Ctrl-click: Single Selection'; + gallery.appendChild(hint); + + let max_size = 300; + + for(let i=0; i
(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,
ComfyUI roop and comfy_mtb)", + "filename": "inswapper_128_fp16.onnx", + "name": "Inswapper-fp16 (face swap)", + "reference": "https://github.com/facefusion/facefusion-assets", + "save_path": "insightface", + "type": "insightface", + "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx" + }, + { + "base": "inswapper", + "description": "[529MB] Checkpoint of the insightface swapper model
(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,
ComfyUI roop and comfy_mtb)", + "filename": "inswapper_128.onnx", + "name": "Inswapper (face swap)", + "reference": "https://github.com/facefusion/facefusion-assets", + "save_path": "insightface", + "type": "insightface", + "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx" + }, + { + "base": "deepbump", + "description": "Checkpoint of the deepbump model to generate height and normal maps textures from an image (requires comfy_mtb)", + "filename": "deepbump256.onnx", + "name": "Deepbump", + "reference": "https://github.com/HugoTini/DeepBump", + "save_path": "deepbump", + "type": "deepbump", + "url": "https://github.com/HugoTini/DeepBump/raw/master/deepbump256.onnx" + }, + { + "base": "face_restore", + "description": "Face restoration", + "filename": "GFPGANv1.3.pth", + "name": "GFPGAN 1.3", + "reference": "https://github.com/TencentARC/GFPGAN", + "save_path": "face_restore", + "type": "face_restore", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth" + }, + { + "base": "face_restore", + "description": "Face restoration", + "filename": "GFPGANv1.4.pth", + "name": "GFPGAN 1.4", + "reference": "https://github.com/TencentARC/GFPGAN", + "save_path": "face_restore", + "type": "face_restore", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" + }, + { + "base": "face_restore", + "description": "Face restoration", + "filename": "RestoreFormer.pth", + "name": "RestoreFormer", + "reference": "https://github.com/TencentARC/GFPGAN", + "save_path": "face_restore", + "type": "face_restore", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth" + }, + { + "base": "SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
NOTE: 14 frames @ 576x1024", + "filename": "svd.safetensors", + "name": "Stable Video Diffusion Image-to-Video", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid", + "save_path": "checkpoints/SVD", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd.safetensors" + }, + { + "base": "zero123", + "description": "Stable Zero123 is a model for view-conditioned image generation based on [a/Zero123](https://github.com/cvlab-columbia/zero123).", + "filename": "stable_zero123.ckpt", + "name": "stabilityai/Stable Zero123", + "reference": "https://huggingface.co/stabilityai/stable-zero123", + "save_path": "checkpoints/zero123", + "type": "zero123", + "url": "https://huggingface.co/stabilityai/stable-zero123/resolve/main/stable_zero123.ckpt" + }, + { + "base": "SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
NOTE: 25 frames @ 576x1024 ", + "filename": "svd_xt.safetensors", + "name": "Stable Video Diffusion Image-to-Video (XT)", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt", + "save_path": "checkpoints/SVD", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors" + }, + { + "base": "SD1.5", + "description": "If you use this embedding with negatives, you can solve the issue of damaging your hands.", + "filename": "negative_hand-neg.pt", + "name": "negative_hand Negative Embedding", + "reference": "https://civitai.com/models/56519/negativehand-negative-embedding", + "save_path": "default", + "type": "embeddings", + "url": "https://civitai.com/api/download/models/60938" + }, + { + "base": "SD1.5", + "description": "The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding.", + "filename": "bad_prompt_version2-neg.pt", + "name": "bad_prompt Negative Embedding", + "reference": "https://civitai.com/models/55700/badprompt-negative-embedding", + "save_path": "default", + "type": "embeddings", + "url": "https://civitai.com/api/download/models/60095" + }, + { + "base": "SD1.5", + "description": "These embedding learn what disgusting compositions and color patterns are, including faulty human anatomy, offensive color schemes, upside-down spatial structures, and more. Placing it in the negative can go a long way to avoiding these things.", + "filename": "ng_deepnegative_v1_75t.pt", + "name": "Deep Negative V1.75", + "reference": "https://civitai.com/models/4629/deep-negative-v1x", + "save_path": "default", + "type": "embeddings", + "url": "https://civitai.com/api/download/models/5637" + }, + { + "base": "SD1.5", + "description": "This embedding should be used in your NEGATIVE prompt. Adjust the strength as desired (seems to scale well without any distortions), the strength required may vary based on positive and negative prompts.", + "filename": "easynegative.safetensors", + "name": "EasyNegative", + "reference": "https://civitai.com/models/7808/easynegative", + "save_path": "default", + "type": "embeddings", + "url": "https://civitai.com/api/download/models/9208" + }, + { + "base": "SDXL", + "description": "[6.9GB] SDXL-Turbo 1.0 fp16", + "filename": "sd_xl_turbo_1.0_fp16.safetensors", + "name": "SDXL-Turbo 1.0 (fp16)", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "save_path": "checkpoints/SDXL-TURBO", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors" + }, + { + "base": "SDXL", + "description": "[13.9GB] SDXL-Turbo 1.0", + "filename": "sd_xl_turbo_1.0.safetensors", + "name": "SDXL-Turbo 1.0", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "save_path": "checkpoints/SDXL-TURBO", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0.safetensors" + }, + { + "base": "SDXL", + "description": "Stable Diffusion XL base model (VAE 0.9)", + "filename": "sd_xl_base_1.0_0.9vae.safetensors", + "name": "sd_xl_base_1.0_0.9vae.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors" + }, + { + "base": "SDXL", + "description": "Stable Diffusion XL base model", + "filename": "sd_xl_base_1.0.safetensors", + "name": "sd_xl_base_1.0.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" + }, + { + "base": "SDXL", + "description": "Stable Diffusion XL refiner model (VAE 0.9)", + "filename": "sd_xl_refiner_1.0_0.9vae.safetensors", + "name": "sd_xl_refiner_1.0_0.9vae.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors" + }, + { + "base": "SDXL", + "description": "Stable Diffusion XL refiner model", + "filename": "sd_xl_refiner_1.0.safetensors", + "name": "stable-diffusion-xl-refiner-1.0", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors" + }, + { + "base": "SDXL", + "description": "[5.14GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "filename": "diffusion_pytorch_model.fp16.safetensors", + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET/fp16)", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "save_path": "unet/xl-inpaint-0.1", + "type": "unet", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors" + }, + { + "base": "SDXL", + "description": "[10.3GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "filename": "diffusion_pytorch_model.safetensors", + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET)", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "save_path": "unet/xl-inpaint-0.1", + "type": "unet", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.safetensors" + }, + { + "base": "SDXL", + "description": "Stable Diffusion XL offset LoRA", + "filename": "sd_xl_offset_example-lora_1.0.safetensors", + "name": "sd_xl_offset_example-lora_1.0.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "save_path": "default", + "type": "lora", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors" + }, + { + "base": "SD1.5", + "description": "Stable Diffusion 1.5 base model", + "filename": "v1-5-pruned-emaonly.ckpt", + "name": "v1-5-pruned-emaonly.ckpt", + "reference": "https://huggingface.co/runwayml/stable-diffusion-v1-5", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt" + }, + { + "base": "SD2", + "description": "Stable Diffusion 2 base model (512)", + "filename": "v2-1_512-ema-pruned.safetensors", + "name": "v2-1_512-ema-pruned.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors" + }, + { + "base": "SD2", + "description": "Stable Diffusion 2 base model (768)", + "filename": "v2-1_768-ema-pruned.safetensors", + "name": "v2-1_768-ema-pruned.safetensors", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors" + }, + { + "base": "SD1.5", + "description": "AbyssOrangeMix2 - hard version (anime style)", + "filename": "AbyssOrangeMix2_hard.safetensors", + "name": "AbyssOrangeMix2 (hard)", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors" + }, + { + "base": "SD1.5", + "description": "AbyssOrangeMix3 - A1 (anime style)", + "filename": "AOM3A1_orangemixs.safetensors", + "name": "AbyssOrangeMix3 A1", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors" + }, + { + "base": "SD1.5", + "description": "AbyssOrangeMix - A3 (anime style)", + "filename": "AOM3A3_orangemixs.safetensors", + "name": "AbyssOrangeMix3 A3", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors" + }, + { + "base": "SD1.5", + "description": "Anything v3 (anime style)", + "filename": "anything-v3-fp16-pruned.safetensors", + "name": "Anything v3 (fp16; pruned)", + "reference": "https://huggingface.co/Linaqruf/anything-v3.0", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors" + }, + { + "base": "SD2.1", + "description": "Waifu Diffusion 1.5 Beta3", + "filename": "wd-illusion-fp16.safetensors", + "name": "Waifu Diffusion 1.5 Beta3 (fp16)", + "reference": "https://huggingface.co/waifu-diffusion/wd-1-5-beta3", + "save_path": "default", + "type": "checkpoints", + "url": "https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors" + }, + { + "base": "SD2.1", + "description": "Mix model (SD2.1 unCLIP + illuminatiDiffusionV1_v11)", + "filename": "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors", + "name": "illuminatiDiffusionV1_v11 unCLIP model", + "reference": "https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP", + "save_path": "default", + "type": "unclip", + "url": "https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors" + }, + { + "base": "SD2.1", + "description": "Mix model (SD2.1 unCLIP + Waifu Diffusion 1.5)", + "filename": "wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors", + "name": "Waifu Diffusion 1.5 unCLIP model", + "reference": "https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP", + "save_path": "default", + "type": "unclip", + "url": "https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors" + }, + { + "base": "SDXL VAE", + "description": "SDXL-VAE", + "filename": "sdxl_vae.safetensors", + "name": "sdxl_vae.safetensors", + "reference": "https://huggingface.co/stabilityai/sdxl-vae", + "save_path": "default", + "type": "VAE", + "url": "https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors" + }, + { + "base": "SD1.5 VAE", + "description": "vae-ft-mse-840000-ema-pruned", + "filename": "vae-ft-mse-840000-ema-pruned.safetensors", + "name": "vae-ft-mse-840000-ema-pruned", + "reference": "https://huggingface.co/stabilityai/sd-vae-ft-mse-original", + "save_path": "default", + "type": "VAE", + "url": "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors" + }, + { + "base": "SD1.5 VAE", + "description": "orangemix vae model", + "filename": "orangemix.vae.pt", + "name": "orangemix.vae", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "save_path": "default", + "type": "VAE", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt" + }, + { + "base": "SD2.1 VAE", + "description": "kl-f8-anime2 vae model", + "filename": "kl-f8-anime2.ckpt", + "name": "kl-f8-anime2", + "reference": "https://huggingface.co/hakurei/waifu-diffusion-v1-4", + "save_path": "default", + "type": "VAE", + "url": "https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt" + }, + { + "base": "SD1.5", + "description": "Latent Consistency LoRA for SD1.5", + "filename": "pytorch_lora_weights.safetensors", + "name": "LCM LoRA SD1.5", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5", + "save_path": "loras/lcm/SD1.5", + "type": "lora", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "base": "SSD-1B", + "description": "Latent Consistency LoRA for SSD-1B", + "filename": "pytorch_lora_weights.safetensors", + "name": "LCM LoRA SSD-1B", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b", + "save_path": "loras/lcm/SSD-1B", + "type": "lora", + "url": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "base": "SSD-1B", + "description": "Latent Consistency LoRA for SDXL", + "filename": "pytorch_lora_weights.safetensors", + "name": "LCM LoRA SDXL", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdxl", + "save_path": "loras/lcm/SDXL", + "type": "lora", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "base": "segmind-vega", + "description": "The Segmind-Vega Model is a distilled version of the Stable Diffusion XL (SDXL), offering a remarkable 70% reduction in size and an impressive 100% speedup while retaining high-quality text-to-image generation capabilities.", + "filename": "segmind-vega.safetensors", + "name": "Segmind-Vega", + "reference": "https://huggingface.co/segmind/Segmind-Vega", + "save_path": "checkpoints/segmind-vega", + "type": "checkpoint", + "url": "https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors" + }, + { + "base": "segmind-vega", + "description": "Segmind-VegaRT a distilled consistency adapter for Segmind-Vega that allows to reduce the number of inference steps to only between 2 - 8 steps.", + "filename": "pytorch_lora_weights.safetensors", + "name": "Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega", + "reference": "https://huggingface.co/segmind/Segmind-VegaRT", + "save_path": "loras/segmind-vega", + "type": "lora", + "url": "https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "base": "SD2.1", + "description": "LORA: Theovercomer8's Contrast Fix (SD2.1)", + "filename": "theovercomer8sContrastFix_sd21768.safetensors", + "name": "Theovercomer8's Contrast Fix (SD2.1)", + "reference": "https://civitai.com/models/8765/theovercomer8s-contrast-fix-sd15sd21-768", + "save_path": "default", + "type": "lora", + "url": "https://civitai.com/api/download/models/10350" + }, + { + "base": "SD1.5", + "description": "LORA: Theovercomer8's Contrast Fix (SD1.5)", + "filename": "theovercomer8sContrastFix_sd15.safetensors", + "name": "Theovercomer8's Contrast Fix (SD1.5)", + "reference": "https://civitai.com/models/8765/theovercomer8s-contrast-fix-sd15sd21-768", + "save_path": "default", + "type": "lora", + "url": "https://civitai.com/api/download/models/10638" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for depth", + "filename": "t2iadapter_depth_sd14v1.pth", + "name": "T2I-Adapter (depth)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for seg", + "filename": "t2iadapter_seg_sd14v1.pth", + "name": "T2I-Adapter (seg)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for sketch", + "filename": "t2iadapter_sketch_sd14v1.pth", + "name": "T2I-Adapter (sketch)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for keypose", + "filename": "t2iadapter_keypose_sd14v1.pth", + "name": "T2I-Adapter (keypose)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for openpose", + "filename": "t2iadapter_openpose_sd14v1.pth", + "name": "T2I-Adapter (openpose)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for color", + "filename": "t2iadapter_color_sd14v1.pth", + "name": "T2I-Adapter (color)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter for canny", + "filename": "t2iadapter_canny_sd14v1.pth", + "name": "T2I-Adapter (canny)", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Adapter", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "ControlNet T2I-Adapter style model. Need to download CLIPVision model.", + "filename": "t2iadapter_style_sd14v1.pth", + "name": "T2I-Style model", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "save_path": "default", + "type": "T2I-Style", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth" + }, + { + "base": "SD1.5", + "description": "TemporalNet was a ControlNet model designed to enhance the temporal consistency of generated outputs", + "filename": "temporalnetversion2.ckpt", + "name": "CiaraRowles/TemporalNet2", + "reference": "https://huggingface.co/CiaraRowles/TemporalNet2", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/CiaraRowles/TemporalNet2/resolve/main/temporalnetversion2.ckpt" + }, + { + "base": "SD1.5", + "description": "This is TemporalNet1XL, it is a re-train of the controlnet TemporalNet1 with Stable Diffusion XL.", + "filename": "diffusion_pytorch_model.safetensors", + "name": 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"url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors" + }, + { + "base": "SDXL", + "description": "Control-LoRA: sketch rank256", + "filename": "control-lora-sketch-rank256.safetensors", + "name": "stabilityai/control-lora-sketch-rank256.safetensors", + "reference": "https://huggingface.co/stabilityai/control-lora", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors" + }, + { + "base": "SDXL", + "description": "[46.2MB] An extremely compactly designed controlnet model (a.k.a. ControlNet-LLLite). Note: The model structure is highly experimental and may be subject to change in the future.", + "filename": "controllllite_v01032064e_sdxl_canny_anime.safetensors", + "name": "kohya-ss/ControlNet-LLLite: SDXL Canny Anime", + "reference": "https://huggingface.co/kohya-ss/controlnet-lllite", + "save_path": "custom_nodes/ControlNet-LLLite-ComfyUI/models", + "type": "controlnet", + "url": "https://huggingface.co/kohya-ss/controlnet-lllite/resolve/main/controllllite_v01032064e_sdxl_canny_anime.safetensors" + }, + { + "base": "SDXL", + "description": "ControlNet openpose model for SDXL", + "filename": "OpenPoseXL2.safetensors", + "name": "SDXL-controlnet: OpenPose (v2)", + "reference": "https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/resolve/main/OpenPoseXL2.safetensors" + }, + { + "base": "SDXL", + "description": "ControlNet softedge model for SDXL", + "filename": "controlnet-sd-xl-1.0-softedge-dexined.safetensors", + "name": "controlnet-SargeZT/controlnet-sd-xl-1.0-softedge-dexined", + "reference": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined/resolve/main/controlnet-sd-xl-1.0-softedge-dexined.safetensors" + }, + { + "base": "SDXL", + "description": "ControlNet depth-zoe model for SDXL", + "filename": "depth-zoe-xl-v1.0-controlnet.safetensors", + "name": "controlnet-SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe", + "reference": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe/resolve/main/depth-zoe-xl-v1.0-controlnet.safetensors" + }, + { + "base": "SD1.5", + "description": "Safetensors/FP16 versions of the new ControlNet-v1-1 checkpoints (ip2p)", + "filename": "control_v11e_sd15_ip2p_fp16.safetensors", + "name": "ControlNet-v1-1 (ip2p; 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You need to this model for Tiled Resample", + "filename": "control_v11f1e_sd15_tile_fp16.safetensors", + "name": "ControlNet-v1-1 (tile; fp16; v11f1e)", + "reference": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors", + "save_path": "default", + "type": "controlnet", + "url": "https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors" + }, + { + "base": "SD1.5", + "description": "GLIGEN textbox model", + "filename": "gligen_sd14_textbox_pruned_fp16.safetensors", + "name": "GLIGEN textbox (fp16; pruned)", + "reference": "https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors", + "save_path": "default", + "type": "gligen", + "url": "https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors" + }, + { + "base": "SAM", + "description": "Segmenty Anything SAM model (ViT-H)", + "filename": "sam_vit_h_4b8939.pth", + "name": "ViT-H 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model", + "filename": "seecoder-v1-0.safetensors", + "name": "seecoder v1.0", + "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder", + "save_path": "seecoders", + "type": "seecoder", + "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-v1-0.safetensors" + }, + { + "base": "SEECODER", + "description": "SeeCoder model", + "filename": "seecoder-pa-v1-0.safetensors", + "name": "seecoder pa v1.0", + "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder", + "save_path": "seecoders", + "type": "seecoder", + "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-pa-v1-0.safetensors" + }, + { + "base": "SEECODER", + "description": "SeeCoder model", + "filename": "seecoder-anime-v1-0.safetensors", + "name": "seecoder anime v1.0", + "reference": "https://huggingface.co/shi-labs/prompt-free-diffusion/tree/main/pretrained/pfd/seecoder", + "save_path": "seecoders", + "type": "seecoder", + "url": "https://huggingface.co/shi-labs/prompt-free-diffusion/resolve/main/pretrained/pfd/seecoder/seecoder-anime-v1-0.safetensors" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8m.pt", + "name": "face_yolov8m (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8m.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8n.pt", + "name": "face_yolov8n (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8n.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8n_v2.pt", + "name": "face_yolov8n_v2 (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8n_v2.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8s.pt", + "name": "face_yolov8s (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/face_yolov8s.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "hand_yolov8n.pt", + "name": "hand_yolov8n (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8n.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "hand_yolov8s.pt", + "name": "hand_yolov8s (bbox)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/bbox", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "person_yolov8m-seg.pt", + "name": "person_yolov8m (segm)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "person_yolov8n-seg.pt", + "name": "person_yolov8n (segm)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8n-seg.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "person_yolov8s-seg.pt", + "name": "person_yolov8s (segm)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8s-seg.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "deepfashion2_yolov8s-seg.pt", + "name": "deepfashion2_yolov8s (segm)", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/deepfashion2_yolov8s-seg.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8m-seg_60.pt", + "name": "face_yolov8m-seg_60.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8m-seg_60.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "face_yolov8n-seg2_60.pt", + "name": "face_yolov8n-seg2_60.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8n-seg2_60.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "hair_yolov8n-seg_60.pt", + "name": "hair_yolov8n-seg_60.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/hair_yolov8n-seg_60.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "skin_yolov8m-seg_400.pt", + "name": "skin_yolov8m-seg_400.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8m-seg_400.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "skin_yolov8n-seg_400.pt", + "name": "skin_yolov8n-seg_400.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_400.pt" + }, + { + "base": "Ultralytics", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "filename": "skin_yolov8n-seg_800.pt", + "name": "skin_yolov8n-seg_800.pt (segm)", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "save_path": "ultralytics/segm", + "type": "Ultralytics", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_800.pt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sd_v14.ckpt", + "name": "animatediff/mmd_sd_v14.ckpt (comfyui-animatediff)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/comfyui-animatediff/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sd_v15.ckpt", + "name": "animatediff/mm_sd_v15.ckpt (comfyui-animatediff)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/comfyui-animatediff/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sd_v14.ckpt", + "name": "animatediff/mmd_sd_v14.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sd_v15.ckpt", + "name": "animatediff/mm_sd_v15.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sd_v15_v2.ckpt", + "name": "animatediff/mm_sd_v15_v2.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt" + }, + { + "base": "SD1.x", + "description": "AnimateDiff SparseCtrl RGB ControlNet model", + "filename": "v3_sd15_sparsectrl_rgb.ckpt", + "name": "animatediff/v3_sd15_sparsectrl_rgb.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "controlnet/SD1.5/animatediff", + "type": "controlnet", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_rgb.ckpt" + }, + { + "base": "SD1.x", + "description": "AnimateDiff SparseCtrl Scribble ControlNet model", + "filename": "v3_sd15_sparsectrl_scribble.ckpt", + "name": "animatediff/v3_sd15_sparsectrl_scribble.ckpt", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "controlnet/SD1.5/animatediff", + "type": "controlnet", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_scribble.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v3_sd15_mm.ckpt", + "name": "animatediff/v3_sd15_mm.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_mm.ckpt" + }, + { + "base": "SD1.x", + "description": "AnimateDiff Adapter LoRA (SD1.5)", + "filename": "v3_sd15_adapter.ckpt", + "name": "animatediff/v3_sd15_adapter.ckpt", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "loras/SD1.5/animatediff", + "type": "lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_adapter.ckpt" + }, + { + "base": "SDXL", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm_sdxl_v10_beta.ckpt", + "name": "animatediff/mm_sdxl_v10_beta.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm-Stabilized_high.pth", + "name": "AD_Stabilized_Motion/mm-Stabilized_high.pth (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_high.pth" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "mm-Stabilized_mid.pth", + "name": "AD_Stabilized_Motion/mm-Stabilized_mid.pth (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_mid.pth" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "temporaldiff-v1-animatediff.ckpt", + "name": "CiaraRowles/temporaldiff-v1-animatediff.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/CiaraRowles/TemporalDiff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/CiaraRowles/TemporalDiff/resolve/main/temporaldiff-v1-animatediff.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_PanLeft.ckpt", + "name": "animatediff/v2_lora_PanLeft.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanLeft.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_PanRight.ckpt", + "name": "animatediff/v2_lora_PanRight.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanRight.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_RollingAnticlockwise.ckpt", + "name": "animatediff/v2_lora_RollingAnticlockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingAnticlockwise.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_RollingClockwise.ckpt", + "name": "animatediff/v2_lora_RollingClockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingClockwise.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_TiltDown.ckpt", + "name": "animatediff/v2_lora_TiltDown.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltDown.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_TiltUp.ckpt", + "name": "animatediff/v2_lora_TiltUp.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltUp.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_ZoomIn.ckpt", + "name": "animatediff/v2_lora_ZoomIn.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomIn.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "v2_lora_ZoomOut.ckpt", + "name": "animatediff/v2_lora_ZoomOut.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/guoyww/animatediff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "type": "motion lora", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomOut.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "lt_long_mm_32_frames.ckpt", + "name": "LongAnimatediff/lt_long_mm_32_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_32_frames.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "lt_long_mm_16_64_frames.ckpt", + "name": "LongAnimatediff/lt_long_mm_16_64_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames.ckpt" + }, + { + "base": "SD1.x", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "lt_long_mm_16_64_frames_v1.1.ckpt", + "name": "LongAnimatediff/lt_long_mm_16_64_frames_v1.1.ckpt (ComfyUI-AnimateDiff-Evolved)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "type": "animatediff", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames_v1.1.ckpt" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter_sd15.safetensors", + "name": "ip-adapter_sd15.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter_sd15_light.safetensors", + "name": "ip-adapter_sd15_light.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter_sd15_vit-G.safetensors", + "name": "ip-adapter_sd15_vit-G.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter-plus_sd15.safetensors", + "name": "ip-adapter-plus_sd15.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter-plus-face_sd15.safetensors", + "name": "ip-adapter-plus-face_sd15.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors" + }, + { + "base": "SD1.5", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter-full-face_sd15.safetensors", + "name": "ip-adapter-full-face_sd15.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors" + }, + { + "base": "SD1.5", + "description": "IP-Adapter-FaceID Model (SD1.5)", + "filename": "ip-adapter-faceid_sd15.bin", + "name": "ip-adapter-faceid_sd15.bin", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin" + }, + { + "base": "SD1.5", + "description": "IP-Adapter-FaceID LoRA Model (SD1.5)", + "filename": "ip-adapter-faceid_sd15_lora.safetensors", + "name": "ip-adapter-faceid_sd15_lora.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "save_path": "loras/ipadapter", + "type": "lora", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors" + }, + { + "base": "SDXL", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "filename": "ip-adapter_sdxl.safetensors", + "name": "ip-adapter_sdxl.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors" + }, + { + "base": "SDXL", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "filename": "ip-adapter_sdxl_vit-h.safetensors", + "name": "ip-adapter_sdxl_vit-h.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors" + }, + { + "base": "SDXL", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "filename": "ip-adapter-plus_sdxl_vit-h.safetensors", + "name": "ip-adapter-plus_sdxl_vit-h.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors" + }, + { + "base": "SDXL", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "filename": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "name": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "reference": "https://huggingface.co/h94/IP-Adapter", + "save_path": "ipadapter", + "type": "IP-Adapter", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors" + }, + { + "base": "SD1.5", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "pfg-novel-n10.pt", + "name": "pfg-novel-n10.pt", + "reference": "https://huggingface.co/furusu/PFG", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "type": "PFG", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-novel-n10.pt" + }, + { + "base": "SD1.5", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "pfg-wd14-n10.pt", + "name": "pfg-wd14-n10.pt", + "reference": "https://huggingface.co/furusu/PFG", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "type": "PFG", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd14-n10.pt" + }, + { + "base": "SD1.5", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "filename": "pfg-wd15beta2-n10.pt", + "name": "pfg-wd15beta2-n10.pt", + "reference": "https://huggingface.co/furusu/PFG", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "type": "PFG", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd15beta2-n10.pt" + }, + { + "base": "GFPGAN", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "GFPGANv1.4.pth", + "name": "GFPGANv1.4.pth", + "reference": "https://github.com/TencentARC/GFPGAN/releases", + "save_path": "facerestore_models", + "type": "GFPGAN", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" + }, + { + "base": "CodeFormer", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "codeformer.pth", + "name": "codeformer.pth", + "reference": "https://github.com/sczhou/CodeFormer/releases", + "save_path": "facerestore_models", + "type": "CodeFormer", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth" + }, + { + "base": "facexlib", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "detection_Resnet50_Final.pth", + "name": "detection_Resnet50_Final.pth", + "reference": "https://github.com/xinntao/facexlib", + "save_path": "facerestore_models", + "type": "facexlib", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" + }, + { + "base": "facexlib", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "detection_mobilenet0.25_Final.pth", + "name": "detection_mobilenet0.25_Final.pth", + "reference": "https://github.com/xinntao/facexlib", + "save_path": "facerestore_models", + "type": "facexlib", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth" + }, + { + "base": "facexlib", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "yolov5l-face.pth", + "name": "yolov5l-face.pth", + "reference": "https://github.com/xinntao/facexlib", + "save_path": "facedetection", + "type": "facexlib", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth" + }, + { + "base": "facexlib", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "filename": "yolov5n-face.pth", + "name": "yolov5n-face.pth", + "reference": "https://github.com/xinntao/facexlib", + "save_path": "facedetection", + "type": "facexlib", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth" + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/.gitignore b/ComfyUI/custom_nodes/ComfyUI-Manager/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8eea45d4e59bc30a5cd8c27062f2b15d25f9ff23 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/.gitignore @@ -0,0 +1,12 @@ +__pycache__/ +.idea/ +.vscode/ +.tmp +.cache +config.ini +snapshots/** +startup-scripts/** +.openart_key +matrix_auth +channels.list +comfyworkflows_sharekey diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/LICENSE.txt b/ComfyUI/custom_nodes/ComfyUI-Manager/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/LICENSE.txt @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc.
{match.group(1)}
" + + def replace_i(match): + return f"{match.group(1)}
" + + def replace_bold(match): + return f"{match.group(1)}" + + def replace_white(match): + return f"{match.group(1)}" + + input_text = input_text.replace('\\[', '[').replace('\\]', ']').replace('<', '<').replace('>', '>') + + result_text = re.sub(pattern_a, replace_a, input_text) + result_text = re.sub(pattern_w, replace_w, result_text) + result_text = re.sub(pattern_i, replace_i, result_text) + result_text = re.sub(pattern_bold, replace_bold, result_text) + result_text = re.sub(pattern_white, replace_white, result_text) + + return result_text.replace("\n", "") + + +def populate_markdown(x): + if 'description' in x: + x['description'] = convert_markdown_to_html(x['description']) + + if 'name' in x: + x['name'] = x['name'].replace('<', '<').replace('>', '>') + + if 'title' in x: + x['title'] = x['title'].replace('<', '<').replace('>', '>') + + +@server.PromptServer.instance.routes.get("/customnode/getlist") +async def fetch_customnode_list(request): + if "skip_update" in request.rel_url.query and request.rel_url.query["skip_update"] == "true": + skip_update = True + else: + skip_update = False + + if request.rel_url.query["mode"] == "local": + channel = 'local' + else: + channel = get_config()['channel_url'] + + json_obj = await get_data_by_mode(request.rel_url.query["mode"], 'custom-node-list.json') + + def is_ignored_notice(code): + global version + + if code is not None and code.startswith('#NOTICE_'): + try: + notice_version = [int(x) for x in code[8:].split('.')] + return notice_version[0] < version[0] or (notice_version[0] == version[0] and notice_version[1] <= version[1]) + except Exception: + return False + else: + False + + + json_obj['custom_nodes'] = [record for record in json_obj['custom_nodes'] if not is_ignored_notice(record.get('author'))] + + check_custom_nodes_installed(json_obj, False, not skip_update) + + for x in json_obj['custom_nodes']: + populate_markdown(x) + + if channel != 'local': + found = 'custom' + + for name, url in get_channel_dict().items(): + if url == channel: + found = name + break + + channel = found + + json_obj['channel'] = channel + + return web.json_response(json_obj, content_type='application/json') + + +@server.PromptServer.instance.routes.get("/alternatives/getlist") +async def fetch_alternatives_list(request): + if "skip_update" in request.rel_url.query and request.rel_url.query["skip_update"] == "true": + skip_update = True + else: + skip_update = False + + alter_json = await get_data_by_mode(request.rel_url.query["mode"], 'alter-list.json') + custom_node_json = await get_data_by_mode(request.rel_url.query["mode"], 'custom-node-list.json') + + fileurl_to_custom_node = {} + + for item in custom_node_json['custom_nodes']: + for fileurl in item['files']: + fileurl_to_custom_node[fileurl] = item + + for item in alter_json['items']: + fileurl = item['id'] + if fileurl in fileurl_to_custom_node: + custom_node = fileurl_to_custom_node[fileurl] + check_a_custom_node_installed(custom_node, not skip_update) + + populate_markdown(item) + populate_markdown(custom_node) + item['custom_node'] = custom_node + + return web.json_response(alter_json, content_type='application/json') + + +def check_model_installed(json_obj): + def process_model(item): + model_path = get_model_path(item) + item['installed'] = 'None' + + if model_path is not None: + if os.path.exists(model_path): + item['installed'] = 'True' + else: + item['installed'] = 'False' + + with concurrent.futures.ThreadPoolExecutor(8) as executor: + for item in json_obj['models']: + executor.submit(process_model, item) + + +@server.PromptServer.instance.routes.get("/externalmodel/getlist") +async def fetch_externalmodel_list(request): + json_obj = await get_data_by_mode(request.rel_url.query["mode"], 'model-list.json') + + check_model_installed(json_obj) + + for x in json_obj['models']: + populate_markdown(x) + + return web.json_response(json_obj, content_type='application/json') + + +@server.PromptServer.instance.routes.get("/snapshot/getlist") +async def get_snapshot_list(request): + snapshots_directory = os.path.join(os.path.dirname(__file__), 'snapshots') + items = [f[:-5] for f in os.listdir(snapshots_directory) if f.endswith('.json')] + items.sort(reverse=True) + return web.json_response({'items': items}, content_type='application/json') + + +@server.PromptServer.instance.routes.get("/snapshot/remove") +async def remove_snapshot(request): + try: + target = request.rel_url.query["target"] + + path = os.path.join(os.path.dirname(__file__), 'snapshots', f"{target}.json") + if os.path.exists(path): + os.remove(path) + + return web.Response(status=200) + except: + return web.Response(status=400) + + +@server.PromptServer.instance.routes.get("/snapshot/restore") +async def remove_snapshot(request): + try: + target = request.rel_url.query["target"] + + path = os.path.join(os.path.dirname(__file__), 'snapshots', f"{target}.json") + if os.path.exists(path): + if not os.path.exists(startup_script_path): + os.makedirs(startup_script_path) + + target_path = os.path.join(startup_script_path, "restore-snapshot.json") + shutil.copy(path, target_path) + + print(f"Snapshot restore scheduled: `{target}`") + return web.Response(status=200) + + print(f"Snapshot file not found: `{path}`") + return web.Response(status=400) + except: + return web.Response(status=400) + + +def get_current_snapshot(): + # Get ComfyUI hash + repo_path = os.path.dirname(folder_paths.__file__) + + if not os.path.exists(os.path.join(repo_path, '.git')): + print(f"ComfyUI update fail: The installed ComfyUI does not have a Git repository.") + return web.Response(status=400) + + repo = git.Repo(repo_path) + comfyui_commit_hash = repo.head.commit.hexsha + + git_custom_nodes = {} + file_custom_nodes = [] + + # Get custom nodes hash + for path in os.listdir(custom_nodes_path): + fullpath = os.path.join(custom_nodes_path, path) + + if os.path.isdir(fullpath): + is_disabled = path.endswith(".disabled") + + try: + git_dir = os.path.join(fullpath, '.git') + + if not os.path.exists(git_dir): + continue + + repo = git.Repo(fullpath) + commit_hash = repo.head.commit.hexsha + url = repo.remotes.origin.url + git_custom_nodes[url] = { + 'hash': commit_hash, + 'disabled': is_disabled + } + + except: + print(f"Failed to extract snapshots for the custom node '{path}'.") + + elif path.endswith('.py'): + is_disabled = path.endswith(".py.disabled") + filename = os.path.basename(path) + item = { + 'filename': filename, + 'disabled': is_disabled + } + + file_custom_nodes.append(item) + + return { + 'comfyui': comfyui_commit_hash, + 'git_custom_nodes': git_custom_nodes, + 'file_custom_nodes': file_custom_nodes, + } + + +def save_snapshot_with_postfix(postfix): + now = datetime.datetime.now() + + date_time_format = now.strftime("%Y-%m-%d_%H-%M-%S") + file_name = f"{date_time_format}_{postfix}" + + path = os.path.join(os.path.dirname(__file__), 'snapshots', f"{file_name}.json") + with open(path, "w") as json_file: + json.dump(get_current_snapshot(), json_file, indent=4) + + +@server.PromptServer.instance.routes.get("/snapshot/get_current") +async def get_current_snapshot_api(request): + try: + return web.json_response(get_current_snapshot(), content_type='application/json') + except: + return web.Response(status=400) + + +@server.PromptServer.instance.routes.get("/snapshot/save") +async def save_snapshot(request): + try: + save_snapshot_with_postfix('snapshot') + return web.Response(status=200) + except: + return web.Response(status=400) + + +def unzip_install(files): + temp_filename = 'manager-temp.zip' + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} + + req = urllib.request.Request(url, headers=headers) + response = urllib.request.urlopen(req) + data = response.read() + + with open(temp_filename, 'wb') as f: + f.write(data) + + with zipfile.ZipFile(temp_filename, 'r') as zip_ref: + zip_ref.extractall(custom_nodes_path) + + os.remove(temp_filename) + except Exception as e: + print(f"Install(unzip) error: {url} / {e}", file=sys.stderr) + return False + + print("Installation was successful.") + return True + + +def download_url_with_agent(url, save_path): + try: + headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} + + req = urllib.request.Request(url, headers=headers) + response = urllib.request.urlopen(req) + data = response.read() + + if not os.path.exists(os.path.dirname(save_path)): + os.makedirs(os.path.dirname(save_path)) + + with open(save_path, 'wb') as f: + f.write(data) + + except Exception as e: + print(f"Download error: {url} / {e}", file=sys.stderr) + return False + + print("Installation was successful.") + return True + + +def copy_install(files, js_path_name=None): + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + if url.endswith(".py"): + download_url(url, custom_nodes_path) + else: + path = os.path.join(js_path, js_path_name) if js_path_name is not None else js_path + if not os.path.exists(path): + os.makedirs(path) + download_url(url, path) + + except Exception as e: + print(f"Install(copy) error: {url} / {e}", file=sys.stderr) + return False + + print("Installation was successful.") + return True + + +def copy_uninstall(files, js_path_name='.'): + for url in files: + if url.endswith("/"): + url = url[:-1] + dir_name = os.path.basename(url) + base_path = custom_nodes_path if url.endswith('.py') else os.path.join(js_path, js_path_name) + file_path = os.path.join(base_path, dir_name) + + try: + if os.path.exists(file_path): + os.remove(file_path) + elif os.path.exists(file_path + ".disabled"): + os.remove(file_path + ".disabled") + except Exception as e: + print(f"Uninstall(copy) error: {url} / {e}", file=sys.stderr) + return False + + print("Uninstallation was successful.") + return True + + +def copy_set_active(files, is_disable, js_path_name='.'): + if is_disable: + action_name = "Disable" + else: + action_name = "Enable" + + for url in files: + if url.endswith("/"): + url = url[:-1] + dir_name = os.path.basename(url) + base_path = custom_nodes_path if url.endswith('.py') else os.path.join(js_path, js_path_name) + file_path = os.path.join(base_path, dir_name) + + try: + if is_disable: + current_name = file_path + new_name = file_path + ".disabled" + else: + current_name = file_path + ".disabled" + new_name = file_path + + os.rename(current_name, new_name) + + except Exception as e: + print(f"{action_name}(copy) error: {url} / {e}", file=sys.stderr) + + return False + + print(f"{action_name} was successful.") + return True + + +def execute_install_script(url, repo_path, lazy_mode=False): + install_script_path = os.path.join(repo_path, "install.py") + requirements_path = os.path.join(repo_path, "requirements.txt") + + if lazy_mode: + install_cmd = ["#LAZY-INSTALL-SCRIPT", sys.executable] + try_install_script(url, repo_path, install_cmd) + else: + if os.path.exists(requirements_path): + print("Install: pip packages") + with open(requirements_path, "r") as requirements_file: + for line in requirements_file: + package_name = line.strip() + if package_name: + install_cmd = [sys.executable, "-m", "pip", "install", package_name] + if package_name.strip() != "": + try_install_script(url, repo_path, install_cmd) + + if os.path.exists(install_script_path): + print(f"Install: install script") + install_cmd = [sys.executable, "install.py"] + try_install_script(url, repo_path, install_cmd) + + return True + + +class GitProgress(RemoteProgress): + def __init__(self): + super().__init__() + self.pbar = tqdm() + + def update(self, op_code, cur_count, max_count=None, message=''): + self.pbar.total = max_count + self.pbar.n = cur_count + self.pbar.pos = 0 + self.pbar.refresh() + + +def is_valid_url(url): + try: + result = urlparse(url) + return all([result.scheme, result.netloc]) + except ValueError: + return False + + +def gitclone_install(files): + print(f"install: {files}") + for url in files: + if not is_valid_url(url): + print(f"Invalid git url: '{url}'") + return False + + if url.endswith("/"): + url = url[:-1] + try: + print(f"Download: git clone '{url}'") + repo_name = os.path.splitext(os.path.basename(url))[0] + repo_path = os.path.join(custom_nodes_path, repo_name) + + # Clone the repository from the remote URL + if platform.system() == 'Windows': + res = run_script([sys.executable, git_script_path, "--clone", custom_nodes_path, url]) + if res != 0: + return False + else: + repo = git.Repo.clone_from(url, repo_path, recursive=True, progress=GitProgress()) + repo.git.clear_cache() + repo.close() + + if not execute_install_script(url, repo_path): + return False + + except Exception as e: + print(f"Install(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print("Installation was successful.") + return True + + +def pip_install(packages): + install_cmd = ['#FORCE', sys.executable, "-m", "pip", "install", '-U'] + packages + try_install_script('pip install via manager', '.', install_cmd) + + +import platform +import subprocess +import time + + +def rmtree(path): + retry_count = 3 + + while True: + try: + retry_count -= 1 + + if platform.system() == "Windows": + run_script(['attrib', '-R', path + '\\*', '/S']) + shutil.rmtree(path) + + return True + + except Exception as ex: + print(f"ex: {ex}") + time.sleep(3) + + if retry_count < 0: + raise ex + + print(f"Uninstall retry({retry_count})") + + +def gitclone_uninstall(files): + import shutil + import os + + print(f"uninstall: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_path, dir_name) + + # safety check + if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '': + print(f"Uninstall(git-clone) error: invalid path '{dir_path}' for '{url}'") + return False + + install_script_path = os.path.join(dir_path, "uninstall.py") + disable_script_path = os.path.join(dir_path, "disable.py") + if os.path.exists(install_script_path): + uninstall_cmd = [sys.executable, "uninstall.py"] + code = run_script(uninstall_cmd, cwd=dir_path) + + if code != 0: + print(f"An error occurred during the execution of the uninstall.py script. Only the '{dir_path}' will be deleted.") + elif os.path.exists(disable_script_path): + disable_script = [sys.executable, "disable.py"] + code = run_script(disable_script, cwd=dir_path) + if code != 0: + print(f"An error occurred during the execution of the disable.py script. Only the '{dir_path}' will be deleted.") + + if os.path.exists(dir_path): + rmtree(dir_path) + elif os.path.exists(dir_path + ".disabled"): + rmtree(dir_path + ".disabled") + except Exception as e: + print(f"Uninstall(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print("Uninstallation was successful.") + return True + + +def gitclone_set_active(files, is_disable): + import os + + if is_disable: + action_name = "Disable" + else: + action_name = "Enable" + + print(f"{action_name}: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + dir_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_path, dir_name) + + # safey check + if dir_path == '/' or dir_path[1:] == ":/" or dir_path == '': + print(f"{action_name}(git-clone) error: invalid path '{dir_path}' for '{url}'") + return False + + if is_disable: + current_path = dir_path + new_path = dir_path + ".disabled" + else: + current_path = dir_path + ".disabled" + new_path = dir_path + + os.rename(current_path, new_path) + + if is_disable: + if os.path.exists(os.path.join(new_path, "disable.py")): + disable_script = [sys.executable, "disable.py"] + try_install_script(url, new_path, disable_script) + else: + if os.path.exists(os.path.join(new_path, "enable.py")): + enable_script = [sys.executable, "enable.py"] + try_install_script(url, new_path, enable_script) + + except Exception as e: + print(f"{action_name}(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print(f"{action_name} was successful.") + return True + + +def gitclone_update(files): + import os + + print(f"Update: {files}") + for url in files: + if url.endswith("/"): + url = url[:-1] + try: + repo_name = os.path.splitext(os.path.basename(url))[0].replace(".git", "") + repo_path = os.path.join(custom_nodes_path, repo_name) + git_pull(repo_path) + + if not execute_install_script(url, repo_path, lazy_mode=True): + return False + + except Exception as e: + print(f"Update(git-clone) error: {url} / {e}", file=sys.stderr) + return False + + print("Update was successful.") + return True + + +@server.PromptServer.instance.routes.post("/customnode/install") +async def install_custom_node(request): + json_data = await request.json() + + install_type = json_data['install_type'] + + print(f"Install custom node '{json_data['title']}'") + + res = False + + if len(json_data['files']) == 0: + return web.Response(status=400) + + if install_type == "unzip": + res = unzip_install(json_data['files']) + + if install_type == "copy": + js_path_name = json_data['js_path'] if 'js_path' in json_data else '.' + res = copy_install(json_data['files'], js_path_name) + + elif install_type == "git-clone": + res = gitclone_install(json_data['files']) + + if 'pip' in json_data: + for pname in json_data['pip']: + install_cmd = [sys.executable, "-m", "pip", "install", pname] + try_install_script(json_data['files'][0], ".", install_cmd) + + if res: + print(f"After restarting ComfyUI, please refresh the browser.") + return web.json_response({}, content_type='application/json') + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.get("/customnode/install/git_url") +async def install_custom_node_git_url(request): + res = False + if "url" in request.rel_url.query: + url = request.rel_url.query['url'] + res = gitclone_install([url]) + + if res: + print(f"After restarting ComfyUI, please refresh the browser.") + return web.Response(status=200) + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.get("/customnode/install/pip") +async def install_custom_node_git_url(request): + res = False + if "packages" in request.rel_url.query: + packages = request.rel_url.query['packages'] + pip_install(packages.split(' ')) + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.post("/customnode/uninstall") +async def uninstall_custom_node(request): + json_data = await request.json() + + install_type = json_data['install_type'] + + print(f"Uninstall custom node '{json_data['title']}'") + + res = False + + if install_type == "copy": + js_path_name = json_data['js_path'] if 'js_path' in json_data else '.' + res = copy_uninstall(json_data['files'], js_path_name) + + elif install_type == "git-clone": + res = gitclone_uninstall(json_data['files']) + + if res: + print(f"After restarting ComfyUI, please refresh the browser.") + return web.json_response({}, content_type='application/json') + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.post("/customnode/update") +async def update_custom_node(request): + json_data = await request.json() + + install_type = json_data['install_type'] + + print(f"Update custom node '{json_data['title']}'") + + res = False + + if install_type == "git-clone": + res = gitclone_update(json_data['files']) + + if res: + print(f"After restarting ComfyUI, please refresh the browser.") + return web.json_response({}, content_type='application/json') + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.get("/comfyui_manager/update_comfyui") +async def update_comfyui(request): + print(f"Update ComfyUI") + + try: + repo_path = os.path.dirname(folder_paths.__file__) + + if not os.path.exists(os.path.join(repo_path, '.git')): + print(f"ComfyUI update fail: The installed ComfyUI does not have a Git repository.") + return web.Response(status=400) + + # version check + repo = git.Repo(repo_path) + + if repo.head.is_detached: + switch_to_default_branch(repo) + + current_branch = repo.active_branch + branch_name = current_branch.name + + remote_name = 'origin' + remote = repo.remote(name=remote_name) + + try: + remote.fetch() + except Exception as e: + if 'detected dubious' in e: + print(f"[ComfyUI-Manager] Try fixing 'dubious repository' error on 'ComfyUI' repository") + subprocess.run(['git', 'config', '--global', '--add', 'safe.directory', comfy_path]) + try: + remote.fetch() + except Exception: + print(f"\n[ComfyUI-Manager] Failed to fixing repository setup. Please execute this command on cmd: \n" + f"-----------------------------------------------------------------------------------------\n" + f'git config --global --add safe.directory "{comfy_path}"\n' + f"-----------------------------------------------------------------------------------------\n") + + commit_hash = repo.head.commit.hexsha + remote_commit_hash = repo.refs[f'{remote_name}/{branch_name}'].object.hexsha + + if commit_hash != remote_commit_hash: + git_pull(repo_path) + execute_install_script("ComfyUI", repo_path) + return web.Response(status=201) + else: + return web.Response(status=200) + except Exception as e: + print(f"ComfyUI update fail: {e}", file=sys.stderr) + pass + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.post("/customnode/toggle_active") +async def toggle_active(request): + json_data = await request.json() + + install_type = json_data['install_type'] + is_disabled = json_data['installed'] == "Disabled" + + print(f"Update custom node '{json_data['title']}'") + + res = False + + if install_type == "git-clone": + res = gitclone_set_active(json_data['files'], not is_disabled) + elif install_type == "copy": + res = copy_set_active(json_data['files'], not is_disabled, json_data.get('js_path', None)) + + if res: + return web.json_response({}, content_type='application/json') + + return web.Response(status=400) + + +@server.PromptServer.instance.routes.post("/model/install") +async def install_model(request): + json_data = await request.json() + + model_path = get_model_path(json_data) + + res = False + + try: + if model_path is not None: + print(f"Install model '{json_data['name']}' into '{model_path}'") + + if json_data['url'].startswith('https://github.com') or json_data['url'].startswith('https://huggingface.co'): + model_dir = get_model_dir(json_data) + download_url(json_data['url'], model_dir) + + return web.json_response({}, content_type='application/json') + else: + res = download_url_with_agent(json_data['url'], model_path) + else: + print(f"Model installation error: invalid model type - {json_data['type']}") + + if res: + return web.json_response({}, content_type='application/json') + except Exception as e: + print(f"[ERROR] {e}", file=sys.stderr) + pass + + return web.Response(status=400) + + +class ManagerTerminalHook: + def write_stderr(self, msg): + server.PromptServer.instance.send_sync("manager-terminal-feedback", {"data": msg}) + + def write_stdout(self, msg): + server.PromptServer.instance.send_sync("manager-terminal-feedback", {"data": msg}) + + +manager_terminal_hook = ManagerTerminalHook() + + +@server.PromptServer.instance.routes.get("/manager/terminal") +async def terminal_mode(request): + if "mode" in request.rel_url.query: + if request.rel_url.query['mode'] == 'true': + sys.__comfyui_manager_terminal_hook.add_hook('cm', manager_terminal_hook) + else: + sys.__comfyui_manager_terminal_hook.remove_hook('cm') + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/manager/preview_method") +async def preview_method(request): + if "value" in request.rel_url.query: + set_preview_method(request.rel_url.query['value']) + write_config() + else: + return web.Response(text=get_current_preview_method(), status=200) + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/manager/badge_mode") +async def badge_mode(request): + if "value" in request.rel_url.query: + set_badge_mode(request.rel_url.query['value']) + write_config() + else: + return web.Response(text=get_config()['badge_mode'], status=200) + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/manager/channel_url_list") +async def channel_url_list(request): + channels = get_channel_dict() + if "value" in request.rel_url.query: + channel_url = channels.get(request.rel_url.query['value']) + if channel_url is not None: + get_config()['channel_url'] = channel_url + write_config() + else: + selected = 'custom' + selected_url = get_config()['channel_url'] + + for name, url in channels.items(): + if url == selected_url: + selected = name + break + + res = {'selected': selected, + 'list': get_channel_list()} + return web.json_response(res, status=200) + + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/manager/notice") +async def get_notice(request): + url = "github.com" + path = "/ltdrdata/ltdrdata.github.io/wiki/News" + + conn = http.client.HTTPSConnection(url) + conn.request("GET", path) + + response = conn.getresponse() + + try: + if response.status == 200: + html_content = response.read().decode('utf-8') + + pattern = re.compile(r'
([\s\S]*?)
