Full Code of Comfy-Org/ComfyUI for AI

gitextract_r6aji11h/

├── .ci/
│   ├── update_windows/
│   │   ├── update.py
│   │   ├── update_comfyui.bat
│   │   └── update_comfyui_stable.bat
│   ├── windows_amd_base_files/
│   │   ├── README_VERY_IMPORTANT.txt
│   │   ├── run_amd_gpu.bat
│   │   └── run_amd_gpu_disable_smart_memory.bat
│   ├── windows_nightly_base_files/
│   │   └── run_nvidia_gpu_fast.bat
│   └── windows_nvidia_base_files/
│       ├── README_VERY_IMPORTANT.txt
│       ├── advanced/
│       │   └── run_nvidia_gpu_disable_api_nodes.bat
│       ├── run_cpu.bat
│       ├── run_nvidia_gpu.bat
│       └── run_nvidia_gpu_fast_fp16_accumulation.bat
├── .coderabbit.yaml
├── .gitattributes
├── .github/
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug-report.yml
│   │   ├── config.yml
│   │   ├── feature-request.yml
│   │   └── user-support.yml
│   ├── PULL_REQUEST_TEMPLATE/
│   │   └── api-node.md
│   ├── scripts/
│   │   └── check-ai-co-authors.sh
│   └── workflows/
│       ├── api-node-template.yml
│       ├── check-ai-co-authors.yml
│       ├── check-line-endings.yml
│       ├── pullrequest-ci-run.yml
│       ├── release-stable-all.yml
│       ├── release-webhook.yml
│       ├── ruff.yml
│       ├── stable-release.yml
│       ├── stale-issues.yml
│       ├── test-build.yml
│       ├── test-ci.yml
│       ├── test-execution.yml
│       ├── test-launch.yml
│       ├── test-unit.yml
│       ├── update-api-stubs.yml
│       ├── update-ci-container.yml
│       ├── update-version.yml
│       ├── windows_release_dependencies.yml
│       ├── windows_release_dependencies_manual.yml
│       ├── windows_release_nightly_pytorch.yml
│       └── windows_release_package.yml
├── .gitignore
├── CODEOWNERS
├── CONTRIBUTING.md
├── LICENSE
├── QUANTIZATION.md
├── README.md
├── alembic.ini
├── alembic_db/
│   ├── README.md
│   ├── env.py
│   ├── script.py.mako
│   └── versions/
│       ├── 0001_assets.py
│       ├── 0002_merge_to_asset_references.py
│       └── 0003_add_metadata_job_id.py
├── api_server/
│   ├── __init__.py
│   ├── routes/
│   │   ├── __init__.py
│   │   └── internal/
│   │       ├── README.md
│   │       ├── __init__.py
│   │       └── internal_routes.py
│   ├── services/
│   │   ├── __init__.py
│   │   └── terminal_service.py
│   └── utils/
│       └── file_operations.py
├── app/
│   ├── __init__.py
│   ├── app_settings.py
│   ├── assets/
│   │   ├── api/
│   │   │   ├── routes.py
│   │   │   ├── schemas_in.py
│   │   │   ├── schemas_out.py
│   │   │   └── upload.py
│   │   ├── database/
│   │   │   ├── models.py
│   │   │   └── queries/
│   │   │       ├── __init__.py
│   │   │       ├── asset.py
│   │   │       ├── asset_reference.py
│   │   │       ├── common.py
│   │   │       └── tags.py
│   │   ├── helpers.py
│   │   ├── scanner.py
│   │   ├── seeder.py
│   │   └── services/
│   │       ├── __init__.py
│   │       ├── asset_management.py
│   │       ├── bulk_ingest.py
│   │       ├── file_utils.py
│   │       ├── hashing.py
│   │       ├── ingest.py
│   │       ├── metadata_extract.py
│   │       ├── path_utils.py
│   │       ├── schemas.py
│   │       └── tagging.py
│   ├── custom_node_manager.py
│   ├── database/
│   │   ├── db.py
│   │   └── models.py
│   ├── frontend_management.py
│   ├── logger.py
│   ├── model_manager.py
│   ├── node_replace_manager.py
│   ├── subgraph_manager.py
│   └── user_manager.py
├── blueprints/
│   ├── .glsl/
│   │   ├── Brightness_and_Contrast_1.frag
│   │   ├── Chromatic_Aberration_16.frag
│   │   ├── Color_Adjustment_15.frag
│   │   ├── Edge-Preserving_Blur_128.frag
│   │   ├── Film_Grain_15.frag
│   │   ├── Glow_30.frag
│   │   ├── Hue_and_Saturation_1.frag
│   │   ├── Image_Blur_1.frag
│   │   ├── Image_Channels_23.frag
│   │   ├── Image_Levels_1.frag
│   │   ├── README.md
│   │   ├── Sharpen_23.frag
│   │   ├── Unsharp_Mask_26.frag
│   │   └── update_blueprints.py
│   ├── Brightness and Contrast.json
│   ├── Canny to Image (Z-Image-Turbo).json
│   ├── Canny to Video (LTX 2.0).json
│   ├── Chromatic Aberration.json
│   ├── Color Adjustment.json
│   ├── Depth to Image (Z-Image-Turbo).json
│   ├── Depth to Video (ltx 2.0).json
│   ├── Edge-Preserving Blur.json
│   ├── Film Grain.json
│   ├── Glow.json
│   ├── Hue and Saturation.json
│   ├── Image Blur.json
│   ├── Image Captioning (gemini).json
│   ├── Image Channels.json
│   ├── Image Edit (Flux.2 Klein 4B).json
│   ├── Image Edit (Qwen 2511).json
│   ├── Image Inpainting (Qwen-image).json
│   ├── Image Levels.json
│   ├── Image Outpainting (Qwen-Image).json
│   ├── Image Upscale(Z-image-Turbo).json
│   ├── Image to Depth Map (Lotus).json
│   ├── Image to Layers(Qwen-Image Layered).json
│   ├── Image to Model (Hunyuan3d 2.1).json
│   ├── Image to Video (Wan 2.2).json
│   ├── Pose to Image (Z-Image-Turbo).json
│   ├── Pose to Video (LTX 2.0).json
│   ├── Prompt Enhance.json
│   ├── Sharpen.json
│   ├── Text to Audio (ACE-Step 1.5).json
│   ├── Text to Image (Z-Image-Turbo).json
│   ├── Text to Video (Wan 2.2).json
│   ├── Unsharp Mask.json
│   ├── Video Captioning (Gemini).json
│   ├── Video Inpaint(Wan2.1 VACE).json
│   ├── Video Stitch.json
│   ├── Video Upscale(GAN x4).json
│   └── put_blueprints_here
├── comfy/
│   ├── audio_encoders/
│   │   ├── audio_encoders.py
│   │   ├── wav2vec2.py
│   │   └── whisper.py
│   ├── cldm/
│   │   ├── cldm.py
│   │   ├── control_types.py
│   │   ├── dit_embedder.py
│   │   └── mmdit.py
│   ├── cli_args.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
│   ├── clip_vision_config_vitl_336.json
│   ├── clip_vision_config_vitl_336_llava.json
│   ├── clip_vision_siglip2_base_naflex.json
│   ├── clip_vision_siglip_384.json
│   ├── clip_vision_siglip_512.json
│   ├── comfy_types/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── examples/
│   │   │   └── example_nodes.py
│   │   └── node_typing.py
│   ├── conds.py
│   ├── context_windows.py
│   ├── controlnet.py
│   ├── diffusers_convert.py
│   ├── diffusers_load.py
│   ├── extra_samplers/
│   │   └── uni_pc.py
│   ├── float.py
│   ├── gligen.py
│   ├── hooks.py
│   ├── image_encoders/
│   │   ├── dino2.py
│   │   ├── dino2_giant.json
│   │   └── dino2_large.json
│   ├── k_diffusion/
│   │   ├── deis.py
│   │   ├── sa_solver.py
│   │   ├── sampling.py
│   │   └── utils.py
│   ├── latent_formats.py
│   ├── ldm/
│   │   ├── ace/
│   │   │   ├── ace_step15.py
│   │   │   ├── attention.py
│   │   │   ├── lyric_encoder.py
│   │   │   ├── model.py
│   │   │   └── vae/
│   │   │       ├── autoencoder_dc.py
│   │   │       ├── music_dcae_pipeline.py
│   │   │       ├── music_log_mel.py
│   │   │       └── music_vocoder.py
│   │   ├── anima/
│   │   │   └── model.py
│   │   ├── audio/
│   │   │   ├── autoencoder.py
│   │   │   ├── dit.py
│   │   │   └── embedders.py
│   │   ├── aura/
│   │   │   └── mmdit.py
│   │   ├── cascade/
│   │   │   ├── common.py
│   │   │   ├── controlnet.py
│   │   │   ├── stage_a.py
│   │   │   ├── stage_b.py
│   │   │   ├── stage_c.py
│   │   │   └── stage_c_coder.py
│   │   ├── chroma/
│   │   │   ├── layers.py
│   │   │   └── model.py
│   │   ├── chroma_radiance/
│   │   │   ├── layers.py
│   │   │   └── model.py
│   │   ├── common_dit.py
│   │   ├── cosmos/
│   │   │   ├── blocks.py
│   │   │   ├── cosmos_tokenizer/
│   │   │   │   ├── layers3d.py
│   │   │   │   ├── patching.py
│   │   │   │   └── utils.py
│   │   │   ├── model.py
│   │   │   ├── position_embedding.py
│   │   │   ├── predict2.py
│   │   │   └── vae.py
│   │   ├── flux/
│   │   │   ├── controlnet.py
│   │   │   ├── layers.py
│   │   │   ├── math.py
│   │   │   ├── model.py
│   │   │   └── redux.py
│   │   ├── genmo/
│   │   │   ├── joint_model/
│   │   │   │   ├── asymm_models_joint.py
│   │   │   │   ├── layers.py
│   │   │   │   ├── rope_mixed.py
│   │   │   │   ├── temporal_rope.py
│   │   │   │   └── utils.py
│   │   │   └── vae/
│   │   │       └── model.py
│   │   ├── hidream/
│   │   │   └── model.py
│   │   ├── hunyuan3d/
│   │   │   ├── model.py
│   │   │   └── vae.py
│   │   ├── hunyuan3dv2_1/
│   │   │   └── hunyuandit.py
│   │   ├── hunyuan_video/
│   │   │   ├── model.py
│   │   │   ├── upsampler.py
│   │   │   ├── vae.py
│   │   │   └── vae_refiner.py
│   │   ├── hydit/
│   │   │   ├── attn_layers.py
│   │   │   ├── controlnet.py
│   │   │   ├── models.py
│   │   │   ├── poolers.py
│   │   │   └── posemb_layers.py
│   │   ├── kandinsky5/
│   │   │   └── model.py
│   │   ├── lightricks/
│   │   │   ├── av_model.py
│   │   │   ├── embeddings_connector.py
│   │   │   ├── latent_upsampler.py
│   │   │   ├── model.py
│   │   │   ├── symmetric_patchifier.py
│   │   │   ├── vae/
│   │   │   │   ├── audio_vae.py
│   │   │   │   ├── causal_audio_autoencoder.py
│   │   │   │   ├── causal_conv3d.py
│   │   │   │   ├── causal_video_autoencoder.py
│   │   │   │   ├── conv_nd_factory.py
│   │   │   │   ├── dual_conv3d.py
│   │   │   │   └── pixel_norm.py
│   │   │   └── vocoders/
│   │   │       └── vocoder.py
│   │   ├── lumina/
│   │   │   ├── controlnet.py
│   │   │   └── model.py
│   │   ├── mmaudio/
│   │   │   └── vae/
│   │   │       ├── __init__.py
│   │   │       ├── activations.py
│   │   │       ├── alias_free_torch.py
│   │   │       ├── autoencoder.py
│   │   │       ├── bigvgan.py
│   │   │       ├── distributions.py
│   │   │       ├── vae.py
│   │   │       └── vae_modules.py
│   │   ├── models/
│   │   │   └── autoencoder.py
│   │   ├── modules/
│   │   │   ├── attention.py
│   │   │   ├── diffusionmodules/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── mmdit.py
│   │   │   │   ├── model.py
│   │   │   │   ├── openaimodel.py
│   │   │   │   ├── upscaling.py
│   │   │   │   └── util.py
│   │   │   ├── distributions/
│   │   │   │   ├── __init__.py
│   │   │   │   └── distributions.py
│   │   │   ├── ema.py
│   │   │   ├── encoders/
│   │   │   │   ├── __init__.py
│   │   │   │   └── noise_aug_modules.py
│   │   │   ├── sdpose.py
│   │   │   ├── sub_quadratic_attention.py
│   │   │   └── temporal_ae.py
│   │   ├── omnigen/
│   │   │   └── omnigen2.py
│   │   ├── pixart/
│   │   │   ├── blocks.py
│   │   │   └── pixartms.py
│   │   ├── qwen_image/
│   │   │   ├── controlnet.py
│   │   │   └── model.py
│   │   ├── util.py
│   │   └── wan/
│   │       ├── model.py
│   │       ├── model_animate.py
│   │       ├── model_multitalk.py
│   │       ├── vae.py
│   │       └── vae2_2.py
│   ├── lora.py
│   ├── lora_convert.py
│   ├── memory_management.py
│   ├── model_base.py
│   ├── model_detection.py
│   ├── model_management.py
│   ├── model_patcher.py
│   ├── model_sampling.py
│   ├── nested_tensor.py
│   ├── ops.py
│   ├── options.py
│   ├── patcher_extension.py
│   ├── pinned_memory.py
│   ├── pixel_space_convert.py
│   ├── quant_ops.py
│   ├── rmsnorm.py
│   ├── sample.py
│   ├── sampler_helpers.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
│   ├── sdxl_clip.py
│   ├── supported_models.py
│   ├── supported_models_base.py
│   ├── t2i_adapter/
│   │   └── adapter.py
│   ├── taesd/
│   │   ├── taehv.py
│   │   └── taesd.py
│   ├── text_encoders/
│   │   ├── ace.py
│   │   ├── ace15.py
│   │   ├── ace_lyrics_tokenizer/
│   │   │   └── vocab.json
│   │   ├── ace_text_cleaners.py
│   │   ├── anima.py
│   │   ├── aura_t5.py
│   │   ├── bert.py
│   │   ├── byt5_config_small_glyph.json
│   │   ├── byt5_tokenizer/
│   │   │   ├── added_tokens.json
│   │   │   ├── special_tokens_map.json
│   │   │   └── tokenizer_config.json
│   │   ├── cosmos.py
│   │   ├── flux.py
│   │   ├── genmo.py
│   │   ├── hidream.py
│   │   ├── hunyuan_image.py
│   │   ├── hunyuan_video.py
│   │   ├── hydit.py
│   │   ├── hydit_clip.json
│   │   ├── hydit_clip_tokenizer/
│   │   │   ├── special_tokens_map.json
│   │   │   ├── tokenizer_config.json
│   │   │   └── vocab.txt
│   │   ├── jina_clip_2.py
│   │   ├── kandinsky5.py
│   │   ├── llama.py
│   │   ├── llama_tokenizer/
│   │   │   ├── tokenizer.json
│   │   │   └── tokenizer_config.json
│   │   ├── long_clipl.py
│   │   ├── longcat_image.py
│   │   ├── lt.py
│   │   ├── lumina2.py
│   │   ├── mt5_config_xl.json
│   │   ├── newbie.py
│   │   ├── omnigen2.py
│   │   ├── ovis.py
│   │   ├── pixart_t5.py
│   │   ├── qwen25_tokenizer/
│   │   │   ├── merges.txt
│   │   │   ├── tokenizer_config.json
│   │   │   └── vocab.json
│   │   ├── qwen_image.py
│   │   ├── qwen_vl.py
│   │   ├── sa_t5.py
│   │   ├── sd2_clip.py
│   │   ├── sd2_clip_config.json
│   │   ├── sd3_clip.py
│   │   ├── spiece_tokenizer.py
│   │   ├── t5.py
│   │   ├── t5_config_base.json
│   │   ├── t5_config_xxl.json
│   │   ├── t5_old_config_xxl.json
│   │   ├── t5_pile_config_xl.json
│   │   ├── t5_pile_tokenizer/
│   │   │   └── tokenizer.model
│   │   ├── t5_tokenizer/
│   │   │   ├── special_tokens_map.json
│   │   │   ├── tokenizer.json
│   │   │   └── tokenizer_config.json
│   │   ├── umt5_config_base.json
│   │   ├── umt5_config_xxl.json
│   │   ├── wan.py
│   │   └── z_image.py
│   ├── utils.py
│   ├── weight_adapter/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── boft.py
│   │   ├── bypass.py
│   │   ├── glora.py
│   │   ├── loha.py
│   │   ├── lokr.py
│   │   ├── lora.py
│   │   └── oft.py
│   └── windows.py
├── comfy_api/
│   ├── feature_flags.py
│   ├── generate_api_stubs.py
│   ├── input/
│   │   ├── __init__.py
│   │   ├── basic_types.py
│   │   └── video_types.py
│   ├── input_impl/
│   │   ├── __init__.py
│   │   └── video_types.py
│   ├── internal/
│   │   ├── __init__.py
│   │   ├── api_registry.py
│   │   ├── async_to_sync.py
│   │   └── singleton.py
│   ├── latest/
│   │   ├── __init__.py
│   │   ├── _caching.py
│   │   ├── _input/
│   │   │   ├── __init__.py
│   │   │   ├── basic_types.py
│   │   │   └── video_types.py
│   │   ├── _input_impl/
│   │   │   ├── __init__.py
│   │   │   └── video_types.py
│   │   ├── _io.py
│   │   ├── _io_public.py
│   │   ├── _ui.py
│   │   ├── _ui_public.py
│   │   ├── _util/
│   │   │   ├── __init__.py
│   │   │   ├── geometry_types.py
│   │   │   ├── image_types.py
│   │   │   └── video_types.py
│   │   └── generated/
│   │       └── ComfyAPISyncStub.pyi
│   ├── torch_helpers/
│   │   ├── __init__.py
│   │   └── torch_compile.py
│   ├── util/
│   │   ├── __init__.py
│   │   └── video_types.py
│   ├── util.py
│   ├── v0_0_1/
│   │   ├── __init__.py
│   │   └── generated/
│   │       └── ComfyAPISyncStub.pyi
│   ├── v0_0_2/
│   │   ├── __init__.py
│   │   └── generated/
│   │       └── ComfyAPISyncStub.pyi
│   └── version_list.py
├── comfy_api_nodes/
│   ├── __init__.py
│   ├── apis/
│   │   ├── __init__.py
│   │   ├── bfl.py
│   │   ├── bria.py
│   │   ├── bytedance.py
│   │   ├── elevenlabs.py
│   │   ├── gemini.py
│   │   ├── grok.py
│   │   ├── hitpaw.py
│   │   ├── hunyuan3d.py
│   │   ├── ideogram.py
│   │   ├── kling.py
│   │   ├── luma.py
│   │   ├── magnific.py
│   │   ├── meshy.py
│   │   ├── minimax.py
│   │   ├── moonvalley.py
│   │   ├── openai.py
│   │   ├── pixverse.py
│   │   ├── recraft.py
│   │   ├── reve.py
│   │   ├── rodin.py
│   │   ├── runway.py
│   │   ├── stability.py
│   │   ├── topaz.py
│   │   ├── tripo.py
│   │   ├── veo.py
│   │   ├── vidu.py
│   │   └── wavespeed.py
│   ├── nodes_bfl.py
│   ├── nodes_bria.py
│   ├── nodes_bytedance.py
│   ├── nodes_elevenlabs.py
│   ├── nodes_gemini.py
│   ├── nodes_grok.py
│   ├── nodes_hitpaw.py
│   ├── nodes_hunyuan3d.py
│   ├── nodes_ideogram.py
│   ├── nodes_kling.py
│   ├── nodes_ltxv.py
│   ├── nodes_luma.py
│   ├── nodes_magnific.py
│   ├── nodes_meshy.py
│   ├── nodes_minimax.py
│   ├── nodes_moonvalley.py
│   ├── nodes_openai.py
│   ├── nodes_pixverse.py
│   ├── nodes_recraft.py
│   ├── nodes_reve.py
│   ├── nodes_rodin.py
│   ├── nodes_runway.py
│   ├── nodes_sora.py
│   ├── nodes_stability.py
│   ├── nodes_topaz.py
│   ├── nodes_tripo.py
│   ├── nodes_veo2.py
│   ├── nodes_vidu.py
│   ├── nodes_wan.py
│   ├── nodes_wavespeed.py
│   └── util/
│       ├── __init__.py
│       ├── _helpers.py
│       ├── client.py
│       ├── common_exceptions.py
│       ├── conversions.py
│       ├── download_helpers.py
│       ├── request_logger.py
│       ├── upload_helpers.py
│       └── validation_utils.py
├── comfy_config/
│   ├── config_parser.py
│   └── types.py
├── comfy_execution/
│   ├── cache_provider.py
│   ├── caching.py
│   ├── graph.py
│   ├── graph_utils.py
│   ├── jobs.py
│   ├── progress.py
│   ├── utils.py
│   └── validation.py
├── comfy_extras/
│   ├── chainner_models/
│   │   └── model_loading.py
│   ├── nodes_ace.py
│   ├── nodes_advanced_samplers.py
│   ├── nodes_align_your_steps.py
│   ├── nodes_apg.py
│   ├── nodes_attention_multiply.py
│   ├── nodes_audio.py
│   ├── nodes_audio_encoder.py
│   ├── nodes_camera_trajectory.py
│   ├── nodes_canny.py
│   ├── nodes_cfg.py
│   ├── nodes_chroma_radiance.py
│   ├── nodes_clip_sdxl.py
│   ├── nodes_color.py
│   ├── nodes_compositing.py
│   ├── nodes_cond.py
│   ├── nodes_context_windows.py
│   ├── nodes_controlnet.py
│   ├── nodes_cosmos.py
│   ├── nodes_custom_sampler.py
│   ├── nodes_dataset.py
│   ├── nodes_differential_diffusion.py
│   ├── nodes_easycache.py
│   ├── nodes_edit_model.py
│   ├── nodes_eps.py
│   ├── nodes_flux.py
│   ├── nodes_freelunch.py
│   ├── nodes_fresca.py
│   ├── nodes_gits.py
│   ├── nodes_glsl.py
│   ├── nodes_hidream.py
│   ├── nodes_hooks.py
│   ├── nodes_hunyuan.py
│   ├── nodes_hunyuan3d.py
│   ├── nodes_hypernetwork.py
│   ├── nodes_hypertile.py
│   ├── nodes_image_compare.py
│   ├── nodes_images.py
│   ├── nodes_ip2p.py
│   ├── nodes_kandinsky5.py
│   ├── nodes_latent.py
│   ├── nodes_load_3d.py
│   ├── nodes_logic.py
│   ├── nodes_lora_debug.py
│   ├── nodes_lora_extract.py
│   ├── nodes_lotus.py
│   ├── nodes_lt.py
│   ├── nodes_lt_audio.py
│   ├── nodes_lt_upsampler.py
│   ├── nodes_lumina2.py
│   ├── nodes_mahiro.py
│   ├── nodes_mask.py
│   ├── nodes_math.py
│   ├── nodes_mochi.py
│   ├── nodes_model_advanced.py
│   ├── nodes_model_downscale.py
│   ├── nodes_model_merging.py
│   ├── nodes_model_merging_model_specific.py
│   ├── nodes_model_patch.py
│   ├── nodes_morphology.py
│   ├── nodes_nag.py
│   ├── nodes_nop.py
│   ├── nodes_optimalsteps.py
│   ├── nodes_pag.py
│   ├── nodes_painter.py
│   ├── nodes_perpneg.py
│   ├── nodes_photomaker.py
│   ├── nodes_pixart.py
│   ├── nodes_post_processing.py
│   ├── nodes_preview_any.py
│   ├── nodes_primitive.py
│   ├── nodes_qwen.py
│   ├── nodes_rebatch.py
│   ├── nodes_replacements.py
│   ├── nodes_resolution.py
│   ├── nodes_rope.py
│   ├── nodes_sag.py
│   ├── nodes_sd3.py
│   ├── nodes_sdpose.py
│   ├── nodes_sdupscale.py
│   ├── nodes_slg.py
│   ├── nodes_stable3d.py
│   ├── nodes_stable_cascade.py
│   ├── nodes_string.py
│   ├── nodes_tcfg.py
│   ├── nodes_textgen.py
│   ├── nodes_tomesd.py
│   ├── nodes_toolkit.py
│   ├── nodes_torch_compile.py
│   ├── nodes_train.py
│   ├── nodes_upscale_model.py
│   ├── nodes_video.py
│   ├── nodes_video_model.py
│   ├── nodes_wan.py
│   ├── nodes_wanmove.py
│   ├── nodes_webcam.py
│   └── nodes_zimage.py
├── comfyui_version.py
├── cuda_malloc.py
├── custom_nodes/
│   └── example_node.py.example
├── execution.py
├── extra_model_paths.yaml.example
├── folder_paths.py
├── hook_breaker_ac10a0.py
├── latent_preview.py
├── main.py
├── manager_requirements.txt
├── middleware/
│   ├── __init__.py
│   └── cache_middleware.py
├── new_updater.py
├── node_helpers.py
├── nodes.py
├── protocol.py
├── pyproject.toml
├── pytest.ini
├── requirements.txt
├── script_examples/
│   ├── basic_api_example.py
│   ├── websockets_api_example.py
│   └── websockets_api_example_ws_images.py
├── server.py
├── tests/
│   ├── README.md
│   ├── __init__.py
│   ├── compare/
│   │   ├── conftest.py
│   │   └── test_quality.py
│   ├── conftest.py
│   ├── execution/
│   │   ├── test_async_nodes.py
│   │   ├── test_execution.py
│   │   ├── test_jobs.py
│   │   ├── test_preview_method.py
│   │   ├── test_progress_isolation.py
│   │   ├── test_public_api.py
│   │   └── testing_nodes/
│   │       └── testing-pack/
│   │           ├── __init__.py
│   │           ├── api_test_nodes.py
│   │           ├── async_test_nodes.py
│   │           ├── conditions.py
│   │           ├── flow_control.py
│   │           ├── specific_tests.py
│   │           ├── stubs.py
│   │           ├── tools.py
│   │           └── util.py
│   └── inference/
│       ├── __init__.py
│       ├── graphs/
│       │   └── default_graph_sdxl1_0.json
│       └── test_inference.py
├── tests-unit/
│   ├── README.md
│   ├── app_test/
│   │   ├── __init__.py
│   │   ├── custom_node_manager_test.py
│   │   ├── frontend_manager_test.py
│   │   ├── model_manager_test.py
│   │   ├── test_migrations.py
│   │   └── user_manager_system_user_test.py
│   ├── assets_test/
│   │   ├── conftest.py
│   │   ├── helpers.py
│   │   ├── queries/
│   │   │   ├── conftest.py
│   │   │   ├── test_asset.py
│   │   │   ├── test_asset_info.py
│   │   │   ├── test_cache_state.py
│   │   │   ├── test_metadata.py
│   │   │   └── test_tags.py
│   │   ├── services/
│   │   │   ├── __init__.py
│   │   │   ├── conftest.py
│   │   │   ├── test_asset_management.py
│   │   │   ├── test_bulk_ingest.py
│   │   │   ├── test_enrich.py
│   │   │   ├── test_ingest.py
│   │   │   ├── test_tag_histogram.py
│   │   │   └── test_tagging.py
│   │   ├── test_assets_missing_sync.py
│   │   ├── test_crud.py
│   │   ├── test_downloads.py
│   │   ├── test_file_utils.py
│   │   ├── test_list_filter.py
│   │   ├── test_metadata_filters.py
│   │   ├── test_prune_orphaned_assets.py
│   │   ├── test_sync_references.py
│   │   ├── test_tags_api.py
│   │   └── test_uploads.py
│   ├── comfy_api_test/
│   │   ├── input_impl_test.py
│   │   └── video_types_test.py
│   ├── comfy_extras_test/
│   │   ├── __init__.py
│   │   ├── image_stitch_test.py
│   │   └── nodes_math_test.py
│   ├── comfy_quant/
│   │   └── test_mixed_precision.py
│   ├── comfy_test/
│   │   ├── folder_path_test.py
│   │   └── model_detection_test.py
│   ├── execution_test/
│   │   ├── preview_method_override_test.py
│   │   ├── test_cache_provider.py
│   │   └── validate_node_input_test.py
│   ├── feature_flags_test.py
│   ├── folder_paths_test/
│   │   ├── __init__.py
│   │   ├── filter_by_content_types_test.py
│   │   ├── misc_test.py
│   │   └── system_user_test.py
│   ├── prompt_server_test/
│   │   ├── __init__.py
│   │   ├── system_user_endpoint_test.py
│   │   └── user_manager_test.py
│   ├── requirements.txt
│   ├── seeder_test/
│   │   └── test_seeder.py
│   ├── server/
│   │   └── utils/
│   │       └── file_operations_test.py
│   ├── server_test/
│   │   └── test_cache_control.py
│   ├── utils/
│   │   ├── extra_config_test.py
│   │   └── json_util_test.py
│   └── websocket_feature_flags_test.py
└── utils/
    ├── __init__.py
    ├── extra_config.py
    ├── install_util.py
    ├── json_util.py
    └── mime_types.py

SYMBOL INDEX (5205 symbols across 233 files)

FILE: .ci/update_windows/update.py
  function pull (line 8) | def pull(repo, remote_name='origin', branch='master'):
  function latest_tag (line 96) | def latest_tag(repo):
  function files_equal (line 130) | def files_equal(file1, file2):
  function file_size (line 136) | def file_size(f):

FILE: alembic_db/env.py
  function run_migrations_offline (line 20) | def run_migrations_offline() -> None:
  function run_migrations_online (line 41) | def run_migrations_online() -> None:

FILE: alembic_db/versions/0001_assets.py
  function upgrade (line 17) | def upgrade() -> None:
  function downgrade (line 144) | def downgrade() -> None:

FILE: alembic_db/versions/0002_merge_to_asset_references.py
  function upgrade (line 21) | def upgrade() -> None:
  function downgrade (line 175) | def downgrade() -> None:

FILE: alembic_db/versions/0003_add_metadata_job_id.py
  function upgrade (line 21) | def upgrade() -> None:
  function downgrade (line 63) | def downgrade() -> None:

FILE: api_server/routes/internal/internal_routes.py
  class InternalRoutes (line 8) | class InternalRoutes:
    method __init__ (line 15) | def __init__(self, prompt_server):
    method setup_routes (line 21) | def setup_routes(self):
    method get_app (line 73) | def get_app(self):

FILE: api_server/services/terminal_service.py
  class TerminalService (line 6) | class TerminalService:
    method __init__ (line 7) | def __init__(self, server):
    method get_terminal_size (line 14) | def get_terminal_size(self):
    method update_size (line 25) | def update_size(self):
    method subscribe (line 42) | def subscribe(self, client_id):
    method unsubscribe (line 45) | def unsubscribe(self, client_id):
    method send_messages (line 48) | def send_messages(self, entries):

FILE: api_server/utils/file_operations.py
  class FileInfo (line 4) | class FileInfo(TypedDict):
  class DirectoryInfo (line 10) | class DirectoryInfo(TypedDict):
  function is_file_info (line 17) | def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
  class FileSystemOperations (line 20) | class FileSystemOperations:
    method walk_directory (line 22) | def walk_directory(directory: str) -> List[FileSystemItem]:

