "ĠPl aza", "ĠFront ier", "Ġdavid jl", "ĠSer ial", "ĠH av", "ĠNut rition", "B i", "Ġâĸ Ī", "ĠJ ays", "lin ux", "Ġhur ry", "Ġv oy", "Ġhop eless", "ĠSte alth", "Ġ ãģ", "ess ors", "tt le", "b org", "ĠSaf ari", "f ell", "Ġw ary", "d ue", "ĠAb ove", "H a", "E LL", "Ġnot or", "ĠW on", "T oo", "Ġoccup ations", "Ġposs essions", "Ġinv iting", "Ġpred ators", "Ġacceler ated", "Ġ15 7", "uter te", "ĠC ube", "e ast", "acc ount", "G ive", "Ġtrans plant", "red ients", "id able", "Ġscreens hots", "ĠG und", "ĠF S", "Ġtravel ers", "Ġsens ory", "ĠF iat", "ĠRock ets", "İ ĭ", "_ {", "F riend", "Ġchar ming", "AL S", "Ġenjoy ment", "m ph", "Ġ5 000", "ĠRE G", "Ù Ĩ", "b ia", "Ġcomp ilation", "ro st", "ĠV P", "ĠSch ne", "201 9", "Ġcop ying", "M ORE", "ĠFl ore", "f alls", "2 15", "t otal", "Ġdis ciples", "d ouble", "Ġexceed ing", "Ġsm ashed", "Ġconcept ual", "ĠRom ania", "ĠB rent", "ĠI CE", "ĠT ou", "Ġg rap", "Ġn ails", "18 9", "ãĥ ĺ", "Ġproc ure", "e ur", "Ġconfir ming", "ĠC ec", "aw i", "ĠEd en", "Ġn g", "Ġengine ered", "at ics", "Ġhook ed", "Ġdisgust ing", "ĠMur der", "ãĤ ¿", "L ibrary", "Ġ16 8", "Al most", "hem atic", "Men u", "ĠNot re", "ĠJ ur", "Ġkidn apped", "Ġhack er", "ĠJ ade", "Ġcreep y", "Ġdraw ings", "ĠSpons or", "Ġcycl ists", "ĠGob lin", "Ġoptim ized", "Ġst aged", "ĠMc D", "bet ween", "A ge", "en o", "S ex", "ĠW ide", "n ings", "av is", "Ġincap able", "ĠK ob", "Ġreward ing", "ĠL one", "oles cent", "Ġcontract ed", "Ġstick y", "J ose", "B all", "f est", "ĠIn put", "ĠRec ently", "Ġto mat", "squ are", "App lication", "Ġnit rogen", "Ġdupl icate", "ĠRec on", "ĠD ear", "L ondon", "Ġint ra", "Ġd ock", "Ġout reach", "ĠM illion", "Ġmamm als", "am pton", "V AL", "Ġsn aps", "Ġd os", "ĠWh ole", "ĠRead y", "T ry", "ĠWinn ipeg", "ear ance", "Ġinc urred", "ren ched", "ĠNS W", "il ot", "rain e", "Ġc ube", "g ot", "Ġrun way", "etermin ed", "ĠHaw ks", "Ġsurviv or", "ĠW ish", "ĠD in", "ĠDE F", "ĠV ault", "18 7", "Ġmush rooms", "Ġcris p", "be y", "ĠDisco very", "Ġdevelopment al", "Ġparad igm", "Ġcha otic", "ĠT su", "Ġ3 33", "b ons", "Ġbacter ial", "Ġcomm its", "Ġcos mic", "Ġme ga", "oc ative", "ĠP aint", "ophob ic", "Ġv ain", "Ġcar ved", "ĠTh ief", "ĠG ul", "ows hip", "Ġc ites", "ĠEd inburgh", "Ġdimin ished", "Ġacknowled ges", "ĠK ills", "Ġmic row", "ĠHer a", "Ġsen iors", "Ġwhere by", "H op", "at ron", "Ġun available", "ĠN ate", "Ġ4 80", "Ġsl ated", "ĠRe becca", "ĠB attery", "Ġgram mar", "Ġhead set", "Ġcurs or", "Ġex cluding", "any e", "aunder ing", "eb in", "Ġfeas ible", "ĠPub lishing", "ĠLab s", "ĠCl iff", "ĠFerr ari", "Ġp ac", "vis ible", "mark ed", "pe ll", "Ġpol ite", "Ġstagger ing", "ĠGal actic", "Ġsuper st", "Ġpar an", "ĠOffic ers", "ãĢ ģ", "Ġspecific s", "ul us", "23 9", "ĠP aste", "AM P", "ĠPan ama", "ĠDe lete", "angu ard", "rest rial", "Ġhero ic", "ĠD y", "ا ÙĦ", "Ġincumb ent", "Ġcr unch", "t ro", "Ġsc oop", "Ġblog ger", "Ġsell ers", "ure n", "Ġmedic ines", "ĠC aps", "ĠAnim ation", "ox y", "Ġout ward", "Ġinqu iries", "22 9", "Ġpsych ologist", "ĠS ask", "ev il", "Ġcontam inated", "ãĤ ¨", "he rence", "Ġbrand ed", "ĠAbd ul", "z h", "Ġparagraph s", "Ġmin s", "Ġcor related", "er b", "Ġimp art", "Ġmil estone", "ĠSol utions", "ot le", "Ġunder cover", "Ġmar ched", "ĠCharg ers", "f ax", "ĠSec rets", "Ġr uth", "we ather", "Ġfemin ine", "Ġsh am", "Ġprest igious", "igg ins", "Ġs ung", "hist ory", "ett le", "gg ie", "Ġout dated", "ol and", "Ġper ceptions", "ĠS ession", "ĠDod gers", "u j", "ĠE ND", "D oc", "Ġdefic iency", "Gr and", "ĠJ oker", "Ġretro spect", "Ġdiagn ostic", "Ġharm less", "Ġro gue", "ĠA val", "E qu", "Ġtrans c", "ĠRoberts on", "ĠDep ending", "ĠBurn s", "iv o", "Ġhost ility", "F eatures", "ĵ ĺ", "Ġdis comfort", "ĠL CD", "spec ified", "ĠEx pect", "3 40", "Ġimper ative", "ĠReg ular", "Ch inese", "Ġstate wide", "Ġsy mm", "Ġlo ops", "Ġaut umn", "N ick", "Ġsh aping", "Ġqu ot", "Ġc herry", "ĠCross ref", "è¦ ļéĨĴ", "Stand ard", "he ed", "ĠD ell", "ĠViet namese", "Ġo st", "ĠV alkyrie", "O A", "Ass ad", "Ġreb ound", "ĠTra ffic", "pl aces", "æ ĺ", "ĠB uc", "17 2", "Ġshel ters", "Ġins isting", "ĠCertain ly", "ĠKenn eth", "ĠT CP", "Ġpen al", "ĠRe play", "he ard", "Ġdial ect", "iz a", "ĠF Y", "it cher", "ĠD L", "Ġspir al", "Ġquarterback s", "Ġh ull", "Ġgo ogle", "Ġto dd", "ĠSter ling", "ĠPl ate", "Ġsp ying", "mb ol", "ĠReal m", "ĠPro ced", "ĠCr ash", "Ġtermin ate", "Ġprotest ing", "C enter", "gu ided", "Ġun cover", "Ġboy cott", "Ġreal izes", "s ound", "Ġpret ending", "ĠV as", "19 80", "Ġfram ed", "Ġ13 9", "Ġdesc ended", "Ġrehab ilitation", "Ġborrow ing", "ĠB uch", "Ġbl ur", "R on", "ĠFro zen", "en za", "Ch ief", "ĠP oor", "Ġtransl ates", "M IN", "Ġ2 12", "J ECT", "Ġerupt ed", "Ġsuccess es", "S EC", "Ġpl ague", "Ġg ems", "d oms", "Ġstret ches", "ĠSp y", "Ġstory telling", "C redit", "ĠP ush", "Ġtra ction", "Ġin effective", "ĠL una", "Ġt apes", "Ġanaly tics", "erc ise", "Ġprogram mes", "ĠCar bon", "Ġbeh old", "he avy", "ĠConserv ation", "ĠF IR", "Ġs ack", "ter min", "ric ks", "Ġhous ed", "Ġunus ually", "I ce", "Ġexecut ing", "ĠMor oc", "ed ay", "Ġed itions", "Ġsm arter", "ĠB A", "Ġout law", "Ġvan ished", "ib a", "AL SE", "ĠSil va", "23 8", "C ould", "Ġphilos opher", "Ġevac uated", "Sec ret", "14 2", "Ġvis as", "ãĤ ¬", "ĠM alt", "ĠClear ly", "ĠN iger", "ĠC airo", "ĠF ist", "3 80", "ĠX ML", "aut o", "it ant", "Ġrein forced", "Rec ord", "ĠSurviv or", "G Hz", "Ġscrew s", "parent s", "Ġo ceans", "ma res", "Ġbra kes", "vas ive", "Ġhell o", "ĠS IM", "rim p", "Ġo re", "ĠArm our", "24 7", "Ġterr ific", "Ġt ones", "14 1", "ĠMin utes", "Ep isode", "Ġcur ves", "Ġinflamm atory", "Ġbat ting", "ĠBeaut iful", "L ay", "Ġunp op", "v able", "Ġr iots", "ĠTact ics", "b augh", "ĠC ock", "Ġorg asm", "ĠS as", "Ġconstruct or", "et z", "G ov", "Ġant agon", "Ġthe at", "Ġde eds", "ha o", "c uts", "ĠMc Cl", "Ġu m", "ĠScient ists", "Ġgrass roots", "ys sey", "\"] =>", "Ġsurf aced", "Ġsh ades", "Ġneighb ours", "Ġad vertis", "oy a", "Ġmer ged", "Up on", "Ġg ad", "Ġanticip ate", "Any way", "Ġsl ogan", "Ġdis respect", "I ran", "ĠT B", "act ed", "Ġsubp oen", "medi ately", "OO OO", "Ġwa iver", "Ġvulner abilities", "ott esville", "ĠHuff ington", "J osh", "ĠD H", "M onday", "ĠEll en", "K now", "x on", "it ems", "22 8", "Ġf ills", "ĠN ike", "Ġcum ulative", "and als", "I r", "Ġ ì", "Ġfr iction", "ig ator", "Ġsc ans", "ĠVi enna", "ld om", "Ġperform ers", "P rim", "Ġb idding", "M ur", "Ġlean ed", "ĠPri x", "al ks", "Ġ[ âĢ¦]", "ĠTw itch", "ĠDevelop er", "ĠG ir", "Ġcall back", "Ab stract", "Ġacc ustomed", "Ġfreed oms", "ĠP G", "ur acy", "Ġl ump", "is man", ",, ,,", "19 92", "ĠR ED", "Ġwor m", "M atch", "ĠPl atinum", "I J", "ĠOwn er", "Tri via", "com pl", "Ġnew born", "Ġfant as", "O wn", "Ġ19 59", "Ġsymp ath", "Ġub iqu", "Ġoutput s", "Ġal lev", "Ġpr ag", "K evin", "Ġfav ors", "Ġbur ial", "Ġn urt", "so lete", "c ache", "Ġ15 6", "Ġunl ocks", "te chn", "M aking", "Ġcon quer", "ad ic", "æ ĸ", "Ġel f", "Ġelect orate", "ĠKurd s", "ĠSt ack", "ĠSam urai", "Ġâ ĺħ", "Ġ{ }", "ĠS aid", "ĠFall out", "Ġkind ness", "ĠCustom s", "ĠBou levard", "Ġhelicop ters", "ot ics", "ĠVe get", "com ment", "Ġcritic ised", "Ġpol ished", "ĠRem ix", "ĠC ultural", "Ġrec ons", "Ġdo i", "at em", "Sc reen", "Ġbar red", "Com ments", "ĠGener ally", "Ġsl ap", "7 20", "V ari", "p ine", "Ġem pt", "Ġh ats", "ĠPlay ing", "l ab", "a verage", "form s", "ĠC otton", "Ġcan s", "ĠD ON", "ĠSom alia", "C rypt", "ĠIncre ases", "E ver", "mod ern", "Ġsur geon", "3 000", "Ġrandom ized", "================================ ================================", "B ern", "im pl", "ĠC OR", "Ġpro claim", "th ouse", "Ġto es", "Ġam ple", "Ġpres erving", "Ġdis bel", "gr and", "B esides", "Ġsil k", "ĠPat tern", "h m", "Ġenter prises", "Ġaffidav it", "ĠAdvis ory", "Ġadvert ised", "ĠRel igious", "se ctions", "psy ch", "ĠField s", "aw ays", "Ġhasht ag", "ĠNight mare", "Ġv ampire", "Ġfore nsic", "rosso ver", "n ar", "Ġn avy", "Ġvac ant", "ĠD uel", "Ġhall way", "Ġface book", "ident ally", "ĠN RA", "Ġm att", "Ġhur ricane", "ĠKir by", "ĠP uzzle", "Ġsk irt", "ou st", "du llah", "Ġanal ogy", "in ion", "Ġtomat oes", "ĠN V", "ĠPe ak", "ĠMe yer", "Ġappoint ments", "Ġm asc", "Ġal ley", "re hend", "Ġchar ities", "Ġund o", "Ġdest inations", "ĠTest ing", "\"> \"", "c ats", "* .", "Ġgest ures", "gener al", "Le ague", "Ġpack ets", "ĠInspect or", "ĠBer g", "Ġfraud ulent", "Ġcritic ize", "F un", "Ġbl aming", "nd ra", "Ġsl ash", "ĠE ston", "Ġpropos ing", "Ġwh ales", "Ġtherap ist", "Ġsub set", "Ġle isure", "EL D", "ĠC VE", "ĠAct ivity", "Ġcul min", "sh op", "ĠD AY", "is cher", "ĠAdmir al", "ĠAtt acks", "Ġ19 58", "Ġmem oir", "Ġfold ed", "Ġsex ist", "Ġ15 3", "ĠL I", "Ġread ings", "Ġembarrass ment", "ĠEmploy ment", "w art", "ch in", "Ġcontin uation", "l ia", "Rec ently", "Ġd uel", "Ġevac uation", "ĠKash mir", "Ġdis position", "ĠR ig", "Ġbol ts", "Ġins urers", "4 67", "M ex", "Ġret aliation", "Ġmis ery", "Ġunre asonable", "r aining", "I mm", "ĠP U", "em er", "Ġgen ital", "ãĤ ³", "ĠC andy", "Ġon ions", "ĠP att", "lin er", "Ġconced ed", "Ġf a", "Ġfor c", "ĠH ernandez", "ĠGe off", "deb ian", "ĠTe ams", "Ġc ries", "Ġhome owners", "23 7", "A BC", "Ġst itch", "Ġstat istic", "Ġhead ers", "ĠBi ology", "Ġmot ors", "ĠG EN", "ĠL ip", "Ġh ates", "Ġhe el", "S elf", "i pl", "ED IT", "ort ing", "Ġann ot", "ĠSpe ech", "old emort", "ĠJ avascript", "ĠLe Bron", "Ġfoot print", "Ġf n", "Ġseiz ures", "n as", "h ide", "Ġ19 54", "ĠBe e", "ĠDecl aration", "ĠKat ie", "Ġreserv ations", "N R", "f emale", "Ġsatur ated", "Ġb iblical", "Ġtroll s", "Dev ice", "ph otos", "Ġdr ums", "ãĥīãĥ© ãĤ´ãĥ³", "N ight", "f ighter", "ĠH ak", "ri ber", "Ġc ush", "Ġdiscipl inary", "ba um", "ĠG H", "ĠSch midt", "ilib rium", "Ġs ixty", "ĠKush ner", "ro ts", "Ġp und", "ĠR ac", "Ġspr ings", "Ġcon ve", "Bus iness", "F all", "Ġqual ifications", "Ġvers es", "Ġnarc iss", "ĠK oh", "ĠW ow", "ĠCharl ottesville", "ed o", "Ġinterrog ation", "ĠW ool", "36 5", "B rian", "Ġâľ ĵ", "Ġalleg es", "ond s", "id ation", "ĠJack ie", "y u", "Ġl akes", "Ġworth while", "Ġcryst als", "ĠJud a", "Ġcomp rehend", "Ġfl ush", "Ġabsor ption", "ĠO C", "Ġfright ened", "ĠCh ocolate", "Mart in", "Ġbu ys", "Ġbu cks", "Ġapp ell", "ĠChampions hips", "Ġlist ener", "ĠDef ensive", "Ġc z", "ud s", "ĠM ate", "Ġre play", "Ġdecor ated", "Ġs unk", "ĠV IP", "ĠAn k", "Ġ19 5", "aa aa", "Nob ody", "ĠMil k", "ĠG ur", "ĠM k", "ĠS ara", "Ġse ating", "ĠW id", "Tr ack", "Ġemploy s", "Ġgig antic", "AP P", "ãĤ §", "in ventory", "Ġtow el", "at che", "l asting", "ĠT L", "Ġlat ency", "Ġkn e", "B er", "me aning", "Ġup held", "Ġplay ground", "Ġm ant", "S ide", "Ġstere o", "Ġnorth west", "Ġexception ally", "Ġr ays", "Ġrec urring", "D rive", "Ġup right", "Ġab duct", "ĠMar athon", "Ġgood bye", "Ġal phabet", "h p", "Ġcourt room", "ring ton", "ot hing", "T ag", "Ġdiplom ats", "Ġbar bar", "ĠAqu a", "18 3", "33 33", "Ġmat urity", "Ġinst ability", "ĠAp ache", "Ġ= ==", "Ġfast ing", "ĠGr id", "Mod Loader", "Ġ15 2", "A bs", "ĠOper ating", "ett i", "Ġacqu aint", "Don nell", "ĠK em", "ĠFor ge", "Ġarm ored", "M il", "Ġphilos ophers", "in vest", "Pl ayers", "â Ī", "Ġmy riad", "Ġcomr ades", "R ot", "Ġremember ing", "Ġcorrespond s", "Ġprogram mers", "ĠLyn n", "Ġo lig", "Ġco herent", "yn chron", "ĠChem ical", "Ġj ugg", "p air", "post s", "E ye", "ĠIn ner", "Ġsem ester", "ott est", "ĠEmir ates", "ric anes", "or ously", "m its", "ĠW is", "Ġd odge", "l ocation", "Ġf aded", "Am azon", "ĠPro ceed", "ĠIN FO", "j ournal", "ĠTru ck", "T en", "Ġ2 17", "Ġstat utes", "m obile", "ĠT ypes", "Rec omm", "b uster", "pe x", "Ġleg ends", "Ġhead ache", "f aced", "ĠWi Fi", "if ty", "ĠH ER", "Ġcirc uits", "ER ROR", "22 6", "ol in", "Ġcyl inder", "osp ace", "ik ers", "P rem", "Qu ant", "Ġconflic ting", "Ġslight est", "Ġfor ged", "ion age", "Step hen", "ĠK ub", "ĠOpp ortun", "ĠHe al", "Ġbl o", "Ġrul ers", "Ġh uh", "Ġsubmar ine", "f y", "ass er", "Ġallow ance", "ĠKas ich", "ĠT as", "ĠAustral ians", "Forge ModLoader", "ĠâĨ ij", "ĠMat rix", "am ins", "Ġ12 00", "ĠAc qu", "23 6", "D ocument", "ĠBre aking", "19 3", "ĠSub st", "ĠRoll er", "ĠPro perties", "ĠN I", "t ier", "Ġcr ushing", "Ġadvoc ating", "Further more", "keep ers", "Ġsex ism", "x d", "Ġcall er", "ĠS ense", "chie ve", "ĠT F", "Ġfuel ed", "Ġreminis cent", "Ġobs ess", "ur st", "Ġup hold", "ĠF ans", "het ics", "Ġâ Ĺ", "ĠB ath", "Ġbe verage", "Ġo scill", "25 4", "Ġpol es", "Ġgrad ual", "Ġex ting", "ĠS uff", "ĠS uddenly", "Ġlik ing", "Ġ19 49", "un ciation", "am ination", "ĠO mar", "ĠL V", "ĠCon sequently", "Ġsynt hes", "ĠG IF", "Ġp ains", "Ġinteract ing", "u ously", "inc re", "Ġrum or", "ĠScient ology", "19 7", "ĠZ ig", "Ġspe lling", "ĠA SS", "Ġexting u", "ms on", "Ġg h", "Ġremark ed", "ĠStrateg ic", "ĠM ON", "å ¥", "g ae", "ĠWH AT", "E ric", "ĠCamp us", "Ġmeth ane", "Ġimag in", "J UST", "ĠAl m", "X T", "i q", "ĠR SS", "Ġwrong doing", "att a", "Ġbig ot", "Ġdemonstr ators", "ĠCal vin", "ĠV illa", "Ġmembr ane", "ĠAw esome", "Ġbenef ic", "26 8", "Ġmagn ificent", "ĠL ots", "G reg", "ĠBor is", "Ġdetain ees", "ĠH erman", "Ġwhis pered", "Ġa we", "Prof essor", "fund ing", "Ġphys iological", "ĠDest ruction", "Ġlim b", "Ġmanip ulated", "Ġbub bles", "Ġpse ud", "Ġhyd ra", "ĠBrist ol", "Ġst ellar", "ĠExp ansion", "ĠK ell", "ĠInterest ingly", "Ġm ans", "Ġdrag ging", "Ġec ological", "ĠF it", "Ġg ent", "Ġbenef ited", "ĠHait i", "Ġpoly g", "ãĥ İ", "Ġ20 30", "Ġpro w", "Ġrecon struction", "Ġwas t", "Ġpsych ic", "ĠGree ks", "Hand ler", "16 2", "ĠP ulse", "Ġsol icit", "Ġsy s", "Ġinflu x", "ĠG entle", "per cent", "Ġprolifer ation", "Ġtax able", "Ġdisreg ard", "Ġesc aping", "Ġg inger", "Ġwith stand", "Ġdevast ated", "ĠD ew", "ser ies", "Ġinject ed", "ela ide", "Ġturn over", "he at", "Ļ Ĥ", "H appy", "ĠSil ent", "ãĤ Ń", "iv ism", "Ġir rational", "AM A", "Ġre ef", "r ub", "Ġ16 2", "Ġbank ers", "ĠEth ics", "v v", "Ġcritic isms", "K n", "18 6", "M ovie", "ĠT ories", "Ġno od", "Ġdist ortion", "F alse", "od ore", "Ġt asty", "Res earch", "ĠU ID", "- )", "Ġdivor ced", "ĠM U", "ĠHay es", "ĠIs n", "ian i", "ĠH Q", "Ġ\" #", "ign ant", "Ġtra umatic", "ĠL ing", "H un", "Ġsab ot", "on line", "r andom", "Ġren amed", "ra red", "K A", "d ead", "é t", "ĠAss istance", "Ġse af", "++++ ++++", "Ġse ldom", "ĠWeb b", "Ġbo olean", "u let", "Ġref rain", "ĠDI Y", "ru le", "Ġshut ting", "Ġutil izing", "load ing", "ĠPar am", "co al", "oot er", "Ġattract ing", "ĠD ol", "Ġher s", "ag netic", "ĠRe ach", "im o", "Ġdisc arded", "ĠP ip", "01 5", "ü r", "Ġm ug", "Im agine", "C OL", "Ġcurs ed", "ĠSh ows", "ĠCurt is", "ĠSach s", "spe aking", "ĠV ista", "ĠFram ework", "ong o", "Ġsub reddit", "Ġcr us", "ĠO val", "R ow", "g rowing", "Ġinstall ment", "Ġgl ac", "ĠAdv ance", "EC K", "ĠLGBT Q", "LE Y", "Ġac et", "Ġsuccess ive", "ĠNic ole", "Ġ19 57", "Qu ote", "Ġcircumst ance", "ack ets", "Ġ14 2", "ort ium", "Ġguess ed", "ĠFr ame", "Ġperpet rators", "ĠAv iation", "ĠBen ch", "Ġhand c", "A p", "Ġ19 56", "25 9", "r and", "Net Message", "d in", "urt les", "h ig", "ĠV III", "ff iti", "ĠSw ords", "b ial", "Ġkidn apping", "dev ice", "Ġb arn", "ĠEl i", "auc as", "S end", "Con structed", "Ġ ½", "Ġneed les", "Ġad vertisements", "Ġv ou", "Ġexhib ited", "ĠFort ress", "As k", "B erry", "TY PE", "Ġcan cers", "ump ing", "ĠTerrit ory", "Ġpr ud", "Ġn as", "Ġathe ist", "Ġbal ances", "ãģ Ł", "ĠSh awn", "& &", "Ġland sc", "ĠR GB", "Ġpet ty", "Ġex cellence", "Ġtransl ations", "Ġpar cel", "ĠChe v", "E ast", "ĠOut put", "im i", "Ġamb ient", "ĠTh reat", "Ġvill ains", "Ġ5 50", "IC A", "Ġtall er", "Ġle aking", "c up", "Ġpol ish", "Ġinfect ious", "ĠK C", "Ġ@ @", "back ground", "Ġbureaucr acy", "ĠS ai", "un less", "it ious", "ĠSky pe", "At l", "ID ENT", "00 8", "Ġhyp ocr", "Ġpit chers", "Ġguess ing", "ĠF INAL", "Bet ween", "Ġvill agers", "Ġ25 2", "f ashion", "ĠTun is", "Be h", "ĠEx c", "ĠM ID", "28 8", "ĠHas kell", "19 6", "ĠN OR", "Ġspec s", "Ġinv ari", "Ġgl ut", "ĠC ars", "Ġimp ulse", "Ġhon ors", "g el", "Ġjurisd ictions", "ĠBund le", "ul as", "Calif ornia", "ĠIncre ase", "Ġp ear", "Ġsing les", "Ġc ues", "Ġunder went", "ĠW S", "Ġexagger ated", "Ġdub ious", "Ġfl ashing", "L OG", ") ].", "J ournal", "t g", "V an", "ĠI stanbul", "ĠIn sp", "ĠFrank en", "D raw", "Ġsad ness", "Ġiron ic", "ĠF ry", "x c", "Ġ16 4", "is ch", "W ay", "ĠProtest ant", "h orn", "Ġun aff", "ĠV iv", "ill as", "ĠProduct ions", "ĠH ogan", "Ġper imeter", "ĠS isters", "Ġspont aneous", "Ġdown side", "Ġdescend ants", "Ġor n", "w orm", "Japan ese", "Ġ19 55", "Ġ15 1", "ĠDo ing", "els en", "umb les", "Ġrad ically", "ĠDr um", "ĠB ach", "Ġli abilities", "ĠO B", "ĠElement ary", "Ġmem e", "yn es", "Ġfinger print", "ĠGr ab", "Ġundert ake", "Mem bers", "ĠRead er", "ĠSim s", "g od", "Ġhypot hetical", "s cient", "ĠA J", "Ġchar ism", "Ġad missions", "ĠMiss ile", "tr ade", "Ġexerc ising", "ĠBack ground", "W ritten", "Ġvoc als", "whe ther", "Ġv i", "ĠW inner", "Ġl itter", "ĠSh ooting", "ST EM", "ãĤ ¡", "ĠA FL", "Ġvari ability", "Ġe ats", "ĠD PS", "b row", "Ġeleph ants", "Ġstr at", "Ġ Å", "Ġsett lers", "Matt hew", "Ġin advert", "H I", "ĠIM F", "ĠGo al", "Ġnerv es", "John son", "ey e", "ablish ment", "Th ursday", "BIL ITY", "H ad", "am oto", "het amine", "ep s", "Ġmit ochond", "Ġcomp ressed", "ĠTre vor", "ĠAnim als", "T ool", "L ock", "Ġtwe ak", "Ġpin ch", "Ġcancell ation", "P ot", "Ġfoc al", "ĠAst ron", "17 3", "ĠA SC", "ĠO THER", "umn i", "Ġdem ise", "d l", "Ù ħ", "Sem itism", "Ġcr acking", "Ġcollabor ative", "Ġexpl ores", "s ql", "Ġher bs", "Ġconfig urations", "m is", "ĠRes ult", "ace y", "ĠSm oke", "Ġsan ct", "el ia", "Ġdeg ener", "Ġdeep est", "Ġscream ed", "Ġn ap", "Soft ware", "ĠST AR", "E F", "ĠX in", "spons ored", "mans hip", "23 3", "Ġprim aries", "Ġfilter ing", "Ġas semble", "m il", "ĠMy ers", "b ows", "Ġpun ched", "M ic", "Ġinnov ations", "Ġfun c", "and o", "Ġfr acking", "ĠV ul", "о Ð", "osh op", "ĠIm mun", "Ġsett ling", "Ġadolesc ents", "Ġreb uilding", "Ġtransform ing", "Ġpar ole", "Ġhar bor", "Ġbook ing", "ot ional", "onge vity", "ĠY o", "b ug", "Ġemer ges", "ĠMethod s", "ĠCh u", "P res", "ĠDun geons", "Ġtra iling", "ĠR um", "ĠH ugh", "å¤ ©", "ĠE ra", "ĠBatt les", "Res ults", "ĠTr ading", "Ġvers a", "c ss", "ax ies", "he et", "Ġgre ed", "19 89", "Ġgard ens", "Ġconting ent", "P ark", "ĠLeaf s", "h ook", "ro be", "Ġdiplom acy", "ĠF uel", "ĠInv asion", "Ġupgr ading", "M ale", "Ġe lic", "Ġrelent less", "ĠCo venant", "ap esh", "ĠT rop", "T y", "pro duction", "art y", "Ġpun ches", "ak o", "cyclop edia", "ĠR abbit", "ĠHD MI", "Ġ14 1", "Ġf oil", "Item Image", "ĠF G", "Ġimplement ations", "ĠP om", "ixt ures", "Ġaw ait", "Ġ3 30", "am us", "Ġumb rella", "Ġfore see", "se par", "Ġcircum cision", "Ġperipher al", "S ay", "ĠExper t", "In c", "Ġwithd rew", "ĠAnd ers", "f ried", "Ġradio active", "ĠOp ening", "Ġboard ing", "ĠN D", "Ġover throw", "Act iv", "W P", "ĠAct s", "× Ļ", "Ġmot ions", "v ic", "ĠM ighty", "ĠDef ender", "a er", "Ġthank ful", "ĠK illing", "ĠBr is", "mo il", "Ġpredict ing", "26 6", "ch oice", "Ġkill ers", "Ġinc ub", "ĠChe st", "ather ing", "Ġpro claimed", "fl ower", "oss om", "umbled ore", "ĠCy cling", "ĠOccup y", "AG ES", "P en", "ĠY ug", "Ġpack aged", "Ġheight ened", "c ot", "st ack", "C ond", "Ġst amps", "m age", "Ġpersu aded", "Ġens l", "ĠCard inal", "Ġsol itary", "Ġpossess ing", "ĠC ork", "Ġev id", "ĠT ay", "Ġbl ues", "Ġextrem ism", "Ġlun ar", "Ġcl own", "Te chn", "Ġfest ivals", "ĠPv P", "ĠL ar", "Ġconsequ ently", "p resent", "Ġsom eday", "ç İĭ", "ĠMet eor", "Ġtour ing", "c ulture", "Ġbe aches", "S hip", "c ause", "ĠFl ood", "ãĥ ¯", "Ġpur ity", "th ose", "Ġem ission", "b olt", "Ġch ord", "ĠScript ure", "L u", "Ġ$ {", "cre ated", "Other s", "25 8", "Ġelement al", "Ġannoy ed", "ĠA E", "d an", "ĠS ag", "Res earchers", "Ġfair y", "âĢĵ âĢĵ", "======== ====", "Sm art", "GG GG", "Ġskelet ons", "Ġpup ils", "link ed", "Ġur gency", "en abled", "ĠF uck", "Ġcoun cill", "r ab", "U AL", "T I", "Ġlif es", "Ġconf essed", "B ug", "Ġharm on", "ĠCON FIG", "ĠNe utral", "D ouble", "Ġst aple", "ĠSH A", "Brit ish", "ĠSN P", "AT OR", "oc o", "Ġswing ing", "ge x", "ole on", "pl ain", "ĠMiss ing", "ĠTro phy", "v ari", "ran ch", "Ġ3 01", "4 40", "00000000 00000000", "Ġrest oring", "Ġha ul", "uc ing", "ner g", "Ġfut ures", "Ġstrateg ist", "quest ion", "Ġlater al", "ĠB ard", "Ġs or", "ĠRhod es", "ĠD owntown", "????? -", "ĠL it", "ĠB ened", "Ġco il", "st reet", "ĠPort al", "FI LE", "ĠG ru", "* ,", "23 1", "ne um", "Ġsuck ed", "Ġr apper", "Ġtend encies", "ĠLaure n", "cell aneous", "26 7", "Ġbrow se", "Ġover c", "head er", "o ise", "Ġbe et", "ĠG le", "St ay", "Ġm um", "Ġtyp ed", "Ġdiscount s", "T alk", "ĠO g", "ex isting", "ĠS ell", "u ph", "C I", "ĠAust rian", "ĠW arm", "Ġdismiss al", "Ġaver ages", "c amera", "Ġalleg iance", "L AN", "=\" #", "Ġcomment ators", "ĠSet ting", "ĠMid west", "Ġpharm ac", "ĠEX P", "Ġstain less", "Ch icago", "Ġt an", "24 4", "Ġcountry side", "ĠV ac", "29 5", "Ġpin ned", "Ġcr ises", "Ġstandard ized", "T ask", "ĠJ ail", "ĠD ocker", "col ored", "f orth", "\" },", "Ġpat rons", "Ġsp ice", "Ġm ourn", "ĠM ood", "Ġlaund ry", "Ġequ ip", "ĠM ole", "y ll", "ĠTH C", "n ation", "ĠSher lock", "Ġiss u", "ĠK re", "ĠAmeric as", "ĠA AA", "Ġsystem atically", "Ġcont ra", "ĠS ally", "Ġrational e", "Ġcar riage", "Ġpe aks", "Ġcontrad iction", "ens ation", "ĠFail ure", "Ġpro ps", "Ġnames pace", "Ġc ove", "field s", "ãĤ ĭ", "Ġw ool", "ĠC atch", "Ġpresum ed", "ĠD iana", "r agon", "ig i", "Ġh amm", "Ġst unt", "ĠG UI", "ĠObserv atory", "ĠSh ore", "Ġsmell s", "ann ah", "Ġcock pit", "ĠD uterte", "8 50", "Ġopp ressed", "bre aker", "ĠCont ribut", "ĠPer u", "ĠMons anto", "ĠAtt empt", "Ġcommand ing", "Ġfr idge", "ĠR in", "ĠChe ss", "ual ity", "Ġo l", "Republic an", "ĠGl ory", "ĠW IN", ".... ...", "ag ent", "read ing", "Ġin h", "J ones", "Ġcl icks", "al an", "Ġ[ ];", "ĠMaj esty", "ĠC ed", "op us", "ate l", "à ª", "AR C", "ĠEc uador", "ãĥ ł", "ĠK uro", "Ġritual s", "Ġcapt ive", "Ġoun ce", "Ġdisag reement", "Ġsl og", "f uel", "P et", "M ail", "Ġexerc ised", "Ġsol ic", "Ġrain fall", "Ġdev otion", "ĠAss essment", "Ġrob otic", "opt ions", "ĠR P", "ĠFam ilies", "ĠFl ames", "Ġassign ments", "00 7", "aked own", "Ġvoc abulary", "Re illy", "Ġc aval", "g ars", "Ġsupp ressed", "ĠS ET", "ĠJohn s", "Ġwar p", "bro ken", "Ġstat ues", "Ġadvoc ated", "Ġ2 75", "Ġper il", "om orph", "ĠF emin", "per fect", "Ġh atch", "L ib", "5 12", "Ġlif elong", "3 13", "Ġche eks", "Ġnum bered", "ĠM ug", "B ody", "ra vel", "We ight", "ĠJ ak", "ĠHe ath", "Ġkiss ing", "ĠJ UST", "Ġw aving", "u pload", "Ġins ider", "ĠPro gressive", "ĠFil ter", "tt a", "ĠBe am", "Ġviol ently", "ip ation", "Ġskept icism", "Ġ19 18", "ĠAnn ie", "ĠS I", "Ġgen etics", "Ġon board", "at l", "ĠFried man", "ĠB ri", "cept ive", "Ġpir ate", "ĠRep orter", "27 8", "Ġmyth ology", "Ġe clipse", "Ġsk ins", "Ġgly ph", "ing ham", "F iles", "C our", "w omen", "Ġreg imes", "Ġphotograp hed", "K at", "ĠMA X", "Offic ials", "Ġunexpected ly", "Ġimpress ions", "F ront", ";;;; ;;;;", "Ġsuprem acy", "Ġs ang", "Ġaggrav ated", "Ġabrupt ly", "ĠS ector", "Ġexc uses", "Ġcost ing", "ide press", "St ack", "ĠR NA", "ob il", "Ġghost s", "ld on", "at ibility", "Top ics", "Ġreim burse", "ĠH M", "ĠDe g", "Ġth ief", "y et", "ogen esis", "le aning", "ĠK ol", "ĠB asketball", "Ġf i", "ĠSee ing", "Ġrecy cling", "Ġ[ -", "Cong ress", "Ġlect ures", "P sy", "Ġne p", "Ġm aid", "Ġori ented", "A X", "Ġrespect ful", "re ne", "fl ush", "ĠUn loaded", "re quest", "gr id", "ĠAltern atively", "ĠHug o", "Ġdec ree", "ĠBuddh ism", "and um", "And roid", "ĠCong o", "ĠJoy ce", "Ġacknowled ging", "hes ive", "ĠTom orrow", "ĠH iro", "th ren", "ĠM aced", "Ġho ax", "ĠIncre ased", "ĠPr adesh", "W ild", "____ __", "16 1", "Ġa unt", "Ġdistribut ing", "ĠT ucker", "ĠSS L", "ĠW olves", "B uilding", "ou lt", "ĠLu o", "ĠY as", "ĠSp ir", "ĠSh ape", "ĠCamb od", "ĠIP v", "Ġm l", "Ġext rad", "39 0", "ĠPenn y", "d ream", "Ġstation ed", "opt ional", "ew orthy", ". ", "ĠWorks hop", "ĠRet ail", "ĠAv atar", "6 25", "N a", "ĠV C", "ĠSec ure", "M Y", "19 88", "oss ip", "Ġpro state", "Ġund en", "Ġg amer", "ĠCont ents", "ĠWar hammer", "ĠSent inel", "3 10", "Ġse gregation", "ĠF lex", "ĠM AY", "Ġdr ills", "ĠDrug s", "Islam ic", "Ġsp ur", "Ġca fe", "Ġimag inary", "Ġgu iding", "Ġsw ings", "ĠThe me", "ob y", "Ġn ud", "Ġbe gging", "Ġstr ongh", "Ġreject ing", "Ġpedest rians", "ĠPro spect", "R are", "s le", "Ġconcess ions", "ĠConst itutional", "Ġbe ams", "Ġfib ers", "p oon", "Ġinstinct s", "pro perty", "ĠB IG", "Sand ers", "im ates", "Ġco ating", "Ġcorps es", "ĠTR UE", "check ed", "Ġ16 6", "A sh", "ĠJ S", "ĠF iction", "Ġcommun al", "Ġener getic", "oooo oooo", "Ġnow adays", "IL D", "ib o", "ĠSU V", "R en", "Ġdwell ing", "Sil ver", "Ġt ally", "ĠM oving", "Ġcow ard", "Ġgener als", "Ġhorn s", "Ġcirc ulated", "Ġrob bed", "ĠUn limited", "Ġharass ed", "Ġinhib it", "Ġcomp oser", "ĠSpot ify", "Ġspread s", "3 64", "Ġsu icidal", "Ġno ises", "ĠSt ur", "Ġs aga", "ĠK ag", "is o", "Ġtheoret ically", "M oney", "Ġsimilar ity", "Ġslic ed", "ut ils", "ing es", "\" -", "Ġan th", "Ġimp ed", "Mod ule", "Through out", "Ġmen us", "comm ittee", "and i", "ob j", "in av", "f ired", "ĠAb dullah", "Ġund ead", "Ġfont s", "H old", "EN G", "Ġsustain ability", "Ġfl ick", "Ġr azor", "ĠF est", "ĠChar acters", "Ġword ing", "Ġpopul ist", "Ġcritic izing", "Ġm use", "v ine", "Ġcard board", "Ġkind ly", "Ġfr inge", "ĠThe ft", "icult ural", "Ġgovern ors", "Ġ ����", "Ġ16 3", "Ġtime out", "ĠA uth", "Child ren", "A U", "Ġred emption", "ĠAl ger", "Ġ19 14", "Ġw aved", "Ġastron auts", "og rams", "Ġsw amp", "ĠFinn ish", "Ġcand le", "Ġton nes", "ut m", "Ġr ay", "Ġsp un", "Ġfear ful", "art icles", "Ġca us", "or ically", "ĠRequ ires", "ĠG ol", "Ġpop e", "Ġinaug ural", "Ġg le", "AD A", "ĠIS IL", "ĠOff ensive", "Ġwatch dog", "Ġbal con", "ent ity", "ĠH oo", "Ġgall on", "AC C", "Ġdoub ling", "Ġimpl ication", "ĠS ight", "Ġdoct r", "---- ---", "Ġ\\ \\", "Ġm alt", "R oll", "Ġâī ¥", "Ġrec ap", "add ing", "u ces", "ĠB end", "fig ure", "Ġtur key", "Ġsoc ietal", "ĠT ickets", "Ġcommer cially", "Ġsp icy", "Ġ2 16", "ĠR amp", "Ġsuperior ity", "à ¯", "ĠTr acker", "C arl", "ĠC oy", "ĠPatri ot", "Ġconsult ed", "Ġlist ings", "Ġsle w", "reens hot", "ĠG one", "Ġ[ ...]", "30 9", "Ġh ottest", "Ø ±", "Ġrock y", "ĠD iaz", "Ġmass age", "Ġpar aly", "Ġp ony", "A z", "Ġcart ridge", "ĠN Z", "Ġsn ack", "ĠLam ar", "ple ment", "ĠLes lie", "Ġm ater", "Ġsn ipp", "24 6", "Ġjoint ly", "ĠBris bane", "ĠiP od", "Ġpump ing", "Ġgo at", "ĠSh aron", "eal ing", "Ġcor on", "Ġan omal", "rah im", "ĠConnect ion", "Ġsculpt ure", "Ġsched uling", "ĠD addy", "at hing", "Ġeyeb rows", "Ġcur ved", "Ġsent iments", "Ġdraft ing", "D rop", "( [", "Ġnom inal", "ĠLeaders hip", "ĠG row", "Ġ17 6", "Ġconstruct ive", "iv ation", "Ġcorrupt ed", "ger ald", "ĠC ros", "ĠChe ster", "ĠL ap", "ãģ ª", "OT H", "D ATA", "Ġal mond", "pro bably", "I mp", "Ġfe ast", "ĠWar craft", "F lor", "Ġcheck point", "Ġtrans cription", "Ġ20 4", "Ġtwe aks", "Ġrel ieve", "S cience", "Ġperform er", "Z one", "Ġtur moil", "ig ated", "hib it", "ĠC afe", "the med", "Ġflu or", "ben ch", "Ġde com", "ĠU nt", "ĠBar rett", "ĠF acts", "Ġt asting", "ĠPTS D", "ĠSe al", "ĠJuda ism", "ĠDynam ic", "ĠC ors", "V e", "ĠM ing", "ĠTrans form", "v on", "ĠDef enders", "ĠTact ical", "ĠV on", "ĠUn ivers", "Ġdist orted", "ĠB reath", "?' \"", "Ġag on", "ĠDead ly", "Ġl an", "ĠCy cle", "orn ed", "Ġrel iably", "Ġgl or", "ĠMon key", "ãĥ ¡", "Ġad ren", "Ġmicrow ave", "ĠAl ban", "irc raft", "dig it", "sm art", "ĠD read", "¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯", "{ {", "ĠRoc hester", "Ġsimpl ified", "Ġinf licted", "Ġtake over", "Ġyour selves", "ad itional", "Ġmus cular", "K S", "Ġing en", "T ax", "ĠFe ature", "27 7", "Ġcru c", "Ġcr ate", "Ġun identified", "Ġacclaim ed", "ĠM anga", "ĠFr ances", "ĠNep al", "ĠG erald", "ĠKu wait", "Ġsl ain", "ĠHe b", "ĠG oku", "ãģ® æ", "28 6", "M rs", "ĠC ody", "ĠSan ctuary", "01 6", "Ġdism ant", "Ġdatas et", "ĠH ond", "b uck", "ĠPat terson", "Ġpal ette", "ĠG D", "ic ol", "ĠL odge", "Ġplanet ary", "ak in", "ĠRegist ered", "ab we", "ĠPeters burg", "Ġha iled", "ĠP iece", "S che", "ĠDO J", "Ġen umer", "18 1", "ĠObs erver", "ĠB old", "f ounded", "com merce", "Ġexplo its", "ĠF inding", "UR N", "ĠS ne", "ĠAc id", "ay ette", "ĠVal ues", "Ġdr astic", "Ġarchitect ural", "Ġ\" .", "× ķ", "ump ed", "Ġwra pping", "Ġwid ow", "ĠSl ayer", "l ace", "on ce", "German y", "av oid", "Ġtem ples", "P AR", "à ´", "ĠLuc ifer", "ĠFl ickr", "l ov", "for ces", "Ġsc outing", "Ġlou der", "tes y", "Ġbefore hand", "Ä ĵ", "ĠNe on", "ĠW ol", "ĠTyp ically", "ĠPolit ico", "-+ -+", "Ġbuild er", "Ġder ive", "K ill", "Ġp oker", "Ġambig uous", "Ġlif ts", "Ġcy t", "Ġrib s", "ood le", "ĠS ounds", "h air", "ĠSynd rome", "t f", "Ġproport ional", "u id", "Ġper taining", "ĠKind le", "ĠNeg ro", "Ġreiter ated", "ĠTon ight", "oth s", "ĠCorn ell", "Ġo wing", "Ġ20 8", "elf are", "oc ating", "ĠB irds", "Sub scribe", "Ġess ays", "Ġburd ens", "Ġillust rations", "ar ious", "ER AL", "ĠCal cul", "Ġx en", "ĠLink edIn", "ĠJ ung", "Ġredes ign", "Con nor", "29 6", "Ġrevers al", "ĠAd elaide", "ĠL L", "Ġs inking", "Ġg um", "US H", "c apt", "ĠGr imm", "Ġfoot steps", "ĠCB D", "isp ers", "Ġpro se", "Wed nesday", "ĠM ovies", "ed in", "Ġoverturn ed", "Ġcontent ious", "US B", "~~~~~~~~ ~~~~~~~~", "ĠCo pper", "Ġpoint less", "N V", "val ues", "olph in", "d ain", "Ġdepos ited", "ĠG W", "Ġpreced ed", "ĠCl a", "ĠGo lem", "ĠN im", "ĠÎ ²", "ĠEngine ers", "m iddle", "Ġfl att", "oper ative", "Ġcouncil s", "imb abwe", "el in", "Ġstress ful", "ĠL D", "Ġres h", "l ake", "Ġwheel chair", "ĠAltern ative", "Ġoptim ize", "oper ation", "Ġpe ek", "Ġones elf", "ig il", "Ġtrans itions", "op athy", "bl ank", "Ġ16 9", "17 1", "________________________________ ________________________________", "Ġl aundering", "En c", "ĠD EC", "Ġwork outs", "Ġsp ikes", "Ġdin osaurs", "Ġdiscrim inatory", "P ool", "R ather", "38 5", "R NA", "tes ters", "et o", "ĠIdent ity", "Ġve in", "ĠBur ton", "Ġarc ade", "4 20", "Ult imately", "ĠSad ly", "à °", "p ill", "Ġcub ic", "ĠSpect rum", "the se", "st ates", "Ġun official", "h awks", "ĠEVER Y", "Ġrain bow", "Ġincarcer ation", "and ing", "Ġsy ll", "ĠEver ton", "Ġ17 9", "ĠSer bia", "Ġ18 9", "m eter", "ĠMic key", "Ġant iqu", "Ġfact ual", "ne ck", "ĠN are", "n orm", "m ust", "Ġhigh ways", "Ġgl am", "Ġdivid ing", "ĠSquad ron", "ĠMar tha", "Ġbirth s", "C over", "//////// ////////", "ĠW ong", "Ph ot", "ĠA LS", "ri o", "ĠNon etheless", "ĠL emon", "Ġ20 6", "ĠE E", "Ġderiv ative", "ĠWW II", "v ote", "Ġthere in", "Ġsepar ating", "44 6", "sy nc", "ĠStre ets", "Ġr att", "Ġmunicip ality", "ĠShort ly", "Ġmon k", ") ,\"", "Ġscr ub", "Ġoper atives", "Ne ither", "Pl ace", "ĠLim it", "F emale", "ĠAct or", "Char acter", "Ġconstit uted", "35 7", "Ġprotest ed", "ĠSt raw", "ĠHe ight", "ild a", "ĠTy ph", "Ġflood s", "Ġcos metic", "W AY", "pert ure", "up on", "t ons", "ess ing", "ĠP ocket", "Ġro oft", "ĠC aucas", "Ġant idepress", "Ġincomp atible", "EC D", "Ġoper a", "ĠCont est", "Ġgener ators", "l 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ed", "V ICE", "Ġcontact ing", "ĠG ent", "he i", "Ġlabel ing", "Ġmerc ury", "ĠL ite", "Ġexp ires", "Ġdest abil", "rit is", "c u", "Ġfeather s", "Ġste er", "Ġprogram med", "ĠV ader", "Go ing", "ĠE lim", "Ġy o", "ĠMic he", "Ġ20 3", "Ġslee ves", "Ġb ully", "ĠHum ans", "36 8", "Ġcomp ress", "ĠBan ner", "AR S", "Ġa while", "Ġcal ib", "Ġspons orship", "ĠDiff iculty", "ĠP apers", "Ġident ifier", "} .", "Ġy og", "ĠSh ia", "Ġclean up", "Ġvib e", "int rodu", "im ming", "Austral ia", "Ġout lines", "ĠY outube", "tr ain", "ĠM akes", "Ġde ported", "Ġcent r", "ĠD ug", "ĠB oulder", "ĠBuff y", "Ġinj unction", "ĠHar ley", "ĠG roups", "ĠD umbledore", "ĠCl ara", "Ġ\" -", "Ġsacrific ed", "ep h", "Sh adow", "ib ling", "Ġfreel ance", "Ġevident ly", "ph al", "Ġret ains", "M ir", "Ġfin ite", "d ar", "ĠC ous", "Ġrep aired", "Ġperiod ic", "Ġchampions hips", "Ġaster oid", "bl ind", "Ġexpress ly", "ĠAst ros", "Ġsc aled", "Ġge ographical", "ĠRap ids", "En joy", "Ġel astic", "ĠMoh amed", "Mark et", "be gin", "Ġdisco vers", "Ġtele communications", "Ġscan ner", "Ġen large", "Ġsh arks", "Ġpsy chedel", "ĠRou ge", "Ġsnap shot", "is ine", "X P", "Ġpestic ides", "ĠL SD", "ĠDist ribution", "re ally", "Ġde gradation", "Ġdisgu ise", "Ġbi om", "ĠEX T", "Ġequ ations", "Ġhaz ards", "ĠComp ared", ") *", "Ġvirt ues", "Ġeld ers", "Ġenh ancing", "ĠAc ross", "er os", "ang ling", "Ġcomb ust", "ucc i", "Ġconc ussion", "Ġcontrace ption", "ĠK ang", "Ġexpress es", "Ġa ux", "ĠP ione", "Ġexhib its", "Deb ug", "OT AL", "ĠAl ready", "ĠWheel er", "Ġexp ands", "? :", "Ġreconc iliation", "Ġpir ates", "Ġpur se", "Ġdiscour age", "Ġspect acle", "R ank", "Ġwra ps", "ĠTh ought", "Ġimp ending", "O pp", "ĠAng lo", "ĠE UR", "Ġscrew ed", "ret ched", "Ġencour agement", "mod els", "Ġconf use", "mm m", "ĠVit amin", "âĸij âĸij", "C ru", "Ġkn ights", "Ġdisc ard", "Ġb ishops", "ĠW ear", "ĠGar rett", "k an", "ãĥ Ł", "Ġmascul ine", "cap ital", "ĠA us", "Ġfat ally", "th anks", "ĠA U", "ĠG ut", "12 00", "Ġ 00000000", "Ġsur rog", "ĠBI OS", "ra its", "ĠWat ts", "Ġresur rection", "ĠElect oral", "ĠT ips", "4 000", "Ġnut rient", "Ġdepict ing", "Ġspr ink", "Ġm uff", "ĠL IM", "ĠS ample", "ps c", "ib i", "gener ated", "Ġspec imens", "Ġdiss atisf", "Ġtail ored", "Ġhold ings", "ĠMonth ly", "ĠE at", "po ons", "Ġne c", "ĠC age", "ĠLot us", "ĠLan tern", "Ġfront ier", "Ġp ensions", "Ġj oked", "ĠHard y", "=-=- =-=-", "r ade", "U ID", "Ġr ails", "Ġem it", "Ġsl ate", "Ġsm ug", "Ġsp it", "ĠCall s", "ĠJac obs", "f eat", "ĠU E", "Ġrest ruct", "Ġregener ation", "Ġenerg ies", "ĠCon nor", "OH N", "ĠChe ese", "Ġg er", "Ġresur rect", "man agement", "N W", "Ġpres ently", "ĠBru ins", "M ember", "ĠM ang", "id an", "Ġboost ing", "w yn", "+ .", "requ isite", "ĠNY PD", "ĠMe gan", "ĠCond itions", "Ġp ics", "nes ium", "ĠR ash", "Ġ17 4", "ĠD ucks", "Ġemb ro", "z u", "on ian", "rel igious", "Ġc raz", "ĠAC A", "ĠZ ucker", "EM A", "ĠPro s", "We apon", "ĠKn ox", "ĠAr duino", "Ġst ove", "Ġheaven s", "ĠP urchase", "Ġher d", "Ġfundra iser", "Dig ital", "5 000", "Ġprop onents", "/ âĢĭ", "Ġj elly", "ĠVis a", "Ġmon ks", "Ġadvance ment", "ĠW er", "Ġ18 7", "e us", "ert ility", "Ġfet al", "Ġ19 36", "L o", "Ġout fits", "Ġstair case", "b omb", "Ġcustom ized", "cl air", "T ree", "Ġm apped", "ĠConsider ing", "ĠTor res", "Ġmeth yl", "Ġapprox imate", "Ġdo om", "ĠHans en", "Ġc rossover", "Ġstand alone", "ä ¼", "Ġinv ites", "Ġgra veyard", "Ġh p", "Donald Trump", "Ġesc ort", "G ar", "Ġpredec essors", "Ġh ay", "Ġen zyme", "ĠStra ight", "vis ors", "I ng", "ane ously", "ĠApp lied", "Ġf ec", "ĠDur ant", "Ġout spoken", "or b", "Ġz eal", "Ġdisgr ace", "' ).", "ĠChe ng", "28 9", "ĠRen a", "ĠSu icide", "29 4", "Ġout raged", "ĠNew man", "ĠN vidia", "ĠA ber", "ĠB ers", "Ġrecre ation", "Wind ow", "ĠD P", "x e", "Ġped oph", "Ġfall out", "ambo o", "Ġpresent ations", "ĠApp s", "Ġh tml", "3 45", "ĠX XX", "Ġrub bing", "ĠLe ather", "Ġhum idity", "se ys", "est ablished", "ĠUn its", "64 6", "Ġrespect able", "A uto", "Ġthri ving", "ĠInn ovation", "ang s", "Ext ra", "reg ulation", "29 8", "p ick", "Ex amples", "ĠC J", "Att ack", "Ġdr acon", "L T", "Ġstick er", "re rs", "Ġsun ny", "I ss", "reg ulated", "d im", "ĠAb stract", "Ġhus bands", "Off ice", "om ination", "it ars", "AN GE", "asc al", "ĠK ris", "ĠInf antry", "Ġm alf", "ĠA the", "ĠR ally", "bal anced", "................ ........", "OU P", "Ġmole cule", "met ics", "ĠSpl it", "ĠInstruct ions", "ĠN ights", "c ards", "Ġt ug", "Ġcon e", "å Ń", "Ġt x", "ĠDisc ussion", "Ġcatast rophe", "pp e", "g io", "Ġcommun ism", "Ġhal ted", "ĠGu ant", "cle an", "ĠSc hed", "ĠK anye", "Ġw ander", "ĠSer iously", "Ġ18 8", "enn ial", "f ollow", "product ive", "ĠFl ow", "ĠS ail", "Ġc raw", "Ġsim ulations", "or u", "ang les", "ĠN olan", "Ġmen stru", "4 70", "Ġ20 7", "aj a", "Ġcas ually", "board ing", "Ġ2 22", "ov y", "ĠN umbers", "um at", "O E", "28 7", "ĠCle mson", "Ġcert s", "Ġsl id", "ĠT ribe", "Ġto ast", "Ġfort unes", "Ġf als", "ĠComm ittees", "Ġg p", "Ġf iery", "ĠN ets", "ĠAn ime", "Pack age", "ĠComp are", "l aughter", "in fect", "Ġatroc ities", "Ġjust ices", "Ġins ults", "ĠVern on", "Ġsh aken", "Ġperson a", "est amp", "36 7", "br ain", "Ġexperiment ing", "K en", "ĠElect ronics", "Ġ16 1", "dom ain", "Ġgraph ical", "b ishop", "Ġwho pping", "ĠEv angel", "Ġadvertis ers", "ĠSpe ar", "Ġb ids", "Ġdestro ys", "ut z", "Ġunders c", "ĠAD D", "Ġan ts", "ĠC um", "ipp les", "ĠF ill", "Ġgl anced", "Ġind icted", "ĠE ff", "Ġmis con", "ĠDes ktop", "Ġab ide", "ãĥ Ģ", "ĠI o", "ĠC oul", "Ġcaps ule", "ĠCh rys", "M ON", "Ġund es", "ĠI RA", "Ġc itation", "Ġdict ate", "ĠNet works", "ĠConf lict", "ĠSt uff", "x a", "is ec", "ĠChem istry", "Ġquarter ly", "William s", "an an", "O pt", "ĠAlexand ria", "out heastern", "ĠSpring field", "ĠBlack s", "Ġge ography", "24 2", "Ġut most", "ĠEx xon", "ab outs", "E VA", "ĠEn able", "ĠBar r", "Ġdisag reed", "ĠCy prus", "Ġdement ia", "Ġlab s", "Ġubiqu itous", "ĠLO VE", "Ġconsolid ated", "s r", "Ġcream y", "ĠTim ber", "Reg ardless", "ĠCert ificate", "Ġ\" ...", "ogen ous", "Capt ain", "Ġinsult ing", "ĠSor os", "ĠInst r", "ĠBulgar ia", "bet ter", "Ġsuck ing", "ĠDavid son", "at z", "Ġcoll ateral", "g if", "Ġplag ued", "ĠC ancel", "ĠGard ner", "R B", "Ġsix teen", "Rem ove", "ur istic", "c ook", "R od", "Ġcompr ising", "f le", ") âĢĶ", "ĠVik ing", "g rowth", "agon al", "Ġsr f", "af ety", "m ot", "N early", "st own", "ĠF actor", "Ġautom obile", "Ġproced ural", "m ask", "amp ires", "Ġdisapp ears", "j ab", "3 15", "Ġ19 51", "ne eded", "Ġd aring", "le ader", "Ġp odium", "Ġun healthy", "Ġm und", "Ġpy ramid", "oc re", "Ġkiss ed", "Ġdream ed", "ĠFant astic", "ĠG ly", "å Ĭ", "Ġgreat ness", "Ġsp ices", "Ġmet ropolitan", "Ġcomp uls", "i ets", "101 6", "ĠSh am", "ĠP yr", "fl ies", "ĠMid night", "Ġswall owed", "Ġgen res", "ĠL ucky", "ĠRew ards", "Ġdisp atch", "ĠI PA", "ĠApp ly", "Ġa ven", "al ities", "3 12", "th ings", "Ġ( ).", "Ġm ates", "ĠS z", "ĠC OP", "ol ate", "O FF", "Ġre charge", "c aps", "ĠYork er", "ic one", "Ġgal axies", "ile aks", "D ave", "ĠP uzz", "ĠCelt ic", "ĠA FC", "27 6", "ĠS ons", "Ġaffirm ative", "H or", "Ġtutorial s", "ĠC ITY", "ĠR osa", "ĠExt ension", "Ser ies", "Ġf ats", "Ġr ab", "l is", "Ġun ic", "Ġe ve", "ĠSp in", "Ġadul thood", "ty p", "Ġsect arian", "Ġcheck out", "ĠCy cl", "S ingle", "Ġmart yr", "Ġch illing", "88 8", "ou fl", "Ġ] ;", "Ġcongest ion", "m k", "ĠWhere as", "Ġ19 38", "ur rencies", "er ion", "Ġbo ast", "ĠPat ients", "Ġch ap", "ĠB D", "real DonaldTrump", "Ġexam ines", "h ov", "Ġstart ling", "ĠBab ylon", "w id", "om ew", "br ance", "ĠOd yssey", "w ig", "Ġtor ch", "ĠV ox", "ĠMo z", "ĠT roll", "ĠAn s", "Similar ly", "ĠF ul", "00 6", "Un less", "ĠAl one", "st ead", "ĠPub lisher", "r ights", "t u", "ĠDoes n", "Ġprofession ally", "Ġcl o", "ic z", "Ġste als", "Ġ á", "19 86", "Ġst urdy", "ĠJoh ann", "Ġmed als", "Ġfil ings", "ĠFr aser", "d one", "Ġmult inational", "Ġf eder", "Ġworth less", "Ġp est", "Yes terday", "ank ind", "Ġg ays", "Ġb orne", "ĠP OS", "Pict ure", "Ġpercent ages", "25 1", "r ame", "Ġpot ions", "AM D", "ĠLeban ese", "Ġr ang", "ĠL SU", "ong s", "Ġpen insula", "ĠCl ause", "AL K", "oh a", "ĠMac Book", "Ġunanim ous", "Ġl enders", "Ġhang s", "Ġfranch ises", "ore rs", "ĠUp dates", "Ġisol ate", "and ro", "S oon", "Ġdisrupt ive", "ĠSur ve", "Ġst itches", "ĠSc orp", "ĠDomin ion", "Ġsupp lying", "Ar g", "Ġtur ret", "ĠL uk", "Ġbr ackets", "* )", "ĠRevolution ary", "ĠHon est", "Ġnot icing", "ĠSh annon", "Ġafford ed", "Ġth a", "ĠJan et", "! --", "ĠNare ndra", "ĠPl ot", "H ol", "se ver", "e enth", "Ġobst ruction", "Ġ10 24", "st aff", "j as", "or get", "sc enes", "l aughs", "ĠF argo", "cr ime", "Ġorche str", "Ġde let", "ili ary", "rie ved", "Ġmilit ar", "ĠGreen e", "âĹ ı", "ãģ ¦", "ĠGu ards", "Ġunle ashed", "ĠWe ber", "Ġadjust able", "Ġcal iber", "Ġmotiv ations", "Ġà ł", "m Ah", "ĠL anka", "hand le", "Ġp ent", "ĠR av", "ĠAng ular", "ĠK au", "umb ing", "Ġphil anthrop", "Ġde hyd", "Ġtox icity", "e er", "ĠY ORK", "w itz", "å ¼", "ĠI E", "commun ity", "ĠA H", "Ġret ali", "Ġmass ively", "ĠDani els", "ĠD EL", "Ġcar cin", "Ur l", "Ġrout ing", "ĠNPC s", "ĠR AF", "ry ce", "Ġwa ived", "ĠGu atem", "Every body", "Ġco venant", "Ġ17 3", "Ġrelax ing", "Ġqu art", "al most", "Ġguard ed", "ĠSold iers", "ĠPL AY", "Ġout going", "L AND", "Ġre write", "ĠM OV", "ĠIm per", "ĠS olution", "Ġphenomen al", "Ġl ongevity", "Ġimp at", "ĠN issan", "ir ie", "Ġod or", "ĠZ ar", "ok s", "Ġmilit ias", "ĠSP EC", "Ġtoler ated", "ars er", "ĠBrad ford", "+ ,", "Ġsur real", "s f", "Can adian", "Ġresemb lance", "Ġcarbohyd rate", "VI EW", "Ġaccess ory", "me al", "larg est", "ieg el", "Some one", "Ġtoug hest", "os o", "Ġfun nel", "Ġcondemn ation", "lu ent", "Ġw ired", "ĠSun set", "Jes us", "ĠP ST", "ĠP ages", "ĠTy coon", "ĠP F", "Ġselect ions", "Ġ à¤", "part isan", "Ġhigh s", "ĠR une", "Ġcraft s", "le ad", "ĠParent s", "Ġre claim", "ek er", "ĠAll ied", "ae per", "Ġlo oming", "Ġbenefic iaries", "ĠH ull", "Stud ents", "Jew ish", "d j", "Ġp act", "tem plate", "ĠOffic ials", "ĠBay lor", "Ġhe mp", "Ġyouth s", "ĠLevel s", "ĠX iao", "ĠC hes", "Ġende avor", "ĠRem oved", "Ġhipp ocamp", "H ell", "ãĤ Ĭ", "80 5", "Ġd inosaur", "ĠWr ath", "ĠIndones ian", "Ġcalcul ator", "ĠD ictionary", "Ġ4 20", "ĠM AG", "( _", "! ,", "t arians", "Ġrestrict ing", "rac use", "Ġweek day", "OU NT", "Ġsh rugged", "leg round", "Ġb ald", "ĠDo ctors", "Ġt outed", "ĠMax well", "Ġ2 14", "Ġdiplom at", "Ġrep ression", "Ġconstitu ency", "v ice", "r anked", "ĠNap oleon", "g ang", "ĠFore ver", "t un", "Ġbul b", "ĠPD T", "ĠC isco", "V EN", "Ġres umed", "Ste ven", "ĠManit oba", "Ġfab ulous", "ĠAg ents", "19 84", "Ġam using", "ĠMyster ies", "Ġor thodox", "fl oor", "Ġquestion naire", "Ġpenet rate", "Ġfilm makers", "ĠUn c", "Ġst amped", "Ġth irteen", "Ġout field", "Ġforward ed", "Ġapp ra", "Ġa ided", "t ry", "Ġunf ocused", "ĠL iz", "ĠWend y", "ĠSc ene", "Ch arg", "Ġreject s", "Ġleft ist", "ĠProv idence", "ĠBr id", "reg n", "Ġprophe cy", "ĠL IVE", "4 99", "Ġfor ge", "ĠF ML", "Ġintrins ic", "ĠF rog", "Ġw ont", "ĠH olt", "Ġfam ed", "CL US", "aeper nick", "ĠH ate", "ĠC ay", "Ġregister ing", "ort ality", "rop y", "ocaly ptic", "a an", "n av", "Ġfasc ist", "IF IED", "Ġimpl icated", "ĠRes ort", "ĠChand ler", "ĠBr ick", "P in", "ys c", "Us age", "ĠHel m", "us ra", "âĺħ âĺħ", "ĠAb bas", "Ġunanim ously", "Ġke eper", "Ġadd icted", "?? ?", "Ġhelm ets", "Ġant ioxid", "aps ed", "80 8", "gi ene", "Ġwa its", "Ġmin ion", "ra ved", "ĠP orsche", "Ġdream ing", "Ġ17 1", "ĠC ain", "Ġun for", "ass o", "ĠConfig uration", "k un", "hard t", "Ġn ested", "ĠL DS", "L ES", "Ġt ying", "en os", "Ġc ue", "ĠMar qu", "sk irts", "Ġclick ed", "Ġexp iration", "ĠAccording ly", "ĠW C", "Ġbless ings", "Ġaddict ive", "ĠN arr", "y x", "ĠJagu ars", "Ġrent s", "ĠS iber", "Ġt ipped", "ous se", "ĠFitz gerald", "Ġhier arch", "out ine", "Ġwa velength", "> .", "ch id", "ĠProcess ing", "/ +", "r anking", "E asy", "ĠConst ruct", "Ġt et", "ins ured", "H UD", "Ġqu oting", "Ġcommun icated", "in x", "Ġin mate", "Ġerect ed", "ĠAbs olutely", "ĠSure ly", "Ġun im", "ĠThr one", "he id", "Ġcl aws", "Ġsuper star", "ĠL enn", "ĠWh is", "U k", "ab ol", "Ġsk et", "ĠN iet", "Ġper ks", "Ġaff inity", "Ġopen ings", "phas is", "Ġdiscrim inate", "T ip", "v c", "Ġgr inding", "ĠJenn y", "Ġast hma", "hol es", "ĠHom er", "Ġreg isters", "ĠGl ad", "Ġcre ations", "Ġlith ium", "Ġappl ause", "unt il", "Just ice", "ĠTur ks", "Ġsc andals", "Ġb ake", "t ank", "M ech", "ĠMe ans", "ĠM aid", "Republic ans", "is al", "wind ows", "ĠSant os", "Ġveget ation", "33 8", "t ri", "Ġfl ux", "ins ert", "Ġclar ified", "Ġmort g", "ĠCh im", "ĠT ort", "Ġdiscl aim", "met al", "ĠAs ide", "Ġindu ction", "Ġinf l", "Ġathe ists", "amp h", "Ġe ther", "ĠV ital", "ĠBu ilt", "M ind", "Ġweapon ry", "S ET", "Ġ18 6", "ad min", "g am", "cont ract", "af a", "Ġderiv atives", "Ġsn acks", "Ġch urn", "E conom", "Ġca pped", "ĠUnder standing", "ĠH ers", "ĠI z", "Ġd uct", "I ENT", "augh ty", "Ġâľ Ķ", "ĠN P", "Ġsa iling", "In itialized", "Ġt ed", "Ġreact ors", "ĠL omb", "Ġcho ke", "ĠW orm", "Ġadm iration", "Ġsw ung", "ens ibly", "Ġr ash", "ĠGo als", "ĠImport ant", "Sh ot", "ĠR as", "Ġtrain ers", "ĠB un", "Work ing", "Ġhar med", "ĠPand ora", "ĠL TE", "Ġmush room", "ĠCH AR", "ĠF ee", "ĠM oy", "B orn", "ol iberal", "ĠMart ial", "Ġgentle men", "Ġling ering", "Offic ial", "Ġgra ffiti", "ĠN ames", "D er", "Ġqu int", "ist rate", "aze era", "ĠNOT ICE", "ĠFlore nce", "Ġpay able", "Ġdep icts", "ĠSpe cies", "He art", "âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ", "Ġencl osed", "Incre ases", "D aily", "ĠL is", "Ġenact ment", "ĠB acon", "ĠSt eele", "dem and", "Ġ18 3", "Ġmouth s", "Ġstr anded", "Ġenhance ment", "01 1", "ĠWh ats", "Ġhe aled", "en y", "ĠR ab", "Ġ3 40", "ĠLab yrinth", "ro ach", "ĠY osh", "ĠCl ippers", "Ġconcert s", "Intern et", "35 5", "Ġstick ers", "Ġter med", "ĠAx e", "Ġgrand parents", "Fr ance", "ĠCl im", "ĠU h", "ul ic", "Ġthr ill", "cent ric", "ĠOver view", "ĠCond uct", "Ġsubstant ive", "Ġ18 2", "m ur", "Ġstr ay", "ĠCo ff", "Ġrep etitive", "ĠFor gotten", "Ġqual ification", "ew itness", "ĠZ imbabwe", "Ġsim ulated", "ĠJ D", "25 3", "ĠW are", "Ġun sc", "T imes", "Ġsum mons", "Ġdis connected", "Ġ18 4", "ci us", "ĠGu jar", "od ka", "Ġer ase", "ĠTob acco", "elect ed", "Ġun cont", "ĠShe pard", "ĠL amp", "Ġalert ed", "Ġoper ative", "arn a", "u int", "Ġneglig ence", "ac ements", "Ġsup ra", "Ġprev ail", "ĠSh ark", "Ġbel ts", "ãģ «", "Ġt ighter", "Engine ers", "Ġin active", "Ġexp onent", "ĠWill ie", "a ples", "Ġhe ir", "ĠH its", "ian n", "ĠS ays", "Ġcurrent s", "ĠBeng al", "Ġar ist", "B uffer", "Ġbree ze", "ĠWes ley", "Col a", "Ġpron oun", "Ġde ed", "ĠK ling", "Ġof t", "Ġinf lict", "Ġpun ishing", "Ġn m", "ik u", "OD UCT", "01 4", "Ġsubsid y", "ĠDE A", "ĠHer bert", "ĠJ al", "B ank", "Ġdef erred", "Ġship ment", "B ott", "Ġal le", "b earing", "HT ML", "Off line", "Ġ2 13", "Ġscroll ing", "Ġsc anned", "ĠLib yan", "ĠT OP", "ch rom", "d t", "col umn", "Psy NetMessage", "Z ero", "Ġtor so", "0 50", "âķ IJ", "Ġimp erson", "ĠSchw artz", "ud ic", "Ġpiss ed", "ĠS app", "25 7", "ĠIS Ps", "og l", "Ġsuper vised", "Ġad olescent", "Ġatt ained", "ĠDel ivery", "ĠB unny", "Ġ19 37", "Ġmini ature", "Ġo s", "Ġ3 70", "60 8", "ĠMour inho", "Ġinn ate", "Ġtem po", "ĠN M", "ĠFall en", "00 9", "Ġprov ocative", "Stream er", "ĠBened ict", "ĠBol she", "Ġt urtle", "ĠPC B", "ĠEqu al", "Direct or", "ĠR end", "Ġflu ids", "Author ities", "Ġcous ins", "requ ency", "ĠNeigh bor", "s ets", "sh ared", "Char les", "pass word", "Ġg ears", "Ġ2 11", "ĠHard ware", "ri ka", "Ġup stream", "H om", "Ġdisproportion ately", "iv ities", "Ġund efined", "Ġelect rons", "Ġcommem or", "Event ually", "Ġ> <", "Ġir responsible", "2 18", "ĠRe leased", "ĠO VER", "ĠI GN", "ĠB read", "st ellar", "ĠS age", "tt ed", "dam age", "ed ition", "ĠPre c", "Ġl ime", "Ġconf inement", "Ġcal orie", "we apon", "Ġdiff ering", "ĠS ina", "m ys", "am d", "Ġintric ate", "k k", "ĠP AT", "ã o", "st ones", "lin ks", "Ġr anch", "Sem itic", "Ġdifferent iate", "ĠS inger", "occup ied", "Ġfort ress", "c md", "Ġinter ception", "ĠAnk ara", "Ġre pt", "ĠSol itaire", "Ġrem ake", "p red", "Ġd ared", "aut ions", "ĠB ACK", "Run ning", "Ġdebug ging", "Ġgraph s", "3 99", "ĠNig el", "Ġb un", "Ġpill ow", "Ġprog ressed", "fashion ed", "Ġob edience", "ER N", "Ġrehe ars", "C ell", "t l", "S her", "Ġher ald", "ĠPay ment", "ĠC ory", "ĠDe pt", "Ġrep ent", "ĠWe ak", "uck land", "Ġple asing", "Ġshort ages", "Ġjur ors", "ĠK ab", "q qa", "Ant i", "Ġw ow", "ĠRC MP", "Ġt sun", "ĠS ic", "Ġcomp rises", "Ġsp ies", "Ġprec inct", "n u", "Ġur ges", "Ġtim ed", "Ġstrip es", "ĠB oots", "Ġy en", "Adv anced", "Ġdisc rete", "ĠArch angel", "employ ment", "D iff", "Ġmon uments", "Ġ20 9", "work er", "Ġ19 6", "ĠI g", "utter stock", "T PS", "J ac", "Ġhomeless ness", "Ġcomment ator", "Ġrac ially", "f ing", "se ed", "E le", "ell ation", "Ġeth anol", "Ġpar ish", "ĠD ong", "ĠAw akening", "Ġdev iation", "ĠB earing", "ĠTsu k", "Ġrec ess", "Ġl ymph", "ĠCann abis", "å ľ", "ĠNEW S", "Ġd ra", "ĠStef an", "ĠWr ong", "ĠS AM", "Ġloose ly", "Ġinterpre ter", "ĠPl ain", "Go vernment", "Ġbigot ry", "Ġgren ades", "ave z", "pict ured", "Ġmand ated", "ĠMon k", "ĠPed ro", "Ġl ava", "27 4", "Ġcyn ical", "ĠScroll s", "l ocks", "M p", "Ġcon gregation", "orn ings", "ph il", "ĠI bid", "Ġf erv", "Ġdisapp earing", "Ġarrog ant", "sy n", "ĠMa ver", "ĠSu it", "24 1", "Ġab bre", "ack ers", "P a", "ĠY el", "Whe never", "Ġ23 5", "ĠV ine", "ĠAn at", "Ġext inct", "LE T", "Ġexecut able", "V ERS", "ox ide", "D NA", "ĠP rel", "Ġresent ment", "Ġcompr ise", "ĠAv iv", "Ġinter ceptions", "Ġprol ific", "IN A", "ĠEr in", "though t", "2 19", "ĠPsychiat ry", "un ky", "chem ist", "H o", "ĠMcC oy", "Ġbr icks", "L os", "ri ly", "ĠUS SR", "Ġr ud", "Ġl aud", "ĠW ise", "ĠEmer ald", "Ġrev ived", "Ġdam ned", "ĠRep air", "id em", "ct ica", "Ġpatri arch", "ĠN urs", "me g", "Ġcheap est", "re ements", "empt y", "ĠCele br", "Ġdepri vation", "ch anted", "ĠTh umbnails", "E nergy", "ĠEth an", "ĠQ ing", "Ġopp oses", "W IND", "v ik", "ĠM au", "ĠS UB", "66 7", "G RE", "ĠVol unte", "nt on", "C ook", "å IJ", "es que", "Ġplum met", "Ġsu ing", "Ġpron ounce", "Ġresist ing", "ĠF ishing", "ĠTri als", "Ġy ell", "Ġ3 10", "Ġin duct", "Ġpersonal ized", "oft en", "R eb", "EM BER", "Ġview point", "Ġexist ential", "() )", "rem ove", "MENT S", "l asses", "Ġev apor", "Ġa isle", "met a", "Ġreflect ive", "Ġentit lement", "Ġdev ised", "mus ic", "asc ade", "Ġwind ing", "off set", "Ġaccess ibility", "ke red", "Bet ter", "ĠJohn ston", "th inking", "S now", "ĠCroat ia", "ĠAt omic", "27 1", "34 8", "Ġtext book", "ĠSix th", "Ġ اÙĦ", "Ġsl ider", "ĠBur ger", "b ol", "S ync", "Ġgrand children", "Ġc erv", "+ )", "Ġe ternity", "Ġtweet ing", "Ġspec ulative", "Ġpiv otal", "ĠW P", "ĠT ER", "ynam ic", "Ġu pl", "ĠC ats", "per haps", "Ġclass mates", "Ġblat ant", "' -", "Ġl akh", "ant ine", "ĠB org", "i om", "/ (", "ĠAthlet ic", "Ġs ar", "OT A", "ĠHoff man", "Never theless", "Ġad orable", "Ġspawn ed", "Ass ociated", "ĠDom estic", "Ġimpl ant", "ĠLux em", "ĠK ens", "Ġp umps", "ĠS AT", "Att ributes", "50 9", "av our", "Ġcentral ized", "ĠT N", "Ġfresh ly", "ĠA chieve", "Ġouts iders", "her ty", "ĠRe e", "ĠT owers", "ĠD art", "ak able", "Ġm p", "ĠHeaven ly", "Ġr ipe", "ĠCarol ine", "ry an", "Ġclass ics", "Ġret iring", "Ġ2 28", "Ġa h", "Ġdeal ings", "Ġpunch ing", "ĠChap man", "O ptions", "max well", "vol ume", "Ġst al", "Ġex ported", "ĠQu ite", "Ġnumer ical", "B urn", "F act", "ĠKey stone", "Ġtrend ing", "Ġalter ing", "ĠAfric ans", "47 8", "ĠM N", "ĠKn ock", "Ġtempt ation", "Ġprest ige", "Over view", "ĠTrad itional", "ĠBah rain", "Priv ate", "ĠH OU", "Ġbar r", "ĠT at", "C ube", "US D", "ĠGrand e", "ĠG at", "ĠFl o", "Ġres ides", "Ġind ec", "vol ent", "Ġperpet ual", "ub es", "Ġworld view", "ĠQuant um", "Ġfil tered", "Ġen su", "orget own", "ERS ON", "ĠM ild", "37 9", "OT T", "à ¥", "Ġvit amins", "Ġrib bon", "Ġsincere ly", "ĠH in", "Ġeight een", "Ġcontradict ory", "Ġgl aring", "Ġexpect ancy", "Ġcons pir", "Ġmon strous", "Ġ3 80", "re ci", "Ġhand ic", "Ġpump ed", "Ġindic ative", "Ġr app", "Ġav ail", "ĠLEG O", "ĠMar ijuana", "19 85", "ert on", "Ġtwent ieth", "################ ################", "ĠSw amp", "Ġval uation", "Ġaffili ates", "adjust ed", "ĠFac ility", "26 2", "Ġenz ymes", "itud inal", "Ġimp rint", "S ite", "Ġinstall er", "ĠT RA", "m ology", "lin ear", "ĠCollect ive", "ig ating", "ĠT oken", "Ġspec ulated", "K N", "ĠC ly", "or ity", "Ġdef er", "Ġinspect ors", "appro ved", "R M", "ĠSun s", "Ġinform ing", "ĠSy racuse", "ib li", "7 65", "Ġgl ove", "Ġauthor ize", "âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦", "ĠCru ise", "Ġcontract ing", "she ll", "IF E", "ĠJew el", "p ract", "ĠPhot oshop", "ĠKnow ing", "h arm", "Ġattract ions", "ad an", "et us", "01 8", "w agen", "Al t", "Ġmultip ly", "Ġequ ilibrium", ": {", "ĠF ighters", "ĠEd gar", "Ġfour teen", "Go vern", "Ġmis use", "Ġab using", "Ġancest ry", "ram er", "64 4", "Ġwor ms", "Ġthick er", "ĠComb ine", "Ġpeas ants", "Ġv ind", "Ġcon quest", "Ġm ocked", "Ġc innamon", "ĠC ald", "ĠGall up", "Ġavoid ance", "Ġincarn ation", "ĠStr at", "Ġt asted", "ent a", "ĠN eal", "p ared", "Ġtermin ology", "ject ion", "Scient ists", "ĠIN S", "ĠDe e", "Ġdirect ories", "R oad", "ĠSh ap", "br ight", "ĠDirect ors", "ĠCol umn", "Ġb ob", "Ġprefer ably", "Ġgl itch", "f urt", "Ġe g", "id is", "C BC", "Ġsur rendered", "Ġtest ament", "33 6", "ug gest", "ĠN il", "an other", "Ġpat hetic", "ĠDon na", "Ġ2 18", "ĠA very", "Ġwhis key", "Ġf ixture", "ĠCon quest", "Ġbet s", "O cc", "ĠLe icester", "] .\"", "Ġ) );", "Ġfl ashes", "45 6", "Ġmask ed", "ge bra", "Ġcomput ed", "che l", "aud er", "Ġdefe ats", "ĠLiber ation", "ĠOs ama", "ĠV ive", "Ch anges", "Ch annel", "Ġtar iffs", "Ġm age", "ĠS ax", "Ġinadvert ently", "ĠC RE", "ĠRe aper", "ink y", "gr ading", "Ġstere otyp", "Ġcur l", "ĠF ANT", "Ġfram eworks", "M om", "ĠAn ch", "Ġflav our", "car bon", "Ġperm itting", "let cher", "ĠMo zilla", "ĠPark ing", "ĠCh amp", "Sc roll", "Ġmurd erer", "Ġrest ed", "Ġow es", "ĠP oss", "AD D", "IF F", "res olution", "ĠMin ing", "Ġcompar ative", "D im", "Ġneighbour ing", "ĠA ST", "ĠT oxic", "Ġbi ases", "Ġgun fire", "ur ous", "ĠMom ent", "19 83", "Ġper vasive", "tt p", "ĠNorm ally", "r ir", "S arah", "ĠAlb any", "Ġun sett", "ĠS MS", "ip ers", "l ayer", "ĠWh ites", "up le", "Ġtur bo", "ĠLe eds", "Ġthat s", "ĠMin er", "M ER", "ĠRe ign", "Ġper me", "ĠBl itz", "Ġ19 34", "Ġintimid ating", "t ube", "Ġecc entric", "ab olic", "box es", "ĠAssoci ates", "v otes", "Ġsim ulate", "um bo", "aster y", "Ġship ments", "FF FF", "an th", "Ġseason ed", "Ġexperiment ation", "âĸ ł", "law s", "Me et", "idd les", "ant ics", "R ating", "IS IS", "h ift", "Ġfront s", "b uf", "01 7", "Ġun att", "ĠD il", "le ases", "ĠGard ens", "77 7", "t ouch", "ve ll", "45 8", "Ġ= ====", "s aving", "Ġer osion", "ĠQu in", "Ġearn s", "Ġaccomplish ment", "ĠWe i", "Ġ< [", "____ _", "Ġir rig", "ĠT eddy", "Ġconqu ered", "ĠArm ored", "Ġassert s", "Ġmanip ulating", "r é", "Ġtranscript s", "G allery", "Ġplot ting", "Ne il", "Ġbetray al", "load er", "ĠS ul", "Ġdispl acement", "Ġroy alty", "ĠW I", "he it", "ĠDev ices", "alle l", "Ġmunicipal ities", "Ġcan al", "St ars", "ĠU AE", "Ġ\" âĢ¦", "ĠC U", "ab ove", "Ġreson ance", "ĠguiActive Un", "add ed", "ĠBra ves", "ĠI bn", "Ġhere by", "ĠB RE", "Ġshare holder", "ĠH ir", "ĠJ i", "Ġstrange ly", "Ġadm ired", "Ġpl ight", "Ġb achelor", "ĠP ole", "cipl inary", "T ony", "ĠArmen ian", "Ġun man", "ĠZion ist", "St age", "isco ver", "Ġautom otive", "Ġs idelines", "Ġsl ick", "ĠRena issance", "ĠF UN", "Im ages", "ĠH aj", "Ġp ing", "Ġshort cut", "ĠBl vd", "ĠLook s", "Ġbur sts", "Ġcl amp", "Ġm ish", "Ġsort ing", "Ġpatri ot", "Ġcorrect ness", "ĠScand inav", "ĠCaval iers", "p ython", "az ar", "Ġ3 75", "ĠJa une", "40 9", "Ġdetrim ental", "Ġstab bing", "Ġpoison ed", "Ġf ountain", "oc ent", "or st", "ĠMar i", "Ġr ains", "ĠO vers", "ĠInst itution", "ud get", "AM Y", "t ale", "ĠK R", "ĠPr ices", "Ġhead aches", "Ġlands l", "ĠA ura", "Bon us", "ĠZ hao", "ĠH ip", "Ġhop s", "ĠKurd istan", "Ġexplo iting", "ry n", "Ġhypocr isy", "op ening", "Ġgun shot", "Ġw ed", "inter stitial", "Inter stitial", "Ġam en", "Bre aking", "Ġmarket ed", "W ire", "ĠC rowd", "Contin ue", "ĠK nown", "ĠEffect ive", "ore an", "iz ons", "Jose ph", "Ġescal ation", "us ername", "Ġcur tain", "AT ES", "ĠP AR", "ĠM iy", "Ġcounter fe", "l ene", "Ġcont enders", "d aily", "ĠAs c", "ĠPhill ip", "most ly", "Ġfil ename", "he ne", "Ġresemb ling", "Ġst aging", "ĠCh loe", "Ġw iring", "H on", "ĠRen ew", "ott age", "ĠHy brid", "m uch", "Ġstro kes", "Ġpolicy makers", "AP TER", "ĠArk ham", "pl ot", "Ġassist ants", "Ġde port", "ĠSe ga", "Ġinflu enza", "ĠC ursed", "ĠK obe", "Ġskin ny", "Prov ider", "ĠR ip", "Ġincrement al", "product s", "B F", "Ġd ome", "ĠC redits", "Ġlos ers", "int s", "ĠBet ty", "ĠTal ent", "ĠD AM", "L v", "E ss", "Ġd ens", "tem p", "J udge", "od ic", "Ġ' (", "UR ES", "ets k", "V O", "Ġretrie ved", "Ġarchitect s", "Ù ĩ", "Ġeth ic", "ĠSecond ary", "st ocks", "ad ia", "Ġ3 25", "ĠOp inion", "Ġsimultane ous", "Ġd izz", "ul p", "Ġsmugg ling", "ipp ery", "R andom", "f acing", "ĠD as", "Ġstock p", "Ġdiscl osures", "po inter", "Ġcor al", "ĠSe lection", "ĠP ike", "ival ent", "Ġruth less", "ĠR im", "Ġensu ing", "ĠExper iment", "Ġcongress man", "Ġbelie ver", "Ġun specified", "ĠM ord", "Ġknowledge able", "ĠV ERY", "T X", "Ġstra ps", "Ġtur f", "apesh ifter", "Ġmar ital", "Ġfl ock", "ãģ Ĩ", "26 3", "AM ES", "ĠOpp osition", "Ġtre asures", "ĠG OD", "Ġmodel ed", "ĠWOR LD", "Ġ( [", "ĠUs age", "H F", "Ġ$ (", "uss ed", "Ġpione er", "E ight", "par se", "b read", "rit z", "ĠMir anda", "ĠK ant", "++ )", "ore n", "Ġprov oked", "Ġbre eds", "ĠIn cludes", "ĠPast ebin", "ĠFl ip", "J ava", "Ġbr ink", "Ġrum ored", "Ġun seen", "Ġgar nered", "ĠDef in", "al ted", "Ġtatt oos", "Ġhes itation", "is itions", "ĠWe aver", "ĠReport ing", "Ġtherap ies", "Ġconsult ants", "Ġresid ual", "ĠMal i", "ĠRom a", "i ago", "ĠRes idents", "ub i", "Ġremed ies", "Ġadapt ive", "ĠAl ive", "ĠBar cl", "Ġwal lets", "c rypt", "etermin ation", "ĠPel osi", "Ġsl ipping", "oton in", "Ġall iances", "pat rick", "ir is", "Ġor th", "ĠPer kins", "ĠDe V", "ĠG ets", "Ġdry ing", "ge e", "fore st", "ĠFor get", "ore m", "33 9", "Ġvague ly", "ĠD ion", "ĠP orn", "ĠH OW", "Ġp neum", "Ġrub ble", "ĠT aste", "enc ia", "ĠG el", "Ġd st", "Ġ24 5", "ĠMoroc co", "inf lamm", "ĠTw ins", "Ġb ots", "d aughter", "ĠB alk", "Ġbre thren", "Ġlog os", "Ġgo bl", "f ps", "Ġsub division", "Ġp awn", "Ġsquee zed", "Ġmor ale", "ĠD W", "' \"", "Ġkn ot", "ook y", "Ġdiv isive", "Ġboost ed", "ch y", "ãĥ IJ", "if act", "Ġnewcom ers", "ĠWrest ling", "Ġsc outs", "w olves", "R at", "Ġnin eteenth", "ĠOs borne", "St ats", "Ġem powered", "Ġpsych opath", "ĠO EM", "ugg age", "ĠP K", "ĠMoh ammad", "P ak", "Ġanarch ists", "ĠExt ract", "est hes", "ĠStock holm", "l oo", "ĠG raph", "Ġdeploy ing", "ĠStr anger", "ĠM old", "Ġstaff er", "Ġdiscount ed", "uck le", "ple ase", "ĠLand ing", "ÃŃ a", "Ġ19 3", "Ġan te", "Ġrep etition", "Ġ+ /-", "Ġpar ody", "Ġlive ly", "AA A", "ĠHor us", "Ġp its", "ind ers", "L OC", "ĠVen ice", "40 6", "ĠDis cover", "â Ĩ", "ellect ual", "Ġp ens", "Ġey el", "ig uous", "Im pl", "Ġj oking", "Ġinv al", "ĠBel fast", "Ġcredit ors", "ĠSky walker", "ov sky", "Ġcease fire", "Ġse als", "is oft", ") ).", "ĠFel ix", "IT S", "Ġt resp", "ĠBlock chain", "ew are", "ĠSch war", "en ne", "mount ed", "ĠBe acon", "les h", "Ġimmense ly", "Ġche ering", "Em ploy", "sc ene", "ish ly", "atche wan", "ĠNic olas", "Ġdr ained", "ĠEx it", "ĠAz erb", "j un", "Ġflo ated", "u ania", "De ep", "Ġsuper v", "Ġmyst ical", "ĠD ollar", "ĠApost le", "ĠR EL", "ĠProv ided", "ĠB ucks", "ãĥ ´", "cut ting", "Ġenhance ments", "ĠPengu ins", "ĠIsa iah", "Ġj erk", "ĠW yn", "Ġst alled", "Ġcryptoc urrencies", "ĠR oland", "sing le", "Ġl umin", "ĠF ellow", "ĠCap acity", "ĠKaz akh", "W N", "Ġfin anced", "38 9", "Ġt id", "Ġcoll usion", "ĠMy r", "î Ģ", "Sen ator", "Ġped iatric", "Ġneat ly", "Ġsandwic hes", "ĠArchitect ure", "Ġt ucked", "Ġbalcon y", "Ġearthqu akes", "qu ire", "F uture", "Ġhe fty", "é Ĺ", "Ġspecial izes", "Ġstress es", "Ġs ender", "Ġmisunder standing", "Ġep ile", "Ġprov oke", "ĠCol ors", "Ġdis may", "uk o", "[ _", "58 6", "ne utral", "Ġdon ating", "ĠRand all", "Mult i", "Ġconvenient ly", "ĠS ung", "ĠC oca", "Ġt ents", "ĠAc celer", "Ġpart nered", "27 2", "ir ming", "ĠB AS", "s ometimes", "Ġobject ed", "ub ric", "p osed", "LC S", "gr ass", "Ġattribut able", "V IS", "Israel i", "Ġrepe ats", "ĠR M", "v ag", "ut a", "in ous", "Ġin ert", "ĠMig uel", "æ Ń", "ĠHawai ian", "B oard", "Ġart ific", "ĠAzerb ai", "as io", "ĠR ent", "A IN", "Ġappl iances", "Ġnational ity", "Ġass hole", "ĠN eb", "Ġnot ch", "h ani", "ĠBr ide", "Av ailability", "Ġintercept ed", "Ġcontin ental", "Ġsw elling", "ĠPers pect", "b ies", ". <", "ith metic", "ĠL ara", "Ġtempt ing", "add r", "Ġoversee ing", "cl ad", "ĠD V", "ĠGing rich", "Ġm un", "ĠApp ropri", "Ġalter ations", "ĠPat reon", "Ġha voc", "Ġdiscipl ines", "Ġnotor iously", "aku ya", "ier i", "? ).", "ĠW ent", "Ġsil icon", "Ġtre mb", "Cont ainer", "K nown", "Ġmort ar", "est e", "ick a", "Ar thur", "ĠPre viously", "ĠMart y", "Ġsp arse", "g ins", "Ġin ward", "ĠParticip ant", "C opy", "ĠM isc", "Ġantib iotic", "ĠRet ro", "Ġel usive", "Ġass ail", "ĠBatt alion", "ĠB ought", "Ġdimin ish", "ĠEuro pa", "s ession", "ĠDanger ous", "ies el", "Ġdisbel ief", "Ġbl asts", "ext reme", "ĠBoy d", "ĠProject s", "ĠGu ys", "Ġunder gone", "Ġgr ill", "ĠDw ight", "Ġ19 7", "US ER", "Ġfiles ystem", "Ġcl ocks", "T aylor", "Ġwra pper", "Ġfold ing", "ous and", "ĠPhilipp ine", "ATION AL", "ĠPer th", "Ġas hes", "Ġaccum ulate", "ĠGate way", "Sh op", "orks hire", "H an", "ĠBar rel", "ĠLe h", "ĠX V", "Ġwh im", "Ġrep o", "ĠC G", "ĠM am", "Ġincorpor ating", "Ġbail out", "Ġlingu istic", "Ġdis integ", "C LE", "Ġcinem atic", "ĠF iber", "S yn", "il ion", "ĠCom pos", "c hens", "Ġne oc", "Ġbo iled", "F INE", "on o", "un cle", "ik en", "ĠB M", "Î ¹", "Ġreceipt s", "Ġdisp osed", "ĠTh irty", "ĠR ough", "ĠA BS", "Ġnot withstanding", "oll en", "# $", "Ġunrel iable", "Ġbl oom", "Ġmedi ocre", "Ġtr am", "ĠTas man", "Ġsh akes", "Ġmanifest o", "ĠM W", "Ġsatisf actory", "Ġsh ores", "Ġcomput ation", "Ġassert ions", "orm ons", "ar ag", "ab it", "Dem ocrats", "ĠL oot", "ĠVol ks", "ha ired", "Ġgrav itational", "S ing", "ĠM iz", "Ġthro ttle", "Ġtyr anny", "ĠView s", "Ġrob ber", "ĠMinor ity", "Ġsh rine", "sc ope", "pur pose", "Ġnucle us", "our cing", "ĠUS DA", "ĠD HS", "w ra", "ĠBow ie", "Sc ale", "ĠB EL", "x i", "I ter", "Ġ( ),", "w right", "Ġsail ors", "ous ed", "NAS A", "ĠPro of", "ĠMin eral", "t oken", "ĠF D", "R ew", "Ġe ll", "6 30", "Ġchance llor", "ĠG os", "Ġamount ed", "ĠRec re", "ome z", "ĠOpt im", "ĠOl ive", "Ġtrack er", "ow ler", "ĠUn ique", "R oot", "Ġmar itime", "ĠQur an", "ĠAd apt", "Ġecosystem s", "ĠRe peat", "ĠS oy", "ĠI MP", "Ġgrad uating", "and em", "P ur", "ĠRes et", "ĠTr ick", "ĠPh illy", "ĠT ue", "ĠMalays ian", "Ġclim ax", "Ġb ury", "Ġcons pic", "ĠSouth ampton", "ĠFl owers", "Ġesc orted", "ĠEduc ational", "ĠI RC", "Ġbrut ally", "e ating", "Ġpill ar", "ĠS ang", "ĠJ ude", "ar ling", "ĠAm nesty", "Ġrem inding", "ĠAdminist rative", "hes da", "Ġfl ashed", "ĠP BS", "per ate", "fe ature", "Ġsw ipe", "Ġgra ves", "oult ry", "26 1", "bre aks", "ĠGu er", "Ġsh rimp", "ĠV oting", "qu ist", "Ġanaly tical", "Ġtables poons", "ĠS OU", "Ġresear ched", "Ġdisrupt ed", "Ġj our", "Ġrepl ica", "Ġcart oons", "b ians", "} )", "c opy", "G ot", "ou ched", "P UT", "Ġsw arm", "not ations", "s aid", "Ġreb uilt", "Ġcollabor ate", "Ġr aging", "Ġn ar", "Ġdem ographics", "ĠD DR", "Ġdist rust", "oss ier", "ĠK ro", "Ġpump kin", "Ġreg rets", "Ġfatal ities", "ĠL ens", "ĠO le", "p d", "Ġpupp et", "ĠOut look", "ĠSt am", "O l", "F air", "U U", "Ġre written", "Ä ±", "Ġfasc inated", "Ġve ctors", "Ġtrib unal", "u ay", "ĠM ats", "ĠCo ins", "[ [", "Ġ18 1", "Ġrend ers", "ĠK aepernick", "Ġesp ionage", "Ġsum m", "Ġd itch", "Acc ount", "Ġspread sheet", "Ġmut ant", "p ast", "40 7", "Ġd ye", "Ġinit iation", "Ġ4 000", "Ġpunish able", "Ġth inner", "ĠKh al", "Ġinter medi", "D un", "ĠGoth am", "Ġeager ly", "Ġvag inal", "p owers", "V W", "ĠWATCH ED", "Ġpred ator", "ams ung", "Ġdispar ity", "Ġ[ *", "Ġam ph", "Ġout skirts", "ĠSpir its", "Ġskelet al", "Ð »", "ĠR ear", "Ġissu ance", "ĠLog ic", "re leased", "Z Z", "ĠB ound", "Ent ry", "Ġex its", "is ol", "ĠFound er", "Ġw re", "ĠGreen land", "ĠM MO", "t aker", "IN C", "ãģ ¾", "Ġhour ly", "hen ko", "Ġfantas ies", "Ġdis ob", "Ġdemol ition", "ãĥ ĭ", "Ġen listed", "rat ulations", "Ġmis guided", "Ġens ured", "Ġdiscour aged", "m ort", "Ġfl ank", "Ġc ess", "Ġreact s", "ĠS ere", "s ensitive", "ĠSer pent", "ass ad", "Ġ24 7", "Ġcalm ly", "b usters", "Ġble ed", "ĠSt ro", "Ġamuse ment", "ĠAntar ctica", "Ġs cept", "ĠG aw", "a q", "ason ic", "Ġsp rawling", "n ative", "atur ated", "ĠBattle field", "IV ERS", "E B", "ĠG ems", "ĠNorth western", "ĠFil ms", "ĠAut omatic", "Ġappre hend", "ãģ ¨", "Ġgui Name", "Ġback end", "Ġevid enced", "ge ant", "01 2", "ĠS iege", "Ġexternal To", "Ġunfocused Range", "ĠguiActiveUn focused", "Ġgui Icon", "ĠexternalTo EVA", "ĠexternalToEVA Only", "F ri", "ch ard", "en aries", "Ġchief s", "Ġc f", "ĠH UD", "Ġcorro bor", "Ġd B", "ĠT aken", "ĠPat ricia", "ra il", "ĠCh arm", "ĠLiber tarian", "rie ve", "Person al", "ĠO UR", "ger ies", "Ġdump ing", "Ġneurolog ical", "it imate", "ĠClint ons", "raft ed", "ĠM olly", "Ġtermin als", "reg ister", "Ġfl are", "Ġenc oded", "Ġautop sy", "p el", "m achine", "Ġexempt ions", "ĠRoy als", "d istance", "Ġdraft s", "Ġl ame", "ĠC unning", "Ġsp ouses", "ĠMark ets", "ĠCar rier", "Ġimp lying", "ĠY ak", "s id", "Ġl oser", "Ġvigil ant", "Ġimpe achment", "Ġaug mented", "ĠEmploy ees", "Ġunint ended", "tern ally", "ĠW att", "Ġrecogn izable", "ess im", "æ Ŀ", "Ġco ated", "r ha", "Ġlie utenant", "ĠLegisl ation", "pub lished", "44 4", "01 3", "Ġide ally", "ĠPass word", "Ġsimpl ify", "ĠMet a", "ĠM RI", "Ġple ading", "organ ized", "hand ler", "Ġun ravel", "cor rect", "Ġ icy", "Ġparan oid", "Ġpass er", "Ġinspect ions", "of er", "ĠHealth care", "28 3", "ĠBr ut", "iol a", "for ge", "ĠMed ieval", "MS N", "ie vers", "ĠProgram ming", "å ī", "Ġ2 23", "m u", "ĠC LE", "ug a", "Ġsho ppers", "Ġinform ative", "ĠPl ans", "Ġsupplement ation", "ĠT ests", "ty ard", "ocy tes", "ĠVeg a", "ĠGujar at", "erman ent", "Ex cept", "ĠL OT", "all a", "ĠC umm", "ĠO sw", "Ġven om", "ĠDeb t", "ĠD OWN", "Ġreun ion", "Ġm uc", "ĠRel ief", "Ġge op", "ĠðŁ ĺ", "al ogue", "An th", "ech o", "Ġcor ros", "Ġrepl ication", "ĠBl azing", "ĠD aughter", "Ġinf lic", "ĠLind sey", "Ù Ī", "28 4", "Ex it", "Ġgl oom", "TA IN", "Ġundermin ing", "Ġadv ising", "h idden", "Ġover flow", "Ġg or", "urd ue", "Ġe choes", "enh agen", "Ġimp uls", "d rug", "c ash", "Ġas ync", "Ġmir ac", "at ts", "p unk", "Ġpiv ot", "ĠLegisl ative", "Ġblog gers", "ĠCl aw", "s burg", "d yl", "ĠRecomm end", "Ġver te", "Ġprohib iting", "ĠPant her", "Jon athan", "Ġo min", "Ġhate ful", "28 1", "ĠOr che", "ĠMurd och", "down s", "Ġas ymm", "G ER", "Al ways", "Ġinform s", "ĠW M", "ĠP ony", "ĠApp endix", "ĠAr lington", "J am", "Ġmedic inal", "ĠS lam", "IT IES", "Ġre aff", "ĠR i", "F G", "S pring", "b ool", "Ġthigh s", "Ġmark ings", "ĠRa qqa", "ĠL ak", "p oll", "ts ky", "ĠMort y", "ĠDef inition", "Ġdeb unk", "end ered", "ĠLe one", "a vers", "Ġmortg ages", "App arently", "N ic", "ha us", "ĠTh ousands", "au ld", "Ġm ash", "sh oot", "Ġdi arr", "Ġconscious ly", "H ero", "e as", "ĠN aturally", "ĠDestroy er", "Ġdash board", "serv ices", "R og", "Ġmillenn ials", "Ġinv ade", "- (", "Ġcomm issions", "ĠA uckland", "Ġbroadcast s", "Ġfront al", "Ġcr ank", "ĠHist oric", "Ġrum ours", "CT V", "Ġster il", "Ġboost er", "rock et", "ãĤ ¼", "ut sche", "ĠP I", "Ġ2 33", "ĠProdu cer", "ĠAnaly tics", "Ġinval uable", "Ġunint ention", "ĠC Y", "Ġscrut in", "Ġg igg", "Ġeng ulf", "Ġprolet ariat", "Ġh acks", "ĠH ew", "ar ak", "ĠSl ime", "ield ing", "ag her", "ĠEll iot", "Ġtele com", "Ġ2 19", "ult an", "ĠAr bor", "ĠSc outs", "B an", "Ġlifes pan", "Ġbl asp", "38 8", "Ġjud iciary", "ĠContin ental", "ask ing", "Mc C", "L ED", "Ġbag gage", "ĠSorce rer", "Ġrem nants", "ĠGriff ith", "ets u", "ĠSub aru", "ĠPerson ality", "des igned", "ush ima", "agn ar", "Ġrec oil", "Ġpass ions", "\\ \":", "Ġte e", "Ġabol ition", "ĠCreat ing", "j ac", "Ġ19 4", "01 9", "Ġpill ars", "ric hed", "/ \"", "t k", "Ġlive lihood", "Ġro asted", "ah on", "ĠH utch", "ass ert", "Ġdivid end", "Ġkn it", "Ġd aunting", "Ġdisturb ance", "Ġsh ale", "Ġcultiv ated", "Ġrefriger ator", "L B", "ĠN ET", "Ġcommercial s", "Ġthink ers", "45 5", "Ġch op", "B road", "Ġsuspic ions", "Ġtag ged", "l ifting", "Ġsty lish", "ĠShield s", "Short ly", "Ġt ails", "A uth", "ST E", "ĠG AME", "Ġse ism", "ĠK is", "olog ne", "Ġcow ork", "Ġforc ibly", "Ġthy roid", "ĠP B", "AN E", "mar ried", "h orse", "Ġpoly mer", "ĠCh al", "od or", "DE BUG", "ĠCon text", "Ġbl iss", "Ġpin point", "ĠMat hemat", "leg ram", "ĠWeek end", "Ġlab elled", "Ġb art", "it les", "Ġest rogen", "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", "\" '", "Ġvis ibly", "Ġouts ider", "aid a", "Are a", "Ġdisse min", "Ġdish onest", "ĠCl osed", "ĠBullet in", "ĠRam sey", "sw ord", "ĠX I", "our ced", "S ame", "34 6", "ĠRe pe", "ĠK ou", "c ake", "em is", "C ache", "ĠMe aning", "ĠEn light", "onom y", "Ġmanifest ation", "sw orth", "J ay", "Ġch ore", "ö r", "D ream", "Ġsanction ed", "Ġcult urally", "ĠA ra", "N av", "Ġthe ological", "Ġstr ut", "ĠV O", "ĠHand book", "Ġconstruct ing", "Ġ ¶", "ĠBenef its", "ĠPsych ological", "s ac", "å ¸", "p olicy", "ĠMat ters", "ĠReport ed", "ĠBy te", "Ġvit ro", "ĠM aiden", "Ġl am", "ĠJenn ings", "Ġgar ment", "ĠRut gers", "ĠStaff ord", "ĠWell ington", "Ġinter mitt", "Ġn pm", "Ġord eal", "Ġplug ged", "o oming", "in ished", "fram ework", "Ġtim ber", "Ġc ass", "Ġ8 50", "il ess", "ĠRed ux", "7 68", "St re", "Ġsurpass ed", "w hel", "Ġparalle ls", "Ġve il", "ĠG I", "ĠR EST", "Ġread iness", "s ort", "Ġmod ifying", "ĠSl ate", "ru ff", "Ġmar ble", "Ġinf rared", "Ġaud itor", "ĠFANT ASY", "ĠP overty", "ĠS PD", "Ġ\" (", "K y", "RA Y", "Ġexecut ions", "ĠBever ly", "ĠMarx ism", "ĠBur st", "ĠK ali", "est ones", "Clear ly", "E ll", "ãģ §", "ĠProceed ings", "T oken", "IF IC", "ñ a", "Cent ral", "ĠH aley", "ĠD rama", "Ġform ations", "OR N", "Book s", "Ġdom inating", "ĠFly ers", "ĠCompan ion", "Ġdiscipl ined", "ĠYug oslav", "ĠSpell s", "Ġv engeance", "Ġland lords", "L en", "ĠO gre", "ano ia", "Ġpier cing", "Ġcon greg", "Ġscore r", "ob ia", "Ġnic kel", "ĠLear ns", "Ġre jo", "Ġmaster piece", "Fl ash", "Ġinhab ited", "ĠOpen GL", "ĠD ud", "ĠI CO", "Ġar ter", "Ġpl ur", "Ġmaster y", "Ġlong standing", "st ed", "Ġw ines", "Ġtelev ised", "ĠSh rine", "ĠBay ern", "Ġâ ĵĺ", "Ġencl osure", "j ohn", "Ġprophe ts", "ĠRes urrection", "ĠOrd ers", "Ġun even", "r als", "Ġd wind", "ĠL ah", "ĠSl oven", "37 8", "Ġins istence", "aff le", "ĠCl one", "Ġhard ship", "ĠCongress man", "Ġple ad", "Ġreview ers", "Ġc ured", "Ġ19 35", "as ley", "f ake", "ĠTh inking", "yd ia", "P ART", "ĠD ota", "o it", "Ġwh ipped", "Ġb ouncing", "ĠHispan ics", "com ings", "Ġcann abin", "ĠCh ambers", "ĠZ ack", "Option al", "Ġco ats", "Ġprow ess", "ĠNort on", "Ġplain ly", "Ġfre ight", "Ġinhib ition", "Ġcl am", "Ġ30 3", "ke f", "ale igh", "L uke", "Ġpsych o", "ator ium", "M ED", "Ġtreat ies", "Ġind isc", "Ġd c", "OP S", "Ġresil ient", "ĠInter state", "Ġsl ack", "Ġmund ane", "Ġestab lishes", "35 9", "Ġstr ained", "Ġn ond", "S us", "Ġcast e", "ar ate", "ie ving", "Ġunfair ly", "Ġpars er", "on ial", "urs ive", "V ia", "ĠOtt o", "ĠAuthor ities", "stro ke", "K R", "ĠMer cy", "Ġfurn ished", "Ġout set", "Ġmet ic", "19 82", "olith ic", "ĠT ent", "og ical", "ĠA ircraft", "Ġh ides", "ĠBec ame", "Ġeduc ators", "re aching", "Ġvol atility", "Ġtodd ler", "ĠNAS CAR", "ĠTw elve", "ĠHigh lights", "Ġgra pe", "Ġspl its", "Ġpe asant", "Ġre neg", "ĠMS I", "Tem p", "st ars", "Ġtre k", "ĠHy de", "b inding", "Ġreal ism", "Ġox ide", "ĠH os", "Ġmount s", "Ġbit ing", "Ġcollaps ing", "Ġpost al", "Ġmuse ums", "Ġdet ached", "Ġrespect ing", "Ġmonop ol", "Ġwork flow", "ĠC ake", "Tem plate", "ĠOrgan isation", "Ġpers istence", "36 9", "C oming", "B rad", "Ġredund ant", "ĠG TA", "Ġb ending", "Ġrev oked", "Ġoff ending", "Ġfram ing", "Ġprint f", "Comm un", "mem bers", "Out side", "Ġconst rued", "Ġc oded", "F ORE", "Ġch ast", "Ch at", "Ind ian", "ĠY ard", "? !\"", "ĠP orts", "ĠX avier", "ĠR ET", "' .\"", "ĠBo at", "iv ated", "ich t", "umer able", "D s", "ĠDun n", "Ġcoff in", "Ġsecure ly", "ĠRapt ors", "ĠB es", "Install ation", "Ġin ception", "ĠHealth y", "end ants", "Ġpsych ologists", "ĠShe ikh", "c ultural", "ĠBlack Berry", "sh ift", "F red", "oc he", "Ġc akes", "ĠS EO", "ĠG ian", "ĠAs ians", "og ging", "e lement", "Ġpund its", "ĠV augh", "ĠG avin", "Ġh itter", "Ġdrown ed", "Ġch alk", "ĠZ ika", "Ġmeas les", "80 2", "âĢ¦ ..", "ĠAW S", "] \"", "Ġdist ort", "ĠM ast", "Ġantib odies", "ĠM ash", "Mem ory", "ĠUg anda", "ĠPro b", "Ġvom iting", "ĠTurn s", "Ġoccup ying", "Ġev asion", "ĠTher apy", "Ġprom o", "Ġelect r", "Ġblue print", "ĠD re", "pr iced", "ĠDep ot", "Ġallev iate", "ĠSom ali", "m arg", "n ine", "Ġnostalg ia", "ĠShe pherd", "Ġcaval ry", "Ġtor ped", "ĠBlood y", "x b", "Ġs ank", "Ġgo alt", "report print", "embed reportprint", "clone embedreportprint", "ĠIn itially", "ĠF ischer", "Ġnot eworthy", "c ern", "Ġin efficient", "raw download", "rawdownload cloneembedreportprint", "c ation", "ĠD ynasty", "l ag", "D ES", "Ġdistinct ly", "ĠEston ia", "Ġopen ness", "Ġg ossip", "ru ck", "W idth", "ĠIb rahim", "Ġpet roleum", "Ġav atar", "ĠH ed", "ath a", "ĠHog warts", "Ġc aves", "67 8", "Ġsafegu ard", "ĠM og", "iss on", "ĠDur ham", "sl aught", "ĠGrad uate", "Ġsub conscious", "ĠEx cellent", "ĠD um", "---- -", "Ġp iles", "ĠW ORK", "ĠG arn", "ĠF ol", "ĠAT M", "Ġavoid s", "ĠT ul", "Ġble ak", "EL Y", "iv ist", "light ly", "P ers", "ĠD ob", "ĠL S", "Ġins anity", "Î µ", "atal ie", "En large", "Ġtw ists", "Ġfault y", "Ġpir acy", "Ġimp over", "Ġrug ged", "ĠF ashion", "Ġs ands", "' ?", "sw ick", "Ġn atives", "Ġhe n", "ĠNo ise", "ãĥ Ĺ", "Ġg reens", "Ġfree zer", "Ġd ynasty", "ĠFather s", "ĠNew ark", "Ġarchae ological", "Ġo t", "ob ar", "Ġblock ade", "Ġall erg", "L V", "Ġdeb it", "ĠR FC", "ĠMil ton", "ĠPress ure", "Ġwill ingly", "Ġdisproportion ate", "Ġopp ressive", "Ġdiamond s", "Ġbelong ings", "19 70", "Ġbell s", "Ġimperial ism", "Ġ2 27", "Ġexpl oding", "ĠE clipse", "Ġ19 19", "Ġr ant", "Ġnom inations", "34 7", "Ġpeace fully", "ric a", "ĠF UCK", "Ġvib ration", "mal ink", "Ġro pes", "ĠIv anka", "ĠBrew ery", "ĠBook er", "ĠOw ens", "go ers", "Serv ices", "ĠSn ape", "Ġ19 1", "39 5", "Ġ2 99", "just ice", "Ġb ri", "Ġdisc s", "Ġprom inently", "Ġvul gar", "Ġsk ipping", "l ves", "Ġtsun ami", "37 4", "ĠU rug", "ĠE id", "rec ated", "p hen", "Ġfault s", "ĠStart ed", "9 50", "Ġp i", "Ġdetect or", "Ġbast ard", "Ġvalid ated", "Space Engineers", "OUR CE", "Ġ( ~", "Ġuns ur", "Ġaff irmed", "Ġfasc ism", "Ġres olving", "ĠCh avez", "ĠC yn", "Ġdet ract", "L ost", "Ġrig ged", "Ġhom age", "ĠBrun o", "55 5", "ec a", "Ġpress es", "Ġhum our", "Ġsp acing", "Ġ' /", "olk ien", "C oun", "OP ER", "T re", "S on", "ĠCambod ia", "ier re", "m ong", "o zy", "Ġliquid ity", "ĠSov iets", "ĠFernand o", "Ġ2 29", "Ġsl ug", "ĠCatal an", "elect ric", "Ġsc enery", "ĠH earth", "Ġconst rained", "Ġgoal ie", "ĠGu idelines", "ĠAm mo", "ĠPear son", "Ġtax ed", "Ġfet us", "Resp onse", "ĠAlex is", "th ia", "G uy", "Ġrecon struct", "Ġextrem es", "Ġconclud ing", "ĠP eg", "ook s", "Ġded uctions", "R ose", "Ġground breaking", "ĠT arg", "ãĥ ģ", "ĠRe ve", "res ource", "Ġmo ons", "Ġelectrom agnetic", "Ġamid st", "ĠVik tor", "N ESS", "B ACK", "Ġcomm ute", "ĠAna heim", "Ġfluct uations", "6 40", "Ġnood les", "ĠCop enhagen", "ĠT ide", "ĠGri zz", "ĠS EE", "Ġpip elines", "Ġsc ars", "end o", "ag us", "ĠE TF", "/ #", "ĠBec ome", "44 8", "Ġvis c", "ĠRecomm ended", "Ġj umper", "Ġcogn ition", "Ġassass in", "Ġwitness ing", "ĠSet up", "Ġl ac", "v im", "IS M", "p ages", "SS L", "35 8", "Ġad ject", "indust rial", "l ore", "cher y", "Ġgl itter", "Ġc alf", "Flor ida", "Ġspoil ers", "Ġsucceed s", "Ġch anting", "Ġslog ans", "ĠTr acy", "Vis it", "rol ogy", "Ġm ornings", "Ġline age", "Ġs ip", "Ġintense ly", "Ġflour ish", "ĠSle eping", "ĠF em", "or por", "ĠK lan", "ĠDar th", "h ack", "ĠNi elsen", "Ġtum ors", "Ġprocure ment", "ĠY orkshire", "Ġra ided", "K Y", "An na", "Ġ// [", "ĠDis order", "ĠMust ang", "ĠW en", "ĠTry ing", "s q", "Ġdeliver ies", "Ġshut ter", "Ġcere bral", "Ġbip olar", "ĠC N", "l ass", "j et", "Ġdeb ating", "> :", "Ġe agle", "gr ades", "ĠD ixon", "UG C", "M AS", "ĠDr aco", "ĠMach ines", "aff er", "Ġem an", " ²", "pr on", "ĠG ym", "Ġcompar atively", "ĠTrib unal", "PR O", "Ġle x", "Ġfert ile", "Ġdep ressing", "Ġsuperf icial", "ess ential", "ĠHun ters", "g p", "Ġprom inence", "L iber", "ĠAn cest", "ote chnology", "Ġm ocking", "ĠTra ff", "ĸ ļ", "Med ium", "I raq", "Ġpsychiat rist", "Quant ity", "ĠL ect", "Ġno isy", "5 20", "G Y", "Ġsl apped", "ĠM TV", "Ġpar a", "p ull", "Mult iple", "as her", "Ġn our", "ĠSe g", "Spe ll", "v ous", "ord ial", "Sen ior", "ĠGold berg", "ĠPl asma", "ne ed", "Ġmess enger", "ere t", "Ġteam ed", "Ġliter acy", "ĠLe ah", "ĠD oyle", "Ġem itted", "U X", "Ġev ade", "Ġm aze", "Ġwrong ly", "ĠL ars", "Ġstere otype", "Ġpled ges", "Ġarom a", "ĠM ET", "Ġac re", "ĠO D", "Ġf f", "Ġbrew eries", "ĠH ilton", "und le", "ĠK ak", "ĠThank fully", "ĠCan ucks", "in ctions", "ĠApp ears", "Ġco er", "Ġundermin ed", "ro vers", "And re", "Ġbl aze", "um ers", "Ġfam ine", "amp hetamine", "ulk an", "Am ount", "Ġdesper ation", "wik ipedia", "develop ment", "ĠCor inth", "uss ia", "Jack son", "L I", "N ative", "R s", "Oh io", "ĠKath leen", "F ortunately", "Ġattend ant", "ĠPre ferred", "ĠDid n", "ĠV s", "M is", "Ġrespond ent", "Ġb oun", "st able", "Ġp aved", "Ġunex pl", "ĠChe ney", "L M", "ĠC ull", "bl own", "Ġconfront ing", "oc ese", "serv ing", "W i", "ĠLith uania", "ann i", "Ġst alk", "h d", "Ġv ener", "AP H", "ynchron ous", "UR R", "um ably", "hist oric", "H alf", "H ay", "Ġresil ience", "spe ction", "Ġabandon ing", "O bs", "ĠDeb bie", "Ġgrad ient", "ĠPl aint", "ĠCan al", "AR CH", "Ġexpans ive", "Ġfun g", "Ġb ounced", "U nd", "Ġprec autions", "Ġclar ification", "Ġd agger", "Ġgri ps", "Ġ µ", "ĠRiver a", "ĠUnd ead", "is ites", "ĠFIR ST", "ñ o", "aud i", "Ġhost ages", "Ġcompl iant", "Ġal umni", "Se ven", "Ġcyber security", "e ither", "Col lect", "Ġinvari ably", "ĠS oci", "Ġlaw maker", "Ġa le", "ĠPerson ally", "N azi", "Ġcustom ization", "ĠPro c", "ĠSask atchewan", "eat uring", "Ġsp ared", "Ġdiscontin ued", "Ġcomput ational", "ĠMotor ola", "Ġsuprem acist", "government al", "Ġparad ise", "ĠDown ing", "ĠNik on", "Ġcat alyst", "ber ra", "Tor onto", "8 75", "bet a", "ĠMac ron", "Ġunreal istic", "ve ctor", "ĠVeh icles", "it iveness", "ĠR V", "ĠCol bert", "s in", "o ji", "ent in", "ĠKr ish", "hell o", "ff ield", "ok y", "ĠT ate", "Ġmap le", "Ġa ids", "chem ical", "33 4", "n uts", "ĠWar p", "Ġx x", "ĠRob b", "umer ous", "_- _", "ft ime", "ĠV W", "Ġw inger", "ĠD ome", "t ools", "ĠP V", "ĠGe orgetown", "Ġg eared", "Ġjihad ists", "Ġc p", "Ġster oids", "M other", "cler osis", "ĠDR M", "nes ia", "Ġl inger", "Ġimm ersive", "ĠC OUN", "Ġoutwe igh", "ens ual", "B and", "Ġtransform s", "mat ched", "ps ons", "ĠJud icial", "f actor", "Ġrefer ral", "Ġodd ly", "ĠW enger", "B ring", "ĠB ows", "60 2", "IC LE", "Ġl ions", "ĠAcad emic", "ĠTh orn", "ĠRa ider", "kef eller", "St orage", "L ower", "ĠOr t", "ĠEqu ality", "AL T", "ĠS OC", "T ypes", "Ġl yn", "ĠAss et", "co at", "TP P", "C VE", "ĠPione er", "app lication", "Mod ern", "ĠH K", "En vironment", "Al right", "R ain", "IP P", "ĠShi ite", "Ġm ound", "ĠAb ilities", "cond ition", "St aff", "Ġcompet ence", "ĠM oor", "ĠDi ablo", "Ġwith held", "Ġost ensibly", "ĠB rom", "Ġms g", "Ġden omin", "ĠRef erences", "ĠF P", "Ġplun ged", "Ġp amph", "m oving", "cent ral", "Ġdown right", "Ġf ading", "T al", "T yp", "ĠTh y", "uk es", "it he", "Ġo ve", "Ġbatt led", "Ġseaf ood", "Ġfig ur", "ĠR D", "c rop", "Ġsqu ads", "{ \\", "à ¹", "ĠE h", "Ġinterview ing", "ĠQ in", "Ġas piring", "PL IC", "Ġcla uses", "ĠG ast", "ĠN ir", "Ġl uggage", "Ġh ose", "Ġsystem d", "Ġdesc ending", "ĠRev ised", "ĠR ails", "al ign", "70 9", "33 7", "Ġf ug", "charg ing", "t ags", "Ġut er", "k ish", "WAR NING", "49 0", "prof its", "Ġvoy age", "Ġa ce", "ĠV anguard", "ĠT anks", "ĠM uk", "Ġ2 26", "S afe", "Ar mor", "Ġvolcan ic", "Ġwom b", "ĠM IL", "Ġbegin ner", "ĠRec ogn", "ĠA AP", "PL AY", ") !", "Ġdetect ing", "c n", "Ġbre aches", "Bas ically", "ĠP ag", "ĠMunicip al", "ĠInd ie", "ĠL af", "ĠDis able", "ĠOl son", "Ġrest rained", "Ġrul ings", "Ġhum ane", "ev ents", "ĠCinem a", "display Text", "ĠH atch", "action Date", "onna issance", "Ġassault ing", "ĠL ug", "CH AT", "Ġvig orous", "ĠPer se", "Ġintoler ance", "ĠSnap chat", "ĠSh arks", "Ġd ummy", "ĠDi agn", "ĠGu itar", "im eters", "40 3", "RE G", "A x", "Ġsepar ates", "ĠMah m", "Ġt v", "j ah", "O OL", "C irc", "ĠWinds or", "uss ian", "Ġintu ition", "Ġdis dain", "ĠDon ovan", "Ġ2 21", "E mb", "Ġcondem ning", "Ġgener osity", "zz y", "Ġpant ies", "ĠPre vent", "Action Code", "AN A", "34 2", "external ActionCode", "Ġspec ifying", "Ġcryst all", "J ere", "Ġru pt", "ĠApp rentice", "Ġprof iling", "Ð º", "St rike", "Ġsid eline", "Ġoblig ated", "Ġocc ult", "Ġbureaucr atic", "ant ically", "rupt ed", "neg ative", "ĠEthiop ia", "ĠC ivic", "Ġins iders", "el igible", "ĠTV s", "ĠB AR", "ĠT I", "i ologist", "ĠA IR", "Ġsubstit uted", "Ar ab", "ĠS aul", "ĠY og", "p rem", "Ġbuild ers", "Ġstation ary", "Ġdoubt ful", "Ġvig orously", "Ġthr illing", "Ph ysical", "ĠCare y", "ĠHyd ra", "geon ing", "ĠS ly", "y ton", "Ġborrow ers", "ĠPark inson", "Ġ ë", "ĠJama ica", "Ġsat ir", "Ġinsurg ents", "ĠF irm", "Ġis ot", "ĠK arn", "our ning", "ak ens", "doc s", "l ittle", "ĠMon aco", "CL ASS", "Tur key", "L y", "ĠCon an", "ass ic", "Ġstar red", "ĠPac ers", "et ies", "Ġt ipping", "M oon", "ĠR w", "s ame", "Ġcav ity", "Ġgo of", "ĠZ o", "Sh ock", "um mer", "Ġemphas izes", "Ġreg rett", "Ġnovel ty", "Ġen vy", "ĠPass ive", "r w", "50 5", "Ġind ifferent", "ĠR ica", "ĠHim self", "ĠFred die", "Ġad ip", "ä¸ Ģ", "Ġbreak out", "Ġhur ried", "ĠHu ang", "ĠD isk", "Ġro aming", "?????- ?????-", "U V", "ĠRick y", "ĠS igma", "Ġmarginal ized", "Ġed its", "Ġ30 4", "mem ory", "Ġspec imen", "29 3", "ãģ ¯", "Ġvert ically", "Ġaud ition", "ĠHe ck", "Ġc aster", "ĠHold ings", "ad al", "ĠC ron", "ĠL iam", "Ġdef lect", "P ick", "ĠDeb ug", "RE F", "Ġvers atility", "ot hes", "class ified", "ĠMah ar", "ĠH ort", "C ounter", "st asy", "not iced", "33 1", "ĠSh im", "f uck", "ĠB ie", "Ġair ing", "ĠPro tein", "ĠHold ing", "Ġspect ators", "ili ated", "ĠThat cher", "n osis", "ãĥ¼ ãĥ³", "Te le", "B oston", "ĠTem pl", "st ay", "Ġdecl arations", "47 9", "Vol ume", "ĠDesign er", "ĠOver watch", "id ae", "Ġon wards", "Ġn ets", "ĠMan ila", "part icularly", "Ġpolit ic", "o other", "Ġport raits", "Ġpave ment", "c ffff", "Ġs aints", "Ġbegin ners", "ES PN", "Ġshort comings", "âķIJ âķIJ", "Ġcom et", "ĠOrgan ic", "qu el", "Ġhospital ized", "Bre ak", "Ġpe el", "dyl ib", "asp x", "ur ances", "ĠT IM", "P g", "Ġread able", "ĠMal ik", "Ġm uzzle", "Ġbench marks", "d al", "ĠV acc", "ĠH icks", "60 9", "ĠB iblical", "he ng", "Ġover load", "ĠCivil ization", "Ġimm oral", "Ġf ries", "ãĤ Ĵ", "Ġreprodu ced", "Ġform ulation", "j ug", "ire z", "g ear", "Ġco ached", "Mp Server", "ĠS J", "ĠK w", "In it", "d eal", "ĠO ro", "ĠL oki", "ĠSong s", "Ġ23 2", "ĠLou ise", "asion ally", "Ġunc ond", "olly wood", "Ġprogress ives", "ĠEn ough", "ĠDo e", "Ġwreck age", "Ġbr ushed", "ĠBase Type", "Ġz oning", "ish able", "het ically", "ĠC aucus", "ĠH ue", "Ġk arma", "ĠSport ing", "Ġtrad er", "Ġseem ing", "ĠCapt ure", "4 30", "b ish", "Ġt unes", "Ġindo ors", "ĠSp here", "ĠD ancing", "TER N", "Ġno b", "ĠG ST", "m aps", "Ġpe ppers", "F it", "Ġoverse es", "ĠRabb i", "ĠR uler", "vert ising", "off ice", "xx x", "Ġra ft", "Ch anged", "Ġtext books", "L inks", "ĠO mn", "ãĢ ij", "Ġinconven ience", "ĠDon etsk", "= ~", "Ġimplicit ly", "Ġboost s", "ĠB ones", "ĠBo om", "Cour tesy", "Ġsens ational", "AN Y", "Ġgre edy", "ed en", "Ġinex per", "ĠL er", "ĠV ale", "Ġtight en", "ĠE AR", "ĠN um", "Ġancest or", "S ent", "ĠH orde", "urg ical", "all ah", "Ġsa p", "amb a", "ĠSp read", "tw itch", "Ġgrand son", "Ġfract ure", "Ġmoder ator", "ĠSe venth", "ĠRe verse", "Ġestim ation", "Cho ose", "Ġpar ach", "Ġbar ric", "ãĢ IJ", "Ġcomp ass", "Ġall ergic", "âĢ ķ", "OT HER", "err illa", "Ġw agon", "Ġz inc", "Ġrub bed", "ĠFull er", "ĠLuxem bourg", "ĠHoo ver", "Ġli ar", "ĠEven ing", "ĠCob b", "est eem", "Ġselect or", "ĠB rawl", "is ance", "ĠE k", "Ġtro op", "Ġg uts", "ĠApp eal", "ĠTibet an", "Ġrout ines", "ĠM ent", "Ġsummar ized", "steam apps", "Ġtr anqu", "Ġ19 29", "or an", "ĠAut hent", "Ġg maxwell", "Ġappre hens", "Ġpo ems", "Ġsa usage", "ĠWeb ster", "ur us", "Ġthem ed", "Ġl ounge", "Ġcharg er", "Sp oiler", "Ġsp illed", "h og", "ĠSu nder", "ĠA in", "ĠAng ry", "Ġdis qual", "ĠFrequ ency", "ĠEther net", "Ġhel per", "Per cent", "Ġhorr ifying", "Ġa il", "ĠAll an", "EE E", "ĠCross ing", "44 9", "Ġh olog", "ĠPuzz les", "ĠGo es", "eren n", "60 4", "ãģ ı", "ĠRaf ael", "Ġatt en", "ĠE manuel", "Ġup ro", "ĠSus p", "P sych", "ĠTr ainer", "ĠN ES", "ĠHun ts", "bec ue", "Ġcounsel or", "R ule", "Ġtox ins", "Ġb anners", "r ifice", "Ġgreet ing", "Ġfren zy", "Ġall ocate", "Ġ* )", "ex pr", "50 3", "ĠCh ick", "ĠT orn", "Ġconsolid ation", "ĠF letcher", "sw itch", "fr ac", "cl ips", "ĠMcK in", "ĠLun ar", "Mon th", "IT CH", "Ġscholar ly", "rap ed", "39 8", "Ġ19 10", "Ġe greg", "Ġin secure", "Ġvict orious", "cffff cc", "Ġsing led", "Ġel ves", "ĠW ond", "bur st", "Ġcam oufl", "ĠBL ACK", "Ġcondition ed", "ç ī", "ans wered", "Ġcompuls ory", "asc ist", "Ġpodcast s", "ĠFrank furt", "bn b", "Ġne oliberal", "ĠKey board", "ĠBel le", "w arm", "Ġtrust s", "Ġins ured", "ĠBu cc", "us able", "60 7", "ĠPl ains", "Ġ18 90", "Ġsabot age", "Ġlod ged", "f elt", "Ġg a", "ĠN arc", "ĠSal em", "Ġsevent y", "ĠBl ank", "p ocket", "Ġwhis per", "Ġm ating", "om ics", "ĠSal man", "ĠK ad", "Ġan gered", "Ġcoll isions", "Ġextraord inarily", "Ġcoerc ion", "G host", "b irds", "è Ģ", "k ok", "Ġper missible", "avor able", "Ġpo inters", "Ġdiss ip", "ac i", "Ġtheat rical", "ĠCos mic", "Ġforget ting", "Ġfinal ized", "å¤ §", "y out", "l ibrary", "Ġbo oming", "ĠBel ieve", "ĠTe acher", "ĠL iv", "ĠGOOD MAN", "ĠDomin ican", "OR ED", "ĠPart ies", "Ġprecip itation", "ĠSl ot", "R oy", "ĠComb ined", "Ġinteg rating", "Ġch rome", "Ġintest inal", "ĠRe bell", "Ġmatch ups", "Ġblock buster", "ĠLore n", "ĠLe vy", "Ġpre aching", "ĠS ending", "ĠPur pose", "ra x", "f if", "Ġauthor itative", "ĠP ET", "ast ical", "Ġdish on", "Ġchat ting", "Ġ\"$ :/", "Connect ion", "Ġrecre ate", "Ġdel inqu", "Ġbro th", "ĠD irty", "ĠAd min", "z man", "Ġscholars hips", "Ġ25 3", "cont act", "als a", "7 67", "c reen", "abb age", "Ġ19 15", "Ġbl ended", "Ġal armed", "L anguage", "35 6", "Ġbl ends", "ĠCh anged", "W olf", "Ġhe pat", "Creat ing", "Ġper secut", "Ġsweet ness", "art e", "Ġforfe iture", "ĠRober to", "im pro", "N FL", "ĠMag net", "Det ailed", "Ġinsign ificant", "ĠPOL IT", "ĠBB Q", "ĠC PS", "Ġse aw", "amin er", "m L", "end if", "f inals", "Ġ26 5", "u ish", "Ġ} )", "ĠPro blems", "Ġem blem", "Ġserious ness", "Ġpars ing", "Ġsubst itution", "Ġpress ured", "Ġrecy cled", "ale b", "Rub y", "Ġprof iciency", "Dri ver", "ĠW ester", ": '", "AF TA", "Ġm antle", "ĠClay ton", "fl ag", "Ġpractition er", "c overed", "ĠSt ruct", "add afi", "4 25", "ĠTown ship", "ĠHyd ro", "Lou is", "34 3", "Ġcond o", "ĠT ao", "Ġutil ization", "Ġnause a", "ĠDem s", "rid ges", "p ause", "Ġform ulas", "Ġchall enger", "37 6", "Ġdefect ive", "ĠRail way", "ĠPub Med", "Ġyog urt", "l bs", "ĠNor folk", "OP E", "ĠMood y", "Ġdistribut or", "Ġscroll s", "Ġextract s", "St an", "Ġv iability", "Ġexp oses", "Ġstar vation", "ĠStep s", "ĠD odd", "f ew", "ST D", "33 2", "Ġclos ures", "Ġcomplement ary", "ĠS asha", "ump y", "Ġmon et", "Ġartic ulate", "ĠDo ct", "k iller", "Ġsc rim", "Ġ2 64", "Ġprost itutes", "Ġse vered", "Ġattach ments", "Ġcool ed", "L ev", "ĠF alk", "f ail", "Ġpolic eman", "ĠD ag", "Ġpray ed", "ĠK ernel", "Ġcl ut", "Ġc ath", "Ġan omaly", "St orm", "em aker", "ĠBreak fast", "ul i", "o ire", "J J", "h z", "Oper ation", "ĠS ick", "35 4", "ĠGuatem ala", "R ate", "Ġexp osures", "f aces", "ĠArch ae", "ra f", "ĠM ia", "Ġ20 25", "Ġop aque", "Ġdisgu ised", "ĠHead quarters", "S ah", "Ġp ots", "9 78", "ĠM alf", "Ġfrown ed", "Ġpoison ous", "ĠCon vers", "ee ks", "Ġcr ab", ".