')
+ match = pattern.search(html_content)
+
+ if match:
+ markdown_content = match.group(1)
+ markdown_content += f"ComfyUI: {comfy_ui_revision}[{comfy_ui_hash[:6]}]({comfy_ui_commit_date})" + # markdown_content += f"
()" + markdown_content += f"
Manager: {version_str}" + + try: + if required_comfyui_revision > int(comfy_ui_revision): + markdown_content = f'
Your ComfyUI is too OUTDATED!!!
' + markdown_content + except: + pass + + return web.Response(text=markdown_content, status=200) + else: + return web.Response(text="Unable to retrieve Notice", status=200) + else: + return web.Response(text="Unable to retrieve Notice", status=200) + finally: + conn.close() + + +@server.PromptServer.instance.routes.get("/manager/reboot") +def restart(self): + try: + sys.stdout.close_log() + except Exception as e: + pass + + return os.execv(sys.executable, [sys.executable] + sys.argv) + + +@server.PromptServer.instance.routes.get("/manager/share_option") +async def share_option(request): + if "value" in request.rel_url.query: + get_config()['share_option'] = request.rel_url.query['value'] + write_config() + else: + return web.Response(text=get_config()['share_option'], status=200) + + return web.Response(status=200) + + +def get_openart_auth(): + if not os.path.exists(os.path.join(comfyui_manager_path, ".openart_key")): + return None + try: + with open(os.path.join(comfyui_manager_path, ".openart_key"), "r") as f: + openart_key = f.read().strip() + return openart_key if openart_key else None + except: + return None + + +def get_matrix_auth(): + if not os.path.exists(os.path.join(comfyui_manager_path, "matrix_auth")): + return None + try: + with open(os.path.join(comfyui_manager_path, "matrix_auth"), "r") as f: + matrix_auth = f.read() + homeserver, username, password = matrix_auth.strip().split("\n") + if not homeserver or not username or not password: + return None + return { + "homeserver": homeserver, + "username": username, + "password": password, + } + except: + return None + + +def get_comfyworkflows_auth(): + if not os.path.exists(os.path.join(comfyui_manager_path, "comfyworkflows_sharekey")): + return None + try: + with open(os.path.join(comfyui_manager_path, "comfyworkflows_sharekey"), "r") as f: + share_key = f.read() + if not share_key.strip(): + return None + return share_key + except: + return None + + +@server.PromptServer.instance.routes.get("/manager/get_openart_auth") +async def api_get_openart_auth(request): + # print("Getting stored Matrix credentials...") + openart_key = get_openart_auth() + if not openart_key: + return web.Response(status=404) + return web.json_response({"openart_key": openart_key}) + + +@server.PromptServer.instance.routes.post("/manager/set_openart_auth") +async def api_set_openart_auth(request): + json_data = await request.json() + openart_key = json_data['openart_key'] + with open(os.path.join(comfyui_manager_path, ".openart_key"), "w") as f: + f.write(openart_key) + return web.Response(status=200) + + +@server.PromptServer.instance.routes.get("/manager/get_matrix_auth") +async def api_get_matrix_auth(request): + # print("Getting stored Matrix credentials...") + matrix_auth = get_matrix_auth() + if not matrix_auth: + return web.Response(status=404) + return web.json_response(matrix_auth) + + +@server.PromptServer.instance.routes.get("/manager/get_comfyworkflows_auth") +async def api_get_comfyworkflows_auth(request): + # Check if the user has provided Matrix credentials in a file called 'matrix_accesstoken' + # in the same directory as the ComfyUI base folder + # print("Getting stored Comfyworkflows.com auth...") + comfyworkflows_auth = get_comfyworkflows_auth() + if not comfyworkflows_auth: + return web.Response(status=404) + return web.json_response({"comfyworkflows_sharekey" : comfyworkflows_auth}) + + +def set_matrix_auth(json_data): + homeserver = json_data['homeserver'] + username = json_data['username'] + password = json_data['password'] + with open(os.path.join(comfyui_manager_path, "matrix_auth"), "w") as f: + f.write("\n".join([homeserver, username, password])) + + +def set_comfyworkflows_auth(comfyworkflows_sharekey): + with open(os.path.join(comfyui_manager_path, "comfyworkflows_sharekey"), "w") as f: + f.write(comfyworkflows_sharekey) + + +def has_provided_matrix_auth(matrix_auth): + return matrix_auth['homeserver'].strip() and matrix_auth['username'].strip() and matrix_auth['password'].strip() + + +def has_provided_comfyworkflows_auth(comfyworkflows_sharekey): + return comfyworkflows_sharekey.strip() + + +@server.PromptServer.instance.routes.post("/manager/share") +async def share_art(request): + # get json data + json_data = await request.json() + + matrix_auth = json_data['matrix_auth'] + comfyworkflows_sharekey = json_data['cw_auth']['cw_sharekey'] + + set_matrix_auth(matrix_auth) + set_comfyworkflows_auth(comfyworkflows_sharekey) + + share_destinations = json_data['share_destinations'] + credits = json_data['credits'] + title = json_data['title'] + description = json_data['description'] + is_nsfw = json_data['is_nsfw'] + prompt = json_data['prompt'] + potential_outputs = json_data['potential_outputs'] + selected_output_index = json_data['selected_output_index'] + + try: + output_to_share = potential_outputs[int(selected_output_index)] + except: + # for now, pick the first output + output_to_share = potential_outputs[0] + + assert output_to_share['type'] in ('image', 'output') + output_dir = folder_paths.get_output_directory() + + if output_to_share['type'] == 'image': + asset_filename = output_to_share['image']['filename'] + asset_subfolder = output_to_share['image']['subfolder'] + + if output_to_share['image']['type'] == 'temp': + output_dir = folder_paths.get_temp_directory() + else: + asset_filename = output_to_share['output']['filename'] + asset_subfolder = output_to_share['output']['subfolder'] + + if asset_subfolder: + asset_filepath = os.path.join(output_dir, asset_subfolder, asset_filename) + else: + asset_filepath = os.path.join(output_dir, asset_filename) + + # get the mime type of the asset + assetFileType = mimetypes.guess_type(asset_filepath)[0] + + if "comfyworkflows" in share_destinations: + share_website_host = "https://comfyworkflows.com" + share_endpoint = f"{share_website_host}/api" + + # get presigned urls + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.post( + f"{share_endpoint}/get_presigned_urls", + json={ + "assetFileName": asset_filename, + "assetFileType": assetFileType, + "workflowJsonFileName" : 'workflow.json', + "workflowJsonFileType" : 'application/json', + + }, + ) as resp: + assert resp.status == 200 + presigned_urls_json = await resp.json() + assetFilePresignedUrl = presigned_urls_json["assetFilePresignedUrl"] + assetFileKey = presigned_urls_json["assetFileKey"] + workflowJsonFilePresignedUrl = presigned_urls_json["workflowJsonFilePresignedUrl"] + workflowJsonFileKey = presigned_urls_json["workflowJsonFileKey"] + + # upload asset + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.put(assetFilePresignedUrl, data=open(asset_filepath, "rb")) as resp: + assert resp.status == 200 + + # upload workflow json + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + async with session.put(workflowJsonFilePresignedUrl, data=json.dumps(prompt['workflow']).encode('utf-8')) as resp: + assert resp.status == 200 + + # make a POST request to /api/upload_workflow with form data key values + async with aiohttp.ClientSession(trust_env=True, connector=aiohttp.TCPConnector(verify_ssl=False)) as session: + form = aiohttp.FormData() + if comfyworkflows_sharekey: + form.add_field("shareKey", comfyworkflows_sharekey) + form.add_field("source", "comfyui_manager") + form.add_field("assetFileKey", assetFileKey) + form.add_field("assetFileType", assetFileType) + form.add_field("workflowJsonFileKey", workflowJsonFileKey) + form.add_field("sharedWorkflowWorkflowJsonString", json.dumps(prompt['workflow'])) + form.add_field("sharedWorkflowPromptJsonString", json.dumps(prompt['output'])) + form.add_field("shareWorkflowCredits", credits) + form.add_field("shareWorkflowTitle", title) + form.add_field("shareWorkflowDescription", description) + form.add_field("shareWorkflowIsNSFW", str(is_nsfw).lower()) + + async with session.post( + f"{share_endpoint}/upload_workflow", + data=form, + ) as resp: + assert resp.status == 200 + upload_workflow_json = await resp.json() + workflowId = upload_workflow_json["workflowId"] + + # check if the user has provided Matrix credentials + if "matrix" in share_destinations: + comfyui_share_room_id = '!LGYSoacpJPhIfBqVfb:matrix.org' + filename = os.path.basename(asset_filepath) + content_type = assetFileType + + try: + from matrix_client.api import MatrixHttpApi + from matrix_client.client import MatrixClient + + homeserver = 'matrix.org' + if matrix_auth: + homeserver = matrix_auth.get('homeserver', 'matrix.org') + homeserver = homeserver.replace("http://", "https://") + if not homeserver.startswith("https://"): + homeserver = "https://" + homeserver + + client = MatrixClient(homeserver) + try: + token = client.login(username=matrix_auth['username'], password=matrix_auth['password']) + if not token: + return web.json_response({"error" : "Invalid Matrix credentials."}, content_type='application/json', status=400) + except: + return web.json_response({"error" : "Invalid Matrix credentials."}, content_type='application/json', status=400) + + matrix = MatrixHttpApi(homeserver, token=token) + with open(asset_filepath, 'rb') as f: + mxc_url = matrix.media_upload(f.read(), content_type, filename=filename)['content_uri'] + + workflow_json_mxc_url = matrix.media_upload(prompt['workflow'], 'application/json', filename='workflow.json')['content_uri'] + + text_content = "" + if title: + text_content += f"{title}\n" + if description: + text_content += f"{description}\n" + if credits: + text_content += f"\ncredits: {credits}\n" + response = matrix.send_message(comfyui_share_room_id, text_content) + response = matrix.send_content(comfyui_share_room_id, mxc_url, filename, 'm.image') + response = matrix.send_content(comfyui_share_room_id, workflow_json_mxc_url, 'workflow.json', 'm.file') + except: + import traceback + traceback.print_exc() + return web.json_response({"error": "An error occurred when sharing your art to Matrix."}, content_type='application/json', status=500) + + return web.json_response({ + "comfyworkflows": { + "url": None if "comfyworkflows" not in share_destinations else f"{share_website_host}/workflows/{workflowId}", + }, + "matrix": { + "success": None if "matrix" not in share_destinations else True + } + }, content_type='application/json', status=200) + + + +def register_api(k, f): + sys.CM_api[k] = f + + +def sanitize(data): + return data.replace("<", "<").replace(">", ">") + + +def lookup_customnode_by_url(data, target): + for x in data['custom_nodes']: + if target in x['files']: + dir_name = os.path.splitext(os.path.basename(target))[0].replace(".git", "") + dir_path = os.path.join(custom_nodes_path, dir_name) + if os.path.exists(dir_path): + x['installed'] = 'True' + elif os.path.exists(dir_path + ".disabled"): + x['installed'] = 'Disabled' + return x + + return None + + +async def _confirm_try_install(sender, custom_node_url, msg): + json_obj = await get_data_by_mode('default', 'custom-node-list.json') + + sender = sanitize(sender) + msg = sanitize(msg) + target = lookup_customnode_by_url(json_obj, custom_node_url) + + if target is not None: + server.PromptServer.instance.send_sync("cm-api-try-install-customnode", + {"sender": sender, "target": target, "msg": msg}) + else: + print(f"[ComfyUI Manager API] Failed to try install - Unknown custom node url '{custom_node_url}'") + + +def confirm_try_install(sender, custom_node_url, msg): + asyncio.run(_confirm_try_install(sender, custom_node_url, msg)) + +register_api('cm.try-install-custom-node', confirm_try_install) + + +import asyncio +async def default_cache_update(): + async def get_cache(filename): + uri = 'https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/' + filename + cache_uri = str(simple_hash(uri)) + '_' + filename + cache_uri = os.path.join(cache_dir, cache_uri) + + json_obj = await get_data(uri) + + with cache_lock: + with open(cache_uri, "w", encoding='utf-8') as file: + json.dump(json_obj, file, indent=4, sort_keys=True) + print(f"[ComfyUI-Manager] default cache updated: {uri}") + + a = get_cache("custom-node-list.json") + b = get_cache("extension-node-map.json") + c = get_cache("model-list.json") + d = get_cache("alter-list.json") + + await asyncio.gather(a, b, c, d) + +threading.Thread(target=lambda: asyncio.run(default_cache_update())).start() + + +WEB_DIRECTORY = "js" +NODE_CLASS_MAPPINGS = {} +__all__ = ['NODE_CLASS_MAPPINGS'] + diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/__init__.cpython-310.pyc b/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..05682da81fa4323ae47566b644f4f20e6c713ceb Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/__init__.cpython-310.pyc differ diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/prestartup_script.cpython-310.pyc b/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/prestartup_script.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..97ba104c680411c43b4d2ad853414ad6cf0a7015 Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI-Manager/__pycache__/prestartup_script.cpython-310.pyc differ diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/alter-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/alter-list.json new file mode 100644 index 0000000000000000000000000000000000000000..62a7240b45e9d757a1d62acf12bc52117f2e9adb --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/alter-list.json @@ -0,0 +1,194 @@ +{ + "items": [ + { + "id":"https://github.com/Fannovel16/comfyui_controlnet_aux", + "tags":"controlnet", + "description": "This extension provides preprocessor nodes for using controlnet." + }, + { + "id":"https://github.com/comfyanonymous/ComfyUI_experiments", + "tags":"Dynamic Thresholding, DT, CFG, controlnet, reference only", + "description": "This experimental nodes contains a 'Reference Only' node and a 'ModelSamplerTonemapNoiseTest' node corresponding to the 'Dynamic Threshold'." + }, + { + "id":"https://github.com/ltdrdata/ComfyUI-Impact-Pack", + "tags":"ddetailer, adetailer, ddsd, DD, loopback scaler, prompt, wildcard, dynamic prompt", + "description": "To implement the feature of automatically detecting faces and enhancing details, various detection nodes and detailers provided by the Impact Pack can be applied. Similarly to Loopback Scaler, it also provides various custom workflows that can apply Ksampler while gradually scaling up." + }, + { + "id":"https://github.com/ltdrdata/ComfyUI-Inspire-Pack", + "tags":"lora block weight, effective block analyzer, lbw, variation seed", + "description": "The Inspire Pack provides the functionality of Lora Block Weight, Variation Seed." + }, + { + "id":"https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py", + "tags":"ddsd", + "description": "This extension provides a feature that generates segment masks on an image using a text prompt. When used in conjunction with Impact Pack, it enables applications such as DDSD." + }, + { + "id":"https://github.com/BadCafeCode/masquerade-nodes-comfyui", + "tags":"ddetailer", + "description": "This extension provides a way to recognize and enhance masks for faces similar to Impact Pack." + }, + { + "id":"https://github.com/BlenderNeko/ComfyUI_Cutoff", + "tags":"cutoff", + "description": "By using this extension, prompts like 'blue hair' can be prevented from interfering with other prompts by blocking the attribute 'blue' from being used in prompts other than 'hair'." + }, + { + "id":"https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb", + "tags":"prompt, weight", + "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. With this extension, various settings can be used to implement prompt processing methods similar to A1111. As this feature is also integrated into ComfyUI Cutoff, please download the Cutoff extension if you plan to use it in conjunction with Cutoff." + }, + { + "id":"https://github.com/shiimizu/ComfyUI_smZNodes", + "tags":"prompt, weight", + "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. This extension helps to reproduce the same embedding as A1111." + }, + { + "id":"https://github.com/BlenderNeko/ComfyUI_Noise", + "tags":"img2img alt, random", + "description": "The extension provides an unsampler that reverses the sampling process, allowing for a function similar to img2img alt to be implemented. Furthermore, ComfyUI uses CPU's Random instead of GPU's Random for better reproducibility compared to A1111. This extension provides the ability to use GPU's Random for Latent Noise. However, since GPU's Random may vary depending on the GPU model, reproducibility on different devices cannot be guaranteed." + }, + { + "id":"https://github.com/BlenderNeko/ComfyUI_SeeCoder", + "tags":"seecoder, prompt-free-diffusion", + "description": "The extension provides seecoder feature." + }, + { + "id":"https://github.com/lilly1987/ComfyUI_node_Lilly", + "tags":"prompt, wildcard", + "description": "This extension provides features such as a wildcard function that randomly selects prompts belonging to a category and the ability to directly load lora from prompts." + }, + { + "id":"https://github.com/Davemane42/ComfyUI_Dave_CustomNode", + "tags":"latent couple", + "description": "ComfyUI already provides the ability to composite latents by default. However, this extension makes it more convenient to use by visualizing the composite area." + }, + { + "id":"https://github.com/LEv145/images-grid-comfy-plugin", + "tags":"X/Y Plot", + "description": "This tool provides a viewer node that allows for checking multiple outputs in a grid, similar to the X/Y Plot extension." + }, + { + "id":"https://github.com/pythongosssss/ComfyUI-WD14-Tagger", + "tags":"deepbooru, clip interrogation", + "description": "This extension generates clip text by taking an image as input and using the Deepbooru model." + }, + { + "id":"https://github.com/szhublox/ambw_comfyui", + "tags":"supermerger", + "description": "This node takes two models, merges individual blocks together at various ratios, and automatically rates each merge, keeping the ratio with the highest score. " + }, + { + "id":"https://github.com/ssitu/ComfyUI_UltimateSDUpscale", + "tags":"upscaler, Ultimate SD Upscale", + "description": "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A. Uses the same script used in the A1111 extension to hopefully replicate images generated using the A1111 webui." + }, + { + "id":"https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py", + "tags":"random, noise", + "description": "A1111 provides KSampler that uses GPU-based random noise. This extension offers KSampler utilizing GPU-based random noise." + }, + { + "id":"https://github.com/space-nuko/nui-suite", + "tags":"prompt, dynamic prompt", + "description": "This extension provides nodes with the functionality of dynamic prompts." + }, + { + "id":"https://github.com/melMass/comfy_mtb", + "tags":"roop", + "description": "This extension provides bunch of nodes including roop" + }, + { + "id":"https://github.com/ssitu/ComfyUI_roop", + "tags":"roop", + "description": "This extension provides nodes for the roop A1111 webui script." + }, + { + "id":"https://github.com/asagi4/comfyui-prompt-control", + "tags":"prompt, prompt editing", + "description": "This extension provides the ability to use prompts like \n\n**a [large::0.1] [cat|dog:0.05] [Installing '${target.title}'`); + } + + disableButtons() { + for(let i in this.install_buttons) { + this.install_buttons[i].disabled = true; + this.install_buttons[i].style.backgroundColor = 'gray'; + } + } + + apply_searchbox(data) { + let keyword = this.search_box.value.toLowerCase(); + for(let i in this.grid_rows) { + let data1 = this.grid_rows[i].data; + let data2 = data1.custom_node; + + if(!data2) + continue; + + let content = data1.tags.toLowerCase() + data1.description.toLowerCase() + data2.author.toLowerCase() + data2.description.toLowerCase() + data2.title.toLowerCase(); + + if(this.filter && this.filter != '*') { + if(this.filter != data2.installed) { + this.grid_rows[i].control.style.display = 'none'; + continue; + } + } + + if(keyword == "") + this.grid_rows[i].control.style.display = null; + else if(content.includes(keyword)) { + this.grid_rows[i].control.style.display = null; + } + else { + this.grid_rows[i].control.style.display = 'none'; + } + } + } + + async invalidateControl() { + this.clear(); + + // splash + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + const msg = $el('div', {id:'custom-message'}, + [$el('br'), + 'The custom node DB is currently being updated, and updates to custom nodes are being checked for.', + $el('br'), + 'NOTE: Update only checks for extensions that have been fetched.', + $el('br')]); + msg.style.height = '100px'; + msg.style.verticalAlign = 'middle'; + this.element.appendChild(msg); + + // invalidate + this.data = (await getAlterList()).items; + + this.element.removeChild(msg); + + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + this.createHeaderControls(); + await this.createGrid(); + this.apply_searchbox(this.data); + this.createBottomControls(); + } + + updateMessage(msg, btn_id) { + this.message_box.innerHTML = msg; + if(btn_id) { + const rebootButton = document.getElementById(btn_id); + const self = this; + rebootButton.addEventListener("click", + function() { + if(rebootAPI()) { + self.close(); + self.manager_dialog.close(); + } + }); + } + } + + invalidate_checks(is_checked, install_state) { + if(is_checked) { + for(let i in this.grid_rows) { + let data = this.grid_rows[i].data; + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + + checkbox.disabled = data.custom_node.installed != install_state; + + if(checkbox.disabled) { + for(let j in buttons) { + buttons[j].style.display = 'none'; + } + } + else { + for(let j in buttons) { + buttons[j].style.display = null; + } + } + } + + this.checkbox_all.disabled = false; + } + else { + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(checkbox.check) + return; // do nothing + } + + // every checkbox is unchecked -> enable all checkbox + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + checkbox.disabled = false; + + for(let j in buttons) { + buttons[j].style.display = null; + } + } + + this.checkbox_all.checked = false; + this.checkbox_all.disabled = true; + } + } + + check_all(is_checked) { + if(is_checked) { + // lookup first checked item's state + let check_state = null; + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(checkbox.checked) { + check_state = this.grid_rows[i].data.custom_node.installed; + } + } + + if(check_state == null) + return; + + // check only same state items + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(this.grid_rows[i].data.custom_node.installed == check_state) + checkbox.checked = true; + } + } + else { + // uncheck all + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + checkbox.checked = false; + checkbox.disabled = false; + + for(let j in buttons) { + buttons[j].style.display = null; + } + } + + this.checkbox_all.disabled = true; + } + } + + async createGrid() { + var grid = document.createElement('table'); + grid.setAttribute('id', 'alternatives-grid'); + + this.grid_rows = {}; + + let self = this; + + var thead = document.createElement('thead'); + var tbody = document.createElement('tbody'); + + var headerRow = document.createElement('tr'); + thead.style.position = "sticky"; + thead.style.top = "0px"; + thead.style.borderCollapse = "collapse"; + thead.style.tableLayout = "fixed"; + + var header0 = document.createElement('th'); + header0.style.width = "20px"; + this.checkbox_all = $el("input",{type:'checkbox', id:'check_all'},[]); + header0.appendChild(this.checkbox_all); + this.checkbox_all.checked = false; + this.checkbox_all.disabled = true; + this.checkbox_all.addEventListener('change', function() { self.check_all.call(self, self.checkbox_all.checked); }); + + var header1 = document.createElement('th'); + header1.innerHTML = ' ID '; + header1.style.width = "20px"; + var header2 = document.createElement('th'); + header2.innerHTML = 'Tags'; + header2.style.width = "10%"; + var header3 = document.createElement('th'); + header3.innerHTML = 'Author'; + header3.style.width = "150px"; + var header4 = document.createElement('th'); + header4.innerHTML = 'Title'; + header4.style.width = "20%"; + var header5 = document.createElement('th'); + header5.innerHTML = 'Description'; + header5.style.width = "50%"; + var header6 = document.createElement('th'); + header6.innerHTML = 'Install'; + header6.style.width = "130px"; + + header1.style.position = "sticky"; + header1.style.top = "0px"; + header2.style.position = "sticky"; + header2.style.top = "0px"; + header3.style.position = "sticky"; + header3.style.top = "0px"; + header4.style.position = "sticky"; + header4.style.top = "0px"; + header5.style.position = "sticky"; + header5.style.top = "0px"; + + thead.appendChild(headerRow); + headerRow.appendChild(header0); + headerRow.appendChild(header1); + headerRow.appendChild(header2); + headerRow.appendChild(header3); + headerRow.appendChild(header4); + headerRow.appendChild(header5); + headerRow.appendChild(header6); + + headerRow.style.backgroundColor = "Black"; + headerRow.style.color = "White"; + headerRow.style.textAlign = "center"; + headerRow.style.width = "100%"; + headerRow.style.padding = "0"; + + grid.appendChild(thead); + grid.appendChild(tbody); + + if(this.data) + for (var i = 0; i < this.data.length; i++) { + const data = this.data[i]; + var dataRow = document.createElement('tr'); + + let data0 = document.createElement('td'); + let checkbox = $el("input",{type:'checkbox', id:`check_${i}`},[]); + data0.appendChild(checkbox); + checkbox.checked = false; + checkbox.addEventListener('change', function() { self.invalidate_checks.call(self, checkbox.checked, data.custom_node?.installed); }); + + var data1 = document.createElement('td'); + data1.style.textAlign = "center"; + data1.innerHTML = i+1; + var data2 = document.createElement('td'); + data2.innerHTML = ` ${data.tags}`; + var data3 = document.createElement('td'); + var data4 = document.createElement('td'); + if(data.custom_node) { + data3.innerHTML = ` ${data.custom_node.author}`; + data4.innerHTML = ` ${data.custom_node.title}`; + } + else { + data3.innerHTML = ` Unknown`; + data4.innerHTML = ` Unknown`; + } + var data5 = document.createElement('td'); + data5.innerHTML = data.description; + var data6 = document.createElement('td'); + data6.style.textAlign = "center"; + + var installBtn = document.createElement('button'); + var installBtn2 = null; + var installBtn3 = null; + + if(data.custom_node) { + this.install_buttons.push(installBtn); + + switch(data.custom_node.installed) { + case 'Disabled': + installBtn3 = document.createElement('button'); + installBtn3.innerHTML = 'Enable'; + installBtn3.style.backgroundColor = 'blue'; + installBtn3.style.color = 'white'; + this.install_buttons.push(installBtn3); + + installBtn.innerHTML = 'Uninstall'; + installBtn.style.backgroundColor = 'red'; + installBtn.style.color = 'white'; + break; + case 'Update': + installBtn2 = document.createElement('button'); + installBtn2.innerHTML = 'Update'; + installBtn2.style.backgroundColor = 'blue'; + installBtn2.style.color = 'white'; + this.install_buttons.push(installBtn2); + + installBtn3 = document.createElement('button'); + installBtn3.innerHTML = 'Disable'; + installBtn3.style.backgroundColor = 'MediumSlateBlue'; + installBtn3.style.color = 'white'; + this.install_buttons.push(installBtn3); + + installBtn.innerHTML = 'Uninstall'; + installBtn.style.backgroundColor = 'red'; + installBtn.style.color = 'white'; + break; + case 'True': + installBtn3 = document.createElement('button'); + installBtn3.innerHTML = 'Disable'; + installBtn3.style.backgroundColor = 'MediumSlateBlue'; + installBtn3.style.color = 'white'; + this.install_buttons.push(installBtn3); + + installBtn.innerHTML = 'Uninstall'; + installBtn.style.backgroundColor = 'red'; + installBtn.style.color = 'white'; + break; + case 'False': + installBtn.innerHTML = 'Install'; + installBtn.style.backgroundColor = 'black'; + installBtn.style.color = 'white'; + break; + default: + installBtn.innerHTML = 'Try Install'; + installBtn.style.backgroundColor = 'Gray'; + installBtn.style.color = 'white'; + } + + let j = i; + if(installBtn2 != null) { + installBtn2.style.width = "120px"; + installBtn2.addEventListener('click', function() { + install_checked_custom_node(self.grid_rows, j, AlternativesInstaller.instance, 'update'); + }); + + data6.appendChild(installBtn2); + } + + if(installBtn3 != null) { + installBtn3.style.width = "120px"; + installBtn3.addEventListener('click', function() { + install_checked_custom_node(self.grid_rows, j, AlternativesInstaller.instance, 'toggle_active'); + }); + + data6.appendChild(installBtn3); + } + + + installBtn.style.width = "120px"; + installBtn.addEventListener('click', function() { + if(this.innerHTML == 'Uninstall') { + if (confirm(`Are you sure uninstall ${data.title}?`)) { + install_checked_custom_node(self.grid_rows, j, AlternativesInstaller.instance, 'uninstall'); + } + } + else { + install_checked_custom_node(self.grid_rows, j, AlternativesInstaller.instance, 'install'); + } + }); + + data6.appendChild(installBtn); + } + + dataRow.style.backgroundColor = "var(--bg-color)"; + dataRow.style.color = "var(--fg-color)"; + dataRow.style.textAlign = "left"; + + dataRow.appendChild(data0); + dataRow.appendChild(data1); + dataRow.appendChild(data2); + dataRow.appendChild(data3); + dataRow.appendChild(data4); + dataRow.appendChild(data5); + dataRow.appendChild(data6); + tbody.appendChild(dataRow); + + let buttons = []; + if(installBtn) { + buttons.push(installBtn); + } + if(installBtn2) { + buttons.push(installBtn2); + } + if(installBtn3) { + buttons.push(installBtn3); + } + + this.grid_rows[i] = {data:data, buttons:buttons, checkbox:checkbox, control:dataRow}; + } + + const panel = document.createElement('div'); + panel.style.width = "100%"; + panel.appendChild(grid); + + function handleResize() { + const parentHeight = self.element.clientHeight; + const gridHeight = parentHeight - 200; + + grid.style.height = gridHeight + "px"; + } + window.addEventListener("resize", handleResize); + + grid.style.position = "relative"; + grid.style.display = "inline-block"; + grid.style.width = "100%"; + grid.style.height = "100%"; + grid.style.overflowY = "scroll"; + this.element.style.height = "85%"; + this.element.style.width = "80%"; + this.element.appendChild(panel); + + handleResize(); + } + + createFilterCombo() { + let combo = document.createElement("select"); + + combo.style.cssFloat = "left"; + combo.style.fontSize = "14px"; + combo.style.padding = "4px"; + combo.style.background = "black"; + combo.style.marginLeft = "2px"; + combo.style.width = "199px"; + combo.id = `combo-manger-filter`; + combo.style.borderRadius = "15px"; + + let items = + [ + { value:'*', text:'Filter: all' }, + { value:'Disabled', text:'Filter: disabled' }, + { value:'Update', text:'Filter: update' }, + { value:'True', text:'Filter: installed' }, + { value:'False', text:'Filter: not-installed' }, + ]; + + items.forEach(item => { + const option = document.createElement("option"); + option.value = item.value; + option.text = item.text; + combo.appendChild(option); + }); + + let self = this; + combo.addEventListener('change', function(event) { + self.filter = event.target.value; + self.apply_searchbox(); + }); + + if(self.filter) { + combo.value = self.filter; + } + + return combo; + } + + createHeaderControls() { + let self = this; + this.search_box = $el('input.cm-search-filter', {type:'text', id:'manager-alternode-search-box', placeholder:'input search keyword', value:this.search_keyword}, []); + this.search_box.style.height = "25px"; + this.search_box.onkeydown = (event) => { + if (event.key === 'Enter') { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + } + if (event.key === 'Escape') { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + } + }; + + let search_button = document.createElement("button"); + search_button.className = "cm-small-button"; + search_button.innerHTML = "Search"; + search_button.onclick = () => { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + }; + search_button.style.display = "inline-block"; + + let filter_control = this.createFilterCombo(); + filter_control.style.display = "inline-block"; + + let cell = $el('td', {width:'100%'}, [filter_control, this.search_box, ' ', search_button]); + let search_control = $el('table', {width:'100%'}, + [ + $el('tr', {}, [cell]) + ] + ); + + cell.style.textAlign = "right"; + this.element.appendChild(search_control); + } + + async createBottomControls() { + var close_button = document.createElement("button"); + close_button.className = "cm-small-button"; + close_button.innerHTML = "Close"; + close_button.onclick = () => { this.close(); } + close_button.style.display = "inline-block"; + + this.message_box = $el('div', {id:'alternatives-installer-message'}, [$el('br'), '']); + this.message_box.style.height = '60px'; + this.message_box.style.verticalAlign = 'middle'; + + this.element.appendChild(this.message_box); + this.element.appendChild(close_button); + } + + async show() { + try { + this.invalidateControl(); + this.element.style.display = "block"; + this.element.style.zIndex = 10001; + } + catch(exception) { + app.ui.dialog.show(`Failed to get alternatives list. / ${exception}`); + console.error(exception); + } + } +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/cm-api.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/cm-api.js new file mode 100644 index 0000000000000000000000000000000000000000..e65cb3480d4eab98b6a27db81148bc8124f91cdc --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/cm-api.js @@ -0,0 +1,54 @@ +import { api } from "../../scripts/api.js"; +import { app } from "../../scripts/app.js"; +import { sleep } from "./common.js"; + +async function tryInstallCustomNode(event) { + let msg = '-= [ComfyUI Manager] extension installation request =-\n\n'; + msg += `The '${event.detail.sender}' extension requires the installation of the '${event.detail.title}' extension. `; + + if(event.detail.target.installed == 'Disabled') { + msg += 'However, the extension is currently disabled. Would you like to enable it and reboot?' + } + else if(event.detail.target.installed == 'True') { + msg += 'However, it seems that the extension is in an import-fail state or is not compatible with the current version. Please address this issue.'; + } + else { + msg += `Would you like to install it and reboot?`; + } + + msg += `\n\nRequest message:\n${event.detail.msg}`; + + if(event.detail.target.installed == 'True') { + alert(msg); + return; + } + + let res = confirm(msg); + if(res) { + if(event.detail.target.installed == 'Disabled') { + const response = await api.fetchApi(`/customnode/toggle_active`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(event.detail.target) + }); + } + else { + await sleep(300); + app.ui.dialog.show(`Installing... '${event.detail.target.title}'`); + + const response = await api.fetchApi(`/customnode/install`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(event.detail.target) + }); + } + + api.fetchApi("/manager/reboot"); + + await sleep(300); + + app.ui.dialog.show(`Rebooting...`); + } +} + +api.addEventListener("cm-api-try-install-customnode", tryInstallCustomNode); diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-manager.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-manager.js new file mode 100644 index 0000000000000000000000000000000000000000..a0e827c833d7f545ad62ff3dd6012fc4264c7cc0 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-manager.js @@ -0,0 +1,1132 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { ShareDialog, SUPPORTED_OUTPUT_NODE_TYPES, getPotentialOutputsAndOutputNodes, ShareDialogChooser, showOpenArtShareDialog, showShareDialog } from "./comfyui-share-common.js"; +import { OpenArtShareDialog } from "./comfyui-share-openart.js"; +import { CustomNodesInstaller } from "./custom-nodes-downloader.js"; +import { AlternativesInstaller } from "./a1111-alter-downloader.js"; +import { SnapshotManager } from "./snapshot.js"; +import { ModelInstaller } from "./model-downloader.js"; +import { manager_instance, setManagerInstance, install_via_git_url, install_pip, rebootAPI } from "./common.js"; + +var docStyle = document.createElement('style'); +docStyle.innerHTML = ` +#cm-manager-dialog { + width: 1000px; + height: 450px; + box-sizing: content-box; + z-index: 10000; +} + +.cm-menu-container { + column-gap: 20px; + display: flex; + flex-wrap: wrap; + justify-content: center; + box-sizing: content-box; +} + +.cm-menu-column { + display: flex; + flex-direction: column; + flex: 1 1 auto; + width: 300px; + box-sizing: content-box; +} + +.cm-title { + background-color: black; + text-align: center; + height: 40px; + width: calc(100% - 10px); + font-weight: bold; + justify-content: center; + align-content: center; + vertical-align: middle; +} + +#cm-channel-badge { + color: white; + background-color: #AA0000; + width: 220px; + height: 23px; + font-size: 13px; + border-radius: 5px; + left: 5px; + top: 5px; + align-content: center; + justify-content: center; + text-align: center; + font-weight: bold; + float: left; + vertical-align: middle; + position: relative; +} + +.cm-notice-board { + width: 310px; + padding: 0px !important; + height: 190px; + overflow: auto; + color: var(--input-text); + border: 1px solid var(--descrip-text); + padding: 10px; + overflow-x: hidden; +} + +.cm-conflicted-nodes-text { + background-color: #CCCC55 !important; + color: #AA3333 !important; + font-size: 10px; + border-radius: 5px; + padding: 10px; +} + +.cm-warn-note { + background-color: #101010 !important; + color: #FF3800 !important; + font-size: 13px; + border-radius: 5px; + padding: 10px; + overflow-x: hidden; + overflow: auto; +} + +.cm-info-note { + background-color: #101010 !important; + color: #FF3800 !important; + font-size: 13px; + border-radius: 5px; + padding: 10px; + overflow-x: hidden; + overflow: auto; +} +`; + +document.head.appendChild(docStyle); + +var update_comfyui_button = null; +var fetch_updates_button = null; +var update_all_button = null; +var badge_mode = "none"; +let share_option = 'all'; + +// copied style from https://github.com/pythongosssss/ComfyUI-Custom-Scripts +const style = ` +#workflowgallery-button { + width: 310px; + height: 27px; + padding: 0px !important; + position: relative; + overflow: hidden; + font-size: 17px !important; +} +#cm-nodeinfo-button { + width: 310px; + height: 27px; + padding: 0px !important; + position: relative; + overflow: hidden; + font-size: 17px !important; +} +#cm-manual-button { + width: 310px; + height: 27px; + position: relative; + overflow: hidden; +} + +.cm-button { + width: 310px; + height: 30px; + position: relative; + overflow: hidden; + font-size: 17px !important; +} + +.cm-experimental-button { + width: 290px; + height: 30px; + position: relative; + overflow: hidden; + font-size: 17px !important; +} + +.cm-experimental { + width: 310px; + border: 1px solid #555; + border-radius: 5px; + padding: 10px; + align-items: center; + text-align: center; + justify-content: center; + box-sizing: border-box; +} + +.cm-experimental-legend { + margin-top: -20px; + margin-left: 95px; + width:100px; + height:20px; + font-size: 13px; + font-weight: bold; + background-color: #990000; + color: #CCFFFF; + border-radius: 5px; +} + +.cm-menu-combo { + cursor: pointer; + width: 310px; + box-sizing: border-box; +} + +.cm-small-button { + width: 120px; + height: 30px; + position: relative; + overflow: hidden; + box-sizing: border-box; + font-size: 17px !important; +} + +#cm-install-customnodes-button { + width: 200px; + height: 30px; + position: relative; + overflow: hidden; + box-sizing: border-box; + font-size: 17px !important; +} + +.cm-search-filter { + width: 200px; + height: 30px !important; + position: relative; + overflow: hidden; + box-sizing: border-box; +} + +#cm-close-button { + width: calc(100% - 65px); + bottom: 10px; + position: absolute; + overflow: hidden; +} + +.pysssss-workflow-arrow-2 { + position: absolute; + top: 0; + bottom: 0; + right: 0; + font-size: 12px; + display: flex; + align-items: center; + width: 24px; + justify-content: center; + background: rgba(255,255,255,0.1); + content: "▼"; +} +.pysssss-workflow-arrow-2:after { + content: "▼"; + } + .pysssss-workflow-arrow-2:hover { + filter: brightness(1.6); + background-color: var(--comfy-menu-bg); + } +.pysssss-workflow-popup-2 ~ .litecontextmenu { + transform: scale(1.3); +} +#workflowgallery-button-menu { + z-index: 10000000000 !important; +} +#cm-manual-button-menu { + z-index: 10000000000 !important; +} +`; + + + +async function init_badge_mode() { + api.fetchApi('/manager/badge_mode') + .then(response => response.text()) + .then(data => { badge_mode = data; }) +} + +async function init_share_option() { + api.fetchApi('/manager/share_option') + .then(response => response.text()) + .then(data => { + share_option = data || 'all'; + }); +} + +async function init_notice(notice) { + api.fetchApi('/manager/notice') + .then(response => response.text()) + .then(data => { + notice.innerHTML = data; + }) +} + +await init_badge_mode(); +await init_share_option(); + +async function fetchNicknames() { + const response1 = await api.fetchApi(`/customnode/getmappings?mode=local`); + const mappings = await response1.json(); + + let result = {}; + let nickname_patterns = []; + + for (let i in mappings) { + let item = mappings[i]; + var nickname; + if (item[1].nickname) { + nickname = item[1].nickname; + } + else if (item[1].title) { + nickname = item[1].title; + } + else { + nickname = item[1].title_aux; + } + + for (let j in item[0]) { + result[item[0][j]] = nickname; + } + + if(item[1].nodename_pattern) { + nickname_patterns.push([item[1].nodename_pattern, nickname]); + } + } + + return [result, nickname_patterns]; +} + +const [nicknames, nickname_patterns] = await fetchNicknames(); + +function getNickname(node, nodename) { + if(node.nickname) { + return node.nickname; + } + else { + if (nicknames[nodename]) { + node.nickname = nicknames[nodename]; + } + else { + for(let i in nickname_patterns) { + let item = nickname_patterns[i]; + if(nodename.match(item[0])) { + node.nickname = item[1]; + } + } + } + + return node.nickname; + } +} + +function drawBadge(node, orig, restArgs) { + let ctx = restArgs[0]; + const r = orig?.apply?.(node, restArgs); + + if (!node.flags.collapsed && badge_mode != 'none' && node.constructor.title_mode != LiteGraph.NO_TITLE) { + let text = ""; + if (badge_mode.startsWith('id_nick')) + text = `#${node.id} `; + + let nick = node.getNickname(); + if (nick) { + if (nick == 'ComfyUI') { + if(badge_mode.endsWith('hide')) { + nick = ""; + } + else { + nick = "🦊" + } + } + + if (nick.length > 25) { + text += nick.substring(0, 23) + ".."; + } + else { + text += nick; + } + } + + if (text != "") { + let fgColor = "white"; + let bgColor = "#0F1F0F"; + let visible = true; + + ctx.save(); + ctx.font = "12px sans-serif"; + const sz = ctx.measureText(text); + ctx.fillStyle = bgColor; + ctx.beginPath(); + ctx.roundRect(node.size[0] - sz.width - 12, -LiteGraph.NODE_TITLE_HEIGHT - 20, sz.width + 12, 20, 5); + ctx.fill(); + + ctx.fillStyle = fgColor; + ctx.fillText(text, node.size[0] - sz.width - 6, -LiteGraph.NODE_TITLE_HEIGHT - 6); + ctx.restore(); + + if (node.has_errors) { + ctx.save(); + ctx.font = "bold 14px sans-serif"; + const sz2 = ctx.measureText(node.type); + ctx.fillStyle = 'white'; + ctx.fillText(node.type, node.size[0] / 2 - sz2.width / 2, node.size[1] / 2); + ctx.restore(); + } + } + } + return r; +} + + +async function updateComfyUI() { + let prev_text = update_comfyui_button.innerText; + update_comfyui_button.innerText = "Updating ComfyUI..."; + update_comfyui_button.disabled = true; + update_comfyui_button.style.backgroundColor = "gray"; + + try { + const response = await api.fetchApi('/comfyui_manager/update_comfyui'); + + if (response.status == 400) { + app.ui.dialog.show('Failed to update ComfyUI.'); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + + if (response.status == 201) { + app.ui.dialog.show('ComfyUI has been successfully updated.'); + app.ui.dialog.element.style.zIndex = 10010; + } + else { + app.ui.dialog.show('ComfyUI is already up to date with the latest version.'); + app.ui.dialog.element.style.zIndex = 10010; + } + + return true; + } + catch (exception) { + app.ui.dialog.show(`Failed to update ComfyUI / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + finally { + update_comfyui_button.disabled = false; + update_comfyui_button.innerText = prev_text; + update_comfyui_button.style.backgroundColor = ""; + } +} + +async function fetchUpdates(update_check_checkbox) { + let prev_text = fetch_updates_button.innerText; + fetch_updates_button.innerText = "Fetching updates..."; + fetch_updates_button.disabled = true; + fetch_updates_button.style.backgroundColor = "gray"; + + try { + var mode = manager_instance.datasrc_combo.value; + + const response = await api.fetchApi(`/customnode/fetch_updates?mode=${mode}`); + + if (response.status != 200 && response.status != 201) { + app.ui.dialog.show('Failed to fetch updates.'); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + + if (response.status == 201) { + app.ui.dialog.show("There is an updated extension available.
NOTE:
Fetch Updates is not an update.
Please update from
See terminal log.
'); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + if(response1.status == 201 || response2.status == 201) { + app.ui.dialog.show("ComfyUI and all extensions have been updated to the latest version.