FILE: app/app_settings.py
  class AppSettings (line 7) | class AppSettings():
    method __init__ (line 8) | def __init__(self, user_manager):
    method get_settings (line 11) | def get_settings(self, request):
    method save_settings (line 30) | def save_settings(self, request, settings):
    method add_routes (line 36) | def add_routes(self, routes):

FILE: app/assets/api/routes.py
  function _require_assets_feature_enabled (line 49) | def _require_assets_feature_enabled(handler):
  function get_query_dict (line 67) | def get_query_dict(request: web.Request) -> dict[str, Any]:
  function register_assets_routes (line 87) | def register_assets_routes(
  function disable_assets_routes (line 98) | def disable_assets_routes() -> None:
  function _build_error_response (line 104) | def _build_error_response(
  function _build_validation_error_response (line 113) | def _build_validation_error_response(code: str, ve: ValidationError) -> ...
  function _validate_sort_field (line 118) | def _validate_sort_field(requested: str | None) -> str:
  function _build_preview_url_from_view (line 127) | def _build_preview_url_from_view(tags: list[str], user_metadata: dict[st...
  function _build_asset_response (line 153) | def _build_asset_response(result: schemas.AssetDetailResult | schemas.Up...
  function head_asset_by_hash (line 184) | async def head_asset_by_hash(request: web.Request) -> web.Response:
  function list_assets_route (line 198) | async def list_assets_route(request: web.Request) -> web.Response:
  function get_asset_route (line 236) | async def get_asset_route(request: web.Request) -> web.Response:
  function download_asset_content (line 271) | async def download_asset_content(request: web.Request) -> web.Response:
  function create_asset_from_hash_route (line 337) | async def create_asset_from_hash_route(request: web.Request) -> web.Resp...
  function upload_asset (line 377) | async def upload_asset(request: web.Request) -> web.Response:
  function update_asset_route (line 480) | async def update_asset_route(request: web.Request) -> web.Response:
  function delete_asset_route (line 518) | async def delete_asset_route(request: web.Request) -> web.Response:
  function get_tags (line 550) | async def get_tags(request: web.Request) -> web.Response:
  function add_asset_tags (line 587) | async def add_asset_tags(request: web.Request) -> web.Response:
  function delete_asset_tags (line 635) | async def delete_asset_tags(request: web.Request) -> web.Response:
  function get_tags_refine (line 682) | async def get_tags_refine(request: web.Request) -> web.Response:
  function seed_assets (line 704) | async def seed_assets(request: web.Request) -> web.Response:
  function get_seed_status (line 754) | async def get_seed_status(request: web.Request) -> web.Response:
  function cancel_seed (line 776) | async def cancel_seed(request: web.Request) -> web.Response:
  function mark_missing_assets (line 786) | async def mark_missing_assets(request: web.Request) -> web.Response:

FILE: app/assets/api/schemas_in.py
  class UploadError (line 16) | class UploadError(Exception):
    method __init__ (line 19) | def __init__(self, status: int, code: str, message: str):
  class AssetValidationError (line 26) | class AssetValidationError(Exception):
    method __init__ (line 29) | def __init__(self, code: str, message: str):
  class ParsedUpload (line 36) | class ParsedUpload:
  class ListAssetsQuery (line 52) | class ListAssetsQuery(BaseModel):
    method _split_csv_tags (line 70) | def _split_csv_tags(cls, v):
    method _parse_metadata_json (line 86) | def _parse_metadata_json(cls, v):
  class UpdateAssetBody (line 100) | class UpdateAssetBody(BaseModel):
    method _validate_at_least_one_field (line 106) | def _validate_at_least_one_field(self):
  class CreateFromHashBody (line 117) | class CreateFromHashBody(BaseModel):
    method _require_blake3 (line 129) | def _require_blake3(cls, v):
    method _normalize_tags_field (line 134) | def _normalize_tags_field(cls, v):
  class TagsRefineQuery (line 151) | class TagsRefineQuery(BaseModel):
    method _split_csv_tags (line 160) | def _split_csv_tags(cls, v):
    method _parse_metadata_json (line 175) | def _parse_metadata_json(cls, v):
  class TagsListQuery (line 189) | class TagsListQuery(BaseModel):
    method normalize_prefix (line 200) | def normalize_prefix(cls, v: str | None) -> str | None:
  class TagsAdd (line 207) | class TagsAdd(BaseModel):
    method normalize_tags (line 213) | def normalize_tags(cls, v: list[str]) -> list[str]:
  class TagsRemove (line 230) | class TagsRemove(TagsAdd):
  class UploadAssetSpec (line 234) | class UploadAssetSpec(BaseModel):
    method _parse_hash (line 259) | def _parse_hash(cls, v):
    method _parse_tags (line 269) | def _parse_tags(cls, v):
    method _parse_metadata_json (line 315) | def _parse_metadata_json(cls, v):
    method _validate_order (line 332) | def _validate_order(self):

FILE: app/assets/api/schemas_out.py
  class Asset (line 7) | class Asset(BaseModel):
    method _serialize_datetime (line 31) | def _serialize_datetime(self, v: datetime | None, _info):
  class AssetCreated (line 35) | class AssetCreated(Asset):
  class AssetsList (line 39) | class AssetsList(BaseModel):
  class TagUsage (line 45) | class TagUsage(BaseModel):
  class TagsList (line 51) | class TagsList(BaseModel):
  class TagsAdd (line 57) | class TagsAdd(BaseModel):
  class TagsRemove (line 64) | class TagsRemove(BaseModel):
  class TagHistogram (line 71) | class TagHistogram(BaseModel):

FILE: app/assets/api/upload.py
  function normalize_and_validate_hash (line 13) | def normalize_and_validate_hash(s: str) -> str:
  function parse_multipart_upload (line 24) | async def parse_multipart_upload(
  function delete_temp_file_if_exists (line 172) | def delete_temp_file_if_exists(tmp_path: str | None) -> None:

FILE: app/assets/database/models.py
  class Asset (line 26) | class Asset(Base):
    method __repr__ (line 56) | def __repr__(self) -> str:
  class AssetReference (line 60) | class AssetReference(Base):
    method __repr__ (line 164) | def __repr__(self) -> str:
  class AssetReferenceMeta (line 169) | class AssetReferenceMeta(Base):
  class AssetReferenceTag (line 201) | class AssetReferenceTag(Base):
  class Tag (line 226) | class Tag(Base):
    method __repr__ (line 245) | def __repr__(self) -> str:

FILE: app/assets/database/queries/asset.py
  function asset_exists_by_hash (line 10) | def asset_exists_by_hash(
  function get_asset_by_hash (line 28) | def get_asset_by_hash(
  function upsert_asset (line 39) | def upsert_asset(
  function bulk_insert_assets (line 81) | def bulk_insert_assets(
  function get_existing_asset_ids (line 93) | def get_existing_asset_ids(
  function update_asset_hash_and_mime (line 109) | def update_asset_hash_and_mime(
  function reassign_asset_references (line 126) | def reassign_asset_references(

FILE: app/assets/database/queries/asset_reference.py
  function _check_is_scalar (line 37) | def _check_is_scalar(v):
  function _scalar_to_row (line 47) | def _scalar_to_row(key: str, ordinal: int, value) -> dict:
  function convert_metadata_to_rows (line 59) | def convert_metadata_to_rows(key: str, value) -> list[dict]:
  function get_reference_by_id (line 77) | def get_reference_by_id(
  function get_reference_with_owner_check (line 84) | def get_reference_with_owner_check(
  function get_reference_by_file_path (line 103) | def get_reference_by_file_path(
  function reference_exists_for_asset_id (line 117) | def reference_exists_for_asset_id(
  function reference_exists (line 131) | def reference_exists(
  function insert_reference (line 146) | def insert_reference(
  function get_or_create_reference (line 177) | def get_or_create_reference(
  function update_reference_timestamps (line 229) | def update_reference_timestamps(
  function list_references_page (line 241) | def list_references_page(
  function fetch_reference_asset_and_tags (line 321) | def fetch_reference_asset_and_tags(
  function fetch_reference_and_asset (line 358) | def fetch_reference_and_asset(
  function update_reference_access_time (line 380) | def update_reference_access_time(
  function update_reference_name (line 398) | def update_reference_name(
  function update_reference_updated_at (line 412) | def update_reference_updated_at(
  function rebuild_metadata_projection (line 426) | def rebuild_metadata_projection(session: Session, ref: AssetReference) -...
  function set_reference_metadata (line 462) | def set_reference_metadata(
  function set_reference_system_metadata (line 478) | def set_reference_system_metadata(
  function delete_reference_by_id (line 495) | def delete_reference_by_id(
  function soft_delete_reference_by_id (line 507) | def soft_delete_reference_by_id(
  function set_reference_preview (line 529) | def set_reference_preview(
  class CacheStateRow (line 550) | class CacheStateRow(NamedTuple):
  function list_references_by_asset_id (line 562) | def list_references_by_asset_id(
  function list_all_file_paths_by_asset_id (line 579) | def list_all_file_paths_by_asset_id(
  function upsert_reference (line 598) | def upsert_reference(
  function mark_references_missing_outside_prefixes (line 655) | def mark_references_missing_outside_prefixes(
  function restore_references_by_paths (line 679) | def restore_references_by_paths(session: Session, file_paths: list[str])...
  function get_unreferenced_unhashed_asset_ids (line 700) | def get_unreferenced_unhashed_asset_ids(session: Session) -> list[str]:
  function delete_assets_by_ids (line 722) | def delete_assets_by_ids(session: Session, asset_ids: list[str]) -> int:
  function get_references_for_prefixes (line 739) | def get_references_for_prefixes(
  function bulk_update_needs_verify (line 797) | def bulk_update_needs_verify(
  function bulk_update_is_missing (line 817) | def bulk_update_is_missing(
  function update_is_missing_by_asset_id (line 837) | def update_is_missing_by_asset_id(
  function delete_references_by_ids (line 853) | def delete_references_by_ids(session: Session, reference_ids: list[str])...
  function delete_orphaned_seed_asset (line 869) | def delete_orphaned_seed_asset(session: Session, asset_id: str) -> bool:
  class UnenrichedReferenceRow (line 886) | class UnenrichedReferenceRow(NamedTuple):
  function get_unenriched_references (line 895) | def get_unenriched_references(
  function bulk_update_enrichment_level (line 945) | def bulk_update_enrichment_level(
  function bulk_insert_references_ignore_conflicts (line 964) | def bulk_insert_references_ignore_conflicts(
  function get_references_by_paths_and_asset_ids (line 983) | def get_references_by_paths_and_asset_ids(
  function get_reference_ids_by_ids (line 1014) | def get_reference_ids_by_ids(

FILE: app/assets/database/queries/common.py
  function calculate_rows_per_statement (line 16) | def calculate_rows_per_statement(cols: int) -> int:
  function iter_chunks (line 21) | def iter_chunks(seq, n: int):
  function iter_row_chunks (line 27) | def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[lis...
  function build_visible_owner_clause (line 34) | def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
  function build_prefix_like_conditions (line 45) | def build_prefix_like_conditions(
  function apply_tag_filters (line 59) | def apply_tag_filters(
  function apply_metadata_filter (line 87) | def apply_metadata_filter(

FILE: app/assets/database/queries/tags.py
  class AddTagsResult (line 27) | class AddTagsResult:
  class RemoveTagsResult (line 34) | class RemoveTagsResult:
  class SetTagsResult (line 41) | class SetTagsResult:
  function validate_tags_exist (line 47) | def validate_tags_exist(session: Session, tags: list[str]) -> None:
  function ensure_tags_exist (line 58) | def ensure_tags_exist(
  function get_reference_tags (line 73) | def get_reference_tags(session: Session, reference_id: str) -> list[str]:
  function set_reference_tags (line 86) | def set_reference_tags(
  function add_tags_to_reference (line 126) | def add_tags_to_reference(
  function remove_tags_from_reference (line 178) | def remove_tags_from_reference(
  function add_missing_tag_for_asset_id (line 210) | def add_missing_tag_for_asset_id(
  function remove_missing_tag_for_asset_id (line 247) | def remove_missing_tag_for_asset_id(
  function list_tags_with_usage (line 261) | def list_tags_with_usage(
  function list_tag_counts_for_filtered_assets (line 338) | def list_tag_counts_for_filtered_assets(
  function bulk_insert_tags_and_meta (line 385) | def bulk_insert_tags_and_meta(

FILE: app/assets/helpers.py
  function select_best_live_path (line 6) | def select_best_live_path(states: Sequence) -> str:
  function escape_sql_like_string (line 26) | def escape_sql_like_string(s: str, escape: str = "!") -> tuple[str, str]:
  function get_utc_now (line 36) | def get_utc_now() -> datetime:
  function normalize_tags (line 41) | def normalize_tags(tags: list[str] | None) -> list[str]:
  function validate_blake3_hash (line 50) | def validate_blake3_hash(s: str) -> str:

FILE: app/assets/scanner.py
  class _RefInfo (line 44) | class _RefInfo(TypedDict):
  class _AssetAccumulator (line 52) | class _AssetAccumulator(TypedDict):
  function get_prefixes_for_root (line 61) | def get_prefixes_for_root(root: RootType) -> list[str]:
  function get_all_known_prefixes (line 74) | def get_all_known_prefixes() -> list[str]:
  function collect_models_files (line 80) | def collect_models_files() -> list[str]:
  function sync_references_with_filesystem (line 102) | def sync_references_with_filesystem(
  function sync_root_safely (line 232) | def sync_root_safely(root: RootType) -> set[str]:
  function mark_missing_outside_prefixes_safely (line 252) | def mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int:
  function collect_paths_for_roots (line 267) | def collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]:
  function build_asset_specs (line 279) | def build_asset_specs(
  function insert_asset_specs (line 349) | def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -...
  function get_unenriched_assets_for_roots (line 367) | def get_unenriched_assets_for_roots(
  function enrich_asset (line 395) | def enrich_asset(
  function enrich_assets_batch (line 514) | def enrich_assets_batch(

FILE: app/assets/seeder.py
  class ScanInProgressError (line 28) | class ScanInProgressError(Exception):
  class State (line 32) | class State(Enum):
  class ScanPhase (line 41) | class ScanPhase(Enum):
  class Progress (line 50) | class Progress:
  class ScanStatus (line 60) | class ScanStatus:
  class _AssetSeeder (line 71) | class _AssetSeeder:
    method __init__ (line 79) | def __init__(self) -> None:
    method disable (line 96) | def disable(self) -> None:
    method is_disabled (line 101) | def is_disabled(self) -> bool:
    method start (line 105) | def start(
    method start_fast (line 151) | def start_fast(
    method start_enrich (line 175) | def start_enrich(
    method cancel (line 199) | def cancel(self) -> bool:
    method stop (line 214) | def stop(self) -> bool:
    method pause (line 222) | def pause(self) -> bool:
    method resume (line 238) | def resume(self) -> bool:
    method restart (line 255) | def restart(
    method wait (line 300) | def wait(self, timeout: float | None = None) -> bool:
    method get_status (line 316) | def get_status(self) -> ScanStatus:
    method shutdown (line 333) | def shutdown(self, timeout: float = 5.0) -> None:
    method mark_missing_outside_prefixes (line 344) | def mark_missing_outside_prefixes(self) -> int:
    method _is_cancelled (line 388) | def _is_cancelled(self) -> bool:
    method _is_paused_or_cancelled (line 392) | def _is_paused_or_cancelled(self) -> bool:
    method _check_pause_and_cancel (line 402) | def _check_pause_and_cancel(self) -> bool:
    method _emit_event (line 417) | def _emit_event(self, event_type: str, data: dict) -> None:
    method _update_progress (line 427) | def _update_progress(
    method _add_error (line 466) | def _add_error(self, message: str) -> None:
    method _log_scan_config (line 472) | def _log_scan_config(self, roots: tuple[RootType, ...]) -> None:
    method _run_scan (line 487) | def _run_scan(self) -> None:
    method _run_fast_phase (line 601) | def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, i...
    method _run_enrich_phase (line 709) | def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool...

FILE: app/assets/services/asset_management.py
  function get_asset_detail (line 44) | def get_asset_detail(
  function update_asset_metadata (line 65) | def update_asset_metadata(
  function delete_asset_reference (line 147) | def delete_asset_reference(
  function set_asset_preview (line 203) | def set_asset_preview(
  function asset_exists (line 234) | def asset_exists(asset_hash: str) -> bool:
  function get_asset_by_hash (line 239) | def get_asset_by_hash(asset_hash: str) -> AssetData | None:
  function list_assets_page (line 245) | def list_assets_page(
  function resolve_hash_to_path (line 283) | def resolve_hash_to_path(
  function resolve_asset_for_download (line 324) | def resolve_asset_for_download(

FILE: app/assets/services/bulk_ingest.py
  class SeedAssetSpec (line 28) | class SeedAssetSpec(TypedDict):
  class AssetRow (line 42) | class AssetRow(TypedDict):
  class ReferenceRow (line 52) | class ReferenceRow(TypedDict):
  class TagRow (line 68) | class TagRow(TypedDict):
  class MetadataRow (line 77) | class MetadataRow(TypedDict):
  class BulkInsertResult (line 90) | class BulkInsertResult:
  function batch_insert_seed_assets (line 98) | def batch_insert_seed_assets(
  function cleanup_unreferenced_assets (line 270) | def cleanup_unreferenced_assets(session: Session) -> int:

FILE: app/assets/services/file_utils.py
  function get_mtime_ns (line 4) | def get_mtime_ns(stat_result: os.stat_result) -> int:
  function get_size_and_mtime_ns (line 11) | def get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tu...
  function verify_file_unchanged (line 17) | def verify_file_unchanged(
  function is_visible (line 39) | def is_visible(name: str) -> bool:
  function list_files_recursively (line 44) | def list_files_recursively(base_dir: str) -> list[str]:

FILE: app/assets/services/hashing.py
  class HashCheckpoint (line 19) | class HashCheckpoint:
  function _open_for_hashing (line 29) | def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], ...
  function compute_blake3_hash (line 51) | def compute_blake3_hash(

FILE: app/assets/services/ingest.py
  function _ingest_file_from_path (line 45) | def _ingest_file_from_path(
  function _register_existing_asset (line 133) | def _register_existing_asset(
  function _update_metadata_with_filename (line 213) | def _update_metadata_with_filename(
  function _sanitize_filename (line 237) | def _sanitize_filename(name: str | None, fallback: str) -> str:
  class HashMismatchError (line 242) | class HashMismatchError(Exception):
  class DependencyMissingError (line 246) | class DependencyMissingError(Exception):
    method __init__ (line 247) | def __init__(self, message: str):
  function upload_from_temp_path (line 252) | def upload_from_temp_path(
  function register_file_in_place (line 364) | def register_file_in_place(
  function create_from_hash (line 430) | def create_from_hash(

FILE: app/assets/services/metadata_extract.py
  class ExtractedMetadata (line 29) | class ExtractedMetadata:
    method to_user_metadata (line 58) | def to_user_metadata(self) -> dict[str, Any]:
    method to_meta_rows (line 104) | def to_meta_rows(self, reference_id: str) -> list[dict]:
  function _read_safetensors_header (line 177) | def _read_safetensors_header(
  function _extract_safetensors_metadata (line 208) | def _extract_safetensors_metadata(
  function extract_file_metadata (line 278) | def extract_file_metadata(

FILE: app/assets/services/path_utils.py
  function get_comfy_models_folders (line 12) | def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
  function resolve_destination_from_tags (line 29) | def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[st...
  function validate_path_within_base (line 61) | def validate_path_within_base(candidate: str, base: str) -> None:
  function compute_relative_filename (line 68) | def compute_relative_filename(file_path: str) -> str | None:
  function get_asset_category_and_relative_path (line 94) | def get_asset_category_and_relative_path(
  function get_name_and_tags_from_asset_path (line 153) | def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list...

FILE: app/assets/services/schemas.py
  class AssetData (line 11) | class AssetData:
  class ReferenceData (line 18) | class ReferenceData:
  class AssetDetailResult (line 34) | class AssetDetailResult:
  class RegisterAssetResult (line 41) | class RegisterAssetResult:
  class IngestResult (line 49) | class IngestResult:
  class TagUsage (line 57) | class TagUsage(NamedTuple):
  class AssetSummaryData (line 64) | class AssetSummaryData:
  class ListAssetsResult (line 71) | class ListAssetsResult:
  class DownloadResolutionResult (line 77) | class DownloadResolutionResult:
  class UploadResult (line 84) | class UploadResult:
  function extract_reference_data (line 91) | def extract_reference_data(ref: AssetReference) -> ReferenceData:
  function extract_asset_data (line 106) | def extract_asset_data(asset: Asset | None) -> AssetData | None:

FILE: app/assets/services/tagging.py
  function apply_tags (line 16) | def apply_tags(
  function remove_tags (line 38) | def remove_tags(
  function list_tags (line 56) | def list_tags(
  function list_tag_histogram (line 81) | def list_tag_histogram(

FILE: app/custom_node_manager.py
  function safe_load_json_file (line 22) | def safe_load_json_file(file_path: str) -> dict:
  class CustomNodeManager (line 34) | class CustomNodeManager:
    method build_translations (line 36) | def build_translations(self):
    method add_routes (line 94) | def add_routes(self, routes, webapp, loadedModules):

FILE: app/database/db.py
  function dependencies_available (line 38) | def dependencies_available():
  function can_create_session (line 45) | def can_create_session():
  function get_alembic_config (line 53) | def get_alembic_config():
  function get_db_path (line 65) | def get_db_path():
  function _acquire_file_lock (line 75) | def _acquire_file_lock(db_path):
  function _is_memory_db (line 94) | def _is_memory_db(db_url):
  function init_db (line 99) | def init_db():
  function _init_memory_db (line 109) | def _init_memory_db(db_url):
  function _init_file_db (line 134) | def _init_file_db(db_url):
  function create_session (line 190) | def create_session():

FILE: app/database/models.py
  class Base (line 14) | class Base(DeclarativeBase):
  function to_dict (line 17) | def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:

FILE: app/frontend_management.py
  function frontend_install_warning_message (line 26) | def frontend_install_warning_message():
  function parse_version (line 33) | def parse_version(version: str) -> tuple[int, int, int]:
  function is_valid_version (line 36) | def is_valid_version(version: str) -> bool:
  function get_installed_frontend_version (line 41) | def get_installed_frontend_version():
  function get_required_frontend_version (line 47) | def get_required_frontend_version():
  function check_frontend_version (line 51) | def check_frontend_version():
  class Asset (line 80) | class Asset(TypedDict):
  class Release (line 84) | class Release(TypedDict):
  class FrontEndProvider (line 96) | class FrontEndProvider:
    method folder_name (line 101) | def folder_name(self) -> str:
    method release_url (line 105) | def release_url(self) -> str:
    method all_releases (line 109) | def all_releases(self) -> list[Release]:
    method latest_release (line 124) | def latest_release(self) -> Release:
    method latest_prerelease (line 131) | def latest_prerelease(self) -> Release:
    method get_release (line 141) | def get_release(self, version: str) -> Release:
  function download_release_asset_zip (line 153) | def download_release_asset_zip(release: Release, destination_path: str) ...
  class FrontendManager (line 183) | class FrontendManager:
    method get_required_frontend_version (line 187) | def get_required_frontend_version(cls) -> str:
    method get_installed_templates_version (line 192) | def get_installed_templates_version(cls) -> str:
    method get_required_templates_version (line 201) | def get_required_templates_version(cls) -> str:
    method default_frontend_path (line 205) | def default_frontend_path(cls) -> str:
    method template_asset_map (line 225) | def template_asset_map(cls) -> Optional[Dict[str, str]]:
    method legacy_templates_path (line 271) | def legacy_templates_path(cls) -> Optional[str]:
    method embedded_docs_path (line 294) | def embedded_docs_path(cls) -> str:
    method parse_version_string (line 307) | def parse_version_string(cls, value: str) -> tuple[str, str, str]:
    method init_frontend_unsafe (line 326) | def init_frontend_unsafe(
    method init_frontend (line 391) | def init_frontend(cls, version_string: str) -> str:
    method template_asset_handler (line 409) | def template_asset_handler(cls):

FILE: app/logger.py
  class LogInterceptor (line 13) | class LogInterceptor(io.TextIOWrapper):
    method __init__ (line 14) | def __init__(self, stream,  *args, **kwargs):
    method write (line 22) | def write(self, data):
    method flush (line 34) | def flush(self):
    method on_flush (line 40) | def on_flush(self, callback):
  function get_logs (line 44) | def get_logs():
  function on_flush (line 48) | def on_flush(callback):
  function setup_logger (line 54) | def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdou...
  function log_startup_warning (line 90) | def log_startup_warning(msg):
  function print_startup_warnings (line 95) | def print_startup_warnings():

FILE: app/model_manager.py
  class ModelFileManager (line 17) | class ModelFileManager:
    method __init__ (line 18) | def __init__(self) -> None:
    method get_cache (line 21) | def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[...
    method set_cache (line 24) | def set_cache(self, key: str, value: tuple[list[dict], dict[str, float...
    method clear_cache (line 27) | def clear_cache(self):
    method add_routes (line 30) | def add_routes(self, routes):
    method get_model_file_list (line 79) | def get_model_file_list(self, folder_name: str):
    method cache_model_file_list_ (line 95) | def cache_model_file_list_(self, folder: str):
    method recursive_search_models_ (line 112) | def recursive_search_models_(self, directory: str, pathIndex: int) -> ...
    method get_model_previews (line 160) | def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
    method __exit__ (line 194) | def __exit__(self, exc_type, exc_value, traceback):

FILE: app/node_replace_manager.py
  class NodeStruct (line 12) | class NodeStruct(TypedDict):
  function copy_node_struct (line 17) | def copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False...
  class NodeReplaceManager (line 27) | class NodeReplaceManager:
    method __init__ (line 30) | def __init__(self):
    method register (line 33) | def register(self, node_replace: NodeReplace):
    method get_replacement (line 37) | def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
    method has_replacement (line 41) | def has_replacement(self, old_node_id: str) -> bool:
    method apply_replacements (line 45) | def apply_replacements(self, prompt: dict[str, NodeStruct]):
    method as_dict (line 97) | def as_dict(self):
    method add_routes (line 104) | def add_routes(self, routes):

FILE: app/subgraph_manager.py
  class Source (line 11) | class Source:
  class SubgraphEntry (line 15) | class SubgraphEntry(TypedDict):
  class CustomNodeSubgraphEntryInfo (line 35) | class CustomNodeSubgraphEntryInfo(TypedDict):
  class SubgraphManager (line 39) | class SubgraphManager:
    method __init__ (line 40) | def __init__(self):
    method _create_entry (line 44) | def _create_entry(self, file: str, source: str, node_pack: str) -> tup...
    method load_entry_data (line 55) | async def load_entry_data(self, entry: SubgraphEntry):
    method sanitize_entry (line 60) | async def sanitize_entry(self, entry: SubgraphEntry | None, remove_dat...
    method sanitize_entries (line 69) | async def sanitize_entries(self, entries: dict[str, SubgraphEntry], re...
    method get_custom_node_subgraphs (line 75) | async def get_custom_node_subgraphs(self, loadedModules, force_reload=...
    method get_blueprint_subgraphs (line 92) | async def get_blueprint_subgraphs(self, force_reload=False):
    method get_all_subgraphs (line 109) | async def get_all_subgraphs(self, loadedModules, force_reload=False):
    method get_subgraph (line 115) | async def get_subgraph(self, id: str, loadedModules):
    method add_routes (line 122) | def add_routes(self, routes, loadedModules):

FILE: app/user_manager.py
  class FileInfo (line 20) | class FileInfo(TypedDict):
  function get_file_info (line 27) | def get_file_info(path: str, relative_to: str) -> FileInfo:
  class UserManager (line 36) | class UserManager():
    method __init__ (line 37) | def __init__(self):
    method get_users_file (line 56) | def get_users_file(self):
    method get_request_user_id (line 59) | def get_request_user_id(self, request):
    method get_request_user_filepath (line 72) | def get_request_user_filepath(self, request, file, type="userdata", cr...
    method add_user (line 105) | def add_user(self, name):
    method add_routes (line 123) | def add_routes(self, routes):

FILE: blueprints/.glsl/update_blueprints.py
  function get_blueprint_files (line 29) | def get_blueprint_files():
  function sanitize_filename (line 34) | def sanitize_filename(name):
  function extract_shaders (line 39) | def extract_shaders():
  function patch_shaders (line 76) | def patch_shaders():
  function main (line 141) | def main():

FILE: comfy/audio_encoders/audio_encoders.py
  class AudioEncoderModel (line 10) | class AudioEncoderModel():
    method __init__ (line 11) | def __init__(self, config):
    method load_sd (line 32) | def load_sd(self, sd):
    method get_sd (line 35) | def get_sd(self):
    method encode_audio (line 38) | def encode_audio(self, audio, sample_rate):
  function load_audio_encoder_from_sd (line 49) | def load_audio_encoder_from_sd(sd, prefix=""):

FILE: comfy/audio_encoders/wav2vec2.py
  class LayerNormConv (line 6) | class LayerNormConv(nn.Module):
    method __init__ (line 7) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 12) | def forward(self, x):
  class LayerGroupNormConv (line 16) | class LayerGroupNormConv(nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 22) | def forward(self, x):
  class ConvNoNorm (line 26) | class ConvNoNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 31) | def forward(self, x):
  class ConvFeatureEncoder (line 36) | class ConvFeatureEncoder(nn.Module):
    method __init__ (line 37) | def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=No...
    method forward (line 60) | def forward(self, x):
  class FeatureProjection (line 69) | class FeatureProjection(nn.Module):
    method __init__ (line 70) | def __init__(self, conv_dim, embed_dim, dtype=None, device=None, opera...
    method forward (line 75) | def forward(self, x):
  class PositionalConvEmbedding (line 81) | class PositionalConvEmbedding(nn.Module):
    method __init__ (line 82) | def __init__(self, embed_dim=768, kernel_size=128, groups=16):
    method forward (line 94) | def forward(self, x):
  class TransformerEncoder (line 102) | class TransformerEncoder(nn.Module):
    method __init__ (line 103) | def __init__(
    method forward (line 129) | def forward(self, x, mask=None):
  class Attention (line 143) | class Attention(nn.Module):
    method __init__ (line 144) | def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device...
    method forward (line 155) | def forward(self, x, mask=None):
  class FeedForward (line 165) | class FeedForward(nn.Module):
    method __init__ (line 166) | def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, oper...
    method forward (line 171) | def forward(self, x):
  class TransformerEncoderLayer (line 178) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 179) | def __init__(
    method forward (line 196) | def forward(self, x, mask=None):
  class Wav2Vec2Model (line 209) | class Wav2Vec2Model(nn.Module):
    method __init__ (line 212) | def __init__(
    method forward (line 241) | def forward(self, x, mask_time_indices=None, return_dict=False):