\" \"", "Ġtre ason", "Ġr anc", "Ġescal ating", "Ġwar r", "Ġmob s", "Ġl amps", "ĠSun shine", "ĠBrun swick", "Ph ones", "Ġspe lled", "ĠSk ip", "Ġ20 50", "Ġ19 11", "ĠPl uto", "ĠAm end", "Ġme ats", "38 7", "Ġst omp", "ĠZh ou", "ĠLevi athan", "ĠHaz ard", "ad v", "ĠOr well", "Ġal oud", "Ġb umper", "ĠAn arch", "ub untu", "ĠSer ious", "f itting", "ĠOption al", "ĠCec il", "RE AM", "Ġser otonin", "Ġcultiv ate", "ag ogue", "} \\", "Ġmos ques", "ĠSun ny", "Ġre active", "rev olution", "ĠL up", "ĠFed ora", "Ġdefense man", "ĠV ID", "ist ine", "Ġdrown ing", "ĠBroad casting", "Ġthr iller", "ĠS cy", "Ġacceler ating", "Ġdirect s", "od ied", "b ike", "d uration", "Ġpain fully", "R edd", "Ġproduct ions", "Ġg ag", "Ġwh ist", "Ġs ock", "Ġinf initely", "ĠConc ern", "ĠCit adel", "Ġlie u", "Ġcand les", "ogene ous", "arg er", "Ġheaven ly", "inflamm atory", "Per formance", "C s", "ruct ose", "az aki", "Ġp essim", "Ġinf erence", "Ġpow d", "ĠZ oe", "Ġpain ts", "Ġd azz", "pt a", "-------- ---", "Ġins pir", "ĠExper imental", "ĠKn ife", "reg or", "b ors", "Ġshow ers", "rom eda", "Ġs aint", "Ġben ign", "ĠJ iang", "Ġenvision ed", "Ġsh roud", "IF T", "H O", "Ġsh uff", "ĠI CC", "Ġse greg", "Ġrevis it", "ighth ouse", "L i", "Ġsub strate", "ĠSe as", "ĠRew ard", "ĠH ep", "ĠBr ass", "s bm", "Ġelim inates", "Ġst amina", "ĠV AT", "ĠLo an", "Ġconst raint", "Ġappropri ated", "Ġp es", "ĠA LE", "r anging", "Ġ40 4", "39 2", "Ġintellectual s", "ach u", "Ġrestruct uring", "ĠLe vin", "Ġrun es", "Ġdelight ful", "Ġcarbohyd rates", "ĠMod els", "ĠExp o", "Ġtransport ing", "all oc", "Ġring ing", "S amsung", "Ġscarce ly", "ĠURL s", "ĠM AS", "Ġprot otypes", "Ġnarr ator", "ĠCPU s", "cd n", "ĠBart on", "Ġdecided ly", "ĠSh u", "ix ir", "oc ious", "ĠMy st", "N intendo", "Ġre use", "Ġforg iven", "F ew", "in ical", "n at", "Ġseam less", "ĠEv a", "ĠE VE", "ĠJ O", "land ers", "Ġso fter", "neg ie", "Ġtrans ient", "Ġorb ital", "Ġfulf il", "ĠK om", "Hop efully", "Ġdynam ically", "ĠHun ger", "å Ľ", "ĠArmen ia", "el man", "ber to", "Ġp ige", "ĠID s", "lim it", "Ġve ins", "Ġso aring", "p acks", "Gold en", "ĠCr ab", "ist or", "ĠR PM", "Ġ$ $", "g ression", "Ġjihad ist", "Ġgam ble", "Ġcare g", "Ġinf lated", "F ace", "ĠFire arms", "ĠEm manuel", "â Ŀ", "Ġsh ocks", "gr ab", "Ġspl end", "ĠHP V", "ab ortion", "Ab ove", "Ent ity", "play ers", "Ġcomm enced", "ul ence", "Ġfulfill ment", "Ġembod iments", "ĠW elfare", "Ġha il", "Ġ< @", "tt en", "Ġcat cher", "ĠJ azeera", "Ġvolcan o", "Ġstabil ize", "ĠHand ler", "Ġintens ified", "ĠAb rams", "Ġhum iliation", "p aced", "60 5", "ĠCent OS", "Spe cific", "Ġhe ed", "ĠC AM", "ĠGal ile", "D ie", "Ġabol ished", "ĠThom son", "ĠTe achers", "ĠW ass", "j ong", "ĠIS BN", "ĠAll ies", "sh ake", "å ·", "v ict", "How ard", "Ġde em", "Ġexceed ingly", "ĠSmart stocks", "ib e", "Ġdoor way", "Ġcompet ed", "ig mat", "Ġnational ists", "Ġg room", "ĠKe en", "Ġdispos able", "de cl", "ĠT olkien", "ĠSche me", "Ġb iod", "Ġav id", "ĠEl on", "ag ar", "ĠT SA", "R oman", "Ġartific ially", "Ġadvis ors", "X L", "ĠInf erno", "36 6", "Ġted ious", "ĠPhot ography", "ĠCar rie", "Ġtro pe", "ĠSand ra", "Ġdec imal", "Que en", "ĠGund am", "ĠO M", "ote ch", "N BA", "Ġ19 32", "Ġent renched", "ĠMar ion", "Ġfr aternity", "Lab our", "Hen ry", "Ġlat itude", "E ither", "Ġenh ances", "ĠPot ential", "Ġsh ines", "id ad", "Ġbread th", "Ġcapac ities", "ĠðŁ ĻĤ", "ĠBron x", "Ġsex es", "Ġdifferent iation", "Ġheavy weight", "ĠT aj", "d ra", "Ġmigr ate", "Ġexhaust ion", "ĠR UN", "els ius", "ĠCu omo", "Ġgu itars", "Ġcl ones", "ĠSom ew", "ĠP ry", "------------ -", "Ġwarr anted", "cy cles", "Ġsalv age", "Ġdis ks", "R ANT", "ĠNGO s", "ĠMart ian", "\":[ {\"", "Ġadd icts", "oj ure", "il let", "Ġamazing ly", "art ments", "p ixel", "ĠGPU s", "Lay out", "è £", "ĠTam il", "ĠBas il", "Ġimpart ial", "ĠSt ructure", "f ork", "b ryce", "Ġr idge", "ĠHamb urg", "ri ous", "Ġbl itz", "cig arettes", "Ġcan ned", "40 2", "Ġiron ically", "Ġcompassion ate", "ĠHaw kins", ". #", "ĠCat hedral", "Ġrall ied", "in ternal", "Ġqu ota", "st akes", "T EXT", "m om", "Ġcomple tes", "Ġ23 8", "Ġsh rug", "ãĥ ij", "ĠN inth", "Ġrev ise", "ĠProv ider", "Ġtre acher", "Ġqu asi", "ĠPR ES", "Ġdep osition", "Ġconfidential ity", "iss ors", "Ġim balance", "Ġspan ning", "Ġang ular", "ĠC ul", "commun ication", "ĠNor a", "ĠGen ius", "op ter", "Ġs acked", "Sp ot", "Ġfine ly", "ĠCH R", "28 2", "w aves", "Pal est", "ĠRo hing", "N L", "è ¿", "Ġsh itty", "ĠSc alia", "4 75", "Pro gress", "Ġreferen cing", "Ġclass rooms", "ab ee", "Ġs od", "hes ion", "70 8", "ĠZucker berg", "ĠFin ish", "ĠScot ia", "ĠSav ior", "ĠInstall ation", "an tha", "( -", "Ġ30 2", "ĠP unk", "Ġcr ater", "yout u", "Ġro ast", "Ġinflu encing", "Ġd up", "ĠJ R", "ĠG rav", "Ġstat ure", "Ġbath rooms", "A side", "W iki", "me an", "ĠZ ak", "ĠOn es", "ĠN ath", "Ġhyper t", "Ġcommence ment", "C ivil", "Ġmoder ately", "Ġdistribut ors", "Ġbreast feeding", "Ġ9 80", "ĠS ik", "ĠC ig", "ĠAM ER", "R IP", "ĠCare er", "ust ing", "Ġmess ed", "Ġe h", "ĠJ ensen", "/ $", "Ġblack mail", "Ġconvers ions", "Ġscientific ally", "Ġmant ra", "p aying", "Ġiv ory", "ĠCour ts", "OU GH", "aunt let", "Ser ial", "B row", "ĠH undreds", "3 23", "Ġpe e", "Ġlin ux", "Ġsub mer", "ĠPrinc ipal", "48 5", "ĠD SL", "ĠCous ins", "Ġdoctr ines", "ĠAthlet ics", "Ġ3 15", "ĠK arma", "Ġatt ent", "ur ger", "Ġpresc ribe", "Ġenc aps", "ĠC ame", "Ġsecret ive", "ĠCr imes", "d n", "C lean", "ĠEgypt ians", "ĠCar penter", "Ġ ll", "H um", "ĠMil o", "Ġcapital ists", "Ġbrief ed", "T we", "ĠBas in", "elve t", "M os", "Ġplun ge", "ĠKa iser", "ĠFu j", "ill in", "Ġsafegu ards", "Ġo ste", "ĠOpportun ity", "ĠM afia", "ĠCall ing", "ap a", "ur ban", "br ush", "ill ard", "c é", "int elligence", "ĠL ob", "ĠDru id", "Ġsm oother", "Ġfoot ing", "Ġmotor ists", "arc ity", "Ġmascul inity", "Ġm ism", "Ġabdom inal", "ĠTa vern", "ĠR oh", "Ġesc apes", "s igned", "Anth ony", "Ġsacrific ing", "Ġintim acy", "Ġan terior", "ĠK od", "Ġmot if", "Ġg raz", "Ġvisual ization", "Ġguitar ist", "ĠTro tsky", "m agic", "D ar", "ĠMor i", "Ġw ards", "Ġtoile ts", "l est", "Ġtele port", "ĠSund ays", "ĠPl at", "ET S", "Ġe Sports", "Pat rick", "ĠK atherine", "en ko", "Ġhas sle", "ĠM ick", "gg les", "Ġh ob", "aint ain", "Ġair borne", "Ġsp ans", "Ġch ili", "Ġa perture", "Ġvolunte ered", "ĠInc ident", "ĠF res", "ĠVeter an", "augh tered", "ing o", "Ġun insured", "CL OSE", "Ġf use", "Ġer otic", "Ġadvert ise", "ra ising", "Text ure", "Ġatt ends", "ĠRE AL", "udd led", "Ġsm oot", "Ġ30 5", "ĠWill is", "Ġbl ond", "An alysis", "ĠV T", "on ica", "Ġstrongh old", "R F", "N M", ". >>", "Ġprosper ous", "Ġbo asted", "29 2", "ĠManufact uring", "PR ESS", "g ren", "Ġpharm acy", "ĠRoc kefeller", "k ai", "Ġth umbs", "ĠH ut", "Ġmother board", "Ġguard ians", "ĠAl ter", "ll ular", "Ġsh ack", "Ġwise ly", "Ġback bone", "erv a", "Ġsu icides", "ĠMcG regor", "ij ah", "E mer", "ĠB rav", "Ġdesign ate", "P OST", "produ ced", "Ġcleans ing", "irl wind", "ex istent", "ĠHum ph", "ĠPay ne", "Ġv ested", "Å ¡", "Ġstring ent", "ion a", "Ġuns ub", "Ġsum med", "ĠHer cules", "sub ject", "ĠR agnar", "ĠN os", "Ġcharacter ization", "Ġsav vy", "ĠDaw son", "ĠCas ino", "Ġf ri", "ĠBar rier", "Ġmis information", "Ġins ulation", "Ġcorrid ors", "Ġair planes", "ĠNo ct", "ah i", "Ġ19 16", "k b", "arm ac", "Ġsh un", "Ġsche ma", "Ġhorr ified", "Ġ23 9", "aund ers", "N B", "i ates", "er ity", "ĠSh ard", "Ġr arity", "Ġgroup ed", "ĠGh ana", "again st", "ĠBi ological", "ĠA ware", "ow ell", "Ï Ħ", "ĠBe au", "sh aw", "H ack", "ĠJul ius", "US S", "ol son", "aun a", "c ru", "ĠMaur ice", "ĠI k", "Ġsequ encing", "Ġradical s", "Ġ( ?,", "v irtual", "Ġany ways", "Ġreper c", "Ġhand lers", "Ġhes itant", "é ĥ", "ĠM F", "ple mentation", "ass ociated", "Ġcampaign ed", "ĠY ue", "ut ations", "ĠY oga", "Ġsim mer", "Ġro ds", "Ġmel ody", "Ġconv oy", "v ideos", "Ġscreen ed", "N eg", "ochem ical", "Ġ( ))", "Ġultr as", "Ġant ip", "ĠIsland ers", "70 4", "Ġfet ish", "Ġridic ulously", "ĠK art", "Ġmitochond rial", "Ġinterf ering", "Build er", "Ġover fl", "Ġac ne", "ĠM ud", "ĠK err", "f lex", "ĠPost al", "ĠBalt ic", "47 7", "ĠPers ons", "our age", "H B", "ĠM use", "ĠImm ortal", "ĠDri ving", "Ġpet itions", "Ġsubsc ript", "Ġs orce", "ĠProcess or", "ut on", "S ony", "Ġph on", "Ġr aced", "ĠAnth rop", "Ġday time", "ĠEx ercise", "Add ing", "Ġeng ages", "ĠQual comm", "Ġmir acles", "Ġmem es", "ĠDr ink", "ĠOri oles", "Ġhair s", "ĠPol ar", "ath om", "Ġsl ippery", "ĠR emy", "Ġcar amel", "ĠY EAR", "Ġal k", "I gn", "a ution", "ĠMer lin", "ĠC ran", "Ġap ologies", "Ġ4 10", "Ġout ing", "ĠMem ories", "app ointed", "Ġcount ered", "u ld", "pos ing", "Ġfire wall", "ĠW ast", "ĠW et", "work ed", "se ller", "Ġrepe aled", "ere o", "ass uming", "BL IC", "m ite", "ĠCEO s", "ĠChap el", "ellig ent", "________________ ________", "D og", "Ġw art", "Ġsubsc riber", "s ports", "Ġbe gged", "ĠM V", "Ġsem if", "eth ical", "Ġpre ach", "Ġrev ital", "Ġpun itive", "Ġshort cuts", "Ġinstit uted", "ĠWars aw", "Ġabdom en", "ĠK ING", "Ġsuper intendent", "Ġf ry", "ĠGe o", "T OR", "Ġcontrad ictions", "apt ic", "Ġlandsc apes", "b ugs", "Ġcl ust", "Ġvol ley", "c ribed", "Ġt andem", "Ġrob es", "WH AT", "Ġpromot er", "Ġel oqu", "review ed", "ĠD K", "ĠPl ato", "Ġf ps", "T ank", "ĠDer rick", "Ġpriorit ize", "as per", "ĠHond uras", "ĠCom pleted", "ne c", "Ġm og", "n ir", "ĠMay o", "DE F", "st all", "in ness", "ĠVolks wagen", "Ġprec aution", "ĠM ell", "i ak", "ist ries", "Ġ24 8", "Ġoverl apping", "Sen ate", "ĠEnh ance", "res y", "rac ial", "OR TS", "ĠM ormons", "Str ong", "ĠCo ch", "Mex ico", "ĠMad uro", "Ġj ars", "Ġcan e", "W ik", "oll a", "iff erence", "Ġphysic ist", "ĠMag gie", "Ġ28 5", "Ġdep iction", "ĠMcL aren", "J u", "Ġsl ows", "Ġcommission ers", "ĠWill ow", "ĠExpl os", "hov ah", "Ġtechn ician", "Ġhom icides", "ĠFl av", "ĠTr uman", "Ġ100 00", "u ctor", "Ġsh ader", "News letter", "45 7", "Ġre ver", "Ġhard ened", "Ġwhere abouts", "Ġrede velop", "Ġcar bs", "Ġtra vers", "Ġsqu irrel", "Ġfoll ower", "Ġs ings", "50 8", "Ġrabb its", "emon ium", "Ġdocument ing", "Ġmisunder stood", ") '", "R ick", "gg ies", "Ġprem ie", "Ġsk ating", "Ġpass ports", "Ġf ists", "aged don", "H aw", "AC P", "0 80", "ĠThough ts", "ĠCarl son", "Ġpriest hood", "h ua", "Ġdun geons", "ĠLo ans", "Ġant is", "Ġfamiliar ity", "ĠS abb", "op al", "ĠIn k", "st rike", "Ġc ram", "Ġlegal ized", "Ġcu isine", "Ġfib re", "Tra vel", "ĠMon ument", "OD Y", "eth y", "Ġinter state", "ĠP UR", "em porary", "ĠArab ian", "develop ed", "Ġsadd le", "Ġg ithub", "ĠOff er", "ĠIS P", "ro let", "ĠSUP ER", "ĠDen is", "Ġmultipl ier", "Ġstir red", "Interest ingly", "Ġcustom ary", "Ġbill ed", "he x", "Ġmultipl ied", "Ġfl ipping", "ĠCros by", "Ġfundament als", "ia e", "ĠPlay ed", "ĠAt om", "am azon", "ĠFl am", "ee z", "activ ated", "Ġtables poon", "Ġliberal ism", "ĠPal in", "ĠP atel", "N um", "ĠT AM", "Ġs urn", "ĠRel oaded", "Ġco ined", "\" ],", "ĠCl ash", "ĠAg u", "Ġprag matic", "ĠActiv ate", "Ġ8 02", "Ġtrail ers", "Ġsil hou", "Ġprob es", "Ġcirc us", "ĠB ain", "ĠLind say", "ĠAb bey", "Del ivery", "Ġconcess ion", "Ġgast ro", "ĠSpr ite", "Ä Ł", "and el", "Ġg imm", "Ġaut obi", "ĠT urtle", "Ġwonder fully", "ĠHar am", "ĠWorld wide", "ĠHand le", "Ġtheor ists", "Ġsle ek", "ĠZh u", "ograph ically", "EG A", "ĠOwn ers", "ath s", "ĠAntar ctic", "n atal", "=\" \"", "fl ags", "`` ``", "Ġs ul", "K h", "Ġpot assium", "Ġlinem an", "Ġcere al", "ĠSe asons", "Ġ20 22", "Ġmat hematic", "Ġastron omers", "prof essional", "Ġf ares", "cknow led", "Ġch i", "Ġyoung sters", "Ġmistaken ly", "Ġhem isphere", "ĠDiv inity", "r one", "Ġ\" ,", "r ings", "Ġattract s", "v ana", "å ¹", "C AP", "Ġplay list", "Ġpor ch", "ãģ £", "Ġincorpor ates", "Ġso ak", "Ġassert ing", "ĠTerror ism", "ĠP ablo", "J a", "ces ter", "Ġfear ing", "ĠPr ayer", "Ġescal ated", "G W", "Ġro be", "ĠBright on", "ac ists", "ĠSym phony", "ĠDwar f", "ĠPar ade", "ĠLe go", "Ġinex pl", "Ġl ords", "le af", "RA G", "l iber", "Ġcig ars", "ĠJe hovah", "60 6", "WIND OWS", "ĠLiber ia", "eb us", "He avy", "Ġl ubric", "ĠR W", "angu ages", "Ġnarrow ed", "com puter", "ĠE mber", "Ġmurder ing", "Ġdown stream", "ĠT uls", "ĠT ables", "Top ic", "ĠAcc uracy", "= /", "l ost", "ĠRe i", "Ġprogress es", "b ear", "Ġestablish ments", "Just in", "ĠPe ach", "ĠG omez", "å ¿", "ĠTri angle", "Id ent", "ĠH ive", "Res ources", "Ġmix es", "ĠAss uming", "M u", "Ġhyp oc", "Ġs ane", "ĠW an", "id ious", "Su ccess", "Ġ io", "Ang el", "Ġdanger ously", "ĠCreat ure", "W ORK", ": [", "ĠKat rina", "List ener", "M iller", "ĠId lib", "h ang", "Ġcircum vent", "h ref", "Ġcel estial", "ĠWe eks", "ĠP ug", "ĠDal ton", "Ġsubpoen a", "uk u", "Ġpers isted", "pe i", "old ing", "ĠDoc uments", "ĠH ast", "ĠC ENT", "Ġprim er", "Ġsyn onymous", "Ġn ib", "om bs", "Ġnot ation", "ĠD ish", "ĠAt mosp", "Ġforb id", "ĠAN G", "pat tern", "l os", "Ġproject iles", "b rown", ".\" ,", "ĠVen om", "Ġfierce ly", "ub lished", "ĠU ran", "ĠNic arag", "4 10", "ĠC AL", "OT OS", "ĠMir acle", "ĠEn chant", "Ġguard ing", "app end", "Att ach", "Ġlevel ed", "Ġcond oms", "ih ilation", "64 9", "Ġnight mares", "ĠTHE Y", "ĠST ART", "ĠK inn", "Ġroomm ate", "Ġhy giene", "o pping", "J ob", "Ġl vl", "ĠV ER", "ĠKe eping", "ab etic", "Ġformat ting", "eral a", "Ġrev isions", "Ġres urg", "T el", "ĠGood man", "35 3", "p od", "Ġind isp", "ĠTrans lation", "Ġg own", "ĠM und", "Ġc is", "Ġby stand", "col lect", "ĠPun jab", "act ively", "ĠG amb", "te ll", "Ġimport ing", "g encies", "Ġloc om", "ĠBr ill", "H oly", "ĠBer ger", "Ġshow down", "Ġrespond ers", "IL Y", "Ġt akedown", "le ted", "Ġmat tered", "Ġpredict ive", "Ġover lay", "G PU", "ĠV ick", "Ġconvey ed", "T ab", "pe er", "Sc an", "Ġdefensive ly", "v ae", "Ġappro ving", "Ġt iers", "ĠV ia", "quer ade", "ĠSaud is", "Ġdemol ished", "ĠProp he", "Ġmon o", "Ġhospital ity", "H AM", "ĠAri el", "M OD", "ĠTor ah", "Ġbl ah", "ĠBel arus", "erent ial", "ĠT uc", "Ġbank er", "39 7", "Ġmosqu it", "ĠScient ist", "ĠMus ical", "Ġh ust", "Sh ift", "Ġtor ment", "Ġstand off", "E duc", "ĠF og", "Ġampl ifier", "Sh ape", "Inst ance", "ĠCrit ics", "Ġda emon", "H ouston", "Ġmatt ress", "ĠID F", "Ġobsc ene", "ĠA mer", "hett i", "Ġcomp iling", "35 2", "vere tt", "ĠRed uction", "ist ration", "ĠBl essed", "ĠB achelor", "3 16", "Ġpr ank", "ĠVul can", "dd ing", "Ġm ourning", "ĠQu int", "ĠBl aster", "test ing", "Ġsed iment", ">> >", "ĠE ternity", "ĠWH ERE", "ĠM aze", "Ġreact ing", "ĠAl v", "oms day", "ĠC RA", "Ġtransl ator", "Ġbog us", "at u", "We bsite", "oll s", "Ġbapt ism", "Ġs ibling", "ĠAut umn", "ve z", "ãģ® é", "gu ards", "Ge org", "assad ors", "ĠFre ud", "Ġcontin ents", "ĠReg istry", "Bern ie", "ĸļ 士", "Ġtoler ant", "ĠU W", "Ġhor ribly", "99 5", "ĠMID I", "Ġimpat ient", "oc ado", "er i", "ĠWor st", "ĠNor ris", "ĠTalk ing", "Ġdef ends", "ens able", "Ġ20 21", "Ġanat omy", "L ew", "Ġdraw er", "ĠCan berra", "Ġpatri otic", "é¾įå ĸļ士", "ĠAv g", "AR M", "Ġundis closed", "Ġfare well", "45 9", "b able", "ĠAll ison", "OL OG", "Ġcon co", "t ight", "ĠAC PI", "ĠM ines", "l ich", "ĠâĶ ľ", "represent ed", "200 000", "Ġenthusi ast", "OT S", "b il", "ĠIng redients", "Ġinvent or", "ĠMy SQL", "³³ Âł", "ĠAB OUT", "with in", "Ġm k", "B ul", "ĠF ake", "Ġdracon ian", "W a", "hel m", "ĠTer ran", "erv ille", "Ġcommon place", "SI ZE", "Ġ\" <", "re place", "ograph s", "ĠSE LECT", "inc ible", "ĠMost ly", "ĠShe ffield", "ĠID E", "ugg le", "Ġcit ations", "h urst", "ĠUn ix", "Ġunle ash", "ĠP iper", "ĠN ano", "Ġsucc umb", "Ġreluct ance", "Ġ25 00", "ĠMer chant", "Ġwire t", "Ġcomb os", "ĠBirth day", "Ġchar coal", "ĠU PS", "ĠFair fax", "Ġdrive way", "ĠT ek", "ĠP itch", "ove re", "Ġtechn icians", "ĠAct ual", "fl ation", "ĠF iscal", "ĠEm pty", "an amo", "Ġmag nesium", "Ġsl ut", "Ġgrow ers", "Invest igators", "( ):", "ĠS atellite", "ĠKe ynes", "miss ive", "l ane", "Ġb orough", "3 44", "ĠTE AM", "ĠBet hesda", "C V", "h ower", "ĠR AD", "Ġch ant", "ĠR iy", "Ġcompos itions", "Ġmild ly", "Ġmedd ling", "Ġag ility", "ane ers", "5 01", "Ġsyn th", "ling er", "29 1", "Ġex claimed", "Part y", "Ġcont amin", "ĠMan or", "ĠResp ond", "Ġpra ising", "Ġman ners", "fle et", "Sum mer", "ĠLy nd", "ĠDef initely", "gr im", "Ġbow ling", "st ri", "ç Ľ", "y nt", "Ġmand ates", "D IV", "Ġreconc ile", "view s", "ĠDam on", "vet te", "F lo", "ĠGreat est", "il on", "ic ia", "Ġportray al", "Ġcush ion", "50 4", "19 79", "oss al", "App lic", "sc ription", "Ġmit igation", "AT S", "p ac", "Ġer ased", "Ġdefic iencies", "ĠHolland e", "ĠX u", "Ġb red", "Ġpregn ancies", "f emin", "Ġem ph", "Ġpl anners", "Ġout per", "utter ing", "Ġperpet rator", "Ġm otto", "ĠEll ison", "ĠNE VER", "Ġadmitted ly", "AR I", "ĠAzerbai jan", "Ġmill isec", "Ġcombust ion", "ĠBott le", "ĠL und", "ĠP s", "ĠD ress", "Ġfabric ated", "Ġbat tered", "Ġs idel", "ĠNot ting", "Fore ign", "ĠJer ome", "0 20", "ĠAr bit", "Ġkn ots", "ĠR IGHT", "M oving", "ãģ Ļ", "Ġsur geries", "Ġcour thouse", "Ġm astered", "Ġhover ing", "ĠBr an", "ĠAl ison", "Ġsaf est", "m ilitary", "Ġbull ied", "Ġbar rage", "Read er", "ES E", "ĠGe ographic", "T ools", "3 14", "ĠGe ek", "ro th", "gl ers", "ĠF IN", "Ï ģ", "ĠA ston", "al tern", "48 8", "Ġveter in", "G amer", "Ġint el", "ren ches", "Sh ield", "Ġam nesty", "ĠB har", "Ġp iled", "Ġhonor able", "ĠInst itutes", "Ġso aked", "Ġcom a", "ĠE FF", "34 1", "by tes", "ĠG mail", "le in", "ĠCanad iens", "m aterial", "I l", "Ġinstruct ors", "ĠK Y", "Ġconce ive", "ub b", "ĠP ossible", "Ġeas ing", "ĠChrist ina", "Ġcar ic", "ĠHD R", "R OM", "Ġsho vel", "de lete", "Ġp uff", "ĠCh anging", "Ġseam lessly", "Att ribute", "Ġacqu isitions", "ak ery", "ĠE F", "Ġaut istic", "ĠT akes", "ĠPow der", "ĠSt ir", "5 10", "ĠBub ble", "sett ings", "ĠF owler", "Ġmust ard", "Ġmore over", "Ġcopyright ed", "ĠLED s", "15 00", "æ ī", "ĠH IS", "en f", "Ġcust od", "ĠH uck", "G i", "Ġim g", "An swer", "C t", "j ay", "ĠInf rastructure", "Ġfeder ally", "L oc", "Ġmicro bes", "Ġover run", "dd s", "ot ent", "adi ator", ">>>> >>>>", "Ġtorn ado", "Ġadj ud", "Ġintrig ued", "Ġs i", "ĠRevel ation", "pro gress", "Ġburgl ary", "ĠSai yan", "ĠK athy", "Ġser pent", "ĠAndre as", "Ġcomp el", "ess ler", "ĠPl astic", "ĠAd vent", "ĠPos itive", "ĠQ t", "ĠHind us", "reg istered", "ular ity", "Ġrighteous ness", "Ġdemon ic", "u itive", "ĠB DS", "ĠGre gg", "c ia", "ĠCrus ade", "ĠSina i", "W ARE", "+ (", "Ġme ll", "Ġder ail", "y ards", "A st", "Ġnotice ably", "ĠO ber", "R am", "Ġun noticed", "Ġse q", "av age", "T s", "Ġ6 40", "Ġconced e", "Ġ] )", "F ill", "Ġcapt ivity", "ĠImprove ment", "ĠCrus ader", "ara oh", "M AP", "æ Ĺ", "Ġstr ide", "al ways", "F ly", "N it", "Ġal gae", "ĠCook ing", "ĠDo ors", "Mal ley", "Ġpolic emen", "ãģ į", "Ġastron aut", "access ible", "49 5", "ĠR AW", "cl iffe", "udic rous", "Ġdep ended", "al ach", "Ġvent ures", "ra ke", "Ġt its", "ĠH ou", "Ġcond om", "ormon al", "Ġind ent", "Ġupload ing", "Foot note", "Import ant", "Ġ27 1", "Ġmind ful", "Ġcont ends", "C ra", "Ġcal ibr", "ĠO ECD", "plug in", "F at", "ĠIS S", "ĠDynam ics", "ans en", "68 6", "' ),", "Ġsp rite", "Ġhand held", "ĠH ipp", "=~ =~", "Tr ust", "Ġsem antics", "ĠBund es", "ĠRen o", "ĠLiter ature", "s ense", "G ary", "ĠA eg", "ĠTr in", "EE K", "Ġcler ic", "ĠSS H", "Ġch rist", "Ġinv ading", "ib u", "Ġen um", "aur a", "Ġal lege", "ĠInc redible", "B BC", "Ġth ru", "Ġsa iled", "Ġem ulate", "Ġin security", "Ġc rou", "Ġaccommod ations", "Ġincompet ent", "Ġsl ips", "ĠEarth qu", "s ama", "IL LE", "Ġi Phones", "as aki", "Ġby e", "Ġar d", "Ġext ras", "Ġsl aughtered", "Ġcrowd funding", "res so", "Ġfil ib", "ĠER ROR", "ĠT LS", "e gg", "ĠIt al", "Ġen list", "ĠCatal onia", "ĠSc ots", "Ġser geant", "Ġdiss olve", "N H", "Ġstand ings", "ri que", "I Q", "Ġbenef iciary", "Ġaqu arium", "You Tube", "ĠPower Shell", "Ġbright est", "ĠWar rant", "S old", "Writ ing", "Ġbegin nings", "ĠRes erved", "ĠLatin os", "head ing", "Ġ4 40", "Ġrooft op", "AT ING", "Ġ3 90", "VP N", "G s", "k ernel", "turn ed", "Ġprefer able", "Ġturn overs", "ĠH els", "S a", "ĠShin ji", "ve h", "ĠMOD ULE", "V iol", "Ġex iting", "Ġj ab", "ĠVan illa", "Ġac ron", "ĠG ap", "ber n", "A k", "ĠMc Gu", "Ġend lessly", "ĠFar age", "ĠNo el", "V a", "M K", "Ġbr ute", "ĠK ru", "ĠES V", "ĠOl ivia", "âĢ ł", "ĠK af", "Ġtrust ing", "Ġh ots", "3 24", "Ġmal aria", "Ġj son", "Ġp ounding", "ort ment", "Count ry", "Ġpostp oned", "Ġunequ iv", "? ),", "ĠRo oney", "udd ing", "ĠLe ap", "ur rence", "sh apeshifter", "ĠH AS", "os ate", "Ġca vern", "Ġconserv atism", "ĠB AD", "Ġmile age", "Ġarrest ing", "V aults", "Ġmix er", "Dem ocratic", "ĠB enson", "Ġauth ored", "8 000", "Ġpro active", "ĠSpirit ual", "t re", "Ġincarcer ated", "ĠS ort", "Ġpe aked", "Ġwield ing", "re ciation", "×Ļ ×", "P atch", "ĠEm my", "Ġex qu", "tt o", "ĠRat io", "ĠP icks", "ĠG ry", "ph ant", "Ġf ret", "Ġeth n", "Ġarch ived", "% -", "c ases", "ĠBl aze", "Ġim b", "c v", "y ss", "im ony", "Ġcount down", "Ġaw akening", "ĠTunis ia", "ĠRe fer", "ĠM J", "Ġun natural", "ĠCar negie", "iz en", "ĠN uggets", "he ss", "Ġev ils", "64 7", "Ġintrodu ctory", "l oving", "ĠMcM ahon", "Ġambig uity", "L abel", "ĠAlm ighty", "Ġcolor ing", "ĠCl aus", "set ting", "N ULL", "ĠF avorite", "ĠS IG", "> (", "ĠSh iva", "ĠMay er", "Ġstorm ed", "ĠCo verage", "we apons", "igh am", "Ġun answered", "Ġle ve", "Ġc oy", "c as", "b ags", "as ured", "Se attle", "ĠSant orum", "ser ious", "Ġcourage ous", "ĠS oup", "Ġconfisc ated", "Ġ// /", "Ġuncon ventional", "Ġmom s", "ĠRohing ya", "ĠOrche stra", "ĠPot ion", "Ġdisc redit", "ĠF IL", "f ixed", "ĠDe er", "do i", "ĠDim ension", "Ġbureaucr ats", "et een", "Ġaction Group", "oh m", "Ġb umps", "ĠUt ility", "Ġsubmar ines", "ren heit", "re search", "ĠShap iro", "Ġsket ches", "Ġde ceptive", "ĠV il", "es ame", "ĠEss entially", "Ġramp age", "isk y", "Ġmut tered", "th ritis", "Ġ23 6", "f et", "b ars", "Ġpup il", "ĠTh ou", "o S", "s ong", "Ġfract ured", "Ġre vert", "pict ure", "Ġcrit erion", "us her", "Ġreperc ussions", "ĠV intage", "ĠSuper intendent", "Offic ers", "Ġflag ged", "Ġbl ames", "Ġin verse", "ograp hers", "Ġmakes hift", "Ġdev oid", "Ġfoss ils", "ĠArist otle", "ĠFund s", "Ġde pleted", "ĠFl u", "ĠY uan", "Ġw oes", "Ġlip id", "Ġsit u", "requ isites", "Ġfurn ish", "ĠSam ar", "Ġshame ful", "Ġadverse ly", "Ġad ept", "Ġrem orse", "Ġmurder ous", "uck les", "ĠE SL", "Ġ3 14", "s ent", "Ġred ef", "ĠC ache", "ĠP urs", "ig ans", "Ġ4 60", "Ġpres criptions", "Ġf res", "F uck", "ocr ates", "Tw enty", "ĠWe ird", "ĠT oggle", "ĠC alled", "itiz ens", "Ġp oultry", "Ġharvest ing", "ãĤ¦ ãĤ¹", "Bott om", "Ġcaution ed", "t n", "39 6", "ĠNik ki", "Ġeval uations", "Ġharass ing", "Ġbind ings", "ĠMon etary", "Ġhit ters", "Ġadvers ary", "un ts", "Ġset back", "Ġenc rypt", "ĠC ait", "Ġl ows", "eng es", "ĠN orn", "Ġbul bs", "Ġbott led", "ĠVoy ager", "3 17", "Ġsp heres", "p olitics", "Ġsubt ract", "Ġsens ations", "Ġapp alling", "Ġ3 16", "Ġenvironment ally", "ĠST EM", "Ġpub lishes", "5 60", "Ġdilig ence", "48 4", "Ġadv ises", "Ġpet rol", "Ġimag ining", "Ġpatrol s", "ĠInt eger", "ĠAs hes", "act us", "ĠRad iant", "ĠL T", "it ability", "ht aking", "Set ting", "Ġnu anced", "ĠRe ef", "ĠDevelop ers", "N i", "pie ces", "99 0", "Lic ense", "Ġlow ers", "ĠOtt oman", "3 27", "oo o", "Ġqu itting", "mark ets", "Beh ind", "Ġbas in", "Ġdoc s", "an ie", "fl ash", "ct l", "Ġcivil ized", "ĠFuk ushima", "\"] ,\"", "ĠK S", "ĠHonest ly", "ar at", "Ġconstruct s", "ĠL ans", "ĠD ire", "ĠLI KE", "ĠTrou ble", "Ġwith holding", "ĠOb livion", "Ġsan ity", "any a", "Con st", "Ġgro cer", "ĠC elsius", "Ġrecount ed", "ĠW ife", "B order", "ate red", "h appy", "Ġspo iler", "Ġlog ically", "H all", "Ġsucceed ing", "Ġpoly morph", "Ġax es", "ĠShot gun", "ĠS lim", "ĠPrin ciples", "ĠL eth", "art a", "Ġsc or", "Sc reenshot", "Ġrelax ation", "#$ #$", "Ġdeter rent", "idd y", "Ġpower less", "Ġles bians", "Ġch ords", "ĠEd ited", "se lected", "Ġseparat ists", "000 2", "Ġair space", "Ġturn around", "Ġc unning", "P ATH", "P oly", "Ġbomb ed", "Ġt ion", "x s", "Ġwith hold", "Ġw aged", "ĠLiber ties", "Fl ag", "Ġcomfort ing", "45 4", "ĠI ris", "are rs", "Ġr ag", "Ġrel ocated", "ĠGu arant", "Ġstrateg ically", "Ġgam ma", "uber ty", "ĠLock heed", "g res", "Ġgr illed", "ĠLow e", "st ats", "ĠR ocks", "Ġsens ing", "Ġrent ing", "ĠGe ological", "ا Ø", "ot rop", "Ġse w", "Ġimproper ly", "48 6", "Ġâĸ ł", "Ġstar ving", "ĠB j", "Disc ussion", "3 28", "ĠCom bo", "ĠFix es", "N AT", "Ġstri ving", "th ora", "Ġharvest ed", "ĠP ing", "Ġplay ful", "Ġaven ues", "Ġoccup ational", "Ġw akes", "ĠCou rier", "Ġdrum mer", "ĠBrow ser", "ĠH outh", "it u", "Ġapp arel", "p aste", "Ġhun ted", "ĠSecond ly", "l ain", "X Y", "ĠP IN", "ic ons", "Ġcock tails", "Ġs izable", "Ġhurd les", "est inal", "ĠRecre ation", "Ġe co", "64 8", "ĠD ied", "m int", "Ġfinger prints", "Ġdis pose", "ĠBos nia", "ts y", "22 00", "Ġins pected", "ĠF ou", "Ġf uss", "Ġamb ush", "ĠR ak", "Ġmanif ested", "Pro secut", "Ġsuff ice", "ren ces", "Ġcompens ated", "ĠC yrus", "Ġgen us", "ĠWolver ine", "ĠTrend s", "Ġh ikes", "ĠSe en", "Ġen rol", "C old", "Ġpol itely", "ĠSl av", "ĠRu pert", "Ġey ewitness", "ĠAl to", "Ġun comp", "Ġposter ior", "M ust", "ĠHer z", "Ġprogress ively", "Ġ23 4", "Ġind ifference", "ĠCunning ham", "Ġacadem ia", "Ġse wer", "Ġast ounding", "ĠA ES", "r ather", "Ġeld est", "Ġclim bs", "ĠAdd s", "Ġout cry", "Ġcont ag", "ĠH ouses", "Ġpe pt", "ĠMel ania", "interest ed", "ĠU CH", "ĠR oots", "ĠHub bard", "ĠT BD", "ĠRoman ian", "fil ename", "St one", "ĠIm pl", "Ġchromos ome", "C le", "d x", "Ġscram bled", "ĠP t", "Ġ24 2", "OP LE", "Ġtremend ously", "St reet", "Ġcra ving", "Ġbund led", "ĠR G", "p ipe", "Ġinj uring", "Ġarc ane", "Part icip", "ĠHero ic", "st y", "Ġto pping", "ĠTemp est", "rent ices", "b h", "Ġpar anoia", "ĠUnic ode", "Ġegreg ious", "Ġ\\ '", "ĠOsw ald", "Ġgra vel", "ĠSim psons", "Ġbl and", "ĠGuant anamo", "Writ er", "lin ers", "ĠD ice", "J C", "Ġpar ity", "Ġs ided", "Ġ23 7", "ĠPyr rha", "at ters", "d k", "F ine", "comp an", "Ġform ulated", "ĠId ol", "il ers", "hem oth", "ĠF av", "Ġintr usion", "Ġcar rots", "ĠL ayer", "ĠH acker", "Ġ ----------------", "Ġmoder ation", "é ģ", "oc oc", "Ġcharacter ize", "ĠTe resa", "Ġsocio economic", "Ġper k", "ĠParticip ation", "tr aining", "ĠPaul o", "ph ys", "Ġtrust worthy", "Ġembod ied", "ĠMer ch", "c urrency", "ĠPrior ity", "Ġte asing", "Ġabsor bing", "Ġunf inished", "ĠCompar ison", "Ġdis ple", "writ ers", "Ġprofess ions", "ĠPengu in", "Ġang rily", "ĠL INK", "68 8", "ĠCor respond", "Ġprev ailed", "Ġcart el", "l p", "as ms", "ĠRed emption", "ĠIslam ists", "effect s", "d ose", "ĠL atter", "ĠHal ifax", "Ġv as", "ĠTop ics", "ĠN amed", "advert ising", "zz a", "IC ES", "Ġret arded", "ach able", "ĠPupp et", "ĠItem Level", "Ġret ract", "Ġident ifiable", "A aron", "ĠB uster", "s ol", "hel le", "as semb", "H ope", "r anged", "B a", "ĠP urch", "é Ģ", "ĠSir i", "Ġarri vals", "Ġ19 12", "Ġshort ened", "Ġ3 12", "Ġdiscrep ancy", "ĠTem perature", "ĠWal ton", "Ġkind erg", "p olit", "Ġrem ix", "Ġconnect ors", "ãĥĺ ãĥ©", "ĠKazakh stan", "dom inated", "Ġsu gars", "im ble", "ĠPan ic", "ĠDem and", "ĠCol ony", "on en", "ĠM ER", "7 75", "ur ia", "aza ar", "ĠDeg ree", "P ri", "Ġsun shine", "Ġ25 1", "Ġpsychedel ic", "Ġdigit ally", "ĠBra un", "Ġsh immer", "Ġsh ave", "ĠTel esc", "ĠAst ral", "ĠVenezuel an", "ĠO G", "Ġc rawling", "Int eg", "ĠFe ather", "Ġunfold ing", "Ġappropri ation", "Ġè£ı è", "ĠMob ility", "ĠN ey", "- .", "b ilt", "L IN", "ĠT ube", "ĠCon versely", "Ġkey boards", "ĠC ao", "Ġover th", "Ġla ure", ">> \\", "ĠV iper", "ach a", "Off set", "ĠR aleigh", "ĠJ ae", "J ordan", "j p", "Ġtotal itarian", "Connect or", "Ġobserv es", "ĠSpart an", "ĠIm mediately", "ĠSc al", "C ool", "Ġt aps", "Ġro ar", "P ast", "Ġch ars", "ĠB ender", "ĠShe ldon", "Ġpain ter", "Ġbe acon", "ĠCreat ures", "Ġdownt urn", "Ġh inder", "ĠAnd romeda", "à Ľ", "cc oli", "ĠF itness", "et rical", "Ġutil izes", "Ġsen ate", "Ġen semble", "Ġche ers", "T W", "Ġaff luent", "k il", "ry lic", "ord ering", "Com puter", "Ġgru esome", "ost ics", "ĠUb isoft", "ĠKel ley", "Ġw rench", "Ġbourgeois ie", "IB LE", "ĠPrest on", "w orn", "ar ist", "reat ing", "Ġst ained", "ar ine", "Ġsl ime", "EN N", "Ġche sts", "Ġground water", "ann ot", "ĠTr ay", "ĠLoc ke", "ĠC TR", "Ġd udes", "ĠEx ternal", "ĠDec oder", "Ġpar amed", "ĠMed line", "80 9", "ĠD inner", "rup al", "g z", "ĠG um", "ĠDem o", "j ee", "Ġd h", "ber man", "arch s", "Ġen qu", "ĠEp stein", "Ġdevast ation", "Ġfriends hips", "ĠAr d", "Ġ23 1", "ĠRub in", "ĠDist ance", "Ġsp urred", "Ġd ossier", "Ġover looking", "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\", "Fore st", "ĠCom es", "\\ \",", "ĠIran ians", "Ġf ixtures", "L aughs", "Ġcur ry", "ĠKing ston", "Ġsqu ash", "Ġcat alogue", "Ġabnormal ities", "Ġdigest ive", ".... .....", "Ġsubord inate", "og ly", "Ġ24 9", "M iddle", "Ġmass ac", "Ġburg ers", "Ġdown stairs", "Ġ19 31", "39 4", "ĠV G", "Ġl asers", "ĠS ikh", "ĠAlex a", "der ived", "Ġcycl ist", "ãģ® éŃĶ", "onel iness", "!!!! !!!!", "Ġbuff s", "leg ate", "Ġrap ing", "Ġrecomm ending", "ro red", "Ġmult icultural", "un ique", "Ġbusiness men", "Ġune asy", "ĠM AP", "Ġdisp ersed", "cipl ine", "J ess", "ĠK erala", "å §", "Ġabst raction", "Sur v", "U h", "Ġprin ters", "ij a", "ow der", "Ġanalog ous", "ĠA SP", "af er", "Ġunfold ed", "Ġlevel ing", "Ġbre ached", "ĠH earing", "Ġn at", "Ġtransl ating", "crit ical", "Ġant agonist", "ĠYes terday", "Ġfuzz y", "w ash", "m ere", "Ġbe wild", "ĠM ae", "V irgin", "ph rase", "Ġsign aled", "ĠH IGH", "Ġprot ester", "Ġgar ner", "unk nown", "Ġk ay", "Ġabduct ed", "Ġst alking", "am n", "Ġdes erving", "ĠR iv", "ĠJ orge", "Ġscratch ing", "ĠS aving", "ip ing", "Ġte ase", "Ġmission ary", "ĠMor row", "T IME", "P resent", "Ġchem otherapy", "tern ess", "ĠH omes", "ĠP urdue", "Ġst aunch", "ĠWhit ney", "ĠTH ERE", "Î ¼", "iat us", "ĠErn est", "ĠDe ploy", "Ġcove ted", "F ML", "ĠDial ogue", "Ġex ited", "f ruit", "Ġner d", "\":\" \",\"", "Ġv ivo", "ru ly", "4 60", "ĠAm en", "rehens ible", "Ġâ ĺ", "D IR", "Ġad herence", "Ġche w", "ĠCo ke", "ĠSerge i", "dig ital", "ĠNe ck", "g ently", "enth al", "/ )", "Ġwe ary", "Ġgu ise", "ĠConc ord", "ĠOn ion", "at cher", "Ġb inge", "ĠDirect ive", "Ġman ned", "ans k", "Ġill usions", "Ġbillion aires", "38 3", "oly n", "odynam ic", "ĠWhe at", "ĠA lic", "Ġcol oured", "ĠN AFTA", "ab o", "Ġmac ros", "ind ependent", "s weet", "Ġsp ac", "ĠK abul", "Ġ Ä", "em e", "Ġdict ated", "Ġsh outs", "= {", "Ġr ipping", "ĠSh ay", "ĠCr icket", "direct ed", "Ġanalys ed", "ĠWAR RANT", "ag ons", "ĠBlaz ers", "Ġche ered", "Ġar ithmetic", "ĠTan z", "37 3", "ĠFl ags", "Ġ29 5", "Ġw itches", "ĠIn cluded", "ĠG ained", "ĠBl ades", "G am", "ĠSam antha", "ĠAtl antis", "ĠPr att", "Ġspo iled", "ĠI B", "ĠRam irez", "Pro bably", "re ro", "ĠN g", "ĠWar lock", "t p", "Ġover he", "Ġadministr ations", "Ġt int", "Ġreg iment", "Ġpist ols", "Ġblank ets", "Ġep ist", "Ġbowl s", "Ġhydra ulic", "Ġde an", "Ġj ung", "Ġasc end", "70 5", "ĠSant iago", "à ®", "Ġun avoid", "ĠSh aman", "re b", "Ġstem ming", "99 8", "ĠM G", "st icks", "esthes ia", "ER O", "Ġmor bid", "ĠGr ill", "ĠP oe", "any l", "Ġdele ting", "ĠSurve illance", "Ġdirect ives", "Ġiter ations", "ĠR ox", "ĠMil ky", "F ather", "Ġpat ented", "44 7", "Ġprec ursor", "Ġm aiden", "ĠP hen", "ĠVe gan", "ĠPat ent", "K elly", "Redd itor", "Ġn ods", "Ġvent ilation", "ĠSchwar z", "Ġw izards", "Ġomin ous", "ĠHe ads", "ĠB G", "Ġl umber", "ĠSp iel", "Ġis Enabled", "Ġancest ral", "ĠSh ips", "Ġwrest ler", "ph i", "Ġy uan", "ĠRebell ion", "Ġice berg", "Ġmag ically", "Ġdivers ion", "ar ro", "yth m", "ĠR iders", "ĠRob bie", "ĠK ara", "ĠMain tenance", "ĠHer b", "Ġhar ms", "p acked", "ĠFe instein", "Ġmarry ing", "Ġbl ending", "ĠR ates", "Ġ18 80", "Ġwr ink", "ĠUn ch", "ĠTor ch", "desc ribed", "Ġhuman oid", "ilit ating", "ĠCon v", "ĠFe ld", "IGH TS", "Ġwhistlebl ower", "ort mund", "ets y", "arre tt", "ĠMon o", "ĠI ke", "ĠC NBC", "ĠW AY", "ĠMD MA", "ĠIndividual s", "Ġsupplement al", "Ġpower house", "ĠSt ru", "F ocus", "aph ael", "ĠCol leg", "att i", "Z A", "Ġp erenn", "ĠSign ature", "ĠRod ney", "Ġcub es", "idd led", "ĠD ante", "ĠIN V", "iling ual", "ĠC th", "Ġso fa", "Ġintimid ate", "ĠR oe", "ĠDi plom", "ĠCount ries", "ays on", "Ġextrad ition", "Ġdis abling", "ĠCard iff", "Ġmemor andum", "ĠTr ace", "Ġ?? ?", "se ctor", "ĠRou hani", "ĠY ates", "ĠFree ze", "Ġbl adder", "M otor", "ĠProm ise", "ant asy", "Ġforesee able", "ĠC ologne", "cont ainer", "ĠTre es", "ĠG ors", "ĠSin clair", "Ġbar ring", "key e", "Ġsl ashed", "ĠStat istical", "é ĩ", "Ġâĸ º", "All ows", "Ġhum ility", "Ġdr illed", "ĠF urn", "44 3", "Ġse wage", "Ġhome page", "Ġcour tyard", "Ġv ile", "Ġsubsid iaries", "aj o", "direct ory", "Ġam mon", "V ers", "charg es", "Ġ} }", "ĠCh ains", "Ġ24 6", "n ob", "Ġper cept", "Ġg rit", "Ġfisher men", "ĠIraq is", "ĠDIS TR", "ĠF ULL", "ĠEval uation", "g raph", "at ial", "Ġcooper ating", "Ġmel an", "Ġenlight ened", "Ġal i", "t ailed", "Ġsal ute", "Ġweak est", "ĠBull dogs", "U A", "ĠAll oy", "Ġsem en", "oc ene", "ĠWilliam son", "s pr", ", âĢĶ", "ĠG F", "itt ens", "Be at", "ĠJ unk", "iph ate", "ĠFarm ers", "ĠBit coins", "ig ers", "d h", "ĠL oyal", "p ayer", "Ġentert ained", "Ġpenn ed", "Ġcoup on", "Que ue", "Ġweaken ing", "c arry", "Ġunderest imate", "Ġshoot out", "Ġcharism atic", "ĠProced ure", "Ġprud ent", "in ances", "Ġric hes", "Ġcort ical", "Ġstr ides", "Ġd rib", "ĠOil ers", "5 40", "ĠPer form", "ĠBang kok", "Ġe uth", "S ER", "Ġsimpl istic", "t ops", "camp aign", "Q uality", "Ġimpover ished", "ĠEisen hower", "Ġaug ment", "ĠH arden", "Ġinterven ed", "Ġlist ens", "ĠK ok", "Ġs age", "Ġrub bish", "ĠD ed", "Ġm ull", "pe lling", "Ġvide ot", "Produ ction", "D J", "m iah", "Ġadapt ations", "Ġmed ically", "Ġboard ed", "Ġarrog ance", "Ġscra pped", "Ġopp ress", "FORM ATION", "Ġj unction", "4 15", "EE EE", "S kill", "Ġsub du", "ĠSug gest", "ĠP ett", "Ġle tt", "ĠMan ip", "ĠC af", "ĠCooper ation", "T her", "Ġreg ained", "¶ æ", "ref lect", "Ġth ugs", "ĠShel by", "Ġdict ates", "ĠWe iner", "ĠH ale", "Ġbatt leground", "s child", "Ġcond ol", "h unt", "osit ories", "Ġacc uses", "Fil ename", "Ġsh ri", "Ġmotiv ate", "Ġreflect ions", "N ull", "ĠL obby", "¥ µ", "ĠS ATA", "ĠBack up", "Ñ ĥ", "n in", "ĠCor rection", "Ġju icy", "ut ra", "ĠP ric", "Ġrest raining", "ĠAir bnb", "ĠAr rest", "Ġappropri ations", "Ġsl opes", "Ġmans laughter", "Ġwork ings", "ĠH uss", "ĠF rey", "Le ave", "ĠHarm ony", "ĠF eder", "Ġ4 30", "Ġt rench", "Ġglad ly", "Ġbull pen", "ĠG au", "b ones", "Ġgro ove", "Ġpre text", "ã ħĭ", "Ġtransm itter", "ĠComp onent", "Ġunder age", "ĠEm pires", "T ile", "Ġo y", "ĠMar vin", "ĠC AS", "Ġbl oss", "Ġrepl icated", "ĠMar iners", "Marc us", "ĠBl ocks", "Ġliber ated", "Ġbutter fly", "Fe el", "Ġfer mentation", "Ġyou tube", "Ġoff end", "ĠTer m", "res ist", "Ġcess ation", "Ġinsurg ency", "Ġb ir", "ĠRa ise", "59 5", "Ġhypothes es", "50 2", "Ġpl aque", "ocr at", "Ġjack ets", "ĠHuff Post", "am ong", "Ġconf er", "48 7", "ĠL illy", "Ġadapt ing", "ĠF ay", "Ġsh oved", "ve c", "Ġref ine", "Ġg on", "Ġgun men", "z ai", "ĠShut tle", "ĠI zan", "Ġ19 13", "Ġple thora", "· ·", "Ġ5 10", "Ġp uberty", "Ġ24 1", "ĠWe alth", "ĠAl ma", "ĠM EM", "ĠAd ults", "C as", "pr ison", "R ace", "Ġwater proof", "Ġathlet icism", "Ġcapital ize", "ĠJu ice", "Ġillum inated", "ĠP ascal", "Ġirrit ation", "ĠWitness es", "ad le", "ĠAst ro", "Ġf ax", "ĠEl vis", "Prim ary", "ĠL ich", "ĠEl ves", "Ġres iding", "Ġst umble", "3 19", "ĠP KK", "Ġadvers aries", "D OS", "ĠR itual", "Ġsm ear", "Ġar son", "ident al", "Ġsc ant", "Ġmon archy", "Ġhal ftime", "Ġresid ue", "Ġind ign", "ĠSh aun", "ĠEl m", "aur i", "A ff", "W ATCH", "ĠLy on", "hel ps", "36 1", "Ġlobby ist", "Ġdimin ishing", "Ġout breaks", "Ġgo ats", "f avorite", "ĠN ah", "son ian", "ĠBo oster", "Ġsand box", "ĠF are", "ĠMalt a", "Ġatt Rot", "ĠM OR", "ld e", "Ġnavig ating", "T ouch", "Ġunt rue", "ĠDis aster", "Ġl udicrous", "Pass word", "ĠJ FK", "blog spot", "4 16", "ĠUN DER", "ern al", "Ġdelay ing", "T OP", "Ġimpl ants", "ĠAV G", "ĠH uge", "att r", "Ġjournal istic", "ĠPe yton", "ĠI A", "R ap", "go al", "ĠProgram me", "Ġsm ashing", "w ives", "print ln", "ĠPl ague", "in us", "EE P", "Ġcru iser", "ĠPar ish", "umin ium", "Ġoccup ants", "ĠJ ihad", "m op", "Ġp int", "Ġhe ct", "ĠMe cca", "direct or", "ĠFund ing", "ĠM ixed", "Ġst ag", "T ier", "Ġg ust", "Ġbright ly", "ors i", "Ġup hill", "R D", "Ġles ions", "ĠBund y", "liv ious", "Ġbi ologist", "ĠFac ulty", "ĠAuthor ization", "Ġ24 4", "All ow", "ï ¸", "ĠGi ul", "Ġpert inent", "ot aur", "es se", "ĠRo of", "Ġunman ned", "35 1", "ĠSh ak", "ĠO rient", "Ġend anger", "D ir", "Ġrepl en", "ed ient", "Ġtail or", "Ġgad gets", "Ġaud ible", "âĺ Ĩ", "N ice", "Ġbomb ard", "ĠR ape", "Ġdef iance", "ĠTW O", "ĠFilip ino", "Ġunaff ected", "erv atives", "Ġso ared", "ĠBol ton", "Ġcomprom ising", "ĠBrew ers", "R AL", "ĠA HL", "icy cle", "Ġv ampires", "Ġdi pped", "oy er", "ĠX III", "Ġsidew ays", "ĠW aste", "ĠD iss", "ĠâĶľ âĶĢâĶĢ", "$ .", "Ġhabit ats", "ĠBe ef", "tr uth", "tr ained", "spl it", "R us", "And y", "ĠB ram", "RE P", "p id", "è£ ħ", "ĠMut ant", "An im", "ĠMar ina", "Ġfut ile", "hig hest", "f requency", "Ġepile psy", "Ġcop ing", "Ġconc ise", "Ġtr acing", "ĠS UN", "pan el", "ĠSoph ie", "ĠCrow ley", "ĠAd olf", "ĠShoot er", "Ġsh aky", "ĠI G", "ĠL ies", "ĠBar ber", "p kg", "Ġupt ake", "Ġpred atory", "UL TS", "/ **", "Ġintox icated", "ĠWest brook", "od der", "he ment", "Ġbas eman", "AP D", "st orage", "ĠFif ty", "ed itor", "G EN", "UT ION", "ir ting", "Ġse wing", "r ift", "Ġag ony", "ĠS ands", "Ġ25 4", "C ash", "Ġl odge", "Ġp unt", "N atural", "ĠIde as", "Ġerrone ous", "ĠSens or", "ĠHann ity", "Ġ19 21", "Ġm ould", "ĠG on", "kay a", "Ġanonym ously", "ĠK EY", "Ġsim ulator", "W inter", "Ġstream ed", "50 7", "? \",", "Ġte ased", "Ġco efficient", "Ġwart ime", "ĠTH R", "' '.", "ĠBank ing", "mp ire", "Ġf andom", "Ġl ia", "G a", "Ġdown hill", "Ġinterpre ting", "Ind ividual", "N orm", "Ġjealous y", "bit coin", "Ġple asures", "ĠToy s", "ĠChev rolet", "ĠAd visor", "IZ E", "Ġrecept ions", "70 6", "C ro", "Ġ26 2", "Ġcit rus", "ir u", "Review er", "ject ed", "U ES", "an z", "19 81", "ĠWork er", "Ġcompl ied", "ores cent", "contin ental", "T on", "ĠPr ism", "ĠShe ep", "Ġ28 8", "n ox", "ĠV og", "O rd", "Ġreal ms", "te k", "Ġirrig ation", "Ġbicy cles", "Ġelectron ically", "p oly", "t all", "() );", "Ġaest hetics", "ĠInteg rated", "Expl ore", "Ġd unk", "47 6", "p ain", "ĠJac ques", "ĠD mit", "Fram es", "Ġreun ited", "Ġhum id", "D ro", "P olitical", "Ġyouth ful", "Ġent ails", "Ġmosqu ito", "36 3", "spe cies", "Ġcoord inating", "ĠMay hem", "ĠMagn us", "M ount", "Impro ved", "ĠST ATE", "ATT LE", "Ġflow ed", "Ġtack led", "Ġfashion ed", "Ġre organ", "iv ari", "f inger", "Ġreluct antly", "et ting", "ĠV and", "you ng", "ĠGar land", "Ġpresum ption", "Ġamen ities", "ĠPle asant", "on ential", "ĠO xy", "Ġmor als", "ĠY ah", "Read y", "Sim on", "En h", "D emon", "Ġcl ich", "Mon itor", "ĠD U", "Ġwel comes", "Ġstand out", "Ġdread ful", "Ġban anas", "Ġball oons", "h ooting", "bas ic", "Ġsuff ix", "Ġd uly", "can o", "Ch ain", "at os", "Ġgeop olitical", "Ġ( &", "ĠGem ini", "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", "Ġacqu itted", "L uck", "prot ect", "10 24", "Ġsc arcity", "Ġmind fulness", "ec ided", "D N", "pr ime", "ĠPres idents", "ĠVID EO", "Ġ( âĪĴ", "add ock", "N OR", "ĠP ru", "p un", "ĠL OL", ")) ))", "ĠL iqu", "ĠS AS", "Ġsty ling", "Ġpunish ments", "Ġnum b", "Ġasc ertain", "ĠRock ies", "f lu", "Th umbnail", "Ġperpet rated", "ĠSem i", "Ġdis arm", "ĠOld er", "ĠEx ception", "Ġexponent ially", "ĠCommun ities", "Ġabol ish", "ĠPart ner", "pt oms", "Ġ7 77", "ĠFo ley", "ĠC ases", "Ġgre ase", "ĠReb irth", "G round", "Ġ; )", "ĠDoct rine", "ik ini", "Y e", "ĠBl ossom", "Ġpers ists", "b ill", "Ġinf usion", "Ġbud dies", "9 11", "ĠPat ient", "Ġdem os", "Ġacquaint ance", "ĠP aw", "at ari", "Ġx ml", "Ġfasc ination", "ĠSer ve", "Ï Ĥ", "br anded", "Ġa z", "Return s", "Ġover shadow", "Ġro am", "Ġspeed y", "n umbered", "hel ial", "Ġdisc iple", "Ġass urances", "g iven", "pect ing", "ĠN atalie", "çĶ °", "Ġmosquit oes", "rote in", "Ġnumer ic", "Ġindepend ents", "Ġtrans itional", "Ġreaction ary", "ĠMech dragon", "do ctor", "Ġshort est", "Ġsequ ential", "ĠB ac", "ĠAccount s", "ãģ Į", "ach y", "ract ive", "ĠReg iment", "Ġbreat htaking", "ffic iency", "ĠB ates", "Ġ3 11", "Ġward robe", "ft s", "ĠBer k", "Sim ply", "ĠRivers ide", "iver ing", "ident ial", "lu cent", "Ġen riched", "ĠCon ver", "ĠG iving", "ãĥ Ļ", "Ġlegal ize", "ĠF TC", "Ġfre aking", "M ix", "Ġter restrial", "es ian", "ci ents", "W ing", "LO AD", "Ġled ge", "ĠViol ent", "ĠMet all", "Ġ30 8", "Ġs outheastern", "hett o", "M eat", "Ġslow down", "Ġret reated", "Jere my", "end as", "**** *", "er ic", "Ġre ins", "opp able", "ĠHuman ity", "ear ances", "rig an", "C amera", "Ġwa ivers", "s oc", "Ġalter ation", "trans form", "ĠC emetery", "50 6", "Ġindef inite", "Ġstim ulating", "y g", "60 3", "ĠS op", "Ġdescript ive", "Ph ase", "ĠEd mund", "Ġpneum onia", "vent us", "A mb", "Ġlabor atories", "ĠEx clusive", "ug ar", "W ere", "Ġmalf unction", "Ġhomosexual s", "Ġ---- ---", "un i", "Ġturb ines", "ĠEqu ity", "D u", "Ġmind ed", "ĠR H", "ĠBlack hawks", "Ġfe ats", "Ġ17 00", "re pl", "36 2", "lad en", "Ġindisp ensable", "ly ss", "tt i", "Ġre el", "Ġdiver ted", "Ġlik eness", "Ġsubscript ions", "Ġfing ert", "Ġfil thy", "dest ruct", "d raft", "ĠBernard ino", "l aunch", "Ġper plex", "ĠS UM", "car b", "Ġswe ater", "ĠVent ure", "ĠJ ag", "ĠCele b", "ĠV oters", "Ġstead fast", "Ġathlet ics", "ĠHans on", "ĠDr ac", "Tr acker", "Ġcomm end", "ĠPres idency", "ĠD ID", "in formed", "Ġweb page", "P retty", "Ġforce fully", "ãĥĥ ãĤ¯", "Ġrel ocation", "Ġsat ire", "â ī", "ĠSunder land", "æ Ħ", "V oice", "???? ????", "Ġinform ant", "Ġbow el", "ĠUn iform", "Ġ ...\"", "Ġpur ge", "Ġpic nic", "ĠU mb", "ĠU PDATE", "ĠSapp hire", "ĠSt all", "le arn", "Ġobject ively", "Ġob liter", "Ġlooph ole", "Ġjour neys", "Ġo mission", "Pro s", "ĠSid ney", "pl oma", "Ġspray ed", "Ġg uru", "Ġtra itor", "Ġtim et", "Ġsn apping", "ĠSe vent", "urn al", "ĠUk ip", "Ġb owed", "por al", "l iberal", "R os", "Quest ions", "i OS", "Ġsummar ize", "ST AT", "Ġ18 50", "ap est", "Ġl ender", "ĠVari able", "br inging", "ĠL ORD", ", )", "Ġcollaps es", "x iety", "ĠN ed", "Y D", "ĠSch a", "Ġantib ody", "Ġdis band", "y re", "ill usion", "Ġro ver", "s hed", "ĠHiro sh", "cc i", "Ġcal am", "ĠMort on", "P interest", "Ġ19 28", "ĠE uras", "ord es", "Ġf ences", "ĠIn ventory", "ĠVal encia", "ĠU d", "ĠT iff", "Ġsqu e", "Ġqu otation", "Ġtroubles ome", "er ker", "QU EST", "ĠKing doms", "s outh", "Ġle vy", "Pr ince", "ĠSt ing", "Ġnick named", "Ġapp e", "Ġphot ographic", "Ġcorp us", "re ference", "ĠT rog", "U nt", ") =(", "ĠLat via", "Ġactiv ating", "Ġlicense e", "Ġdispar ities", "ĠNews letter", "ãĥĥ ãĥĪ", "Ġfree ing", "ĠJe ep", "ĠPer ception", "ins k", "Ġsil icone", "ĠHay den", "Le an", "ĠSuz uki", "ibr arian", "66 8", "Ġsp or", "Ġcorrel ations", "ag hetti", "Ġtu ber", "ĠIP CC", "il us", "ĠV u", "Ġwealth iest", "ĠCarb uncle", "an za", "Ġfool ed", "ĠZ ur", "Ġd addy", "ran o", "il ian", "Ġknock out", "f man", "requ ired", "ĠWik ileaks", "ĠD uffy", "ON T", "Ġins ol", "ĠObject s", "Ġb ou", "ĠNord ic", "ĠIns ert", "sc an", "Ġd ancers", "Ġid iots", "major ity", "ĠNev ille", "ĠFree BSD", "Ġt art", "pan ic", "69 0", "Ġcoc oa", "Ġsam pled", "Ġlook up", "Ind ust", "Ġinject ions", "gen re", "Ġa u", "Ġroad way", "Ġgen itals", "K ind", "ĠEx aminer", "ĠY az", "F resh", "Ġpar alysis", "ĠAl uminum", "Ġre ap", "ok é", "Ġsl oppy", "ĠTun nel", "pos ium", "ner y", "en ic", "Ġher bal", "ĠOut er", "ĠBuild er", "Ġinc ur", "Ġide ologies", "Ġback ups", "cons uming", "ĠDet ect", "de ck", "ĠKN OW", "ĠG ret", "ĠM IC", "Ġtough ness", "ĠEx hibit", "Ġh ive", "L es", "ĠSCH OOL", "ĠAt ari", "ald e", "ĠN ull", "and estine", "m ouse", "Ġbrig ade", "48 9", "Ġrev ol", "ĠLaw son", "ĠW ah", "op oly", "eb ted", "ĠS aunders", "Ġ3 13", "ĠW inc", "Ġtab oo", "ĠHel met", "Ġw edge", "ch ip", "ĠT ina", "b g", "Ġinf uri", "r n", "Ġanomal ies", "ĠSy nc", "ĠEx am", "ĠComm it", "ĠDi ary", "ĠALS O", "ĠDe bor", "omed ical", "Ġcomprehens ion", "6 55", "Ġempower ing", "Ġ ire", "Ġju ices", "ĠE TH", "ĠBox ing", "=\" /", "Ġfacilit ated", "p oke", "ĠPars ons", "ĠMod er", "tra vel", "Ġcivil izations", "Ġliber tarians", "Ġrun e", "ĠCl arks", "at hed", "Ġcampaign ers", "ĠDis patch", "ĠFah renheit", "ĠCap com", "-------- --", "Ġl ace", "Ġdr aining", "Ġl iner", "ĠArt ificial", "é n", "t ask", "] ).", "ĠGM O", "ĠOper ator", "ord inary", "ĠInf luence", "ĠU ps", "Ġpot ency", "uss en", "osp ons", "ĠSw im", "ĠDead line", "Un ity", "Ġcul inary", "Ġenlight enment", "Ġwe arer", "Ġmin ed", "Ġp ly", "Ġinc est", "ĠDVD s", "W alk", "B TC", "Tr ade", "Ġdev al", "ib and", "ĠOvers ight", "Palest inian", "Ġd art", "Ġm ul", "L R", "Ġrem ovable", "ĠReal ms", "ì Ŀ", "Ġmisc ar", "ĠV ulkan", "68 5", "è re", "ĠS ap", "Ġmer ging", "ĠCar ly", "che ster", "Ġbr isk", "Ġlux urious", "ĠGener ator", "Ġbit terness", "Ġed ible", "Ġ24 3", "T G", "Ġrect angle", "With No", "bel ow", "J enn", "Ġdark est", "Ġh itch", "Ġdos age", "Ġsc aven", "ĠK eller", "ĠIllust rated", "Certain ly", "ĠMaver icks", "Marg inal", "Ġdiarr hea", "Ġenorm ously", "Ġ9 99", "sh r", "qu art", "Ġadam ant", "ĠM ew", "Ġren ovation", "Ġcerv ical", "ĠPercent age", "en ers", "ĠKim ber", "Ġflo ats", "Ġde x", "ĠW itcher", "ĠSwan sea", "d m", "Ġsal ty", "y ellow", "Ġca pe", "ĠDr ain", "ĠPaul a", "ĠTol edo", "les i", "Mag azine", "ĠW ick", "ĠM n", "ĠA ck", "ĠR iding", "AS ON", "Ġhom ophobic", "AR P", "Ġwand ered", "C PU", "ood oo", "ĠP ipe", "Ġtight ening", "ĠBut t", "3 18", "Ġdesert ed", "S ession", "Ġfacilit ating", "J ump", "Ġemer gencies", "OW ER", "Ġexhaust ive", "ĠAF TER", "Ġheart beat", "ĠLab el", "ack y", "ĠCert ified", "ilt ration", "Z e", "ĠU tt", "Ġ13 00", "Ġpres ume", "ĠDis p", "Ġsur ged", "Ġdoll s", "Col umb", "Ġchim pan", "ĠR azor", "Ġt icks", "Ġcouncill or", "Ġpilgr image", "ĠReb els", "ĠQ C", "ĠA uction", "x ia", "ik k", "b red", "Ġinsert ion", "Ġco arse", "d B", "SE E", "ĠZ ap", "ĠF oo", "Ġcontem por", "ĠQuarter ly", "ot ions", "ĠAl chemist", "ĠT rey", "ĠDu o", "S weet", "80 4", "ĠGi ov", "Ġfun n", "N in", "h off", "Ġram ifications", "Ġ19 22", "ĠExper ts", "az es", "Ġgar ments", "ar ial", "ĠN ab", "Ġ25 7", "ĠV ed", "Ġhum orous", "ĠPom pe", "Ġn ylon", "Ġlur king", "ĠSerge y", "ĠMatt is", "Ġmisogyn y", "ĠComp onents", "ĠWatch ing", "ĠF olk", "ract ical", "B ush", "Ġt aped", "Ġgroup ing", "Ġbe ads", "Ġ20 48", "Ġcon du", "quer que", "Read ing", "Ġgriev ances", "Ult ra", "Ġend point", "H ig", "ĠSt atic", "ĠScar borough", "L ua", "ĠMess i", "a qu", "ĠPsy Net", "ĠR udd", "Ġa venue", "v p", "J er", "Ġsh ady", "ĠRes ist", "ĠArt emis", "Ġcare less", "Ġbro kers", "Ġtemper ament", "Ġ5 20", "T ags", "ĠTurn ing", "Ġut tered", "Ġp edd", "Ġimpro vised", "Ġ: (", "Ġtab l", "Ġpl ains", "16 00", "press ure", "ĠEss ence", "marg in", "friend s", "ĠRest oration", "Ġpoll ut", "ĠPok er", "ĠAugust ine", "ĠC IS", "ĠSE AL", "or ama", "Ġth wart", "se ek", "Ġp agan", " º", "cp u", "Ġg arn", "Ġass ortment", "ĠI LCS", "t ower", "Recomm ended", "Ġun born", "ĠRandom Redditor", "ĠRandomRedditor WithNo", "Ġparaly zed", "Ġeru ption", "Ġinter sect", "ĠSt oke", "ĠS co", "B ind", "å ¾", "ĠP NG", "ĠNeg ative", "ĠNO AA", "Le on", "Ġall oy", "ĠL ama", "ĠD iversity", "5 75", "Ġunderest imated", "ĠSc or", "Ġm ural", "Ġb usted", "so on", "l if", "Ġnone x", "Ġall ergy", "ĠUnder world", "ĠR ays", "ĠBl asio", "Ġh rs", "ĠD ir", "Ġ3 27", "by ter", "Ġrepl acements", "Ġactiv ates", "ri ved", "M H", "Ġp ans", "ĠH I", "Ġlong itudinal", "Ġnu isance", "al er", "Ġsw ell", "ĠS igned", "s ci", "ĠIs les", "ĠA GA", "Ġdef iant", "Ġson ic", "oc on", "K C", "ĠA im", "t ie", "ah ah", "Ġm L", "D X", "Ġb isc", "ĠBill board", "ĠSY STEM", "NE Y", "ga ard", "Ġdist ressed", "former ly", "Al an", "Ġche fs", "Ġopt ics", "ĠC omet", "ĠAM C", "Ġredes igned", "irm ation", "Ġsight ings", "38 2", "3 11", "ĠW B", "Ġcont raction", "ĠT OTAL", "D ual", "Ġstart led", "Ġunderstand ably", "Ġsung lasses", "ETH OD", "Ġd ocker", "Ġsurf ing", "ĠH EL", "ĠSl ack", "ton es", "Ġsh alt", "Vis ual", "49 8", "Dep artment", "c ussion", "Ġunrest ricted", "Ġt ad", "Ġre name", "employ ed", "Ġeduc ating", "Ġgrin ned", "bed room", "ĠActiv ities", "ĠV elvet", "ĠSW AT", "Ġsh uffle", "ig or", "Ġsatur ation", "F inding", "c ream", "ic ter", "Ġv odka", "tr acking", "te c", "Ġfore ground", "iest a", "Ġve hement", "ĠEC B", "ĠT ie", "E y", "Ġt urtles", "ĠRail road", "ĠKat z", "ĠFram es", "Ġmen ace", "ĠFell owship", "ĠEss ential", "ugg ish", "Ġdri p", "ch witz", "ĠKy oto", "s b", "ĠN ina", "Param eter", "Ġal arms", "ĠCl aud", "Ġpione ering", "Ġchief ly", "ĠSc ream", "Col lection", "Ġthank fully", "ĠRonald o", "åŃ IJ", "st rip", "ĠDisney land", "com mercial", "See ing", "S oul", "Ġevac uate", "Ġc iv", "ĠAs he", "Ġdiv ides", "ĠD agger", "rehens ive", "Ġber ries", "ĠD F", "Ġs ushi", "Ġplur ality", "W I", "Ġdisadvant aged", "Ġbatt alion", "ob iles", "45 1", "Ġcl ing", "Ġunden iable", "ĠL ounge", "Ġha unt", "p he", "Ġquant ify", "Ġdiff ered", "Ġ[* ]", "ĠV iz", "c um", "sl ave", "Ġvide og", "Ġqu ar", "Ġbund les", "ĠAl onso", "t ackle", "Ġneur onal", "Ġlandsl ide", "conf irmed", "ĠDep th", "Ġrenew ables", "B ear", "ĠMaced onia", "Ġjer seys", "Ġb unk", "ĠSp awn", "ĠControl s", "ĠBuch anan", "Ġrobot ics", "Ġemphas izing", "ĠTut orial", "h yp", "ist on", "Ġmonument al", "æ °", "ĠCar ry", "Ġt bsp", "en ance", "H ill", "art hed", "Ġro tten", "De an", "Ġtw isting", "Ġgood will", "Ġimm ersion", "L iving", "Ġbr ushes", "ĠC GI", "ĠAt k", "tr aditional", "Ġph antom", "ĠSt amina", "Ġexpans ions", "ĠMar in", "Ġembark ed", "ĠE g", "int estinal", "ĠPE OPLE", "ĠBo oth", "ĠApp alach", "Ġreleg ated", "V T", "M IT", "Ġmust er", "Ġwithdraw ing", "Ġmicrosc ope", "ĠG athering", "ĠC rescent", "ĠArgent ine", "ĠDec re", "ĠDomin ic", "Ġbud s", "ant age", "ĠI on", "Ġwid ened", "ONS ORED", "ĠGl oves", "iann opoulos", "raz en", "fe el", "Ġrepay ment", "Ġhind sight", "ĠRE ALLY", "ĠPist ol", "ĠBra h", "Ġwat ts", "Ġsurv ives", "Ġfl urry", "iss y", "Al ert", "ĠUrug uay", "Ph oenix", "S low", "ĠG rave", "ĠF ir", "Ġmanage able", "Ġtar iff", "ĠU DP", "ĠPist ons", "ĠNiger ian", "Ġstrike outs", "Ġcos metics", "whel ming", "f ab", "c ape", "pro xy", "Ġre think", "Ġover coming", "sim ple", "Ġw oo", "Ġdistract ing", "ĠSt anton", "ĠTuls a", "ĠD ock", "65 9", "Ġdisc ord", "ĠEm acs", "ĠV es", "ĠR OB", "Ġreass uring", "Ġcons ortium", "Muslim s", "3 21", "Ġprompt s", "se i", "ĠH itch", "imp osed", "ĠF ool", "Ġindisc rim", "wr ong", "bu querque", "D avis", "! ]", "Ġtim eless", "ĠNE ED", "Ġpestic ide", "Ġrally ing", "ĠCal der", "Ġå ¤", "Ġx p", "ĠUn le", "ĠEx port", "lu aj", "B uff", ") [", "Ġsq or", "S audi", "Ġis tg", "Ġindul ge", "pro c", "Ġdisg usted", "Ġcomp ounded", "Ġn em", "Ġschool ing", "ĠC ure", "process ing", "S ol", "Ġpro verb", "it ized", "ĠAlv arez", "Ġscar f", "Ġrect angular", "re ve", "Ġh ormonal", "ĠSt ress", "itiz en", "Ġ4 25", "girl s", "ĠNo ir", "ĠR app", "Ġmar ches", "ch urch", "ĠUs es", "Ġ40 5", "ĠBer m", "Ġord inances", "ĠJud gment", "Charg es", "ĠZ in", "Ġdust y", "Ġstraw berries", "Ġper ce", "ĠTh ur", "ĠDebor ah", "net flix", "ĠLam bert", "Ġam used", "ĠGu ang", "Y OU", "R GB", "ĠC CTV", "Ġf iat", "r ang", "Ġf ederation", "ĠM ant", "ĠB ust", "ĠM are", "respect ive", "ĠM igration", "ĠB IT", "59 0", "Ġpatriot ism", "Ġout lining", "reg ion", "ĠJos é", "Ġbl asting", "ĠEz ra", "B s", "Ġundermin es", "ĠSm ooth", "Ġcl ashed", "rad io", "Ġtransition ing", "ĠBucc aneers", "ĠOw l", "Ġplug s", "Ġh iatus", "ĠPin ball", "Ġm ig", "ĠNut r", "ĠWolf e", "Ġinteg ers", "Ġor bits", "ĠEd win", "ĠDirect X", "b ite", "Ġbl azing", "v r", "Ed ge", "ĠP ID", "ex it", "ĠCom ed", "ĠPath finder", "ĠGu id", "ĠSign s", "ĠZ er", "ĠAg enda", "Ġreimburse ment", "M esh", "i Phone", "ĠMar cos", "ĠS ites", "h ate", "en burg", "Ġs ockets", "p end", "Bat man", "v ir", "ĠSH OW", "Ġprovision al", "con n", "ĠDeath s", "AT IVE", "Pro file", "sy m", "J A", "Ġnin ja", "inst alled", "id ates", "eb ra", "ĠOm aha", "Ġse izing", "ĠBe asts", "Ġsal ts", "M ission", "Gener ally", "ĠTr ilogy", "he on", "leg ates", "Ġd ime", "Ġf aire", "par able", "G raph", "Ġtotal ing", "Ġdiagram s", "ĠYan uk", "ple t", "ĠMe h", "Ġmyth ical", "ĠStep hens", "aut ical", "ochem istry", "Ġkil ograms", "Ġel bows", "anc ock", "ĠB CE", "ĠPr ague", "Ġimpro v", "ĠDev in", "Ġ\" \\", "par alle", "Ġsuprem acists", "ĠB illion", "Ġreg imen", "inn acle", "Ġrequ isite", "ang an", "ĠBur lington", "ain ment", "ĠObject ive", "oms ky", "G V", "Ġun ilateral", "Ġt c", "Ġh ires", "ment al", "Ġinvol untary", "Ġtrans pl", "ĠASC II", " ¨", "Ev ents", "Ġdoub ted", "ĠKa plan", "ĠCour age", "ig on", "ĠMan aging", "ĠT art", "Ġfalse hood", "ĠV iolet", "Ġair s", "Ġfertil izer", "Brit ain", "Ġaqu atic", "ou f", "W ords", "ĠHart ford", "Ġeven ings", "ĠV engeance", "qu ite", "G all", "ĠP ret", "Ġp df", "ĠL M", "ĠSo chi", "ĠInter cept", "9 20", "Ġprofit ability", "ĠId le", "ĠMac Donald", "ĠEst ablishment", "um sy", "Ġgather ings", "ĠN aj", "Charl ie", "Ġas cent", "ĠProt ector", "Ġal gebra", "Ġbi os", "for ums", "EL S", "Introdu ced", "Ġ3 35", "Ġastron omy", "Cont ribut", "ĠPol ic", "Pl atform", "Ġcontain ment", "w rap", "Ġcoron ary", "ĠJ elly", "man ager", "Ġheart breaking", "c air", "ĠChe ro", "c gi", "Med ical", "ĠAccount ability", "! !\"", "oph ile", "Ġpsych otic", "ĠRest rict", "Ġequ itable", "iss ues", "Ġ19 05", "ĠN ek", "c ised", "ĠTr acking", "Ġo zone", "Ġcook er", "ros is", "Ġre open", "Ġinf inity", "ĠPharm aceutical", "ens ional", "Att empt", "ĠR ory", "Mar co", "Ġawa its", "H OW", "t reated", "Ġbol st", "Ġreve red", "Ġp ods", "opp ers", "00 10", "Ġampl itude", "ric an", "SP ONSORED", "Ġtrou sers", "Ġhal ves", "ĠK aine", "ĠCut ler", "ĠA UTH", "Ġsplend id", "Ġprevent ive", "ĠDud ley", "if acts", "umin ati", "ĠY in", "Ġad mon", "ĠV ag", "Ġin verted", "Ġhast ily", "ĠH ague", "L yn", "Ġled ger", "Ġastron omical", "get ting", "Ġcirc a", "ĠC ic", "ĠTenn is", "Lim ited", "Ġd ru", "ĠBY U", "Ġtrave llers", "Ġp ane", "ĠInt ro", "Ġpatient ly", "Ġa iding", "Ġlo os", "ĠT ough", "Ġ29 3", "Ġconsum es", "Source File", "Ġ\"\" \"", "Ġbond ing", "Ġtil ted", "Ġmenstru al", "ĠCel estial", "UL AR", "Plug in", "Ġrisk ing", "N az", "ĠRiy adh", "Ġacc redited", "Ġsk irm", "é Ľ", "Ġexam iner", "Ġmess ing", "Ġnear ing", "ĠC hern", "ĠBeck ham", "Ġsw apped", "Ġgo ose", "K ay", "Ġlo fty", "ĠWal let", "Ġ[ '", "Ġap ocalypse", "Ġb amboo", "ĠSP ACE", "ĠEl ena", "Ġ30 6", "ac ons", "Ġtight ened", "Ġadolesc ence", "Ġrain y", "Ġvandal ism", "ĠNew town", "Ġcon ject", "c akes", "Ġche ated", "Ġmoder ators", "par ams", "E FF", "Ġdece it", "ĠST L", "ĠTanz ania", "ĠR I", "Ġ19 23", "ĠEx ile", "the l", "Ġthe olog", "Ġquir ky", "ĠIr vine", "Ġneed y", "or is", "U m", "K a", "Ġmail box", "3 22", "Ġb os", "ĠPet ra", "K ING", "Ġenlarg ed", "O ften", "Ġbad ass", "Ġ3 43", "ĠPl aces", "ĠC AD", "Ġpr istine", "Ġinterven ing", "d irection", "Ġl az", "ĠD SM", "Ġproject ing", "ĠF unk", "ag og", "pay ment", "n ov", "Ġch atter", "AR B", "Ġexam inations", "ĠHouse hold", "ĠG us", "F ord", "4 14", "B oss", "Ġmy stic", "Ġle aps", "ĠB av", "ul z", "b udget", "Foot ball", "Ġsubsid ized", "Ġfirst hand", "Ġcoinc ide", "oc ular", "Con n", "ĠColl abor", "Ġfool s", "am ura", "ah ar", "r ists", "Ġsw ollen", "Ġexp ended", "ĠP au", "s up", "Ġsp ar", "Ġkey note", "s uff", "Ġunequ al", "Ġprogress ing", "str ings", "ĠGamer gate", "Dis ney", "ĠEle ven", "om nia", "Ġscript ed", "Ġear ners", "bro ther", "ĠEn abled", "æ ³", "Ġlar vae", "ĠL OC", "m ess", "Wil son", "ĠTem plate", "success fully", "Ġparam ount", "Ġcamoufl age", "Ġbind s", "ĠQu iet", "ĠSh utterstock", "r ush", "Ġmasc ot", "fort une", "ĠCol t", "ĠBe yon", "hab i", "Ġha irc", "Ġ26 7", "ĠDe us", "Ġtw itch", "Ġconcent rating", "Ġn ipples", "c ible", "Ġg ir", "N Z", "M ath", "n ih", "Requ ired", "Ġp onder", "ĠS AN", "Ġwedd ings", "Ġl oneliness", "N ES", "ĠMah jong", "69 5", "add le", "ĠGar ner", "ĠC OUR", "Br idge", "Ġsp ree", "ĠCald well", "Ġbri bery", "Ġ���� ����", "plug ins", "Ġr acket", "Ġchamp agne", "vers ible", "V ote", "Ġmod ifiers", "May or", "6 80", "Ġassemb lies", "ĠS ultan", "ĠN ing", "ĠLad ies", "Ġsulf ur", "Ġor bs", "Ġ---- -", "____ ___", "ĠJournal ism", "Ġes ports", "Ġl ush", "Ġh ue", "Ġspect ral", "H onest", "ãĥ ı", "Ġbus hes", "Ġrein forcement", "Ġre opened", "ĠWhe els", "ĠM org", "rie ving", "Ġaux iliary", "Ġj Query", "ĠB AT", "tes que", "Ġver tex", "p ure", "f rey", "ãĤ º", "d os", "Ġty ph", "Ġc ull", "Ġe q", "Ġdec on", "Ġtoss ing", "Ġdispar ate", "ĠBr igham", "print f", "led ged", "Ġsu nd", "Ġco zy", "Ġhepat itis", "per forming", "Ġav al", "ĠG G", "f uture", "Ġpet ertodd", "ĠKos ovo", "Ġmagn ets", "Al ready", "ĠEd ison", "ĠCe res", "ĠRA ID", "Ġbrill iance", "57 6", "Ġder ives", "Ġhypert ension", "ĠÎ Ķ", "Ġlamb da", "Ġfl air", "Ġmission aries", "Ġrap es", "ĠSt arter", "ĠMon ths", "Ġdef y", "Ġseism ic", "ĠR aphael", "Ġeuro zone", "65 6", "z sche", "Ġscr atched", "Ġb ows", "ĠLenn on", "ĠGa ia", "Ġdri pping", "f acts", "A le", "Ġfrog s", "ĠBre ast", "ogene ity", "ĠProsecut or", "Ġampl ified", "ĠHod g", "ĠF n", "Th ousands", "ĠNI H", "ĠMonitor ing", "FT WARE", "ĠPri ebus", "ĠG rowing", "hun ter", "Ġdiagn ose", "ĠM ald", "ĠL R", "Ġcrown ed", "Ġburst ing", "Ġdiss olution", "j avascript", "Ġuseful ness", "ĠExec ution", ": (", "ĠIv ory", "a ah", "Ġpersecut ed", "viol ence", "ist as", "ĠCr ate", "Ġimpuls es", "ĠSp ani", "ed es", "Hand le", "ĠZ erg", "think able", "Last ly", "Ġspont aneously", "Ġinconven ient", "Ġdismiss ing", "Ġpl otted", "Ġeight y", "Ġ7 37", "r ish", "ĠThor nton", "ath am", "Ġsit com", "V en", "Rec ipe", "t el", "l und", "Ġcle ars", "ĠSas uke", "Ġ25 8", "Ġopt ing", "Ġen raged", "est hetic", "ĠA e", "uch s", "Pre p", "Fl ow", "Ġrun off", "ĠE ating", "ĠG iles", "ĠAct ing", "res ources", "ib aba", "Ġr pm", "Ġske wed", "ĠBl anc", "ĠS akuya", "Ġhot ter", "Ġ19 24", "op ian", "ck o", "Ġcr umbling", "Ġcapt ains", "ĠAppropri ations", "le aders", "dro pping", "an uts", "Ġrevers ing", "ĠP ose", "ĠS ek", "Sc ot", "ĠIde a", "c ise", "ĠSloven ia", "Ġ3 17", "Do ctor", "Ġcro cod", "ald i", "Se a", "ĠFar rell", "Ġmerc enaries", "ĠR NC", "ĠGu ess", "Ġp acing", "M achine", "Streamer Bot", "ĠChar ity", "Ġ29 8", "Ġcann ons", "ĠTob y", "TPP StreamerBot", "ĠPass ion", "cf g", "Th om", "Ġbad ges", "ĠBern stein", ". âĢĵ", "ĠP OP", "ĠCon j", "Ġinitial ization", "Ġbiod iversity", "D ub", "Ġfeud al", "Ġdisclaim er", "Ġc row", "Ġign ition", "ar f", "S HA", "Ġk Hz", "h azard", "ĠArt ists", "oe uv", "67 9", "ĠRud y", "N ine", "ĠRam adan", "å ½", "itt o", "Ġadren aline", "C ert", "Ġsmell ed", "Ġimp unity", "Ġag endas", "ĠRe born", "ĠCon cent", "ĠSe ems", "Ġo mega", "ĠDust in", "Ġback er", "ĠSau ce", "ĠBoy le", "W IN", "Ġsp ins", "Ġpa uses", "u pt", "Ġshred ded", "Ġstra pped", "ĠCor ruption", "Ġscr atches", "Ġn i", "Ġatt ire", "ĠS AF", "Factory Reloaded", "ĠI PS", "Ġ( %", "Ġsem inar", "f ocus", "c ivil", "Ġ18 60", "int osh", "Ġcontin ual", "Ġabbre vi", "ĠS ok", "oc obo", "X M", "Ġfr antic", "Ġunavoid able", "Ġar tery", "Ġannot ations", "b ath", "Cl imate", "Ġd ors", "ĠSl ide", "co ord", "ĠRel oad", "ĠL DL", "ĠLove craft", "Ġunim agin", "Ġresemb led", "Ġbarr acks", "n p", "Ġsurrog ate", "Ġcategor ized", "ãĤ ©", "Ġvacc inated", "Ġdrain age", "Ġind ist", "ĠWhats App", "Ġ18 70", "oler ance", "inv oke", "am orph", "Ġrecon nect", "Ġem anc", "Ġblind ness", "Ġ12 80", "intern et", "c ollar", "Ġalt ru", "Ġab yss", "ĠT RI", "65 7", "Ġinf used", "HE AD", "Ġforest ry", "ĠWood y", "ĠC i", "w i", "s am", "78 4", "hol iday", "Ġmog ul", "ĠF ees", "ĠD EN", "In ternal", "ur bed", "f usc", "at om", "ĠIll usion", "Ġpoll ed", "Ġfl ap", "Ġco ax", "L GBT", "An aly", "ĠSect ions", "ĠCalif orn", "em n", "Ġh ither", "ĠN IGHT", "Ġn ailed", "ĠPip eline", "39 1", "o of", "ĠPr imal", "vere nd", "Ġsl ashing", "Ġret ri", "avi our", "Ġdepart ing", "g il", "IS C", "Ġmid way", "Ġultras ound", "Ġbeh aving", "ĠT ara", "class es", "V irtual", "ĠColon ial", "Ġstri pping", "Ġorchestr ated", "ĠGra ves", "45 2", "ĠIron ically", "ĠWrit ers", "Ġl ends", "ĠMan z", "Ġra ven", "Ġoxid ative", "Ġ26 6", "EL F", "act ually", "asc ar", "D raft", "Ġfavour able", "Ġhumili ating", "Ġf idelity", "ĠH of", "ĠX uan", "49 6", "Ġlay ered", "at is", "79 0", "Ġpay check", "it on", "K ar", "ĠVM ware", "ĠFar mer", "Ġserv ic", "gl omer", "Ġsl ump", "ĠFab ric", "ĠD OC", "est ing", "Ġreass ure", "Ġph yl", "v olt", "it ory", "R ules", "Ġoxid ation", "Ġpri zed", "Ġmist ress", "ĠDj ango", "WAR N", "å ij", "Ġenc ode", "ĠFeed back", "Ġstupid ity", "I an", "ĠYugoslav ia", "× ¨", "ac l", "UT E", "19 77", "Ġqual ifies", "Ġpuls es", "pret ty", "Ġfro ze", "Ġs s", "Iter ator", "Ġur gently", "Ġm ailed", "ĠCh am", "Ġsust aining", "Ġbas il", "Ġpupp ies", "il ant", "ĠP LEASE", "l ap", "ace ous", "F ear", "ĠMaster y", "aut omatic", "ĠT AG", "Ġant im", "ag les", "47 3", "fram es", "Ġwh ispers", "ĠWho ever", "Ġbra very", "ĠUK IP", "ract ions", "\"\" \"", "Ġt ame", "Ġpart ed", "every thing", "CON T", "Ġind ebted", "Ġadd r", "re k", "IR ED", "Ġem inent", "cl inton", "Ġo usted", "Ġreview er", "Ġmelt down", "Ġre arr", "ĠY ao", "the real", "aby te", "Ġst umbling", "Ġbat ches", "Ġ25 9", "Ġcontrace ptive", "Ġprost itute", "ens is", "De cl", "ĠSt rikes", "M ilitary", "ĠO ath", "v acc", "pp ings", "05 2", "Ġpart Name", "amp ing", "Rep orts", "K I", "CH R", "Ġsubt ly", "sw ers", "Bl ake", "us ual", "Ġcontest ants", "Ġcart ridges", "ĠGRE AT", "Ġbl ush", "ĠâĢ º", "47 2", "Ġreason ed", "ãĥ ¤", "paralle led", "Ġd yn", "ag ate", "Ġnight ly", "å Ĩ", "55 6", "Ġsem antic", "ĠAdv oc", "Ġ !!", "Ġdisag rees", "ĠB W", "V eh", "Ġharm ing", "Ġembr aces", "Ġstri ves", "Ġin land", "ĠK ard", "Ġhe ats", "ĠGin ny", "ut an", "ern aut", "yl ene", "ĠE lev", "J D", "Ġh ars", "ĠStar r", "Ġsk ysc", "Ġcollabor ators", "Us ually", "Ġrev olutions", "ĠSTAT S", "Ġdism antle", "Ġconfident ly", "Ġkin etic", "Al i", "Ġpercent ile", "Ġextract ing", "ill ian", "est ead", "Ġphysic ists", "ĠMarsh al", "Ġfell owship", "Ġd ashed", "ĠU R", "ĠSi oux", "ĠComp act", "am ide", "P ython", "ĠLe igh", "ĠPharm ac", "ist rates", "her ical", "Ġf ue", "ĠE min", "Ġ( {", "ĠNeighbor hood", "Ġdisrupt ing", "ĠD up", "Ġg land", "ĠSe v", "ĠMar ian", "arg on", "ĠD und", "Ġ< !--", "Ġstr and", "Ġstadium s", "z os", "Ġpsych osis", "ĠR ack", "Ġbrilliant ly", "ï¸ ı", "Ġsubmer ged", "ĠInst it", "ĠCh ow", "Ġc ages", "ĠH ats", "ĠU rs", "Ġdil uted", "us at", "ien ne", "ĠMembers hip", "ĠBur k", "Ġ ie", "Ġarche type", "D rug", "ult on", "ĠSp ock", "ĠMcK ay", "ĠDep end", "F eatured", "S oc", "19 78", "ĠB ere", "Ġrelent lessly", "Ġcripp ling", "Ġar thritis", "çĶ Ł", "ĠTrop ical", "ĠBul g", "ĠCher yl", "Ġadm irable", "Ġsub title", "Over ride", "Ġorig inating", "ĠC CP", "Ġsw ore", "ĠSo le", "ĠDis orders", "3 29", "Ġprocess ion", "Ġref urb", "Ġimm ersed", "requ ently", "Ġskept ics", "Ġcer amic", "m itter", "en stein", "b elt", "ĠT IT", "b idden", "Ġf ir", "m ist", "> ]", "Ġwe ave", "ĠParad ox", "Ġentr usted", "ĠBarcl ays", "Ġnovel ist", "og ie", "80 6", "Ġnin ety", "Ġdisag reements", "@@@@ @@@@", "ĠAus chwitz", "c ars", "ĠL ET", "t ub", "arant ine", "P OS", "Ġback story", "Ġcheer ful", "ĠR ag", "ek a", "bi ased", "Ġinexper ienced", "ak ra", "ĠW itt", "t an", "Ġrap ist", "Ġplate au", "ch al", "ĠInqu is", "exp ression", "Ġc ipher", "Ġsh aving", "add en", "re ly", "( \\", "ism a", "ĠReg ulatory", "CH AR", "ily n", "N VIDIA", "G U", "Ġmur m", "la us", "Christ opher", "Ġcontract ual", "ĠPro xy", "ĠJa ime", "ĠMethod ist", "Ġstew ards", "st a", "per ia", "Ġphys iology", "Ġbump ed", "Ġf ructose", "Austral ian", "ĠMet allic", "ĠMas querade", "ar b", "Ġprom ul", "Ġdown fall", "Ġbut cher", "Ġb our", "ĠIN FORMATION", "ĠB is", "pect s", "ad ena", "Ġcontempl ating", "ar oo", "cent ered", "ĠPe aks", "Us ed", "Ġmod em", "Ġg enders", "Ġ8 000", "37 1", "Ġm aternity", "ĠR az", "Ġrock ing", "Ġhandgun s", "ĠD ACA", "Aut om", "ĠN ile", "Ġtum ult", "ĠBenef it", "ĠAppro ach", "works hop", "ĠLe aving", "G er", "inst ead", "Ġvibr ations", "Ġrep ositories", "49 7", "ĠA unt", "ĠJ ub", "ĠExp edition", "Al pha", "Ġs ans", "Ġoverd ue", "Ġoverc rowd", "Ġlegisl atures", "Ġp aternal", "ĠLeon ardo", "Ġexp ressive", "Ġdistract ions", "Ġsil enced", "tr ust", "Ġb iking", "Ġ5 60", "Ġpropri et", "Ġimp osition", "Ġcon glomer", "Ġ= ================================================================", "ĠTe aching", "ĠY ose", "int ensive", "T own", "Ġtroll ing", "ĠGr ac", "ĠAS US", "Y o", "Ġspecial s", "ĠNep h", "ĠGod zilla", "Dat abase", "ĠHe gel", "Ġ27 2", "19 76", "ĠGl oria", "Ġdis emb", "ĠInvestig ations", "ĠB ane", "ag ements", "St range", "Ġtre asury", "ĠPl ays", "Ġundes irable", "Ġwid ening", "Ġverb ally", "Ġinf ancy", "Ġcut ter", "f ml", "Ġ21 00", "prot otype", "f ine", "Ġdec riminal", "Ġdysfunction al", "Ġbes ie", "ĠErn st", "z eb", "Ġnort heastern", "Ġa ust", "por ate", "ĠMar lins", "Ġsegreg ated", "ew orld", "ĠMa her", "Ġtra verse", "Ġmon astery", "ur gy", "G ear", "s and", "Com pl", "ĠE MP", "Ġpl ent", "ĠMer cer", "Ġ27 6", "TA BLE", "Config uration", "H undreds", "Ġpr ic", "Ġcollabor ating", "ĠPar amount", "ĠCumm ings", "Ġ( <", "Ġrecord er", "Ġfl ats", "Ġ4 16", "wh ose", "Font Size", "ĠOr bit", "Y R", "Ġwr ists", "Ġb akery", ") }", "ĠB ounty", "ĠLanc aster", "Ġend ings", "acc ording", "ĠSal am", "e asy", "75 5", "ĠBur r", "ĠBarn ett", "onom ous", "Un ion", "Ġpreced ence", "ĠScholars hip", "ĠU X", "Ġroll out", "Ġbo on", "al m", "ĠCan ter", "æ µ", "Ġround ing", "Ġcl ad", "Ġv ap", "ĠF eatured", "is ations", "Ġ5 40", "pol ice", "Ġunsett ling", "Ġdr ifting", "ĠLum ia", "ĠObama Care", "ĠF avor", "Hy per", "ĠRoth schild", "ĠMil iband", "an aly", "ĠJul iet", "H u", "Ġrec alling", "a head", "69 6", "Ġunf avorable", "Ġd ances", "O x", "Ġleg ality", "Ġ40 3", "rom ancer", "Ġinqu ire", "ĠM oves", "\\ \">", "ĠVari ant", "ĠMess iah", "ĠL CS", "ĠBah á", "75 6", "Ġeyeb row", "Ġ ¥", "ĠMc F", "ĠFort y", "M as", "Ġpan icked", "Ġtransform ations", "q q", "Ġrev olves", "ring e", "ĠA i", "ax e", "Ġon ward", "ĠC FR", "ĠB are", "log in", "Ġliqu ids", "Ġde comp", "second ary", "il an", "ĠCon vert", "ami ya", "Ġprosecut ing", "Ġâī ¡", "ĠYork ers", "ĠByr ne", "sl ow", "aw ei", "J ean", "Ġ26 9", "ĠSky dragon", "Ġ é", "ĠNicarag ua", "ĠHuck abee", "ĠHigh ly", "Ġamph ib", "ĠPast or", "ĠL ets", "Ġbl urred", "Ġvisc eral", "ĠC BO", "Ġcollabor ated", "z ig", "Leg al", "Ġapart heid", "Ġbr id", "Ġpres et", "ĠD ET", "ĠAM A", "× Ķ", "arch ing", "auc uses", "build er", "Ġpo etic", "Ġem ulator", "ĠMole cular", "Ġhon oring", "ise um", "Ġtract or", "ĠCl uster", "ĠCal m", "ared evil", "Ġsidew alks", "Ġviol in", "Ġgeneral ized", "ĠAle c", "Ġemb argo", "Ġfast ball", "ĠHT TPS", "ĠL ack", "ĠCh ill", "ri ver", "C hel", "ĠSw arm", "ĠLev ine", "ro ying", "L aunch", "Ġkick er", "Ġadd itive", "ĠDe als", "W idget", "cont aining", "Ġescal ate", "ĠOP EN", "Ġtwe aked", "Ġst ash", "Ġsp arks", "ĠEs sex", "ĠE cc", "Ġconv ict", "Ġblog ging", "I ER", "ĠH L", "Ġmurd erers", "75 9", "ĠH ib", "Ġde pl", "ĠJ ord", "S ac", "Ġdis sect", "ĠHow e", "os her", "Ġcustom izable", "ĠFran z", "Ġat ro", "Ä ĩ", "Ġ000 4", "Ġout post", "R oss", "Ġglyph osate", "ĠHast ings", "ĠBE FORE", "Ġsh ove", "o pped", "ĠSc ala", "Ġam ulet", "an ian", "Ġexacerb ated", "Ġe ater", "47 1", "UM E", "Ġpul p", "izont al", "ĠZ am", "ĠAT I", "imm une", "aby tes", "Ġunnecess arily", "ĠC AT", "ĠAx is", "Ġvisual ize", "à ī", "ĠRad ical", "f m", "Doc uments", "ĠFor rest", "Ġcontext ual", "ĠSy mbol", "Ġtent ative", "ĠDO ES", "ĠGood s", "Ġintermitt ent", "} :", "medi ated", "Ġridic ule", "Ġathe ism", "Ġpath ogens", "ĠM um", "Ġre introdu", "Ġ30 7", "i HUD", "Ġflash light", "Ġsw earing", "Ġp engu", "B u", "Ġrot ated", "ĠCr ane", "Ġ() );", "Ġfashion able", "Ġendors ing", "46 3", ") [", "Ġingest ion", "Ġcook s", "Ġ9 50", "ot omy", "ĠIm am", "Ġk a", "Ġte aser", "ĠGhost s", "ĠãĤ µ", "19 69", "Ï ĥ", "ub by", "Ġconver ter", "zan ne", "end e", "ĠPre par", "ĠNic kel", "ĠChim era", "h im", "ĠTyr ann", "ĠSabb ath", "ĠNich ols", "Ġra pt", "ih ar", "Ġshe lling", "Ġillum inate", "Ġdent ist", "ut or", "ĠInteg ration", "Ġwh ims", "ĠLiter ary", "Be aut", "Ġp archment", "ag ara", "Br and", "Ġder og", "âĢ¦ )", "ĠNor se", "Ġunw itting", "Ġc uc", "Ġborder line", "Ġupset ting", "Ġrec ourse", "Ġd raped", "ĠRad ar", "Ġcold er", "ĠPep si", "im inary", "], [", "65 8", "V i", "ĠF rem", "ĠP es", "Ġveter inary", "ĠT ED", "ĠEp idem", "n ova", "k id", "Ġdev out", "o ct", "j ad", "M oh", "ĠP AY", "Ġge ometric", "Ġ3 23", "Ġcircum ference", "ich ick", "19 75", "ĠY uri", "ĠSh all", "ĠH over", "un in", "S pr", "Ġg raft", "ĠHapp iness", "Ġdisadvant ages", "att acks", "Ġhub s", "ĠStar Craft", "é ĸ", "Ġgall eries", "ĠKor ra", "Ġgrocer ies", "ĠGors uch", "Ġrap ists", "Ġfun gi", "ĠTyph oon", "V ector", "ĠEm press", "b attle", "4 68", "Ġparas ite", "ĠBom ber", "S G", "ex ist", "ĠP f", "Ġun se", "Ġsurge ons", "B irth", "ĠUn sure", "ĠPrint ed", "ĠBehavior al", "ĠA ster", "Pak istan", "Ġun ethical", "Ġs v", "ĠIo T", "Ġlay outs", "P ain", "Ġconst ants", "ĠL W", "ĠB ake", "Ġtow els", "Ġdeterior ation", "ĠBol ivia", "Ġblind ed", "ĠW arden", "ĠMist ress", "Ġon stage", "Ġcl ans", "ĠB EST", "19 60", "Ġant ique", "Ġrhet orical", "ĠPer cy", "ĠRw anda", ", .", "B ruce", "Ġtra umat", "ĠParliament ary", "Ġfoot note", "id ia", "ĠLear ned", "se eking", "gen ic", "Ġdim ensional", "H ide", "èĢ ħ", "Ġintrig ue", "in se", "Ġle ases", "Ġapp rentices", "w ashing", "Ġ19 26", "V ILLE", "Ġsw oop", "s cl", "Ġbed rooms", "on ics", "ĠCr unch", "comp atible", "Ġincap ac", "ĠYemen i", "ash tra", "z hou", "d anger", "Ġmanifest ations", "ĠDem ons", "AA F", "Secret ary", "ACT ED", "L OD", "Ġam y", "ra per", "eth nic", "4 17", "Ġpos itives", "Ġ27 3", "ĠRefuge es", "Ġus b", "ĠV ald", "odd y", "ĠMahm oud", "As ia", "Ġskull s", "ĠEx odus", "ĠComp et", "ĠL IC", "ĠM ansion", "ĠA me", "Ġconsolid ate", "storm s", "ont ent", "99 6", "Ġcl en", "Ġm ummy", "fl at", "75 8", "ĠV OL", "oter ic", "n en", "ĠMin ute", "S ov", "Ġfin er", "R h", "ly cer", "Ġreinforce ments", "ĠJohann es", "ĠGall agher", "Ġgym n", "S uddenly", "Ġext ortion", "k r", "i ator", "T a", "Ġhippocamp us", "N PR", "ĠComput ing", "Ġsquare ly", "Ġmod elling", "ĠFor ums", "ĠL isp", "ĠKrish na", "Ġ3 24", "Ġr ushes", "Ġens ued", "Ġcre eping", "on te", "n ai", "il ater", "ĠHorn ets", "Ġob livious", "IN ST", "55 9", "Ġjeopard y", "Ġdistingu ishing", "j ured", "Ġbeg s", "sim ilar", "ph ot", "5 30", "ĠPark way", "Ġs inks", "ĠHearth stone", "ib ur", "ĠBat on", "Av oid", "Ġd ancer", "Ġmag istrate", "ary n", "Ġdisturb ances", "ĠRom ero", "Ġpar aph", "Ġmis chief", "âĸ ĵ", "ĠSh aria", "Ġur inary", "r oute", "iv as", "f itted", "Ġeject ed", "ĠAl buquerque", "Ġ4 70", "Ġirrit ated", "ĠZ ip", "ĠB iol", "à į", "Ġden ounce", "Ġbin aries", "ĠVer se", "Ġopp os", "ĠKend rick", "ĠG PL", "Ġsp ew", "ĠEl ijah", "ĠE as", "Ġdr ifted", "so far", "Ġannoy ance", "ĠB ET", "47 4", "ĠSt rongh", "it ates", "ĠCogn itive", "oph one", "ĠIdent ification", "ocr ine", "connect ion", "Ġbox er", "ĠAS D", "ĠAre as", "Y ang", "t ch", "ull ah", "Ġdece ive", "Comb at", "ep isode", "cre te", "W itness", "Ġcondol ences", "ht ar", "Ġhe als", "Ġbuck ets", "ĠLA W", "B lu", "Ġsl ab", "ĠOR DER", "oc l", "att on", "ĠSteven son", "ĠG inger", "ĠFriend ly", "ĠVander bilt", "sp irit", "ig l", "ĠReg arding", "ĠPR OG", "Ġse aling", "start ing", "Ġcard inal", "ĠV ec", "ĠBe ir", "Ġmillisec onds", "we ak", "per se", "Ġster ile", "ĠCont emporary", "ĠPh ant", "ĠCl o", "Ġout p", "Ġex iled", "Ġ27 7", "Ġself ie", "Ġman ic", "Ġn ano", "ter ms", "Alex ander", "Ġres olves", "Ġmillenn ia", "Ġexpl odes", "Ġconst ellation", "Ġadul tery", "m otion", "D OC", "Ġbroad casters", "Ġkinderg arten", "ĠMay weather", "ĠE co", "ich o", "Ġ28 7", "l aun", "Ġm ute", "Ġdisc reet", "Ġpres chool", "Ġpre empt", "De lete", "ĠFre ed", "P i", "H K", "Ġblock er", "ĠC umber", "Ġw rought", "d ating", "Ġins urer", "Ġquot as", "Ġpre ached", "Ġev iction", "ĠReg ina", "ĠP ens", "Ġsevent een", "ĠN ass", "D ick", "Ġfold s", "Ġd otted", "ĠA ad", "Un iversal", "Ġp izz", "ĠG uru", "Ġso ils", "Ġno vice", "ĠNe ander", "Ġst ool", "Ġdeton ated", "ĠPik achu", "ĠMass ive", "IV ER", "ĠAb del", "Ġsubdu ed", "Ġtall est", "Ġprec arious", "Ġa y", "r ification", "ĠOb j", "c ale", "Ġun question", "cul osis", "ad as", "igr ated", "D ays", "Ġque ens", "ĠGaz ette", "ĠCol our", "ĠBow man", "ĠJ J", "ï ve", "Ġdomin ates", "Stud ent", "Ġm u", "Ġback log", "ĠElect ro", "Tr uth", "48 3", "Ġcond ensed", "r ules", "ĠCons piracy", "Ġacron ym", "hand led", "ĠMat te", "j ri", "ĠImp ossible", "l ude", "cre ation", "Ġwar med", "ĠSl ave", "Ġmis led", "Ġfer ment", "ĠK ah", "ink i", "ke leton", "cy l", "ĠKar in", "Hun ter", "Reg ister", "ĠSur rey", "Ġst ares", "ĠW idth", "ĠN ay", "ĠSk i", "Ġblack list", "uck et", "Ġexp ulsion", "im et", "Ġret weet", "vant age", "Fe ature", "Ġtro opers", "Ġhom ers", "9 69", "Ġconting ency", "ĠW TC", "ĠBrew er", "fore ign", "W are", "S olar", "Ġund ue", "RE C", "ulner able", "path ic", "ĠBo ise", "Ġ3 22", "Ġarous ed", "ĠY ing", "ä¸ į", "uel ess", "Ġp as", "Ġmor p", "Ġfl oral", "Ex press", "ud ging", "k B", "ĠGr anted", "Ø ¯", "ĠMich a", "ĠGoth ic", "ĠSPEC IAL", "ĠRic ardo", "F ran", "Ġadminister ing", "6 20", "por a", "Ġ ®", "Ġcomprom ises", "Ġb itten", "Ac cept", "Th irty", "Ð ²", "Ġmater ially", "ĠTer r", "ig matic", "ch ains", "Ġdo ve", "stad t", "Mar vel", "FA ULT", "Ġwind shield", "Ġ3 36", "ad ier", "Ġsw apping", "Ġflaw less", "ĠPred ator", "ĠMiche le", "Ġprop ulsion", "ĠPsych ic", "Ġassign ing", "Ġfabric ation", "Ġbar ley", "l ust", "Ġtow ering", "Ġalter cation", "ĠBent ley", "Sp here", "Ġtun a", "ĠClass es", "Fre edom", "un er", "L ady", "v oice", "Ġcool est", "or r", "Ġpal p", "$ {", "Ġhyster ia", "ĠMet atron", "p ants", "Ġspawn ing", "Exper ts", "ĠInvest ors", "ĠAn archy", "Ġshr unk", "ĠVict im", "Ġ28 9", "Ġec stasy", "ĠB inding", "58 5", "ĠMel ody", "57 8", "ot ally", "ĠE tsy", "lig a", "Ġapplaud ed", "Ġswe ating", "Ġredist ributed", "Ġpop corn", "Ġsem inal", "f ur", "ĠNeuro science", "R and", "ĠO st", "ĠMadd en", "ĠIncre asing", "ĠDaw kins", "ĠSub way", "Ġar sen", "cons erv", "B UR", "Ġsp iked", "ĠLy ft", "ĠImper ium", "ĠDrop box", "Ġfav oured", "Ġencomp asses", "gh ost", "Ġins pires", "Ġbur geoning", "ĠY oshi", "ĠVert ical", "ĠAud itor", "Ġint ending", "Ġfilib uster", "Bl oom", "f ac", "ĠCav s", "ign ing", "Ġcowork ers", "ĠBarb arian", "rem ember", "FL AG", "Ġaudit ory", "ason ry", "Col lege", "Ġmut ed", "gem ony", "ob in", "ĠPsych o", "9 68", "Ġlav ish", "Ġhierarch ical", "ĠDr one", "ou k", "Ġcripp led", "ĠMax im", "Sl ot", "Ġqu iz", "ĠV id", "if ling", "Ġarchae ologists", "Ġabandon ment", "d ial", "le on", "ĠF as", "T ed", "Ġr aspberry", "Ġmaneu vers", "Ġbehavi ours", "Ġins ure", "Ġrem od", "Sw itch", "h oe", "Ġsp aced", "Ġafford ability", "ĠF ern", "not ation", "ĠBal anced", "Ġoccup ies", "en vironment", "Ġneck lace", "Ġsed an", "F U", "ĠBrav o", "Ġab users", "ĠAn ita", "met adata", "ĠG ithub", "ait o", "ĠF aster", "ĠWass erman", "ĠF lesh", "Ġth orn", "r arily", "ĠMer ry", "w ine", "Ġpopul ace", "ĠL ann", "Ġrepair ing", "Ġpsy che", "Ġmod ulation", "aw aru", "âĢĭ âĢĭ", "ari j", "Ġdecor ations", "Ġapolog ise", "ĠG arg", "app ly", "Ġgive away", "ĠFl an", "ĠWy att", "U ber", "Ġauthor ised", "ĠMor al", "HAHA HAHA", "activ ate", "Ġtorped o", "ĠF AR", "Ġam assed", "ĠA ram", "ark in", "ĠVict ims", "st ab", "Ġo m", "ĠE CO", "Ġopio ids", "Ġpurpose ly", "ĠV est", "Ġer g", "at an", "ĠSur gery", "Ġcorrect ing", "ĠOrt iz", "ĠBe et", "Ġrev oke", "Ġfre eway", "ĠH iggins", "F ail", "ĠFar ms", "ĠAT P", "h ound", "Ġp oking", "ĠCommun ists", "mon ster", "iment ary", "Ġunlock ing", "Ġunf it", "we ed", "en ario", "at ical", "ĠEnlight enment", "ĠN G", "ĠComp ensation", "de en", "ĠWid ow", "ĠCind y", "ĠAfter wards", "Ġ6 000", "ikh ail", "ag ically", "Ġrat ified", "Ġcasual ty", "H OME", "p sey", "f ee", "Ġspark ling", "Ġd é", "Ġconcert ed", "C atal", "Ġcomp lying", "ĠA res", "ĠD ent", "Sh ut", "Ġsk im", "ad minist", "Ġhost ilities", "ĠG ins", "Ġ6 08", "Ġm uddy", "ĠMc Int", "ĠDec ay", "5 25", "Ġconspic uous", "ĠEx posure", "Ġresc ind", "Ġwear able", "Ġ3 28", "our met", "ah s", "ĠRob ots", "Ġe clips", "inst ance", "ĠRE PORT", "ĠApp l", "0 30", "ĠSk ies", "01 00", "Ġfall acy", "S ocket", "ĠRece iver", "Ġsol ves", "ĠButter fly", "ĠSho pping", "ĠFI RE", "65 4", "Med ic", "Ġsing ers", "ĠNeed less", "'' ''", "isher s", "ĠD ive", "58 8", "Ġselect ively", "Ġcl umsy", "88 9", "Ġpurch aser", "ear ned", "ard y", "Ġbenef iting", "eng lish", "Ġyield ing", "ĠP our", "Ġspin ach", "Ġdel ve", "ĠC rom", "6 10", "Ġexport ing", "ĠMA KE", "Ġ26 3", "Ġg rop", "Ġenv oy", "ĠInqu iry", "ĠLu igi", "d ry", "ĠT uring", "Thumbnail Image", "ĠVar iety", "Ġfac et", "Ġfl uffy", "Ġexcerpt s", "Ġsh orth", "ĠOl sen", "CL UD", "Ġrel iant", "ĠUN C", "T our", "Ġbat hing", "Comp any", "Ġglobal ization", "P red", "ĠMalf oy", "Ġh oc", "j am", "craft ed", "ĠBond s", "ĠKiss inger", "Eng land", "Ġorder ly", "cat entry", "Ġ26 1", "Ġexch anging", "ĠInt ent", "ĠAmend ments", "D OM", "Ġst out", "³³³³³³³³ ³³³³³³³³", "ĠAir bus", "Ġ27 8", "hy de", "P oll", "Item ThumbnailImage", "Ġlooph oles", "ĠPill ar", "Ġexpl or", "St retch", "A part", "Ġun married", "Lim it", "ĠTransform ers", "Ġintellect ually", "unct ure", "18 00", "Ġd arn", "B razil", "Ġleft over", "ber us", "f red", "Mine craft", "3 26", "ĠForm s", "Ġproof s", "ĠDes igned", "Ġindex es", "ĠSupp ose", "EM S", "ĠL oving", "ĠBon nie", "im ating", "OT US", "Ġconduct or", "Ġbehav ed", "ĠF ren", "Ġsy nerg", "Ġmillenn ium", "Ġcater ing", "ĠL auder", "W r", "ĠY iannopoulos", "ĠAT F", "Ġensl aved", "Ġawaken ed", "D VD", "ĠED ITION", "ĠConc ert", "ĠChall enger", "ĠH aku", "umer ic", "Ġdep recated", "ĠSH AR", "4 12", "Ġdy stop", "Ġtremb ling", "Ġdread ed", "ĠSp ac", "p adding", "Re pl", "ĠG arrison", "M ini", "Ġun paralleled", "am ar", "URR ENT", "w reck", "c ertain", "t al", "ĠC LS", "app ings", "Ġsens ed", "Ġf encing", "ĠPas o", "ĠDes k", "Ġsc off", "Ġcontem plate", "ĠL iga", "l iquid", "75 7", "Ġapp rentice", "ĠUCH IJ", "5 70", "ĠTh ousand", "ĠIll um", "Ġchampion ed", "ãĤ Į", "Ġelect ors", "Ġ3 98", "ĠH ancock", "round ed", "ĠJ OHN", "Ġuns atisf", "Ġqual ifier", "ĠGad get", "EN E", "Ġdead liest", "ĠPl ants", "Ġ ions", "Ġacc ents", "Ġtwe aking", "Ġsh aved", "F REE", "ĠCh aser", "Again st", "9 60", "Ġmeth amphetamine", "Ġnormal ized", "Ġ$ \\", "ĠPre cision", "ĠGu am", "Ġch oked", "ĠX II", "ĠCast ing", "Tor rent", "Ġscal p", "ĠJagu ar", "w it", "Ġsem ic", "ix ie", "ĠG ould", "Ġconf ines", "N usra", "ĠL on", "ĠJ ugg", "y cle", "ĠCod ec", "E gypt", "Ġrest rain", "ĠAl iens", "Ġch oking", "ĠD unk", "ĠBell a", "ab c", "Ġsl ang", "Ġneuro trans", "s av", "Ġempower ment", "â ĨĴ", "Ġclim bers", "ĠM im", "ĠF ra", "ros se", "Cap ital", "ĠCth ulhu", "Inter face", "Ġprof icient", "ĠIN TO", "Ġ3 18", "ront al", "5 80", "ĠDes pair", "K enn", "Ġscrim mage", "ĠCo at", "as ions", "Ġwall paper", "ĠJ ol", "Ġresurg ence", "Ġant iv", "ĠB alls", "² ¾", "Ġbuff ers", "Ġsub system", "ĠSt ellar", "ĠL ung", "A IDS", "Ġerad icate", "Ġblat antly", "Ġbehav es", "ĠN un", "Ġant ics", "ex port", "DE V", "w b", "Ġph p", "ĠInteg rity", "Ġexplore r", "Ġrev olving", "auth ored", "g ans", "Ġbas k", "Ġas ynchronous", "å į", "TH ING", "69 8", "G ene", "ĠR acer", "ĠN ico", "iss ued", "Ġser mon", "p ossibly", "Ġsize of", "Ġentrepreneur ial", "ox in", "ĠMin erva", "Ġpl atoon", "n os", "ri ks", "A UT", "ĠAval anche", "ĠDes c", "ij 士", "ĠP oc", "Ġconf erred", "Î »", "Ġpat ched", "F BI", "66 2", "Ġfract ures", "Ġdetect s", "Ġded icate", "Ġconstitu ent", "Ġcos mos", "W T", "Ġswe ats", "Ġspr ung", "b ara", "s olid", "Ġuns us", "Ġbul ky", "ĠPhilipp e", "ĠFen rir", "Ġtherap ists", "ore al", "^^ ^^", "Ġtotal ed", "Ġboo ze", "ĠR PC", "Prosecut ors", "Ġdis eng", "ĠSh ared", "Ġmotor cycles", "Ġinvent ions", "Ġlett uce", "ĠMer ge", "ĠJ C", "Ġspiritual ity", "ĠWAR NING", "Ġunl ucky", "ĠT ess", "Ġtong ues", "ĠD UI", "T umblr", "Ġle ans", "Ġinv aders", "Ġcan opy", "ĠHur ricanes", "ĠB ret", "ĠAP PLIC", "id ine", "ick le", "Reg arding", "Ġve ggies", "Ġe jac", "ju ven", "F ish", "D EM", "ĠD ino", "Th row", "ĠCheck ing", "be ard", "( &", "Ġj ails", "Ġh r", "trans fer", "iv ating", "Ġfle ets", "ĠIm ag", "ĠMc Donnell", "Ġsnipp et", "Is a", "ĠCh att", "ĠSt ain", "ĠSet FontSize", "ĠO y", "ĠMathemat ics", "49 4", "Ġelectro ly", "ĠG ott", "ĠBr as", "B OOK", "ĠF inger", "d ump", "Ġmut ants", "Ġrent als", "Ġinter tw", "Ġc reek", "ail a", "Bro ther", "ĠDisc ord", "pe e", "raw ler", "Ġcar p", "Ġ27 9", "ãĤ· ãĥ£", "rel ations", "Ġcontr asts", "Col umn", "Ġrec onnaissance", "Ġun know", "Ġl ooting", "Ġregul ates", "Ġopt imum", "ĠChero kee", "ĠA ry", "Lat est", "Ġroad side", "Ġd anced", "ĠUnic orn", "A cknowled", "Ġuncont roll", "ĠM US", "at io", "ch ance", "ha ven", "VAL UE", "Ġfavour ites", "Ġceremon ial", "b inary", "pe ed", "wood s", "EM P", "Ġv ascular", "Ġcontempl ated", "Ġbar ren", "ĠL IST", "Y ellow", "ospons ors", "Ġwhisk y", "ĠM amm", "ĠDeV os", "min imum", "H ung", "44 2", "P ic", "ĠSnap dragon", "77 6", "Ġcar ving", "Ġund ecided", "Ġadvantage ous", "Ġpal ms", "ĠA Q", "Ġst arch", "L oop", "Ġpadd le", "Ġfl aming", "ĠHor izons", "An imation", "bo ost", "Ġprob abilities", "ĠM ish", "Ġex odus", "ĠEditor ial", "Ġfung us", "Ġdissent ing", "ĠDel icious", "rog ram", "ĠD yn", "d isk", "t om", "Ġfab rics", "ĠC ove", "ĠB ans", "Ġsoft en", "ĠCON S", "Ġin eligible", "Ġestim ating", "ĠLex ington", "pract ice", "of i", "Ġshe dding", "ĠN ope", "Ġbreat hed", "ĠCorinth ians", "y ne", "ek i", "B ull", "Ġatt aching", "reens hots", "Ġanaly se", "ĠK appa", "Ġuns ustainable", "Ġinter pol", "ank y", "he mer", "Ġprot agonists", "Ġform atted", "ĠBry ce", "ĠAch illes", "ĠAb edin", "sh ock", "Ġb um", "b os", "qu a", "ĠW arn", "q t", "ĠDi abetes", "8 64", "ĠIn visible", "Ġvan ish", "Ġtrans mitting", "Ġmur ky", "ĠFe i", "Ġawa ited", "ĠJur assic", "umm ies", "Ġmen acing", "g all", "C ath", "B uilt", "ild o", "ĠV otes", "Ġon t", "Ġmun itions", "ĠFre em", "ÃŃ n", "Ġdec ency", "lo pp", "ie ved", "ĠG ord", "Ġun thinkable", "ĠNews week", "Ġ3 21", "He at", "Ġpresent er", "ji ang", "Ġpl ank", "ĠAval on", "Ġben z", "ĠR out", "Ġslam ming", "ĠD ai", "ou ter", "ĠCook ie", "ĠAlic ia", "ge y", "Ġvan ity", "Ġow l", "á µ", "t ested", "ĠAw akens", "Ġcan v", "Ġblind ly", "ĠRid ley", "ĠEm ails", "Requ ires", "ĠSer bian", "ograp hed", "if rame", "eter ia", "Ġaltern ating", "qu iet", "Ġsoc iology", "ĠUn lock", "ĠCommun ism", "Ġo ps", "Ġatt ribution", "Ġab duction", "ĠAb ram", "Ġsidel ined", "ĠB OOK", "Ġref ining", "ĠFe eling", "ĠOs lo", "ĠPru itt", "r ack", "ang ible", "Ġcaut iously", "ĠM ARK", "eed s", "M ouse", "ĠStep h", "ĠP air", "S ab", "99 7", "ĠBa al", "B ec", "Ġcomm a", "ĠP all", "ĠG ael", "Ġmisunder stand", "ĠP esh", "Order able", "Ġdis mal", "ĠSh iny", "% \"", "Ġreal istically", "Ġpat io", "ĠG w", "ĠVirt ue", "Ġexhaust ing", "wh atever", "oph ys", "y ip", "4 18", "Ad just", "ĠWa iting", "ess on", "ĠMaz da", "ĠDo zens", "Ġstream lined", "Ġincompet ence", "ĠM eth", "Ġeth os", "ON ES", "Ġincent iv", "Ġgr itty", "ĠBut cher", "Head er", "Ġexp onential", "à Ł", "Ġcorrel ate", "Ġcons ensual", "s ounding", "R ing", "Orig in", "Ġcon clusive", "fe et", "ac ly", "ĠF ernandez", "Buy able", "Ġd ucks", "aunt lets", "Ġel ong", "Ġ28 6", "Ġsim ul", "G as", "ĠK irst", "Ġprot r", "ĠRob o", "ĠAo E", "op ol", "Ġpsych ologically", "sp in", "ilater ally", "ĠCon rad", "W ave", "44 1", "ĠAd vertisement", "ĠHarm on", "ĠOri ental", "is Special", "Ġpresum ptive", "Ġw il", "ĠK ier", "ne a", "Ġp pm", "Ġhar bour", "ĠW ired", "comp any", "Ġcor oner", "atur days", "ĠP roud", "ĠN EXT", "ĠFl ake", "val ued", "ce iver", "Ġfra ught", "Ġc asing", "Ġrun away", "Ġg in", "ĠLaure nt", "ĠHar lem", "ĠCur iosity", "qu ished", "Ġneuro science", "ĠH ulu", "Ġborrow er", "Ġpetition er", "ĠCo oldown", "W ARD", "Ġinv oking", "conf idence", "For ward", "Ġst s", "pop ulation", "Delivery Date", "Fil m", "ĠC ov", "quick Ship", "quickShip Available", "prim ary", "isSpecial Orderable", "inventory Quantity", "channel Availability", "BO X", "ĠMulti player", "ĠJen ner", "77 8", "ĠM d", "Ġ~ /.", "M N", "Ġchild ish", "Ġantioxid ant", "ĠChrom ebook", "Ġ27 4", "Ġscreen play", "Ġadvent urous", "ĠRelations hip", "respons ive", "ming ton", "Ġcorner stone", "ĠF ey", "F IR", "Ġrook ies", "ĠF eaturing", "Ġorig inate", "Ġelectro des", "ant es", "Ġscript ures", "Ġgl ued", "Ġdiscont ent", "Ġaff licted", "lay out", "B rave", "Ġm osa", "ĠQuant ity", "ĠH ik", "w inner", "H ours", "Ġent ail", "ĠCell s", "olog ue", "Ġv il", "Ġpre acher", "Ġdecor ative", "d ifferent", "Ġprejud ices", "ĠSm oking", "ĠNotting ham", "so Type", "Ġrhyth ms", "ĠAl ph", "bl ast", "Ste el", "ĠDaniel le", "Ġstr ife", "Ġrem atch", "so DeliveryDate", "ĠF ork", "t rip", "ol ulu", "hes es", "C G", "ĠPOLIT ICO", "ost a", "ĠDr ift", "é¾įå ¥", "é¾įå¥ ij士", "Ġvet ting", "ĠJin ping", "ĠRec ession", "Min or", "ĠF raud", "enf ranch", "Ġconven ed", "ĠNA ACP", "ĠMill ions", "ĠFarm ing", "ĠW oo", "ĠFl are", "rit o", "imm igrant", "Ġvac ancy", "ĠHE AD", "ĠV aj", "eg al", "ĠV igil", "Stud y", "Ġru ining", "Ġr acks", "Ġhe ater", "ĠRand olph", "ĠBr ush", "ĠT ir", "Ø ¨", "Ġc ov", "% ]", "Ġrecount s", "ĠO PT", "ĠM elt", "Ġtr uce", "Ġcas inos", "Ġcrus ade", "Ġcarn age", "Ġstri pe", "ĠK yl", "Text ures", "Ġ6 98", "Ġpro clamation", "Ġgood ies", "Ġ........ ..", "pro claimed", "P olit", "Ġtop ical", "Ġspecial ize", "ĠA min", "g m", "Ġanch ored", "Ġbear ings", "s ample", "ĠHigh land", "ĠAut ism", "Ġmerc enary", "Ġinterview er", "L ER", "ĠSom ers", "Ġembry o", "ĠAss y", "Ġ28 1", "ĠEd iting", "ĠCh osen", "6 60", "Ġp ci", "ĠThunder bolt", "BI LL", "Ġchuck led", "jri wal", "h of", "Ġearth ly", "() {", "ind ependence", "Ġdisp ers", "ĠV endor", "ĠG areth", "Ġp als", "P enn", "ĠSub mit", "ic um", "Th u", "Ġcl andestine", "Ġcann ibal", "ĠCl erk", "E Stream", "gal itarian", "âĻ ¥", "g ew", "Ġhor rend", "ĠL ov", "ĠRe action", "ocr in", "Class ic", "Ġecho ing", "Ġdiscl osing", "ĠIns ight", "og un", "ĠInc arn", "upload s", "pp erc", "guy en", "Ġ19 01", "ĠB ars", "68 7", "Ġb ribes", "ĠFres no", "ur at", "ĠRe ese", "Ġintr usive", "Ġgri pping", "ĠBlue print", "ĠR asm", "un ia", "man aged", "ĠHeb do", "Ġ3 45", "Ġdec oding", "Ġpo ets", "Ġj aws", "ĠF IGHT", "am eless", "ĠMead ows", "ĠHar baugh", "Inter view", "ĠH osp", "ĠB RA", "Ġdelet ion", "m ob", "W alker", "ĠMoon light", "ĠJ ed", "ĠSoph ia", "Ġus ur", "Ġfortun ately", "ĠPut ting", "ĠF old", "Ġsan itation", "Ġpart isans", "IS ON", "B ow", "ĠCON C", "ĠRed uced", "ĠS utton", "Ġtouch screen", "Ġembry os", "âĢ¢âĢ¢ âĢ¢âĢ¢", "ĠK rug", "com bat", "ĠPet roleum", "Ġam d", "ĠCos mos", "Ġpresc ribing", "Ġconform ity", "ours es", "Ġplent iful", "Ġdis illusion", "ĠEc ology", "itt al", "Ġf anc", "Ġassass inated", "regn ancy", "Ġperenn ial", "ĠBul lets", "Ġst ale", "Ġc ached", "ĠJud ith", "ĠDise ases", "All en", "Ġl as", "Ġsh ards", "ĠSu arez", "ĠFriend ship", "inter face", "ĠSupp orters", "add ons", "46 2", "ĠIm ran", "ĠW im", "Ġnew found", "ĠM b", "An imal", "Ġd arling", "and e", "Ġrh y", "ĠTw isted", "pos al", "yn ski", "Var ious", "× ľ", "ĠK iw", "uy omi", "Ġwell being", "ĠL au", "an os", "Ġunm ist", "Ġmac OS", "Ġrest room", "ĠOl iv", "ĠAir ways", "Ġtimet able", "9 80", "Ġrad ios", "v oy", "ias co", "Ġcloud y", "ĠDraw ing", "Any thing", "Sy ria", "ĠH ert", "st aking", "Ġun checked", "Ġb razen", "ĠN RS", "69 7", "onom ic", "est ablish", "Ġl eng", "Ġdi agonal", "ĠF ior", "L air", "ĠSt ard", "Ġdef icient", "jo ining", "be am", "Ġomn ip", "Ġbl ender", "Ġsun rise", "Mo ore", "ĠF ault", "ĠCost ume", "ĠM ub", "Fl ags", "an se", "Ġpay out", "ĠGovern ors", "ĠD illon", "ĠBan ana", "N ar", "Ġtra iled", "Ġimperial ist", "um ann", "ats uki", "4 35", "ĠRoad s", "Ġsl ur", "ĠIde ally", "Ġt renches", "C trl", "Ġmir rored", "ĠZ el", "ĠC rest", "Comp at", "ĠRoll s", "sc rib", "ĠTra ils", "omet ers", "w inter", "Ġimm ortality", "il ated", "Ġcontrad icts", "un iversal", "ill ions", "ĠM ama", "opt im", "AT URE", "Ġge o", "et ter", "ĠCar lo", "4 24", "Ġcanon ical", "ĠStrongh old", "n ear", "Ġperf ume", "Ġorche stra", "od iac", "Ġup he", "Ġreign ing", "vers ive", "Ġc aucuses", "ĠD EM", "Ġinsult ed", "Ġ---- --", "ĠCr ush", "Ġroot ing", "ĠWra ith", "Ġwh ore", "Ġto fu", "C md", "ĠB ree", "Ġ$ _", "Ġr ive", "ĠAd vertising", "Ġw att", "ĠH O", "Ġpersu asive", "ĠParam eters", "Ġobserv ational", "ĠN CT", "ĠMo j", "ĠSal on", "Ġtr unc", "Ġexqu isite", "ĠMar a", "Ġpo op", "ĠAN N", "Ex c", "ĠWonder ful", "ĠT aco", "Ġhome owner", "ĠSmith sonian", "orpor ated", "mm mm", "Ġlo af", "ĠYam ato", "ĠInd o", "Ġcl inging", "á s", "Ġimm utable", "h ub", "Or ange", "Ġfingert ips", "ĠWood en", "ĠK idd", "ĠJ PM", "ĠDam n", "C ow", "c odes", "48 2", "Ġiniti ating", "ĠEl k", "ĠCut ting", "Ġabsent ee", "ĠV ance", "ĠLil ith", "G UI", "Ġobsc ured", "Ġdwar ves", "ĠCh op", "ĠB oko", "Val ues", "Ġmult imedia", "Ġbrew ed", "Reg ular", "CRIP TION", "ĠMort al", "Ġa pex", "Ġtravel er", "Ġbo ils", "Ġspray ing", "Rep resent", "ĠStars hip", "4 28", "Ġdisappro val", "Ġshadow y", "Ġlament ed", "ĠRe place", "ĠFran ç", "67 7", "d or", "Ġunst oppable", "Ġcoh orts", "gy n", "ĠClass ics", "ĠAm ph", "Ġsl uggish", "ĠAdd iction", "ĠPad res", "Ġins cription", "Ġin human", "min us", "ĠJere miah", "at ars", "Ter ror", "ĠT os", "ĠSh arma", "ast a", "c atch", "Ġpl umbing", "ĠTim bers", "Sh ar", "H al", "ĠO sc", "Ġcou pling", "hum ans", "Ġsp onge", "Ġid ols", "ĠSp a", "ĠAdv ocate", "ĠBe ats", "lu a", "Ġtick ing", "Ġload er", "ĠG ron", "8 10", "Ġstim ulated", "Ġside bar", "ĠManufact urer", "ore And", "19 73", "Ġpra ises", "ĠFl ores", "dis able", "ĠElect rical", "ra ise", "E th", "Ġmigr ated", "Ġlect urer", "K ids", "ĠCa vern", "Ġk ettle", "Ġgly c", "ĠMand ela", "ĠF ully", "å§ «", "FIN EST", "Ġsquee zing", "ĠRy der", "amp oo", "oreAnd Online", "Inst oreAndOnline", "Buyable InstoreAndOnline", "Ġcommem orate", "ĠRamp age", "Aust in", "ĠSh roud", "ĠRu ins", "9 15", "ĠK H", "Ġwater front", "ĠE SC", "b aby", "ĠC out", "ĠEm blem", "Ġequival ents", "49 2", "Un ique", "ĠNiet zsche", "brow ser", "Ġim itation", "ĠWere wolf", "ĠKir in", "ac as", "' ,\"", "Ġà ¾", "Review ed", "Ġc unt", "Ġvo ic", "ĠLen ovo", "Ġbond ed", "48 1", "Ġinhib itors", "Ġendeav ors", "ĠHav ana", "ĠSt out", "ĠJ olly", "A ctor", "*/ (", "Ġoccur rences", "ĠT ens", "Incre ased", "ĠACT ION", "Ġ ãĢĮ", "ĠRank ings", "ĠB reat", "Ġ30 9", "D ou", "Ġimpact ing", "ĠDuc hess", "pre fix", "Q B", "Ġsummon ing", "Ġbest owed", "ĠKe pler", "ĠPOW ER", "c ube", "ĠK its", "ĠG rip", "Ġop ium", "Ġrep utable", "t oc", "ich ael", "ĠR ipple", "Ġcaf é", "ĠZ oom", "ĠBur ma", "Ġwa ive", "Ġst alls", "Ġdem eanor", "inc erity", "Ġfluor ide", "ĠSH OULD", "Par is", "Ġlong ing", "Ġpl at", "Ġgross ly", "Ġbull s", "Ġshowc asing", "ex pected", "ĠG addafi", "engine ering", "Re peat", "ĠK ut", "Ġconce ivable", "Ġtrim med", "osc ope", "ĠCand idate", "ĠT ears", "rol og", "Lew is", "S UP", "Ġroad map", "Ġsal iva", "Ġtrump et", "Jim my", "Ġmirac ulous", "Ġcolon ization", "Ġam put", "ĠGN OME", "ate ch", "D ifferent", "ĠE LE", "ĠGovern ments", "ĠA head", "ãħĭ ãħĭ", "word press", "L IB", "ĠIn clude", "ĠDor othy", "0 45", "ĠColomb ian", "Ġle ased", "88 4", "Ġde grading", "ĠDa isy", "i ations", "Ġbapt ized", "Ġsurn ame", "co x", "Ġblink ed", "ãĥ ¢", "Ġpoll en", "Ġder mat", "Ġre gex", "ĠNich olson", "ĠE ater", "ç ľ", "rad or", "Ġnarrow er", "Ġhur ricanes", "Ġhalluc inations", "r idden", "ISS ION", "ĠFire fly", "Ġattain ment", "Ġnom inate", "Ġav ocado", "ĠM eredith", "Ġt s", "Ġreve rence", "Ġe uph", "Ġcr ates", "ĠT EXT", "Ġ4 43", "Ġ3 19", "J SON", "iqu ette", "Ġshort stop", "ic key", "Ġpro pelled", "Ġap i", "ĠTh ieves", "77 9", "Ġovers aw", "Ġcol i", "ĠNic ola", "Ġover cl", "ik awa", "ĠC yr", "Ġ38 4", "78 9", "ĠAll ows", "10 27", "Det roit", "TR Y", "set up", "ĠSocial ism", "Sov iet", "s usp", "ĠAP R", "ĠShut down", "Ġal uminium", "zb ek", "ĠL over", "GGGG GGGG", "Ġdemocr acies", "Ġ19 08", "ĠMer rill", "ĠFranco is", "gd ala", "Ġtraff ickers", "ĠT il", "ĠGo at", "Ġsp ed", "ĠRes erv", "Ġpro d", "55 2", "Ġc ac", "ĠUn iv", "ĠSch we", "Ġsw irling", "ĠWild erness", "ĠEgg s", "Ġsadd ened", "Ġarch aic", "H yd", "Ġexcess ively", "B RE", "Ġaer ospace", "ĠVo ices", "Cra ig", "Ġign ited", "In itially", "ĠMc A", "Ġhand set", "Ġreform ing", "Ġfrust rations", "ĠDead pool", "ĠBel ichick", "ract or", "ĠRagnar ok", "ĠD rupal", "ĠApp roximately", "19 20", "ĠHub ble", "arm or", "ĠSar as", "ĠJon as", "Ġnostalg ic", "Ġfeas ibility", "Sah aran", "Ġorb iting", "Ġ9 70", "R u", "Ġsh in", "ĠInvestig ators", "Ġinconsist encies", "ĠP AN", "B G", "Ġgraz ing", "Ġdetect ors", "ĠStart up", "ĠFun ny", "ĠNa omi", "Consider ing", "Ġh og", "ut f", "ce mic", "Ġfort ified", "ĠFun ctions", "Ġcod ec", "nut rition", "H at", "\" !", "micro soft", "55 8", "ĠTh in", "ĠA CE", "Al ias", "ĠO PS", "p apers", "P K", "ãĢ İ", "Ġimpro bable", "N orthern", "equ al", "Ġlook out", "Ġty res", "ĠMod ified", "ĠK op", "Abs olutely", "Ġbuild up", "sil ver", "Ġaud i", "Ġgro tesque", "ĠSab er", "ĠPres byter", "ON Y", "Ġglac iers", "ĠSho als", "ĠK ass", "ĠH RC", "ĠNic ol", "ĠL unch", "ĠF oss", "âĸ Ĵ", "AD RA", "ĠOne Plus", "o ing", "ground s", "Ġincident al", "Ġdatas ets", "68 9", "ĠClarks on", "Ġassemb ling", "ĠCorrect ions", "Ġdrink ers", "Ġqual ifiers", "Ġle ash", "Ġunf ounded", "ĠH undred", "Ġkick off", "T i", "Ġrecon cil", "ĠGr ants", "ĠCompl iance", "ĠDexter ity", "Ġ19 06", "w arn", "D allas", "Max imum", "n ard", "av ia", "be aut", "ens itivity", "tr ace", "Ġpione ers", "ĠF ract", "ãĢ ı", "Ġpre cept", "Ġgloss y", "ĠI EEE", "Ac ross", "Ġ6 80", "S leep", "che on", "Ġsatir ical", "ĠMin otaur", "ĠCla ude", "Ġr é", "ape go", "Ġcar rot", "ĠSem in", "ino a", "Ġz o", "Ind ependent", "Ġdiagn oses", "ĠC ue", "M AR", "Ġrend ition", "ĠK ik", "Ġpath ology", "Ġselect s", "Link edIn", "Ġass ay", "ĠD res", "Ġtext ual", "post ed", "IT AL", "ĠM aul", "N eal", "Ġinter connected", "Ġerr atic", "ĠVir us", "Ġ5 30", "Ġenvironmental ists", "ĠP helps", "Ġeng agements", "ĠIN ST", "Ġeconom ical", "nox ious", "Ġg earing", "izz y", "Ġfavor ably", "ĠMcG ill", "T erm", "Ġh anged", "Ġball park", "ĠRe yes", "Ġbe ware", "ĠP sal", "ĠMass acre", "q i", "Ġin accessible", "acly sm", "Ġfr ay", "ill ac", "Ġbitter ly", "ĠCert ification", "Mich igan", "Ġir respective", "al ore", "Em pty", "Ġendorse ments", "Ġund et", "f g", "equ ipped", "Ġmerc iless", "ĠC ust", "Ġimm ature", "Ġvou cher", "ĠBlack well", "Ñ ı", "h awk", "dis ciplinary", "ile e", "ĠMak oto", "ĠD ude", "ãĥĩ ãĤ£", "Y ears", "Ġin ver", "Ġsh aman", "ĠY ong", "ip el", "ell en", "ĠCath y", "br ids", "Ġs arc", "65 1", "N ear", "Ġground work", "Ġam az", "Ġ4 15", "ĠHunting ton", "hew s", "ĠB ung", "Ġarbit rarily", "ĠW it", "ĠAl berto", "Ġdis qualified", "best os", "46 1", "Ġp c", "Ġ28 4", "ro bat", "Rob in", "Ġh ugs", "ĠTrans ition", "ĠOcc asionally", "Ġ3 26", "ĠWh ilst", "ĠLe y", "Ġspaces hip", "cs v", "Ġun successfully", "ĠA u", "le ck", "ĠWing ed", "ĠGrizz lies", ". �", "Ġne arer", "ĠSorce ress", "ĠInd igo", "El se", "8 40", "let es", "Co ach", "Ġup bringing", "ĠK es", "Ġseparat ist", "Ġrac ists", "Ġch ained", "Ġabst inence", "lear ning", "Ġrein stated", "Ġsymm etry", "Ġremind ers", "ĠChe vy", "Ġm ont", "Ġexempl ary", "ĠT OR", "Z X", "Ġqual itative", "ĠSt amp", "ĠSav annah", "ĠRoss i", "Ġp aed", "Ġdispens aries", "ĠWall s", "ĠCh ronic", "Ġcompliment ary", "ĠBeir ut", "Ġ+ ---", "igs list", "Ġcrypt ographic", "mas ters", "ĠCap itals", "Ġmax imal", "Ġent ropy", "Point s", "Ġcombat ants", "l ip", "ĠGl ob", "ĠB MC", "ph ase", "th ank", "HT TP", "Ġcomm uter", "Ġ\\( \\", ".. /", "ĠReg ener", "ĠDO I", "ĠActiv ision", "Ġsl it", "os al", "RE M", "Ġch ants", "Y u", "Ke ys", "Bre xit", "ĠFor ced", "Ari zona", "Ġsquad ron", "IS O", "ĠMal one", "Ġ3 38", "Ġcontrast ing", "Ġt idal", "Ġlib el", "Ġimpl anted", "Ġupro ar", "ĠC ater", "Ġpropos itions", "M anchester", "ĠEuro s", "it amin", "G il", "ĠEl ven", "ĠSe ek", "ĠB ai", "Ġredevelop ment", "ĠTown s", "ĠL ub", "! \",", "al on", "K rist", "Ġmeas urable", "Ġimagin able", "Ġapost les", "Y N", "7 60", "Ġster oid", "Ġspecific ity", "ĠL ocated", "ĠBeck er", "ĠE du", "ĠDiet ary", "uts ch", "ĠMar ilyn", "Ġbl ister", "ĠM EP", "ĠK oz", "ĠC MS", "y ahoo", "ĠCar ney", "Ġbo asting", "ĠC aleb", "By te", "read s", "ad en", "Pro blem", "ĠWood ward", "S we", "S up", "ĠK GB", "Set up", "Ġtac it", "Ġret ribution", "Ġd ues", "ĠM ü", ". ?", "ä¸ Ń", "p ots", "Ġcame o", "ĠP AL", "educ ation", "A my", "like ly", "g ling", "Ġconstitution ally", "ĠHam m", "ĠSpe ak", "Ġwid gets", "br ate", "Ġcra ppy", "ĠI ter", "Ġanticip ating", "ĠB out", "P ixel", "ĠY ep", "ĠLaur ie", "Ġh ut", "Ġbullet in", "ĠSal vation", "Ġch ats", "ear able", "Honest ly", "AL TH", "onse qu", "c ult", "isco very", "ovy ch", "Ġse lves", "ĠSat oshi", "S ounds", "Ġconver gence", "ĠRosen berg", "19 74", "Ġnas al", "Ġfull est", "Ġfer ocious", "x us", "ist e", "AM S", "Ġlobb ied", "Ġso othing", "ĠGun n", "t oday", "0 24", "Ġinspir ational", "ĠN BN", "p b", "g ewater", "or ah", "all owed", "ĠCol iseum", "Ġspecial izing", "Ġinsane ly", "ĠT ape", "del ay", "Ġt arn", "ĠP ound", "Ġmel anch", "Ġdeploy ments", "il and", "Ġless en", "Ġfur ry", "ĠUE FA", "Ġblood shed", "ĠMe ier", "ither ing", "Ġhe irs", "ĠJ aw", "ax ter", "ĠPublic ations", "Ġal ters", "int ention", "ĠWinc hester", "d etermination", "ĠLif etime", "th in", "Mon ster", "7 80", "Ġapprox imation", "Ġsuper markets", "ĠSecond s", "or os", "h uge", "Ġb ribe", "ĠLIM ITED", "un ed", "Ġmis interpret", "ĠIn jury", "Ġ3 67", "Ġthreshold s", "ĠCarn ival", "Ġgastro intestinal", "Ġguid eline", "Ġde ceived", "f eatures", "Ġpurported ly", "ĠRon nie", "ĠNew t", "Ġsp acious", "as us", "Ġsuperhero es", "ĠCyn thia", "le gged", "k amp", "ch io", "Ġth umbnail", "ĠShir ley", "ill ation", "Ġshe ds", "ĠZ y", "E PA", "Ġdam s", "Ġy awn", "n ah", "ĠPe ggy", "ĠE rie", "ĠJu ventus", "ĠF ountain", "r x", "don ald", "al bum", "ĠComp rehensive", "Ġc aching", "ĠU z", "ulner ability", "ĠPrinc iple", "ĠJ ian", "ing ers", "cast s", "ĠOs iris", "ch art", "t ile", "ĠTiff any", "ĠPatt on", "ĠWh ip", "Ġovers ized", "J e", "ĠCind erella", "ĠB orders", "ĠDa esh", "M ah", "Ġdog ma", "Ġcommun ists", "v u", "Coun cil", "Ġfresh water", "Ġw ounding", "Ġdeb acle", "Ġyoung ster", "Ġthread ed", "ĠB ots", "ĠSav ings", "ãģ Ĥ", "ol ing", "oh o", "Ġillum ination", "M RI", "Ġlo osen", "tr ump", "ag ency", "ur ion", "Ġmoment arily", "ĠCh un", "ĠBud apest", "ĠAl ley", "D isk", "Ġaston ished", "ĠCon quer", "ĠAccount ing", "h aving", "ĠWe in", "ĠAl right", "Ġrev olver", "Ġdel usion", "Ġrelic s", "Ġad herent", "qu ant", "Ġhand made", "or io", "Ġcomb ating", "c oded", "Ġquad ru", "re th", "N ik", "ĠTrib al", "ĠMyster ious", "Ġin hal", "ĠWin ning", "ĠClass ification", "ch anged", "Ġun ab", "Ġsc orn", "icip ated", "w l", "ond uctor", "Ġrein forcing", "ĠChild hood", "an ova", "Ġadventure r", "Ġdoctor al", "ĠStrateg ies", "Ġengulf ed", "ĠEnc ounter", "Ġl ashes", "Crit ical", "ric ular", "ĠU TF", "oci ation", "check ing", "ĠConsult ing", "Run time", "per iod", "ĠAs gard", "Ġdist illed", "ĠPas adena", "ĠD ying", "ĠCOUN TY", "Ġgran ite", "Ġsm ack", "Ġparach ute", "ĠS UR", "Virgin ia", "ĠF urious", "78 7", "ĠO kin", "Ġcam el", "ĠM bps", "19 72", "ĠCh ao", "ĠC yan", "j oice", "ef er", "ĠW rap", "ĠDeb ate", "S eg", "Ġfore arm", "ĠIgn ore", "Ġtim estamp", "Ġprob ing", "ĠNo on", "ĠGra il", "f en", "Ġdorm ant", "ĠFirst ly", "ĠE ighth", "ĠH UN", "ĠDes ire", "or as", "Girl s", "ĠDes mond", "z ar", "am ines", "O AD", "exec ute", "Ġbo obs", "ĠAT L", "_ (", "Chel sea", "Ġmasturb ation", "ĠCo C", "Ġdestroy er", "ĠCh omsky", "Ġsc atter", "ĠAss ets", "79 6", "ĠC argo", "Ġrecept ive", "ĠSc ope", "Ġmarket ers", "Ġlaun chers", "Ġax le", "ĠSE A", "se q", "ĠM off", "f inding", "ĠGib bs", "Georg ia", "extreme ly", "N J", "Ġlab orers", "st als", "Ġmed iation", "ĠH edge", "at own", "Ġi od", "des pite", "v ill", "J ane", "ex istence", "Ġcoinc ided", "ĠUt ilities", "ĠChe ap", "Ġlog istical", "Ġcul mination", "ĠNic otine", "p ak", "F older", "Ġrod ents", "st uff", "Ġlaw fully", "Ġreper to", "io ch", "j j", "Dial ogue", "HH HH", "lic tion", "Look s", "Ġ29 7", "Ġtur rets", "ĠAb andon", "Ġinc ess", "ĠTraff ord", "Ġcur led", "Ġprefer ring", "Ġprivat ization", "Ġir resist", "ĠP anda", "ĠSh ake", "ĠMc Gr", "ãĥ Ħ", "und ers", "Ġdiscrim inated", "Ġbart ender", "I LE", "Atl antic", "Ġprop ensity", "ĠW iz", "ĠG im", "con ference", "Ġrein forces", "G h", "w agon", "Ġe erie", "F al", "Ġhug ged", "rac ist", "R IC", "F u", "Ġf iller", "ĠSt ub", "Ġeng raved", "ĠWrest le", "Ġimagin ative", "ĠPe er", "ĠFact ors", "an us", "ĠDrac ula", "mon itor", "Ġrou ters", "ib ia", "ĠBoo lean", "end ale", "ĠSl aughter", "ĠSh ack", "R FC", "ĠSpiel berg", "S ax", "ĠPH OTO", "ĠCl over", "ĠR ae", "Dep ending", "ĠMem or", "ar am", "Ġpier ced", "Ġcur tains", "v ale", "ĠInqu isition", "ĠP oke", "Ġforecast ing", "Ġcompl ains", "S ense", "ĠHer mes", "isc overed", "Ġb ible", "ĠMor ph", "Ġg erm", "78 5", "D ON", "Ġcon gen", "Ġcr ane", "ĠD PR", "Ġrespect fully", "R oom", "ĠN aw", "ĠDal ai", "re ason", "ĠAng us", "Educ ation", "ĠTitan ic", "Ë ľ", "Ġo val", "un ited", "Ġthird s", "Ġmoist ur", "ĠC PC", "M iami", "Ġtent acles", "ĠPol aris", "ex c", "ex clusive", "ĠPra irie", "Ġcol ossal", "ĠBl end", "sur prisingly", "ÃŃ s", "Ġindo ctr", "Ġbas al", "ĠMP EG", "und o", "Spl it", "Develop ment", "Ġlan tern", "19 71", "Ġprov ocation", "Ġang uish", "ĠB ind", "ĠLe ia", "duc ers", "ipp y", "conserv ancy", "Ġinitial ize", "ĠTw ice", "ĠSu k", "Ġpred ic", "Ġdi ploma", "Ġsoc iop", "Ing redients", "Ġhamm ered", "ĠIr ma", "Q aida", "Ġglim ps", "ĠB ian", "Ġst acking", "Ġf end", "gov track", "Ġun n", "dem ocratic", "ig ree", "Ġ5 80", "Ġ29 4", "Ġstraw berry", "ID ER", "Ġcher ished", "ĠH ots", "Ġinfer red", "Ġ8 08", "ĠS ocrates", "O regon", "ĠR oses", "ĠFO IA", "Ġins ensitive", "Ġ40 8", "Recomm end", "ĠSh ine", "Ġpain staking", "UG E", "ĠHell er", "ĠEnter prises", "I OR", "ad j", "N RS", "L G", "Ġalien ated", "Ġacknowled gement", "ĠA UD", "ĠRen eg", "Ġvou chers", "Ġ9 60", "Ġm oot", "ĠDim ensions", "Ġc abbage", "B right", "g at", "ĠK lu", "Ġlat ent", "Ġz e", "ĠM eng", "Ġdis perse", "Ġpand emonium", "H Q", "Ġvirt uous", "ĠLoc ations", "ee per", "prov ided", "Ġse ams", "ĠW T", "iz o", "PR OV", "Ġtit anium", "Ġrecol lection", "Ġcr an", "Ġ7 80", "ĠN F", "49 1", "64 2", "p acking", "59 8", "text ure", "Sp ider", "fre edom", "cipl ed", "ĠTAM ADRA", "âĻ ¦", "aut hent", "ĠW ANT", "r ified", "Ġr ites", "Ġuter us", "k iss", "Ġâī ¤", "Ġsk illet", "Ġdis enfranch", "ĠGa al", "Comp an", "Ġage ing", "gu ide", "B alt", "Ġiter ator", "Ġdiscretion ary", "t ips", "Ġprim ates", "ĠTechn ique", "ĠPay ments", "az el", "ĠR OCK", "stant ial", "0 60", "Ġd mg", "ĠJack ets", "ĠPlay off", "Ġnurs ery", "ĠSy mb", "art on", "Ġannex ation", "Color ado", "Ġco ils", "ĠSh oes", "âĦ¢ :", "ĠRo z", "COM PLE", "ĠEve rest", "ĠTri umph", "J oy", "G rid", "à ¼", "process or", "ĠPros per", "ĠSever us", "ĠSelect ed", "r g", "ĠTay yip", "St ra", "Ġski ing", "Ġ? )", "Ġpe g", "Tes la", "Ġtime frame", "Ġmaster mind", "ĠN B", "scient ific", "ĠSh it", "gener ic", "IN TER", "N UM", "Ġst roll", "ĠEn ix", "ĠM MR", "ĠE MS", "m ovie", "Ĥ ª", "Ġminim izing", "idd ling", "Ġilleg itimate", "Ġprot otyp", "Ġpremature ly", "Ġmanual s", "obb ies", "ĠCass idy", "D EC", "des ktop", "Ġaer os", "Ġscreen ings", "Ġdeb ilitating", "ĠGr ind", "nature conservancy", "Ġf ades", "ter mination", "assets adobe", "F actor", "Ġdefinitive ly", "P oké", "ap ult", "ĠLaf ayette", "C orn", "ĠCor al", "Ġstagn ant", "T ue", "Ġdissatisf action", "G ender", "Ġkid neys", "ĠG ow", "ĠDef eat", "ĠAsh ton", "Ġcart els", "Ġfore closure", "ĠExpl ore", "stre ngth", "ot in", "Ġveterin arian", "Ġf umble", "Ġpar ap", "ĠSt rait", "r ils", "Ġpr ick", "ĠBerm uda", "ĠAm munition", "skin ned", "Ġab ound", "ĠB raz", "Ġshar per", "ĠAsc ension", "Ġ9 78", "Ġpreview s", "Ġcommun ion", "ĠX Y", "Ġph ony", "Ġnewcom er", "Ġ3 32", ".\" ,\"", "Ġredist ribution", "Prot ect", "ĠSo f", "K al", "Ġlip stick", "w orst", "Ġtang led", "Ġretrospect ive", "int eger", "Ġvolunte ering", "Ġ19 07", "Ġ --------------------", "ic hen", "Ġunve iling", "Ġsen seless", "Ġfisher ies", "\\ -", "Ġh inges", "Ġcalcul us", "My th", "Ġund efeated", "Ġoptim izations", "Ġdep ress", "Ġbill board", "ĠY ad", "ĠPy ramid", "Is n", "I de", "Ġleg ion", "ĠK ramer", "ent anyl", "Ġpenet rating", "ĠHaw th", "ĠPR ODUCT", "ĠGer ard", "ĠP act", "ĠIn cluding", "ĠEl ias", "ĠEl aine", "vis ual", "Ġhum ming", "Ġcond esc", "ĠF asc", "ä¸ Ĭ", "Ġe galitarian", "Ġdev s", "ĠD ahl", "O ps", "D H", "ĠB ounce", "id ated", "ald o", "Ġrepublic an", "Ġh amb", "ĠS ett", "ograph ies", "CH APTER", "Ġtrans sexual", "Ġsky rocket", "ans wer", "Ġmark up", "Ø ª", "Ġhero ine", "Comp are", "ĠT av", "Be ast", "Ġsuccess ors", "Ġna ïve", "ĠBuck ley", "st ress", "me at", "Ġdownload able", "Ġindex ed", "Ġsc aff", "ĠL ump", "ĠHom o", "Stud io", "In sp", "Ġr acked", "far ious", "ĠPet ty", "Ex ternal", "Ġ19 09", "W ars", "com mit", "put ers", "Ġun ob", "ĠEr r", "ĠE G", "ĠAl am", "ĠSiber ia", "ĠAtmosp heric", "IS TER", "ĠSatan ic", "trans lation", "ĠL oud", "tra umatic", "l ique", "Ġreson ate", "ĠWel ch", "Ġspark ing", "ĠT OM", "t one", "Ġout l", "Ġhandc uffed", "ĠSer ie", "8 01", "Ġland marks", "ĠRee ves", "Ġsoft ened", "Ġdazz ling", "ĠW anted", "month s", "Mag ikarp", "Ġunt reated", "ĠBed ford", "M i", "ĠDynam o", "O re", "79 5", "Ġwrong ful", "Ġl ured", "Ġcort isol", "Ġve x", "d rawn", "ile t", "Download ha", "ĠF action", "Ġlab yrinth", "Ġhij acked", "w aters", "er ick", "Ġsuper iors", "ĠRow ling", "ĠGu inness", "Ġt d", "99 2", "Ġune arthed", "Ġcentr if", "Ġsham eless", "P od", "ĠF ib", "Ġ icing", "Ġpredict or", "Ġ29 2", "fore station", "con struct", "C and", "@ #", "Ġag itated", "Ġre pr", "OV A", "Ġkn itting", "ĠLim a", "Ġf odder", "68 4", "ĠPerson a", "k l", "7 01", "Ġbreak up", "á ¸", "Ġapp alled", "Ġantidepress ants", "ĠSus sex", "Har ris", "ĠTher mal", "ee ee", "U pload", "Ġg ulf", "Ġdoor step", "ĠSh ank", "L U", "ĠM EN", "ĠP ond", "s orry", "Ġmis fortune", "n ance", "Ġb ona", "M ut", "Ġde graded", "ĠL OG", "ĠN ess", "an imal", "Ġa version", "und own", "Ġsupplement ed", "ĠC ups", "Ġ50 4", "Ġdep rive", "ĠSpark le", "Å Ĥ", "ĠMed itation", "auth ors", "ĠSab an", "ĠN aked", "air d", "ĠMand arin", "ĠScript ures", "ĠPerson nel", "ĠMahar ashtra", "Ġ19 03", "ĠP ai", "ĠMir age", "omb at", "Access ory", "Ġfrag mented", "T ogether", "Ġbelie vable", "ĠGl adiator", "al igned", "ĠSl ug", "M AT", "Ġconvert ible", "ĠBour bon", "amer on", "ĠRe hab", "nt ax", "Ġpowd ered", "pill ar", "Ġsm oker", "ĠMans on", "ĠB F", "5 11", "ĠGood ell", "ĠD AR", "m ud", "g art", "Ġob edient", "ĠTrans mission", "ĠDon ation", "8 80", "Ġbother ing", "Material s", "ãĤ ±", "dest roy", "Ġfore going", "Ġanarch ism", "ĠK ry", "ice ps", "Ġl ittered", "ĠSch iff", "Ġanecd otal", "un its", "Ġf ian", "ĠSt im", "ĠS OME", "ĠInv aders", "Ġbehaviour al", "ĠVent ures", "Ġsub lime", "Ġfru ition", "ĠPen alty", "Ġcorros ion", "¶ ħ", "Ġlik ened", "Ġbesie ged", "ween ey", "ĠCre ep", "Ġlinem en", "mult i", "ic ably", "ud der", "Ġvital ity", "Ġshort fall", "ĠP ants", "ap ist", "H idden", "ĠDro ps", "med ical", "Ġpron unciation", "ĠN RL", "Ġinsight ful", "J V", "ĠBe ard", "ĠCh ou", "Ġchar ms", "Ġb ins", "Ġamb assadors", "ĠS aturdays", "Ġinhib itor", "ĠFr anch", "6 01", "', '", "ĠCon or", "art ney", "ĠX peria", "g rave", "be es", "ĠProtest ants", "Ġso aking", "ĠM andal", "Ġph ased", "Ġ6 60", "Ġsc ams", "Ġbuzz ing", "ĠItal ians", "ĠLoren zo", "ĠJ A", "Ġhes itated", "Ġcl iffs", "ĠG OT", "ingu ishable", "Ġk o", "Ġinter ruption", "Z ip", "Lear ning", "Ġundersc ores", "ĠBl ink", "K u", "57 9", "ĠAut ob", "I RE", "Ġwater ing", "Ġpast ry", "8 20", "Ġvision ary", "ĠTempl ar", "awa ited", "Ġpist on", "Ġant id", "current ly", "Ġp ard", "Ġw aging", "Ġnob ility", "ĠY us", "Ġinject ing", "f aith", "ĠP ASS", "å º", "Ġret ake", "ĠPR OC", "Ġcat hedral", "b ash", "Ġwrest lers", "Ġpartner ing", "Ġn oses", "Ġ3 58", "Trans form", "am en", "Ġb outs", "ĠId eal", "ĠConstant in", "Ġse p", "ĠMon arch", "att en", "ĠPe oples", "mod ified", "Ġmor atorium", "Ġpen chant", "Ġoffensive ly", "Ġprox ies", "ok ane", "ĠTaiwan ese", "ĠP oo", "ĠH OME", "us ional", "Ġver bs", "ĠO man", "vis ory", "Ġpersu asion", "Ġmult it", "Ġsc issors", "G ay", "ow ay", "oph ysical", "l us", "gn u", "Ġap ocalyptic", "Ġabsurd ity", "Ġplay book", "Ġautobi ography", "I UM", "Ġsne aking", "ĠSim ulation", "pp s", "ell ery", "Plan et", "Ġright fully", "Ġn iece", "ĠN EC", "ĠIP O", "ĠDis closure", "lean or", "ous y", "ST ER", "Ġ28 2", "Cru z", "Ch all", "64 3", "ĠSurv ive", "ĠF atal", "ĠAm id", "ap o", "We apons", "D EN", "7 70", "ĠGreen wald", "Ġlin en", "al os", "Ġpollut ants", "ĠPCI e", "k at", "Ġp aw", "ĠK raft", "C hem", "ĠTermin ator", "Ġre incarn", "Ġ] [", "ĠSe eds", "Ġsilhou ette", "ĠSt ores", "Ġgro oming", "ĠD irection", "ĠIs abel", "ĠBr idges", "ðŁ ij", "E ED", "ĠM orsi", "Ġval ves", "ĠRank ed", "ĠPh arma", "ĠOrgan izations", "Ġpenet rated", "ĠRod ham", "ĠProt oss", "Ġove rest", "Ġex asper", "ĠT J", "Ġ 000000", "Ġtrick le", "Ġbour bon", "WH O", "Ġw retched", "Ġmicrosc opic", "Ġcheck list", "Ġad orned", "R oyal", "Ad minist", "ĠRet irement", "ĠHig hest", "We ather", "ile ge", "Ġincre ments", "ĠC osponsors", "Ġmas se", "ĠS inn", "r f", "Ġh ordes", "as sembly", "75 4", "ĠNat asha", "ĠTY PE", "ĠGEN ERAL", "Ġarr anging", "Ġ40 7", "l ator", "Ġg lean", "Ġdisc redited", "Ġclin icians", "UN E", "Ġachie ves", "ĠEm erson", "com plex", "= [", "Ġprincip ally", "Ġfra il", "p icked", "Ġthan king", "Ġre cl", "ĠL AST", "Ġsupp ressing", "il ic", "Ġantidepress ant", "ĠLis bon", "Ġth or", "Ġsp a", "Ġking doms", "ĠPear ce", "em o", "Ġpl ung", "Ġdiv est", "Ġ ********************************", "b is", "osp els", "ad r", "Sp irit", "hall a", "P ink", "end ez", "Ġresurrect ed", "esc ape", "ĠRosen stein", "Ġge ological", "Ġnecess ities", "Ġcarn iv", "ĠE lys", "ĠBar ney", "Ġ29 6", "dig y", "ST ON", "D OWN", "Ġmil estones", "Ġk er", "Ġdismant ling", "Ġre prim", "Ġcross ings", "19 45", "Ġpatri archy", "Ġblasp hemy", "Ġ3 59", "met ry", "ĠOb esity", "ĠDiff erences", "bl ocking", "ãĥķ ãĤ¡", "ich ita", "ĠSab ha", "ph alt", "ĠCol o", "ual a", "effic ients", "ĠMed ina", "con sole", "55 7", "ĠHann ibal", "ĠHab it", "ĠF ever", "Ġthen ce", "Ġsyn agogue", "Ġessential s", "Ġw ink", "ĠTr ader", "ID A", "ĠSp oiler", "ĠIceland ic", "ĠHay ward", "Ġpe ac", "Ġmal ice", "Ġflash back", "Ġth w", "Ġlay offs", "L iquid", "Ġtro oper", "Ġh inge", "ĠRead ers", "Ph ill", "ĠB auer", "Cre ated", "Ġaud its", "ac compan", "Ġunsus pecting", "ier a", "6666 6666", "Ġbro ch", "Ġapprehend ed", "ĠM alk", "cer ning", "ĠCod ex", "O VER", "M arsh", "ĠD eng", "ĠExp ression", "Ġdisrespect ful", "Ġasc ending", "t ests", "ĠPlaint iff", "ster y", "ĠAl ibaba", "din and", "ĠDem psey", "Applic ations", "mor al", "Ġthrough put", "Ġquar rel", "Ġm ills", "Ġhe mor", "ĠC ASE", "terror ist", "st im", "ifest yle", "ro zen", "CE PT", "Ar k", "u ci", "lect ic", "Ġirrit ating", "she ets", "A y", "Ġrede emed", "Ġhorn y", "ĠTe ach", "ĠS ear", "dem ocracy", "4 65", "ĠRest ore", "Ġstand by", "ĠP is", "iff in", "Ġsleep y", "Ġextr ater", "Ġcompl iments", "Fram eworks", "Ġinstall s", "Ġb anging", "sur face", "found land", "Ġmetaph ysical", "Ġ28 3", "oul s", "dev ices", "Ar gs", "ĠSac rifice", "ĠMcC orm", "es on", "Cons ervative", "ĠM ikhail", "see ing", "is ively", "ĠRo oms", "ĠGener ic", "Ġenthusi astically", "Ġgri pped", "Ġcomed ic", "ĠElectric ity", "Ġgu errilla", "Ġdec oration", "ĠPerspect ive", "Ġconsult ations", "Ġun amb", "Ġplag iar", "Ġmagic ian", "Ġe rection", "ĠTour ism", "or ied", "ro xy", "11 00", "T am", "Ī è", "Î ³", "× ª", "ĠPred ators", "Nit rome", "Ġtelesc opes", "project s", "Ġun protected", "Ġst ocked", "ĠEnt reprene", "nex pected", "Ġwast ewater", "V ill", "Ġint imately", "Ġi Cloud", "ĠConst able", "Ġspo of", "Ġne farious", "Ġfin s", "Ġcens or", "ĠMod es", "ĠEs per", "ar bon", "Ġinter sections", "Ġlaud ed", "Ġphys i", "Ġgener ously", "ĠThe Nitrome", "ĠTheNitrome Fan", "Ġar isen", "ĠÙ Ī", "Ġg lands", "ĠPav ilion", "ĠGu pta", "Ġuniform ly", "Ġr amps", "ri et", "ĠWH EN", "ĠVan essa", "Ġrout ed", "Ġlim p", "ĠC PI", "p ter", "int uitive", "Ġv aping", "Ġexperiment ed", "ĠOlymp us", "ĠAm on", "Ġsight ing", "Ġinfiltr ate", "ĠGentle man", "Ġsign ings", "ĠMe ow", "ĠNav igation", "che cks", "4 33", "Ġel apsed", "ĠBulg arian", "esp ie", "ĠS OM", "d uring", "Ġsp ills", "anc a", "ĠPly mouth", "M AL", "Ġdomest ically", "ĠWater gate", "ĠF AM", "k illed", "ed ited", "ĠYour self", "Ġsynchron ization", "ĠPract ices", "ST EP", "Ġgen omes", "ĠQ R", "not ice", "Ġloc ating", "z in", "Ġ3 29", "al cohol", "Ġk itten", "V o", "Ġr inse", "Ġgrapp le", "ĠSc rew", "ĠD ul", "A IR", "Ġle asing", "ĠCaf é", "Ġro ses", "ĠRes pect", "Ġmis lead", "Ġperfect ed", "Ġnud ity", "Ġnon partisan", "ĠCons umption", "Report ing", "Ġnu ances", "Ġdeduct ible", "ĠSh ots", "Ġ3 77", "Ġæ ľ", "ano oga", "Ben ef", "ĠB am", "ĠS amp", "if ix", "Ġgal van", "ĠMed als", "rad ius", "Ġno bles", "Ġe aves", "igr ate", "K T", "ĠHar bour", "u ers", "Ġrisk ed", "re q", "Ġneuro t", "get table", "ain a", "Rom ney", "Ġunder pin", "Ġlo ft", "ĠSub committee", "ĠMong ol", "b iz", "Ġmanif ests", "ass isted", "ĠG aga", "Ġsy nergy", "Ġreligious ly", "ĠPre f", "ĠG erry", "T AG", "ĠCho i", "4 66", "beh ind", "ĠO u", "Gold Magikarp", "Ġhemor rh", "R iver", "Ġtend on", "Ġinj ure", "ĠF iona", "Ġp ag", "Ġag itation", "|| ||", "ur an", "ĠE SA", "Ġest eem", "Ġdod ging", "Ġ4 12", "r ss", "Ġce ases", "ex cluding", "Ġint akes", "Ġinsert s", "Ġemb old", "ĠO ral", "up uncture", "4 11", "ĠUn ified", "ĠDe le", "Ġfurn ace", "ĠCoy otes", "ĠBr ach", "L abor", "Ġhand shake", "Ġbru ises", "Gr ade", "éĹ ĺ", "ĠGram my", "ile en", "St ates", "ĠScandinav ian", "ĠKard ash", "8 66", "Ġeffort lessly", "ĠDI RECT", "ĠTH EN", "ĠMe i", "ert ation", "19 68", "Ġgro in", "w itch", "Requ irements", "98 5", "Ġroof s", "Ġest ates", "ĠH F", "Ġha ha", "Ġdense ly", "ĠO CT", "Ġpl astics", "Ġincident ally", "ĠTr acks", "ĠTax es", "Ġch anted", "Ġforce ful", "ĠBie ber", "ĠK ahn", "K ent", "ĠC ot", "lic ts", "F ed", "Ġhide ous", "ĠVer d", "ĠSynd icate", "ĠIl legal", "J et", "ĠD AV", "re asonable", "c rew", "Ġfundamental ist", "Ġtruth ful", "ĠJ ing", "Ġl il", "Ġdown ed", "Ġen chanted", "ĠPolic ies", "ĠMcM aster", "ĠH are", "ides how", "Ġpar ams", "en cers", "gorith m", "Ġallow ances", "Ġturb ulent", "Ġcomplex ities", "ĠK T", "Ġ3 37", "ĠGen etic", "F UN", "D oug", "t ick", "Ġg igs", "ument hal", "Ġpatriarch al", "Ġcal c", ", ...", "Ġc out", "ĠGu an", "Ġpath ological", "ĠR ivals", "Ġunder rated", "Ġflu orescent", "ĠJ iu", "arna ev", "ĠQu an", "Ġ4 29", "Ġ à¨", "M ario", "Con struct", "ĠC itation", "ĠR acial", "ĠR SA", "ĠF idel", "Ġ3 95", "Person ally", "C ause", "à »", "rad ical", "in en", "Ġvehement ly", "ĠPap a", "Ġintern ship", "Ġfl akes", "ĠRe ck", "Luck ily", "B ra", "20 20", "rav ings", "R N", "W onder", "Ser iously", "Ġre usable", "Ġpoll uted", "ĠP eng", "le igh", "ind le", "Ġcircuit ry", "ĠMad onna", "ĠB ART", "Res idents", "att ribute", "Phil adelphia", "Cl ub", "Ġplan ner", "Ġfr antically", "Ġfaith fully", "ĠTerrit ories", "ĠL AT", "ĠAnders en", "an u", "ĠP ARK", "ĠS ora", "i age", "ĠPlay offs", "ĠG CC", "4 27", "Ġab norm", "ĠL ever", "Ġdisob edience", "As ync", "ĠShe a", "V ert", "Ġsk irts", "ĠSaw yer", "x p", "Ġwors ening", "Ġsc apego", "ĠAng le", "oth al", "Ġtro ve", "ĠSt y", "ĠN guyen", "mar ine", "ide on", "Dep ths", "Bl og", "ĠIll uminati", "Ġtract s", "Ġorgan ise", "Ġo str", "F s", "Ġlever aging", "ĠD aredevil", "as ar", "Ġl ang", "Ġex termin", "urs ions", "ĠRom o", "ãĤ¤ ãĥĪ", "Ġcont ended", "Ġencounter ing", "ĠTable t", "ĠAltern ate", "sk ill", "Ġswe ets", "Ġco hesive", "cap acity", "Ġrep ud", "Ġl izard", "ro o", "Ġpilgr ims", "ĠR uff", "ĠInstr ument", "ĠLog o", "uit ous", "E H", "Ġsales man", "Ġank les", "L ed", "ĠPat ty", "ud os", "Own er", "Ġdiscrep ancies", "k j", "M U", "Ġuncond itional", "Dragon Magazine", "i ard", "O ak", "ĠConvers ation", "be er", "ĠOs aka", "D elta", "us ky", "Ġsecret ion", "Ġpl aza", "Ġm ing", "Ġde pletion", "ĠM ous", "ĠI TS", "ĠH imal", "ĠFle ming", "Ġcyt ok", "ĠH ick", "Ġbat ters", "ĠInt ellectual", "6 75", "é r", "IS ION", "ĠQu entin", "ĠCh apters", "ih adi", "Ġco aster", "WAY S", "ĠL izard", "ĠY or", "and ering", "S kin", "ha ust", "ab by", "Ġportray ing", "Ġwield ed", "d ash", "Ġprop onent", "Ġr ipple", "Ġgrap hene", "Ġfly er", "Ġrec urrent", "Ġdev ils", "Ġwater fall", "æĺ ¯", "go o", "Text Color", "Ġtam pering", "IV ES", "TR UMP", "ĠAb el", "ĠS AL", "ĠHend ricks", "ĠLu cius", "b ots", "Ġ40 96", "IST ORY", "Gu est", "ĠN X", "in ant", "Ben z", "ĠLoad ed", "ĠCle ver", "t reatment", "Ġta vern", "Ġ3 39", "ĠT NT", "ific antly", "Tem perature", "F el", "Ġunder world", "ĠJud ges", "Ġ< +", "Ġst ump", "Ġoccup ancy", "Ġab er", "ĠF inder", ") \",", "ĠN unes", "res et", "in et", "ect omy", "Ġwell ness", "ĠP eb", "quart ered", "and an", "Ġneg atives", "ĠTh iel", "ĠCl ip", "ĠL TD", "Ġbl ight", "Ġreperto ire", "K yle", "Ġqu er", "ĠC es", "Ġha pl", "98 9", "ĠTh ames", "isc opal", "Des k", "ivari ate", "ĠEx cellence", "found ation", "Ġâ ĩ", "X i", "Ġmyster iously", "esty les", "Ġper ish", "ĠEng els", "ĠDE AD", "09 0", "}} }", "ĠUn real", "Ġrest less", "ID ES", "orth odox", "ĠInter mediate", "Ġdin ners", "ĠTr out", "ĠSe ym", "ĠHall s", "og ged", "Ġtraged ies", "Ġdid nt", "67 6", "Ġail ments", "Ġobserv able", "ĠV ide", "ad apt", "ĠD usk", "Ġprofessional ism", "ĠPres cott", "ĠInd ies", "p ox", "ĠMe hran", "W ide", "Ġend emic", "ĠPar an", "B ird", "Ġped als", "ĠI U", "ĠAdam ant", "ĠH urt", "Ġcorrel ates", "urd en", "Ġspons oring", "cl imate", "ĠUnivers ities", "ĠK not", "enn es", "ĠDam ian", "ĠAx el", "S port", "Ġbar b", "ĠS no", "sh own", "ste en", "ud ence", "Ġnon violent", "Ġhom ophobia", "Ġbiom ass", "ĠDet ail", "Ġsrf N", "ĠT une", "accompan ied", "I ENCE", "Al bert", "ĠMong o", "z x", "ĠCer berus", "or bit", "c ens", "Ġsl ay", "SH ARE", "H Y", "Ġb rawl", "ĠPro be", "Ġnonex istent", "ĠClare nce", "ĠBlack burn", "Ġport als", "ĠR ita", "ĠRem ain", "ĠLe vant", "Ġtrick ed", "ĠF erry", "aver ing", "ĠStraw berry", "ĠAn swers", "Ġhorrend ous", "ĠA man", "Supp lement", "ĠT oad", "Ġpe eled", "Ġman oeuv", "ĠU zbek", "mond s", "ĠH ector", "Ġ40 2", "pe es", "fix es", "Ġd j", "Ġres umes", "Ġaccount ant", "Ġadvers ity", "Ġham pered", "ĠL arson", "Ġd oping", "part s", "H ur", "Ġbe arded", "Ġy r", "ĠPlug in", "å¥ ³", "Ġ/ **", "rol ley", "Ġwaters hed", "ĠSub mission", "if lower", "AS C", "Ġcho ir", "Ġsculpt ures", "m A", "incre asing", "ai i", "Ġsne akers", "Ġconfront s", "ĠEle phant", "ĠEl ixir", "Ġrec al", "ĠT TL", "w idget", "ĠW ax", "ĠGr ayson", "Ġha irst", "Ġhumili ated", "ĠWAR N", "app iness", "ĠT TC", "F uel", "Ġpol io", "Ġcomplex es", "Ġbab e", "ĠX IV", "P F", "). [", "P arts", "Ġ4 35", "M eg", "ĠY ards", "ĠAL P", "Ġy ells", "Ġprin ces", "Ġbull ies", "ĠCapital ism", "ex empt", "FA Q", "ĠSp onge", "ĠAl a", "Ġpleas antly", "Ġbu f", "Ġden ote", "Ġunp ublished", "Ġkne eling", "asc a", "Ġl apse", "al ien", "99 4", "Ġrefere es", "ĠLaw yers", "S anta", "Ġpuzz ling", "ĠProm etheus", "ĠPh araoh", "ĠDel ay", "Ġfacilit ates", "ĠC ES", "Ġjew els", "Ġbook let", "ond ing", "Ġpolar ization", "ĠMor an", "ĠSal ad", "ĠS OS", "ĠAdv ice", "PH OTOS", "IC AN", "iat ures", "ex press", "ĠWonder land", "ĠC ODE", "ĠCL ASS", "9 75", "Ġg rep", "ĠD iesel", "ĠGl ac", "! ?\"", "Ġr m", "o ine", "disc rimination", "ĠN urse", "m allow", "Ġv ortex", "ĠCons ortium", "Ġlarge Download", "stra ight", "augh lin", "G rad", "Ġpublic ized", "ĠW aves", "ĠRed d", "Ġfest ivities", "ĠM ane", "ar ov", "Ġfleet ing", "ĠDr unk", "ug en", "C ele", "Ġchromos omes", "ĠD OT", "-+-+ -+-+", "Ġbus iest", "ĠBe aver", "Sy rian", "ĠK yr", "k as", "ĠCross Ref", "19 50", "76 01", "Ġrepe aling", "ĠWin ners", "ĠMac ro", "ĠD OD", "bl ance", "S ort", "64 1", "Ġmet re", "ĠD irk", "Ġgo ggles", "Ġdraw backs", "Ġcomplain ant", "Ġauthor izing", "Ġantit rust", "oper ated", "Ġm ah", "Ġexagger ation", "Am azing", "ĠSer aph", "Ġha ze", "w ow", "Ġextingu ished", "Ġcan yon", "ĠB osh", "Ġv ents", "Ġsc rape", "Cor rect", "4 26", "Ġav g", "Dem and", "ĠâĪ ¼", "Ġmicrobi ota", "\"} ],\"", "ĠSt ev", "B io", "ĠPlan es", "Ġsuggest ive", "Ġdec ipher", "ĠRefuge e", "ĠKe jriwal", "ĠGreen peace", "Ġdecl ass", "ĠSound ers", "Ġth o", "Ġdec rypt", "Ġbr ushing", "ĠJane iro", "ip op", "S i", "8 77", "ĠGeoff rey", "Ġc pu", "ĠHaz el", "Ġview points", "Ġcris py", "ĠNot ification", "Ġsold er", "ĠMod est", "ĠHem isphere", "Ġcass ette", "in cludes", "Ġident ifiers", "ĠC ALL", "in cent", "T odd", "ĠSwe ep", "Ġ3 34", "b oss", "Ġsm ir", "gin x", "Ġtown ship", "Ġg rieving", "ĠMos que", "Net flix", "AS ED", "ĠMillenn ials", "oc om", "19 67", "Ġbold ly", "s leep", "Ġes che", "arij uana", "Ġsw irl", "ĠPen al", "Ġneglig ent", "ĠStephen son", "K ER", "ĠZ oro", "ris is", "Ġlocal ization", "ĠSeym our", "ĠAng lic", "red itation", "prot ection", "ĠPa ige", "Ġo mit", "ĠR ousse", "ĠT ub", "Ġinv itations", "t ty", "Ġm oss", "ph ysical", "C redits", "Ġan archy", "Ġchild care", "Ġl ull", "ĠM ek", "ĠL anguages", "lat est", "ĠSan ford", "Ġus ability", "Ġdiff use", "ĠD ATA", "Ġsp rites", "ĠVeget a", "ĠProm otion", "ãĥ¼ ãĤ¯", "rict ing", "z ee", "Tur kish", "ĠTD s", "pro ven", "57 1", "Ġsmug glers", "707 10", "Ġreform ed", "ĠLo is", "Ġun fl", "ĠWITH OUT", "ĠReturn ing", "ann ie", "ĠTom as", "Fr anc", "ĠProf it", "ĠSER V", "ĠR umble", "ik uman", "es an", "Ġt esters", "Ġgad get", "Ġbrace let", "ĠF SA", "comp onent", "Ġparamed ics", "Ġj an", "ĠRem em", "ĠSk inner", "Ġl ov", "ĠQu ake", "rom a", "Ġfl ask", "Pr inc", "Ġover power", "Ġlod ging", "ĠK KK", "ret te", "Ġabsor bs", "w rote", "Ġ ,\"", "K ings", "ĠH ail", "ĠFall ing", "xt ap", "ĠHel ena", "ire ns", "L arry", "Ġpamph let", "ĠC PR", "G ro", "ĠHirosh ima", "Ġhol istic", "\". [", "Ġdet achment", "Ġas pire", "Ġcompl icit", "ĠGreen wood", "Ġresp awn", "ĠSt upid", "ĠFin ished", "f al", "b ass", "Ġab hor", "Ġmock ery", "ĠFe ast", "VID EO", "Ġcon sec", "ĠHung ry", "P ull", "ĠH ust", "it ance", "? ãĢį", ") --", "ĠPar allel", "con v", "4 69", "ha ar", "w ant", "P aper", "m ins", "ĠTor o", "ĠTR UMP", "ĠR ai", "D W", "ĠW icked", "ĠL ep", "Ġfun ky", "Ġdetrim ent", "ios is", "ache v", "Ġde grade", "im ilation", "Ġret ard", "Ġfrag mentation", "Ġcow boy", "ĠY PG", "ĠH AL", "Parent s", "ĠS ieg", "ĠStra uss", "ĠRub ber", "× IJ", "Fr ag", "Ġp t", "Ġoption ally", "ĠZ IP", "ĠTrans cript", "ĠD well", "88 2", "M erc", "ĠM OT", "ãĥ¯ ãĥ³", "Ġhun ts", "Ġexec utes", "In cludes", "Ġacid ic", "ĠRespons ibility", "ĠD umb", "we i", "And erson", "ĠJas per", "ight on", "abs olutely", "Ad ult", "Ġpl under", "Mor ning", "ĠT ours", "ĠD ane", "Î º", "ĠT EST", "ĠG ina", "Ġcan ine", "aw an", "Ġsocial ists", "ĠS oda", "Ġimp etus", "ĠSupplement ary", "oli ath", "ĠKinn ikuman", "mitted ly", "second s", "Ġorganis ers", "Ġdocument aries", "Vari able", "GRE EN", "Ġres orts", "Ġbr agging", "Ġ3 68", "Art ist", "w k", "bl ers", "Un common", "ĠRet rieved", "Ġhect ares", "Ġtox in", "r ank", "Ġfaith s", "ĠG raphic", "Ġve c", "ĠL IA", "Af rican", "Ġard ent", "end iary", "L ake", "ĠD OS", "cient ious", "ĠOk awaru", "ĠAll y", "ĠTim eline", "D ash", "ĠI c", "contin ue", "Ġt idy", "Ġinstinct ively", "ĠP ossibly", "ĠOut door", "ĠWould n", "Ġl ich", "ĠBr ay", "ĠA X", "Ġà ī", "Ġ+ #", "\\ '", "Direct ory", "ab iding", "Ġf eral", "ic ative", "but t", "Ġper verse", "S alt", "Ġwar ped", "Ġnin eteen", "Ġcabin ets", "Ġsrf Attach", "ĠSl oan", "Ġpower ing", "reg ation", "F light", "se vere", "Ġst ren", "Ġc og", "ap ache", "Ġâ Ŀ", "Ġcaf eteria", "p aces", "ĠGrim oire", "uton ium", "Ġr aining", "Ġcir cling", "Ġlineback ers", "c redit", "Ġrep atri", "ĠCam den", "lic ense", "Ġly ric", "Ġdescript or", "Ġval leys", "Ġre q", "Ġback stage", "ĠPro hibition", "ĠK et", "Op ening", "S ym", "æĸ ¹", "Ġserv ings", "Ġoverse en", "Ġaster oids", "ĠMod s", "ĠSpr inger", "ĠCont ainer", "è »", "ĠM ens", "Ġmult im", "Ġfire fighter", "pe c", "Ġchlor ine", "Ð ¼", "end i", "Ġsp aring", "Ġpolyg amy", "ĠR N", "ĠP ell", "Ġt igers", "Ġflash y", "ĠMad ame", "S word", "Ġpref rontal", "Ġpre requisite", "uc a", "Ġw ifi", "Ġmiscon ception", "Ġharsh ly", "ĠStream ing", "ot om", "ĠGiul iani", "foot ed", "Ġtub ing", "ind ividual", "z ek", "n uclear", "m ol", "Ġright ful", "49 3", "Ġspecial ization", "Ġpassion ately", "ĠVel ocity", "ĠAv ailability", "T enn", "Ġl atch", "ĠSome body", "Ġhel ium", "cl aw", "Ġdi pping", "XX X", "Ġinter personal", "7 10", "Ġsub ter", "Ġbi ologists", "ĠLight ing", "Ġopt ic", "Ġden im", "end on", "ĠC orm", "Ġ3 41", "ĠC oup", "Ġfear less", "Ġal ot", "ĠCliff ord", "ĠRun time", "ĠProv ision", "up dated", "lene ck", "Ġneur on", "Ġgrad ing", "ĠC t", "sequ ence", "in ia", "con cept", "Ġro aring", "ri val", "ĠCaucas ian", "Ġmon og", "key es", "Ġappell ate", "Ġlia ison", "EStream Frame", "ĠPl um", "! .", "Ġsp herical", "Ġper ished", "Ġbl ot", "Ġben ches", "Ġ4 11", "Ġpione ered", "Ġhur led", "Jenn ifer", "ĠYose mite", "Ch air", "Ġreef s", "Ġelect or", "ĠAnt hem", "65 2", "Ġun install", "Ġimp ede", "Ġbl inking", "Ġgot o", "Dec re", "A ren", "Ġstabil ization", "ĠDis abled", "ĠYanuk ovych", "Ġoutlaw ed", "ĠVent ura", "ten ess", "Ġplant ation", "Ġy acht", "ĠHu awei", "Ġsol vent", "Ġgr acious", "Ġcur iously", "Ġcapac itor", "Ġc x", "ĠRef lex", "Ph ys", "ĠC f", "pt in", "cons ervative", "Ġinv ocation", "c our", "F N", "ĠNew ly", "H our", "As ian", "ĠLe ading", "ĠAer ospace", "An ne", "Ġpre natal", "Ġdeterior ating", "H CR", "ĠNorm andy", "ol ini", "ĠAm bro", "9 10", "Ġset backs", "ĠT RE", "Ġs ig", "ĠSc ourge", "59 7", "79 8", "Game play", "Ġm sec", "M X", "Ġprice y", "ĠL LP", "aker u", "Ġover arching", "ĠB ale", "Ġworld ly", "Cl ark", "Ġscen ic", "Ġdisl iked", "ĠCont rolled", "T ickets", "ĠE W", "ab ies", "ĠPl enty", "Non etheless", "Ġart isan", "Trans fer", "ĠF amous", "Ġinf ield", "ble y", "Ġunres olved", "ĠML A", "ãĤ Ĥ", "Cor rection", "Ġdemocr at", "ĠMore no", "ro cal", "il ings", "Ġsail or", "Ġr ife", "h ung", "Ġtrop es", "Ġsn atched", "ĠL IN", "ĠB ib", "ES A", "ĠPre v", "ĠCam el", "run time", "Ġob noxious", "4 37", "Ġsum mers", "Ġunexpl ained", "ĠWal ters", "cal iber", "Ġg ull", "ĠEnd urance", "ä½ ľ", "Ġ3 47", "Ir ish", "Ġaer obic", "Ġcr amped", "ĠHon olulu", "à ©", "us erc", "ec ast", "AC Y", "ĠQu ery", "ãĤ¹ ãĥĪ", "Bet a", "Ġsuscept ibility", "ĠSh iv", "ĠLim baugh", "Ġà ĸ", "ĠN XT", "ĠM uss", "ĠBrit ons", "ES CO", "EG IN", "Ġ% %", "Ġsec ession", "ĠPat ron", "ĠLu a", "n aires", "ĠJPM organ", "us b", "ocy te", "Ġcouncill ors", "ĠLi ang", "f arm", "Ġnerv ously", "Ġattract iveness", "ĠK ov", "j ump", "Pl ot", "Ġst ains", "ĠStat ue", "ĠApost les", "he ter", "ĠSUP PORT", "Ġoverwhel m", "Y ES", "Ġ29 1", "d ensity", "Ġtra pping", "M it", "Ġf ide", "ĠPam ela", "atl antic", "Dam n", "Ġp ts", "OP A", "Ġserv icing", "Ġoverfl owing", "ul o", "ĠE rit", "t icket", "light ing", "ĠH mm", "ãĥ¼ ãĥ«", "im oto", "Ġchuck le", "4 23", "ãģ ķ", "sh ape", "Ġque ues", "Ġanch ors", "ãĤ¼ ãĤ¦ãĤ¹", "F er", "Ġaw oke", "Ġ6 66", "h ands", "Ġdiver gence", "Ġ50 5", "T ips", "Ġdep ot", "Ġske w", "ĠDel iver", "op ot", "Ġdiv ul", "ĠE B", "uns igned", "ĠUn i", "X box", "Ġfor ks", "Ġ7 02", "å ¯", "Ġpromot ers", "ĠV apor", "Ġlev ied", "sl ot", "Ġpig ment", "Ġcyl inders", "C RE", "Ġsn atch", "Ġperpet ually", "Ġl icking", "ĠFe et", "ĠKra ken", "ĠHold en", "ĠCLS ID", "m r", "Ġproject or", "Ġden otes", "Ġchap el", "ĠTor rent", "b ler", "R oute", "ĠDef endant", "ĠPublisher s", "ĠM ales", "ĠInn ov", "ĠAg ility", "rit er", "ty mology", "st ores", "L ind", "Ġf olly", "ĠZur ich", "B le", "Ġnurt ure", "Ġcoast line", "uch in", "D omin", "Ġfri vol", "ĠCons olid", "res ults", "M J", "Ġphyl ogen", "Ġha uled", "ĠW iley", "ĠJess ie", "ĠPrep are", "ĠE ps", "Ġtreasure r", "I AS", "Ġcolon ists", "Ġin und", "ĠWW F", "ĠCon verted", "6 000", "out side", "ĠApp earance", "ĠRel ic", "ĠM ister", "s aw", "Ġresult ant", "Ġadject ive", "ĠLaure l", "ĠHind i", "b da", "Pe ace", "Ġreb irth", "Ġmembr anes", "Ġforward ing", "Ġcoll ided", "ĠCar olyn", "K ansas", "5 99", "ĠSolid GoldMagikarp", "Be ck", "Ġstress ing", "ĠGo o", "ĠCooper ative", "Ġf s", "ĠAr chie", "L iter", "ĠK lopp", "J erry", "Ġfoot wear", "War ren", "Ġsc ree", "h are", "Under standing", "P ed", "Ġanth ology", "ĠAnn ounce", "M ega", "Ġflu ent", "Ġbond age", "ĠDisc ount", "il ial", "C art", "ĠNight mares", "Sh am", "ĠB oll", "uss ie", "H ttp", "Atl anta", "Ġun recogn", "ĠB id", "Ġunder grad", "Ġforg iving", "ĠGl over", "AAAA AAAA", "4 45", "V G", "pa io", "kill ers", "Ġrespons ibly", "Ġmobil ize", "Ġeffect ed", "ĠL umin", "Ġk ale", "Ġinfring ing", "ann ounced", "Ġf itt", "b atch", "ĠT ackle", "ĠL ime", "ĠAP P", "uke mia", "Ġrub y", "Ġex oner", "ĠCas ual", "0 70", "Ġpel vic", "Ġautom ate", "ĠK ear", "ĠCoast al", "Ġcre ed", "Ġbored om", "ĠSt un", "ri ott", "Ĥ İ", "Ġregener ate", "Ġcomed ians", "ĠOP ER", "Sp ons", "id ium", "on is", "L ocated", "05 7", "Ġsusp ense", "ĠD ating", "C ass", "Ġneoc ons", "ĠShin zo", "Ġaw oken", "ch rist", "ĠMess ages", "att led", "ĠSpr ay", "ĠSp ice", "C W", "Ġshield ing", "ĠG aul", "Am id", "Ġparam ilitary", "Ġmult if", "ĠTan ner", "il k", "Ġgodd amn", "g ements", "Ġbe friend", "m obi", "Ġ3 88", "fold er", "acc a", "Ġins in", "g ap", "N ev", "fif th", "Ġpsychiat ry", "b anks", "TH IS", "Ġhar b", "ac qu", "Ġfac ade", "ĠPower Point", "80 3", "Ġbl uff", "Sh ares", "Ġfavor ing", "El izabeth", "Ãį Ãį", "Ġr anger", "77 2", "ĠAr che", "h ak", "ĠGen etics", "ĠF EMA", "Ġev olves", "Ġest e", "ĠP ets", "ĠM é", "ĠInterest ing", "ĠCanter bury", "ch apter", "ĠStar fleet", "Sp anish", "Ġdraw back", "ĠNor wich", "9 70", "n orth", "ag anda", "Ġtransform ative", "ram ids", "bi ology", "ad ay", "Ġpropag ation", "ĠGam ma", "ĠDen ise", "ĠCalcul ator", "ent imes", "ĠB ett", "Ġapp endix", "ĠHD D", "AK ING", "Ġst igmat", "Ġhol ster", "Ġord inarily", "Ch ance", "ĠCont rary", "Ġad hesive", "Ġgather s", "6 12", "re au", "ony ms", "ew ays", "Ġindu ces", "Ġinterchange able", "se m", "Wh it", "Ġtr ance", "Ġincorpor ation", "ĠExt ras", "Fin ancial", "Ġawkward ly", "ĠStur geon", "ĠH Y", "Norm ally", "ĠEnd ing", "ĠAss ist", "enc rypted", "Ġsub jug", "Ġn os", "Ġfan atic", "C ub", "C U", "?\" .", "Ġirre versible", "å Ĥ", "03 1", "ĠH AR", "sp read", "ul ia", "= $", "Sc ope", "L ots", "Ġlif estyles", "ol on", "Ġf eds", "Ġcongrat ulate", "web kit", "Ġindist inguishable", "ĠSw ing", "Ġcommand ments", "qu ila", "ab ella", "m ethyl", "ann abin", "Ġo vere", "Ġlob ster", "ĠQU EST", "ĠCONT IN", "bern atorial", ":::: ::::", "ĠTra ve", "ĠSam oa", "AN I", "75 2", "Ð ´", "userc ontent", "ĠMod erate", "y eah", "ĠK itt", "Ġwe e", "Ġstuff ing", "ĠInter vention", "ĠD ign", "Ġware houses", "ĠF iji", "Ġpel lets", "Ġtake away", "ĠT ABLE", "ĠClass ical", "col lection", "Ġland fall", "ĠMus cle", "Ġsett les", "ĠAD V", "Ġ3 44", "L aura", "Ġf ared", "ĠPart ial", "4 36", "oss ibility", "ĠD aly", "ĠT arant", "ĠFu ji", "am l", "c ence", "55 1", "ĠProced ures", "ĠO CD", "ĠU D", "t in", "Q UI", "ach o", "4 38", "Ġgl itches", "Ġenchant ment", "Ġcalcul ates", "IR O", "ĠH ua", "alys es", "ĠL ift", "um o", "Ġle apt", "Ġhypothes ized", "ĠGust av", "it ans", "VERS ION", "æ ł", "Rog er", "Ġr and", "ĠAd apter", "Ġ3 31", "ĠPet ition", "k ies", "M ars", "Ġunder cut", "ze es", "ĠLy ons", "ĠDH CP", "Miss ing", "Ġretire es", "Ġins idious", "el i", "> )", ". ãĢį", "Ġfinal ists", "ĠA ure", "Ġacc user", "Ġwas tes", "ĠY s", "ĠL ori", "Ġconstitu encies", "Ġsupp er", "Ġmay hem", "or ange", "Ġmis placed", "Ġmanager ial", "Ġex ce", "ĠCL I", "Ġprim al", "ĠL ent", "Cry stal", "h over", "ĠN TS", "end um", "Ġd w", "ĠAl c", "n ostic", "Ġpres erves", "ĠTs arnaev", "Ġtri pled", "rel ative", "Arc ade", "k illing", "ĠW EEK", "ĠH anna", "D ust", "Com pleted", "ģ «", "Ġappro ves", "ĠSur f", "ĠLuther an", "ven ants", "Ġrobber ies", "we ights", "soft ware", "at ana", "ug al", "Ġgrav y", "ĠC ance", "OLOG Y", "ly ak", "Ton ight", "Ġunve il", "Ġ19 04", "ĠMin ion", "ent ious", "st ice", "pack ages", "ĠG EAR", "Ġg ol", "ĠHutch inson", "ĠProf ession", "ĠG UN", "ĠDiff erence", "ĠTsuk uyomi", "ĠLes bian", "6 70", "Ġfug itive", "ĠPlan etary", "-------------------------------- ------------------------", "Ġacc rued", "Ġch icks", "Ġsto pp", "Ġblock ers", "C od", "Ġcomment ers", "ĠSomew here", "ĠPhot ographer", "the me", "Ġmay oral", "w u", "Ġanten nas", "Ġrev amped", "ĠSubject s", "it é", "im ura", "Ġentr ances", "liter ally", "Ġten ets", "ĠO MG", "ĠMP H", "ĠDon key", "ĠOff ense", "Ġ\" +", "Sn ap", "ĠAF B", "Ġan imate", "ĠS od", "His panic", "Ġinconsist ency", "D b", "F Y", "Ex port", "Ġa pe", "Ġpear l", "ib el", "ĠPAC s", "Ġ{ \\", "Ġact u", "ĠHS BC", "camp us", "Ġpay off", "Ġde ities", "ĠN ato", "ou ple", "Ġcens ored", "ĠCl ojure", "Ġconf ounding", "en i", "Ġreck on", "op he", "Ġspot ting", "Ġsign ifies", "Ġprop el", "Ġfest ive", "S uggest", "Ġpled ging", "ĠB erman", "Ġrebell ious", "Ġovershadow ed", "Ġinfiltr ated", "j obs", "67 2", "Ġscal able", "Ġdomin ion", "ĠNew foundland", "ĠMead ow", "Ġpart itions", "AM I", "Ġsupplement ary", "str ument", "Ġhair y", "Ġperpet uate", "Ġnuts hell", "ĠPot ato", "ĠHob bit", "Ġcur ses", "Flo at", "Ġquiet er", "Ġfuel ing", "Ġcaps ules", "ĠL ust", "ĠH aunted", "Exec utive", "Ġchild birth", "G re", "Ġrad iant", "å İ", "Ġm alls", "Ġin ept", "ĠWarrant y", "Ġspect ator", "E h", "t hens", "Ġculmin ating", "æ ©", "ary a", "ãĤ ®", "ilit arian", "ĠOR IG", "ĠSp ending", "pt ives", "ĠS iren", "ĠRec ording", "ay ne", "Ġv im", "Ġspr ang", "T ang", "ĠM FT", "mor ning", "ĠWe ed", "m peg", "cess ion", "ĠCh ung", "7 30", "w arning", "56 2", "handed ly", "P oor", "P olitics", ": #", "Ġp ian", "Ġfec es", "ĠDocument ation", "Ġban ished", "Ġ3 99", "ĠAR C", "Ġhe inous", "J ake", "ĠAm ir", "way ne", "v re", "os henko", "Ġnotebook s", "Ġfound ational", "Ġmarvel ous", "ixt ape", "Ġwithdraw als", "Ġh orde", "ĠD habi", "is able", "ĠK D", "Ġcontag ious", "ĠD ip", "ĠAr rows", "Ġpronoun s", "Ġmorph ine", "ĠB US", "68 2", "Ġk osher", "fin ished", "ĠInstr uments", "Ġf used", "yd en", "ĠSal mon", "F ab", "aff ected", "K EN", "C ENT", "Dom ain", "Ġpoke mon", "ĠDr inking", "G rowing", "ĠInvestig ative", "ĠA ether", "em i", "Ġtabl oid", "Ġrep ro", "ĠNot withstanding", "ĠBers erker", "Ġdram as", "Ġclich é", "Ġb ung", "ĠU RI", "ĠD os", "0 44", "Ġpast ors", "Ġl s", "Ġac rylic", "aun ts", "Ed ward", "Ġmajor ities", "B ang", "Ġfield ing", "ĠRepl acement", "ĠAl chemy", "pp ard", "ĠRome o", "ĠSan ct", "ĠLav rov", "ib ble", "Inst ruct", "Ġimp ractical", "ĠPlay boy", "ce phal", "Ġsw aps", "Ġk an", "ĠThe o", "Ġillust rating", "Ġdismant led", "ĠTrans gender", "ĠG uth", "UG H", "Ġtriumph ant", "Ġencomp ass", "Ġbook mark", "udd in", "j er", "Ġpred icate", "ES H", "Ġwhen ce", "ĠAB E", "Ġnon profits", "Se qu", "Ġdi abetic", "Ġp end", "Ġheart felt", "sh i", "Ġinter acts", "ĠTele com", "Ġbombard ment", "dep ending", "ĠLow ry", "ĠAd mission", "ĠBl ooming", "ust ration", "ene gger", "B rew", "Ġmol ten", "ĠNer d", "P IN", "âĸ Ģ", "ave ment", "Ġtou red", "Ġco efficients", "ĠTray von", "ans son", "Ġsand y", "t old", "fl ows", "Ġpop ulous", "ĠT inder", "ĠBl iss", "R achel", "Min imum", "Ġcontest ant", "ĠRed uce", "ĠMor se", "ĠGrass ley", "ĠClick er", "Ġexp r", "Ġs incerity", "Ġmar qu", "Ġelic it", "ĠPro position", "ĠDemon ic", "Ġtac os", "G reek", "Ġpost war", "Ġin sofar", "ĠP ork", "Ġ35 2", "doctor al", "walk ing", "Ġmid term", "ĠSam my", "sight ed", "ĠTR ANS", "ic i", "AL D", "ĠUS L", "ĠF ISA", "ĠAm pl", "ĠAlex andra", "ine lli", "Tr ain", "Ġsign ify", "ĠVers us", "Ġob fusc", "Ġk h", "Ġagg ro", "ĠRen ault", "Ġ3 48", "5 18", "ox icity", "0 22", "ĠTw ist", "Ġgoof y", "D ynamic", "Ġbrief ings", "m ight", "8 99", "Ġderog atory", "T ro", "Ġfor ging", "ĠKor an", "ĠMar ried", "ĠBuc s", "Ġpal ate", "ĠCon version", "m able", "4 13", "Ġ( _", "Ġs iph", "ĠN EO", "col lege", "Ġmarg inally", "Ġfl irt", "ĠTra ps", "ĠP ace", "é »Ĵ", "Ġgoalt ender", "Ġforb ids", "Ġcler ks", "ĠT ant", "ĠRobb ins", "ĠPrint ing", "Ġpremie red", "Ġmagn ification", "ĠT G", "ĠR ouse", "ĠM ock", "odynam ics", "Ġpre clude", "ism o", "ĠPul itzer", "Ġaval anche", "ĠK odi", "rib une", "ĠL ena", "Elect ric", "Ġref inery", "Ġend owed", "Ġcounsel ors", "Ġd olphin", "ĠM ith", "Ġarm oured", "hib ited", "Beg in", "ĠP W", "O il", "ĠV or", "ĠShar if", "ĠFraz ier", "est ate", "Ġj ams", "Pro xy", "Ġband its", "ĠPresbyter ian", "ĠPrem iere", "t iny", "ĠCru el", "Test ing", "Ġhom er", "ĠV ERS", "ĠPro l", "ĠDep osit", "ĠCoff in", "Ġsemin ars", "Ġs ql", "ĠDef endants", "Altern atively", "ĠR ats", "ç «", "ethy st", "' >", "Ġiss uer", "58 9", "Ġch aired", "ĠAccess ories", "man ent", "Ġmar row", "ĠPrim ordial", "C N", "Ġlimit less", "ĠCarn age", "Ġund rafted", "q v", "IN ESS", "on ew", "Ġco hesion", "98 7", "Ġne cks", "Ġfootball er", "ĠG ER", "Ġdetect able", "ĠSupport ing", "ĠCS V", "oc ally", "k Hz", "Ġund e", "Ġsh one", "Ġbud ding", "tra k", "Stand ing", "ĠStar craft", "ĠKem p", "Ben ch", "Ġthw arted", "ĠGround s", "ath i", "L isa", "Dial og", "ĠS X", "V ision", "Ġingen ious", "Ù IJ", "Ġfost ering", "ĠZ a", "ĠIn gram", "Ġ\" @", "N aturally", "6 16", "0 35", "ĠF AC", "H mm", "55 4", "Ġacceler ator", "ĠV end", "Ġsun screen", "Ġtuber culosis", "rav iolet", "ĠFunction al", "ĠEr rors", "ed ar", "19 66", "ĠSpect re", "ĠRec ipes", "88 5", "ĠM ankind", "L iverpool", "Ġ| --", "Ġsubst itutes", "ĠX T", "w ired", "Ġinc o", "ĠAf gh", "E va", "ic c", "S ong", "K night", "Ġdilig ently", "ĠBroad cast", "A id", "Ġaf ar", "ĠH MS", "aton in", "ĠGr ateful", "Ġfire place", "ĠOm ni", "e uro", "ĠF RE", "ĠSh ib", "ĠDig est", "t oggle", "Ġheads ets", "Ġdiff usion", "ĠSqu irrel", "ĠF N", "Ġdark ened", "out her", "Ġsleep s", "ĠX er", "gun s", "Ġset ups", "Ġpars ed", "Ġmamm oth", "ĠCur ious", "g ob", "ĠFitz patrick", "ĠEm il", "im ov", "........ .....", "ĠB enny", "Second ly", "Ġheart y", "Ġcons on", "st ained", "Ġgal actic", "cl ave", "Ġplummet ed", "Ġp ests", "Ġsw at", "Ġrefer rals", "ĠLion el", "h oly", "Ġunder dog", "ĠSl ater", "ĠProv ide", "ĠAm ar", "ress or", "å Į", "ong a", "Ġtim id", "Ġp iety", "ĠD ek", "Ġsur ging", "az o", "Ġ6 10", "Ġdes ks", "ĠSp okane", "ĠAn field", "Ġwars hips", "ĠCob ra", "Ġar ming", "clus ively", "ĠBad ge", "ag ascar", "ĠPR ESS", "ĠMcK enzie", "ĠFer dinand", "burn ing", "Af ee", "Ġtyr ann", "ĠI w", "ĠBo one", "100 7", "ĠRe pt", "Ċ Âł", "Ġcar avan", "ĠD ill", "ĠBundes liga", "Ch uck", "Ġheal er", "ãĥ¼ãĥ Ĩ", "ĠH obby", "Ġneg ate", "Ġcrit iques", "section al", "mop olitan", "Ġd x", "Ġouts ourcing", "ĠC ipher", "t ap", "Sh arp", "Ġup beat", "Ġhang ar", "Ġcru ising", "ĠNi agara", "Ġ3 42", "ill us", "ĠS v", "Ġsubt itles", "Ġsqu ared", "Ġbook store", "Ġrevolution aries", "ĠCarl ton", "ab al", "Ut ah", "Ġdesp ise", "ĠU M", "cons ider", "aid o", "Ġc arts", "ĠT urtles", "Tr aining", "Ġhonor ary", " ¢", "Ġtri angles", "4 22", "Ġreprint ed", "Ġgrace ful", "ĠMong olia", "Ġdisrupt ions", "ĠB oh", "Ġ3 49", "Ġdr ains", "Ġcons ulate", "Ġb ends", "Ġm afia", "ur on", "ĠF ulton", "m isc", "Ġren al", "Ġin action", "ck ing", "Ġphot ons", "Ġbru ised", "ĠC odes", "og i", "Ġn ests", "ĠLove ly", "ĠLib re", "ĠD aryl", "Ġ# ##", "S ys", ". ,\"", "Ġfree zes", "est ablishment", "and owski", "Ġcum bers", "ĠSt arg", "ĠBom bs", "Ġleg ions", "Ġhand writing", "Ġgr un", "ĠC ah", "sequ ent", "Ġm oth", "ĠMS M", "Ins ert", "F if", "Ġmot el", "Ġdex ter", "ĠB ild", "hearted ly", "Ġpro pe", "ĠText ure", "ĠJ unction", "ynt hesis", "oc ard", "ĠVer a", "ĠBar th", "Ġμ g", "Ġl ashed", "Ġ35 1", "ĠZ amb", "ĠSt aples", "ĠCort ex", "ĠCork er", "Ġcontinu um", "ĠWR ITE", "unt a", "rid or", "Ġde ems", "0 33", "ĠG OLD", "p as", "Ġrep ressive", "ãĥĨ ãĤ£", "Ġbaff led", "Sc ar", "Ġc rave", "Ġ ______", "Ġentrepreneurs hip", "ĠDirector ate", "Ġ' [", "Ġv ines", "Ġasc ended", "ĠGR OUP", "ĠGood bye", "Ġdo gged", "ãĥ´ ãĤ¡", "Man ufact", "Ġunimagin able", "ri ots", "ier rez", "Ġrel ativity", "ĠCraft ing", "ra ught", "ud en", "c ookie", "Ġassass ins", "Ġdissatisf ied", "ac ci", "Ġcondu it", "Sp read", "ĠR ican", "n ice", "izz le", "Ġsc ares", "ĠWH Y", "ph ans", "5 35", "Ġprot racted", "ĠKrist en", "5 36", "ĠSc rib", "ĠNe h", "Ġtwent ies", "Ġpredic ament", "Ġhandc uffs", "Ġfruit ful", "ĠU L", "ĠLud wig", "Ġatt est", "ĠBre aker", "Ġbi ologically", "ĠDeal er", "Ġrenov ations", "f w", "ess en", "Al ice", "ĠHen ri", "Ġun ilaterally", "ĠS idd", "h ai", "ĠSt retch", "S ales", "Ġcumbers ome", "ĠJ avier", "Ġtrend y", "Ġrot ting", "ĠChall enges", "Ġscra ps", "Ġfac ets", "ĠVer onica", "ĠVer ge", "ĠS ana", "Al ien", "ĠR ih", "Ġrad ial", "ect ar", "Ġ6 30", "cl i", "Mar ie", "Ġwild fire", "ĠCat o", "h ander", "Ġwait ress", "Ġch ops", "ĠS ECTION", "Ġblunt ly", "ĠCat alog", "n ian", "stud y", "Ġpat rolling", "ĠT enth", "nex us", "ĠN ON", "op sy", "Ġsc athing", "s ie", "Ġdeterior ated", "V B", "Naz is", "Ġdep ictions", "Ġauthent icated", "ĠCon ce", "k rit", "Ġpromul g", "ĠL ONG", "U FC", "ĠVis itors", "ĠRec all", "Ġrehab ilit", "ĠSL I", "Ġglac ier", "ĠB ite", "Ġ50 3", "Ġvom it", "Ġfer mented", "ĠKh alid", "Ġgrad ed", "ĠMag icka", "ĠIch igo", "power ful", "ic ators", "75 3", "Ġsh rew", "Ġ35 6", "Ġlegal izing", "Ġall otted", "ĠArch demon", "ith ing", "igg urat", "V OL", "Le od", "Ġo ily", "Ġindu cing", "Ġamy gdala", "Ġadm ins", "ĠAcqu isition", "C AN", "Ġsche matic", "Ġmo an", "ĠCamer oon", "Ġt ink", "Ġmer ry", "Ġbutter flies", "ĠGo ff", "Ġworks pace", "ĠCor ona", "Ġj avascript", "ĠD olphin", "ĠCant or", "4 64", "to e", "AP S", "ĠAg ing", "Ġpadd ed", "ĠZ heng", "ĠHe ld", "Ġest ranged", "Ġ7 70", ". }", "ĠDun ham", "Ġsm okes", "Ġcap itals", "und ai", "Sh in", "ĠFound ing", "Ġent itle", "Ġcenter piece", "D iscover", "Ġthere to", "al ert", "ĠN ou", "ĠAnaly st", "l c", "F H", "FI ELD", "ĠP OV", "gr ay", "Ġar cs", "ĠH OT", "Ġr s", "Ġoblig atory", "ĠArchitect s", "ĠS ven", "ĠF EC", "0 200", "Christ mas", "ĠAlban ia", "rat om", "58 7", "Ġhard ships", "Ġaut os", "ĠCharg es", "Ġap es", "Ġ3 76", "wal let", "Ġintox ication", "Ġgobl in", "Ġ5 70", "++++++++ ++++++++", "ĠYel p", "ĠMag netic", "ĠBr iggs", "R ail", "Ġspawn s", "ĠW iggins", "Ġshowc ased", "Ġres orted", "ub en", "Ġwh ipping", "Ġim itate", "Ġdigest ion", "ĠUS PS", "ĠG est", "Ġye a", "ĠT ight", "ind al", "ic as", "` .", "C AST", "'' ;", "ĠF et", "opath ic", "In valid", "Ġregrett ed", "Ġbro ccoli", "ĠSc ores", "e ve", "Ġpost ings", "Ġaccum ulating", "Ġneed less", "elf th", "Ġmay ors", "Ġsc rib", "Ġanecd otes", "Ġbot ched", "ĠRib bon", "ĠConstant ine", "i uses", "ess es", "Ġdev ise", "Comp ared", "Ġp udding", "Ġg arg", "Ġev oke", "79 7", "Ġdet ox", "9 09", "ĠPie ces", "ĠMcC artney", "Ġmet ast", "ĠK rypt", "P OR", "Ġt ending", "ĠMerch ants", "Pro of", "ĠV arg", "ĠPort able", "ãĥ¼ãĥĨ ãĤ£", "B rain", "25 00", "Ġfol iage", "Ø ¹", "Ġment ors", "ĠA ires", "Ġminimal ist", "Ġing ested", "ĠTro jan", "ĠQ ian", "inv olved", "0 27", "Ġer oded", "RA FT", "Ġbl urry", "M ob", "Ġbuff et", "ĠFn atic", "ae a", "KN OWN", "ĠIn it", "s afety", "en um", "ACT ION", "ĠCrus her", "ĠD ates", "Ġ ................", "c alling", "ak ov", "Ġvent ured", "Ġ5 55", "au ga", "H art", "ĠA ero", "M AC", "Ġthin ly", "Ġar ra", "ST ATE", "ild e", "ĠJac qu", "ĠFem ales", "Ġthe orem", "Ġ3 46", "Ġsmart est", "ĠPU BLIC", "ĠK ron", "ĠB its", "ĠV essel", "ĠTele phone", "Ġdec ap", "Ġadj unct", "ĠS EN", "mer ga", "Ġred acted", "Ġpre historic", "Ġexplan atory", "ĠRun s", "ĠUtt ar", "ĠM anny", "ĠAUTH OR", "ĠUnle ashed", "ĠBow ling", "be ans", "79 3", "Ġunivers es", "Ġsens it", "ĠK ung", "re peat", "ctr l", "Ġp aced", "Ġfull er", "Cl ock", "Ġrec omb", "ĠF aul", "ĠB unker", "Ġpool ed", "Ġan a", "ĠM outh", "LL OW", "hum ane", "Ġbull do", "ĠMicha els", "f am", "Ġwreck ed", "Ġport rays", "ĠWh ale", "ĠH es", "Ġguess es", "ĠBrow se", "ĠL APD", "Ġconsequ ential", "ĠInn ocent", "ĠD RAG", "Ġtrans gress", "ĠO aks", "Ġtri via", "ĠRes on", "ĠA DS", "-- +", "ĠT oll", "Ġgrasp ing", "ĠTHE M", "ĠT ags", "ĠCon clusion", "Ġpract icable", "Ġho op", "Ġunintention ally", "Ġign ite", "ĠM ov", "ur ized", "le hem", "Ter min", "Ġcolour ful", "ĠLin ear", "ĠEll ie", "G y", "Ġman power", "Ġj s", "Ġem oji", "ĠSHAR ES", "_ .", "0000 7", "Ġsophistic ation", "Ġunders core", "Ġpract ise", "Ġbl ob", "op ens", "Uk raine", "Ke eping", "Y C", "J R", "ult imate", "Cl aim", "Ġautom obiles", "99 3", "ste el", "Ġpart ing", "ĠL ank", "... ?", "Ġ38 5", "Ġremem brance", "Ġe ased", "Ġcov ari", "ĠS ind", "Effect ive", "Ġdisse mination", "ĠMo ose", "ĠCl apper", "br ates", "App ly", "Ġinv is", "Ġwors ened", "âĢĶ -", "Ġlegisl ator", "ĠL ol", "ĠRow e", "Ġdealers hip", "um ar", "id ences", "Ġinvestig ates", "Ġc ascade", "Ġbid der", "ĠB EN", "Iron ically", "Ġpres iding", "Ġd ing", "Ġcontrad icted", "Ġshut s", "ĠF IX", "Ġ3 66", "Dist rict", "Ġsin ful", "ĠChar isma", "o ops", "Ġtot ality", "Ġrest itution", "ĠOpt imus", "ĠD ah", "Ġcl ueless", "urn ed", "Ġnut rit", "Ġland owners", "Ġfl ushed", "Ġbroad en", "m ie", "Ġprint ln", "Ġn ig", "ĠCorp us", "J en", "Ġprot o", "ĠWik imedia", "ĠPal o", "C OR", "Ġstory lines", "Ġevangel icals", "ĠDar rell", "Ġrot or", "ĠH W", "sk illed", "ery l", "Ġbe gg", "ĠBl umenthal", "Ġwe aving", "Ġdown wards", "ĠJack et", "ĠANG EL", "Te chnology", "Ġes oteric", "alde hyde", "Ġfur iously", "Ġforeign er", "We ak", "CH O", "ĠH ound", "Exper ience", "ĠPlay station", "ĠM IA", "ĠU ng", "cl oth", "ag all", "Ġcal ming", "iz ens", "St ruct", "ĠW itches", "ĠCeleb ration", "Ġ........ ......", "pt roller", "ĠTC U", "Ġb unny", "ãĥ į", "ut orial", "Ġup scale", "ĠSt a", "ĠCol ossus", "Ġchlor ide", "ĠZ ac", "ĠRe asons", "ĠBrook ings", "ĠWH ITE", "][ /", "ĠL ose", "9 05", "Ġunders ide", "ern els", "Ġv ape", "do zen", "upp et", "ĠST OP", "mat ical", "ĠStat ements", "hed dar", "P AC", "Custom er", "Ġmem os", "ĠP J", "end ars", "ĠLim its", "l augh", "Ġstabil ized", "ĠALE C", "Y A", "Up grade", "al am", "Ġtechn o", "Ġan ew", "fore seen", "Ġcolleg iate", "ĠPy ro", "ĠD ism", "Ġfront line", "Ġammon ia", "I U", "Qu ite", "John ny", "ass in", "G OP", "ĠSt yles", "ĠSovere ign", "acter ial", "5 49", "ĠR IP", "ĠL ists", "Ġ3 64", "ĠRece p", "s ocket", "ĠByr d", "ĠCand le", "An cient", "Ġappell ant", "en forcement", "ace a", "ans ki", "Ġold s", "88 6", "Ġsl urs", "Ġem pires", "Ġbuck le", "Ġalien ation", "ĠAber deen", "Ġunic orn", "Ġoverr iding", "ĠL X", "pp a", "Ġdesp ised", "ĠB ugs", "ĠB ST", "S outhern", "5 33", "Ġhall mark", "ĠPost er", "Ġstem med", "Ġprincip als", "ĠT ECH", "ĠSand wich", "It aly", "Ġche esy", "ĠSet TextColor", "ĠProt ective", "ĠC ohn", "J O", "apt op", "Re ason", "Lead er", "ĠUnder stand", "ĠFr idays", "ĠContin uous", "Ġcl ipping", "ĠR ye", "Ġber th", "tim er", "ann is", "re act", "Ġbuff alo", "ĠPar as", "Ġ6 55", "Ġpres ided", "ĠSun rise", "Ġve ts", "Ġcl oves", "ĠMcC ull", "Stre ngth", "G AN", "Ġill iter", "ĠPric ing", "l é", "Ġresist or", "Ġbr un", "ĠSuff olk", "Ñ ĭ", "ĠL iver", "Re leased", "Ġwhat s", "8 60", "ĠMe asures", "Ġden ouncing", "ĠRy zen", "Ġsou ven", "Ġcareg ivers", "ch ini", "ĠScar lett", "Ġt rough", "Cong ratulations", "Ġtax is", "ĠTrad ition", "j it", "Ġtable top", "Ġhither to", "Ġdis information", "off ensive", "h ra", "ĠDISTR ICT", "Ġcompl icate", "chen ko", "ĠRecon struction", "Ġpalp able", "Ġa usp", "Ġ4 28", "Ġshowc ases", "ĠPublic ation", "know ledge", "inn on", "4 19", "Ġretri eval", "and ers", "Ġref ute", "Ġinqu ired", "g ur", "Ġneg ativity", "Ġcons erve", "Ġafter life", "Ġpres upp", "ĠGill espie", "Ġm t", "ĠD N", "T ap", "Ġper pend", "ĠS my", "does n", "Ġsp illing", "Ġhyp ers", "K ate", "® ,", "ke pt", "ĠP owered", "Ġj a", "ĠK lux", "ard e", "ab an", "Ġ4 44", "Ġflatt ened", "ĠImprove ments", "urg a", "ĠK und", "Ġins cribed", "Ġfac ult", "Ġunpre pared", "ĠCons umers", "Ġsatisf ies", "Ġpul monary", "Ġinf iltration", "Ġex ternally", "Ġcongrat ulations", "ag han", "Ġair liner", "Ġfl ung", "Ġfly ers", "G D", "Ġsnipp ets", "Ġrec ursive", "Ġmaster ing", "L ex", "Ġovert ly", "v g", "Ġluck ily", "Ġenc ro", "ĠLanc et", "ĠAbyss al", "function al", "Ġs ow", "Ġsqu id", "Ġnar ration", "Ġn aughty", "ĠHon our", "ĠSpart ans", "Ġsh atter", "ĠTac oma", "ĠCal ories", "ĠR aces", "Sub mit", "Ġpurpose fully", "w av", "ĠY ok", "F est", "ĠG err", "Met ro", "Ġit iner", "f amous", "Ġ\" {", "in line", "was her", "Iss ue", "ĠCL IENT", "oz o", "Vers ions", "7 25", "ĠGl ock", "Ġshield ed", "ĠPC R", "ENC Y", "ĠWe ld", "ĠSim pl", "Ġredirect ed", "ĠK ham", "Ġ( >", "Ġlab ou", "Ġdi apers", "ss l", "Ġcell ar", "organ isms", "ore sc", "ĠBer ks", "did n", "Sh ipping", "C hest", "Ġund one", "Ġmillion aire", "Ġc ords", "ĠYoung er", "appropri ately", "Ġsequ els", "u ve", "ant icipated", "Ġle wd", "ĠSh irt", "ĠDmit ry", "V eter", "Ġsl aying", "ĠY ar", "Ġcompl ication", "I owa", "ĠEric a", "ĠBL M", "g irlfriend", "b odied", "6 26", "19 63", "Ġintermedi ary", "Ġcons olation", "M ask", "ĠSi em", "ow an", "Beg inning", "Ġfix me", "Ġculmin ated", "Ġcon duc", "ĠVolunte er", "Ġpos itional", "Ġgre ets", "ĠDefin itions", "Ġthink er", "Ġingen uity", "Ġfresh men", "ĠMom ents", "Ġ35 7", "ate urs", "ĠFed Ex", "s g", "69 4", "Ġdwind ling", "ĠBO X", "sel age", "Ġt mp", "Ġst en", "ĠS ut", "Ġneighbourhood s", "Ġclass mate", "f ledged", "Ġleft ists", "Ġclim ates", "ATH ER", "ĠScy the", "ul iffe", "Ġs ag", "Ġho pped", "ĠF t", "ĠE ck", "ĠC K", "ĠDo omsday", "k ids", "Ġgas ped", "Ġmon iker", "ĠL od", "ĠC FL", "t ions", "r ums", "fol ios", "Ġm d", "Ġunc anny", "Ġtrans ports", "ĠLab rador", "Ġrail ways", "Ġappl iance", "ĠCTR L", "æ Ģ", "Pop ulation", "ĠConfeder acy", "Ġunb earable", "Ġdors al", "ĠIn form", "op ted", "ĠK ILL", "Mar x", "Ġhypoc ritical", "q us", "ĠN umerous", "ĠGeorg ian", "ĠAmbro se", "ĠL och", "Ġgu bernatorial", "ĠX eon", "ĠSupp orts", "ens er", "ee ly", "ĠAven ger", "19 65", "Ar my", "Ġju xtap", "Ġcho pping", "ĠSpl ash", "ĠS ustainable", "ĠFin ch", "Ġ18 61", "ict ive", "at meal", "ĠG ohan", "Ġlights aber", "ĠG PA", "ug u", "ĠRE PL", "vari able", "Ġher pes", "Ġdesert s", "ac iously", "Ġsitu ational", "week ly", "ob l", "Ġtext ile", "ĠCorn wall", "Ġcontrace ptives", "ĠA ke", "] -", "ä¹ ĭ", ": ,", "ĠW em", "ĠB ihar", "Ġ' .", "Ġbe re", "Ġanal ogue", "ĠCook ies", "Ġtake off", "Whe el", "Ġmaj estic", "Ġcomm uting", "0 23", "ĠCor pse", "ass ment", "min i", "Ġgor illa", "ĠAl as", "ere e", "Ġacquaint ances", "ĠAd vantage", "Ġspirit ually", "Ġey ed", "pm wiki", "ĠE nder", "Ġtrans lucent", "Ġnight time", "ĠIM AGES", "5 45", "ĠK amp", "ĠFre ak", "Ġ ig", "Port land", "4 32", "ĠM ata", "Ġmar ines", "Ġh ors", "ater asu", "ĠAtt ribution", "Ġ-------- -", "Ġk ins", "ĠBEL OW", "++ +", "Ġre eling", "ol ed", "Ġcl utter", "ĠRel ative", "Ġ4 27", "B US", "Ġa vert", "ĠChe ong", "ĠA ble", "ĠPry or", "Develop er", "Ġen cyclopedia", "ĠUSA F", "ĠG arry", "Sp ain", "Bl ocks", "Ġexp osition", "ĠGamer Gate", "W OR", "Ġstockp ile", "Ġclot hed", "ĠT one", "ĠR ue", "t umblr", "Ġtreacher ous", "Ġf rying", "Ñ Į", "ĠS ph", "Ġrest raints", "Ġemb odies", "ĠG es", "S afety", "Ġnegoti ators", "min ing", "ĠAppalach ian", "L OS", "ĠJenn a", "Ġpass ers", "ç ĭ", "sn ap", "Ġshort en", "creat or", "Ġinn umerable", "uther land", "67 4", "ĠW OM", "ĠAs cend", "ĠArm ory", "ĠTrans action", "K ick", "Ġsuit case", "day Name", "Ġwaste ful", "mar riage", "ĠMcC abe", "ite ch", "ĠO ss", "Cl osure", "ĠTreasure r", "Ġindec ent", "ĠD ull", "Ġresid ences", "19 59", "ĠS ettlement", "Ham ilton", "Ġself ies", "ĠRank ing", "ĠBark ley", "ĠB ore", "ĠW CS", "ĠMar itime", "ĠH uh", "ĠForest ry", "Ġcultiv ating", "ĠBall ard", "Ġg arrison", "ĠSD L", "9 30", "Ġnas cent", "Ġirresist ible", "Ġaw fully", "\\/ \\/", "Ġequ ate", "Ġanthrop ology", "ĠSylv ia", "Ġintest ine", "Ġinnoc uous", "cess ive", "ag ra", "ĠMet roid", "G rant", "8 55", "ģ ĸ", "Ġ\" _", "ãĥĥ ãĥī", "Ġappra isal", "ĠFred dy", "04 6", "Ġ40 6", "Ġ18 30", "Ġd ocking", "St atic", "Ġp ont", "ĠVolt age", "ĠSt ead", "ĠMort gage", "ĠJon ah", "Y L", "CLASS IFIED", "Ġas bestos", "nik ov", "Ġcoll agen", "ĠOrb ital", "P ocket", "7 99", "Ġhy brids", "inc hes", "Ġinv oice", "und y", "Ġinequ alities", "T rend", "w ashed", "B ALL", "Ġluc id", "ĠComment ary", "Ġw itty", "Br andon", "Ġbru ising", "Ġ6 20", "es cent", "box ing", "P OL", "Ġ3 78", "R ect", "Ġlic ences", "ĠMcG ee", "p ressed", "D anny", "Ġj ammed", "ord inate", "Ġle th", "Ġdistingu ishes", "ĠYam aha", "IL S", "ĠH ume", "ĠC ategories", "Rober ts", "Ch art", "Ġbeet le", "ĠGra veyard", "Ġ($ )", "o ÄŁ", "Ġtw ilight", "are lla", "á ½", "Ġbooth s", "ĠH HS", "ĠFeld man", "Ġexcav ation", "Ġphilosoph ies", "at ography", "ĠGar age", "te chnology", "Ġunfor gettable", "Ġver ifying", "Ġsubord inates", "E ls", "Ġne b", "G aming", "EN A", "ĠAchieve ment", "it ters", "ĠG abe", "Ġd umps", "for cer", "Ġpo ignant", "ĠM BA", "ĠHe idi", "ime i", "Ġm ages", "Ġliber ate", "Ġcircum cised", "ĠMer maid", "ĠMat th", "t ogether", "ĠW ichita", "Ġstore front", "ĠAd in", "V II", "Four th", "Ġexplore rs", "W ER", "Not able", "Bro ok", "m ens", "F aith", "-------- -", "ĠJ ou", "¬ ¼", "Ġpine apple", "Ġam alg", "el n", "ark able", "ĠãĤµ ãĥ¼ãĥĨãĤ£", "ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³", "Ġov arian", "ĠE choes", "Ġhairc ut", "Ġp av", "Ġch illed", "anas ia", "Ġsty led", "Ġd ab", "ni per", "Ġminister ial", "ĠD UP", "T an", "Ġsul ph", "ĠD eter", "ĠBo hem", "od an", "Ġeduc ator", "â ĵĺ", "sp ir", "Ch icken", "ĠE leanor", "Ġqu i", "Ġheav iest", "Ġgrasp ed", "U RA", "Ġcro oked", "Jess ica", "pro blem", "Ġpred etermined", "Ġman iac", "Ġbreath s", "ĠLauder dale", "Ġh obbies", "y z", "Cr ime", "Ġcharism a", "d L", "Ġle aping", "Ġk ittens", "Ang elo", "ĠJ ACK", "ĠSu zanne", "Ġhal ting", "ENT ION", "Ġswall owing", "ĠEarthqu ake", "Ġeight eenth", "ĠN IC", "ĠIN F", "ĠCons cious", "Ġparticular s", "circ le", "7 40", "Ġbene volent", "Ġ7 47", "Ġ4 90", "Ġr undown", "ĠVal erie", "ĠB UR", "Ġcivil isation", "ĠS chn", "W B", "ot ide", "intern ational", "Ġj ohn", "Ġ19 02", "Ġpe anuts", "Ġflav ored", "k us", "Ġro ared", "Ġcut off", "é £", "Ġorn ament", "Ġarchitect ures", "Ġ3 69", "ol or", "ĠWild e", "ĠC RC", "ĠAdjust ed", "Ġprov oking", "land ish", "Ġrational ity", "Ġjust ifies", "Ġdisp el", "Ġa meric", "ĠPol es", "Ø ©", "Ġen vis", "ĠD oodle", "ä½ ¿", "igs aw", "auld ron", "Techn ical", "T een", "up hem", "ĠX iang", "Ġdetract ors", "ĠZ i", "ĠJournal ists", "Ġconduc ive", "ĠVolunte ers", "Ġs d", "Know ing", "Ġtrans missions", "ĠPL AN", "ĠL IB", "Ġall uded", "Ġob e", "Ġd ope", "ĠGold stein", "Ġwavelength s", "ĠDest ination", "nd a", "ug i", "Ġattent ive", "ĠLe an", "ral tar", "Ġman g", "mb uds", "ak ings", "b ender", "Ġacc ol", "Ġcraw led", "N OW", "Min nesota", "Ġflour ished", "ĠZ up", "ĠSuper visor", "ĠOliv ier", "Ex cellent", "Ġwid en", "D one", "Ġw ig", "Ġmiscon ceptions", "Cor p", "W an", "Ġvener able", "ĠNot ably", "ĠKling on", "an imate", "Bo ost", "ĠS AY", "miss ing", "ibli ography", "mel on", "Ġpay day", "Ø ³", "bo le", "Ġve iled", "ĠAl phabet", "It alian", "Ġever lasting", "ĠR IS", "ĠC ree", "rom pt", "Ġh ating", "Ġgrin ning", "Ġge ographically", "OS H", "Ġwe eping", "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", "Ġimpe cc", "Let ter", "Ġblo ated", "PL A", "ĠFe in", "Ġper sever", "Th under", "Ġa ur", "ĠR L", "Ġpit falls", "âĸ º", "Ġpredomin ant", "Ġ5 25", "7 18", "AP E", "7 14", "Ġfarm land", "ĠQ iao", "Ġv iolet", "ĠBah amas", "Ġinflic ting", "ĠE fficiency", "Ġhome brew", "Ġundert ook", "Ġcur ly", "ĠHard ing", "man ia", "59 6", "Ġtem pered", "Ġhar rowing", "ĠP ledge", "ĠFranken stein", "è ª", "M otion", "Ġpredict ably", "ĠExpl osion", "oc using", "er d", "col o", "FF ER", "Ġback field", "ĠV IDE", "ue bl", "N arr", "ĠArg ument", "Ġgen omic", "Ġbout ique", "Ġbatt ed", "ĠB inary", "Ġg amb", "ĠRh ythm", "67 3", "Ġa float", "ĠOlymp ia", "Y ING", "Ġend if", "is in", "Ġwin ters", "Ġsc attering", "I v", "D istance", "Ġtr u", "ĠCom fort", "Ġne xus", "Ġair flow", "ĠByz antine", "p ayers", "con i", "ĠB etsy", "D eal", "ĠN ug", "ĠContin ent", "red ibly", "Ġoptim izing", "al beit", "Ġec static", "ĠPro to", "ç ·", "iv ot", "âĸ Ħ", "em p", "rou nder", "Ġcl out", "ĠI ST", "66 3", "ĠDoll ars", "ĠD AC", "Ġsubsc ribed", "Ġrehears al", "Ġam ps", "ĠSh ang", "es m", "Ġspr inkle", "Ġassail ant", "ĠO o", "ĠCoin base", "T act", "Ġret ina", "Ġn uns", "R ON", "att o", "Ġj ug", "ĠSV G", "Ġb ikini", "ĠFI LE", "ĠFound ers", "ep ort", "ĠK P", "Ġrest ores", "ĠTh ick", "Ġash ore", "Ġappro vals", "R ender", "M AG", "G raham", "ĠCort ana", "ãĥ³ ãĤ¸", "ss h", "or ians", "ars ity", "ĠInsp ired", "u pper", "Ġsign alling", "Ġreb uke", "Ġfl ares", "Ġdownt ime", "Stud ies", "Ġstagn ation", "ĠSequ ence", "Ġgr unt", "Ġass ures", "ĠPL A", "59 2", "Ġintra ven", "d epend", "Sus an", "ĠManz iel", "Man ia", "Cont ract", "Ġsl ams", "Ġcult ured", "Ġcred itor", "L IST", "ĠH UM", "ĠChatt anooga", "serv ed", "Ġclo aked", "ĠF TP", "p owder", "ĠSt ella", "uct ive", "Ġcheap ly", "ĠMU CH", "ĠGalile o", "Ġsu ites", "spe ech", "Ġdeliber ations", "ĠCh ips", "« ĺ", "Bal ance", "ĠWyn ne", "ĠAk ron", "Ass et", "Ġhon oured", "Ġed ged", "Like wise", "anim ous", "ĠW age", "ĠEz ek", "ad vertisement", "ĠRT X", "ĠM AD", "Ġmigr ating", "ĠS QU", "Ġ4 75", "Ed ited", "Ġshorth and", "ĠBas ics", "Ġcro tch", "ĠEV EN", "Ġv m", "effic iency", "Ġcal ves", "ĠF rie", "ĠBrill iant", "Ġstri kers", "Ġrepent ance", "Ġarter ies", "r l", "B ed", "h ap", "Ġcrypt ography", "ĠSab res", "Ġ4 14", "vi ks", "ih ara", "aps es", "T alking", "Ġintertw ined", "Ġdoc ks", "Ġalle le", "ĠArt ifact", "ĠH IM", "t orn", "ç ķ", "Ġop acity", "ĠE ly", "os uke", "Ġn ipple", "Ġhand written", "ĠV K", "ĠChamber lain", "ĠLa os", "ig raph", "g row", "Ġtr illions", "Ġdescend ant", "ĠSail or", "as uring", "Ġce ilings", "ĠWare house", "f lying", "ĠGl ow", "Ġn ont", "Ġmiscar riage", "Ġrig s", "Ġmin istries", "Ġelabor ated", "Ġdel usional", "ĠHum ane", "Ġ3 79", "n ets", "Ġblack out", "add ers", "Ġn p", "ĠT ire", "ro sc", "Ġsub div", "Ġlink age", "Ġchron ological", "ĠHER O", "Ġres ettlement", "ĠVin yl", "Ġpast oral", "ĠMob il", "ĠBar bar", "Co oldown", "ĠF ritz", "c riminal", "re pe", "Ġbell ig", "ĠBre ed", "Ġ4 18", "Ġsem blance", "ij k", "Ġcur tail", "Ġclin ch", "cont ained", "ĠProm pt", "ast on", "Ġw i", "Ġpursu its", "5 15", "ĠGl oss", "Ġfl ips", "Ġcoup ons", "Ġcl oning", "ĠLike ly", "Rem oved", "ĠQu artz", "r ices", "ĠSpe ars", "Ġp ious", "Ġdep reciation", "ĠD are", "oun ces", "am az", "O nt", "Ġp innacle", "d ocker", "0 26", "ĠW yr", "ĠPro per", "Ë Ī", "n il", "By tes", "Ġseek er", "t rial", "Ġunf olds", "ĠMar se", "Ġextravag ant", "ĠSurviv ors", "RED ACTED", "ĠSpeed way", "ĠCra igslist", "sub mit", "ĠGener ations", "Ġup holding", "Ġblood stream", "ĠMiss ions", "ĠL awn", "Ġlim bo", "ene i", "H uh", "ĠWild cats", "pre p", "ĠMark us", "ĠFor bidden", "rit ic", "IN O", "Ġexhib iting", "requ ent", "ch uk", "Ġhabit ual", "ĠComp atibility", "Dr ag", "RIP T", "uj ah", "GR OUND", "Ġdelinqu ent", "Ġburn er", "Ġcontempor aries", "Ġgimm ick", "load s", "Ġno zzle", "p odcast", "ĠW ak", "ĠStat en", "ĠK uh", "ãģ ĵ", "inter rupted", "Ġinv incible", "ĠBurn ett", "cig arette", "ĠPeb ble", "ĠTem porary", "ĠMar ino", "58 2", "Ġwast eland", "ident ly", "T x", "Ġr ite", "ĠPan asonic", "ĠM iddles", "ĠHort on", "ae us", "Ġc uring", "Ġm ats", "Ġadj ourn", "Ġfears ome", "pe z", "bo ats", "Ġpro pell", "Ġconflic ted", "ĠAng er", "Ġinsurg ent", "K arl", "Ġco ales", "Ġsouth western", "Ġdis su", "ĠO vert", "******** ****", "Ġbox ed", "ĠBr une", "aa a", "Ġgard ening", "ĠEng el", "tr acks", "Ġpur ified", "Ġplace holder", "ĠL ikes", "Ġd an", "G ab", "Ġe ct", "ĠF aw", "ĠEl iot", "Ġ' ,", "otrop ic", "ĠRu in", "hed on", "Ġca ul", "Ġa ft", "ĠCad illac", "gh a", "ass ian", "ud eb", "ĠT ick", "Ġadjust s", "AR GET", "5 37", "isc he", "ant y", "ĠFried rich", "ĠBl izz", "ĠA OL", "Camp aign", "Ġmamm al", "ĠVe il", "ĠK ev", "ĠMaur it", "ĠDam ien", "N ation", "E astern", "Ġ{ :", "Ġ= ================================", "Ġstereotyp ical", "Ġatt ic", "ĠCy borg", "requ ire", "Ġaward ing", "ĠPap ua", "bt n", "b ent", "B oo", "Ġ( =", "ĠX ander", "ĠSomers et", "Ġcatch y", "Ġcert ify", "STR UCT", "Ġit al", "Ġt ides", "ĠBr ands", "G ray", "comp etitive", "Ġcur ator", "ĠD G", "omin ium", "ĠGM Os", "ci ating", "ĠCarm en", "ow ard", "Balt imore", "Ġr gb", "C u", "Ġwip es", "spe ll", "IT NESS", "Ġsummar izes", "ĠRe vis", "Ġwhistlebl owers", "ĠBre ach", "Ġcro chet", "k os", "ews ki", "Ġrep et", "Ġcrim son", "ĠKar achi", "read able", "dim ension", "ĠI gor", "ild ed", "ĠZ ed", "ĠKe ane", "ĠCos metic", "DE P", "Ġretreat ing", "ĠU A", "ens ical", "Ġd usk", "ĠDick ens", "Ġaren as", "ĠPass age", "level s", "Ġcur v", "P ope", "Ġch ores", "ĠEl ise", "ĠComp ass", "b ub", "Ġmamm alian", "ĠSans krit", "ĠAN C", "ĠCr ack", "Q ual", "L aun", "amp unk", "Ġlearn ers", "Ġglam orous", "Ġfur the", "erm ott", "c and", "Gener ic", "Ġnarr ated", "Ġdisorder ly", "ĠTrans actions", "ĠDet ention", "ĠR oku", "Ä į", "Ġunder statement", "ĠS aur", "ĠRodrig o", "ĠAS AP", "S in", "Ġre joice", "Method s", "Ġelectro de", "Ġworsh ipped", "Ġid i", "ĠPhys icians", "Ġpop up", "Ġde ft", "ĠRem oval", "ĠBu enos", "ver bs", "Ġfun k", "ush a", "rict ion", "ore a", "ĠBang alore", "ĠKen obi", "zz i", "Ġnorm ative", "Ġgobl ins", "Ġcaf es", "ĠUN CLASSIFIED", "ĠF ired", "S IGN", "Ġs clerosis", "ĠV oter", "ĠSon ny", "ĠExt end", "ĠEV s", "Ar senal", "Ġp si", "Ġwid est", "ĠT us", "Ġlo oms", "Ġjust ifying", "ĠGr anger", "è ¯", "Ref er", "58 3", "Ġflour ishing", "ab re", "Ġr ave", "ĠCont ra", "Ġ18 98", "Add s", "Ġf ul", "ĠCo oke", "some one", "= #", "67 1", "Ġy ak", "Ġar te", "ĠMis cellaneous", "ĠDet ection", "ĠCl ancy", "â ģ", "ass ies", "Ġval iant", "ĠFemin ist", "cor ruption", "V el", "P ear", "Ġsucc inct", "Ġquick est", "k w", "Ġsp itting", "ĠL ibraries", "åħ ī", "ant z", "D ad", "ĠSpec ifications", "rup ulous", "and r", "RES ULTS", "Ġsnow ball", "Ġpred is", "ĠB axter", "ĠNurs ing", "ĠCh aff", "s we", "Ġout age", "Ġnest ing", "Ġnotor iety", "tr igger", "on ite", "j on", "Ġf ou", "ook ed", "ĠCelebr ity", "re ality", "Ġfat ig", "Ġhug ging", "Ġbother s", "ĠPan zer", "ĠCh andra", "fig ured", "Ġvol ts", "ĠCloud s", "Ġfee ble", "ĠCur ve", "ĠAs us", "78 6", "abs or", "ĠV ICE", "ĠH ess", "Ġmanufact ures", "Ġgri zz", "ĠPower ful", "ac id", "Ġsub sections", "ĠKrug man", "ĠAl ps", "is u", "Ġsequ est", "ĠUlt ron", "ĠT inker", "ĠGo ose", "Ġmism atch", "Att orney", "Ġmorph ology", "ĠSix ers", "ut tered", "ĠE LECT", "gr an", "Rus sell", "ĠG SL", "Ġfort night", "Ġ. )", "Ġapost le", "pr one", "el ist", "Unt itled", "ĠIm plementation", "ist ors", "Ġtank er", "Ġpl ush", "Ġattend ants", "ĠT ik", "ĠGreen wich", "ĠY on", "ĠSP L", "cell s", "unt led", "S olution", "ĠQu é", "Ġvac ated", "Ġupt ick", "ĠMer idian", "æ ĥ", "ĠDr ill", "9 25", "58 4", "Ġrenov ated", "ĠKub rick", "zy k", "Ġl ousy", "pp el", "ohyd rate", "ĠI zzy", "lesi astical", "CC C", "ĠAj ax", "Ġad apters", "ĠPetra eus", "Ġaffirm ation", "ĠST OR", "le ms", "ad oes", "ĠConstantin ople", "Ġp onies", "Ġl ighthouse", "Ġadherent s", "ĠBre es", "omorph ic", "Fight ing", "Ġpl aster", "ĠP VC", "ĠOb st", "Ġdear ly", "ĠTo oth", "icks on", "Ġsh aming", "P lex", "A gg", "ĠâĢ¦ \"", "Ġsub reddits", "Ġpige on", "ĠResident ial", "ĠPass ing", "Ġl um", "ĠP ension", "Ġpessim istic", "Ġ4 32", "z inski", "c ade", "0 75", "Ġapolog ised", "iy ah", "Put ting", "Ġgloom y", "ĠLy me", "=-=-=-=- =-=-=-=-", "ĠT ome", "ĠPsych iatric", "ĠH IT", "c ms", "ap olog", "Ġbreak er", "Ġdeep en", "Ġtheor ist", "ĠHigh lands", "Ġb aker", "Ġst aples", "Ġinterf ered", "ĠAb ortion", "jo ined", "ch u", "Ġform ulate", "Ġvacc inations", "Ġban ter", "phe us", "Ġoutfield er", "ĠM eter", "Ġ# ####", "Ġ18 95", "Ġnarrow ing", "ĠST ORY", "f p", "ĠC ST", "ign ore", "Ġproclaim ing", "ĠR U", "ĠB ALL", "yn a", "65 3", "Ġpos it", "P RE", "59 4", "ĠRegist rar", "ĠPil grim", "ic io", "Ġpre tt", "Ġlif eless", "Ġ__ _", "Ne igh", "ĠCh urches", "orn o", "Ġor cs", "Ġkind red", "ĠAud it", "Ġmillenn ial", "ĠPers ia", "g ravity", "ĠDis ability", "ĠD ARK", "W s", "od on", "Ġgrand daughter", "ĠBro oke", "ĠA DA", "ER A", "Ġpick ups", "ĠWil kinson", "ĠSh ards", "ĠN K", "Ġexp el", "ĠKis lyak", "Ġj argon", "Ġpolar ized", "ian e", "Pub lisher", "Ġreb utt", "Ġapprehens ion", "ĠK essler", "Ġpr ism", "F UL", "19 64", "ĠL oll", "ä ¿", "le thal", "Å Ł", "Ġg hetto", "Ġb oulder", "ĠSlow ly", "ĠOsc ars", "ĠInst ruction", "ĠUl tr", "ĠM oe", "N ich", "ĠP ATH", "( *", "ĠRE LEASE", "un ing", "rou se", "en eg", "Ġre imb", "ĠDet ected", "Do S", "Ġster ling", "Ġaggreg ation", "ĠLone ly", "ĠAtt end", "hig her", "Ġairst rike", "ks on", "SE LECT", "Ġdef lation", "ĠHer rera", "C ole", "rit ch", "Ġadvis able", "F ax", "Ġwork around", "Ġp id", "mort em", "ers en", "Ġtyp o", "Ġal um", "78 2", "ĠJam al", "script s", "Ġcapt ives", "ĠPres ence", "ĠLie berman", "angel o", "Ġalcohol ism", "ass i", "Ġrec ite", "Ġgap ing", "Ġbask ets", "ĠG ou", "Brow ser", "ne au", "Ġcorrect ive", "und a", "sc oring", "ĠX D", "Ġfil ament", "Ġdeep ening", "ĠStain less", "Int eger", "Ġbu ggy", "Ġten ancy", "ĠMub arak", "Ġt uple", "ĠD roid", "ĠS itting", "Ġforfe it", "ĠRasm ussen", "ixt ies", "es i", "ĠKim mel", "Ġmetic ulously", "Ġap opt", "ĠS eller", "08 8", "ec ake", "hem atically", "T N", "Ġmind less", "Ġdig s", "ĠAcc ord", "ons ense", "em ing", "br ace", "Ġe Book", "ĠDist ribut", "ĠInvest ments", "w t", "] ),", "beh avior", "56 3", "Ġbl inding", "ĠPro testers", "top ia", "Ġreb orn", "ĠKel vin", "ĠDo ver", "ĠD airy", "ĠOut s", "Ġ[ /", "Ï Ģ", "b p", "ĠVan ity", "ĠRec ap", "ĠHOU SE", "ĠF ACE", "Ġ4 22", "69 2", "ĠAnt ioch", "cook ed", "Ġcoll ide", "Ġa pr", "Ġsle eper", "ĠJar vis", "Ġalternative ly", "ĠLe aves", "ĠM aw", "Ġantiqu ity", "ĠAdin ida", "Ġab user", "Poké mon", "Ġass orted", "ĠRev ision", "ĠP iano", "ĠG ideon", "O cean", "Ġsal on", "Ġbust ling", "ogn itive", "ĠRah man", "Ġwa iter", "Ġpres ets", "ĠO sh", "ĠG HC", "oper ator", "Ġrept iles", "Ġ4 13", "ĠG arr", "ĠCh ak", "Ġhas hes", "Ġfail ings", "Ġfolk lore", "Ġab l", "ĠC ena", "ĠMac Arthur", "ĠCOUR T", "Ġperipher y", "app ers", "Ġreck oned", "ĠInf lu", "ĠC ET", "Ġ3 72", "ĠDefin itive", "ass ault", "4 21", "Ġreservoir s", "Ġd ives", "ĠCo il", "DA Q", "Ġvivid ly", "ĠR J", "ĠBel lev", "Ġec lectic", "ĠShow down", "ĠK M", "ip ed", "reet ings", "ĠAs uka", "L iberal", "ĠÏ Ħ", "Ġbystand ers", "ĠGood win", "uk ong", "S it", "ĠT rem", "Ġcrim inally", "ĠCirc us", "ch rome", "88 7", "Ġnan op", "ĠOb i", "ĠL OW", "o gh", "ĠAuth ors", "ob yl", "Ur ban", "Ġt i", "ĠWe ir", "t rap", "ag y", "Ġparent heses", "Ġout numbered", "Ġcounter productive", "ĠTob ias", "ub is", "P arser", "ST AR", "Ġsyn aptic", "ĠG ears", "Ġh iber", "Ġdebunk ed", "Ġex alted", "aw atts", "H OU", "Ch urch", "ĠPix ie", "ĠU ri", "ĠForm ation", "ĠPred iction", "C EO", "Ġthro tt", "ĠBrit ann", "ĠMad agascar", "ë ĭ", "Ġbill boards", "ĠRPG s", "ĠBe es", "complete ly", "F IL", "Ġdoes nt", "ĠGreen berg", "re ys", "Ġsl ing", "Ġempt ied", "ĠPix ar", "ĠDh arma", "l uck", "ingu ished", "Ġend ot", "Ġbab ys", "05 9", "che st", "r ats", "Ġr idden", "Ġbeet les", "Ġillum inating", "Ġfict itious", "ĠProv incial", "Ġ7 68", "Ġshe pherd", "ĠR ender", "Ġ18 96", "C rew", "Ġmold ed", "ĠXia omi", "ĠSp iral", "Ġdel im", "Ġorgan ising", "Ġho ops", "ĠBe i", "z hen", "Ġfuck in", "Ġdec ad", "Ġun biased", "am my", "sw ing", "Ġsmugg led", "Ġk ios", "ĠP ERSON", "ĠInquis itor", "Ġsnow y", "Ġscrap ing", "ĠBurg ess", "P tr", "ag ame", "R W", "Ġdro id", "ĠL ys", "ĠCass andra", "Jac ob", "Ġ35 4", "Ġpast ure", "Ġfr anc", "ĠScot ch", "ĠEnd s", "ĠI GF", "def inition", "Ġhyster ical", "ĠBrown e", "77 1", "Ġmobil ization", "æ ķ", "iqu eness", "Th or", "Ġspear headed", "Ġembro iled", "Ġconject ure", "jud icial", "Ch oice", "Ġpaper back", "P ir", "Ġrec overs", "ĠSur ge", "ĠSh ogun", "ĠPed iatrics", "ãģ ł", "Ġsweep s", "ĠLabor atories", "ĠP acks", "al us", "add in", "Ġhead lights", "g ra", "Ev idence", "COL OR", "Ad min", "Ĭ ±", "Ġconco ct", "s ufficient", "Ġun marked", "Ġrich ness", "Ġdiss ertation", "Ġseason ing", "Ġg ib", "ĠM ages", "un ctions", "ĠN id", "che at", "ĠTM Z", "c itizens", "ĠCatholic ism", "n b", "Ġdisemb ark", "ĠPROG RAM", "a ques", "Ty ler", "Or g", "ĠSl ay", "ĠN ero", "ĠTown send", "IN TON", "te le", "Ġmes mer", "9 01", "Ġfire ball", "ev idence", "aff iliated", "ĠFrench man", "ĠAugust a", "0 21", "Ġs led", "Ġre used", "ĠImmun ity", "Ġwrest le", "assemb led", "Mar ia", "Ġgun shots", "ĠBarb ie", "Ġcannabin oids", "ĠTo ast", "ĠK inder", "IR D", "Ġre juven", "Ġg ore", "Ġrupt ure", "Ġbre aching", "ĠCart oon", "Ġ4 55", "ĠPale o", "6 14", "Ġspe ars", "ĠAm es", "ab us", "Mad ison", "GR OUP", "Ġab orted", "y ah", "Ġfel on", "Ġcaus ation", "Ġprep aid", "Ġp itted", "op lan", "ĠShel ley", "ĠRus so", "ĠP agan", "Ġwill fully", "ĠCan aver", "und rum", "ĠSal ary", "ĠAr paio", "read er", "ĠR ational", "ĠOver se", "ĠCa uses", "Ġ* .", "Ġw ob", "Ke ith", "ĠCons ent", "man ac", "77 3", "6 23", "Ġfate ful", "et imes", "Ġspir ited", "ĠD ys", "Ġhe gemony", "Ġboy cot", "ĠEn rique", "em outh", "Ġtim elines", "ĠSah ara", "ĠRel ax", "ĠQuin cy", "ĠLess ons", "ĠE QU", "SE A", "N K", "ĠCost co", "Incre ase", "Ġmotiv ating", "ĠCh ong", "am aru", "ĠDiv ide", "Ġped igree", "ĠTasman ia", "ĠPrel ude", "L as", "9 40", "57 4", "Ġch au", "ĠSp iegel", "un ic", "-- >", "ĠPhil ips", "ĠKaf ka", "Ġuphe aval", "Ġsent imental", "Ġsa x", "ĠAk ira", "ser ial", "Mat rix", "Ġelect ing", "Ġcomment er", "ĠNeb ula", "ple ts", "ĠNad u", "ĠAd ren", "Ġen shr", "ĠR AND", "fin ancial", "ĠCly de", "uther ford", "Ġsign age", "Ġde line", "Ġphosph ate", "rovers ial", "f ascist", "ĠV all", "ĠBeth lehem", "Ġfor s", "Ġeng lish", "S olid", "N ature", "Ġv a", "ĠGu ests", "Ġtant al", "Ġauto immune", ";;;;;;;; ;;;;", "ĠTot ally", "ĠO v", "Ġdef ences", "ĠCoc onut", "Ġtranqu il", "Ġpl oy", "Ġflav ours", "ĠFl ask", "ãĤ¨ ãĥ«", "ĠWest on", "ĠVol vo", "8 70", "Ġmicro phones", "ver bal", "R PG", "Ġi ii", "; }", "0 28", "Ġhead lined", "Ġprim ed", "Ġho ard", "ĠSh ad", "ĠEN TER", "Ġtri angular", "Ġcap it", "l ik", "ĠAn cients", "Ġl ash", "Ġconv ol", "Ġcolon el", "en emy", "G ra", "Ġpub s", "ut ters", "Ġassign s", "ĠPen et", "ĠMon strous", "ĠBow en", "il ver", "H aunted", "ĠD ing", "start ed", "pl in", "Ġcontamin ants", "ĠDO E", "ff en", "ĠTechn ician", "R y", "Ġrob bers", "Ġhot line", "ĠGuard iola", "ĠKau fman", "row er", "ĠDres den", "ĠAl pine", "E lf", "Ġf mt", "ĠS ard", "urs es", "g pu", "Un ix", "Ġunequiv ocally", "ĠCitizens hip", "qu ad", "m ire", "ĠS weeney", "B attery", "6 15", "Ġpanc akes", "Ġo ats", "M aps", "ĠCont rast", "mbuds man", "ĠE PS", "Ġsub committee", "Ġsour cing", "Ġs izing", "ĠBuff er", "ĠMand atory", "Ġmoder ates", "ĠPattern s", "ĠCh ocobo", "ĠZ an", "ĠSTAT ES", "ĠJud ging", "ĠIn her", "* :", "Ġb il", "ĠY en", "Ġexh ilar", "oll ower", "z ers", "Ġsn ug", "max imum", "Ġdesp icable", "ĠP ACK", "ĠAn nex", "Ġsarcast ic", "Ġlate x", "Ġt amp", "ĠS ao", "b ah", "ĠRe verend", "ĠChin atown", "ĠA UT", "d ocumented", "ĠGA BA", "ĠCan aan", "ĠÙ ħ", "Ġgovern s", "pre v", "E sc", "ĠEst imates", "OS P", "Ġendeav our", "ĠCl osing", "omet ime", "every one", "Ġwor sen", "Ġsc anners", "Ġdev iations", "ĠRobot ics", "ĠCom pton", "Ġsorce rer", "Ġend ogenous", "Ġem ulation", "ĠPier cing", "ĠA ph", "ĠS ocket", "Ġb ould", "ĠO U", "ĠBorder lands", "Ġ18 63", "G ordon", "ĠW TO", "Ġrestrict s", "Ġmosa ic", "Ġmel odies", "ç Ħ", "T ar", "Ġdis son", "ĠProv ides", "Ġ ......", "b ek", "F IX", "Ġbro om", "ans hip", "Do ctors", "Ġner ds", "ĠReg ions", "na issance", "Ġmet e", "Ġcre pt", "pl ings", "Ġgirlfriend s", "kn it", "ig ent", "ow e", "Ġus hered", "ĠB az", "M obil", "4 34", "ĠPres ents", "orig in", "Ġins omnia", "ĠA ux", "4 39", "ĠCh ili", "irs ch", "G AME", "Ġgest ation", "alg ia", "rom ising", "$ ,", "c row", "ĠIn spection", "at omic", "Rel ations", "J OHN", "rom an", "ĠClock work", "ĠBak r", "m one", "M ET", "Ġthirst y", "Ġb c", "Ġfacult ies", "R um", "Ġnu ance", "ĠD arius", "ple ting", "fter s", "etch up", "Reg istration", "ĠK E", "R ah", "Ġpref erential", "ĠL ash", "ĠH H", "Val id", "ĠN AV", "Ġstar ve", "ĠG ong", "z ynski", "ĠAct ress", "Ġw ik", "Ġun accompanied", "lv l", "Br ide", "AD S", "ĠCommand o", "ĠVaugh n", "Wal let", "Ġho pping", "ĠV ie", "Ġcave ats", "Ġal as", "if led", "ab use", "66 1", "Ġib n", "Ġg ul", "Ġrob bing", "t il", "IL A", "Ġmit igating", "Ġapt ly", "Ġty rant", "Ġmid day", "ĠGil more", "ĠDe cker", "Ġ§ §", "part ial", "Ex actly", "Ġphen otype", "Ġ[+ ]", "ĠP lex", "ĠI ps", "vers ions", "Ġe book", "Ġch ic", "g ross", "\":\" \"},{\"", "ĠSur prisingly", "M organ", "Ġresid ues", "ĠConf ederation", "in feld", "Ġl yr", "mod erate", "Ġperpend icular", "V K", "Ġsynchron ized", "Ġrefres hed", "Ġad ore", "ĠTor ment", "ol ina", "Ġ26 00", "Item Tracker", "Ġp ies", "ĠF AT", "ĠR HP", "0 48", "ĠRES P", "ĠB J", "all ows", "P and", "Ġunw elcome", "ĠV oc", "ĠBast ard", "ĠO W", "ĠL AR", "ĠHeal er", "Environment al", "ĠKen yan", "ĠTr ance", "ĠP ats", "Ġali ases", "ĠGar field", "Ġcampaign er", "Ġadvance ments", "ĠOkin awa", "ĠC oh", "ows ky", "Ġstar ved", "Ġsize able", "Ġ: -)", "Ġm RNA", "Ġsusp ensions", "ist ar", "Scot land", "Pr in", "-------------------------------- ----------------", "Ġ50 2", "Ġteasp oons", "Ġ10 50", "Ġcoerc ive", "ĠMason ic", "edd ed", "ĠPass enger", "Ġl att", "Ġbr aces", "ĠSt eal", "ĠNY T", "ĠK ats", "ĠCel est", "ae z", "T u", "ĠCoul ter", "ðŁ ĺ", "Fl ickr", "ĠWil mington", "ith s", "++ ;", "Ġv