To apply the updated custom node, please ComfyUI. And refresh browser."); + + const rebootButton = document.getElementById('cm-reboot-button'); + rebootButton.addEventListener("click", + function() { + if(rebootAPI()) { + manager_dialog.close(); + } + }); + + app.ui.dialog.element.style.zIndex = 10010; + } + else { + app.ui.dialog.show('ComfyUI and all extensions are already up-to-date with the latest versions.'); + app.ui.dialog.element.style.zIndex = 10010; + } + + return true; + } + catch (exception) { + app.ui.dialog.show(`Failed to update ComfyUI or several extensions / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + finally { + update_all_button.disabled = false; + update_all_button.innerText = prev_text; + update_all_button.style.backgroundColor = ""; + } +} + +function newDOMTokenList(initialTokens) { + const tmp = document.createElement(`div`); + + const classList = tmp.classList; + if (initialTokens) { + initialTokens.forEach(token => { + classList.add(token); + }); + } + + return classList; + } + +/** + * Check whether the node is a potential output node (img, gif or video output) + */ +const isOutputNode = (node) => { + return [ + "VHS_VideoCombine", + "PreviewImage", + "SaveImage", + "ADE_AnimateDiffCombine", + "SaveAnimatedWEBP", + ].includes(node.type); +} + +// ----------- +class ManagerMenuDialog extends ComfyDialog { + createControlsMid() { + let self = this; + + update_comfyui_button = + $el("button.cm-button", { + type: "button", + textContent: "Update ComfyUI", + onclick: + () => updateComfyUI() + }); + + fetch_updates_button = + $el("button.cm-button", { + type: "button", + textContent: "Fetch Updates", + onclick: + () => fetchUpdates(this.update_check_checkbox) + }); + + update_all_button = + $el("button.cm-button", { + type: "button", + textContent: "Update All", + onclick: + () => updateAll(this.update_check_checkbox, self) + }); + + const res = + [ + $el("button.cm-button", { + type: "button", + textContent: "Install Custom Nodes", + onclick: + () => { + if(!CustomNodesInstaller.instance) + CustomNodesInstaller.instance = new CustomNodesInstaller(app, self); + CustomNodesInstaller.instance.show(CustomNodesInstaller.ShowMode.NORMAL); + } + }), + + $el("button.cm-button", { + type: "button", + textContent: "Install Missing Custom Nodes", + onclick: + () => { + if(!CustomNodesInstaller.instance) + CustomNodesInstaller.instance = new CustomNodesInstaller(app, self); + CustomNodesInstaller.instance.show(CustomNodesInstaller.ShowMode.MISSING_NODES); + } + }), + + $el("button.cm-button", { + type: "button", + textContent: "Install Models", + onclick: + () => { + if(!ModelInstaller.instance) + ModelInstaller.instance = new ModelInstaller(app, self); + ModelInstaller.instance.show(); + } + }), + + $el("br", {}, []), + update_all_button, + update_comfyui_button, + fetch_updates_button, + + $el("br", {}, []), + $el("button.cm-button", { + type: "button", + textContent: "Alternatives of A1111", + onclick: + () => { + if(!AlternativesInstaller.instance) + AlternativesInstaller.instance = new AlternativesInstaller(app, self); + AlternativesInstaller.instance.show(); + } + }) + ]; + + return res; + } + + createControlsLeft() { + let self = this; + + this.update_check_checkbox = $el("input",{type:'checkbox', id:"skip_update_check"},[]) + const uc_checkbox_text = $el("label",{for:"skip_update_check"},[" Skip update check"]) + uc_checkbox_text.style.color = "var(--fg-color)"; + uc_checkbox_text.style.cursor = "pointer"; + this.update_check_checkbox.checked = true; + + // db mode + this.datasrc_combo = document.createElement("select"); + this.datasrc_combo.className = "cm-menu-combo"; + this.datasrc_combo.appendChild($el('option', { value: 'cache', text: 'DB: Channel (1day cache)' }, [])); + this.datasrc_combo.appendChild($el('option', { value: 'local', text: 'DB: Local' }, [])); + this.datasrc_combo.appendChild($el('option', { value: 'url', text: 'DB: Channel (remote)' }, [])); + + // preview method + let preview_combo = document.createElement("select"); + preview_combo.className = "cm-menu-combo"; + preview_combo.appendChild($el('option', { value: 'auto', text: 'Preview method: Auto' }, [])); + preview_combo.appendChild($el('option', { value: 'taesd', text: 'Preview method: TAESD (slow)' }, [])); + preview_combo.appendChild($el('option', { value: 'latent2rgb', text: 'Preview method: Latent2RGB (fast)' }, [])); + preview_combo.appendChild($el('option', { value: 'none', text: 'Preview method: None (very fast)' }, [])); + + api.fetchApi('/manager/preview_method') + .then(response => response.text()) + .then(data => { preview_combo.value = data; }) + + preview_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/preview_method?value=${event.target.value}`); + }); + + // nickname + let badge_combo = document.createElement("select"); + badge_combo.className = "cm-menu-combo"; + badge_combo.appendChild($el('option', { value: 'none', text: 'Badge: None' }, [])); + badge_combo.appendChild($el('option', { value: 'nick', text: 'Badge: Nickname' }, [])); + badge_combo.appendChild($el('option', { value: 'nick_hide', text: 'Badge: Nickname (hide built-in)' }, [])); + badge_combo.appendChild($el('option', { value: 'id_nick', text: 'Badge: #ID Nickname' }, [])); + badge_combo.appendChild($el('option', { value: 'id_nick_hide', text: 'Badge: #ID Nickname (hide built-in)' }, [])); + + api.fetchApi('/manager/badge_mode') + .then(response => response.text()) + .then(data => { badge_combo.value = data; badge_mode = data; }); + + badge_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/badge_mode?value=${event.target.value}`); + badge_mode = event.target.value; + app.graph.setDirtyCanvas(true); + }); + + // channel + let channel_combo = document.createElement("select"); + channel_combo.className = "cm-menu-combo"; + api.fetchApi('/manager/channel_url_list') + .then(response => response.json()) + .then(async data => { + try { + let urls = data.list; + for (let i in urls) { + if (urls[i] != '') { + let name_url = urls[i].split('::'); + channel_combo.appendChild($el('option', { value: name_url[0], text: `Channel: ${name_url[0]}` }, [])); + } + } + + channel_combo.addEventListener('change', function (event) { + api.fetchApi(`/manager/channel_url_list?value=${event.target.value}`); + }); + + channel_combo.value = data.selected; + } + catch (exception) { + + } + }); + + // share + let share_combo = document.createElement("select"); + share_combo.className = "cm-menu-combo"; + const share_options = [ + ['none', 'None'], + ['openart', 'OpenArt AI'], + ['matrix', 'Matrix Server'], + ['comfyworkflows', 'ComfyWorkflows'], + ['all', 'All'], + ]; + for (const option of share_options) { + share_combo.appendChild($el('option', { value: option[0], text: `Share: ${option[1]}` }, [])); + } + + api.fetchApi('/manager/share_option') + .then(response => response.text()) + .then(data => { + share_combo.value = data || 'all'; + share_option = data || 'all'; + }); + + share_combo.addEventListener('change', function (event) { + const value = event.target.value; + share_option = value; + api.fetchApi(`/manager/share_option?value=${value}`); + const shareButton = document.getElementById("shareButton"); + if (value === 'none') { + shareButton.style.display = "none"; + } else { + shareButton.style.display = "inline-block"; + } + }); + + return [ + $el("div", {}, [this.update_check_checkbox, uc_checkbox_text]), + $el("br", {}, []), + this.datasrc_combo, + channel_combo, + preview_combo, + badge_combo, + share_combo, + $el("br", {}, []), + $el("button.cm-button", { + type: "button", + textContent: "Install via Git URL", + onclick: () => { + var url = prompt("Please enter the URL of the Git repository to install", ""); + + if (url !== null) { + install_via_git_url(url, self); + } + } + }), + + $el("br", {}, []), + $el("filedset.cm-experimental", {}, [ + $el("legend.cm-experimental-legend", {}, ["EXPERIMENTAL"]), + $el("button.cm-experimental-button", { + type: "button", + textContent: "Snapshot Manager", + onclick: + () => { + if(!SnapshotManager.instance) + SnapshotManager.instance = new SnapshotManager(app, self); + SnapshotManager.instance.show(); + } + }), + $el("button.cm-experimental-button", { + type: "button", + textContent: "Install PIP packages", + onclick: + () => { + var url = prompt("Please enumerate the pip packages to be installed.\n\nExample: insightface opencv-python-headless>=4.1.1\n", ""); + + if (url !== null) { + install_pip(url, self); + } + } + }) + ]), + ]; + } + + createControlsRight() { + const elts = [ + $el("button.cm-button", { + id: 'cm-manual-button', + type: "button", + textContent: "Community Manual", + onclick: () => { window.open("https://blenderneko.github.io/ComfyUI-docs/", "comfyui-community-manual"); } + }, [ + $el("div.pysssss-workflow-arrow-2", { + id: `cm-manual-button-arrow`, + onclick: (e) => { + e.preventDefault(); + e.stopPropagation(); + + LiteGraph.closeAllContextMenus(); + const menu = new LiteGraph.ContextMenu( + [ + { + title: "Comfy Custom Node How To", + callback: () => { window.open("https://github.com/chrisgoringe/Comfy-Custom-Node-How-To/wiki/aaa_index", "comfyui-community-manual1"); }, + }, + { + title: "ComfyUI Guide To Making Custom Nodes", + callback: () => { window.open("https://github.com/Suzie1/ComfyUI_Guide_To_Making_Custom_Nodes/wiki", "comfyui-community-manual2"); }, + }, + { + title: "ComfyUI Examples", + callback: () => { window.open("https://comfyanonymous.github.io/ComfyUI_examples", "comfyui-community-manual3"); }, + }, + { + title: "Close", + callback: () => { + LiteGraph.closeAllContextMenus(); + }, + } + ], + { + event: e, + scale: 1.3, + }, + window + ); + // set the id so that we can override the context menu's z-index to be above the comfyui manager menu + menu.root.id = "cm-manual-button-menu"; + menu.root.classList.add("pysssss-workflow-popup-2"); + }, + }) + ]), + + $el("button", { + id: 'workflowgallery-button', + type: "button", + style: { + ...(localStorage.getItem("wg_last_visited") ? {height: '50px'} : {}) + }, + onclick: (e) => { + const last_visited_site = localStorage.getItem("wg_last_visited") + if (!!last_visited_site) { + window.open(last_visited_site, "comfyui-workflow-gallery"); + } else { + this.handleWorkflowGalleryButtonClick(e) + } + }, + }, [ + $el("p", { + textContent: 'Workflow Gallery', + style: { + 'text-align': 'center', + 'color': 'white', + 'font-size': '18px', + 'margin': 0, + 'padding': 0, + } + }, [ + $el("p", { + id: 'workflowgallery-button-last-visited-label', + textContent: `(${localStorage.getItem("wg_last_visited") ? localStorage.getItem("wg_last_visited").split('/')[2] : ''})`, + style: { + 'text-align': 'center', + 'color': 'white', + 'font-size': '12px', + 'margin': 0, + 'padding': 0, + } + }) + ]), + $el("div.pysssss-workflow-arrow-2", { + id: `comfyworkflows-button-arrow`, + onclick: this.handleWorkflowGalleryButtonClick + }) + ]), + + $el("button.cm-button", { + id: 'cm-nodeinfo-button', + type: "button", + textContent: "Nodes Info", + onclick: () => { window.open("https://ltdrdata.github.io/", "comfyui-node-info"); } + }), + $el("br", {}, []), + ]; + + var textarea = document.createElement("div"); + textarea.className = "cm-notice-board"; + elts.push(textarea); + + init_notice(textarea); + + return elts; + } + + constructor() { + super(); + + const close_button = $el("button", { id: "cm-close-button", type: "button", textContent: "Close", onclick: () => this.close() }); + + const content = + $el("div.comfy-modal-content", + [ + $el("tr.cm-title", {}, [ + $el("font", {size:6, color:"white"}, [`ComfyUI Manager Menu`])] + ), + $el("br", {}, []), + $el("div.cm-menu-container", + [ + $el("div.cm-menu-column", [...this.createControlsLeft()]), + $el("div.cm-menu-column", [...this.createControlsMid()]), + $el("div.cm-menu-column", [...this.createControlsRight()]) + ]), + + $el("br", {}, []), + close_button, + ] + ); + + content.style.width = '100%'; + content.style.height = '100%'; + + this.element = $el("div.comfy-modal", { id:'cm-manager-dialog', parent: document.body }, [ content ]); + } + + show() { + this.element.style.display = "block"; + } + + handleWorkflowGalleryButtonClick(e) { + e.preventDefault(); + e.stopPropagation(); + LiteGraph.closeAllContextMenus(); + + // Modify the style of the button so that the UI can indicate the last + // visited site right away. + const modifyButtonStyle = (url) => { + const workflowGalleryButton = document.getElementById('workflowgallery-button'); + workflowGalleryButton.style.height = '50px'; + const lastVisitedLabel = document.getElementById('workflowgallery-button-last-visited-label'); + lastVisitedLabel.textContent = `(${url.split('/')[2]})`; + } + + const menu = new LiteGraph.ContextMenu( + [ + { + title: "Share your art", + callback: () => { + if (share_option === 'openart') { + showOpenArtShareDialog(); + return; + } else if (share_option === 'matrix' || share_option === 'comfyworkflows') { + showShareDialog(share_option); + return; + } + + if (!ShareDialogChooser.instance) { + ShareDialogChooser.instance = new ShareDialogChooser(); + } + ShareDialogChooser.instance.show(); + }, + }, + { + title: "Open 'openart.ai'", + callback: () => { + const url = "https://openart.ai/workflows/dev"; + localStorage.setItem("wg_last_visited", url); + window.open(url, "comfyui-workflow-gallery"); + modifyButtonStyle(url); + }, + }, + { + title: "Open 'comfyworkflows.com'", + callback: () => { + const url = "https://comfyworkflows.com/"; + localStorage.setItem("wg_last_visited", url); + window.open(url, "comfyui-workflow-gallery"); + modifyButtonStyle(url); + }, + }, + { + title: "Close", + callback: () => { + LiteGraph.closeAllContextMenus(); + }, + } + ], + { + event: e, + scale: 1.3, + }, + window + ); + // set the id so that we can override the context menu's z-index to be above the comfyui manager menu + menu.root.id = "workflowgallery-button-menu"; + menu.root.classList.add("pysssss-workflow-popup-2"); + } +} + + +app.registerExtension({ + name: "Comfy.ManagerMenu", + init() { + $el("style", { + textContent: style, + parent: document.head, + }); + }, + async setup() { + const menu = document.querySelector(".comfy-menu"); + const separator = document.createElement("hr"); + + separator.style.margin = "20px 0"; + separator.style.width = "100%"; + menu.append(separator); + + const managerButton = document.createElement("button"); + managerButton.textContent = "Manager"; + managerButton.onclick = () => { + if(!manager_instance) + setManagerInstance(new ManagerMenuDialog()); + manager_instance.show(); + } + menu.append(managerButton); + + + const shareButton = document.createElement("button"); + shareButton.id = "shareButton"; + shareButton.textContent = "Share"; + shareButton.onclick = () => { + if (share_option === 'openart') { + showOpenArtShareDialog(); + return; + } else if (share_option === 'matrix' || share_option === 'comfyworkflows') { + showShareDialog(share_option); + return; + } + + if(!ShareDialogChooser.instance) { + ShareDialogChooser.instance = new ShareDialogChooser(); + } + ShareDialogChooser.instance.show(); + } + // make the background color a gradient of blue to green + shareButton.style.background = "linear-gradient(90deg, #00C9FF 0%, #92FE9D 100%)"; + shareButton.style.color = "black"; + + // Load share option from local storage to determine whether to show + // the share button. + const shouldShowShareButton = share_option !== 'none'; + shareButton.style.display = shouldShowShareButton ? "inline-block" : "none"; + + menu.append(shareButton); + }, + + async beforeRegisterNodeDef(nodeType, nodeData, app) { + this._addExtraNodeContextMenu(nodeType, app); + }, + async nodeCreated(node, app) { + if(!node.badge_enabled) { + node.getNickname = function () { return getNickname(node, node.comfyClass.trim()) }; + const orig = node.__proto__.onDrawForeground; + node.onDrawForeground = function (ctx) { + drawBadge(node, orig, arguments) + }; + node.badge_enabled = true; + } + }, + async loadedGraphNode(node, app) { + if(!node.badge_enabled) { + const orig = node.onDrawForeground; + node.getNickname = function () { return getNickname(node, node.type.trim()) }; + node.onDrawForeground = function (ctx) { drawBadge(node, orig, arguments) }; + } + }, + + _addExtraNodeContextMenu(node, app) { + const origGetExtraMenuOptions = node.prototype.getExtraMenuOptions; + node.prototype.getExtraMenuOptions = function (_, options) { + origGetExtraMenuOptions?.apply?.(this, arguments); + if (isOutputNode(node)) { + const { potential_outputs } = getPotentialOutputsAndOutputNodes([this]); + const hasOutput = potential_outputs.length > 0; + + // Check if the previous menu option is `null`. If it's not, + // then we need to add a `null` as a separator. + if (options[options.length - 1] !== null) { + options.push(null); + } + + options.push({ + content: "🏞️ Share Output", + disabled: !hasOutput, + callback: (obj) => { + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(); + } + const shareButton = document.getElementById("shareButton"); + if (shareButton) { + const currentNode = this; + if (!OpenArtShareDialog.instance) { + OpenArtShareDialog.instance = new OpenArtShareDialog(); + } + OpenArtShareDialog.instance.selectedNodeId = currentNode.id; + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(share_option); + } + ShareDialog.instance.selectedNodeId = currentNode.id; + shareButton.click(); + } + } + }, null); + } + } + }, +}); diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-common.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-common.js new file mode 100644 index 0000000000000000000000000000000000000000..9875f0cd04d7921b259e303c955271446a89d04e --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-common.js @@ -0,0 +1,976 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { OpenArtShareDialog } from "./comfyui-share-openart.js"; + +export const SUPPORTED_OUTPUT_NODE_TYPES = [ + "PreviewImage", + "SaveImage", + "VHS_VideoCombine", + "ADE_AnimateDiffCombine", + "SaveAnimatedWEBP", +] + +var docStyle = document.createElement('style'); +docStyle.innerHTML = ` +.cm-menu-container { + column-gap: 20px; + display: flex; + flex-wrap: wrap; + justify-content: center; +} + +.cm-menu-column { + display: flex; + flex-direction: column; +} + +.cm-title { + padding: 10px 10px 0 10p; + background-color: black; + text-align: center; + height: 45px; +} +`; +document.head.appendChild(docStyle); + +export function getPotentialOutputsAndOutputNodes(nodes) { + const potential_outputs = []; + const potential_output_nodes = []; + + // iterate over the array of nodes to find the ones that are marked as SaveImage + // TODO: Add support for AnimateDiffCombine, etc. nodes that save videos/gifs, etc. + for (let i = 0; i < nodes.length; i++) { + const node = nodes[i]; + if (!SUPPORTED_OUTPUT_NODE_TYPES.includes(node.type)) { + continue; + } + + if (node.type === "SaveImage") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title, "node_id": node.id }); + } + } + } + else if (node.type === "PreviewImage") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title, "node_id": node.id }); + } + } + } + else if (node.type === "VHS_VideoCombine") { + // check if node has a 'widgets' array property, with type 'image' + if (node.hasOwnProperty("widgets") && Array.isArray(node.widgets)) { + // iterate over the widgets array and add each image to the potential_outputs array + for (let j = 0; j < node.widgets.length; j++) { + if (node.widgets[j].type === "image") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = parseURLPath(widgetValue); + + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "node_id": node.id , "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "value": widgetValue, "format": parsedURLVals.format } }); + } + } else if (node.widgets[j].type === "preview") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = widgetValue.params; + + if(!parsedURLVals.format?.startsWith('image')) { + // video isn't supported format + continue; + } + + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "node_id": node.id , "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "value": `/view?filename=${parsedURLVals.filename}&subfolder=${parsedURLVals.subfolder}&type=${parsedURLVals.type}&format=${parsedURLVals.format}`, "format": parsedURLVals.format } }); + } + } + } + } + } + else if (node.type === "ADE_AnimateDiffCombine") { + // check if node has a 'widgets' array property, with type 'image' + if (node.hasOwnProperty("widgets") && Array.isArray(node.widgets)) { + // iterate over the widgets array and add each image to the potential_outputs array + for (let j = 0; j < node.widgets.length; j++) { + if (node.widgets[j].type === "image") { + const widgetValue = node.widgets[j].value; + const parsedURLVals = parseURLPath(widgetValue); + // ensure that the parsedURLVals have 'filename', 'subfolder', 'type', and 'format' properties + if (parsedURLVals.hasOwnProperty("filename") && parsedURLVals.hasOwnProperty("subfolder") && parsedURLVals.hasOwnProperty("type") && parsedURLVals.hasOwnProperty("format")) { + if (parsedURLVals.type !== "output") { + // TODO + continue; + } + potential_output_nodes.push(node); + potential_outputs.push({ "type": "output", 'title': node.title, "output": { "filename": parsedURLVals.filename, "subfolder": parsedURLVals.subfolder, "type": parsedURLVals.type, "value": widgetValue, "format": parsedURLVals.format } }); + } + } + } + } + } + else if (node.type === "SaveAnimatedWEBP") { + // check if node has an 'images' array property + if (node.hasOwnProperty("images") && Array.isArray(node.images)) { + // iterate over the images array and add each image to the potential_outputs array + for (let j = 0; j < node.images.length; j++) { + potential_output_nodes.push(node); + potential_outputs.push({ "type": "image", "image": node.images[j], "title": node.title }); + } + } + } + } + + // Note: make sure that two arrays are the same length + return { potential_outputs, potential_output_nodes }; +} + + +export function parseURLPath(urlPath) { + // Extract the query string from the URL path + var queryString = urlPath.split('?')[1]; + + // Use the URLSearchParams API to parse the query string + var params = new URLSearchParams(queryString); + + // Create an object to store the parsed parameters + var parsedParams = {}; + + // Iterate over each parameter and add it to the object + for (var pair of params.entries()) { + parsedParams[pair[0]] = pair[1]; + } + + // Return the object with the parsed parameters + return parsedParams; +} + + +export const showOpenArtShareDialog = () => { + if (!OpenArtShareDialog.instance) { + OpenArtShareDialog.instance = new OpenArtShareDialog(); + } + + return app.graphToPrompt() + .then(prompt => { + // console.log({ prompt }) + return app.graph._nodes; + }) + .then(nodes => { + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + OpenArtShareDialog.instance.show({ potential_outputs, potential_output_nodes}); + }) +} + +export const showShareDialog = async (share_option) => { + if (!ShareDialog.instance) { + ShareDialog.instance = new ShareDialog(share_option); + } + return app.graphToPrompt() + .then(prompt => { + // console.log({ prompt }) + return app.graph._nodes; + }) + .then(nodes => { + // console.log({ nodes }); + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + if (potential_outputs.length === 0) { + if (potential_output_nodes.length === 0) { + // todo: add support for other output node types (animatediff combine, etc.) + const supported_nodes_string = SUPPORTED_OUTPUT_NODE_TYPES.join(", "); + alert(`No supported output node found (${supported_nodes_string}). To share this workflow, please add an output node to your graph and re-run your prompt.`); + } else { + alert("To share this, first run a prompt. Once it's done, click 'Share'.\n\nNOTE: Images of the Share target can only be selected in the PreviewImage, SaveImage, and VHS_VideoCombine nodes. In the case of VHS_VideoCombine, only the image/gif and image/webp formats are supported."); + } + return false; + } + ShareDialog.instance.show({ potential_outputs, potential_output_nodes, share_option }); + return true; + }); +} + +export class ShareDialogChooser extends ComfyDialog { + static instance = null; + constructor() { + super(); + this.element = $el("div.comfy-modal", { + parent: document.body, style: { + 'overflow-y': "auto", + } + }, + [$el("div.comfy-modal-content", + {}, + [...this.createButtons()]), + ]); + this.selectedNodeId = null; + } + createButtons() { + const buttons = [ + { + key: "openart", + textContent: "OpenArt AI", + website: "https://openart.ai/workflows/", + description: "Share ComfyUI workflows and art on OpenArt.ai", + onclick: () => { + showOpenArtShareDialog(); + this.close(); + } + }, + { + key: "matrix", + textContent: "Matrix Server", + website: "https://app.element.io/#/room/%23comfyui_space%3Amatrix.org", + description: "Share your art on the official ComfyUI matrix server", + onclick: async () => { + showShareDialog('matrix').then((suc) => { + suc && this.close(); + }) + } + }, + { + key: "comfyworkflows", + textContent: "ComfyWorkflows", + website: "https://comfyworkflows.com", + description: "Share ComfyUI art on comfyworkflows.com", + onclick: () => { + showShareDialog('comfyworkflows').then((suc) => { + suc && this.close(); + }) + } + }, + ]; + + function createShareButtonsWithDescriptions() { + // Responsive container + const container = $el("div", { + style: { + display: "flex", + 'flex-wrap': 'wrap', + 'justify-content': 'space-around', + 'padding': '20px', + } + }); + + buttons.forEach(b => { + const button = $el("button", { + type: "button", + textContent: b.textContent, + onclick: b.onclick, + style: { + 'width': '25%', + 'minWidth': '200px', + 'background-color': b.backgroundColor || '', + 'border-radius': '5px', + 'cursor': 'pointer', + 'padding': '5px 5px', + 'margin-bottom': '5px', + 'transition': 'background-color 0.3s', + } + }); + button.addEventListener('mouseover', () => { + button.style.backgroundColor = '#007BFF'; // Change color on hover + }); + button.addEventListener('mouseout', () => { + button.style.backgroundColor = b.backgroundColor || ''; + }); + + const description = $el("p", { + textContent: b.description, + style: { + 'text-align': 'left', + color: 'white', + 'font-size': '14px', + 'margin-bottom': '10px', + }, + }); + + const websiteLink = $el("a", { + textContent: "🌐 Website", + href: b.website, + target: "_blank", + style: { + color: 'white', + 'margin-left': '10px', + 'font-size': '12px', + 'text-decoration': 'none', + 'align-self': 'center', + }, + }); + + // Add highlight to the website link + websiteLink.addEventListener('mouseover', () => { + websiteLink.style.opacity = '0.7'; + }); + + websiteLink.addEventListener('mouseout', () => { + websiteLink.style.opacity = '1'; + }); + + const buttonLinkContainer = $el("div", { + style: { + display: 'flex', + 'align-items': 'center', + 'margin-bottom': '10px', + } + }, [button, websiteLink]); + + const column = $el("div", { + style: { + 'flex-basis': '100%', + 'margin': '10px', + 'padding': '20px', + 'border': '1px solid #ddd', + 'border-radius': '5px', + 'box-shadow': '0 2px 4px rgba(0, 0, 0, 0.1)', + } + }, [buttonLinkContainer, description]); + + container.appendChild(column); + }); + + return container; + } + + return [ + $el("p", { + textContent: 'Choose a platform to share your workflow', + style: { + 'text-align': 'center', + 'color': 'white', + 'font-size': '18px', + 'margin-bottom': '10px', + }, + } + ), + + $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("div.cm-menu-column", [ + createShareButtonsWithDescriptions(), + $el("br", {}, []), + ]), + ]), + $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("button", { + type: "button", + style: { + margin: "0 25px", + width: "100%", + }, + textContent: "Close", + onclick: () => { + this.close() + } + }), + $el("br", {}, []), + ]), + ]; + } + show() { + this.element.style.display = "block"; + this.element.style.zIndex = 10001; + } +} +export class ShareDialog extends ComfyDialog { + static instance = null; + static matrix_auth = { homeserver: "matrix.org", username: "", password: "" }; + static cw_sharekey = ""; + + constructor(share_option) { + super(); + this.share_option = share_option; + this.element = $el("div.comfy-modal", { + parent: document.body, style: { + 'overflow-y': "auto", + } + }, + [$el("div.comfy-modal-content", + {}, + [...this.createButtons()]), + ]); + this.selectedOutputIndex = 0; + } + + createButtons() { + this.radio_buttons = $el("div", { + id: "selectOutputImages", + }, []); + + this.is_nsfw_checkbox = $el("input", { type: 'checkbox', id: "is_nsfw" }, []) + const is_nsfw_checkbox_text = $el("label", { + }, [" Is this NSFW?"]) + this.is_nsfw_checkbox.style.color = "var(--fg-color)"; + this.is_nsfw_checkbox.checked = false; + + this.matrix_destination_checkbox = $el("input", { type: 'checkbox', id: "matrix_destination" }, []) + const matrix_destination_checkbox_text = $el("label", {}, [" ComfyUI Matrix server"]) + this.matrix_destination_checkbox.style.color = "var(--fg-color)"; + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; //true; + + this.comfyworkflows_destination_checkbox = $el("input", { type: 'checkbox', id: "comfyworkflows_destination" }, []) + const comfyworkflows_destination_checkbox_text = $el("label", {}, [" ComfyWorkflows.com"]) + this.comfyworkflows_destination_checkbox.style.color = "var(--fg-color)"; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + + this.matrix_homeserver_input = $el("input", { type: 'text', id: "matrix_homeserver", placeholder: "matrix.org", value: ShareDialog.matrix_auth.homeserver || 'matrix.org' }, []); + this.matrix_username_input = $el("input", { type: 'text', placeholder: "Username", value: ShareDialog.matrix_auth.username || '' }, []); + this.matrix_password_input = $el("input", { type: 'password', placeholder: "Password", value: ShareDialog.matrix_auth.password || '' }, []); + + this.cw_sharekey_input = $el("input", { type: 'text', placeholder: "Share key (found on your profile page)", value: ShareDialog.cw_sharekey || '' }, []); + this.cw_sharekey_input.style.width = "100%"; + + this.credits_input = $el("input", { + type: "text", + placeholder: "This will be used to give credits", + required: false, + }, []); + + this.title_input = $el("input", { + type: "text", + placeholder: "ex: My awesome art", + required: false + }, []); + + this.description_input = $el("textarea", { + placeholder: "ex: Trying out a new workflow... ", + required: false, + }, []); + + this.share_button = $el("button", { + type: "submit", + textContent: "Share", + style: { + backgroundColor: "blue" + } + }, []); + + this.final_message = $el("div", { + style: { + color: "white", + textAlign: "center", + // marginTop: "10px", + // backgroundColor: "black", + padding: "10px", + } + }, []); + + this.share_finalmessage_container = $el("div.cm-menu-container", { + id: "comfyui-share-finalmessage-container", + style: { + display: "none", + } + }, [ + $el("div.cm-menu-column", [ + this.final_message, + $el("button", { + type: "button", + textContent: "Close", + onclick: () => { + // Reset state + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + this.share_button.textContent = "Share"; + this.share_button.style.display = "inline-block"; + this.final_message.innerHTML = ""; + this.final_message.style.color = "white"; + this.credits_input.value = ""; + this.title_input.value = ""; + this.description_input.value = ""; + this.is_nsfw_checkbox.checked = false; + this.selectedOutputIndex = 0; + + // hide the final message + this.share_finalmessage_container.style.display = "none"; + + // show the share container + this.share_container.style.display = "flex"; + + this.close() + } + }), + ]) + ]); + this.share_container = $el("div.cm-menu-container", { + id: "comfyui-share-container" + }, [ + $el("div.cm-menu-column", [ + $el("details", { + style: { + border: "1px solid #999", + padding: "5px", + borderRadius: "5px", + backgroundColor: "#222" + } + }, [ + $el("summary", { + style: { + color: "white", + cursor: "pointer", + } + }, [`Matrix account`]), + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Homeserver", + style: { + marginRight: "10px", + } + }, []), + this.matrix_homeserver_input, + ]), + + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Username", + style: { + marginRight: "10px", + } + }, []), + this.matrix_username_input, + ]), + + $el("div", { + style: { + display: "flex", + flexDirection: "row", + } + }, [ + $el("div", { + textContent: "Password", + style: { + marginRight: "10px", + } + }, []), + this.matrix_password_input, + ]), + + ]), + $el("details", { + style: { + border: "1px solid #999", + marginTop: "10px", + padding: "5px", + borderRadius: "5px", + backgroundColor: "#222" + } + }, [ + $el("summary", { + style: { + color: "white", + cursor: "pointer", + } + }, [`Comfyworkflows.com account`]), + $el("h4", { + textContent: "Share key (found on your profile page)", + }, []), + $el("p", { size: 3, color: "white" }, ["When provided, your art will be saved to your account."]), + this.cw_sharekey_input, + ]), + + $el("div", {}, [ + $el("p", { + size: 3, color: "white", style: { + color: 'white' + } + }, [`Select where to share your art:`]), + this.matrix_destination_checkbox, + matrix_destination_checkbox_text, + $el("br", {}, []), + this.comfyworkflows_destination_checkbox, + comfyworkflows_destination_checkbox_text, + ]), + + $el("h4", { + textContent: "Credits (optional)", + size: 3, + color: "white", + style: { + color: 'white' + } + }, []), + this.credits_input, + // $el("br", {}, []), + + $el("h4", { + textContent: "Title (optional)", + size: 3, + color: "white", + style: { + color: 'white' + } + }, []), + this.title_input, + // $el("br", {}, []), + + $el("h4", { + textContent: "Description (optional)", + size: 3, + color: "white", + style: { + color: 'white' + } + }, []), + this.description_input, + $el("br", {}, []), + + $el("div", {}, [this.is_nsfw_checkbox, is_nsfw_checkbox_text]), + // $el("br", {}, []), + + // this.final_message, + // $el("br", {}, []), + ]), + $el("div.cm-menu-column", [ + this.radio_buttons, + $el("br", {}, []), + + this.share_button, + + $el("button", { + type: "button", + textContent: "Close", + onclick: () => { + // Reset state + this.matrix_destination_checkbox.checked = this.share_option === 'matrix'; + this.comfyworkflows_destination_checkbox.checked = this.share_option !== 'matrix'; + this.share_button.textContent = "Share"; + this.share_button.style.display = "inline-block"; + this.final_message.innerHTML = ""; + this.final_message.style.color = "white"; + this.credits_input.value = ""; + this.title_input.value = ""; + this.description_input.value = ""; + this.is_nsfw_checkbox.checked = false; + this.selectedOutputIndex = 0; + + // hide the final message + this.share_finalmessage_container.style.display = "none"; + + // show the share container + this.share_container.style.display = "flex"; + + this.close() + } + }), + $el("br", {}, []), + ]), + ]); + + // get the user's existing matrix auth and share key + ShareDialog.matrix_auth = { homeserver: "matrix.org", username: "", password: "" }; + try { + api.fetchApi(`/manager/get_matrix_auth`) + .then(response => response.json()) + .then(data => { + ShareDialog.matrix_auth = data; + this.matrix_homeserver_input.value = ShareDialog.matrix_auth.homeserver; + this.matrix_username_input.value = ShareDialog.matrix_auth.username; + this.matrix_password_input.value = ShareDialog.matrix_auth.password; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + + // get the user's existing comfyworkflows share key + ShareDialog.cw_sharekey = ""; + try { + // console.log("Fetching comfyworkflows share key") + api.fetchApi(`/manager/get_comfyworkflows_auth`) + .then(response => response.json()) + .then(data => { + ShareDialog.cw_sharekey = data.comfyworkflows_sharekey; + this.cw_sharekey_input.value = ShareDialog.cw_sharekey; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + + this.share_button.onclick = async () => { + const prompt = await app.graphToPrompt(); + const nodes = app.graph._nodes; + + // console.log({ prompt, nodes }); + + const destinations = []; + if (this.matrix_destination_checkbox.checked) { + destinations.push("matrix"); + } + if (this.comfyworkflows_destination_checkbox.checked) { + destinations.push("comfyworkflows"); + } + + // if destinations includes matrix, make an api call to /manager/check_matrix to ensure that the user has configured their matrix settings + if (destinations.includes("matrix")) { + let definedMatrixAuth = !!this.matrix_homeserver_input.value && !!this.matrix_username_input.value && !!this.matrix_password_input.value; + if (!definedMatrixAuth) { + alert("Please set your Matrix account details."); + return; + } + } + + if (destinations.includes("comfyworkflows") && !this.cw_sharekey_input.value && !confirm("You have NOT set your ComfyWorkflows.com share key. Your art will NOT be connected to your account (it will be shared anonymously). Continue?")) { + return; + } + + const { potential_outputs, potential_output_nodes } = getPotentialOutputsAndOutputNodes(nodes); + + // console.log({ potential_outputs, potential_output_nodes }) + + if (potential_outputs.length === 0) { + if (potential_output_nodes.length === 0) { + // todo: add support for other output node types (animatediff combine, etc.) + const supported_nodes_string = SUPPORTED_OUTPUT_NODE_TYPES.join(", "); + alert(`No supported output node found (${supported_nodes_string}). To share this workflow, please add an output node to your graph and re-run your prompt.`); + } else { + alert("To share this, first run a prompt. Once it's done, click 'Share'.\n\nNOTE: Images of the Share target can only be selected in the PreviewImage, SaveImage, and VHS_VideoCombine nodes. In the case of VHS_VideoCombine, only the image/gif and image/webp formats are supported."); + } + this.selectedOutputIndex = 0; + this.close(); + return; + } + + // Change the text of the share button to "Sharing..." to indicate that the share process has started + this.share_button.textContent = "Sharing..."; + + const response = await api.fetchApi(`/manager/share`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + matrix_auth: { + homeserver: this.matrix_homeserver_input.value, + username: this.matrix_username_input.value, + password: this.matrix_password_input.value, + }, + cw_auth: { + cw_sharekey: this.cw_sharekey_input.value, + }, + share_destinations: destinations, + credits: this.credits_input.value, + title: this.title_input.value, + description: this.description_input.value, + is_nsfw: this.is_nsfw_checkbox.checked, + prompt, + potential_outputs, + selected_output_index: this.selectedOutputIndex, + // potential_output_nodes + }) + }); + + if (response.status != 200) { + try { + const response_json = await response.json(); + if (response_json.error) { + alert(response_json.error); + this.close(); + return; + } else { + alert("Failed to share your art. Please try again."); + this.close(); + return; + } + } catch (e) { + alert("Failed to share your art. Please try again."); + this.close(); + return; + } + } + + const response_json = await response.json(); + + if (response_json.comfyworkflows.url) { + this.final_message.innerHTML = "Your art has been shared: " + response_json.comfyworkflows.url + ""; + if (response_json.matrix.success) { + this.final_message.innerHTML += "
Your art has been shared in the ComfyUI Matrix server's #share channel!"; + } + } else { + if (response_json.matrix.success) { + this.final_message.innerHTML = "Your art has been shared in the ComfyUI Matrix server's #share channel!"; + } + } + + this.final_message.style.color = "green"; + + // hide #comfyui-share-container and show #comfyui-share-finalmessage-container + this.share_container.style.display = "none"; + this.share_finalmessage_container.style.display = "block"; + + // hide the share button + this.share_button.textContent = "Shared!"; + this.share_button.style.display = "none"; + // this.close(); + } + + const res = + [ + $el("tr.td", { width: "100%" }, [ + $el("font", { size: 6, color: "white" }, [`Share your art`]), + ]), + $el("br", {}, []), + + this.share_finalmessage_container, + this.share_container, + ]; + + res[0].style.padding = "10px 10px 10px 10px"; + res[0].style.backgroundColor = "black"; //"linear-gradient(90deg, #00C9FF 0%, #92FE9D 100%)"; + res[0].style.textAlign = "center"; + res[0].style.height = "45px"; + return res; + } + + show({potential_outputs, potential_output_nodes, share_option}) { + // Sort `potential_output_nodes` by node ID to make the order always + // consistent, but we should also keep `potential_outputs` in the same + // order as `potential_output_nodes`. + const potential_output_to_order = {}; + potential_output_nodes.forEach((node, index) => { + if (node.id in potential_output_to_order) { + potential_output_to_order[node.id][1].push(potential_outputs[index]); + } else { + potential_output_to_order[node.id] = [node, [potential_outputs[index]]]; + } + }) + // Sort the object `potential_output_to_order` by key (node ID) + const sorted_potential_output_to_order = Object.fromEntries( + Object.entries(potential_output_to_order).sort((a, b) => a[0].id - b[0].id) + ); + const sorted_potential_outputs = [] + const sorted_potential_output_nodes = [] + for (const [key, value] of Object.entries(sorted_potential_output_to_order)) { + sorted_potential_output_nodes.push(value[0]); + sorted_potential_outputs.push(...value[1]); + } + potential_output_nodes = sorted_potential_output_nodes; + potential_outputs = sorted_potential_outputs; + + // console.log({ potential_outputs, potential_output_nodes }) + this.radio_buttons.innerHTML = ""; // clear the radio buttons + let is_radio_button_checked = false; // only check the first radio button if multiple images from the same node + const new_radio_buttons = $el("div", { + id: "selectOutput-Options", + style: { + 'overflow-y': 'scroll', + 'max-height': '400px', + } + }, potential_outputs.map((output, index) => { + const {node_id} = output; + const radio_button = $el("input", { type: 'radio', name: "selectOutputImages", value: index, required: index === 0 }, []) + let radio_button_img; + if (output.type === "image" || output.type === "temp") { + radio_button_img = $el("img", { src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, style: { width: "auto", height: "100px" } }, []); + } else if (output.type === "output") { + radio_button_img = $el("img", { src: output.output.value, style: { width: "auto", height: "100px" } }, []); + } else { + // unsupported output type + // this should never happen + // TODO + radio_button_img = $el("img", { src: "", style: { width: "auto", height: "100px" } }, []); + } + const radio_button_text = $el("label", { + // style: { + // color: 'white' + // } + }, [output.title]) + radio_button.style.color = "var(--fg-color)"; + + // Make the radio button checked if it's the selected node, + // otherwise make the first radio button checked. + if (this.selectedNodeId) { + if (this.selectedNodeId === node_id && !is_radio_button_checked) { + radio_button.checked = true; + is_radio_button_checked = true; + } + } else { + radio_button.checked = index === 0; + } + + if (radio_button.checked) { + this.selectedOutputIndex = index; + } + + radio_button.onchange = () => { + this.selectedOutputIndex = parseInt(radio_button.value); + }; + + return $el("div", { + style: { + display: "flex", + 'align-items': 'center', + 'justify-content': 'space-between', + 'margin-bottom': '10px', + } + }, [radio_button, radio_button_text, radio_button_img]); + })); + const header = $el("h3", { + textContent: "Select an image to share", + size: 3, + color: "white", + style: { + 'text-align': 'center', + color: 'white', + backgroundColor: 'black', + padding: '10px', + 'margin-top': '0px', + } + }, [ + $el("p", { + textContent: "Scroll to see all outputs", + size: 2, + color: "white", + style: { + 'text-align': 'center', + color: 'white', + 'margin-bottom': '5px', + 'font-style': 'italic', + 'font-size': '12px', + }, + }, []) + ]); + this.radio_buttons.appendChild(header); + // this.radio_buttons.appendChild(subheader); + this.radio_buttons.appendChild(new_radio_buttons); + this.element.style.display = "block"; + + share_option = share_option || this.share_option; + if (share_option === 'comfyworkflows') { + this.matrix_destination_checkbox.checked = false; + this.comfyworkflows_destination_checkbox.checked = true; + } else { + this.matrix_destination_checkbox.checked = true; + this.comfyworkflows_destination_checkbox.checked = false; + } + } +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-openart.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-openart.js new file mode 100644 index 0000000000000000000000000000000000000000..5a7f12739f2186503841c998c2d7b4c2dcbe02ec --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/comfyui-share-openart.js @@ -0,0 +1,747 @@ +import {app} from "../../scripts/app.js"; +import {api} from "../../scripts/api.js"; +import {ComfyDialog, $el} from "../../scripts/ui.js"; + +const LOCAL_STORAGE_KEY = "openart_comfy_workflow_key"; +const DEFAULT_HOMEPAGE_URL = "https://openart.ai/workflows/dev?developer=true"; +//const DEFAULT_HOMEPAGE_URL = "http://localhost:8080/workflows/dev?developer=true"; + +const API_ENDPOINT = "https://openart.ai/api"; +//const API_ENDPOINT = "http://localhost:8080/api"; + +const style = ` + .openart-share-dialog a { + color: #f8f8f8; + } + .openart-share-dialog a:hover { + color: #007bff; + } + .output_label { + border: 5px solid transparent; + } + .output_label:hover { + border: 5px solid #59E8C6; + } + .output_label.checked { + border: 5px solid #59E8C6; + } +`; + +// Shared component styles +const sectionStyle = { + marginBottom: 0, + padding: 0, + borderRadius: "8px", + boxShadow: "0 2px 4px rgba(0, 0, 0, 0.05)", + display: "flex", + flexDirection: "column", + justifyContent: "center", +}; + +export class OpenArtShareDialog extends ComfyDialog { + static instance = null; + + constructor() { + super(); + $el("style", { + textContent: style, + parent: document.head, + }); + this.element = $el( + "div.comfy-modal.openart-share-dialog", + { + parent: document.body, + style: { + "overflow-y": "auto", + }, + }, + [$el("div.comfy-modal-content", {}, [...this.createButtons()])] + ); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.uploadedImages = []; + this.selectedFile = null; + } + + async readKey() { + let key = "" + try { + key = await api.fetchApi(`/manager/get_openart_auth`) + .then(response => response.json()) + .then(data => { + return data.openart_key; + }) + .catch(error => { + // console.log(error); + }); + } catch (error) { + // console.log(error); + } + return key || ""; + } + + async saveKey(value) { + await api.fetchApi(`/manager/set_openart_auth`, { + method: 'POST', + headers: {'Content-Type': 'application/json'}, + body: JSON.stringify({ + openart_key: value + }) + }); + } + + createButtons() { + const inputStyle = { + display: "block", + minWidth: "500px", + width: "100%", + padding: "10px", + margin: "10px 0", + borderRadius: "4px", + border: "1px solid #ddd", + boxSizing: "border-box", + }; + + const hyperLinkStyle = { + display: "block", + marginBottom: "15px", + fontWeight: "bold", + fontSize: "14px", + }; + + const labelStyle = { + color: "#f8f8f8", + display: "block", + margin: "10px 0 0 0", + fontWeight: "bold", + textDecoration: "none", + }; + + const buttonStyle = { + padding: "10px 80px", + margin: "10px 5px", + borderRadius: "4px", + border: "none", + cursor: "pointer", + color: "#fff", + backgroundColor: "#007bff", + }; + + // upload images input + this.uploadImagesInput = $el("input", { + type: "file", + multiple: false, + style: inputStyle, + accept: "image/*", + }); + + this.uploadImagesInput.addEventListener("change", async (e) => { + const file = e.target.files[0]; + if (!file) { + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + return; + } + const reader = new FileReader(); + reader.onload = async (e) => { + const imgData = e.target.result; + this.previewImage.src = imgData; + this.previewImage.style.display = "block"; + this.selectedFile = null + // Once user uploads an image, we uncheck all radio buttons + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 0.35; + this.uploadImagesInput.style.opacity = 1; + }; + reader.readAsDataURL(file); + }); + + // preview image + this.previewImage = $el("img", { + src: "", + style: { + width: "100%", + maxHeight: "100px", + objectFit: "contain", + display: "none", + marginTop: '10px', + }, + }); + + this.keyInput = $el("input", { + type: "password", + placeholder: "Copy & paste your API key", + style: inputStyle, + }); + this.NameInput = $el("input", { + type: "text", + placeholder: "Title (required)", + style: inputStyle, + }); + this.descriptionInput = $el("textarea", { + placeholder: "Description (optional)", + style: { + ...inputStyle, + minHeight: "100px", + }, + }); + + // Header Section + const headerSection = $el("h3", { + textContent: "Share your workflow to OpenArt", + size: 3, + color: "white", + style: { + 'text-align': 'center', + color: 'white', + margin: '0 0 10px 0', + } + }); + + // LinkSection + this.communityLink = $el("a", { + style: hyperLinkStyle, + href: DEFAULT_HOMEPAGE_URL, + target: "_blank" + }, ["👉 Check out thousands of workflows shared from the community"]) + this.getAPIKeyLink = $el("a", { + style: { + ...hyperLinkStyle, + color: "#59E8C6" + }, + href: DEFAULT_HOMEPAGE_URL, + target: "_blank" + }, ["👉 Get your API key here"]) + const linkSection = $el( + "div", + { + style: { + marginTop: "10px", + display: "flex", + flexDirection: "column", + }, + }, + [ + this.communityLink, + this.getAPIKeyLink, + ] + ); + + // Account Section + const accountSection = $el("div", {style: sectionStyle}, [ + $el("label", {style: labelStyle}, ["1️⃣ OpenArt API Key"]), + this.keyInput, + ]); + + // Output Upload Section + const outputUploadSection = $el("div", {style: sectionStyle}, [ + $el("label", { + style: { + ...labelStyle, + margin: "10px 0 0 0" + } + }, ["2️⃣ Image/Thumbnail (Required)"]), + this.previewImage, + this.uploadImagesInput, + ]); + + // Outputs Section + this.outputsSection = $el("div", { + id: "selectOutputs", + }, []); + + // Additional Inputs Section + const additionalInputsSection = $el("div", {style: sectionStyle}, [ + $el("label", {style: labelStyle}, ["3️⃣ Workflow Information"]), + this.NameInput, + this.descriptionInput, + ]); + + // OpenArt Contest Section + this.joinContestCheckbox = $el("input", { + type: 'checkbox', + id: "join_contest" + }, []) + this.joinContestDescription = $el("a", { + style: { + ...hyperLinkStyle, + display: 'inline-block', + color: "#59E8C6", + fontSize: '12px', + marginLeft: '10px', + marginBottom: 0, + }, + href: "https://contest.openart.ai/", + target: "_blank" + }, ["🏆 I'm participating in the OpenArt workflow contest"]) + this.joinContestLabel = $el("label", { + style: { + display: 'flex', + alignItems: 'center', + cursor: 'pointer', + } + }, [this.joinContestCheckbox, this.joinContestDescription]) + const contestSection = $el("div", {style: sectionStyle}, [ + this.joinContestLabel, + ]); + + // Message Section + this.message = $el( + "div", + { + style: { + color: "#ff3d00", + textAlign: "center", + padding: "10px", + fontSize: "20px", + }, + }, + [] + ); + + this.shareButton = $el("button", { + type: "submit", + textContent: "Share", + style: buttonStyle, + onclick: () => { + this.handleShareButtonClick(); + }, + }); + + // Share and Close Buttons + const buttonsSection = $el( + "div", + { + style: { + textAlign: "right", + marginTop: "20px", + display: "flex", + justifyContent: "space-between", + }, + }, + [ + $el("button", { + type: "button", + textContent: "Close", + style: { + ...buttonStyle, + backgroundColor: undefined, + }, + onclick: () => { + this.close(); + }, + }), + this.shareButton, + ] + ); + + // Composing the full layout + const layout = [ + headerSection, + linkSection, + accountSection, + outputUploadSection, + this.outputsSection, + additionalInputsSection, + contestSection, + this.message, + buttonsSection, + ]; + + return layout; + } + + async fetchApi(path, options, statusText) { + if (statusText) { + this.message.textContent = statusText; + } + const addSearchParams = (url, params = {}) => + new URL( + `${url.origin}${url.pathname}?${new URLSearchParams([ + ...Array.from(url.searchParams.entries()), + ...Object.entries(params), + ])}` + ); + + const fullPath = addSearchParams(new URL(API_ENDPOINT + path), { + workflow_api_key: this.keyInput.value, + }); + + const response = await fetch(fullPath, options); + + if (!response.ok) { + throw new Error(response.statusText); + } + + if (statusText) { + this.message.textContent = ""; + } + const data = await response.json(); + return { + ok: response.ok, + statusText: response.statusText, + status: response.status, + data, + }; + } + + async uploadThumbnail(uploadFile) { + const form = new FormData(); + form.append("file", uploadFile); + try { + const res = await this.fetchApi( + `/workflows/upload_thumbnail`, + { + method: "POST", + body: form, + }, + "Uploading thumbnail..." + ); + + if (res.ok && res.data) { + const {image_url, width, height} = res.data; + this.uploadedImages.push({ + url: image_url, + width, + height, + }); + } + } catch (e) { + if (e?.response?.status === 413) { + throw new Error("File size is too large (max 20MB)"); + } else { + throw new Error("Error uploading thumbnail: " + e.message); + } + } + } + + async handleShareButtonClick() { + this.message.textContent = ""; + await this.saveKey(this.keyInput.value); + try { + this.shareButton.disabled = true; + this.shareButton.textContent = "Sharing..."; + await this.share(); + } catch (e) { + alert(e.message); + } + this.shareButton.disabled = false; + this.shareButton.textContent = "Share"; + } + + async share() { + const prompt = await app.graphToPrompt(); + const workflowJSON = prompt["workflow"]; + const workflowAPIJSON = prompt["output"]; + const form_values = { + name: this.NameInput.value, + description: this.descriptionInput.value, + }; + + if (!this.keyInput.value) { + throw new Error("API key is required"); + } + + if (!this.uploadImagesInput.files[0] && !this.selectedFile) { + throw new Error("Thumbnail is required"); + } + + if (!form_values.name) { + throw new Error("Title is required"); + } + + const current_snapshot = await api.fetchApi(`/snapshot/get_current`) + .then(response => response.json()) + .catch(error => { + // console.log(error); + }); + + + if (!this.uploadedImages.length) { + if (this.selectedFile) { + await this.uploadThumbnail(this.selectedFile); + } else { + for (const file of this.uploadImagesInput.files) { + try { + await this.uploadThumbnail(file); + } catch (e) { + this.uploadedImages = []; + throw new Error(e.message); + } + } + + if (this.uploadImagesInput.files.length === 0) { + throw new Error("No thumbnail uploaded"); + } + + if (this.uploadImagesInput.files.length === 0) { + throw new Error("No thumbnail uploaded"); + } + } + } + + const join_contest = this.joinContestCheckbox.checked; + + try { + const response = await this.fetchApi( + "/workflows/publish", + { + method: "POST", + headers: {"Content-Type": "application/json"}, + body: JSON.stringify({ + workflow_json: workflowJSON, + upload_images: this.uploadedImages, + form_values, + advanced_config: { + workflow_api_json: workflowAPIJSON, + snapshot: current_snapshot, + }, + join_contest, + }), + }, + "Uploading workflow..." + ); + + if (response.ok) { + const {workflow_id} = response.data; + if (workflow_id) { + const url = `https://openart.ai/workflows/-/-/${workflow_id}`; + this.message.innerHTML = `Workflow has been shared successfully. Click here to view it.`; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + this.uploadedImages = []; + this.NameInput.value = ""; + this.descriptionInput.value = ""; + this.radioButtons.forEach((ele) => { + ele.checked = false; + ele.parentElement.classList.remove("checked"); + }); + this.selectedOutputIndex = 0; + this.selectedNodeId = null; + this.selectedFile = null; + } + } + } catch (e) { + throw new Error("Error sharing workflow: " + e.message); + } + } + + async fetchImageBlob(url) { + const response = await fetch(url); + const blob = await response.blob(); + return blob; + } + + async show({potential_outputs, potential_output_nodes} = {}) { + // Sort `potential_output_nodes` by node ID to make the order always + // consistent, but we should also keep `potential_outputs` in the same + // order as `potential_output_nodes`. + const potential_output_to_order = {}; + potential_output_nodes.forEach((node, index) => { + if (node.id in potential_output_to_order) { + potential_output_to_order[node.id][1].push(potential_outputs[index]); + } else { + potential_output_to_order[node.id] = [node, [potential_outputs[index]]]; + } + }) + // Sort the object `potential_output_to_order` by key (node ID) + const sorted_potential_output_to_order = Object.fromEntries( + Object.entries(potential_output_to_order).sort((a, b) => a[0].id - b[0].id) + ); + const sorted_potential_outputs = [] + const sorted_potential_output_nodes = [] + for (const [key, value] of Object.entries(sorted_potential_output_to_order)) { + sorted_potential_output_nodes.push(value[0]); + sorted_potential_outputs.push(...value[1]); + } + potential_output_nodes = sorted_potential_output_nodes; + potential_outputs = sorted_potential_outputs; + + this.message.innerHTML = ""; + this.message.textContent = ""; + this.element.style.display = "block"; + this.previewImage.src = ""; + this.previewImage.style.display = "none"; + const key = await this.readKey(); + this.keyInput.value = key; + this.uploadedImages = []; + + // If `selectedNodeId` is provided, we will select the corresponding radio + // button for the node. In addition, we move the selected radio button to + // the top of the list. + if (this.selectedNodeId) { + const index = potential_output_nodes.findIndex(node => node.id === this.selectedNodeId); + if (index >= 0) { + this.selectedOutputIndex = index; + } + } + + this.radioButtons = []; + const new_radio_buttons = $el("div", + { + id: "selectOutput-Options", + style: { + 'overflow-y': 'scroll', + 'max-height': '200px', + + 'display': 'grid', + 'grid-template-columns': 'repeat(auto-fit, minmax(100px, 1fr))', + 'grid-template-rows': 'auto', + 'grid-column-gap': '10px', + 'grid-row-gap': '10px', + 'margin-bottom': '10px', + 'padding': '10px', + 'border-radius': '8px', + 'box-shadow': '0 2px 4px rgba(0, 0, 0, 0.05)', + 'background-color': 'var(--bg-color)', + } + }, + potential_outputs.map((output, index) => { + const {node_id} = output; + const radio_button = $el("input", { + type: 'radio', + name: "selectOutputImages", + value: index, + required: index === 0 + }, []) + let radio_button_img; + let filename; + if (output.type === "image" || output.type === "temp") { + radio_button_img = $el("img", { + src: `/view?filename=${output.image.filename}&subfolder=${output.image.subfolder}&type=${output.image.type}`, + style: { + width: "100px", + height: "100px", + objectFit: "cover", + borderRadius: "5px" + } + }, []); + filename = output.image.filename + } else if (output.type === "output") { + radio_button_img = $el("img", { + src: output.output.value, + style: { + width: "auto", + height: "100px", + objectFit: "cover", + borderRadius: "5px" + } + }, []); + filename = output.filename + } else { + // unsupported output type + // this should never happen + // TODO + radio_button_img = $el("img", { + src: "", + style: {width: "auto", height: "100px"} + }, []); + } + const radio_button_text = $el("span", { + style: { + color: 'gray', + display: 'block', + fontSize: '12px', + overflowX: 'hidden', + textOverflow: 'ellipsis', + textWrap: 'nowrap', + maxWidth: '100px', + } + }, [output.title]) + const node_id_chip = $el("span", { + style: { + color: '#FBFBFD', + display: 'block', + backgroundColor: 'rgba(0, 0, 0, 0.5)', + fontSize: '12px', + overflowX: 'hidden', + padding: '2px 3px', + textOverflow: 'ellipsis', + textWrap: 'nowrap', + maxWidth: '100px', + position: 'absolute', + top: '3px', + left: '3px', + borderRadius: '3px', + } + }, [`Node: ${node_id}`]) + radio_button.style.color = "var(--fg-color)"; + radio_button.checked = this.selectedOutputIndex === index; + + radio_button.onchange = async () => { + this.selectedOutputIndex = parseInt(radio_button.value); + + // Remove the "checked" class from all radio buttons + this.radioButtons.forEach((ele) => { + ele.parentElement.classList.remove("checked"); + }); + radio_button.parentElement.classList.add("checked"); + + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }) + + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + }; + + if (radio_button.checked) { + this.fetchImageBlob(radio_button_img.src).then((blob) => { + const file = new File([blob], filename, { + type: blob.type, + }); + this.previewImage.src = radio_button_img.src; + this.previewImage.style.display = "block"; + this.selectedFile = file; + }) + // Add the opacity style toggle here to indicate that they only need + // to upload one image or choose one from the outputs. + this.outputsSection.style.opacity = 1; + this.uploadImagesInput.style.opacity = 0.35; + } + + this.radioButtons.push(radio_button); + + return $el(`label.output_label${radio_button.checked ? '.checked' : ''}`, { + style: { + display: "flex", + flexDirection: "column", + alignItems: "center", + justifyContent: "center", + marginBottom: "10px", + cursor: "pointer", + position: 'relative', + } + }, [radio_button_img, radio_button_text, radio_button, node_id_chip]); + }) + ); + + const header = + $el("p", { + textContent: this.radioButtons.length === 0 ? "Queue Prompt to see the outputs" : "Or choose one from the outputs (scroll to see all)", + size: 2, + color: "white", + style: { + color: 'white', + margin: '0 0 5px 0', + fontSize: '12px', + }, + }, []) + this.outputsSection.innerHTML = ""; + this.outputsSection.appendChild(header); + this.outputsSection.appendChild(new_radio_buttons); + } +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/common.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/common.js new file mode 100644 index 0000000000000000000000000000000000000000..471951793c2c84282dbdc645131d5b7b64bc76a6 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/common.js @@ -0,0 +1,147 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" + +export async function sleep(ms) { + return new Promise(resolve => setTimeout(resolve, ms)); +} + +export function rebootAPI() { + if (confirm("Are you sure you'd like to reboot the server?")) { + try { + api.fetchApi("/manager/reboot"); + } + catch(exception) { + + } + return true; + } + + return false; +} + +export async function install_checked_custom_node(grid_rows, target_i, caller, mode) { + if(caller) { + let failed = ''; + + caller.disableButtons(); + + for(let i in grid_rows) { + if(!grid_rows[i].checkbox.checked && i != target_i) + continue; + + var target; + + if(grid_rows[i].data.custom_node) { + target = grid_rows[i].data.custom_node; + } + else { + target = grid_rows[i].data; + } + + caller.startInstall(target); + + try { + const response = await api.fetchApi(`/customnode/${mode}`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(target) + }); + + if(response.status == 400) { + app.ui.dialog.show(`${mode} failed: ${target.title}`); + app.ui.dialog.element.style.zIndex = 10010; + continue; + } + + const status = await response.json(); + app.ui.dialog.close(); + target.installed = 'True'; + continue; + } + catch(exception) { + failed += `
${target.title}`; + } + } + + if(failed != '') { + app.ui.dialog.show(`${mode} failed: ${failed}`); + app.ui.dialog.element.style.zIndex = 10010; + } + + await caller.invalidateControl(); + caller.updateMessage("
To apply the installed/updated/disabled/enabled custom node, please ComfyUI. And refresh browser.", 'cm-reboot-button'); + } +}; + +export var manager_instance = null; + +export function setManagerInstance(obj) { + manager_instance = obj; +} + +function isValidURL(url) { + if(url.includes('&')) + return false; + + const pattern = /^(https?|ftp):\/\/[^\s/$.?#].[^\s]*$/; + return pattern.test(url); +} + +export async function install_pip(packages) { + if(packages.includes('&')) + app.ui.dialog.show(`Invalid PIP package enumeration: '${packages}'`); + + const res = await api.fetchApi(`/customnode/install/pip?packages=${packages}`); + + if(res.status == 200) { + app.ui.dialog.show(`PIP package installation is processed.