FILE: comfy/audio_encoders/whisper.py
  class WhisperFeatureExtractor (line 9) | class WhisperFeatureExtractor(nn.Module):
    method __init__ (line 10) | def __init__(self, n_mels=128, device=None):
    method __call__ (line 30) | def __call__(self, audio):
  class MultiHeadAttention (line 54) | class MultiHeadAttention(nn.Module):
    method __init__ (line 55) | def __init__(self, d_model: int, n_heads: int, dtype=None, device=None...
    method forward (line 68) | def forward(
  class EncoderLayer (line 87) | class EncoderLayer(nn.Module):
    method __init__ (line 88) | def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, ...
    method forward (line 98) | def forward(
  class AudioEncoder (line 118) | class AudioEncoder(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 144) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class WhisperLargeV3 (line 162) | class WhisperLargeV3(nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 183) | def forward(self, audio):

FILE: comfy/cldm/cldm.py
  class OptimizedAttention (line 19) | class OptimizedAttention(nn.Module):
    method __init__ (line 20) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 28) | def forward(self, x):
  class QuickGELU (line 34) | class QuickGELU(nn.Module):
    method forward (line 35) | def forward(self, x: torch.Tensor):
  class ResBlockUnionControlnet (line 38) | class ResBlockUnionControlnet(nn.Module):
    method __init__ (line 39) | def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
    method attention (line 48) | def attention(self, x: torch.Tensor):
    method forward (line 51) | def forward(self, x: torch.Tensor):
  class ControlledUnetModel (line 56) | class ControlledUnetModel(UNetModel):
  class ControlNet (line 60) | class ControlNet(nn.Module):
    method __init__ (line 61) | def __init__(
    method union_controlnet_merge (line 353) | def union_controlnet_merge(self, hint, control_type, emb, context):
    method make_zero_conv (line 380) | def make_zero_conv(self, channels, operations=None, dtype=None, device...
    method forward (line 383) | def forward(self, x, hint, timesteps, context, y=None, **kwargs):

FILE: comfy/cldm/dit_embedder.py
  class ControlNetEmbedder (line 11) | class ControlNetEmbedder(nn.Module):
    method __init__ (line 13) | def __init__(
    method forward (line 88) | def forward(

FILE: comfy/cldm/mmdit.py
  class ControlNet (line 5) | class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
    method __init__ (line 6) | def __init__(
    method forward (line 36) | def forward(

FILE: comfy/cli_args.py
  class EnumAction (line 7) | class EnumAction(argparse.Action):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method __call__ (line 30) | def __call__(self, parser, namespace, values, option_string=None):
  class LatentPreviewMethod (line 96) | class LatentPreviewMethod(enum.Enum):
    method from_string (line 103) | def from_string(cls, value: str):
  class PerformanceFeature (line 161) | class PerformanceFeature(enum.Enum):
  function is_valid_directory (line 206) | def is_valid_directory(path: str) -> str:
  function enables_dynamic_vram (line 265) | def enables_dynamic_vram():

FILE: comfy/clip_model.py
  function clip_preprocess (line 6) | def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.4082...
  function siglip2_flex_calc_resolution (line 25) | def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, ep...
  function siglip2_preprocess (line 42) | def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, ...
  class CLIPAttention (line 58) | class CLIPAttention(torch.nn.Module):
    method __init__ (line 59) | def __init__(self, embed_dim, heads, dtype, device, operations):
    method forward (line 69) | def forward(self, x, mask=None, optimized_attention=None):
  class CLIPMLP (line 82) | class CLIPMLP(torch.nn.Module):
    method __init__ (line 83) | def __init__(self, embed_dim, intermediate_size, activation, dtype, de...
    method forward (line 89) | def forward(self, x):
  class CLIPLayer (line 95) | class CLIPLayer(torch.nn.Module):
    method __init__ (line 96) | def __init__(self, embed_dim, heads, intermediate_size, intermediate_a...
    method forward (line 103) | def forward(self, x, mask=None, optimized_attention=None):
  class CLIPEncoder (line 109) | class CLIPEncoder(torch.nn.Module):
    method __init__ (line 110) | def __init__(self, num_layers, embed_dim, heads, intermediate_size, in...
    method forward (line 114) | def forward(self, x, mask=None, intermediate_output=None):
  class CLIPEmbeddings (line 138) | class CLIPEmbeddings(torch.nn.Module):
    method __init__ (line 139) | def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtyp...
    method forward (line 144) | def forward(self, input_tokens, dtype=torch.float32):
  class CLIPTextModel_ (line 148) | class CLIPTextModel_(torch.nn.Module):
    method __init__ (line 149) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 163) | def forward(self, input_tokens=None, attention_mask=None, embeds=None,...
  class CLIPTextModel (line 192) | class CLIPTextModel(torch.nn.Module):
    method __init__ (line 193) | def __init__(self, config_dict, dtype, device, operations):
    method get_input_embeddings (line 201) | def get_input_embeddings(self):
    method set_input_embeddings (line 204) | def set_input_embeddings(self, embeddings):
    method forward (line 207) | def forward(self, *args, **kwargs):
  function siglip2_pos_embed (line 212) | def siglip2_pos_embed(embed_weight, embeds, orig_shape):
  class Siglip2Embeddings (line 219) | class Siglip2Embeddings(torch.nn.Module):
    method __init__ (line 220) | def __init__(self, embed_dim, num_channels=3, patch_size=14, image_siz...
    method forward (line 226) | def forward(self, pixel_values):
  class CLIPVisionEmbeddings (line 234) | class CLIPVisionEmbeddings(torch.nn.Module):
    method __init__ (line 235) | def __init__(self, embed_dim, num_channels=3, patch_size=14, image_siz...
    method forward (line 259) | def forward(self, pixel_values):
  class CLIPVision (line 266) | class CLIPVision(torch.nn.Module):
    method __init__ (line 267) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 289) | def forward(self, pixel_values, attention_mask=None, intermediate_outp...
  class LlavaProjector (line 301) | class LlavaProjector(torch.nn.Module):
    method __init__ (line 302) | def __init__(self, in_dim, out_dim, dtype, device, operations):
    method forward (line 307) | def forward(self, x):
  class CLIPVisionModelProjection (line 310) | class CLIPVisionModelProjection(torch.nn.Module):
    method __init__ (line 311) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 324) | def forward(self, *args, **kwargs):

FILE: comfy/clip_vision.py
  class Output (line 13) | class Output:
    method __getitem__ (line 14) | def __getitem__(self, key):
    method __setitem__ (line 16) | def __setitem__(self, key, item):
  class ClipVisionModel (line 28) | class ClipVisionModel():
    method __init__ (line 29) | def __init__(self, json_config):
    method load_sd (line 52) | def load_sd(self, sd):
    method get_sd (line 55) | def get_sd(self):
    method encode_image (line 58) | def encode_image(self, image, crop=True):
  function convert_to_transformers (line 80) | def convert_to_transformers(sd, prefix):
  function load_clipvision_from_sd (line 106) | def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
  function load (line 151) | def load(ckpt_path):

FILE: comfy/comfy_types/__init__.py
  class UnetApplyFunction (line 6) | class UnetApplyFunction(Protocol):
    method __call__ (line 9) | def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torc...
  class UnetApplyConds (line 13) | class UnetApplyConds(TypedDict):
  class UnetParams (line 22) | class UnetParams(TypedDict):

FILE: comfy/comfy_types/examples/example_nodes.py
  class ExampleNode (line 5) | class ExampleNode(ComfyNodeABC):
    method INPUT_TYPES (line 16) | def INPUT_TYPES(s) -> InputTypeDict:
    method execute (line 27) | def execute(self, input_int: int):

FILE: comfy/comfy_types/node_typing.py
  class StrEnum (line 10) | class StrEnum(str, Enum):
    method __str__ (line 13) | def __str__(self) -> str:
  class IO (line 17) | class IO(StrEnum):
    method __ne__ (line 65) | def __ne__(self, value: object) -> bool:
  class RemoteInputOptions (line 75) | class RemoteInputOptions(TypedDict):
  class MultiSelectOptions (line 90) | class MultiSelectOptions(TypedDict):
  class InputTypeOptions (line 97) | class InputTypeOptions(TypedDict):
  class HiddenInputTypeDict (line 183) | class HiddenInputTypeDict(TypedDict):
  class InputTypeDict (line 198) | class InputTypeDict(TypedDict):
  class ComfyNodeABC (line 215) | class ComfyNodeABC(ABC):
    method INPUT_TYPES (line 248) | def INPUT_TYPES(s) -> InputTypeDict:
  class CheckLazyMixin (line 325) | class CheckLazyMixin:
    method check_lazy_status (line 328) | def check_lazy_status(self, **kwargs) -> list[str]:
  class FileLocator (line 346) | class FileLocator(TypedDict):

FILE: comfy/conds.py
  function is_equal (line 7) | def is_equal(x, y):
  class CONDRegular (line 26) | class CONDRegular:
    method __init__ (line 27) | def __init__(self, cond):
    method _copy_with (line 30) | def _copy_with(self, cond):
    method process_cond (line 33) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 36) | def can_concat(self, other):
    method concat (line 44) | def concat(self, others):
    method size (line 50) | def size(self):
  class CONDNoiseShape (line 54) | class CONDNoiseShape(CONDRegular):
    method process_cond (line 55) | def process_cond(self, batch_size, area, **kwargs):
  class CONDCrossAttn (line 65) | class CONDCrossAttn(CONDRegular):
    method can_concat (line 66) | def can_concat(self, other):
    method concat (line 82) | def concat(self, others):
  class CONDConstant (line 98) | class CONDConstant(CONDRegular):
    method __init__ (line 99) | def __init__(self, cond):
    method process_cond (line 102) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 105) | def can_concat(self, other):
    method concat (line 110) | def concat(self, others):
    method size (line 113) | def size(self):
  class CONDList (line 117) | class CONDList(CONDRegular):
    method __init__ (line 118) | def __init__(self, cond):
    method process_cond (line 121) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 128) | def can_concat(self, other):
    method concat (line 137) | def concat(self, others):
    method size (line 147) | def size(self):  # hackish implementation to make the mem estimation work

FILE: comfy/context_windows.py
  class ContextWindowABC (line 17) | class ContextWindowABC(ABC):
    method __init__ (line 18) | def __init__(self):
    method get_tensor (line 22) | def get_tensor(self, full: torch.Tensor) -> torch.Tensor:
    method add_window (line 29) | def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torc...
  class ContextHandlerABC (line 35) | class ContextHandlerABC(ABC):
    method __init__ (line 36) | def __init__(self):
    method should_use_context (line 40) | def should_use_context(self, model: BaseModel, conds: list[list[dict]]...
    method get_resized_cond (line 44) | def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, wi...
    method execute (line 48) | def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: ...
  class IndexListContextWindow (line 53) | class IndexListContextWindow(ContextWindowABC):
    method __init__ (line 54) | def __init__(self, index_list: list[int], dim: int=0, total_frames: in...
    method get_tensor (line 61) | def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain...
    method add_window (line 73) | def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=Non...
    method get_region_index (line 80) | def get_region_index(self, num_regions: int) -> int:
  class IndexListCallbacks (line 85) | class IndexListCallbacks:
    method init_callbacks (line 92) | def init_callbacks(self):
  function slice_cond (line 96) | def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.T...
  class ContextSchedule (line 141) | class ContextSchedule:
  class ContextFuseMethod (line 146) | class ContextFuseMethod:
  class IndexListContextHandler (line 151) | class IndexListContextHandler(ContextHandlerABC):
    method __init__ (line 152) | def __init__(self, context_schedule: ContextSchedule, fuse_method: Con...
    method should_use_context (line 168) | def should_use_context(self, model: BaseModel, conds: list[list[dict]]...
    method prepare_control_objects (line 177) | def prepare_control_objects(self, control: ControlBase, device=None) -...
    method get_resized_cond (line 182) | def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, wi...
    method set_step (line 264) | def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
    method get_context_windows (line 271) | def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, mo...
    method execute (line 277) | def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: ...
    method evaluate_context_windows (line 314) | def evaluate_context_windows(self, calc_cond_batch: Callable, model: B...
    method combine_context_window_results (line 339) | def combine_context_window_results(self, x_in: torch.Tensor, sub_conds...
  function _prepare_sampling_wrapper (line 369) | def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor...
  function create_prepare_sampling_wrapper (line 381) | def create_prepare_sampling_wrapper(model: ModelPatcher):
  function _sampler_sample_wrapper (line 389) | def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callba...
  function create_sampler_sample_wrapper (line 403) | def create_sampler_sample_wrapper(model: ModelPatcher):
  function match_weights_to_dim (line 411) | def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: ...
  function get_shape_for_dim (line 420) | def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]:
  class ContextSchedules (line 430) | class ContextSchedules:
  function create_windows_uniform_looped (line 438) | def create_windows_uniform_looped(num_frames: int, handler: IndexListCon...
  function create_windows_uniform_standard (line 457) | def create_windows_uniform_standard(num_frames: int, handler: IndexListC...
  function create_windows_static_standard (line 505) | def create_windows_static_standard(num_frames: int, handler: IndexListCo...
  function create_windows_batched (line 524) | def create_windows_batched(num_frames: int, handler: IndexListContextHan...
  function create_windows_default (line 537) | def create_windows_default(num_frames: int, handler: IndexListContextHan...
  function get_matching_context_schedule (line 549) | def get_matching_context_schedule(context_schedule: str) -> ContextSched...
  function get_context_weights (line 556) | def get_context_weights(length: int, full_length: int, idxs: list[int], ...
  function create_weights_flat (line 560) | def create_weights_flat(length: int, **kwargs) -> list[float]:
  function create_weights_pyramid (line 564) | def create_weights_pyramid(length: int, **kwargs) -> list[float]:
  function create_weights_overlap_linear (line 575) | def create_weights_overlap_linear(length: int, full_length: int, idxs: l...
  class ContextFuseMethods (line 589) | class ContextFuseMethods:
  function get_matching_fuse_method (line 606) | def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:
  function ordered_halving (line 613) | def ordered_halving(val):
  function get_missing_indexes (line 625) | def get_missing_indexes(windows: list[list[int]], num_frames: int) -> li...
  function does_window_roll_over (line 636) | def does_window_roll_over(window: list[int], num_frames: int) -> tuple[b...
  function shift_window_to_start (line 646) | def shift_window_to_start(window: list[int], num_frames: int):
  function shift_window_to_end (line 654) | def shift_window_to_end(window: list[int], num_frames: int):
  function apply_freenoise (line 665) | def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, ...

FILE: comfy/controlnet.py
  function broadcast_image_to (line 46) | def broadcast_image_to(tensor, target_batch_size, batched_number):
  class StrengthType (line 63) | class StrengthType(Enum):
  class ControlBase (line 67) | class ControlBase:
    method __init__ (line 68) | def __init__(self):
    method set_cond_hint (line 89) | def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_rang...
    method pre_run (line 102) | def pre_run(self, model, percent_to_timestep_function):
    method set_previous_controlnet (line 107) | def set_previous_controlnet(self, controlnet):
    method cleanup (line 111) | def cleanup(self):
    method get_models (line 119) | def get_models(self):
    method get_extra_hooks (line 125) | def get_extra_hooks(self):
    method copy_to (line 133) | def copy_to(self, c):
    method inference_memory_requirements (line 150) | def inference_memory_requirements(self, dtype):
    method control_merge (line 155) | def control_merge(self, control, control_prev, output_dtype):
    method set_extra_arg (line 196) | def set_extra_arg(self, argument, value=None):
  class ControlNet (line 200) | class ControlNet(ControlBase):
    method __init__ (line 201) | def __init__(self, control_model=None, global_average_pooling=False, c...
    method get_control (line 218) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method copy (line 280) | def copy(self):
    method get_models (line 287) | def get_models(self):
    method pre_run (line 292) | def pre_run(self, model, percent_to_timestep_function):
    method cleanup (line 296) | def cleanup(self):
  class QwenFunControlNet (line 301) | class QwenFunControlNet(ControlNet):
    method get_control (line 302) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method pre_run (line 313) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 317) | def copy(self):
  class ControlLoraOps (line 324) | class ControlLoraOps:
    class Linear (line 325) | class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
      method __init__ (line 326) | def __init__(self, in_features: int, out_features: int, bias: bool =...
      method forward (line 336) | def forward(self, input):
    class Conv2d (line 345) | class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
      method __init__ (line 346) | def __init__(
      method forward (line 378) | def forward(self, input):
  class ControlLora (line 387) | class ControlLora(ControlNet):
    method __init__ (line 388) | def __init__(self, control_weights, global_average_pooling=False, mode...
    method pre_run (line 394) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 428) | def copy(self):
    method cleanup (line 433) | def cleanup(self):
    method get_models (line 438) | def get_models(self):
    method inference_memory_requirements (line 442) | def inference_memory_requirements(self, dtype):
  function controlnet_config (line 445) | def controlnet_config(sd, model_options={}):
  function controlnet_load_state_dict (line 465) | def controlnet_load_state_dict(control_model, sd):
  function load_controlnet_mmdit (line 476) | def load_controlnet_mmdit(sd, model_options={}):
  class ControlNetSD35 (line 497) | class ControlNetSD35(ControlNet):
    method pre_run (line 498) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 505) | def copy(self):
  function load_controlnet_sd35 (line 512) | def load_controlnet_sd35(sd, model_options={}):
  function load_controlnet_hunyuandit (line 573) | def load_controlnet_hunyuandit(controlnet_data, model_options={}):
  function load_controlnet_flux_xlabs_mistoline (line 584) | def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_opti...
  function load_controlnet_flux_instantx (line 593) | def load_controlnet_flux_instantx(sd, model_options={}):
  function load_controlnet_qwen_instantx (line 617) | def load_controlnet_qwen_instantx(sd, model_options={}):
  function load_controlnet_qwen_fun (line 634) | def load_controlnet_qwen_fun(sd, model_options={}):
  function convert_mistoline (line 680) | def convert_mistoline(sd):
  function load_controlnet_state_dict (line 684) | def load_controlnet_state_dict(state_dict, model=None, model_options={}):
  function load_controlnet (line 839) | def load_controlnet(ckpt_path, model=None, model_options={}):
  class T2IAdapter (line 851) | class T2IAdapter(ControlBase):
    method __init__ (line 852) | def __init__(self, t2i_model, channels_in, compression_ratio, upscale_...
    method scale_image_to (line 863) | def scale_image_to(self, width, height):
    method get_control (line 869) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method copy (line 904) | def copy(self):
  function load_t2i_adapter (line 909) | def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options

FILE: comfy/diffusers_convert.py
  function reshape_weight_for_sd (line 61) | def reshape_weight_for_sd(w, conv3d=False):
  function convert_vae_state_dict (line 69) | def convert_vae_state_dict(vae_state_dict):
  function cat_tensors (line 119) | def cat_tensors(tensors):
  function convert_text_enc_state_dict_v20 (line 135) | def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
  function convert_text_enc_state_dict (line 188) | def convert_text_enc_state_dict(text_enc_dict):

FILE: comfy/diffusers_load.py
  function first_file (line 5) | def first_file(path, filenames):
  function load_diffusers (line 12) | def load_diffusers(model_path, output_vae=True, output_clip=True, embedd...

FILE: comfy/extra_samplers/uni_pc.py
  class NoiseScheduleVP (line 10) | class NoiseScheduleVP:
    method __init__ (line 11) | def __init__(
    method marginal_log_mean_coeff (line 129) | def marginal_log_mean_coeff(self, t):
    method marginal_alpha (line 142) | def marginal_alpha(self, t):
    method marginal_std (line 148) | def marginal_std(self, t):
    method marginal_lambda (line 154) | def marginal_lambda(self, t):
    method inverse_lambda (line 162) | def inverse_lambda(self, lamb):
  function model_wrapper (line 181) | def model_wrapper(
  class UniPC (line 352) | class UniPC:
    method __init__ (line 353) | def __init__(
    method dynamic_thresholding_fn (line 373) | def dynamic_thresholding_fn(self, x0, t=None):
    method noise_prediction_fn (line 384) | def noise_prediction_fn(self, x, t):
    method data_prediction_fn (line 390) | def data_prediction_fn(self, x, t):
    method model_fn (line 405) | def model_fn(self, x, t):
    method get_time_steps (line 414) | def get_time_steps(self, skip_type, t_T, t_0, N, device):
    method get_orders_and_timesteps_for_singlestep_solver (line 431) | def get_orders_and_timesteps_for_singlestep_solver(self, steps, order,...
    method denoise_to_zero_fn (line 462) | def denoise_to_zero_fn(self, x, s):
    method multistep_uni_pc_update (line 468) | def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, ...
    method multistep_uni_pc_vary_update (line 477) | def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list...
    method multistep_uni_pc_bh_update (line 579) | def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, ...
    method sample (line 700) | def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip...
  function interpolate_fn (line 766) | def interpolate_fn(x, xp, yp):
  function expand_dims (line 808) | def expand_dims(v, dims):
  class SigmaConvert (line 821) | class SigmaConvert:
    method marginal_log_mean_coeff (line 823) | def marginal_log_mean_coeff(self, sigma):
    method marginal_alpha (line 826) | def marginal_alpha(self, t):
    method marginal_std (line 829) | def marginal_std(self, t):
    method marginal_lambda (line 832) | def marginal_lambda(self, t):
  function predict_eps_sigma (line 840) | def predict_eps_sigma(model, input, sigma_in, **kwargs):
  function sample_unipc (line 846) | def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, d...
  function sample_unipc_bh2 (line 872) | def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=Non...

FILE: comfy/float.py
  function calc_mantissa (line 3) | def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_...
  function manual_stochastic_round_to_float8 (line 14) | def manual_stochastic_round_to_float8(x, dtype, generator=None):
  function stochastic_rounding (line 50) | def stochastic_rounding(value, dtype, seed=0):
  function stochastic_float_to_fp4_e2m1 (line 71) | def stochastic_float_to_fp4_e2m1(x, generator):
  function to_blocked (line 99) | def to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor:
  function stochastic_round_quantize_nvfp4_block (line 140) | def stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator):
  function stochastic_round_quantize_nvfp4 (line 157) | def stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0):
  function stochastic_round_quantize_nvfp4_by_block (line 177) | def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16...
  function stochastic_round_quantize_mxfp8_by_block (line 214) | def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):

FILE: comfy/gligen.py
  class GatedCrossAttentionDense (line 9) | class GatedCrossAttentionDense(nn.Module):
    method __init__ (line 10) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 32) | def forward(self, x, objs):
  class GatedSelfAttentionDense (line 42) | class GatedSelfAttentionDense(nn.Module):
    method __init__ (line 43) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 69) | def forward(self, x, objs):
  class GatedSelfAttentionDense2 (line 82) | class GatedSelfAttentionDense2(nn.Module):
    method __init__ (line 83) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 105) | def forward(self, x, objs):
  class FourierEmbedder (line 136) | class FourierEmbedder():
    method __init__ (line 137) | def __init__(self, num_freqs=64, temperature=100):
    method __call__ (line 144) | def __call__(self, x, cat_dim=-1):
  class PositionNet (line 153) | class PositionNet(nn.Module):
    method __init__ (line 154) | def __init__(self, in_dim, out_dim, fourier_freqs=8):
    method forward (line 175) | def forward(self, boxes, masks, positive_embeddings):
  class Gligen (line 198) | class Gligen(nn.Module):
    method __init__ (line 199) | def __init__(self, modules, position_net, key_dim):
    method _set_position (line 207) | def _set_position(self, boxes, masks, positive_embeddings):
    method set_position (line 215) | def set_position(self, latent_image_shape, position_params, device):
    method set_empty (line 246) | def set_empty(self, latent_image_shape, device):
  function load_gligen (line 259) | def load_gligen(sd):

FILE: comfy/hooks.py
  class EnumHookMode (line 29) | class EnumHookMode(enum.Enum):
  class EnumHookType (line 39) | class EnumHookType(enum.Enum):
  class EnumWeightTarget (line 49) | class EnumWeightTarget(enum.Enum):
  class EnumHookScope (line 53) | class EnumHookScope(enum.Enum):
  class _HookRef (line 64) | class _HookRef:
  function default_should_register (line 68) | def default_should_register(hook: Hook, model: ModelPatcher, model_optio...
  function create_target_dict (line 73) | def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[...
  class Hook (line 82) | class Hook:
    method __init__ (line 83) | def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=No...
    method strength (line 99) | def strength(self):
    method initialize_timesteps (line 102) | def initialize_timesteps(self, model: BaseModel):
    method reset (line 106) | def reset(self):
    method clone (line 109) | def clone(self):
    method should_register (line 119) | def should_register(self, model: ModelPatcher, model_options: dict, ta...
    method add_hook_patches (line 122) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method __eq__ (line 125) | def __eq__(self, other: Hook):
    method __hash__ (line 128) | def __hash__(self):
  class WeightHook (line 131) | class WeightHook(Hook):
    method __init__ (line 137) | def __init__(self, strength_model=1.0, strength_clip=1.0):
    method strength_model (line 147) | def strength_model(self):
    method strength_clip (line 151) | def strength_clip(self):
    method add_hook_patches (line 154) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method clone (line 182) | def clone(self):
  class ObjectPatchHook (line 191) | class ObjectPatchHook(Hook):
    method __init__ (line 192) | def __init__(self, object_patches: dict[str]=None,
    method clone (line 198) | def clone(self):
    method add_hook_patches (line 203) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class AdditionalModelsHook (line 206) | class AdditionalModelsHook(Hook):
    method __init__ (line 212) | def __init__(self, models: list[ModelPatcher]=None, key: str=None):
    method clone (line 217) | def clone(self):
    method add_hook_patches (line 223) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class TransformerOptionsHook (line 229) | class TransformerOptionsHook(Hook):
    method __init__ (line 233) | def __init__(self, transformers_dict: dict[str, dict[str, dict[str, li...
    method clone (line 241) | def clone(self):
    method add_hook_patches (line 247) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method on_apply_hooks (line 263) | def on_apply_hooks(self, model: ModelPatcher, transformer_options: dic...
  class InjectionsHook (line 270) | class InjectionsHook(Hook):
    method __init__ (line 271) | def __init__(self, key: str=None, injections: list[PatcherInjection]=N...
    method clone (line 278) | def clone(self):
    method add_hook_patches (line 284) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class HookGroup (line 287) | class HookGroup:
    method __init__ (line 294) | def __init__(self):
    method __len__ (line 298) | def __len__(self):
    method add (line 301) | def add(self, hook: Hook):
    method remove (line 306) | def remove(self, hook: Hook):
    method get_type (line 311) | def get_type(self, hook_type: EnumHookType):
    method contains (line 314) | def contains(self, hook: Hook):
    method is_subset_of (line 317) | def is_subset_of(self, other: HookGroup):
    method new_with_common_hooks (line 322) | def new_with_common_hooks(self, other: HookGroup):
    method clone (line 329) | def clone(self):
    method clone_and_combine (line 335) | def clone_and_combine(self, other: HookGroup):
    method set_keyframes_on_hooks (line 342) | def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup):
    method get_hooks_for_clip_schedule (line 350) | def get_hooks_for_clip_schedule(self):
    method reset (line 399) | def reset(self):
    method combine_all_hooks (line 404) | def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) ->...
  class HookKeyframe (line 426) | class HookKeyframe:
    method __init__ (line 427) | def __init__(self, strength: float, start_percent=0.0, guarantee_steps...
    method get_effective_guarantee_steps (line 434) | def get_effective_guarantee_steps(self, max_sigma: torch.Tensor):
    method clone (line 440) | def clone(self):
  class HookKeyframeGroup (line 446) | class HookKeyframeGroup:
    method __init__ (line 447) | def __init__(self):
    method strength (line 457) | def strength(self):
    method reset (line 462) | def reset(self):
    method add (line 470) | def add(self, keyframe: HookKeyframe):
    method _set_first_as_current (line 476) | def _set_first_as_current(self):
    method has_guarantee_steps (line 482) | def has_guarantee_steps(self):
    method has_index (line 488) | def has_index(self, index: int):
    method is_empty (line 491) | def is_empty(self):
    method clone (line 494) | def clone(self):
    method initialize_timesteps (line 501) | def initialize_timesteps(self, model: BaseModel):
    method prepare_current_keyframe (line 505) | def prepare_current_keyframe(self, curr_t: float, transformer_options:...
  class InterpolationMethod (line 540) | class InterpolationMethod:
    method get_weights (line 549) | def get_weights(cls, num_from: float, num_to: float, length: int, meth...
  function get_sorted_list_via_attr (line 568) | def get_sorted_list_via_attr(objects: list, attr: str) -> list:
  function create_transformer_options_from_hooks (line 591) | def create_transformer_options_from_hooks(model: ModelPatcher, hooks: Ho...
  function create_hook_lora (line 602) | def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: floa...
  function create_hook_model_as_lora (line 609) | def create_hook_model_as_lora(weights_model, weights_clip, strength_mode...
  function get_patch_weights_from_model (line 628) | def get_patch_weights_from_model(model: ModelPatcher, discard_model_samp...
  function load_hook_lora_for_models (line 640) | def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dic...
  function _combine_hooks_from_values (line 672) | def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dic...
  function conditioning_set_values_with_hooks (line 692) | def conditioning_set_values_with_hooks(conditioning, values={}, append_h...
  function set_hooks_for_conditioning (line 708) | def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True...
  function set_timesteps_for_conditioning (line 713) | def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,flo...
  function set_mask_for_conditioning (line 719) | def set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: s...
  function combine_conditioning (line 731) | def combine_conditioning(conds: list):
  function combine_with_new_conds (line 737) | def combine_with_new_conds(conds: list, new_conds: list):
  function set_conds_props (line 743) | def set_conds_props(conds: list, strength: float, set_cond_area: str,
  function set_conds_props_and_combine (line 758) | def set_conds_props_and_combine(conds: list, new_conds: list, strength: ...
  function set_default_conds_and_combine (line 773) | def set_default_conds_and_combine(conds: list, new_conds: list,

FILE: comfy/image_encoders/dino2.py
  class Dino2AttentionOutput (line 7) | class Dino2AttentionOutput(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, devic...
    method forward (line 12) | def forward(self, x):
  class Dino2AttentionBlock (line 16) | class Dino2AttentionBlock(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, op...
    method forward (line 22) | def forward(self, x, mask, optimized_attention):
  class LayerScale (line 26) | class LayerScale(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, dim, dtype, device, operations):
    method forward (line 31) | def forward(self, x):
  class Dinov2MLP (line 34) | class Dinov2MLP(torch.nn.Module):
    method __init__ (line 35) | def __init__(self, hidden_size: int, dtype, device, operations):
    method forward (line 43) | def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
  class SwiGLUFFN (line 49) | class SwiGLUFFN(torch.nn.Module):
    method __init__ (line 50) | def __init__(self, dim, dtype, device, operations):
    method forward (line 59) | def forward(self, x):
  class Dino2Block (line 66) | class Dino2Block(torch.nn.Module):
    method __init__ (line 67) | def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, oper...
    method forward (line 79) | def forward(self, x, optimized_attention):
  class Dino2Encoder (line 85) | class Dino2Encoder(torch.nn.Module):
    method __init__ (line 86) | def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, ...
    method forward (line 91) | def forward(self, x, intermediate_output=None):
  class Dino2PatchEmbeddings (line 106) | class Dino2PatchEmbeddings(torch.nn.Module):
    method __init__ (line 107) | def __init__(self, dim, num_channels=3, patch_size=14, image_size=518,...
    method forward (line 119) | def forward(self, pixel_values):
  class Dino2Embeddings (line 123) | class Dino2Embeddings(torch.nn.Module):
    method __init__ (line 124) | def __init__(self, dim, dtype, device, operations):
    method forward (line 134) | def forward(self, pixel_values):
  class Dinov2Model (line 142) | class Dinov2Model(torch.nn.Module):
    method __init__ (line 143) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 155) | def forward(self, pixel_values, attention_mask=None, intermediate_outp...