ending", "Ġneg ro", "ĠPh i", "ĠYellow stone", "Call back", "Ġsh ampoo", "ĠSh ades", "w at", "Ġsuper human", "Ġridic uled", "Ġhol iest", "om bo", "Ġintern s", "Ġh one", "ĠPar agu", "UR I", "Ġd angling", "ãĤ »", "so v", "ict ional", "av ailability", "Ġrev ocation", "Ġd ow", "in ic", "ĠTHE IR", "Ġis o", "Ġout ings", "ĠLeth al", "Ġ) ))", "Ġinacc ur", "Ġout landish", "Ġan us", "let ico", "id on", "l ol", "Ġun regulated", "Ġsuccumb ed", "Ġc uff", "ĠWast eland", "let al", "Ġsub str", "Ġcoff ers", "Ġautom akers", "ov i", "ĠX ue", "ĠDayton a", "Ġjar ring", "Ġf umes", "Ġdisband ed", "z ik", "itt on", "Ġstriking ly", "Ġsp ores", "Ad apter", ".) :", "ĠLynd on", "ival ry", "Ġor ally", "Ġtumult uous", "Ġdisple asure", "Ġcon es", "or rect", "Ġappe ase", "Ġder by", "ĠTrip oli", "ĠAl ess", "Ġp oked", "ĠGu ilty", "v P", "En ough", "Ġorig inals", "6 99", "Ġrabb i", "Ġproverb ial", "Ġpostp one", "el ope", "ĠMist y", "Ġstaff ed", "ĠUn employment", "redit ary", "Ġdilig ent", "re comm", "me asures", "as in", "8 25", "Ġpond s", "Ġmm ol", "ĠS AR", "ĠC ARE", "Ġ3 71", "Ġclen ched", "ĠCors air", "Ġcaric ature", "z n", "att ach", "ĠSch ro", "spe ak", "p ainted", "ĠS uc", "ĠE NT", "Ġcell ul", "ĠP aid", "di agn", "WH ERE", "Ġtext ed", "B arn", "Ġret racted", "ĠRe ferred", "S av", "Ġup keep", "Ġwork places", "ĠTok ens", "Ġampl ify", "cl inical", "Ġmult ic", "mber g", "Ġconvol uted", "Reg ion", "5 65", "ĠTop ic", "Ġsn ail", "Ġsal ine", "Ġins urrection", "ĠPet r", "f orts", "B AT", "ĠNav ajo", "Ġrud imentary", "ĠLak sh", "OND ON", "Me asure", "Ġtransform er", "ĠGodd ard", "Ġcoinc ides", "ir in", "R ex", "ĠB ok", "qu it", "Ġshotgun s", "Ġprolet arian", "Ġsc orp", "ĠAd a", "5 14", "Ġsl ander", "record ed", "Ġemb ell", "ris ome", "Ġapolog izing", "ĠMul cair", "ĠGib raltar", "Cl a", "Ġall ot", "ĠAtt ention", "Ġ4 33", "le ave", "Ġwh ine", "ĠIss a", "ĠFa ust", "ĠBar ron", "hen y", "Ġvictim ized", "J ews", "Ġnurt uring", "ett el", "W inged", "ĠSub tle", "Ġflavor ful", "ĠRep s", "eng ed", "call back", "Ġdirection al", "Ġcl asp", "ĠDirect ions", "plan et", "icult ure", "Hel per", "ic ion", "ac ia", "Ġç ¥ŀ", "Ġsur ges", "Ġcan oe", "ĠPrem iership", "be en", "Ġdef ied", "ĠTro oper", "Ġtrip od", "Ġgas p", "ĠE uph", "ĠAd s", "vern ight", "high ly", "R ole", "Ġent angled", "ĠZe it", "6 18", "ĠRust y", "Ġhaven s", "ĠVaugh an", "HA EL", "ĠSER VICE", "/ ,", "Ġstr icken", "Ġdel usions", "Ġb is", "ĠH af", "Ġgrat ification", "Ġent icing", "UN CH", "Ad ams", "ĠOL ED", "ĠBeet le", "Ġ18 99", "ĠSO FTWARE", "ateg or", "V L", "ĠTot em", "ĠG ators", "AT URES", "Ġimped ance", "Reg istered", "ĠC ary", "ĠAer ial", "on ne", "en ium", "Ġd red", "ĠBe g", "Ġconcurrent ly", "Ġsuper power", "ĠX an", "j ew", "imes ter", "ĠDick inson", "âĶ ģ", "F la", "Ġp ree", "ĠRoll ins", "© ¶æ", "Ġden omination", "ĠL ana", "5 16", "Ġinc iting", "sc ribed", "j uries", "ĠWond ers", "app roximately", "Ġsusp ending", "Ġmountain ous", "ĠL augh", "oid al", "N s", "Det ect", ") =", "ĠL uthor", "ĠSchwarz enegger", "ĠMull er", "ĠDev i", "ec ycle", "J ar", "6 13", "ĠL ongh", "B ah", "ĠSP ORTS", "n w", "Ġref inement", "Ġwater ways", "Ġd iner", "Bl ade", "68 3", "F ac", "Ġinitial s", "Ġro g", "Ġparan ormal", "B UT", "Ġ[ (", "ĠSw anson", "ĠM esh", "âĸ ¬", "Impro ve", "ĠRad iation", "ĠEst her", "ĠE sk", "ĠA ly", "ik y", "Ġir rad", "ĠBuck ingham", "Ġref ill", "Ġ. _", "Re pe", "CON CLUS", "Ġdifferent iated", "Ġchi rop", "ĠAt kins", "Pat tern", "Ġexc ise", "Ġcab al", "N SA", "ĠST A", "ĠS IL", "ĠPar aly", "Ġr ye", "ĠHow ell", "ĠCount down", "ness es", "alys ed", "Ġres ize", "ãĤ ½", "Ġbudget ary", "ĠStr as", "w ang", "Ġap iece", "Ġprecinct s", "Ġpe ach", "Ġsky line", "Ġ35 3", "pop ular", "App earances", "ĠMechan ics", "ĠDev Online", "S ullivan", "Z en", "Ġp u", "op olis", "5 44", "Ġde form", "Ġcounter act", "ĠL ange", "Ġ4 17", "Con sole", "77 4", "Ġnodd ing", "Ġpopul ism", "Ġhe p", "Ġcoun selling", "compl iance", "U FF", "Ġunden iably", "Ġrail ing", "ĠHor owitz", "ĠSim one", "ĠBung ie", "Ġa k", "ĠTal ks", "x ff", "fl ake", "Cr ash", "Ġsweat y", "Ġban quet", "ĠOFF IC", "Ġinvent ive", "Ġastron omer", "ĠStam ford", "ĠSc are", "ĠGRE EN", "olic ited", "Ġr usher", "Ġcent rist", "ight ing", "Ġsub class", "Ġdis av", "Ġdef und", "ĠN anto", "oci ate", "m ast", "Ġpac if", "Ġm end", "e ers", "imm igration", "ESS ION", "Ġnumber ing", "Ġlaugh able", "ĠEnd ed", "v iation", "em ark", "P itt", "Ġmetic ulous", "ĠL F", "Ġcongrat ulated", "ĠBir ch", "Ġsway ed", "Ġsemif inals", "Ġhum ankind", "m atter", "ĠEqu ip", "opa usal", "S aid", "ĠLay out", "Ġvo icing", "Ġth ug", "Ġporn ographic", "I PS", "Ġmo aning", "Ġgriev ance", "Ġconf essions", "esc al", "TEXT URE", "Aut hent", "os aurus", "P urchase", "Ġreleg ation", "al ter", "ĠÂł Âł", "Ġr iddled", "Ġo gre", "ĠLow ell", "Occ up", "E at", "ĠHy der", "ĠAdvis er", "Com merce", "H unt", "ĠOr th", "ĠComp etitive", "ĠCL A", "CD C", "Ġsal ads", "F le", "Ġindustrial ized", "` ,", "ĠO WN", "Ġbec k", "ĠPart icularly", "oub t", "Ġm M", "ĠHuss ain", "ĠChen nai", "Ġ9 20", "Ġappoint ing", "ĠCull en", ",,,, ,,,,", "Ġp ores", "ver ified", "Ġbi ochemical", "em ate", "Ġcoward ly", "ĠHels inki", "ĠEthiop ian", "S OURCE", "ER C", "est ro", "Ġbi otech", "ĠS our", "Ġbrew er", "Bloom berg", "Ġintens ify", "Gl ass", "an co", "ĠF DR", "gre SQL", "ĠF ires", "©¶æ ¥µ", "ec o", "100 1", "ĠHom eless", "Ġinstant aneous", "ĠH aste", "ig el", "D iamond", "Ġp aving", "Ġland fill", "Ġd ads", "h oun", ": ]", "Ġinc endiary", "ĠLiving ston", "ĠHil bert", "ĠChe cks", "st yles", "in ators", "ĠCl ive", "ph rine", "Ġchimpan zees", "Ġp all", "ĠJ M", "ĠAad haar", "ð Ŀ", "Ġachie vable", "dis abled", "P ET", "OOOO OOOO", "M ot", "Ġint angible", "Ġbal let", "ĠWe bs", "ĠEst imated", "Effect s", "Ġb ailed", "Josh ua", "Ġturb ulence", "Ġoccup ant", "ĠDay light", "Ġ36 1", "me et", "Ġstat ically", "Ġon look", "Ġk i", "il legal", "Ġvel vet", "Ġdehyd ration", "Ġacqu ies", "ĠRe z", "ak ura", "ĠU pton", "at ro", "Ġincomp rehensible", "Ġback door", "ĠRh ino", "7 27", "Ġmath s", ") +", "Ġhe resy", "Ġd f", "ĠRoc he", "ĠL ydia", "Ġpanc reat", "re ply", "arre ll", "Ġsolicit ation", "Ġcirc adian", "BI P", "Ġfor ay", "Ġcrypt ic", "iz u", "ime o", "ĠTom ato", "ĠH oms", "ex amination", "Ġqu arry", "ĠVal iant", "ĠJer icho", "ĠIN CLUD", "Ġ18 40", "5 19", "Ġres ists", "Ġsnap shots", "ĠSp ur", "ĠAnt iqu", "Log in", "Ġbest selling", "Ġant ic", "ĠS utherland", "ãĤ¢ ãĥ«", "Ġ~ /", "ĠP arm", "è ĥ", "P ages", "int ensity", "Ġimm obil", "Ġ18 65", "zz o", "Ġn ifty", "Ġf entanyl", "ĠPres ervation", "op hen", "Ġd arts", "ĠD inosaur", "po inters", "ĠR ite", "s uggest", "aware ness", "ĠSher idan", "Ġst ances", "Ġsor cery", "Ġper jury", "ĠNik ola", "ie ver", "Ġf iance", "ĠJordan ian", "ĠBall oon", "Ġn ab", "Ġk b", "Ġhuman ities", "ĠTan aka", "hill ary", "Ġconsult ancy", "ĠZ ub", "Ġrem ission", "Ġconf id", "CH Q", "ĠF ug", "Ġimpro vis", "Y ep", "/ _", "Ġunwilling ness", "Ġport folios", "05 5", "ĠInstruct or", "aim an", "Ġclaim ants", "M bps", "ĠBy e", "re ceived", "T weet", "Ġind emn", "ri z", "am ara", "N at", "Ġeval uates", "ĠL ur", "ep ad", "FO X", "ĠTh ro", "Ġrust y", "Ġbed rock", "ĠOp rah", "J B", "Ġmanip ulative", "Ġwill ful", "Ġrel apse", "Ġext ant", "The me", "S ensor", "ĠSt ability", "go vern", "Ġpo ppy", "Ġkn ack", "Ġins ulated", "ĠT ile", "ĠExt rem", "Ġunt old", "Ġconver ge", "Ġref uel", "ig roup", "Ġdistort ions", "Ġrav aged", "Ġmechan ically", "ĠRe illy", "ĠN ose", "ĠIncarn ation", "ĠBeck y", "abb ling", "Ġt aco", "Ġr ake", "Ġmelanch oly", "Ġillust rious", "ĠDart mouth", "Gu ide", "ĠR azer", "ĠBen z", "Ult imate", "ĠSur prise", "Ġpage ant", "off er", "Who ever", "Ġw iser", "Ġchem ist", "ĠHE LL", "ĠBul k", "Ġpl utonium", "ĠCO VER", "Ö ¼", "f ailed", "Ġtire lessly", "Ġinf ertility", "ĠTr ident", "ĠShow time", "ĠC iv", "V ice", "requ ires", "itt ance", "Ġun controlled", "interest ing", "56 1", "Ġinnov ate", "ateg ic", "L ie", "ĠS elling", "U l", "Ġsav ior", "ĠT osh", "Ġsw ast", "P ASS", "Ġr ink", "Ġcard io", "ĠI ro", "ud i", "Ġv antage", "Ġv ans", "ĠNi ño", "+ =", "Ġpropag ate", "< ?", "Ġmethod ological", "204 39", "Ġtrig lycer", "Ġing rained", "ĠAn notations", "arr anted", "6 17", "ĠS odium", "ĠA AC", "techn ical", "mult ipl", "Ġ3 73", "å ĭ", "Ġdec isively", "Ġboost ers", "Ġdessert s", "ĠGren ade", "Ġtest ifying", "ĠSc ully", "ID s", "Ġlock down", "ĠSc her", "ĠR é", "ĠWhit man", "ĠRams ay", "rem ote", "Ġh ikers", "ĠHy undai", "Ġcons cientious", "Ġcler ics", "ĠSiber ian", "ut i", "is bury", "Ġrel ayed", "Ġqu artz", "ĠC BI", "seek ers", "ull a", "Ġweld ing", "ĠSh al", "ble acher", "T ai", "ĠSam son", "Ġt umble", "ĠInvest or", "Ġsub contract", "ĠShin ra", "ow icz", "j andro", "d ad", "Ġtermin ating", "ĠNe ural", "ä» £", "Ġleak age", "ĠMid lands", "ĠCaucas us", "í ķ", "c it", "ll an", "iv ably", "ĠAlb ion", "Ġ4 57", "Ġregist rations", "Ġcomr ade", "Ġclip board", "0 47", "Ġdiscour aging", "ĠO ops", "Ad apt", "Ġem path", "n v", "ĠPR OT", "ĠDon n", "ĠP ax", "ĠB ayer", "t is", "Squ are", "Ġfoot prints", "part icip", "ĠChile an", "B rend", "ind ucing", "M agn", "Ġclub house", "ĠMagn um", "Ġenc amp", "ĠEth nic", "uch a", "ere y", "Ġw atered", "ĠCal ais", "Ġcomplex ion", "Ġsect s", "Ġren ters", "Ġbr as", "oÄŁ an", "Time out", "Man agement", "Ġinf ographic", "P okemon", "Cl ar", "Ġloc ality", "Ġfl ora", "as el", "P ont", "Ġpop ulate", "ĠO ng", "Ġsubs istence", "Ġa uctions", "ĠMcA uliffe", "ĠL OOK", "br inger", "Ġtit an", "Ġmanif old", "ĠâĹ ı", "Ġcalibr ated", "Ġcal iphate", "ĠSH E", "ĠCommission ers", "ce ivable", "j c", "W inner", "5 24", "Ġcond one", "Other wise", "Ġp iling", "Ġem body", "ĠCrime an", "ut ics", "ĠEx hibition", "Ġ4 26", "e ering", "Ġv ying", "ĠH UGE", "* =-", "Ġprin cipled", "à ¦", "Ġquir ks", "ĠEdit ors", "put ing", "G ES", "ĠF TA", "ठ¾", "add on", "ĠH AM", "ĠFrie za", "W oman", ". $", "Ġc rib", "ĠHer od", "Ġtim ers", "ĠSp aces", "ĠMac intosh", "at aka", "Ġgl ide", "Ġsmell ing", "ĠB AL", "Ġun su", "Ġcond os", "Ġbicy cl", "ĠRev ival", "55 3", "Ġjugg ling", "H ug", "ĠKardash ian", "ĠBalk ans", "mult iple", "Ġnutrit ious", "oc ry", "19 00", "Ġinteg rates", "Ġad joining", "ĠF older", "roll ment", "ven ient", "Ġu ber", "y i", "Ġwh iff", "ĠJu ven", "ĠB orough", "net te", "Ġb ilingual", "ĠSp arks", "ph thal", "man ufact", "Ġt outing", "ĠPH I", "Ke efe", "Rew ard", "Ġinf all", "ĠTem per", "typ ically", "ĠNik ol", "Ġregular s", "Ġpseud onym", "Ġexhib itions", "Ġbl aster", "Ġ40 9", "w arming", "Ġrever ber", "Ġrecip rocal", "Ġ6 70", "ip ient", "b ett", "ĠBe gins", "Ġit ching", "ĠPh ar", "Ass uming", "Ġem itting", "ĠML G", "Ġbirth place", "Ġt aunt", "ĠL uffy", "ĠAm it", "Ġcir cled", "ĠN ost", "enn ett", "Ġde forestation", "ĠHist orically", "ĠEvery day", "Ġovert ake", "79 2", "Ġn un", "ĠLuc ia", "Ġaccompan ies", "ĠSe eking", "ĠTr ash", "an ism", "R ogue", "Ġnorth western", "ĠSupplement al", "ĠNY U", "ĠF RI", "ĠSat isf", "x es", "5 17", "Ġreass ured", "Ġspor adic", "Ġ7 01", "Ġmed ial", "Ġcannabin oid", "Ġbarbar ic", "Ġep is", "ĠExplos ive", "ĠD ough", "Ġuns olved", "Support ed", "Ġacknowled gment", "sp awn", "Ġkit chens", "Ġ- =", "talk ing", "ic ist", "ĠPeg asus", "ĠPS U", "Ġphot on", "ĠAuthent ication", "R G", "@# &", "76 2", "ĠCl air", "Ġdi aper", "Ġbr ist", "ĠProsecut ors", "ĠJ em", "6 28", "ĠEvery where", "ĠJean ne", "equ ality", "ãĥ© ãĥ³", "object s", "ĠPel icans", "Ġ39 2", "Ġbl u", "b ys", "ĠA go", "Ġinstruction al", "Ġdiscrim inating", "ĠTR AN", "ĠCorn el", "ag os", "Ġty re", "Ġas piration", "ĠBrid gewater", "\": -", "! \".", "ĠEn s", "ĠCoc o", "P ie", "Ġdet ach", "ĠC ouch", "Ġphys ique", "ĠOccup ations", "osc opic", "en ough", "B uzz", "App earance", "Y P", "Ġrac er", "Ġcompl icity", "r pm", "T oy", "Ġinterrupt s", "ĠCat alyst", "Ġut ilitarian", "imp act", "Ġsp aghetti", "Ġp orous", "Ġeste emed", "Ġinc iner", "ĠI OC", "7 48", "Ġesp resso", "ĠSm ile", "abil ia", "6 35", "Ġmathematic ian", "Ġ4 24", "ĠK L", "ĠH IP", "Ġover heard", "ĠT ud", "ĠT ec", "Ġqu izz", "Ġfl attering", "Ġcon n", "âĢ İ", "Ġatt aches", "ĠR OS", "ĠAC S", "Ġt cp", "ĠSh ame", "sk ip", "res pected", "ĠTrin idad", "gr ain", "Ġfooth old", "ĠUnch arted", "ĠJul io", "z l", "av ored", "ĠAn xiety", "er rors", "ĠCent auri", "its ch", "D addy", "Ġclutch ing", "ĠIm plement", "ĠGut ierrez", "Ġ7 60", "Ġtele portation", "end ra", "Ġrevers ible", "st ros", "Ad venture", "08 3", "Ġliber ating", "Ġas phalt", "ĠSp end", "AR DS", "im sy", "PR ES", "ĠEmer ging", "Ġwild fires", "Ġtechn ologically", "Ġem its", "ĠART ICLE", "Ġirregular ities", "Ġcher ish", "çī Ī", "Ġst ink", "ĠR ost", "Econom ic", "Ġcough ing", "ĠMcC ann", "pro perties", "ilant ro", "Ġreneg oti", "Trans lation", "Ġin quest", "ĠGra pe", "oot ers", "gu i", "ĠSwords man", "ace ae", "h itting", "Ġr c", "Ġexert ed", "ĠS AP", "it ent", "Ġperil ous", "Ġobsc urity", "Ġassass inate", "Ġab original", "Ġresc uing", "ĠSh attered", "lock ing", "all ion", "Ch anging", "ĠHar rington", "ĠB ord", "ĠAfgh ans", "Jam ie", "aret z", "ĠAugust us", "Ġ38 6", "8 30", "Ġj og", "ok ingly", "Tr igger", "ĠH OR", "Stat istics", "Ġviewers hip", "Ġadd itives", "h ur", "Ġmaxim izing", "ĠR ove", "ĠLou ie", "ĠBuck et", "ĠCHR IST", "ou sel", "Ġstre aks", "ir ted", "Ġt ert", "Ġcolonial ism", "Ġbur ying", "y k", "Cond ition", "ĠDPR K", "By Id", "75 1", "âĹ ¼", "Ġwor risome", "Ġvoc ational", "sl ice", "Ġsa ils", "ĠCorrection al", "95 4", "Ġt ul", "K id", "l uster", "Ġfam ilial", "ĠSp it", "ĠEp iscopal", "Specific ally", "ĠVol cano", "run s", "q s", "Ġve tted", "Ġcram med", "t rop", "here r", "Thank fully", "Ġper cussion", "Ġor anges", "Ġround up", "Ġ4 99", "x ious", "Char acters", "ĠZion ism", "ĠR ao", "ÃĽ ÃĽ", "W F", "Ġunintention al", "ONE Y", "Gr ab", "Com mercial", "Ġglut amate", "ĠMcK enna", "ru ciating", "ning ton", "ih u", "Ch an", "ĠSw ap", "Ġleaf lets", "Ġfunction ally", "er ous", "F arm", "Ġcal oric", "ĠLiter ally", "con cert", "Ġshe nan", "Ġrep aid", "ey es", "Ġbas hing", "ĠG orge", "Ġcollabor ations", "Ġun account", "itch ie", "Ġteam work", "pp elin", "Ġpip ing", "Ġmin ced", "Ġd iam", "ri eg", "Ġmasc ara", "Ġsuck er", "ĠMo ons", "App s", "ĠPe ck", "Ġper v", "ĠFl oat", "o ley", "ĠN ish", "im ize", "Ġarom atic", "u in", "end ish", "! /", "ĠB icycle", "ĠAS IC", "ile ged", "ĠQuad ro", "ios yn", "Ġlock out", "ĠW ink", "SP EC", "Attempt s", "Ġseed ed", "red o", "ias is", "Ġsn ag", "ãĥķ ãĤ©", "ãĤ ¶", "Ġground ing", "Ġrelie ver", "Ġfrivol ous", "ĠG ifts", "ĠF aces", "Es pecially", "Ġmicrobi ome", "im ag", "ĠSch l", "ĠP les", "ĠBle ach", "ĠIr win", "ĠE aton", "ĠDisc iple", "Ġmultipl ication", "Ġcoer ced", "Ġ4 19", "st h", "E vil", "B omb", "Ġex orc", "Ġstag gered", "L ESS", "Ġinert ia", "ĠED IT", "Ġgo b", "Tr aditional", "Ġclass y", "Lear y", "ĠP AGE", "yr s", "Ġtrans porter", "Ġmat ured", "Ġhij ab", "Ġbi ome", "Where as", "Ġex termination", "ĠT ues", "ĠT akeru", "ĠAud rey", "er ial", "ĠAd en", "aff les", "Ġnarciss istic", "ĠB aird", "UT F", "I re", "ĠCon nie", "Ch amp", "Ġwhis pering", "ĠH att", "D K", "Ġdis infect", "Ġdeduct ed", "Ġpart ake", "Ġdown grade", "ĠEs ports", "ĠContin uing", "Ġdemocr atically", "icro bial", "itt a", "Ġlim estone", "Ġexempt ed", "ĠFren zy", "H erm", "7 28", "Ġfled gling", "Met a", "765 61", "69 3", "% :", "w ake", "5 26", "ĠDis cipline", "Ġvirgin ity", "ĠLeg ions", "ĠFrank ie", "int ent", "Ġrest rooms", "ĠRou ter", "da q", "Ġobjection able", "âĨ ij", "w ark", "ĠRah ul", "g ain", "activ ation", "abs olute", "ĠAccess ed", "Ġ24 00", "ogg les", "Ġsecond ly", "ĠDEF ENSE", "Ġpost age", "wra pper", "sh arp", "7 29", "Ġcommun icates", "Ġadd on", "ĠMil itia", "H ong", "Ġsl umped", "ĠJP EG", "ĠI car", "ad ish", "68 1", "Ġmaj esty", "ĠWolf gang", "ĠEl astic", "u per", "Ġv iz", "Ġunconscious ly", "ĠST D", "ĠS ass", "Ġflower ing", "ĠHel ic", "ĠDra per", "ĠAm ateur", "Ġman ure", "Ġdis ingen", "ĠLe i", "br ing", "9 49", "Ġinhib ited", "Ġhead quartered", "Ġen igmatic", "�� �", "Ġred ress", "R H", "Ġratt led", "Ġd iction", "l io", "ĠT BA", "ĠSN AP", "C alling", "Ġfasc ists", "ĠD ove", "iew icz", "0 36", "Ġco asts", "ĠR ect", "Ġ) ]", "L ot", "6 29", "ĠS EM", "ĠPeters en", "ĠExpl ain", "ĠBo ards", "ĠBe zos", "ĠJ ournals", "Ġ20 24", "p arser", "Ġmist rust", "Ġgr ate", "ĠL ocked", "bo a", "S aint", "g aming", "Ġvow el", "in ately", "bl ow", "All ah", "Ġun matched", "Ġb ordering", "ĠExp end", "n r", "Or acle", "rou ch", "Ġcont iguous", "ac us", "Ġdist raught", "58 1", "Ġanat omical", "O X", "ap ixel", "8 33", "ĠPL US", "Ġres usc", "Ġab iding", "57 3", "Ġvac ancies", "Em ily", "Ġhyp othal", "ĠWer ner", "ĠWe e", "ĠDJ s", "5 13", "Ġwitch craft", "Ġac upuncture", "ent ary", "benef it", "Product s", "ĠP SP", "ĠMP G", "ĠJ inn", "ĠJ arrett", "Ġ4 45", "ĠIm aging", "ĠP yth", "Fin ish", "Ġte x", "Ġjuven iles", "Ġhero ism", "Ġdoubt less", "ĠA ki", "ĠT end", "ĠPatri arch", "Ġbit ters", "ĠTele communications", "it atively", "ag na", "Ġr g", "ĠS OLD", "Ġcomp ulsion", "ĠN asa", "ĠKath ryn", "Ġmillion aires", "Ġintrins ically", "Ġbolst ered", "time out", "fl o", "Ġtut or", "p our", "Stat ement", "Ġ{ *", "ĠRud olph", "ĠKimber ly", "rog ens", "adi q", "] +", "Ġindign ation", "Ġfract uring", "ĠRe leases", "ĠGr ain", "pro tein", "L ago", "Ġvac ations", "Ġboot ed", "ĠTH REE", "ĠH G", "oresc ence", "Ġt f", "Ġso ar", "iosyn cr", "Ġgl ances", "ĠSp oon", "ĠJ ury", "ĠCow boy", "Ġcreat ively", "Hig her", "Ġsolic itor", "Ġhaw k", "ac io", "89 6", "Ġsuperf lu", "Ġbombs hell", "ct ure", "Ġbroker age", "Ġraid ing", "Ġf rench", "Ġang led", "Trans action", "ĠGen ocide", "u pe", "ĠHait ian", "57 2", "! :", "Ġunwitting ly", "iter ator", "sc roll", "Ġtall ied", "Ġbi omedical", "ĠC ARD", "Ġe uphem", "Ġbrain storm", "a quin", "K o", "Mic helle", "ĠR unes", "ĠBall istic", "ud ers", "Ġmod esty", "ĠiP ads", "ĠEzek iel", "Y E", "Ġstars hip", "Ġpower fully", "Ġper l", "ĠSh ade", "ĠQu art", "ĠE EG", "Ġfisher man", "OS ED", "ĠTyp ical", "df x", "Ġmes hes", "Ġet ched", "worth iness", "Ġtopp led", "Ġ3 96", "or ius", "We iss", "Ġmy sql", "ĠVal halla", "Ù Ĵ", "le asing", "Ġrec omp", "rap nel", "S el", "04 3", "Ġder ailed", "ĠGu ides", "IR T", "Ġde human", "ĠBritt any", "\" ))", "Ġex claim", "Ġb alk", "Ġ8 40", "CLA IM", "int el", "L AB", "Ġpe gged", "Ġast roph", "sm oking", "Ġrig ging", "Ġfix ation", "Ġcat apult", "ins ide", "ĠC ascade", "ĠBolshe vik", "G aza", "Dep th", "Ġloud spe", "Ġalmond s", "me yer", "l eness", "j en", "f resh", "Ġunbeat en", "ĠSqu id", "ĠPres umably", "Tim er", "B W", "Ġro sters", "Ġell ipt", "ĠHar riet", "dat abase", "ĠMut ual", "ĠComm odore", "uk ed", "kn ife", "ĠCOMM UN", "h ya", "Ġmel ts", "arch ives", "Ġrat ification", "Ġmultip lying", "Ġinter oper", "Ġasc ert", "w ings", "ver ting", "ĠScorp ion", "ay e", "ĠPorts mouth", "ĠM TA", "n it", "iaz ep", "Ġqu arantine", "Ġslides how", "Ġcent imeters", "Ġsyn opsis", "Ġsp ate", "th irst", "Ġnom inating", "ĠMel vin", "Pre view", "Ġthro b", "Ġgener ational", "ĠRad ius", "rest ling", "put able", "aw ar", "N ECT", "Ġunlaw fully", "ĠRevel ations", "Wik ipedia", "sur v", "Ġeye ing", "ij n", "ĠF W", "Ġbr unt", "Ġinter stellar", "Ġcl itor", "ĠCroat ian", "ĠCh ic", "ev a", "ĠDis app", "ĠA kin", "iner ies", "d ust", "Interest ed", "Ġgen esis", "ĠE ucl", "ö n", "p icking", "Ġmut ated", "Ġdisappro ve", "ĠHD L", "Ġ6 25", "Ì ¶", "c ancer", "Ġsqu ats", "Ġle vers", "Disc uss", "= ]", "D ex", "ĠVIDE OS", "A UD", "Ġtrans act", "ĠKin ect", "ĠK uala", "ĠC yp", "7 47", "Ġsh attering", "Ġarsen ic", "ĠInt ake", "ĠAngel o", "ĠQu it", "ĠK he", "Ġ18 93", "M aker", "0 29", "ĠPain ting", "Dis able", "9 16", "Ġanal ges", "Ġtact ile", "Ġprop hes", "Ġd iced", "ĠTravel s", "ĠHe ader", "ĠClub s", "Ass istant", "Ġinc rim", "Ġd ips", "Ġcruc ifix", "ĠShan ahan", "ĠInter pret", "Ġ40 90", "al ogy", "abb a", "Ġsimul ac", "hus band", "S IM", "Ġrecy cle", "uc er", "ed ged", "Ġre naissance", "ĠBomb ay", "Cath olic", "ĠL INE", "ĠCl othing", "re ports", "Ġpl aus", "Ġd ag", "ĠM ace", "Z I", "Ġintr uder", "ĠVeter inary", "g ru", "Ġsne aky", "ĠS ie", "ĠC innamon", "P OSE", "Ġcou rier", "ĠC NS", "Ġemanc ipation", "s it", "Ġplay through", "ĠFac ilities", "v irt", "ĠG auntlet", "Thom pson", "Ġunbeliev ably", "Param eters", "Ġst itching", "ign e", "ĠTH ESE", "Priv acy", "Ġshenan igans", "Ġvit ri", "ĠVal id", "59 1", "Ń ·", "ĠProt otype", "ink a", "SC P", "ĠT id", "è Ī", "old ed", "Ġindividual ity", "Ġbark ing", "Ġm ars", "ĠW D", "Ġ8 20", "Ġt ir", "Ġsl apping", "Ġdisgr untled", "ĠAng ola", "ri us", "ĠTorn ado", "ĠTh urs", "Ġcapt cha", "Ġang st", "ĠP og", "ĠAssass ins", "ĠAd idas", "Ġjoy ful", "Ġwh ining", "Emer gency", "Ġphosph orus", "Ġatt rition", "oph on", "ĠTimber wolves", "ĠJ ah", "ĠBr inging", "ĠW ad", "ĠEn sure", "oh l", "ĠX ie", "omm el", "c mp", "Ġz ipper", "Ġrel at", "ĠCor ridor", "m ilo", "T ING", "Av g", "Ġcro pped", "] }", "Ġr aged", "ĠLump ur", "ĠGuer rero", "our ke", "N ut", "Ġoff sets", "og lu", "dr m", "Ġmort als", "lat able", "Ġdismiss ive", "ä¸ ī", "Ġthro ats", "Ġchips et", "ĠSpot light", "Catal og", "art ist", "G b", "Ġch illy", "Ġst oked", "Ġ3 74", "W ard", "L atin", "Ġf iasco", "Ġble ach", "Ġb rav", "Enh anced", "Ġin oc", "ĠFior ina", "_ >", "Ġle ukemia", "Ġel uc", "Ġannoun cer", "ĠLith uan", "ĠArm ageddon", "å ĩ", "Len in", "ĠR uk", "Ġpe pp", "ĠRom antic", "ĠP IT", "ĠInter stellar", "ĠAt kinson", "R aid", "J s", "Go al", "C ourse", "Ġvan ishing", "es ley", "ĠR ounds", "Els a", "59 3", "Ġredund ancy", "ĠST AND", "Ġprop hetic", "Ġhabit able", "ry u", "Ġfaint ly", "M ODE", "Ġfl anked", "IR C", "Aw esome", "Ġsp urious", "ĠZ ah", "ĠMS G", "Ġsh ading", "Ġmotiv ational", "ĠSant ana", "ĠS PR", "Ġexc ruciating", "om ial", "ĠM iko", "ĠLe opard", "A byss", "Ġ[ |", "d irty", "Ġbath s", "Ġdem oral", "and re", "P B", "Ġun ification", "Ġsac rament", "Ġ[ &", "Ġpric eless", "Ġgel atin", "Ġeman ating", "ĠAll aah", "98 6", "Ġout burst", "Ġer as", "ĠX VI", "ĠSP I", "O tt", "ĠLaz arus", "PL IED", "F lying", "blog s", "W isconsin", "R aven", "Ġreb ate", "Ġcreep s", "ĠSp an", "ĠPain ter", "ĠKir a", "ĠAm os", "ĠCor vette", "Cons umer", "ĠRec over", "ck i", "Ġpes ky", "ĠIn vention", "Compan ies", "Ġchalleng ers", "ad emic", "ĠUkrain ians", "ĠNeuro log", "ĠFors aken", "Ġent rants", "Ġemb attled", "Ġdef unct", "ĠGlac ier", "Ġpo isons", "ĠH orses", "m akes", "ĠD irt", "Ġ4 23", "hh h", "ĠTrans formation", "QUI RE", "................ ..", "Ġtrave ller", "ĠSe xy", "ĠK ern", "ip olar", "Ġransom ware", "oooooooo oooooooo", "E c", "rub y", "Prof essional", "ĠOut break", "arg ument", "G rey", "ĠFif a", "ĠCH O", "ĠFOR M", "ĠAm trak", "- [", "Ġcr adle", "Ġantioxid ants", "ãģ®å ®", "7 36", "ĠNAS L", "ĠContribut ions", "Ind iana", "ĠST EP", "C SS", "Ġsal ient", "Ġall ocations", "yr ights", "Ġm ashed", "ĠCut ter", "Sex ual", "Ġp ounded", "Ġfan base", "Ġc asc", "ĠTrans parency", "Ġanaly tic", "ĠSummon er", "× ŀ", "ĠAD C", "det ail", "Ġvan quished", "Ġcr abs", "ar ie", "Dest roy", "ĠS ack", "Ġtrans istor", "Al abama", "ĠK oen", "ĠFisher ies", "c one", "Ġannex ed", "ĠM GM", "es a", "Ġf aked", "ĠCong ratulations", "Ġhind ered", "Ġcorrection al", "ĠI TV", "lee ve", "Ġin appropriately", "lic ks", "Ġtresp ass", "Ġp aws", "Ġnegoti ator", "ĠChrist ensen", "lim its", "ĠDian ne", "Ġeleg ance", "ĠContract s", "an ke", "Ob j", "Ġvigil ance", "Ġcast les", "ĠN AD", "ĠHol o", "Ġemph atically", "ĠTit us", "ĠServ ing", "ĠRich ie", "ĠP igs", "5 68", "Ġanim osity", "ĠAtt ributes", "ĠU riel", "M Q", "my ra", "ĠApplic ant", "Ġpsychiat rists", "ĠV ij", "ĠAb by", "ag ree", "P ush", "Ġk Wh", "hib a", "Ġinc ite", "ĠWe asley", "ĠTax i", "minist ic", "hy per", "ĠF arn", "Ġ6 01", "ĠNation wide", "F ake", "95 2", "Ġma ize", "Ġinteract ed", "Ġtransition ed", "Ġparas itic", "Ġharm onic", "Ġdec aying", "Ġbas eless", "ns ics", "Ġtrans pired", "Ġabund antly", "ĠFore nsic", "Ġtread mill", "ĠJ av", "ab and", "Ġssh d", "Ġfront man", "ĠJak arta", "oll er", "dro ps", "ĠSERV ICES", "rompt u", "oph ical", "h ospital", "bled on", "6 45", "Ġmid range", "ĠEV ENT", "cul ated", "raw led", "Ġper ched", "Ġover board", "ĠPe el", "ĠP wr", "ĠCar th", "ĠCOM PLE", "co e", "sh all", "Ġdeter rence", "M ETHOD", "ĠAbs ent", "M EN", "Ġs ill", "ĠLE VEL", "Y ork", "Ġsin ners", "ĠOP EC", "ĠN ur", "ĠDesign s", "se lection", "Ġunw orthy", "CH A", "Ġstreng thens", "88 3", "ed ly", "Ġslic ing", "Ġmal nutrition", "Ġfilm making", "ĠPol k", "ur ated", "Ġ4 21", "bre akers", "!' \"", "Ġwet lands", "ĠDisc rimination", "Ġallow able", "Ġste ered", "ĠSic ily", "S AM", "Ġmust ache", "Ġm ids", "Ġcl ipped", "Ġcirc ulate", "Ġbr ittle", "ĠBuild ings", "ra ised", "ĠRound up", "Ġwealth ier", "Ġoverw rite", "Ġover powered", "ĠGerr ard", "s ites", "PD ATED", "Ġacute ly", "ĠGam ble", "Ġp im", "ĠK us", "Typ ically", "De ploy", "ĠMoroc can", "p otion", "com be", "Ġvigil ante", "Ġ36 3", "St ew", "ĠB agg", "Ġres ided", "ĠSp o", "Ġrem nant", "Ġempt iness", "br ainer", "Ġout patient", "pri ority", "Ġle ptin", "ĠPay ton", "ĠGle aming", "ĠS hed", "ĠPol o", "ĠMormon ism", "rest ricted", "arl ane", "w x", "Ġcreat ine", "ĠAn on", "ĠST UD", "ĠJ UL", "ĠT ee", "5 28", "08 9", "Ġhat ched", "Dis patch", "ĠCompos ite", "Ġ45 1", "p uff", "ĠX COM", "ĠOr n", "ĠTH ANK", "END ED", "ĠAshe ville", "Ġà ľ", "Ġman go", "ĠS lightly", "world ly", "ĠW ander", "ĠExp and", "ĠCh r", "M ist", "Ġorthodox y", "ĠUN ESCO", "reg ate", "Else where", "k ie", "ir led", "Ġtopp le", "Ġadopt ive", "ĠLeg s", "d ress", "ĠS agan", "b are", "ĠGl ou", "Cr unch", "Ġhelp ers", "Ġchron ically", "ĠH uma", "1 0000", "Ġaccommod ating", "äº Ķ", "Ġwrink les", "Ġdod ged", "four th", "Ġpre con", "Ġcompress or", "ĠK are", "Ġev ict", "ĠWar wick", "im ar", "Ġmodern ization", "Ġband wagon", "Ġref uted", "Ġnet ted", "ĠNa ples", "ĠGen ie", "per ors", "Ġfield ed", "Ġde re", "ĠPar ables", "le es", "Ġtr out", "asp ers", "Ġn ihil", "Ġhapp iest", "Ġflo ppy", "ĠLo ft", "ĠHe ard", "Ġun ison", "Ġl ug", "ĠRed mond", "class ic", "Supp orters", "SH IP", "G MT", "Ġfue lled", "ç IJ", "Ġd d", "ĠEmin em", "Ġ18 97", "NY SE", "Ġsecret aries", "ĠF IA", "ĠCanaver al", "F avorite", "Ġp omp", "Ġdetain ee", "ers hip", "aim on", "i our", "ĠA pex", "Ġplant ations", "am ia", "ac ion", "R ust", "Ġtow ed", "ĠTru ly", "5 77", "Ġshel tered", "r ider", "W o", "Ġl air", "ĠInt elligent", "impro ve", "m atically", "Ġet iquette", "ad ra", "all o", "ĠJun o", "any thing", "ĠStru ggle", "ĠPred ict", "ĠGr imes", "ĠAMER ICA", "ct x", "ĠSit uation", "W OOD", "Ġsol uble", "me ier", "Ġintoler able", "ang ering", "Ġun interrupted", "Ġtool tip", "Ġinterrog ated", "Ġgun ned", "ĠSne ak", "æŃ ¦", "Ġt ether", "Ġcr umble", "L ens", "Ġclust ered", "ĠSy l", "ĠHas an", "Ġdystop ian", "w ana", "Ġjoy stick", "ĠTh ib", "amm u", "Tom orrow", "5 46", "Ġoverc ame", "Ġminim ized", "cept or", "Run ner", "ENG TH", "ĠBrend a", "ĠAchieve ments", "Ġtor ches", "Ġrapp ort", "ĠInvestig ator", "ĠHand ling", "rel ation", "g rey", "8 15", "Ġk cal", "ĠComm ands", "d q", "Ġcur ls", "Ġbe arer", "Ġcyn icism", "it ri", "ĠUse ful", "B ee", "D CS", "Ġab ras", "P ract", "BIL ITIES", "7 12", "Ġdebug ger", "Ġdebt or", "ĠL ia", "ĠK ers", "Ġexacerb ate", "ĠSt acy", "ĠB land", "ĠSc enes", "Ġbranch ing", "âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ", "ape ake", "Ġs alsa", "Ġmish and", "ĠKon ami", "ĠN ib", "Ġanecd ote", "Ġagree able", "Ï ī", "ĠNath aniel", "ĠHe isman", "ĠB eware", "Ġ18 86", "spect ive", "69 1", "5 22", "Ġinhib its", "Ġhas hing", "Ġ18 89", "å° Ĩ", "v ich", "P ure", "Ġsolid ly", "Ġaspir in", "im aru", "Ġstreet car", "ĠU CS", "ĠJ udd", "Ġflash backs", "p ins", "Ġ14 40", "ĠUN HCR", "ĠSym ptoms", "T IT", "5 38", "F ra", "% );", "Ġo oz", "Ġcur few", "Ġcal med", "Ġparticip ates", "Te X", "Ġnons ensical", "Ġfull back", "ĠDe L", "mon key", "h ari", "Ġmetabol ites", "Ġloot ed", "ĠAL WAYS", "ĠB CC", "L t", "oc het", "B one", "Ġveto ed", "Ġg cc", "ĠCL ICK", "Ġ18 88", "s af", "Ġstiff ness", "Ġlow ly", "ĠGe h", "vers on", "ors et", "Ġun foreseen", "Ġan esthesia", "ĠOpt ical", "Ġrecon structed", "ĠT up", "sh ows", "NEW S", "ĠNewsp aper", "ĠA SA", "ter a", "N umbers", "Ġinexpl icable", "× ij", "Ġhard ness", "unt arily", "ĠA cer", "grad ient", "ARD IS", "Ġwood land", "Ġmetaph ors", "ĠWem bley", "ĠPa vel", "phil is", "Ġre writing", "Ġpercept ual", "Ġ10 70", "worm s", "ĠDown s", "Ġunsur prisingly", "Ġtag ging", "fl ame", "Ġlit res", "Ġboun ces", "ĠB abe", "sh ut", "Ġoverd oses", "ĠShe ila", "ĠCh au", "ĠBl ess", "Capt ure", "ĠSign ificant", "ĠSc ion", "Ġ38 9", "ĠMc H", "ĠTitan ium", "ĠMe al", "amed a", "ag ents", "agg ressive", "B illy", "76 3", "ĠS aying", "DER R", "it one", "Coll ins", "B ound", "Ġbol ted", "ĠDM CA", "95 3", "Ġun iqueness", "Ġep igen", "un ci", "ant am", "Ġreck oning", "ch airs", "OG R", "ĠSen egal", "Ġ18 62", "re levant", "Ġ ¯", "Ġpharm acies", "ĠG eral", "v ier", "Y an", "OR PG", "Ġrab id", "b ending", "ĠUN ITED", "Ġ4 65", "As sembly", "Ġwe ep", "Ġbe hest", "ĠMother s", "ĠJ ace", "h id", "Ġwh irlwind", "ĠUN IVERS", "Ġut opian", "Ġkidn ap", "Ph ilipp", "K in", "89 3", "Ġlivest ream", "ĠM ISS", "Ġsub versive", "ĠTechn iques", "ĠJUST ICE", "ĠB ASE", "Ġ38 7", "Ġassail ants", "ĠHard core", "Ġsprink led", "ĠP se", "é ļ", "print ed", "ĠH au", "OR GE", "ĠT OUR", "Ġl aced", "Ġit ch", "G iving", "Ġport ed", "78 1", "//////////////// ////////////////", "bre eding", "Ġlog ger", "ĠH OL", "inn ie", "First ly", "Ġembry onic", "Ġdeleg ated", "p ai", "O IL", "Ġcentr ally", "ĠR x", "ĠSc outing", "D utch", "Ġhe reditary", "ĠCru iser", "s at", "5 29", "ĠMar riott", "other mal", "Ġprohib itions", "E arn", "ĠSt 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================================================ FILE: models/prompt_expansion/put_prompt_expansion_here ================================================ ================================================ FILE: models/safety_checker/put_safety_checker_models_here ================================================ ================================================ FILE: models/style_models/put_t2i_style_model_here ================================================ ================================================ FILE: models/unet/put_unet_files_here ================================================ ================================================ FILE: models/upscale_models/put_esrgan_and_other_upscale_models_here ================================================ ================================================ FILE: models/vae/put_vae_here ================================================ ================================================ FILE: models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here ================================================ ================================================ FILE: modules/__init__.py ================================================ ================================================ FILE: modules/anisotropic.py ================================================ import torch Tensor = torch.Tensor Device = torch.DeviceObjType Dtype = torch.Type pad = torch.nn.functional.pad def _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]: ky, kx = _unpack_2d_ks(kernel_size) return (ky - 1) // 2, (kx - 1) // 2 def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: if isinstance(kernel_size, int): ky = kx = kernel_size else: assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.' ky, kx = kernel_size ky = int(ky) kx = int(kx) return ky, kx def gaussian( window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None ) -> Tensor: batch_size = sigma.shape[0] x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) return gauss / gauss.sum(-1, keepdim=True) def get_gaussian_kernel1d( kernel_size: int, sigma: float | Tensor, force_even: bool = False, *, device: Device | None = None, dtype: Dtype | None = None, ) -> Tensor: return gaussian(kernel_size, sigma, device=device, dtype=dtype) def get_gaussian_kernel2d( kernel_size: tuple[int, int] | int, sigma: tuple[float, float] | Tensor, force_even: bool = False, *, device: Device | None = None, dtype: Dtype | None = None, ) -> Tensor: sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype) ksize_y, ksize_x = _unpack_2d_ks(kernel_size) sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None] kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None] kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None] return kernel_y * kernel_x.view(-1, 1, ksize_x) def _bilateral_blur( input: Tensor, guidance: Tensor | None, kernel_size: tuple[int, int] | int, sigma_color: float | Tensor, sigma_space: tuple[float, float] | Tensor, border_type: str = 'reflect', color_distance_type: str = 'l1', ) -> Tensor: if isinstance(sigma_color, Tensor): sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1) ky, kx = _unpack_2d_ks(kernel_size) pad_y, pad_x = _compute_zero_padding(kernel_size) padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type) unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) if guidance is None: guidance = input unfolded_guidance = unfolded_input else: padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type) unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) diff = unfolded_guidance - guidance.unsqueeze(-1) if color_distance_type == "l1": color_distance_sq = diff.abs().sum(1, keepdim=True).square() elif color_distance_type == "l2": color_distance_sq = diff.square().sum(1, keepdim=True) else: raise ValueError("color_distance_type only acceps l1 or l2") color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx) space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype) space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky) kernel = space_kernel * color_kernel out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1) return out def bilateral_blur( input: Tensor, kernel_size: tuple[int, int] | int = (13, 13), sigma_color: float | Tensor = 3.0, sigma_space: tuple[float, float] | Tensor = 3.0, border_type: str = 'reflect', color_distance_type: str = 'l1', ) -> Tensor: return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) def adaptive_anisotropic_filter(x, g=None): if g is None: g = x s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True) s = s + 1e-5 guidance = (g - m) / s y = _bilateral_blur(x, guidance, kernel_size=(13, 13), sigma_color=3.0, sigma_space=3.0, border_type='reflect', color_distance_type='l1') return y def joint_bilateral_blur( input: Tensor, guidance: Tensor, kernel_size: tuple[int, int] | int, sigma_color: float | Tensor, sigma_space: tuple[float, float] | Tensor, border_type: str = 'reflect', color_distance_type: str = 'l1', ) -> Tensor: return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) class _BilateralBlur(torch.nn.Module): def __init__( self, kernel_size: tuple[int, int] | int, sigma_color: float | Tensor, sigma_space: tuple[float, float] | Tensor, border_type: str = 'reflect', color_distance_type: str = "l1", ) -> None: super().__init__() self.kernel_size = kernel_size self.sigma_color = sigma_color self.sigma_space = sigma_space self.border_type = border_type self.color_distance_type = color_distance_type def __repr__(self) -> str: return ( f"{self.__class__.__name__}" f"(kernel_size={self.kernel_size}, " f"sigma_color={self.sigma_color}, " f"sigma_space={self.sigma_space}, " f"border_type={self.border_type}, " f"color_distance_type={self.color_distance_type})" ) class BilateralBlur(_BilateralBlur): def forward(self, input: Tensor) -> Tensor: return bilateral_blur( input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type ) class JointBilateralBlur(_BilateralBlur): def forward(self, input: Tensor, guidance: Tensor) -> Tensor: return joint_bilateral_blur( input, guidance, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type, ) ================================================ FILE: modules/async_worker.py ================================================ import threading from extras.inpaint_mask import generate_mask_from_image, SAMOptions from modules.patch import PatchSettings, patch_settings, patch_all import modules.config patch_all() class AsyncTask: def __init__(self, args): from modules.flags import Performance, MetadataScheme, ip_list, disabled from modules.util import get_enabled_loras from modules.config import default_max_lora_number import args_manager self.args = args.copy() self.yields = [] self.results = [] self.last_stop = False self.processing = False self.performance_loras = [] if len(args) == 0: return args.reverse() self.generate_image_grid = args.pop() self.prompt = args.pop() self.negative_prompt = args.pop() self.style_selections = args.pop() self.performance_selection = Performance(args.pop()) self.steps = self.performance_selection.steps() self.original_steps = self.steps self.aspect_ratios_selection = args.pop() self.image_number = args.pop() self.output_format = args.pop() self.seed = int(args.pop()) self.read_wildcards_in_order = args.pop() self.sharpness = args.pop() self.cfg_scale = args.pop() self.base_model_name = args.pop() self.refiner_model_name = args.pop() self.refiner_switch = args.pop() self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in range(default_max_lora_number)]) self.input_image_checkbox = args.pop() self.current_tab = args.pop() self.uov_method = args.pop() self.uov_input_image = args.pop() self.outpaint_selections = args.pop() self.inpaint_input_image = args.pop() self.inpaint_additional_prompt = args.pop() self.inpaint_mask_image_upload = args.pop() self.disable_preview = args.pop() self.disable_intermediate_results = args.pop() self.disable_seed_increment = args.pop() self.black_out_nsfw = args.pop() self.adm_scaler_positive = args.pop() self.adm_scaler_negative = args.pop() self.adm_scaler_end = args.pop() self.adaptive_cfg = args.pop() self.clip_skip = args.pop() self.sampler_name = args.pop() self.scheduler_name = args.pop() self.vae_name = args.pop() self.overwrite_step = args.pop() self.overwrite_switch = args.pop() self.overwrite_width = args.pop() self.overwrite_height = args.pop() self.overwrite_vary_strength = args.pop() self.overwrite_upscale_strength = args.pop() self.mixing_image_prompt_and_vary_upscale = args.pop() self.mixing_image_prompt_and_inpaint = args.pop() self.debugging_cn_preprocessor = args.pop() self.skipping_cn_preprocessor = args.pop() self.canny_low_threshold = args.pop() self.canny_high_threshold = args.pop() self.refiner_swap_method = args.pop() self.controlnet_softness = args.pop() self.freeu_enabled = args.pop() self.freeu_b1 = args.pop() self.freeu_b2 = args.pop() self.freeu_s1 = args.pop() self.freeu_s2 = args.pop() self.debugging_inpaint_preprocessor = args.pop() self.inpaint_disable_initial_latent = args.pop() self.inpaint_engine = args.pop() self.inpaint_strength = args.pop() self.inpaint_respective_field = args.pop() self.inpaint_advanced_masking_checkbox = args.pop() self.invert_mask_checkbox = args.pop() self.inpaint_erode_or_dilate = args.pop() self.save_final_enhanced_image_only = args.pop() if not args_manager.args.disable_image_log else False self.save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False self.metadata_scheme = MetadataScheme( args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS self.cn_tasks = {x: [] for x in ip_list} for _ in range(modules.config.default_controlnet_image_count): cn_img = args.pop() cn_stop = args.pop() cn_weight = args.pop() cn_type = args.pop() if cn_img is not None: self.cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) self.debugging_dino = args.pop() self.dino_erode_or_dilate = args.pop() self.debugging_enhance_masks_checkbox = args.pop() self.enhance_input_image = args.pop() self.enhance_checkbox = args.pop() self.enhance_uov_method = args.pop() self.enhance_uov_processing_order = args.pop() self.enhance_uov_prompt_type = args.pop() self.enhance_ctrls = [] for _ in range(modules.config.default_enhance_tabs): enhance_enabled = args.pop() enhance_mask_dino_prompt_text = args.pop() enhance_prompt = args.pop() enhance_negative_prompt = args.pop() enhance_mask_model = args.pop() enhance_mask_cloth_category = args.pop() enhance_mask_sam_model = args.pop() enhance_mask_text_threshold = args.pop() enhance_mask_box_threshold = args.pop() enhance_mask_sam_max_detections = args.pop() enhance_inpaint_disable_initial_latent = args.pop() enhance_inpaint_engine = args.pop() enhance_inpaint_strength = args.pop() enhance_inpaint_respective_field = args.pop() enhance_inpaint_erode_or_dilate = args.pop() enhance_mask_invert = args.pop() if enhance_enabled: self.enhance_ctrls.append([ enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert ]) self.should_enhance = self.enhance_checkbox and (self.enhance_uov_method != disabled.casefold() or len(self.enhance_ctrls) > 0) self.images_to_enhance_count = 0 self.enhance_stats = {} async_tasks = [] class EarlyReturnException(BaseException): pass def worker(): global async_tasks import os import traceback import math import numpy as np import torch import time import shared import random import copy import cv2 import modules.default_pipeline as pipeline import modules.core as core import modules.flags as flags import modules.patch import ldm_patched.modules.model_management import extras.preprocessors as preprocessors import modules.inpaint_worker as inpaint_worker import modules.constants as constants import extras.ip_adapter as ip_adapter import extras.face_crop import fooocus_version from extras.censor import default_censor from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name from modules.private_logger import log from extras.expansion import safe_str from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, parse_lora_references_from_prompt, apply_wildcards) from modules.upscaler import perform_upscale from modules.flags import Performance from modules.meta_parser import get_metadata_parser pid = os.getpid() print(f'Started worker with PID {pid}') try: async_gradio_app = shared.gradio_root flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}''' if async_gradio_app.share: flag += f''' or {async_gradio_app.share_url}''' print(flag) except Exception as e: print(e) def progressbar(async_task, number, text): print(f'[Fooocus] {text}') async_task.yields.append(['preview', (number, text, None)]) def yield_result(async_task, imgs, progressbar_index, black_out_nsfw, censor=True, do_not_show_finished_images=False): if not isinstance(imgs, list): imgs = [imgs] if censor and (modules.config.default_black_out_nsfw or black_out_nsfw): progressbar(async_task, progressbar_index, 'Checking for NSFW content ...') imgs = default_censor(imgs) async_task.results = async_task.results + imgs if do_not_show_finished_images: return async_task.yields.append(['results', async_task.results]) return def build_image_wall(async_task): results = [] if len(async_task.results) < 2: return for img in async_task.results: if isinstance(img, str) and os.path.exists(img): img = cv2.imread(img) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) if not isinstance(img, np.ndarray): return if img.ndim != 3: return results.append(img) H, W, C = results[0].shape for img in results: Hn, Wn, Cn = img.shape if H != Hn: return if W != Wn: return if C != Cn: return cols = float(len(results)) ** 0.5 cols = int(math.ceil(cols)) rows = float(len(results)) / float(cols) rows = int(math.ceil(rows)) wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8) for y in range(rows): for x in range(cols): if y * cols + x < len(results): img = results[y * cols + x] wall[y * H:y * H + H, x * W:x * W + W, :] = img # must use deep copy otherwise gradio is super laggy. Do not use list.append() . async_task.results = async_task.results + [wall] return def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, positive_cond, negative_cond, task, loras, tiled, use_expansion, width, height, base_progress, preparation_steps, total_count, show_intermediate_results, persist_image=True): if async_task.last_stop is not False: ldm_patched.modules.model_management.interrupt_current_processing() if 'cn' in goals: for cn_flag, cn_path in [ (flags.cn_canny, controlnet_canny_path), (flags.cn_cpds, controlnet_cpds_path) ]: for cn_img, cn_stop, cn_weight in async_task.cn_tasks[cn_flag]: positive_cond, negative_cond = core.apply_controlnet( positive_cond, negative_cond, pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop) imgs = pipeline.process_diffusion( positive_cond=positive_cond, negative_cond=negative_cond, steps=steps, switch=switch, width=width, height=height, image_seed=task['task_seed'], callback=callback, sampler_name=async_task.sampler_name, scheduler_name=final_scheduler_name, latent=initial_latent, denoise=denoising_strength, tiled=tiled, cfg_scale=async_task.cfg_scale, refiner_swap_method=async_task.refiner_swap_method, disable_preview=async_task.disable_preview ) del positive_cond, negative_cond # Save memory if inpaint_worker.current_task is not None: imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps) if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, current_progress, 'Checking for NSFW content ...') imgs = default_censor(imgs) progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...') img_paths = save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image) yield_result(async_task, img_paths, current_progress, async_task.black_out_nsfw, False, do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results) return imgs, img_paths, current_progress def apply_patch_settings(async_task): patch_settings[pid] = PatchSettings( async_task.sharpness, async_task.adm_scaler_end, async_task.adm_scaler_positive, async_task.adm_scaler_negative, async_task.controlnet_softness, async_task.adaptive_cfg ) def save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image=True) -> list: img_paths = [] for x in imgs: d = [('Prompt', 'prompt', task['log_positive_prompt']), ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']), ('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])), ('Performance', 'performance', async_task.performance_selection.value), ('Steps', 'steps', async_task.steps), ('Resolution', 'resolution', str((width, height))), ('Guidance Scale', 'guidance_scale', async_task.cfg_scale), ('Sharpness', 'sharpness', async_task.sharpness), ('ADM Guidance', 'adm_guidance', str(( modules.patch.patch_settings[pid].positive_adm_scale, modules.patch.patch_settings[pid].negative_adm_scale, modules.patch.patch_settings[pid].adm_scaler_end))), ('Base Model', 'base_model', async_task.base_model_name), ('Refiner Model', 'refiner_model', async_task.refiner_model_name), ('Refiner Switch', 'refiner_switch', async_task.refiner_switch)] if async_task.refiner_model_name != 'None': if async_task.overwrite_switch > 0: d.append(('Overwrite Switch', 'overwrite_switch', async_task.overwrite_switch)) if async_task.refiner_swap_method != flags.refiner_swap_method: d.append(('Refiner Swap Method', 'refiner_swap_method', async_task.refiner_swap_method)) if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr: d.append( ('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg)) if async_task.clip_skip > 1: d.append(('CLIP Skip', 'clip_skip', async_task.clip_skip)) d.append(('Sampler', 'sampler', async_task.sampler_name)) d.append(('Scheduler', 'scheduler', async_task.scheduler_name)) d.append(('VAE', 'vae', async_task.vae_name)) d.append(('Seed', 'seed', str(task['task_seed']))) if async_task.freeu_enabled: d.append(('FreeU', 'freeu', str((async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2)))) for li, (n, w) in enumerate(loras): if n != 'None': d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}')) metadata_parser = None if async_task.save_metadata_to_images: metadata_parser = modules.meta_parser.get_metadata_parser(async_task.metadata_scheme) metadata_parser.set_data(task['log_positive_prompt'], task['positive'], task['log_negative_prompt'], task['negative'], async_task.steps, async_task.base_model_name, async_task.refiner_model_name, loras, async_task.vae_name) d.append(('Metadata Scheme', 'metadata_scheme', async_task.metadata_scheme.value if async_task.save_metadata_to_images else async_task.save_metadata_to_images)) d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) img_paths.append(log(x, d, metadata_parser, async_task.output_format, task, persist_image)) return img_paths def apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress): for task in async_task.cn_tasks[flags.cn_canny]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) if not async_task.skipping_cn_preprocessor: cn_img = preprocessors.canny_pyramid(cn_img, async_task.canny_low_threshold, async_task.canny_high_threshold) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_cpds]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) if not async_task.skipping_cn_preprocessor: cn_img = preprocessors.cpds(cn_img) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_ip]: cn_img, cn_stop, cn_weight = task cn_img = HWC3(cn_img) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_ip_face]: cn_img, cn_stop, cn_weight = task cn_img = HWC3(cn_img) if not async_task.skipping_cn_preprocessor: cn_img = extras.face_crop.crop_image(cn_img) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) all_ip_tasks = async_task.cn_tasks[flags.cn_ip] + async_task.cn_tasks[flags.cn_ip_face] if len(all_ip_tasks) > 0: pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks) def apply_vary(async_task, uov_method, denoising_strength, uov_input_image, switch, current_progress, advance_progress=False): if 'subtle' in uov_method: denoising_strength = 0.5 if 'strong' in uov_method: denoising_strength = 0.85 if async_task.overwrite_vary_strength > 0: denoising_strength = async_task.overwrite_vary_strength shape_ceil = get_image_shape_ceil(uov_input_image) if shape_ceil < 1024: print(f'[Vary] Image is resized because it is too small.') shape_ceil = 1024 elif shape_ceil > 2048: print(f'[Vary] Image is resized because it is too big.') shape_ceil = 2048 uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil) initial_pixels = core.numpy_to_pytorch(uov_input_image) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') candidate_vae, _ = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((width, height))}.') return uov_input_image, denoising_strength, initial_latent, width, height, current_progress def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, denoising_strength, inpaint_respective_field, switch, inpaint_disable_initial_latent, current_progress, skip_apply_outpaint=False, advance_progress=False): if not skip_apply_outpaint: inpaint_image, inpaint_mask = apply_outpaint(async_task, inpaint_image, inpaint_mask) inpaint_worker.current_task = inpaint_worker.InpaintWorker( image=inpaint_image, mask=inpaint_mask, use_fill=denoising_strength > 0.99, k=inpaint_respective_field ) if async_task.debugging_inpaint_preprocessor: yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), 100, async_task.black_out_nsfw, do_not_show_finished_images=True) raise EarlyReturnException if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE Inpaint encoding ...') inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill) inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image) inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask) candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) latent_inpaint, latent_mask = core.encode_vae_inpaint( mask=inpaint_pixel_mask, vae=candidate_vae, pixels=inpaint_pixel_image) latent_swap = None if candidate_vae_swap is not None: if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE SD15 encoding ...') latent_swap = core.encode_vae( vae=candidate_vae_swap, pixels=inpaint_pixel_fill)['samples'] if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') latent_fill = core.encode_vae( vae=candidate_vae, pixels=inpaint_pixel_fill)['samples'] inpaint_worker.current_task.load_latent( latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap) if inpaint_parameterized: pipeline.final_unet = inpaint_worker.current_task.patch( inpaint_head_model_path=inpaint_head_model_path, inpaint_latent=latent_inpaint, inpaint_latent_mask=latent_mask, model=pipeline.final_unet ) if not inpaint_disable_initial_latent: initial_latent = {'samples': latent_fill} B, C, H, W = latent_fill.shape height, width = H * 8, W * 8 final_height, final_width = inpaint_worker.current_task.image.shape[:2] print(f'Final resolution is {str((final_width, final_height))}, latent is {str((width, height))}.') return denoising_strength, initial_latent, width, height, current_progress def apply_outpaint(async_task, inpaint_image, inpaint_mask): if len(async_task.outpaint_selections) > 0: H, W, C = inpaint_image.shape if 'top' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', constant_values=255) if 'bottom' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', constant_values=255) H, W, C = inpaint_image.shape if 'left' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant', constant_values=255) if 'right' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant', constant_values=255) inpaint_image = np.ascontiguousarray(inpaint_image.copy()) inpaint_mask = np.ascontiguousarray(inpaint_mask.copy()) async_task.inpaint_strength = 1.0 async_task.inpaint_respective_field = 1.0 return inpaint_image, inpaint_mask def apply_upscale(async_task, uov_input_image, uov_method, switch, current_progress, advance_progress=False): H, W, C = uov_input_image.shape if advance_progress: current_progress += 1 progressbar(async_task, current_progress, f'Upscaling image from {str((W, H))} ...') uov_input_image = perform_upscale(uov_input_image) print(f'Image upscaled.') if '1.5x' in uov_method: f = 1.5 elif '2x' in uov_method: f = 2.0 else: f = 1.0 shape_ceil = get_shape_ceil(H * f, W * f) if shape_ceil < 1024: print(f'[Upscale] Image is resized because it is too small.') uov_input_image = set_image_shape_ceil(uov_input_image, 1024) shape_ceil = 1024 else: uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f) image_is_super_large = shape_ceil > 2800 if 'fast' in uov_method: direct_return = True elif image_is_super_large: print('Image is too large. Directly returned the SR image. ' 'Usually directly return SR image at 4K resolution ' 'yields better results than SDXL diffusion.') direct_return = True else: direct_return = False if direct_return: return direct_return, uov_input_image, None, None, None, None, None, current_progress tiled = True denoising_strength = 0.382 if async_task.overwrite_upscale_strength > 0: denoising_strength = async_task.overwrite_upscale_strength initial_pixels = core.numpy_to_pytorch(uov_input_image) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') candidate_vae, _ = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) initial_latent = core.encode_vae( vae=candidate_vae, pixels=initial_pixels, tiled=True) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((width, height))}.') return direct_return, uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress def apply_overrides(async_task, steps, height, width): if async_task.overwrite_step > 0: steps = async_task.overwrite_step switch = int(round(async_task.steps * async_task.refiner_switch)) if async_task.overwrite_switch > 0: switch = async_task.overwrite_switch if async_task.overwrite_width > 0: width = async_task.overwrite_width if async_task.overwrite_height > 0: height = async_task.overwrite_height return steps, switch, width, height def process_prompt(async_task, prompt, negative_prompt, base_model_additional_loras, image_number, disable_seed_increment, use_expansion, use_style, use_synthetic_refiner, current_progress, advance_progress=False): prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='') negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='') prompt = prompts[0] negative_prompt = negative_prompts[0] if prompt == '': # disable expansion when empty since it is not meaningful and influences image prompt use_expansion = False extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else [] if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Loading models ...') lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, async_task.performance_selection) loras, prompt = parse_lora_references_from_prompt(prompt, async_task.loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames) loras += async_task.performance_loras pipeline.refresh_everything(refiner_model_name=async_task.refiner_model_name, base_model_name=async_task.base_model_name, loras=loras, base_model_additional_loras=base_model_additional_loras, use_synthetic_refiner=use_synthetic_refiner, vae_name=async_task.vae_name) pipeline.set_clip_skip(async_task.clip_skip) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Processing prompts ...') tasks = [] for i in range(image_number): if disable_seed_increment: task_seed = async_task.seed % (constants.MAX_SEED + 1) else: task_seed = (async_task.seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not task_rng = random.Random(task_seed) # may bind to inpaint noise in the future task_prompt = apply_wildcards(prompt, task_rng, i, async_task.read_wildcards_in_order) task_prompt = apply_arrays(task_prompt, i) task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, async_task.read_wildcards_in_order) task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt in extra_positive_prompts] task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt in extra_negative_prompts] positive_basic_workloads = [] negative_basic_workloads = [] task_styles = async_task.style_selections.copy() if use_style: placeholder_replaced = False for j, s in enumerate(task_styles): if s == random_style_name: s = get_random_style(task_rng) task_styles[j] = s p, n, style_has_placeholder = apply_style(s, positive=task_prompt) if style_has_placeholder: placeholder_replaced = True positive_basic_workloads = positive_basic_workloads + p negative_basic_workloads = negative_basic_workloads + n if not placeholder_replaced: positive_basic_workloads = [task_prompt] + positive_basic_workloads else: positive_basic_workloads.append(task_prompt) negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative. positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt) negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt) tasks.append(dict( task_seed=task_seed, task_prompt=task_prompt, task_negative_prompt=task_negative_prompt, positive=positive_basic_workloads, negative=negative_basic_workloads, expansion='', c=None, uc=None, positive_top_k=len(positive_basic_workloads), negative_top_k=len(negative_basic_workloads), log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts), log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts), styles=task_styles )) if use_expansion: if advance_progress: current_progress += 1 for i, t in enumerate(tasks): progressbar(async_task, current_progress, f'Preparing Fooocus text #{i + 1} ...') expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed']) print(f'[Prompt Expansion] {expansion}') t['expansion'] = expansion t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy. if advance_progress: current_progress += 1 for i, t in enumerate(tasks): progressbar(async_task, current_progress, f'Encoding positive #{i + 1} ...') t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k']) if advance_progress: current_progress += 1 for i, t in enumerate(tasks): if abs(float(async_task.cfg_scale) - 1.0) < 1e-4: t['uc'] = pipeline.clone_cond(t['c']) else: progressbar(async_task, current_progress, f'Encoding negative #{i + 1} ...') t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k']) return tasks, use_expansion, loras, current_progress def apply_freeu(async_task): print(f'FreeU is enabled!') pipeline.final_unet = core.apply_freeu( pipeline.final_unet, async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2 ) def patch_discrete(unet, scheduler_name): return core.opModelSamplingDiscrete.patch(unet, scheduler_name, False)[0] def patch_edm(unet, scheduler_name): return core.opModelSamplingContinuousEDM.patch(unet, scheduler_name, 120.0, 0.002)[0] def patch_samplers(async_task): final_scheduler_name = async_task.scheduler_name if async_task.scheduler_name in ['lcm', 'tcd']: final_scheduler_name = 'sgm_uniform' if pipeline.final_unet is not None: pipeline.final_unet = patch_discrete(pipeline.final_unet, async_task.scheduler_name) if pipeline.final_refiner_unet is not None: pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet, async_task.scheduler_name) elif async_task.scheduler_name == 'edm_playground_v2.5': final_scheduler_name = 'karras' if pipeline.final_unet is not None: pipeline.final_unet = patch_edm(pipeline.final_unet, async_task.scheduler_name) if pipeline.final_refiner_unet is not None: pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet, async_task.scheduler_name) return final_scheduler_name def set_hyper_sd_defaults(async_task, current_progress, advance_progress=False): print('Enter Hyper-SD mode.') if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Downloading Hyper-SD components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in Hyper-SD mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'dpmpp_sde_gpu' async_task.scheduler_name = 'karras' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def set_lightning_defaults(async_task, current_progress, advance_progress=False): print('Enter Lightning mode.') if advance_progress: current_progress += 1 progressbar(async_task, 1, 'Downloading Lightning components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in Lightning mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'euler' async_task.scheduler_name = 'sgm_uniform' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def set_lcm_defaults(async_task, current_progress, advance_progress=False): print('Enter LCM mode.') if advance_progress: current_progress += 1 progressbar(async_task, 1, 'Downloading LCM components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in LCM mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'lcm' async_task.scheduler_name = 'lcm' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def apply_image_input(async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner): if (async_task.current_tab == 'uov' or ( async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \ and async_task.uov_method != flags.disabled.casefold() and async_task.uov_input_image is not None: async_task.uov_input_image, skip_prompt_processing, async_task.steps = prepare_upscale( async_task, goals, async_task.uov_input_image, async_task.uov_method, async_task.performance_selection, async_task.steps, 1, skip_prompt_processing=skip_prompt_processing) if (async_task.current_tab == 'inpaint' or ( async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \ and isinstance(async_task.inpaint_input_image, dict): inpaint_image = async_task.inpaint_input_image['image'] inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0] if async_task.inpaint_advanced_masking_checkbox: if isinstance(async_task.inpaint_mask_image_upload, dict): if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray) and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray) and async_task.inpaint_mask_image_upload['image'].ndim == 3): async_task.inpaint_mask_image_upload = np.maximum( async_task.inpaint_mask_image_upload['image'], async_task.inpaint_mask_image_upload['mask']) if isinstance(async_task.inpaint_mask_image_upload, np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3: H, W, C = inpaint_image.shape async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload, width=W, height=H) async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2) async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype( np.uint8) * 255 inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload) if int(async_task.inpaint_erode_or_dilate) != 0: inpaint_mask = erode_or_dilate(inpaint_mask, async_task.inpaint_erode_or_dilate) if async_task.invert_mask_checkbox: inpaint_mask = 255 - inpaint_mask inpaint_image = HWC3(inpaint_image) if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ and (np.any(inpaint_mask > 127) or len(async_task.outpaint_selections) > 0): progressbar(async_task, 1, 'Downloading upscale models ...') modules.config.downloading_upscale_model() if inpaint_parameterized: progressbar(async_task, 1, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( async_task.inpaint_engine) base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') if async_task.refiner_model_name == 'None': use_synthetic_refiner = True async_task.refiner_switch = 0.8 else: inpaint_head_model_path, inpaint_patch_model_path = None, None print(f'[Inpaint] Parameterized inpaint is disabled.') if async_task.inpaint_additional_prompt != '': if async_task.prompt == '': async_task.prompt = async_task.inpaint_additional_prompt else: async_task.prompt = async_task.inpaint_additional_prompt + '\n' + async_task.prompt goals.append('inpaint') if async_task.current_tab == 'ip' or \ async_task.mixing_image_prompt_and_vary_upscale or \ async_task.mixing_image_prompt_and_inpaint: goals.append('cn') progressbar(async_task, 1, 'Downloading control models ...') if len(async_task.cn_tasks[flags.cn_canny]) > 0: controlnet_canny_path = modules.config.downloading_controlnet_canny() if len(async_task.cn_tasks[flags.cn_cpds]) > 0: controlnet_cpds_path = modules.config.downloading_controlnet_cpds() if len(async_task.cn_tasks[flags.cn_ip]) > 0: clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip') if len(async_task.cn_tasks[flags.cn_ip_face]) > 0: clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters( 'face') if async_task.current_tab == 'enhance' and async_task.enhance_input_image is not None: goals.append('enhance') skip_prompt_processing = True async_task.enhance_input_image = HWC3(async_task.enhance_input_image) return base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner def prepare_upscale(async_task, goals, uov_input_image, uov_method, performance, steps, current_progress, advance_progress=False, skip_prompt_processing=False): uov_input_image = HWC3(uov_input_image) if 'vary' in uov_method: goals.append('vary') elif 'upscale' in uov_method: goals.append('upscale') if 'fast' in uov_method: skip_prompt_processing = True steps = 0 else: steps = performance.steps_uov() if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Downloading upscale models ...') modules.config.downloading_upscale_model() return uov_input_image, skip_prompt_processing, steps def prepare_enhance_prompt(prompt: str, fallback_prompt: str): if safe_str(prompt) == '' or len(remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')) == 0: prompt = fallback_prompt return prompt def stop_processing(async_task, processing_start_time): async_task.processing = False processing_time = time.perf_counter() - processing_start_time print(f'Processing time (total): {processing_time:.2f} seconds') def process_enhance(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, inpaint_disable_initial_latent, inpaint_engine, inpaint_respective_field, inpaint_strength, prompt, negative_prompt, final_scheduler_name, goals, height, img, mask, preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, show_intermediate_results=True, persist_image=True): base_model_additional_loras = [] inpaint_head_model_path = None inpaint_parameterized = inpaint_engine != 'None' # inpaint_engine = None, improve detail initial_latent = None prompt = prepare_enhance_prompt(prompt, async_task.prompt) negative_prompt = prepare_enhance_prompt(negative_prompt, async_task.negative_prompt) if 'vary' in goals: img, denoising_strength, initial_latent, width, height, current_progress = apply_vary( async_task, async_task.enhance_uov_method, denoising_strength, img, switch, current_progress) if 'upscale' in goals: direct_return, img, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale( async_task, img, async_task.enhance_uov_method, switch, current_progress) if direct_return: d = [('Upscale (Fast)', 'upscale_fast', '2x')] if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, current_progress, 'Checking for NSFW content ...') img = default_censor(img) progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...') uov_image_path = log(img, d, output_format=async_task.output_format, persist_image=persist_image) yield_result(async_task, uov_image_path, current_progress, async_task.black_out_nsfw, False, do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results) return current_progress, img, prompt, negative_prompt if 'inpaint' in goals and inpaint_parameterized: progressbar(async_task, current_progress, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( inpaint_engine) if inpaint_patch_model_path not in base_model_additional_loras: base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] progressbar(async_task, current_progress, 'Preparing enhance prompts ...') # positive and negative conditioning aren't available here anymore, process prompt again tasks_enhance, use_expansion, loras, current_progress = process_prompt( async_task, prompt, negative_prompt, base_model_additional_loras, 1, True, use_expansion, use_style, use_synthetic_refiner, current_progress) task_enhance = tasks_enhance[0] # TODO could support vary, upscale and CN in the future # if 'cn' in goals: # apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width) if async_task.freeu_enabled: apply_freeu(async_task) patch_samplers(async_task) if 'inpaint' in goals: denoising_strength, initial_latent, width, height, current_progress = apply_inpaint( async_task, None, inpaint_head_model_path, img, mask, inpaint_parameterized, inpaint_strength, inpaint_respective_field, switch, inpaint_disable_initial_latent, current_progress, True) imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, task_enhance['c'], task_enhance['uc'], task_enhance, loras, tiled, use_expansion, width, height, current_progress, preparation_steps, total_count, show_intermediate_results, persist_image) del task_enhance['c'], task_enhance['uc'] # Save memory return current_progress, imgs[0], prompt, negative_prompt def enhance_upscale(all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, prompt, negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=True): # reset inpaint worker to prevent tensor size issues and not mix upscale and inpainting inpaint_worker.current_task = None current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting)) goals_enhance = [] img, skip_prompt_processing, steps = prepare_upscale( async_task, goals_enhance, img, async_task.enhance_uov_method, async_task.performance_selection, enhance_steps, current_progress) steps, _, _, _ = apply_overrides(async_task, steps, height, width) exception_result = '' if len(goals_enhance) > 0: try: current_progress, img, prompt, negative_prompt = process_enhance( all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, False, 'None', 0.0, 0.0, prompt, negative_prompt, final_scheduler_name, goals_enhance, height, img, None, preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image) except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False # also skip all enhance steps for this image, but add the steps to the progress bar if async_task.enhance_uov_processing_order == flags.enhancement_uov_before: done_steps_inpainting += len(async_task.enhance_ctrls) * enhance_steps exception_result = 'continue' else: print('User stopped') exception_result = 'break' finally: done_steps_upscaling += steps return current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result @torch.no_grad() @torch.inference_mode() def handler(async_task: AsyncTask): preparation_start_time = time.perf_counter() async_task.processing = True async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections] base_model_additional_loras = [] async_task.uov_method = async_task.uov_method.casefold() async_task.enhance_uov_method = async_task.enhance_uov_method.casefold() if fooocus_expansion in async_task.style_selections: use_expansion = True async_task.style_selections.remove(fooocus_expansion) else: use_expansion = False use_style = len(async_task.style_selections) > 0 if async_task.base_model_name == async_task.refiner_model_name: print(f'Refiner disabled because base model and refiner are same.') async_task.refiner_model_name = 'None' current_progress = 0 if async_task.performance_selection == Performance.EXTREME_SPEED: set_lcm_defaults(async_task, current_progress, advance_progress=True) elif async_task.performance_selection == Performance.LIGHTNING: set_lightning_defaults(async_task, current_progress, advance_progress=True) elif async_task.performance_selection == Performance.HYPER_SD: set_hyper_sd_defaults(async_task, current_progress, advance_progress=True) print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}') print(f'[Parameters] CLIP Skip = {async_task.clip_skip}') print(f'[Parameters] Sharpness = {async_task.sharpness}') print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}') print(f'[Parameters] ADM Scale = ' f'{async_task.adm_scaler_positive} : ' f'{async_task.adm_scaler_negative} : ' f'{async_task.adm_scaler_end}') print(f'[Parameters] Seed = {async_task.seed}') apply_patch_settings(async_task) print(f'[Parameters] CFG = {async_task.cfg_scale}') initial_latent = None denoising_strength = 1.0 tiled = False width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2] width, height = int(width), int(height) skip_prompt_processing = False inpaint_worker.current_task = None inpaint_parameterized = async_task.inpaint_engine != 'None' inpaint_image = None inpaint_mask = None inpaint_head_model_path = None use_synthetic_refiner = False controlnet_canny_path = None controlnet_cpds_path = None clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None goals = [] tasks = [] current_progress = 1 if async_task.input_image_checkbox: base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner = apply_image_input( async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner) # Load or unload CNs progressbar(async_task, current_progress, 'Loading control models ...') pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path]) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path) async_task.steps, switch, width, height = apply_overrides(async_task, async_task.steps, height, width) print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}') print(f'[Parameters] Steps = {async_task.steps} - {switch}') progressbar(async_task, current_progress, 'Initializing ...') loras = async_task.loras if not skip_prompt_processing: tasks, use_expansion, loras, current_progress = process_prompt(async_task, async_task.prompt, async_task.negative_prompt, base_model_additional_loras, async_task.image_number, async_task.disable_seed_increment, use_expansion, use_style, use_synthetic_refiner, current_progress, advance_progress=True) if len(goals) > 0: current_progress += 1 progressbar(async_task, current_progress, 'Image processing ...') should_enhance = async_task.enhance_checkbox and (async_task.enhance_uov_method != flags.disabled.casefold() or len(async_task.enhance_ctrls) > 0) if 'vary' in goals: async_task.uov_input_image, denoising_strength, initial_latent, width, height, current_progress = apply_vary( async_task, async_task.uov_method, denoising_strength, async_task.uov_input_image, switch, current_progress) if 'upscale' in goals: direct_return, async_task.uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale( async_task, async_task.uov_input_image, async_task.uov_method, switch, current_progress, advance_progress=True) if direct_return: d = [('Upscale (Fast)', 'upscale_fast', '2x')] if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, 100, 'Checking for NSFW content ...') async_task.uov_input_image = default_censor(async_task.uov_input_image) progressbar(async_task, 100, 'Saving image to system ...') uov_input_image_path = log(async_task.uov_input_image, d, output_format=async_task.output_format) yield_result(async_task, uov_input_image_path, 100, async_task.black_out_nsfw, False, do_not_show_finished_images=True) return if 'inpaint' in goals: try: denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, async_task.inpaint_strength, async_task.inpaint_respective_field, switch, async_task.inpaint_disable_initial_latent, current_progress, advance_progress=True) except EarlyReturnException: return if 'cn' in goals: apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress) if async_task.debugging_cn_preprocessor: return if async_task.freeu_enabled: apply_freeu(async_task) # async_task.steps can have value of uov steps here when upscale has been applied steps, _, _, _ = apply_overrides(async_task, async_task.steps, height, width) images_to_enhance = [] if 'enhance' in goals: async_task.image_number = 1 images_to_enhance += [async_task.enhance_input_image] height, width, _ = async_task.enhance_input_image.shape # input image already provided, processing is skipped steps = 0 yield_result(async_task, async_task.enhance_input_image, current_progress, async_task.black_out_nsfw, False, async_task.disable_intermediate_results) all_steps = steps * async_task.image_number if async_task.enhance_checkbox and async_task.enhance_uov_method != flags.disabled.casefold(): enhance_upscale_steps = async_task.performance_selection.steps() if 'upscale' in async_task.enhance_uov_method: if 'fast' in async_task.enhance_uov_method: enhance_upscale_steps = 0 else: enhance_upscale_steps = async_task.performance_selection.steps_uov() enhance_upscale_steps, _, _, _ = apply_overrides(async_task, enhance_upscale_steps, height, width) enhance_upscale_steps_total = async_task.image_number * enhance_upscale_steps all_steps += enhance_upscale_steps_total if async_task.enhance_checkbox and len(async_task.enhance_ctrls) != 0: enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width) all_steps += async_task.image_number * len(async_task.enhance_ctrls) * enhance_steps all_steps = max(all_steps, 1) print(f'[Parameters] Denoising Strength = {denoising_strength}') if isinstance(initial_latent, dict) and 'samples' in initial_latent: log_shape = initial_latent['samples'].shape else: log_shape = f'Image Space {(height, width)}' print(f'[Parameters] Initial Latent shape: {log_shape}') preparation_time = time.perf_counter() - preparation_start_time print(f'Preparation time: {preparation_time:.2f} seconds') final_scheduler_name = patch_samplers(async_task) print(f'Using {final_scheduler_name} scheduler.') async_task.yields.append(['preview', (current_progress, 'Moving model to GPU ...', None)]) processing_start_time = time.perf_counter() preparation_steps = current_progress total_count = async_task.image_number def callback(step, x0, x, total_steps, y): if step == 0: async_task.callback_steps = 0 async_task.callback_steps += (100 - preparation_steps) / float(all_steps) async_task.yields.append(['preview', ( int(current_progress + async_task.callback_steps), f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{total_count} ...', y)]) show_intermediate_results = len(tasks) > 1 or async_task.should_enhance persist_image = not async_task.should_enhance or not async_task.save_final_enhanced_image_only for current_task_id, task in enumerate(tasks): progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{async_task.image_number} ...') execution_start_time = time.perf_counter() try: imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, async_task.steps, switch, task['c'], task['uc'], task, loras, tiled, use_expansion, width, height, current_progress, preparation_steps, async_task.image_number, show_intermediate_results, persist_image) current_progress = int(preparation_steps + (100 - preparation_steps) / float(all_steps) * async_task.steps * (current_task_id + 1)) images_to_enhance += imgs except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False continue else: print('User stopped') break del task['c'], task['uc'] # Save memory execution_time = time.perf_counter() - execution_start_time print(f'Generating and saving time: {execution_time:.2f} seconds') if not async_task.should_enhance: print(f'[Enhance] Skipping, preconditions aren\'t met') stop_processing(async_task, processing_start_time) return progressbar(async_task, current_progress, 'Processing enhance ...') active_enhance_tabs = len(async_task.enhance_ctrls) should_process_enhance_uov = async_task.enhance_uov_method != flags.disabled.casefold() enhance_uov_before = False enhance_uov_after = False if should_process_enhance_uov: active_enhance_tabs += 1 enhance_uov_before = async_task.enhance_uov_processing_order == flags.enhancement_uov_before enhance_uov_after = async_task.enhance_uov_processing_order == flags.enhancement_uov_after total_count = len(images_to_enhance) * active_enhance_tabs async_task.images_to_enhance_count = len(images_to_enhance) base_progress = current_progress current_task_id = -1 done_steps_upscaling = 0 done_steps_inpainting = 0 enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width) exception_result = None for index, img in enumerate(images_to_enhance): async_task.enhance_stats[index] = 0 enhancement_image_start_time = time.perf_counter() last_enhance_prompt = async_task.prompt last_enhance_negative_prompt = async_task.negative_prompt if enhance_uov_before: current_task_id += 1 persist_image = not async_task.save_final_enhanced_image_only or active_enhance_tabs == 0 current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale( all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, async_task.prompt, async_task.negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image) async_task.enhance_stats[index] += 1 if exception_result == 'continue': continue elif exception_result == 'break': break # inpaint for all other tabs for enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert in async_task.enhance_ctrls: current_task_id += 1 current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting)) progressbar(async_task, current_progress, f'Preparing enhancement {current_task_id + 1}/{total_count} ...') enhancement_task_start_time = time.perf_counter() is_last_enhance_for_image = (current_task_id + 1) % active_enhance_tabs == 0 and not enhance_uov_after persist_image = not async_task.save_final_enhanced_image_only or is_last_enhance_for_image extras = {} if enhance_mask_model == 'sam': print(f'[Enhance] Searching for "{enhance_mask_dino_prompt_text}"') elif enhance_mask_model == 'u2net_cloth_seg': extras['cloth_category'] = enhance_mask_cloth_category mask, dino_detection_count, sam_detection_count, sam_detection_on_mask_count = generate_mask_from_image( img, mask_model=enhance_mask_model, extras=extras, sam_options=SAMOptions( dino_prompt=enhance_mask_dino_prompt_text, dino_box_threshold=enhance_mask_box_threshold, dino_text_threshold=enhance_mask_text_threshold, dino_erode_or_dilate=async_task.dino_erode_or_dilate, dino_debug=async_task.debugging_dino, max_detections=enhance_mask_sam_max_detections, model_type=enhance_mask_sam_model, )) if len(mask.shape) == 3: mask = mask[:, :, 0] if int(enhance_inpaint_erode_or_dilate) != 0: mask = erode_or_dilate(mask, enhance_inpaint_erode_or_dilate) if enhance_mask_invert: mask = 255 - mask if async_task.debugging_enhance_masks_checkbox: async_task.yields.append(['preview', (current_progress, 'Loading ...', mask)]) yield_result(async_task, mask, current_progress, async_task.black_out_nsfw, False, async_task.disable_intermediate_results) async_task.enhance_stats[index] += 1 print(f'[Enhance] {dino_detection_count} boxes detected') print(f'[Enhance] {sam_detection_count} segments detected in boxes') print(f'[Enhance] {sam_detection_on_mask_count} segments applied to mask') if enhance_mask_model == 'sam' and (dino_detection_count == 0 or not async_task.debugging_dino and sam_detection_on_mask_count == 0): print(f'[Enhance] No "{enhance_mask_dino_prompt_text}" detected, skipping') continue goals_enhance = ['inpaint'] try: current_progress, img, enhance_prompt_processed, enhance_negative_prompt_processed = process_enhance( all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_respective_field, enhance_inpaint_strength, enhance_prompt, enhance_negative_prompt, final_scheduler_name, goals_enhance, height, img, mask, preparation_steps, enhance_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image) async_task.enhance_stats[index] += 1 if (should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_after and async_task.enhance_uov_prompt_type == flags.enhancement_uov_prompt_type_last_filled): if enhance_prompt_processed != '': last_enhance_prompt = enhance_prompt_processed if enhance_negative_prompt_processed != '': last_enhance_negative_prompt = enhance_negative_prompt_processed except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False continue else: print('User stopped') exception_result = 'break' break finally: done_steps_inpainting += enhance_steps enhancement_task_time = time.perf_counter() - enhancement_task_start_time print(f'Enhancement time: {enhancement_task_time:.2f} seconds') if exception_result == 'break': break if enhance_uov_after: current_task_id += 1 # last step in enhance, always save persist_image = True current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale( all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, last_enhance_prompt, last_enhance_negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image) async_task.enhance_stats[index] += 1 if exception_result == 'continue': continue elif exception_result == 'break': break enhancement_image_time = time.perf_counter() - enhancement_image_start_time print(f'Enhancement image time: {enhancement_image_time:.2f} seconds') stop_processing(async_task, processing_start_time) return while True: time.sleep(0.01) if len(async_tasks) > 0: task = async_tasks.pop(0) try: handler(task) if task.generate_image_grid: build_image_wall(task) task.yields.append(['finish', task.results]) pipeline.prepare_text_encoder(async_call=True) except: traceback.print_exc() task.yields.append(['finish', task.results]) finally: if pid in modules.patch.patch_settings: del modules.patch.patch_settings[pid] pass threading.Thread(target=worker, daemon=True).start() ================================================ FILE: modules/auth.py ================================================ import json import hashlib import modules.constants as constants from os.path import exists def auth_list_to_dict(auth_list): auth_dict = {} for auth_data in auth_list: if 'user' in auth_data: if 'hash' in auth_data: auth_dict |= {auth_data['user']: auth_data['hash']} elif 'pass' in auth_data: auth_dict |= {auth_data['user']: hashlib.sha256(bytes(auth_data['pass'], encoding='utf-8')).hexdigest()} return