To apply the pip packages, please click the button in ComfyUI.`); + + const rebootButton = document.getElementById('cm-reboot-button'); + const self = this; + + rebootButton.addEventListener("click", rebootAPI); + + app.ui.dialog.element.style.zIndex = 10010; + } + else { + app.ui.dialog.show(`Failed to install '${packages}'
See terminal log.`); + app.ui.dialog.element.style.zIndex = 10010; + } +} + +export async function install_via_git_url(url, manager_dialog) { + if(!url) { + return; + } + + if(!isValidURL(url)) { + app.ui.dialog.show(`Invalid Git url '${url}'`); + app.ui.dialog.element.style.zIndex = 10010; + return; + } + + app.ui.dialog.show(`Wait...
Installing '${url}'`); + app.ui.dialog.element.style.zIndex = 10010; + + const res = await api.fetchApi(`/customnode/install/git_url?url=${url}`); + + if(res.status == 200) { + app.ui.dialog.show(`'${url}' is installed
To apply the installed custom node, please ComfyUI.`); + + const rebootButton = document.getElementById('cm-reboot-button'); + const self = this; + + rebootButton.addEventListener("click", + function() { + if(rebootAPI()) { + manager_dialog.close(); + } + }); + + app.ui.dialog.element.style.zIndex = 10010; + } + else { + app.ui.dialog.show(`Failed to install '${url}'
See terminal log.`); + app.ui.dialog.element.style.zIndex = 10010; + } +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/custom-nodes-downloader.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/custom-nodes-downloader.js new file mode 100644 index 0000000000000000000000000000000000000000..fda3476e58820165911088a071cf05470daa4d03 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/custom-nodes-downloader.js @@ -0,0 +1,770 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { install_checked_custom_node, manager_instance, rebootAPI } from "./common.js"; + + +async function getCustomNodes() { + var mode = manager_instance.datasrc_combo.value; + + var skip_update = ""; + if(manager_instance.update_check_checkbox.checked) + skip_update = "&skip_update=true"; + + const response = await api.fetchApi(`/customnode/getlist?mode=${mode}${skip_update}`); + + const data = await response.json(); + return data; +} + +async function getCustomnodeMappings() { + var mode = manager_instance.datasrc_combo.value; + + const response = await api.fetchApi(`/customnode/getmappings?mode=${mode}`); + + const data = await response.json(); + return data; +} + +async function getConflictMappings() { + var mode = manager_instance.datasrc_combo.value; + + const response = await api.fetchApi(`/customnode/getmappings?mode=${mode}`); + + const data = await response.json(); + + let node_to_extensions_map = {}; + + for(let k in data) { + for(let i in data[k][0]) { + let node = data[k][0][i]; + let l = node_to_extensions_map[node]; + if(!l) { + l = []; + node_to_extensions_map[node] = l; + } + l.push(k); + } + } + + let conflict_map = {}; + for(let node in node_to_extensions_map) { + if(node_to_extensions_map[node].length > 1) { + for(let i in node_to_extensions_map[node]) { + let extension = node_to_extensions_map[node][i]; + let l = conflict_map[extension]; + + if(!l) { + l = []; + conflict_map[extension] = l; + } + + for(let j in node_to_extensions_map[node]) { + let extension2 = node_to_extensions_map[node][j]; + if(extension != extension2) + l.push([node, extension2]); + } + } + } + } + + return conflict_map; +} + +async function getUnresolvedNodesInComponent() { + try { + var mode = manager_instance.datasrc_combo.value; + + const response = await api.fetchApi(`/component/get_unresolved`); + + const data = await response.json(); + return data.nodes; + } + catch { + return []; + } +} + +export class CustomNodesInstaller extends ComfyDialog { + static instance = null; + + install_buttons = []; + message_box = null; + data = null; + + static ShowMode = { + NORMAL: 0, + MISSING_NODES: 1, + UPDATE: 2, + }; + + clear() { + this.install_buttons = []; + this.message_box = null; + this.data = null; + } + + constructor(app, manager_dialog) { + super(); + this.manager_dialog = manager_dialog; + this.search_keyword = ''; + this.element = $el("div.comfy-modal", { parent: document.body }, []); + } + + startInstall(target) { + const self = CustomNodesInstaller.instance; + + self.updateMessage(`
Installing '${target.title}'`); + } + + disableButtons() { + for(let i in this.install_buttons) { + this.install_buttons[i].disabled = true; + this.install_buttons[i].style.backgroundColor = 'gray'; + } + } + + apply_searchbox(data) { + let keyword = this.search_box.value.toLowerCase(); + for(let i in this.grid_rows) { + let data = this.grid_rows[i].data; + let content = data.author.toLowerCase() + data.description.toLowerCase() + data.title.toLowerCase() + data.reference.toLowerCase(); + + if(this.filter && this.filter != '*') { + if(this.filter != data.installed) { + this.grid_rows[i].control.style.display = 'none'; + continue; + } + } + + if(keyword == "") + this.grid_rows[i].control.style.display = null; + else if(content.includes(keyword)) { + this.grid_rows[i].control.style.display = null; + } + else { + this.grid_rows[i].control.style.display = 'none'; + } + } + } + + async filter_missing_node(data) { + const mappings = await getCustomnodeMappings(); + + + // build regex->url map + const regex_to_url = []; + for (let i in data) { + if(data[i]['nodename_pattern']) { + let item = {regex: new RegExp(data[i].nodename_pattern), url: data[i].files[0]}; + regex_to_url.push(item); + } + } + + // build name->url map + const name_to_url = {}; + for (const url in mappings) { + const names = mappings[url]; + for(const name in names[0]) { + name_to_url[names[0][name]] = url; + } + } + + const registered_nodes = new Set(); + for (let i in LiteGraph.registered_node_types) { + registered_nodes.add(LiteGraph.registered_node_types[i].type); + } + + const missing_nodes = new Set(); + const workflow = app.graph.serialize(); + const group_nodes = workflow.extra && workflow.extra.groupNodes ? workflow.extra.groupNodes : []; + let nodes = workflow.nodes; + + for (let i in group_nodes) { + let group_node = group_nodes[i]; + nodes = nodes.concat(group_node.nodes); + } + + for (let i in nodes) { + const node_type = nodes[i].type; + if(node_type.startsWith('workflow/')) + continue; + + if (!registered_nodes.has(node_type)) { + const url = name_to_url[node_type.trim()]; + if(url) + missing_nodes.add(url); + else { + for(let j in regex_to_url) { + if(regex_to_url[j].regex.test(node_type)) { + missing_nodes.add(regex_to_url[j].url); + } + } + } + } + } + + let unresolved_nodes = await getUnresolvedNodesInComponent(); + for (let i in unresolved_nodes) { + let node_type = unresolved_nodes[i]; + const url = name_to_url[node_type]; + if(url) + missing_nodes.add(url); + } + + return data.filter(node => node.files.some(file => missing_nodes.has(file))); + } + + async invalidateControl() { + this.clear(); + + // splash + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + const msg = $el('div', {id:'custom-message'}, + [$el('br'), + 'The custom node DB is currently being updated, and updates to custom nodes are being checked for.', + $el('br'), + 'NOTE: Update only checks for extensions that have been fetched.', + $el('br')]); + msg.style.height = '100px'; + msg.style.verticalAlign = 'middle'; + msg.style.color = "var(--fg-color)"; + + this.element.appendChild(msg); + + // invalidate + let data = await getCustomNodes(); + this.data = data.custom_nodes; + this.channel = data.channel; + + this.conflict_mappings = await getConflictMappings(); + + if(this.show_mode == CustomNodesInstaller.ShowMode.MISSING_NODES) + this.data = await this.filter_missing_node(this.data); + + this.element.removeChild(msg); + + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + this.createHeaderControls(); + await this.createGrid(); + this.apply_searchbox(this.data); + this.createBottomControls(); + } + + updateMessage(msg, btn_id) { + this.message_box.innerHTML = msg; + if(btn_id) { + const rebootButton = document.getElementById(btn_id); + const self = this; + rebootButton.addEventListener("click", + function() { + if(rebootAPI()) { + self.close(); + self.manager_dialog.close(); + } + }); + console.log(rebootButton); + } + } + + invalidate_checks(is_checked, install_state) { + if(is_checked) { + for(let i in this.grid_rows) { + let data = this.grid_rows[i].data; + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + + checkbox.disabled = data.installed != install_state; + + if(checkbox.disabled) { + for(let j in buttons) { + buttons[j].style.display = 'none'; + } + } + else { + for(let j in buttons) { + buttons[j].style.display = null; + } + } + } + + this.checkbox_all.disabled = false; + } + else { + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(checkbox.check) + return; // do nothing + } + + // every checkbox is unchecked -> enable all checkbox + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + checkbox.disabled = false; + + for(let j in buttons) { + buttons[j].style.display = null; + } + } + + this.checkbox_all.checked = false; + this.checkbox_all.disabled = true; + } + } + + check_all(is_checked) { + if(is_checked) { + // lookup first checked item's state + let check_state = null; + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(checkbox.checked) { + check_state = this.grid_rows[i].data.installed; + } + } + + if(check_state == null) + return; + + // check only same state items + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + if(this.grid_rows[i].data.installed == check_state) + checkbox.checked = true; + } + } + else { + // uncheck all + for(let i in this.grid_rows) { + let checkbox = this.grid_rows[i].checkbox; + let buttons = this.grid_rows[i].buttons; + checkbox.checked = false; + checkbox.disabled = false; + + for(let j in buttons) { + buttons[j].style.display = null; + } + } + + this.checkbox_all.disabled = true; + } + } + + async createGrid() { + var grid = document.createElement('table'); + grid.setAttribute('id', 'custom-nodes-grid'); + + this.grid_rows = {}; + + let self = this; + + var thead = document.createElement('thead'); + var tbody = document.createElement('tbody'); + + var headerRow = document.createElement('tr'); + thead.style.position = "sticky"; + thead.style.top = "0px"; + thead.style.borderCollapse = "collapse"; + thead.style.tableLayout = "fixed"; + + var header0 = document.createElement('th'); + header0.style.width = "20px"; + this.checkbox_all = $el("input",{type:'checkbox', id:'check_all'},[]); + header0.appendChild(this.checkbox_all); + this.checkbox_all.checked = false; + this.checkbox_all.disabled = true; + this.checkbox_all.addEventListener('change', function() { self.check_all.call(self, self.checkbox_all.checked); }); + + var header1 = document.createElement('th'); + header1.innerHTML = ' ID '; + header1.style.width = "20px"; + var header2 = document.createElement('th'); + header2.innerHTML = 'Author'; + header2.style.width = "150px"; + var header3 = document.createElement('th'); + header3.innerHTML = 'Name'; + header3.style.width = "20%"; + var header4 = document.createElement('th'); + header4.innerHTML = 'Description'; + header4.style.width = "60%"; +// header4.classList.add('expandable-column'); + var header5 = document.createElement('th'); + header5.innerHTML = 'Install'; + header5.style.width = "130px"; + + header0.style.position = "sticky"; + header0.style.top = "0px"; + header1.style.position = "sticky"; + header1.style.top = "0px"; + header2.style.position = "sticky"; + header2.style.top = "0px"; + header3.style.position = "sticky"; + header3.style.top = "0px"; + header4.style.position = "sticky"; + header4.style.top = "0px"; + header5.style.position = "sticky"; + header5.style.top = "0px"; + + thead.appendChild(headerRow); + headerRow.appendChild(header0); + headerRow.appendChild(header1); + headerRow.appendChild(header2); + headerRow.appendChild(header3); + headerRow.appendChild(header4); + headerRow.appendChild(header5); + + headerRow.style.backgroundColor = "Black"; + headerRow.style.color = "White"; + headerRow.style.textAlign = "center"; + headerRow.style.width = "100%"; + headerRow.style.padding = "0"; + + grid.appendChild(thead); + grid.appendChild(tbody); + + if(this.data) + for (var i = 0; i < this.data.length; i++) { + const data = this.data[i]; + let dataRow = document.createElement('tr'); + + let data0 = document.createElement('td'); + let checkbox = $el("input",{type:'checkbox', id:`check_${i}`},[]); + data0.appendChild(checkbox); + checkbox.checked = false; + checkbox.addEventListener('change', function() { self.invalidate_checks.call(self, checkbox.checked, data.installed); }); + + var data1 = document.createElement('td'); + data1.style.textAlign = "center"; + data1.innerHTML = i+1; + + var data2 = document.createElement('td'); + data2.style.maxWidth = "100px"; + data2.className = "cm-node-author" + data2.textContent = ` ${data.author}`; + data2.style.whiteSpace = "nowrap"; + data2.style.overflow = "hidden"; + data2.style.textOverflow = "ellipsis"; + + var data3 = document.createElement('td'); + data3.style.maxWidth = "200px"; + data3.style.wordWrap = "break-word"; + data3.className = "cm-node-name" + data3.innerHTML = ` ${data.title}`; + if(data.installed == 'Fail') + data3.innerHTML = ' (IMPORT FAILED)' + data3.innerHTML; + + var data4 = document.createElement('td'); + data4.innerHTML = data.description; + data4.className = "cm-node-desc" + + let conflicts = this.conflict_mappings[data.files[0]]; + if(conflicts) { + let buf = '
Conflicted Nodes:
';
+ for(let k in conflicts) {
+ let node_name = conflicts[k][0];
+
+ let extension_name = conflicts[k][1].split('/').pop();
+ if(extension_name.endsWith('/')) {
+ extension_name = extension_name.slice(0, -1);
+ }
+ if(node_name.endsWith('.git')) {
+ extension_name = extension_name.slice(0, -4);
+ }
+
+ buf += `${node_name} [${extension_name}], `;
+ }
+
+ if(buf.endsWith(', ')) {
+ buf = buf.slice(0, -2);
+ }
+ buf += "
To apply the installed model, please click the 'Refresh' button on the main menu."); + } + } +} + +async function getModelList() { + var mode = manager_instance.datasrc_combo.value; + + const response = await api.fetchApi(`/externalmodel/getlist?mode=${mode}`); + + const data = await response.json(); + return data; +} + +export class ModelInstaller extends ComfyDialog { + static instance = null; + + install_buttons = []; + message_box = null; + data = null; + + clear() { + this.install_buttons = []; + this.message_box = null; + this.data = null; + } + + constructor(app, manager_dialog) { + super(); + this.manager_dialog = manager_dialog; + this.search_keyword = ''; + this.element = $el("div.comfy-modal", { parent: document.body }, []); + } + + createControls() { + return [ + $el("button.cm-small-button", { + type: "button", + textContent: "Close", + onclick: () => { this.close(); } + }) + ]; + } + + startInstall(target) { + const self = ModelInstaller.instance; + + self.updateMessage(`
Installing '${target.name}'`); + + for(let i in self.install_buttons) { + self.install_buttons[i].disabled = true; + self.install_buttons[i].style.backgroundColor = 'gray'; + } + } + + apply_searchbox(data) { + let keyword = this.search_box.value.toLowerCase(); + for(let i in this.grid_rows) { + let data = this.grid_rows[i].data; + let content = data.name.toLowerCase() + data.type.toLowerCase() + data.base.toLowerCase() + data.description.toLowerCase(); + + if(this.filter && this.filter != '*') { + if(this.filter != data.installed) { + this.grid_rows[i].control.style.display = 'none'; + continue; + } + } + + if(keyword == "") + this.grid_rows[i].control.style.display = null; + else if(content.includes(keyword)) { + this.grid_rows[i].control.style.display = null; + } + else { + this.grid_rows[i].control.style.display = 'none'; + } + } + } + + async invalidateControl() { + this.clear(); + this.data = (await getModelList()).models; + + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + await this.createHeaderControls(); + + if(this.search_keyword) { + this.search_box.value = this.search_keyword; + } + + await this.createGrid(); + await this.createBottomControls(); + + this.apply_searchbox(this.data); + } + + updateMessage(msg, btn_id) { + this.message_box.innerHTML = msg; + if(btn_id) { + const rebootButton = document.getElementById(btn_id); + const self = this; + rebootButton.addEventListener("click", + function() { + if(rebootAPI()) { + self.close(); + self.manager_dialog.close(); + } + }); + } + } + + async createGrid(models_json) { + var grid = document.createElement('table'); + grid.setAttribute('id', 'external-models-grid'); + + var thead = document.createElement('thead'); + var tbody = document.createElement('tbody'); + + var headerRow = document.createElement('tr'); + thead.style.position = "sticky"; + thead.style.top = "0px"; + thead.style.borderCollapse = "collapse"; + thead.style.tableLayout = "fixed"; + + var header1 = document.createElement('th'); + header1.innerHTML = ' ID '; + header1.style.width = "20px"; + var header2 = document.createElement('th'); + header2.innerHTML = 'Type'; + header2.style.width = "100px"; + var header3 = document.createElement('th'); + header3.innerHTML = 'Base'; + header3.style.width = "100px"; + var header4 = document.createElement('th'); + header4.innerHTML = 'Name'; + header4.style.width = "30%"; + var header5 = document.createElement('th'); + header5.innerHTML = 'Filename'; + header5.style.width = "20%"; + header5.style.tableLayout = "fixed"; + var header6 = document.createElement('th'); + header6.innerHTML = 'Description'; + header6.style.width = "50%"; + var header_down = document.createElement('th'); + header_down.innerHTML = 'Download'; + header_down.style.width = "50px"; + + thead.appendChild(headerRow); + headerRow.appendChild(header1); + headerRow.appendChild(header2); + headerRow.appendChild(header3); + headerRow.appendChild(header4); + headerRow.appendChild(header5); + headerRow.appendChild(header6); + headerRow.appendChild(header_down); + + headerRow.style.backgroundColor = "Black"; + headerRow.style.color = "White"; + headerRow.style.textAlign = "center"; + headerRow.style.width = "100%"; + headerRow.style.padding = "0"; + + grid.appendChild(thead); + grid.appendChild(tbody); + + this.grid_rows = {}; + + if(this.data) + for (var i = 0; i < this.data.length; i++) { + const data = this.data[i]; + var dataRow = document.createElement('tr'); + var data1 = document.createElement('td'); + data1.style.textAlign = "center"; + data1.innerHTML = i+1; + var data2 = document.createElement('td'); + data2.innerHTML = ` ${data.type}`; + var data3 = document.createElement('td'); + data3.innerHTML = ` ${data.base}`; + var data4 = document.createElement('td'); + data4.className = "cm-node-name"; + data4.innerHTML = ` ${data.name}`; + var data5 = document.createElement('td'); + data5.className = "cm-node-filename"; + data5.innerHTML = ` ${data.filename}`; + data5.style.wordBreak = "break-all"; + var data6 = document.createElement('td'); + data6.className = "cm-node-desc"; + data6.innerHTML = data.description; + data6.style.wordBreak = "break-all"; + var data_install = document.createElement('td'); + var installBtn = document.createElement('button'); + data_install.style.textAlign = "center"; + + installBtn.innerHTML = 'Install'; + this.install_buttons.push(installBtn); + + switch(data.installed) { + case 'True': + installBtn.innerHTML = 'Installed'; + installBtn.style.backgroundColor = 'green'; + installBtn.style.color = 'white'; + installBtn.disabled = true; + break; + default: + installBtn.innerHTML = 'Install'; + installBtn.style.backgroundColor = 'black'; + installBtn.style.color = 'white'; + break; + } + + installBtn.style.width = "100px"; + + installBtn.addEventListener('click', function() { + install_model(data); + }); + + data_install.appendChild(installBtn); + + dataRow.style.backgroundColor = "var(--bg-color)"; + dataRow.style.color = "var(--fg-color)"; + dataRow.style.textAlign = "left"; + + dataRow.appendChild(data1); + dataRow.appendChild(data2); + dataRow.appendChild(data3); + dataRow.appendChild(data4); + dataRow.appendChild(data5); + dataRow.appendChild(data6); + dataRow.appendChild(data_install); + tbody.appendChild(dataRow); + + this.grid_rows[i] = {data:data, control:dataRow}; + } + + let self = this; + const panel = document.createElement('div'); + panel.style.width = "100%"; + panel.appendChild(grid); + + function handleResize() { + const parentHeight = self.element.clientHeight; + const gridHeight = parentHeight - 200; + + grid.style.height = gridHeight + "px"; + } + window.addEventListener("resize", handleResize); + + grid.style.position = "relative"; + grid.style.display = "inline-block"; + grid.style.width = "100%"; + grid.style.height = "100%"; + grid.style.overflowY = "scroll"; + this.element.style.height = "85%"; + this.element.style.width = "80%"; + this.element.appendChild(panel); + + handleResize(); + } + + createFilterCombo() { + let combo = document.createElement("select"); + + combo.style.cssFloat = "left"; + combo.style.fontSize = "14px"; + combo.style.padding = "4px"; + combo.style.background = "black"; + combo.style.marginLeft = "2px"; + combo.style.width = "199px"; + combo.id = `combo-manger-filter`; + combo.style.borderRadius = "15px"; + + let items = + [ + { value:'*', text:'Filter: all' }, + { value:'True', text:'Filter: installed' }, + { value:'False', text:'Filter: not-installed' }, + ]; + + items.forEach(item => { + const option = document.createElement("option"); + option.value = item.value; + option.text = item.text; + combo.appendChild(option); + }); + + let self = this; + combo.addEventListener('change', function(event) { + self.filter = event.target.value; + self.apply_searchbox(); + }); + + return combo; + } + + createHeaderControls() { + let self = this; + this.search_box = $el('input.cm-search-filter', {type:'text', id:'manager-model-search-box', placeholder:'input search keyword', value:this.search_keyword}, []); + this.search_box.style.height = "25px"; + this.search_box.onkeydown = (event) => { + if (event.key === 'Enter') { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + } + if (event.key === 'Escape') { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + } + }; + + let search_button = document.createElement("button"); + search_button.className = "cm-small-button"; + search_button.innerHTML = "Search"; + search_button.onclick = () => { + self.search_keyword = self.search_box.value; + self.apply_searchbox(); + }; + search_button.style.display = "inline-block"; + + let filter_control = this.createFilterCombo(); + filter_control.style.display = "inline-block"; + + let cell = $el('td', {width:'100%'}, [filter_control, this.search_box, ' ', search_button]); + let search_control = $el('table', {width:'100%'}, + [ + $el('tr', {}, [cell]) + ] + ); + + cell.style.textAlign = "right"; + this.element.appendChild(search_control); + } + + async createBottomControls() { + var close_button = document.createElement("button"); + close_button.className = "cm-small-button"; + close_button.innerHTML = "Close"; + close_button.onclick = () => { this.close(); } + close_button.style.display = "inline-block"; + + this.message_box = $el('div', {id:'custom-download-message'}, [$el('br'), '']); + this.message_box.style.height = '60px'; + this.message_box.style.verticalAlign = 'middle'; + + this.element.appendChild(this.message_box); + this.element.appendChild(close_button); + } + + async show() { + try { + this.invalidateControl(); + this.element.style.display = "block"; + this.element.style.zIndex = 10001; + } + catch(exception) { + app.ui.dialog.show(`Failed to get external model list. / ${exception}`); + } + } +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/snapshot.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/snapshot.js new file mode 100644 index 0000000000000000000000000000000000000000..e4a720eaa4610c3abf00c034a1c76d18dde84b4a --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/snapshot.js @@ -0,0 +1,292 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { manager_instance, rebootAPI } from "./common.js"; + + +async function restore_snapshot(target) { + if(SnapshotManager.instance) { + try { + const response = await api.fetchApi(`/snapshot/restore?target=${target}`, { cache: "no-store" }); + if(response.status == 400) { + app.ui.dialog.show(`Restore snapshot failed: ${target.title} / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + } + + app.ui.dialog.close(); + return true; + } + catch(exception) { + app.ui.dialog.show(`Restore snapshot failed: ${target.title} / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + SnapshotManager.instance.updateMessage("
To apply the snapshot, please ComfyUI. And refresh browser.", 'cm-reboot-button'); + } + } +} + +async function remove_snapshot(target) { + if(SnapshotManager.instance) { + try { + const response = await api.fetchApi(`/snapshot/remove?target=${target}`, { cache: "no-store" }); + if(response.status == 400) { + app.ui.dialog.show(`Remove snapshot failed: ${target.title} / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + } + + app.ui.dialog.close(); + return true; + } + catch(exception) { + app.ui.dialog.show(`Restore snapshot failed: ${target.title} / ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + } + } +} + +async function save_current_snapshot() { + try { + const response = await api.fetchApi('/snapshot/save', { cache: "no-store" }); + app.ui.dialog.close(); + return true; + } + catch(exception) { + app.ui.dialog.show(`Backup snapshot failed: ${exception}`); + app.ui.dialog.element.style.zIndex = 10010; + return false; + } + finally { + await SnapshotManager.instance.invalidateControl(); + SnapshotManager.instance.updateMessage("
Current snapshot saved."); + } +} + +async function getSnapshotList() { + const response = await api.fetchApi(`/snapshot/getlist`); + const data = await response.json(); + return data; +} + +export class SnapshotManager extends ComfyDialog { + static instance = null; + + restore_buttons = []; + message_box = null; + data = null; + + clear() { + this.restore_buttons = []; + this.message_box = null; + this.data = null; + } + + constructor(app, manager_dialog) { + super(); + this.manager_dialog = manager_dialog; + this.search_keyword = ''; + this.element = $el("div.comfy-modal", { parent: document.body }, []); + } + + async remove_item() { + caller.disableButtons(); + + await caller.invalidateControl(); + } + + createControls() { + return [ + $el("button.cm-small-button", { + type: "button", + textContent: "Close", + onclick: () => { this.close(); } + }) + ]; + } + + startRestore(target) { + const self = SnapshotManager.instance; + + self.updateMessage(`
Restore snapshot '${target.name}'`); + + for(let i in self.restore_buttons) { + self.restore_buttons[i].disabled = true; + self.restore_buttons[i].style.backgroundColor = 'gray'; + } + } + + async invalidateControl() { + this.clear(); + this.data = (await getSnapshotList()).items; + + while (this.element.children.length) { + this.element.removeChild(this.element.children[0]); + } + + await this.createGrid(); + await this.createBottomControls(); + } + + updateMessage(msg, btn_id) { + this.message_box.innerHTML = msg; + if(btn_id) { + const rebootButton = document.getElementById(btn_id); + const self = this; + rebootButton.onclick = function() { + if(rebootAPI()) { + self.close(); + self.manager_dialog.close(); + } + }; + } + } + + async createGrid(models_json) { + var grid = document.createElement('table'); + grid.setAttribute('id', 'snapshot-list-grid'); + + var thead = document.createElement('thead'); + var tbody = document.createElement('tbody'); + + var headerRow = document.createElement('tr'); + thead.style.position = "sticky"; + thead.style.top = "0px"; + thead.style.borderCollapse = "collapse"; + thead.style.tableLayout = "fixed"; + + var header1 = document.createElement('th'); + header1.innerHTML = ' ID '; + header1.style.width = "20px"; + var header2 = document.createElement('th'); + header2.innerHTML = 'Datetime'; + header2.style.width = "100%"; + var header_button = document.createElement('th'); + header_button.innerHTML = 'Action'; + header_button.style.width = "100px"; + + thead.appendChild(headerRow); + headerRow.appendChild(header1); + headerRow.appendChild(header2); + headerRow.appendChild(header_button); + + headerRow.style.backgroundColor = "Black"; + headerRow.style.color = "White"; + headerRow.style.textAlign = "center"; + headerRow.style.width = "100%"; + headerRow.style.padding = "0"; + + grid.appendChild(thead); + grid.appendChild(tbody); + + this.grid_rows = {}; + + if(this.data) + for (var i = 0; i < this.data.length; i++) { + const data = this.data[i]; + var dataRow = document.createElement('tr'); + var data1 = document.createElement('td'); + data1.style.textAlign = "center"; + data1.innerHTML = i+1; + var data2 = document.createElement('td'); + data2.innerHTML = ` ${data}`; + var data_button = document.createElement('td'); + data_button.style.textAlign = "center"; + + var restoreBtn = document.createElement('button'); + restoreBtn.innerHTML = 'Restore'; + restoreBtn.style.width = "100px"; + restoreBtn.style.backgroundColor = 'blue'; + + restoreBtn.addEventListener('click', function() { + restore_snapshot(data); + }); + + var removeBtn = document.createElement('button'); + removeBtn.innerHTML = 'Remove'; + removeBtn.style.width = "100px"; + removeBtn.style.backgroundColor = 'red'; + + removeBtn.addEventListener('click', function() { + remove_snapshot(data); + }); + + data_button.appendChild(restoreBtn); + data_button.appendChild(removeBtn); + + dataRow.style.backgroundColor = "var(--bg-color)"; + dataRow.style.color = "var(--fg-color)"; + dataRow.style.textAlign = "left"; + + dataRow.appendChild(data1); + dataRow.appendChild(data2); + dataRow.appendChild(data_button); + tbody.appendChild(dataRow); + + this.grid_rows[i] = {data:data, control:dataRow}; + } + + let self = this; + const panel = document.createElement('div'); + panel.style.width = "100%"; + panel.appendChild(grid); + + function handleResize() { + const parentHeight = self.element.clientHeight; + const gridHeight = parentHeight - 200; + + grid.style.height = gridHeight + "px"; + } + window.addEventListener("resize", handleResize); + + grid.style.position = "relative"; + grid.style.display = "inline-block"; + grid.style.width = "100%"; + grid.style.height = "100%"; + grid.style.overflowY = "scroll"; + this.element.style.height = "85%"; + this.element.style.width = "80%"; + this.element.appendChild(panel); + + handleResize(); + } + + async createBottomControls() { + var close_button = document.createElement("button"); + close_button.className = "cm-small-button"; + close_button.innerHTML = "Close"; + close_button.onclick = () => { this.close(); } + close_button.style.display = "inline-block"; + + var save_button = document.createElement("button"); + save_button.className = "cm-small-button"; + save_button.innerHTML = "Save snapshot"; + save_button.onclick = () => { save_current_snapshot(); } + save_button.style.display = "inline-block"; + save_button.style.horizontalAlign = "right"; + + this.message_box = $el('div', {id:'custom-download-message'}, [$el('br'), '']); + this.message_box.style.height = '60px'; + this.message_box.style.verticalAlign = 'middle'; + + this.element.appendChild(this.message_box); + this.element.appendChild(close_button); + this.element.appendChild(save_button); + } + + async show() { + try { + this.invalidateControl(); + this.element.style.display = "block"; + this.element.style.zIndex = 10001; + } + catch(exception) { + app.ui.dialog.show(`Failed to get external model list. / ${exception}`); + } + } +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/js/terminal.js b/ComfyUI/custom_nodes/ComfyUI-Manager/js/terminal.js new file mode 100644 index 0000000000000000000000000000000000000000..091959fa5f60ae880b1f19f5ea014c56d9f56415 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/js/terminal.js @@ -0,0 +1,81 @@ +import {app} from "../../scripts/app.js"; +import {ComfyWidgets} from "../../scripts/widgets.js"; +// Node that add notes to your project + +let terminal_node; +let log_mode = false; + +app.registerExtension({ + name: "Comfy.Manager.Terminal", + + registerCustomNodes() { + class TerminalNode { + color = "#222222"; + bgcolor = "#000000"; + groupcolor = LGraphCanvas.node_colors.black.groupcolor; + constructor() { + this.logs = []; + + if (!this.properties) { + this.properties = {}; + this.properties.text=""; + } + + ComfyWidgets.STRING(this, "", ["", {default:this.properties.text, multiline: true}], app) + ComfyWidgets.BOOLEAN(this, "mode", ["", {default:true, label_on:'Logging', label_off:'Stop'}], app) + ComfyWidgets.INT(this, "lines", ["", {default:500, min:10, max:10000, steps:1}], app) + + let self = this; + Object.defineProperty(this.widgets[1], 'value', { + set: (v) => { + api.fetchApi(`/manager/terminal?mode=${v}`, {}); + log_mode = v; + }, + get: () => { + return log_mode; + } + }); + + this.serialize_widgets = false; + this.isVirtualNode = true; + + if(terminal_node) { + try { + terminal_node.widgets[0].value = 'The output of this node is disabled because another terminal node has appeared.'; + } + catch {} + } + terminal_node = this; + } + } + + // Load default visibility + LiteGraph.registerNodeType( + "Terminal Log //CM", + Object.assign(TerminalNode, { + title_mode: LiteGraph.NORMAL_TITLE, + title: "Terminal Log (Manager)", + collapsable: true, + }) + ); + + TerminalNode.category = "utils"; + }, +}); + + +import { api } from "../../scripts/api.js"; + +function terminalFeedback(event) { + if(terminal_node) { + terminal_node.logs.push(event.detail.data); + if(terminal_node.logs.length > terminal_node.widgets[2].value) { + terminal_node.logs.shift(); + if(terminal_node.logs[0] == '' || terminal_node.logs[0] == '\n') + terminal_node.logs.shift(); + } + terminal_node.widgets[0].value = [...terminal_node.logs].reverse().join('').trim(); + } +} + +api.addEventListener("manager-terminal-feedback", terminalFeedback); diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/json-checker.py b/ComfyUI/custom_nodes/ComfyUI-Manager/json-checker.py new file mode 100644 index 0000000000000000000000000000000000000000..a6c1317ff0a7f5fc80b18a79a6b39034b315671d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/json-checker.py @@ -0,0 +1,23 @@ +import json +import argparse + +def check_json_syntax(file_path): + try: + with open(file_path, 'r') as file: + json_str = file.read() + json.loads(json_str) + print(f"[ OK ] {file_path}") + except json.JSONDecodeError as e: + print(f"[FAIL] {file_path}\n\n {e}\n") + except FileNotFoundError: + print(f"[FAIL] {file_path}\n\n File not found\n") + +def main(): + parser = argparse.ArgumentParser(description="JSON File Syntax Checker") + parser.add_argument("file_path", type=str, help="Path to the JSON file for syntax checking") + + args = parser.parse_args() + check_json_syntax(args.file_path) + +if __name__ == "__main__": + main() diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/misc/custom-nodes.jpg b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/custom-nodes.jpg new file mode 100644 index 0000000000000000000000000000000000000000..10482f1b7b55a617f3c9cabba55b318b5ab4d714 Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/custom-nodes.jpg differ diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/misc/main.jpg b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/main.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ec31f76fcaf06cbd2b2b51d98f4d244bcd34fcb6 Binary files /dev/null and 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/dev/null and b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/share.jpg differ diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/misc/snapshot.jpg b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/snapshot.jpg new file mode 100644 index 0000000000000000000000000000000000000000..33269564bbd2b286994c78a92bf09905d251907b Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI-Manager/misc/snapshot.jpg differ diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..34f4d981c18719e37426aed0c888a9004ae36fef --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/model-list.json @@ -0,0 +1,1653 @@ +{ + "models": [ + { + "name": "TAESDXL Decoder", + "type": "TAESD", + "base": "SDXL", + "save_path": "vae_approx", + "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", + "reference": "https://github.com/madebyollin/taesd", + "filename": "taesdxl_decoder.pth", + "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth" + }, + { + "name": "TAESDXL Encoder", + "type": "TAESD", + "base": "SDXL", + "save_path": "vae_approx", + "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", + "reference": "https://github.com/madebyollin/taesd", + "filename": "taesdxl_encoder.pth", + "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_encoder.pth" + }, + { + "name": "TAESD Decoder", + "type": "TAESD", + "base": "SD1.x", + "save_path": "vae_approx", + "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.", + "reference": "https://github.com/madebyollin/taesd", + "filename": "taesd_decoder.pth", + "url": "https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth" + }, + { + "name": "TAESD Encoder", + "type": "TAESD", + "base": "SD1.x", + "save_path": "vae_approx", + "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.", + "reference": "https://github.com/madebyollin/taesd", + "filename": "taesd_encoder.pth", + "url": "https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth" + }, + { + "name": "RealESRGAN x2", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "RealESRGAN x2 upscaler model", + "reference": "https://huggingface.co/ai-forever/Real-ESRGAN", + "filename": "RealESRGAN_x2.pth", + "url": "https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth" + }, + { + "name": "RealESRGAN x4", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "RealESRGAN x4 upscaler model", + "reference": "https://huggingface.co/ai-forever/Real-ESRGAN", + "filename": "RealESRGAN_x4.pth", + "url": "https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth" + }, + { + "name": "ESRGAN x4", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "ESRGAN x4 upscaler model", + "reference": "https://huggingface.co/Afizi/ESRGAN_4x.pth", + "filename": "ESRGAN_4x.pth", + "url": "https://huggingface.co/Afizi/ESRGAN_4x.pth/resolve/main/ESRGAN_4x.pth" + }, + { + "name": "4x_foolhardy_Remacri", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "4x_foolhardy_Remacri upscaler model", + "reference": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri", + "filename": "4x_foolhardy_Remacri.pth", + "url": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth" + }, + { + "name": "4x-AnimeSharp", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "4x-AnimeSharp upscaler model", + "reference": "https://huggingface.co/konohashinobi4/4xAnimesharp", + "filename": "4x-AnimeSharp.pth", + "url": "https://huggingface.co/konohashinobi4/4xAnimesharp/resolve/main/4x-AnimeSharp.pth" + }, + { + "name": "4x-UltraSharp", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "4x-UltraSharp upscaler model", + "reference": "https://upscale.wiki/wiki/Model_Database", + "filename": "4x-UltraSharp.pth", + "url": "https://huggingface.co/datasets/Kizi-Art/Upscale/resolve/fa98e357882a23b8e7928957a39462fbfaee1af5/4x-UltraSharp.pth" + }, + { + "name": "4x_NMKD-Siax_200k", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "4x_NMKD-Siax_200k upscaler model", + "reference": "https://huggingface.co/gemasai/4x_NMKD-Siax_200k", + "filename": "4x_NMKD-Siax_200k.pth", + "url": "https://huggingface.co/gemasai/4x_NMKD-Siax_200k/resolve/main/4x_NMKD-Siax_200k.pth" + }, + { + "name": "8x_NMKD-Superscale_150000_G", + "type": "upscale", + "base": "upscale", + "save_path": "default", + "description": "8x_NMKD-Superscale_150000_G upscaler model", + "reference": "https://huggingface.co/uwg/upscaler", + "filename": "8x_NMKD-Superscale_150000_G.pth", + "url": "https://huggingface.co/uwg/upscaler/resolve/main/ESRGAN/8x_NMKD-Superscale_150000_G.pth" + }, + { + "name": "Inswapper-fp16 (face swap)", + "type": "insightface", + "base" : "inswapper", + "save_path": "insightface", + "description": "[264MB] Checkpoint of the insightface swapper model
(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,
ComfyUI roop and comfy_mtb)", + "reference": "https://github.com/facefusion/facefusion-assets", + "filename": "inswapper_128_fp16.onnx", + "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx" + }, + { + "name": "Inswapper (face swap)", + "type": "insightface", + "base" : "inswapper", + "save_path": "insightface", + "description": "[529MB] Checkpoint of the insightface swapper model
(used by ComfyUI-FaceSwap, comfyui-reactor-node, CharacterFaceSwap,
ComfyUI roop and comfy_mtb)", + "reference": "https://github.com/facefusion/facefusion-assets", + "filename": "inswapper_128.onnx", + "url": "https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx" + }, + { + "name": "Deepbump", + "type": "deepbump", + "base": "deepbump", + "save_path": "deepbump", + "description": "Checkpoint of the deepbump model to generate height and normal maps textures from an image (requires comfy_mtb)", + "reference": "https://github.com/HugoTini/DeepBump", + "filename": "deepbump256.onnx", + "url": "https://github.com/HugoTini/DeepBump/raw/master/deepbump256.onnx" + }, + { + "name": "GFPGAN 1.3", + "type": "face_restore", + "base": "face_restore", + "save_path": "face_restore", + "description": "Face restoration", + "reference": "https://github.com/TencentARC/GFPGAN", + "filename": "GFPGANv1.3.pth", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth" + }, + { + "name": "GFPGAN 1.4", + "type": "face_restore", + "base": "face_restore", + "save_path": "face_restore", + "description": "Face restoration", + "reference": "https://github.com/TencentARC/GFPGAN", + "filename": "GFPGANv1.4.pth", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" + }, + { + "name": "RestoreFormer", + "type": "face_restore", + "base": "face_restore", + "save_path": "face_restore", + "description": "Face restoration", + "reference": "https://github.com/TencentARC/GFPGAN", + "filename": "RestoreFormer.pth", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth" + }, + { + "name": "Stable Video Diffusion Image-to-Video", + "type": "checkpoints", + "base": "SVD", + "save_path": "checkpoints/SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
NOTE: 14 frames @ 576x1024", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid", + "filename": "svd.safetensors", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd.safetensors" + }, + { + "name": "stabilityai/Stable Zero123", + "type": "zero123", + "base": "zero123", + "save_path": "checkpoints/zero123", + "description": "Stable Zero123 is a model for view-conditioned image generation based on [a/Zero123](https://github.com/cvlab-columbia/zero123).", + "reference": "https://huggingface.co/stabilityai/stable-zero123", + "filename": "stable_zero123.ckpt", + "url": "https://huggingface.co/stabilityai/stable-zero123/resolve/main/stable_zero123.ckpt" + }, + { + "name": "Stable Video Diffusion Image-to-Video (XT)", + "type": "checkpoints", + "base": "SVD", + "save_path": "checkpoints/SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