FILE: comfy/k_diffusion/deis.py
  function edm2t (line 13) | def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
  function cal_poly (line 22) | def cal_poly(prev_t, j, taus):
  function t2alpha_fn (line 33) | def t2alpha_fn(beta_0, beta_1, t):
  function cal_intergrand (line 38) | def cal_intergrand(beta_0, beta_1, taus):
  function get_deis_coeff_list (line 54) | def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):

FILE: comfy/k_diffusion/sa_solver.py
  function compute_exponential_coeffs (line 10) | def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_...
  function compute_simple_stochastic_adams_b_coeffs (line 53) | def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, c...
  function compute_stochastic_adams_b_coeffs (line 69) | def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lam...
  function get_tau_interval_func (line 103) | def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: flo...

FILE: comfy/k_diffusion/sampling.py
  function append_zero (line 19) | def append_zero(x):
  function get_sigmas_karras (line 23) | def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
  function get_sigmas_exponential (line 32) | def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
  function get_sigmas_polyexponential (line 38) | def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='...
  function get_sigmas_vp (line 45) | def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
  function get_sigmas_laplace (line 52) | def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device=...
  function to_d (line 63) | def to_d(x, sigma, denoised):
  function get_ancestral_step (line 68) | def get_ancestral_step(sigma_from, sigma_to, eta=1.):
  function default_noise_sampler (line 78) | def default_noise_sampler(x, seed=None):
  class BatchedBrownianTree (line 91) | class BatchedBrownianTree:
    method __init__ (line 94) | def __init__(self, x, t0, t1, seed=None, **kwargs):
    method sort (line 115) | def sort(a, b):
    method __call__ (line 118) | def __call__(self, t0, t1):
  class BrownianTreeNoiseSampler (line 127) | class BrownianTreeNoiseSampler:
    method __init__ (line 142) | def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambd...
    method __call__ (line 147) | def __call__(self, sigma, sigma_next):
  function sigma_to_half_log_snr (line 152) | def sigma_to_half_log_snr(sigma, model_sampling):
  function half_log_snr_to_sigma (line 160) | def half_log_snr_to_sigma(half_log_snr, model_sampling):
  function offset_first_sigma_for_snr (line 168) | def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e...
  function ei_h_phi_1 (line 179) | def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:
  function ei_h_phi_2 (line 184) | def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:
  function sample_euler (line 190) | def sample_euler(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_euler_ancestral (line 216) | def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=N...
  function sample_euler_ancestral_RF (line 240) | def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callbac...
  function sample_heun (line 268) | def sample_heun(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_dpm_2 (line 303) | def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_dpm_2_ancestral (line 339) | def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=N...
  function sample_dpm_2_ancestral_RF (line 371) | def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callbac...
  function linear_multistep_coeff (line 404) | def linear_multistep_coeff(order, t, i, j):
  function sample_lms (line 418) | def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable...
  class PIDStepSizeController (line 441) | class PIDStepSizeController:
    method __init__ (line 443) | def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0...
    method limiter (line 452) | def limiter(self, x):
    method propose_step (line 455) | def propose_step(self, error):
  class DPMSolver (line 470) | class DPMSolver(nn.Module):
    method __init__ (line 473) | def __init__(self, model, extra_args=None, eps_callback=None, info_cal...
    method t (line 480) | def t(self, sigma):
    method sigma (line 483) | def sigma(self, t):
    method eps (line 486) | def eps(self, eps_cache, key, x, t, *args, **kwargs):
    method dpm_solver_1_step (line 495) | def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
    method dpm_solver_2_step (line 502) | def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
    method dpm_solver_3_step (line 512) | def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cach...
    method dpm_solver_fast (line 525) | def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., ...
    method dpm_solver_adaptive (line 564) | def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, a...
  function sample_dpm_fast (line 619) | def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, ...
  function sample_dpm_adaptive (line 631) | def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None,...
  function sample_dpmpp_2s_ancestral (line 646) | def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callbac...
  function sample_dpmpp_2s_ancestral_RF (line 684) | def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, call...
  function sample_dpmpp_sde (line 735) | def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, d...
  function sample_dpmpp_2m (line 792) | def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, di...
  function sample_dpmpp_2m_sde (line 818) | def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_2m_sde_heun (line 872) | def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback...
  function sample_dpmpp_3m_sde (line 877) | def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_3m_sde_gpu (line 939) | def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=...
  function sample_dpmpp_2m_sde_heun_gpu (line 949) | def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, call...
  function sample_dpmpp_2m_sde_gpu (line 959) | def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=...
  function sample_dpmpp_sde_gpu (line 969) | def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=Non...
  function DDPMSampler_step (line 978) | def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
  function generic_step_sampler (line 988) | def generic_step_sampler(model, x, sigmas, extra_args=None, callback=Non...
  function sample_ddpm (line 1005) | def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_lcm (line 1009) | def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable...
  function sample_heunpp2 (line 1027) | def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_ipndm (line 1085) | def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_ipndm_v (line 1128) | def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_deis (line 1195) | def sample_deis(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_euler_ancestral_cfg_pp (line 1244) | def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, cal...
  function sample_euler_cfg_pp (line 1288) | def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_2s_ancestral_cfg_pp (line 1294) | def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, ...
  function sample_dpmpp_2m_cfg_pp (line 1337) | def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=N...
  function res_multistep (line 1370) | def res_multistep(model, x, sigmas, extra_args=None, callback=None, disa...
  function sample_res_multistep (line 1434) | def sample_res_multistep(model, x, sigmas, extra_args=None, callback=Non...
  function sample_res_multistep_cfg_pp (line 1438) | def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callb...
  function sample_res_multistep_ancestral (line 1442) | def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, ca...
  function sample_res_multistep_ancestral_cfg_pp (line 1446) | def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=N...
  function sample_gradient_estimation (line 1451) | def sample_gradient_estimation(model, x, sigmas, extra_args=None, callba...
  function sample_gradient_estimation_cfg_pp (line 1495) | def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None,...
  function sample_er_sde (line 1500) | def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disa...
  function sample_seeds_2 (line 1566) | def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_exp_heun_2_x0 (line 1628) | def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=Non...
  function sample_exp_heun_2_x0_sde (line 1634) | def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback...
  function sample_seeds_3 (line 1640) | def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_sa_solver (line 1706) | def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, d...
  function sample_sa_solver_pece (line 1810) | def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=No...

FILE: comfy/k_diffusion/utils.py
  function hf_datasets_augs_helper (line 15) | def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
  function append_dims (line 21) | def append_dims(x, target_dims):
  function n_params (line 32) | def n_params(module):
  function download_file (line 37) | def download_file(path, url, digest=None):
  function train_mode (line 52) | def train_mode(model, mode=True):
  function eval_mode (line 63) | def eval_mode(model):
  function ema_update (line 70) | def ema_update(model, averaged_model, decay):
  class EMAWarmup (line 88) | class EMAWarmup:
    method __init__ (line 104) | def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1.,...
    method state_dict (line 113) | def state_dict(self):
    method load_state_dict (line 117) | def load_state_dict(self, state_dict):
    method get_value (line 125) | def get_value(self):
    method step (line 131) | def step(self):
  class InverseLR (line 136) | class InverseLR(optim.lr_scheduler._LRScheduler):
    method __init__ (line 153) | def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_l...
    method get_lr (line 163) | def get_lr(self):
    method _get_closed_form_lr (line 170) | def _get_closed_form_lr(self):
  class ExponentialLR (line 177) | class ExponentialLR(optim.lr_scheduler._LRScheduler):
    method __init__ (line 194) | def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
    method get_lr (line 204) | def get_lr(self):
    method _get_closed_form_lr (line 211) | def _get_closed_form_lr(self):
  function rand_log_normal (line 218) | def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.f...
  function rand_log_logistic (line 223) | def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=f...
  function rand_log_uniform (line 233) | def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=to...
  function rand_v_diffusion (line 240) | def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float...
  function rand_split_log_normal (line 248) | def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dt...
  class FolderOfImages (line 258) | class FolderOfImages(data.Dataset):
    method __init__ (line 264) | def __init__(self, root, transform=None):
    method __repr__ (line 270) | def __repr__(self):
    method __len__ (line 273) | def __len__(self):
    method __getitem__ (line 276) | def __getitem__(self, key):
  class CSVLogger (line 284) | class CSVLogger:
    method __init__ (line 285) | def __init__(self, filename, columns):
    method write (line 294) | def write(self, *args):
  function tf32_mode (line 299) | def tf32_mode(cudnn=None, matmul=None):

FILE: comfy/latent_formats.py
  class LatentFormat (line 3) | class LatentFormat:
    method process_in (line 13) | def process_in(self, latent):
    method process_out (line 16) | def process_out(self, latent):
  class SD15 (line 19) | class SD15(LatentFormat):
    method __init__ (line 20) | def __init__(self, scale_factor=0.18215):
  class SDXL (line 31) | class SDXL(LatentFormat):
    method __init__ (line 34) | def __init__(self):
  class SDXL_Playground_2_5 (line 46) | class SDXL_Playground_2_5(LatentFormat):
    method __init__ (line 47) | def __init__(self):
    method process_in (line 61) | def process_in(self, latent):
    method process_out (line 66) | def process_out(self, latent):
  class SD_X4 (line 72) | class SD_X4(LatentFormat):
    method __init__ (line 73) | def __init__(self):
  class SC_Prior (line 82) | class SC_Prior(LatentFormat):
    method __init__ (line 85) | def __init__(self):
  class SC_B (line 106) | class SC_B(LatentFormat):
    method __init__ (line 108) | def __init__(self):
  class SD3 (line 117) | class SD3(LatentFormat):
    method __init__ (line 119) | def __init__(self):
    method process_in (line 143) | def process_in(self, latent):
    method process_out (line 146) | def process_out(self, latent):
  class StableAudio1 (line 149) | class StableAudio1(LatentFormat):
  class Flux (line 153) | class Flux(SD3):
    method __init__ (line 155) | def __init__(self):
    method process_in (line 179) | def process_in(self, latent):
    method process_out (line 182) | def process_out(self, latent):
  class Flux2 (line 185) | class Flux2(LatentFormat):
    method __init__ (line 189) | def __init__(self):
    method process_in (line 228) | def process_in(self, latent):
    method process_out (line 231) | def process_out(self, latent):
  class Mochi (line 234) | class Mochi(LatentFormat):
    method __init__ (line 238) | def __init__(self):
    method process_in (line 266) | def process_in(self, latent):
    method process_out (line 271) | def process_out(self, latent):
  class LTXV (line 276) | class LTXV(LatentFormat):
    method __init__ (line 281) | def __init__(self):
  class LTXAV (line 415) | class LTXAV(LTXV):
    method __init__ (line 416) | def __init__(self):
  class HunyuanVideo (line 420) | class HunyuanVideo(LatentFormat):
  class Cosmos1CV8x8x8 (line 446) | class Cosmos1CV8x8x8(LatentFormat):
  class Wan21 (line 471) | class Wan21(LatentFormat):
    method __init__ (line 496) | def __init__(self):
    method process_in (line 510) | def process_in(self, latent):
    method process_out (line 515) | def process_out(self, latent):
  class Wan22 (line 520) | class Wan22(Wan21):
    method __init__ (line 578) | def __init__(self):
  class HunyuanImage21 (line 598) | class HunyuanImage21(LatentFormat):
  class HunyuanImage21Refiner (line 672) | class HunyuanImage21Refiner(LatentFormat):
    method process_in (line 677) | def process_in(self, latent):
    method process_out (line 686) | def process_out(self, latent):
  class HunyuanVideo15 (line 696) | class HunyuanVideo15(LatentFormat):
  class Hunyuan3Dv2 (line 739) | class Hunyuan3Dv2(LatentFormat):
  class Hunyuan3Dv2_1 (line 744) | class Hunyuan3Dv2_1(LatentFormat):
  class Hunyuan3Dv2mini (line 749) | class Hunyuan3Dv2mini(LatentFormat):
  class ACEAudio (line 754) | class ACEAudio(LatentFormat):
  class ACEAudio15 (line 758) | class ACEAudio15(LatentFormat):
  class ChromaRadiance (line 762) | class ChromaRadiance(LatentFormat):
    method __init__ (line 766) | def __init__(self):
    method process_in (line 774) | def process_in(self, latent):
    method process_out (line 777) | def process_out(self, latent):
  class ZImagePixelSpace (line 781) | class ZImagePixelSpace(ChromaRadiance):

FILE: comfy/ldm/ace/ace_step15.py
  function get_silence_latent (line 10) | def get_silence_latent(length, device):
  function get_layer_class (line 71) | def get_layer_class(operations, layer_name):
  class RotaryEmbedding (line 76) | class RotaryEmbedding(nn.Module):
    method __init__ (line 77) | def __init__(self, dim, max_position_embeddings=32768, base=1000000.0,...
    method _set_cos_sin_cache (line 87) | def _set_cos_sin_cache(self, seq_len, device, dtype):
    method forward (line 95) | def forward(self, x, seq_len=None):
  function rotate_half (line 103) | def rotate_half(x):
  function apply_rotary_pos_emb (line 108) | def apply_rotary_pos_emb(q, k, cos, sin):
  class MLP (line 115) | class MLP(nn.Module):
    method __init__ (line 116) | def __init__(self, hidden_size, intermediate_size, dtype=None, device=...
    method forward (line 124) | def forward(self, x):
  class TimestepEmbedding (line 127) | class TimestepEmbedding(nn.Module):
    method __init__ (line 128) | def __init__(self, in_channels: int, time_embed_dim: int, scale: float...
    method forward (line 139) | def forward(self, t, dtype=None):
  class AceStepAttention (line 147) | class AceStepAttention(nn.Module):
    method __init__ (line 148) | def __init__(
    method forward (line 179) | def forward(
  class AceStepDiTLayer (line 252) | class AceStepDiTLayer(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 289) | def forward(
  class AceStepEncoderLayer (line 327) | class AceStepEncoderLayer(nn.Module):
    method __init__ (line 328) | def __init__(
    method forward (line 349) | def forward(self, hidden_states, position_embeddings, attention_mask=N...
  class AceStepLyricEncoder (line 365) | class AceStepLyricEncoder(nn.Module):
    method __init__ (line 366) | def __init__(
    method forward (line 401) | def forward(self, inputs_embeds, attention_mask=None):
  class AceStepTimbreEncoder (line 417) | class AceStepTimbreEncoder(nn.Module):
    method __init__ (line 418) | def __init__(
    method unpack_timbre_embeddings (line 454) | def unpack_timbre_embeddings(self, timbre_embs_packed, refer_audio_ord...
    method forward (line 485) | def forward(self, refer_audio_acoustic_hidden_states_packed, refer_aud...
  function pack_sequences (line 506) | def pack_sequences(hidden1, hidden2, mask1, mask2):
  class AceStepConditionEncoder (line 523) | class AceStepConditionEncoder(nn.Module):
    method __init__ (line 524) | def __init__(
    method forward (line 572) | def forward(
  class AceStepDiTModel (line 602) | class AceStepDiTModel(nn.Module):
    method __init__ (line 603) | def __init__(
    method forward (line 668) | def forward(
  class AttentionPooler (line 721) | class AttentionPooler(nn.Module):
    method __init__ (line 722) | def __init__(self, hidden_size, num_layers, head_dim, rms_norm_eps, dt...
    method forward (line 738) | def forward(self, x):
  class FSQ (line 753) | class FSQ(nn.Module):
    method __init__ (line 754) | def __init__(
    method bound (line 791) | def bound(self, z):
    method _indices_to_codes (line 801) | def _indices_to_codes(self, indices):
    method codes_to_indices (line 806) | def codes_to_indices(self, zhat):
    method forward (line 810) | def forward(self, z):
  class ResidualFSQ (line 821) | class ResidualFSQ(nn.Module):
    method __init__ (line 822) | def __init__(
    method get_output_from_indices (line 873) | def get_output_from_indices(self, indices, dtype=torch.float32):
    method forward (line 886) | def forward(self, x):
  class AceStepAudioTokenizer (line 913) | class AceStepAudioTokenizer(nn.Module):
    method __init__ (line 914) | def __init__(
    method forward (line 945) | def forward(self, hidden_states):
    method tokenize (line 951) | def tokenize(self, x):
  class AudioTokenDetokenizer (line 967) | class AudioTokenDetokenizer(nn.Module):
    method __init__ (line 968) | def __init__(
    method forward (line 995) | def forward(self, x):
  class AceStepConditionGenerationModel (line 1011) | class AceStepConditionGenerationModel(nn.Module):
    method __init__ (line 1012) | def __init__(
    method prepare_condition (line 1076) | def prepare_condition(
    method forward (line 1113) | def forward(self, x, timestep, context, lyric_embed=None, refer_audio=...

FILE: comfy/ldm/ace/attention.py
  class Attention (line 24) | class Attention(nn.Module):
    method __init__ (line 25) | def __init__(
    method forward (line 131) | def forward(
  class CustomLiteLAProcessor2_0 (line 149) | class CustomLiteLAProcessor2_0:
    method __init__ (line 152) | def __init__(self):
    method apply_rotary_emb (line 157) | def apply_rotary_emb(
    method __call__ (line 187) | def __call__(
  class CustomerAttnProcessor2_0 (line 327) | class CustomerAttnProcessor2_0:
    method apply_rotary_emb (line 332) | def apply_rotary_emb(
    method __call__ (line 362) | def __call__(
  function val2list (line 457) | def val2list(x: list or tuple or any, repeat_time=1) -> list:  # type: i...
  function val2tuple (line 464) | def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int...
  function t2i_modulate (line 476) | def t2i_modulate(x, shift, scale):
  function get_same_padding (line 480) | def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[...
  class ConvLayer (line 487) | class ConvLayer(nn.Module):
    method __init__ (line 488) | def __init__(
    method forward (line 537) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class GLUMBConv (line 546) | class GLUMBConv(nn.Module):
    method __init__ (line 547) | def __init__(
    method forward (line 606) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LinearTransformerBlock (line 621) | class LinearTransformerBlock(nn.Module):
    method __init__ (line 625) | def __init__(
    method forward (line 694) | def forward(

FILE: comfy/ldm/ace/lyric_encoder.py
  class ConvolutionModule (line 9) | class ConvolutionModule(nn.Module):
    method __init__ (line 12) | def __init__(self,
    method forward (line 79) | def forward(
  class PositionwiseFeedForward (line 136) | class PositionwiseFeedForward(torch.nn.Module):
    method __init__ (line 149) | def __init__(
    method forward (line 164) | def forward(self, xs: torch.Tensor) -> torch.Tensor:
  class Swish (line 174) | class Swish(torch.nn.Module):
    method forward (line 177) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MultiHeadedAttention (line 181) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 191) | def __init__(self,
    method forward_qkv (line 209) | def forward_qkv(
    method forward_attention (line 237) | def forward_attention(
    method forward (line 279) | def forward(
  class RelPositionMultiHeadedAttention (line 331) | class RelPositionMultiHeadedAttention(MultiHeadedAttention):
    method __init__ (line 340) | def __init__(self,
    method rel_shift (line 357) | def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 381) | def forward(
  function subsequent_mask (line 450) | def subsequent_mask(
  function subsequent_chunk_mask (line 486) | def subsequent_chunk_mask(
  function add_optional_chunk_mask (line 523) | def add_optional_chunk_mask(xs: torch.Tensor,
  class ConformerEncoderLayer (line 597) | class ConformerEncoderLayer(nn.Module):
    method __init__ (line 617) | def __init__(
    method forward (line 649) | def forward(
  class EspnetRelPositionalEncoding (line 729) | class EspnetRelPositionalEncoding(torch.nn.Module):
    method __init__ (line 743) | def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5...
    method extend_pe (line 752) | def extend_pe(self, x: torch.Tensor):
    method forward (line 784) | def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = ...
    method position_encoding (line 800) | def position_encoding(self,
  class LinearEmbed (line 826) | class LinearEmbed(torch.nn.Module):
    method __init__ (line 836) | def __init__(self, idim: int, odim: int, dropout_rate: float,
    method position_encoding (line 847) | def position_encoding(self, offset: Union[int, torch.Tensor],
    method forward (line 851) | def forward(
  function make_pad_mask (line 889) | def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
  class ConformerEncoder (line 918) | class ConformerEncoder(torch.nn.Module):
    method __init__ (line 921) | def __init__(
    method forward_layers (line 1011) | def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
    method forward (line 1018) | def forward(

FILE: comfy/ldm/ace/model.py
  function cross_norm (line 29) | def cross_norm(hidden_states, controlnet_input):
  class Qwen2RotaryEmbedding (line 38) | class Qwen2RotaryEmbedding(nn.Module):
    method __init__ (line 39) | def __init__(self, dim, max_position_embeddings=2048, base=10000, dtyp...
    method _set_cos_sin_cache (line 53) | def _set_cos_sin_cache(self, seq_len, device, dtype):
    method forward (line 63) | def forward(self, x, seq_len=None):
  class T2IFinalLayer (line 74) | class T2IFinalLayer(nn.Module):
    method __init__ (line 79) | def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, ...
    method unpatchfy (line 87) | def unpatchfy(
    method forward (line 107) | def forward(self, x, t, output_length):
  class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 140) | def forward(self, latent):
  class ACEStepTransformer2DModel (line 147) | class ACEStepTransformer2DModel(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward_lyric_encoder (line 258) | def forward_lyric_encoder(
    method encode (line 270) | def encode(
    method decode (line 307) | def decode(
    method forward (line 349) | def forward(self,
    method _forward (line 370) | def _forward(

FILE: comfy/ldm/ace/vae/autoencoder_dc.py
  class RMSNorm (line 12) | class RMSNorm(ops.RMSNorm):
    method __init__ (line 13) | def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False):
    method forward (line 18) | def forward(self, x):
  function get_normalization (line 26) | def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5):
  function get_activation (line 39) | def get_activation(activation_type):
  class ResBlock (line 52) | class ResBlock(nn.Module):
    method __init__ (line 53) | def __init__(
    method forward (line 68) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class SanaMultiscaleAttentionProjection (line 82) | class SanaMultiscaleAttentionProjection(nn.Module):
    method __init__ (line 83) | def __init__(
    method forward (line 102) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class SanaMultiscaleLinearAttention (line 107) | class SanaMultiscaleLinearAttention(nn.Module):
    method __init__ (line 108) | def __init__(
    method apply_linear_attention (line 148) | def apply_linear_attention(self, query, key, value):
    method apply_quadratic_attention (line 157) | def apply_quadratic_attention(self, query, key, value):
    method forward (line 164) | def forward(self, hidden_states):
  class EfficientViTBlock (line 219) | class EfficientViTBlock(nn.Module):
    method __init__ (line 220) | def __init__(
    method forward (line 246) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class GLUMBConv (line 252) | class GLUMBConv(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 276) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  function get_block (line 299) | def get_block(
  class DCDownBlock2d (line 323) | class DCDownBlock2d(nn.Module):
    method __init__ (line 324) | def __init__(self, in_channels: int, out_channels: int, downsample: bo...
    method forward (line 346) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class DCUpBlock2d (line 362) | class DCUpBlock2d(nn.Module):
    method __init__ (line 363) | def __init__(
    method forward (line 385) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class Encoder (line 403) | class Encoder(nn.Module):
    method __init__ (line 404) | def __init__(
    method forward (line 474) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class Decoder (line 489) | class Decoder(nn.Module):
    method __init__ (line 490) | def __init__(
    method forward (line 563) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class AutoencoderDC (line 581) | class AutoencoderDC(nn.Module):
    method __init__ (line 582) | def __init__(
    method encode (line 630) | def encode(self, x: torch.Tensor) -> torch.Tensor:
    method decode (line 635) | def decode(self, z: torch.Tensor) -> torch.Tensor:
    method forward (line 641) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: comfy/ldm/ace/vae/music_dcae_pipeline.py
  class MusicDCAE (line 14) | class MusicDCAE(torch.nn.Module):
    method __init__ (line 15) | def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_co...
    method forward_mel (line 38) | def forward_mel(self, audios):
    method encode (line 47) | def encode(self, audios, audio_lengths=None, sr=None):
    method decode (line 74) | def decode(self, latents, audio_lengths=None, sr=None):
    method forward (line 95) | def forward(self, audios, audio_lengths=None, sr=None):

FILE: comfy/ldm/ace/vae/music_log_mel.py
  class LinearSpectrogram (line 13) | class LinearSpectrogram(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 32) | def forward(self, y: Tensor) -> Tensor:
  class LogMelSpectrogram (line 65) | class LogMelSpectrogram(nn.Module):
    method __init__ (line 66) | def __init__(
    method compress (line 99) | def compress(self, x: Tensor) -> Tensor:
    method decompress (line 102) | def decompress(self, x: Tensor) -> Tensor:
    method forward (line 105) | def forward(self, x: Tensor, return_linear: bool = False) -> Tensor:

FILE: comfy/ldm/ace/vae/music_vocoder.py
  function drop_path (line 20) | def drop_path(
  class DropPath (line 45) | class DropPath(nn.Module):
    method __init__ (line 48) | def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
    method forward (line 53) | def forward(self, x):
    method extra_repr (line 56) | def extra_repr(self):
  class LayerNorm (line 60) | class LayerNorm(nn.Module):
    method __init__ (line 67) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l...
    method forward (line 77) | def forward(self, x):
  class ConvNeXtBlock (line 90) | class ConvNeXtBlock(nn.Module):
    method __init__ (line 105) | def __init__(
    method forward (line 137) | def forward(self, x, apply_residual: bool = True):
  class ParallelConvNeXtBlock (line 159) | class ParallelConvNeXtBlock(nn.Module):
    method __init__ (line 160) | def __init__(self, kernel_sizes: List[int], *args, **kwargs):
    method forward (line 169) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ConvNeXtEncoder (line 176) | class ConvNeXtEncoder(nn.Module):
    method __init__ (line 177) | def __init__(
    method forward (line 237) | def forward(
  function get_padding (line 248) | def get_padding(kernel_size, dilation=1):
  class ResBlock1 (line 252) | class ResBlock1(torch.nn.Module):
    method __init__ (line 253) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
    method forward (line 326) | def forward(self, x):
    method remove_weight_norm (line 335) | def remove_weight_norm(self):
  class HiFiGANGenerator (line 342) | class HiFiGANGenerator(nn.Module):
    method __init__ (line 343) | def __init__(
    method forward (line 430) | def forward(self, x, template=None):
    method remove_weight_norm (line 456) | def remove_weight_norm(self):
  class ADaMoSHiFiGANV1 (line 465) | class ADaMoSHiFiGANV1(nn.Module):
    method __init__ (line 466) | def __init__(
    method decode (line 526) | def decode(self, mel):
    method encode (line 532) | def encode(self, x):
    method forward (line 535) | def forward(self, mel):

FILE: comfy/ldm/anima/model.py
  function rotate_half (line 7) | def rotate_half(x):
  function apply_rotary_pos_emb (line 13) | def apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):
  class RotaryEmbedding (line 20) | class RotaryEmbedding(nn.Module):
    method __init__ (line 21) | def __init__(self, head_dim):
    method forward (line 28) | def forward(self, x, position_ids):
  class Attention (line 42) | class Attention(nn.Module):
    method __init__ (line 43) | def __init__(self, query_dim, context_dim, n_heads, head_dim, device=N...
    method forward (line 62) | def forward(self, x, mask=None, context=None, position_embeddings=None...
    method init_weights (line 86) | def init_weights(self):
  class TransformerBlock (line 90) | class TransformerBlock(nn.Module):
    method __init__ (line 91) | def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0,...
    method forward (line 125) | def forward(self, x, context, target_attention_mask=None, source_atten...
    method init_weights (line 138) | def init_weights(self):
  class LLMAdapter (line 143) | class LLMAdapter(nn.Module):
    method __init__ (line 144) | def __init__(
    method forward (line 171) | def forward(self, source_hidden_states, target_input_ids, target_atten...
  class Anima (line 193) | class Anima(MiniTrainDIT):
    method __init__ (line 194) | def __init__(self, *args, **kwargs):
    method preprocess_text_embeds (line 198) | def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=...
    method forward (line 210) | def forward(self, x, timesteps, context, **kwargs):

FILE: comfy/ldm/audio/autoencoder.py
  function vae_sample (line 10) | def vae_sample(mean, scale):
  class VAEBottleneck (line 20) | class VAEBottleneck(nn.Module):
    method __init__ (line 21) | def __init__(self):
    method encode (line 25) | def encode(self, x, return_info=False, **kwargs):
    method decode (line 39) | def decode(self, x):
  function snake_beta (line 43) | def snake_beta(x, alpha, beta):
  class SnakeBeta (line 47) | class SnakeBeta(nn.Module):
    method __init__ (line 49) | def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha...
    method forward (line 67) | def forward(self, x):
  function WNConv1d (line 77) | def WNConv1d(*args, **kwargs):
  function WNConvTranspose1d (line 80) | def WNConvTranspose1d(*args, **kwargs):
  function get_activation (line 83) | def get_activation(activation: Literal["elu", "snake", "none"], antialia...
  class ResidualUnit (line 99) | class ResidualUnit(nn.Module):
    method __init__ (line 100) | def __init__(self, in_channels, out_channels, dilation, use_snake=Fals...
    method forward (line 116) | def forward(self, x):
  class EncoderBlock (line 124) | class EncoderBlock(nn.Module):
    method __init__ (line 125) | def __init__(self, in_channels, out_channels, stride, use_snake=False,...
    method forward (line 140) | def forward(self, x):
  class DecoderBlock (line 143) | class DecoderBlock(nn.Module):
    method __init__ (line 144) | def __init__(self, in_channels, out_channels, stride, use_snake=False,...
    method forward (line 173) | def forward(self, x):
  class OobleckEncoder (line 176) | class OobleckEncoder(nn.Module):
    method __init__ (line 177) | def __init__(self,
    method forward (line 206) | def forward(self, x):
  class OobleckDecoder (line 210) | class OobleckDecoder(nn.Module):
    method __init__ (line 211) | def __init__(self,
    method forward (line 250) | def forward(self, x):
  class AudioOobleckVAE (line 254) | class AudioOobleckVAE(nn.Module):
    method __init__ (line 255) | def __init__(self,
    method encode (line 271) | def encode(self, x):
    method decode (line 274) | def decode(self, x):