auth_dict def load_auth_data(filename=None): auth_dict = None if filename != None and exists(filename): with open(filename, encoding='utf-8') as auth_file: try: auth_obj = json.load(auth_file) if isinstance(auth_obj, list) and len(auth_obj) > 0: auth_dict = auth_list_to_dict(auth_obj) except Exception as e: print('load_auth_data, e: ' + str(e)) return auth_dict auth_dict = load_auth_data(constants.AUTH_FILENAME) auth_enabled = auth_dict != None def check_auth(user, password): if user not in auth_dict: return False else: return hashlib.sha256(bytes(password, encoding='utf-8')).hexdigest() == auth_dict[user] ================================================ FILE: modules/config.py ================================================ import os import json import math import numbers import args_manager import tempfile import modules.flags import modules.sdxl_styles from modules.model_loader import load_file_from_url from modules.extra_utils import makedirs_with_log, get_files_from_folder, try_eval_env_var from modules.flags import OutputFormat, Performance, MetadataScheme def get_config_path(key, default_value): env = os.getenv(key) if env is not None and isinstance(env, str): print(f"Environment: {key} = {env}") return env else: return os.path.abspath(default_value) wildcards_max_bfs_depth = 64 config_path = get_config_path('config_path', "./config.txt") config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt") config_dict = {} always_save_keys = [] visited_keys = [] try: with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file: config_dict.update(json.load(json_file)) except Exception as e: print(f'Load default preset failed.') print(e) try: if os.path.exists(config_path): with open(config_path, "r", encoding="utf-8") as json_file: config_dict.update(json.load(json_file)) always_save_keys = list(config_dict.keys()) except Exception as e: print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}') print('Please make sure that:') print(f'1. The file "{config_path}" is a valid text file, and you have access to read it.') print('2. Use "\\\\" instead of "\\" when describing paths.') print('3. There is no "," before the last "}".') print('4. All key/value formats are correct.') def try_load_deprecated_user_path_config(): global config_dict if not os.path.exists('user_path_config.txt'): return try: deprecated_config_dict = json.load(open('user_path_config.txt', "r", encoding="utf-8")) def replace_config(old_key, new_key): if old_key in deprecated_config_dict: config_dict[new_key] = deprecated_config_dict[old_key] del deprecated_config_dict[old_key] replace_config('modelfile_path', 'path_checkpoints') replace_config('lorafile_path', 'path_loras') replace_config('embeddings_path', 'path_embeddings') replace_config('vae_approx_path', 'path_vae_approx') replace_config('upscale_models_path', 'path_upscale_models') replace_config('inpaint_models_path', 'path_inpaint') replace_config('controlnet_models_path', 'path_controlnet') replace_config('clip_vision_models_path', 'path_clip_vision') replace_config('fooocus_expansion_path', 'path_fooocus_expansion') replace_config('temp_outputs_path', 'path_outputs') if deprecated_config_dict.get("default_model", None) == 'juggernautXL_version6Rundiffusion.safetensors': os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') print('Config updated successfully in silence. ' 'A backup of previous config is written to "user_path_config-deprecated.txt".') return if input("Newer models and configs are available. " "Download and update files? [Y/n]:") in ['n', 'N', 'No', 'no', 'NO']: config_dict.update(deprecated_config_dict) print('Loading using deprecated old models and deprecated old configs.') return else: os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') print('Config updated successfully by user. ' 'A backup of previous config is written to "user_path_config-deprecated.txt".') return except Exception as e: print('Processing deprecated config failed') print(e) return try_load_deprecated_user_path_config() def get_presets(): preset_folder = 'presets' presets = ['initial'] if not os.path.exists(preset_folder): print('No presets found.') return presets return presets + [f[:f.index(".json")] for f in os.listdir(preset_folder) if f.endswith('.json')] def update_presets(): global available_presets available_presets = get_presets() def try_get_preset_content(preset): if isinstance(preset, str): preset_path = os.path.abspath(f'./presets/{preset}.json') try: if os.path.exists(preset_path): with open(preset_path, "r", encoding="utf-8") as json_file: json_content = json.load(json_file) print(f'Loaded preset: {preset_path}') return json_content else: raise FileNotFoundError except Exception as e: print(f'Load preset [{preset_path}] failed') print(e) return {} available_presets = get_presets() preset = args_manager.args.preset config_dict.update(try_get_preset_content(preset)) def get_path_output() -> str: """ Checking output path argument and overriding default path. """ global config_dict path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True) if args_manager.args.output_path: print(f'Overriding config value path_outputs with {args_manager.args.output_path}') config_dict['path_outputs'] = path_output = args_manager.args.output_path return path_output def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False): global config_dict, visited_keys, always_save_keys if key not in visited_keys: visited_keys.append(key) if key not in always_save_keys: always_save_keys.append(key) v = os.getenv(key) if v is not None: print(f"Environment: {key} = {v}") config_dict[key] = v else: v = config_dict.get(key, None) if isinstance(v, str): if make_directory: makedirs_with_log(v) if os.path.exists(v) and os.path.isdir(v): return v if not as_array else [v] elif isinstance(v, list): if make_directory: for d in v: makedirs_with_log(d) if all([os.path.exists(d) and os.path.isdir(d) for d in v]): return v if v is not None: print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.') if isinstance(default_value, list): dp = [] for path in default_value: abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path)) dp.append(abs_path) os.makedirs(abs_path, exist_ok=True) else: dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value)) os.makedirs(dp, exist_ok=True) if as_array: dp = [dp] config_dict[key] = dp return dp paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True) paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True) path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/') path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/') path_vae = get_dir_or_set_default('path_vae', '../models/vae/') path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/') path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/') path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/') path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/') path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion') path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/') path_safety_checker = get_dir_or_set_default('path_safety_checker', '../models/safety_checker/') path_sam = get_dir_or_set_default('path_sam', '../models/sam/') path_outputs = get_path_output() def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False, expected_type=None): global config_dict, visited_keys if key not in visited_keys: visited_keys.append(key) v = os.getenv(key) if v is not None: v = try_eval_env_var(v, expected_type) print(f"Environment: {key} = {v}") config_dict[key] = v if key not in config_dict: config_dict[key] = default_value return default_value v = config_dict.get(key, None) if not disable_empty_as_none: if v is None or v == '': v = 'None' if validator(v): return v else: if v is not None: print(f'Failed to load config key: {json.dumps({key:v})} is invalid; will use {json.dumps({key:default_value})} instead.') config_dict[key] = default_value return default_value def init_temp_path(path: str | None, default_path: str) -> str: if args_manager.args.temp_path: path = args_manager.args.temp_path if path != '' and path != default_path: try: if not os.path.isabs(path): path = os.path.abspath(path) os.makedirs(path, exist_ok=True) print(f'Using temp path {path}') return path except Exception as e: print(f'Could not create temp path {path}. Reason: {e}') print(f'Using default temp path {default_path} instead.') os.makedirs(default_path, exist_ok=True) return default_path default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus') temp_path = init_temp_path(get_config_item_or_set_default( key='temp_path', default_value=default_temp_path, validator=lambda x: isinstance(x, str), expected_type=str ), default_temp_path) temp_path_cleanup_on_launch = get_config_item_or_set_default( key='temp_path_cleanup_on_launch', default_value=True, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_base_model_name = default_model = get_config_item_or_set_default( key='default_model', default_value='model.safetensors', validator=lambda x: isinstance(x, str), expected_type=str ) previous_default_models = get_config_item_or_set_default( key='previous_default_models', default_value=[], validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x), expected_type=list ) default_refiner_model_name = default_refiner = get_config_item_or_set_default( key='default_refiner', default_value='None', validator=lambda x: isinstance(x, str), expected_type=str ) default_refiner_switch = get_config_item_or_set_default( key='default_refiner_switch', default_value=0.8, validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1, expected_type=numbers.Number ) default_loras_min_weight = get_config_item_or_set_default( key='default_loras_min_weight', default_value=-2, validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10, expected_type=numbers.Number ) default_loras_max_weight = get_config_item_or_set_default( key='default_loras_max_weight', default_value=2, validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10, expected_type=numbers.Number ) default_loras = get_config_item_or_set_default( key='default_loras', default_value=[ [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ] ], validator=lambda x: isinstance(x, list) and all( len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number) or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x), expected_type=list ) default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras] default_max_lora_number = get_config_item_or_set_default( key='default_max_lora_number', default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5, validator=lambda x: isinstance(x, int) and x >= 1, expected_type=int ) default_cfg_scale = get_config_item_or_set_default( key='default_cfg_scale', default_value=7.0, validator=lambda x: isinstance(x, numbers.Number), expected_type=numbers.Number ) default_sample_sharpness = get_config_item_or_set_default( key='default_sample_sharpness', default_value=2.0, validator=lambda x: isinstance(x, numbers.Number), expected_type=numbers.Number ) default_sampler = get_config_item_or_set_default( key='default_sampler', default_value='dpmpp_2m_sde_gpu', validator=lambda x: x in modules.flags.sampler_list, expected_type=str ) default_scheduler = get_config_item_or_set_default( key='default_scheduler', default_value='karras', validator=lambda x: x in modules.flags.scheduler_list, expected_type=str ) default_vae = get_config_item_or_set_default( key='default_vae', default_value=modules.flags.default_vae, validator=lambda x: isinstance(x, str), expected_type=str ) default_styles = get_config_item_or_set_default( key='default_styles', default_value=[ "Fooocus V2", "Fooocus Enhance", "Fooocus Sharp" ], validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x), expected_type=list ) default_prompt_negative = get_config_item_or_set_default( key='default_prompt_negative', default_value='', validator=lambda x: isinstance(x, str), disable_empty_as_none=True, expected_type=str ) default_prompt = get_config_item_or_set_default( key='default_prompt', default_value='', validator=lambda x: isinstance(x, str), disable_empty_as_none=True, expected_type=str ) default_performance = get_config_item_or_set_default( key='default_performance', default_value=Performance.SPEED.value, validator=lambda x: x in Performance.values(), expected_type=str ) default_image_prompt_checkbox = get_config_item_or_set_default( key='default_image_prompt_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_enhance_checkbox = get_config_item_or_set_default( key='default_enhance_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_advanced_checkbox = get_config_item_or_set_default( key='default_advanced_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_developer_debug_mode_checkbox = get_config_item_or_set_default( key='default_developer_debug_mode_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_image_prompt_advanced_checkbox = get_config_item_or_set_default( key='default_image_prompt_advanced_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_max_image_number = get_config_item_or_set_default( key='default_max_image_number', default_value=32, validator=lambda x: isinstance(x, int) and x >= 1, expected_type=int ) default_output_format = get_config_item_or_set_default( key='default_output_format', default_value='png', validator=lambda x: x in OutputFormat.list(), expected_type=str ) default_image_number = get_config_item_or_set_default( key='default_image_number', default_value=2, validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number, expected_type=int ) checkpoint_downloads = get_config_item_or_set_default( key='checkpoint_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()), expected_type=dict ) lora_downloads = get_config_item_or_set_default( key='lora_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()), expected_type=dict ) embeddings_downloads = get_config_item_or_set_default( key='embeddings_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()), expected_type=dict ) vae_downloads = get_config_item_or_set_default( key='vae_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()), expected_type=dict ) available_aspect_ratios = get_config_item_or_set_default( key='available_aspect_ratios', default_value=modules.flags.sdxl_aspect_ratios, validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1, expected_type=list ) default_aspect_ratio = get_config_item_or_set_default( key='default_aspect_ratio', default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0], validator=lambda x: x in available_aspect_ratios, expected_type=str ) default_inpaint_engine_version = get_config_item_or_set_default( key='default_inpaint_engine_version', default_value='v2.6', validator=lambda x: x in modules.flags.inpaint_engine_versions, expected_type=str ) default_selected_image_input_tab_id = get_config_item_or_set_default( key='default_selected_image_input_tab_id', default_value=modules.flags.default_input_image_tab, validator=lambda x: x in modules.flags.input_image_tab_ids, expected_type=str ) default_uov_method = get_config_item_or_set_default( key='default_uov_method', default_value=modules.flags.disabled, validator=lambda x: x in modules.flags.uov_list, expected_type=str ) default_controlnet_image_count = get_config_item_or_set_default( key='default_controlnet_image_count', default_value=4, validator=lambda x: isinstance(x, int) and x > 0, expected_type=int ) default_ip_images = {} default_ip_stop_ats = {} default_ip_weights = {} default_ip_types = {} for image_count in range(default_controlnet_image_count): image_count += 1 default_ip_images[image_count] = get_config_item_or_set_default( key=f'default_ip_image_{image_count}', default_value='None', validator=lambda x: x == 'None' or isinstance(x, str) and os.path.exists(x), expected_type=str ) if default_ip_images[image_count] == 'None': default_ip_images[image_count] = None default_ip_types[image_count] = get_config_item_or_set_default( key=f'default_ip_type_{image_count}', default_value=modules.flags.default_ip, validator=lambda x: x in modules.flags.ip_list, expected_type=str ) default_end, default_weight = modules.flags.default_parameters[default_ip_types[image_count]] default_ip_stop_ats[image_count] = get_config_item_or_set_default( key=f'default_ip_stop_at_{image_count}', default_value=default_end, validator=lambda x: isinstance(x, float) and 0 <= x <= 1, expected_type=float ) default_ip_weights[image_count] = get_config_item_or_set_default( key=f'default_ip_weight_{image_count}', default_value=default_weight, validator=lambda x: isinstance(x, float) and 0 <= x <= 2, expected_type=float ) default_inpaint_advanced_masking_checkbox = get_config_item_or_set_default( key='default_inpaint_advanced_masking_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_inpaint_method = get_config_item_or_set_default( key='default_inpaint_method', default_value=modules.flags.inpaint_option_default, validator=lambda x: x in modules.flags.inpaint_options, expected_type=str ) default_cfg_tsnr = get_config_item_or_set_default( key='default_cfg_tsnr', default_value=7.0, validator=lambda x: isinstance(x, numbers.Number), expected_type=numbers.Number ) default_clip_skip = get_config_item_or_set_default( key='default_clip_skip', default_value=2, validator=lambda x: isinstance(x, int) and 1 <= x <= modules.flags.clip_skip_max, expected_type=int ) default_overwrite_step = get_config_item_or_set_default( key='default_overwrite_step', default_value=-1, validator=lambda x: isinstance(x, int), expected_type=int ) default_overwrite_switch = get_config_item_or_set_default( key='default_overwrite_switch', default_value=-1, validator=lambda x: isinstance(x, int), expected_type=int ) default_overwrite_upscale = get_config_item_or_set_default( key='default_overwrite_upscale', default_value=-1, validator=lambda x: isinstance(x, numbers.Number) ) example_inpaint_prompts = get_config_item_or_set_default( key='example_inpaint_prompts', default_value=[ 'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes' ], validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x), expected_type=list ) example_enhance_detection_prompts = get_config_item_or_set_default( key='example_enhance_detection_prompts', default_value=[ 'face', 'eye', 'mouth', 'hair', 'hand', 'body' ], validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x), expected_type=list ) default_enhance_tabs = get_config_item_or_set_default( key='default_enhance_tabs', default_value=3, validator=lambda x: isinstance(x, int) and 1 <= x <= 5, expected_type=int ) default_enhance_uov_method = get_config_item_or_set_default( key='default_enhance_uov_method', default_value=modules.flags.disabled, validator=lambda x: x in modules.flags.uov_list, expected_type=int ) default_enhance_uov_processing_order = get_config_item_or_set_default( key='default_enhance_uov_processing_order', default_value=modules.flags.enhancement_uov_before, validator=lambda x: x in modules.flags.enhancement_uov_processing_order, expected_type=int ) default_enhance_uov_prompt_type = get_config_item_or_set_default( key='default_enhance_uov_prompt_type', default_value=modules.flags.enhancement_uov_prompt_type_original, validator=lambda x: x in modules.flags.enhancement_uov_prompt_types, expected_type=int ) default_sam_max_detections = get_config_item_or_set_default( key='default_sam_max_detections', default_value=0, validator=lambda x: isinstance(x, int) and 0 <= x <= 10, expected_type=int ) default_black_out_nsfw = get_config_item_or_set_default( key='default_black_out_nsfw', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_save_only_final_enhanced_image = get_config_item_or_set_default( key='default_save_only_final_enhanced_image', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_save_metadata_to_images = get_config_item_or_set_default( key='default_save_metadata_to_images', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_metadata_scheme = get_config_item_or_set_default( key='default_metadata_scheme', default_value=MetadataScheme.FOOOCUS.value, validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x], expected_type=str ) metadata_created_by = get_config_item_or_set_default( key='metadata_created_by', default_value='', validator=lambda x: isinstance(x, str), expected_type=str ) example_inpaint_prompts = [[x] for x in example_inpaint_prompts] example_enhance_detection_prompts = [[x] for x in example_enhance_detection_prompts] default_invert_mask_checkbox = get_config_item_or_set_default( key='default_invert_mask_checkbox', default_value=False, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_inpaint_mask_model = get_config_item_or_set_default( key='default_inpaint_mask_model', default_value='isnet-general-use', validator=lambda x: x in modules.flags.inpaint_mask_models, expected_type=str ) default_enhance_inpaint_mask_model = get_config_item_or_set_default( key='default_enhance_inpaint_mask_model', default_value='sam', validator=lambda x: x in modules.flags.inpaint_mask_models, expected_type=str ) default_inpaint_mask_cloth_category = get_config_item_or_set_default( key='default_inpaint_mask_cloth_category', default_value='full', validator=lambda x: x in modules.flags.inpaint_mask_cloth_category, expected_type=str ) default_inpaint_mask_sam_model = get_config_item_or_set_default( key='default_inpaint_mask_sam_model', default_value='vit_b', validator=lambda x: x in modules.flags.inpaint_mask_sam_model, expected_type=str ) default_describe_apply_prompts_checkbox = get_config_item_or_set_default( key='default_describe_apply_prompts_checkbox', default_value=True, validator=lambda x: isinstance(x, bool), expected_type=bool ) default_describe_content_type = get_config_item_or_set_default( key='default_describe_content_type', default_value=[modules.flags.describe_type_photo], validator=lambda x: all(k in modules.flags.describe_types for k in x), expected_type=list ) config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))] # mapping config to meta parameter possible_preset_keys = { "default_model": "base_model", "default_refiner": "refiner_model", "default_refiner_switch": "refiner_switch", "previous_default_models": "previous_default_models", "default_loras_min_weight": "default_loras_min_weight", "default_loras_max_weight": "default_loras_max_weight", "default_loras": "", "default_cfg_scale": "guidance_scale", "default_sample_sharpness": "sharpness", "default_cfg_tsnr": "adaptive_cfg", "default_clip_skip": "clip_skip", "default_sampler": "sampler", "default_scheduler": "scheduler", "default_overwrite_step": "steps", "default_overwrite_switch": "overwrite_switch", "default_performance": "performance", "default_image_number": "image_number", "default_prompt": "prompt", "default_prompt_negative": "negative_prompt", "default_styles": "styles", "default_aspect_ratio": "resolution", "default_save_metadata_to_images": "default_save_metadata_to_images", "checkpoint_downloads": "checkpoint_downloads", "embeddings_downloads": "embeddings_downloads", "lora_downloads": "lora_downloads", "vae_downloads": "vae_downloads", "default_vae": "vae", # "default_inpaint_method": "inpaint_method", # disabled so inpaint mode doesn't refresh after every preset change "default_inpaint_engine_version": "inpaint_engine_version", } REWRITE_PRESET = False if REWRITE_PRESET and isinstance(args_manager.args.preset, str): save_path = 'presets/' + args_manager.args.preset + '.json' with open(save_path, "w", encoding="utf-8") as json_file: json.dump({k: config_dict[k] for k in possible_preset_keys}, json_file, indent=4) print(f'Preset saved to {save_path}. Exiting ...') exit(0) def add_ratio(x): a, b = x.replace('*', ' ').split(' ')[:2] a, b = int(a), int(b) g = math.gcd(a, b) return f'{a}×{b} \U00002223 {a // g}:{b // g}' default_aspect_ratio = add_ratio(default_aspect_ratio) available_aspect_ratios_labels = [add_ratio(x) for x in available_aspect_ratios] # Only write config in the first launch. if not os.path.exists(config_path): with open(config_path, "w", encoding="utf-8") as json_file: json.dump({k: config_dict[k] for k in always_save_keys}, json_file, indent=4) # Always write tutorials. with open(config_example_path, "w", encoding="utf-8") as json_file: cpa = config_path.replace("\\", "\\\\") json_file.write(f'You can modify your "{cpa}" using the below keys, formats, and examples.\n' f'Do not modify this file. Modifications in this file will not take effect.\n' f'This file is a tutorial and example. Please edit "{cpa}" to really change any settings.\n' + 'Remember to split the paths with "\\\\" rather than "\\", ' 'and there is no "," before the last "}". \n\n\n') json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4) model_filenames = [] lora_filenames = [] vae_filenames = [] wildcard_filenames = [] def get_model_filenames(folder_paths, extensions=None, name_filter=None): if extensions is None: extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'] files = [] if not isinstance(folder_paths, list): folder_paths = [folder_paths] for folder in folder_paths: files += get_files_from_folder(folder, extensions, name_filter) return files def update_files(): global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets model_filenames = get_model_filenames(paths_checkpoints) lora_filenames = get_model_filenames(paths_loras) vae_filenames = get_model_filenames(path_vae) wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt']) available_presets = get_presets() return def downloading_inpaint_models(v): assert v in modules.flags.inpaint_engine_versions load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth', model_dir=path_inpaint, file_name='fooocus_inpaint_head.pth' ) head_file = os.path.join(path_inpaint, 'fooocus_inpaint_head.pth') patch_file = None if v == 'v1': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch', model_dir=path_inpaint, file_name='inpaint.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint.fooocus.patch') if v == 'v2.5': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch', model_dir=path_inpaint, file_name='inpaint_v25.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint_v25.fooocus.patch') if v == 'v2.6': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch', model_dir=path_inpaint, file_name='inpaint_v26.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint_v26.fooocus.patch') return head_file, patch_file def downloading_sdxl_lcm_lora(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors', model_dir=paths_loras[0], file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value ) return modules.flags.PerformanceLoRA.EXTREME_SPEED.value def downloading_sdxl_lightning_lora(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors', model_dir=paths_loras[0], file_name=modules.flags.PerformanceLoRA.LIGHTNING.value ) return modules.flags.PerformanceLoRA.LIGHTNING.value def downloading_sdxl_hyper_sd_lora(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors', model_dir=paths_loras[0], file_name=modules.flags.PerformanceLoRA.HYPER_SD.value ) return modules.flags.PerformanceLoRA.HYPER_SD.value def downloading_controlnet_canny(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors', model_dir=path_controlnet, file_name='control-lora-canny-rank128.safetensors' ) return os.path.join(path_controlnet, 'control-lora-canny-rank128.safetensors') def downloading_controlnet_cpds(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_xl_cpds_128.safetensors', model_dir=path_controlnet, file_name='fooocus_xl_cpds_128.safetensors' ) return os.path.join(path_controlnet, 'fooocus_xl_cpds_128.safetensors') def downloading_ip_adapters(v): assert v in ['ip', 'face'] results = [] load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/clip_vision_vit_h.safetensors', model_dir=path_clip_vision, file_name='clip_vision_vit_h.safetensors' ) results += [os.path.join(path_clip_vision, 'clip_vision_vit_h.safetensors')] load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_ip_negative.safetensors', model_dir=path_controlnet, file_name='fooocus_ip_negative.safetensors' ) results += [os.path.join(path_controlnet, 'fooocus_ip_negative.safetensors')] if v == 'ip': load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus_sdxl_vit-h.bin', model_dir=path_controlnet, file_name='ip-adapter-plus_sdxl_vit-h.bin' ) results += [os.path.join(path_controlnet, 'ip-adapter-plus_sdxl_vit-h.bin')] if v == 'face': load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus-face_sdxl_vit-h.bin', model_dir=path_controlnet, file_name='ip-adapter-plus-face_sdxl_vit-h.bin' ) results += [os.path.join(path_controlnet, 'ip-adapter-plus-face_sdxl_vit-h.bin')] return results def downloading_upscale_model(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_upscaler_s409985e5.bin', model_dir=path_upscale_models, file_name='fooocus_upscaler_s409985e5.bin' ) return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin') def downloading_safety_checker_model(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/stable-diffusion-safety-checker.bin', model_dir=path_safety_checker, file_name='stable-diffusion-safety-checker.bin' ) return os.path.join(path_safety_checker, 'stable-diffusion-safety-checker.bin') def download_sam_model(sam_model: str) -> str: match sam_model: case 'vit_b': return downloading_sam_vit_b() case 'vit_l': return downloading_sam_vit_l() case 'vit_h': return downloading_sam_vit_h() case _: raise ValueError(f"sam model {sam_model} does not exist.") def downloading_sam_vit_b(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_b_01ec64.pth', model_dir=path_sam, file_name='sam_vit_b_01ec64.pth' ) return os.path.join(path_sam, 'sam_vit_b_01ec64.pth') def downloading_sam_vit_l(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_l_0b3195.pth', model_dir=path_sam, file_name='sam_vit_l_0b3195.pth' ) return os.path.join(path_sam, 'sam_vit_l_0b3195.pth') def downloading_sam_vit_h(): load_file_from_url( url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_h_4b8939.pth', model_dir=path_sam, file_name='sam_vit_h_4b8939.pth' ) return os.path.join(path_sam, 'sam_vit_h_4b8939.pth') ================================================ FILE: modules/constants.py ================================================ # as in k-diffusion (sampling.py) MIN_SEED = 0 MAX_SEED = 2**63 - 1 AUTH_FILENAME = 'auth.json' ================================================ FILE: modules/core.py ================================================ import os import einops import torch import numpy as np import ldm_patched.modules.model_management import ldm_patched.modules.model_detection import ldm_patched.modules.model_patcher import ldm_patched.modules.utils import ldm_patched.modules.controlnet import modules.sample_hijack import ldm_patched.modules.samplers import ldm_patched.modules.latent_formats from ldm_patched.modules.sd import load_checkpoint_guess_config from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \ ControlNetApplyAdvanced from ldm_patched.contrib.external_freelunch import FreeU_V2 from ldm_patched.modules.sample import prepare_mask from modules.lora import match_lora from modules.util import get_file_from_folder_list from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip from modules.config import path_embeddings from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM opEmptyLatentImage = EmptyLatentImage() opVAEDecode = VAEDecode() opVAEEncode = VAEEncode() opVAEDecodeTiled = VAEDecodeTiled() opVAEEncodeTiled = VAEEncodeTiled() opControlNetApplyAdvanced = ControlNetApplyAdvanced() opFreeU = FreeU_V2() opModelSamplingDiscrete = ModelSamplingDiscrete() opModelSamplingContinuousEDM = ModelSamplingContinuousEDM() class StableDiffusionModel: def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None): self.unet = unet self.vae = vae self.clip = clip self.clip_vision = clip_vision self.filename = filename self.vae_filename = vae_filename self.unet_with_lora = unet self.clip_with_lora = clip self.visited_loras = '' self.lora_key_map_unet = {} self.lora_key_map_clip = {} if self.unet is not None: self.lora_key_map_unet = model_lora_keys_unet(self.unet.model, self.lora_key_map_unet) self.lora_key_map_unet.update({x: x for x in self.unet.model.state_dict().keys()}) if self.clip is not None: self.lora_key_map_clip = model_lora_keys_clip(self.clip.cond_stage_model, self.lora_key_map_clip) self.lora_key_map_clip.update({x: x for x in self.clip.cond_stage_model.state_dict().keys()}) @torch.no_grad() @torch.inference_mode() def refresh_loras(self, loras): assert isinstance(loras, list) if self.visited_loras == str(loras): return self.visited_loras = str(loras) if self.unet is None: return print(f'Request to load LoRAs {str(loras)} for model [{self.filename}].') loras_to_load = [] for filename, weight in loras: if filename == 'None': continue if os.path.exists(filename): lora_filename = filename else: lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras) if not os.path.exists(lora_filename): print(f'Lora file not found: {lora_filename}') continue loras_to_load.append((lora_filename, weight)) self.unet_with_lora = self.unet.clone() if self.unet is not None else None self.clip_with_lora = self.clip.clone() if self.clip is not None else None for lora_filename, weight in loras_to_load: lora_unmatch = ldm_patched.modules.utils.load_torch_file(lora_filename, safe_load=False) lora_unet, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_unet) lora_clip, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_clip) if len(lora_unmatch) > 12: # model mismatch continue if len(lora_unmatch) > 0: print(f'Loaded LoRA [{lora_filename}] for model [{self.filename}] ' f'with unmatched keys {list(lora_unmatch.keys())}') if self.unet_with_lora is not None and len(lora_unet) > 0: loaded_keys = self.unet_with_lora.add_patches(lora_unet, weight) print(f'Loaded LoRA [{lora_filename}] for UNet [{self.filename}] ' f'with {len(loaded_keys)} keys at weight {weight}.') for item in lora_unet: if item not in loaded_keys: print("UNet LoRA key skipped: ", item) if self.clip_with_lora is not None and len(lora_clip) > 0: loaded_keys = self.clip_with_lora.add_patches(lora_clip, weight) print(f'Loaded LoRA [{lora_filename}] for CLIP [{self.filename}] ' f'with {len(loaded_keys)} keys at weight {weight}.') for item in lora_clip: if item not in loaded_keys: print("CLIP LoRA key skipped: ", item) @torch.no_grad() @torch.inference_mode() def apply_freeu(model, b1, b2, s1, s2): return opFreeU.patch(model=model, b1=b1, b2=b2, s1=s1, s2=s2)[0] @torch.no_grad() @torch.inference_mode() def load_controlnet(ckpt_filename): return ldm_patched.modules.controlnet.load_controlnet(ckpt_filename) @torch.no_grad() @torch.inference_mode() def apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent): return opControlNetApplyAdvanced.apply_controlnet(positive=positive, negative=negative, control_net=control_net, image=image, strength=strength, start_percent=start_percent, end_percent=end_percent) @torch.no_grad() @torch.inference_mode() def load_model(ckpt_filename, vae_filename=None): unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings, vae_filename_param=vae_filename) return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename) @torch.no_grad() @torch.inference_mode() def generate_empty_latent(width=1024, height=1024, batch_size=1): return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0] @torch.no_grad() @torch.inference_mode() def decode_vae(vae, latent_image, tiled=False): if tiled: return opVAEDecodeTiled.decode(samples=latent_image, vae=vae, tile_size=512)[0] else: return opVAEDecode.decode(samples=latent_image, vae=vae)[0] @torch.no_grad() @torch.inference_mode() def encode_vae(vae, pixels, tiled=False): if tiled: return opVAEEncodeTiled.encode(pixels=pixels, vae=vae, tile_size=512)[0] else: return opVAEEncode.encode(pixels=pixels, vae=vae)[0] @torch.no_grad() @torch.inference_mode() def encode_vae_inpaint(vae, pixels, mask): assert mask.ndim == 3 and pixels.ndim == 4 assert mask.shape[-1] == pixels.shape[-2] assert mask.shape[-2] == pixels.shape[-3] w = mask.round()[..., None] pixels = pixels * (1 - w) + 0.5 * w latent = vae.encode(pixels) B, C, H, W = latent.shape latent_mask = mask[:, None, :, :] latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round() latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent) return latent, latent_mask class VAEApprox(torch.nn.Module): def __init__(self): super(VAEApprox, self).__init__() self.conv1 = torch.nn.Conv2d(4, 8, (7, 7)) self.conv2 = torch.nn.Conv2d(8, 16, (5, 5)) self.conv3 = torch.nn.Conv2d(16, 32, (3, 3)) self.conv4 = torch.nn.Conv2d(32, 64, (3, 3)) self.conv5 = torch.nn.Conv2d(64, 32, (3, 3)) self.conv6 = torch.nn.Conv2d(32, 16, (3, 3)) self.conv7 = torch.nn.Conv2d(16, 8, (3, 3)) self.conv8 = torch.nn.Conv2d(8, 3, (3, 3)) self.current_type = None def forward(self, x): extra = 11 x = torch.nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2)) x = torch.nn.functional.pad(x, (extra, extra, extra, extra)) for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8]: x = layer(x) x = torch.nn.functional.leaky_relu(x, 0.1) return x VAE_approx_models = {} @torch.no_grad() @torch.inference_mode() def get_previewer(model): global VAE_approx_models from modules.config import path_vae_approx is_sdxl = isinstance(model.model.latent_format, ldm_patched.modules.latent_formats.SDXL) vae_approx_filename = os.path.join(path_vae_approx, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth') if vae_approx_filename in VAE_approx_models: VAE_approx_model = VAE_approx_models[vae_approx_filename] else: sd = torch.load(vae_approx_filename, map_location='cpu', weights_only=True) VAE_approx_model = VAEApprox() VAE_approx_model.load_state_dict(sd) del sd VAE_approx_model.eval() if ldm_patched.modules.model_management.should_use_fp16(): VAE_approx_model.half() VAE_approx_model.current_type = torch.float16 else: VAE_approx_model.float() VAE_approx_model.current_type = torch.float32 VAE_approx_model.to(ldm_patched.modules.model_management.get_torch_device()) VAE_approx_models[vae_approx_filename] = VAE_approx_model @torch.no_grad() @torch.inference_mode() def preview_function(x0, step, total_steps): with torch.no_grad(): x_sample = x0.to(VAE_approx_model.current_type) x_sample = VAE_approx_model(x_sample) * 127.5 + 127.5 x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')[0] x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8) return x_sample return preview_function @torch.no_grad() @torch.inference_mode() def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu', scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1, previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False): if sigmas is not None: sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device()) latent_image = latent["samples"] if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) if isinstance(noise_mean, torch.Tensor): noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] previewer = get_previewer(model) if previewer_start is None: previewer_start = 0 if previewer_end is None: previewer_end = steps def callback(step, x0, x, total_steps): ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() y = None if previewer is not None and not disable_preview: y = previewer(x0, previewer_start + step, previewer_end) if callback_function is not None: callback_function(previewer_start + step, x0, x, previewer_end, y) disable_pbar = False modules.sample_hijack.current_refiner = refiner modules.sample_hijack.refiner_switch_step = refiner_switch ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked try: samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed, sigmas=sigmas) out = latent.copy() out["samples"] = samples finally: modules.sample_hijack.current_refiner = None return out @torch.no_grad() @torch.inference_mode() def pytorch_to_numpy(x): return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x] @torch.no_grad() @torch.inference_mode() def numpy_to_pytorch(x): y = x.astype(np.float32) / 255.0 y = y[None] y = np.ascontiguousarray(y.copy()) y = torch.from_numpy(y).float() return y ================================================ FILE: modules/default_pipeline.py ================================================ import modules.core as core import os import torch import modules.patch import modules.config import modules.flags import ldm_patched.modules.model_management import ldm_patched.modules.latent_formats import modules.inpaint_worker import extras.vae_interpose as vae_interpose from extras.expansion import FooocusExpansion from ldm_patched.modules.model_base import SDXL, SDXLRefiner from modules.sample_hijack import clip_separate from modules.util import get_file_from_folder_list, get_enabled_loras model_base = core.StableDiffusionModel() model_refiner = core.StableDiffusionModel() final_expansion = None final_unet = None final_clip = None final_vae = None final_refiner_unet = None final_refiner_vae = None loaded_ControlNets = {} @torch.no_grad() @torch.inference_mode() def refresh_controlnets(model_paths): global loaded_ControlNets cache = {} for p in model_paths: if p is not None: if p in loaded_ControlNets: cache[p] = loaded_ControlNets[p] else: cache[p] = core.load_controlnet(p) loaded_ControlNets = cache return @torch.no_grad() @torch.inference_mode() def assert_model_integrity(): error_message = None if not isinstance(model_base.unet_with_lora.model, SDXL): error_message = 'You have selected base model other than SDXL. This is not supported yet.' if error_message is not None: raise NotImplementedError(error_message) return True @torch.no_grad() @torch.inference_mode() def refresh_base_model(name, vae_name=None): global model_base filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) vae_filename = None if vae_name is not None and vae_name != modules.flags.default_vae: vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae) if model_base.filename == filename and model_base.vae_filename == vae_filename: return model_base = core.load_model(filename, vae_filename) print(f'Base model loaded: {model_base.filename}') print(f'VAE loaded: {model_base.vae_filename}') return @torch.no_grad() @torch.inference_mode() def refresh_refiner_model(name): global model_refiner filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) if model_refiner.filename == filename: return model_refiner = core.StableDiffusionModel() if name == 'None': print(f'Refiner unloaded.') return model_refiner = core.load_model(filename) print(f'Refiner model loaded: {model_refiner.filename}') if isinstance(model_refiner.unet.model, SDXL): model_refiner.clip = None model_refiner.vae = None elif isinstance(model_refiner.unet.model, SDXLRefiner): model_refiner.clip = None model_refiner.vae = None else: model_refiner.clip = None return @torch.no_grad() @torch.inference_mode() def synthesize_refiner_model(): global model_base, model_refiner print('Synthetic Refiner Activated') model_refiner = core.StableDiffusionModel( unet=model_base.unet, vae=model_base.vae, clip=model_base.clip, clip_vision=model_base.clip_vision, filename=model_base.filename ) model_refiner.vae = None model_refiner.clip = None model_refiner.clip_vision = None return @torch.no_grad() @torch.inference_mode() def refresh_loras(loras, base_model_additional_loras=None): global model_base, model_refiner if not isinstance(base_model_additional_loras, list): base_model_additional_loras = [] model_base.refresh_loras(loras + base_model_additional_loras) model_refiner.refresh_loras(loras) return @torch.no_grad() @torch.inference_mode() def clip_encode_single(clip, text, verbose=False): cached = clip.fcs_cond_cache.get(text, None) if cached is not None: if verbose: print(f'[CLIP Cached] {text}') return cached tokens = clip.tokenize(text) result = clip.encode_from_tokens(tokens, return_pooled=True) clip.fcs_cond_cache[text] = result if verbose: print(f'[CLIP Encoded] {text}') return result @torch.no_grad() @torch.inference_mode() def clone_cond(conds): results = [] for c, p in conds: p = p["pooled_output"] if isinstance(c, torch.Tensor): c = c.clone() if isinstance(p, torch.Tensor): p = p.clone() results.append([c, {"pooled_output": p}]) return results @torch.no_grad() @torch.inference_mode() def clip_encode(texts, pool_top_k=1): global final_clip if final_clip is None: return None if not isinstance(texts, list): return None if len(texts) == 0: return None cond_list = [] pooled_acc = 0 for i, text in enumerate(texts): cond, pooled = clip_encode_single(final_clip, text) cond_list.append(cond) if i < pool_top_k: pooled_acc += pooled return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]] @torch.no_grad() @torch.inference_mode() def set_clip_skip(clip_skip: int): global final_clip if final_clip is None: return final_clip.clip_layer(-abs(clip_skip)) return @torch.no_grad() @torch.inference_mode() def clear_all_caches(): final_clip.fcs_cond_cache = {} @torch.no_grad() @torch.inference_mode() def prepare_text_encoder(async_call=True): if async_call: # TODO: make sure that this is always called in an async way so that users cannot feel it. pass assert_model_integrity() ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher]) return @torch.no_grad() @torch.inference_mode() def refresh_everything(refiner_model_name, base_model_name, loras, base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None): global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion final_unet = None final_clip = None final_vae = None final_refiner_unet = None final_refiner_vae = None if use_synthetic_refiner and refiner_model_name == 'None': print('Synthetic Refiner Activated') refresh_base_model(base_model_name, vae_name) synthesize_refiner_model() else: refresh_refiner_model(refiner_model_name) refresh_base_model(base_model_name, vae_name) refresh_loras(loras, base_model_additional_loras=base_model_additional_loras) assert_model_integrity() final_unet = model_base.unet_with_lora final_clip = model_base.clip_with_lora final_vae = model_base.vae final_refiner_unet = model_refiner.unet_with_lora final_refiner_vae = model_refiner.vae if final_expansion is None: final_expansion = FooocusExpansion() prepare_text_encoder(async_call=True) clear_all_caches() return refresh_everything( refiner_model_name=modules.config.default_refiner_model_name, base_model_name=modules.config.default_base_model_name, loras=get_enabled_loras(modules.config.default_loras), vae_name=modules.config.default_vae, ) @torch.no_grad() @torch.inference_mode() def vae_parse(latent): if final_refiner_vae is None: return latent result = vae_interpose.parse(latent["samples"]) return {'samples': result} @torch.no_grad() @torch.inference_mode() def calculate_sigmas_all(sampler, model, scheduler, steps): from ldm_patched.modules.samplers import calculate_sigmas_scheduler discard_penultimate_sigma = False if sampler in ['dpm_2', 'dpm_2_ancestral']: steps += 1 discard_penultimate_sigma = True sigmas = calculate_sigmas_scheduler(model, scheduler, steps) if discard_penultimate_sigma: sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) return sigmas @torch.no_grad() @torch.inference_mode() def calculate_sigmas(sampler, model, scheduler, steps, denoise): if denoise is None or denoise > 0.9999: sigmas = calculate_sigmas_all(sampler, model, scheduler, steps) else: new_steps = int(steps / denoise) sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps) sigmas = sigmas[-(steps + 1):] return sigmas @torch.no_grad() @torch.inference_mode() def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'): assert refiner_swap_method in ['joint', 'separate', 'vae'] if final_refiner_vae is not None and final_refiner_unet is not None: if denoise > 0.9: return final_vae, final_refiner_vae else: if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 return final_vae, None else: return final_refiner_vae, None return final_vae, final_refiner_vae @torch.no_grad() @torch.inference_mode() def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False): target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \ = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip assert refiner_swap_method in ['joint', 'separate', 'vae'] if final_refiner_vae is not None and final_refiner_unet is not None: # Refiner Use Different VAE (then it is SD15) if denoise > 0.9: refiner_swap_method = 'vae' else: refiner_swap_method = 'joint' if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 target_unet, target_vae, target_refiner_unet, target_refiner_vae \ = final_unet, final_vae, None, None print(f'[Sampler] only use Base because of partial denoise.') else: positive_cond = clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip) negative_cond = clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip) target_unet, target_vae, target_refiner_unet, target_refiner_vae \ = final_refiner_unet, final_refiner_vae, None, None print(f'[Sampler] only use Refiner because of partial denoise.') print(f'[Sampler] refiner_swap_method = {refiner_swap_method}') if latent is None: initial_latent = core.generate_empty_latent(width=width, height=height, batch_size=1) else: initial_latent = latent minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise) sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max() sigma_min = float(sigma_min.cpu().numpy()) sigma_max = float(sigma_max.cpu().numpy()) print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}') modules.patch.BrownianTreeNoiseSamplerPatched.global_init( initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()), sigma_min, sigma_max, seed=image_seed, cpu=False) decoded_latent = None if refiner_swap_method == 'joint': sampled_latent = core.ksampler( model=target_unet, refiner=target_refiner_unet, positive=positive_cond, negative=negative_cond, latent=initial_latent, steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True, seed=image_seed, denoise=denoise, callback_function=callback, cfg=cfg_scale, sampler_name=sampler_name, scheduler=scheduler_name, refiner_switch=switch, previewer_start=0, previewer_end=steps, disable_preview=disable_preview ) decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled) if refiner_swap_method == 'separate': sampled_latent = core.ksampler( model=target_unet, positive=positive_cond, negative=negative_cond, latent=initial_latent, steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False, seed=image_seed, denoise=denoise, callback_function=callback, cfg=cfg_scale, sampler_name=sampler_name, scheduler=scheduler_name, previewer_start=0, previewer_end=steps, disable_preview=disable_preview ) print('Refiner swapped by changing ksampler. Noise preserved.') target_model = target_refiner_unet if target_model is None: target_model = target_unet print('Use base model to refine itself - this may because of developer mode.') sampled_latent = core.ksampler( model=target_model, positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), latent=sampled_latent, steps=steps, start_step=switch, last_step=steps, disable_noise=True, force_full_denoise=True, seed=image_seed, denoise=denoise, callback_function=callback, cfg=cfg_scale, sampler_name=sampler_name, scheduler=scheduler_name, previewer_start=switch, previewer_end=steps, disable_preview=disable_preview ) target_model = target_refiner_vae if target_model is None: target_model = target_vae decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) if refiner_swap_method == 'vae': modules.patch.patch_settings[os.getpid()].eps_record = 'vae' if modules.inpaint_worker.current_task is not None: modules.inpaint_worker.current_task.unswap() sampled_latent = core.ksampler( model=target_unet, positive=positive_cond, negative=negative_cond, latent=initial_latent, steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=True, seed=image_seed, denoise=denoise, callback_function=callback, cfg=cfg_scale, sampler_name=sampler_name, scheduler=scheduler_name, previewer_start=0, previewer_end=steps, disable_preview=disable_preview ) print('Fooocus VAE-based swap.') target_model = target_refiner_unet if target_model is None: target_model = target_unet print('Use base model to refine itself - this may because of developer mode.') sampled_latent = vae_parse(sampled_latent) k_sigmas = 1.4 sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=target_model.model, steps=steps, denoise=denoise)[switch:] * k_sigmas len_sigmas = len(sigmas) - 1 noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True) if modules.inpaint_worker.current_task is not None: modules.inpaint_worker.current_task.swap() sampled_latent = core.ksampler( model=target_model, positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), latent=sampled_latent, steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True, seed=image_seed+1, denoise=denoise, callback_function=callback, cfg=cfg_scale, sampler_name=sampler_name, scheduler=scheduler_name, previewer_start=switch, previewer_end=steps, sigmas=sigmas, noise_mean=noise_mean, disable_preview=disable_preview ) target_model = target_refiner_vae if target_model is None: target_model = target_vae decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) images = core.pytorch_to_numpy(decoded_latent) modules.patch.patch_settings[os.getpid()].eps_record = None return images ================================================ FILE: modules/extra_utils.py ================================================ import os from ast import literal_eval def makedirs_with_log(path): try: os.makedirs(path, exist_ok=True) except OSError as error: print(f'Directory {path} could not be created, reason: {error}') def get_files_from_folder(folder_path, extensions=None, name_filter=None): if not os.path.isdir(folder_path): raise ValueError("Folder path is not a valid directory.") filenames = [] for root, _, files in os.walk(folder_path, topdown=False): relative_path = os.path.relpath(root, folder_path) if relative_path == ".": relative_path = "" for filename in sorted(files, key=lambda s: s.casefold()): _, file_extension = os.path.splitext(filename) if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _): path = os.path.join(relative_path, filename) filenames.append(path) return filenames def try_eval_env_var(value: str, expected_type=None): try: value_eval = value if expected_type is bool: value_eval = value.title() value_eval = literal_eval(value_eval) if expected_type is not None and not isinstance(value_eval, expected_type): return value return value_eval except: return value ================================================ FILE: modules/flags.py ================================================ from enum import IntEnum, Enum disabled = 'Disabled' enabled = 'Enabled' subtle_variation = 'Vary (Subtle)' strong_variation = 'Vary (Strong)' upscale_15 = 'Upscale (1.5x)' upscale_2 = 'Upscale (2x)' upscale_fast = 'Upscale (Fast 2x)' uov_list = [disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast] enhancement_uov_before = "Before First Enhancement" enhancement_uov_after = "After Last Enhancement" enhancement_uov_processing_order = [enhancement_uov_before, enhancement_uov_after] enhancement_uov_prompt_type_original = 'Original Prompts' enhancement_uov_prompt_type_last_filled = 'Last Filled Enhancement Prompts' enhancement_uov_prompt_types = [enhancement_uov_prompt_type_original, enhancement_uov_prompt_type_last_filled] CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"] # fooocus: a1111 (Civitai) KSAMPLER = { "euler": "Euler", "euler_ancestral": "Euler a", "heun": "Heun", "heunpp2": "", "dpm_2": "DPM2", "dpm_2_ancestral": "DPM2 a", "lms": "LMS", "dpm_fast": "DPM fast", "dpm_adaptive": "DPM adaptive", "dpmpp_2s_ancestral": "DPM++ 2S a", "dpmpp_sde": "DPM++ SDE", "dpmpp_sde_gpu": "DPM++ SDE", "dpmpp_2m": "DPM++ 2M", "dpmpp_2m_sde": "DPM++ 2M SDE", "dpmpp_2m_sde_gpu": "DPM++ 2M SDE", "dpmpp_3m_sde": "", "dpmpp_3m_sde_gpu": "", "ddpm": "", "lcm": "LCM", "tcd": "TCD", "restart": "Restart" } SAMPLER_EXTRA = { "ddim": "DDIM", "uni_pc": "UniPC", "uni_pc_bh2": "" } SAMPLERS = KSAMPLER | SAMPLER_EXTRA KSAMPLER_NAMES = list(KSAMPLER.keys()) SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd", "edm_playground_v2.5"] SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys()) sampler_list = SAMPLER_NAMES scheduler_list = SCHEDULER_NAMES clip_skip_max = 12 default_vae = 'Default (model)' refiner_swap_method = 'joint' default_input_image_tab = 'uov_tab' input_image_tab_ids = ['uov_tab', 'ip_tab', 'inpaint_tab', 'describe_tab', 'enhance_tab', 'metadata_tab'] cn_ip = "ImagePrompt" cn_ip_face = "FaceSwap" cn_canny = "PyraCanny" cn_cpds = "CPDS" ip_list = [cn_ip, cn_canny, cn_cpds, cn_ip_face] default_ip = cn_ip default_parameters = { cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0) } # stop, weight output_formats = ['png', 'jpeg', 'webp'] inpaint_mask_models = ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam'] inpaint_mask_cloth_category = ['full', 'upper', 'lower'] inpaint_mask_sam_model = ['vit_b', 'vit_l', 'vit_h'] inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6'] inpaint_option_default = 'Inpaint or Outpaint (default)' inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)' inpaint_option_modify = 'Modify Content (add objects, change background, etc.)' inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify] describe_type_photo = 'Photograph' describe_type_anime = 'Art/Anime' describe_types = [describe_type_photo, describe_type_anime] sdxl_aspect_ratios = [ '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152', '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960', '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768', '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640', '1664*576', '1728*576' ] class MetadataScheme(Enum): FOOOCUS = 'fooocus' A1111 = 'a1111' metadata_scheme = [ (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value), (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value), ] class OutputFormat(Enum): PNG = 'png' JPEG = 'jpeg' WEBP = 'webp' @classmethod def list(cls) -> list: return list(map(lambda c: c.value, cls)) class PerformanceLoRA(Enum): QUALITY = None SPEED = None EXTREME_SPEED = 'sdxl_lcm_lora.safetensors' LIGHTNING = 'sdxl_lightning_4step_lora.safetensors' HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors' class Steps(IntEnum): QUALITY = 60 SPEED = 30 EXTREME_SPEED = 8 LIGHTNING = 4 HYPER_SD = 4 @classmethod def keys(cls) -> list: return list(map(lambda c: c, Steps.__members__)) class StepsUOV(IntEnum): QUALITY = 36 SPEED = 18 EXTREME_SPEED = 8 LIGHTNING = 4 HYPER_SD = 4 class Performance(Enum): QUALITY = 'Quality' SPEED = 'Speed' EXTREME_SPEED = 'Extreme Speed' LIGHTNING = 'Lightning' HYPER_SD = 'Hyper-SD' @classmethod def list(cls) -> list: return list(map(lambda c: (c.name, c.value), cls)) @classmethod def values(cls) -> list: return list(map(lambda c: c.value, cls)) @classmethod def by_steps(cls, steps: int | str): return cls[Steps(int(steps)).name] @classmethod def has_restricted_features(cls, x) -> bool: if isinstance(x, Performance): x = x.value return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value] def steps(self) -> int | None: return Steps[self.name].value if self.name in Steps.__members__ else None def steps_uov(self) -> int | None: return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None def lora_filename(self) -> str | None: return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None ================================================ FILE: modules/gradio_hijack.py ================================================ """gr.Image() component.""" from __future__ import annotations import warnings from pathlib import Path from typing import Any, Literal import numpy as np import PIL import PIL.ImageOps import gradio.routes import importlib from gradio_client import utils as client_utils from gradio_client.documentation import document, set_documentation_group from gradio_client.serializing import ImgSerializable from PIL import Image as _Image # using _ to minimize namespace pollution from gradio import processing_utils, utils, Error from gradio.components.base import IOComponent, _Keywords, Block from gradio.deprecation import warn_style_method_deprecation from gradio.events import ( Changeable, Clearable, Editable, EventListenerMethod, Selectable, Streamable, Uploadable, ) from gradio.interpretation import TokenInterpretable set_documentation_group("component") _Image.init() # fixes https://github.com/gradio-app/gradio/issues/2843 @document() class Image( Editable, Clearable, Changeable, Streamable, Selectable, Uploadable, IOComponent, ImgSerializable, TokenInterpretable, ): """ Creates an image component that can be used to upload/draw images (as an input) or display images (as an output). Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a {dict} with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`. Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image. Examples-format: a {str} filepath to a local file that contains the image. Demos: image_mod, image_mod_default_image Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan """ def __init__( self, value: str | _Image.Image | np.ndarray | None = None, *, shape: tuple[int, int] | None = None, height: int | None = None, width: int | None = None, image_mode: Literal[ "1", "L", "P", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F" ] = "RGB", invert_colors: bool = False, source: Literal["upload", "webcam", "canvas"] = "upload", tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None, type: Literal["numpy", "pil", "filepath"] = "numpy", label: str | None = None, every: float | None = None, show_label: bool | None = None, show_download_button: bool = True, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool = True, streaming: bool = False, elem_id: str | None = None, elem_classes: list[str] | str | None = None, mirror_webcam: bool = True, brush_radius: float | None = None, brush_color: str = "#000000", mask_opacity: float = 0.7, show_share_button: bool | None = None, **kwargs, ): """ Parameters: value: A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. shape: (width, height) shape to crop and resize image when passed to function. If None, matches input image size. Pass None for either width or height to only crop and resize the other. height: Height of the displayed image in pixels. width: Width of the displayed image in pixels. image_mode: "RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. invert_colors: whether to invert the image as a preprocessing step. source: Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools. tool: Tools used for editing. "editor" allows a full screen editor (and is the default if source is "upload" or "webcam"), "select" provides a cropping and zoom tool, "sketch" allows you to create a binary sketch (and is the default if source="canvas"), and "color-sketch" allows you to created a sketch in different colors. "color-sketch" can be used with source="upload" or "webcam" to allow sketching on an image. "sketch" can also be used with "upload" or "webcam" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys "image" and "mask" respectively. type: The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. label: component name in interface. every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. show_label: if True, will display label. show_download_button: If True, will display button to download image. container: If True, will place the component in a container - providing some extra padding around the border. scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output. visible: If False, component will be hidden. streaming: If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'. elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. mirror_webcam: If True webcam will be mirrored. Default is True. brush_radius: Size of the brush for Sketch. Default is None which chooses a sensible default brush_color: Color of the brush for Sketch as hex string. Default is "#000000". mask_opacity: Opacity of mask drawn on image, as a value between 0 and 1. show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise. """ self.brush_radius = brush_radius self.brush_color = brush_color self.mask_opacity = mask_opacity self.mirror_webcam = mirror_webcam valid_types = ["numpy", "pil", "filepath"] if type not in valid_types: raise ValueError( f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}" ) self.type = type self.shape = shape self.height = height self.width = width self.image_mode = image_mode valid_sources = ["upload", "webcam", "canvas"] if source not in valid_sources: raise ValueError( f"Invalid value for parameter `source`: {source}. Please choose from one of: {valid_sources}" ) self.source = source if tool is None: self.tool = "sketch" if source == "canvas" else "editor" else: self.tool = tool self.invert_colors = invert_colors self.streaming = streaming self.show_download_button = show_download_button if streaming and source != "webcam": raise ValueError("Image streaming only available if source is 'webcam'.") self.select: EventListenerMethod """ Event listener for when the user clicks on a pixel within the image. Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel. See EventData documentation on how to use this event data. """ self.show_share_button = ( (utils.get_space() is not None) if show_share_button is None else show_share_button ) IOComponent.__init__( self, label=label, every=every, show_label=show_label, container=container, scale=scale, min_width=min_width, interactive=interactive, visible=visible, elem_id=elem_id, elem_classes=elem_classes, value=value, **kwargs, ) TokenInterpretable.__init__(self) def get_config(self): return { "image_mode": self.image_mode, "shape": self.shape, "height": self.height, "width": self.width, "source": self.source, "tool": self.tool, "value": self.value, "streaming": self.streaming, "mirror_webcam": self.mirror_webcam, "brush_radius": self.brush_radius, "brush_color": self.brush_color, "mask_opacity": self.mask_opacity, "selectable": self.selectable, "show_share_button": self.show_share_button, "show_download_button": self.show_download_button, **IOComponent.get_config(self), } @staticmethod def update( value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE, height: int | None = None, width: int | None = None, label: str | None = None, show_label: bool | None = None, show_download_button: bool | None = None, container: bool | None = None, scale: int | None = None, min_width: int | None = None, interactive: bool | None = None, visible: bool | None = None, brush_radius: float | None = None, brush_color: str | None = None, mask_opacity: float | None = None, show_share_button: bool | None = None, ): return { "height": height, "width": width, "label": label, "show_label": show_label, "show_download_button": show_download_button, "container": container, "scale": scale, "min_width": min_width, "interactive": interactive, "visible": visible, "value": value, "brush_radius": brush_radius, "brush_color": brush_color, "mask_opacity": mask_opacity, "show_share_button": show_share_button, "__type__": "update", } def _format_image( self, im: _Image.Image | None ) -> np.ndarray | _Image.Image | str | None: """Helper method to format an image based on self.type""" if im is None: return im fmt = im.format if self.type == "pil": return im elif self.type == "numpy": return np.array(im) elif self.type == "filepath": path = self.pil_to_temp_file( im, dir=self.DEFAULT_TEMP_DIR, format=fmt or "png" ) self.temp_files.add(path) return path else: raise ValueError( "Unknown type: " + str(self.type) + ". Please choose from: 'numpy', 'pil', 'filepath'." ) def preprocess( self, x: str | dict[str, str] ) -> np.ndarray | _Image.Image | str | dict | None: """ Parameters: x: base64 url data, or (if tool == "sketch") a dict of image and mask base64 url data Returns: image in requested format, or (if tool == "sketch") a dict of image and mask in requested format """ if x is None: return x mask = None if self.tool == "sketch" and self.source in ["upload", "webcam"]: if isinstance(x, dict): x, mask = x["image"], x["mask"] assert isinstance(x, str) try: im = processing_utils.decode_base64_to_image(x) except PIL.UnidentifiedImageError: raise Error("Unsupported image type in input") with warnings.catch_warnings(): warnings.simplefilter("ignore") im = im.convert(self.image_mode) if self.shape is not None: im = processing_utils.resize_and_crop(im, self.shape) if self.invert_colors: im = PIL.ImageOps.invert(im) if ( self.source == "webcam" and self.mirror_webcam is True and self.tool != "color-sketch" ): im = PIL.ImageOps.mirror(im) if self.tool == "sketch" and self.source in ["upload", "webcam"]: if mask is not None: mask_im = processing_utils.decode_base64_to_image(mask) if mask_im.mode == "RGBA": # whiten any opaque pixels in the mask alpha_data = mask_im.getchannel("A").convert("L") mask_im = _Image.merge("RGB", [alpha_data, alpha_data, alpha_data]) return { "image": self._format_image(im), "mask": self._format_image(mask_im), } else: return { "image": self._format_image(im), "mask": None, } return self._format_image(im) def postprocess( self, y: np.ndarray | _Image.Image | str | Path | None ) -> str | None: """ Parameters: y: image as a numpy array, PIL Image, string/Path filepath, or string URL Returns: base64 url data """ if y is None: return None if isinstance(y, np.ndarray): return processing_utils.encode_array_to_base64(y) elif isinstance(y, _Image.Image): return processing_utils.encode_pil_to_base64(y) elif isinstance(y, (str, Path)): return client_utils.encode_url_or_file_to_base64(y) else: raise ValueError("Cannot process this value as an Image") def set_interpret_parameters(self, segments: int = 16): """ Calculates interpretation score of image subsections by splitting the image into subsections, then using a "leave one out" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value. Parameters: segments: Number of interpretation segments to split image into. """ self.interpretation_segments = segments return self def _segment_by_slic(self, x): """ Helper method that segments an image into superpixels using slic. Parameters: x: base64 representation of an image """ x = processing_utils.decode_base64_to_image(x) if self.shape is not None: x = processing_utils.resize_and_crop(x, self.shape) resized_and_cropped_image = np.array(x) try: from skimage.segmentation import slic except (ImportError, ModuleNotFoundError) as err: raise ValueError( "Error: running this interpretation for images requires scikit-image, please install it first." ) from err try: segments_slic = slic( resized_and_cropped_image, self.interpretation_segments, compactness=10, sigma=1, start_label=1, ) except TypeError: # For skimage 0.16 and older segments_slic = slic( resized_and_cropped_image, self.interpretation_segments, compactness=10, sigma=1, ) return segments_slic, resized_and_cropped_image def tokenize(self, x): """ Segments image into tokens, masks, and leave-one-out-tokens Parameters: x: base64 representation of an image Returns: tokens: list of tokens, used by the get_masked_input() method leave_one_out_tokens: list of left-out tokens, used by the get_interpretation_neighbors() method masks: list of masks, used by the get_interpretation_neighbors() method """ segments_slic, resized_and_cropped_image = self._segment_by_slic(x) tokens, masks, leave_one_out_tokens = [], [], [] replace_color = np.mean(resized_and_cropped_image, axis=(0, 1)) for segment_value in np.unique(segments_slic): mask = segments_slic == segment_value image_screen = np.copy(resized_and_cropped_image) image_screen[segments_slic == segment_value] = replace_color leave_one_out_tokens.append( processing_utils.encode_array_to_base64(image_screen) ) token = np.copy(resized_and_cropped_image) token[segments_slic != segment_value] = 0 tokens.append(token) masks.append(mask) return tokens, leave_one_out_tokens, masks def get_masked_inputs(self, tokens, binary_mask_matrix): masked_inputs = [] for binary_mask_vector in binary_mask_matrix: masked_input = np.zeros_like(tokens[0], dtype=int) for token, b in zip(tokens, binary_mask_vector): masked_input = masked_input + token * int(b) masked_inputs.append(processing_utils.encode_array_to_base64(masked_input)) return masked_inputs def get_interpretation_scores( self, x, neighbors, scores, masks, tokens=None, **kwargs ) -> list[list[float]]: """ Returns: A 2D array representing the interpretation score of each pixel of the image. """ x = processing_utils.decode_base64_to_image(x) if self.shape is not None: x = processing_utils.resize_and_crop(x, self.shape) x = np.array(x) output_scores = np.zeros((x.shape[0], x.shape[1])) for score, mask in zip(scores, masks): output_scores += score * mask max_val, min_val = np.max(output_scores), np.min(output_scores) if max_val > 0: output_scores = (output_scores - min_val) / (max_val - min_val) return output_scores.tolist() def style(self, *, height: int | None = None, width: int | None = None, **kwargs): """ This method is deprecated. Please set these arguments in the constructor instead. """ warn_style_method_deprecation() if height is not None: self.height = height if width is not None: self.width = width return self def check_streamable(self): if self.source != "webcam": raise ValueError("Image streaming only available if source is 'webcam'.") def as_example(self, input_data: str | None) -> str: if input_data is None: return "" elif ( self.root_url ): # If an externally hosted image, don't convert to absolute path return input_data return str(utils.abspath(input_data)) all_components = [] if not hasattr(Block, 'original__init__'): Block.original_init = Block.__init__ def blk_ini(self, *args, **kwargs): all_components.append(self) return Block.original_init(self, *args, **kwargs) Block.__init__ = blk_ini gradio.routes.asyncio = importlib.reload(gradio.routes.asyncio) if not hasattr(gradio.routes.asyncio, 'original_wait_for'): gradio.routes.asyncio.original_wait_for = gradio.routes.asyncio.wait_for def patched_wait_for(fut, timeout): del timeout return gradio.routes.asyncio.original_wait_for(fut, timeout=65535) gradio.routes.asyncio.wait_for = patched_wait_for ================================================ FILE: modules/hash_cache.py ================================================ import json import os from concurrent.futures import ThreadPoolExecutor from multiprocessing import cpu_count import args_manager from modules.util import sha256, HASH_SHA256_LENGTH, get_file_from_folder_list hash_cache_filename = 'hash_cache.txt' hash_cache = {} def sha256_from_cache(filepath): global hash_cache if filepath not in hash_cache: print(f"[Cache] Calculating sha256 for {filepath}") hash_value = sha256(filepath) print(f"[Cache] sha256 for {filepath}: {hash_value}") hash_cache[filepath] = hash_value save_cache_to_file(filepath, hash_value) return hash_cache[filepath] def load_cache_from_file(): global hash_cache try: if os.path.exists(hash_cache_filename): with open(hash_cache_filename, 'rt', encoding='utf-8') as fp: for line in fp: entry = json.loads(line) for filepath, hash_value in entry.items(): if not os.path.exists(filepath) or not isinstance(hash_value, str) and len(hash_value) != HASH_SHA256_LENGTH: print(f'[Cache] Skipping invalid cache entry: {filepath}') continue hash_cache[filepath] = hash_value except Exception as e: print(f'[Cache] Loading failed: {e}') def save_cache_to_file(filename=None, hash_value=None): global hash_cache if filename is not None and hash_value is not None: items = [(filename, hash_value)] mode = 'at' else: items = sorted(hash_cache.items()) mode = 'wt' try: with open(hash_cache_filename, mode, encoding='utf-8') as fp: for filepath, hash_value in items: json.dump({filepath: hash_value}, fp) fp.write('\n') except Exception as e: print(f'[Cache] Saving failed: {e}') def init_cache(model_filenames, paths_checkpoints, lora_filenames, paths_loras): load_cache_from_file() if args_manager.args.rebuild_hash_cache: max_workers = args_manager.args.rebuild_hash_cache if args_manager.args.rebuild_hash_cache > 0 else cpu_count() rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers) # write cache to file again for sorting and cleanup of invalid cache entries save_cache_to_file() def rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers=cpu_count()): def thread(filename, paths): filepath = get_file_from_folder_list(filename, paths) sha256_from_cache(filepath) print('[Cache] Rebuilding hash cache') with ThreadPoolExecutor(max_workers=max_workers) as executor: for model_filename in model_filenames: executor.submit(thread, model_filename, paths_checkpoints) for lora_filename in lora_filenames: executor.submit(thread, lora_filename, paths_loras) print('[Cache] Done') ================================================ FILE: modules/html.py ================================================ progress_html = '''
*text*