NOTE: 25 frames @ 576x1024 ", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt", + "filename": "svd_xt.safetensors", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors" + }, + { + "name": "negative_hand Negative Embedding", + "type": "embeddings", + "base": "SD1.5", + "save_path": "default", + "description": "If you use this embedding with negatives, you can solve the issue of damaging your hands.", + "reference": "https://civitai.com/models/56519/negativehand-negative-embedding", + "filename": "negative_hand-neg.pt", + "url": "https://civitai.com/api/download/models/60938" + }, + { + "name": "bad_prompt Negative Embedding", + "type": "embeddings", + "base": "SD1.5", + "save_path": "default", + "description": "The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding.", + "reference": "https://civitai.com/models/55700/badprompt-negative-embedding", + "filename": "bad_prompt_version2-neg.pt", + "url": "https://civitai.com/api/download/models/60095" + }, + { + "name": "Deep Negative V1.75", + "type": "embeddings", + "base": "SD1.5", + "save_path": "default", + "description": "These embedding learn what disgusting compositions and color patterns are, including faulty human anatomy, offensive color schemes, upside-down spatial structures, and more. Placing it in the negative can go a long way to avoiding these things.", + "reference": "https://civitai.com/models/4629/deep-negative-v1x", + "filename": "ng_deepnegative_v1_75t.pt", + "url": "https://civitai.com/api/download/models/5637" + }, + { + "name": "EasyNegative", + "type": "embeddings", + "base": "SD1.5", + "save_path": "default", + "description": "This embedding should be used in your NEGATIVE prompt. Adjust the strength as desired (seems to scale well without any distortions), the strength required may vary based on positive and negative prompts.", + "reference": "https://civitai.com/models/7808/easynegative", + "filename": "easynegative.safetensors", + "url": "https://civitai.com/api/download/models/9208" + }, + { + "name": "SDXL-Turbo 1.0 (fp16)", + "type": "checkpoints", + "base": "SDXL", + "save_path": "checkpoints/SDXL-TURBO", + "description": "[6.9GB] SDXL-Turbo 1.0 fp16", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "filename": "sd_xl_turbo_1.0_fp16.safetensors", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors" + }, + { + "name": "SDXL-Turbo 1.0", + "type": "checkpoints", + "base": "SDXL", + "save_path": "checkpoints/SDXL-TURBO", + "description": "[13.9GB] SDXL-Turbo 1.0", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "filename": "sd_xl_turbo_1.0.safetensors", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0.safetensors" + }, + { + "name": "sd_xl_base_1.0_0.9vae.safetensors", + "type": "checkpoints", + "base": "SDXL", + "save_path": "default", + "description": "Stable Diffusion XL base model (VAE 0.9)", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "filename": "sd_xl_base_1.0_0.9vae.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors" + }, + { + "name": "sd_xl_base_1.0.safetensors", + "type": "checkpoints", + "base": "SDXL", + "save_path": "default", + "description": "Stable Diffusion XL base model", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "filename": "sd_xl_base_1.0.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" + }, + { + "name": "sd_xl_refiner_1.0_0.9vae.safetensors", + "type": "checkpoints", + "base": "SDXL", + "save_path": "default", + "description": "Stable Diffusion XL refiner model (VAE 0.9)", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0", + "filename": "sd_xl_refiner_1.0_0.9vae.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors" + }, + { + "name": "stable-diffusion-xl-refiner-1.0", + "type": "checkpoints", + "base": "SDXL", + "save_path": "default", + "description": "Stable Diffusion XL refiner model", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0", + "filename": "sd_xl_refiner_1.0.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors" + }, + { + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET/fp16)", + "type": "unet", + "base": "SDXL", + "save_path": "unet/xl-inpaint-0.1", + "description": "[5.14GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "filename": "diffusion_pytorch_model.fp16.safetensors", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors" + }, + { + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET)", + "type": "unet", + "base": "SDXL", + "save_path": "unet/xl-inpaint-0.1", + "description": "[10.3GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "filename": "diffusion_pytorch_model.safetensors", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.safetensors" + }, + { + "name": "sd_xl_offset_example-lora_1.0.safetensors", + "type": "lora", + "base": "SDXL", + "save_path": "default", + "description": "Stable Diffusion XL offset LoRA", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", + "filename": "sd_xl_offset_example-lora_1.0.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors" + }, + { + "name": "v1-5-pruned-emaonly.ckpt", + "type": "checkpoints", + "base": "SD1.5", + "save_path": "default", + "description": "Stable Diffusion 1.5 base model", + "reference": "https://huggingface.co/runwayml/stable-diffusion-v1-5", + "filename": "v1-5-pruned-emaonly.ckpt", + "url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt" + }, + { + "name": "v2-1_512-ema-pruned.safetensors", + "type": "checkpoints", + "base": "SD2", + "save_path": "default", + "description": "Stable Diffusion 2 base model (512)", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base", + "filename": "v2-1_512-ema-pruned.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors" + }, + { + "name": "v2-1_768-ema-pruned.safetensors", + "type": "checkpoints", + "base": "SD2", + "save_path": "default", + "description": "Stable Diffusion 2 base model (768)", + "reference": "https://huggingface.co/stabilityai/stable-diffusion-2-1", + "filename": "v2-1_768-ema-pruned.safetensors", + "url": "https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors" + }, + { + "name": "AbyssOrangeMix2 (hard)", + "type": "checkpoints", + "base": "SD1.5", + "save_path": "default", + "description": "AbyssOrangeMix2 - hard version (anime style)", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "filename": "AbyssOrangeMix2_hard.safetensors", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors" + }, + { + "name": "AbyssOrangeMix3 A1", + "type": "checkpoints", + "base": "SD1.5", + "save_path": "default", + "description": "AbyssOrangeMix3 - A1 (anime style)", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "filename": "AOM3A1_orangemixs.safetensors", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors" + }, + { + "name": "AbyssOrangeMix3 A3", + "type": "checkpoints", + "base": "SD1.5", + "save_path": "default", + "description": "AbyssOrangeMix - A3 (anime style)", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "filename": "AOM3A3_orangemixs.safetensors", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors" + }, + { + "name": "Anything v3 (fp16; pruned)", + "type": "checkpoints", + "base": "SD1.5", + "save_path": "default", + "description": "Anything v3 (anime style)", + "reference": "https://huggingface.co/Linaqruf/anything-v3.0", + "filename": "anything-v3-fp16-pruned.safetensors", + "url": "https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors" + }, + { + "name": "Waifu Diffusion 1.5 Beta3 (fp16)", + "type": "checkpoints", + "base": "SD2.1", + "save_path": "default", + "description": "Waifu Diffusion 1.5 Beta3", + "reference": "https://huggingface.co/waifu-diffusion/wd-1-5-beta3", + "filename": "wd-illusion-fp16.safetensors", + "url": "https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors" + }, + { + "name": "illuminatiDiffusionV1_v11 unCLIP model", + "type": "unclip", + "base": "SD2.1", + "save_path": "default", + "description": "Mix model (SD2.1 unCLIP + illuminatiDiffusionV1_v11)", + "reference": "https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP", + "filename": "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors", + "url": "https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors" + }, + { + "name": "Waifu Diffusion 1.5 unCLIP model", + "type": "unclip", + "base": "SD2.1", + "save_path": "default", + "description": "Mix model (SD2.1 unCLIP + Waifu Diffusion 1.5)", + "reference": "https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP", + "filename": "wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors", + "url": "https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors" + }, + { + "name": "sdxl_vae.safetensors", + "type": "VAE", + "base": "SDXL VAE", + "save_path": "default", + "description": "SDXL-VAE", + "reference": "https://huggingface.co/stabilityai/sdxl-vae", + "filename": "sdxl_vae.safetensors", + "url": "https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors" + }, + { + "name": "vae-ft-mse-840000-ema-pruned", + "type": "VAE", + "base": "SD1.5 VAE", + "save_path": "default", + "description": "vae-ft-mse-840000-ema-pruned", + "reference": "https://huggingface.co/stabilityai/sd-vae-ft-mse-original", + "filename": "vae-ft-mse-840000-ema-pruned.safetensors", + "url": "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors" + }, + { + "name": "orangemix.vae", + "type": "VAE", + "base": "SD1.5 VAE", + "save_path": "default", + "description": "orangemix vae model", + "reference": "https://huggingface.co/WarriorMama777/OrangeMixs", + "filename": "orangemix.vae.pt", + "url": "https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt" + }, + { + "name": "kl-f8-anime2", + "type": "VAE", + "base": "SD2.1 VAE", + "save_path": "default", + "description": "kl-f8-anime2 vae model", + "reference": "https://huggingface.co/hakurei/waifu-diffusion-v1-4", + "filename": "kl-f8-anime2.ckpt", + "url": "https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt" + }, + { + "name": "LCM LoRA SD1.5", + "type": "lora", + "base": "SD1.5", + "save_path": "loras/lcm/SD1.5", + "description": "Latent Consistency LoRA for SD1.5", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "LCM LoRA SSD-1B", + "type": "lora", + "base": "SSD-1B", + "save_path": "loras/lcm/SSD-1B", + "description": "Latent Consistency LoRA for SSD-1B", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "LCM LoRA SDXL", + "type": "lora", + "base": "SSD-1B", + "save_path": "loras/lcm/SDXL", + "description": "Latent Consistency LoRA for SDXL", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdxl", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "Segmind-Vega", + "type": "checkpoint", + "base": "segmind-vega", + "save_path": "checkpoints/segmind-vega", + "description": "The Segmind-Vega Model is a distilled version of the Stable Diffusion XL (SDXL), offering a remarkable 70% reduction in size and an impressive 100% speedup while retaining high-quality text-to-image generation capabilities.", + "reference": "https://huggingface.co/segmind/Segmind-Vega", + "filename": "segmind-vega.safetensors", + "url": "https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors" + }, + { + "name": "Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega", + "type": "lora", + "base": "segmind-vega", + "save_path": "loras/segmind-vega", + "description": "Segmind-VegaRT a distilled consistency adapter for Segmind-Vega that allows to reduce the number of inference steps to only between 2 - 8 steps.", + "reference": "https://huggingface.co/segmind/Segmind-VegaRT", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "Theovercomer8's Contrast Fix (SD2.1)", + "type": "lora", + "base": "SD2.1", + "save_path": "default", + "description": "LORA: Theovercomer8's Contrast Fix (SD2.1)", + "reference": "https://civitai.com/models/8765/theovercomer8s-contrast-fix-sd15sd21-768", + "filename": "theovercomer8sContrastFix_sd21768.safetensors", + "url": "https://civitai.com/api/download/models/10350" + }, + { + "name": "Theovercomer8's Contrast Fix (SD1.5)", + "type": "lora", + "base": "SD1.5", + "save_path": "default", + "description": "LORA: Theovercomer8's Contrast Fix (SD1.5)", + "reference": "https://civitai.com/models/8765/theovercomer8s-contrast-fix-sd15sd21-768", + "filename": "theovercomer8sContrastFix_sd15.safetensors", + "url": "https://civitai.com/api/download/models/10638" + }, + { + "name": "T2I-Adapter (depth)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for depth", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_depth_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth" + }, + { + "name": "T2I-Adapter (seg)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for seg", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_seg_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth" + }, + { + "name": "T2I-Adapter (sketch)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for sketch", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_sketch_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth" + }, + { + "name": "T2I-Adapter (keypose)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for keypose", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_keypose_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth" + }, + { + "name": "T2I-Adapter (openpose)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for openpose", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_openpose_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth" + }, + { + "name": "T2I-Adapter (color)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for color", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_color_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth" + }, + { + "name": "T2I-Adapter (canny)", + "type": "T2I-Adapter", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter for canny", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_canny_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth" + }, + { + "name": "T2I-Style model", + "type": "T2I-Style", + "base": "SD1.5", + "save_path": "default", + "description": "ControlNet T2I-Adapter style model. Need to download CLIPVision model.", + "reference": "https://huggingface.co/TencentARC/T2I-Adapter", + "filename": "t2iadapter_style_sd14v1.pth", + "url": "https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth" + }, + { + "name": "CiaraRowles/TemporalNet2", + "type": "controlnet", + "base": "SD1.5", + "save_path": "default", + "description": "TemporalNet was a ControlNet model designed to enhance the temporal consistency of generated outputs", + "reference": "https://huggingface.co/CiaraRowles/TemporalNet2", + "filename": "temporalnetversion2.ckpt", + "url": "https://huggingface.co/CiaraRowles/TemporalNet2/resolve/main/temporalnetversion2.ckpt" + }, + { + "name": "CiaraRowles/TemporalNet1XL (1.0)", + "type": "controlnet", + "base": "SD1.5", + "save_path": "controlnet/TemporalNet1XL", + "description": "This is TemporalNet1XL, it is a re-train of the controlnet TemporalNet1 with Stable Diffusion XL.", + "reference": "https://huggingface.co/CiaraRowles/controlnet-temporalnet-sdxl-1.0", + "filename": "diffusion_pytorch_model.safetensors", + "url": "https://huggingface.co/CiaraRowles/controlnet-temporalnet-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors" + }, + { + "name": "CLIPVision model (stabilityai/clip_vision_g)", + "type": "clip_vision", + "base": "SDXL", + "save_path": "clip_vision/SDXL", + "description": "[3.69GB] clip_g vision model", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "clip_vision_g.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/revision/clip_vision_g.safetensors" + }, + { + "name": "CLIPVision model (openai/clip-vit-large)", + "type": "clip_vision", + "base": "SD1.5", + "save_path": "clip_vision/SD1.5", + "description": "[1.7GB] CLIPVision model (needed for styles model)", + "reference": "https://huggingface.co/openai/clip-vit-large-patch14", + "filename": "pytorch_model.bin", + "url": "https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin" + }, + { + "name": "CLIPVision model (IP-Adapter)", + "type": "clip_vision", + "base": "SD1.5", + "save_path": "clip_vision/SD1.5", + "description": "[2.5GB] CLIPVision model (needed for IP-Adapter)", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "pytorch_model.bin", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin" + }, + { + "name": "CLIPVision model (IP-Adapter)", + "type": "clip_vision", + "base": "SDXL", + "save_path": "clip_vision/SDXL", + "description": "[3.69GB] CLIPVision model (needed for IP-Adapter)", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "pytorch_model.bin", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/pytorch_model.bin" + }, + { + "name": "stabilityai/control-lora-canny-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: canny rank128", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-canny-rank128.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-canny-rank128.safetensors" + }, + { + "name": "stabilityai/control-lora-depth-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: depth rank128", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-depth-rank128.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-depth-rank128.safetensors" + }, + { + "name": "stabilityai/control-lora-recolor-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: recolor rank128", + "reference": 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"base": "Ultralytics", + "save_path": "ultralytics/bbox", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "hand_yolov8n.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8n.pt" + }, + { + "name": "hand_yolov8s (bbox)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/bbox", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "hand_yolov8s.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/hand_yolov8s.pt" + }, + { + "name": "person_yolov8m (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "person_yolov8m-seg.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8m-seg.pt" + }, + { + "name": "person_yolov8n (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "person_yolov8n-seg.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8n-seg.pt" + }, + { + "name": "person_yolov8s (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "person_yolov8s-seg.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/person_yolov8s-seg.pt" + }, + { + "name": "deepfashion2_yolov8s (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://huggingface.co/Bingsu/adetailer/tree/main", + "filename": "deepfashion2_yolov8s-seg.pt", + "url": "https://huggingface.co/Bingsu/adetailer/resolve/main/deepfashion2_yolov8s-seg.pt" + }, + + { + "name": "face_yolov8m-seg_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "face_yolov8m-seg_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8m-seg_60.pt" + }, + { + "name": "face_yolov8n-seg2_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "face_yolov8n-seg2_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8n-seg2_60.pt" + }, + { + "name": "hair_yolov8n-seg_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "hair_yolov8n-seg_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/hair_yolov8n-seg_60.pt" + }, + { + "name": "skin_yolov8m-seg_400.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8m-seg_400.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8m-seg_400.pt" + }, + { + "name": "skin_yolov8n-seg_400.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8n-seg_400.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_400.pt" + }, + { + "name": "skin_yolov8n-seg_800.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8n-seg_800.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_800.pt" + }, + + { + "name": "animatediff/mmd_sd_v14.ckpt (comfyui-animatediff)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/comfyui-animatediff/models", + "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v14.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" + }, + { + "name": "animatediff/mm_sd_v15.ckpt (comfyui-animatediff)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/comfyui-animatediff/models", + "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v15.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" + }, + + { + "name": "animatediff/mmd_sd_v14.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v14.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" + }, + { + "name": "animatediff/mm_sd_v15.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v15.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" + }, + { + "name": "animatediff/mm_sd_v15_v2.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v15_v2.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt" + }, + { + "name": "animatediff/v3_sd15_sparsectrl_rgb.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "controlnet", + "base": "SD1.x", + "save_path": "controlnet/SD1.5/animatediff", + "description": "AnimateDiff SparseCtrl RGB ControlNet model", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_sparsectrl_rgb.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_rgb.ckpt" + }, + { + "name": "animatediff/v3_sd15_sparsectrl_scribble.ckpt", + "type": "controlnet", + "base": "SD1.x", + "save_path": "controlnet/SD1.5/animatediff", + "description": "AnimateDiff SparseCtrl Scribble ControlNet model", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_sparsectrl_scribble.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_scribble.ckpt" + }, + { + "name": "animatediff/v3_sd15_mm.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_mm.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_mm.ckpt" + }, + { + "name": "animatediff/v3_sd15_adapter.ckpt", + "type": "lora", + "base": "SD1.x", + "save_path": "loras/SD1.5/animatediff", + "description": "AnimateDiff Adapter LoRA (SD1.5)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_adapter.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_adapter.ckpt" + }, + + { + "name": "animatediff/mm_sdxl_v10_beta.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SDXL", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sdxl_v10_beta.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt" + }, + { + "name": "AD_Stabilized_Motion/mm-Stabilized_high.pth (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "filename": "mm-Stabilized_high.pth", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_high.pth" + }, + { + "name": "AD_Stabilized_Motion/mm-Stabilized_mid.pth (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "filename": "mm-Stabilized_mid.pth", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_mid.pth" + }, + { + "name": "CiaraRowles/temporaldiff-v1-animatediff.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/CiaraRowles/TemporalDiff", + "filename": "temporaldiff-v1-animatediff.ckpt", + "url": "https://huggingface.co/CiaraRowles/TemporalDiff/resolve/main/temporaldiff-v1-animatediff.ckpt" + }, + + { + "name": "animatediff/v2_lora_PanLeft.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_PanLeft.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanLeft.ckpt" + }, + { + "name": "animatediff/v2_lora_PanRight.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_PanRight.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanRight.ckpt" + }, + { + "name": "animatediff/v2_lora_RollingAnticlockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_RollingAnticlockwise.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingAnticlockwise.ckpt" + }, + { + "name": "animatediff/v2_lora_RollingClockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_RollingClockwise.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingClockwise.ckpt" + }, + { + "name": "animatediff/v2_lora_TiltDown.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_TiltDown.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltDown.ckpt" + }, + { + "name": "animatediff/v2_lora_TiltUp.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_TiltUp.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltUp.ckpt" + }, + { + "name": "animatediff/v2_lora_ZoomIn.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_ZoomIn.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomIn.ckpt" + }, + { + "name": "animatediff/v2_lora_ZoomOut.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_ZoomOut.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomOut.ckpt" + }, + { + "name": "LongAnimatediff/lt_long_mm_32_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_32_frames.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_32_frames.ckpt" + }, + { + "name": "LongAnimatediff/lt_long_mm_16_64_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_16_64_frames.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames.ckpt" + }, + { + "name": "LongAnimatediff/lt_long_mm_16_64_frames_v1.1.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_16_64_frames_v1.1.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames_v1.1.ckpt" + }, + { + "name": "ip-adapter_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors" + }, + { + "name": "ip-adapter_sd15_light.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15_light.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors" + }, + { + "name": "ip-adapter_sd15_vit-G.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15_vit-G.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors" + }, + { + "name": "ip-adapter-plus_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors" + }, + { + "name": "ip-adapter-plus-face_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus-face_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors" + }, + { + "name": "ip-adapter-full-face_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-full-face_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors" + }, + { + "name": "ip-adapter-faceid_sd15.bin", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "IP-Adapter-FaceID Model (SD1.5)", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "filename": "ip-adapter-faceid_sd15.bin", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin" + }, + { + "name": "ip-adapter-faceid_sd15_lora.safetensors", + "type": "lora", + "base": "SD1.5", + "save_path": "loras/ipadapter", + "description": "IP-Adapter-FaceID LoRA Model (SD1.5)", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "filename": "ip-adapter-faceid_sd15_lora.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors" + }, + { + "name": "ip-adapter_sdxl.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sdxl.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors" + }, + { + "name": "ip-adapter_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors" + }, + { + "name": "ip-adapter-plus_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors" + }, + { + "name": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors" + }, + + { + "name": "pfg-novel-n10.pt", + "type": "PFG", + "base": "SD1.5", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/furusu/PFG", + "filename": "pfg-novel-n10.pt", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-novel-n10.pt" + }, + { + "name": "pfg-wd14-n10.pt", + "type": "PFG", + "base": "SD1.5", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/furusu/PFG", + "filename": "pfg-wd14-n10.pt", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd14-n10.pt" + }, + { + "name": "pfg-wd15beta2-n10.pt", + "type": "PFG", + "base": "SD1.5", + "save_path": "custom_nodes/pfg-ComfyUI/models", + "description": "Pressing 'install' directly downloads the model from the pfg-ComfyUI/models extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/furusu/PFG", + "filename": "pfg-wd15beta2-n10.pt", + "url": "https://huggingface.co/furusu/PFG/resolve/main/pfg-wd15beta2-n10.pt" + }, + { + "name": "GFPGANv1.4.pth", + "type": "GFPGAN", + "base": "GFPGAN", + "save_path": "facerestore_models", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/TencentARC/GFPGAN/releases", + "filename": "GFPGANv1.4.pth", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" + }, + { + "name": "codeformer.pth", + "type": "CodeFormer", + "base": "CodeFormer", + "save_path": "facerestore_models", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/sczhou/CodeFormer/releases", + "filename": "codeformer.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth" + }, + { + "name": "detection_Resnet50_Final.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facerestore_models", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "detection_Resnet50_Final.pth", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" + }, + { + "name": "detection_mobilenet0.25_Final.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facerestore_models", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "detection_mobilenet0.25_Final.pth", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth" + }, + { + "name": "yolov5l-face.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facedetection", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "yolov5l-face.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth" + }, + { + "name": "yolov5n-face.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facedetection", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "yolov5n-face.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth" + } + ] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json new file mode 100644 index 0000000000000000000000000000000000000000..8fa4e96f8b23de1330129339d9743698719e984d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/custom-node-list.json @@ -0,0 +1,284 @@ +{ + "custom_nodes": [ + { + "author": "#NOTICE_1.13", + "title": "NOTICE: This channel is not the default channel.", + "reference": "https://github.com/ltdrdata/ComfyUI-Manager", + "files": [], + "install_type": "git-clone", + "description": "If you see this message, your ComfyUI-Manager is outdated.\nDev channel provides only the list of the developing nodes. If you want to find the complete node list, please go to the Default channel." + }, + + + { + "author": "ZHO-ZHO-ZHO", + "title": "ComfyUI ArtGallery（WIP)", + "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-ArtGallery", + "files": [ + "https://github.com/ZHO-ZHO-ZHO/ComfyUI-ArtGallery" + ], + "install_type": "git-clone", + "description": "Visualize the selection of prompts:Choosing prompts has never really been suitable for humans, especially when there are a lot of prompts to choose from. Selecting corresponding visual references is a more suitable way." + }, + { + "author": "foglerek", + "title": "comfyui-cem-tools", + "reference": "https://github.com/foglerek/comfyui-cem-tools", + "files": [ + "https://github.com/foglerek/comfyui-cem-tools" + ], + "install_type": "git-clone", + "description": "Nodes:ProcessImageBatch" + }, + { + "author": "komojini", + "title": "ComfyUI_Prompt_Template_CustomNodes", + "reference": "https://github.com/komojini/ComfyUI_Prompt_Template_CustomNodes", + "files": [ + "https://github.com/komojini/ComfyUI_Prompt_Template_CustomNodes/raw/main/prompt_with_template.py" + ], + "install_type": "copy", + "description": "Nodes:Prompt with Template" + }, + { + "author": "talesofai", + "title": "comfyui-supersave [WIP]", + "reference": "https://github.com/talesofai/comfyui-supersave", + "files": [ + "https://github.com/talesofai/comfyui-supersave" + ], + "install_type": "git-clone", + "description": "WIP" + }, + { + "author": "Sai-ComfyUI", + "title": "ComfyUI-MS-Nodes [WIP]", + "reference": "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes", + "files": [ + "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes" + ], + "install_type": "git-clone", + "description": "WIP" + }, + { + "author": "eigenpunk", + "title": "ComfyUI-audio", + "reference": "https://github.com/eigenpunk/ComfyUI-audio", + "files": [ + "https://github.com/eigenpunk/ComfyUI-audio" + ], + "install_type": "git-clone", + "description": "generative audio tools for ComfyUI. highly experimental-expect things to break." + }, + { + "author": "Jaxkr", + "title": "comfyui-terminal-command [UNSAFE]", + "reference": "https://github.com/Jaxkr/comfyui-terminal-command", + "files": [ + "https://github.com/Jaxkr/comfyui-terminal-command" + ], + "install_type": "git-clone", + "description": "Nodes: Run Terminal Command. [w/This node is an unsafe node that includes the capability to execute terminal commands.]" + }, + { + "author": "BlueDangerX", + "title": "ComfyUI-BDXNodes [WIP]", + "reference": "https://github.com/BlueDangerX/ComfyUI-BDXNodes", + "files": [ + "https://github.com/BlueDangerX/ComfyUI-BDXNodes" + ], + "install_type": "git-clone", + "description": "Nodes: Node Jumper. Various quality of life testing nodes" + }, + { + "author": "ilovejohnwhite", + "title": "TatToolkit", + "reference": "https://github.com/ilovejohnwhite/UncleBillyGoncho", + "files": [ + "https://github.com/ilovejohnwhite/UncleBillyGoncho" + ], + "install_type": "git-clone", + "description": "Nodes:UWU TTK Preprocessor, Pixel Perfect Resolution, Generation Resolution From Image, Generation Resolution From Latent, Enchance And Resize Hint Images, ..." + }, + { + "author": "IvanZhd", + "title": "comfyui-codeformer [WIP]", + "reference": "https://github.com/IvanZhd/comfyui-codeformer", + "files": [ + "https://github.com/IvanZhd/comfyui-codeformer" + ], + "install_type": "git-clone", + "description": "Nodes:Image Inverter" + }, + { + "author": "hinablue", + "title": "ComfyUI 3D Pose Editor", + "reference": "https://github.com/hinablue/ComfyUI_3dPoseEditor", + "files": [ + "https://github.com/hinablue/ComfyUI_3dPoseEditor" + ], + "install_type": "git-clone", + "description": "Nodes:3D Pose Editor" + }, + { + "author": "alt-key-project", + "title": "Dream Project Video Batches [WIP]", + "reference": "https://github.com/alt-key-project/comfyui-dream-video-batches", + "files": [ + "https://github.com/alt-key-project/comfyui-dream-video-batches" + ], + "install_type": "git-clone", + "description": "NOTE: This is currently work in progress. Expect nodes to break (or be broken) until 1.0 release." + }, + { + "author": "oyvindg", + "title": "ComfyUI-TrollSuite", + "reference": "https://github.com/oyvindg/ComfyUI-TrollSuite", + "files": [ + "https://github.com/oyvindg/ComfyUI-TrollSuite" + ], + "install_type": "git-clone", + "description": "Nodes: BinaryImageMask, ImagePadding, LoadLastCreatedImage, RandomMask, TransparentImage." + }, + { + "author": "romeobuilderotti", + "title": "ComfyUI-EZ-Pipes", + "reference": "https://github.com/romeobuilderotti/ComfyUI-EZ-Pipes", + "files": [ + "https://github.com/romeobuilderotti/ComfyUI-EZ-Pipes" + ], + "install_type": "git-clone", + "description": "ComfyUI-EZ-Pipes is a set of custom pipe nodes for ComfyUI. It provides a set of Input/Edit/Output nodes for each pipe type." + }, + { + "author": "wormley", + "title": "comfyui-wormley-nodes", + "reference": "https://github.com/wormley/comfyui-wormley-nodes", + "files": [ + "https://github.com/wormley/comfyui-wormley-nodes" + ], + "install_type": "git-clone", + "description": "Nodes: CheckpointVAELoaderSimpleText, CheckpointVAESelectorText, LoRA_Tag_To_Stack" + }, + { + "author": "dnl13", + "title": "ComfyUI-dnl13-seg", + "reference": "https://github.com/dnl13/ComfyUI-dnl13-seg", + "files": [ + "https://github.com/dnl13/ComfyUI-dnl13-seg" + ], + "install_type": "git-clone", + "description": "After discovering @storyicon implementation here of Segment Anything, I realized its potential as a powerful tool for ComfyUI if implemented correctly. I delved into the SAM and Dino models. The following is my own adaptation of sam_hq for ComfyUI." + }, + { + "author": "phineas-pta", + "title": "comfy-trt-test [WIP]", + "reference": "https://github.com/phineas-pta/comfy-trt-test", + "files": [ + "https://github.com/phineas-pta/comfy-trt-test" + ], + "install_type": "git-clone", + "description": "Test project for ComfyUI TensorRT Support.\nNOT WORKING YET.\nnot automatic yet, do not use ComfyUI-Manager to install !!!.\nnot beginner-friendly yet, still intended to technical users\nNOTE: The reason for registration in the Manager is for guidance, and for detailed installation instructions, please visit the repository." + }, + { + "author": "Brandelan", + "title": "ComfyUI_bd_customNodes", + "reference": "https://github.com/Brandelan/ComfyUI_bd_customNodes", + "files": [ + "https://github.com/Brandelan/ComfyUI_bd_customNodes" + ], + "install_type": "git-clone", + "description": "Nodes: BD Random Range, BD Settings, BD Sequencer." + }, + { + "author": "Jordach", + "title": "comfy-consistency-vae", + "reference": "https://github.com/Jordach/comfy-consistency-vae", + "files": [ + "https://github.com/Jordach/comfy-consistency-vae" + ], + "install_type": "git-clone", + "description": "Nodes: Comfy_ConsistencyVAE" + }, + { + "author": "gameltb", + "title": "ComfyUI_stable_fast", + "reference": "https://github.com/gameltb/ComfyUI_stable_fast", + "files": [ + "https://github.com/gameltb/ComfyUI_stable_fast" + ], + "install_type": "git-clone", + "description": "Nodes:ApplyStableFastUnet. Experimental usage of stable-fast." + }, + { + "author": "jn-jairo", + "title": "jn_node_suite_comfyui [WIP]", + "reference": "https://github.com/jn-jairo/jn_node_suite_comfyui", + "files": [ + "https://github.com/jn-jairo/jn_node_suite_comfyui" + ], + "install_type": "git-clone", + "description": "Image manipulation nodes, Temperature control nodes, Tiling nodes, Primitive and operation nodes, ..." + }, + { + "author": "PluMaZero", + "title": "ComfyUI-SpaceFlower", + "reference": "https://github.com/PluMaZero/ComfyUI-SpaceFlower", + "files": [ + "https://github.com/PluMaZero/ComfyUI-SpaceFlower" + ], + "install_type": "git-clone", + "description": "Nodes: SpaceFlower_Prompt, SpaceFlower_HangulPrompt, ..." + }, + { + "author": "laksjdjf", + "title": "ssd-1b-comfyui", + "reference": "https://github.com/laksjdjf/ssd-1b-comfyui", + "files": [ + "https://github.com/laksjdjf/ssd-1b-comfyui" + ], + "install_type": "git-clone", + "description": "Experimental node for SSD-1B. This node is not need for latest comfyui." + }, + { + "author": "flowtyone", + "title": "comfyui-flowty-lcm", + "reference": "https://github.com/flowtyone/comfyui-flowty-lcm", + "files": [ + "https://github.com/flowtyone/comfyui-flowty-lcm" + ], + "install_type": "git-clone", + "description": "This is a comfyui early testing node for LCM, adapted from [a/https://github.com/0xbitches/sd-webui-lcm](https://github.com/0xbitches/sd-webui-lcm). It uses the diffusers backend unfortunately and not comfy's model loading mechanism. But the intention here is just to be able to execute lcm inside comfy.\nNOTE: 0xbitches's 'Latent Consistency Model for ComfyUI' is original implementation." + }, + { + "author": "doucx", + "title": "ComfyUI_WcpD_Utility_Kit", + "reference": "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit", + "files": [ + "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit" + ], + "install_type": "git-clone", + "description": "Nodes: MergeStrings, ExecStrAsCode, RandnLatentImage. [w/NOTE: This extension includes the ability to execute code as a string in nodes. Be cautious during installation, as it can pose a security risk.]" + }, + { + "author": "WSJUSA", + "title": "pre-comfyui-stablsr", + "reference": "https://github.com/WSJUSA/Comfyui-StableSR", + "files": [ + "https://github.com/WSJUSA/Comfyui-StableSR" + ], + "install_type": "git-clone", + "description": "This is a development respository for debugging migration of StableSR to Comfyui" + }, + { + "author": "Dr.Lt.Data", + "title": "ComfyUI-Workflow-Component [WIP]", + "reference": "https://github.com/ltdrdata/ComfyUI-Workflow-Component", + "files": [ + "https://github.com/ltdrdata/ComfyUI-Workflow-Component" + ], + "install_type": "git-clone", + "description": "This extension provides the capability to use ComfyUI Workflow as a component and the ability to use the Image Refiner functionality based on components. NOTE: This is an experimental extension feature with no consideration for backward compatibility and can be highly unstable." + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json new file mode 100644 index 0000000000000000000000000000000000000000..7b664576ba30a71f886b82a96709a9d7b9a2a974 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/extension-node-map.json @@ -0,0 +1,546 @@ +{ + "https://github.com/BlueDangerX/ComfyUI-BDXNodes": [ + [ + "BDXTestInt", + "ColorMatch", + "ColorToMask", + "ConditioningMultiCombine", + "ConditioningSetMaskAndCombine", + "ConditioningSetMaskAndCombine3", + "ConditioningSetMaskAndCombine4", + "ConditioningSetMaskAndCombine5", + "CreateAudioMask", + "CreateFadeMask", + "CreateFluidMask", + "CreateGradientMask", + "CreateTextMask", + "CrossFadeImages", + "EmptyLatentImagePresets", + "GrowMaskWithBlur", + "SomethingToString", + "VRAM_Debug" + ], + { + "author": "BlueDangerX", + "title": "BDXNodes", + "title_aux": "ComfyUI-BDXNodes [WIP]" + } + ], + "https://github.com/Brandelan/ComfyUI_bd_customNodes": [ + [ + "BD Random Range", + "BD Random Settings", + "BD Sequencer", + "BD Settings" + ], + { + "title_aux": "ComfyUI_bd_customNodes" + } + ], + "https://github.com/IvanZhd/comfyui-codeformer": [ + [ + "RedBeanie_CustomImageInverter" + ], + { + "title_aux": "comfyui-codeformer [WIP]" + } + ], + "https://github.com/Jaxkr/comfyui-terminal-command": [ + [ + "Terminal" + ], + { + "title_aux": "comfyui-terminal-command [UNSAFE]" + } + ], + "https://github.com/Jordach/comfy-consistency-vae": [ + [ + "Comfy_ConsistencyVAE" + ], + { + "title_aux": "comfy-consistency-vae" + } + ], + "https://github.com/PluMaZero/ComfyUI-SpaceFlower": [ + [ + "SpaceFlower_HangulPrompt", + "SpaceFlower_Prompt" + ], + { + "title_aux": "ComfyUI-SpaceFlower" + } + ], + "https://github.com/Sai-ComfyUI/ComfyUI-MS-Nodes": [ + [ + "FloatMath", + "MS_Boolean", + "MS_Float", + "MS_GenerateSeed", + "MS_NP_Vector3", + "PowerFractalCrossHatchNode", + "PowerFractalNoiseNode", + "VectorMath" + ], + { + "title_aux": "ComfyUI-MS-Nodes [WIP]" + } + ], + "https://github.com/WSJUSA/Comfyui-StableSR": [ + [ + "ColorFix", + "StableSRUpscalerPipe" + ], + { + "author": "WSJUSA", + "description": "This module enables StableSR in Comgfyui. Ported work of sd-webui-stablesr. Original work for Auotmaatic1111 version of this module and StableSR credit to LIightChaser and Jianyi Wang.", + "nickname": "StableSR", + "title": "StableSR", + "title_aux": "pre-comfyui-stablsr" + } + ], + "https://github.com/ZHO-ZHO-ZHO/ComfyUI-ArtGallery": [ + [ + "ArtGallery_Zho", + "ArtistsImage_Zho", + "CamerasImage_Zho", + "FilmsImage_Zho", + "MovementsImage_Zho", + "StylesImage_Zho" + ], + { + "title_aux": "ComfyUI ArtGallery\uff08WIP)" + } + ], + "https://github.com/alt-key-project/comfyui-dream-video-batches": [ + [ + "Blended Transition [DVB]", + "Calculation [DVB]", + "Create Frame Set [DVB]", + "Divide [DVB]", + "Fade From Black [DVB]", + "Fade To Black [DVB]", + "Float Input [DVB]", + "For Each Done [DVB]", + "For Each Filename [DVB]", + "Frame Set Append [DVB]", + "Frame Set Frame Dimensions Scaled [DVB]", + "Frame Set Index Offset [DVB]", + "Frame Set Merger [DVB]", + "Frame Set Reindex [DVB]", + "Frame Set Repeat [DVB]", + "Frame Set Reverse [DVB]", + "Frame Set Split Beginning [DVB]", + "Frame Set Split End [DVB]", + "Frame Set Splitter [DVB]", + "Generate Inbetween Frames [DVB]", + "Int Input [DVB]", + "Linear Camera Pan [DVB]", + "Linear Camera Roll [DVB]", + "Linear Camera Zoom [DVB]", + "Load Image From Path [DVB]", + "Multiply [DVB]", + "Sine Camera Pan [DVB]", + "Sine Camera Roll [DVB]", + "Sine Camera Zoom [DVB]", + "String Input [DVB]", + "Text Input [DVB]", + "Trace Memory Allocation [DVB]", + "Unwrap Frame Set [DVB]" + ], + { + "title_aux": "Dream Project Video Batches [WIP]" + } + ], + "https://github.com/comfyanonymous/ComfyUI": [ + [ + "BasicScheduler", + "CLIPLoader", + "CLIPMergeSimple", + "CLIPSave", + "CLIPSetLastLayer", + "CLIPTextEncode", + "CLIPTextEncodeSDXL", + "CLIPTextEncodeSDXLRefiner", + "CLIPVisionEncode", + "CLIPVisionLoader", + "Canny", + "CheckpointLoader", + "CheckpointLoaderSimple", + "CheckpointSave", + "ConditioningAverage", + "ConditioningCombine", + "ConditioningConcat", + "ConditioningSetArea", + "ConditioningSetAreaPercentage", + "ConditioningSetMask", + "ConditioningSetTimestepRange", + "ConditioningZeroOut", + "ControlNetApply", + "ControlNetApplyAdvanced", + "ControlNetLoader", + "CropMask", + "DiffControlNetLoader", + "DiffusersLoader", + "DualCLIPLoader", + "EmptyImage", + "EmptyLatentImage", + "ExponentialScheduler", + "FeatherMask", + "FlipSigmas", + "FreeU", + "FreeU_V2", + "GLIGENLoader", + "GLIGENTextBoxApply", + "GrowMask", + "HyperTile", + "HypernetworkLoader", + "ImageBatch", + "ImageBlend", + "ImageBlur", + "ImageColorToMask", + "ImageCompositeMasked", + "ImageCrop", + "ImageInvert", + "ImageOnlyCheckpointLoader", + "ImagePadForOutpaint", + "ImageQuantize", + "ImageScale", + "ImageScaleBy", + "ImageScaleToTotalPixels", + "ImageSharpen", + "ImageToMask", + "ImageUpscaleWithModel", + "InvertMask", + "JoinImageWithAlpha", + "KSampler", + "KSamplerAdvanced", + "KSamplerSelect", + "KarrasScheduler", + "LatentAdd", + "LatentBatch", + "LatentBlend", + "LatentComposite", + "LatentCompositeMasked", + "LatentCrop", + "LatentFlip", + "LatentFromBatch", + "LatentInterpolate", + "LatentMultiply", + "LatentRotate", + "LatentSubtract", + "LatentUpscale", + "LatentUpscaleBy", + "LoadImage", + "LoadImageMask", + "LoadLatent", + "LoraLoader", + "LoraLoaderModelOnly", + "MaskComposite", + "MaskToImage", + "ModelMergeAdd", + "ModelMergeBlocks", + "ModelMergeSimple", + "ModelMergeSubtract", + "ModelSamplingContinuousEDM", + "ModelSamplingDiscrete", + "PatchModelAddDownscale", + "PerpNeg", + "PolyexponentialScheduler", + "PorterDuffImageComposite", + "PreviewImage", + "RebatchImages", + "RebatchLatents", + "RepeatImageBatch", + "RepeatLatentBatch", + "RescaleCFG", + "SDTurboScheduler", + "SVD_img2vid_Conditioning", + "SamplerCustom", + "SamplerDPMPP_2M_SDE", + "SamplerDPMPP_SDE", + "SaveAnimatedPNG", + "SaveAnimatedWEBP", + "SaveImage", + "SaveLatent", + "SelfAttentionGuidance", + "SetLatentNoiseMask", + "SolidMask", + "SplitImageWithAlpha", + "SplitSigmas", + "StableZero123_Conditioning", + "StyleModelApply", + "StyleModelLoader", + "TomePatchModel", + "UNETLoader", + "UpscaleModelLoader", + "VAEDecode", + "VAEDecodeTiled", + "VAEEncode", + "VAEEncodeForInpaint", + "VAEEncodeTiled", + "VAELoader", + "VAESave", + "VPScheduler", + "VideoLinearCFGGuidance", + "unCLIPCheckpointLoader", + "unCLIPConditioning" + ], + { + "title_aux": "ComfyUI" + } + ], + "https://github.com/dnl13/ComfyUI-dnl13-seg": [ + [ + "Automatic Segmentation (dnl13)", + "BatchSelector (dnl13)", + "Combine Images By Mask (dnl13)", + "Dinov1 Model Loader (dnl13)", + "Mask with prompt (dnl13)", + "RGB (dnl13)", + "SAM Model Loader (dnl13)" + ], + { + "title_aux": "ComfyUI-dnl13-seg" + } + ], + "https://github.com/doucx/ComfyUI_WcpD_Utility_Kit": [ + [ + "ExecStrAsCode", + "KSamplerAdvancedWithDenoise", + "MergeStrings", + "RandnLatentImage", + "StrTuple" + ], + { + "title_aux": "ComfyUI_WcpD_Utility_Kit" + } + ], + "https://github.com/eigenpunk/ComfyUI-audio": [ + [ + "ApplyVoiceFixer", + "BatchAudio", + "ClipAudio", + "CombineImageWithAudio", + "ConcatAudio", + "ConvertAudio", + "FlattenAudioBatch", + "LoadAudio", + "MusicgenGenerate", + "MusicgenHFGenerate", + "MusicgenHFLoader", + "MusicgenLoader", + "PreviewAudio", + "SaveAudio", + "SpectrogramImage", + "TortoiseTTSGenerate", + "TortoiseTTSLoader", + "VALLEXGenerator", + "VALLEXLoader", + "VALLEXVoicePromptFromAudio", + "VALLEXVoicePromptLoader" + ], + { + "title_aux": "ComfyUI-audio" + } + ], + "https://github.com/flowtyone/comfyui-flowty-lcm": [ + [ + "LCMSampler" + ], + { + "title_aux": "comfyui-flowty-lcm" + } + ], + "https://github.com/foglerek/comfyui-cem-tools": [ + [ + "ProcessImageBatch" + ], + { + "title_aux": "comfyui-cem-tools" + } + ], + "https://github.com/gameltb/ComfyUI_stable_fast": [ + [ + "ApplyStableFastUnet", + "ApplyTensorRTControlNet", + "ApplyTensorRTUnet", + "ApplyTensorRTVaeDecoder" + ], + { + "title_aux": "ComfyUI_stable_fast" + } + ], + "https://github.com/hinablue/ComfyUI_3dPoseEditor": [ + [ + "Hina.PoseEditor3D" + ], + { + "title_aux": "ComfyUI 3D Pose Editor" + } + ], + "https://github.com/ilovejohnwhite/UncleBillyGoncho": [ + [ + "CannyEdgePreprocessor", + "HintImageEnchance", + "ImageGenResolutionFromImage", + "ImageGenResolutionFromLatent", + "LineArtPreprocessor", + "PiDiNetPreprocessor", + "PixelPerfectResolution", + "SuckerPunch", + "UWU_Preprocessor", + "VooDooNode", + "VooDooNode2" + ], + { + "title_aux": "TatToolkit" + } + ], + "https://github.com/jn-jairo/jn_node_suite_comfyui": [ + [ + "JN_AreaInfo", + "JN_AreaNormalize", + "JN_AreaWidthHeight", + "JN_AreaXY", + "JN_Blip", + "JN_BlipLoader", + "JN_BooleanOperation", + "JN_Condition", + "JN_CoolDown", + "JN_CoolDownOutput", + "JN_CropFace", + "JN_DatetimeFormat", + "JN_DatetimeInfo", + "JN_DatetimeNow", + "JN_Dump", + "JN_DumpOutput", + "JN_FaceRestoreModelLoader", + "JN_FaceRestoreWithModel", + "JN_FirstActive", + "JN_ImageAddMask", + "JN_ImageBatch", + "JN_ImageCenterArea", + "JN_ImageCrop", + "JN_ImageGrid", + "JN_ImageInfo", + "JN_ImageSharpness", + "JN_ImageSquare", + "JN_ImageUncrop", + "JN_KSampler", + "JN_KSamplerAdvancedParams", + "JN_KSamplerFaceRestoreParams", + "JN_KSamplerResizeInputParams", + "JN_KSamplerResizeOutputParams", + "JN_KSamplerSeamlessParams", + "JN_KSamplerTileParams", + "JN_LoadImageDirectory", + "JN_LogicOperation", + "JN_MaskInfo", + "JN_MathOperation", + "JN_MathOperationArray", + "JN_PrimitiveArrayInfo", + "JN_PrimitiveBatchToArray", + "JN_PrimitiveBoolean", + "JN_PrimitiveFloat", + "JN_PrimitiveInt", + "JN_PrimitivePrompt", + "JN_PrimitiveString", + "JN_PrimitiveStringMultiline", + "JN_PrimitiveStringToArray", + "JN_PrimitiveToArray", + "JN_PrimitiveToBoolean", + "JN_PrimitiveToFloat", + "JN_PrimitiveToInt", + "JN_PrimitiveToString", + "JN_RemoveBackground", + "JN_Seamless", + "JN_SeamlessBorder", + "JN_SeamlessBorderCrop", + "JN_SelectItem", + "JN_Sleep", + "JN_SleepOutput", + "JN_SliceOperation", + "JN_StopIf", + "JN_StopIfOutput", + "JN_TextConcatenation", + "JN_TextReplace", + "JN_TimedeltaFormat", + "JN_TimedeltaInfo", + "JN_VAEPatch" + ], + { + "title_aux": "jn_node_suite_comfyui [WIP]" + } + ], + "https://github.com/komojini/ComfyUI_Prompt_Template_CustomNodes/raw/main/prompt_with_template.py": [ + [ + "ObjectPromptWithTemplate", + "PromptWithTemplate" + ], + { + "title_aux": "ComfyUI_Prompt_Template_CustomNodes" + } + ], + "https://github.com/laksjdjf/ssd-1b-comfyui": [ + [ + "SSD-1B-Loader" + ], + { + "title_aux": "ssd-1b-comfyui" + } + ], + "https://github.com/ltdrdata/ComfyUI-Workflow-Component": [ + [ + "ComboToString", + "ExecutionBlocker", + "ExecutionControlString", + "ExecutionOneOf", + "ExecutionSwitch", + "InputUnzip", + "InputZip", + "LoopControl", + "LoopCounterCondition", + "OptionalTest", + "TensorToCPU" + ], + { + "title_aux": "ComfyUI-Workflow-Component [WIP]" + } + ], + "https://github.com/oyvindg/ComfyUI-TrollSuite": [ + [ + "BinaryImageMask", + "ImagePadding", + "LoadLastImage", + "RandomMask", + "TransparentImage" + ], + { + "title_aux": "ComfyUI-TrollSuite" + } + ], + "https://github.com/phineas-pta/comfy-trt-test": [ + [ + "TRT_Lora_Loader", + "TRT_Torch_Compile", + "TRT_Unet_Loader" + ], + { + "author": "PTA", + "description": "attempt to use TensorRT with ComfyUI, not yet compatible with ComfyUI-Manager, see README for instructions", + "nickname": "comfy trt test", + "title": "TensorRT with ComfyUI (work-in-progress)", + "title_aux": "comfy-trt-test [WIP]" + } + ], + "https://github.com/wormley/comfyui-wormley-nodes": [ + [ + "CheckpointVAELoaderSimpleText", + "CheckpointVAESelectorText", + "LoRA_Tag_To_Stack" + ], + { + "title_aux": "comfyui-wormley-nodes" + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/model-list.json @@ -0,0 +1,3 @@ +{ + "models": [] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh new file mode 100644 index 0000000000000000000000000000000000000000..f9589f3c57268b258caa19e8569cc6f1d1882eae --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/dev/scan.sh @@ -0,0 +1,3 @@ +#!/bin/bash +rm ~/.tmp/dev/*.py > /dev/null 2>&1 +python ../../scanner.py ~/.tmp/dev diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json new file mode 100644 index 0000000000000000000000000000000000000000..1f0cfc688aa5321f50ca10767640f92254770965 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json @@ -0,0 +1,14 @@ +{ + "custom_nodes": [ + { + "author": "BlenderNeko", + "title": "ComfyUI Noise [Dr.Lt.Data hotfix version]", + "reference": "https://github.com/BlenderNeko/ComfyUI_Noise", + "files": [ + "https://github.com/BlenderNeko/ComfyUI_Noise" + ], + "install_type": "git-clone", + "description": "There is an issue when using a mask with the Unsampler node in the current ComfyUI Noise extension, causing errors. This fork provides a HOTFIX for this issue. Once [PR-13]((https://github.com/BlenderNeko/ComfyUI_Noise/pull/13) is merged in the future, this fork will become obsolete." + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json @@ -0,0 +1,3 @@ +{ + "models": [] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh new file mode 100644 index 0000000000000000000000000000000000000000..5d8d8c48b6e3f48dc1491738c1226f574909c05d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/scan.sh @@ -0,0 +1,4 @@ +#!/bin/bash +source ../../../../venv/bin/activate +rm .tmp/*.py > /dev/null +python ../../scanner.py diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/alter-list.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json new file mode 100644 index 0000000000000000000000000000000000000000..080644f6c22f39e72b3e3450fdc67c54303e5ab0 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/custom-node-list.json @@ -0,0 +1,184 @@ +{ + "custom_nodes": [ + { + "author": "#NOTICE_1.13", + "title": "NOTICE: This channel is not the default channel.", + "reference": "https://github.com/ltdrdata/ComfyUI-Manager", + "files": [], + "install_type": "git-clone", + "description": "If you see this message, your ComfyUI-Manager is outdated.\nLegacy channel provides only the list of the deprecated nodes. If you want to find the complete node list, please go to the Default channel." + }, + + { + "author": "bvhari", + "title": "ComfyUI_PerpNeg [WIP]", + "reference": "https://github.com/bvhari/ComfyUI_PerpNeg", + "files": [ + "https://github.com/bvhari/ComfyUI_PerpNeg" + ], + "install_type": "git-clone", + "description": "Nodes: KSampler (Advanced + Perp-Neg). Implementation of [a/Perp-Neg](https://perp-neg.github.io/)\nIncludes Tonemap and CFG Rescale optionsComfyUI custom node to convert latent to RGB.[w/WARNING: Experimental code, might have incompatibilities and edge cases.]\nNOTE: In the latest version of ComfyUI, this extension is included as built-in." + }, + { + "author": "laksjdjf", + "title": "IPAdapter-ComfyUI", + "reference": "https://github.com/laksjdjf/IPAdapter-ComfyUI", + "files": [ + "https://github.com/laksjdjf/IPAdapter-ComfyUI" + ], + "install_type": "git-clone", + "description": "This custom nodes provides loader of the IP-Adapter model.[w/NOTE: To use this extension node, you need to download the [a/ip-adapter_sd15.bin](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin) file and place it in the %%**custom_nodes/IPAdapter-ComfyUI/models**%% directory. Additionally, you need to download the 'Clip vision model' from the 'Install models' menu as well.]