FILE: comfy/ldm/audio/dit.py
  class FourierFeatures (line 14) | class FourierFeatures(nn.Module):
    method __init__ (line 15) | def __init__(self, in_features, out_features, std=1., dtype=None, devi...
    method forward (line 21) | def forward(self, input):
  class LayerNorm (line 26) | class LayerNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, dim, bias=False, fix_scale=False, dtype=None, devic...
    method forward (line 40) | def forward(self, x):
  class GLU (line 46) | class GLU(nn.Module):
    method __init__ (line 47) | def __init__(
    method forward (line 63) | def forward(self, x):
  class AbsolutePositionalEmbedding (line 74) | class AbsolutePositionalEmbedding(nn.Module):
    method __init__ (line 75) | def __init__(self, dim, max_seq_len):
    method forward (line 81) | def forward(self, x, pos = None, seq_start_pos = None):
  class ScaledSinusoidalEmbedding (line 95) | class ScaledSinusoidalEmbedding(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, theta = 10000):
    method forward (line 106) | def forward(self, x, pos = None, seq_start_pos = None):
  class RotaryEmbedding (line 119) | class RotaryEmbedding(nn.Module):
    method __init__ (line 120) | def __init__(
    method forward_from_seq_len (line 152) | def forward_from_seq_len(self, seq_len, device, dtype):
    method forward (line 158) | def forward(self, t):
  function rotate_half (line 178) | def rotate_half(x):
  function apply_rotary_pos_emb (line 183) | def apply_rotary_pos_emb(t, freqs, scale = 1):
  class FeedForward (line 203) | class FeedForward(nn.Module):
    method __init__ (line 204) | def __init__(
    method forward (line 253) | def forward(self, x):
  class Attention (line 256) | class Attention(nn.Module):
    method __init__ (line 257) | def __init__(
    method forward (line 294) | def forward(
  class ConformerModule (line 376) | class ConformerModule(nn.Module):
    method __init__ (line 377) | def __init__(
    method forward (line 395) | def forward(self, x):
  class TransformerBlock (line 412) | class TransformerBlock(nn.Module):
    method __init__ (line 413) | def __init__(
    method forward (line 485) | def forward(
  class ContinuousTransformer (line 534) | class ContinuousTransformer(nn.Module):
    method __init__ (line 535) | def __init__(
    method forward (line 601) | def forward(
  class AudioDiffusionTransformer (line 670) | class AudioDiffusionTransformer(nn.Module):
    method __init__ (line 671) | def __init__(self,
    method _forward (line 776) | def _forward(
    method forward (line 872) | def forward(

FILE: comfy/ldm/audio/embedders.py
  class LearnedPositionalEmbedding (line 11) | class LearnedPositionalEmbedding(nn.Module):
    method __init__ (line 14) | def __init__(self, dim: int):
    method forward (line 20) | def forward(self, x: Tensor) -> Tensor:
  function TimePositionalEmbedding (line 27) | def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
  class NumberEmbedder (line 34) | class NumberEmbedder(nn.Module):
    method __init__ (line 35) | def __init__(
    method forward (line 44) | def forward(self, x: Union[List[float], Tensor]) -> Tensor:
  class Conditioner (line 56) | class Conditioner(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 70) | def forward(self, x):
  class NumberConditioner (line 73) | class NumberConditioner(Conditioner):
    method __init__ (line 77) | def __init__(self,
    method forward (line 89) | def forward(self, floats, device=None):

FILE: comfy/ldm/aura/mmdit.py
  function modulate (line 15) | def modulate(x, shift, scale):
  function find_multiple (line 19) | def find_multiple(n: int, k: int) -> int:
  class MLP (line 25) | class MLP(nn.Module):
    method __init__ (line 26) | def __init__(self, dim, hidden_dim=None, dtype=None, device=None, oper...
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MultiHeadLayerNorm (line 44) | class MultiHeadLayerNorm(nn.Module):
    method __init__ (line 45) | def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
    method forward (line 52) | def forward(self, hidden_states):
  class SingleAttention (line 63) | class SingleAttention(nn.Module):
    method __init__ (line 64) | def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=N...
    method forward (line 88) | def forward(self, c, transformer_options={}):
  class DoubleAttention (line 104) | class DoubleAttention(nn.Module):
    method __init__ (line 105) | def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=N...
    method forward (line 147) | def forward(self, c, x, transformer_options={}):
  class MMDiTBlock (line 180) | class MMDiTBlock(nn.Module):
    method __init__ (line 181) | def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, d...
    method forward (line 210) | def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
  class DiTBlock (line 241) | class DiTBlock(nn.Module):
    method __init__ (line 243) | def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, devi...
    method forward (line 258) | def forward(self, cx, global_cond, transformer_options={}, **kwargs):
  class TimestepEmbedder (line 275) | class TimestepEmbedder(nn.Module):
    method __init__ (line 276) | def __init__(self, hidden_size, frequency_embedding_size=256, dtype=No...
    method timestep_embedding (line 286) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 300) | def forward(self, t, dtype):
  class MMDiT (line 306) | class MMDiT(nn.Module):
    method __init__ (line 307) | def __init__(
    method extend_pe (line 371) | def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
    method pe_selection_index_based_on_dim (line 386) | def pe_selection_index_based_on_dim(self, h, w):
    method unpatchify (line 399) | def unpatchify(self, x, h, w):
    method patchify (line 408) | def patchify(self, x):
    method apply_pos_embeds (line 422) | def apply_pos_embeds(self, x, h, w):
    method forward (line 439) | def forward(self, x, timestep, context, transformer_options={}, **kwar...
    method _forward (line 446) | def _forward(self, x, timestep, context, transformer_options={}, **kwa...

FILE: comfy/ldm/cascade/common.py
  class OptimizedAttention (line 24) | class OptimizedAttention(nn.Module):
    method __init__ (line 25) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 35) | def forward(self, q, k, v, transformer_options={}):
  class Attention2D (line 44) | class Attention2D(nn.Module):
    method __init__ (line 45) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 50) | def forward(self, x, kv, self_attn=False, transformer_options={}):
  function LayerNorm2d_op (line 61) | def LayerNorm2d_op(operations):
  class GlobalResponseNorm (line 70) | class GlobalResponseNorm(nn.Module):
    method __init__ (line 72) | def __init__(self, dim, dtype=None, device=None):
    method forward (line 77) | def forward(self, x):
  class ResBlock (line 83) | class ResBlock(nn.Module):
    method __init__ (line 84) | def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None...
    method forward (line 97) | def forward(self, x, x_skip=None):
  class AttnBlock (line 106) | class AttnBlock(nn.Module):
    method __init__ (line 107) | def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtyp...
    method forward (line 117) | def forward(self, x, kv, transformer_options={}):
  class FeedForwardBlock (line 123) | class FeedForwardBlock(nn.Module):
    method __init__ (line 124) | def __init__(self, c, dropout=0.0, dtype=None, device=None, operations...
    method forward (line 135) | def forward(self, x):
  class TimestepBlock (line 140) | class TimestepBlock(nn.Module):
    method __init__ (line 141) | def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=No...
    method forward (line 148) | def forward(self, x, t):

FILE: comfy/ldm/cascade/controlnet.py
  class CNetResBlock (line 24) | class CNetResBlock(nn.Module):
    method __init__ (line 25) | def __init__(self, c, dtype=None, device=None, operations=None):
    method forward (line 36) | def forward(self, x):
  class ControlNet (line 40) | class ControlNet(nn.Module):
    method __init__ (line 41) | def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_m...
    method forward (line 87) | def forward(self, x):

FILE: comfy/ldm/cascade/stage_a.py
  class vector_quantize (line 27) | class vector_quantize(Function):
    method forward (line 29) | def forward(ctx, x, codebook):
    method backward (line 44) | def backward(ctx, grad_output, grad_indices):
  class VectorQuantize (line 59) | class VectorQuantize(nn.Module):
    method __init__ (line 60) | def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):
    method _laplace_smoothing (line 80) | def _laplace_smoothing(self, x, epsilon):
    method _updateEMA (line 84) | def _updateEMA(self, z_e_x, indices):
    method idx2vq (line 95) | def idx2vq(self, idx, dim=-1):
    method forward (line 101) | def forward(self, x, get_losses=True, dim=-1):
  class ResBlock (line 121) | class ResBlock(nn.Module):
    method __init__ (line 122) | def __init__(self, c, c_hidden):
    method _norm (line 141) | def _norm(self, x, norm):
    method forward (line 144) | def forward(self, x):
  class StageA (line 160) | class StageA(nn.Module):
    method __init__ (line 161) | def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_lat...
    method encode (line 205) | def encode(self, x, quantize=False):
    method decode (line 214) | def decode(self, x):
    method forward (line 219) | def forward(self, x, quantize=False):
  class Discriminator (line 225) | class Discriminator(nn.Module):
    method __init__ (line 226) | def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):
    method forward (line 243) | def forward(self, x, cond=None):

FILE: comfy/ldm/cascade/stage_b.py
  class StageB (line 24) | class StageB(nn.Module):
    method __init__ (line 25) | def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280,...
    method gen_r_embedding (line 158) | def gen_r_embedding(self, r, max_positions=10000):
    method gen_c_embeddings (line 169) | def gen_c_embeddings(self, clip):
    method _down_encode (line 176) | def _down_encode(self, x, r_embed, clip, transformer_options={}):
    method _up_decode (line 202) | def _up_decode(self, level_outputs, r_embed, clip, transformer_options...
    method forward (line 231) | def forward(self, x, r, effnet, clip, pixels=None, transformer_options...
    method update_weights_ema (line 252) | def update_weights_ema(self, src_model, beta=0.999):

FILE: comfy/ldm/cascade/stage_c.py
  class UpDownBlock2d (line 25) | class UpDownBlock2d(nn.Module):
    method __init__ (line 26) | def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device...
    method forward (line 34) | def forward(self, x):
  class StageC (line 40) | class StageC(nn.Module):
    method __init__ (line 41) | def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=204...
    method gen_r_embedding (line 162) | def gen_r_embedding(self, r, max_positions=10000):
    method gen_c_embeddings (line 173) | def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
    method _down_encode (line 185) | def _down_encode(self, x, r_embed, clip, cnet=None, transformer_option...
    method _up_decode (line 216) | def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transfor...
    method forward (line 250) | def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control...
    method update_weights_ema (line 269) | def update_weights_ema(self, src_model, beta=0.999):

FILE: comfy/ldm/cascade/stage_c_coder.py
  class EfficientNetEncoder (line 27) | class EfficientNetEncoder(nn.Module):
    method __init__ (line 28) | def __init__(self, c_latent=16):
    method forward (line 38) | def forward(self, x):
  class Previewer (line 46) | class Previewer(nn.Module):
    method __init__ (line 47) | def __init__(self, c_in=16, c_hidden=512, c_out=3):
    method forward (line 85) | def forward(self, x):
  class StageC_coder (line 88) | class StageC_coder(nn.Module):
    method __init__ (line 89) | def __init__(self):
    method encode (line 94) | def encode(self, x):
    method decode (line 97) | def decode(self, x):

FILE: comfy/ldm/chroma/layers.py
  class ChromaModulationOut (line 14) | class ChromaModulationOut(ModulationOut):
    method from_offset (line 16) | def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> Modulat...
  class Approximator (line 26) | class Approximator(nn.Module):
    method __init__ (line 27) | def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layer...
    method device (line 35) | def device(self):
    method forward (line 39) | def forward(self, x: Tensor) -> Tensor:
  class LastLayer (line 50) | class LastLayer(nn.Module):
    method __init__ (line 51) | def __init__(self, hidden_size: int, patch_size: int, out_channels: in...
    method forward (line 56) | def forward(self, x: Tensor, vec: Tensor) -> Tensor:

FILE: comfy/ldm/chroma/model.py
  class ChromaParams (line 26) | class ChromaParams:
  class Chroma (line 48) | class Chroma(nn.Module):
    method __init__ (line 53) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method get_modulations (line 115) | def get_modulations(self, tensor: torch.Tensor, block_type: str, *, id...
    method forward_orig (line 143) | def forward_orig(
    method forward (line 270) | def forward(self, x, timestep, context, guidance, control=None, transf...
    method _forward (line 277) | def _forward(self, x, timestep, context, guidance, control=None, trans...

FILE: comfy/ldm/chroma_radiance/layers.py
  class NerfEmbedder (line 8) | class NerfEmbedder(nn.Module):
    method __init__ (line 17) | def __init__(
    method fetch_pos (line 48) | def fetch_pos(self, patch_size: int, device: torch.device, dtype: torc...
    method forward (line 102) | def forward(self, inputs: torch.Tensor) -> torch.Tensor:
  class NerfGLUBlock (line 135) | class NerfGLUBlock(nn.Module):
    method __init__ (line 139) | def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, ...
    method forward (line 150) | def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
  class NerfFinalLayer (line 176) | class NerfFinalLayer(nn.Module):
    method __init__ (line 177) | def __init__(self, hidden_size, out_channels, dtype=None, device=None,...
    method forward (line 182) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class NerfFinalLayerConv (line 188) | class NerfFinalLayerConv(nn.Module):
    method __init__ (line 189) | def __init__(self, hidden_size: int, out_channels: int, dtype=None, de...
    method forward (line 201) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: comfy/ldm/chroma_radiance/model.py
  class ChromaRadianceParams (line 28) | class ChromaRadianceParams(ChromaParams):
  class ChromaRadiance (line 42) | class ChromaRadiance(Chroma):
    method __init__ (line 47) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method _nerf_final_layer (line 166) | def _nerf_final_layer(self) -> nn.Module:
    method img_in (line 174) | def img_in(self, img: Tensor) -> Tensor:
    method forward_nerf (line 179) | def forward_nerf(
    method forward_tiled_nerf (line 223) | def forward_tiled_nerf(
    method radiance_get_override_params (line 260) | def radiance_get_override_params(self, overrides: dict) -> ChromaRadia...
    method _apply_x0_residual (line 282) | def _apply_x0_residual(self, predicted, noisy, timesteps):
    method _forward (line 288) | def _forward(

FILE: comfy/ldm/common_dit.py
  function pad_to_patch_size (line 5) | def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):

FILE: comfy/ldm/cosmos/blocks.py
  function get_normalization (line 29) | def get_normalization(name: str, channels: int, weight_args={}, operatio...
  class BaseAttentionOp (line 38) | class BaseAttentionOp(nn.Module):
    method __init__ (line 39) | def __init__(self):
  class Attention (line 43) | class Attention(nn.Module):
    method __init__ (line 74) | def __init__(
    method cal_qkv (line 128) | def cal_qkv(
    method forward (line 173) | def forward(
  class FeedForward (line 194) | class FeedForward(nn.Module):
    method __init__ (line 215) | def __init__(
    method forward (line 237) | def forward(self, x: torch.Tensor):
  class GPT2FeedForward (line 247) | class GPT2FeedForward(FeedForward):
    method __init__ (line 248) | def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias...
    method forward (line 260) | def forward(self, x: torch.Tensor):
  function modulate (line 270) | def modulate(x, shift, scale):
  class Timesteps (line 274) | class Timesteps(nn.Module):
    method __init__ (line 275) | def __init__(self, num_channels):
    method forward (line 279) | def forward(self, timesteps):
  class TimestepEmbedding (line 294) | class TimestepEmbedding(nn.Module):
    method __init__ (line 295) | def __init__(self, in_features: int, out_features: int, use_adaln_lora...
    method forward (line 308) | def forward(self, sample: torch.Tensor) -> torch.Tensor:
  class FourierFeatures (line 323) | class FourierFeatures(nn.Module):
    method __init__ (line 343) | def __init__(self, num_channels, bandwidth=1, normalize=False):
    method forward (line 349) | def forward(self, x, gain: float = 1.0):
  class PatchEmbed (line 366) | class PatchEmbed(nn.Module):
    method __init__ (line 381) | def __init__(
    method forward (line 408) | def forward(self, x):
  class FinalLayer (line 431) | class FinalLayer(nn.Module):
    method __init__ (line 436) | def __init__(
    method forward (line 466) | def forward(
  class VideoAttn (line 489) | class VideoAttn(nn.Module):
    method __init__ (line 516) | def __init__(
    method forward (line 544) | def forward(
  function adaln_norm_state (line 582) | def adaln_norm_state(norm_state, x, scale, shift):
  class DITBuildingBlock (line 587) | class DITBuildingBlock(nn.Module):
    method __init__ (line 609) | def __init__(
    method forward (line 663) | def forward(
  class GeneralDITTransformerBlock (line 725) | class GeneralDITTransformerBlock(nn.Module):
    method __init__ (line 753) | def __init__(
    method forward (line 785) | def forward(

FILE: comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py
  class CausalConv3d (line 59) | class CausalConv3d(nn.Module):
    method __init__ (line 60) | def __init__(
    method _replication_pad (line 98) | def _replication_pad(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 104) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalUpsample3d (line 109) | class CausalUpsample3d(nn.Module):
    method __init__ (line 110) | def __init__(self, in_channels: int) -> None:
    method forward (line 116) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalDownsample3d (line 129) | class CausalDownsample3d(nn.Module):
    method __init__ (line 130) | def __init__(self, in_channels: int) -> None:
    method forward (line 141) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalHybridUpsample3d (line 149) | class CausalHybridUpsample3d(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward (line 188) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalHybridDownsample3d (line 211) | class CausalHybridDownsample3d(nn.Module):
    method __init__ (line 212) | def __init__(
    method forward (line 251) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalResnetBlock3d (line 275) | class CausalResnetBlock3d(nn.Module):
    method __init__ (line 276) | def __init__(
    method forward (line 303) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalResnetBlockFactorized3d (line 318) | class CausalResnetBlockFactorized3d(nn.Module):
    method __init__ (line 319) | def __init__(
    method forward (line 372) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalAttnBlock (line 387) | class CausalAttnBlock(nn.Module):
    method __init__ (line 388) | def __init__(self, in_channels: int, num_groups: int) -> None:
    method forward (line 407) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalTemporalAttnBlock (line 427) | class CausalTemporalAttnBlock(nn.Module):
    method __init__ (line 428) | def __init__(self, in_channels: int, num_groups: int) -> None:
    method forward (line 445) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class EncoderBase (line 479) | class EncoderBase(nn.Module):
    method __init__ (line 480) | def __init__(
    method patcher3d (line 561) | def patcher3d(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 567) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DecoderBase (line 607) | class DecoderBase(nn.Module):
    method __init__ (line 608) | def __init__(
    method unpatcher3d (line 696) | def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 703) | def forward(self, z):
  class EncoderFactorized (line 734) | class EncoderFactorized(nn.Module):
    method __init__ (line 735) | def __init__(
    method forward (line 863) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DecoderFactorized (line 888) | class DecoderFactorized(nn.Module):
    method __init__ (line 889) | def __init__(
    method forward (line 1020) | def forward(self, z):

FILE: comfy/ldm/cosmos/cosmos_tokenizer/patching.py
  class Patcher (line 39) | class Patcher(torch.nn.Module):
    method __init__ (line 49) | def __init__(self, patch_size=1, patch_method="haar"):
    method forward (line 65) | def forward(self, x):
    method _dwt (line 73) | def _dwt(self, x, mode="reflect", rescale=False):
    method _haar (line 97) | def _haar(self, x):
    method _arrange (line 102) | def _arrange(self, x):
  class Patcher3D (line 112) | class Patcher3D(Patcher):
    method __init__ (line 115) | def __init__(self, patch_size=1, patch_method="haar"):
    method _dwt (line 123) | def _dwt(self, x, wavelet, mode="reflect", rescale=False):
    method _haar (line 161) | def _haar(self, x):
    method _arrange (line 168) | def _arrange(self, x):
  class UnPatcher (line 181) | class UnPatcher(torch.nn.Module):
    method __init__ (line 191) | def __init__(self, patch_size=1, patch_method="haar"):
    method forward (line 207) | def forward(self, x):
    method _idwt (line 215) | def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
    method _ihaar (line 252) | def _ihaar(self, x):
    method _iarrange (line 257) | def _iarrange(self, x):
  class UnPatcher3D (line 267) | class UnPatcher3D(UnPatcher):
    method __init__ (line 270) | def __init__(self, patch_size=1, patch_method="haar"):
    method _idwt (line 273) | def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
    method _ihaar (line 362) | def _ihaar(self, x):
    method _iarrange (line 368) | def _iarrange(self, x):

FILE: comfy/ldm/cosmos/cosmos_tokenizer/utils.py
  function time2batch (line 26) | def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
  function batch2time (line 31) | def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor:
  function space2batch (line 35) | def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
  function batch2space (line 40) | def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch....
  function cast_tuple (line 44) | def cast_tuple(t: Any, length: int = 1) -> Any:
  function replication_pad (line 48) | def replication_pad(x):
  function divisible_by (line 52) | def divisible_by(num: int, den: int) -> bool:
  function is_odd (line 56) | def is_odd(n: int) -> bool:
  function nonlinearity (line 60) | def nonlinearity(x):
  function Normalize (line 65) | def Normalize(in_channels, num_groups=32):
  class CausalNormalize (line 71) | class CausalNormalize(torch.nn.Module):
    method __init__ (line 72) | def __init__(self, in_channels, num_groups=1):
    method forward (line 82) | def forward(self, x):
  function exists (line 91) | def exists(v):
  function default (line 95) | def default(*args):
  function round_ste (line 102) | def round_ste(z: torch.Tensor) -> torch.Tensor:
  function log (line 108) | def log(t, eps=1e-5):
  function entropy (line 112) | def entropy(prob):

FILE: comfy/ldm/cosmos/model.py
  class DataType (line 43) | class DataType(Enum):
  class GeneralDIT (line 48) | class GeneralDIT(nn.Module):
    method __init__ (line 91) | def __init__(
    method build_pos_embed (line 213) | def build_pos_embed(self, device=None, dtype=None):
    method prepare_embedded_sequence (line 250) | def prepare_embedded_sequence(
    method decoder_head (line 310) | def decoder_head(
    method forward_before_blocks (line 342) | def forward_before_blocks(
    method forward (line 423) | def forward(
    method _forward (line 459) | def _forward(

FILE: comfy/ldm/cosmos/position_embedding.py
  function normalize (line 24) | def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: flo...
  class VideoPositionEmb (line 43) | class VideoPositionEmb(nn.Module):
    method forward (line 44) | def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor...
    method generate_embeddings (line 53) | def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torc...
  class VideoRopePosition3DEmb (line 57) | class VideoRopePosition3DEmb(VideoPositionEmb):
    method __init__ (line 58) | def __init__(
    method generate_embeddings (line 100) | def generate_embeddings(
  class LearnablePosEmbAxis (line 166) | class LearnablePosEmbAxis(VideoPositionEmb):
    method __init__ (line 167) | def __init__(
    method generate_embeddings (line 192) | def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torc...

FILE: comfy/ldm/cosmos/predict2.py
  function apply_rotary_pos_emb (line 18) | def apply_rotary_pos_emb(
  class GPT2FeedForward (line 29) | class GPT2FeedForward(nn.Module):
    method __init__ (line 30) | def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, o...
    method forward (line 40) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function torch_attention_op (line 48) | def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor,...
  class Attention (line 78) | class Attention(nn.Module):
    method __init__ (line 110) | def __init__(
    method compute_qkv (line 154) | def compute_qkv(
    method compute_attention (line 184) | def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch...
    method forward (line 188) | def forward(
  class Timesteps (line 204) | class Timesteps(nn.Module):
    method __init__ (line 205) | def __init__(self, num_channels: int):
    method forward (line 209) | def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor:
  class TimestepEmbedding (line 226) | class TimestepEmbedding(nn.Module):
    method __init__ (line 227) | def __init__(self, in_features: int, out_features: int, use_adaln_lora...
    method forward (line 242) | def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optiona...
  class PatchEmbed (line 257) | class PatchEmbed(nn.Module):
    method __init__ (line 272) | def __init__(
    method forward (line 297) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class FinalLayer (line 322) | class FinalLayer(nn.Module):
    method __init__ (line 327) | def __init__(
    method forward (line 357) | def forward(
  class Block (line 388) | class Block(nn.Module):
    method __init__ (line 409) | def __init__(
    method forward (line 456) | def forward(
  class MiniTrainDIT (line 573) | class MiniTrainDIT(nn.Module):
    method __init__ (line 608) | def __init__(
    method build_pos_embed (line 718) | def build_pos_embed(self, device=None, dtype=None) -> None:
    method prepare_embedded_sequence (line 755) | def prepare_embedded_sequence(
    method unpatchify (line 808) | def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor:
    method forward (line 818) | def forward(self,
    method _forward (line 832) | def _forward(

FILE: comfy/ldm/cosmos/vae.py
  class IdentityDistribution (line 30) | class IdentityDistribution(torch.nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward (line 34) | def forward(self, parameters):
  class GaussianDistribution (line 38) | class GaussianDistribution(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
    method sample (line 44) | def sample(self, mean, logvar):
    method forward (line 48) | def forward(self, parameters):
  class ContinuousFormulation (line 54) | class ContinuousFormulation(Enum):
  class CausalContinuousVideoTokenizer (line 59) | class CausalContinuousVideoTokenizer(nn.Module):
    method __init__ (line 60) | def __init__(
    method encode (line 103) | def encode(self, x):
    method decode (line 117) | def decode(self, z):

FILE: comfy/ldm/flux/controlnet.py
  class MistolineCondDownsamplBlock (line 14) | class MistolineCondDownsamplBlock(nn.Module):
    method __init__ (line 15) | def __init__(self, dtype=None, device=None, operations=None):
    method forward (line 39) | def forward(self, x):
  class MistolineControlnetBlock (line 42) | class MistolineControlnetBlock(nn.Module):
    method __init__ (line 43) | def __init__(self, hidden_size, dtype=None, device=None, operations=No...
    method forward (line 48) | def forward(self, x):
  class ControlNetFlux (line 52) | class ControlNetFlux(Flux):
    method __init__ (line 53) | def __init__(self, latent_input=False, num_union_modes=0, mistoline=Fa...
    method forward_orig (line 109) | def forward_orig(
    method forward (line 182) | def forward(self, x, timesteps, context, y=None, guidance=None, hint=N...

FILE: comfy/ldm/flux/layers.py
  class EmbedND (line 12) | class EmbedND(nn.Module):
    method __init__ (line 13) | def __init__(self, dim: int, theta: int, axes_dim: list):
    method forward (line 19) | def forward(self, ids: Tensor) -> Tensor:
  function timestep_embedding (line 29) | def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: fl...
  class MLPEmbedder (line 50) | class MLPEmbedder(nn.Module):
    method __init__ (line 51) | def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None...
    method forward (line 57) | def forward(self, x: Tensor) -> Tensor:
  class YakMLP (line 60) | class YakMLP(nn.Module):
    method __init__ (line 61) | def __init__(self, hidden_size: int, intermediate_size: int, dtype=Non...
    method forward (line 70) | def forward(self, x: Tensor) -> Tensor:
  function build_mlp (line 74) | def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=F...
  class QKNorm (line 91) | class QKNorm(torch.nn.Module):
    method __init__ (line 92) | def __init__(self, dim: int, dtype=None, device=None, operations=None):
    method forward (line 97) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
  class SelfAttention (line 103) | class SelfAttention(nn.Module):
    method __init__ (line 104) | def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = Fals...
  class ModulationOut (line 115) | class ModulationOut:
  class Modulation (line 121) | class Modulation(nn.Module):
    method __init__ (line 122) | def __init__(self, dim: int, double: bool, bias=True, dtype=None, devi...
    method forward (line 128) | def forward(self, vec: Tensor) -> tuple:
  function apply_mod (line 139) | def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
  class SiLUActivation (line 153) | class SiLUActivation(nn.Module):
    method __init__ (line 154) | def __init__(self):
    method forward (line 158) | def forward(self, x: Tensor) -> Tensor:
  class DoubleStreamBlock (line 163) | class DoubleStreamBlock(nn.Module):
    method __init__ (line 164) | def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float,...
    method forward (line 192) | def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, a...
  class SingleStreamBlock (line 261) | class SingleStreamBlock(nn.Module):
    method __init__ (line 267) | def __init__(
    method forward (line 316) | def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, ...
  class LastLayer (line 358) | class LastLayer(nn.Module):
    method __init__ (line 359) | def __init__(self, hidden_size: int, patch_size: int, out_channels: in...
    method forward (line 365) | def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Ten...

FILE: comfy/ldm/flux/math.py
  function attention (line 10) | def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, tr...
  function rope (line 17) | def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
  function _apply_rope1 (line 32) | def _apply_rope1(x: Tensor, freqs_cis: Tensor):
  function _apply_rope (line 43) | def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
  function apply_rope (line 51) | def apply_rope(xq, xk, freqs_cis):
  function apply_rope1 (line 56) | def apply_rope1(x, freqs_cis):

FILE: comfy/ldm/flux/model.py
  class FluxParams (line 22) | class FluxParams:
  function invert_slices (line 47) | def invert_slices(slices, length):
  class Flux (line 63) | class Flux(nn.Module):
    method __init__ (line 68) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method forward_orig (line 147) | def forward_orig(
    method process_img (line 314) | def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_...
    method forward (line 344) | def forward(self, x, timestep, context, y=None, guidance=None, ref_lat...
    method _forward (line 351) | def _forward(self, x, timestep, context, y=None, guidance=None, ref_la...