''' def make_progress_html(number, text): return progress_html.replace('*number*', str(number)).replace('*text*', text) ================================================ FILE: modules/inpaint_worker.py ================================================ import torch import numpy as np from PIL import Image, ImageFilter from modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil from modules.upscaler import perform_upscale import cv2 inpaint_head_model = None class InpaintHead(torch.nn.Module): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device='cpu')) def __call__(self, x): x = torch.nn.functional.pad(x, (1, 1, 1, 1), "replicate") return torch.nn.functional.conv2d(input=x, weight=self.head) current_task = None def box_blur(x, k): x = Image.fromarray(x) x = x.filter(ImageFilter.BoxBlur(k)) return np.array(x) def max_filter_opencv(x, ksize=3): # Use OpenCV maximum filter # Make sure the input type is int16 return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16)) def morphological_open(x): # Convert array to int16 type via threshold operation x_int16 = np.zeros_like(x, dtype=np.int16) x_int16[x > 127] = 256 for i in range(32): # Use int16 type to avoid overflow maxed = max_filter_opencv(x_int16, ksize=3) - 8 x_int16 = np.maximum(maxed, x_int16) # Clip negative values to 0 and convert back to uint8 type x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8) return x_uint8 def up255(x, t=0): y = np.zeros_like(x).astype(np.uint8) y[x > t] = 255 return y def imsave(x, path): x = Image.fromarray(x) x.save(path) def regulate_abcd(x, a, b, c, d): H, W = x.shape[:2] if a < 0: a = 0 if a > H: a = H if b < 0: b = 0 if b > H: b = H if c < 0: c = 0 if c > W: c = W if d < 0: d = 0 if d > W: d = W return int(a), int(b), int(c), int(d) def compute_initial_abcd(x): indices = np.where(x) a = np.min(indices[0]) b = np.max(indices[0]) c = np.min(indices[1]) d = np.max(indices[1]) abp = (b + a) // 2 abm = (b - a) // 2 cdp = (d + c) // 2 cdm = (d - c) // 2 l = int(max(abm, cdm) * 1.15) a = abp - l b = abp + l + 1 c = cdp - l d = cdp + l + 1 a, b, c, d = regulate_abcd(x, a, b, c, d) return a, b, c, d def solve_abcd(x, a, b, c, d, k): k = float(k) assert 0.0 <= k <= 1.0 H, W = x.shape[:2] if k == 1.0: return 0, H, 0, W while True: if b - a >= H * k and d - c >= W * k: break add_h = (b - a) < (d - c) add_w = not add_h if b - a == H: add_w = True if d - c == W: add_h = True if add_h: a -= 1 b += 1 if add_w: c -= 1 d += 1 a, b, c, d = regulate_abcd(x, a, b, c, d) return a, b, c, d def fooocus_fill(image, mask): current_image = image.copy() raw_image = image.copy() area = np.where(mask < 127) store = raw_image[area] for k, repeats in [(512, 2), (256, 2), (128, 4), (64, 4), (33, 8), (15, 8), (5, 16), (3, 16)]: for _ in range(repeats): current_image = box_blur(current_image, k) current_image[area] = store return current_image class InpaintWorker: def __init__(self, image, mask, use_fill=True, k=0.618): a, b, c, d = compute_initial_abcd(mask > 0) a, b, c, d = solve_abcd(mask, a, b, c, d, k=k) # interested area self.interested_area = (a, b, c, d) self.interested_mask = mask[a:b, c:d] self.interested_image = image[a:b, c:d] # super resolution if get_image_shape_ceil(self.interested_image) < 1024: self.interested_image = perform_upscale(self.interested_image) # resize to make images ready for diffusion self.interested_image = set_image_shape_ceil(self.interested_image, 1024) self.interested_fill = self.interested_image.copy() H, W, C = self.interested_image.shape # process mask self.interested_mask = up255(resample_image(self.interested_mask, W, H), t=127) # compute filling if use_fill: self.interested_fill = fooocus_fill(self.interested_image, self.interested_mask) # soft pixels self.mask = morphological_open(mask) self.image = image # ending self.latent = None self.latent_after_swap = None self.swapped = False self.latent_mask = None self.inpaint_head_feature = None return def load_latent(self, latent_fill, latent_mask, latent_swap=None): self.latent = latent_fill self.latent_mask = latent_mask self.latent_after_swap = latent_swap return def patch(self, inpaint_head_model_path, inpaint_latent, inpaint_latent_mask, model): global inpaint_head_model if inpaint_head_model is None: inpaint_head_model = InpaintHead() sd = torch.load(inpaint_head_model_path, map_location='cpu', weights_only=True) inpaint_head_model.load_state_dict(sd) feed = torch.cat([ inpaint_latent_mask, model.model.process_latent_in(inpaint_latent) ], dim=1) inpaint_head_model.to(device=feed.device, dtype=feed.dtype) inpaint_head_feature = inpaint_head_model(feed) def input_block_patch(h, transformer_options): if transformer_options["block"][1] == 0: h = h + inpaint_head_feature.to(h) return h m = model.clone() m.set_model_input_block_patch(input_block_patch) return m def swap(self): if self.swapped: return if self.latent is None: return if self.latent_after_swap is None: return self.latent, self.latent_after_swap = self.latent_after_swap, self.latent self.swapped = True return def unswap(self): if not self.swapped: return if self.latent is None: return if self.latent_after_swap is None: return self.latent, self.latent_after_swap = self.latent_after_swap, self.latent self.swapped = False return def color_correction(self, img): fg = img.astype(np.float32) bg = self.image.copy().astype(np.float32) w = self.mask[:, :, None].astype(np.float32) / 255.0 y = fg * w + bg * (1 - w) return y.clip(0, 255).astype(np.uint8) def post_process(self, img): a, b, c, d = self.interested_area content = resample_image(img, d - c, b - a) result = self.image.copy() result[a:b, c:d] = content result = self.color_correction(result) return result def visualize_mask_processing(self): return [self.interested_fill, self.interested_mask, self.interested_image] ================================================ FILE: modules/launch_util.py ================================================ import os import importlib import importlib.util import shutil import subprocess import sys import re import logging import importlib.metadata import packaging.version from packaging.requirements import Requirement logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh... logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) re_requirement = re.compile(r"\s*([-\w]+)\s*(?:==\s*([-+.\w]+))?\s*") python = sys.executable default_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == "1") index_url = os.environ.get('INDEX_URL', "") modules_path = os.path.dirname(os.path.realpath(__file__)) script_path = os.path.dirname(modules_path) def is_installed(package): try: spec = importlib.util.find_spec(package) except ModuleNotFoundError: return False return spec is not None def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str: if desc is not None: print(desc) run_kwargs = { "args": command, "shell": True, "env": os.environ if custom_env is None else custom_env, "encoding": 'utf8', "errors": 'ignore', } if not live: run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE result = subprocess.run(**run_kwargs) if result.returncode != 0: error_bits = [ f"{errdesc or 'Error running command'}.", f"Command: {command}", f"Error code: {result.returncode}", ] if result.stdout: error_bits.append(f"stdout: {result.stdout}") if result.stderr: error_bits.append(f"stderr: {result.stderr}") raise RuntimeError("\n".join(error_bits)) return (result.stdout or "") def run_pip(command, desc=None, live=default_command_live): try: index_url_line = f' --index-url {index_url}' if index_url != '' else '' return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live) except Exception as e: print(e) print(f'CMD Failed {desc}: {command}') return None def requirements_met(requirements_file): with open(requirements_file, "r", encoding="utf8") as file: for line in file: line = line.strip() if line == "" or line.startswith('#'): continue requirement = Requirement(line) package = requirement.name try: version_installed = importlib.metadata.version(package) installed_version = packaging.version.parse(version_installed) # Check if the installed version satisfies the requirement if installed_version not in requirement.specifier: print(f"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}") return False except Exception as e: print(f"Error checking version for {package}: {e}") return False return True def delete_folder_content(folder, prefix=None): result = True for filename in os.listdir(folder): file_path = os.path.join(folder, filename) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) elif os.path.isdir(file_path): shutil.rmtree(file_path) except Exception as e: print(f'{prefix}Failed to delete {file_path}. Reason: {e}') result = False return result ================================================ FILE: modules/localization.py ================================================ import json import os current_translation = {} localization_root = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'language') def localization_js(filename): global current_translation if isinstance(filename, str): full_name = os.path.abspath(os.path.join(localization_root, filename + '.json')) if os.path.exists(full_name): try: with open(full_name, encoding='utf-8') as f: current_translation = json.load(f) assert isinstance(current_translation, dict) for k, v in current_translation.items(): assert isinstance(k, str) assert isinstance(v, str) except Exception as e: print(str(e)) print(f'Failed to load localization file {full_name}') # current_translation = {k: 'XXX' for k in current_translation.keys()} # use this to see if all texts are covered return f"window.localization = {json.dumps(current_translation)}" def dump_english_config(components): all_texts = [] for c in components: label = getattr(c, 'label', None) value = getattr(c, 'value', None) choices = getattr(c, 'choices', None) info = getattr(c, 'info', None) if isinstance(label, str): all_texts.append(label) if isinstance(value, str): all_texts.append(value) if isinstance(info, str): all_texts.append(info) if isinstance(choices, list): for x in choices: if isinstance(x, str): all_texts.append(x) if isinstance(x, tuple): for y in x: if isinstance(y, str): all_texts.append(y) config_dict = {k: k for k in all_texts if k != "" and 'progress-container' not in k} full_name = os.path.abspath(os.path.join(localization_root, 'en.json')) with open(full_name, "w", encoding="utf-8") as json_file: json.dump(config_dict, json_file, indent=4) return ================================================ FILE: modules/lora.py ================================================ def match_lora(lora, to_load): patch_dict = {} loaded_keys = set() for x in to_load: real_load_key = to_load[x] if real_load_key in lora: patch_dict[real_load_key] = ('fooocus', lora[real_load_key]) loaded_keys.add(real_load_key) continue alpha_name = "{}.alpha".format(x) alpha = None if alpha_name in lora.keys(): alpha = lora[alpha_name].item() loaded_keys.add(alpha_name) regular_lora = "{}.lora_up.weight".format(x) diffusers_lora = "{}_lora.up.weight".format(x) transformers_lora = "{}.lora_linear_layer.up.weight".format(x) A_name = None if regular_lora in lora.keys(): A_name = regular_lora B_name = "{}.lora_down.weight".format(x) mid_name = "{}.lora_mid.weight".format(x) elif diffusers_lora in lora.keys(): A_name = diffusers_lora B_name = "{}_lora.down.weight".format(x) mid_name = None elif transformers_lora in lora.keys(): A_name = transformers_lora B_name ="{}.lora_linear_layer.down.weight".format(x) mid_name = None if A_name is not None: mid = None if mid_name is not None and mid_name in lora.keys(): mid = lora[mid_name] loaded_keys.add(mid_name) patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) loaded_keys.add(A_name) loaded_keys.add(B_name) ######## loha hada_w1_a_name = "{}.hada_w1_a".format(x) hada_w1_b_name = "{}.hada_w1_b".format(x) hada_w2_a_name = "{}.hada_w2_a".format(x) hada_w2_b_name = "{}.hada_w2_b".format(x) hada_t1_name = "{}.hada_t1".format(x) hada_t2_name = "{}.hada_t2".format(x) if hada_w1_a_name in lora.keys(): hada_t1 = None hada_t2 = None if hada_t1_name in lora.keys(): hada_t1 = lora[hada_t1_name] hada_t2 = lora[hada_t2_name] loaded_keys.add(hada_t1_name) loaded_keys.add(hada_t2_name) patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) loaded_keys.add(hada_w1_a_name) loaded_keys.add(hada_w1_b_name) loaded_keys.add(hada_w2_a_name) loaded_keys.add(hada_w2_b_name) ######## lokr lokr_w1_name = "{}.lokr_w1".format(x) lokr_w2_name = "{}.lokr_w2".format(x) lokr_w1_a_name = "{}.lokr_w1_a".format(x) lokr_w1_b_name = "{}.lokr_w1_b".format(x) lokr_t2_name = "{}.lokr_t2".format(x) lokr_w2_a_name = "{}.lokr_w2_a".format(x) lokr_w2_b_name = "{}.lokr_w2_b".format(x) lokr_w1 = None if lokr_w1_name in lora.keys(): lokr_w1 = lora[lokr_w1_name] loaded_keys.add(lokr_w1_name) lokr_w2 = None if lokr_w2_name in lora.keys(): lokr_w2 = lora[lokr_w2_name] loaded_keys.add(lokr_w2_name) lokr_w1_a = None if lokr_w1_a_name in lora.keys(): lokr_w1_a = lora[lokr_w1_a_name] loaded_keys.add(lokr_w1_a_name) lokr_w1_b = None if lokr_w1_b_name in lora.keys(): lokr_w1_b = lora[lokr_w1_b_name] loaded_keys.add(lokr_w1_b_name) lokr_w2_a = None if lokr_w2_a_name in lora.keys(): lokr_w2_a = lora[lokr_w2_a_name] loaded_keys.add(lokr_w2_a_name) lokr_w2_b = None if lokr_w2_b_name in lora.keys(): lokr_w2_b = lora[lokr_w2_b_name] loaded_keys.add(lokr_w2_b_name) lokr_t2 = None if lokr_t2_name in lora.keys(): lokr_t2 = lora[lokr_t2_name] loaded_keys.add(lokr_t2_name) if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) #glora a1_name = "{}.a1.weight".format(x) a2_name = "{}.a2.weight".format(x) b1_name = "{}.b1.weight".format(x) b2_name = "{}.b2.weight".format(x) if a1_name in lora: patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) loaded_keys.add(a1_name) loaded_keys.add(a2_name) loaded_keys.add(b1_name) loaded_keys.add(b2_name) w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) w_norm = lora.get(w_norm_name, None) b_norm = lora.get(b_norm_name, None) if w_norm is not None: loaded_keys.add(w_norm_name) patch_dict[to_load[x]] = ("diff", (w_norm,)) if b_norm is not None: loaded_keys.add(b_norm_name) patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) diff_name = "{}.diff".format(x) diff_weight = lora.get(diff_name, None) if diff_weight is not None: patch_dict[to_load[x]] = ("diff", (diff_weight,)) loaded_keys.add(diff_name) diff_bias_name = "{}.diff_b".format(x) diff_bias = lora.get(diff_bias_name, None) if diff_bias is not None: patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) loaded_keys.add(diff_bias_name) remaining_dict = {x: y for x, y in lora.items() if x not in loaded_keys} return patch_dict, remaining_dict ================================================ FILE: modules/meta_parser.py ================================================ import json import re from abc import ABC, abstractmethod from pathlib import Path import gradio as gr from PIL import Image import fooocus_version import modules.config import modules.sdxl_styles from modules.flags import MetadataScheme, Performance, Steps from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS from modules.hash_cache import sha256_from_cache from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)' re_param = re.compile(re_param_code) re_imagesize = re.compile(r"^(\d+)x(\d+)$") def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool, inpaint_mode: str): loaded_parameter_dict = raw_metadata if isinstance(raw_metadata, str): loaded_parameter_dict = json.loads(raw_metadata) assert isinstance(loaded_parameter_dict, dict) results = [len(loaded_parameter_dict) > 0] get_image_number('image_number', 'Image Number', loaded_parameter_dict, results) get_str('prompt', 'Prompt', loaded_parameter_dict, results) get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results) get_list('styles', 'Styles', loaded_parameter_dict, results) performance = get_str('performance', 'Performance', loaded_parameter_dict, results) get_steps('steps', 'Steps', loaded_parameter_dict, results) get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results) get_resolution('resolution', 'Resolution', loaded_parameter_dict, results) get_number('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results) get_number('sharpness', 'Sharpness', loaded_parameter_dict, results) get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results) get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results) get_number('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results) get_number('clip_skip', 'CLIP Skip', loaded_parameter_dict, results, cast_type=int) get_str('base_model', 'Base Model', loaded_parameter_dict, results) get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results) get_number('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results) get_str('sampler', 'Sampler', loaded_parameter_dict, results) get_str('scheduler', 'Scheduler', loaded_parameter_dict, results) get_str('vae', 'VAE', loaded_parameter_dict, results) get_seed('seed', 'Seed', loaded_parameter_dict, results) get_inpaint_engine_version('inpaint_engine_version', 'Inpaint Engine Version', loaded_parameter_dict, results, inpaint_mode) get_inpaint_method('inpaint_method', 'Inpaint Mode', loaded_parameter_dict, results) if is_generating: results.append(gr.update()) else: results.append(gr.update(visible=True)) results.append(gr.update(visible=False)) get_freeu('freeu', 'FreeU', loaded_parameter_dict, results) # prevent performance LoRAs to be added twice, by performance and by lora performance_filename = None if performance is not None and performance in Performance.values(): performance = Performance(performance) performance_filename = performance.lora_filename() for i in range(modules.config.default_max_lora_number): get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename) return results def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None: try: h = source_dict.get(key, source_dict.get(fallback, default)) assert isinstance(h, str) results.append(h) return h except: results.append(gr.update()) return None def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) h = eval(h) assert isinstance(h, list) results.append(h) except: results.append(gr.update()) def get_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None, cast_type=float): try: h = source_dict.get(key, source_dict.get(fallback, default)) assert h is not None h = cast_type(h) results.append(h) except: results.append(gr.update()) def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) assert h is not None h = int(h) h = min(h, modules.config.default_max_image_number) results.append(h) except: results.append(1) def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) assert h is not None h = int(h) # if not in steps or in steps and performance is not the same performance_name = source_dict.get('performance', '').replace(' ', '_').replace('-', '_').casefold() performance_candidates = [key for key in Steps.keys() if key.casefold() == performance_name and Steps[key] == h] if len(performance_candidates) == 0: results.append(h) return results.append(-1) except: results.append(-1) def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) width, height = eval(h) formatted = modules.config.add_ratio(f'{width}*{height}') if formatted in modules.config.available_aspect_ratios_labels: results.append(formatted) results.append(-1) results.append(-1) else: results.append(gr.update()) results.append(int(width)) results.append(int(height)) except: results.append(gr.update()) results.append(gr.update()) results.append(gr.update()) def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) assert h is not None h = int(h) results.append(False) results.append(h) except: results.append(gr.update()) results.append(gr.update()) def get_inpaint_engine_version(key: str, fallback: str | None, source_dict: dict, results: list, inpaint_mode: str, default=None) -> str | None: try: h = source_dict.get(key, source_dict.get(fallback, default)) assert isinstance(h, str) and h in modules.flags.inpaint_engine_versions if inpaint_mode != modules.flags.inpaint_option_detail: results.append(h) else: results.append(gr.update()) results.append(h) return h except: results.append(gr.update()) results.append('empty') return None def get_inpaint_method(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None: try: h = source_dict.get(key, source_dict.get(fallback, default)) assert isinstance(h, str) and h in modules.flags.inpaint_options results.append(h) for i in range(modules.config.default_enhance_tabs): results.append(h) return h except: results.append(gr.update()) for i in range(modules.config.default_enhance_tabs): results.append(gr.update()) def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) p, n, e = eval(h) results.append(float(p)) results.append(float(n)) results.append(float(e)) except: results.append(gr.update()) results.append(gr.update()) results.append(gr.update()) def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None): try: h = source_dict.get(key, source_dict.get(fallback, default)) b1, b2, s1, s2 = eval(h) results.append(True) results.append(float(b1)) results.append(float(b2)) results.append(float(s1)) results.append(float(s2)) except: results.append(False) results.append(gr.update()) results.append(gr.update()) results.append(gr.update()) results.append(gr.update()) def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None): try: split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ') enabled = True name = split_data[0] weight = split_data[1] if len(split_data) == 3: enabled = split_data[0] == 'True' name = split_data[1] weight = split_data[2] if name == performance_filename: raise Exception weight = float(weight) results.append(enabled) results.append(name) results.append(weight) except: results.append(True) results.append('None') results.append(1) def parse_meta_from_preset(preset_content): assert isinstance(preset_content, dict) preset_prepared = {} items = preset_content for settings_key, meta_key in modules.config.possible_preset_keys.items(): if settings_key == "default_loras": loras = getattr(modules.config, settings_key) if settings_key in items: loras = items[settings_key] for index, lora in enumerate(loras[:modules.config.default_max_lora_number]): preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora)) elif settings_key == "default_aspect_ratio": if settings_key in items and items[settings_key] is not None: default_aspect_ratio = items[settings_key] width, height = default_aspect_ratio.split('*') else: default_aspect_ratio = getattr(modules.config, settings_key) width, height = default_aspect_ratio.split('×') height = height[:height.index(" ")] preset_prepared[meta_key] = (width, height) else: preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[settings_key] is not None else getattr(modules.config, settings_key) if settings_key == "default_styles" or settings_key == "default_aspect_ratio": preset_prepared[meta_key] = str(preset_prepared[meta_key]) return preset_prepared class MetadataParser(ABC): def __init__(self): self.raw_prompt: str = '' self.full_prompt: str = '' self.raw_negative_prompt: str = '' self.full_negative_prompt: str = '' self.steps: int = Steps.SPEED.value self.base_model_name: str = '' self.base_model_hash: str = '' self.refiner_model_name: str = '' self.refiner_model_hash: str = '' self.loras: list = [] self.vae_name: str = '' @abstractmethod def get_scheme(self) -> MetadataScheme: raise NotImplementedError @abstractmethod def to_json(self, metadata: dict | str) -> dict: raise NotImplementedError @abstractmethod def to_string(self, metadata: dict) -> str: raise NotImplementedError def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name, refiner_model_name, loras, vae_name): self.raw_prompt = raw_prompt self.full_prompt = full_prompt self.raw_negative_prompt = raw_negative_prompt self.full_negative_prompt = full_negative_prompt self.steps = steps self.base_model_name = Path(base_model_name).stem base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints) self.base_model_hash = sha256_from_cache(base_model_path) if refiner_model_name not in ['', 'None']: self.refiner_model_name = Path(refiner_model_name).stem refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints) self.refiner_model_hash = sha256_from_cache(refiner_model_path) self.loras = [] for (lora_name, lora_weight) in loras: if lora_name != 'None': lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras) lora_hash = sha256_from_cache(lora_path) self.loras.append((Path(lora_name).stem, lora_weight, lora_hash)) self.vae_name = Path(vae_name).stem class A1111MetadataParser(MetadataParser): def get_scheme(self) -> MetadataScheme: return MetadataScheme.A1111 fooocus_to_a1111 = { 'raw_prompt': 'Raw prompt', 'raw_negative_prompt': 'Raw negative prompt', 'negative_prompt': 'Negative prompt', 'styles': 'Styles', 'performance': 'Performance', 'steps': 'Steps', 'sampler': 'Sampler', 'scheduler': 'Scheduler', 'vae': 'VAE', 'guidance_scale': 'CFG scale', 'seed': 'Seed', 'resolution': 'Size', 'sharpness': 'Sharpness', 'adm_guidance': 'ADM Guidance', 'refiner_swap_method': 'Refiner Swap Method', 'adaptive_cfg': 'Adaptive CFG', 'clip_skip': 'Clip skip', 'overwrite_switch': 'Overwrite Switch', 'freeu': 'FreeU', 'base_model': 'Model', 'base_model_hash': 'Model hash', 'refiner_model': 'Refiner', 'refiner_model_hash': 'Refiner hash', 'lora_hashes': 'Lora hashes', 'lora_weights': 'Lora weights', 'created_by': 'User', 'version': 'Version' } def to_json(self, metadata: str) -> dict: metadata_prompt = '' metadata_negative_prompt = '' done_with_prompt = False *lines, lastline = metadata.strip().split("\n") if len(re_param.findall(lastline)) < 3: lines.append(lastline) lastline = '' for line in lines: line = line.strip() if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"): done_with_prompt = True line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip() if done_with_prompt: metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line else: metadata_prompt += ('' if metadata_prompt == '' else "\n") + line found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt) data = { 'prompt': prompt, 'negative_prompt': negative_prompt } for k, v in re_param.findall(lastline): try: if v != '' and v[0] == '"' and v[-1] == '"': v = unquote(v) m = re_imagesize.match(v) if m is not None: data['resolution'] = str((m.group(1), m.group(2))) else: data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v except Exception: print(f"Error parsing \"{k}: {v}\"") # workaround for multiline prompts if 'raw_prompt' in data: data['prompt'] = data['raw_prompt'] raw_prompt = data['raw_prompt'].replace("\n", ', ') if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles: found_styles.append(modules.sdxl_styles.fooocus_expansion) if 'raw_negative_prompt' in data: data['negative_prompt'] = data['raw_negative_prompt'] data['styles'] = str(found_styles) # try to load performance based on steps, fallback for direct A1111 imports if 'steps' in data and 'performance' in data is None: try: data['performance'] = Performance.by_steps(data['steps']).value except ValueError | KeyError: pass if 'sampler' in data: data['sampler'] = data['sampler'].replace(' Karras', '') # get key for k, v in SAMPLERS.items(): if v == data['sampler']: data['sampler'] = k break for key in ['base_model', 'refiner_model', 'vae']: if key in data: if key == 'vae': self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae') else: self.add_extension_to_filename(data, modules.config.model_filenames, key) lora_data = '' if 'lora_weights' in data and data['lora_weights'] != '': lora_data = data['lora_weights'] elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2: lora_data = data['lora_hashes'] if lora_data != '': for li, lora in enumerate(lora_data.split(', ')): lora_split = lora.split(': ') lora_name = lora_split[0] lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1] for filename in modules.config.lora_filenames: path = Path(filename) if lora_name == path.stem: data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}' break return data def to_string(self, metadata: dict) -> str: data = {k: v for _, k, v in metadata} width, height = eval(data['resolution']) sampler = data['sampler'] scheduler = data['scheduler'] if sampler in SAMPLERS and SAMPLERS[sampler] != '': sampler = SAMPLERS[sampler] if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras': sampler += f' Karras' generation_params = { self.fooocus_to_a1111['steps']: self.steps, self.fooocus_to_a1111['sampler']: sampler, self.fooocus_to_a1111['seed']: data['seed'], self.fooocus_to_a1111['resolution']: f'{width}x{height}', self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'], self.fooocus_to_a1111['sharpness']: data['sharpness'], self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'], self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem, self.fooocus_to_a1111['base_model_hash']: self.base_model_hash, self.fooocus_to_a1111['performance']: data['performance'], self.fooocus_to_a1111['scheduler']: scheduler, self.fooocus_to_a1111['vae']: Path(data['vae']).stem, # workaround for multiline prompts self.fooocus_to_a1111['raw_prompt']: self.raw_prompt, self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt, } if self.refiner_model_name not in ['', 'None']: generation_params |= { self.fooocus_to_a1111['refiner_model']: self.refiner_model_name, self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash } for key in ['adaptive_cfg', 'clip_skip', 'overwrite_switch', 'refiner_swap_method', 'freeu']: if key in data: generation_params[self.fooocus_to_a1111[key]] = data[key] if len(self.loras) > 0: lora_hashes = [] lora_weights = [] for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras): # workaround for Fooocus not knowing LoRA name in LoRA metadata lora_hashes.append(f'{lora_name}: {lora_hash}') lora_weights.append(f'{lora_name}: {lora_weight}') lora_hashes_string = ', '.join(lora_hashes) lora_weights_string = ', '.join(lora_weights) generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string generation_params[self.fooocus_to_a1111['version']] = data['version'] if modules.config.metadata_created_by != '': generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by generation_params_text = ", ".join( [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if v is not None]) positive_prompt_resolved = ', '.join(self.full_prompt) negative_prompt_resolved = ', '.join(self.full_negative_prompt) negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else "" return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip() @staticmethod def add_extension_to_filename(data, filenames, key): for filename in filenames: path = Path(filename) if data[key] == path.stem: data[key] = filename break class FooocusMetadataParser(MetadataParser): def get_scheme(self) -> MetadataScheme: return MetadataScheme.FOOOCUS def to_json(self, metadata: dict) -> dict: for key, value in metadata.items(): if value in ['', 'None']: continue if key in ['base_model', 'refiner_model']: metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames) elif key.startswith('lora_combined_'): metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames) elif key == 'vae': metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames) else: continue return metadata def to_string(self, metadata: list) -> str: for li, (label, key, value) in enumerate(metadata): # remove model folder paths from metadata if key.startswith('lora_combined_'): name, weight = value.split(' : ') name = Path(name).stem value = f'{name} : {weight}' metadata[li] = (label, key, value) res = {k: v for _, k, v in metadata} res['full_prompt'] = self.full_prompt res['full_negative_prompt'] = self.full_negative_prompt res['steps'] = self.steps res['base_model'] = self.base_model_name res['base_model_hash'] = self.base_model_hash if self.refiner_model_name not in ['', 'None']: res['refiner_model'] = self.refiner_model_name res['refiner_model_hash'] = self.refiner_model_hash res['vae'] = self.vae_name res['loras'] = self.loras if modules.config.metadata_created_by != '': res['created_by'] = modules.config.metadata_created_by return json.dumps(dict(sorted(res.items()))) @staticmethod def replace_value_with_filename(key, value, filenames): for filename in filenames: path = Path(filename) if key.startswith('lora_combined_'): name, weight = value.split(' : ') if name == path.stem: return f'{filename} : {weight}' elif value == path.stem: return filename return None def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser: match metadata_scheme: case MetadataScheme.FOOOCUS: return FooocusMetadataParser() case MetadataScheme.A1111: return A1111MetadataParser() case _: raise NotImplementedError def read_info_from_image(file) -> tuple[str | None, MetadataScheme | None]: items = (file.info or {}).copy() parameters = items.pop('parameters', None) metadata_scheme = items.pop('fooocus_scheme', None) exif = items.pop('exif', None) if parameters is not None and is_json(parameters): parameters = json.loads(parameters) elif exif is not None: exif = file.getexif() # 0x9286 = UserComment parameters = exif.get(0x9286, None) # 0x927C = MakerNote metadata_scheme = exif.get(0x927C, None) if is_json(parameters): parameters = json.loads(parameters) try: metadata_scheme = MetadataScheme(metadata_scheme) except ValueError: metadata_scheme = None # broad fallback if isinstance(parameters, dict): metadata_scheme = MetadataScheme.FOOOCUS if isinstance(parameters, str): metadata_scheme = MetadataScheme.A1111 return parameters, metadata_scheme def get_exif(metadata: str | None, metadata_scheme: str): exif = Image.Exif() # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py # 0x9286 = UserComment exif[0x9286] = metadata # 0x0131 = Software exif[0x0131] = 'Fooocus v' + fooocus_version.version # 0x927C = MakerNote exif[0x927C] = metadata_scheme return exif ================================================ FILE: modules/model_loader.py ================================================ import os from urllib.parse import urlparse from typing import Optional def load_file_from_url( url: str, *, model_dir: str, progress: bool = True, file_name: Optional[str] = None, ) -> str: """Download a file from `url` into `model_dir`, using the file present if possible. Returns the path to the downloaded file. """ domain = os.environ.get("HF_MIRROR", "https://huggingface.co").rstrip('/') url = str.replace(url, "https://huggingface.co", domain, 1) os.makedirs(model_dir, exist_ok=True) if not file_name: parts = urlparse(url) file_name = os.path.basename(parts.path) cached_file = os.path.abspath(os.path.join(model_dir, file_name)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') from torch.hub import download_url_to_file download_url_to_file(url, cached_file, progress=progress) return cached_file ================================================ FILE: modules/ops.py ================================================ import torch import contextlib @contextlib.contextmanager def use_patched_ops(operations): op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm'] backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names} try: for op_name in op_names: setattr(torch.nn, op_name, getattr(operations, op_name)) yield finally: for op_name in op_names: setattr(torch.nn, op_name, backups[op_name]) return ================================================ FILE: modules/patch.py ================================================ import os import torch import time import math import ldm_patched.modules.model_base import ldm_patched.ldm.modules.diffusionmodules.openaimodel import ldm_patched.modules.model_management import modules.anisotropic as anisotropic import ldm_patched.ldm.modules.attention import ldm_patched.k_diffusion.sampling import ldm_patched.modules.sd1_clip import modules.inpaint_worker as inpaint_worker import ldm_patched.ldm.modules.diffusionmodules.openaimodel import ldm_patched.ldm.modules.diffusionmodules.model import ldm_patched.modules.sd import ldm_patched.controlnet.cldm import ldm_patched.modules.model_patcher import ldm_patched.modules.samplers import ldm_patched.modules.args_parser import warnings import safetensors.torch import modules.constants as constants from ldm_patched.modules.samplers import calc_cond_uncond_batch from ldm_patched.k_diffusion.sampling import BatchedBrownianTree from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control from modules.patch_precision import patch_all_precision from modules.patch_clip import patch_all_clip class PatchSettings: def __init__(self, sharpness=2.0, adm_scaler_end=0.3, positive_adm_scale=1.5, negative_adm_scale=0.8, controlnet_softness=0.25, adaptive_cfg=7.0): self.sharpness = sharpness self.adm_scaler_end = adm_scaler_end self.positive_adm_scale = positive_adm_scale self.negative_adm_scale = negative_adm_scale self.controlnet_softness = controlnet_softness self.adaptive_cfg = adaptive_cfg self.global_diffusion_progress = 0 self.eps_record = None patch_settings = {} def calculate_weight_patched(self, patches, weight, key): for p in patches: alpha = p[0] v = p[1] strength_model = p[2] if strength_model != 1.0: weight *= strength_model if isinstance(v, list): v = (self.calculate_weight(v[1:], v[0].clone(), key),) if len(v) == 1: patch_type = "diff" elif len(v) == 2: patch_type = v[0] v = v[1] if patch_type == "diff": w1 = v[0] if alpha != 0.0: if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lora": mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) if v[2] is not None: alpha *= v[2] / mat2.shape[0] if v[3] is not None: mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) try: weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape( weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "fooocus": w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32) w1 = (w1 / 255.0) * (w_max - w_min) + w_min if alpha != 0.0: if w1.shape != weight.shape: print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) else: weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) elif patch_type == "lokr": w1 = v[0] w2 = v[1] w1_a = v[3] w1_b = v[4] w2_a = v[5] w2_b = v[6] t2 = v[7] dim = None if w1 is None: dim = w1_b.shape[0] w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) else: w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) if w2 is None: dim = w2_b.shape[0] if t2 is None: w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) else: w2 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) else: w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) if len(w2.shape) == 4: w1 = w1.unsqueeze(2).unsqueeze(2) if v[2] is not None and dim is not None: alpha *= v[2] / dim try: weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "loha": w1a = v[0] w1b = v[1] if v[2] is not None: alpha *= v[2] / w1b.shape[0] w2a = v[3] w2b = v[4] if v[5] is not None: # cp decomposition t1 = v[5] t2 = v[6] m1 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) m2 = torch.einsum('i j k l, j r, i p -> p r k l', ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) else: m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) try: weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) except Exception as e: print("ERROR", key, e) elif patch_type == "glora": if v[4] is not None: alpha *= v[4] / v[0].shape[0] a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) else: print("patch type not recognized", patch_type, key) return weight class BrownianTreeNoiseSamplerPatched: transform = None tree = None @staticmethod def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): if ldm_patched.modules.model_management.directml_enabled: cpu = True t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max)) BrownianTreeNoiseSamplerPatched.transform = transform BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) def __init__(self, *args, **kwargs): pass @staticmethod def __call__(sigma, sigma_next): transform = BrownianTreeNoiseSamplerPatched.transform tree = BrownianTreeNoiseSamplerPatched.tree t0, t1 = transform(torch.as_tensor(sigma)), transform(torch.as_tensor(sigma_next)) return tree(t0, t1) / (t1 - t0).abs().sqrt() def compute_cfg(uncond, cond, cfg_scale, t): pid = os.getpid() mimic_cfg = float(patch_settings[pid].adaptive_cfg) real_cfg = float(cfg_scale) real_eps = uncond + real_cfg * (cond - uncond) if cfg_scale > patch_settings[pid].adaptive_cfg: mimicked_eps = uncond + mimic_cfg * (cond - uncond) return real_eps * t + mimicked_eps * (1 - t) else: return real_eps def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None): pid = os.getpid() if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False): final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0] if patch_settings[pid].eps_record is not None: patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu() return final_x0 positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options) positive_eps = x - positive_x0 negative_eps = x - negative_x0 alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0) positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha) final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress) if patch_settings[pid].eps_record is not None: patch_settings[pid].eps_record = (final_eps / timestep).cpu() return x - final_eps def round_to_64(x): h = float(x) h = h / 64.0 h = round(h) h = int(h) h = h * 64 return h def sdxl_encode_adm_patched(self, **kwargs): clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor) width = kwargs.get("width", 1024) height = kwargs.get("height", 1024) target_width = width target_height = height pid = os.getpid() if kwargs.get("prompt_type", "") == "negative": width = float(width) * patch_settings[pid].negative_adm_scale height = float(height) * patch_settings[pid].negative_adm_scale elif kwargs.get("prompt_type", "") == "positive": width = float(width) * patch_settings[pid].positive_adm_scale height = float(height) * patch_settings[pid].positive_adm_scale def embedder(number_list): h = self.embedder(torch.tensor(number_list, dtype=torch.float32)) h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) return h width, height = int(width), int(height) target_width, target_height = round_to_64(target_width), round_to_64(target_height) adm_emphasized = embedder([height, width, 0, 0, target_height, target_width]) adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width]) clip_pooled = clip_pooled.to(adm_emphasized) final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1) return final_adm def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): if inpaint_worker.current_task is not None: latent_processor = self.inner_model.inner_model.process_latent_in inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x) inpaint_mask = inpaint_worker.current_task.latent_mask.to(x) if getattr(self, 'energy_generator', None) is None: # avoid bad results by using different seeds. self.energy_generator = torch.Generator(device='cpu').manual_seed((seed + 1) % constants.MAX_SEED) energy_sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(x.shape) - 1)) current_energy = torch.randn( x.size(), dtype=x.dtype, generator=self.energy_generator, device="cpu").to(x) * energy_sigma x = x * inpaint_mask + (inpaint_latent + current_energy) * (1.0 - inpaint_mask) out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) out = out * inpaint_mask + inpaint_latent * (1.0 - inpaint_mask) else: out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) return out def timed_adm(y, timesteps): if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632: y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None] y_with_adm = y[..., :2816].clone() y_without_adm = y[..., 2816:].clone() return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask) return y def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs): t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) pid = os.getpid() guided_hint = self.input_hint_block(hint, emb, context) y = timed_adm(y, timesteps) outs = [] hs = [] if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for module, zero_conv in zip(self.input_blocks, self.zero_convs): if guided_hint is not None: h = module(h, emb, context) h += guided_hint guided_hint = None else: h = module(h, emb, context) outs.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) outs.append(self.middle_block_out(h, emb, context)) if patch_settings[pid].controlnet_softness > 0: for i in range(10): k = 1.0 - float(i) / 9.0 outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k) return outs def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): self.current_step = 1.0 - timesteps.to(x) / 999.0 patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0]) y = timed_adm(y, timesteps) transformer_options["original_shape"] = list(x.shape) transformer_options["transformer_index"] = 0 transformer_patches = transformer_options.get("patches", {}) num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) time_context = kwargs.get("time_context", None) assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) h = x for id, module in enumerate(self.input_blocks): transformer_options["block"] = ("input", id) h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'input') if "input_block_patch" in transformer_patches: patch = transformer_patches["input_block_patch"] for p in patch: h = p(h, transformer_options) hs.append(h) if "input_block_patch_after_skip" in transformer_patches: patch = transformer_patches["input_block_patch_after_skip"] for p in patch: h = p(h, transformer_options) transformer_options["block"] = ("middle", 0) h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) hsp = hs.pop() hsp = apply_control(hsp, control, 'output') if "output_block_patch" in transformer_patches: patch = transformer_patches["output_block_patch"] for p in patch: h, hsp = p(h, hsp, transformer_options) h = torch.cat([h, hsp], dim=1) del hsp if len(hs) > 0: output_shape = hs[-1].shape else: output_shape = None h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) def patched_load_models_gpu(*args, **kwargs): execution_start_time = time.perf_counter() y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs) moving_time = time.perf_counter() - execution_start_time if moving_time > 0.1: print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds') return y def build_loaded(module, loader_name): original_loader_name = loader_name + '_origin' if not hasattr(module, original_loader_name): setattr(module, original_loader_name, getattr(module, loader_name)) original_loader = getattr(module, original_loader_name) def loader(*args, **kwargs): result = None try: result = original_loader(*args, **kwargs) except Exception as e: result = None exp = str(e) + '\n' for path in list(args) + list(kwargs.values()): if isinstance(path, str): if os.path.exists(path): exp += f'File corrupted: {path} \n' corrupted_backup_file = path + '.corrupted' if os.path.exists(corrupted_backup_file): os.remove(corrupted_backup_file) os.replace(path, corrupted_backup_file) if os.path.exists(path): os.remove(path) exp += f'Fooocus has tried to move the corrupted file to {corrupted_backup_file} \n' exp += f'You may try again now and Fooocus will download models again. \n' raise ValueError(exp) return result setattr(module, loader_name, loader) return def patch_all(): if ldm_patched.modules.model_management.directml_enabled: ldm_patched.modules.model_management.lowvram_available = True ldm_patched.modules.model_management.OOM_EXCEPTION = Exception patch_all_precision() patch_all_clip() if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'): ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched ldm_patched.modules.samplers.sampling_function = patched_sampling_function warnings.filterwarnings(action='ignore', module='torchsde') build_loaded(safetensors.torch, 'load_file') build_loaded(torch, 'load') return ================================================ FILE: modules/patch_clip.py ================================================ # Consistent with Kohya/A1111 to reduce differences between model training and inference. import os import torch import ldm_patched.controlnet.cldm import ldm_patched.k_diffusion.sampling import ldm_patched.ldm.modules.attention import ldm_patched.ldm.modules.diffusionmodules.model import ldm_patched.ldm.modules.diffusionmodules.openaimodel import ldm_patched.ldm.modules.diffusionmodules.openaimodel import ldm_patched.modules.args_parser import ldm_patched.modules.model_base import ldm_patched.modules.model_management import ldm_patched.modules.model_patcher import ldm_patched.modules.samplers import ldm_patched.modules.sd import ldm_patched.modules.sd1_clip import ldm_patched.modules.clip_vision import ldm_patched.modules.ops as ops from modules.ops import use_patched_ops from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection def patched_encode_token_weights(self, token_weight_pairs): to_encode = list() max_token_len = 0 has_weights = False for x in token_weight_pairs: tokens = list(map(lambda a: a[0], x)) max_token_len = max(len(tokens), max_token_len) has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) to_encode.append(tokens) sections = len(to_encode) if has_weights or sections == 0: to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) out, pooled = self.encode(to_encode) if pooled is not None: first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device()) else: first_pooled = pooled output = [] for k in range(0, sections): z = out[k:k + 1] if has_weights: original_mean = z.mean() z_empty = out[-1] for i in range(len(z)): for j in range(len(z[i])): weight = token_weight_pairs[k][j][1] if weight != 1.0: z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] new_mean = z.mean() z = z * (original_mean / new_mean) output.append(z) if len(output) == 0: return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, special_tokens=None, layer_norm_hidden_state=True, **kwargs): torch.nn.Module.__init__(self) assert layer in self.LAYERS if special_tokens is None: special_tokens = {"start": 49406, "end": 49407, "pad": 49407} if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), "sd1_clip_config.json") config = CLIPTextConfig.from_json_file(textmodel_json_config) self.num_layers = config.num_hidden_layers with use_patched_ops(ops.manual_cast): with modeling_utils.no_init_weights(): self.transformer = CLIPTextModel(config) if dtype is not None: self.transformer.to(dtype) self.transformer.text_model.embeddings.to(torch.float32) if freeze: self.freeze() self.max_length = max_length self.layer = layer self.layer_idx = None self.special_tokens = special_tokens self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) self.enable_attention_masks = False self.layer_norm_hidden_state = layer_norm_hidden_state if layer == "hidden": assert layer_idx is not None assert abs(layer_idx) < self.num_layers self.clip_layer(layer_idx) self.layer_default = (self.layer, self.layer_idx) def patched_SDClipModel_forward(self, tokens): backup_embeds = self.transformer.get_input_embeddings() device = backup_embeds.weight.device tokens = self.set_up_textual_embeddings(tokens, backup_embeds) tokens = torch.LongTensor(tokens).to(device) attention_mask = None if self.enable_attention_masks: attention_mask = torch.zeros_like(tokens) max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 for x in range(attention_mask.shape[0]): for y in range(attention_mask.shape[1]): attention_mask[x, y] = 1 if tokens[x, y] == max_token: break outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, output_hidden_states=self.layer == "hidden") self.transformer.set_input_embeddings(backup_embeds) if self.layer == "last": z = outputs.last_hidden_state elif self.layer == "pooled": z = outputs.pooler_output[:, None, :] else: z = outputs.hidden_states[self.layer_idx] if self.layer_norm_hidden_state: z = self.transformer.text_model.final_layer_norm(z) if hasattr(outputs, "pooler_output"): pooled_output = outputs.pooler_output.float() else: pooled_output = None if self.text_projection is not None and pooled_output is not None: pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() return z.float(), pooled_output def patched_ClipVisionModel__init__(self, json_config): config = CLIPVisionConfig.from_json_file(json_config) self.load_device = ldm_patched.modules.model_management.text_encoder_device() self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False): self.dtype = torch.float16 else: self.dtype = torch.float32 with use_patched_ops(ops.manual_cast): with modeling_utils.no_init_weights(): self.model = CLIPVisionModelWithProjection(config) self.model.to(self.dtype) self.patcher = ldm_patched.modules.model_patcher.ModelPatcher( self.model, load_device=self.load_device, offload_device=self.offload_device ) def patched_ClipVisionModel_encode_image(self, image): ldm_patched.modules.model_management.load_model_gpu(self.patcher) pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device)) outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) for k in outputs: t = outputs[k] if t is not None: if k == 'hidden_states': outputs["penultimate_hidden_states"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device()) outputs["hidden_states"] = None else: outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device()) return outputs def patch_all_clip(): ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__ ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__ ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image return ================================================ FILE: modules/patch_precision.py ================================================ # Consistent with Kohya to reduce differences between model training and inference. import torch import math import einops import numpy as np import ldm_patched.ldm.modules.diffusionmodules.openaimodel import ldm_patched.modules.model_sampling import ldm_patched.modules.sd1_clip from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): # Consistent with Kohya to reduce differences between model training and inference. if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=timesteps.device) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = einops.repeat(timesteps, 'b -> b d', d=dim) return embedding def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): # Consistent with Kohya to reduce differences between model training and inference. if given_betas is not None: betas = given_betas else: betas = make_beta_schedule( beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32) self.set_sigmas(sigmas) alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32) self.set_alphas_cumprod(alphas_cumprod) return def patch_all_precision(): ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule return ================================================ FILE: modules/private_logger.py ================================================ import os import args_manager import modules.config import json import urllib.parse from PIL import Image from PIL.PngImagePlugin import PngInfo from modules.flags import OutputFormat from modules.meta_parser import MetadataParser, get_exif from modules.util import generate_temp_filename log_cache = {} def get_current_html_path(output_format=None): output_format = output_format if output_format else modules.config.default_output_format date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, extension=output_format) html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') return html_name def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None, persist_image=True) -> str: path_outputs = modules.config.temp_path if args_manager.args.disable_image_log or not persist_image else modules.config.path_outputs output_format = output_format if output_format else modules.config.default_output_format date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format) os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True) parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else '' image = Image.fromarray(img) if output_format == OutputFormat.PNG.value: if parsed_parameters != '': pnginfo = PngInfo() pnginfo.add_text('parameters', parsed_parameters) pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value) else: pnginfo = None image.save(local_temp_filename, pnginfo=pnginfo) elif output_format == OutputFormat.JPEG.value: image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) elif output_format == OutputFormat.WEBP.value: image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) else: image.save(local_temp_filename) if args_manager.args.disable_image_log: return local_temp_filename html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') css_styles = ( "" ) js = ( """""" ) begin_part = f"Fooocus Log {date_string}{css_styles}{js}

Fooocus Log {date_string} (private)

\n

Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.

\n\n" end_part = f'\n' middle_part = log_cache.get(html_name, "") if middle_part == "": if os.path.exists(html_name): existing_split = open(html_name, 'r', encoding='utf-8').read().split('') if len(existing_split) == 3: middle_part = existing_split[1] else: middle_part = existing_split[0] div_name = only_name.replace('.', '_') item = f"
\n" item += f"" item += "" item += "
{only_name}
" for label, key, value in metadata: value_txt = str(value).replace('\n', '
') item += f"\n" if task is not None and 'positive' in task and 'negative' in task: full_prompt_details = f"""
Positive{', '.join(task['positive'])}
Negative{', '.join(task['negative'])}
""" item += f"\n" item += "
{label}{value_txt}
Full raw prompt{full_prompt_details}
" js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='') item += f"
" item += "
\n\n" middle_part = item + middle_part with open(html_name, 'w', encoding='utf-8') as f: f.write(begin_part + middle_part + end_part) print(f'Image generated with private log at: {html_name}') log_cache[html_name] = middle_part return local_temp_filename ================================================ FILE: modules/sample_hijack.py ================================================ import torch import ldm_patched.modules.samplers import ldm_patched.modules.model_management from collections import namedtuple from ldm_patched.contrib.external_align_your_steps import AlignYourStepsScheduler from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler from ldm_patched.k_diffusion import sampling as k_diffusion_sampling from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler from ldm_patched.modules.model_base import SDXLRefiner, SDXL from ldm_patched.modules.conds import CONDRegular from ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models from ldm_patched.modules.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \ create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_model_conds current_refiner = None refiner_switch_step = -1 @torch.no_grad() @torch.inference_mode() def clip_separate_inner(c, p, target_model=None, target_clip=None): if target_model is None or isinstance(target_model, SDXLRefiner): c = c[..., -1280:].clone() elif isinstance(target_model, SDXL): c = c.clone() else: p = None c = c[..., :768].clone() final_layer_norm = target_clip.cond_stage_model.clip_l.transformer.text_model.final_layer_norm final_layer_norm_origin_device = final_layer_norm.weight.device final_layer_norm_origin_dtype = final_layer_norm.weight.dtype c_origin_device = c.device c_origin_dtype = c.dtype final_layer_norm.to(device='cpu', dtype=torch.float32) c = c.to(device='cpu', dtype=torch.float32) c = torch.chunk(c, int(c.size(1)) // 77, 1) c = [final_layer_norm(ci) for ci in c] c = torch.cat(c, dim=1) final_layer_norm.to(device=final_layer_norm_origin_device, dtype=final_layer_norm_origin_dtype) c = c.to(device=c_origin_device, dtype=c_origin_dtype) return c, p @torch.no_grad() @torch.inference_mode() def clip_separate(cond, target_model=None, target_clip=None): results = [] for c, px in cond: p = px.get('pooled_output', None) c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) p = {} if p is None else {'pooled_output': p.clone()} results.append([c, p]) return results @torch.no_grad() @torch.inference_mode() def clip_separate_after_preparation(cond, target_model=None, target_clip=None): results = [] for x in cond: p = x.get('pooled_output', None) c = x['model_conds']['c_crossattn'].cond c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) result = {'model_conds': {'c_crossattn': CONDRegular(c)}} if p is not None: result['pooled_output'] = p.clone() results.append(result) return results @torch.no_grad() @torch.inference_mode() def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): global current_refiner positive = positive[:] negative = negative[:] resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) model_wrap = wrap_model(model) calculate_start_end_timesteps(model, negative) calculate_start_end_timesteps(model, positive) if latent_image is not None: latent_image = model.process_latent_in(latent_image) if hasattr(model, 'extra_conds'): positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) #make sure each cond area has an opposite one with the same area for c in positive: create_cond_with_same_area_if_none(negative, c) for c in negative: create_cond_with_same_area_if_none(positive, c) # pre_run_control(model, negative + positive) pre_run_control(model, positive) # negative is not necessary in Fooocus, 0.5s faster. apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'): positive_refiner = clip_separate_after_preparation(positive, target_model=current_refiner.model) negative_refiner = clip_separate_after_preparation(negative, target_model=current_refiner.model) positive_refiner = encode_model_conds(current_refiner.model.extra_conds, positive_refiner, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) negative_refiner = encode_model_conds(current_refiner.model.extra_conds, negative_refiner, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) def refiner_switch(): cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))) extra_args["cond"] = positive_refiner extra_args["uncond"] = negative_refiner # clear ip-adapter for refiner extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()} models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype()) ldm_patched.modules.model_management.load_models_gpu( [current_refiner] + models, model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory) model_wrap.inner_model = current_refiner.model print('Refiner Swapped') return def callback_wrap(step, x0, x, total_steps): if step == refiner_switch_step and current_refiner is not None: refiner_switch() if callback is not None: # residual_noise_preview = x - x0 # residual_noise_preview /= residual_noise_preview.std() # residual_noise_preview *= x0.std() callback(step, x0, x, total_steps) samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar) return model.process_latent_out(samples.to(torch.float32)) @torch.no_grad() @torch.inference_mode() def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps): if scheduler_name == "karras": sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "exponential": sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) elif scheduler_name == "normal": sigmas = normal_scheduler(model, steps) elif scheduler_name == "simple": sigmas = simple_scheduler(model, steps) elif scheduler_name == "ddim_uniform": sigmas = ddim_scheduler(model, steps) elif scheduler_name == "sgm_uniform": sigmas = normal_scheduler(model, steps, sgm=True) elif scheduler_name == "turbo": sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0] elif scheduler_name == "align_your_steps": model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1' sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0] else: raise TypeError("error invalid scheduler") return sigmas ldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked ldm_patched.modules.samplers.sample = sample_hacked ================================================ FILE: modules/sdxl_styles.py ================================================ import os import re import json import math from modules.extra_utils import get_files_from_folder from random import Random # cannot use modules.config - validators causing circular imports styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/')) def normalize_key(k): k = k.replace('-', ' ') words = k.split(' ') words = [w[:1].upper() + w[1:].lower() for w in words] k = ' '.join(words) k = k.replace('3d', '3D') k = k.replace('Sai', 'SAI') k = k.replace('Mre', 'MRE') k = k.replace('(s', '(S') return k styles = {} styles_files = get_files_from_folder(styles_path, ['.json']) for x in ['sdxl_styles_fooocus.json', 'sdxl_styles_sai.json', 'sdxl_styles_mre.json', 'sdxl_styles_twri.json', 'sdxl_styles_diva.json', 'sdxl_styles_marc_k3nt3l.json']: if x in styles_files: styles_files.remove(x) styles_files.append(x) for styles_file in styles_files: try: with open(os.path.join(styles_path, styles_file), encoding='utf-8') as f: for entry in json.load(f): name = normalize_key(entry['name']) prompt = entry['prompt'] if 'prompt' in entry else '' negative_prompt = entry['negative_prompt'] if 'negative_prompt' in entry else '' styles[name] = (prompt, negative_prompt) except Exception as e: print(str(e)) print(f'Failed to load style file {styles_file}') style_keys = list(styles.keys()) fooocus_expansion = 'Fooocus V2' random_style_name = 'Random Style' legal_style_names = [fooocus_expansion, random_style_name] + style_keys def get_random_style(rng: Random) -> str: return rng.choice(list(styles.items()))[0] def apply_style(style, positive): p, n = styles[style] return p.replace('{prompt}', positive).splitlines(), n.splitlines(), '{prompt}' in p def get_words(arrays, total_mult, index): if len(arrays) == 1: return [arrays[0].split(',')[index]] else: words = arrays[0].split(',') word = words[index % len(words)] index -= index % len(words) index /= len(words) index = math.floor(index) return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index) def apply_arrays(text, index): arrays = re.findall(r'\[\[(.*?)\]\]', text) if len(arrays) == 0: return text print(f'[Arrays] processing: {text}') mult = 1 for arr in arrays: words = arr.split(',') mult *= len(words) index %= mult chosen_words = get_words(arrays, mult, index) i = 0 for arr in arrays: text = text.replace(f'[[{arr}]]', chosen_words[i], 1) i = i+1 return text ================================================ FILE: modules/style_sorter.py ================================================ import os import gradio as gr import modules.localization as localization import json all_styles = [] def try_load_sorted_styles(style_names, default_selected): global all_styles all_styles = style_names try: if os.path.exists('sorted_styles.json'): with open('sorted_styles.json', 'rt', encoding='utf-8') as fp: sorted_styles = [] for x in json.load(fp): if x in all_styles: sorted_styles.append(x) for x in all_styles: if x not in sorted_styles: sorted_styles.append(x) all_styles = sorted_styles except Exception as e: print('Load style sorting failed.') print(e) unselected = [y for y in all_styles if y not in default_selected] all_styles = default_selected + unselected return def sort_styles(selected): global all_styles unselected = [y for y in all_styles if y not in selected] sorted_styles = selected + unselected try: with open('sorted_styles.json', 'wt', encoding='utf-8') as fp: json.dump(sorted_styles, fp, indent=4) except Exception as e: print('Write style sorting failed.') print(e) all_styles = sorted_styles return gr.CheckboxGroup.update(choices=sorted_styles) def localization_key(x): return x + localization.current_translation.get(x, '') def search_styles(selected, query): unselected = [y for y in all_styles if y not in selected] matched = [y for y in unselected if query.lower() in localization_key(y).lower()] if len(query.replace(' ', '')) > 0 else [] unmatched = [y for y in unselected if y not in matched] sorted_styles = matched + selected + unmatched return gr.CheckboxGroup.update(choices=sorted_styles) ================================================ FILE: modules/ui_gradio_extensions.py ================================================ # based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/modules/ui_gradio_extensions.py import os import gradio as gr import args_manager from modules.localization import localization_js GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse modules_path = os.path.dirname(os.path.realpath(__file__)) script_path = os.path.dirname(modules_path) def webpath(fn): if fn.startswith(script_path): web_path = os.path.relpath(fn, script_path).replace('\\', '/') else: web_path = os.path.abspath(fn) return f'file={web_path}?{os.path.getmtime(fn)}' def javascript_html(): script_js_path = webpath('javascript/script.js') context_menus_js_path = webpath('javascript/contextMenus.js') localization_js_path = webpath('javascript/localization.js') zoom_js_path = webpath('javascript/zoom.js') edit_attention_js_path = webpath('javascript/edit-attention.js') viewer_js_path = webpath('javascript/viewer.js') image_viewer_js_path = webpath('javascript/imageviewer.js') samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg')) head = f'\n' head += f'\n' head += f'\n' head += f'\n' head += f'\n' head += f'\n' head += f'\n' head += f'\n' head += f'\n' if args_manager.args.theme: head += f'\n' return head def css_html(): style_css_path = webpath('css/style.css') head = f'' return head def reload_javascript(): js = javascript_html() css = css_html() def template_response(*args, **kwargs): res = GradioTemplateResponseOriginal(*args, **kwargs) res.body = res.body.replace(b'', f'{js}'.encode("utf8")) res.body = res.body.replace(b'', f'{css}'.encode("utf8")) res.init_headers() return res gr.routes.templates.TemplateResponse = template_response ================================================ FILE: modules/upscaler.py ================================================ from collections import OrderedDict import modules.core as core import torch from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN from modules.config import downloading_upscale_model opImageUpscaleWithModel = ImageUpscaleWithModel() model = None def perform_upscale(img): global model print(f'Upscaling image with shape {str(img.shape)} ...') if model is None: model_filename = downloading_upscale_model() sd = torch.load(model_filename, weights_only=True) sdo = OrderedDict() for k, v in sd.items(): sdo[k.replace('residual_block_', 'RDB')] = v del sd model = ESRGAN(sdo) model.cpu() model.eval() img = core.numpy_to_pytorch(img) img = opImageUpscaleWithModel.upscale(model, img)[0] img = core.pytorch_to_numpy(img)[0] return img ================================================ FILE: modules/util.py ================================================ from pathlib import Path import numpy as np import datetime import random import math import os import cv2 import re from typing import List, Tuple, AnyStr, NamedTuple import json import hashlib from PIL import Image import modules.config import modules.sdxl_styles from modules.flags import Performance LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) # Regexp compiled once. Matches entries with the following pattern: # # LORAS_PROMPT_PATTERN = re.compile(r"()", re.X) HASH_SHA256_LENGTH = 10 def erode_or_dilate(x, k): k = int(k) if k > 0: return cv2.dilate(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=k) if k < 0: return cv2.erode(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=-k) return x def resample_image(im, width, height): im = Image.fromarray(im) im = im.resize((int(width), int(height)), resample=LANCZOS) return np.array(im) def resize_image(im, width, height, resize_mode=1): """ Resizes an image with the specified resize_mode, width, and height. Args: resize_mode: The mode to use when resizing the image. 0: Resize the image to the specified width and height. 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. im: The image to resize. width: The width to resize the image to. height: The height to resize the image to. """ im = Image.fromarray(im) def resize(im, w, h): return im.resize((w, h), resample=LANCZOS) if resize_mode == 0: res = resize(im, width, height) elif resize_mode == 1: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio > src_ratio else im.width * height // im.height src_h = height if ratio <= src_ratio else im.height * width // im.width resized = resize(im, src_w, src_h) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) else: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio < src_ratio else im.width * height // im.height src_h = height if ratio >= src_ratio else im.height * width // im.width resized = resize(im, src_w, src_h) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) if ratio < src_ratio: fill_height = height // 2 - src_h // 2 if fill_height > 0: res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) elif ratio > src_ratio: fill_width = width // 2 - src_w // 2 if fill_width > 0: res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) return np.array(res) def get_shape_ceil(h, w): return math.ceil(((h * w) ** 0.5) / 64.0) * 64.0 def get_image_shape_ceil(im): H, W = im.shape[:2] return get_shape_ceil(H, W) def set_image_shape_ceil(im, shape_ceil): shape_ceil = float(shape_ceil) H_origin, W_origin, _ = im.shape H, W = H_origin, W_origin for _ in range(256): current_shape_ceil = get_shape_ceil(H, W) if abs(current_shape_ceil - shape_ceil) < 0.1: break k = shape_ceil / current_shape_ceil H = int(round(float(H) * k / 64.0) * 64) W = int(round(float(W) * k / 64.0) * 64) if H == H_origin and W == W_origin: return im return resample_image(im, width=W, height=H) def HWC3(x): assert x.dtype == np.uint8 if x.ndim == 2: x = x[:, :, None] assert x.ndim == 3 H, W, C = x.shape assert C == 1 or C == 3 or C == 4 if C == 3: return x if C == 1: return np.concatenate([x, x, x], axis=2) if C == 4: color = x[:, :, 0:3].astype(np.float32) alpha = x[:, :, 3:4].astype(np.float32) / 255.0 y = color * alpha + 255.0 * (1.0 - alpha) y = y.clip(0, 255).astype(np.uint8) return y def remove_empty_str(items, default=None): items = [x for x in items if x != ""] if len(items) == 0 and default is not None: return [default] return items def join_prompts(*args, **kwargs): prompts = [str(x) for x in args if str(x) != ""] if len(prompts) == 0: return "" if len(prompts) == 1: return prompts[0] return ', '.join(prompts) def generate_temp_filename(folder='./outputs/', extension='png'): current_time = datetime.datetime.now() date_string = current_time.strftime("%Y-%m-%d") time_string = current_time.strftime("%Y-%m-%d_%H-%M-%S") random_number = random.randint(1000, 9999) filename = f"{time_string}_{random_number}.{extension}" result = os.path.join(folder, date_string, filename) return date_string, os.path.abspath(result), filename def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH): if use_addnet_hash: with open(filename, "rb") as file: sha256_value = addnet_hash_safetensors(file) else: sha256_value = calculate_sha256(filename) return sha256_value[:length] if length is not None else sha256_value def addnet_hash_safetensors(b): """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py""" hash_sha256 = hashlib.sha256() blksize = 1024 * 1024 b.seek(0) header = b.read(8) n = int.from_bytes(header, "little") offset = n + 8 b.seek(offset) for chunk in iter(lambda: b.read(blksize), b""): hash_sha256.update(chunk) return hash_sha256.hexdigest() def calculate_sha256(filename) -> str: hash_sha256 = hashlib.sha256() blksize = 1024 * 1024 with open(filename, "rb") as f: for chunk in iter(lambda: f.read(blksize), b""): hash_sha256.update(chunk) return hash_sha256.hexdigest() def quote(text): if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text): return text return json.dumps(text, ensure_ascii=False) def unquote(text): if len(text) == 0 or text[0] != '"' or text[-1] != '"': return text try: return json.loads(text) except Exception: return text def unwrap_style_text_from_prompt(style_text, prompt): """ Checks the prompt to see if the style text is wrapped around it. If so, returns True plus the prompt text without the style text. Otherwise, returns False with the original prompt. Note that the "cleaned" version of the style text is only used for matching purposes here. It isn't returned; the original style text is not modified. """ stripped_prompt = prompt stripped_style_text = style_text if "{prompt}" in stripped_style_text: # Work out whether the prompt is wrapped in the style text. If so, we # return True and the "inner" prompt text that isn't part of the style. try: left, right = stripped_style_text.split("{prompt}", 2) except ValueError as e: # If the style text has multple "{prompt}"s, we can't split it into # two parts. This is an error, but we can't do anything about it. print(f"Unable to compare style text to prompt:\n{style_text}") print(f"Error: {e}") return False, prompt, '' left_pos = stripped_prompt.find(left) right_pos = stripped_prompt.find(right) if 0 <= left_pos < right_pos: real_prompt = stripped_prompt[left_pos + len(left):right_pos] prompt = stripped_prompt.replace(left + real_prompt + right, '', 1) if prompt.startswith(", "): prompt = prompt[2:] if prompt.endswith(", "): prompt = prompt[:-2] return True, prompt, real_prompt else: # Work out whether the given prompt starts with the style text. If so, we # return True and the prompt text up to where the style text starts. if stripped_prompt.endswith(stripped_style_text): prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)] if prompt.endswith(", "): prompt = prompt[:-2] return True, prompt, prompt return False, prompt, '' def extract_original_prompts(style, prompt, negative_prompt): """ Takes a style and compares it to the prompt and negative prompt. If the style matches, returns True plus the prompt and negative prompt with the style text removed. Otherwise, returns False with the original prompt and negative prompt. """ if not style.prompt and not style.negative_prompt: return False, prompt, negative_prompt match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt( style.prompt, prompt ) if not match_positive: return False, prompt, negative_prompt, '' match_negative, extracted_negative, _ = unwrap_style_text_from_prompt( style.negative_prompt, negative_prompt ) if not match_negative: return False, prompt, negative_prompt, '' return True, extracted_positive, extracted_negative, real_prompt def extract_styles_from_prompt(prompt, negative_prompt): extracted = [] applicable_styles = [] for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items(): applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt)) real_prompt = '' while True: found_style = None for style in applicable_styles: is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts( style, prompt, negative_prompt ) if is_match: found_style = style prompt = new_prompt negative_prompt = new_neg_prompt if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt: real_prompt = new_real_prompt break if not found_style: break applicable_styles.remove(found_style) extracted.append(found_style.name) # add prompt expansion if not all styles could be resolved if prompt != '': if real_prompt != '': extracted.append(modules.sdxl_styles.fooocus_expansion) else: # find real_prompt when only prompt expansion is selected first_word = prompt.split(', ')[0] first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)] if len(first_word_positions) > 1: real_prompt = prompt[:first_word_positions[-1]] extracted.append(modules.sdxl_styles.fooocus_expansion) if real_prompt.endswith(', '): real_prompt = real_prompt[:-2] return list(reversed(extracted)), real_prompt, negative_prompt class PromptStyle(NamedTuple): name: str prompt: str negative_prompt: str def is_json(data: str) -> bool: try: loaded_json = json.loads(data) assert isinstance(loaded_json, dict) except (ValueError, AssertionError): return False return True def get_filname_by_stem(lora_name, filenames: List[str]) -> str | None: for filename in filenames: path = Path(filename) if lora_name == path.stem: return filename return None def get_file_from_folder_list(name, folders): if not isinstance(folders, list): folders = [folders] for folder in folders: filename = os.path.abspath(os.path.realpath(os.path.join(folder, name))) if os.path.isfile(filename): return filename return os.path.abspath(os.path.realpath(os.path.join(folders[0], name))) def get_enabled_loras(loras: list, remove_none=True) -> list: return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)] def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5, skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True, lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]: # prevent unintended side effects when returning without detection loras = loras.copy() if lora_filenames is None: lora_filenames = [] found_loras = [] prompt_without_loras = '' cleaned_prompt = '' for token in prompt.split(','): matches = LORAS_PROMPT_PATTERN.findall(token) if len(matches) == 0: prompt_without_loras += token + ', ' continue for match in matches: lora_name = match[1] + '.safetensors' if not skip_file_check: lora_name = get_filname_by_stem(match[1], lora_filenames) if lora_name is not None: found_loras.append((lora_name, float(match[2]))) token = token.replace(match[0], '') prompt_without_loras += token + ', ' if prompt_without_loras != '': cleaned_prompt = prompt_without_loras[:-2] if prompt_cleanup: cleaned_prompt = cleanup_prompt(prompt_without_loras) new_loras = [] lora_names = [lora[0] for lora in loras] for found_lora in found_loras: if deduplicate_loras and (found_lora[0] in lora_names or found_lora in new_loras): continue new_loras.append(found_lora) if len(new_loras) == 0: return loras, cleaned_prompt updated_loras = [] for lora in loras + new_loras: if lora[0] != "None": updated_loras.append(lora) return updated_loras[:loras_limit], cleaned_prompt def remove_performance_lora(filenames: list, performance: Performance | None): loras_without_performance = filenames.copy() if performance is None: return loras_without_performance performance_lora = performance.lora_filename() for filename in filenames: path = Path(filename) if performance_lora == path.name: loras_without_performance.remove(filename) return loras_without_performance def cleanup_prompt(prompt): prompt = re.sub(' +', ' ', prompt) prompt = re.sub(',+', ',', prompt) cleaned_prompt = '' for token in prompt.split(','): token = token.strip() if token == '': continue cleaned_prompt += token + ', ' return cleaned_prompt[:-2] def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str: for _ in range(modules.config.wildcards_max_bfs_depth): placeholders = re.findall(r'__([\w-]+)__', wildcard_text) if len(placeholders) == 0: return wildcard_text print(f'[Wildcards] processing: {wildcard_text}') for placeholder in placeholders: try: matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder] words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines() words = [x for x in words if x != ''] assert len(words) > 0 if read_wildcards_in_order: wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1) else: wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1) except: print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. ' f'Using "{placeholder}" as a normal word.') wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder) print(f'[Wildcards] {wildcard_text}') print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}') return wildcard_text def get_image_size_info(image: np.ndarray, aspect_ratios: list) -> str: try: image = Image.fromarray(np.uint8(image)) width, height = image.size ratio = round(width / height, 2) gcd = math.gcd(width, height) lcm_ratio = f'{width // gcd}:{height // gcd}' size_info = f'Image Size: {width} x {height}, Ratio: {ratio}, {lcm_ratio}' closest_ratio = min(aspect_ratios, key=lambda x: abs(ratio - float(x.split('*')[0]) / float(x.split('*')[1]))) recommended_width, recommended_height = map(int, closest_ratio.split('*')) recommended_ratio = round(recommended_width / recommended_height, 2) recommended_gcd = math.gcd(recommended_width, recommended_height) recommended_lcm_ratio = f'{recommended_width // recommended_gcd}:{recommended_height // recommended_gcd}' size_info = f'{width} x {height}, {ratio}, {lcm_ratio}' size_info += f'\n{recommended_width} x {recommended_height}, {recommended_ratio}, {recommended_lcm_ratio}' return size_info except Exception as e: return f'Error reading image: {e}' ================================================ FILE: presets/.gitignore ================================================ *.json !anime.json !default.json !lcm.json !playground_v2.5.json !pony_v6.json !realistic.json !sai.json ================================================ FILE: presets/anime.json ================================================ { "default_model": "animaPencilXL_v500.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_loras": [ [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 6.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus V2", "Fooocus Semi Realistic", "Fooocus Masterpiece" ], "default_aspect_ratio": "896*1152", "default_overwrite_step": -1, "checkpoint_downloads": { "animaPencilXL_v500.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/animaPencilXL_v500.safetensors" }, "embeddings_downloads": {}, "lora_downloads": {}, "previous_default_models": [ "animaPencilXL_v400.safetensors", "animaPencilXL_v310.safetensors", "animaPencilXL_v300.safetensors", "animaPencilXL_v260.safetensors", "animaPencilXL_v210.safetensors", "animaPencilXL_v200.safetensors", "animaPencilXL_v100.safetensors" ] } ================================================ FILE: presets/default.json ================================================ { "default_model": "juggernautXL_v8Rundiffusion.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_loras": [ [ true, "sd_xl_offset_example-lora_1.0.safetensors", 0.1 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 4.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus V2", "Fooocus Enhance", "Fooocus Sharp" ], "default_aspect_ratio": "1152*896", "default_overwrite_step": -1, "checkpoint_downloads": { "juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors" }, "embeddings_downloads": {}, "lora_downloads": { "sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors" }, "previous_default_models": [ "juggernautXL_version8Rundiffusion.safetensors", "juggernautXL_version7Rundiffusion.safetensors", "juggernautXL_v7Rundiffusion.safetensors", "juggernautXL_version6Rundiffusion.safetensors", "juggernautXL_v6Rundiffusion.safetensors" ] } ================================================ FILE: presets/lcm.json ================================================ { "default_model": "juggernautXL_v8Rundiffusion.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_loras": [ [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 4.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Extreme Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus V2", "Fooocus Enhance", "Fooocus Sharp" ], "default_aspect_ratio": "1152*896", "default_overwrite_step": -1, "checkpoint_downloads": { "juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors" }, "embeddings_downloads": {}, "lora_downloads": {}, "previous_default_models": [ "juggernautXL_version8Rundiffusion.safetensors", "juggernautXL_version7Rundiffusion.safetensors", "juggernautXL_v7Rundiffusion.safetensors", "juggernautXL_version6Rundiffusion.safetensors", "juggernautXL_v6Rundiffusion.safetensors" ] } ================================================ FILE: presets/playground_v2.5.json ================================================ { "default_model": "playground-v2.5-1024px-aesthetic.fp16.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_loras": [ [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 2.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m", "default_scheduler": "edm_playground_v2.5", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus V2" ], "default_aspect_ratio": "1024*1024", "default_overwrite_step": -1, "default_inpaint_engine_version": "None", "checkpoint_downloads": { "playground-v2.5-1024px-aesthetic.fp16.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors" }, "embeddings_downloads": {}, "lora_downloads": {}, "previous_default_models": [] } ================================================ FILE: presets/pony_v6.json ================================================ { "default_model": "ponyDiffusionV6XL.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_vae": "ponyDiffusionV6XL_vae.safetensors", "default_loras": [ [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 7.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus Pony" ], "default_aspect_ratio": "896*1152", "default_overwrite_step": -1, "default_inpaint_engine_version": "None", "checkpoint_downloads": { "ponyDiffusionV6XL.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL.safetensors" }, "embeddings_downloads": {}, "lora_downloads": {}, "vae_downloads": { "ponyDiffusionV6XL_vae.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL_vae.safetensors" } } ================================================ FILE: presets/realistic.json ================================================ { "default_model": "realisticStockPhoto_v20.safetensors", "default_refiner": "None", "default_refiner_switch": 0.5, "default_loras": [ [ true, "SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors", 0.25 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 3.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "unrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label", "default_styles": [ "Fooocus V2", "Fooocus Photograph", "Fooocus Negative" ], "default_aspect_ratio": "896*1152", "default_overwrite_step": -1, "checkpoint_downloads": { "realisticStockPhoto_v20.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/realisticStockPhoto_v20.safetensors" }, "embeddings_downloads": {}, "lora_downloads": { "SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors": "https://huggingface.co/mashb1t/fav_models/resolve/main/fav/SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors" }, "previous_default_models": ["realisticStockPhoto_v10.safetensors"] } ================================================ FILE: presets/sai.json ================================================ { "default_model": "sd_xl_base_1.0_0.9vae.safetensors", "default_refiner": "sd_xl_refiner_1.0_0.9vae.safetensors", "default_refiner_switch": 0.75, "default_loras": [ [ true, "sd_xl_offset_example-lora_1.0.safetensors", 0.5 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ], [ true, "None", 1.0 ] ], "default_cfg_scale": 7.0, "default_sample_sharpness": 2.0, "default_sampler": "dpmpp_2m_sde_gpu", "default_scheduler": "karras", "default_performance": "Speed", "default_prompt": "", "default_prompt_negative": "", "default_styles": [ "Fooocus V2", "Fooocus Cinematic" ], "default_aspect_ratio": "1152*896", "default_overwrite_step": -1, "checkpoint_downloads": { "sd_xl_base_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors", "sd_xl_refiner_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors" }, "embeddings_downloads": {}, "lora_downloads": { "sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors" }, "previous_default_models": [] } ================================================ FILE: readme.md ================================================
# Fooocus [>>> Click Here to Install Fooocus <<<](#download) Fooocus is an image generating software (based on [Gradio](https://www.gradio.app/) ). Fooocus presents a rethinking of image generator designs. The software is offline, open source, and free, while at the same time, similar to many online image generators like Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images. Fooocus has also simplified the installation: between pressing "download" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia). **Recently many fake websites exist on Google when you search “fooocus”. Do not trust those – here is the only official source of Fooocus.** # Project Status: Limited Long-Term Support (LTS) with Bug Fixes Only The Fooocus project, built entirely on the **Stable Diffusion XL** architecture, is now in a state of limited long-term support (LTS) with bug fixes only. As the existing functionalities are considered as nearly free of programmartic issues (Thanks to [mashb1t](https://github.com/mashb1t)'s huge efforts), future updates will focus exclusively on addressing any bugs that may arise. **There are no current plans to migrate to or incorporate newer model architectures.** However, this may change during time with the development of open-source community. For example, if the community converge to one single dominant method for image generation (which may really happen in half or one years given the current status), Fooocus may also migrate to that exact method. For those interested in utilizing newer models such as **Flux**, we recommend exploring alternative platforms such as [WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge) (also from us), [ComfyUI/SwarmUI](https://github.com/comfyanonymous/ComfyUI). Additionally, several [excellent forks of Fooocus](https://github.com/lllyasviel/Fooocus?tab=readme-ov-file#forks) are available for experimentation. Again, recently many fake websites exist on Google when you search “fooocus”. Do **NOT** get Fooocus from those websites – this page is the only official source of Fooocus. We never have any website like such as “fooocus.com”, “fooocus.net”, “fooocus.co”, “fooocus.ai”, “fooocus.org”, “fooocus.pro”, “fooocus.one”. Those websites are ALL FAKE. **They have ABSOLUTLY no relationship to us. Fooocus is a 100% non-commercial offline open-source software.** # Features Below is a quick list using Midjourney's examples: | Midjourney | Fooocus | | - | - | | High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Unknown method) | High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Fooocus has an offline GPT-2 based prompt processing engine and lots of sampling improvements so that results are always beautiful, no matter if your prompt is as short as “house in garden” or as long as 1000 words) | | V1 V2 V3 V4 | Input Image -> Upscale or Variation -> Vary (Subtle) / Vary (Strong)| | U1 U2 U3 U4 | Input Image -> Upscale or Variation -> Upscale (1.5x) / Upscale (2x) | | Inpaint / Up / Down / Left / Right (Pan) | Input Image -> Inpaint or Outpaint -> Inpaint / Up / Down / Left / Right
(Fooocus uses its own inpaint algorithm and inpaint models so that results are more satisfying than all other software that uses standard SDXL inpaint method/model) | | Image Prompt | Input Image -> Image Prompt
(Fooocus uses its own image prompt algorithm so that result quality and prompt understanding are more satisfying than all other software that uses standard SDXL methods like standard IP-Adapters or Revisions) | | --style | Advanced -> Style | | --stylize | Advanced -> Advanced -> Guidance | | --niji | [Multiple launchers: "run.bat", "run_anime.bat", and "run_realistic.bat".](https://github.com/lllyasviel/Fooocus/discussions/679)
Fooocus support SDXL models on Civitai
(You can google search “Civitai” if you do not know about it) | | --quality | Advanced -> Quality | | --repeat | Advanced -> Image Number | | Multi Prompts (::) | Just use multiple lines of prompts | | Prompt Weights | You can use " I am (happy:1.5)".