NOTE: Use ComfyUI_IPAdapter_plus instead of this." + }, + { + "author": "RockOfFire", + "title": "CR Animation Nodes", + "reference": "https://github.com/RockOfFire/CR_Animation_Nodes", + "files": [ + "https://github.com/RockOfFire/CR_Animation_Nodes" + ], + "install_type": "git-clone", + "description": "A comprehensive suite of nodes to enhance your animations. These nodes include some features similar to Deforum, and also some new ideas.
NOTE: This node is merged into Comfyroll Custom Nodes." + }, + { + "author": "tkoenig89", + "title": "Load Image with metadata", + "reference": "https://github.com/tkoenig89/ComfyUI_Load_Image_With_Metadata", + "files": [ + "https://github.com/tkoenig89/ComfyUI_Load_Image_With_Metadata" + ], + "install_type": "git-clone", + "description": "A custom node for comfy ui to read generation data from images (prompt, seed, size...). This could be used when upscaling generated images to use the original prompt and seed." + }, + { + "author": "LucianoCirino", + "title": "Efficiency Nodes for ComfyUI [LEGACY]", + "reference": "https://github.com/LucianoCirino/efficiency-nodes-comfyui", + "files": [ + "https://github.com/LucianoCirino/efficiency-nodes-comfyui" + ], + "install_type": "git-clone", + "description": "A collection of ComfyUI custom nodes to help streamline workflows and reduce total node count.
NOTE: This repository is the original repository but is no longer maintained. Please use the forked version by jags." + }, + { + "author": "GeLi1989", + "title": "roop nodes for ComfyUI", + "reference": "https://github.com/GeLi1989/GK-beifen-ComfyUI_roop", + "files": [ + "https://github.com/GeLi1989/GK-beifen-ComfyUI_roop" + ], + "install_type": "git-clone", + "description": "ComfyUI nodes for the roop A1111 webui script. NOTE: Need to download model to use this node. NOTE: This is removed." + }, + { + "author": "ProDALOR", + "title": "comfyui_u2net", + "reference": "https://github.com/ProDALOR/comfyui_u2net", + "files": [ + "https://github.com/ProDALOR/comfyui_u2net" + ], + "install_type": "git-clone", + "description": "Nodes: Load U2Net model, U2Net segmentation, To mask, Segmentation to mask, U2NetBaseNormalization, U2NetMaxNormalization. NOTE: This is removed." + }, + { + "author": "FizzleDorf", + "title": "AIT", + "reference": "https://github.com/FizzleDorf/AIT", + "files": [ + "https://github.com/FizzleDorf/AIT" + ], + "install_type": "git-clone", + "description": "Nodes: Load AITemplate, Load AITemplate (ControlNet), VAE Decode (AITemplate), VAE Encode (AITemplate), VAE Encode (AITemplate, Inpaint). Experimental usage of AITemplate. NOTE: This is deprecated extension. Use ComfyUI-AIT instead of this." + }, + { + "author": "chenbaiyujason", + "title": "sc-node-comfyui", + "reference": "https://github.com/chenbaiyujason/sc-node-comfyui", + "files": [ + "https://github.com/chenbaiyujason/sc-node-comfyui" + ], + "install_type": "git-clone", + "description": "Nodes for GPT interaction and text manipulation" + }, + { + "author": "asd417", + "title": "CheckpointTomeLoader", + "reference": "https://github.com/asd417/tomeSD_for_Comfy", + "files": [ + "https://github.com/ltdrdata/ComfyUI-tomeSD-installer" + ], + "install_type": "git-clone", + "description": "tomeSD(https://github.com/dbolya/tomesd) applied to ComfyUI stable diffusion UI using custom node. Note:In vanilla ComfyUI, the TomePatchModel node is provided as a built-in feature." + }, + { + "author": "gamert", + "title": "ComfyUI_tagger", + "reference": "https://github.com/gamert/ComfyUI_tagger", + "pip": ["gradio"], + "files": [ + "https://github.com/gamert/ComfyUI_tagger" + ], + "install_type": "git-clone", + "description": "Nodes: CLIPTextEncodeTaggerDD, ImageTaggerDD.
WARNING: Installing the current version is causing an issue where ComfyUI fails to start.
" + }, + { + "author": "Fannovel16", + "title": "ControlNet Preprocessors", + "reference": "https://github.com/Fannovel16/comfy_controlnet_preprocessors", + "files": [ + "https://github.com/Fannovel16/comfy_controlnet_preprocessors" + ], + "install_type": "git-clone", + "description": "ControlNet Preprocessors. (To use this extension, you need to download the required model file from Install Models)NOTE: Please uninstall this custom node and instead install 'ComfyUI's ControlNet Auxiliary Preprocessors' from the default channel.
To use nodes belonging to controlnet v1 such as Canny_Edge_Preprocessor, MIDAS_Depth_Map_Preprocessor, Uniformer_SemSegPreprocessor, etc., you need to copy the config.yaml.example file to config.yaml and change skip_v1: True to skip_v1: False.
It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.
" + }, + { + "author": "comfyanonymous", + "title": "ComfyUI_experiments/advanced_model_merging", + "reference": "https://github.com/comfyanonymous/ComfyUI_experiments", + "files": [ + "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/advanced_model_merging.py" + ], + "install_type": "copy", + "description": "This provides a detailed model merge feature based on block weight. ModelMergeBlock, in vanilla ComfyUI, allows for adjusting the ratios of input/middle/output layers, but this node provides ratio adjustments for all blocks within each layer.It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.
" + }, + { + "author": "comfyanonymous", + "title": "ComfyUI_experiments/sdxl_model_merging", + "reference": "https://github.com/comfyanonymous/ComfyUI_experiments", + "files": [ + "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/sdxl_model_merging.py" + ], + "install_type": "copy", + "description": "These nodes provide the capability to merge SDXL base models.It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.
" + }, + { + "author": "comfyanonymous", + "title": "ComfyUI_experiments/reference_only", + "reference": "https://github.com/comfyanonymous/ComfyUI_experiments", + "files": [ + "https://github.com/comfyanonymous/ComfyUI_experiments/raw/master/reference_only.py" + ], + "install_type": "copy", + "description": "This node provides functionality corresponding to Reference only in Controlnet.It is recommended to use the integrated custom nodes in the default channel for update support rather than installing individual nodes.
" + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/model-list.json @@ -0,0 +1,3 @@ +{ + "models": [] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/alter-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/alter-list.json new file mode 100644 index 0000000000000000000000000000000000000000..072c3bb5e8bd05b6f14f6df25386dc1e1010a137 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/alter-list.json @@ -0,0 +1,4 @@ +{ + "items": [ + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/custom-node-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/custom-node-list.json new file mode 100644 index 0000000000000000000000000000000000000000..bbe212a5cd2315c445ce586165395d3ed48974a1 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/custom-node-list.json @@ -0,0 +1,700 @@ +{ + "custom_nodes": [ + { + "author": "#NOTICE_1.13", + "title": "NOTICE: This channel is not the default channel.", + "reference": "https://github.com/ltdrdata/ComfyUI-Manager", + "files": [], + "install_type": "git-clone", + "description": "If you see this message, your ComfyUI-Manager is outdated.\nRecent channel provides only the list of the latest nodes. If you want to find the complete node list, please go to the Default channel." + }, + + + + { + "author": "Ryuukeisyou", + "title": "comfyui_face_parsing", + "reference": "https://github.com/Ryuukeisyou/comfyui_face_parsing", + "files": [ + "https://github.com/Ryuukeisyou/comfyui_face_parsing" + ], + "install_type": "git-clone", + "description": "This is a set of custom nodes for ComfyUI. The nodes utilize the [a/face parsing model](https://huggingface.co/jonathandinu/face-parsing) to provide detailed segmantation of face. To improve face segmantation accuracy, [a/yolov8 face model](https://huggingface.co/Bingsu/adetailer/) is used to first extract face from an image. There are also auxiliary nodes for image and mask processing. A guided filter is also provided for skin smoothing." + }, + { + "author": "florestefano1975", + "title": "comfyui-prompt-composer", + "reference": "https://github.com/florestefano1975/comfyui-prompt-composer", + "files": [ + "https://github.com/florestefano1975/comfyui-prompt-composer" + ], + "install_type": "git-clone", + "description": "A suite of tools for prompt management. Combining nodes helps the user sequence strings for prompts, also creating logical groupings if necessary. Individual nodes can be chained together in any order." + }, + { + "author": "ai-liam", + "title": "LiamUtil", + "reference": "https://github.com/ai-liam/comfyui_liam_util", + "files": [ + "https://github.com/ai-liam/comfyui_liam_util" + ], + "install_type": "git-clone", + "description": "Nodes: LiamLoadImage. This node provides the capability to load images from a URL." + }, + { + "author": "jesenzhang", + "title": "ComfyUI_StreamDiffusion", + "reference": "https://github.com/jesenzhang/ComfyUI_StreamDiffusion", + "files": [ + "https://github.com/jesenzhang/ComfyUI_StreamDiffusion" + ], + "install_type": "git-clone", + "description": "This is a simple implementation StreamDiffusion(A Pipeline-Level Solution for Real-Time Interactive Generation) for ComfyUI" + }, + { + "author": "an90ray", + "title": "ComfyUI-DareMerge", + "reference": "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes", + "files": [ + "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes" + ], + "install_type": "git-clone", + "description": "Nodes: RErouter, String (RE), Int (RE)" + }, + { + "author": "Crystian", + "title": "Crystools", + "reference": "https://github.com/crystian/ComfyUI-Crystools", + "files": [ + "https://github.com/crystian/ComfyUI-Crystools" + ], + "install_type": "git-clone", + "description": "You can see the metadata and compare between two images, compare between two JSONs, show any value to console/display, pipes, and more!\nThis provides a better nodes to load images, previews, etc, and see \"hidden\" data, but without load a new workflow." + }, + { + "author": "54rt1n", + "title": "ComfyUI-DareMerge", + "reference": "https://github.com/54rt1n/ComfyUI-DareMerge", + "files": [ + "https://github.com/54rt1n/ComfyUI-DareMerge" + ], + "install_type": "git-clone", + "description": "Merge two checkpoint models by dare ties [a/(https://github.com/yule-BUAA/MergeLM)](https://github.com/yule-BUAA/MergeLM), sort of." + }, + { + "author": "Kangkang625", + "title": "ComfyUI-Paint-by-Example", + "reference": "https://github.com/Kangkang625/ComfyUI-paint-by-example", + "pip": ["diffusers"], + "files": [ + "https://github.com/Kangkang625/ComfyUI-paint-by-example" + ], + "install_type": "git-clone", + "description": "This repo is a simple implementation of [a/Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example) based on its [a/huggingface pipeline](https://huggingface.co/Fantasy-Studio/Paint-by-Example)." + }, + { + "author": "pkpk", + "title": "ComfyUI-SaveAVIF", + "reference": "https://github.com/pkpkTech/ComfyUI-SaveAVIF", + "files": [ + "https://github.com/pkpkTech/ComfyUI-SaveAVIF" + ], + "install_type": "git-clone", + "description": "A custom node on ComfyUI that saves images in AVIF format. Workflow can be loaded from images saved at this node." + }, + { + "author": "styler00dollar", + "title": "ComfyUI-deepcache", + "reference": "https://github.com/styler00dollar/ComfyUI-deepcache", + "files": [ + "https://github.com/styler00dollar/ComfyUI-deepcache" + ], + "install_type": "git-clone", + "description": "This extension provides nodes for [a/DeepCache: Accelerating Diffusion Models for Free](https://arxiv.org/abs/2312.00858)\nNOTE:Original code can be found [a/here](https://gist.github.com/laksjdjf/435c512bc19636e9c9af4ee7bea9eb86). Full credit to laksjdjf for sharing the code. " + }, + { + "author": "edenartlab", + "title": "eden_comfy_pipelines", + "reference": "https://github.com/edenartlab/eden_comfy_pipelines", + "files": [ + "https://github.com/edenartlab/eden_comfy_pipelines" + ], + "install_type": "git-clone", + "description": "Nodes:CLIP Interrogator." + }, + { + "author": "Limitex", + "title": "ComfyUI-Diffusers", + "reference": "https://github.com/Limitex/ComfyUI-Diffusers", + "files": [ + "https://github.com/Limitex/ComfyUI-Diffusers" + ], + "install_type": "git-clone", + "description": "This extension enables the use of the diffuser pipeline in ComfyUI." + }, + { + "author": "Limitex", + "title": "ComfyUI-Calculation", + "reference": "https://github.com/Limitex/ComfyUI-Calculation", + "files": [ + "https://github.com/Limitex/ComfyUI-Calculation" + ], + "install_type": "git-clone", + "description": "Nodes: Center Calculation. Improved Numerical Calculation for ComfyUI" + }, + { + "author": "Comfyui_GPT_Story", + "title": "junglehu", + "reference": "https://github.com/junglehu/Comfyui_Story_LLmA", + "files": [ + "https://github.com/junglehu/Comfyui_Story_LLmA" + ], + "install_type": "git-clone", + "description": "GPT+Comfyui Generate coherent pictures" + }, + { + "author": "HarroweD and quadmoon", + "title": "Harronode", + "reference": "https://github.com/NotHarroweD/Harronode", + "nodename_pattern": "Harronode", + "files": [ + "https://github.com/NotHarroweD/Harronode.git" + ], + "install_type": "git-clone", + "description": "Harronode is a custom node designed to build prompts easily for use with the Harrlogos SDXL LoRA. This Node simplifies the process of crafting prompts and makes all built in activation terms available at your fingertips." + }, + { + "author": "styler00dollar", + "title": "ComfyUI-sudo-latent-upscale", + "reference": "https://github.com/styler00dollar/ComfyUI-sudo-latent-upscale", + "files": [ + "https://github.com/styler00dollar/ComfyUI-sudo-latent-upscale" + ], + "install_type": "git-clone", + "description": "Directly upscaling inside the latent space. Model was trained for SD1.5 and drawn content. Might add new architectures or update models at some point. This took heavy inspriration from [city96/SD-Latent-Upscaler](https://github.com/city96/SD-Latent-Upscaler) and [Ttl/ComfyUi_NNLatentUpscale](https://github.com/Ttl/ComfyUi_NNLatentUpscale). " + }, + { + "author": "thecooltechguy", + "title": "ComfyUI-ComfyRun", + "reference": "https://github.com/thecooltechguy/ComfyUI-ComfyRun", + "files": [ + "https://github.com/thecooltechguy/ComfyUI-ComfyRun" + ], + "install_type": "git-clone", + "description": "The easiest way to run & share any ComfyUI workflow [a/https://comfyrun.com](https://comfyrun.com)" + }, + { + "author": "ceruleandeep", + "title": "ComfyUI LLaVA Captioner", + "reference": "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner", + "files": [ + "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner" + ], + "install_type": "git-clone", + "description": "A ComfyUI extension for chatting with your images. Runs on your own system, no external services used, no filter. Uses the [a/LLaVA multimodal LLM](https://llava-vl.github.io/) so you can give instructions or ask questions in natural language. It's maybe as smart as GPT3.5, and it can see." + }, + { + "author": "jitcoder", + "title": "LoraInfo", + "reference": "https://github.com/jitcoder/lora-info", + "files": [ + "https://github.com/jitcoder/lora-info" + ], + "install_type": "git-clone", + "description": "Shows Lora information from CivitAI and outputs trigger words and example prompt" + }, + { + "author": "ttulttul", + "title": "ComfyUI Iterative Mixing Nodes", + "reference": "https://github.com/ttulttul/ComfyUI-Iterative-Mixer", + "files": [ + "https://github.com/ttulttul/ComfyUI-Iterative-Mixer" + ], + "install_type": "git-clone", + "description": "Nodes: Iterative Mixing KSampler, Batch Unsampler, Iterative Mixing KSampler Advanced" + }, + { + "author": "OpenArt-AI", + "title": "ComfyUI Assistant", + "reference": "https://github.com/OpenArt-AI/ComfyUI-Assistant", + "files": [ + "https://github.com/OpenArt-AI/ComfyUI-Assistant" + ], + "install_type": "git-clone", + "description": "ComfyUI Assistant is your one stop plugin for everything you need to get started with comfy-ui. Now it provides useful courses, tutorials, and basic templates." + }, + { + "author": "shockz0rz", + "title": "comfy-easy-grids", + "reference": "https://github.com/shockz0rz/comfy-easy-grids", + "files": [ + "https://github.com/shockz0rz/comfy-easy-grids" + ], + "install_type": "git-clone", + "description": "A set of custom nodes for creating image grids, sequences, and batches in ComfyUI." + }, + { + "author": "CosmicLaca", + "title": "Primere nodes for ComfyUI", + "reference": "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes", + "files": [ + "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes" + ], + "install_type": "git-clone", + "description": "This extension provides various utility nodes. Inputs(prompt, styles, dynamic, merger, ...), Outputs(style pile), Dashboard(selectors, loader, switch, ...), Networks(LORA, Embedding, Hypernetwork), Visuals(visual selectors, )" + }, + { + "author": "ZHO-ZHO-ZHO", + "title": "ComfyUI-Gemini", + "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini", + "files": [ + "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini" + ], + "install_type": "git-clone", + "description": "Using Gemini-pro & Gemini-pro-vision in ComfyUI." + }, + { + "author": "ZHO-ZHO-ZHO", + "title": "comfyui-portrait-master-zh-cn", + "reference": "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn", + "files": [ + "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn" + ], + "install_type": "git-clone", + "description": "ComfyUI Portrait Master 简体中文版." + }, + { + "author": "Continue7777", + "title": "comfyui-easyapi-nodes", + "reference": "https://github.com/Continue7777/comfyui-yoy", + "files": [ + "https://github.com/Continue7777/comfyui-yoy" + ], + "install_type": "git-clone", + "description": "This plugin gives you the ability to generate a real Product rendering, commonly referred to as the mockup.The most important is that we can provide it for you,cause we have a complete supply chain.You can buy products with your designs or sell them. We can provide you dropshipping services." + }, + { + "author": "RenderRift", + "title": "ComfyUI-RenderRiftNodes", + "reference": "https://github.com/RenderRift/ComfyUI-RenderRiftNodes", + "files": [ + "https://github.com/RenderRift/ComfyUI-RenderRiftNodes" + ], + "install_type": "git-clone", + "description": "Nodes:RR_Date_Folder_Format, RR_Image_Metadata_Overlay, RR_VideoPathMetaExtraction, RR_DisplayMetaOptions. This extension provides nodes designed to enhance the Animatediff workflow." + }, + { + "author": "Haoming02", + "title": "ComfyUI Floodgate", + "reference": "https://github.com/Haoming02/comfyui-floodgate", + "files": [ + "https://github.com/Haoming02/comfyui-floodgate" + ], + "install_type": "git-clone", + "description": "This is an Extension for ComfyUI, which allows you to control the logic flow with just one click!" + }, + { + "author": "Trung0246", + "title": "ComfyUI-0246", + "reference": "https://github.com/Trung0246/ComfyUI-0246", + "files": [ + "https://github.com/Trung0246/ComfyUI-0246" + ], + "install_type": "git-clone", + "description": "Random nodes for ComfyUI I made to solve my struggle with ComfyUI (ex: pipe, process). Have varying quality." + }, + { + "author": "violet-chen", + "title": "comfyui-psd2png", + "reference": "https://github.com/violet-chen/comfyui-psd2png", + "files": [ + "https://github.com/violet-chen/comfyui-psd2png" + ], + "install_type": "git-clone", + "description": "Nodes: Psd2Png." + }, + { + "author": "IDGallagher", + "title": "IG Interpolation Nodes", + "reference": "https://github.com/IDGallagher/ComfyUI-IG-Nodes", + "files": [ + "https://github.com/IDGallagher/ComfyUI-IG-Nodes" + ], + "install_type": "git-clone", + "description": "Custom nodes to aid in the exploration of Latent Space" + }, + { + "author": "rcfcu2000", + "title": "zhihuige-nodes-comfyui", + "reference": "https://github.com/rcfcu2000/zhihuige-nodes-comfyui", + "files": [ + "https://github.com/rcfcu2000/zhihuige-nodes-comfyui" + ], + "install_type": "git-clone", + "description": "Nodes: Combine ZHGMasks, Cover ZHGMasks, ZHG FaceIndex, ZHG SaveImage, ZHG SmoothEdge, ZHG GetMaskArea, ..." + }, + { + "author": "rcsaquino", + "title": "rcsaquino/comfyui-custom-nodes", + "reference": "https://github.com/rcsaquino/comfyui-custom-nodes", + "files": [ + "https://github.com/rcsaquino/comfyui-custom-nodes" + ], + "install_type": "git-clone", + "description": "Nodes: VAE Processor, VAE Loader, Background Remover" + }, + { + "author": "mozman", + "title": "ComfyUI_mozman_nodes", + "reference": "https://github.com/mozman/ComfyUI_mozman_nodes", + "files": [ + "https://github.com/mozman/ComfyUI_mozman_nodes" + ], + "install_type": "git-clone", + "description": "This extension provides styler nodes for SDXL.\n\nNOTE: Due to the dynamic nature of node name definitions, ComfyUI-Manager cannot recognize the node list from this extension. The Missing nodes and Badge features are not available for this extension." + }, + { + "author": "meap158", + "title": "ComfyUI-Background-Replacement", + "reference": "https://github.com/meap158/ComfyUI-Background-Replacement", + "files": [ + "https://github.com/meap158/ComfyUI-Background-Replacement" + ], + "install_type": "git-clone", + "description": "Instantly replace your image's background." + }, + { + "author": "florestefano1975", + "title": "comfyui-portrait-master", + "reference": "https://github.com/florestefano1975/comfyui-portrait-master", + "files": [ + "https://github.com/florestefano1975/comfyui-portrait-master" + ], + "install_type": "git-clone", + "description": "ComfyUI Portrait Master. A node designed to help AI image creators to generate prompts for human portraits." + }, + { + "author": "deroberon", + "title": "StableZero123-comfyui", + "reference": "https://github.com/deroberon/StableZero123-comfyui", + "files": [ + "https://github.com/deroberon/StableZero123-comfyui" + ], + "install_type": "git-clone", + "description": "StableZero123 is a node wrapper that uses the model and technique provided [here](https://github.com/SUDO-AI-3D/zero123plus/). It uses the Zero123plus model to generate 3D views using just one image." + }, + { + "author": "dmarx", + "title": "ComfyUI-Keyframed", + "reference": "https://github.com/dmarx/ComfyUI-Keyframed", + "files": [ + "https://github.com/dmarx/ComfyUI-Keyframed" + ], + "install_type": "git-clone", + "description": "ComfyUI nodes to facilitate parameter/prompt keyframing using comfyui nodes for defining and manipulating parameter curves. Essentially provides a ComfyUI interface to the [a/keyframed](https://github.com/dmarx/keyframed) library." + }, + { + "author": "TripleHeadedMonkey", + "title": "ComfyUI_MileHighStyler", + "reference": "https://github.com/TripleHeadedMonkey/ComfyUI_MileHighStyler", + "files": [ + "https://github.com/TripleHeadedMonkey/ComfyUI_MileHighStyler" + ], + "install_type": "git-clone", + "description": "This extension provides various SDXL Prompt Stylers. See: [a/youtube](https://youtu.be/WBHI-2uww7o?si=dijvDaUI4nmx4VkF)" + }, + { + "author": "Extraltodeus", + "title": "sigmas_tools_and_the_golden_scheduler", + "reference": "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler", + "files": [ + "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler" + ], + "install_type": "git-clone", + "description": "A few nodes to mix sigmas and a custom scheduler that uses phi, then one using eval() to be able to schedule with custom formulas." + }, + { + "author": "BennyKok", + "title": "ComfyUI Deploy", + "reference": "https://github.com/BennyKok/comfyui-deploy", + "files": [ + "https://github.com/BennyKok/comfyui-deploy" + ], + "install_type": "git-clone", + "description": "Open source comfyui deployment platform, a vercel for generative workflow infra." + }, + { + "author": "Rui", + "title": "RUI-Nodes", + "reference": "https://github.com/rui40000/RUI-Nodes", + "files": [ + "https://github.com/rui40000/RUI-Nodes" + ], + "install_type": "git-clone", + "description": "Rui's workflow-specific custom node, written using GPT." + }, + { + "author": "SpaceKendo", + "title": "Text to video for Stable Video Diffusion in ComfyUI", + "reference": "https://github.com/SpaceKendo/ComfyUI-svd_txt2vid", + "files": [ + "https://github.com/SpaceKendo/ComfyUI-svd_txt2vid" + ], + "install_type": "git-clone", + "description": "This is node replaces the init_image conditioning for the [a/Stable Video Diffusion](https://github.com/Stability-AI/generative-models) image to video model with text embeds, together with a conditioning frame. The conditioning frame is a set of latents." + }, + { + "author": "NimaNzrii", + "title": "comfyui-photoshop", + "reference": "https://github.com/NimaNzrii/comfyui-photoshop", + "files": [ + "https://github.com/NimaNzrii/comfyui-photoshop" + ], + "install_type": "git-clone", + "description": "Photoshop node inside of ComfyUi, send and get data from Photoshop" + }, + { + "author": "NimaNzrii", + "title": "comfyui-popup_preview", + "reference": "https://github.com/NimaNzrii/comfyui-popup_preview", + "files": [ + "https://github.com/NimaNzrii/comfyui-popup_preview" + ], + "install_type": "git-clone", + "description": "popup preview for comfyui" + }, + + + { + "author": "AI2lab", + "title": "comfyUI-tool-2lab", + "reference": "https://github.com/AI2lab/comfyUI-tool-2lab", + "files": [ + "https://github.com/AI2lab/comfyUI-tool-2lab" + ], + "install_type": "git-clone", + "description": "Integrate non-painting capabilities into comfyUI, including data, algorithms, video processing, large models, etc., to facilitate the construction of more powerful workflows." + }, + { + "author": "LZC", + "title": "Hayo comfyui nodes", + "reference": "https://github.com/1shadow1/hayo_comfyui_nodes", + "files": [ + "https://github.com/1shadow1/hayo_comfyui_nodes/raw/main/LZCNodes.py" + ], + "install_type": "copy", + "description": "Nodes:tensor_trans_pil, Make Transparent mask, MergeImages, words_generatee, load_PIL image" + }, + { + "author": "MNeMoNiCuZ", + "title": "ComfyUI-mnemic-nodes", + "reference": "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes", + "files": [ + "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes" + ], + "install_type": "git-clone", + "description": "Nodes:Save Text File" + }, + { + "author": "vienteck", + "title": "ComfyUI-Chat-GPT-Integration", + "reference": "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration", + "files": [ + "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration" + ], + "install_type": "git-clone", + + "description": "This extension is a reimagined version based on the [a/ComfyUI-QualityOfLifeSuit_Omar92](https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92) extension, and it supports integration with ChatGPT through the new OpenAI API.\nNOTE: See detailed installation instructions on the [a/repository](https://github.com/vienteck/ComfyUI-Chat-GPT-Integration)." + }, + { + "author": "Haoming02", + "title": "ComfyUI Tab Handler", + "reference": "https://github.com/Haoming02/comfyui-tab-handler", + "files": [ + "https://github.com/Haoming02/comfyui-tab-handler" + ], + "install_type": "git-clone", + "description": "This is an Extension for ComfyUI, which moves the menu to the specified corner on startup." + }, + { + "author": "glibsonoran", + "title": "Plush-for-ComfyUI", + "reference": "https://github.com/glibsonoran/Plush-for-ComfyUI", + "files": [ + "https://github.com/glibsonoran/Plush-for-ComfyUI" + ], + "install_type": "git-clone", + "description": "Nodes: Style Prompt, OAI Dall_e Image. Plush contains two OpenAI enabled nodes: Style Prompt: Takes your prompt and the art style you specify and generates a prompt from ChatGPT3 or 4 that Stable Diffusion can use to generate an image in that style. OAI Dall_e 3: Takes your prompt and parameters and produces a Dall_e3 image in ComfyUI." + }, + { + "author": "Aegis72", + "title": "AegisFlow Utility Nodes", + "reference": "https://github.com/aegis72/aegisflow_utility_nodes", + "files": [ + "https://github.com/aegis72/aegisflow_utility_nodes" + ], + "install_type": "git-clone", + "description": "These nodes will be placed in comfyui/custom_nodes/aegisflow and contains the image passer (accepts an image as either wired or wirelessly, input and passes it through. Latent passer does the same for latents, and the Preprocessor chooser allows a passthrough image and 10 controlnets to be passed in AegisFlow Shima. The inputs on the Preprocessor chooser should not be renamed if you intend to accept image inputs wirelessly through UE nodes. It can be done, but the send node input regex for each controlnet preprocessor column must also be changed." + }, + { + "author": "concarne000", + "title": "ConCarneNode", + "reference": "https://github.com/concarne000/ConCarneNode", + "files": [ + "https://github.com/concarne000/ConCarneNode" + ], + "install_type": "git-clone", + "description": "Nodes:Bing Image Grabber node for ComfyUI." + }, + { + "author": "Haoming02", + "title": "ComfyUI Menu Anchor", + "reference": "https://github.com/Haoming02/comfyui-menu-anchor", + "files": [ + "https://github.com/Haoming02/comfyui-menu-anchor" + ], + "install_type": "git-clone", + "description": "This is an Extension for ComfyUI, which moves the menu to the specified corner on startup." + }, + { + "author": "glifxyz", + "title": "ComfyUI-GlifNodes", + "reference": "https://github.com/glifxyz/ComfyUI-GlifNodes", + "files": [ + "https://github.com/glifxyz/ComfyUI-GlifNodes" + ], + "install_type": "git-clone", + "description": "Nodes:Consistency VAE Decoder." + }, + { + "author": "kijai", + "title": "Marigold depth estimation in ComfyUI", + "reference": "https://github.com/kijai/ComfyUI-Marigold", + "files": [ + "https://github.com/kijai/ComfyUI-Marigold" + ], + "install_type": "git-clone", + "description": "This is a wrapper node for Marigold depth estimation: [https://github.com/prs-eth/Marigold](https://github.com/kijai/ComfyUI-Marigold). Currently using the same diffusers pipeline as in the original implementation, so in addition to the custom node, you need the model in diffusers format.\nNOTE: See details in repo to install." + }, + { + "author": "spacepxl", + "title": "ComfyUI-Image-Filters", + "reference": "https://github.com/spacepxl/ComfyUI-Image-Filters", + "files": [ + "https://github.com/spacepxl/ComfyUI-Image-Filters" + ], + "install_type": "git-clone", + "description": "Image and matte filtering nodes for ComfyUI `image/filters/*`" + }, + { + "author": "Haoming02", + "title": "ComfyUI Clear Screen", + "reference": "https://github.com/Haoming02/comfyui-clear-screen", + "files": [ + "https://github.com/Haoming02/comfyui-clear-screen" + ], + "install_type": "git-clone", + "description": "This is an Extension for ComfyUI, which adds a button, CLS, to clear the console window." + }, + { + "author": "deroberon", + "title": "demofusion-comfyui", + "reference": "https://github.com/deroberon/demofusion-comfyui", + "files": [ + "https://github.com/deroberon/demofusion-comfyui" + ], + "install_type": "git-clone", + "description": "The Demofusion Custom Node is a wrapper that adapts the work and implementation of the [a/DemoFusion](https://ruoyidu.github.io/demofusion/demofusion.html) technique created and implemented by Ruoyi Du to the Comfyui environment." + }, + { + "author": "Haoming02", + "title": "comfyui-prompt-format", + "reference": "https://github.com/Haoming02/comfyui-prompt-format", + "files": [ + "https://github.com/Haoming02/comfyui-prompt-format" + ], + "install_type": "git-clone", + "description": "This is an Extension for ComfyUI, which helps formatting texts." + }, + { + "author": "brianfitzgerald", + "title": "StyleAligned for ComfyUI", + "reference": "https://github.com/brianfitzgerald/style_aligned_comfy", + "files": [ + "https://github.com/brianfitzgerald/style_aligned_comfy" + ], + "install_type": "git-clone", + "description": "Implementation of the [a/StyleAligned](https://style-aligned-gen.github.io/) paper for ComfyUI. This node allows you to apply a consistent style to all images in a batch; by default it will use the first image in the batch as the style reference, forcing all other images to be consistent with it." + }, + { + "author": "MitoshiroPJ", + "title": "ComfyUI Slothful Attention", + "reference": "https://github.com/MitoshiroPJ/comfyui_slothful_attention", + "files": [ + "https://github.com/MitoshiroPJ/comfyui_slothful_attention" + ], + "install_type": "git-clone", + "description": "This custom node allow controlling output without training. The reducing method is similar to [a/Spatial-Reduction Attention](https://paperswithcode.com/method/spatial-reduction-attention), but generating speed may not be increased on typical image sizes due to overheads. (In some cases, slightly slower)" + }, + { + "author": "aria1th", + "title": "ComfyUI-LogicUtils", + "reference": "https://github.com/aria1th/ComfyUI-LogicUtils", + "files": [ + "https://github.com/aria1th/ComfyUI-LogicUtils" + ], + "install_type": "git-clone", + "description": "Nodes:UniformRandomFloat..., RandomShuffleInt, YieldableIterator..., LogicGate..., Add..., MergeString, MemoryNode, ..." + }, + { + "author": "modusCell", + "title": "Preset Dimensions", + "reference": "https://github.com/modusCell/ComfyUI-dimension-node-modusCell", + "files": [ + "https://github.com/modusCell/ComfyUI-dimension-node-modusCell" + ], + "install_type": "git-clone", + "description": "Simple node for sharing latent image size between nodes. Preset dimensions for SD and XL." + }, + { + "author": "mmaker", + "title": "Color Enhance", + "reference": "https://git.mmaker.moe/mmaker/sd-webui-color-enhance", + "files": [ + "https://git.mmaker.moe/mmaker/sd-webui-color-enhance" + ], + "install_type": "git-clone", + "description": "Node: Color Enhance, Color Blend. This is the same algorithm GIMP/GEGL uses for color enhancement. The gist of this implementation is that it converts the color space to CIELCh(ab) and normalizes the chroma (or [colorfulness](https://en.wikipedia.org/wiki/Colorfulness)] component. Original source can be found in the link below." + }, + { + "author": "bruefire", + "title": "ComfyUI Sequential Image Loader", + "reference": "https://github.com/bruefire/ComfyUI-SeqImageLoader", + "files": [ + "https://github.com/bruefire/ComfyUI-SeqImageLoader" + ], + "install_type": "git-clone", + "description": "This is an extension node for ComfyUI that allows you to load frames from a video in bulk and perform masking and sketching on each frame through a GUI." + }, + { + "author": "yolain", + "title": "ComfyUI Easy Use", + "reference": "https://github.com/yolain/ComfyUI-Easy-Use", + "files": [ + "https://github.com/yolain/ComfyUI-Easy-Use" + ], + "install_type": "git-clone", + "description": "To enhance the usability of ComfyUI, optimizations and integrations have been implemented for several commonly used nodes." + }, + { + "author": "tzwm", + "title": "ComfyUI Browser", + "reference": "https://github.com/tzwm/comfyui-browser", + "files": [ + "https://github.com/tzwm/comfyui-browser" + ], + "install_type": "git-clone", + "description": "This is an image/video/workflow browser and manager for ComfyUI. You could add image/video/workflow to collections and load it to ComfyUI. You will be able to use your collections everywhere." + } + ] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/extension-node-map.