FILE: comfy/ldm/flux/redux.py
  class ReduxImageEncoder (line 6) | class ReduxImageEncoder(torch.nn.Module):
    method __init__ (line 7) | def __init__(
    method forward (line 23) | def forward(self, sigclip_embeds) -> torch.Tensor:

FILE: comfy/ldm/genmo/joint_model/asymm_models_joint.py
  function modulated_rmsnorm (line 33) | def modulated_rmsnorm(x, scale, eps=1e-6):
  function residual_tanh_gated_rmsnorm (line 41) | def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6):
  class AsymmetricAttention (line 53) | class AsymmetricAttention(nn.Module):
    method __init__ (line 54) | def __init__(
    method forward (line 105) | def forward(
  class AsymmetricJointBlock (line 160) | class AsymmetricJointBlock(nn.Module):
    method __init__ (line 161) | def __init__(
    method forward (line 223) | def forward(
    method ff_block_x (line 277) | def ff_block_x(self, x, scale_x, gate_x):
    method ff_block_y (line 283) | def ff_block_y(self, y, scale_y, gate_y):
  class FinalLayer (line 290) | class FinalLayer(nn.Module):
    method __init__ (line 295) | def __init__(
    method forward (line 313) | def forward(self, x, c):
  class AsymmDiTJoint (line 321) | class AsymmDiTJoint(nn.Module):
    method __init__ (line 328) | def __init__(
    method embed_x (line 443) | def embed_x(self, x: torch.Tensor) -> torch.Tensor:
    method prepare (line 453) | def prepare(
    method forward (line 487) | def forward(

FILE: comfy/ldm/genmo/joint_model/layers.py
  function _ntuple (line 17) | def _ntuple(n):
  class TimestepEmbedder (line 29) | class TimestepEmbedder(nn.Module):
    method __init__ (line 30) | def __init__(
    method timestep_embedding (line 51) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 63) | def forward(self, t, out_dtype):
  class FeedForward (line 71) | class FeedForward(nn.Module):
    method __init__ (line 72) | def __init__(
    method forward (line 94) | def forward(self, x):
  class PatchEmbed (line 100) | class PatchEmbed(nn.Module):
    method __init__ (line 101) | def __init__(
    method forward (line 133) | def forward(self, x):

FILE: comfy/ldm/genmo/joint_model/rope_mixed.py
  function centers (line 9) | def centers(start: float, stop, num, dtype=None, device=None):
  function create_position_matrix (line 27) | def create_position_matrix(
  function compute_mixed_rotation (line 68) | def compute_mixed_rotation(

FILE: comfy/ldm/genmo/joint_model/temporal_rope.py
  function apply_rotary_emb_qk_real (line 7) | def apply_rotary_emb_qk_real(

FILE: comfy/ldm/genmo/joint_model/utils.py
  function modulate (line 11) | def modulate(x, shift, scale):
  function pool_tokens (line 15) | def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -...
  class AttentionPool (line 36) | class AttentionPool(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 59) | def forward(self, x, mask):

FILE: comfy/ldm/genmo/vae/model.py
  function cast_tuple (line 22) | def cast_tuple(t, length=1):
  class GroupNormSpatial (line 26) | class GroupNormSpatial(ops.GroupNorm):
    method forward (line 31) | def forward(self, x: torch.Tensor, *, chunk_size: int = 8):
  class PConv3d (line 40) | class PConv3d(ops.Conv3d):
    method __init__ (line 41) | def __init__(
    method forward (line 68) | def forward(self, x: torch.Tensor):
  class Conv1x1 (line 85) | class Conv1x1(ops.Linear):
    method __init__ (line 88) | def __init__(self, in_features: int, out_features: int, *args, **kwargs):
    method forward (line 91) | def forward(self, x: torch.Tensor):
  class DepthToSpaceTime (line 106) | class DepthToSpaceTime(nn.Module):
    method __init__ (line 107) | def __init__(
    method extra_repr (line 117) | def extra_repr(self):
    method forward (line 120) | def forward(self, x: torch.Tensor):
  function norm_fn (line 149) | def norm_fn(
  class ResBlock (line 156) | class ResBlock(nn.Module):
    method __init__ (line 159) | def __init__(
    method forward (line 201) | def forward(self, x: torch.Tensor):
  class Attention (line 215) | class Attention(nn.Module):
    method __init__ (line 216) | def __init__(
    method forward (line 232) | def forward(
  class AttentionBlock (line 276) | class AttentionBlock(nn.Module):
    method __init__ (line 277) | def __init__(
    method forward (line 286) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalUpsampleBlock (line 290) | class CausalUpsampleBlock(nn.Module):
    method __init__ (line 291) | def __init__(
    method forward (line 321) | def forward(self, x):
  function block_fn (line 328) | def block_fn(channels, *, affine: bool = True, has_attention: bool = Fal...
  class DownsampleBlock (line 333) | class DownsampleBlock(nn.Module):
    method __init__ (line 334) | def __init__(
    method forward (line 378) | def forward(self, x):
  function add_fourier_features (line 382) | def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):
  class FourierFeatures (line 409) | class FourierFeatures(nn.Module):
    method __init__ (line 410) | def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
    method forward (line 416) | def forward(self, inputs):
  class Decoder (line 428) | class Decoder(nn.Module):
    method __init__ (line 429) | def __init__(
    method forward (line 509) | def forward(self, x):
  class LatentDistribution (line 532) | class LatentDistribution:
    method __init__ (line 533) | def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
    method sample (line 544) | def sample(self, temperature=1.0, generator: torch.Generator = None, n...
    method mode (line 560) | def mode(self):
  class Encoder (line 563) | class Encoder(nn.Module):
    method __init__ (line 564) | def __init__(
    method temporal_downsample (line 637) | def temporal_downsample(self):
    method spatial_downsample (line 641) | def spatial_downsample(self):
    method forward (line 644) | def forward(self, x) -> LatentDistribution:
  class VideoVAE (line 673) | class VideoVAE(nn.Module):
    method __init__ (line 674) | def __init__(self):
    method encode (line 707) | def encode(self, x):
    method decode (line 710) | def decode(self, x):

FILE: comfy/ldm/hidream/model.py
  class EmbedND (line 21) | class EmbedND(nn.Module):
    method __init__ (line 22) | def __init__(self, theta: int, axes_dim: List[int]):
    method forward (line 27) | def forward(self, ids: torch.Tensor) -> torch.Tensor:
  class PatchEmbed (line 36) | class PatchEmbed(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 49) | def forward(self, latent):
  class PooledEmbed (line 54) | class PooledEmbed(nn.Module):
    method __init__ (line 55) | def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None,...
    method forward (line 59) | def forward(self, pooled_embed):
  class TimestepEmbed (line 63) | class TimestepEmbed(nn.Module):
    method __init__ (line 64) | def __init__(self, hidden_size, frequency_embedding_size=256, dtype=No...
    method forward (line 69) | def forward(self, timesteps, wdtype):
  function attention (line 75) | def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tenso...
  class HiDreamAttnProcessor_flashattn (line 79) | class HiDreamAttnProcessor_flashattn:
    method __call__ (line 82) | def __call__(
  class HiDreamAttention (line 148) | class HiDreamAttention(nn.Module):
    method __init__ (line 149) | def __init__(
    method forward (line 197) | def forward(
  class FeedForwardSwiGLU (line 215) | class FeedForwardSwiGLU(nn.Module):
    method __init__ (line 216) | def __init__(
    method forward (line 237) | def forward(self, x):
  class MoEGate (line 242) | class MoEGate(nn.Module):
    method __init__ (line 243) | def __init__(self, embed_dim, num_routed_experts=4, num_activated_expe...
    method reset_parameters (line 258) | def reset_parameters(self) -> None:
    method forward (line 263) | def forward(self, hidden_states):
  class MOEFeedForwardSwiGLU (line 287) | class MOEFeedForwardSwiGLU(nn.Module):
    method __init__ (line 288) | def __init__(
    method forward (line 307) | def forward(self, x):
    method moe_infer (line 328) | def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
  class TextProjection (line 349) | class TextProjection(nn.Module):
    method __init__ (line 350) | def __init__(self, in_features, hidden_size, dtype=None, device=None, ...
    method forward (line 354) | def forward(self, caption):
  class BlockType (line 359) | class BlockType:
  class HiDreamImageSingleTransformerBlock (line 364) | class HiDreamImageSingleTransformerBlock(nn.Module):
    method __init__ (line 365) | def __init__(
    method forward (line 405) | def forward(
  class HiDreamImageTransformerBlock (line 437) | class HiDreamImageTransformerBlock(nn.Module):
    method __init__ (line 438) | def __init__(
    method forward (line 483) | def forward(
  class HiDreamImageBlock (line 527) | class HiDreamImageBlock(nn.Module):
    method __init__ (line 528) | def __init__(
    method forward (line 552) | def forward(
  class HiDreamImageTransformer2DModel (line 571) | class HiDreamImageTransformer2DModel(nn.Module):
    method __init__ (line 572) | def __init__(
    method expand_timesteps (line 654) | def expand_timesteps(self, timesteps, batch_size, device):
    method unpatchify (line 668) | def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]...
    method patchify (line 679) | def patchify(self, x, max_seq, img_sizes=None):
    method forward (line 704) | def forward(self,
    method _forward (line 720) | def _forward(

FILE: comfy/ldm/hunyuan3d/model.py
  class Hunyuan3Dv2 (line 13) | class Hunyuan3Dv2(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 70) | def forward(self, x, timestep, context, guidance=None, transformer_opt...
    method _forward (line 77) | def _forward(self, x, timestep, context, guidance=None, transformer_op...

FILE: comfy/ldm/hunyuan3d/vae.py
  function fps (line 18) | def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, s...
  class PointCrossAttention (line 62) | class PointCrossAttention(nn.Module):
    method __init__ (line 63) | def __init__(self,
    method sample_points_and_latents (line 113) | def sample_points_and_latents(self, point_cloud: torch.Tensor, feature...
    method forward (line 199) | def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
    method subsample (line 219) | def subsample(self, pc, num_query, input_pc_size: int):
    method handle_features (line 247) | def handle_features(self, features, idx_pc, input_pc_size, batch_size:...
  function normalize_mesh (line 258) | def normalize_mesh(mesh, scale = 0.9999):
  function sample_pointcloud (line 270) | def sample_pointcloud(mesh, num = 200000):
  function detect_sharp_edges (line 277) | def detect_sharp_edges(mesh, threshold=0.985):
  function sharp_sample_pointcloud (line 297) | def sharp_sample_pointcloud(mesh, num = 16384):
  function load_surface_sharpedge (line 317) | def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096,...
  class SharpEdgeSurfaceLoader (line 377) | class SharpEdgeSurfaceLoader:
    method __init__ (line 380) | def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
    method __call__ (line 386) | def __call__(self, mesh_input, device = "cuda"):
    method _load_mesh (line 391) | def _load_mesh(mesh_input):
  class DiagonalGaussianDistribution (line 407) | class DiagonalGaussianDistribution:
    method __init__ (line 408) | def __init__(self, params: torch.Tensor, feature_dim: int = -1):
    method sample (line 416) | def sample(self):
  class VanillaVolumeDecoder (line 427) | class VanillaVolumeDecoder():
    method __call__ (line 429) | def __call__(self, latents: torch.Tensor, geo_decoder: callable, octre...
  class FourierEmbedder (line 459) | class FourierEmbedder(nn.Module):
    method __init__ (line 496) | def __init__(self,
    method get_dims (line 529) | def get_dims(self, input_dim):
    method forward (line 535) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CrossAttentionProcessor (line 555) | class CrossAttentionProcessor:
    method __call__ (line 556) | def __call__(self, attn, q, k, v):
  class DropPath (line 560) | class DropPath(nn.Module):
    method __init__ (line 564) | def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
    method forward (line 569) | def forward(self, x):
    method extra_repr (line 588) | def extra_repr(self):
  class MLP (line 592) | class MLP(nn.Module):
    method __init__ (line 593) | def __init__(
    method forward (line 607) | def forward(self, x):
  class QKVMultiheadCrossAttention (line 610) | class QKVMultiheadCrossAttention(nn.Module):
    method __init__ (line 611) | def __init__(
    method forward (line 625) | def forward(self, q, kv):
  class MultiheadCrossAttention (line 646) | class MultiheadCrossAttention(nn.Module):
    method __init__ (line 647) | def __init__(
    method forward (line 674) | def forward(self, x, data):
  class ResidualCrossAttentionBlock (line 687) | class ResidualCrossAttentionBlock(nn.Module):
    method __init__ (line 688) | def __init__(
    method forward (line 717) | def forward(self, x: torch.Tensor, data: torch.Tensor):
  class QKVMultiheadAttention (line 723) | class QKVMultiheadAttention(nn.Module):
    method __init__ (line 724) | def __init__(
    method forward (line 737) | def forward(self, qkv):
  class MultiheadAttention (line 751) | class MultiheadAttention(nn.Module):
    method __init__ (line 752) | def __init__(
    method forward (line 774) | def forward(self, x):
  class ResidualAttentionBlock (line 781) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 782) | def __init__(
    method forward (line 805) | def forward(self, x: torch.Tensor):
  class Transformer (line 811) | class Transformer(nn.Module):
    method __init__ (line 812) | def __init__(
    method forward (line 840) | def forward(self, x: torch.Tensor):
  class CrossAttentionDecoder (line 846) | class CrossAttentionDecoder(nn.Module):
    method __init__ (line 848) | def __init__(
    method forward (line 886) | def forward(self, queries=None, query_embeddings=None, latents=None):
  class ShapeVAE (line 899) | class ShapeVAE(nn.Module):
    method __init__ (line 900) | def __init__(
    method decode (line 966) | def decode(self, latents, **kwargs):
    method encode (line 978) | def encode(self, surface):

FILE: comfy/ldm/hunyuan3dv2_1/hunyuandit.py
  class GELU (line 8) | class GELU(nn.Module):
    method __init__ (line 10) | def __init__(self, dim_in: int, dim_out: int, operations, device, dtype):
    method gelu (line 14) | def gelu(self, gate: torch.Tensor) -> torch.Tensor:
    method forward (line 21) | def forward(self, hidden_states):
  class FeedForward (line 28) | class FeedForward(nn.Module):
    method __init__ (line 30) | def __init__(self, dim: int, dim_out = None, mult: int = 4,
    method forward (line 47) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class AddAuxLoss (line 52) | class AddAuxLoss(torch.autograd.Function):
    method forward (line 55) | def forward(ctx, x, loss):
    method backward (line 63) | def backward(ctx, grad_output):
  class MoEGate (line 73) | class MoEGate(nn.Module):
    method __init__ (line 75) | def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, a...
    method forward (line 86) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class MoEBlock (line 114) | class MoEBlock(nn.Module):
    method __init__ (line 115) | def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, drop...
    method forward (line 130) | def forward(self, hidden_states) -> torch.Tensor:
    method moe_infer (line 160) | def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
  class Timesteps (line 190) | class Timesteps(nn.Module):
    method __init__ (line 191) | def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0,
    method forward (line 214) | def forward(self, timesteps: torch.Tensor):
  class TimestepEmbedder (line 235) | class TimestepEmbedder(nn.Module):
    method __init__ (line 236) | def __init__(self, hidden_size, frequency_embedding_size = 256, cond_p...
    method forward (line 251) | def forward(self, timesteps, condition):
  class MLP (line 264) | class MLP(nn.Module):
    method __init__ (line 265) | def __init__(self, *, width: int, operations = None, device = None, dt...
    method forward (line 272) | def forward(self, x):
  class CrossAttention (line 275) | class CrossAttention(nn.Module):
    method __init__ (line 276) | def __init__(
    method forward (line 317) | def forward(self, x, y):
  class Attention (line 353) | class Attention(nn.Module):
    method __init__ (line 355) | def __init__(
    method forward (line 391) | def forward(self, x):
  class HunYuanDiTBlock (line 422) | class HunYuanDiTBlock(nn.Module):
    method __init__ (line 423) | def __init__(
    method forward (line 491) | def forward(self, hidden_states, conditioning=None, text_states=None, ...
  class FinalLayer (line 519) | class FinalLayer(nn.Module):
    method __init__ (line 521) | def __init__(self, final_hidden_size, out_channels, operations, use_fp...
    method forward (line 532) | def forward(self, x):
  class HunYuanDiTPlain (line 538) | class HunYuanDiTPlain(nn.Module):
    method __init__ (line 541) | def __init__(
    method forward (line 607) | def forward(self, x, t, context, transformer_options = {}, **kwargs):

FILE: comfy/ldm/hunyuan_video/model.py
  class HunyuanVideoParams (line 27) | class HunyuanVideoParams:
  class SelfAttentionRef (line 49) | class SelfAttentionRef(nn.Module):
    method __init__ (line 50) | def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, devic...
  class TokenRefinerBlock (line 56) | class TokenRefinerBlock(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 85) | def forward(self, x, c, mask, transformer_options={}):
  class IndividualTokenRefiner (line 98) | class IndividualTokenRefiner(nn.Module):
    method __init__ (line 99) | def __init__(
    method forward (line 122) | def forward(self, x, c, mask, transformer_options={}):
  class TokenRefiner (line 134) | class TokenRefiner(nn.Module):
    method __init__ (line 135) | def __init__(
    method forward (line 152) | def forward(
  class ByT5Mapper (line 173) | class ByT5Mapper(nn.Module):
    method __init__ (line 174) | def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=Fals...
    method forward (line 183) | def forward(self, x):
  class HunyuanVideo (line 196) | class HunyuanVideo(nn.Module):
    method __init__ (line 201) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method forward_orig (line 289) | def forward_orig(
    method img_ids (line 459) | def img_ids(self, x):
    method img_ids_2d (line 471) | def img_ids_2d(self, x):
    method forward (line 481) | def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fe...
    method _forward (line 488) | def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_f...

FILE: comfy/ldm/hunyuan_video/upsampler.py
  class SRResidualCausalBlock3D (line 9) | class SRResidualCausalBlock3D(nn.Module):
    method __init__ (line 10) | def __init__(self, channels: int):
    method forward (line 20) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SRModel3DV2 (line 23) | class SRModel3DV2(nn.Module):
    method __init__ (line 24) | def __init__(
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Upsampler (line 49) | class Upsampler(nn.Module):
    method __init__ (line 50) | def __init__(
    method forward (line 81) | def forward(self, z):
  class HunyuanVideo15SRModel (line 104) | class HunyuanVideo15SRModel():
    method __init__ (line 105) | def __init__(self, model_type, config):
    method load_sd (line 114) | def load_sd(self, sd):
    method get_sd (line 117) | def get_sd(self):
    method resample_latent (line 120) | def resample_latent(self, latent):

FILE: comfy/ldm/hunyuan_video/vae.py
  class PixelShuffle2D (line 8) | class PixelShuffle2D(nn.Module):
    method __init__ (line 9) | def __init__(self, in_dim, out_dim, op=ops.Conv2d):
    method forward (line 14) | def forward(self, x):
  class PixelUnshuffle2D (line 22) | class PixelUnshuffle2D(nn.Module):
    method __init__ (line 23) | def __init__(self, in_dim, out_dim, op=ops.Conv2d):
    method forward (line 28) | def forward(self, x):
  class Encoder (line 36) | class Encoder(nn.Module):
    method __init__ (line 37) | def __init__(self, in_channels, z_channels, block_out_channels, num_re...
    method forward (line 71) | def forward(self, x):
  class Decoder (line 89) | class Decoder(nn.Module):
    method __init__ (line 90) | def __init__(self, z_channels, out_channels, block_out_channels, num_r...
    method forward (line 126) | def forward(self, z):

FILE: comfy/ldm/hunyuan_video/vae_refiner.py
  class RMS_norm (line 11) | class RMS_norm(nn.Module):
    method __init__ (line 12) | def __init__(self, dim):
    method forward (line 18) | def forward(self, x):
  class DnSmpl (line 21) | class DnSmpl(nn.Module):
    method __init__ (line 22) | def __init__(self, ic, oc, tds, refiner_vae, op):
    method forward (line 32) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class UpSmpl (line 81) | class UpSmpl(nn.Module):
    method __init__ (line 82) | def __init__(self, ic, oc, tus, refiner_vae, op):
    method forward (line 91) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class Encoder (line 136) | class Encoder(nn.Module):
    method __init__ (line 137) | def __init__(self, in_channels, z_channels, block_out_channels, num_re...
    method forward (line 184) | def forward(self, x):
  class Decoder (line 232) | class Decoder(nn.Module):
    method __init__ (line 233) | def __init__(self, z_channels, out_channels, block_out_channels, num_r...
    method forward (line 278) | def forward(self, z):

FILE: comfy/ldm/hydit/attn_layers.py
  function reshape_for_broadcast (line 7) | def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Ten...
  function rotate_half (line 49) | def rotate_half(x):
  function apply_rotary_emb (line 54) | def apply_rotary_emb(
  class CrossAttention (line 96) | class CrossAttention(nn.Module):
    method __init__ (line 100) | def __init__(self,
    method forward (line 134) | def forward(self, x, y, freqs_cis_img=None):
  class Attention (line 174) | class Attention(nn.Module):
    method __init__ (line 178) | def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_...
    method forward (line 198) | def forward(self, x, freqs_cis_img=None):

FILE: comfy/ldm/hydit/controlnet.py
  class HunYuanControlNet (line 17) | class HunYuanControlNet(nn.Module):
    method __init__ (line 47) | def __init__(
    method forward (line 203) | def forward(

FILE: comfy/ldm/hydit/models.py
  function calc_rope (line 14) | def calc_rope(x, patch_size, head_size):
  function modulate (line 26) | def modulate(x, shift, scale):
  class HunYuanDiTBlock (line 30) | class HunYuanDiTBlock(nn.Module):
    method __init__ (line 34) | def __init__(self,
    method _forward (line 89) | def _forward(self, x, c=None, text_states=None, freq_cis_img=None, ski...
    method forward (line 117) | def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip...
  class FinalLayer (line 123) | class FinalLayer(nn.Module):
    method __init__ (line 127) | def __init__(self, final_hidden_size, c_emb_size, patch_size, out_chan...
    method forward (line 136) | def forward(self, x, c):
  class HunYuanDiT (line 143) | class HunYuanDiT(nn.Module):
    method __init__ (line 173) | def __init__(self,
    method forward (line 274) | def forward(self,
    method unpatchify (line 404) | def unpatchify(self, x, h, w):

FILE: comfy/ldm/hydit/poolers.py
  class AttentionPool (line 6) | class AttentionPool(nn.Module):
    method __init__ (line 7) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 17) | def forward(self, x):

FILE: comfy/ldm/hydit/posemb_layers.py
  function _to_tuple (line 6) | def _to_tuple(x):
  function get_fill_resize_and_crop (line 13) | def get_fill_resize_and_crop(src, tgt):
  function get_meshgrid (line 34) | def get_meshgrid(start, *args):
  function get_2d_sincos_pos_embed (line 64) | def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, ex...
  function get_2d_sincos_pos_embed_from_grid (line 83) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 94) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function get_2d_rotary_pos_embed (line 120) | def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
  function get_2d_rotary_pos_embed_from_grid (line 145) | def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
  function get_1d_rotary_pos_embed (line 161) | def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta...
  function calc_sizes (line 195) | def calc_sizes(rope_img, patch_size, th, tw):
  function init_image_posemb (line 209) | def init_image_posemb(rope_img,

FILE: comfy/ldm/kandinsky5/model.py
  function attention (line 10) | def attention(q, k, v, heads, transformer_options={}):
  function apply_scale_shift_norm (line 20) | def apply_scale_shift_norm(norm, x, scale, shift):
  function apply_gate_sum (line 23) | def apply_gate_sum(x, out, gate):
  function get_shift_scale_gate (line 26) | def get_shift_scale_gate(params):
  function get_freqs (line 30) | def get_freqs(dim, max_period=10000.0):
  class TimeEmbeddings (line 34) | class TimeEmbeddings(nn.Module):
    method __init__ (line 35) | def __init__(self, model_dim, time_dim, max_period=10000.0, operation_...
    method forward (line 46) | def forward(self, timestep, dtype):
  class TextEmbeddings (line 53) | class TextEmbeddings(nn.Module):
    method __init__ (line 54) | def __init__(self, text_dim, model_dim, operation_settings=None):
    method forward (line 60) | def forward(self, text_embed):
  class VisualEmbeddings (line 65) | class VisualEmbeddings(nn.Module):
    method __init__ (line 66) | def __init__(self, visual_dim, model_dim, patch_size, operation_settin...
    method forward (line 72) | def forward(self, x):
  class Modulation (line 88) | class Modulation(nn.Module):
    method __init__ (line 89) | def __init__(self, time_dim, model_dim, num_params, operation_settings...
    method forward (line 94) | def forward(self, x):
  class SelfAttention (line 98) | class SelfAttention(nn.Module):
    method __init__ (line 99) | def __init__(self, num_channels, head_dim, operation_settings=None):
    method _compute_qk (line 115) | def _compute_qk(self, x, freqs, proj_fn, norm_fn):
    method _forward (line 119) | def _forward(self, x, freqs, transformer_options={}):
    method _forward_chunked (line 126) | def _forward_chunked(self, x, freqs, transformer_options={}):
    method forward (line 141) | def forward(self, x, freqs, transformer_options={}):
  class CrossAttention (line 148) | class CrossAttention(SelfAttention):
    method get_qkv (line 149) | def get_qkv(self, x, context):
    method forward (line 155) | def forward(self, x, context, transformer_options={}):
  class FeedForward (line 161) | class FeedForward(nn.Module):
    method __init__ (line 162) | def __init__(self, dim, ff_dim, operation_settings=None):
    method _forward (line 170) | def _forward(self, x):
    method _forward_chunked (line 173) | def _forward_chunked(self, x):
    method forward (line 180) | def forward(self, x):
  class OutLayer (line 187) | class OutLayer(nn.Module):
    method __init__ (line 188) | def __init__(self, model_dim, time_dim, visual_dim, patch_size, operat...
    method forward (line 196) | def forward(self, visual_embed, time_embed):
  class TransformerEncoderBlock (line 213) | class TransformerEncoderBlock(nn.Module):
    method __init__ (line 214) | def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_se...
    method forward (line 225) | def forward(self, x, time_embed, freqs, transformer_options={}):
  class TransformerDecoderBlock (line 239) | class TransformerDecoderBlock(nn.Module):
    method __init__ (line 240) | def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_se...
    method forward (line 254) | def forward(self, visual_embed, text_embed, time_embed, freqs, transfo...
  class Kandinsky5 (line 274) | class Kandinsky5(nn.Module):
    method __init__ (line 275) | def __init__(
    method rope_encode_1d (line 311) | def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None...
    method rope_encode_3d (line 318) | def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=Non...
    method forward_orig (line 362) | def forward_orig(self, x, timestep, context, y, freqs, freqs_text, tra...
    method _forward (line 389) | def _forward(self, x, timestep, context, y, time_dim_replace=None, tra...
    method forward (line 408) | def forward(self, x, timestep, context, y, time_dim_replace=None, tran...

FILE: comfy/ldm/lightricks/av_model.py
  class CompressedTimestep (line 20) | class CompressedTimestep:
    method __init__ (line 24) | def __init__(self, tensor: torch.Tensor, patches_per_frame: int):
    method expand (line 46) | def expand(self):
    method expand_for_computation (line 55) | def expand_for_computation(self, scale_shift_table: torch.Tensor, batc...
  class BasicAVTransformerBlock (line 83) | class BasicAVTransformerBlock(nn.Module):
    method __init__ (line 84) | def __init__(
    method get_ada_values (line 202) | def get_ada_values(
    method get_av_ca_ada_values (line 216) | def get_av_ca_ada_values(
    method _apply_text_cross_attention (line 237) | def _apply_text_cross_attention(
    method forward (line 256) | def forward(
  class LTXAVModel (line 378) | class LTXAVModel(LTXVModel):
    method __init__ (line 381) | def __init__(
    method _init_model_components (line 448) | def _init_model_components(self, device, dtype, **kwargs):
    method preprocess_text_embeds (line 563) | def preprocess_text_embeds(self, context, unprocessed=False):
    method _init_transformer_blocks (line 582) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 605) | def _init_output_components(self, device, dtype):
    method separate_audio_and_video_latents (line 621) | def separate_audio_and_video_latents(self, x, audio_length):
    method recombine_audio_and_video_latents (line 640) | def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):
    method _process_input (line 663) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _prepare_timestep (line 689) | def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwar...
    method _prepare_context (line 789) | def _prepare_context(self, context, batch_size, x, attention_mask=None):
    method _prepare_positional_embeddings (line 811) | def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_d...
    method _process_transformer_blocks (line 850) | def _process_transformer_blocks(
    method _process_output (line 952) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
    method forward (line 984) | def forward(

FILE: comfy/ldm/lightricks/embeddings_connector.py
  class BasicTransformerBlock1D (line 16) | class BasicTransformerBlock1D(nn.Module):
    method __init__ (line 46) | def __init__(
    method forward (line 83) | def forward(self, hidden_states, attention_mask=None, pe=None) -> torc...
  class Embeddings1DConnector (line 112) | class Embeddings1DConnector(nn.Module):
    method __init__ (line 115) | def __init__(
    method get_fractional_positions (line 169) | def get_fractional_positions(self, indices_grid):
    method precompute_freqs (line 179) | def precompute_freqs(self, indices_grid, spacing):
    method generate_freq_grid (line 212) | def generate_freq_grid(self, spacing, dtype, device):
    method precompute_freqs_cis (line 239) | def precompute_freqs_cis(self, indices_grid, spacing="exp", out_dtype=...
    method forward (line 254) | def forward(

FILE: comfy/ldm/lightricks/latent_upsampler.py
  function _rational_for_scale (line 8) | def _rational_for_scale(scale: float) -> Tuple[int, int]:
  class PixelShuffleND (line 17) | class PixelShuffleND(nn.Module):
    method __init__ (line 18) | def __init__(self, dims, upscale_factors=(2, 2, 2)):
    method forward (line 24) | def forward(self, x):
  class BlurDownsample (line 48) | class BlurDownsample(nn.Module):
    method __init__ (line 54) | def __init__(self, dims: int, stride: int):
    method forward (line 67) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SpatialRationalResampler (line 92) | class SpatialRationalResampler(nn.Module):
    method __init__ (line 100) | def __init__(self, mid_channels: int, scale: float):
    method forward (line 110) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ResBlock (line 120) | class ResBlock(nn.Module):
    method __init__ (line 121) | def __init__(
    method forward (line 136) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LatentUpsampler (line 147) | class LatentUpsampler(nn.Module):
    method __init__ (line 160) | def __init__(
    method forward (line 223) | def forward(self, latent: torch.Tensor) -> torch.Tensor:
    method from_config (line 269) | def from_config(cls, config):
    method config (line 281) | def config(self):

FILE: comfy/ldm/lightricks/model.py
  function _log_base (line 20) | def _log_base(x, base):
  class LTXRopeType (line 23) | class LTXRopeType(str, Enum):
    method from_dict (line 30) | def from_dict(cls, kwargs, default=None):
  class LTXFrequenciesPrecision (line 36) | class LTXFrequenciesPrecision(str, Enum):
    method from_dict (line 43) | def from_dict(cls, kwargs, default=None):
  function get_timestep_embedding (line 49) | def get_timestep_embedding(
  class TimestepEmbedding (line 101) | class TimestepEmbedding(nn.Module):
    method __init__ (line 102) | def __init__(
    method forward (line 139) | def forward(self, sample, condition=None):
  class Timesteps (line 154) | class Timesteps(nn.Module):
    method __init__ (line 155) | def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale...
    method forward (line 162) | def forward(self, timesteps):
  class PixArtAlphaCombinedTimestepSizeEmbeddings (line 173) | class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
    method __init__ (line 181) | def __init__(
    method forward (line 198) | def forward(self, timestep, resolution, aspect_ratio, batch_size, hidd...
  class AdaLayerNormSingle (line 204) | class AdaLayerNormSingle(nn.Module):
    method __init__ (line 215) | def __init__(
    method forward (line 232) | def forward(
  class PixArtAlphaTextProjection (line 245) | class PixArtAlphaTextProjection(nn.Module):
    method __init__ (line 252) | def __init__(
    method forward (line 271) | def forward(self, caption):
  class NormSingleLinearTextProjection (line 278) | class NormSingleLinearTextProjection(nn.Module):
    method __init__ (line 281) | def __init__(
    method forward (line 296) | def forward(self, caption):
  class GELU_approx (line 302) | class GELU_approx(nn.Module):
    method __init__ (line 303) | def __init__(self, dim_in, dim_out, dtype=None, device=None, operation...
    method forward (line 307) | def forward(self, x):
  class FeedForward (line 311) | class FeedForward(nn.Module):
    method __init__ (line 312) | def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype...
    method forward (line 321) | def forward(self, x):
  function apply_rotary_emb (line 324) | def apply_rotary_emb(input_tensor, freqs_cis):
  function apply_interleaved_rotary_emb (line 333) | def apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs):  #...
  function apply_split_rotary_emb (line 343) | def apply_split_rotary_emb(input_tensor, cos, sin):
  class CrossAttention (line 361) | class CrossAttention(nn.Module):
    method __init__ (line 362) | def __init__(
    method forward (line 400) | def forward(self, x, context=None, mask=None, pe=None, k_pe=None, tran...
  class BasicTransformerBlock (line 433) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 434) | def __init__(
    method forward (line 470) | def forward(self, x, context=None, attention_mask=None, timestep=None,...
  function compute_prompt_timestep (line 490) | def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, h...
  function apply_cross_attention_adaln (line 512) | def apply_cross_attention_adaln(
  function get_fractional_positions (line 531) | def get_fractional_positions(indices_grid, max_pos):
  function generate_freq_grid_np (line 542) | def generate_freq_grid_np(positional_embedding_theta, positional_embeddi...
  function generate_freq_grid_pytorch (line 559) | def generate_freq_grid_pytorch(positional_embedding_theta, positional_em...
  function generate_freqs (line 580) | def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_gr...
  function interleaved_freqs_cis (line 599) | def interleaved_freqs_cis(freqs, pad_size):
  function split_freqs_cis (line 609) | def split_freqs_cis(freqs, pad_size, num_attention_heads):
  class LTXBaseModel (line 630) | class LTXBaseModel(torch.nn.Module, ABC):
    method __init__ (line 638) | def __init__(
    method _init_common_components (line 701) | def _init_common_components(self, device, dtype):
    method _init_model_components (line 744) | def _init_model_components(self, device, dtype, **kwargs):
    method _init_transformer_blocks (line 749) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 754) | def _init_output_components(self, device, dtype):
    method _process_input (line 759) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _build_guide_self_attention_mask (line 763) | def _build_guide_self_attention_mask(self, x, transformer_options, mer...
    method _process_transformer_blocks (line 772) | def _process_transformer_blocks(self, x, context, attention_mask, time...
    method _process_output (line 777) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
    method _prepare_timestep (line 781) | def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwar...
    method _prepare_context (line 805) | def _prepare_context(self, context, batch_size, x, attention_mask=None):
    method _precompute_freqs_cis (line 813) | def _precompute_freqs_cis(
    method _prepare_positional_embeddings (line 838) | def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_d...
    method _prepare_attention_mask (line 852) | def _prepare_attention_mask(self, attention_mask, x_dtype):
    method forward (line 860) | def forward(
    method _forward (line 887) | def _forward(
  class LTXVModel (line 944) | class LTXVModel(LTXBaseModel):
    method __init__ (line 947) | def __init__(
    method _init_model_components (line 989) | def _init_model_components(self, device, dtype, **kwargs):
    method _init_transformer_blocks (line 993) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 1011) | def _init_output_components(self, device, dtype):
    method _process_input (line 1020) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _build_guide_self_attention_mask (line 1074) | def _build_guide_self_attention_mask(self, x, transformer_options, mer...
    method _downsample_mask_to_latent (line 1174) | def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):
    method _build_self_attention_mask (line 1238) | def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked...
    method _process_transformer_blocks (line 1276) | def _process_transformer_blocks(self, x, context, attention_mask, time...
    method _process_output (line 1306) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):

FILE: comfy/ldm/lightricks/symmetric_patchifier.py
  function latent_to_pixel_coords (line 9) | def latent_to_pixel_coords(
  class Patchifier (line 36) | class Patchifier(ABC):
    method __init__ (line 37) | def __init__(self, patch_size: int, start_end: bool=False):
    method patchify (line 43) | def patchify(
    method unpatchify (line 49) | def unpatchify(
    method patch_size (line 60) | def patch_size(self):
    method get_latent_coords (line 63) | def get_latent_coords(
  class SymmetricPatchifier (line 97) | class SymmetricPatchifier(Patchifier):
    method patchify (line 98) | def patchify(
    method unpatchify (line 113) | def unpatchify(
  class AudioPatchifier (line 135) | class AudioPatchifier(Patchifier):
    method __init__ (line 136) | def __init__(self, patch_size: int,
    method copy_with_shift (line 151) | def copy_with_shift(self, shift):
    method _get_audio_latent_time_in_sec (line 157) | def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int,...
    method patchify (line 165) | def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor,...
    method unpatchify (line 185) | def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq:...