Fooocus uses A1111's reweighting algorithm so that results are better than ComfyUI if users directly copy prompts from Civitai. (Because if prompts are written in ComfyUI's reweighting, users are less likely to copy prompt texts as they prefer dragging files)
To use embedding, you can use "(embedding:file_name:1.1)" | | --no | Advanced -> Negative Prompt | | --ar | Advanced -> Aspect Ratios | | InsightFace | Input Image -> Image Prompt -> Advanced -> FaceSwap | | Describe | Input Image -> Describe | Below is a quick list using LeonardoAI's examples: | LeonardoAI | Fooocus | | - | - | | Prompt Magic | Advanced -> Style -> Fooocus V2 | | Advanced Sampler Parameters (like Contrast/Sharpness/etc) | Advanced -> Advanced -> Sampling Sharpness / etc | | User-friendly ControlNets | Input Image -> Image Prompt -> Advanced | Also, [click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117) # Download ### Windows You can directly download Fooocus with: **[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/v2.5.0/Fooocus_win64_2-5-0.7z)** After you download the file, please uncompress it and then run the "run.bat". ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/c49269c4-c274-4893-b368-047c401cc58c) The first time you launch the software, it will automatically download models: 1. It will download [default models](#models) to the folder "Fooocus\models\checkpoints" given different presets. You can download them in advance if you do not want automatic download. 2. Note that if you use inpaint, at the first time you inpaint an image, it will download [Fooocus's own inpaint control model from here](https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch) as the file "Fooocus\models\inpaint\inpaint_v26.fooocus.patch" (the size of this file is 1.28GB). After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). After Fooocus 2.3.0 you can also switch presets directly in the browser. Keep in mind to add these arguments if you want to change the default behavior: * Use `--disable-preset-selection` to disable preset selection in the browser. * Use `--always-download-new-model` to download missing models on preset switch. Default is fallback to `previous_default_models` defined in the corresponding preset, also see terminal output. ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447) If you already have these files, you can copy them to the above locations to speed up installation. Note that if you see **"MetadataIncompleteBuffer" or "PytorchStreamReader"**, then your model files are corrupted. Please download models again. Below is a test on a relatively low-end laptop with **16GB System RAM** and **6GB VRAM** (Nvidia 3060 laptop). The speed on this machine is about 1.35 seconds per iteration. Pretty impressive – nowadays laptops with 3060 are usually at very acceptable price. ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/938737a5-b105-4f19-b051-81356cb7c495) Besides, recently many other software report that Nvidia driver above 532 is sometimes 10x slower than Nvidia driver 531. If your generation time is very long, consider download [Nvidia Driver 531 Laptop](https://www.nvidia.com/download/driverResults.aspx/199991/en-us/) or [Nvidia Driver 531 Desktop](https://www.nvidia.com/download/driverResults.aspx/199990/en-us/). Note that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and **8GB system memory (8GB RAM)**. This requires using Microsoft’s Virtual Swap technique, which is automatically enabled by your Windows installation in most cases, so you often do not need to do anything about it. However, if you are not sure, or if you manually turned it off (would anyone really do that?), or **if you see any "RuntimeError: CPUAllocator"**, you can enable it here:
Click here to see the image instructions. ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) **And make sure that you have at least 40GB free space on each drive if you still see "RuntimeError: CPUAllocator" !**
Please open an issue if you use similar devices but still cannot achieve acceptable performances. Note that the [minimal requirement](#minimal-requirement) for different platforms is different. See also the common problems and troubleshoots [here](troubleshoot.md). ### Colab (Last tested - 2024 Aug 12 by [mashb1t](https://github.com/mashb1t)) | Colab | Info | --- | --- | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official In Colab, you can modify the last line to `!python entry_with_update.py --share --always-high-vram` or `!python entry_with_update.py --share --always-high-vram --preset anime` or `!python entry_with_update.py --share --always-high-vram --preset realistic` for Fooocus Default/Anime/Realistic Edition. You can also change the preset in the UI. Please be aware that this may lead to timeouts after 60 seconds. If this is the case, please wait until the download has finished, change the preset to initial and back to the one you've selected or reload the page. Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab. Using `--always-high-vram` shifts resource allocation from RAM to VRAM and achieves the overall best balance between performance, flexibility and stability on the default T4 instance. Please find more information [here](https://github.com/lllyasviel/Fooocus/pull/1710#issuecomment-1989185346). Thanks to [camenduru](https://github.com/camenduru) for the template! ### Linux (Using Anaconda) If you want to use Anaconda/Miniconda, you can git clone https://github.com/lllyasviel/Fooocus.git cd Fooocus conda env create -f environment.yaml conda activate fooocus pip install -r requirements_versions.txt Then download the models: download [default models](#models) to the folder "Fooocus\models\checkpoints". **Or let Fooocus automatically download the models** using the launcher: conda activate fooocus python entry_with_update.py Or, if you want to open a remote port, use conda activate fooocus python entry_with_update.py --listen Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. ### Linux (Using Python Venv) Your Linux needs to have **Python 3.10** installed, and let's say your Python can be called with the command **python3** with your venv system working; you can git clone https://github.com/lllyasviel/Fooocus.git cd Fooocus python3 -m venv fooocus_env source fooocus_env/bin/activate pip install -r requirements_versions.txt See the above sections for model downloads. You can launch the software with: source fooocus_env/bin/activate python entry_with_update.py Or, if you want to open a remote port, use source fooocus_env/bin/activate python entry_with_update.py --listen Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. ### Linux (Using native system Python) If you know what you are doing, and your Linux already has **Python 3.10** installed, and your Python can be called with the command **python3** (and Pip with **pip3**), you can git clone https://github.com/lllyasviel/Fooocus.git cd Fooocus pip3 install -r requirements_versions.txt See the above sections for model downloads. You can launch the software with: python3 entry_with_update.py Or, if you want to open a remote port, use python3 entry_with_update.py --listen Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. ### Linux (AMD GPUs) Note that the [minimal requirement](#minimal-requirement) for different platforms is different. Same with the above instructions. You need to change torch to the AMD version pip uninstall torch torchvision torchaudio torchtext functorch xformers pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6 AMD is not intensively tested, however. The AMD support is in beta. Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. ### Windows (AMD GPUs) Note that the [minimal requirement](#minimal-requirement) for different platforms is different. Same with Windows. Download the software and edit the content of `run.bat` as: .\python_embeded\python.exe -m pip uninstall torch torchvision torchaudio torchtext functorch xformers -y .\python_embeded\python.exe -m pip install torch-directml .\python_embeded\python.exe -s Fooocus\entry_with_update.py --directml pause Then run the `run.bat`. AMD is not intensively tested, however. The AMD support is in beta. For AMD, use `.\python_embeded\python.exe Fooocus\entry_with_update.py --directml --preset anime` or `.\python_embeded\python.exe Fooocus\entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition. ### Mac Note that the [minimal requirement](#minimal-requirement) for different platforms is different. Mac is not intensively tested. Below is an unofficial guideline for using Mac. You can discuss problems [here](https://github.com/lllyasviel/Fooocus/pull/129). You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or a newer version. Fooocus runs on Apple silicon computers via [PyTorch](https://pytorch.org/get-started/locally/) MPS device acceleration. Mac Silicon computers don't come with a dedicated graphics card, resulting in significantly longer image processing times compared to computers with dedicated graphics cards. 1. Install the conda package manager and pytorch nightly. Read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide for instructions. Make sure pytorch recognizes your MPS device. 1. Open the macOS Terminal app and clone this repository with `git clone https://github.com/lllyasviel/Fooocus.git`. 1. Change to the new Fooocus directory, `cd Fooocus`. 1. Create a new conda environment, `conda env create -f environment.yaml`. 1. Activate your new conda environment, `conda activate fooocus`. 1. Install the packages required by Fooocus, `pip install -r requirements_versions.txt`. 1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant amount of time, depending on your internet connection. Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. ### Docker See [docker.md](docker.md) ### Download Previous Version See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405). ## Minimal Requirement Below is the minimal requirement for running Fooocus locally. If your device capability is lower than this spec, you may not be able to use Fooocus locally. (Please let us know, in any case, if your device capability is lower but Fooocus still works.) | Operating System | GPU | Minimal GPU Memory | Minimal System Memory | [System Swap](troubleshoot.md) | Note | |-------------------|------------------------------|------------------------------|---------------------------|--------------------------------|----------------------------------------------------------------------------| | Windows/Linux | Nvidia RTX 4XXX | 4GB | 8GB | Required | fastest | | Windows/Linux | Nvidia RTX 3XXX | 4GB | 8GB | Required | usually faster than RTX 2XXX | | Windows/Linux | Nvidia RTX 2XXX | 4GB | 8GB | Required | usually faster than GTX 1XXX | | Windows/Linux | Nvidia GTX 1XXX | 8GB (* 6GB uncertain) | 8GB | Required | only marginally faster than CPU | | Windows/Linux | Nvidia GTX 9XX | 8GB | 8GB | Required | faster or slower than CPU | | Windows/Linux | Nvidia GTX < 9XX | Not supported | / | / | / | | Windows | AMD GPU | 8GB (updated 2023 Dec 30) | 8GB | Required | via DirectML (* ROCm is on hold), about 3x slower than Nvidia RTX 3XXX | | Linux | AMD GPU | 8GB | 8GB | Required | via ROCm, about 1.5x slower than Nvidia RTX 3XXX | | Mac | M1/M2 MPS | Shared | Shared | Shared | about 9x slower than Nvidia RTX 3XXX | | Windows/Linux/Mac | only use CPU | 0GB | 32GB | Required | about 17x slower than Nvidia RTX 3XXX | * AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows. * Nvidia GTX 1XXX 6GB uncertain: Some people report 6GB success on GTX 10XX, but some other people report failure cases. *Note that Fooocus is only for extremely high quality image generating. We will not support smaller models to reduce the requirement and sacrifice result quality.* ## Troubleshoot See the common problems [here](troubleshoot.md). ## Default Models Given different goals, the default models and configs of Fooocus are different: | Task | Windows | Linux args | Main Model | Refiner | Config | |-----------| --- | --- |-----------------------------| --- |--------------------------------------------------------------------------------| | General | run.bat | | juggernautXL_v8Rundiffusion | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/default.json) | | Realistic | run_realistic.bat | --preset realistic | realisticStockPhoto_v20 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) | | Anime | run_anime.bat | --preset anime | animaPencilXL_v500 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json) | Note that the download is **automatic** - you do not need to do anything if the internet connection is okay. However, you can download them manually if you (or move them from somewhere else) have your own preparation. ## UI Access and Authentication In addition to running on localhost, Fooocus can also expose its UI in two ways: * Local UI listener: use `--listen` (specify port e.g. with `--port 8888`). * API access: use `--share` (registers an endpoint at `.gradio.live`). In both ways the access is unauthenticated by default. You can add basic authentication by creating a file called `auth.json` in the main directory, which contains a list of JSON objects with the keys `user` and `pass` (see example in [auth-example.json](./auth-example.json)). ## List of "Hidden" Tricks
Click to see a list of tricks. Those are based on SDXL and are not very up-to-date with latest models. 1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processing and "raw" mode, or the LeonardoAI's Prompt Magic). 2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!) 3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Draw Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!) 4. We implemented a carefully tuned variation of Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.) 5. We modified the style templates a bit and added the "cinematic-default". 6. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora. 7. The parameters of samplers are carefully tuned. 8. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than those from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results. 9. Separated prompts for two different text encoders seem unnecessary. Separated prompts for the base model and refiner may work, but the effects are random, and we refrain from implementing this. 10. The DPM family seems well-suited for XL since XL sometimes generates overly smooth texture, but the DPM family sometimes generates overly dense detail in texture. Their joint effect looks neutral and appealing to human perception. 11. A carefully designed system for balancing multiple styles as well as prompt expansion. 12. Using automatic1111's method to normalize prompt emphasizing. This significantly improves results when users directly copy prompts from civitai. 13. The joint swap system of the refiner now also supports img2img and upscale in a seamless way. 14. CFG Scale and TSNR correction (tuned for SDXL) when CFG is bigger than 10.
## Customization After the first time you run Fooocus, a config file will be generated at `Fooocus\config.txt`. This file can be edited to change the model path or default parameters. For example, an edited `Fooocus\config.txt` (this file will be generated after the first launch) may look like this: ```json { "path_checkpoints": "D:\\Fooocus\\models\\checkpoints", "path_loras": "D:\\Fooocus\\models\\loras", "path_embeddings": "D:\\Fooocus\\models\\embeddings", "path_vae_approx": "D:\\Fooocus\\models\\vae_approx", "path_upscale_models": "D:\\Fooocus\\models\\upscale_models", "path_inpaint": "D:\\Fooocus\\models\\inpaint", "path_controlnet": "D:\\Fooocus\\models\\controlnet", "path_clip_vision": "D:\\Fooocus\\models\\clip_vision", "path_fooocus_expansion": "D:\\Fooocus\\models\\prompt_expansion\\fooocus_expansion", "path_outputs": "D:\\Fooocus\\outputs", "default_model": "realisticStockPhoto_v10.safetensors", "default_refiner": "", "default_loras": [["lora_filename_1.safetensors", 0.5], ["lora_filename_2.safetensors", 0.5]], "default_cfg_scale": 3.0, "default_sampler": "dpmpp_2m", "default_scheduler": "karras", "default_negative_prompt": "low quality", "default_positive_prompt": "", "default_styles": [ "Fooocus V2", "Fooocus Photograph", "Fooocus Negative" ] } ``` Many other keys, formats, and examples are in `Fooocus\config_modification_tutorial.txt` (this file will be generated after the first launch). Consider twice before you really change the config. If you find yourself breaking things, just delete `Fooocus\config.txt`. Fooocus will go back to default. A safer way is just to try "run_anime.bat" or "run_realistic.bat" - they should already be good enough for different tasks. ~Note that `user_path_config.txt` is deprecated and will be removed soon.~ (Edit: it is already removed.) ### All CMD Flags ``` entry_with_update.py [-h] [--listen [IP]] [--port PORT] [--disable-header-check [ORIGIN]] [--web-upload-size WEB_UPLOAD_SIZE] [--hf-mirror HF_MIRROR] [--external-working-path PATH [PATH ...]] [--output-path OUTPUT_PATH] [--temp-path TEMP_PATH] [--cache-path CACHE_PATH] [--in-browser] [--disable-in-browser] [--gpu-device-id DEVICE_ID] [--async-cuda-allocation | --disable-async-cuda-allocation] [--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16] [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2] [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16] [--vae-in-cpu] [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32] [--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack] [--preview-option [none,auto,fast,taesd]] [--attention-split | --attention-quad | --attention-pytorch] [--disable-xformers] [--always-gpu | --always-high-vram | --always-normal-vram | --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]] [--always-offload-from-vram] [--pytorch-deterministic] [--disable-server-log] [--debug-mode] [--is-windows-embedded-python] [--disable-server-info] [--multi-user] [--share] [--preset PRESET] [--disable-preset-selection] [--language LANGUAGE] [--disable-offload-from-vram] [--theme THEME] [--disable-image-log] [--disable-analytics] [--disable-metadata] [--disable-preset-download] [--disable-enhance-output-sorting] [--enable-auto-describe-image] [--always-download-new-model] [--rebuild-hash-cache [CPU_NUM_THREADS]] ``` ## Inline Prompt Features ### Wildcards Example prompt: `__color__ flower` Processed for positive and negative prompt. Selects a random wildcard from a predefined list of options, in this case the `wildcards/color.txt` file. The wildcard will be replaced with a random color (randomness based on seed). You can also disable randomness and process a wildcard file from top to bottom by enabling the checkbox `Read wildcards in order` in Developer Debug Mode. Wildcards can be nested and combined, and multiple wildcards can be used in the same prompt (example see `wildcards/color_flower.txt`). ### Array Processing Example prompt: `[[red, green, blue]] flower` Processed only for positive prompt. Processes the array from left to right, generating a separate image for each element in the array. In this case 3 images would be generated, one for each color. Increase the image number to 3 to generate all 3 variants. Arrays can not be nested, but multiple arrays can be used in the same prompt. Does support inline LoRAs as array elements! ### Inline LoRAs Example prompt: `flower ` Processed only for positive prompt. Applies a LoRA to the prompt. The LoRA file must be located in the `models/loras` directory. ## Advanced Features [Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117) ## Forks Below are some Forks to Fooocus: | Fooocus' forks | | - | | [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control)
[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus)
[MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE)
[mashb1t/Fooocus](https://github.com/mashb1t/Fooocus)
and so on ... | ## Thanks Many thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus. The project starts from a mixture of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) codebases. Also, thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom! ## Update Log The log is [here](update_log.md). ## Localization/Translation/I18N You can put json files in the `language` folder to translate the user interface. For example, below is the content of `Fooocus/language/example.json`: ```json { "Generate": "生成", "Input Image": "入力画像", "Advanced": "고급", "SAI 3D Model": "SAI 3D Modèle" } ``` If you add `--language example` arg, Fooocus will read `Fooocus/language/example.json` to translate the UI. For example, you can edit the ending line of Windows `run.bat` as .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example Or `run_anime.bat` as .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example --preset anime Or `run_realistic.bat` as .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example --preset realistic For practical translation, you may create your own file like `Fooocus/language/jp.json` or `Fooocus/language/cn.json` and then use flag `--language jp` or `--language cn`. Apparently, these files do not exist now. **We need your help to create these files!** Note that if no `--language` is given and at the same time `Fooocus/language/default.json` exists, Fooocus will always load `Fooocus/language/default.json` for translation. By default, the file `Fooocus/language/default.json` does not exist. ================================================ FILE: requirements_docker.txt ================================================ torch==2.1.0 torchvision==0.16.0 ================================================ FILE: requirements_versions.txt ================================================ torchsde==0.2.6 einops==0.8.0 transformers==4.42.4 safetensors==0.4.3 accelerate==0.32.1 pyyaml==6.0.1 pillow==10.4.0 scipy==1.14.0 tqdm==4.66.4 psutil==6.0.0 pytorch_lightning==2.3.3 omegaconf==2.3.0 gradio==3.41.2 pygit2==1.15.1 opencv-contrib-python-headless==4.10.0.84 httpx==0.27.0 onnxruntime==1.18.1 timm==1.0.7 numpy==1.26.4 tokenizers==0.19.1 packaging==24.1 rembg==2.0.57 groundingdino-py==0.4.0 segment_anything==1.0 ================================================ FILE: sdxl_styles/sdxl_styles_diva.json ================================================ [ { "name": "cinematic-diva", "prompt": "UHD, 8K, ultra detailed, a cinematic photograph of {prompt}, beautiful lighting, great composition", "negative_prompt": "ugly, deformed, noisy, blurry, NSFW" }, { "name": "Abstract Expressionism", "prompt": "Abstract Expressionism Art, {prompt}, High contrast, minimalistic, colorful, stark, dramatic, expressionism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" }, { "name": "Academia", "prompt": "Academia, {prompt}, preppy Ivy League style, stark, dramatic, chic boarding school, academia", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt" }, { "name": "Action Figure", "prompt": "Action Figure, {prompt}, plastic collectable action figure, collectable toy action figure", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Adorable 3D Character", "prompt": "Adorable 3D Character, {prompt}, 3D render, adorable character, 3D art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic" }, { "name": "Adorable Kawaii", "prompt": "Adorable Kawaii, {prompt}, pretty, cute, adorable, kawaii", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, gothic, dark, moody, monochromatic" }, { "name": "Art Deco", "prompt": "Art Deco, {prompt}, sleek, geometric forms, art deco style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Art Nouveau", "prompt": "Art Nouveau, beautiful art, {prompt}, sleek, organic forms, long, sinuous, art nouveau style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, industrial, mechanical" }, { "name": "Astral Aura", "prompt": "Astral Aura, {prompt}, astral, colorful aura, vibrant energy", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Avant-garde", "prompt": "Avant-garde, {prompt}, unusual, experimental, avant-garde art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Baroque", "prompt": "Baroque, {prompt}, dramatic, exuberant, grandeur, baroque art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Bauhaus-Style Poster", "prompt": "Bauhaus-Style Poster, {prompt}, simple geometric shapes, clean lines, primary colors, Bauhaus-Style Poster", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Blueprint Schematic Drawing", "prompt": "Blueprint Schematic Drawing, {prompt}, technical drawing, blueprint, schematic", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Caricature", "prompt": "Caricature, {prompt}, exaggerated, comical, caricature", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" }, { "name": "Cel Shaded Art", "prompt": "Cel Shaded Art, {prompt}, 2D, flat color, toon shading, cel shaded style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Character Design Sheet", "prompt": "Character Design Sheet, {prompt}, character reference sheet, character turn around", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Classicism Art", "prompt": "Classicism Art, {prompt}, inspired by Roman and Greek culture, clarity, harmonious, classicism art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Color Field Painting", "prompt": "Color Field Painting, {prompt}, abstract, simple, geometic, color field painting style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Colored Pencil Art", "prompt": "Colored Pencil Art, {prompt}, colored pencil strokes, light color, visible paper texture, colored pencil art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Conceptual Art", "prompt": "Conceptual Art, {prompt}, concept art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Constructivism", "prompt": "Constructivism Art, {prompt}, minimalistic, geometric forms, constructivism art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Cubism", "prompt": "Cubism Art, {prompt}, flat geometric forms, cubism art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Dadaism", "prompt": "Dadaism Art, {prompt}, satirical, nonsensical, dadaism art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Dark Fantasy", "prompt": "Dark Fantasy Art, {prompt}, dark, moody, dark fantasy style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, bright, sunny" }, { "name": "Dark Moody Atmosphere", "prompt": "Dark Moody Atmosphere, {prompt}, dramatic, mysterious, dark moody atmosphere", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful, bright" }, { "name": "DMT Art Style", "prompt": "DMT Art Style, {prompt}, bright colors, surreal visuals, swirling patterns, DMT art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Doodle Art", "prompt": "Doodle Art Style, {prompt}, drawing, freeform, swirling patterns, doodle art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Double Exposure", "prompt": "Double Exposure Style, {prompt}, double image ghost effect, image combination, double exposure style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Dripping Paint Splatter Art", "prompt": "Dripping Paint Splatter Art, {prompt}, dramatic, paint drips, splatters, dripping paint", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Expressionism", "prompt": "Expressionism Art Style, {prompt}, movement, contrast, emotional, exaggerated forms, expressionism art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Faded Polaroid Photo", "prompt": "Faded Polaroid Photo, {prompt}, analog, old faded photo, old polaroid", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful" }, { "name": "Fauvism", "prompt": "Fauvism Art, {prompt}, painterly, bold colors, textured brushwork, fauvism art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Flat 2D Art", "prompt": "Flat 2D Art, {prompt}, simple flat color, 2-dimensional, Flat 2D Art Style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic" }, { "name": "Fortnite Art Style", "prompt": "Fortnite Art Style, {prompt}, 3D cartoon, colorful, Fortnite Art Style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" }, { "name": "Futurism", "prompt": "Futurism Art Style, {prompt}, dynamic, dramatic, Futurism Art Style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Glitchcore", "prompt": "Glitchcore Art Style, {prompt}, dynamic, dramatic, distorted, vibrant colors, glitchcore art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Glo-fi", "prompt": "Glo-fi Art Style, {prompt}, dynamic, dramatic, vibrant colors, glo-fi art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Googie Art Style", "prompt": "Googie Art Style, {prompt}, dynamic, dramatic, 1950's futurism, bold boomerang angles, Googie art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Graffiti Art", "prompt": "Graffiti Art Style, {prompt}, dynamic, dramatic, vibrant colors, graffiti art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Harlem Renaissance Art", "prompt": "Harlem Renaissance Art Style, {prompt}, dynamic, dramatic, 1920s African American culture, Harlem Renaissance art style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "High Fashion", "prompt": "High Fashion, {prompt}, dynamic, dramatic, haute couture, elegant, ornate clothing, High Fashion", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Idyllic", "prompt": "Idyllic, {prompt}, peaceful, happy, pleasant, happy, harmonious, picturesque, charming", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Impressionism", "prompt": "Impressionism, {prompt}, painterly, small brushstrokes, visible brushstrokes, impressionistic style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Infographic Drawing", "prompt": "Infographic Drawing, {prompt}, diagram, infographic", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Ink Dripping Drawing", "prompt": "Ink Dripping Drawing, {prompt}, ink drawing, dripping ink", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant" }, { "name": "Japanese Ink Drawing", "prompt": "Japanese Ink Drawing, {prompt}, ink drawing, inkwash, Japanese Ink Drawing", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant" }, { "name": "Knolling Photography", "prompt": "Knolling Photography, {prompt}, flat lay photography, object arrangment, knolling photography", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Light Cheery Atmosphere", "prompt": "Light Cheery Atmosphere, {prompt}, happy, joyful, cheerful, carefree, gleeful, lighthearted, pleasant atmosphere", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, monochromatic, dark, moody" }, { "name": "Logo Design", "prompt": "Logo Design, {prompt}, dynamic graphic art, vector art, minimalist, professional logo design", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Luxurious Elegance", "prompt": "Luxurious Elegance, {prompt}, extravagant, ornate, designer, opulent, picturesque, lavish", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Macro Photography", "prompt": "Macro Photography, {prompt}, close-up, macro 100mm, macro photography", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Mandola Art", "prompt": "Mandola art style, {prompt}, complex, circular design, mandola", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Marker Drawing", "prompt": "Marker Drawing, {prompt}, bold marker lines, visibile paper texture, marker drawing", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic" }, { "name": "Medievalism", "prompt": "Medievalism, {prompt}, inspired by The Middle Ages, medieval art, elaborate patterns and decoration, Medievalism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Minimalism", "prompt": "Minimalism, {prompt}, abstract, simple geometic shapes, hard edges, sleek contours, Minimalism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Neo-Baroque", "prompt": "Neo-Baroque, {prompt}, ornate and elaborate, dynaimc, Neo-Baroque", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Neo-Byzantine", "prompt": "Neo-Byzantine, {prompt}, grand decorative religious style, Orthodox Christian inspired, Neo-Byzantine", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Neo-Futurism", "prompt": "Neo-Futurism, {prompt}, high-tech, curves, spirals, flowing lines, idealistic future, Neo-Futurism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Neo-Impressionism", "prompt": "Neo-Impressionism, {prompt}, tiny dabs of color, Pointillism, painterly, Neo-Impressionism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic" }, { "name": "Neo-Rococo", "prompt": "Neo-Rococo, {prompt}, curved forms, naturalistic ornamentation, elaborate, decorative, gaudy, Neo-Rococo", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Neoclassicism", "prompt": "Neoclassicism, {prompt}, ancient Rome and Greece inspired, idealic, sober colors, Neoclassicism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Op Art", "prompt": "Op Art, {prompt}, optical illusion, abstract, geometric pattern, impression of movement, Op Art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Ornate and Intricate", "prompt": "Ornate and Intricate, {prompt}, decorative, highly detailed, elaborate, ornate, intricate", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Pencil Sketch Drawing", "prompt": "Pencil Sketch Drawing, {prompt}, black and white drawing, graphite drawing", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Pop Art 2", "prompt": "Pop Art, {prompt}, vivid colors, flat color, 2D, strong lines, Pop Art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" }, { "name": "Rococo", "prompt": "Rococo, {prompt}, flamboyant, pastel colors, curved lines, elaborate detail, Rococo", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Silhouette Art", "prompt": "Silhouette Art, {prompt}, high contrast, well defined, Silhouette Art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Simple Vector Art", "prompt": "Simple Vector Art, {prompt}, 2D flat, simple shapes, minimalistic, professional graphic, flat color, high contrast, Simple Vector Art", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic" }, { "name": "Sketchup", "prompt": "Sketchup, {prompt}, CAD, professional design, Sketchup", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, photograph" }, { "name": "Steampunk 2", "prompt": "Steampunk, {prompt}, retrofuturistic science fantasy, steam-powered tech, vintage industry, gears, neo-victorian, steampunk", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Surrealism", "prompt": "Surrealism, {prompt}, expressive, dramatic, organic lines and forms, dreamlike and mysterious, Surrealism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" }, { "name": "Suprematism", "prompt": "Suprematism, {prompt}, abstract, limited color palette, geometric forms, Suprematism", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" }, { "name": "Terragen", "prompt": "Terragen, {prompt}, beautiful massive landscape, epic scenery, Terragen", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Tranquil Relaxing Atmosphere", "prompt": "Tranquil Relaxing Atmosphere, {prompt}, calming style, soothing colors, peaceful, idealic, Tranquil Relaxing Atmosphere", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, oversaturated" }, { "name": "Sticker Designs", "prompt": "Vector Art Stickers, {prompt}, professional vector design, sticker designs, Sticker Sheet", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Vibrant Rim Light", "prompt": "Vibrant Rim Light, {prompt}, bright rim light, high contrast, bold edge light", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Volumetric Lighting", "prompt": "Volumetric Lighting, {prompt}, light depth, dramatic atmospheric lighting, Volumetric Lighting", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" }, { "name": "Watercolor 2", "prompt": "Watercolor style painting, {prompt}, visible paper texture, colorwash, watercolor", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" }, { "name": "Whimsical and Playful", "prompt": "Whimsical and Playful, {prompt}, imaginative, fantastical, bight colors, stylized, happy, Whimsical and Playful", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, drab, boring, moody" } ] ================================================ FILE: sdxl_styles/sdxl_styles_fooocus.json ================================================ [ { "name": "Fooocus Enhance", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)" }, { "name": "Fooocus Semi Realistic", "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)" }, { "name": "Fooocus Sharp", "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy", "negative_prompt": "anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" }, { "name": "Fooocus Masterpiece", "prompt": "(masterpiece), (best quality), (ultra-detailed), {prompt}, illustration, disheveled hair, detailed eyes, perfect composition, moist skin, intricate details, earrings", "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality" }, { "name": "Fooocus Photograph", "prompt": "photograph {prompt}, 50mm . cinematic 4k epic detailed 4k epic detailed photograph shot on kodak detailed cinematic hbo dark moody, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", "negative_prompt": "Brad Pitt, bokeh, depth of field, blurry, cropped, regular face, saturated, contrast, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" }, { "name": "Fooocus Negative", "negative_prompt": "deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch" }, { "name": "Fooocus Cinematic", "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" }, { "name": "Fooocus Pony", "prompt": "score_9, score_8_up, score_7_up, {prompt}", "negative_prompt": "score_6, score_5, score_4" } ] ================================================ FILE: sdxl_styles/sdxl_styles_marc_k3nt3l.json ================================================ [ { "name": "MK Chromolithography", "prompt": "Chromolithograph {prompt}. Vibrant colors, intricate details, rich color saturation, meticulous registration, multi-layered printing, decorative elements, historical charm, artistic reproductions, commercial posters, nostalgic, ornate compositions.", "negative_prompt": "monochromatic, simple designs, limited color palette, imprecise registration, minimalistic, modern aesthetic, digital appearance." }, { "name": "MK Cross Processing Print", "prompt": "Cross processing print {prompt}. Experimental color shifts, unconventional tonalities, vibrant and surreal hues, heightened contrasts, unpredictable results, artistic unpredictability, retro and vintage feel, dynamic color interplay, abstract and dreamlike.", "negative_prompt": "predictable color tones, traditional processing, realistic color representation, subdued contrasts, standard photographic aesthetics." }, { "name": "MK Dufaycolor Photograph", "prompt": "Dufaycolor photograph {prompt}. Vintage color palette, distinctive color rendering, soft and dreamy atmosphere, historical charm, unique color process, grainy texture, evocative mood, nostalgic aesthetic, hand-tinted appearance, artistic patina.", "negative_prompt": "modern color reproduction, hyperrealistic tones, sharp and clear details, digital precision, contemporary aesthetic." }, { "name": "MK Herbarium", "prompt": "Herbarium drawing{prompt}. Botanical accuracy, old botanical book illustration, detailed illustrations, pressed plants, delicate and precise linework, scientific documentation, meticulous presentation, educational purpose, organic compositions, timeless aesthetic, naturalistic beauty.", "negative_prompt": "abstract representation, vibrant colors, artistic interpretation, chaotic compositions, fantastical elements, digital appearance." }, { "name": "MK Punk Collage", "prompt": "punk collage style {prompt} . mixed media, papercut,textured paper, overlapping, ripped posters, safety pins, chaotic layers, graffiti-style elements, anarchy symbols, vintage photos, cut-and-paste aesthetic, bold typography, distorted images, political messages, urban decay, distressed textures, newspaper clippings, spray paint, rebellious icons, DIY spirit, vivid colors, punk band logos, edgy and raw compositions, ", "negative_prompt": "conventional,blurry, noisy, low contrast" }, { "name": "MK mosaic", "prompt": "mosaic style {prompt} . fragmented, assembled, colorful, highly detailed", "negative_prompt": "whole, unbroken, monochrome" }, { "name": "MK Van Gogh", "prompt": "Oil painting by Van Gogh {prompt} . Expressive, impasto, swirling brushwork, vibrant, brush strokes, Brushstroke-heavy, Textured, Impasto, Colorful, Dynamic, Bold, Distinctive, Vibrant, Whirling, Expressive, Dramatic, Swirling, Layered, Intense, Contrastive, Atmospheric, Luminous, Textural, Evocative, SpiraledVan Gogh style", "negative_prompt": "realistic, photorealistic, calm, straight lines, signature, frame, text, watermark" }, { "name": "MK Coloring Book", "prompt": "centered black and white high contrast line drawing, coloring book style,{prompt} . monochrome, blank white background", "negative_prompt": "greyscale, gradients,shadows,shadow, colored, Red, Blue, Yellow, Green, Orange, Purple, Pink, Brown, Gray, Beige, Turquoise, Lavender, Cyan, Magenta, Olive, Indigo, black background" }, { "name": "MK Singer Sargent", "prompt": "Oil painting by John Singer Sargent {prompt}. Elegant, refined, masterful technique,realistic portrayal, subtle play of light, captivating expression, rich details, harmonious colors, skillful composition, brush strokes, chiaroscuro.", "negative_prompt": "realistic, photorealistic, abstract, overly stylized, excessive contrasts, distorted,bright colors,disorder." }, { "name": "MK Pollock", "prompt": "Oil painting by Jackson Pollock {prompt}. Abstract expressionism, drip painting, chaotic composition, energetic, spontaneous, unconventional technique, dynamic, bold, distinctive, vibrant, intense, expressive, energetic, layered, non-representational, gestural.", "negative_prompt": "(realistic:1.5), (photorealistic:1.5), representational, calm, ordered composition, precise lines, detailed forms, subdued colors, quiet, static, traditional, figurative." }, { "name": "MK Basquiat", "prompt": "Artwork by Jean-Michel Basquiat {prompt}. Neo-expressionism, street art influence, graffiti-inspired, raw, energetic, bold colors, dynamic composition, chaotic, layered, textural, expressive, spontaneous, distinctive, symbolic,energetic brushstrokes.", "negative_prompt": "(realistic:1.5), (photorealistic:1.5), calm, precise lines, conventional composition, subdued" }, { "name": "MK Andy Warhol", "prompt": "Artwork in the style of Andy Warhol {prompt}. Pop art, vibrant colors, bold compositions, repetition of iconic imagery, celebrity culture, commercial aesthetics, mass production influence, stylized simplicity, cultural commentary, graphical elements, distinctive portraits.", "negative_prompt": "subdued colors, realistic, lack of repetition, minimalistic." }, { "name": "MK Halftone print", "prompt": "Halftone print of {prompt}. Dot matrix pattern, grayscale tones, vintage aesthetic, newspaper print vibe, stylized dots, visual texture, black and white contrasts, retro appearance, artistic pointillism,pop culture, (Roy Lichtenstein style:1.5).", "negative_prompt": "smooth gradients, continuous tones, vibrant colors." }, { "name": "MK Gond Painting", "prompt": "Gond painting {prompt}. Intricate patterns, vibrant colors, detailed motifs, nature-inspired themes, tribal folklore, fine lines, intricate detailing, storytelling compositions, mystical and folkloric, cultural richness.", "negative_prompt": "monochromatic, abstract shapes, minimalistic." }, { "name": "MK Albumen Print", "prompt": "Albumen print {prompt}. Sepia tones, fine details, subtle tonal gradations, delicate highlights, vintage aesthetic, soft and muted atmosphere, historical charm, rich textures, meticulous craftsmanship, classic photographic technique, vignetting.", "negative_prompt": "vibrant colors, high contrast, modern, digital appearance, sharp details, contemporary style." }, { "name": "MK Aquatint Print", "prompt": "Aquatint print {prompt}. Soft tonal gradations, atmospheric effects, velvety textures, rich contrasts, fine details, etching process, delicate lines, nuanced shading, expressive and moody atmosphere, artistic depth.", "negative_prompt": "sharp contrasts, bold lines, minimalistic." }, { "name": "MK Anthotype Print", "prompt": "Anthotype print {prompt}. Monochrome dye, soft and muted colors, organic textures, ephemeral and delicate appearance, low details, watercolor canvas, low contrast, overexposed, silhouette, textured paper.", "negative_prompt": "vibrant synthetic dyes, bold and saturated colors." }, { "name": "MK Inuit Carving", "prompt": "A sculpture made of ivory, {prompt} made of . Sculptures, Inuit art style, intricate carvings, natural materials, storytelling motifs, arctic wildlife themes, symbolic representations, cultural traditions, earthy tones, harmonious compositions, spiritual and mythological elements.", "negative_prompt": "abstract, vibrant colors." }, { "name": "MK Bromoil Print", "prompt": "Bromoil print {prompt}. Painterly effects, sepia tones, textured surfaces, rich contrasts, expressive brushwork, tonal variations, vintage aesthetic, atmospheric mood, handmade quality, artistic experimentation, darkroom craftsmanship, vignetting.", "negative_prompt": "smooth surfaces, minimal brushwork, contemporary digital appearance." }, { "name": "MK Calotype Print", "prompt": "Calotype print {prompt}. Soft focus, subtle tonal range, paper negative process, fine details, vintage aesthetic, artistic experimentation, atmospheric mood, early photographic charm, handmade quality, vignetting.", "negative_prompt": "sharp focus, bold contrasts, modern aesthetic, digital photography." }, { "name": "MK Color Sketchnote", "prompt": "Color sketchnote {prompt}. Hand-drawn elements, vibrant colors, visual hierarchy, playful illustrations, varied typography, graphic icons, organic and dynamic layout, personalized touches, creative expression, engaging storytelling.", "negative_prompt": "monochromatic, geometric layout." }, { "name": "MK Cibulak Porcelain", "prompt": "A sculpture made of blue pattern porcelain of {prompt}. Classic design, blue and white color scheme, intricate detailing, floral motifs, onion-shaped elements, historical charm, rococo, white ware, cobalt blue, underglaze pattern, fine craftsmanship, traditional elegance, delicate patterns, vintage aesthetic, Meissen, Blue Onion pattern, Cibulak.", "negative_prompt": "tea, teapot, cup, teacup,bright colors, bold and modern design, absence of intricate detailing, lack of floral motifs, non-traditional shapes." }, { "name": "MK Alcohol Ink Art", "prompt": "Alcohol ink art {prompt}. Fluid and vibrant colors, unpredictable patterns, organic textures, translucent layers, abstract compositions, ethereal and dreamy effects, free-flowing movement, expressive brushstrokes, contemporary aesthetic, wet textured paper.", "negative_prompt": "monochromatic, controlled patterns." }, { "name": "MK One Line Art", "prompt": "One line art {prompt}. Continuous and unbroken black line, minimalistic, simplicity, economical use of space, flowing and dynamic, symbolic representations, contemporary aesthetic, evocative and abstract, white background.", "negative_prompt": "disjointed lines, complexity, complex detailing." }, { "name": "MK Blacklight Paint", "prompt": "Blacklight paint {prompt}. Fluorescent pigments, vibrant and surreal colors, ethereal glow, otherworldly effects, dynamic and psychedelic compositions, neon aesthetics, transformative in ultraviolet light, contemporary and experimental.", "negative_prompt": "muted colors, traditional and realistic compositions." }, { "name": "MK Carnival Glass", "prompt": "A sculpture made of Carnival glass, {prompt}. Iridescent surfaces, vibrant colors, intricate patterns, opalescent hues, reflective and prismatic effects, Art Nouveau and Art Deco influences, vintage charm, intricate detailing, lustrous and luminous appearance, Carnival Glass style.", "negative_prompt": "non-iridescent surfaces, muted colors, absence of intricate patterns, lack of opalescent hues, modern and minimalist aesthetic." }, { "name": "MK Cyanotype Print", "prompt": "Cyanotype print {prompt}. Prussian blue tones, distinctive coloration, high contrast, blueprint aesthetics, atmospheric mood, sun-exposed paper, silhouette effects, delicate details, historical charm, handmade and experimental quality.", "negative_prompt": "vibrant colors, low contrast, modern and polished appearance." }, { "name": "MK Cross-Stitching", "prompt": "Cross-stitching {prompt}. Intricate patterns, embroidery thread, sewing, fine details, precise stitches, textile artistry, symmetrical designs, varied color palette, traditional and contemporary motifs, handmade and crafted,canvas, nostalgic charm.", "negative_prompt": "paper, paint, ink, photography." }, { "name": "MK Encaustic Paint", "prompt": "Encaustic paint {prompt}. Textured surfaces, translucent layers, luminous quality, wax medium, rich color saturation, fluid and organic shapes, contemporary and historical influences, mixed media elements, atmospheric depth.", "negative_prompt": "flat surfaces, opaque layers, lack of wax medium, muted color palette, absence of textured surfaces, non-mixed media." }, { "name": "MK Embroidery", "prompt": "Embroidery {prompt}. Intricate stitching, embroidery thread, fine details, varied thread textures, textile artistry, embellished surfaces, diverse color palette, traditional and contemporary motifs, handmade and crafted, tactile and ornate.", "negative_prompt": "minimalist, monochromatic." }, { "name": "MK Gyotaku", "prompt": "Gyotaku {prompt}. Fish impressions, realistic details, ink rubbings, textured surfaces, traditional Japanese art form, nature-inspired compositions, artistic representation of marine life, black and white contrasts, cultural significance.", "negative_prompt": "photography." }, { "name": "MK Luminogram", "prompt": "Luminogram {prompt}. Photogram technique, ethereal and abstract effects, light and shadow interplay, luminous quality, experimental process, direct light exposure, unique and unpredictable results, artistic experimentation.", "negative_prompt": "" }, { "name": "MK Lite Brite Art", "prompt": "Lite Brite art {prompt}. Luminous and colorful designs, pixelated compositions, retro aesthetic, glowing effects, creative patterns, interactive and playful, nostalgic charm, vibrant and dynamic arrangements.", "negative_prompt": "monochromatic." }, { "name": "MK Mokume-gane", "prompt": "Mokume-gane {prompt}. Wood-grain patterns, mixed metal layers, intricate and organic designs, traditional Japanese metalwork, harmonious color combinations, artisanal craftsmanship, unique and layered textures, cultural and historical significance.", "negative_prompt": "uniform metal surfaces." }, { "name": "Pebble Art", "prompt": "a sculpture made of peebles, {prompt}. Pebble art style,natural materials, textured surfaces, balanced compositions, organic forms, harmonious arrangements, tactile and 3D effects, beach-inspired aesthetic, creative storytelling, artisanal craftsmanship.", "negative_prompt": "non-natural materials, lack of textured surfaces, imbalanced compositions, absence of organic forms, non-tactile appearance." }, { "name": "MK Palekh", "prompt": "Palekh art {prompt}. Miniature paintings, intricate details, vivid colors, folkloric themes, lacquer finish, storytelling compositions, symbolic elements, Russian folklore influence, cultural and historical significance.", "negative_prompt": "large-scale paintings." }, { "name": "MK Suminagashi", "prompt": "Suminagashi {prompt}. Floating ink patterns, marbled effects, delicate and ethereal designs, water-based ink, fluid and unpredictable compositions, meditative process, monochromatic or subtle color palette, Japanese artistic tradition.", "negative_prompt": "vibrant and bold color palette." }, { "name": "MK Scrimshaw", "prompt": "A Scrimshaw engraving of {prompt}. Intricate engravings on a spermwhale's teeth, marine motifs, detailed scenes, nautical themes, black and white contrasts, historical craftsmanship, artisanal carving, storytelling compositions, maritime heritage.", "negative_prompt": "colorful, modern." }, { "name": "MK Shibori", "prompt": "Shibori {prompt}. Textured fabric, intricate patterns, resist-dyeing technique, indigo or vibrant colors, organic and flowing designs, Japanese textile art, cultural tradition, tactile and visual interest.", "negative_prompt": "monochromatic." }, { "name": "MK Vitreous Enamel", "prompt": "A sculpture made of Vitreous enamel {prompt}. Smooth and glossy surfaces, vibrant colors, glass-like finish, durable and resilient, intricate detailing, traditional and contemporary applications, artistic craftsmanship, jewelry and decorative objects, , Vitreous enamel, colored glass.", "negative_prompt": "rough surfaces, muted colors." }, { "name": "MK Ukiyo-e", "prompt": "Ukiyo-e {prompt}. Woodblock prints, vibrant colors, intricate details, depictions of landscapes, kabuki actors, beautiful women, cultural scenes, traditional Japanese art, artistic craftsmanship, historical significance.", "negative_prompt": "absence of woodblock prints, muted colors, lack of intricate details, non-traditional Japanese themes, absence of cultural scenes." }, { "name": "MK vintage-airline-poster", "prompt": "vintage airline poster {prompt} . classic aviation fonts, pastel colors, elegant aircraft illustrations, scenic destinations, distressed textures, retro travel allure", "negative_prompt": "modern fonts, bold colors, hyper-realistic, sleek design" }, { "name": "MK vintage-travel-poster", "prompt": "vintage travel poster {prompt} . retro fonts, muted colors, scenic illustrations, iconic landmarks, distressed textures, nostalgic vibes", "negative_prompt": "modern fonts, vibrant colors, hyper-realistic, sleek design" }, { "name": "MK bauhaus-style", "prompt": "Bauhaus-inspired {prompt} . minimalism, geometric precision, primary colors, sans-serif typography, asymmetry, functional design", "negative_prompt": "ornate, intricate, excessive detail, complex patterns, serif typography" }, { "name": "MK afrofuturism", "prompt": "Afrofuturism illustration {prompt} . vibrant colors, futuristic elements, cultural symbolism, cosmic imagery, dynamic patterns, empowering narratives", "negative_prompt": "monochromatic" }, { "name": "MK atompunk", "prompt": "Atompunk illustation, {prompt} . retro-futuristic, atomic age aesthetics, sleek lines, metallic textures, futuristic technology, optimism, energy", "negative_prompt": "organic, natural textures, rustic, dystopian" }, { "name": "MK constructivism", "prompt": "Constructivism {prompt} . geometric abstraction, bold colors, industrial aesthetics, dynamic compositions, utilitarian design, revolutionary spirit", "negative_prompt": "organic shapes, muted colors, ornate elements, traditional" }, { "name": "MK chicano-art", "prompt": "Chicano art {prompt} . bold colors, cultural symbolism, muralism, lowrider aesthetics, barrio life, political messages, social activism, Mexico", "negative_prompt": "monochromatic, minimalist, mainstream aesthetics" }, { "name": "MK de-stijl", "prompt": "De Stijl Art {prompt} . neoplasticism, primary colors, geometric abstraction, horizontal and vertical lines, simplicity, harmony, utopian ideals", "negative_prompt": "complex patterns, muted colors, ornate elements, asymmetry" }, { "name": "MK dayak-art", "prompt": "Dayak art sculpture of {prompt} . intricate patterns, nature-inspired motifs, vibrant colors, traditional craftsmanship, cultural symbolism, storytelling", "negative_prompt": "minimalist, monochromatic, modern" }, { "name": "MK fayum-portrait", "prompt": "Fayum portrait {prompt} . encaustic painting, realistic facial features, warm earth tones, serene expressions, ancient Egyptian influences", "negative_prompt": "abstract, vibrant colors, exaggerated features, modern" }, { "name": "MK illuminated-manuscript", "prompt": "Illuminated manuscript {prompt} . intricate calligraphy, rich colors, detailed illustrations, gold leaf accents, ornate borders, religious, historical, medieval", "negative_prompt": "modern typography, minimalist design, monochromatic, abstract themes" }, { "name": "MK kalighat-painting", "prompt": "Kalighat painting {prompt} . bold lines, vibrant colors, narrative storytelling, cultural motifs, flat compositions, expressive characters", "negative_prompt": "subdued colors, intricate details, realistic portrayal, modern aesthetics" }, { "name": "MK madhubani-painting", "prompt": "Madhubani painting {prompt} . intricate patterns, vibrant colors, nature-inspired motifs, cultural storytelling, symmetry, folk art aesthetics", "negative_prompt": "abstract, muted colors, minimalistic design, modern aesthetics" }, { "name": "MK pictorialism", "prompt": "Pictorialism illustration{prompt} . soft focus, atmospheric effects, artistic interpretation, tonality, muted colors, evocative storytelling", "negative_prompt": "sharp focus, high contrast, realistic depiction, vivid colors" }, { "name": "MK pichwai-painting", "prompt": "Pichwai painting {prompt} . intricate detailing, vibrant colors, religious themes, nature motifs, devotional storytelling, gold leaf accents", "negative_prompt": "minimalist, subdued colors, abstract design" }, { "name": "MK patachitra-painting", "prompt": "Patachitra painting {prompt} . bold outlines, vibrant colors, intricate detailing, mythological themes, storytelling, traditional craftsmanship", "negative_prompt": "subdued colors, minimalistic, abstract, modern aesthetics" }, { "name": "MK samoan-art-inspired", "prompt": "Samoan art-inspired wooden sculpture {prompt} . traditional motifs, natural elements, bold colors, cultural symbolism, storytelling, craftsmanship", "negative_prompt": "modern aesthetics, minimalist, abstract" }, { "name": "MK tlingit-art", "prompt": "Tlingit art {prompt} . formline design, natural elements, animal motifs, bold colors, cultural storytelling, traditional craftsmanship, Alaska traditional art, (totem:1.5)", "negative_prompt": "" }, { "name": "MK adnate-style", "prompt": "Painting by Adnate {prompt} . realistic portraits, street art, large-scale murals, subdued color palette, social narratives", "negative_prompt": "abstract, vibrant colors, small-scale art" }, { "name": "MK ron-english-style", "prompt": "Painting by Ron English {prompt} . pop-surrealism, cultural subversion, iconic mash-ups, vibrant and bold colors, satirical commentary", "negative_prompt": "traditional, monochromatic" }, { "name": "MK shepard-fairey-style", "prompt": "Painting by Shepard Fairey {prompt} . street art, political activism, iconic stencils, bold typography, high contrast, red, black, and white color palette", "negative_prompt": "traditional, muted colors" } ] ================================================ FILE: sdxl_styles/sdxl_styles_mre.json ================================================ [ { "name": "mre-cinematic-dynamic", "prompt": "epic cinematic shot of dynamic {prompt} in motion. main subject of high budget action movie. raw photo, motion blur. best quality, high resolution", "negative_prompt": "static, still, motionless, sluggish. drawing, painting, illustration, rendered. low budget. low quality, low resolution" }, { "name": "mre-spontaneous-picture", "prompt": "spontaneous picture of {prompt}, taken by talented amateur. best quality, high resolution. magical moment, natural look. simple but good looking", "negative_prompt": "overthinked. low quality, low resolution" }, { "name": "mre-artistic-vision", "prompt": "powerful artistic vision of {prompt}. breathtaking masterpiece made by great artist. best quality, high resolution", "negative_prompt": "insignificant, flawed, made by bad artist. low quality, low resolution" }, { "name": "mre-dark-dream", "prompt": "dark and unsettling dream showing {prompt}. best quality, high resolution. created by genius but depressed mad artist. grim beauty", "negative_prompt": "naive, cheerful. comfortable, casual, boring, cliche. low quality, low resolution" }, { "name": "mre-gloomy-art", "prompt": "astonishing gloomy art made mainly of shadows and lighting, forming {prompt}. masterful usage of lighting, shadows and chiaroscuro. made by black-hearted artist, drawing from darkness. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-bad-dream", "prompt": "picture from really bad dream about terrifying {prompt}, true horror. bone-chilling vision. mad world that shouldn't exist. best quality, high resolution", "negative_prompt": "nice dream, pleasant experience. low quality, low resolution" }, { "name": "mre-underground", "prompt": "uncanny caliginous vision of {prompt}, created by remarkable underground artist. best quality, high resolution. raw and brutal art, careless but impressive style. inspired by darkness and chaos", "negative_prompt": "photography, mainstream, civilized. low quality, low resolution" }, { "name": "mre-surreal-painting", "prompt": "surreal painting representing strange vision of {prompt}. harmonious madness, synergy with chance. unique artstyle, mindbending art, magical surrealism. best quality, high resolution", "negative_prompt": "photography, illustration, drawing. realistic, possible. logical, sane. low quality, low resolution" }, { "name": "mre-dynamic-illustration", "prompt": "insanely dynamic illustration of {prompt}. best quality, high resolution. crazy artstyle, careless brushstrokes, emotional and fun", "negative_prompt": "photography, realistic. static, still, slow, boring. low quality, low resolution" }, { "name": "mre-undead-art", "prompt": "long forgotten art created by undead artist illustrating {prompt}, tribute to the death and decay. miserable art of the damned. wretched and decaying world. best quality, high resolution", "negative_prompt": "alive, playful, living. low quality, low resolution" }, { "name": "mre-elemental-art", "prompt": "art illustrating insane amounts of raging elemental energy turning into {prompt}, avatar of elements. magical surrealism, wizardry. best quality, high resolution", "negative_prompt": "photography, realistic, real. low quality, low resolution" }, { "name": "mre-space-art", "prompt": "winner of inter-galactic art contest illustrating {prompt}, symbol of the interstellar singularity. best quality, high resolution. artstyle previously unseen in the whole galaxy", "negative_prompt": "created by human race, low quality, low resolution" }, { "name": "mre-ancient-illustration", "prompt": "sublime ancient illustration of {prompt}, predating human civilization. crude and simple, but also surprisingly beautiful artwork, made by genius primeval artist. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-brave-art", "prompt": "brave, shocking, and brutally true art showing {prompt}. inspired by courage and unlimited creativity. truth found in chaos. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-heroic-fantasy", "prompt": "heroic fantasy painting of {prompt}, in the dangerous fantasy world. airbrush over oil on canvas. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-dark-cyberpunk", "prompt": "dark cyberpunk illustration of brutal {prompt} in a world without hope, ruled by ruthless criminal corporations. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-lyrical-geometry", "prompt": "geometric and lyrical abstraction painting presenting {prompt}. oil on metal. best quality, high resolution", "negative_prompt": "photography, realistic, drawing, rendered. low quality, low resolution" }, { "name": "mre-sumi-e-symbolic", "prompt": "big long brushstrokes of deep black sumi-e turning into symbolic painting of {prompt}. master level raw art. best quality, high resolution", "negative_prompt": "photography, rendered. low quality, low resolution" }, { "name": "mre-sumi-e-detailed", "prompt": "highly detailed black sumi-e painting of {prompt}. in-depth study of perfection, created by a master. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-manga", "prompt": "manga artwork presenting {prompt}. created by japanese manga artist. highly emotional. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-anime", "prompt": "anime artwork illustrating {prompt}. created by japanese anime studio. highly emotional. best quality, high resolution", "negative_prompt": "low quality, low resolution" }, { "name": "mre-comic", "prompt": "breathtaking illustration from adult comic book presenting {prompt}. fabulous artwork. best quality, high resolution", "negative_prompt": "deformed, ugly, low quality, low resolution" } ] ================================================ FILE: sdxl_styles/sdxl_styles_sai.json ================================================ [ { "name": "sai-3d-model", "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting" }, { "name": "sai-analog film", "prompt": "analog film photo {prompt} . faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" }, { "name": "sai-anime", "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast" }, { "name": "sai-cinematic", "prompt": "cinematic film still {prompt} . shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" }, { "name": "sai-comic book", "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed", "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo" }, { "name": "sai-craft clay", "prompt": "play-doh style {prompt} . sculpture, clay art, centered composition, Claymation", "negative_prompt": "sloppy, messy, grainy, highly detailed, ultra textured, photo" }, { "name": "sai-digital art", "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", "negative_prompt": "photo, photorealistic, realism, ugly" }, { "name": "sai-enhance", "prompt": "breathtaking {prompt} . award-winning, professional, highly detailed", "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy" }, { "name": "sai-fantasy art", "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white" }, { "name": "sai-isometric", "prompt": "isometric style {prompt} . vibrant, beautiful, crisp, detailed, ultra detailed, intricate", "negative_prompt": "deformed, mutated, ugly, disfigured, blur, blurry, noise, noisy, realistic, photographic" }, { "name": "sai-line art", "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics", "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic" }, { "name": "sai-lowpoly", "prompt": "low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition", "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" }, { "name": "sai-neonpunk", "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" }, { "name": "sai-origami", "prompt": "origami style {prompt} . paper art, pleated paper, folded, origami art, pleats, cut and fold, centered composition", "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" }, { "name": "sai-photographic", "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly" }, { "name": "sai-pixel art", "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic" }, { "name": "sai-texture", "prompt": "texture {prompt} top down close-up", "negative_prompt": "ugly, deformed, noisy, blurry" } ] ================================================ FILE: sdxl_styles/sdxl_styles_twri.json ================================================ [ { "name": "ads-advertising", "prompt": "advertising poster style {prompt} . Professional, modern, product-focused, commercial, eye-catching, highly detailed", "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive" }, { "name": "ads-automotive", "prompt": "automotive advertisement style {prompt} . sleek, dynamic, professional, commercial, vehicle-focused, high-resolution, highly detailed", "negative_prompt": "noisy, blurry, unattractive, sloppy, unprofessional" }, { "name": "ads-corporate", "prompt": "corporate branding style {prompt} . professional, clean, modern, sleek, minimalist, business-oriented, highly detailed", "negative_prompt": "noisy, blurry, grungy, sloppy, cluttered, disorganized" }, { "name": "ads-fashion editorial", "prompt": "fashion editorial style {prompt} . high fashion, trendy, stylish, editorial, magazine style, professional, highly detailed", "negative_prompt": "outdated, blurry, noisy, unattractive, sloppy" }, { "name": "ads-food photography", "prompt": "food photography style {prompt} . appetizing, professional, culinary, high-resolution, commercial, highly detailed", "negative_prompt": "unappetizing, sloppy, unprofessional, noisy, blurry" }, { "name": "ads-gourmet food photography", "prompt": "gourmet food photo of {prompt} . soft natural lighting, macro details, vibrant colors, fresh ingredients, glistening textures, bokeh background, styled plating, wooden tabletop, garnished, tantalizing, editorial quality", "negative_prompt": "cartoon, anime, sketch, grayscale, dull, overexposed, cluttered, messy plate, deformed" }, { "name": "ads-luxury", "prompt": "luxury product style {prompt} . elegant, sophisticated, high-end, luxurious, professional, highly detailed", "negative_prompt": "cheap, noisy, blurry, unattractive, amateurish" }, { "name": "ads-real estate", "prompt": "real estate photography style {prompt} . professional, inviting, well-lit, high-resolution, property-focused, commercial, highly detailed", "negative_prompt": "dark, blurry, unappealing, noisy, unprofessional" }, { "name": "ads-retail", "prompt": "retail packaging style {prompt} . vibrant, enticing, commercial, product-focused, eye-catching, professional, highly detailed", "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive" }, { "name": "artstyle-abstract", "prompt": "abstract style {prompt} . non-representational, colors and shapes, expression of feelings, imaginative, highly detailed", "negative_prompt": "realistic, photographic, figurative, concrete" }, { "name": "artstyle-abstract expressionism", "prompt": "abstract expressionist painting {prompt} . energetic brushwork, bold colors, abstract forms, expressive, emotional", "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, monochrome" }, { "name": "artstyle-art deco", "prompt": "art deco style {prompt} . geometric shapes, bold colors, luxurious, elegant, decorative, symmetrical, ornate, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist" }, { "name": "artstyle-art nouveau", "prompt": "art nouveau style {prompt} . elegant, decorative, curvilinear forms, nature-inspired, ornate, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist" }, { "name": "artstyle-constructivist", "prompt": "constructivist style {prompt} . geometric shapes, bold colors, dynamic composition, propaganda art style", "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, abstract expressionism" }, { "name": "artstyle-cubist", "prompt": "cubist artwork {prompt} . geometric shapes, abstract, innovative, revolutionary", "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" }, { "name": "artstyle-expressionist", "prompt": "expressionist {prompt} . raw, emotional, dynamic, distortion for emotional effect, vibrant, use of unusual colors, detailed", "negative_prompt": "realism, symmetry, quiet, calm, photo" }, { "name": "artstyle-graffiti", "prompt": "graffiti style {prompt} . street art, vibrant, urban, detailed, tag, mural", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" }, { "name": "artstyle-hyperrealism", "prompt": "hyperrealistic art {prompt} . extremely high-resolution details, photographic, realism pushed to extreme, fine texture, incredibly lifelike", "negative_prompt": "simplified, abstract, unrealistic, impressionistic, low resolution" }, { "name": "artstyle-impressionist", "prompt": "impressionist painting {prompt} . loose brushwork, vibrant color, light and shadow play, captures feeling over form", "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" }, { "name": "artstyle-pointillism", "prompt": "pointillism style {prompt} . composed entirely of small, distinct dots of color, vibrant, highly detailed", "negative_prompt": "line drawing, smooth shading, large color fields, simplistic" }, { "name": "artstyle-pop art", "prompt": "pop Art style {prompt} . bright colors, bold outlines, popular culture themes, ironic or kitsch", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, minimalist" }, { "name": "artstyle-psychedelic", "prompt": "psychedelic style {prompt} . vibrant colors, swirling patterns, abstract forms, surreal, trippy", "negative_prompt": "monochrome, black and white, low contrast, realistic, photorealistic, plain, simple" }, { "name": "artstyle-renaissance", "prompt": "renaissance style {prompt} . realistic, perspective, light and shadow, religious or mythological themes, highly detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, modernist, minimalist, abstract" }, { "name": "artstyle-steampunk", "prompt": "steampunk style {prompt} . antique, mechanical, brass and copper tones, gears, intricate, detailed", "negative_prompt": "deformed, glitch, noisy, low contrast, anime, photorealistic" }, { "name": "artstyle-surrealist", "prompt": "surrealist art {prompt} . dreamlike, mysterious, provocative, symbolic, intricate, detailed", "negative_prompt": "anime, photorealistic, realistic, deformed, glitch, noisy, low contrast" }, { "name": "artstyle-typography", "prompt": "typographic art {prompt} . stylized, intricate, detailed, artistic, text-based", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" }, { "name": "artstyle-watercolor", "prompt": "watercolor painting {prompt} . vibrant, beautiful, painterly, detailed, textural, artistic", "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" }, { "name": "futuristic-biomechanical", "prompt": "biomechanical style {prompt} . blend of organic and mechanical elements, futuristic, cybernetic, detailed, intricate", "negative_prompt": "natural, rustic, primitive, organic, simplistic" }, { "name": "futuristic-biomechanical cyberpunk", "prompt": "biomechanical cyberpunk {prompt} . cybernetics, human-machine fusion, dystopian, organic meets artificial, dark, intricate, highly detailed", "negative_prompt": "natural, colorful, deformed, sketch, low contrast, watercolor" }, { "name": "futuristic-cybernetic", "prompt": "cybernetic style {prompt} . futuristic, technological, cybernetic enhancements, robotics, artificial intelligence themes", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval" }, { "name": "futuristic-cybernetic robot", "prompt": "cybernetic robot {prompt} . android, AI, machine, metal, wires, tech, futuristic, highly detailed", "negative_prompt": "organic, natural, human, sketch, watercolor, low contrast" }, { "name": "futuristic-cyberpunk cityscape", "prompt": "cyberpunk cityscape {prompt} . neon lights, dark alleys, skyscrapers, futuristic, vibrant colors, high contrast, highly detailed", "negative_prompt": "natural, rural, deformed, low contrast, black and white, sketch, watercolor" }, { "name": "futuristic-futuristic", "prompt": "futuristic style {prompt} . sleek, modern, ultramodern, high tech, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vintage, antique" }, { "name": "futuristic-retro cyberpunk", "prompt": "retro cyberpunk {prompt} . 80's inspired, synthwave, neon, vibrant, detailed, retro futurism", "negative_prompt": "modern, desaturated, black and white, realism, low contrast" }, { "name": "futuristic-retro futurism", "prompt": "retro-futuristic {prompt} . vintage sci-fi, 50s and 60s style, atomic age, vibrant, highly detailed", "negative_prompt": "contemporary, realistic, rustic, primitive" }, { "name": "futuristic-sci-fi", "prompt": "sci-fi style {prompt} . futuristic, technological, alien worlds, space themes, advanced civilizations", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval" }, { "name": "futuristic-vaporwave", "prompt": "vaporwave style {prompt} . retro aesthetic, cyberpunk, vibrant, neon colors, vintage 80s and 90s style, highly detailed", "negative_prompt": "monochrome, muted colors, realism, rustic, minimalist, dark" }, { "name": "game-bubble bobble", "prompt": "Bubble Bobble style {prompt} . 8-bit, cute, pixelated, fantasy, vibrant, reminiscent of Bubble Bobble game", "negative_prompt": "realistic, modern, photorealistic, violent, horror" }, { "name": "game-cyberpunk game", "prompt": "cyberpunk game style {prompt} . neon, dystopian, futuristic, digital, vibrant, detailed, high contrast, reminiscent of cyberpunk genre video games", "negative_prompt": "historical, natural, rustic, low detailed" }, { "name": "game-fighting game", "prompt": "fighting game style {prompt} . dynamic, vibrant, action-packed, detailed character design, reminiscent of fighting video games", "negative_prompt": "peaceful, calm, minimalist, photorealistic" }, { "name": "game-gta", "prompt": "GTA-style artwork {prompt} . satirical, exaggerated, pop art style, vibrant colors, iconic characters, action-packed", "negative_prompt": "realistic, black and white, low contrast, impressionist, cubist, noisy, blurry, deformed" }, { "name": "game-mario", "prompt": "Super Mario style {prompt} . vibrant, cute, cartoony, fantasy, playful, reminiscent of Super Mario series", "negative_prompt": "realistic, modern, horror, dystopian, violent" }, { "name": "game-minecraft", "prompt": "Minecraft style {prompt} . blocky, pixelated, vibrant colors, recognizable characters and objects, game assets", "negative_prompt": "smooth, realistic, detailed, photorealistic, noise, blurry, deformed" }, { "name": "game-pokemon", "prompt": "Pokémon style {prompt} . vibrant, cute, anime, fantasy, reminiscent of Pokémon series", "negative_prompt": "realistic, modern, horror, dystopian, violent" }, { "name": "game-retro arcade", "prompt": "retro arcade style {prompt} . 8-bit, pixelated, vibrant, classic video game, old school gaming, reminiscent of 80s and 90s arcade games", "negative_prompt": "modern, ultra-high resolution, photorealistic, 3D" }, { "name": "game-retro game", "prompt": "retro game art {prompt} . 16-bit, vibrant colors, pixelated, nostalgic, charming, fun", "negative_prompt": "realistic, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" }, { "name": "game-rpg fantasy game", "prompt": "role-playing game (RPG) style fantasy {prompt} . detailed, vibrant, immersive, reminiscent of high fantasy RPG games", "negative_prompt": "sci-fi, modern, urban, futuristic, low detailed" }, { "name": "game-strategy game", "prompt": "strategy game style {prompt} . overhead view, detailed map, units, reminiscent of real-time strategy video games", "negative_prompt": "first-person view, modern, photorealistic" }, { "name": "game-streetfighter", "prompt": "Street Fighter style {prompt} . vibrant, dynamic, arcade, 2D fighting game, highly detailed, reminiscent of Street Fighter series", "negative_prompt": "3D, realistic, modern, photorealistic, turn-based strategy" }, { "name": "game-zelda", "prompt": "Legend of Zelda style {prompt} . vibrant, fantasy, detailed, epic, heroic, reminiscent of The Legend of Zelda series", "negative_prompt": "sci-fi, modern, realistic, horror" }, { "name": "misc-architectural", "prompt": "architectural style {prompt} . clean lines, geometric shapes, minimalist, modern, architectural drawing, highly detailed", "negative_prompt": "curved lines, ornate, baroque, abstract, grunge" }, { "name": "misc-disco", "prompt": "disco-themed {prompt} . vibrant, groovy, retro 70s style, shiny disco balls, neon lights, dance floor, highly detailed", "negative_prompt": "minimalist, rustic, monochrome, contemporary, simplistic" }, { "name": "misc-dreamscape", "prompt": "dreamscape {prompt} . surreal, ethereal, dreamy, mysterious, fantasy, highly detailed", "negative_prompt": "realistic, concrete, ordinary, mundane" }, { "name": "misc-dystopian", "prompt": "dystopian style {prompt} . bleak, post-apocalyptic, somber, dramatic, highly detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, cheerful, optimistic, vibrant, colorful" }, { "name": "misc-fairy tale", "prompt": "fairy tale {prompt} . magical, fantastical, enchanting, storybook style, highly detailed", "negative_prompt": "realistic, modern, ordinary, mundane" }, { "name": "misc-gothic", "prompt": "gothic style {prompt} . dark, mysterious, haunting, dramatic, ornate, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, cheerful, optimistic" }, { "name": "misc-grunge", "prompt": "grunge style {prompt} . textured, distressed, vintage, edgy, punk rock vibe, dirty, noisy", "negative_prompt": "smooth, clean, minimalist, sleek, modern, photorealistic" }, { "name": "misc-horror", "prompt": "horror-themed {prompt} . eerie, unsettling, dark, spooky, suspenseful, grim, highly detailed", "negative_prompt": "cheerful, bright, vibrant, light-hearted, cute" }, { "name": "misc-kawaii", "prompt": "kawaii style {prompt} . cute, adorable, brightly colored, cheerful, anime influence, highly detailed", "negative_prompt": "dark, scary, realistic, monochrome, abstract" }, { "name": "misc-lovecraftian", "prompt": "lovecraftian horror {prompt} . eldritch, cosmic horror, unknown, mysterious, surreal, highly detailed", "negative_prompt": "light-hearted, mundane, familiar, simplistic, realistic" }, { "name": "misc-macabre", "prompt": "macabre style {prompt} . dark, gothic, grim, haunting, highly detailed", "negative_prompt": "bright, cheerful, light-hearted, cartoonish, cute" }, { "name": "misc-manga", "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style" }, { "name": "misc-metropolis", "prompt": "metropolis-themed {prompt} . urban, cityscape, skyscrapers, modern, futuristic, highly detailed", "negative_prompt": "rural, natural, rustic, historical, simple" }, { "name": "misc-minimalist", "prompt": "minimalist style {prompt} . simple, clean, uncluttered, modern, elegant", "negative_prompt": "ornate, complicated, highly detailed, cluttered, disordered, messy, noisy" }, { "name": "misc-monochrome", "prompt": "monochrome {prompt} . black and white, contrast, tone, texture, detailed", "negative_prompt": "colorful, vibrant, noisy, blurry, deformed" }, { "name": "misc-nautical", "prompt": "nautical-themed {prompt} . sea, ocean, ships, maritime, beach, marine life, highly detailed", "negative_prompt": "landlocked, desert, mountains, urban, rustic" }, { "name": "misc-space", "prompt": "space-themed {prompt} . cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed", "negative_prompt": "earthly, mundane, ground-based, realism" }, { "name": "misc-stained glass", "prompt": "stained glass style {prompt} . vibrant, beautiful, translucent, intricate, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" }, { "name": "misc-techwear fashion", "prompt": "techwear fashion {prompt} . futuristic, cyberpunk, urban, tactical, sleek, dark, highly detailed", "negative_prompt": "vintage, rural, colorful, low contrast, realism, sketch, watercolor" }, { "name": "misc-tribal", "prompt": "tribal style {prompt} . indigenous, ethnic, traditional patterns, bold, natural colors, highly detailed", "negative_prompt": "modern, futuristic, minimalist, pastel" }, { "name": "misc-zentangle", "prompt": "zentangle {prompt} . intricate, abstract, monochrome, patterns, meditative, highly detailed", "negative_prompt": "colorful, representative, simplistic, large fields of color" }, { "name": "papercraft-collage", "prompt": "collage style {prompt} . mixed media, layered, textural, detailed, artistic", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" }, { "name": "papercraft-flat papercut", "prompt": "flat papercut style {prompt} . silhouette, clean cuts, paper, sharp edges, minimalist, color block", "negative_prompt": "3D, high detail, noise, grainy, blurry, painting, drawing, photo, disfigured" }, { "name": "papercraft-kirigami", "prompt": "kirigami representation of {prompt} . 3D, paper folding, paper cutting, Japanese, intricate, symmetrical, precision, clean lines", "negative_prompt": "painting, drawing, 2D, noisy, blurry, deformed" }, { "name": "papercraft-paper mache", "prompt": "paper mache representation of {prompt} . 3D, sculptural, textured, handmade, vibrant, fun", "negative_prompt": "2D, flat, photo, sketch, digital art, deformed, noisy, blurry" }, { "name": "papercraft-paper quilling", "prompt": "paper quilling art of {prompt} . intricate, delicate, curling, rolling, shaping, coiling, loops, 3D, dimensional, ornamental", "negative_prompt": "photo, painting, drawing, 2D, flat, deformed, noisy, blurry" }, { "name": "papercraft-papercut collage", "prompt": "papercut collage of {prompt} . mixed media, textured paper, overlapping, asymmetrical, abstract, vibrant", "negative_prompt": "photo, 3D, realistic, drawing, painting, high detail, disfigured" }, { "name": "papercraft-papercut shadow box", "prompt": "3D papercut shadow box of {prompt} . layered, dimensional, depth, silhouette, shadow, papercut, handmade, high contrast", "negative_prompt": "painting, drawing, photo, 2D, flat, high detail, blurry, noisy, disfigured" }, { "name": "papercraft-stacked papercut", "prompt": "stacked papercut art of {prompt} . 3D, layered, dimensional, depth, precision cut, stacked layers, papercut, high contrast", "negative_prompt": "2D, flat, noisy, blurry, painting, drawing, photo, deformed" }, { "name": "papercraft-thick layered papercut", "prompt": "thick layered papercut art of {prompt} . deep 3D, volumetric, dimensional, depth, thick paper, high stack, heavy texture, tangible layers", "negative_prompt": "2D, flat, thin paper, low stack, smooth texture, painting, drawing, photo, deformed" }, { "name": "photo-alien", "prompt": "alien-themed {prompt} . extraterrestrial, cosmic, otherworldly, mysterious, sci-fi, highly detailed", "negative_prompt": "earthly, mundane, common, realistic, simple" }, { "name": "photo-film noir", "prompt": "film noir style {prompt} . monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful" }, { "name": "photo-glamour", "prompt": "glamorous photo {prompt} . high fashion, luxurious, extravagant, stylish, sensual, opulent, elegance, stunning beauty, professional, high contrast, detailed", "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy, sketch, low contrast, dull, plain, modest" }, { "name": "photo-hdr", "prompt": "HDR photo of {prompt} . High dynamic range, vivid, rich details, clear shadows and highlights, realistic, intense, enhanced contrast, highly detailed", "negative_prompt": "flat, low contrast, oversaturated, underexposed, overexposed, blurred, noisy" }, { "name": "photo-iphone photographic", "prompt": "iphone photo {prompt} . large depth of field, deep depth of field, highly detailed", "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, shallow depth of field, bokeh" }, { "name": "photo-long exposure", "prompt": "long exposure photo of {prompt} . Blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed", "negative_prompt": "static, noisy, deformed, shaky, abrupt, flat, low contrast" }, { "name": "photo-neon noir", "prompt": "neon noir {prompt} . cyberpunk, dark, rainy streets, neon signs, high contrast, low light, vibrant, highly detailed", "negative_prompt": "bright, sunny, daytime, low contrast, black and white, sketch, watercolor" }, { "name": "photo-silhouette", "prompt": "silhouette style {prompt} . high contrast, minimalistic, black and white, stark, dramatic", "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, color, realism, photorealistic" }, { "name": "photo-tilt-shift", "prompt": "tilt-shift photo of {prompt} . selective focus, miniature effect, blurred background, highly detailed, vibrant, perspective control", "negative_prompt": "blurry, noisy, deformed, flat, low contrast, unrealistic, oversaturated, underexposed" } ] ================================================ FILE: shared.py ================================================ gradio_root = None ================================================ FILE: tests/__init__.py ================================================ import sys import pathlib sys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve()) ================================================ FILE: tests/test_extra_utils.py ================================================ import numbers import os import unittest import modules.flags from modules import extra_utils class TestUtils(unittest.TestCase): def test_try_eval_env_var(self): test_cases = [ { "input": ("foo", str), "output": "foo" }, { "input": ("1", int), "output": 1 }, { "input": ("1.0", float), "output": 1.0 }, { "input": ("1", numbers.Number), "output": 1 }, { "input": ("1.0", numbers.Number), "output": 1.0 }, { "input": ("true", bool), "output": True }, { "input": ("True", bool), "output": True }, { "input": ("false", bool), "output": False }, { "input": ("False", bool), "output": False }, { "input": ("True", str), "output": "True" }, { "input": ("False", str), "output": "False" }, { "input": ("['a', 'b', 'c']", list), "output": ['a', 'b', 'c'] }, { "input": ("{'a':1}", dict), "output": {'a': 1} }, { "input": ("('foo', 1)", tuple), "output": ('foo', 1) } ] for test in test_cases: value, expected_type = test["input"] expected = test["output"] actual = extra_utils.try_eval_env_var(value, expected_type) self.assertEqual(expected, actual) ================================================ FILE: tests/test_utils.py ================================================ import os import unittest import modules.flags from modules import util class TestUtils(unittest.TestCase): def test_can_parse_tokens_with_lora(self): test_cases = [ { "input": ("some prompt, very cool, , cool ", [], 5, True), "output": ( [('hey-lora.safetensors', 0.4), ('you-lora.safetensors', 0.2)], 'some prompt, very cool, cool'), }, # Test can not exceed limit { "input": ("some prompt, very cool, , cool ", [], 1, True), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt, very cool, cool' ), }, # test Loras from UI take precedence over prompt { "input": ( "some prompt, very cool, , , , , , ", [("hey-lora.safetensors", 0.4)], 5, True ), "output": ( [ ('hey-lora.safetensors', 0.4), ('l1.safetensors', 0.4), ('l2.safetensors', -0.2), ('l3.safetensors', 0.3), ('l4.safetensors', 0.5) ], 'some prompt, very cool' ) }, # test correct matching even if there is no space separating loras in the same token { "input": ("some prompt, very cool, ", [], 3, True), "output": ( [ ('hey-lora.safetensors', 0.4), ('you-lora.safetensors', 0.2) ], 'some prompt, very cool' ), }, # test deduplication, also selected loras are never overridden with loras in prompt { "input": ( "some prompt, very cool, ", [('you-lora.safetensors', 0.3)], 3, True ), "output": ( [ ('you-lora.safetensors', 0.3), ('hey-lora.safetensors', 0.4) ], 'some prompt, very cool' ), }, { "input": (", , , and ", [], 6, True), "output": ( [], ', , , and ' ) } ] for test in test_cases: prompt, loras, loras_limit, skip_file_check = test["input"] expected = test["output"] actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, skip_file_check=skip_file_check) self.assertEqual(expected, actual) def test_can_parse_tokens_and_strip_performance_lora(self): lora_filenames = [ 'hey-lora.safetensors', modules.flags.PerformanceLoRA.EXTREME_SPEED.value, modules.flags.PerformanceLoRA.LIGHTNING.value, os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value) ] test_cases = [ { "input": ("some prompt, ", [], 5, True, modules.flags.Performance.QUALITY), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt' ), }, { "input": ("some prompt, ", [], 5, True, modules.flags.Performance.SPEED), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt' ), }, { "input": ("some prompt, , ", [], 5, True, modules.flags.Performance.EXTREME_SPEED), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt' ), }, { "input": ("some prompt, , ", [], 5, True, modules.flags.Performance.LIGHTNING), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt' ), }, { "input": ("some prompt, , ", [], 5, True, modules.flags.Performance.HYPER_SD), "output": ( [('hey-lora.safetensors', 0.4)], 'some prompt' ), } ] for test in test_cases: prompt, loras, loras_limit, skip_file_check, performance = test["input"] lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance) expected = test["output"] actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames) self.assertEqual(expected, actual) ================================================ FILE: troubleshoot.md ================================================ Below are many common problems that people encountered: ### RuntimeError: CPUAllocator See also the section: **System Swap** ### Model loaded, then paused, then nothing happens See also the section: **System Swap** ### Segmentation Fault See also the section: **System Swap** ### Aborted See also the section: **System Swap** ### core dumped See also the section: **System Swap** ### Killed See also the section: **System Swap** ### ^C, then quit See also the section: **System Swap** ### adm 2816, then stuck See also the section: **System Swap** ### Connection errored out See also the section: **System Swap** ### Error 1006 See also the section: **System Swap** ### WinError 10060 See also the section: **System Swap** ### Read timed out See also the section: **System Swap** ### No error, but the console close in a flash. Cannot find any error. See also the section: **System Swap** ### Model loading is extremely slow (more than 1 minute) See also the section: **System Swap** ### System Swap All above problems are caused by the fact that you do not have enough System Swap. Please make sure that you have at least 40GB System Swap. In fact, it does not need so much Swap, but 40Gb should be safe for you to run Fooocus in 100% success. (If you have more than 64GB RAM, then *perhaps* you do not need any System Swap, but we are not exactly sure about this.) Also, if your system swap is on HDD, the speed of model loading will be very slow. Please try best to put system swap on SSD. If you are using Linux/Mac, please follow your provider's instructions to set Swap Space. Herein, the "provider" refers to Ubuntu official, CentOS official, Mac official, etc. If you are using Windows, you can set Swap here: ![swap](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) If you use both HDD and SSD, you *may* test some settings on the above step 7 to try best to put swap area on SSD, so that the speed of model loading will be faster. **Important: Microsoft Windows 10/11 by default automate system swap for you so that you do not need to touch this dangerous setting. If you do not have enough system swap, just make sure that you have at least 40GB free space on each disk.** The Microsoft Windows 10/11 will automatically make swap areas for you. Also, if you obtain Microsoft Windows 10/11 from some unofficial Chinese or Russian provider, they may have modified the default setting of system swap to advertise some "Enhanced Windows 10/11" (but actually they are just making things worse rather than improve things). In those cases, you may need to manually check if your system swap setting is consistent to the above screenshot. Finally, note that you need to restart computer to activate any changes in system swap. ### MetadataIncompleteBuffer See also the section: **Model corrupted** ### PytorchStreamReader failed See also the section: **Model corrupted** ### Model corrupted If you see Model Corrupted, then your model is corrupted. Fooocus will re-download corrupted models for you if your internet connection is good. Otherwise, you may also manually download models. You can find model url and their local location in the console each time a model download is requested. ### UserWarning: The operator 'aten::std_mean.correction' is not currently supported on the DML This is a warning that you can ignore. ### Torch not compiled with CUDA enabled You are not following the official installation guide. Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. ### subprocess-exited-with-error Please use python 3.10 Also, you are not following the official installation guide. Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. ### SSL: CERTIFICATE_VERIFY_FAILED Are you living in China? If yes, please consider turn off VPN, and/or try to download models manually. If you get this error elsewhere in the world, then you may need to look at [this search](https://www.google.com/search?q=SSL+Certificate+Error). We cannot give very specific guide to fix this since the cause can vary a lot. ### CUDA kernel errors might be asynchronously reported at some other API call A very small amount of devices does have this problem. The cause can be complicated but usually can be resolved after following these steps: 1. Make sure that you are using official version and latest version installed from [here](https://github.com/lllyasviel/Fooocus#download). (Some forks and other versions are more likely to cause this problem.) 2. Upgrade your Nvidia driver to the latest version. (Usually the version of your Nvidia driver should be 53X, not 3XX or 4XX.) 3. If things still do not work, then perhaps it is a problem with CUDA 12. You can use CUDA 11 and Xformers to try to solve this problem. We have prepared all files for you, and please do NOT install any CUDA or other environment on you own. The only one official way to do this is: (1) Backup and delete your `python_embeded` folder (near the `run.bat`); (2) Download the "previous_old_xformers_env.7z" from the [release page](https://github.com/lllyasviel/Fooocus/releases/tag/release), decompress it, and put the newly extracted `python_embeded` folder near your `run.bat`; (3) run Fooocus. 4. If it still does not work, please open an issue for us to take a look. ### Found no NVIDIA driver on your system Please upgrade your Nvidia Driver. If you are using AMD, please follow official installation guide. ### NVIDIA driver too old Please upgrade your Nvidia Driver. ### I am using Mac, the speed is very slow. Some MAC users may need `--disable-offload-from-vram` to speed up model loading. Besides, the current support for MAC is very experimental, and we encourage users to also try Diffusionbee or Drawingthings: they are developed only for MAC. ### I am using Nvidia with 8GB VRAM, I get CUDA Out Of Memory It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I am using Nvidia with 6GB VRAM, I get CUDA Out Of Memory It is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I am using Nvidia with 4GB VRAM with Float16 support, like RTX 3050, I get CUDA Out Of Memory It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I am using Nvidia with 4GB VRAM without Float16 support, like GTX 960, I get CUDA Out Of Memory Supporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I am using AMD GPU on Windows, I get CUDA Out Of Memory Current AMD support is very experimental for Windows. If you see this, then perhaps you cannot use Fooocus on this device on Windows. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. Besides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I am using AMD GPU on Linux, I get CUDA Out Of Memory Current AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). ### I tried flags like --lowvram or --gpu-only or --bf16 or so on, and things are not getting any better? Please remove these flags if you are mislead by some wrong tutorials. In most cases these flags are making things worse and introducing more problems. ### Fooocus suddenly becomes very slow and I have not changed anything Are you accidentally running two Fooocus at the same time? ================================================ FILE: update_log.md ================================================ # [2.5.5](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.5) * Fix colab inpaint issue by moving an import statement # [2.5.4](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.4) * Fix validation for default_ip_image_* and default_inpaint_mask_sam_model * Fix enhance mask debugging in combination with image sorting * Fix loading of checkpoints and LoRAs when using multiple directories in config and then switching presets # [2.5.3](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.3) * Only load weights from non-safetensors files, preventing harmful code injection * Add checkbox for applying/resetting styles when describing images, also allowing multiple describe content types # [2.5.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.2) * Fix not adding positive prompt when styles didn't have a {prompt} placeholder in the positive prompt * Extend config settings for input image, see list in [PR](https://github.com/lllyasviel/Fooocus/pull/3382) # [2.5.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.1) * Update download URL in readme * Increase speed of metadata loading * Fix reading of metadata from jpeg, jpg and webp (exif) * Fix debug preprocessor * Update attributes and add inline prompt features section to readme * Add checkbox, config and handling for saving only the final enhanced image. Use config `default_save_only_final_enhanced_image`, default False. * Add sorting of final images when enhanced is enabled. Use argument `--disable-enhance-output-sorting` to disable. # [2.5.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.0) This version includes various package updates. If the auto-update doesn't work you can do one of the following: 1. Open a terminal in the Fooocus folder (location of config.txt) and run `git pull` 2. Update packages - Windows (installation through zip file): open a terminal in the Fooocus folder (location of config.txt) `..\python_embeded\python.exe -m pip install -r .\requirements_versions.txt` (Windows using embedded python, installation method zip file) or download Fooocus again (zip file attached to this release) - other: manually update the packages using `python.exe -m pip install -r requirements_versions.txt` or use the docker image --- * Update python dependencies, add segment_anything * Add enhance feature, which offers easy image refinement steps (similar to adetailer, but based on dynamic image detection instead of specific mask detection models). See [documentation](https://github.com/lllyasviel/Fooocus/discussions/3281). * Rewrite async worker code, make code much more reusable to allow iterations and improve reusability * Improve GroundingDINO and SAM image masking * Fix inference tensor version counter tracking issue for GroundingDINO after using Enhance (see [discussion](https://github.com/lllyasviel/Fooocus/discussions/3213)) * Move checkboxes Enable Mask Upload and Invert Mask When Generating from Developer Debug Mode to Inpaint Or Outpaint * Add persistent model cache for metadata. Use `--rebuild-hash-cache X` (X = int, number of CPU cores, default all) to manually rebuild the cache for all non-cached hashes * Rename `--enable-describe-uov-image` to `--enable-auto-describe-image`, now also works for enhance image upload * Rename checkbox `Enable Mask Upload` to `Enable Advanced Masking Features` to better hint to mask auto-generation feature * Get upscale model filepath by calling downloading_upscale_model() to ensure the model exists * Rename tab titles and translations from singular to plural * Rename document to documentation * Update default models to latest versions * animaPencilXL_v400 => animaPencilXL_v500 * DreamShaperXL_Turbo_dpmppSdeKarras => DreamShaperXL_Turbo_v2_1 * SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4 => SDXL_FILM_PHOTOGRAPHY_STYLE_V1 * Add preset for pony_v6 (using ponyDiffusionV6XL) * Add style `Fooocus Pony` * Add restart sampler ([paper](https://arxiv.org/abs/2306.14878)) * Add config option for default_inpaint_engine_version, sets inpaint engine for pony_v6 and playground_v2.5 to None for improved results (incompatible with inpaint engine) * Add image editor functionality to mask upload (same as for inpaint, now correctly resizes and allows more detailed mask creation) # [2.4.3](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.3) * Fix alphas_cumprod setter for TCD sampler * Add parser for env var strings to expected config value types to allow override of all non-path config keys # [2.4.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.2) * Fix some small bugs (tcd scheduler when gamma is 0, chown in Dockerfile, update cmd args in readme, translation for aspect ratios, vae default after file reload) * Fix performance LoRA replacement when data is loaded from history log and inline prompt * Add support and preset for playground v2.5 (only works with performance Quality or Speed, use with scheduler edm_playground_v2) * Make textboxes (incl. positive prompt) resizable * Hide intermediate images when performance of Gradio would bottleneck the generation process (Extreme Speed, Lightning, Hyper-SD) # [2.4.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.1) * Fix some small bugs (e.g. adjust clip skip default value from 1 to 2, add type check to aspect ratios js update function) * Add automated docker build on push to main, tagged with `edge`. See [available docker images](https://github.com/lllyasviel/Fooocus/pkgs/container/fooocus). # [2.4.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.0) * Change settings tab elements to be more compact * Add clip skip slider * Add select for custom VAE * Add new style "Random Style" * Update default anime model to animaPencilXL_v310 * Add button to reconnect the UI after Fooocus crashed without having to configure everything again (no page reload required) * Add performance "hyper-sd" (based on [Hyper-SDXL 4 step LoRA](https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors)) * Add [AlignYourSteps](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) scheduler by Nvidia, see * Add [TCD](https://github.com/jabir-zheng/TCD) sampler and scheduler (based on sgm_uniform) * Add NSFW image censoring (disables intermediate image preview while generating). Set config value `default_black_out_nsfw` to True to always enable. * Add argument `--enable-describe-uov-image` to automatically describe uploaded images for upscaling * Add inline lora prompt references with subfolder support, example prompt: `colorful bird ` * Add size and aspect ratio recommendation on image describe * Add inpaint brush color picker, helpful when image and mask brush have the same color * Add automated Docker image build using Github Actions on each release. * Add full raw prompts to history logs * Change code ownership from @lllyasviel to @mashb1t for automated issue / MR notification # [2.3.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.1) * Remove positive prompt from anime prefix to not reset prompt after switching presets * Fix image number being reset to 1 when switching preset, now doesn't reset anymore * Fix outpainting dimension calculation when extending left/right * Fix LoRA compatibility for LoRAs in a1111 metadata scheme # [2.3.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.0) * Add performance "lightning" (based on [SDXL-Lightning 4 step LoRA](https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/sdxl_lightning_4step_lora.safetensors)) * Add preset selection to UI, disable with argument `--disable-preset-selection`. Use `--always-download-new-model` to download missing models on preset switch. * Improve face swap consistency by switching later in the process to (synthetic) refiner * Add temp path cleanup on startup * Add support for wildcard subdirectories * Add scrollable 2 column layout for styles for better structure * Improve Colab resource needs for T4 instances (default), positively tested with all image prompt features * Improve anime preset, now uses style `Fooocus Semi Realistic` instead of `Fooocus Negative` (less wet look images) # [2.2.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.1) * Fix some small bugs (e.g. image grid, upscale fast 2x, LoRA weight width in Firefox) * Allow prompt weights in array syntax * Add steps override and metadata scheme to history log # [2.2.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.0) * Isolate every image generation to truly allow multi-user usage * Add array support, changes the main prompt when increasing the image number. Syntax: `[[red, green, blue]] flower` * Add optional metadata to images, allowing you to regenerate and modify them later with the same parameters * Now supports native PNG, JPG and WEBP image generation * Add Docker support # [2.1.865](https://github.com/lllyasviel/Fooocus/releases/tag/2.1.865) * Various bugfixes * Add authentication to --listen # 2.1.864 * New model list. See also discussions. # 2.1.861 (requested update) (2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems. * Show image preview in Style when mouse hover. # 2.1.860 (requested update) * Allow upload inpaint mask in developer mode. # 2.1.857 (requested update) * Begin to support 8GB AMD GPU on Windows. # 2.1.854 * Add a button to copy parameters to clipboard in log. * Allow users to load parameters directly by pasting parameters to prompt. # 2.1.853 * Add Marc K3nt3L's styles. Thanks [Marc K3nt3L](https://github.com/K3nt3L)! # 2.1.852 * New Log System: Log system now uses tables. If this is breaking some other browser extension or javascript developments, see also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405). # 2.1.846 * Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846. # 2.1.843 * Many improvements to Canvas. Thanks CanvasZoom author! # 2.1.841 * Backend maintain. * Fix some potential frozen after model mismatch. * Fix crash when cfg=1 when using anime preset. * Added some guidelines for troubleshoot the "CUDA kernel errors asynchronously" problem. * Fix inpaint device problem in `--always-gpu` mode. # 2.1.839 * Maintained some computation codes in backend for efficiency. * Added a note about Seed Breaking Change. **Seed Breaking Change**: Note that 2.1.825-2.1.839 is seed breaking change. The computation float point is changed and some seeds may give slightly different results. The minor change in 2.1.825-2.1.839 do not influence image quality. See also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405). # 2.1.837 * Fix some precision-related problems. # 2.1.836 * Avoid blip tokenizer download from torch hub # 2.1.831 * Input Image -> Describe (Midjourney Describe) # 2.1.830 * SegmindVega support. # 2.1.829 * Change SDE tree to CPU on AMD/DirectMl to avoid potential problems. # 2.1.828 * Allow to disable gradio analytics. * Use html table in log. * fix some SSL problems. # 2.1.826 * New backend. * FP8 support (see also the new cmd flag list in Readme, eg, --unet-in-fp8-e4m3fn and --unet-in-fp8-e5m2). * Fix some MPS problems. * GLoRA support. * Turbo scheduler. # 2.1.823 (2023 Nov 26) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid December. However, you may still see updates if other collaborators are fixing bugs or solving problems. * Fix some potential problem when LoRAs has clip keys and user want to load those LoRAs to refiners. # 2.1.822 * New inpaint system (inpaint beta test ends). # 2.1.821 * New UI for LoRAs. * Improved preset system: normalized preset keys and file names. * Improved session system: now multiple users can use one Fooocus at the same time without seeing others' results. * Improved some computation related to model precision. * Improved config loading system with user-friendly prints. # 2.1.820 * support "--disable-image-log" to prevent writing images and logs to hard drive. # 2.1.819 * Allow disabling preview in dev tools. # 2.1.818 * Fix preset lora failed to load when the weight is exactly one. # 2.1.817 * support "--theme dark" and "--theme light". * added preset files "default" and "lcm", these presets exist but will not create launcher files (will not be exposed to users) to keep entry clean. The "--preset lcm" is equivalent to select "Extreme Speed" in UI, but will likely to make some online service deploying easier. # 2.1.815 * Multiple loras in preset. # 2.1.814 * Allow using previous preset of official SAI SDXL by modify the args to '--preset sai'. ~Note that this preset will set inpaint engine back to previous v1 to get same results like before. To change the inpaint engine to v2.6, use the dev tools -> inpaint engine -> v2.6.