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/extension-node-map.json new file mode 100644 index 0000000000000000000000000000000000000000..bd5d8a2c7454659da3eff6dff612a24c536b4d48 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/extension-node-map.json @@ -0,0 +1,6416 @@ +{ + "https://gist.github.com/alkemann/7361b8eb966f29c8238fd323409efb68/raw/f9605be0b38d38d3e3a2988f89248ff557010076/alkemann.py": [ + [ + "Int to Text", + "Save A1 Image", + "Seed With Text" + ], + { + "title_aux": "alkemann nodes" + } + ], + "https://git.mmaker.moe/mmaker/sd-webui-color-enhance": [ + [ + "MMakerColorBlend", + "MMakerColorEnhance" + ], + { + "title_aux": "Color Enhance" + } + ], + "https://github.com/0xbitches/ComfyUI-LCM": [ + [ + "LCM_Sampler", + "LCM_Sampler_Advanced", + "LCM_img2img_Sampler", + "LCM_img2img_Sampler_Advanced" + ], + { + "title_aux": "Latent Consistency Model for ComfyUI" + } + ], + "https://github.com/1shadow1/hayo_comfyui_nodes/raw/main/LZCNodes.py": [ + [ + "LoadPILImages", + "MergeImages", + "make_transparentmask", + "tensor_trans_pil", + "words_generatee" + ], + { + "title_aux": "Hayo comfyui nodes" + } + ], + "https://github.com/42lux/ComfyUI-safety-checker": [ + [ + "Safety Checker" + ], + { + "title_aux": "ComfyUI-safety-checker" + } + ], + "https://github.com/54rt1n/ComfyUI-DareMerge": [ + [ + "DareModelMerger" + ], + { + "title_aux": "ComfyUI-DareMerge" + } + ], + "https://github.com/80sVectorz/ComfyUI-Static-Primitives": [ + [ + "FloatStaticPrimitive", + "IntStaticPrimitive", + "StringMlStaticPrimitive", + "StringStaticPrimitive" + ], + { + "title_aux": "ComfyUI-Static-Primitives" + } + ], + "https://github.com/AIrjen/OneButtonPrompt": [ + [ + "CreatePromptVariant", + "OneButtonPrompt", + "SavePromptToFile" + ], + { + "title_aux": "One Button Prompt" + } + ], + "https://github.com/AbdullahAlfaraj/Comfy-Photoshop-SD": [ + [ + "APS_LatentBatch", + "APS_Seed", + "ContentMaskLatent", + "ControlNetScript", + "ControlnetUnit", + "GaussianLatentImage", + "GetConfig", + "LoadImageBase64", + "LoadImageWithMetaData", + "LoadLorasFromPrompt", + "MaskExpansion" + ], + { + "title_aux": "Comfy-Photoshop-SD" + } + ], + "https://github.com/AbyssYuan0/ComfyUI_BadgerTools": [ + [ + "FloatToInt-badger", + "FloatToString-badger", + "ImageNormalization-badger", + "ImageOverlap-badger", + "ImageScaleToSide-badger", + "IntToString-badger", + "StringToFizz-badger", + "TextListToString-badger", + "getImageSide-badger" + ], + { + "title_aux": "ComfyUI_BadgerTools" + } + ], + "https://github.com/Acly/comfyui-tooling-nodes": [ + [ + "ETN_ApplyMaskToImage", + "ETN_CropImage", + "ETN_LoadImageBase64", + "ETN_LoadMaskBase64", + "ETN_SendImageWebSocket" + ], + { + "title_aux": "ComfyUI Nodes for External Tooling" + } + ], + "https://github.com/Amorano/Jovimetrix": [ + [], + { + "author": "amorano", + "description": "Webcams, GLSL shader, Media Streaming, Tick animation, Image manipulation,", + "nodename_pattern": " \\(jov\\)$", + "title": "Jovimetrix", + "title_aux": "Jovimetrix Composition Nodes" + } + ], + "https://github.com/ArtBot2023/CharacterFaceSwap": [ + [ + "Color Blend", + "Crop Face", + "Exclude Facial Feature", + "Generation Parameter Input", + "Generation Parameter Output", + "Image Full BBox", + "Load BiseNet", + "Load RetinaFace", + "Mask Contour", + "Segment Face", + "Uncrop Face" + ], + { + "title_aux": "Character Face Swap" + } + ], + "https://github.com/ArtVentureX/comfyui-animatediff": [ + [ + "AnimateDiffCombine", + "AnimateDiffLoraLoader", + "AnimateDiffModuleLoader", + "AnimateDiffSampler", + "AnimateDiffSlidingWindowOptions", + "ImageSizeAndBatchSize", + "LoadVideo" + ], + { + "title_aux": "AnimateDiff" + } + ], + "https://github.com/AustinMroz/ComfyUI-SpliceTools": [ + [ + "LogSigmas", + "SpliceDenoised", + "SpliceLatents", + "TemporalSplice" + ], + { + "title_aux": "SpliceTools" + } + ], + "https://github.com/BadCafeCode/masquerade-nodes-comfyui": [ + [ + "Blur", + "Change Channel Count", + "Combine Masks", + "Constant Mask", + "Convert Color Space", + "Create QR Code", + "Create Rect Mask", + "Cut By Mask", + "Get Image Size", + "Image To Mask", + "Make Image Batch", + "Mask By Text", + "Mask Morphology", + "Mask To Region", + "MasqueradeIncrementer", + "Mix Color By Mask", + "Mix Images By Mask", + "Paste By Mask", + "Prune By Mask", + "Separate Mask Components", + "Unary Image Op", + "Unary Mask Op" + ], + { + "title_aux": "Masquerade Nodes" + } + ], + "https://github.com/Beinsezii/bsz-cui-extras": [ + [ + "BSZAbsoluteHires", + "BSZAspectHires", + "BSZColoredLatentImageXL", + "BSZCombinedHires", + "BSZHueChromaXL", + "BSZInjectionKSampler", + "BSZLatentDebug", + "BSZLatentFill", + "BSZLatentGradient", + "BSZLatentHSVAImage", + "BSZLatentOffsetXL", + "BSZLatentRGBAImage", + "BSZLatentbuster", + "BSZPixelbuster", + "BSZPixelbusterHelp", + "BSZPrincipledConditioning", + "BSZPrincipledSampler", + "BSZPrincipledScale", + "BSZStrangeResample" + ], + { + "title_aux": "bsz-cui-extras" + } + ], + "https://github.com/BennyKok/comfyui-deploy": [ + [ + "ComfyUIDeployExternalImage", + "ComfyUIDeployExternalImageAlpha", + "ComfyUIDeployExternalText" + ], + { + "author": "BennyKok", + "description": "", + "nickname": "Comfy Deploy", + "title": "comfyui-deploy", + "title_aux": "ComfyUI Deploy" + } + ], + "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions/raw/main/EXT_AudioManipulation.py": [ + [ + "BatchJoinAudio", + "CutAudio", + "DuplicateAudio", + "JoinAudio", + "ResampleAudio", + "ReverseAudio", + "StretchAudio" + ], + { + "title_aux": "Waveform Extensions" + } + ], + "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb": [ + [ + "BNK_AddCLIPSDXLParams", + "BNK_AddCLIPSDXLRParams", + "BNK_CLIPTextEncodeAdvanced", + "BNK_CLIPTextEncodeSDXLAdvanced" + ], + { + "title_aux": "Advanced CLIP Text Encode" + } + ], + "https://github.com/BlenderNeko/ComfyUI_Cutoff": [ + [ + "BNK_CutoffBasePrompt", + "BNK_CutoffRegionsToConditioning", + "BNK_CutoffRegionsToConditioning_ADV", + "BNK_CutoffSetRegions" + ], + { + "title_aux": "ComfyUI Cutoff" + } + ], + "https://github.com/BlenderNeko/ComfyUI_Noise": [ + [ + "BNK_DuplicateBatchIndex", + "BNK_GetSigma", + "BNK_InjectNoise", + "BNK_NoisyLatentImage", + "BNK_SlerpLatent", + "BNK_Unsampler" + ], + { + "title_aux": "ComfyUI Noise" + } + ], + "https://github.com/BlenderNeko/ComfyUI_SeeCoder": [ + [ + "ConcatConditioning", + "SEECoderImageEncode" + ], + { + "title_aux": "SeeCoder [WIP]" + } + ], + "https://github.com/BlenderNeko/ComfyUI_TiledKSampler": [ + [ + "BNK_TiledKSampler", + "BNK_TiledKSamplerAdvanced" + ], + { + "title_aux": "Tiled sampling for ComfyUI" + } + ], + "https://github.com/CaptainGrock/ComfyUIInvisibleWatermark/raw/main/Invisible%20Watermark.py": [ + [ + "Apply Invisible Watermark", + "Extract Watermark" + ], + { + "title_aux": "ComfyUIInvisibleWatermark" + } + ], + "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes": [ + [ + "LoadImageFromPath" + ], + { + "title_aux": "ComfyUI_Ib_CustomNodes" + } + ], + "https://github.com/Clybius/ComfyUI-Latent-Modifiers": [ + [ + "Latent Diffusion Mega Modifier" + ], + { + "title_aux": "ComfyUI-Latent-Modifiers" + } + ], + "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes": [ + [ + "PrimereAnyDetailer", + "PrimereAnyOutput", + "PrimereCKPT", + "PrimereCKPTLoader", + "PrimereCLIPEncoder", + "PrimereClearPrompt", + "PrimereDynamicParser", + "PrimereEmbedding", + "PrimereEmbeddingHandler", + "PrimereEmbeddingKeywordMerger", + "PrimereHypernetwork", + "PrimereImageSegments", + "PrimereLCMSelector", + "PrimereLORA", + "PrimereLYCORIS", + "PrimereLatentNoise", + "PrimereLoraKeywordMerger", + "PrimereLoraStackMerger", + "PrimereLycorisKeywordMerger", + "PrimereLycorisStackMerger", + "PrimereMetaRead", + "PrimereMetaSave", + "PrimereModelKeyword", + "PrimereNetworkTagLoader", + "PrimerePrompt", + "PrimerePromptSwitch", + "PrimereResolution", + "PrimereResolutionMultiplier", + "PrimereSamplers", + "PrimereSeed", + "PrimereStepsCfg", + "PrimereStyleLoader", + "PrimereStylePile", + "PrimereTextOutput", + "PrimereVAE", + "PrimereVAELoader", + "PrimereVAESelector", + "PrimereVisualCKPT", + "PrimereVisualEmbedding", + "PrimereVisualHypernetwork", + "PrimereVisualLORA", + "PrimereVisualLYCORIS", + "PrimereVisualStyle" + ], + { + "title_aux": "Primere nodes for ComfyUI" + } + ], + "https://github.com/Danand/ComfyUI-ComfyCouple": [ + [ + "Attention couple", + "Comfy Couple" + ], + { + "author": "Rei D.", + "description": "If you want to draw two different characters together without blending their features, so you could try to check out this custom node.", + "nickname": "Danand", + "title": "Comfy Couple", + "title_aux": "ComfyUI-ComfyCouple" + } + ], + "https://github.com/Davemane42/ComfyUI_Dave_CustomNode": [ + [ + "ABGRemover", + "ConditioningStretch", + "ConditioningUpscale", + "MultiAreaConditioning", + "MultiLatentComposite" + ], + { + "title_aux": "Visual Area Conditioning / Latent composition" + } + ], + "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes": [ + [ + "ABSNode_DF", + "Absolute value", + "Ceil", + "CeilNode_DF", + "Conditioning area scale by ratio", + "ConditioningSetArea with tuples", + "ConditioningSetAreaEXT_DF", + "ConditioningSetArea_DF", + "CosNode_DF", + "Cosines", + "Divide", + "DivideNode_DF", + "EmptyLatentImage_DF", + "Float", + "Float debug print", + "Float2Tuple_DF", + "FloatDebugPrint_DF", + "FloatNode_DF", + "Floor", + "FloorNode_DF", + "Get image size", + "Get latent size", + "GetImageSize_DF", + "GetLatentSize_DF", + "Image scale by ratio", + "Image scale to side", + "ImageScale_Ratio_DF", + "ImageScale_Side_DF", + "Int debug print", + "Int to float", + "Int to tuple", + "Int2Float_DF", + "IntDebugPrint_DF", + "Integer", + "IntegerNode_DF", + "Latent Scale by ratio", + "Latent Scale to side", + "LatentComposite with tuples", + "LatentScale_Ratio_DF", + "LatentScale_Side_DF", + "MultilineStringNode_DF", + "Multiply", + "MultiplyNode_DF", + "PowNode_DF", + "Power", + "Random", + "RandomFloat_DF", + "SinNode_DF", + "Sinus", + "SqrtNode_DF", + "Square root", + "String debug print", + "StringNode_DF", + "Subtract", + "SubtractNode_DF", + "Sum", + "SumNode_DF", + "TanNode_DF", + "Tangent", + "Text", + "Text box", + "Tuple", + "Tuple debug print", + "Tuple multiply", + "Tuple swap", + "Tuple to floats", + "Tuple to ints", + "Tuple2Float_DF", + "TupleDebugPrint_DF", + "TupleNode_DF" + ], + { + "title_aux": "Derfuu_ComfyUI_ModdedNodes" + } + ], + "https://github.com/Electrofried/ComfyUI-OpenAINode": [ + [ + "OpenAINode" + ], + { + "title_aux": "OpenAINode" + } + ], + "https://github.com/EllangoK/ComfyUI-post-processing-nodes": [ + [ + "ArithmeticBlend", + "AsciiArt", + "Blend", + "Blur", + "CannyEdgeMask", + "ChromaticAberration", + "ColorCorrect", + "ColorTint", + "Dissolve", + "Dither", + "DodgeAndBurn", + "FilmGrain", + "Glow", + "HSVThresholdMask", + "KMeansQuantize", + "KuwaharaBlur", + "Parabolize", + "PencilSketch", + "PixelSort", + "Pixelize", + "Quantize", + "Sharpen", + "SineWave", + "Solarize", + "Vignette" + ], + { + "title_aux": "ComfyUI-post-processing-nodes" + } + ], + "https://github.com/Extraltodeus/LoadLoraWithTags": [ + [ + "LoraLoaderTagsQuery" + ], + { + "title_aux": "LoadLoraWithTags" + } + ], + "https://github.com/Extraltodeus/noise_latent_perlinpinpin": [ + [ + "NoisyLatentPerlin" + ], + { + "title_aux": "noise latent perlinpinpin" + } + ], + "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler": [ + [ + "Get sigmas as float", + "Graph sigmas", + "Manual scheduler", + "Merge sigmas by average", + "Merge sigmas gradually", + "Multiply sigmas", + "Split and concatenate sigmas", + "The Golden Scheduler" + ], + { + "title_aux": "sigmas_tools_and_the_golden_scheduler" + } + ], + "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation": [ + [ + "AMT VFI", + "CAIN VFI", + "EISAI VFI", + "FILM VFI", + "FLAVR VFI", + "GMFSS Fortuna VFI", + "IFRNet VFI", + "IFUnet VFI", + "KSampler Gradually Adding More Denoise (efficient)", + "M2M VFI", + "Make Interpolation State List", + "RIFE VFI", + "STMFNet VFI", + "Sepconv VFI" + ], + { + "title_aux": "ComfyUI Frame Interpolation" + } + ], + "https://github.com/Fannovel16/ComfyUI-Loopchain": [ + [ + "EmptyLatentImageLoop", + "FolderToImageStorage", + "ImageStorageExportLoop", + "ImageStorageImport", + "ImageStorageReset", + "LatentStorageExportLoop", + "LatentStorageImport", + "LatentStorageReset" + ], + { + "title_aux": "ComfyUI Loopchain" + } + ], + "https://github.com/Fannovel16/ComfyUI-MotionDiff": [ + [ + "EmptyMotionData", + "ExportSMPLTo3DSoftware", + "MotionCLIPTextEncode", + "MotionDataVisualizer", + "MotionDiffLoader", + "MotionDiffSimpleSampler", + "RenderSMPLMesh", + "SMPLLoader", + "SaveSMPL", + "SmplifyMotionData" + ], + { + "title_aux": "ComfyUI MotionDiff" + } + ], + "https://github.com/Fannovel16/ComfyUI-Video-Matting": [ + [ + "Robust Video Matting" + ], + { + "title_aux": "ComfyUI-Video-Matting" + } + ], + "https://github.com/Fannovel16/comfyui_controlnet_aux": [ + [ + "AIO_Preprocessor", + "AnimalPosePreprocessor", + "AnimeFace_SemSegPreprocessor", + "AnimeLineArtPreprocessor", + "BAE-NormalMapPreprocessor", + "BinaryPreprocessor", + "CannyEdgePreprocessor", + "ColorPreprocessor", + "DWPreprocessor", + "DensePosePreprocessor", + "FakeScribblePreprocessor", + "HEDPreprocessor", + "HintImageEnchance", + "ImageGenResolutionFromImage", + "ImageGenResolutionFromLatent", + "InpaintPreprocessor", + "LeReS-DepthMapPreprocessor", + "LineArtPreprocessor", + "LineartStandardPreprocessor", + "M-LSDPreprocessor", + "Manga2Anime_LineArt_Preprocessor", + "MediaPipe-FaceMeshPreprocessor", + "MiDaS-DepthMapPreprocessor", + "MiDaS-NormalMapPreprocessor", + "OneFormer-ADE20K-SemSegPreprocessor", + "OneFormer-COCO-SemSegPreprocessor", + "OpenposePreprocessor", + "PiDiNetPreprocessor", + "PixelPerfectResolution", + "SAMPreprocessor", + "ScribblePreprocessor", + "Scribble_XDoG_Preprocessor", + "SemSegPreprocessor", + "ShufflePreprocessor", + "TilePreprocessor", + "UniFormer-SemSegPreprocessor", + "Zoe-DepthMapPreprocessor" + ], + { + "author": "tstandley", + "title_aux": "ComfyUI's ControlNet Auxiliary Preprocessors" + } + ], + "https://github.com/Feidorian/feidorian-ComfyNodes": [ + [], + { + "nodename_pattern": "^Feidorian_", + "title_aux": "feidorian-ComfyNodes" + } + ], + "https://github.com/Fictiverse/ComfyUI_Fictiverse": [ + [ + "Add Noise to Image with Mask", + "Color correction", + "Displace Image with Depth", + "Displace Images with Mask", + "Zoom Image with Depth" + ], + { + "title_aux": "ComfyUI Fictiverse Nodes" + } + ], + "https://github.com/FizzleDorf/ComfyUI-AIT": [ + [ + "AIT_Unet_Loader", + "AIT_VAE_Encode_Loader" + ], + { + "title_aux": "ComfyUI-AIT" + } + ], + "https://github.com/FizzleDorf/ComfyUI_FizzNodes": [ + [ + "AbsCosWave", + "AbsSinWave", + "BatchGLIGENSchedule", + "BatchPromptSchedule", + "BatchPromptScheduleEncodeSDXL", + "BatchPromptScheduleLatentInput", + "BatchPromptScheduleNodeFlowEnd", + "BatchPromptScheduleSDXLLatentInput", + "BatchStringSchedule", + "BatchValueSchedule", + "BatchValueScheduleLatentInput", + "CalculateFrameOffset", + "ConcatStringSingle", + "CosWave", + "FizzFrame", + "FizzFrameConcatenate", + "ImageBatchFromValueSchedule", + "Init FizzFrame", + "InvCosWave", + "InvSinWave", + "Lerp", + "PromptSchedule", + "PromptScheduleEncodeSDXL", + "PromptScheduleNodeFlow", + "PromptScheduleNodeFlowEnd", + "SawtoothWave", + "SinWave", + "SquareWave", + "StringConcatenate", + "StringSchedule", + "TriangleWave", + "ValueSchedule", + "convertKeyframeKeysToBatchKeys" + ], + { + "title_aux": "FizzNodes" + } + ], + "https://github.com/GMapeSplat/ComfyUI_ezXY": [ + [ + "ConcatenateString", + "ItemFromDropdown", + "IterationDriver", + "JoinImages", + "LineToConsole", + "NumberFromList", + "NumbersToList", + "PlotImages", + "StringFromList", + "StringToLabel", + "StringsToList", + "ezMath", + "ezXY_AssemblePlot", + "ezXY_Driver" + ], + { + "title_aux": "ezXY scripts and nodes" + } + ], + "https://github.com/GTSuya-Studio/ComfyUI-Gtsuya-Nodes": [ + [ + "Danbooru (ID)", + "Danbooru (Random)", + "Random File From Path", + "Replace Strings", + "Simple Wildcards", + "Simple Wildcards (Dir.)", + "Wildcards Nodes" + ], + { + "title_aux": "ComfyUI-GTSuya-Nodes" + } + ], + "https://github.com/Gourieff/comfyui-reactor-node": [ + [ + "ReActorFaceSwap", + "ReActorLoadFaceModel", + "ReActorSaveFaceModel" + ], + { + "title_aux": "ReActor Node for ComfyUI" + } + ], + "https://github.com/Haoming02/comfyui-diffusion-cg": [ + [ + "Hook Recenter", + "Hook Recenter XL", + "Normalization", + "NormalizationXL", + "Tensor Debug", + "Unhook Recenter" + ], + { + "title_aux": "ComfyUI Diffusion Color Grading" + } + ], + "https://github.com/Haoming02/comfyui-floodgate": [ + [ + "FloodGate" + ], + { + "title_aux": "ComfyUI Floodgate" + } + ], + "https://github.com/IDGallagher/ComfyUI-IG-Nodes": [ + [ + "IG Analyze SSIM", + "IG Explorer", + "IG Float", + "IG Folder", + "IG Int", + "IG Load Images", + "IG Multiply", + "IG Path Join", + "IG String" + ], + { + "author": "IDGallagher", + "description": "Custom nodes to aid in the exploration of Latent Space", + "nickname": "IG Interpolation Nodes", + "title": "IG Interpolation Nodes", + "title_aux": "IG Interpolation Nodes" + } + ], + "https://github.com/JPS-GER/ComfyUI_JPS-Nodes": [ + [ + "Conditioning Switch (JPS)", + "ControlNet Switch (JPS)", + "Crop Image Square (JPS)", + "Crop Image TargetSize (JPS)", + "Disable Enable Switch (JPS)", + "Enable Disable Switch (JPS)", + "Generation Settings (JPS)", + "Generation Settings Pipe (JPS)", + "Generation TXT IMG Settings (JPS)", + "Get Date Time String (JPS)", + "Get Image Size (JPS)", + "IP Adapter Settings (JPS)", + "IP Adapter Settings Pipe (JPS)", + "Image Switch (JPS)", + "Images Masks MultiPipe (JPS)", + "Integer Switch (JPS)", + "Largest Int (JPS)", + "Latent Switch (JPS)", + "Lora Loader (JPS)", + "Mask Switch (JPS)", + "Model Switch (JPS)", + "Multiply Float Float (JPS)", + "Multiply Int Float (JPS)", + "Multiply Int Int (JPS)", + "Resolution Multiply (JPS)", + "Revision Settings (JPS)", + "Revision Settings Pipe (JPS)", + "SDXL Basic Settings (JPS)", + "SDXL Basic Settings Pipe (JPS)", + "SDXL Fundamentals MultiPipe (JPS)", + "SDXL Prompt Handling (JPS)", + "SDXL Prompt Handling Plus (JPS)", + "SDXL Prompt Styler (JPS)", + "SDXL Recommended Resolution Calc (JPS)", + "SDXL Resolutions (JPS)", + "Sampler Scheduler Settings (JPS)", + "Substract Int Int (JPS)", + "Text Concatenate (JPS)", + "VAE Switch (JPS)" + ], + { + "author": "JPS", + "description": "Various nodes to handle SDXL Resolutions, SDXL Basic Settings, IP Adapter Settings, Revision Settings, SDXL Prompt Styler, Crop Image to Square, Crop Image to Target Size, Get Date-Time String, Resolution Multiply, Largest Integer, 5-to-1 Switches for Integer, Images, Latents, Conditioning, Model, VAE, ControlNet", + "nickname": "JPS Custom Nodes", + "title": "JPS Custom Nodes for ComfyUI", + "title_aux": "JPS Custom Nodes for ComfyUI" + } + ], + "https://github.com/Jcd1230/rembg-comfyui-node": [ + [ + "Image Remove Background (rembg)" + ], + { + "title_aux": "Rembg Background Removal Node for ComfyUI" + } + ], + "https://github.com/Jordach/comfy-plasma": [ + [ + "JDC_AutoContrast", + "JDC_BlendImages", + "JDC_BrownNoise", + "JDC_Contrast", + "JDC_EqualizeGrey", + "JDC_GaussianBlur", + "JDC_GreyNoise", + "JDC_Greyscale", + "JDC_ImageLoader", + "JDC_ImageLoaderMeta", + "JDC_PinkNoise", + "JDC_Plasma", + "JDC_PlasmaSampler", + "JDC_PowerImage", + "JDC_RandNoise", + "JDC_ResizeFactor" + ], + { + "title_aux": "comfy-plasma" + } + ], + "https://github.com/Kaharos94/ComfyUI-Saveaswebp": [ + [ + "Save_as_webp" + ], + { + "title_aux": "ComfyUI-Saveaswebp" + } + ], + "https://github.com/Kangkang625/ComfyUI-paint-by-example": [ + [ + "PaintbyExamplePipeLoader", + "PaintbyExampleSampler" + ], + { + "title_aux": "ComfyUI-Paint-by-Example" + } + ], + "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet": [ + [ + "ACN_AdvancedControlNetApply", + "ACN_DefaultUniversalWeights", + "ACN_SparseCtrlIndexMethodNode", + "ACN_SparseCtrlLoaderAdvanced", + "ACN_SparseCtrlMergedLoaderAdvanced", + "ACN_SparseCtrlRGBPreprocessor", + "ACN_SparseCtrlSpreadMethodNode", + "ControlNetLoaderAdvanced", + "CustomControlNetWeights", + "CustomT2IAdapterWeights", + "DiffControlNetLoaderAdvanced", + "LatentKeyframe", + "LatentKeyframeBatchedGroup", + "LatentKeyframeGroup", + "LatentKeyframeTiming", + "LoadImagesFromDirectory", + "ScaledSoftControlNetWeights", + "ScaledSoftMaskedUniversalWeights", + "SoftControlNetWeights", + "SoftT2IAdapterWeights", + "TimestepKeyframe" + ], + { + "title_aux": "ComfyUI-Advanced-ControlNet" + } + ], + "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved": [ + [ + "ADE_AnimateDiffCombine", + "ADE_AnimateDiffLoRALoader", + "ADE_AnimateDiffLoaderV1Advanced", + "ADE_AnimateDiffLoaderWithContext", + "ADE_AnimateDiffModelSettings", + "ADE_AnimateDiffModelSettingsAdvancedAttnStrengths", + "ADE_AnimateDiffModelSettingsSimple", + "ADE_AnimateDiffModelSettings_Release", + "ADE_AnimateDiffUniformContextOptions", + "ADE_AnimateDiffUniformContextOptionsExperimental", + "ADE_AnimateDiffUnload", + "ADE_EmptyLatentImageLarge", + "AnimateDiffLoaderV1", + "CheckpointLoaderSimpleWithNoiseSelect" + ], + { + "title_aux": "AnimateDiff Evolved" + } + ], + "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite": [ + [ + "VHS_DuplicateImages", + "VHS_DuplicateLatents", + "VHS_GetImageCount", + "VHS_GetLatentCount", + "VHS_LoadAudio", + "VHS_LoadImages", + "VHS_LoadImagesPath", + "VHS_LoadVideo", + "VHS_LoadVideoPath", + "VHS_MergeImages", + "VHS_MergeLatents", + "VHS_SelectEveryNthImage", + "VHS_SelectEveryNthLatent", + "VHS_SplitImages", + "VHS_SplitLatents", + "VHS_VideoCombine" + ], + { + "title_aux": "ComfyUI-VideoHelperSuite" + } + ], + "https://github.com/LEv145/images-grid-comfy-plugin": [ + [ + "GridAnnotation", + "ImageCombine", + "ImagesGridByColumns", + "ImagesGridByRows", + "LatentCombine" + ], + { + "title_aux": "ImagesGrid" + } + ], + "https://github.com/Lerc/canvas_tab": [ + [ + "Canvas_Tab", + "Send_To_Editor" + ], + { + "author": "Lerc", + "description": "This extension provides a full page image editor with mask support. There are two nodes, one to receive images from the editor and one to send images to the editor.", + "nickname": "Canvas Tab", + "title": "Canvas Tab", + "title_aux": "Canvas Tab" + } + ], + "https://github.com/Limitex/ComfyUI-Calculation": [ + [ + "CenterCalculation", + "CreateQRCode" + ], + { + "title_aux": "ComfyUI-Calculation" + } + ], + "https://github.com/Limitex/ComfyUI-Diffusers": [ + [ + "DiffusersClipTextEncode", + "DiffusersModelMakeup", + "DiffusersPipelineLoader", + "DiffusersSampler", + "DiffusersSaveImage", + "DiffusersSchedulerLoader", + "DiffusersVaeLoader", + "StreamDiffusionCreateStream", + "StreamDiffusionFastSampler", + "StreamDiffusionSampler", + "StreamDiffusionWarmup" + ], + { + "title_aux": "ComfyUI-Diffusers" + } + ], + "https://github.com/LonicaMewinsky/ComfyUI-MakeFrame": [ + [ + "BreakFrames", + "BreakGrid", + "GetKeyFrames", + "MakeGrid", + "RandomImageFromDir" + ], + { + "title_aux": "ComfyBreakAnim" + } + ], + "https://github.com/LonicaMewinsky/ComfyUI-RawSaver": [ + [ + "SaveTifImage" + ], + { + "title_aux": "ComfyUI-RawSaver" + } + ], + "https://github.com/M1kep/ComfyLiterals": [ + [ + "Checkpoint", + "Float", + "Int", + "KepStringLiteral", + "Lora", + "Operation", + "String" + ], + { + "title_aux": "ComfyLiterals" + } + ], + "https://github.com/M1kep/ComfyUI-KepOpenAI": [ + [ + "KepOpenAI_ImageWithPrompt" + ], + { + "title_aux": "ComfyUI-KepOpenAI" + } + ], + "https://github.com/M1kep/ComfyUI-OtherVAEs": [ + [ + "OtherVAE_Taesd" + ], + { + "title_aux": "ComfyUI-OtherVAEs" + } + ], + "https://github.com/M1kep/Comfy_KepKitchenSink": [ + [ + "KepRotateImage" + ], + { + "title_aux": "Comfy_KepKitchenSink" + } + ], + "https://github.com/M1kep/Comfy_KepListStuff": [ + [ + "Empty Images", + "Image Overlay", + "ImageListLoader", + "Join Float Lists", + "Join Image Lists", + "KepStringList", + "KepStringListFromNewline", + "Kep_JoinListAny", + "Kep_RepeatList", + "Kep_ReverseList", + "Kep_VariableImageBuilder", + "List Length", + "Range(Num Steps) - Float", + "Range(Num Steps) - Int", + "Range(Step) - Float", + "Range(Step) - Int", + "Stack Images", + "XYAny", + "XYImage" + ], + { + "title_aux": "Comfy_KepListStuff" + } + ], + "https://github.com/M1kep/Comfy_KepMatteAnything": [ + [ + "MatteAnything_DinoBoxes", + "MatteAnything_GenerateVITMatte", + "MatteAnything_InitSamPredictor", + "MatteAnything_LoadDINO", + "MatteAnything_LoadVITMatteModel", + "MatteAnything_SAMLoader", + "MatteAnything_SAMMaskFromBoxes", + "MatteAnything_ToTrimap" + ], + { + "title_aux": "Comfy_KepMatteAnything" + } + ], + "https://github.com/M1kep/KepPromptLang": [ + [ + "Build Gif", + "Special CLIP Loader" + ], + { + "title_aux": "KepPromptLang" + } + ], + "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes": [ + [ + "Save Text File_mne" + ], + { + "title_aux": "ComfyUI-mnemic-nodes" + } + ], + "https://github.com/ManglerFTW/ComfyI2I": [ + [ + "Color Transfer", + "Combine and Paste", + "Inpaint Segments", + "Mask Ops" + ], + { + "author": "ManglerFTW", + "title": "ComfyI2I", + "title_aux": "ComfyI2I" + } + ], + "https://github.com/MitoshiroPJ/comfyui_slothful_attention": [ + [ + "NearSightedAttention", + "NearSightedAttentionSimple", + "NearSightedTile", + "SlothfulAttention" + ], + { + "title_aux": "ComfyUI Slothful Attention" + } + ], + "https://github.com/NicholasMcCarthy/ComfyUI_TravelSuite": [ + [ + "LatentTravel" + ], + { + "title_aux": "ComfyUI_TravelSuite" + } + ], + "https://github.com/NimaNzrii/comfyui-photoshop": [ + [ + "PhotoshopToComfyUI" + ], + { + "title_aux": "comfyui-photoshop" + } + ], + "https://github.com/NimaNzrii/comfyui-popup_preview": [ + [ + "PreviewPopup" + ], + { + "title_aux": "comfyui-popup_preview" + } + ], + "https://github.com/Niutonian/ComfyUi-NoodleWebcam": [ + [ + "WebcamNode" + ], + { + "title_aux": "ComfyUi-NoodleWebcam" + } + ], + "https://github.com/NotHarroweD/Harronode": [ + [ + "Harronode" + ], + { + "author": "HarroweD and quadmoon (https://github.com/traugdor)", + "description": "This extension to ComfyUI will build a prompt for the Harrlogos LoRA for SDXL.", + "nickname": "Harronode", + "nodename_pattern": "Harronode", + "title": "Harrlogos Prompt Builder Node", + "title_aux": "Harronode" + } + ], + "https://github.com/Nourepide/ComfyUI-Allor": [ + [ + "AlphaChanelAdd", + "AlphaChanelAddByMask", + "AlphaChanelAsMask", + "AlphaChanelRemove", + "AlphaChanelRestore", + "ClipClamp", + "ClipVisionClamp", + "ClipVisionOutputClamp", + "ConditioningClamp", + "ControlNetClamp", + "GligenClamp", + "ImageBatchCopy", + "ImageBatchFork", + "ImageBatchGet", + "ImageBatchJoin", + "ImageBatchPermute", + "ImageBatchRemove", + "ImageClamp", + "ImageCompositeAbsolute", + "ImageCompositeAbsoluteByContainer", + "ImageCompositeRelative", + "ImageCompositeRelativeByContainer", + "ImageContainer", + "ImageContainerInheritanceAdd", + "ImageContainerInheritanceMax", + "ImageContainerInheritanceScale", + "ImageContainerInheritanceSum", + "ImageDrawArc", + "ImageDrawArcByContainer", + "ImageDrawChord", + "ImageDrawChordByContainer", + "ImageDrawEllipse", + "ImageDrawEllipseByContainer", + "ImageDrawLine", + "ImageDrawLineByContainer", + "ImageDrawPieslice", + "ImageDrawPiesliceByContainer", + "ImageDrawPolygon", + "ImageDrawRectangle", + "ImageDrawRectangleByContainer", + "ImageDrawRectangleRounded", + "ImageDrawRectangleRoundedByContainer", + "ImageEffectsAdjustment", + "ImageEffectsGrayscale", + "ImageEffectsLensBokeh", + "ImageEffectsLensChromaticAberration", + "ImageEffectsLensOpticAxis", + "ImageEffectsLensVignette", + "ImageEffectsLensZoomBurst", + "ImageEffectsNegative", + "ImageEffectsSepia", + "ImageFilterBilateralBlur", + "ImageFilterBlur", + "ImageFilterBoxBlur", + "ImageFilterContour", + "ImageFilterDetail", + "ImageFilterEdgeEnhance", + "ImageFilterEdgeEnhanceMore", + "ImageFilterEmboss", + "ImageFilterFindEdges", + "ImageFilterGaussianBlur", + "ImageFilterGaussianBlurAdvanced", + "ImageFilterMax", + "ImageFilterMedianBlur", + "ImageFilterMin", + "ImageFilterMode", + "ImageFilterRank", + "ImageFilterSharpen", + "ImageFilterSmooth", + "ImageFilterSmoothMore", + "ImageFilterStackBlur", + "ImageNoiseBeta", + "ImageNoiseBinomial", + "ImageNoiseBytes", + "ImageNoiseGaussian", + "ImageSegmentation", + "ImageSegmentationCustom", + "ImageSegmentationCustomAdvanced", + "ImageText", + "ImageTextMultiline", + "ImageTextMultilineOutlined", + "ImageTextOutlined", + "ImageTransformCropAbsolute", + "ImageTransformCropCorners", + "ImageTransformCropRelative", + "ImageTransformPaddingAbsolute", + "ImageTransformPaddingRelative", + "ImageTransformResizeAbsolute", + "ImageTransformResizeClip", + "ImageTransformResizeRelative", + "ImageTransformRotate", + "ImageTransformTranspose", + "LatentClamp", + "MaskClamp", + "ModelClamp", + "StyleModelClamp", + "UpscaleModelClamp", + "VaeClamp" + ], + { + "title_aux": "Allor Plugin" + } + ], + "https://github.com/Nuked88/ComfyUI-N-Nodes": [ + [ + "DynamicPrompt", + "Float Variable", + "FrameInterpolator", + "GPT Loader Simple", + "GPTSampler", + "Integer Variable", + "LoadFramesFromFolder", + "LoadVideo", + "SaveVideo", + "SetMetadataForSaveVideo", + "String Variable" + ], + { + "title_aux": "ComfyUI-N-Nodes" + } + ], + "https://github.com/Off-Live/ComfyUI-off-suite": [ + [ + "Cached Image Load From URL", + "Crop Center wigh SEGS", + "Crop Center with SEGS", + "GW Number Formatting", + "Image Crop Fit", + "Image Resize Fit", + "OFF SEGS to Image", + "Watermarking" + ], + { + "title_aux": "ComfyUI-off-suite" + } + ], + "https://github.com/Onierous/QRNG_Node_ComfyUI/raw/main/qrng_node.py": [ + [ + "QRNG_Node_CSV" + ], + { + "title_aux": "QRNG_Node_ComfyUI" + } + ], + "https://github.com/PCMonsterx/ComfyUI-CSV-Loader": [ + [ + "Load Artists CSV", + "Load Artmovements CSV", + "Load Characters CSV", + "Load Colors CSV", + "Load Composition CSV", + "Load Lighting CSV", + "Load Negative CSV", + "Load Positive CSV", + "Load Settings CSV", + "Load Styles CSV" + ], + { + "title_aux": "ComfyUI-CSV-Loader" + } + ], + "https://github.com/ParmanBabra/ComfyUI-Malefish-Custom-Scripts": [ + [ + "CSVPromptsLoader", + "CombinePrompt", + "MultiLoraLoader", + "RandomPrompt" + ], + { + "title_aux": "ComfyUI-Malefish-Custom-Scripts" + } + ], + "https://github.com/Pfaeff/pfaeff-comfyui": [ + [ + "AstropulsePixelDetector", + "BackgroundRemover", + "ImagePadForBetterOutpaint", + "Inpainting", + "InpaintingPipelineLoader" + ], + { + "title_aux": "pfaeff-comfyui" + } + ], + "https://github.com/RenderRift/ComfyUI-RenderRiftNodes": [ + [ + "AnalyseMetadata", + "DateIntegerNode", + "DisplayMetaOptions", + "LoadImageWithMeta", + "MetadataOverlayNode", + "VideoPathMetaExtraction" + ], + { + "title_aux": "ComfyUI-RenderRiftNodes" + } + ], + "https://github.com/Ryuukeisyou/comfyui_face_parsing": [ + [ + "BBoxListItemSelect(FaceParsing)", + "BBoxResize(FaceParsing)", + "ColorAdjust(FaceParsing)", + "FaceBBoxDetect(FaceParsing)", + "FaceBBoxDetectorLoader(FaceParsing)", + "FaceParse(FaceParsing)", + "FaceParsingModelLoader(FaceParsing)", + "FaceParsingProcessorLoader(FaceParsing)", + "FaceParsingResultsParser(FaceParsing)", + "GuidedFilter(FaceParsing)", + "ImageCropWithBBox(FaceParsing)", + "ImageInsertWithBBox(FaceParsing)", + "ImageListSelect(FaceParsing)", + "ImagePadWithBBox(FaceParsing)", + "ImageResizeCalculator(FaceParsing)", + "ImageSize(FaceParsing)", + "MaskComposite(FaceParsing)", + "MaskListComposite(FaceParsing)", + "MaskListSelect(FaceParsing)" + ], + { + "title_aux": "comfyui_face_parsing" + } + ], + "https://github.com/SLAPaper/ComfyUI-Image-Selector": [ + [ + "ImageDuplicator", + "ImageSelector", + "LatentDuplicator", + "LatentSelector" + ], + { + "title_aux": "ComfyUI-Image-Selector" + } + ], + "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes": [ + [ + "MSSqlSelectNode", + "MSSqlTableNode" + ], + { + "title_aux": "LexMSDBNodes" + } + ], + "https://github.com/SOELexicon/ComfyUI-LexTools": [ + [ + "AgeClassifierNode", + "ArtOrHumanClassifierNode", + "DocumentClassificationNode", + "FoodCategoryClassifierNode", + "ImageAspectPadNode", + "ImageCaptioning", + "ImageFilterByFloatScoreNode", + "ImageFilterByIntScoreNode", + "ImageQualityScoreNode", + "ImageRankingNode", + "ImageScaleToMin", + "MD5ImageHashNode", + "SamplerPropertiesNode", + "ScoreConverterNode", + "SeedIncrementerNode", + "SegformerNode", + "SegformerNodeMasks", + "SegformerNodeMergeSegments", + "StepCfgIncrementNode" + ], + { + "title_aux": "ComfyUI-LexTools" + } + ], + "https://github.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI/raw/master/custom_nodes/clip_text_encoder_a1111.py": [ + [ + "CLIPTextEncodeA1111", + "RerouteTextForCLIPTextEncodeA1111" + ], + { + "title_aux": "ComfyUI A1111-like Prompt Custom Node Solution" + } + ], + "https://github.com/Scholar01/ComfyUI-Keyframe": [ + [ + "KeyframeApply", + "KeyframeInterpolationPart", + "KeyframePart" + ], + { + "title_aux": "SComfyUI-Keyframe" + } + ], + "https://github.com/SeargeDP/SeargeSDXL": [ + [ + "SeargeAdvancedParameters", + "SeargeCheckpointLoader", + "SeargeConditionMixing", + "SeargeConditioningMuxer2", + "SeargeConditioningMuxer5", + "SeargeConditioningParameters", + "SeargeControlnetAdapterV2", + "SeargeControlnetModels", + "SeargeCustomAfterUpscaling", + "SeargeCustomAfterVaeDecode", + "SeargeCustomPromptMode", + "SeargeDebugPrinter", + "SeargeEnablerInputs", + "SeargeFloatConstant", + "SeargeFloatMath", + "SeargeFloatPair", + "SeargeFreeU", + "SeargeGenerated1", + "SeargeGenerationParameters", + "SeargeHighResolution", + "SeargeImage2ImageAndInpainting", + "SeargeImageAdapterV2", + "SeargeImageSave", + "SeargeImageSaving", + "SeargeInput1", + "SeargeInput2", + "SeargeInput3", + "SeargeInput4", + "SeargeInput5", + "SeargeInput6", + "SeargeInput7", + "SeargeIntegerConstant", + "SeargeIntegerMath", + "SeargeIntegerPair", + "SeargeIntegerScaler", + "SeargeLatentMuxer3", + "SeargeLoraLoader", + "SeargeLoras", + "SeargeMagicBox", + "SeargeModelSelector", + "SeargeOperatingMode", + "SeargeOutput1", + "SeargeOutput2", + "SeargeOutput3", + "SeargeOutput4", + "SeargeOutput5", + "SeargeOutput6", + "SeargeOutput7", + "SeargeParameterProcessor", + "SeargePipelineStart", + "SeargePipelineTerminator", + "SeargePreviewImage", + "SeargePromptAdapterV2", + "SeargePromptCombiner", + "SeargePromptStyles", + "SeargePromptText", + "SeargeSDXLBasePromptEncoder", + "SeargeSDXLImage2ImageSampler", + "SeargeSDXLImage2ImageSampler2", + "SeargeSDXLPromptEncoder", + "SeargeSDXLRefinerPromptEncoder", + "SeargeSDXLSampler", + "SeargeSDXLSampler2", + "SeargeSDXLSamplerV3", + "SeargeSamplerAdvanced", + "SeargeSamplerInputs", + "SeargeSaveFolderInputs", + "SeargeSeparator", + "SeargeStylePreprocessor", + "SeargeTextInputV2", + "SeargeUpscaleModelLoader", + "SeargeUpscaleModels", + "SeargeVAELoader" + ], + { + "title_aux": "SeargeSDXL" + } + ], + "https://github.com/Ser-Hilary/SDXL_sizing/raw/main/conditioning_sizing_for_SDXL.py": [ + [ + "get_aspect_from_image", + "get_aspect_from_ints", + "sizing_node", + "sizing_node_basic", + "sizing_node_unparsed" + ], + { + "title_aux": "SDXL_sizing" + } + ], + "https://github.com/Smuzzies/comfyui_chatbox_overlay/raw/main/chatbox_overlay.py": [ + [ + "Chatbox Overlay" + ], + { + "title_aux": "Chatbox Overlay node for ComfyUI" + } + ], + "https://github.com/SoftMeng/ComfyUI_Mexx_Poster": [ + [ + "ComfyUI_Mexx_Poster" + ], + { + "title_aux": "ComfyUI_Mexx_Poster" + } + ], + "https://github.com/SoftMeng/ComfyUI_Mexx_Styler": [ + [ + "MexxSDXLPromptStyler", + "MexxSDXLPromptStylerAdvanced" + ], + { + "title_aux": "ComfyUI_Mexx_Styler" + } + ], + "https://github.com/SpaceKendo/ComfyUI-svd_txt2vid": [ + [ + "SVD_txt2vid_ConditioningwithLatent" + ], + { + "title_aux": "Text to video for Stable Video Diffusion in ComfyUI" + } + ], + "https://github.com/Stability-AI/stability-ComfyUI-nodes": [ + [ + "ColorBlend", + "ControlLoraSave", + "GetImageSize" + ], + { + "title_aux": "stability-ComfyUI-nodes" + } + ], + "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes": [ + [ + "CR 3D Camera Drone", + "CR 3D Camera Static", + "CR 3D Polygon", + "CR 3D Solids", + "CR ASCII Pattern", + "CR Add Annotation", + "CR Alternate Latents", + "CR Apply Annotations", + "CR Apply ControlNet", + "CR Apply LoRA Stack", + "CR Apply Model Merge", + "CR Apply Multi Upscale", + "CR Apply Multi-ControlNet", + "CR Arabic Text RTL", + "CR Aspect Ratio", + "CR Aspect Ratio Banners", + "CR Aspect Ratio SDXL", + "CR Batch Images From List", + "CR Batch Process Switch", + "CR Binary Pattern", + "CR Binary To List", + "CR Central Schedule", + "CR Check Job Complete", + "CR Checker Pattern", + "CR Clamp Value", + "CR Clip Input Switch", + "CR Color Bars", + "CR Color Gradient", + "CR Color Panel", + "CR Color Tint", + "CR Combine Schedules", + "CR Comic Panel Templates", + "CR Comic Panel Templates (Advanced)", + "CR Comic Panel Templates Advanced", + "CR Composite Text", + "CR Conditioning Input Switch", + "CR Conditioning Mixer", + "CR Continuous Rotation", + "CR Continuous Track", + "CR Continuous Zoom", + "CR ControlNet Input Switch", + "CR Current Frame", + "CR Cycle Images", + "CR Cycle Images Simple", + "CR Cycle LoRAs", + "CR Cycle Models", + "CR Cycle Styles", + "CR Cycle Text", + "CR Cycle Text Simple", + "CR Debatch Frames", + "CR Display Font", + "CR Draw OBJ", + "CR Draw Perspective Text", + "CR Draw Pie", + "CR Draw Shape", + "CR Draw Text", + "CR Encode Scheduled Prompts", + "CR Feathered Border", + "CR Float Range List", + "CR Float To Integer", + "CR Float To String", + "CR Font File List", + "CR Gradient Float", + "CR Gradient Integer", + "CR Halftone Filter", + "CR Halftone Grid", + "CR Hires Fix Process Switch", + "CR Image Border", + "CR Image Grid Panel", + "CR Image Input Switch", + "CR Image Input Switch (4 way)", + "CR Image List", + "CR Image List Simple", + "CR Image Output", + "CR Image Panel", + "CR Image Pipe Edit", + "CR Image Pipe In", + "CR Image Pipe Out", + "CR Image Size", + "CR Image Transition", + "CR Image XY Panel", + "CR Img2Img Process Switch", + "CR Increment Float", + "CR Increment Integer", + "CR Index", + "CR Index Increment", + "CR Index Multiply", + "CR Index Reset", + "CR Input Text List", + "CR Integer Multiple", + "CR Integer Range List", + "CR Integer To String", + "CR Interpolate Latents", + "CR Interpolate Prompt Weights", + "CR Interpolate Rotation", + "CR Interpolate Track", + "CR Interpolate Zoom", + "CR Intertwine Lists", + "CR Job Current Frame", + "CR Job List", + "CR Job Scheduler", + "CR Keyframe List", + "CR Latent Batch Size", + "CR Latent Input Switch", + "CR List Schedule", + "CR LoRA List", + "CR LoRA Stack", + "CR Load Animation Frames", + "CR Load Flow Frames", + "CR Load Image List", + "CR Load Image List Plus", + "CR Load LoRA", + "CR Load Prompt Style", + "CR Load Schedule From File", + "CR Load Scheduled ControlNets", + "CR Load Scheduled LoRAs", + "CR Load Scheduled Models", + "CR Load Text List", + "CR Load Workflow", + "CR Load XY Annotation From File", + "CR Mask Text", + "CR Model Input Switch", + "CR Model List", + "CR Model Merge Stack", + "CR Module Input", + "CR Module Output", + "CR Module Pipe Loader", + "CR Multi Upscale Stack", + "CR Multi-ControlNet Stack", + "CR Multi-Panel Meme Template", + "CR Multiline Text", + "CR Output Flow Frames", + "CR Output Schedule To File", + "CR Overlay Text", + "CR Overlay Transparent Image", + "CR Page Layout", + "CR Pipe Switch", + "CR Polygons", + "CR Popular Meme Templates", + "CR Prompt List", + "CR Prompt List Keyframes", + "CR Prompt Scheduler", + "CR Prompt Text", + "CR Prompt Weight Scheduler", + "CR Radial Gradient", + "CR Radial Gradient Map", + "CR Random Hex Color", + "CR Random LoRA Stack", + "CR Random Multiline Colors", + "CR Random Multiline Values", + "CR Random Panel Codes", + "CR Random RGB", + "CR Random RGB Gradient", + "CR Random Shape Pattern", + "CR Random Weight LoRA", + "CR SD1.5 Aspect Ratio", + "CR SDXL Aspect Ratio", + "CR SDXL Base Prompt Encoder", + "CR SDXL Prompt Mix Presets", + "CR SDXL Style Text", + "CR Save Text To File", + "CR Schedule Camera Movements", + "CR Schedule ControlNets", + "CR Schedule Input Switch", + "CR Schedule Styles", + "CR Schedule To ScheduleList", + "CR Seed", + "CR Seed to Int", + "CR Select Model", + "CR Set Value On Boolean", + "CR Simple Annotations", + "CR Simple Banner", + "CR Simple Binary Pattern", + "CR Simple Binary Pattern Simple", + "CR Simple Image Compare", + "CR Simple Image Watermark", + "CR Simple Meme Template", + "CR Simple Prompt List", + "CR Simple Prompt List Keyframes", + "CR Simple Prompt Scheduler", + "CR Simple Schedule", + "CR Simple Text Panel", + "CR Simple Text Scheduler", + "CR Simple Text Watermark", + "CR Simple Titles", + "CR Simple Value Scheduler", + "CR Spawn Workflow Instance", + "CR Split String", + "CR Starburst Colors", + "CR Starburst Lines", + "CR String To Combo", + "CR String To Number", + "CR Strobe Images", + "CR Style Bars", + "CR Style List", + "CR Switch Model and CLIP", + "CR System TrueType Font", + "CR Text Input Switch", + "CR Text Input Switch (4 way)", + "CR Text List", + "CR Text List Cross Join", + "CR Text List Simple", + "CR Text List To String", + "CR Text Scheduler", + "CR Thumbnail Preview", + "CR Trigger", + "CR Upscale Image", + "CR VAE Input Switch", + "CR Value", + "CR Value Scheduler", + "CR Vignette Filter", + "CR XY From Folder", + "CR XY Grid", + "CR XY Index", + "CR XY Interpolate", + "CR XY List", + "CR XY Save Grid Image", + "CR XYZ Index", + "CR XYZ Interpolate", + "CR XYZ List" + ], + { + "author": "Suzie1", + "description": "165 custom nodes for Graphics, Animation, IO, Aspect Ratio, Model Merge, ControlNet, LoRA, XY Grid, and Utilities.", + "nickname": "Comfyroll Studio", + "title": "Comfyroll Studio", + "title_aux": "ComfyUI_Comfyroll_CustomNodes" + } + ], + "https://github.com/Sxela/ComfyWarp": [ + [ + "ExtractOpticalFlow", + "LoadFrame", + "LoadFrameFromDataset", + "LoadFrameFromFolder", + "LoadFramePairFromDataset", + "LoadFrameSequence", + "MakeFrameDataset", + "MixConsistencyMaps", + "OffsetNumber", + "ResizeToFit", + "SaveFrame", + "WarpFrame" + ], + { + "title_aux": "ComfyWarp" + } + ], + "https://github.com/TGu-97/ComfyUI-TGu-utils": [ + [ + "MPNReroute", + "MPNSwitch", + "PNSwitch" + ], + { + "title_aux": "TGu Utilities" + } + ], + "https://github.com/THtianhao/ComfyUI-FaceChain": [ + [ + "FCStyleLoraLoad", + "FC_CropAndPaste", + "FC_CropBottom", + "FC_CropFace", + "FC_CropMask", + "FC_FaceDetection", + "FC_FaceFusion", + "FC_MaskOP", + "FC_ReplaceImage", + "FC_Segment", + "FC_StyleLoraLoad" + ], + { + "title_aux": "ComfyUI-FaceChain" + } + ], + "https://github.com/THtianhao/ComfyUI-Portrait-Maker": [ + [ + "PM_BoxCropImage", + "PM_ColorTransfer", + "PM_ExpandMaskBox", + "PM_FaceFusion", + "PM_FaceShapMatch", + "PM_FaceSkin", + "PM_GetImageInfo", + "PM_ImageResizeTarget", + "PM_ImageScaleShort", + "PM_MakeUpTransfer", + "PM_MaskDilateErode", + "PM_MaskMerge2Image", + "PM_PortraitEnhancement", + "PM_RatioMerge2Image", + "PM_ReplaceBoxImg", + "PM_RetinaFace", + "PM_Similarity", + "PM_SkinRetouching", + "PM_SuperColorTransfer", + "PM_SuperMakeUpTransfer" + ], + { + "title_aux": "ComfyUI-Portrait-Maker" + } + ], + "https://github.com/TRI3D-LC/tri3d-comfyui-nodes": [ + [ + "tri3d-atr-parse", + "tri3d-atr-parse-batch", + "tri3d-dwpose", + "tri3d-extract-hand", + "tri3d-extract-parts-batch", + "tri3d-extract-parts-batch2", + "tri3d-extract-parts-mask-batch", + "tri3d-fuzzification", + "tri3d-interaction-canny", + "tri3d-pose-adaption", + "tri3d-pose-to-image", + "tri3d-position-hands", + "tri3d-position-parts-batch", + "tri3d-skin-feathered-padded-mask", + "tri3d-swap-pixels" + ], + { + "title_aux": "tri3d-comfyui-nodes" + } + ], + "https://github.com/TeaCrab/ComfyUI-TeaNodes": [ + [ + "TC_ColorFill", + "TC_EqualizeCLAHE", + "TC_ImageResize", + "TC_ImageScale", + "TC_MaskBG_DIS", + "TC_RandomColorFill", + "TC_SizeApproximation" + ], + { + "title_aux": "ComfyUI-TeaNodes" + } + ], + "https://github.com/TheBarret/ZSuite": [ + [ + "ZSuite: Prompter", + "ZSuite: RF Noise", + "ZSuite: SeedMod" + ], + { + "title_aux": "ZSuite" + } + ], + "https://github.com/TinyTerra/ComfyUI_tinyterraNodes": [ + [ + "ttN busIN", + "ttN busOUT", + "ttN compareInput", + "ttN concat", + "ttN debugInput", + "ttN float", + "ttN