FILE: comfy/ldm/lightricks/vae/audio_vae.py
  class AudioVAEComponentConfig (line 22) | class AudioVAEComponentConfig:
    method from_metadata (line 29) | def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig":
  class ModelDeviceManager (line 47) | class ModelDeviceManager:
    method __init__ (line 50) | def __init__(self, module: torch.nn.Module):
    method ensure_model_loaded (line 55) | def ensure_model_loaded(self) -> None:
    method move_to_load_device (line 62) | def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
    method load_device (line 66) | def load_device(self):
  class AudioLatentNormalizer (line 70) | class AudioLatentNormalizer:
    method __init__ (line 73) | def __init__(self, patchfier: AudioPatchifier, statistics_processor: t...
    method normalize (line 77) | def normalize(self, latents: torch.Tensor) -> torch.Tensor:
    method denormalize (line 84) | def denormalize(self, latents: torch.Tensor) -> torch.Tensor:
  class AudioPreprocessor (line 92) | class AudioPreprocessor:
    method __init__ (line 95) | def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_len...
    method resample (line 101) | def resample(self, waveform: torch.Tensor, source_rate: int) -> torch....
    method waveform_to_mel (line 106) | def waveform_to_mel(
  class AudioVAE (line 132) | class AudioVAE(torch.nn.Module):
    method __init__ (line 135) | def __init__(self, state_dict: dict, metadata: dict):
    method encode (line 173) | def encode(self, audio: dict) -> torch.Tensor:
    method decode (line 203) | def decode(self, latents: torch.Tensor) -> torch.Tensor:
    method target_shape_from_latents (line 219) | def target_shape_from_latents(self, latents_shape):
    method num_of_latents_from_frames (line 231) | def num_of_latents_from_frames(self, frames_number: int, frame_rate: i...
    method run_vocoder (line 234) | def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:
    method sample_rate (line 246) | def sample_rate(self) -> int:
    method mel_hop_length (line 250) | def mel_hop_length(self) -> int:
    method mel_bins (line 254) | def mel_bins(self) -> int:
    method latent_channels (line 258) | def latent_channels(self) -> int:
    method latent_frequency_bins (line 262) | def latent_frequency_bins(self) -> int:
    method latents_per_second (line 266) | def latents_per_second(self) -> float:
    method output_sample_rate (line 270) | def output_sample_rate(self) -> int:
    method memory_required (line 281) | def memory_required(self, input_shape):

FILE: comfy/ldm/lightricks/vae/causal_audio_autoencoder.py
  class StringConvertibleEnum (line 14) | class StringConvertibleEnum(Enum):
    method str_to_enum (line 23) | def str_to_enum(cls, value):
  class AttentionType (line 76) | class AttentionType(StringConvertibleEnum):
  class CausalityAxis (line 84) | class CausalityAxis(StringConvertibleEnum):
  function Normalize (line 93) | def Normalize(in_channels, *, num_groups=32, normtype="group"):
  class CausalConv2d (line 102) | class CausalConv2d(nn.Module):
    method __init__ (line 111) | def __init__(
    method forward (line 157) | def forward(self, x):
  function make_conv2d (line 163) | def make_conv2d(
  class Upsample (line 213) | class Upsample(nn.Module):
    method __init__ (line 214) | def __init__(self, in_channels, with_conv, causality_axis: CausalityAx...
    method forward (line 221) | def forward(self, x):
  class Downsample (line 254) | class Downsample(nn.Module):
    method __init__ (line 261) | def __init__(self, in_channels, with_conv, causality_axis: CausalityAx...
    method forward (line 274) | def forward(self, x):
  class ResnetBlock (line 298) | class ResnetBlock(nn.Module):
    method __init__ (line 299) | def __init__(
    method forward (line 338) | def forward(self, x, temb):
  class AttnBlock (line 361) | class AttnBlock(nn.Module):
    method __init__ (line 362) | def __init__(self, in_channels, norm_type="group"):
    method forward (line 372) | def forward(self, x):
  function make_attn (line 399) | def make_attn(in_channels, attn_type="vanilla", norm_type="group"):
  class Encoder (line 419) | class Encoder(nn.Module):
    method __init__ (line 420) | def __init__(
    method forward (line 532) | def forward(self, x):
  class Decoder (line 588) | class Decoder(nn.Module):
    method __init__ (line 589) | def __init__(
    method _adjust_output_shape (line 691) | def _adjust_output_shape(self, decoded_output, target_shape):
    method get_config (line 735) | def get_config(self):
    method forward (line 746) | def forward(self, latent_features, target_shape=None):
  class processor (line 807) | class processor(nn.Module):
    method __init__ (line 808) | def __init__(self):
    method un_normalize (line 813) | def un_normalize(self, x):
    method normalize (line 816) | def normalize(self, x):
  class CausalAudioAutoencoder (line 820) | class CausalAudioAutoencoder(nn.Module):
    method __init__ (line 821) | def __init__(self, config=None):
    method get_default_config (line 850) | def get_default_config(self):
    method get_config (line 886) | def get_config(self):
    method encode (line 896) | def encode(self, x):
    method decode (line 899) | def decode(self, x, target_shape=None):

FILE: comfy/ldm/lightricks/vae/causal_conv3d.py
  class CausalConv3d (line 9) | class CausalConv3d(nn.Module):
    method __init__ (line 10) | def __init__(
    method forward (line 52) | def forward(self, x, causal: bool = True):
    method weight (line 89) | def weight(self):

FILE: comfy/ldm/lightricks/vae/causal_video_autoencoder.py
  function in_meta_context (line 19) | def in_meta_context():
  function mark_conv3d_ended (line 22) | def mark_conv3d_ended(module):
  function split2 (line 29) | def split2(tensor, split_point, dim=2):
  function add_exchange_cache (line 32) | def add_exchange_cache(dest, cache_in, new_input, dim=2):
  class Encoder (line 43) | class Encoder(nn.Module):
    method __init__ (line 68) | def __init__(
    method _forward_chunk (line 236) | def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch....
    method forward_orig (line 286) | def forward_orig(self, sample: torch.FloatTensor, device=None) -> torc...
    method forward (line 314) | def forward(self, *args, **kwargs):
  function get_max_chunk_size (line 332) | def get_max_chunk_size(device: torch.device) -> int:
  class Decoder (line 345) | class Decoder(nn.Module):
    method __init__ (line 370) | def __init__(
    method decode_output_shape (line 535) | def decode_output_shape(self, input_shape):
    method run_up (line 539) | def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_...
    method forward_orig (line 586) | def forward_orig(
    method forward (line 646) | def forward(self, *args, **kwargs):
  class UNetMidBlock3D (line 658) | class UNetMidBlock3D(nn.Module):
    method __init__ (line 682) | def __init__(
    method forward (line 725) | def forward(
  class SpaceToDepthDownsample (line 754) | class SpaceToDepthDownsample(nn.Module):
    method __init__ (line 755) | def __init__(self, dims, in_channels, out_channels, stride, spatial_pa...
    method forward (line 770) | def forward(self, x, causal: bool = True):
  class DepthToSpaceUpsample (line 825) | class DepthToSpaceUpsample(nn.Module):
    method __init__ (line 826) | def __init__(
    method forward (line 853) | def forward(self, x, causal: bool = True, timestep: Optional[torch.Ten...
  class LayerNorm (line 893) | class LayerNorm(nn.Module):
    method __init__ (line 894) | def __init__(self, dim, eps, elementwise_affine=True) -> None:
    method forward (line 898) | def forward(self, x):
  class ResnetBlock3D (line 905) | class ResnetBlock3D(nn.Module):
    method __init__ (line 918) | def __init__(
    method _feed_spatial_noise (line 1019) | def _feed_spatial_noise(
    method forward (line 1033) | def forward(
  function patchify (line 1100) | def patchify(x, patch_size_hw, patch_size_t=1):
  function unpatchify (line 1121) | def unpatchify(x, patch_size_hw, patch_size_t=1):
  class processor (line 1140) | class processor(nn.Module):
    method __init__ (line 1141) | def __init__(self):
    method un_normalize (line 1146) | def un_normalize(self, x):
    method normalize (line 1149) | def normalize(self, x):
  class VideoVAE (line 1152) | class VideoVAE(nn.Module):
    method __init__ (line 1155) | def __init__(self, version=0, config=None):
    method get_default_config (line 1197) | def get_default_config(self, version):
    method encode (line 1297) | def encode(self, x, device=None):
    method decode_output_shape (line 1302) | def decode_output_shape(self, input_shape):
    method decode (line 1305) | def decode(self, x, output_buffer=None):

FILE: comfy/ldm/lightricks/vae/conv_nd_factory.py
  function make_conv_nd (line 9) | def make_conv_nd(
  function make_linear_nd (line 75) | def make_linear_nd(

FILE: comfy/ldm/lightricks/vae/dual_conv3d.py
  class DualConv3d (line 10) | class DualConv3d(nn.Module):
    method __init__ (line 11) | def __init__(
    method reset_parameters (line 86) | def reset_parameters(self):
    method forward (line 97) | def forward(self, x, use_conv3d=False, skip_time_conv=False):
    method forward_with_3d (line 103) | def forward_with_3d(self, x, skip_time_conv):
    method forward_with_2d (line 133) | def forward_with_2d(self, x, skip_time_conv):
    method weight (line 185) | def weight(self):
  function test_dual_conv3d_consistency (line 189) | def test_dual_conv3d_consistency():

FILE: comfy/ldm/lightricks/vae/pixel_norm.py
  class PixelNorm (line 5) | class PixelNorm(nn.Module):
    method __init__ (line 6) | def __init__(self, dim=1, eps=1e-8):
    method forward (line 11) | def forward(self, x):

FILE: comfy/ldm/lightricks/vocoders/vocoder.py
  function get_padding (line 13) | def get_padding(kernel_size, dilation=1):
  function _sinc (line 23) | def _sinc(x: torch.Tensor):
  function kaiser_sinc_filter1d (line 31) | def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
  class LowPassFilter1d (line 56) | class LowPassFilter1d(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 81) | def forward(self, x):
  class UpSample1d (line 88) | class UpSample1d(nn.Module):
    method __init__ (line 89) | def __init__(self, ratio=2, kernel_size=None, persistent=True, window_...
    method forward (line 125) | def forward(self, x):
  class DownSample1d (line 135) | class DownSample1d(nn.Module):
    method __init__ (line 136) | def __init__(self, ratio=2, kernel_size=None):
    method forward (line 149) | def forward(self, x):
  class Activation1d (line 153) | class Activation1d(nn.Module):
    method __init__ (line 154) | def __init__(
    method forward (line 167) | def forward(self, x):
  class Snake (line 179) | class Snake(nn.Module):
    method __init__ (line 180) | def __init__(
    method forward (line 193) | def forward(self, x):
  class SnakeBeta (line 200) | class SnakeBeta(nn.Module):
    method __init__ (line 201) | def __init__(
    method forward (line 220) | def forward(self, x):
  class AMPBlock1 (line 234) | class AMPBlock1(torch.nn.Module):
    method __init__ (line 235) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activa...
    method forward (line 303) | def forward(self, x):
  class ResBlock1 (line 318) | class ResBlock1(torch.nn.Module):
    method __init__ (line 319) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
    method forward (line 379) | def forward(self, x):
  class ResBlock2 (line 389) | class ResBlock2(torch.nn.Module):
    method __init__ (line 390) | def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
    method forward (line 413) | def forward(self, x):
  class Vocoder (line 421) | class Vocoder(torch.nn.Module):
    method __init__ (line 428) | def __init__(self, config=None):
    method get_default_config (line 504) | def get_default_config(self):
    method forward (line 522) | def forward(self, x):
  class _STFTFn (line 563) | class _STFTFn(nn.Module):
    method __init__ (line 572) | def __init__(self, filter_length: int, hop_length: int, win_length: int):
    method forward (line 580) | def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
  class MelSTFT (line 608) | class MelSTFT(nn.Module):
    method __init__ (line 618) | def __init__(
    method mel_spectrogram (line 634) | def mel_spectrogram(
  class VocoderWithBWE (line 656) | class VocoderWithBWE(torch.nn.Module):
    method __init__ (line 663) | def __init__(self, config):
    method _compute_mel (line 692) | def _compute_mel(self, audio):
    method forward (line 699) | def forward(self, mel_spec):

FILE: comfy/ldm/lumina/controlnet.py
  class ZImageControlTransformerBlock (line 6) | class ZImageControlTransformerBlock(JointTransformerBlock):
    method __init__ (line 7) | def __init__(
    method forward (line 27) | def forward(self, c, x, **kwargs):
  class ZImage_Control (line 34) | class ZImage_Control(torch.nn.Module):
    method __init__ (line 35) | def __init__(
    method forward (line 127) | def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
    method forward_noise_refiner_block (line 141) | def forward_noise_refiner_block(self, layer_id, control_context, x, x_...
    method forward_control_block (line 159) | def forward_control_block(self, layer_id, control_context, x, x_attn_m...

FILE: comfy/ldm/lumina/model.py
  function invert_slices (line 20) | def invert_slices(slices, length):
  function modulate (line 36) | def modulate(x, scale, timestep_zero_index=None):
  function apply_gate (line 51) | def apply_gate(gate, x, timestep_zero_index=None):
  function clamp_fp16 (line 69) | def clamp_fp16(x):
  class JointAttention (line 74) | class JointAttention(nn.Module):
    method __init__ (line 77) | def __init__(
    method forward (line 123) | def forward(
  class FeedForward (line 169) | class FeedForward(nn.Module):
    method __init__ (line 170) | def __init__(
    method _forward_silu_gating (line 219) | def _forward_silu_gating(self, x1, x3):
    method forward (line 222) | def forward(self, x):
  class JointTransformerBlock (line 226) | class JointTransformerBlock(nn.Module):
    method __init__ (line 227) | def __init__(
    method forward (line 299) | def forward(
  class FinalLayer (line 355) | class FinalLayer(nn.Module):
    method __init__ (line 360) | def __init__(self, hidden_size, patch_size, out_channels, z_image_modu...
    method forward (line 393) | def forward(self, x, c, timestep_zero_index=None):
  function p