~ (update: it is not needed now after some tests.) # 2.1.813 * Allow preset to set default inpaint engine. # 2.1.812 * Allow preset to set default performance. * heunpp2 sampler. # 2.1.810 * Added hints to config_modification_tutorial.txt * Removed user hacked aspect ratios in I18N english templates, but it will still be read like before. * fix some style sorting problem again (perhaps should try Gradio 4.0 later). * Refreshed I18N english templates with more keys. # 2.1.809 * fix some sorting problem. # 2.1.808 * Aspect ratios now show aspect ratios. * Added style search. * Added style sorting/ordering/favorites. # 2.1.807 * Click on image to see it in full screen. # 2.1.806 * Fix some lora problems related to clip. # 2.1.805 * Responsive UI for small screens. * Added skip preprocessor in dev tools. # 2.1.802 * Default inpaint engine changed to v2.6. You can still use inpaint engine v1 in dev tools. * Fix some VRAM problems. # 2.1.799 * Added 'Extreme Speed' performance mode (based on LCM). The previous complicated settings are not needed now. # 2.1.798 * added lcm scheduler - LCM may need to set both sampler and scheduler to "lcm". Other than that, see the description in 2.1.782 logs. # 2.1.797 * fixed some dependency problems with facexlib and filterpy. # 2.1.793 * Added many javascripts to improve user experience. Now users with small screen will always see full canvas without needing to scroll. # 2.1.790 * Face swap (in line with Midjourney InsightFace): Input Image -> Image Prompt -> Advanced -> FaceSwap * The performance is super high. Use it carefully and never use it in any illegal things! * This implementation will crop faces for you and you do NOT need to crop faces before feeding images into Fooocus. (If you previously manually crop faces from images for other software, you do not need to do that now in Fooocus.) # 2.1.788 * Fixed some math problems in previous versions. * Inpaint engine v2.6 join the beta test of Fooocus inpaint models. Use it in dev tools -> inpaint engine -> v2.6 . # 2.1.785 * The `user_path_config.txt` is deprecated since 2.1.785. If you are using it right now, please use the new `config.txt` instead. See also the new documentation in the Readme. * The paths in `user_path_config.txt` will still be loaded in recent versions, but it will be removed soon. * We use very user-friendly method to automatically transfer your path settings from `user_path_config.txt` to `config.txt` and usually you do not need to do anything. * The new `config.txt` will never save default values so the default value changes in scripts will not be prevented by old config files. # 2.1.782 2.1.782 is mainly an update for a new LoRA system that supports both SDXL loras and SD1.5 loras. Now when you load a lora, the following things will happen: 1. try to load the lora to the base model, if failed (model mismatch), then try to load the lora to refiner. 2. try to load the lora to refiner, if failed (model mismatch) then do nothing. In this way, Fooocus 2.1.782 can benefit from all models and loras from CivitAI with both SDXL and SD1.5 ecosystem, using the unique Fooocus swap algorithm, to achieve extremely high quality results (although the default setting is already very high quality), especially in some anime use cases, if users really want to play with all these things. Recently the community also developed LCM loras. Users can use it by setting the sampler as 'LCM', scheduler as 'sgm_uniform' (Update in 2.1.798: scheduler should also be "lcm"), the forced overwrite of sampling step as 4 to 8, and CFG guidance as 1.0, in dev tools. Do not forget to change the LCM lora weight to 1.0 (many people forget this and report failure cases). Also, set refiner to None. If LCM's feedback in the artists community is good (not the feedback in the programmer community of Stable Diffusion), Fooocus may add some other shortcuts in the future. # 2.1.781 (2023 Oct 26) Hi all, the feature updating of Fooocus will (really, really, this time) be paused for about two or three weeks because we really have some other workloads. Thanks for the passion of you all (and we in fact have kept updating even after last pausing announcement a week ago, because of many great feedbacks) - see you soon and we will come back in mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems. * Disable refiner to speed up when new users mistakenly set same model to base and refiner. # 2.1.779 * Disable image grid by default because many users reports performance issues. For example, https://github.com/lllyasviel/Fooocus/issues/829 and so on. The image grid will cause problem when user hard drive is not super fast, or when user internet connection is not very good (eg, run in remote). The option is moved to dev tools if users want to use it. We will take a look at it later. # 2.1.776 * Support Ctrl+Up/Down Arrow to change prompt emphasizing weights. # 2.1.750 * New UI: now you can get each image during generating. # 2.1.743 * Improved GPT2 by removing some tokens that may corrupt styles. # 2.1.741 Style Updates: * "Default (Slightly Cinematic)" as renamed to "Fooocus Cinematic". * "Default (Slightly Cinematic)" is canceled from default style selections. * Added "Fooocus Sharp". This style combines many CivitAI prompts that reduces SDXL blurry and improves sharpness in a relatively natural way. * Added "Fooocus Enhance". This style mainly use the very popular [default negative prompts from JuggernautXL](https://civitai.com/models/133005) and some other enhancing words. JuggernautXL's negative prompt has been proved to be very effective in many recent image posts on CivitAI to improve JuggernautXL and many other models. * "Fooocus Sharp" and "Fooocus Enhance" and "Fooocus V2" becomes the new default set of styles. * Removed the default text in the "negative prompt" input area since it is not necessary now. * You can reproduce previous results by using "Fooocus Cinematic". * "Fooocus Sharp" and "Fooocus Enhance" may undergo minor revision in future updates. # 2.1.739 * Added support for authentication in --share mode (via auth.json). # 2.1.737 * Allowed customizing resolutions in config. Modifying this will make results worse if you do not understand how Positional Encoding works. You have been warned. If you do not know why numbers must be transformed with many Sin and Cos functions (yes, those Trigonometric functions that you learn in junior high school) before they are fed to SDXL, we do not encourage you to change this - you will become a victim of Positional Encoding. You are likely to suffer from an easy-to-fail tool, rather than getting more control. Your knowledge gained from SD1.5 (for example, resolution numbers divided by 8 or 64 are good enough for UNet) does not work in SDXL. The SDXL uses Positional Encoding. The SD1.5 does not use Positional Encoding. They are completely different. Your knowledge gained from other resources (for example, resolutions around 1024 are good enough for SDXL) is wrong. The SDXL uses Positional Encoding. People who say "all resolutions around 1024 are good" do not understand what is Positional Encoding. They are not intentionally misleading. They are just not aware of the fact that SDXL is using Positional Encoding. The number 1152 must be exactly 1152, not 1152-1, not 1152+1, not 1152-8, not 1152+8. The number 1152 must be exactly 1152. Just Google what is a Positional Encoding. Again, if you do not understand how Positional Encoding works, just do not change the resolution numbers. # 2.1.735 * Fixed many problems related to torch autocast. # 2.1.733 * Increased allowed random seed range. # 2.1.728 * Fixed some potential numerical problems since 2.1.723 # 2.1.723 * Improve Fooocus Anime a bit by using better SD1.5 refining formulation. # 2.1.722 * Now it is possible to translate 100% all texts in the UI. # 2.1.721 * Added language/en.json to make translation easier. # 2.1.720 * Added Canvas Zoom to inpaint canvas * Fixed the problem that image will be cropped in UI when the uploaded image is too wide. # 2.1.719 * I18N # 2.1.718 * Corrected handling dash in wildcard names, more wildcards (extended-color). # 2.1.717 * Corrected displaying multi-line prompts in Private Log. # 2.1.716 * Added support for nested wildcards, more wildcards (flower, color_flower). # 2.1.714 * Fixed resolution problems. # 2.1.712 * Cleaned up Private Log (most users won't need information about raw prompts). # 2.1.711 * Added more information about prompts in Private Log. * Made wildcards in negative prompt use different seed. # 2.1.710 * Added information about wildcards usage in console log. # 2.1.709 * Allowed changing default values of advanced checkbox and image number. # 2.1.707 * Updated Gradio to v3.41.2. # 2.1.703 * Fixed many previous problems related to inpaint. # 2.1.702 * Corrected reading empty negative prompt from config (it shouldn't turn into None). # 2.1.701 * Updated FreeU node to v2 (gives less overcooked results). # 2.1.699 * Disabled smart memory management (solves some memory issues). # 2.1.698 * Added support for loading model files from subfolders. # 2.1.696 * Improved wildcards implementation (using same wildcard multiple times will now return different values). **(2023 Oct 18) Again, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads - we will come back in early or mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems.** # 2.1.695 (requested emergency bug fix) * Reduced 3.4GB RAM use when swapping base model. * Reduced 372MB VRAM use in VAE decoding after using control model in image prompt. * Note that Official ComfyUI (d44a2de) will run out of VRAM when using sdxl and control-lora on 2060 6GB that does not support float16 at resolution 1024. Fooocus 2.1.695 succeeded in outputting images without OOM using exactly same devices. (2023 Oct 17) Announcement of update being paused. # 2.1.693 * Putting custom styles before pre-defined styles. * Avoided the consusion between Fooocus Anime preset and Fooocus Anime style (Fooocus Anime style is renamed to Fooocus Masterpiece because it does not make images Anime-looking if not using with Fooocus Anime preset). * Fixed some minor bugs in Fooocus Anime preset's prompt emphasizing of commas. * Supported and documented embedding grammar (and wildcards grammar). * This release is a relative stable version and many features are determined now. # 2.1.687 * Added support for wildcards (using files from wildcards folder - try prompts like `__color__ sports car` with different seeds). # 2.1.682 * Added support for custom styles (loaded from JSON files placed in sdxl_styles folder). # 2.1.681 * Added support for generate hotkey (CTRL+ENTER). * Added support for generate forever (RMB on Generate button). * Added support for playing sound when generation is finished ('notification.ogg' or 'notification.mp3'). # 2.1.62 * Preset system. Added anime and realistic support. # 2.1.52 * removed pygit2 dependency (expect auto update) so that people will never have permission denied problems. # 2.1.50 * Begin to support sd1.5 as refiner. This method scale sigmas given SD15/Xl latent scale and is probably the most correct way to do it. I am going to write a discussion soon. # 2.1.25 AMD support on Linux and Windows. # 2.1.0 * Image Prompt * Finished the "Moving from Midjourney" Table # 2.0.85 * Speed Up Again # 2.0.80 * Improved the scheduling of ADM guidance and CFG mimicking for better visual quality in high frequency domain and small objects. # 2.0.80 * Rework many patches and some UI details. * Speed up processing. * Move Colab to independent branch. * Implemented CFG Scale and TSNR correction when CFG is bigger than 10. * Implemented Developer Mode with more options to debug. ### 2.0.72 (2023 sep 21) The feature updating of Fooocus will be paused for about two or three weeks because we have some events and travelling - we will come back in early or mid October. ### 2.0.72 * Allow users to choose path of models. ### 2.0.65 * Inpaint model released. ### 2.0.50 * Variation/Upscale (Midjourney Toolbar) implemented. ### 2.0.16 * Virtual memory system implemented. Now Colab can run both base model and refiner model with 7.8GB RAM + 5.3GB VRAM, and it never crashes. * If you are lucky enough to read this line, keep in mind that ComfyUI cannot do this. This is very reasonable that Fooocus is more optimized because it only need to handle a fixed pipeline, but ComfyUI need to consider arbitrary pipelines. * But if we just consider the optimization of this fixed workload, after 2.0.16, Fooocus has become the most optimized SDXL app, outperforming ComfyUI. ### 2.0.0 * V2 released. * completely rewrite text processing pipeline (higher image quality and prompt understanding). * support multi-style. * In 100 tests (prompts written by ChatGPT), V2 default results outperform V1 default results in 87 cases, evaluated by two human. * In 100 tests (prompts written by ChatGPT), V2 prompt understanding outperform V1 prompt understanding in 81 cases, evaluated by two human, in both default setting and multi/single style mode. * Because the above number is above 80%, we view this as a major update and directly jump to 2.0.0. * Some other things are renamed. ### 1.0.67 * Use dynamic weighting and lower weights for prompt expansion. ### 1.0.64 * Fixed a small OOM problem. ### 1.0.62 * Change prompt expansion to suffix mode for better balance of semantic and style (and debugging). ### 1.0.60 * Tune the balance between style and Prompt Expansion. ### 1.0.56 * Begin to use magic split. ### 1.0.55 * Minor changes of Prompt Expansion. ### 1.0.52 * Reduce the semantic corruption of Prompt Expansion. ### 1.0.51 * Speed up Prompt Expansion a bit. ### 1.0.50 * Prompt expansion and a "Raw mode" to turn it off (similar to Midjourney's "raw"). ### 1.0.45 * Reworked SAG, removed unnecessary patch * Reworked anisotropic filters for faster compute. * Replaced with guided anisotropic filter for less distortion. ### 1.0.41 (The update of Fooocus will be paused for a period of time for AUTOMATIC1111 sd-webui 1.6.X, and some features will also be implemented as webui extensions) ### 1.0.40 * Behaviors reverted to 1.0.36 again (refiner steps). The 1.0.36 is too perfect and too typical; beating 1.0.36 is just impossible. ### 1.0.39 * Reverted unstable changes between 1.0.37 and 1.0.38 . * Increased refiner steps to half of sampling steps. ### 1.0.36 * Change gaussian kernel to anisotropic kernel. ### 1.0.34 * Random seed restoring. ### 1.0.33 * Hide items in log when images are removed. ### 1.0.32 * Fooocus private log ### 1.0.31 * Fix typo and UI. ### 1.0.29 * Added "Advanced->Advanced->Advanced" block for future development. ### 1.0.29 * Fix overcook problem in 1.0.28 ### 1.0.28 * SAG implemented ### 1.0.27 * Fix small problem in textbox css ### 1.0.25 * support sys.argv --listen --share --port ### 1.0.24 * Taller input textbox. ### 1.0.23 * Added some hints on linux after UI start so users know the App does not fail. ### 1.0.20 * Support linux. ### 1.0.20 * Speed-up text encoder. ### 1.0.20 * Re-write UI to use async codes: (1) for faster start, and (2) for better live preview. * Removed opencv dependency * Plan to support Linux soon ### 1.0.19 * Unlock to allow changing model. ### 1.0.17 * Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.) ### 1.0.16 * Implemented "Fooocus/outputs" folder for saving user results. * Ignored cv2 errors when preview fails. * Mentioned future AMD support in Readme. * Created this log. ### 1.0.15 Publicly available. ### 1.0.0 Initial Version. ================================================ FILE: webui.py ================================================ import gradio as gr import random import os import json import time import shared import modules.config import fooocus_version import modules.html import modules.async_worker as worker import modules.constants as constants import modules.flags as flags import modules.gradio_hijack as grh import modules.style_sorter as style_sorter import modules.meta_parser import args_manager import copy import launch from extras.inpaint_mask import SAMOptions from modules.sdxl_styles import legal_style_names from modules.private_logger import get_current_html_path from modules.ui_gradio_extensions import reload_javascript from modules.auth import auth_enabled, check_auth from modules.util import is_json def get_task(*args): args = list(args) args.pop(0) return worker.AsyncTask(args=args) def generate_clicked(task: worker.AsyncTask): import ldm_patched.modules.model_management as model_management with model_management.interrupt_processing_mutex: model_management.interrupt_processing = False # outputs=[progress_html, progress_window, progress_gallery, gallery] if len(task.args) == 0: return execution_start_time = time.perf_counter() finished = False yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \ gr.update(visible=True, value=None), \ gr.update(visible=False, value=None), \ gr.update(visible=False) worker.async_tasks.append(task) while not finished: time.sleep(0.01) if len(task.yields) > 0: flag, product = task.yields.pop(0) if flag == 'preview': # help bad internet connection by skipping duplicated preview if len(task.yields) > 0: # if we have the next item if task.yields[0][0] == 'preview': # if the next item is also a preview # print('Skipped one preview for better internet connection.') continue percentage, title, image = product yield gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \ gr.update(visible=True, value=image) if image is not None else gr.update(), \ gr.update(), \ gr.update(visible=False) if flag == 'results': yield gr.update(visible=True), \ gr.update(visible=True), \ gr.update(visible=True, value=product), \ gr.update(visible=False) if flag == 'finish': if not args_manager.args.disable_enhance_output_sorting: product = sort_enhance_images(product, task) yield gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=True, value=product) finished = True # delete Fooocus temp images, only keep gradio temp images if args_manager.args.disable_image_log: for filepath in product: if isinstance(filepath, str) and os.path.exists(filepath): os.remove(filepath) execution_time = time.perf_counter() - execution_start_time print(f'Total time: {execution_time:.2f} seconds') return def sort_enhance_images(images, task): if not task.should_enhance or len(images) <= task.images_to_enhance_count: return images sorted_images = [] walk_index = task.images_to_enhance_count for index, enhanced_img in enumerate(images[:task.images_to_enhance_count]): sorted_images.append(enhanced_img) if index not in task.enhance_stats: continue target_index = walk_index + task.enhance_stats[index] if walk_index < len(images) and target_index <= len(images): sorted_images += images[walk_index:target_index] walk_index += task.enhance_stats[index] return sorted_images def inpaint_mode_change(mode, inpaint_engine_version): assert mode in modules.flags.inpaint_options # inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, # inpaint_disable_initial_latent, inpaint_engine, # inpaint_strength, inpaint_respective_field if mode == modules.flags.inpaint_option_detail: return [ gr.update(visible=True), gr.update(visible=False, value=[]), gr.Dataset.update(visible=True, samples=modules.config.example_inpaint_prompts), False, 'None', 0.5, 0.0 ] if inpaint_engine_version == 'empty': inpaint_engine_version = modules.config.default_inpaint_engine_version if mode == modules.flags.inpaint_option_modify: return [ gr.update(visible=True), gr.update(visible=False, value=[]), gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), True, inpaint_engine_version, 1.0, 0.0 ] return [ gr.update(visible=False, value=''), gr.update(visible=True), gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), False, inpaint_engine_version, 1.0, 0.618 ] reload_javascript() title = f'Fooocus {fooocus_version.version}' if isinstance(args_manager.args.preset, str): title += ' ' + args_manager.args.preset shared.gradio_root = gr.Blocks(title=title).queue() with shared.gradio_root: currentTask = gr.State(worker.AsyncTask(args=[])) inpaint_engine_state = gr.State('empty') with gr.Row(): with gr.Column(scale=2): with gr.Row(): progress_window = grh.Image(label='Preview', show_label=True, visible=False, height=768, elem_classes=['main_view']) progress_gallery = gr.Gallery(label='Finished Images', show_label=True, object_fit='contain', height=768, visible=False, elem_classes=['main_view', 'image_gallery']) progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False, elem_id='progress-bar', elem_classes='progress-bar') gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768, elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'], elem_id='final_gallery') with gr.Row(): with gr.Column(scale=17): prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt', autofocus=True, lines=3) default_prompt = modules.config.default_prompt if isinstance(default_prompt, str) and default_prompt != '': shared.gradio_root.load(lambda: default_prompt, outputs=prompt) with gr.Column(scale=3, min_width=0): generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True) reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False) load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False) skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', elem_id='skip_button', visible=False) stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) def stop_clicked(currentTask): import ldm_patched.modules.model_management as model_management currentTask.last_stop = 'stop' if (currentTask.processing): model_management.interrupt_current_processing() return currentTask def skip_clicked(currentTask): import ldm_patched.modules.model_management as model_management currentTask.last_stop = 'skip' if (currentTask.processing): model_management.interrupt_current_processing() return currentTask stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever') skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False) with gr.Row(elem_classes='advanced_check_row'): input_image_checkbox = gr.Checkbox(label='Input Image', value=modules.config.default_image_prompt_checkbox, container=False, elem_classes='min_check') enhance_checkbox = gr.Checkbox(label='Enhance', value=modules.config.default_enhance_checkbox, container=False, elem_classes='min_check') advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check') with gr.Row(visible=modules.config.default_image_prompt_checkbox) as image_input_panel: with gr.Tabs(selected=modules.config.default_selected_image_input_tab_id): with gr.Tab(label='Upscale or Variation', id='uov_tab') as uov_tab: with gr.Row(): with gr.Column(): uov_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False) with gr.Column(): uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=modules.config.default_uov_method) gr.HTML('\U0001F4D4 Documentation') with gr.Tab(label='Image Prompt', id='ip_tab') as ip_tab: with gr.Row(): ip_images = [] ip_types = [] ip_stops = [] ip_weights = [] ip_ctrls = [] ip_ad_cols = [] for image_count in range(modules.config.default_controlnet_image_count): image_count += 1 with gr.Column(): ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300, value=modules.config.default_ip_images[image_count]) ip_images.append(ip_image) ip_ctrls.append(ip_image) with gr.Column(visible=modules.config.default_image_prompt_advanced_checkbox) as ad_col: with gr.Row(): ip_stop = gr.Slider(label='Stop At', minimum=0.0, maximum=1.0, step=0.001, value=modules.config.default_ip_stop_ats[image_count]) ip_stops.append(ip_stop) ip_ctrls.append(ip_stop) ip_weight = gr.Slider(label='Weight', minimum=0.0, maximum=2.0, step=0.001, value=modules.config.default_ip_weights[image_count]) ip_weights.append(ip_weight) ip_ctrls.append(ip_weight) ip_type = gr.Radio(label='Type', choices=flags.ip_list, value=modules.config.default_ip_types[image_count], container=False) ip_types.append(ip_type) ip_ctrls.append(ip_type) ip_type.change(lambda x: flags.default_parameters[x], inputs=[ip_type], outputs=[ip_stop, ip_weight], queue=False, show_progress=False) ip_ad_cols.append(ad_col) ip_advanced = gr.Checkbox(label='Advanced', value=modules.config.default_image_prompt_advanced_checkbox, container=False) gr.HTML('* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1). \U0001F4D4 Documentation') def ip_advance_checked(x): return [gr.update(visible=x)] * len(ip_ad_cols) + \ [flags.default_ip] * len(ip_types) + \ [flags.default_parameters[flags.default_ip][0]] * len(ip_stops) + \ [flags.default_parameters[flags.default_ip][1]] * len(ip_weights) ip_advanced.change(ip_advance_checked, inputs=ip_advanced, outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, queue=False, show_progress=False) with gr.Tab(label='Inpaint or Outpaint', id='inpaint_tab') as inpaint_tab: with gr.Row(): with gr.Column(): inpaint_input_image = grh.Image(label='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas', show_label=False) inpaint_advanced_masking_checkbox = gr.Checkbox(label='Enable Advanced Masking Features', value=modules.config.default_inpaint_advanced_masking_checkbox) inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, label='Method') inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False) outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction') example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Documentation') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) with gr.Column(visible=modules.config.default_inpaint_advanced_masking_checkbox) as inpaint_mask_generation_col: inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", mask_opacity=1, elem_id='inpaint_mask_canvas') invert_mask_checkbox = gr.Checkbox(label='Invert Mask When Generating', value=modules.config.default_invert_mask_checkbox) inpaint_mask_model = gr.Dropdown(label='Mask generation model', choices=flags.inpaint_mask_models, value=modules.config.default_inpaint_mask_model) inpaint_mask_cloth_category = gr.Dropdown(label='Cloth category', choices=flags.inpaint_mask_cloth_category, value=modules.config.default_inpaint_mask_cloth_category, visible=False) inpaint_mask_dino_prompt_text = gr.Textbox(label='Detection prompt', value='', visible=False, info='Use singular whenever possible', placeholder='Describe what you want to detect.') example_inpaint_mask_dino_prompt_text = gr.Dataset( samples=modules.config.example_enhance_detection_prompts, label='Detection Prompt Quick List', components=[inpaint_mask_dino_prompt_text], visible=modules.config.default_inpaint_mask_model == 'sam') example_inpaint_mask_dino_prompt_text.click(lambda x: x[0], inputs=example_inpaint_mask_dino_prompt_text, outputs=inpaint_mask_dino_prompt_text, show_progress=False, queue=False) with gr.Accordion("Advanced options", visible=False, open=False) as inpaint_mask_advanced_options: inpaint_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model) inpaint_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05) inpaint_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05) inpaint_mask_sam_max_detections = gr.Slider(label="Maximum number of detections", info="Set to 0 to detect all", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True) generate_mask_button = gr.Button(value='Generate mask from image') def generate_mask(image, mask_model, cloth_category, dino_prompt_text, sam_model, box_threshold, text_threshold, sam_max_detections, dino_erode_or_dilate, dino_debug): from extras.inpaint_mask import generate_mask_from_image extras = {} sam_options = None if mask_model == 'u2net_cloth_seg': extras['cloth_category'] = cloth_category elif mask_model == 'sam': sam_options = SAMOptions( dino_prompt=dino_prompt_text, dino_box_threshold=box_threshold, dino_text_threshold=text_threshold, dino_erode_or_dilate=dino_erode_or_dilate, dino_debug=dino_debug, max_detections=sam_max_detections, model_type=sam_model ) mask, _, _, _ = generate_mask_from_image(image, mask_model, extras, sam_options) return mask inpaint_mask_model.change(lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] + [gr.update(visible=x == 'sam')] * 2 + [gr.Dataset.update(visible=x == 'sam', samples=modules.config.example_enhance_detection_prompts)], inputs=inpaint_mask_model, outputs=[inpaint_mask_cloth_category, inpaint_mask_dino_prompt_text, inpaint_mask_advanced_options, example_inpaint_mask_dino_prompt_text], queue=False, show_progress=False) with gr.Tab(label='Describe', id='describe_tab') as describe_tab: with gr.Row(): with gr.Column(): describe_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False) with gr.Column(): describe_methods = gr.CheckboxGroup( label='Content Type', choices=flags.describe_types, value=modules.config.default_describe_content_type) describe_apply_styles = gr.Checkbox(label='Apply Styles', value=modules.config.default_describe_apply_prompts_checkbox) describe_btn = gr.Button(value='Describe this Image into Prompt') describe_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='describe_image_size', visible=False) gr.HTML('\U0001F4D4 Documentation') def trigger_show_image_properties(image): value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios) return gr.update(value=value, visible=True) describe_input_image.upload(trigger_show_image_properties, inputs=describe_input_image, outputs=describe_image_size, show_progress=False, queue=False) with gr.Tab(label='Enhance', id='enhance_tab') as enhance_tab: with gr.Row(): with gr.Column(): enhance_input_image = grh.Image(label='Use with Enhance, skips image generation', source='upload', type='numpy') gr.HTML('\U0001F4D4 Documentation') with gr.Tab(label='Metadata', id='metadata_tab') as metadata_tab: with gr.Column(): metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='pil') metadata_json = gr.JSON(label='Metadata') metadata_import_button = gr.Button(value='Apply Metadata') def trigger_metadata_preview(file): parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file) results = {} if parameters is not None: results['parameters'] = parameters if isinstance(metadata_scheme, flags.MetadataScheme): results['metadata_scheme'] = metadata_scheme.value return results metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image, outputs=metadata_json, queue=False, show_progress=True) with gr.Row(visible=modules.config.default_enhance_checkbox) as enhance_input_panel: with gr.Tabs(): with gr.Tab(label='Upscale or Variation'): with gr.Row(): with gr.Column(): enhance_uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=modules.config.default_enhance_uov_method) enhance_uov_processing_order = gr.Radio(label='Order of Processing', info='Use before to enhance small details and after to enhance large areas.', choices=flags.enhancement_uov_processing_order, value=modules.config.default_enhance_uov_processing_order) enhance_uov_prompt_type = gr.Radio(label='Prompt', info='Choose which prompt to use for Upscale or Variation.', choices=flags.enhancement_uov_prompt_types, value=modules.config.default_enhance_uov_prompt_type, visible=modules.config.default_enhance_uov_processing_order == flags.enhancement_uov_after) enhance_uov_processing_order.change(lambda x: gr.update(visible=x == flags.enhancement_uov_after), inputs=enhance_uov_processing_order, outputs=enhance_uov_prompt_type, queue=False, show_progress=False) gr.HTML('\U0001F4D4 Documentation') enhance_ctrls = [] enhance_inpaint_mode_ctrls = [] enhance_inpaint_engine_ctrls = [] enhance_inpaint_update_ctrls = [] for index in range(modules.config.default_enhance_tabs): with gr.Tab(label=f'#{index + 1}') as enhance_tab_item: enhance_enabled = gr.Checkbox(label='Enable', value=False, elem_classes='min_check', container=False) enhance_mask_dino_prompt_text = gr.Textbox(label='Detection prompt', info='Use singular whenever possible', placeholder='Describe what you want to detect.', interactive=True, visible=modules.config.default_enhance_inpaint_mask_model == 'sam') example_enhance_mask_dino_prompt_text = gr.Dataset( samples=modules.config.example_enhance_detection_prompts, label='Detection Prompt Quick List', components=[enhance_mask_dino_prompt_text], visible=modules.config.default_enhance_inpaint_mask_model == 'sam') example_enhance_mask_dino_prompt_text.click(lambda x: x[0], inputs=example_enhance_mask_dino_prompt_text, outputs=enhance_mask_dino_prompt_text, show_progress=False, queue=False) enhance_prompt = gr.Textbox(label="Enhancement positive prompt", placeholder="Uses original prompt instead if empty.", elem_id='enhance_prompt') enhance_negative_prompt = gr.Textbox(label="Enhancement negative prompt", placeholder="Uses original negative prompt instead if empty.", elem_id='enhance_negative_prompt') with gr.Accordion("Detection", open=False): enhance_mask_model = gr.Dropdown(label='Mask generation model', choices=flags.inpaint_mask_models, value=modules.config.default_enhance_inpaint_mask_model) enhance_mask_cloth_category = gr.Dropdown(label='Cloth category', choices=flags.inpaint_mask_cloth_category, value=modules.config.default_inpaint_mask_cloth_category, visible=modules.config.default_enhance_inpaint_mask_model == 'u2net_cloth_seg', interactive=True) with gr.Accordion("SAM Options", visible=modules.config.default_enhance_inpaint_mask_model == 'sam', open=False) as sam_options: enhance_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model, interactive=True) enhance_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05, interactive=True) enhance_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05, interactive=True) enhance_mask_sam_max_detections = gr.Slider(label="Maximum number of detections", info="Set to 0 to detect all", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True) with gr.Accordion("Inpaint", visible=True, open=False): enhance_inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, label='Method', interactive=True) enhance_inpaint_disable_initial_latent = gr.Checkbox( label='Disable initial latent in inpaint', value=False) enhance_inpaint_engine = gr.Dropdown(label='Inpaint Engine', value=modules.config.default_inpaint_engine_version, choices=flags.inpaint_engine_versions, info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.') enhance_inpaint_strength = gr.Slider(label='Inpaint Denoising Strength', minimum=0.0, maximum=1.0, step=0.001, value=1.0, info='Same as the denoising strength in A1111 inpaint. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') enhance_inpaint_respective_field = gr.Slider(label='Inpaint Respective Field', minimum=0.0, maximum=1.0, step=0.001, value=0.618, info='The area to inpaint. ' 'Value 0 is same as "Only Masked" in A1111. ' 'Value 1 is same as "Whole Image" in A1111. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') enhance_inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, always processed before any mask invert)') enhance_mask_invert = gr.Checkbox(label='Invert Mask', value=False) gr.HTML('\U0001F4D4 Documentation') enhance_ctrls += [ enhance_enabled, enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert ] enhance_inpaint_mode_ctrls += [enhance_inpaint_mode] enhance_inpaint_engine_ctrls += [enhance_inpaint_engine] enhance_inpaint_update_ctrls += [[ enhance_inpaint_mode, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field ]] enhance_inpaint_mode.change(inpaint_mode_change, inputs=[enhance_inpaint_mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field ], show_progress=False, queue=False) enhance_mask_model.change( lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] + [gr.update(visible=x == 'sam')] * 2 + [gr.Dataset.update(visible=x == 'sam', samples=modules.config.example_enhance_detection_prompts)], inputs=enhance_mask_model, outputs=[enhance_mask_cloth_category, enhance_mask_dino_prompt_text, sam_options, example_enhance_mask_dino_prompt_text], queue=False, show_progress=False) switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}" down_js = "() => {viewer_to_bottom();}" input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox, outputs=image_input_panel, queue=False, show_progress=False, _js=switch_js) ip_advanced.change(lambda: None, queue=False, show_progress=False, _js=down_js) current_tab = gr.Textbox(value='uov', visible=False) uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False) inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False) ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False) describe_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False) enhance_tab.select(lambda: 'enhance', outputs=current_tab, queue=False, _js=down_js, show_progress=False) metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False) enhance_checkbox.change(lambda x: gr.update(visible=x), inputs=enhance_checkbox, outputs=enhance_input_panel, queue=False, show_progress=False, _js=switch_js) with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column: with gr.Tab(label='Settings'): if not args_manager.args.disable_preset_selection: preset_selection = gr.Dropdown(label='Preset', choices=modules.config.available_presets, value=args_manager.args.preset if args_manager.args.preset else "initial", interactive=True) performance_selection = gr.Radio(label='Performance', choices=flags.Performance.values(), value=modules.config.default_performance, elem_classes=['performance_selection']) with gr.Accordion(label='Aspect Ratios', open=False, elem_id='aspect_ratios_accordion') as aspect_ratios_accordion: aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False, choices=modules.config.available_aspect_ratios_labels, value=modules.config.default_aspect_ratio, info='width × height', elem_classes='aspect_ratios') aspect_ratios_selection.change(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}') shared.gradio_root.load(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}') image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number) output_format = gr.Radio(label='Output Format', choices=flags.OutputFormat.list(), value=modules.config.default_output_format) negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.", info='Describing what you do not want to see.', lines=2, elem_id='negative_prompt', value=modules.config.default_prompt_negative) seed_random = gr.Checkbox(label='Random', value=True) image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False) # workaround for https://github.com/gradio-app/gradio/issues/5354 def random_checked(r): return gr.update(visible=not r) def refresh_seed(r, seed_string): if r: return random.randint(constants.MIN_SEED, constants.MAX_SEED) else: try: seed_value = int(seed_string) if constants.MIN_SEED <= seed_value <= constants.MAX_SEED: return seed_value except ValueError: pass return random.randint(constants.MIN_SEED, constants.MAX_SEED) seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed], queue=False, show_progress=False) def update_history_link(): if args_manager.args.disable_image_log: return gr.update(value='') return gr.update(value=f'\U0001F4DA History Log') history_link = gr.HTML() shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False) with gr.Tab(label='Styles', elem_classes=['style_selections_tab']): style_sorter.try_load_sorted_styles( style_names=legal_style_names, default_selected=modules.config.default_styles) style_search_bar = gr.Textbox(show_label=False, container=False, placeholder="\U0001F50E Type here to search styles ...", value="", label='Search Styles') style_selections = gr.CheckboxGroup(show_label=False, container=False, choices=copy.deepcopy(style_sorter.all_styles), value=copy.deepcopy(modules.config.default_styles), label='Selected Styles', elem_classes=['style_selections']) gradio_receiver_style_selections = gr.Textbox(elem_id='gradio_receiver_style_selections', visible=False) shared.gradio_root.load(lambda: gr.update(choices=copy.deepcopy(style_sorter.all_styles)), outputs=style_selections) style_search_bar.change(style_sorter.search_styles, inputs=[style_selections, style_search_bar], outputs=style_selections, queue=False, show_progress=False).then( lambda: None, _js='()=>{refresh_style_localization();}') gradio_receiver_style_selections.input(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False).then( lambda: None, _js='()=>{refresh_style_localization();}') with gr.Tab(label='Models'): with gr.Group(): with gr.Row(): base_model = gr.Dropdown(label='Base Model (SDXL only)', choices=modules.config.model_filenames, value=modules.config.default_base_model_name, show_label=True) refiner_model = gr.Dropdown(label='Refiner (SDXL or SD 1.5)', choices=['None'] + modules.config.model_filenames, value=modules.config.default_refiner_model_name, show_label=True) refiner_switch = gr.Slider(label='Refiner Switch At', minimum=0.1, maximum=1.0, step=0.0001, info='Use 0.4 for SD1.5 realistic models; ' 'or 0.667 for SD1.5 anime models; ' 'or 0.8 for XL-refiners; ' 'or any value for switching two SDXL models.', value=modules.config.default_refiner_switch, visible=modules.config.default_refiner_model_name != 'None') refiner_model.change(lambda x: gr.update(visible=x != 'None'), inputs=refiner_model, outputs=refiner_switch, show_progress=False, queue=False) with gr.Group(): lora_ctrls = [] for i, (enabled, filename, weight) in enumerate(modules.config.default_loras): with gr.Row(): lora_enabled = gr.Checkbox(label='Enable', value=enabled, elem_classes=['lora_enable', 'min_check'], scale=1) lora_model = gr.Dropdown(label=f'LoRA {i + 1}', choices=['None'] + modules.config.lora_filenames, value=filename, elem_classes='lora_model', scale=5) lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight, maximum=modules.config.default_loras_max_weight, step=0.01, value=weight, elem_classes='lora_weight', scale=5) lora_ctrls += [lora_enabled, lora_model, lora_weight] with gr.Row(): refresh_files = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button') with gr.Tab(label='Advanced'): guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01, value=modules.config.default_cfg_scale, info='Higher value means style is cleaner, vivider, and more artistic.') sharpness = gr.Slider(label='Image Sharpness', minimum=0.0, maximum=30.0, step=0.001, value=modules.config.default_sample_sharpness, info='Higher value means image and texture are sharper.') gr.HTML('\U0001F4D4 Documentation') dev_mode = gr.Checkbox(label='Developer Debug Mode', value=modules.config.default_developer_debug_mode_checkbox, container=False) with gr.Column(visible=modules.config.default_developer_debug_mode_checkbox) as dev_tools: with gr.Tab(label='Debug Tools'): adm_scaler_positive = gr.Slider(label='Positive ADM Guidance Scaler', minimum=0.1, maximum=3.0, step=0.001, value=1.5, info='The scaler multiplied to positive ADM (use 1.0 to disable). ') adm_scaler_negative = gr.Slider(label='Negative ADM Guidance Scaler', minimum=0.1, maximum=3.0, step=0.001, value=0.8, info='The scaler multiplied to negative ADM (use 1.0 to disable). ') adm_scaler_end = gr.Slider(label='ADM Guidance End At Step', minimum=0.0, maximum=1.0, step=0.001, value=0.3, info='When to end the guidance from positive/negative ADM. ') refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method, choices=['joint', 'separate', 'vae']) adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01, value=modules.config.default_cfg_tsnr, info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR ' '(effective when real CFG > mimicked CFG).') clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1, value=modules.config.default_clip_skip, info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).') sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list, value=modules.config.default_sampler) scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list, value=modules.config.default_scheduler) vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames, value=modules.config.default_vae, show_label=True) generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch', info='(Experimental) This may cause performance problems on some computers and certain internet conditions.', value=False) overwrite_step = gr.Slider(label='Forced Overwrite of Sampling Step', minimum=-1, maximum=200, step=1, value=modules.config.default_overwrite_step, info='Set as -1 to disable. For developer debugging.') overwrite_switch = gr.Slider(label='Forced Overwrite of Refiner Switch Step', minimum=-1, maximum=200, step=1, value=modules.config.default_overwrite_switch, info='Set as -1 to disable. For developer debugging.') overwrite_width = gr.Slider(label='Forced Overwrite of Generating Width', minimum=-1, maximum=2048, step=1, value=-1, info='Set as -1 to disable. For developer debugging. ' 'Results will be worse for non-standard numbers that SDXL is not trained on.') overwrite_height = gr.Slider(label='Forced Overwrite of Generating Height', minimum=-1, maximum=2048, step=1, value=-1, info='Set as -1 to disable. For developer debugging. ' 'Results will be worse for non-standard numbers that SDXL is not trained on.') overwrite_vary_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Vary"', minimum=-1, maximum=1.0, step=0.001, value=-1, info='Set as negative number to disable. For developer debugging.') overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"', minimum=-1, maximum=1.0, step=0.001, value=modules.config.default_overwrite_upscale, info='Set as negative number to disable. For developer debugging.') disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw, interactive=not modules.config.default_black_out_nsfw, info='Disable preview during generation.') disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', value=flags.Performance.has_restricted_features(modules.config.default_performance), info='Disable intermediate results during generation, only show final gallery.') disable_seed_increment = gr.Checkbox(label='Disable seed increment', info='Disable automatic seed increment when image number is > 1.', value=False) read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False) black_out_nsfw = gr.Checkbox(label='Black Out NSFW', value=modules.config.default_black_out_nsfw, interactive=not modules.config.default_black_out_nsfw, info='Use black image if NSFW is detected.') black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x), inputs=black_out_nsfw, outputs=disable_preview, queue=False, show_progress=False) if not args_manager.args.disable_image_log: save_final_enhanced_image_only = gr.Checkbox(label='Save only final enhanced image', value=modules.config.default_save_only_final_enhanced_image) if not args_manager.args.disable_metadata: save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images, info='Adds parameters to generated images allowing manual regeneration.') metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme, info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.', visible=modules.config.default_save_metadata_to_images) save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], queue=False, show_progress=False) with gr.Tab(label='Control'): debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False, info='See the results from preprocessors.') skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=False, info='Do not preprocess images. (Inputs are already canny/depth/cropped-face/etc.)') mixing_image_prompt_and_vary_upscale = gr.Checkbox(label='Mixing Image Prompt and Vary/Upscale', value=False) mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint', value=False) controlnet_softness = gr.Slider(label='Softness of ControlNet', minimum=0.0, maximum=1.0, step=0.001, value=0.25, info='Similar to the Control Mode in A1111 (use 0.0 to disable). ') with gr.Tab(label='Canny'): canny_low_threshold = gr.Slider(label='Canny Low Threshold', minimum=1, maximum=255, step=1, value=64) canny_high_threshold = gr.Slider(label='Canny High Threshold', minimum=1, maximum=255, step=1, value=128) with gr.Tab(label='Inpaint'): debugging_inpaint_preprocessor = gr.Checkbox(label='Debug Inpaint Preprocessing', value=False) debugging_enhance_masks_checkbox = gr.Checkbox(label='Debug Enhance Masks', value=False, info='Show enhance masks in preview and final results') debugging_dino = gr.Checkbox(label='Debug GroundingDINO', value=False, info='Use GroundingDINO boxes instead of more detailed SAM masks') inpaint_disable_initial_latent = gr.Checkbox(label='Disable initial latent in inpaint', value=False) inpaint_engine = gr.Dropdown(label='Inpaint Engine', value=modules.config.default_inpaint_engine_version, choices=flags.inpaint_engine_versions, info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.') inpaint_strength = gr.Slider(label='Inpaint Denoising Strength', minimum=0.0, maximum=1.0, step=0.001, value=1.0, info='Same as the denoising strength in A1111 inpaint. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') inpaint_respective_field = gr.Slider(label='Inpaint Respective Field', minimum=0.0, maximum=1.0, step=0.001, value=0.618, info='The area to inpaint. ' 'Value 0 is same as "Only Masked" in A1111. ' 'Value 1 is same as "Whole Image" in A1111. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, always processed before any mask invert)') dino_erode_or_dilate = gr.Slider(label='GroundingDINO Box Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, processed before SAM)') inpaint_mask_color = gr.ColorPicker(label='Inpaint brush color', value='#FFFFFF', elem_id='inpaint_brush_color') inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, inpaint_advanced_masking_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate] inpaint_advanced_masking_checkbox.change(lambda x: [gr.update(visible=x)] * 2, inputs=inpaint_advanced_masking_checkbox, outputs=[inpaint_mask_image, inpaint_mask_generation_col], queue=False, show_progress=False) inpaint_mask_color.change(lambda x: gr.update(brush_color=x), inputs=inpaint_mask_color, outputs=inpaint_input_image, queue=False, show_progress=False) with gr.Tab(label='FreeU'): freeu_enabled = gr.Checkbox(label='Enabled', value=False) freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01) freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02) freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99) freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95) freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2] def dev_mode_checked(r): return gr.update(visible=r) dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools], queue=False, show_progress=False) def refresh_files_clicked(): modules.config.update_files() results = [gr.update(choices=modules.config.model_filenames)] results += [gr.update(choices=['None'] + modules.config.model_filenames)] results += [gr.update(choices=[flags.default_vae] + modules.config.vae_filenames)] if not args_manager.args.disable_preset_selection: results += [gr.update(choices=modules.config.available_presets)] for i in range(modules.config.default_max_lora_number): results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()] return results refresh_files_output = [base_model, refiner_model, vae_name] if not args_manager.args.disable_preset_selection: refresh_files_output += [preset_selection] refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls, queue=False, show_progress=False) state_is_generating = gr.State(False) load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections, performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection, overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, clip_skip, base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name, seed_random, image_seed, inpaint_engine, inpaint_engine_state, inpaint_mode] + enhance_inpaint_mode_ctrls + [generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls if not args_manager.args.disable_preset_selection: def preset_selection_change(preset, is_generating, inpaint_mode): preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {} preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content) default_model = preset_prepared.get('base_model') previous_default_models = preset_prepared.get('previous_default_models', []) checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {}) embeddings_downloads = preset_prepared.get('embeddings_downloads', {}) lora_downloads = preset_prepared.get('lora_downloads', {}) vae_downloads = preset_prepared.get('vae_downloads', {}) preset_prepared['base_model'], preset_prepared['checkpoint_downloads'] = launch.download_models( default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads, vae_downloads) if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '': del preset_prepared['prompt'] return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating, inpaint_mode) def inpaint_engine_state_change(inpaint_engine_version, *args): if inpaint_engine_version == 'empty': inpaint_engine_version = modules.config.default_inpaint_engine_version result = [] for inpaint_mode in args: if inpaint_mode != modules.flags.inpaint_option_detail: result.append(gr.update(value=inpaint_engine_version)) else: result.append(gr.update()) return result preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=True) \ .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \ .then(lambda: None, _js='()=>{refresh_style_localization();}') \ .then(inpaint_engine_state_change, inputs=[inpaint_engine_state] + enhance_inpaint_mode_ctrls, outputs=enhance_inpaint_engine_ctrls, queue=False, show_progress=False) performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 + [gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 + [gr.update(value=flags.Performance.has_restricted_features(x))] * 1, inputs=performance_selection, outputs=[ guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive, adm_scaler_negative, refiner_switch, refiner_model, sampler_name, scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results ], queue=False, show_progress=False) output_format.input(lambda x: gr.update(output_format=x), inputs=output_format) advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column, queue=False, show_progress=False) \ .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False) inpaint_mode.change(inpaint_mode_change, inputs=[inpaint_mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field ], show_progress=False, queue=False) # load configured default_inpaint_method default_inpaint_ctrls = [inpaint_mode, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field] for mode, disable_initial_latent, engine, strength, respective_field in [default_inpaint_ctrls] + enhance_inpaint_update_ctrls: shared.gradio_root.load(inpaint_mode_change, inputs=[mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, disable_initial_latent, engine, strength, respective_field ], show_progress=False, queue=False) generate_mask_button.click(fn=generate_mask, inputs=[inpaint_input_image, inpaint_mask_model, inpaint_mask_cloth_category, inpaint_mask_dino_prompt_text, inpaint_mask_sam_model, inpaint_mask_box_threshold, inpaint_mask_text_threshold, inpaint_mask_sam_max_detections, dino_erode_or_dilate, debugging_dino], outputs=inpaint_mask_image, show_progress=True, queue=True) ctrls = [currentTask, generate_image_grid] ctrls += [ prompt, negative_prompt, style_selections, performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, read_wildcards_in_order, sharpness, guidance_scale ] ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls ctrls += [input_image_checkbox, current_tab] ctrls += [uov_method, uov_input_image] ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image] ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw] ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, clip_skip] ctrls += [sampler_name, scheduler_name, vae_name] ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength] ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint] ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold] ctrls += [refiner_swap_method, controlnet_softness] ctrls += freeu_ctrls ctrls += inpaint_ctrls if not args_manager.args.disable_image_log: ctrls += [save_final_enhanced_image_only] if not args_manager.args.disable_metadata: ctrls += [save_metadata_to_images, metadata_scheme] ctrls += ip_ctrls ctrls += [debugging_dino, dino_erode_or_dilate, debugging_enhance_masks_checkbox, enhance_input_image, enhance_checkbox, enhance_uov_method, enhance_uov_processing_order, enhance_uov_prompt_type] ctrls += enhance_ctrls def parse_meta(raw_prompt_txt, is_generating): loaded_json = None if is_json(raw_prompt_txt): loaded_json = json.loads(raw_prompt_txt) if loaded_json is None: if is_generating: return gr.update(), gr.update(), gr.update() else: return gr.update(), gr.update(visible=True), gr.update(visible=False) return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True) prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False) load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=False) def trigger_metadata_import(file, state_is_generating): parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file) if parameters is None: print('Could not find metadata in the image!') parsed_parameters = {} else: metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) parsed_parameters = metadata_parser.to_json(parameters) return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating, inpaint_mode) metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \ .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True), outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \ .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \ .then(fn=get_task, inputs=ctrls, outputs=currentTask) \ .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \ .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False), outputs=[generate_button, stop_button, skip_button, state_is_generating]) \ .then(fn=update_history_link, outputs=history_link) \ .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed') reset_button.click(lambda: [worker.AsyncTask(args=[]), False, gr.update(visible=True, interactive=True)] + [gr.update(visible=False)] * 6 + [gr.update(visible=True, value=[])], outputs=[currentTask, state_is_generating, generate_button, reset_button, stop_button, skip_button, progress_html, progress_window, progress_gallery, gallery], queue=False) for notification_file in ['notification.ogg', 'notification.mp3']: if os.path.exists(notification_file): gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False) break def trigger_describe(modes, img, apply_styles): describe_prompts = [] styles = set() if flags.describe_type_photo in modes: from extras.interrogate import default_interrogator as default_interrogator_photo describe_prompts.append(default_interrogator_photo(img)) styles.update(["Fooocus V2", "Fooocus Enhance", "Fooocus Sharp"]) if flags.describe_type_anime in modes: from extras.wd14tagger import default_interrogator as default_interrogator_anime describe_prompts.append(default_interrogator_anime(img)) styles.update(["Fooocus V2", "Fooocus Masterpiece"]) if len(styles) == 0 or not apply_styles: styles = gr.update() else: styles = list(styles) if len(describe_prompts) == 0: describe_prompt = gr.update() else: describe_prompt = ', '.join(describe_prompts) return describe_prompt, styles describe_btn.click(trigger_describe, inputs=[describe_methods, describe_input_image, describe_apply_styles], outputs=[prompt, style_selections], show_progress=True, queue=True) \ .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \ .then(lambda: None, _js='()=>{refresh_style_localization();}') if args_manager.args.enable_auto_describe_image: def trigger_auto_describe(mode, img, prompt, apply_styles): # keep prompt if not empty if prompt == '': return trigger_describe(mode, img, apply_styles) return gr.update(), gr.update() uov_input_image.upload(trigger_auto_describe, inputs=[describe_methods, uov_input_image, prompt, describe_apply_styles], outputs=[prompt, style_selections], show_progress=True, queue=True) \ .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \ .then(lambda: None, _js='()=>{refresh_style_localization();}') enhance_input_image.upload(lambda: gr.update(value=True), outputs=enhance_checkbox, queue=False, show_progress=False) \ .then(trigger_auto_describe, inputs=[describe_methods, enhance_input_image, prompt, describe_apply_styles], outputs=[prompt, style_selections], show_progress=True, queue=True) \ .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \ .then(lambda: None, _js='()=>{refresh_style_localization();}') def dump_default_english_config(): from modules.localization import dump_english_config dump_english_config(grh.all_components) # dump_default_english_config() shared.gradio_root.launch( inbrowser=args_manager.args.in_browser, server_name=args_manager.args.listen, server_port=args_manager.args.port, share=args_manager.args.share, auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None, allowed_paths=[modules.config.path_outputs], blocked_paths=[constants.AUTH_FILENAME] ) ================================================ FILE: wildcards/.gitignore ================================================ *.txt !animal.txt !artist.txt !color.txt !color_flower.txt !extended-color.txt !flower.txt !nationality.txt ================================================ FILE: wildcards/animal.txt ================================================ Alligator Ant Antelope Armadillo Badger Bat Bear Beaver Bison Boar Bobcat Bull Camel Chameleon Cheetah Chicken Chihuahua Chimpanzee Chinchilla Chipmunk Komodo Dragon Cow Coyote Crocodile Crow Deer Dinosaur Dolphin Donkey Duck Eagle Eel Elephant Elk Emu Falcon Ferret Flamingo Flying Squirrel Giraffe Goose Guinea pig Hawk Hedgehog Hippopotamus Horse Hummingbird Hyena Jackal Jaguar Jellyfish Kangaroo King Cobra Koala bear Leopard Lion Lizard Magpie Marten Meerkat Mole Monkey Moose Mouse Octopus Okapi Orangutan Ostrich Otter Owl Panda Pangolin Panther Penguin Pig Porcupine Possum Puma Quokka Rabbit Raccoon Raven Reindeer Rhinoceros Seal Shark Sheep Snail Snake Sparrow Spider Squirrel Swallow Tiger Walrus Whale Wolf Wombat Yak Zebra ================================================ FILE: wildcards/artist.txt ================================================ A. J. Casson Aaron Douglas Aaron Horkey Aaron Jasinski Aaron Siskind Abbott Fuller Graves Abbott Handerson Thayer Abigail Larson Abraham Pether Abram Efimovich Arkhipov Adam Elsheimer Adam Hughes Adam Martinakis Adi Granov Adolf Hirémy-Hirschl Adolph Gottlieb Adolph Menzel Adonna Khare Adriaen van Ostade Adriaen van Utrecht Adrian Donoghue Adrian Ghenie Adrian Smith Adrian Tomine Adrianus Eversen Afarin Sajedi Agnes Cecile Agnes Lawrence Pelton Agnes Martin Agostino Arrivabene Akihiko Yoshida Akira Toriyama Akos Major Al Capp Al Feldstein Al Williamson Alain Laboile Alan Bean Alan Kenny Alan Lee Alan Moore Alan Parry Alasdair McLellan Albert Benois Albert Bierstadt Albert Dubois-Pillet Albert Edelfelt Albert Gleizes Albert Goodwin Albert Joseph Moore Albert Lynch Albert Marquet Albert Pinkham Ryder Alberto Biasi Alberto Burri Alberto Giacometti Alberto Seveso Alberto Sughi Albrecht Anker Albrecht Dürer Alec Soth Alejandro Burdisio Aleksey Savrasov Aleksi Briclot Alena Aenami Alessandro Allori Alessandro Gottardo Alessio Albi Alex Alemany Alex Andreev Alex Colville Alex Garant Alex Grey Alex Gross Alex Hirsch Alex Horley Alex Howitt Alex Katz Alex Maleev Alex Prager Alex Ross Alex Russell Flint Alex Timmermans Alex Toth Alexander Archipenko Alexander Jansson Alexander McQueen Alexander Millar Alexandr Averin Alexandre Benois Alexandre Cabanel Alexandre Calame Alexandre-Évariste Fragonard Alexej von Jawlensky Alfred Augustus Glendening Alfred Cheney Johnston Alfred Eisenstaedt Alfred Guillou Alfred Kubin Alfred Munnings Alfred Parsons Alfred Sisley Alfred Stevens Alfredo Jaar Algernon Blackwood Alice Bailly Alice Neel Alice Pasquini Alice Rahon Alison Bechdel Aliza Razell Allie Brosh Alma Woodsey Thomas Alois Arnegger Alphonse Mucha Alphonse Osbert Alson Skinner Clark Alvar Aalto Álvaro Siza Alvin Langdon Coburn Alyssa Monks Amanda Clark Amanda Sage Amandine van Ray Ambrosius Benson Ambrosius Bosschaert Amedeo Modigliani Amy Earles Amy Judd Amy Sillman Anato Finnstark Anatoly Metlan Anders Zorn Ando Fuchs Andre de Dienes Andre Derain André Kertész Andre Kohn André Lhote André Masson Andre Norton Andre-Charles Boulle Andrea Kowch Andrea Mantegna Andreas Achenbach Andreas Franke Andreas Levers Andreas Rocha Andreas Vesalius Andrei Markin Andrew Atroshenko Andrew Ferez Andrew Macara Andrew Robinson Andrew Wyeth Andrey Remnev Android Jones Andy Fairhurst Andy Goldsworthy Andy Kehoe Andy Singer Andy Warhol Angela Barrett Angelina Wrona Angus McKie Anja Millen Anja Percival Anka Zhuravleva Ann Stookey Anna Ancher Anna Bocek Anna Dittmann Anne Bachelier Anne Brigman Anne Dewailly Anne Geddes Anne McCaffrey Anne Packard Anne Rothenstein Anne Stokes Anne Truitt Anne-Louis Girodet Anni Albers Annibale Carracci Annick Bouvattier Annie Leibovitz Annie Soudain Annie Swynnerton Ansel Adams Anselm Kiefer Antanas Sutkus Anthony Thieme Anthony van Dyck Antoine Blanchard Anton Fadeev Anton Mauve Anton Otto Fischer Anton Pieck Anton Raphael Mengs Anton Semenov Antonello da Messina Antoni Gaudí Antonio Canova Antonio J. Manzanedo Antonio Mancini Antonio Mora Antony Gormley Apollinary Vasnetsov Apollonia Saintclair Aquirax Uno Archibald Thorburn Aries Moross Aristarkh Lentulov Aristide Maillol Armand Guillaumin Armand Point Arnold Böcklin Aron Demetz Aron Wiesenfeld Arshile Gorky Art Frahm Art Spiegelman Artemisia Gentileschi Arthur Adams Arthur Boyd Arthur Dove Arthur Elgort Arthur Hacker Arthur Hughes Arthur Lismer Arthur Rackham Arthur Sarnoff Arthur Stanley Wilkinson Arthur Streeton Arthur Tress Arthur Wardle Artur Bordalo Arturo Souto Ary Scheffer Asaf Hanuka Asger Jorn Asher Brown Durand Ashley Wood Atelier Olschinsky Atey Ghailan Aubrey Beardsley Audrey Kawasaki August Friedrich Schenck August Macke August Sander Auguste Herbin Auguste Toulmouche Augustus Edwin Mulready Augustus John Austin Briggs Austin Osman Spare Ayami Kojima Barbara Hepworth Barbara Kruger Barbara Stauffacher Solomon Barbara Takenaga Barclay Shaw Barkley L. Hendricks Barnett Newman Barry McGee Barry Windsor Smith Bart Sears Bartolomé Esteban Murillo Basil Gogos Bastien Lecouffe-Deharme Bayard Wu Beatrix Potter Beauford Delaney Becky Cloonan Bella Kotak Ben Aronson Ben Goossens Ben Hatke Ben Nicholson Ben Quilty Ben Shahn Ben Templesmith Benedick Bana Benoit B. Mandelbrot Bernard Buffet Bernardo Bellotto Bernardo Strozzi Berndnaut Smilde Bernie Wrightson Bert Hardy Bert Stern Berthe Morisot Bertil Nilsson Beth Conklin Bettina Rheims Bhupen Khakhar Bill Brandt Bill Brauer Bill Carman Bill Durgin Bill Gekas Bill Henson Bill Jacklin Bill Medcalf Bill Sienkiewicz Bill Traylor Bill Viola Bill Ward Bill Watterson Billy Childish Bjarke Ingels Bo Bartlett Bo Chen Bob Byerley Bob Clampett Bob Eggleton Bob Ross Bojan Jevtic Boris Grigoriev Boris Groh Boris Kustodiev Boris Vallejo Brad Kunkle Brad Rigney Brandon Mably Brandon Woelfel Brent Heighton Brett Weston Brett Whiteley Brian Bolland Brian Despain Brian Froud Brian K. Vaughan Brian Kesinger Brian M. Viveros Brian Mashburn Brian Stelfreeze Brian Sum Briana Mora Brice Marden Bridget Bate Tichenor Bridget Riley Briton Rivière Brooke DiDonato Brooke Shaden Brothers Grimm Brothers Hildebrandt Bruce Davidson Bruce Munro Bruce Nauman Bruce Pennington Bruce Timm Bruno Catalano Bruno Munari Bruno Walpoth Bryan Hitch Buckminster Fuller Burt Glinn Butcher Billy C. R. W. Nevinson Cai Guo-Qiang Caia Koopman Callie Fink Camilla d'Errico Camille Claudel Camille Corot Camille Pissarro Camille Walala Candido Portinari Caras Ionut Carel Willink Carl Barks Carl Gustav Carus Carl Holsoe Carl Kleiner Carl Larsson Carl Moll Carl Spitzweg Carlo Crivelli Carlo Dolci Carlo Scarpa Carlos Cruz-Diez Carlos Schwabe Carne Griffiths Carolina Herrera Carolyn Blish Carrie Ann Baade Carrie Graber Carrie Mae Weems Carsten Holler Carsten Meyerdierks Casey Baugh Casey Childs Casey Weldon Caspar David Friedrich Catherine Hyde Catherine Nolin Cathy Wilkes Catrin Welz-Stein Cecil Beaton Cecilia Beaux Cecily Brown Cedric Seaut Cerith Wyn Evans CFA Voysey Chad Knight Chaïm Soutine Chantal Joffe Charles Addams Charles Angrand Charles Blackman Charles Burns Charles Camoin Charles Courtney Curran Charles Demuth Charles Dwyer Charles E. Burchfield Charles Ginner Charles Gwathmey Charles Le Brun Charles Maurice Detmold Charles Mellin Charles Reiffel Charles Rennie Mackintosh Charles Robinson Charles Schulz Charles Sheeler Charles Spencelayh Charles Tunnicliffe Charles Vess Charles Willson Peale Charles Wysocki Charles-Amable Lenoir Charles-Francois Daubigny Charley Harper Charlie Bowater Charline von Heyl Chen Zhen Chesley Bonestell Chiara Bautista Chie Yoshii Chiharu Shiota Chiho Aoshima Childe Hassam Chip Zdarsky Chris Bachalo Chris Claremont Chris Cunningham Chris Dyer Chris Foss Chris Friel Chris Leib Chris Mars Chris Moore Chris Ofili Chris Riddell Chris Samnee Chris Uminga Chris Van Allsburg Chris Ware Christian Boltanski Christian Schad Christian Schloe Christine Ellger Christoffer Relander Christophe Jacrot Christophe Staelens Christophe Vacher Christopher Balaskas Christopher Ryan McKenney Christopher Wool Chuck Close Chung Shek Cicely Mary Barker Cildo Meireles Cindy Sherman Claes Oldenburg Clara Peeters Clarence Gagnon Claude Cahun Claude Lorrain Claude Monet Claudia Tremblay Clay Mann Clayton Crain Clemens Ascher Cleon Peterson Cliff Chiang Clifford Coffin Clint Langley Clive Barker Clive Madgwick Clyde Caldwell Clyfford Still Coby Whitmore Coles Phillips Colin Campbell Cooper Conrad Roset Conrad Shawcross Constant Permeke Constantin Brancusi Constantin Joffe Cornelia Parker Cornelis Springer Cory Arcangel Cory Loftis Craig Davison Craig McCracken Craig Mullins Craigie Aitchison Cuno Amiet Cyril Rolando Daan Roosegaarde Daido Moriyama Dain Yoon Dale Chihuly Damien Hirst Dan Colen Dan Flavin Dan Hillier Dan Matutina Dan McPharlin Dan Mumford Dan Piraro Dan Witz Dana Schutz Danh Võ Daniel Arsham Daniel Buren Daniel Clowes Daniel F. Gerhartz Daniel Garber Daniel Jaems Daniel Libeskind Daniel Merriam Daniel Ridgway Knight Daniela Uhlig Danny Lyon Danny Roberts Dante Gabriel Rossetti Daria Endresen Daria Petrilli Dariusz Klimczak Darwyn Cooke Dave Coverly Dave Dorman Dave Gibbons Dave McKean Dave Stevens David A. Hardy David Aja David Alfaro Siqueiros David B. Mattingly David Bailey David Bates David Bomberg David Brayne David Brown Milne David Burdeny David Burliuk David Chipperfield David Choe David Downton David Driskell David Finch David Goldblatt David Hammons David Hettinger David Hockney David Inshaw David LaChapelle David Ligare David Lynch David Mann David Michael Bowers David Mould David Nordahl David Palumbo David Plowden David Renshaw David Sims David Spriggs David Teniers the Younger David Tindle David Tutwiler David Walker David Welker David Wiesner David Wojnarowicz David Yarrow Davide Sorrenti Dean Cornwell Dean Ellis Debbie Criswell Debbie Fleming Caffery Deborah Azzopardi Deborah Turbeville Dee Nickerson Deirdre Sullivan-Beeman Delphin Enjolras Denis Sarazhin Dennis Stock Denys Lasdun Derek Gores Desmond Morris Diane Arbus Didier Lourenço Diego Dayer Diego Rivera Diego Velázquez Dima Dmitriev Dimitra Milan Dimitry Roulland Dino Valls Dmitri Danish Dmitry Kustanovich Dmitry Spiros Do Ho Suh Dod Procter Don Bergland Don Blanding Don Bluth Don Lawrence Don Maitz Donald Judd Donato Giancola Donna Huanca Dora Carrington Dora Maar Dorina Costras Dorothea Lange Dorothea Sharp Dorothea Tanning Dorothy Johnstone Dorothy Lathrop Doug Aitken Doug Hyde Douglas Smith Dr. Seuss Drew Struzan Duffy Sheridan Dustin Nguyen Duy Huynh E. H. Shepard Eadweard Muybridge Earl Norem Eastman Johnson Ebru Sidar Ed Binkley Ed Brubaker Ed Emshwiller Ed Freeman Ed Mell Ed Piskor Ed Roth Eddie Campbell Eddie Colla Eddie Mendoza Edgar Degas Edgar Maxence Edmondo Senatore Edmund Dulac Edmund Leighton Edmund Tarbell Edoardo Tresoldi Édouard Manet Édouard Vuillard Eduard Gaertner Eduard Veith Edvard Munch Edward Atkinson Hornel Edward Bawden Edward Blair Wilkins Edward Burne-Jones Edward Cucuel Edward Gorey Edward Henry Potthast Edward Hopper Edward Julius Detmold Edward Lear Edward Moran Edward Okuń Edward Poynter Edward Robert Hughes Edward Seago Edward Steichen Edward Wadsworth Edward Weston Edwin Austin Abbey Edwin Henry Landseer Edwin Lord Weeks Eero Saarinen Egon Schiele Eiichiro Oda Eiko Ojala Eileen Agar Eileen Gray Eilif Peterssen El Anatsui El Lissitzky Elaine de Kooning Eleanor Fortescue-Brickdale Eleanor Vere Boyle Elena Paraskeva Elihu Vedder Elisabeth Sonrel Élisabeth Vigée Le Brun Elizabeth Catlett Elizabeth Gadd Elke Vogelsang Ellen Jewett Ellen von Unwerth Elliott Erwitt Ellsworth Kelly Elly Smallwood Elsa Beskow Elsa Bleda Emek Golan Emerico Imre Toth Emil Alzamora Emil Carlsen Emil Ferris Emil Melmoth Emil Nolde Émile Bernard Emile Claus Émile Gallé Emilia Wilk Emiliano Ponzi Emily Balivet Emily Carr Emily Kame Kngwarreye Emma Geary Emma Ríos Emmanuelle Moureaux Enki Bilal Enoch Bolles Ephraim Moses Lilien Eric Canete Eric Carle Eric Fischl Eric Ravilious Eric Wallis Eric Zener Erica Hopper Erich Heckel Erik Johansson Erik Jones Erin Hanson Ernest Lawson Ernest Meissonier Ernest Zacharevic Ernesto Neto Ernie Barnes Ernst Barlach Ernst Fuchs Ernst Haas Ernst Haeckel Ernst Ludwig Kirchner Ernst Wilhelm Nay Erwin Blumenfeld Esaias van de Velde Esao Andrews Etam Cru Etel Adnan Ethan Van Sciver Étienne Adolphe Piot Ettore Sottsass Ettore Tito Euan Uglow Eugène Atget Eugène Boudin Eugene Delacroix Eugene Galien-Laloue Eugène Girardet Eugène Giraud Eugène Grasset Eugene von Guerard Eustache Le Sueur Eva Rothschild Eve Arnold Eve Ventrue Evelyn De Morgan Evelyn Dunbar Everett Shinn Evgeni Gordiets Evgeny Lushpin Eyvind Earle Ezra Stoller Fabian Perez Fabio Hurtado Fairfield Porter Faith Ringgold Fan Ho Fang Lijun Farel Dalrymple Fatima Ronquillo Fay Godwin Felice Casorati Felicia Simion Felicien Rops Felipe Pantone Felix Gonzalez-Torres Felix Vallotton Fenghua Zhong Ferdinand Hodler Ferdinand Keller Ferdinand Knab Ferenc Pinter Fern Isabel Coppedge Fernand Cormon Fernand Fonssagrives Fernand Khnopff Fernand Leger Fernand Toussaint Fernando Amorsolo Fernando Botero Ferris Plock Filip Hodas Filippino Lippi Filippo Brunelleschi Fintan Magee Firmin Baes Fletcher Sibthorp Flora Borsi Florence Harrison Florian Nicolle Floris Arntzenius Ford Madox Brown Fra Angelico Frances MacDonald Francesca Woodman Francesco Borromini Francesco Clemente Francesco Guardi Francesco Hayez Francesco Solimena Francine Van Hove Francis Bacon Francis Coates Jones Francis Davis Millet Francis Picabia Francisco de Zurbaran Francisco Goya Franco Fontana François Boucher Francois Schuiten Frank Auerbach Frank Bramley Frank Cadogan Cowper Frank Cho Frank Frazetta Frank Gehry Frank Holl Frank Lloyd Wright Frank McCarthy Frank Miller Frank Montague Moore Frank Quitely Frank Stella Frank Thorne Frank Weston Benson Frank Xavier Leyendecker Franklin Booth Franklin Carmichael Frans Floris Frans Francken the Younger Frans Hals Frans Snyders Frantisek Kupka Franz Kline Franz Marc Franz Sedlacek Franz Stuck Franz West Franz Xaver Winterhalter Fred Calleri Fred Stein Fred Tomaselli Frederic Bazille Frederic Edwin Church Frederic Remington Frederick Arthur Bridgman Frederick Carl Frieseke Frederick Cayley Robinson Frederick Goodall Frederick Judd Waugh Frederick Lord Leighton Frederick McCubbin Frederick Sandys Frida Kahlo Friedensreich Regentag Dunkelbunt Hundertwasser Frieke Janssens Frits Thaulow Frits Van den Berghe Fritz Henle Fritz Scholder Gabriel Dawe Gabriel Metsu Gabriel Pacheco Gabriel von Max Gabriele Dell'otto Gabriele Münter Gabriele Viertel Gaetano Pesce Gail Potocki Gail Simone Gareth Pugh Gari Melchers Garry Winogrand Gary Baseman Gary Bunt Gary Hume Gary Larson Gary Panter Gaston Bussière Gaston Lachaise Gediminas Pranckevicius Gemma Correll Gene Luen Yang Genndy Tartakovsky Geof Darrow Geof Kern Geoff Johns Georg Baselitz Georg Jensen George Ault George Barbier George Bellows George Birrell George Caleb Bingham George Callaghan George Catlin George Christakis George Clausen George Condo George Cruikshank George Digalakis George Elgar Hicks George Frederic Watts George Goodwin Kilburne George Grosz George Henry Boughton George Herriman George Hillyard Swinstead George Hurrell George Inness George Lucas George Luks George Morland George Pemba George Perez George Platt Lynes George Romney George Segal George Stefanescu George Stubbs George Tice George Tooker George Underwood Georges Braque Georges Clairin Georges de La Tour Georges Lacombe Georges Ribemont-Dessaignes Georges Rouault Georges Rousse Georges Seurat Georgia O'Keeffe Georgy Kurasov Gerald Brom Gerald Harvey Jones Gerard David Gerard ter Borch Gerard van Honthorst Gerd Arntz Gerda Taro Gerda Wegener Gerhard Gluck Gerhard Munthe Gerhard Richter Germaine Krull Gertrude Abercrombie Gertrude Käsebier Ghada Amer Giacomo Balla Gian Lorenzo Bernini Gianni Strino Gifford Beal Gil Elvgren Gilbert Garcin Gilbert Williams Gino Severini Giorgio Barbarelli da Castelfranco Giorgio de Chirico Giorgio Morandi Giorgio Vasari Giovanni Battista Gaulli Giovanni Battista Piranesi Giovanni Battista Tiepolo Giovanni Battista Venanzi Giovanni Boldini Giovanni Domenico Tiepolo Giovanni Segantini Giuseppe Arcimboldo Giuseppe de Nittis Gjon Mili Glen Keane Glenn Fabry Go Nagai Godfrey Kneller Godfried Schalcken Gordon Parks Goro Fujita Gottfried Helnwein Govaert Flinck Grace Cossington Smith Graham Sutherland Grandma Moses Grant Morrison Grant Wood Granville Redmond Grayson Perry Greg Girard Greg Hildebrandt Greg Land Greg Olsen Greg Rucka Greg Rutkowski Greg Simkins Greg Tocchini Grégoire Guillemin Gregory Colbert Gregory Crewdson Grete Stern Grigory Gluckmann Gris Grimly Guido Borelli da Caluso Guido Crepax Guido Reni Guido van Helten Guillaume Seignac Guillem H. Pongiluppi Guo Pei Gustav Klimt Gustave Baumann Gustave Buchet Gustave Caillebotte Gustave Courbet Gustave Doré Gustave Loiseau Gustave Moreau Gustave Van de Woestijne Guy Aroch Guy Billout Guy Carleton Wiggins Guy Denning Guy Rose Gwen John Gwenda Morgan György Kepes H. R. (Hans Ruedi) Giger H.P. Lovecraft Hajime Sorayama Hal Foster Hale Woodruff Hannah Hoch Hans Andersen Brendekilde Hans Baldung Hans Baluschek Hans Bellmer Hans Christian Andersen Hans Haacke Hans Hartung Hans Hofmann Hans Holbein the Elder Hans Holbein the Younger Hans Makart Hans Memling Hans Thoma Hans Zatzka Harald Sohlberg Harold Cazneaux Harold Edgerton Harold Gilman Harold Harvey Haroon Mirza Harriet Backer Harriet Lee-Merrion Harry Bertoia Harry Callahan Harry Clarke Harry Watrous Harumi Hironaka Harvey Kurtzman Harvey Stein Hassan Hajjaj Hasui Kawase Hayao Miyazaki Hayv Kahraman He Jiaying Heather Theurer Hector Guimard Hedi Slimane Hein Gorny Heiner Luepke Heinrich Kley Heinrich Lefler Helen Allingham Helen Frankenthaler Helen Levitt Helene Knoop Helene Schjerfbeck Helio Oiticica Helmut Newton Hendrick Avercamp Hendrick Cornelisz Vroom Hendrick Goltzius Hendrick ter Brugghen Hendrik Kerstens Henri Cartier-Bresson Henri de Toulouse-Lautrec Henri Fantin-Latour Henri Le Fauconnier Henri Le Sidaner Henri Lebasque Henri Manguin Henri Matisse Henri Rousseau Henri-Edmond Cross Henrietta Harris Henriette Grindat Henriëtte Ronner-Knip Henry Asencio Henry Darger Henry Fuseli Henry Justice Ford Henry Moore Henry Moret Henry Ossawa Tanner Henry Raeburn Henry Scott Tuke Herb Lubalin Herb Ritts Herb Trimpe Herbert Bayer Herbert List Herman Brood Hervé Guibert Heywood Hardy Hideyuki Kikuchi Hieronymus Bosch Hikari Shimoda Hilma af Klint Hippolyte Flandrin Hirohiko Araki Hiroshi Nagai Hiroshi Sugimoto Hiroshi Yoshida Hiroyuki-Mitsume Takahashi Honoré Daumier Hope Gangloff Horace Pippin Horace Vernet Horst P. Horst Howard Arkley Howard Chaykin Howard Finster Howard Hodgkin Howard Pyle Howard Schatz Howard Terpning Howardena Pindell Hsiao-Ron Cheng Hubert Robert Hugh Ferriss Hugh Kretschmer Hugues Merle Hyacinthe Rigaud Iain Faulkner Ian Davenport Ian Howorth Ian McQue Ian Miller Ida Rentoul Outhwaite Igor Morski Igor Zenin Ikenaga Yasunari Ildiko Neer Ilse Bing Ilya Kuvshinov Ilya Mashkov Ilya Repin Inessa Garmash Ingrid Baars Ingrid Endel Inio Asano Inna Mosina Irene Sheri Iris van Herpen Irma Stern Irving Penn Iryna Yermolova Isaac Cordal Isaac Julien Isaac Levitan Isaac Maimon Isaiah Zagar Isamu Noguchi Ismail Inceoglu Ito Shinsui Ivan Aivazovsky Ivan Albright Ivan Bilibin Ivan Fedorovich Choultse Ivan Marchuk Ivan Shishkin Iwona Lifsches J. J. Grandville J. Scott Campbell J.C. Leyendecker J.M.W. Turner Jacek Malczewski Jacek Yerka Jack Butler Yeats Jack Davis Jack Gaughan Jack Hughes Jack Kirby Jack Ohman Jack Spencer Jack Vettriano Jack Whitten Jackson Pollock Jacob Hashimoto Jacob Jordaens Jacob Lawrence Jacob van Ruisdael Jacopo Bassano Jacques Henri Lartigue Jacques Tardi Jacques Villon Jacques-Firmin Beauvarlet Jacques-Laurent Agasse Jacques-Louis David Jake Wood-Evans Jakub Różalski Jakub Schikaneder James Abbott McNeill Whistler James Bullough James C. Christensen James Ensor James Gilleard James Gillray James Gurney James Jean James Lee Byars James McIntosh Patrick James Nares James Paick James Pradier James Rosenquist James Stokoe James Thomas Watts James Tissot James Turrell James Wilson Morrice Jamie Baldridge Jamie Hawkesworth Jamie Heiden Jamie Hewlett Jamie McKelvie Jan Brueghel the Elder Jan Davidsz de Heem Jan Ditlev Jan Frans van Bloemen Jan Mankes Jan Matejko Jan Pietersz Saenredam Jan Saudek Jan Steen Jan Toorop Jan Urschel Jan van Eyck Jan van Goyen Jan van Kessel the Elder Jan van Scorel Jane Crowther Jane Graverol Jane Newland Janek Sedlar Janet Delaney Janet Echelman Janice Sung Janine Antoni Janne Kahila Januz Miralles Jarek Kubicki Jasmine Becket-Griffith Jason A. Engle Jason deCaires Taylor Jason Edmiston Jason Middlebrook Jason Pearson Jasper Francis Cropsey Jasper Johns Jaume Plensa Jay Anacleto Jay DeFeo Jean Arp Jean Auguste Dominique Ingres Jean Cocteau Jean Delville Jean Dubuffet Jean Fouquet Jean Giraud Jean Jullien Jean Metzinger Jean Nouvel Jean Restout the Younger Jean-Antoine Watteau Jean-Baptiste Carpeaux Jean-Baptiste Monge Jean-François Millet Jean-Gabriel Domergue Jean-Honoré Fragonard Jean-Joseph Benjamin-Constant Jean-Léon Gérôme Jean-Louis Forain Jean-Michel Basquiat Jean-Paul Riopelle Jean-Sebastien Rossbach Jeanloup Sieff Jeannette Guichard-Bunel JeeYoung Lee Jeff Danziger Jeff Easley Jeff Kinney Jeff Koons Jeff Legg Jeff Lemire Jeff Rowland Jeff Simpson Jeff Smith Jeff Soto Jeff Wall Jeffrey Catherine Jones Jeffrey Smart Jeffrey Smith Jeffrey T. Larson Jennifer Rubell Jenny Saville Jeppe Hein Jeremiah Ketner Jeremy Geddes Jeremy Lipking Jeremy Mann Jerry Pinkney Jerry Schatzberg Jerry Siegel Jesper Ejsing Jessica Drossin Jessica Rossier Jessie Willcox Smith Jhonen Vasquez Jillian Tamaki Jim Burns Jim Davis Jim Dine Jim Holland Jim Lee Jim Lively Jim Mahfood Jim Woodring Jimmy Lawlor Jindrich Styrsky Jo Ann Callis Joachim Beuckelaer Joachim Brohm Joachim Patinir Joachim Wtewael Joan Eardley Joan Miró Joan Mitchell Joana Vasconcelos Joao Ruas Joaquín Sorolla Jocelyn Hobbie Jody Bergsma Joe Fenton Joe Jusko Joe Kubert Joe Madureira Joe Quesada Joe Shuster Joel Meyerowitz Joel Rea Joel Robison Joel Sternfeld Johan Christian Dahl Johan Messely Johannes Itten Johannes Jan Schoonhoven Johannes Vermeer Johannes Voss Johfra Bosschart John Anster Fitzgerald John Atkinson Grimshaw John Batho John Bauer John Berkey John Blanche John Brack John Bratby John Buscema John Butler Yeats John Cassaday John Chamberlain John Closterman John Collier John Constable John Crome John Currin John Duncan John Everett Millais John Frederick Kensett John French Sloan John Harris John Hejduk John Henry Twachtman John Holcroft John Howe John Hoyland John James Audubon John Kenn Mortensen John La Farge John Larriva John Lavery John Lowrie Morrison John Lurie John Martin John Mckinstry John Melhuish Strudwick John Pawson John Perceval John Philip Falter John Piper John Pitre John Reuss John Roddam Spencer Stanhope John Ruskin John Salminen John Sell Cotman John Singer Sargent John Sloane John T. Biggers John Tenniel John Trumbull John Watkiss John Wayne Gacy John White Alexander John Wilhelm John William Godward John William Waterhouse Johnson Tsang Jon Burgerman Jon Foster Jon J Muth Jon Klassen Jon McNaught Jonas Burgert Jonas Lie Jonathan Lasker Jonathan Meese Jonathan Wolstenholme Joram Roukes Josan Gonzalez José Clemente Orozco Joseba Elorza Josef Albers Josef Capek Josef Kote Joseph Beuys Joseph Clement Coll Joseph Cornell Joseph Ducreux Joseph Farquharson Joseph Lorusso Joseph Stella Josephine Wall Josh Adamski Josh Keyes Joshua Reynolds Joyce Kozloff József Rippl-Rónai Juan Gris Judith Leyster Judy Chicago Juergen Teller Jules Bastien-Lepage Jules Breton Jules Cheret Jules Pascin Jules Tavernier Julian Opie Julian Schnabel Juliana Nan Julie Bell Julie Blackmon Julie Mehretu Julio Larraz Julio Le Parc Jun Kaneko Junji Ito Junko Mizuno Jusepe de Ribera Justin Gaffrey Justin Gerard Justin Roiland Kadir Nelson Kaethe Butcher Kaja Foglio Kara Walker Karel Thole Karen Knorr Karen Wallis Karl Blossfeldt Karl Friedrich Schinkel Karl Gerstner Karl Knaths Karl Schmidt-Rottluff Karol Bak Kate Beaton Kate Greenaway Katerina Belkina Käthe Kollwitz Kathrin Longhurst Kathryn Morris Trotter Kati Horna Katia Chausheva Katsuhiro Otomo Katsushika Hokusai Kawanabe Kyōsai Kay Nielsen Kay Sage Kazimir Malevich Kazuki Takamatsu Kazuo Koike Kazuo Shiraga Kees Scherer Kees van Dongen Kehinde Wiley Keith Haring Keith Mallett Keith Negley Keith Parkinson Kelly Freas Kelly McKernan Kelly Sue Deconnick Kelly Vivanco Ken Kelly Ken Sugimori Kengo Kuma Kenne Gregoire Kenneth Noland Kenneth Rocafort Kenny Scharf Kenro Izu Kent Monkman Kentaro Miura Kerby Rosanes Kerry James Marshall Kestutis Kasparavicius Kevin McNeal Kevin Sloan Kieron Gillen Kiki Smith Kilian Eng Kim Jung Gi Kim Keever Kirsty Mitchell Kishin Shinoyama Kitagawa Utamaro Kitty Lange Kielland Klaus Janson Klaus Pillon Klaus Wittmann Koloman Moser Konstantin Korovin Konstantin Somov Konstantin Yuon Koson Ohara Krenz Cushart Kris Knight Kuno Veeber Kurt Hutton Kurt Schwitters Kurt Wenner Kuzma Petrov-Vodkin Kyffin Williams Kylli Sparre L. Birge Harrison L. S. Lowry Larry Carlson Larry Elmore Larry Fink Larry Poons Larry Sultan László Moholy-Nagy Laura Makabresku Laure Albin Guillot Laurel Burch Lauren Faust Laurent Baheux Laurent Grasso Laurie Greasley Laurie Lipton Lavinia Fontana Lawren Harris Lawrence Alma-Tadema Lawrence Weiner Leandro Erlich Leanne Surfleet Lee Bogle Lee Bontecou Lee Jeffries Lee Krasner Lee Madgwick Leiji Matsumoto Leo Putz Léon Bakst Leon Kossoff Leon Spilliaert Leonardo da Vinci Leonetto Cappiello Leonid Afremov Leonor Fini Leonora Carrington LeRoy Neiman Les Edwards Lesser Ury Leszek Bujnowski Leticia Gillett Lev Lagorio Lewis Morley Li Wei Liam Gillick Liam Sharp Liam Wong Lilia Alvarado Lilla Cabot Perry Lillian Bassman Linnea Strid Lisa Frank Lisa Holloway Lise Deharme Lisette Model Liu Ye Lois Greenfield Lois van Baarle Lorenz Hideyoshi Loretta Lux Lori Earley Lorser Feitelson Loui Jover Louis Anquetin Louis Aston Knight Louis Comfort Tiffany Louis Faurer Louis Icart Louis Janmot Louis Kahn Louis Majorelle Louis Marcoussis Louis Rhead Louis Stettner Louis Valtat Louis Wain Louis Weldon Hawkins Louise Bourgeois Louise Dahl-Wolfe Lovis Corinth Lowell Herrero Luca della Robbia Luca Giordano Lucas Cranach the Elder Lucas Cranach the Younger Lucian Freud Lucien Clergue Lucien Pissarro Lucio Fontana Lucy Glendinning Lucy Grossmith Lucy Madox Brown Ludwig Mies van der Rohe Luigi Loir Luis Ricardo Falero Luis Royo Luke Fildes Lygia Clark Lynd Ward Lynda Barry Lynda Benglis Lynette Yiadom-Boakye Lyonel Feininger Lyubov Popova M.C. Escher M.F. Husain M.W. Kaluta Mab Graves Magali Villeneuve Maggi Hambling Magnus Enckell Maia Flore Makoto Shinkai Makoto Shinkhai Malcolm Howie Malcolm Liepke Malcolm Teasdale Malick Sidibé Mamoru Oshii Man Ray Mandy Disher Mandy Jurgens Mao Hamaguchi Marat Safin Marc Chagall Marc Davis Marc Lagrange Marc Quinn Marc Silvestri Marc Simonetti Marcel Breuer Marcel Duchamp Marcel Mouly Marcin Sobas Marco Mazzoni Marek Okon Margaret Keane Margaret Macdonald Mackintosh Margaret Modlin Margaret Olley Margaret Preston Maria Kreyn Maria Lassnig Maria Sibylla Merian Marianna Rothen Marianne Breslauer Marianne North Marianne Stokes Marianne von Werefkin Marie Laurencin Marie Severin Marie Spartali Stillman Marilyn Minter Marina Abramović Mario Sorrenti Mario Testino Marius Borgeaud Marjane Satrapi Marjorie Miller Mark Arian Mark Briscoe Mark Brooks Mark Catesby Mark Demsteader Mark Gertler Mark Henson Mark Keathley Mark Kostabi Mark Lague Mark Lovett Mark Rothko Mark Ryden Mark Seliger Mark Steinmetz Mark Tobey Marko Manev Marlene Dumas Marsden Hartley Marta Bevacqua Martin Ansin Martin Creed Martin Deschambault Martin Grelle Martin Johnson Heade Martin Kippenberger Martin Parr Martin Puryear Martin Rak Martin Schongauer Martin Stranka Martin Whatson Martin Wittfooth Martina Hoffman Martine Johanna Martiros Saryan Mary Anning Mary Beale Mary Blair Mary Bradish Titcomb Mary Cassatt Mary Ellen Mark Mary Fedden Mary Heilmann Mary Jane Ansell Masaaki Sasamoto Masamune Shirow Mat Collishaw Mati Klarwein Matt Bors Matt Fraction Matt Groening Matthew Barney Matthias Grünewald Matthias Jung Matti Suuronen Mattias Adolfsson Maurice de Vlaminck Maurice Denis Maurice Prendergast Maurice Sapiro Maurice Sendak Maurice Utrillo Maurizio Cattelan Max Beckmann Max Dupain Max Ernst Max Fleischer Max Pechstein Max Rive Max Weber Maxfield Parrish Maxime Maufra Maximilian Pirner Maximilien Luce Maya Lin Maynard Dixon Medardo Rosso Meghan Howland Mehmed Siyah-Kalem Melissa Launay Melvin Sokolsky Meredith Marsone Méret Oppenheim Meryl McMaster Michael Ancher Michael Borremans Michael Carson Michael Cheval Michael Cho Michael Craig-Martin Michael Creese Michael Deforge Michael Eastman Michael Garmash Michael Heizer Michael Hussar Michael Hutter Michael Kenna Michael Malm Michael Page Michael Parkes Michael Shainblum Michael Shapcott Michael Sowa Michael Sweerts Michael Vincent Manalo Michael Wesely Michael Whelan Michal Karcz Michal Lisowski Michelangelo Pistoletto Mickalene Thomas Miho Hirano Mikalojus Konstantinas Ciurlionis Mike Allred Mike Campau Mike Dargas Mike Deodato Mike Judge Mike Kelley Mike Mayhew Mike Mignola Mike Ploog Mike Winkelmann Mike Worrall Mikhail Larionov Mikhail Nesterov Mikhail Vrubel Miki Asai Mikko Lagerstedt Miles Aldridge Milo Manara Milton Avery Milton Glaser Mimmo Rotella Minjae Lee Miriam Schapiro Miroslav Tichý Misha Gordin Miss Aniela Moise Kisling Mona Hatoum Monia Merlo Mordecai Ardon Morris Hirshfield Morris Louis Mort Künstler Moses Soyer Moshe Safdie Myles Birket Foster Myoung Ho Lee N.C. Wyeth N/A Affandi N/A Archillect N/A Artgerm N/A Balthus N/A Banksy N/A Beeple N/A Bordalo II N/A Brassaï N/A Bronzino N/A Byam Shaw N/A Canaletto N/A Caravaggio N/A Craola N/A Domenichino N/A Duccio N/A El Greco N/A Giorgione N/A Giotto N/A Guercino N/A Hergé N/A Hiroshige N/A HUSH N/A Kunisada N/A Kurzgesagt N/A Masaccio N/A Michelangelo N/A Moebius N/A OSGEMEOS N/A Parmigianino N/A Pinturicchio N/A Pontormo N/A Raphael N/A RHADS N/A ROA N/A Rone N/A Sardax N/A Sparth N/A teamLab N/A theCHAMBA N/A Tintoretto N/A Titian N/A Tom of Finland N/A Yuumei Nadav Kander Nam June Paik Nan Goldin Naoki Urasawa Naoko Takeuchi Naomi Okubo Naomi Tydeman Naoto Hattori Natalia Drepina Natalia Goncharova Natalia Rak Natalie Shau Nathan Coley Nathan Spoor Nathan Wirth ND Stevenson Neal Adams Neil Gaiman Neil Welliver Nele Zirnite Nell Dorr Nelleke Pieters Nicholas Hely Hutchinson Nicholas Hilliard Nicholas Hughes Nicholas Roerich Nick Alm Nick Knight Nick Veasey Nick Walker Nickolas Muray Nicola Samori Nicolaes Maes Nicolaes van Verendael Nicolas Bruno Nicolas de Stael Nicolas Delort Nicolas Mignard Nicolas Poussin Nicole Eisenman Nicoletta Ceccoli Nigel van Wieck Nike Savvas Nikolai Ge Nikolai Lockertsen Nikolay Makovsky Nikolina Petolas Nina Leen Njideka Akunyili Crosby Noah Bradley Nobuyoshi Araki Noell Oszvald Nora Heysen Noriyoshi Ohrai Norman Ackroyd Norman Bluhm Norman Cornish Norman Foster Norman Lindsay Norman Parkinson Norman Rockwell Octavio Ocampo Odd Nerdrum Odilon Redon Ohara Koson Okuda San Miguel Olafur Eliasson Oleg Oprisco Oleg Shuplyak Olive Cotton Oliver Jeffers Oliver Wetter Olivia Locher Olivier Bonhomme Olivier Valsecchi Orazio Gentileschi Osamu Tezuka Oscar Niemeyer Oskar Fischinger Oskar Kokoschka Oskar Schlemmer Ossip Zadkine Oswaldo Guayasamin Otto Dix Otto Marseus van Schrieck Otto Piene Pablo Picasso Pam Hawkes Pamela Colman Smith Paolo Roversi Paolo Soleri Paolo Uccello Paolo Veronese Pascale Campion Pat Steir Patrice Murciano Patricia Piccinini Patricia Polacco Patrick Brown Patrick Caulfield Patrick Demarchelier Patrick Dougherty Patrick Heron Patrick McHale Patrick Nagel Patrick Woodroffe Patty Maher Paul Barson Paul Cadmus Paul Catherall Paul Cézanne Paul Chabas Paul Corfield Paul Delvaux Paul Fusco Paul Gauguin Paul Gustav Fischer Paul Hedley Paul Henry Paul Klee Paul Laffoley Paul Lehr Paul Lovering Paul Nash Paul Pelletier Paul Poiret Paul Rader Paul Rand Paul Ranson Paul Rudolph Paul Sérusier Paul Signac Paul Strand Paul Wonner Paula Modersohn-Becker Paula Rego Paula Scher Paulus Potter Pawel Kuczynski Peder Balke Peder Mork Monsted Pegi Nicol MacLeod Pendleton Ward Percy Tarrant Peregrine Heathcote Perry Rhodan Pete Turner Peter Andrew Jones Peter Bagge Peter Basch Peter Blake Peter Coulson Peter Doig Peter Eisenman Peter Elson Peter Gric Peter Holme III Peter Howson Peter Kemp Peter Lely Peter Lindbergh Peter Lippmann Peter Max Peter Milligan Peter Mitchev Peter Mohrbacher Peter Paul Rubens Peter Saville Peter Sculthorpe Peter Sedgley Peter Wileman Peter Zumthor Petra Cortright Petrina Hicks Phil Foglio Phil Jimenez Phil Koch Phil Noto Philip Guston Philip McKay Philip Pearlstein Philip Taaffe Philip Treacy Philip Wilson Steer Philip-Lorca diCorcia Philipp Otto Runge Philippe Buchet Philippe de Champaigne Philippe Druillet Philippe Halsman Philippe Parreno Phoebe Anna Traquair Phyllida Barlow Piero della Francesca Piero di Cosimo Pierre Bonnard Pierre Huyghe Pierre Pellegrini Pierre Puvis de Chavannes Pierre Soulages Pierre-Auguste Renoir Piet Hein Eek Piet Mondrian Pieter Aertsen Pieter Bruegel the Elder Pieter Brueghel the Younger Pieter Claesz Pieter de Hooch Pieter Hugo Pieter Jansz Saenredam Pieter Nason Pieter-Jansz van Asch Pietro Antonio Rotari Pietro da Cortona Pietro da Rimini Pino Daeni Pipilotti Rist PJ Crook Pol Ledent Polixeni Papapetrou Pompeo Batoni Posuka Demizu Prudence Heward Q Hayashida Qian Xuan Quentin Blake Quint Buchholz R. Kenton Nelson Rachel Maclean Rachel Ruysch Rachel Whiteread Rafael Albuquerque Rafael Zabaleta Ragnar Kjartansson Raimonds Staprans Raimundo de Madrazo y Garreta Raina Telgemeier Ralph Bakshi Ralph Blakelock Ralph Horsley Ralph McQuarrie Ralph Steadman Ramon Casas Randolph Caldecott Randolph Stanley Hewton Range Murata Raoul De Keyser Raoul Dufy Raphael Kirchner Raphael Lacoste Raphael Soyer Raphaelle Peale Rashad Alakbarov Ravi Zupa Ray Caesar Ray Collins Ray Donley Ray Eames Ray Metzker Raymond Briggs Raymond Duchamp-Villon Raymond Leech Raymond Pettibon Raymond Swanland Raynald Leclerc Rebeca Saray Rebecca Guay Rebecca Louise Law Rebecca Sugar Regina Valluzzi Reginald Marsh Rembrandt van Rijn Remedios Varo Ren Hang Renato Guttuso Rene Burri René Lalique Rene Magritte Reylia Slaby Ricardo Bofill Richard Anderson Richard Bergh Richard Billingham Richard Burlet Richard Corben Richard Dadd Richard Deacon Richard Diebenkorn Richard Doyle Richard E. Miller Richard Eurich Richard Hamilton Richard Lindner Richard Long Richard McGuire Richard Meier Richard Misrach Richard Mosse Richard Parkes Bonington Richard Phillips Richard Pousette-Dart Richard S. Johnson Richard Scarry Richard Schmid Richard Serra Richard Tuttle Rick Amor Rick Guidice Rick Owens Rimel Neffati Rinko Kawauchi Rob Browning Rob Gonsalves Rob Guillory Rob Hefferan Rob Liefeld Robby Cavanaugh Robert Antoine Pinchon Robert Bateman Robert Bissell Robert Campin Robert Crumb Robert Delaunay Robert Gillmor Robert Hagan Robert Henri Robert Indiana Robert Irwin Robert Mapplethorpe Robert McCall Robert McGinnis Robert Motherwell Robert Rauschenberg Robert S. Duncanson Robert Smithson Robert Stivers Robert Vonnoh Robert Williams Roberto Ferri Roberto Matta Rockwell Kent Rodney Matthews Rodney Smith Roger de La Fresnaye Roger Deakins Roger Dean Rogier van der Weyden Roland Topor Rolf Armstrong Romaine Brooks Roman Vishniac Romare Bearden Romero Britto Romina Ressia Ron Arad Ron English Ron Mueck Ron Walotsky Ronald Balfour Ronald Searle Ronald Wimberly Roni Horn Rosa Bonheur Rosalba Carriera Rose Wylie Ross Tran Roxy Paine Roy DeCarava Roy Lichtenstein Roz Chast Ruan Jia Rudolf Ernst Rudy Siswanto Rufino Tamayo Rui Palha Rumiko Takahashi Rupert Bunny Rupert Vandervell Rupi Kaur Ruslan Lobanov Russ Mills Russell Dauterman Ruth Bernhard Ruth Orkin Ryan Hewett Ryan McGinley Ryan Ottley Ryan Stegman Ryohei Fuke Ryohei Hase Ryoji Ikeda Sabbas Apterus Sacha Goldberger Sailor Moon Sally Mann Sally Storch Salomon van Ruysdael Salvador Dalí Salvator Rosa Sam Bosma Sam Francis Sam Gilliam Sam Guay Sam Kieth Sam Spratt Sam Toft Samantha Keely Smith Samuel Melton Fisher Samuel Peploe Samuel van Hoogstraten Sandra Chevrier Sandra Dieckmann Sandro Botticelli Sandy Skoglund Sanford Robinson Gifford Santiago Calatrava Santiago Caruso Santiago Rusinol Sarah Andersen Sarah Lucas Sarah Morris Sarah Sze Satoshi Kon Saturno Butto Saul Leiter Saul Steinberg Scarlett Hooft Graafland Scott Kolins Scott Listfield Scott McCloud Scott Naismith Scott Rohlfs Sean Scully Sean Yoro Sebastian Errazuriz Sebastiano del Piombo Serge Attukwei Clottey Serge Marshennikov Serge Najjar Sergey Musin Sergio Aragonés Sergio Larraín Sergio Toppi Seth Globepainter Shaun Tan Sheila Hicks Shepard Fairey Sherree Valentine Daines Sherry Akrami Shigenori Soejima Shilin Huang Shinji Aramaki Shintaro Kago Shirin Neshat Shohei Otomo Shotaro Ishinomori Shozo Shimamoto Shuzo Oshimi Sidney Nolan Sidney Sime Signe Vilstrup Silvestro Lega Simeon Solomon Simon Birch Simon Bisley Simon Prades Simon Stalenhag Simon Vouet Simone Bianchi Sir James Guthrie Siya Oum Skottie Young Slawomir Maniak Slim Aarons Sofonisba Anguissola Sol LeWitt Sonia Delaunay Sopheap Pich Sophie Anderson Sou Fujimoto Spencer Tunick Squeak Carnwath Stan Berenstain Stanhope Forbes Stanisław Szukalski Stanisław Wyspiański Stanley Donwood Stanley Kubrick Stanley Spencer Stanley William Hayter Stasia Burrington Stefan Gesell Stephan Martinière Stephanie Rew Stephen Darbishire Stephen Gammell Stephen Hillenburg Stephen Mackey Stephen Ormandy Stephen Shore Stephen Shortridge Stephen Wiltshire Stephen Youll Steve Argyle Steve Dillon Steve Ditko Steve Epting Steve Hanks Steve Henderson Steve Hillier Steve Lieber Steve McCurry Steve Sack Steven Holl Steven Klein Steven Meisel Steven Outram Storm Thorgerson Stuart Davis Stuart Haygarth Stuart Immonen Subodh Gupta Sudersan Pattnaik Sui Ishida Susan Seddon Boulet Suzanne Valadon Sven Nordqvist Sverre Fehn Syd Mead Sydney Prior Hall Tadao Ando Taiyō Matsumoto Takashi Murakami Takato Yamamoto Takeshi Obata Talbot Hughes Tamara de Lempicka Tami Bone Tanya Shatseva Tara McPherson Taras Loboda Tarsila do Amaral Tatiana Suarez Tatsuo Miyajima Tatsuro Kiuchi Taylor Wessing Ted Nasmith Temmie Chang Terada Katsuya Terry Dodson Terry O'Neill Terry Oakes Terry Redlin Tetsuya Nomura Teun Hocks Tex Avery Theo van Doesburg Theo van Rysselberghe Theodor Kittelsen Théodore Chassériau Théodore Géricault Theodore Robinson Théophile Steinlen Thiago Valdi Thom Mayne Thomas Barbey Thomas Benjamin Kennington Thomas Birch Thomas Blackshear Thomas Cole Thomas Dewing Thomas Dodd Thomas Eakins Thomas Edwin Mostyn Thomas Gainsborough Thomas Hart Benton Thomas Heatherwick Thomas Kinkade Thomas Lawrence Thomas Leuthard Thomas Moran Thomas Nast Thomas Saliot Thomas Sully Thomas W Schaller Thomas Wrede Thurston Hopkins Tiago Hoisel Tibor Nagy Tiffany Bozic Tim Burton Tim Doyle Tim Etchells Tim Hildebrandt Tim Holtz Tim Okamura Tim Shumate Tim Walker Tim White Timothy Easton Titus Kaphar TJ Drysdale Toby Fox Todd Hido Todd McFarlane Todd Nauck Todd Schorr Tokujin Yoshioka Tom Bagshaw Tom Chambers Tom Everhart Tom Fruin Tom Grummett Tom Hammick Tom Killion Tom Roberts Tom Thomson Tom Wesselmann Tom Whalen Tomás Saraceno Tomasz Alen Kopera Tomer Hanuka Tomma Abts Tomokazu Matsuyama Ton Dubbeldam Toni Frissell Tony Conrad Tony Cragg Tony DiTerlizzi Tony Fitzpatrick Tony Moore Tony Orrico Tony Oursler Tony Sart Tooth Wu Toshiko Okanoue Toshio Saeki Tove Jansson Toyo Ito Tracey Emin Tracie Grimwood Tran Nguyen Travis Louie Trevor Brown Trish Mistric Tristan Eaton Troy Brooks Truls Espedal Tsutomu Nihei Tyler Edlin Tyler Rayburn Tyler Shields Ub Iwerks Uemura Shoen Umberto Boccioni Ursula von Rydingsvard Utagawa Kuniyoshi Valentin de Boulogne Valentin Rekunenko Valentin Serov Valerie Hegarty Valerio Olgiati Vanessa Beecroft Vanessa Bell Vania Zouravliov Vasily Vereshchagin Verner Panton Veronika Pinke Vicente Romero Redondo Victo Ngai Victor Brauner Victor Enrich Victor Horta Victor Moscoso Victor Nizovtsev Victor Pasmore Victor Vasarely Victoria Crowe Victoria Selbach Vija Celmins Vik Muniz Viktor Vasnetsov Vilhelm Hammershoi Vincent Callebaut Vincent Desiderio Vincent Di Fate Vincent van Gogh Virgil Finlay Virginia Frances Sterrett Vito Acconci Vittorio Matteo Corcos Vittorio Reggianini Vivian Maier Viviane Sassen Vivienne Westwood Vladimir Kush Vladimir Tatlin Vladimir Volegov Vytautas Kairiukstis W. Eugene Smith W. Heath Robinson Wade Guyton Wadim Kashin Walker Evans Walt Disney Walt Kelly Walter Crane Walter Ernest Webster Walter Gropius Walter Langley Walter Launt Palmer Walter Percy Day Walter Sickert Wangechi Mutu Warren Ellis Warwick Goble Wassily Kandinsky Wayne Barlowe Wayne Thiebaud Wendy Froud Wes Anderson Wifredo Lam Wilhelmina Barns-Graham Will Barnet Will Eisner Willard Metcalf Willem Basse Willem Claesz. Heda Willem de Kooning Willem Haenraets Willem Kalf Willem van Aelst Willem van Haecht Willi Baumeister William Blake William Dyce William Eggleston William Etty William Gropper William Henry Hunt William Henry Margetson William Hogarth William Holman Hunt William James Glackens William Kay Blacklock William Kentridge William Klein William Langson Lathrop William Larkin William Morris William Nicholson William Oxer William Powell Frith William Russell Flint William S. Burroughs William Stanley Haseltine William Steig William Stout William Timlin William Trost Richards William Turner William Wegman William Wendt William Whitaker William Wray William Zorach William-Adolphe Bouguereau Wim Delvoye Wim Wenders Winifred Knights Winslow Homer Winsor McCay Wlad Safronow Wolfgang Suschitzky Wong Kar-wai Worthington Whittredge Wu Guanzhong Xiaofei Yue Xu Bing Yaacov Agam Yanjun Cheng Yasuo Kuniyoshi Yasushi Nirasawa Yayoi Kusama Yiannis Moralis Yigal Ozeri Yinka Shonibare Yoann Lossel Yoh Nagao Yoji Shinkawa Yoshitaka Amano Yoshiyuki Tomino Yosuke Ueno Yuko Shimizu Yuri Shwedoff Yusuke Murata Yves Klein Yves Tanguy Yvonne Coomber Zack Snyder Zaha Hadid Zdzisław Beksiński Zena Holloway Zhang Jingna Zhang Kechun Zhichao Cai Zinaida Serebriakova Zoe Buckman ================================================ FILE: wildcards/color.txt ================================================ aqua black blue fuchsia gray green lime maroon navy olive orange purple red silver teal white yellow ================================================ FILE: wildcards/color_flower.txt ================================================ __color__ __flower__ ================================================ FILE: wildcards/extended-color.txt ================================================ aliceblue antiquewhite aqua aquamarine azure beige bisque black blanchedalmond blue blueviolet brown burlywood cadetblue chartreuse chocolate coral cornflowerblue cornsilk crimson cyan darkblue darkcyan darkgoldenrod darkgray darkgreen darkgrey darkkhaki darkmagenta darkolivegreen darkorange darkorchid darkred darksalmon darkseagreen darkslateblue darkslategray darkslategrey darkturquoise darkviolet deeppink deepskyblue dimgray dimgrey dodgerblue firebrick floralwhite forestgreen fuchsia gainsboro ghostwhite gold goldenrod gray green greenyellow grey honeydew hotpink indianred indigo ivory khaki lavender lavenderblush lawngreen lemonchiffon lightblue lightcoral lightcyan lightgoldenrodyellow lightgray lightgreen lightgrey lightpink lightsalmon lightseagreen lightskyblue lightslategray lightslategrey lightsteelblue lightyellow lime limegreen linen magenta maroon mediumaquamarine mediumblue mediumorchid mediumpurple mediumseagreen mediumslateblue mediumspringgreen mediumturquoise mediumvioletred midnightblue mintcream mistyrose moccasin navajowhite navy oldlace olive olivedrab orange orangered orchid palegoldenrod palegreen paleturquoise palevioletred papayawhip peachpuff peru pink plum powderblue purple red rosybrown royalblue saddlebrown salmon sandybrown seagreen seashell sienna silver skyblue slateblue slategray slategrey snow springgreen steelblue tan teal thistle tomato turquoise violet wheat white whitesmoke yellow yellowgreen ================================================ FILE: wildcards/flower.txt ================================================ Acacia Achillea Adam's-needle African Boxwood African Lily Agapanthus Ageratum Ageratum houstonim Allium Alpina Alstroemeria Amaranthus hypochondriacus Amaryllis Ammi majus Anconitum Anemone Anigozanthus Annual Delphinium Anthurium Antirrhinum majus Artichoke thistle Asparagus Aster Astilbe Baby's Breath Bachelor's Button Banksia Bellflower Big Flax Bighead Knapweed Billy Buttons Bird of Paradise Blazing Star Blue Lace Flower Boronia Bouvardia Boxwood African Diosma Buckthorn Variegated Buddleia Bupleurum Butterfly Bush Butterfly Orchid Calla Lily Campanula Candytuft Canterbury Bells Carnation Carthamus Casa Blanca Caspia Cattleya Celosia Celosia argenta Centaurea cyanus Chamelaucium Chimney Bells Chrysanthemum Chrysanthemum x morifolium Clarkia Cockscomb Crested Coffee Bean Berry Common Myrtle Common Yarrow Cone Flower Consolida ambigua Convallaria Cordyline Cosmos Cornflower Craspedia Curly Willow Cymbidium Cymbidium Orchid Daffodil Dahlia Daisy Mums Delphinium Belladonna Delphinium Pacific Giant Dendrobium Dendrobium Orchid Dianthus barbatus Dianthus caryophyllus Dianthus caryophyllus nana Erica spp Eucalyptus seeded Eucalyptus silver dollar Eustoma grandiflorum False Bird of Paradise False Spirea Farewell-To-Spring Fernleaf Yarrow Feverfew Flamingo Flower Flax New Zealand Floss Flower Foxtail Fern Freesia Freesia x hybrida Fuji Mums Gardenia Gay Feather Genista Gerbera Gerbera Ruby Red Ginger Gladiolus Gladiolus hybrid nanus Goat's Beard Godetia Golden Rod Guersney Lily Gyp Gypsophila paniculata Hanging Helicona Heath Heather Helianthus annuus Heliconia spp. Hippeastrum Hydrangea Iberis amara Inca Lily Iris Japhette Orchid Jonquil Knapweed Lace fern Larkspur Lathyrus odoratus Lavandula Lavender Liatris Lilac Lily Lilly-of-the-Valley Lily Casa Blanca Lily of the Field Lily of the Nile Lily Stargazer Limonium Lisianthus Marguerite daisy Mattholia incana Melaleuca Memosa Misty Blue Limonium Moluccella laevis Monkshood Montbretia Monte Cassino Moon orchid Musa Myrsine Myrtle Myrtus Nephrolepis Nerine Nerine Lily Nigella Ornithogalum Paeonia Painted Tongue Paper Reed Papyrus lion's head Peony Peruvian Lily Phalaenopsis Philodendron Phlox Pincushion Flower Pink Mink Pitt Pittosporum Pixie Carnation Polianthes tuberosa Pompon Chrysanthemum Poppy Anemone Porium Pussy Willow Queen Anne's Lace Ranunculus Red Ribbons Rice flower Rose Rose Bridal Pink Rose Bridal White Rose Champagne Rose Diadem Rose Emblem Rose Kardinal Rose Lady Liberty Rose Lavanda Rose Osiana Rose Royalty Safari Sunset Safflower Sage Perennial Salix Salmon Reagan Sansevieria Saponaria Satin Flowers Saxicola Scabiosa Schinus Sea lavender Shell Flowers Snake Plant Snapdragon Solidago Solidaster Speedwell Spider Lily Spider Mums Spray Carnation Sprengeri Fern Star of Bethlehem Statice Stenamezon Stephanotis Strawberry banksia Strawflower Summer poinsettia Summer's Darling Sunflower Sweet Pea Sweet William Sword Fern Syringa vulgaris Tailflowers Tassel flower Thouroughwax Throatwort Tracelium Tree Fern Trumpet Lily Tuberose Tulip Tulipa Veronica Wattle Waxflower Wild Plantain Willow curly Windflower Wolfsbane Zantedeschia Zinna Zinnia elegans ================================================ FILE: wildcards/nationality.txt ================================================ Afghan Albanian Algerian American Andorran Angolan Antiguans Argentine Armenian Australian Austrian Azerbaijani Bahamian Bahraini Bangladeshi Barbadian Barbudans Batswana Belarusian Belgian Belizean Beninese Bhutanese Bolivian Bosnian Brazilian British Bruneian Bulgarian Burkinabe Burmese Burundian Cambodian Cameroonian Canadian Cape Verdean Central African Chadian Chilean Chinese Colombian Comoran Congolese Costa Rican Croatian Cuban Cypriot Czech Danish Djibouti Dominican Dutch East Timorese Ecuadorean Egyptian Emirati Equatorial Guinean Eritrean Estonian Ethiopian Fijian Filipino Finnish French Gabonese Gambian Georgian German Ghanaian Greek Grenadian Guatemalan Guinea-Bissauan Guinean Guyanese Haitian Herzegovinian Honduran Hungarian Icelander Indian Indonesian Iranian Iraqi Irish Israeli Italian Ivorian Jamaican Japanese Jordanian Kazakhstani Kenyan Kittian and Nevisian Kuwaiti Kyrgyz Laotian Latvian Lebanese Liberian Libyan Liechtensteiner Lithuanian Luxembourger Macedonian Malagasy Malawian Malaysian Maldivan Malian Maltese Marshallese Mauritanian Mauritian Mexican Micronesian Moldovan Monacan Mongolian Montenegrin Moroccan Mosotho Motswana Mozambican Namibian Nauruan Nepalese New Zealander Nicaraguan Nigerian Nigerien North Korean Northern Irish Norwegian Omani Pakistani Palauan Palestinian Panamanian Papua New Guinean Paraguayan Peruvian Polish Portuguese Qatari Romanian Russian Rwandan Saint Lucian Salvadoran Samoan San Marinese Sao Tomean Saudi Scottish Senegalese Serbian Seychellois Sierra Leonean Singaporean Slovakian Slovenian Solomon Islander Somali South African South Korean Spanish Sri Lankan Sudanese Surinamer Swazi Swedish Swiss Syrian Taiwanese Tajik Tanzanian Thai Togolese Tongan Trinidadian or Tobagonian Tunisian Turkish Tuvaluan Ugandan Ukrainian Uruguayan Uzbekistani Vanuatuan Venezuelan Vietnamese Welsh Yemenite Zambian Zimbabwean