[
  {
    "path": ".ci/update_windows/update.py",
    "chars": 5782,
    "preview": "import pygit2\r\nfrom datetime import datetime\r\nimport sys\r\nimport os\r\nimport shutil\r\nimport filecmp\r\n\r\ndef pull(repo, rem"
  },
  {
    "path": ".ci/update_windows/update_comfyui.bat",
    "chars": 276,
    "preview": "@echo off\r\n..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\\r\nif exist update_new.py (\r\n  move /y update_new.py updat"
  },
  {
    "path": ".ci/update_windows/update_comfyui_stable.bat",
    "chars": 294,
    "preview": "@echo off\r\n..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\ --stable\r\nif exist update_new.py (\r\n  move /y update_new"
  },
  {
    "path": ".ci/windows_amd_base_files/README_VERY_IMPORTANT.txt",
    "chars": 958,
    "preview": "As of the time of writing this you need this driver for best results:\r\nhttps://www.amd.com/en/resources/support-articles"
  },
  {
    "path": ".ci/windows_amd_base_files/run_amd_gpu.bat",
    "chars": 82,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build\r\npause\r\n"
  },
  {
    "path": ".ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat",
    "chars": 105,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --disable-smart-memory\r\npause\r\n"
  },
  {
    "path": ".ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat",
    "chars": 89,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --fast\r\npause\r\n"
  },
  {
    "path": ".ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt",
    "chars": 1030,
    "preview": "HOW TO RUN:\r\n\r\nif you have a NVIDIA gpu:\r\n\r\nrun_nvidia_gpu.bat\r\n\r\nif you want to enable the fast fp16 accumulation (fast"
  },
  {
    "path": ".ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat",
    "chars": 316,
    "preview": "..\\python_embeded\\python.exe -s ..\\ComfyUI\\main.py --windows-standalone-build --disable-api-nodes\r\necho If you see this "
  },
  {
    "path": ".ci/windows_nvidia_base_files/run_cpu.bat",
    "chars": 88,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --cpu --windows-standalone-build\r\npause\r\n"
  },
  {
    "path": ".ci/windows_nvidia_base_files/run_nvidia_gpu.bat",
    "chars": 292,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build\r\necho If you see this and ComfyUI did not star"
  },
  {
    "path": ".ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat",
    "chars": 317,
    "preview": ".\\python_embeded\\python.exe -s ComfyUI\\main.py --windows-standalone-build --fast fp16_accumulation\r\necho If you see this"
  },
  {
    "path": ".coderabbit.yaml",
    "chars": 3875,
    "preview": "# yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json\nlanguage: \"en-US\"\nearly_access: false\n"
  },
  {
    "path": ".gitattributes",
    "chars": 112,
    "preview": "/web/assets/** linguist-generated\n/web/** linguist-vendored\ncomfy_api_nodes/apis/__init__.py linguist-generated\n"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/bug-report.yml",
    "chars": 3105,
    "preview": "name: Bug Report\ndescription: \"Something is broken inside of ComfyUI. (Do not use this if you're just having issues and "
  },
  {
    "path": ".github/ISSUE_TEMPLATE/config.yml",
    "chars": 689,
    "preview": "blank_issues_enabled: true\ncontact_links:\n  - name: ComfyUI Frontend Issues\n    url: https://github.com/Comfy-Org/ComfyU"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/feature-request.yml",
    "chars": 1584,
    "preview": "name: Feature Request\ndescription: \"You have an idea for something new you would like to see added to ComfyUI's core.\"\nl"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/user-support.yml",
    "chars": 1988,
    "preview": "name: User Support\ndescription: \"Use this if you need help with something, or you're experiencing an issue.\"\nlabels: [ \""
  },
  {
    "path": ".github/PULL_REQUEST_TEMPLATE/api-node.md",
    "chars": 396,
    "preview": "<!-- API_NODE_PR_CHECKLIST: do not remove -->\n\n## API Node PR Checklist\n\n### Scope\n- [ ] **Is API Node Change**\n\n### Pri"
  },
  {
    "path": ".github/scripts/check-ai-co-authors.sh",
    "chars": 3200,
    "preview": "#!/usr/bin/env bash\n# Checks pull request commits for AI agent Co-authored-by trailers.\n# Exits non-zero when any are fo"
  },
  {
    "path": ".github/workflows/api-node-template.yml",
    "chars": 2244,
    "preview": "name: Append API Node PR template\n\non:\n  pull_request_target:\n    types: [opened, reopened, synchronize, ready_for_revie"
  },
  {
    "path": ".github/workflows/check-ai-co-authors.yml",
    "chars": 485,
    "preview": "name: Check AI Co-Authors\n\non:\n  pull_request:\n    branches: ['*']\n\njobs:\n  check-ai-co-authors:\n    name: Check for AI "
  },
  {
    "path": ".github/workflows/check-line-endings.yml",
    "chars": 1235,
    "preview": "name: Check for Windows Line Endings\n\non:\n  pull_request:\n    branches: ['*'] # Trigger on all pull requests to any bran"
  },
  {
    "path": ".github/workflows/pullrequest-ci-run.yml",
    "chars": 1937,
    "preview": "# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' lab"
  },
  {
    "path": ".github/workflows/release-stable-all.yml",
    "chars": 1870,
    "preview": "name: \"Release Stable All Portable Versions\"\n\non:\n  workflow_dispatch:\n    inputs:\n      git_tag:\n        description: '"
  },
  {
    "path": ".github/workflows/release-webhook.yml",
    "chars": 6601,
    "preview": "name: Release Webhook\n\non:\n  release:\n    types: [published]\n\njobs:\n  send-webhook:\n    runs-on: ubuntu-latest\n    env:\n"
  },
  {
    "path": ".github/workflows/ruff.yml",
    "chars": 973,
    "preview": "name: Python Linting\n\non: [push, pull_request]\n\njobs:\n  ruff:\n    name: Run Ruff\n    runs-on: ubuntu-latest\n\n    steps:\n"
  },
  {
    "path": ".github/workflows/stable-release.yml",
    "chars": 5358,
    "preview": "\nname: \"Release Stable Version\"\n\non:\n  workflow_call:\n    inputs:\n      git_tag:\n        description: 'Git tag'\n        "
  },
  {
    "path": ".github/workflows/stale-issues.yml",
    "chars": 691,
    "preview": "name: 'Close stale issues'\non:\n  schedule:\n    # Run daily at 430 am PT\n    - cron: '30 11 * * *'\npermissions:\n  issues:"
  },
  {
    "path": ".github/workflows/test-build.yml",
    "chars": 765,
    "preview": "name: Build package\n\n#\n# This workflow is a test of the python package build.\n# Install Python dependencies across diffe"
  },
  {
    "path": ".github/workflows/test-ci.yml",
    "chars": 3101,
    "preview": "# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of Co"
  },
  {
    "path": ".github/workflows/test-execution.yml",
    "chars": 839,
    "preview": "name: Execution Tests\n\non:\n  push:\n    branches: [ main, master, release/** ]\n  pull_request:\n    branches: [ main, mast"
  },
  {
    "path": ".github/workflows/test-launch.yml",
    "chars": 1788,
    "preview": "name: Test server launches without errors\n\non:\n  push:\n    branches: [ main, master, release/** ]\n  pull_request:\n    br"
  },
  {
    "path": ".github/workflows/test-unit.yml",
    "chars": 798,
    "preview": "name: Unit Tests\n\non:\n  push:\n    branches: [ main, master, release/** ]\n  pull_request:\n    branches: [ main, master, r"
  },
  {
    "path": ".github/workflows/update-api-stubs.yml",
    "chars": 1833,
    "preview": "name: Generate Pydantic Stubs from api.comfy.org\n\non:\n  schedule:\n    - cron: '0 0 * * 1'\n  workflow_dispatch:\n\njobs:\n  "
  },
  {
    "path": ".github/workflows/update-ci-container.yml",
    "chars": 2113,
    "preview": "name: \"CI: Update CI Container\"\n\non:\n  release:\n    types: [published]\n  workflow_dispatch:\n    inputs:\n      version:\n "
  },
  {
    "path": ".github/workflows/update-version.yml",
    "chars": 1859,
    "preview": "name: Update Version File\n\non:\n  pull_request:\n    paths:\n      - \"pyproject.toml\"\n    branches:\n      - master\n      - "
  },
  {
    "path": ".github/workflows/windows_release_dependencies.yml",
    "chars": 2530,
    "preview": "name: \"Windows Release dependencies\"\n\non:\n  workflow_dispatch:\n    inputs:\n      xformers:\n        description: 'xformer"
  },
  {
    "path": ".github/workflows/windows_release_dependencies_manual.yml",
    "chars": 2295,
    "preview": "name: \"Windows Release dependencies Manual\"\n\non:\n  workflow_dispatch:\n    inputs:\n      torch_dependencies:\n        desc"
  },
  {
    "path": ".github/workflows/windows_release_nightly_pytorch.yml",
    "chars": 3737,
    "preview": "name: \"Windows Release Nightly pytorch\"\n\non:\n  workflow_dispatch:\n    inputs:\n      cu:\n        description: 'cuda versi"
  },
  {
    "path": ".github/workflows/windows_release_package.yml",
    "chars": 3666,
    "preview": "name: \"Windows Release packaging\"\n\non:\n  workflow_dispatch:\n    inputs:\n      cu:\n        description: 'cuda version'\n  "
  },
  {
    "path": ".gitignore",
    "chars": 383,
    "preview": "__pycache__/\n*.py[cod]\n/output/\n/input/\n!/input/example.png\n/models/\n/temp/\n/custom_nodes/\n!custom_nodes/example_node.py"
  },
  {
    "path": "CODEOWNERS",
    "chars": 47,
    "preview": "# Admins\n* @comfyanonymous @kosinkadink @guill\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "chars": 1876,
    "preview": "# Contributing to ComfyUI\n\nWelcome, and thank you for your interest in contributing to ComfyUI!\n\nThere are several ways "
  },
  {
    "path": "LICENSE",
    "chars": 35149,
    "preview": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
  },
  {
    "path": "QUANTIZATION.md",
    "chars": 7690,
    "preview": "# The Comfy guide to Quantization\n\n\n## How does quantization work?\n\nQuantization aims to map a high-precision value x_f "
  },
  {
    "path": "README.md",
    "chars": 28839,
    "preview": "<div align=\"center\">\n\n# ComfyUI\n**The most powerful and modular visual AI engine and application.**\n\n\n[![Website][websit"
  },
  {
    "path": "alembic.ini",
    "chars": 3230,
    "preview": "# A generic, single database configuration.\n\n[alembic]\n# path to migration scripts\n# Use forward slashes (/) also on win"
  },
  {
    "path": "alembic_db/README.md",
    "chars": 133,
    "preview": "## Generate new revision\n\n1. Update models in `/app/database/models.py`\n2. Run `alembic revision --autogenerate -m \"{you"
  },
  {
    "path": "alembic_db/env.py",
    "chars": 1914,
    "preview": "from sqlalchemy import engine_from_config\nfrom sqlalchemy import pool\n\nfrom alembic import context\n\n# this is the Alembi"
  },
  {
    "path": "alembic_db/script.py.mako",
    "chars": 689,
    "preview": "\"\"\"${message}\n\nRevision ID: ${up_revision}\nRevises: ${down_revision | comma,n}\nCreate Date: ${create_date}\n\n\"\"\"\nfrom typ"
  },
  {
    "path": "alembic_db/versions/0001_assets.py",
    "chars": 8823,
    "preview": "\"\"\"\nInitial assets schema\nRevision ID: 0001_assets\nRevises: None\nCreate Date: 2025-12-10 00:00:00\n\"\"\"\n\nfrom alembic impo"
  },
  {
    "path": "alembic_db/versions/0002_merge_to_asset_references.py",
    "chars": 13024,
    "preview": "\"\"\"\nMerge AssetInfo and AssetCacheState into unified asset_references table.\n\nThis migration drops old tables and create"
  },
  {
    "path": "alembic_db/versions/0003_add_metadata_job_id.py",
    "chars": 3450,
    "preview": "\"\"\"\nAdd system_metadata and job_id columns to asset_references.\nChange preview_id FK from assets.id to asset_references."
  },
  {
    "path": "api_server/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "api_server/routes/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "api_server/routes/internal/README.md",
    "chars": 220,
    "preview": "# ComfyUI Internal Routes\n\nAll routes under the `/internal` path are designated for **internal use by ComfyUI only**. Th"
  },
  {
    "path": "api_server/routes/internal/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "api_server/routes/internal/internal_routes.py",
    "chars": 3048,
    "preview": "from aiohttp import web\nfrom typing import Optional\nfrom folder_paths import folder_names_and_paths, get_directory_by_ty"
  },
  {
    "path": "api_server/services/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "api_server/services/terminal_service.py",
    "chars": 1709,
    "preview": "from app.logger import on_flush\nimport os\nimport shutil\n\n\nclass TerminalService:\n    def __init__(self, server):\n       "
  },
  {
    "path": "api_server/utils/file_operations.py",
    "chars": 1367,
    "preview": "import os\nfrom typing import List, Union, TypedDict, Literal\nfrom typing_extensions import TypeGuard\nclass FileInfo(Type"
  },
  {
    "path": "app/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "app/app_settings.py",
    "chars": 2255,
    "preview": "import os\nimport json\nfrom aiohttp import web\nimport logging\n\n\nclass AppSettings():\n    def __init__(self, user_manager)"
  },
  {
    "path": "app/assets/api/routes.py",
    "chars": 28115,
    "preview": "import asyncio\nimport functools\nimport json\nimport logging\nimport os\nimport urllib.parse\nimport uuid\nfrom typing import "
  },
  {
    "path": "app/assets/api/schemas_in.py",
    "chars": 10990,
    "preview": "import json\nfrom dataclasses import dataclass\nfrom typing import Any, Literal\n\nfrom app.assets.helpers import validate_b"
  },
  {
    "path": "app/assets/api/schemas_out.py",
    "chars": 2067,
    "preview": "from datetime import datetime\nfrom typing import Any\n\nfrom pydantic import BaseModel, ConfigDict, Field, field_serialize"
  },
  {
    "path": "app/assets/api/upload.py",
    "chars": 6338,
    "preview": "import logging\nimport os\nimport uuid\nfrom typing import Callable\n\nfrom aiohttp import web\n\nimport folder_paths\nfrom app."
  },
  {
    "path": "app/assets/database/models.py",
    "chars": 8998,
    "preview": "from __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\nfrom typing import Any\n\nfrom sqlalchemy im"
  },
  {
    "path": "app/assets/database/queries/__init__.py",
    "chars": 4012,
    "preview": "from app.assets.database.queries.asset import (\n    asset_exists_by_hash,\n    bulk_insert_assets,\n    get_asset_by_hash,"
  },
  {
    "path": "app/assets/database/queries/asset.py",
    "chars": 3818,
    "preview": "import sqlalchemy as sa\nfrom sqlalchemy import select\nfrom sqlalchemy.dialects import sqlite\nfrom sqlalchemy.orm import "
  },
  {
    "path": "app/assets/database/queries/asset_reference.py",
    "chars": 30213,
    "preview": "\"\"\"Query functions for the unified AssetReference table.\n\nThis module replaces the separate asset_info.py and cache_stat"
  },
  {
    "path": "app/assets/database/queries/common.py",
    "chars": 4299,
    "preview": "\"\"\"Shared utilities for database query modules.\"\"\"\n\nimport os\nfrom decimal import Decimal\nfrom typing import Iterable, S"
  },
  {
    "path": "app/assets/database/queries/tags.py",
    "chars": 13042,
    "preview": "from dataclasses import dataclass\nfrom typing import Iterable, Sequence\n\nimport sqlalchemy as sa\nfrom sqlalchemy import "
  },
  {
    "path": "app/assets/helpers.py",
    "chars": 2032,
    "preview": "import os\nfrom datetime import datetime, timezone\nfrom typing import Sequence\n\n\ndef select_best_live_path(states: Sequen"
  },
  {
    "path": "app/assets/scanner.py",
    "chars": 19665,
    "preview": "import logging\nimport os\nfrom pathlib import Path\nfrom typing import Callable, Literal, TypedDict\n\nimport folder_paths\nf"
  },
  {
    "path": "app/assets/seeder.py",
    "chars": 27202,
    "preview": "\"\"\"Background asset seeder with thread management and cancellation support.\"\"\"\n\nimport logging\nimport os\nimport threadin"
  },
  {
    "path": "app/assets/services/__init__.py",
    "chars": 1994,
    "preview": "from app.assets.services.asset_management import (\n    asset_exists,\n    delete_asset_reference,\n    get_asset_by_hash,\n"
  },
  {
    "path": "app/assets/services/asset_management.py",
    "chars": 11372,
    "preview": "import contextlib\nimport mimetypes\nimport os\nfrom typing import Sequence\n\n\nfrom app.assets.database.models import Asset\n"
  },
  {
    "path": "app/assets/services/bulk_ingest.py",
    "chars": 8369,
    "preview": "from __future__ import annotations\n\nimport os\nimport uuid\nfrom dataclasses import dataclass\nfrom datetime import datetim"
  },
  {
    "path": "app/assets/services/file_utils.py",
    "chars": 2267,
    "preview": "import os\n\n\ndef get_mtime_ns(stat_result: os.stat_result) -> int:\n    \"\"\"Extract mtime in nanoseconds from a stat result"
  },
  {
    "path": "app/assets/services/hashing.py",
    "chars": 3076,
    "preview": "import io\nimport os\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass\nfrom typing import IO, Any, "
  },
  {
    "path": "app/assets/services/ingest.py",
    "chars": 14205,
    "preview": "import contextlib\nimport logging\nimport mimetypes\nimport os\nfrom typing import Any, Sequence\n\nfrom sqlalchemy.orm import"
  },
  {
    "path": "app/assets/services/metadata_extract.py",
    "chars": 10987,
    "preview": "\"\"\"Metadata extraction for asset scanning.\n\nTier 1: Filesystem metadata (zero parsing)\nTier 2: Safetensors header metada"
  },
  {
    "path": "app/assets/services/path_utils.py",
    "chars": 6188,
    "preview": "import os\nfrom pathlib import Path\nfrom typing import Literal\n\nimport folder_paths\nfrom app.assets.helpers import normal"
  },
  {
    "path": "app/assets/services/schemas.py",
    "chars": 2349,
    "preview": "from dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import Any, NamedTuple\n\nfrom app.assets.data"
  },
  {
    "path": "app/assets/services/tagging.py",
    "chars": 2598,
    "preview": "from typing import Sequence\n\nfrom app.assets.database.queries import (\n    AddTagsResult,\n    RemoveTagsResult,\n    add_"
  },
  {
    "path": "app/custom_node_manager.py",
    "chars": 5330,
    "preview": "from __future__ import annotations\n\nimport os\nimport folder_paths\nimport glob\nfrom aiohttp import web\nimport json\nimport"
  },
  {
    "path": "app/database/db.py",
    "chars": 5839,
    "preview": "import logging\nimport os\nimport shutil\nfrom app.logger import log_startup_warning\nfrom utils.install_util import get_mis"
  },
  {
    "path": "app/database/models.py",
    "chars": 932,
    "preview": "from typing import Any\nfrom datetime import datetime\nfrom sqlalchemy import MetaData\nfrom sqlalchemy.orm import Declarat"
  },
  {
    "path": "app/frontend_management.py",
    "chars": 13886,
    "preview": "from __future__ import annotations\nimport argparse\nimport logging\nimport os\nimport re\nimport sys\nimport tempfile\nimport "
  },
  {
    "path": "app/logger.py",
    "chars": 2849,
    "preview": "from collections import deque\nfrom datetime import datetime\nimport io\nimport logging\nimport sys\nimport threading\n\nlogs ="
  },
  {
    "path": "app/model_manager.py",
    "chars": 7887,
    "preview": "from __future__ import annotations\n\nimport os\nimport base64\nimport json\nimport time\nimport logging\nimport folder_paths\ni"
  },
  {
    "path": "app/node_replace_manager.py",
    "chars": 4997,
    "preview": "from __future__ import annotations\n\nfrom aiohttp import web\n\nfrom typing import TYPE_CHECKING, TypedDict\nif TYPE_CHECKIN"
  },
  {
    "path": "app/subgraph_manager.py",
    "chars": 5245,
    "preview": "from __future__ import annotations\n\nfrom typing import TypedDict\nimport os\nimport folder_paths\nimport glob\nfrom aiohttp "
  },
  {
    "path": "app/user_manager.py",
    "chars": 19001,
    "preview": "from __future__ import annotations\nimport json\nimport os\nimport re\nimport uuid\nimport glob\nimport shutil\nimport logging\n"
  },
  {
    "path": "blueprints/.glsl/Brightness_and_Contrast_1.frag",
    "chars": 981,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // Brightness slider -100..1"
  },
  {
    "path": "blueprints/.glsl/Chromatic_Aberration_16.frag",
    "chars": 2158,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;      "
  },
  {
    "path": "blueprints/.glsl/Color_Adjustment_15.frag",
    "chars": 2730,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // temperature (-100 to 100)"
  },
  {
    "path": "blueprints/.glsl/Edge-Preserving_Blur_128.frag",
    "chars": 2744,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0;   // Blur radius (0–20, defa"
  },
  {
    "path": "blueprints/.glsl/Film_Grain_15.frag",
    "chars": 3837,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; /"
  },
  {
    "path": "blueprints/.glsl/Glow_30.frag",
    "chars": 3504,
    "preview": "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0;    "
  },
  {
    "path": "blueprints/.glsl/Hue_and_Saturation_1.frag",
    "chars": 6311,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform int u_int0;      // Mode: 0=Master, 1=Reds, "
  },
  {
    "path": "blueprints/.glsl/Image_Blur_1.frag",
    "chars": 3017,
    "preview": "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int B"
  },
  {
    "path": "blueprints/.glsl/Image_Channels_23.frag",
    "chars": 618,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 f"
  },
  {
    "path": "blueprints/.glsl/Image_Levels_1.frag",
    "chars": 2335,
    "preview": "#version 300 es\nprecision highp float;\n\n// Levels Adjustment\n// u_int0:   channel      (0=RGB, 1=R, 2=G, 3=B)         de"
  },
  {
    "path": "blueprints/.glsl/README.md",
    "chars": 742,
    "preview": "# GLSL Shader Sources\n\nThis folder contains the GLSL fragment shaders extracted from blueprint JSON files for easier edi"
  },
  {
    "path": "blueprints/.glsl/Sharpen_23.frag",
    "chars": 920,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  "
  },
  {
    "path": "blueprints/.glsl/Unsharp_Mask_26.frag",
    "chars": 1879,
    "preview": "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0;  "
  },
  {
    "path": "blueprints/.glsl/update_blueprints.py",
    "chars": 5197,
    "preview": "#!/usr/bin/env python3\n\"\"\"\nShader Blueprint Updater\n\nSyncs GLSL shader files between this folder and blueprint JSON file"
  },
  {
    "path": "blueprints/Brightness and Contrast.json",
    "chars": 5784,
    "preview": "{\"revision\": 0, \"last_node_id\": 140, \"last_link_id\": 0, \"nodes\": [{\"id\": 140, \"type\": \"916dff42-6166-4d45-b028-04eaf69fb"
  },
  {
    "path": "blueprints/Canny to Image (Z-Image-Turbo).json",
    "chars": 19621,
    "preview": "{\"id\": \"e046dd74-e2a7-4f31-a75b-5e11a8c72d4e\", \"revision\": 0, \"last_node_id\": 18, \"last_link_id\": 32, \"nodes\": [{\"id\": 1"
  },
  {
    "path": "blueprints/Canny to Video (LTX 2.0).json",
    "chars": 45351,
    "preview": "{\"id\": \"02f6166f-32f8-4673-b861-76be1464cba5\", \"revision\": 0, \"last_node_id\": 155, \"last_link_id\": 391, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Chromatic Aberration.json",
    "chars": 6647,
    "preview": "{\"revision\": 0, \"last_node_id\": 19, \"last_link_id\": 0, \"nodes\": [{\"id\": 19, \"type\": \"2c5ef154-2bde-496d-bc8b-9dcf42f2913"
  },
  {
    "path": "blueprints/Color Adjustment.json",
    "chars": 9232,
    "preview": "{\"revision\": 0, \"last_node_id\": 14, \"last_link_id\": 0, \"nodes\": [{\"id\": 14, \"type\": \"36677b92-5dd8-47a5-9380-4da982c1894"
  },
  {
    "path": "blueprints/Depth to Image (Z-Image-Turbo).json",
    "chars": 31219,
    "preview": "{\"id\": \"e046dd74-e2a7-4f31-a75b-5e11a8c72d4e\", \"revision\": 0, \"last_node_id\": 76, \"last_link_id\": 259, \"nodes\": [{\"id\": "
  },
  {
    "path": "blueprints/Depth to Video (ltx 2.0).json",
    "chars": 67090,
    "preview": "{\"id\": \"ec176c82-4db5-4ab9-b5a0-8aa8e5684a81\", \"revision\": 0, \"last_node_id\": 191, \"last_link_id\": 433, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Edge-Preserving Blur.json",
    "chars": 8129,
    "preview": "{\"revision\": 0, \"last_node_id\": 136, \"last_link_id\": 0, \"nodes\": [{\"id\": 136, \"type\": \"c6dc0f88-416b-4db1-bed1-442d793de"
  },
  {
    "path": "blueprints/Film Grain.json",
    "chars": 10742,
    "preview": "{\"revision\": 0, \"last_node_id\": 22, \"last_link_id\": 0, \"nodes\": [{\"id\": 22, \"type\": \"3324cf54-bcff-405f-a4bf-c5122c72fe5"
  },
  {
    "path": "blueprints/Glow.json",
    "chars": 10127,
    "preview": "{\"revision\": 0, \"last_node_id\": 37, \"last_link_id\": 0, \"nodes\": [{\"id\": 37, \"type\": \"0a99445a-aaf8-4a7f-aec3-d7d710ae149"
  },
  {
    "path": "blueprints/Hue and Saturation.json",
    "chars": 14476,
    "preview": "{\"revision\": 0, \"last_node_id\": 11, \"last_link_id\": 0, \"nodes\": [{\"id\": 11, \"type\": \"c64f83e9-aa5d-4031-89f1-0704e39299f"
  },
  {
    "path": "blueprints/Image Blur.json",
    "chars": 7506,
    "preview": "{\"revision\": 0, \"last_node_id\": 8, \"last_link_id\": 0, \"nodes\": [{\"id\": 8, \"type\": \"198632a3-ee76-4aab-9ce7-a69c624eaff9\""
  },
  {
    "path": "blueprints/Image Captioning (gemini).json",
    "chars": 5341,
    "preview": "{\"revision\": 0, \"last_node_id\": 231, \"last_link_id\": 0, \"nodes\": [{\"id\": 231, \"type\": \"e3e78497-720e-45a2-b4fb-c7bfdb80d"
  },
  {
    "path": "blueprints/Image Channels.json",
    "chars": 4413,
    "preview": "{\"revision\": 0, \"last_node_id\": 29, \"last_link_id\": 0, \"nodes\": [{\"id\": 29, \"type\": \"4c9d6ea4-b912-40e5-8766-6793a9758c5"
  },
  {
    "path": "blueprints/Image Edit (Flux.2 Klein 4B).json",
    "chars": 22997,
    "preview": "{\"id\": \"6686cb78-8003-4289-b969-929755e9a84d\", \"revision\": 0, \"last_node_id\": 81, \"last_link_id\": 179, \"nodes\": [{\"id\": "
  },
  {
    "path": "blueprints/Image Edit (Qwen 2511).json",
    "chars": 19963,
    "preview": "{\"id\": \"d84b7d1a-a73f-4e31-bd16-983ac0cf5f1b\", \"revision\": 0, \"last_node_id\": 17, \"last_link_id\": 32, \"nodes\": [{\"id\": 1"
  },
  {
    "path": "blueprints/Image Inpainting (Qwen-image).json",
    "chars": 23094,
    "preview": "{\"id\": \"84318cde-a839-41d4-8632-df6d7c50ffc5\", \"revision\": 0, \"last_node_id\": 256, \"last_link_id\": 403, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Image Levels.json",
    "chars": 10250,
    "preview": "{\"revision\": 0, \"last_node_id\": 139, \"last_link_id\": 0, \"nodes\": [{\"id\": 139, \"type\": \"75bf8a72-aad8-4f3e-83ee-380e70248"
  },
  {
    "path": "blueprints/Image Outpainting (Qwen-Image).json",
    "chars": 31642,
    "preview": "{\"id\": \"8f79c27f-bec4-412e-9b82-7c5b3b778ecf\", \"revision\": 0, \"last_node_id\": 255, \"last_link_id\": 401, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Image Upscale(Z-image-Turbo).json",
    "chars": 16400,
    "preview": "{\"id\": \"bf8108f3-d857-46c9-aef5-0e8ad2a64bf5\", \"revision\": 0, \"last_node_id\": 95, \"last_link_id\": 115, \"nodes\": [{\"id\": "
  },
  {
    "path": "blueprints/Image to Depth Map (Lotus).json",
    "chars": 11713,
    "preview": "{\"id\": \"6af0a6c1-0161-4528-8685-65776e838d44\", \"revision\": 0, \"last_node_id\": 75, \"last_link_id\": 245, \"nodes\": [{\"id\": "
  },
  {
    "path": "blueprints/Image to Layers(Qwen-Image Layered).json",
    "chars": 17502,
    "preview": "{\"id\": \"1a761372-7c82-4016-b9bf-fa285967e1e9\", \"revision\": 0, \"last_node_id\": 83, \"last_link_id\": 0, \"nodes\": [{\"id\": 83"
  },
  {
    "path": "blueprints/Image to Model (Hunyuan3d 2.1).json",
    "chars": 9216,
    "preview": "{\"id\": \"8fe311ec-2147-47a8-b618-7bd6fb6d4f9d\", \"revision\": 0, \"last_node_id\": 23, \"last_link_id\": 24, \"nodes\": [{\"id\": 1"
  },
  {
    "path": "blueprints/Image to Video (Wan 2.2).json",
    "chars": 28973,
    "preview": "{\"id\": \"ec7da562-7e21-4dac-a0d2-f4441e1efd3b\", \"revision\": 0, \"last_node_id\": 119, \"last_link_id\": 231, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Pose to Image (Z-Image-Turbo).json",
    "chars": 16594,
    "preview": "{\"id\": \"e046dd74-e2a7-4f31-a75b-5e11a8c72d4e\", \"revision\": 0, \"last_node_id\": 26, \"last_link_id\": 46, \"nodes\": [{\"id\": 1"
  },
  {
    "path": "blueprints/Pose to Video (LTX 2.0).json",
    "chars": 48922,
    "preview": "{\"id\": \"01cd475b-52df-43bf-aafa-484a5976d2d2\", \"revision\": 0, \"last_node_id\": 160, \"last_link_id\": 410, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Prompt Enhance.json",
    "chars": 3316,
    "preview": "{\"revision\": 0, \"last_node_id\": 15, \"last_link_id\": 0, \"nodes\": [{\"id\": 15, \"type\": \"24d8bbfd-39d4-4774-bff0-3de40cc7a47"
  },
  {
    "path": "blueprints/Sharpen.json",
    "chars": 4557,
    "preview": "{\"revision\": 0, \"last_node_id\": 25, \"last_link_id\": 0, \"nodes\": [{\"id\": 25, \"type\": \"621ba4e2-22a8-482d-a369-023753198b7"
  },
  {
    "path": "blueprints/Text to Audio (ACE-Step 1.5).json",
    "chars": 18513,
    "preview": "{\"id\": \"67979fed-a490-450a-83f4-c7c0105d450e\", \"revision\": 0, \"last_node_id\": 110, \"last_link_id\": 288, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Text to Image (Z-Image-Turbo).json",
    "chars": 13356,
    "preview": "{\"id\": \"1c3eaa76-5cfa-4dc7-8571-97a570324e01\", \"revision\": 0, \"last_node_id\": 34, \"last_link_id\": 40, \"nodes\": [{\"id\": 5"
  },
  {
    "path": "blueprints/Text to Video (Wan 2.2).json",
    "chars": 22547,
    "preview": "{\"id\": \"ec7da562-7e21-4dac-a0d2-f4441e1efd3b\", \"revision\": 0, \"last_node_id\": 116, \"last_link_id\": 188, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Unsharp Mask.json",
    "chars": 6954,
    "preview": "{\"revision\": 0, \"last_node_id\": 30, \"last_link_id\": 0, \"nodes\": [{\"id\": 30, \"type\": \"d99ba3f5-8a56-4365-8e45-3f3ea7c572a"
  },
  {
    "path": "blueprints/Video Captioning (Gemini).json",
    "chars": 7237,
    "preview": "{\"revision\": 0, \"last_node_id\": 233, \"last_link_id\": 0, \"nodes\": [{\"id\": 233, \"type\": \"dcf32045-0ee4-4efc-9aca-9f26f3a15"
  },
  {
    "path": "blueprints/Video Inpaint(Wan2.1 VACE).json",
    "chars": 30007,
    "preview": "{\"id\": \"2f429c60-2e03-4117-908b-31e1fab04bba\", \"revision\": 0, \"last_node_id\": 229, \"last_link_id\": 366, \"nodes\": [{\"id\":"
  },
  {
    "path": "blueprints/Video Stitch.json",
    "chars": 6738,
    "preview": "{\"revision\": 0, \"last_node_id\": 84, \"last_link_id\": 0, \"nodes\": [{\"id\": 84, \"type\": \"8e8aa94a-647e-436d-8440-8ee4691864d"
  },
  {
    "path": "blueprints/Video Upscale(GAN x4).json",
    "chars": 4791,
    "preview": "{\"revision\": 0, \"last_node_id\": 13, \"last_link_id\": 0, \"nodes\": [{\"id\": 13, \"type\": \"cf95b747-3e17-46cb-8097-cac60ff9b2e"
  },
  {
    "path": "blueprints/put_blueprints_here",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "comfy/audio_encoders/audio_encoders.py",
    "chars": 3489,
    "preview": "from .wav2vec2 import Wav2Vec2Model\nfrom .whisper import WhisperLargeV3\nimport comfy.model_management\nimport comfy.ops\ni"
  },
  {
    "path": "comfy/audio_encoders/wav2vec2.py",
    "chars": 10639,
    "preview": "import torch\nimport torch.nn as nn\nfrom comfy.ldm.modules.attention import optimized_attention_masked\n\n\nclass LayerNormC"
  },
  {
    "path": "comfy/audio_encoders/whisper.py",
    "chars": 5953,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio\nfrom typing import Optional\nfrom co"
  },
  {
    "path": "comfy/cldm/cldm.py",
    "chars": 19828,
    "preview": "#taken from: https://github.com/lllyasviel/ControlNet\n#and modified\n\nimport torch\nimport torch.nn as nn\n\nfrom ..ldm.modu"
  },
  {
    "path": "comfy/cldm/control_types.py",
    "chars": 207,
    "preview": "UNION_CONTROLNET_TYPES = {\n    \"openpose\": 0,\n    \"depth\": 1,\n    \"hed/pidi/scribble/ted\": 2,\n    \"canny/lineart/anime_l"
  },
  {
    "path": "comfy/cldm/dit_embedder.py",
    "chars": 4204,
    "preview": "import math\nfrom typing import List, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nfrom torch import Tensor\n\nfrom "
  },
  {
    "path": "comfy/cldm/mmdit.py",
    "chars": 2555,
    "preview": "import torch\nfrom typing import Optional\nimport comfy.ldm.modules.diffusionmodules.mmdit\n\nclass ControlNet(comfy.ldm.mod"
  },
  {
    "path": "comfy/cli_args.py",
    "chars": 17661,
    "preview": "import argparse\nimport enum\nimport os\nimport comfy.options\n\n\nclass EnumAction(argparse.Action):\n    \"\"\"\n    Argparse act"
  },
  {
    "path": "comfy/clip_config_bigg.json",
    "chars": 524,
    "preview": "{\n  \"architectures\": [\n    \"CLIPTextModel\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"bos_token_id\": 0,\n  \"dropout\": 0.0,\n  \"eo"
  },
  {
    "path": "comfy/clip_model.py",
    "chars": 16257,
    "preview": "import torch\nfrom comfy.ldm.modules.attention import optimized_attention_for_device\nimport comfy.ops\nimport math\n\ndef cl"
  },
  {
    "path": "comfy/clip_vision.py",
    "chars": 7665,
    "preview": "from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace\nimport os\nimport json\nimport logging"
  },
  {
    "path": "comfy/clip_vision_config_g.json",
    "chars": 419,
    "preview": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1664,\n  \"image_size\": 224,\n  \"i"
  },
  {
    "path": "comfy/clip_vision_config_h.json",
    "chars": 419,
    "preview": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1280,\n  \"image_size\": 224,\n  \"i"
  },
  {
    "path": "comfy/clip_vision_config_vitl.json",
    "chars": 424,
    "preview": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"quick_gelu\",\n  \"hidden_size\": 1024,\n  \"image_size\": 224"
  },
  {
    "path": "comfy/clip_vision_config_vitl_336.json",
    "chars": 423,
    "preview": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"quick_gelu\",\n  \"hidden_size\": 1024,\n  \"image_size\": 336"
  },
  {
    "path": "comfy/clip_vision_config_vitl_336_llava.json",
    "chars": 453,
    "preview": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"quick_gelu\",\n  \"hidden_size\": 1024,\n  \"image_size\": 336"
  },
  {
    "path": "comfy/clip_vision_siglip2_base_naflex.json",
    "chars": 336,
    "preview": "{\n  \"num_channels\": 3,\n  \"hidden_act\": \"gelu_pytorch_tanh\",\n  \"hidden_size\": 1152,\n  \"image_size\": -1,\n  \"intermediate_s"
  },
  {
    "path": "comfy/clip_vision_siglip_384.json",
    "chars": 314,
    "preview": "{\n  \"num_channels\": 3,\n  \"hidden_act\": \"gelu_pytorch_tanh\",\n  \"hidden_size\": 1152,\n  \"image_size\": 384,\n  \"intermediate_"
  },
  {
    "path": "comfy/clip_vision_siglip_512.json",
    "chars": 314,
    "preview": "{\n  \"num_channels\": 3,\n  \"hidden_act\": \"gelu_pytorch_tanh\",\n  \"hidden_size\": 1152,\n  \"image_size\": 512,\n  \"intermediate_"
  },
  {
    "path": "comfy/comfy_types/README.md",
    "chars": 1350,
    "preview": "# Comfy Typing\n## Type hinting for ComfyUI Node development\n\nThis module provides type hinting and concrete convenience "
  },
  {
    "path": "comfy/comfy_types/__init__.py",
    "chars": 1251,
    "preview": "import torch\nfrom typing import Callable, Protocol, TypedDict, Optional, List\nfrom .node_typing import IO, InputTypeDict"
  },
  {
    "path": "comfy/comfy_types/examples/example_nodes.py",
    "chars": 781,
    "preview": "from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict\nfrom inspect import cleandoc\n\n\nclass ExampleNode(ComfyNode"
  },
  {
    "path": "comfy/comfy_types/node_typing.py",
    "chars": 15256,
    "preview": "\"\"\"Comfy-specific type hinting\"\"\"\n\nfrom __future__ import annotations\nfrom typing import Literal, TypedDict, Optional\nfr"
  },
  {
    "path": "comfy/conds.py",
    "chars": 4522,
    "preview": "import torch\nimport math\nimport comfy.utils\nimport logging\n\n\ndef is_equal(x, y):\n    if torch.is_tensor(x) and torch.is_"
  },
  {
    "path": "comfy/context_windows.py",
    "chars": 33967,
    "preview": "from __future__ import annotations\nfrom typing import TYPE_CHECKING, Callable\nimport torch\nimport numpy as np\nimport col"
  },
  {
    "path": "comfy/controlnet.py",
    "chars": 44581,
    "preview": "\"\"\"\n    This file is part of ComfyUI.\n    Copyright (C) 2024 Comfy\n\n    This program is free software: you can redistrib"
  },
  {
    "path": "comfy/diffusers_convert.py",
    "chars": 6796,
    "preview": "import re\nimport torch\nimport logging\n\n# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts"
  },
  {
    "path": "comfy/diffusers_load.py",
    "chars": 1437,
    "preview": "import os\n\nimport comfy.sd\n\ndef first_file(path, filenames):\n    for f in filenames:\n        p = os.path.join(path, f)\n "
  },
  {
    "path": "comfy/extra_samplers/uni_pc.py",
    "chars": 38202,
    "preview": "#code taken from: https://github.com/wl-zhao/UniPC and modified\n\nimport torch\nimport math\nimport logging\n\nfrom tqdm.auto"
  },
  {
    "path": "comfy/float.py",
    "chars": 9109,
    "preview": "import torch\n\ndef calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):\n    mantiss"
  },
  {
    "path": "comfy/gligen.py",
    "chars": 10800,
    "preview": "import math\nimport torch\nfrom torch import nn\nfrom .ldm.modules.attention import CrossAttention, FeedForward\nimport comf"
  },
  {
    "path": "comfy/hooks.py",
    "chars": 32752,
    "preview": "from __future__ import annotations\nfrom typing import TYPE_CHECKING, Callable\nimport enum\nimport math\nimport torch\nimpor"
  },
  {
    "path": "comfy/image_encoders/dino2.py",
    "chars": 7120,
    "preview": "import torch\nfrom comfy.text_encoders.bert import BertAttention\nimport comfy.model_management\nfrom comfy.ldm.modules.att"
  },
  {
    "path": "comfy/image_encoders/dino2_giant.json",
    "chars": 512,
    "preview": "{\n  \"attention_probs_dropout_prob\": 0.0,\n  \"drop_path_rate\": 0.0,\n  \"hidden_act\": \"gelu\",\n  \"hidden_dropout_prob\": 0.0,\n"
  },
  {
    "path": "comfy/image_encoders/dino2_large.json",
    "chars": 538,
    "preview": "{\n  \"hidden_size\": 1024,\n  \"use_mask_token\": true,\n  \"patch_size\": 14,\n  \"image_size\": 518,\n  \"num_channels\": 3,\n  \"num_"
  },
  {
    "path": "comfy/k_diffusion/deis.py",
    "chars": 5799,
    "preview": "#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py\n#under Apache 2 license\nimport t"
  },
  {
    "path": "comfy/k_diffusion/sa_solver.py",
    "chars": 5620,
    "preview": "# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019)\n# Conference: https://proceedings.neurips.cc/paper"
  },
  {
    "path": "comfy/k_diffusion/sampling.py",
    "chars": 82593,
    "preview": "import math\nfrom functools import partial\n\nfrom scipy import integrate\nimport torch\nfrom torch import nn\nimport torchsde"
  },
  {
    "path": "comfy/k_diffusion/utils.py",
    "chars": 12718,
    "preview": "from contextlib import contextmanager\nimport hashlib\nimport math\nfrom pathlib import Path\nimport shutil\nimport urllib\nim"
  },
  {
    "path": "comfy/latent_formats.py",
    "chars": 30530,
    "preview": "import torch\n\nclass LatentFormat:\n    scale_factor = 1.0\n    latent_channels = 4\n    latent_dimensions = 2\n    latent_rg"
  },
  {
    "path": "comfy/ldm/ace/ace_step15.py",
    "chars": 46957,
    "preview": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport itertools\nfrom comfy.ldm.modules.a"
  },
  {
    "path": "comfy/ldm/ace/attention.py",
    "chars": 30379,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py\n# Copyright 2024 The HuggingFace Tea"
  },
  {
    "path": "comfy/ldm/ace/lyric_encoder.py",
    "chars": 43151,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py\nfrom typing import "
  },
  {
    "path": "comfy/ldm/ace/model.py",
    "chars": 17216,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py\n\n# Copyright 2024 The Hug"
  },
  {
    "path": "comfy/ldm/ace/vae/autoencoder_dc.py",
    "chars": 22909,
    "preview": "# Rewritten from diffusers\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Tuple, "
  },
  {
    "path": "comfy/ldm/ace/vae/music_dcae_pipeline.py",
    "chars": 3622,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py\nimport torch\nfrom .aut"
  },
  {
    "path": "comfy/ldm/ace/vae/music_log_mel.py",
    "chars": 3090,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py\nimport torch\nimport torch.nn"
  },
  {
    "path": "comfy/ldm/ace/vae/music_vocoder.py",
    "chars": 18149,
    "preview": "# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py\nimport torch\nfrom torch impo"
  },
  {
    "path": "comfy/ldm/anima/model.py",
    "chars": 9793,
    "preview": "from comfy.ldm.cosmos.predict2 import MiniTrainDIT\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\nd"
  },
  {
    "path": "comfy/ldm/audio/autoencoder.py",
    "chars": 9969,
    "preview": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nimport torch\nfrom torch import nn\nfrom typing i"
  },
  {
    "path": "comfy/ldm/audio/dit.py",
    "chars": 31491,
    "preview": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nfrom comfy.ldm.modules.attention import optimiz"
  },
  {
    "path": "comfy/ldm/audio/embedders.py",
    "chars": 3406,
    "preview": "# code adapted from: https://github.com/Stability-AI/stable-audio-tools\n\nimport torch\nimport torch.nn as nn\nfrom torch i"
  },
  {
    "path": "comfy/ldm/aura/mmdit.py",
    "chars": 20461,
    "preview": "#AuraFlow MMDiT\n#Originally written by the AuraFlow Authors\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torc"
  }
]

// ... and 518 more files (download for full content)