hiresfixScale", + "ttN imageOutput", + "ttN imageREMBG", + "ttN int", + "ttN multiModelMerge", + "ttN pipe2BASIC", + "ttN pipe2DETAILER", + "ttN pipeEDIT", + "ttN pipeEncodeConcat", + "ttN pipeIN", + "ttN pipeKSampler", + "ttN pipeKSamplerAdvanced", + "ttN pipeKSamplerSDXL", + "ttN pipeLoader", + "ttN pipeLoaderSDXL", + "ttN pipeLoraStack", + "ttN pipeOUT", + "ttN seed", + "ttN seedDebug", + "ttN text", + "ttN text3BOX_3WAYconcat", + "ttN text7BOX_concat", + "ttN textDebug", + "ttN xyPlot" + ], + { + "author": "tinyterra", + "description": "This extension offers various pipe nodes, fullscreen image viewer based on node history, dynamic widgets, interface customization, and more.", + "nickname": "ttNodes", + "nodename_pattern": "^ttN ", + "title": "tinyterraNodes", + "title_aux": "tinyterraNodes" + } + ], + "https://github.com/TripleHeadedMonkey/ComfyUI_MileHighStyler": [ + [ + "menus" + ], + { + "title_aux": "ComfyUI_MileHighStyler" + } + ], + "https://github.com/Tropfchen/ComfyUI-Embedding_Picker": [ + [ + "EmbeddingPicker" + ], + { + "title_aux": "Embedding Picker" + } + ], + "https://github.com/Tropfchen/ComfyUI-yaResolutionSelector": [ + [ + "YARS", + "YARSAdv" + ], + { + "title_aux": "YARS: Yet Another Resolution Selector" + } + ], + "https://github.com/Trung0246/ComfyUI-0246": [ + [ + "0246.Beautify", + "0246.BoxRange", + "0246.CastReroute", + "0246.Convert", + "0246.Count", + "0246.Highway", + "0246.HighwayBatch", + "0246.Hold", + "0246.Hub", + "0246.Junction", + "0246.JunctionBatch", + "0246.Loop", + "0246.Merge", + "0246.Pick", + "0246.RandomInt", + "0246.Script", + "0246.ScriptImbue", + "0246.ScriptNode", + "0246.ScriptPlan", + "0246.ScriptRule", + "0246.Stringify" + ], + { + "author": "Trung0246", + "description": "Random nodes for ComfyUI I made to solve my struggle with ComfyUI (ex: pipe, process). Have varying quality.", + "nickname": "ComfyUI-0246", + "title": "ComfyUI-0246", + "title_aux": "ComfyUI-0246" + } + ], + "https://github.com/Ttl/ComfyUi_NNLatentUpscale": [ + [ + "NNLatentUpscale" + ], + { + "title_aux": "ComfyUI Neural network latent upscale custom node" + } + ], + "https://github.com/Umikaze-job/select_folder_path_easy": [ + [ + "SelectFolderPathEasy" + ], + { + "title_aux": "select_folder_path_easy" + } + ], + "https://github.com/WASasquatch/ASTERR": [ + [ + "ASTERR", + "SaveASTERR" + ], + { + "title_aux": "ASTERR" + } + ], + "https://github.com/WASasquatch/ComfyUI_Preset_Merger": [ + [ + "Preset_Model_Merge" + ], + { + "title_aux": "ComfyUI Preset Merger" + } + ], + "https://github.com/WASasquatch/FreeU_Advanced": [ + [ + "FreeU (Advanced)" + ], + { + "title_aux": "FreeU_Advanced" + } + ], + "https://github.com/WASasquatch/PPF_Noise_ComfyUI": [ + [ + "Blend Latents (PPF Noise)", + "Cross-Hatch Power Fractal (PPF Noise)", + "Images as Latents (PPF Noise)", + "Perlin Power Fractal Latent (PPF Noise)" + ], + { + "title_aux": "PPF_Noise_ComfyUI" + } + ], + "https://github.com/WASasquatch/PowerNoiseSuite": [ + [ + "Blend Latents (PPF Noise)", + "Cross-Hatch Power Fractal (PPF Noise)", + "Cross-Hatch Power Fractal Settings (PPF Noise)", + "Images as Latents (PPF Noise)", + "Latent Adjustment (PPF Noise)", + "Latents to CPU (PPF Noise)", + "Linear Cross-Hatch Power Fractal (PPF Noise)", + "Perlin Power Fractal Latent (PPF Noise)", + "Perlin Power Fractal Settings (PPF Noise)", + "Power KSampler Advanced (PPF Noise)", + "Power-Law Noise (PPF Noise)" + ], + { + "title_aux": "Power Noise Suite for ComfyUI" + } + ], + "https://github.com/WASasquatch/WAS_Extras": [ + [ + "BLVAEEncode", + "CLIPTextEncodeList", + "CLIPTextEncodeSequence2", + "ConditioningBlend", + "DebugInput", + "KSamplerSeq", + "KSamplerSeq2", + "VAEEncodeForInpaint (WAS)", + "VividSharpen" + ], + { + "title_aux": "WAS_Extras" + } + ], + "https://github.com/WASasquatch/was-node-suite-comfyui": [ + [ + "BLIP Analyze Image", + "BLIP Model Loader", + "Blend Latents", + "Bounded Image Blend", + "Bounded Image Blend with Mask", + "Bounded Image Crop", + "Bounded Image Crop with Mask", + "Bus Node", + "CLIP Input Switch", + "CLIP Vision Input Switch", + "CLIPSeg Batch Masking", + "CLIPSeg Masking", + "CLIPSeg Model Loader", + "CLIPTextEncode (BlenderNeko Advanced + NSP)", + "CLIPTextEncode (NSP)", + "Cache Node", + "Checkpoint Loader", + "Checkpoint Loader (Simple)", + "Conditioning Input Switch", + "Constant Number", + "Control Net Model Input Switch", + "Convert Masks to Images", + "Create Grid Image", + "Create Grid Image from Batch", + "Create Morph Image", + "Create Morph Image from Path", + "Create Video from Path", + "Debug Number to Console", + "Dictionary to Console", + "Diffusers Hub Model Down-Loader", + "Diffusers Model Loader", + "Export API", + "Image Analyze", + "Image Aspect Ratio", + "Image Batch", + "Image Blank", + "Image Blend", + "Image Blend by Mask", + "Image Blending Mode", + "Image Bloom Filter", + "Image Bounds", + "Image Bounds to Console", + "Image Canny Filter", + "Image Chromatic Aberration", + "Image Color Palette", + "Image Crop Face", + "Image Crop Location", + "Image Crop Square Location", + "Image Displacement Warp", + "Image Dragan Photography Filter", + "Image Edge Detection Filter", + "Image Film Grain", + "Image Filter Adjustments", + "Image Flip", + "Image Generate Gradient", + "Image Gradient Map", + "Image High Pass Filter", + "Image History Loader", + "Image Input Switch", + "Image Levels Adjustment", + "Image Load", + "Image Lucy Sharpen", + "Image Median Filter", + "Image Mix RGB Channels", + "Image Monitor Effects Filter", + "Image Nova Filter", + "Image Padding", + "Image Paste Crop", + "Image Paste Crop by Location", + "Image Paste Face", + "Image Perlin Noise", + "Image Perlin Power Fractal", + "Image Pixelate", + "Image Power Noise", + "Image Rembg (Remove Background)", + "Image Remove Background (Alpha)", + "Image Remove Color", + "Image Resize", + "Image Rotate", + "Image Rotate Hue", + "Image SSAO (Ambient Occlusion)", + "Image SSDO (Direct Occlusion)", + "Image Save", + "Image Seamless Texture", + "Image Select Channel", + "Image Select Color", + "Image Shadows and Highlights", + "Image Size to Number", + "Image Stitch", + "Image Style Filter", + "Image Threshold", + "Image Tiled", + "Image Transpose", + "Image Voronoi Noise Filter", + "Image fDOF Filter", + "Image to Latent Mask", + "Image to Noise", + "Image to Seed", + "Images to Linear", + "Images to RGB", + "Inset Image Bounds", + "Integer place counter", + "KSampler (WAS)", + "KSampler Cycle", + "Latent Input Switch", + "Latent Noise Injection", + "Latent Size to Number", + "Latent Upscale by Factor (WAS)", + "Load Cache", + "Load Image Batch", + "Load Lora", + "Load Text File", + "Logic Boolean", + "Lora Input Switch", + "Lora Loader", + "Mask Arbitrary Region", + "Mask Batch", + "Mask Batch to Mask", + "Mask Ceiling Region", + "Mask Crop Dominant Region", + "Mask Crop Minority Region", + "Mask Crop Region", + "Mask Dilate Region", + "Mask Dominant Region", + "Mask Erode Region", + "Mask Fill Holes", + "Mask Floor Region", + "Mask Gaussian Region", + "Mask Invert", + "Mask Minority Region", + "Mask Paste Region", + "Mask Smooth Region", + "Mask Threshold Region", + "Masks Add", + "Masks Combine Batch", + "Masks Combine Regions", + "Masks Subtract", + "MiDaS Depth Approximation", + "MiDaS Mask Image", + "MiDaS Model Loader", + "Model Input Switch", + "Number Counter", + "Number Input Condition", + "Number Input Switch", + "Number Multiple Of", + "Number Operation", + "Number PI", + "Number to Float", + "Number to Int", + "Number to Seed", + "Number to String", + "Number to Text", + "Prompt Multiple Styles Selector", + "Prompt Styles Selector", + "Random Number", + "SAM Image Mask", + "SAM Model Loader", + "SAM Parameters", + "SAM Parameters Combine", + "Samples Passthrough (Stat System)", + "Save Text File", + "Seed", + "String to Text", + "Tensor Batch to Image", + "Text Add Token by Input", + "Text Add Tokens", + "Text Compare", + "Text Concatenate", + "Text Dictionary Update", + "Text File History Loader", + "Text Find and Replace", + "Text Find and Replace Input", + "Text Find and Replace by Dictionary", + "Text Input Switch", + "Text List", + "Text List Concatenate", + "Text List to Text", + "Text Load Line From File", + "Text Multiline", + "Text Parse A1111 Embeddings", + "Text Parse Noodle Soup Prompts", + "Text Parse Tokens", + "Text Random Line", + "Text Random Prompt", + "Text Shuffle", + "Text String", + "Text String Truncate", + "Text to Conditioning", + "Text to Console", + "Text to Number", + "Text to String", + "True Random.org Number Generator", + "Upscale Model Loader", + "Upscale Model Switch", + "VAE Input Switch", + "Video Dump Frames", + "Write to GIF", + "Write to Video", + "unCLIP Checkpoint Loader" + ], + { + "title_aux": "WAS Node Suite" + } + ], + "https://github.com/WebDev9000/WebDev9000-Nodes": [ + [ + "IgnoreBraces", + "SettingsSwitch" + ], + { + "title_aux": "WebDev9000-Nodes" + } + ], + "https://github.com/YMC-GitHub/ymc-node-suite-comfyui": [ + [ + "canvas-util-cal-size", + "conditioning-util-input-switch", + "cutoff-region-util", + "hks-util-cal-denoise-step", + "img-util-get-image-size", + "img-util-switch-input-image", + "io-image-save", + "io-text-save", + "io-util-file-list-get", + "io-util-file-list-get-text", + "number-util-random-num", + "pipe-util-to-basic-pipe", + "region-util-get-by-center-and-size", + "region-util-get-by-lt", + "region-util-get-crop-location-from-center-size-text", + "region-util-get-pad-out-location-by-size", + "text-preset-colors", + "text-util-join-text", + "text-util-loop-text", + "text-util-path-list", + "text-util-prompt-add-prompt", + "text-util-prompt-adv-dup", + "text-util-prompt-adv-search", + "text-util-prompt-del", + "text-util-prompt-dup", + "text-util-prompt-join", + "text-util-prompt-search", + "text-util-prompt-shuffle", + "text-util-prompt-std", + "text-util-prompt-unweight", + "text-util-random-text", + "text-util-search-text", + "text-util-show-text", + "text-util-switch-text", + "xyz-util-txt-to-int" + ], + { + "title_aux": "ymc-node-suite-comfyui" + } + ], + "https://github.com/YOUR-WORST-TACO/ComfyUI-TacoNodes": [ + [ + "Example", + "TacoAnimatedLoader", + "TacoGifMaker", + "TacoImg2ImgAnimatedLoader", + "TacoImg2ImgAnimatedProcessor", + "TacoLatent" + ], + { + "title_aux": "ComfyUI-TacoNodes" + } + ], + "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI": [ + [ + "MergeBlockWeighted" + ], + { + "title_aux": "MergeBlockWeighted_fo_ComfyUI" + } + ], + "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Gemini": [ + [ + "ConcatText_Zho", + "DisplayText_Zho", + "Gemini_API_Chat_Zho", + "Gemini_API_S_Chat_Zho", + "Gemini_API_S_Vsion_ImgURL_Zho", + "Gemini_API_S_Zho", + "Gemini_API_Vsion_ImgURL_Zho", + "Gemini_API_Zho" + ], + { + "title_aux": "ComfyUI-Gemini" + } + ], + "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite": [ + [ + "AlphaChanelAddByMask", + "ImageCompositeBy_BG_Zho", + "ImageCompositeBy_Zho", + "ImageComposite_BG_Zho", + "ImageComposite_Zho", + "RGB_Image_Zho", + "Text_Image_Frame_Zho", + "Text_Image_Multiline_Zho", + "Text_Image_Zho" + ], + { + "title_aux": "ComfyUI-Text_Image-Composite [WIP]" + } + ], + "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn": [ + [ + "PortraitMaster_\u4e2d\u6587\u7248" + ], + { + "title_aux": "comfyui-portrait-master-zh-cn" + } + ], + "https://github.com/ZaneA/ComfyUI-ImageReward": [ + [ + "ImageRewardLoader", + "ImageRewardScore" + ], + { + "title_aux": "ImageReward" + } + ], + "https://github.com/Zuellni/ComfyUI-ExLlama": [ + [ + "ZuellniExLlamaGenerator", + "ZuellniExLlamaLoader", + "ZuellniTextPreview", + "ZuellniTextReplace" + ], + { + "title_aux": "ComfyUI-ExLlama" + } + ], + "https://github.com/Zuellni/ComfyUI-PickScore-Nodes": [ + [ + "ZuellniPickScoreImageProcessor", + "ZuellniPickScoreLoader", + "ZuellniPickScoreSelector", + "ZuellniPickScoreTextProcessor" + ], + { + "title_aux": "ComfyUI PickScore Nodes" + } + ], + "https://github.com/a1lazydog/ComfyUI-AudioScheduler": [ + [ + "AmplitudeToGraph", + "AmplitudeToNumber", + "AudioToAmplitudeGraph", + "AudioToFFTs", + "BatchAmplitudeSchedule", + "ClipAmplitude", + "GateNormalizedAmplitude", + "LoadAudio", + "NormalizeAmplitude", + "NormalizedAmplitudeDrivenString", + "NormalizedAmplitudeToGraph", + "NormalizedAmplitudeToNumber", + "TransientAmplitudeBasic" + ], + { + "title_aux": "ComfyUI-AudioScheduler" + } + ], + "https://github.com/adieyal/comfyui-dynamicprompts": [ + [ + "DPCombinatorialGenerator", + "DPFeelingLucky", + "DPJinja", + "DPMagicPrompt", + "DPOutput", + "DPRandomGenerator" + ], + { + "title_aux": "DynamicPrompts Custom Nodes" + } + ], + "https://github.com/aegis72/aegisflow_utility_nodes": [ + [ + "Aegisflow Image Pass", + "Aegisflow Latent Pass", + "Aegisflow Model Pass", + "Aegisflow VAE Pass", + "Aegisflow controlnet preprocessor bus", + "Brightness & Contrast_Ally", + "Gaussian Blur_Ally", + "Image Flip_ally", + "Placeholder Tuple", + "aegisflow Multi_Pass" + ], + { + "title_aux": "AegisFlow Utility Nodes" + } + ], + "https://github.com/ai-liam/comfyui_liam_util": [ + [ + "LiamLoadImage" + ], + { + "title_aux": "LiamUtil" + } + ], + "https://github.com/aianimation55/ComfyUI-FatLabels": [ + [ + "FatLabels" + ], + { + "title_aux": "Comfy UI FatLabels" + } + ], + "https://github.com/alpertunga-bile/prompt-generator-comfyui": [ + [ + "Prompt Generator" + ], + { + "title_aux": "prompt-generator" + } + ], + "https://github.com/alsritter/asymmetric-tiling-comfyui": [ + [ + "Asymmetric_Tiling_KSampler" + ], + { + "title_aux": "asymmetric-tiling-comfyui" + } + ], + "https://github.com/alt-key-project/comfyui-dream-project": [ + [ + "Analyze Palette [Dream]", + "Beat Curve [Dream]", + "Big Float Switch [Dream]", + "Big Image Switch [Dream]", + "Big Int Switch [Dream]", + "Big Latent Switch [Dream]", + "Big Palette Switch [Dream]", + "Big Text Switch [Dream]", + "Boolean To Float [Dream]", + "Boolean To Int [Dream]", + "Build Prompt [Dream]", + "CSV Curve [Dream]", + "CSV Generator [Dream]", + "Calculation [Dream]", + "Common Frame Dimensions [Dream]", + "Compare Palettes [Dream]", + "FFMPEG Video Encoder [Dream]", + "File Count [Dream]", + "Finalize Prompt [Dream]", + "Float Input [Dream]", + "Float to Log Entry [Dream]", + "Frame Count Calculator [Dream]", + "Frame Counter (Directory) [Dream]", + "Frame Counter (Simple) [Dream]", + "Frame Counter Info [Dream]", + "Frame Counter Offset [Dream]", + "Frame Counter Time Offset [Dream]", + "Image Brightness Adjustment [Dream]", + "Image Color Shift [Dream]", + "Image Contrast Adjustment [Dream]", + "Image Motion [Dream]", + "Image Sequence Blend [Dream]", + "Image Sequence Loader [Dream]", + "Image Sequence Saver [Dream]", + "Image Sequence Tweening [Dream]", + "Int Input [Dream]", + "Int to Log Entry [Dream]", + "Laboratory [Dream]", + "Linear Curve [Dream]", + "Log Entry Joiner [Dream]", + "Log File [Dream]", + "Noise from Area Palettes [Dream]", + "Noise from Palette [Dream]", + "Palette Color Align [Dream]", + "Palette Color Shift [Dream]", + "Sample Image Area as Palette [Dream]", + "Sample Image as Palette [Dream]", + "Saw Curve [Dream]", + "Sine Curve [Dream]", + "Smooth Event Curve [Dream]", + "String Input [Dream]", + "String Tokenizer [Dream]", + "String to Log Entry [Dream]", + "Text Input [Dream]", + "Triangle Curve [Dream]", + "Triangle Event Curve [Dream]", + "WAV Curve [Dream]" + ], + { + "title_aux": "Dream Project Animation Nodes" + } + ], + "https://github.com/alt-key-project/comfyui-dream-video-batches": [ + [ + "Blended Transition [DVB]", + "Calculation [DVB]", + "Create Frame Set [DVB]", + "Divide [DVB]", + "Fade From Black [DVB]", + "Fade To Black [DVB]", + "Float Input [DVB]", + "For Each Done [DVB]", + "For Each Filename [DVB]", + "Frame Set Append [DVB]", + "Frame Set Frame Dimensions Scaled [DVB]", + "Frame Set Index Offset [DVB]", + "Frame Set Merger [DVB]", + "Frame Set Reindex [DVB]", + "Frame Set Repeat [DVB]", + "Frame Set Reverse [DVB]", + "Frame Set Split Beginning [DVB]", + "Frame Set Split End [DVB]", + "Frame Set Splitter [DVB]", + "Generate Inbetween Frames [DVB]", + "Int Input [DVB]", + "Linear Camera Pan [DVB]", + "Linear Camera Roll [DVB]", + "Linear Camera Zoom [DVB]", + "Load Image From Path [DVB]", + "Multiply [DVB]", + "Sine Camera Pan [DVB]", + "Sine Camera Roll [DVB]", + "Sine Camera Zoom [DVB]", + "String Input [DVB]", + "Text Input [DVB]", + "Trace Memory Allocation [DVB]", + "Unwrap Frame Set [DVB]" + ], + { + "title_aux": "Dream Video Batches" + } + ], + "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes": [ + [ + "CLIPTextEncode (RE)", + "CLIPTextEncodeSDXL (RE)", + "CLIPTextEncodeSDXLRefiner (RE)", + "Int (RE)", + "RErouter <=", + "RErouter =>", + "String (RE)" + ], + { + "title_aux": "ComfyUI-DareMerge" + } + ], + "https://github.com/andersxa/comfyui-PromptAttention": [ + [ + "CLIPAttentionMaskEncode" + ], + { + "title_aux": "CLIP Directional Prompt Attention" + } + ], + "https://github.com/asagi4/ComfyUI-CADS": [ + [ + "CADS" + ], + { + "title_aux": "ComfyUI-CADS" + } + ], + "https://github.com/asagi4/comfyui-prompt-control": [ + [ + "EditableCLIPEncode", + "FilterSchedule", + "LoRAScheduler", + "PCSplitSampling", + "PromptControlSimple", + "PromptToSchedule", + "ScheduleToCond", + "ScheduleToModel" + ], + { + "title_aux": "ComfyUI prompt control" + } + ], + "https://github.com/asagi4/comfyui-utility-nodes": [ + [ + "MUJinjaRender", + "MUSimpleWildcard" + ], + { + "title_aux": "asagi4/comfyui-utility-nodes" + } + ], + "https://github.com/aszc-dev/ComfyUI-CoreMLSuite": [ + [ + "Core ML Converter", + "Core ML LCM Converter", + "Core ML LoRA Loader", + "CoreMLModelAdapter", + "CoreMLSampler", + "CoreMLSamplerAdvanced", + "CoreMLUNetLoader" + ], + { + "title_aux": "Core ML Suite for ComfyUI" + } + ], + "https://github.com/avatechai/avatar-graph-comfyui": [ + [ + "ApplyMeshTransformAsShapeKey", + "B_ENUM", + "B_VECTOR3", + "B_VECTOR4", + "Combine Points", + "CreateShapeFlow", + "ExportBlendshapes", + "ExportGLTF", + "Extract Boundary Points", + "Image Alpha Mask Merge", + "ImageBridge", + "LoadImageFromRequest", + "LoadImageWithAlpha", + "SAM MultiLayer", + "Save Image With Workflow" + ], + { + "author": "Avatech Limited", + "description": "Include nodes for sam + bpy operation, that allows workflow creations for generative 2d character rig.", + "nickname": "Avatar Graph", + "title": "Avatar Graph", + "title_aux": "avatar-graph-comfyui" + } + ], + "https://github.com/azazeal04/ComfyUI-Styles": [ + [ + "menus" + ], + { + "title_aux": "ComfyUI-Styles" + } + ], + "https://github.com/badjeff/comfyui_lora_tag_loader": [ + [ + "LoraTagLoader" + ], + { + "title_aux": "LoRA Tag Loader for ComfyUI" + } + ], + "https://github.com/banodoco/steerable-motion": [ + [ + "BatchCreativeInterpolation" + ], + { + "title_aux": "Steerable Motion" + } + ], + "https://github.com/bash-j/mikey_nodes": [ + [ + "AddMetaData", + "Batch Crop Image", + "Batch Crop Resize Inplace", + "Batch Load Images", + "Batch Resize Image for SDXL", + "Checkpoint Loader Simple Mikey", + "CinematicLook", + "Empty Latent Ratio Custom SDXL", + "Empty Latent Ratio Select SDXL", + "EvalFloats", + "FileNamePrefix", + "FileNamePrefixDateDirFirst", + "Float to String", + "HaldCLUT", + "Image Caption", + "ImageBorder", + "ImageOverlay", + "ImagePaste", + "Int to String", + "LMStudioPrompt", + "Load Image Based on Number", + "LoraSyntaxProcessor", + "Mikey Sampler", + "Mikey Sampler Base Only", + "Mikey Sampler Base Only Advanced", + "Mikey Sampler Tiled", + "Mikey Sampler Tiled Base Only", + "MikeySamplerTiledAdvanced", + "MikeySamplerTiledAdvancedBaseOnly", + "OobaPrompt", + "PresetRatioSelector", + "Prompt With SDXL", + "Prompt With Style", + "Prompt With Style V2", + "Prompt With Style V3", + "Range Float", + "Range Integer", + "Ratio Advanced", + "Resize Image for SDXL", + "Save Image If True", + "Save Image With Prompt Data", + "Save Images Mikey", + "Save Images No Display", + "SaveMetaData", + "SearchAndReplace", + "Seed String", + "Style Conditioner", + "Style Conditioner Base Only", + "Text2InputOr3rdOption", + "TextCombinations", + "TextCombinations3", + "TextConcat", + "TextPreserve", + "Upscale Tile Calculator", + "Wildcard Processor", + "WildcardAndLoraSyntaxProcessor", + "WildcardOobaPrompt" + ], + { + "title_aux": "Mikey Nodes" + } + ], + "https://github.com/bedovyy/ComfyUI_NAIDGenerator": [ + [ + "GenerateNAID", + "Img2ImgOptionNAID", + "InpaintingOptionNAID", + "MaskImageToNAID", + "ModelOptionNAID", + "PromptToNAID" + ], + { + "title_aux": "ComfyUI_NAIDGenerator" + } + ], + "https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py": [ + [ + "CLIPSeg", + "CombineSegMasks" + ], + { + "title_aux": "CLIPSeg" + } + ], + "https://github.com/bmad4ever/comfyui_ab_samplercustom": [ + [ + "AB SamplerCustom (experimental)" + ], + { + "title_aux": "comfyui_ab_sampler" + } + ], + "https://github.com/bmad4ever/comfyui_bmad_nodes": [ + [ + "AdaptiveThresholding", + "Add String To Many", + "AddAlpha", + "AdjustRect", + "AnyToAny", + "BoundingRect (contours)", + "BuildColorRangeAdvanced (hsv)", + "BuildColorRangeHSV (hsv)", + "CLAHE", + "CLIPEncodeMultiple", + "CLIPEncodeMultipleAdvanced", + "ChameleonMask", + "CheckpointLoader (dirty)", + "CheckpointLoaderSimple (dirty)", + "Color (RGB)", + "Color (hexadecimal)", + "Color Clip", + "Color Clip (advanced)", + "Color Clip ADE20k", + "ColorDictionary", + "ColorDictionary (custom)", + "Conditioning (combine multiple)", + "Conditioning (combine selective)", + "Conditioning Grid (cond)", + "Conditioning Grid (string)", + "Conditioning Grid (string) Advanced", + "Contour To Mask", + "Contours", + "ControlNetHadamard", + "ControlNetHadamard (manual)", + "ConvertImg", + "CopyMakeBorder", + "CreateRequestMetadata", + "DistanceTransform", + "Draw Contour(s)", + "EqualizeHistogram", + "ExtendColorList", + "ExtendCondList", + "ExtendFloatList", + "ExtendImageList", + "ExtendIntList", + "ExtendLatentList", + "ExtendMaskList", + "ExtendModelList", + "ExtendStringList", + "FadeMaskEdges", + "Filter Contour", + "FindComplementaryColor", + "FindThreshold", + "FlatLatentsIntoSingleGrid", + "Framed Mask Grab Cut", + "Framed Mask Grab Cut 2", + "FromListGet1Color", + "FromListGet1Cond", + "FromListGet1Float", + "FromListGet1Image", + "FromListGet1Int", + "FromListGet1Latent", + "FromListGet1Mask", + "FromListGet1Model", + "FromListGet1String", + "FromListGetColors", + "FromListGetConds", + "FromListGetFloats", + "FromListGetImages", + "FromListGetInts", + "FromListGetLatents", + "FromListGetMasks", + "FromListGetModels", + "FromListGetStrings", + "Get Contour from list", + "Get Models", + "Get Prompt", + "HypernetworkLoader (dirty)", + "ImageBatchToList", + "InRange (hsv)", + "InnerCylinder (remap)", + "Inpaint", + "Input/String to Int Array", + "KMeansColor", + "Load 64 Encoded Image", + "LoraLoader (dirty)", + "MaskGrid N KSamplers Advanced", + "MaskOuterBlur", + "Merge Latent Batch Gridwise", + "MonoMerge", + "MorphologicOperation", + "MorphologicSkeletoning", + "NaiveAutoKMeansColor", + "OtsuThreshold", + "OuterCylinder (remap)", + "RGB to HSV", + "Rect Grab Cut", + "Remap", + "RemapInsideParabolas (remap)", + "RemapInsideParabolasAdvanced (remap)", + "RemapQuadrilateral (remap)", + "Repeat Into Grid (image)", + "Repeat Into Grid (latent)", + "RequestInputs", + "SampleColorHSV", + "Save Image (api)", + "SeamlessClone", + "SeamlessClone (simple)", + "SetRequestStateToComplete", + "String", + "String to Float", + "String to Integer", + "ToColorList", + "ToCondList", + "ToFloatList", + "ToImageList", + "ToIntList", + "ToLatentList", + "ToMaskList", + "ToModelList", + "ToStringList", + "UnGridify (image)", + "VAEEncodeBatch" + ], + { + "title_aux": "Bmad Nodes" + } + ], + "https://github.com/bmad4ever/comfyui_lists_cartesian_product": [ + [ + "AnyListCartesianProduct" + ], + { + "title_aux": "Lists Cartesian Product" + } + ], + "https://github.com/bradsec/ComfyUI_ResolutionSelector": [ + [ + "ResolutionSelector" + ], + { + "title_aux": "ResolutionSelector for ComfyUI" + } + ], + "https://github.com/braintacles/braintacles-comfyui-nodes": [ + [ + "CLIPTextEncodeSDXL-Multi-IO", + "CLIPTextEncodeSDXL-Pipe", + "Empty Latent Image from Aspect-Ratio", + "Random Find and Replace", + "VAE Decode Pipe", + "VAE Decode Tiled Pipe", + "VAE Encode Pipe", + "VAE Encode Tiled Pipe" + ], + { + "title_aux": "braintacles-nodes" + } + ], + "https://github.com/brianfitzgerald/style_aligned_comfy": [ + [ + "StyleAlignedBatchAlign", + "StyleAlignedReferenceSampler" + ], + { + "title_aux": "StyleAligned for ComfyUI" + } + ], + "https://github.com/bronkula/comfyui-fitsize": [ + [ + "FS: Crop Image Into Even Pieces", + "FS: Fit Image And Resize", + "FS: Fit Size From Image", + "FS: Fit Size From Int", + "FS: Image Region To Mask", + "FS: Load Image And Resize To Fit", + "FS: Pick Image From Batch", + "FS: Pick Image From Batches", + "FS: Pick Image From List" + ], + { + "title_aux": "comfyui-fitsize" + } + ], + "https://github.com/bruefire/ComfyUI-SeqImageLoader": [ + [ + "VFrame Loader With Mask Editor", + "Video Loader With Mask Editor" + ], + { + "title_aux": "ComfyUI Sequential Image Loader" + } + ], + "https://github.com/budihartono/comfyui_otonx_nodes": [ + [ + "OTX Integer Multiple Inputs 4", + "OTX Integer Multiple Inputs 5", + "OTX Integer Multiple Inputs 6", + "OTX KSampler Feeder", + "OTX Versatile Multiple Inputs 4", + "OTX Versatile Multiple Inputs 5", + "OTX Versatile Multiple Inputs 6" + ], + { + "title_aux": "Otonx's Custom Nodes" + } + ], + "https://github.com/bvhari/ComfyUI_ImageProcessing": [ + [ + "BilateralFilter", + "Brightness", + "Gamma", + "Hue", + "Saturation", + "SigmoidCorrection", + "UnsharpMask" + ], + { + "title_aux": "ImageProcessing" + } + ], + "https://github.com/bvhari/ComfyUI_LatentToRGB": [ + [ + "LatentToRGB" + ], + { + "title_aux": "LatentToRGB" + } + ], + "https://github.com/bvhari/ComfyUI_PerpWeight": [ + [ + "CLIPTextEncodePerpWeight" + ], + { + "title_aux": "ComfyUI_PerpWeight" + } + ], + "https://github.com/catscandrive/comfyui-imagesubfolders/raw/main/loadImageWithSubfolders.py": [ + [ + "LoadImagewithSubfolders" + ], + { + "title_aux": "Image loader with subfolders" + } + ], + "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner": [ + [ + "LlavaCaptioner" + ], + { + "title_aux": "ComfyUI LLaVA Captioner" + } + ], + "https://github.com/chflame163/ComfyUI_MSSpeech_TTS": [ + [ + "Input Trigger", + "MicrosoftSpeech_TTS", + "Play Sound", + "Play Sound (loop)" + ], + { + "title_aux": "ComfyUI_MSSpeech_TTS" + } + ], + "https://github.com/chibiace/ComfyUI-Chibi-Nodes": [ + [ + "ConditionText", + "ConditionTextMulti", + "ImageAddText", + "ImageSimpleResize", + "ImageSizeInfo", + "ImageTool", + "Int2String", + "LoadEmbedding", + "LoadImageExtended", + "Loader", + "Prompts", + "RandomResolutionLatent", + "SaveImages", + "SeedGenerator", + "SimpleSampler", + "TextSplit", + "Textbox", + "Wildcards" + ], + { + "title_aux": "ComfyUI-Chibi-Nodes" + } + ], + "https://github.com/chrisgoringe/cg-image-picker": [ + [ + "Preview Chooser", + "Preview Chooser Fabric" + ], + { + "author": "chrisgoringe", + "description": "Custom nodes that preview images and pause the workflow to allow the user to select one or more to progress", + "nickname": "Image Chooser", + "title": "Image Chooser", + "title_aux": "Image chooser" + } + ], + "https://github.com/chrisgoringe/cg-noise": [ + [ + "Hijack", + "KSampler Advanced with Variations", + "KSampler with Variations", + "UnHijack" + ], + { + "title_aux": "Variation seeds" + } + ], + "https://github.com/chrisgoringe/cg-use-everywhere": [ + [ + "Seed Everywhere" + ], + { + "nodename_pattern": "(^(Prompts|Anything) Everywhere|Simple String)", + "title_aux": "Use Everywhere (UE Nodes)" + } + ], + "https://github.com/city96/ComfyUI_ColorMod": [ + [ + "ColorModEdges", + "ColorModPivot", + "LoadImageHighPrec", + "PreviewImageHighPrec", + "SaveImageHighPrec" + ], + { + "title_aux": "ComfyUI_ColorMod" + } + ], + "https://github.com/city96/ComfyUI_DiT": [ + [ + "DiTCheckpointLoader", + "DiTCheckpointLoaderSimple", + "DiTLabelCombine", + "DiTLabelSelect", + "DiTSampler" + ], + { + "title_aux": "ComfyUI_DiT [WIP]" + } + ], + "https://github.com/city96/ComfyUI_ExtraModels": [ + [ + "DiTCondLabelEmpty", + "DiTCondLabelSelect", + "DitCheckpointLoader", + "ExtraVAELoader", + "PixArtCheckpointLoader", + "PixArtDPMSampler", + "PixArtLoraLoader", + "PixArtResolutionSelect", + "PixArtT5TextEncode", + "T5TextEncode", + "T5v11Loader" + ], + { + "title_aux": "Extra Models for ComfyUI" + } + ], + "https://github.com/city96/ComfyUI_NetDist": [ + [ + "FetchRemote", + "QueueRemote" + ], + { + "title_aux": "ComfyUI_NetDist" + } + ], + "https://github.com/city96/SD-Advanced-Noise": [ + [ + "LatentGaussianNoise", + "MathEncode" + ], + { + "title_aux": "SD-Advanced-Noise" + } + ], + "https://github.com/city96/SD-Latent-Interposer": [ + [ + "LatentInterposer" + ], + { + "title_aux": "Latent-Interposer" + } + ], + "https://github.com/city96/SD-Latent-Upscaler": [ + [ + "LatentUpscaler" + ], + { + "title_aux": "SD-Latent-Upscaler" + } + ], + "https://github.com/civitai/comfy-nodes": [ + [ + "CivitAI_Checkpoint_Loader", + "CivitAI_Lora_Loader" + ], + { + "title_aux": "comfy-nodes" + } + ], + "https://github.com/comfyanonymous/ComfyUI": [ + [ + "BasicScheduler", + "CLIPLoader", + "CLIPMergeSimple", + "CLIPSave", + "CLIPSetLastLayer", + "CLIPTextEncode", + "CLIPTextEncodeSDXL", + "CLIPTextEncodeSDXLRefiner", + "CLIPVisionEncode", + "CLIPVisionLoader", + "Canny", + "CheckpointLoader", + "CheckpointLoaderSimple", + "CheckpointSave", + "ConditioningAverage", + "ConditioningCombine", + "ConditioningConcat", + "ConditioningSetArea", + "ConditioningSetAreaPercentage", + "ConditioningSetMask", + "ConditioningSetTimestepRange", + "ConditioningZeroOut", + "ControlNetApply", + "ControlNetApplyAdvanced", + "ControlNetLoader", + "CropMask", + "DiffControlNetLoader", + "DiffusersLoader", + "DualCLIPLoader", + "EmptyImage", + "EmptyLatentImage", + "ExponentialScheduler", + "FeatherMask", + "FlipSigmas", + "FreeU", + "FreeU_V2", + "GLIGENLoader", + "GLIGENTextBoxApply", + "GrowMask", + "HyperTile", + "HypernetworkLoader", + "ImageBatch", + "ImageBlend", + "ImageBlur", + "ImageColorToMask", + "ImageCompositeMasked", + "ImageCrop", + "ImageInvert", + "ImageOnlyCheckpointLoader", + "ImagePadForOutpaint", + "ImageQuantize", + "ImageScale", + "ImageScaleBy", + "ImageScaleToTotalPixels", + "ImageSharpen", + "ImageToMask", + "ImageUpscaleWithModel", + "InvertMask", + "JoinImageWithAlpha", + "KSampler", + "KSamplerAdvanced", + "KSamplerSelect", + "KarrasScheduler", + "LatentAdd", + "LatentBatch", + "LatentBlend", + "LatentComposite", + "LatentCompositeMasked", + "LatentCrop", + "LatentFlip", + "LatentFromBatch", + "LatentInterpolate", + "LatentMultiply", + "LatentRotate", + "LatentSubtract", + "LatentUpscale", + "LatentUpscaleBy", + "LoadImage", + "LoadImageMask", + "LoadLatent", + "LoraLoader", + "LoraLoaderModelOnly", + "MaskComposite", + "MaskToImage", + "ModelMergeAdd", + "ModelMergeBlocks", + "ModelMergeSimple", + "ModelMergeSubtract", + "ModelSamplingContinuousEDM", + "ModelSamplingDiscrete", + "PatchModelAddDownscale", + "PerpNeg", + "PolyexponentialScheduler", + "PorterDuffImageComposite", + "PreviewImage", + "RebatchImages", + "RebatchLatents", + "RepeatImageBatch", + "RepeatLatentBatch", + "RescaleCFG", + "SDTurboScheduler", + "SVD_img2vid_Conditioning", + "SamplerCustom", + "SamplerDPMPP_2M_SDE", + "SamplerDPMPP_SDE", + "SaveAnimatedPNG", + "SaveAnimatedWEBP", + "SaveImage", + "SaveLatent", + "SelfAttentionGuidance", + "SetLatentNoiseMask", + "SolidMask", + "SplitImageWithAlpha", + "SplitSigmas", + "StableZero123_Conditioning", + "StyleModelApply", + "StyleModelLoader", + "TomePatchModel", + "UNETLoader", + "UpscaleModelLoader", + "VAEDecode", + "VAEDecodeTiled", + "VAEEncode", + "VAEEncodeForInpaint", + "VAEEncodeTiled", + "VAELoader", + "VAESave", + "VPScheduler", + "VideoLinearCFGGuidance", + "unCLIPCheckpointLoader", + "unCLIPConditioning" + ], + { + "title_aux": "ComfyUI" + } + ], + "https://github.com/comfyanonymous/ComfyUI_experiments": [ + [ + "ModelMergeBlockNumber", + "ModelMergeSDXL", + "ModelMergeSDXLDetailedTransformers", + "ModelMergeSDXLTransformers", + "ModelSamplerTonemapNoiseTest", + "ReferenceOnlySimple", + "RescaleClassifierFreeGuidanceTest", + "TonemapNoiseWithRescaleCFG" + ], + { + "title_aux": "ComfyUI_experiments" + } + ], + "https://github.com/concarne000/ConCarneNode": [ + [ + "BingImageGrabber", + "Zephyr" + ], + { + "title_aux": "ConCarneNode" + } + ], + "https://github.com/coreyryanhanson/ComfyQR": [ + [ + "comfy-qr-by-image-size", + "comfy-qr-by-module-size", + "comfy-qr-by-module-split", + "comfy-qr-mask_errors" + ], + { + "title_aux": "ComfyQR" + } + ], + "https://github.com/coreyryanhanson/ComfyQR-scanning-nodes": [ + [ + "comfy-qr-read", + "comfy-qr-validate" + ], + { + "title_aux": "ComfyQR-scanning-nodes" + } + ], + "https://github.com/cubiq/ComfyUI_IPAdapter_plus": [ + [ + "IPAdapterApply", + "IPAdapterApplyEncoded", + "IPAdapterBatchEmbeds", + "IPAdapterEncoder", + "IPAdapterLoadEmbeds", + "IPAdapterModelLoader", + "IPAdapterSaveEmbeds", + "InsightFaceLoader", + "PrepImageForClipVision", + "PrepImageForInsightFace" + ], + { + "title_aux": "ComfyUI_IPAdapter_plus" + } + ], + "https://github.com/cubiq/ComfyUI_SimpleMath": [ + [ + "SimpleMath", + "SimpleMathDebug" + ], + { + "title_aux": "Simple Math" + } + ], + "https://github.com/cubiq/ComfyUI_essentials": [ + [ + "ConsoleDebug+", + "ExtractKeyframes+", + "GetImageSize+", + "ImageCASharpening+", + "ImageCrop+", + "ImageDesaturate+", + "ImageEnhanceDifference+", + "ImageExpandBatch+", + "ImageFlip+", + "ImageFromBatch+", + "ImagePosterize+", + "ImageResize+", + "MaskBatch+", + "MaskBlur+", + "MaskExpandBatch+", + "MaskFlip+", + "MaskFromBatch+", + "MaskFromColor+", + "MaskPreview+", + "ModelCompile+", + "SimpleMath+", + "StableZero123_Increments", + "TransitionMask+" + ], + { + "title_aux": "ComfyUI Essentials" + } + ], + "https://github.com/dagthomas/comfyui_dagthomas": [ + [ + "CSL", + "CSVPromptGenerator", + "PromptGenerator" + ], + { + "title_aux": "SDXL Auto Prompter" + } + ], + "https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py": [ + [ + "KSamplerAdvancedGPU", + "KSamplerGPU" + ], + { + "title_aux": "KSampler GPU" + } + ], + "https://github.com/daxthin/DZ-FaceDetailer": [ + [ + "DZ_Face_Detailer" + ], + { + "title_aux": "DZ-FaceDetailer" + } + ], + "https://github.com/deroberon/StableZero123-comfyui": [ + [ + "SDZero ImageSplit", + "Stablezero123", + "Stablezero123WithDepth" + ], + { + "title_aux": "StableZero123-comfyui" + } + ], + "https://github.com/deroberon/demofusion-comfyui": [ + [ + "Batch Unsampler", + "Demofusion", + "Demofusion From Single File", + "Iterative Mixing KSampler" + ], + { + "title_aux": "demofusion-comfyui" + } + ], + "https://github.com/dimtoneff/ComfyUI-PixelArt-Detector": [ + [ + "PixelArtAddDitherPattern", + "PixelArtDetectorConverter", + "PixelArtDetectorSave", + "PixelArtDetectorToImage", + "PixelArtLoadPalettes" + ], + { + "title_aux": "ComfyUI PixelArt Detector" + } + ], + "https://github.com/diontimmer/ComfyUI-Vextra-Nodes": [ + [ + "Add Text To Image", + "Apply Instagram Filter", + "Create Solid Color", + "Flatten Colors", + "Generate Noise Image", + "GlitchThis Effect", + "Hue Rotation", + "Load Picture Index", + "Pixel Sort", + "Play Sound At Execution", + "Prettify Prompt Using distilgpt2", + "Swap Color Mode" + ], + { + "title_aux": "ComfyUI-Vextra-Nodes" + } + ], + "https://github.com/dmarx/ComfyUI-Keyframed": [ + [ + "Example", + "KfAddCurveToPGroup", + "KfAddCurveToPGroupx10", + "KfApplyCurveToCond", + "KfConditioningAdd", + "KfConditioningAddx10", + "KfCurveConstant", + "KfCurveDraw", + "KfCurveFromString", + "KfCurveFromYAML", + "KfCurveInverse", + "KfCurveToAcnLatentKeyframe", + "KfCurvesAdd", + "KfCurvesAddx10", + "KfCurvesDivide", + "KfCurvesMultiply", + "KfCurvesMultiplyx10", + "KfCurvesSubtract", + "KfDebug_Clip", + "KfDebug_Cond", + "KfDebug_Curve", + "KfDebug_Float", + "KfDebug_Image", + "KfDebug_Int", + "KfDebug_Latent", + "KfDebug_Model", + "KfDebug_Passthrough", + "KfDebug_Segs", + "KfDebug_String", + "KfDebug_Vae", + "KfDrawSchedule", + "KfEvaluateCurveAtT", + "KfGetCurveFromPGroup", + "KfGetScheduleConditionAtTime", + "KfGetScheduleConditionSlice", + "KfKeyframedCondition", + "KfKeyframedConditionWithText", + "KfPGroupCurveAdd", + "KfPGroupCurveMultiply", + "KfPGroupDraw", + "KfPGroupProd", + "KfPGroupSum", + "KfSetCurveLabel", + "KfSetKeyframe", + "KfSinusoidalAdjustAmplitude", + "KfSinusoidalAdjustFrequency", + "KfSinusoidalAdjustPhase", + "KfSinusoidalAdjustWavelength", + "KfSinusoidalEntangledZeroOneFromFrequencyx2", + "KfSinusoidalEntangledZeroOneFromFrequencyx3", + "KfSinusoidalEntangledZeroOneFromFrequencyx4", + "KfSinusoidalEntangledZeroOneFromFrequencyx5", + "KfSinusoidalEntangledZeroOneFromFrequencyx6", + "KfSinusoidalEntangledZeroOneFromFrequencyx7", + "KfSinusoidalEntangledZeroOneFromFrequencyx8", + "KfSinusoidalEntangledZeroOneFromFrequencyx9", + "KfSinusoidalEntangledZeroOneFromWavelengthx2", + "KfSinusoidalEntangledZeroOneFromWavelengthx3", + "KfSinusoidalEntangledZeroOneFromWavelengthx4", + "KfSinusoidalEntangledZeroOneFromWavelengthx5", + "KfSinusoidalEntangledZeroOneFromWavelengthx6", + "KfSinusoidalEntangledZeroOneFromWavelengthx7", + "KfSinusoidalEntangledZeroOneFromWavelengthx8", + "KfSinusoidalEntangledZeroOneFromWavelengthx9", + "KfSinusoidalGetAmplitude", + "KfSinusoidalGetFrequency", + "KfSinusoidalGetPhase", + "KfSinusoidalGetWavelength", + "KfSinusoidalWithFrequency", + "KfSinusoidalWithWavelength" + ], + { + "title_aux": "ComfyUI-Keyframed" + } + ], + "https://github.com/drago87/ComfyUI_Dragos_Nodes": [ + [ + "file_padding", + "image_info", + "lora_loader", + "vae_loader" + ], + { + "title_aux": "ComfyUI_Dragos_Nodes" + } + ], + "https://github.com/drustan-hawk/primitive-types": [ + [ + "float", + "int", + "string", + "string_multiline" + ], + { + "title_aux": "primitive-types" + } + ], + "https://github.com/ealkanat/comfyui_easy_padding": [ + [ + "comfyui-easy-padding" + ], + { + "title_aux": "ComfyUI Easy Padding" + } + ], + "https://github.com/edenartlab/eden_comfy_pipelines": [ + [ + "CLIP_Interrogator" + ], + { + "title_aux": "eden_comfy_pipelines" + } + ], + "https://github.com/evanspearman/ComfyMath": [ + [ + "CM_BoolBinaryOperation", + "CM_BoolToInt", + "CM_BoolUnaryOperation", + "CM_BreakoutVec2", + "CM_BreakoutVec3", + "CM_BreakoutVec4", + "CM_ComposeVec2", + "CM_ComposeVec3", + "CM_ComposeVec4", + "CM_FloatBinaryCondition", + "CM_FloatBinaryOperation", + "CM_FloatToInt", + "CM_FloatToNumber", + "CM_FloatUnaryCondition", + "CM_FloatUnaryOperation", + "CM_IntBinaryCondition", + "CM_IntBinaryOperation", + "CM_IntToBool", + "CM_IntToFloat", + "CM_IntToNumber", + "CM_IntUnaryCondition", + "CM_IntUnaryOperation", + "CM_NearestSDXLResolution", + "CM_NumberBinaryCondition", + "CM_NumberBinaryOperation", + "CM_NumberToFloat", + "CM_NumberToInt", + "CM_NumberUnaryCondition", + "CM_NumberUnaryOperation", + "CM_SDXLResolution", + "CM_Vec2BinaryCondition", + "CM_Vec2BinaryOperation", + "CM_Vec2ScalarOperation", + "CM_Vec2ToScalarBinaryOperation", + "CM_Vec2ToScalarUnaryOperation", + "CM_Vec2UnaryCondition", + "CM_Vec2UnaryOperation", + "CM_Vec3BinaryCondition", + "CM_Vec3BinaryOperation", + "CM_Vec3ScalarOperation", + "CM_Vec3ToScalarBinaryOperation", + "CM_Vec3ToScalarUnaryOperation", + "CM_Vec3UnaryCondition", + "CM_Vec3UnaryOperation", + "CM_Vec4BinaryCondition", + "CM_Vec4BinaryOperation", + "CM_Vec4ScalarOperation", + "CM_Vec4ToScalarBinaryOperation", + "CM_Vec4ToScalarUnaryOperation", + "CM_Vec4UnaryCondition", + "CM_Vec4UnaryOperation" + ], + { + "title_aux": "ComfyMath" + } + ], + "https://github.com/fearnworks/ComfyUI_FearnworksNodes/raw/main/fw_nodes.py": [ + [ + "Count Files in Directory (FW)", + "Count Tokens (FW)", + "Token Count Ranker(FW)", + "Trim To Tokens (FW)" + ], + { + "title_aux": "Fearnworks Custom Nodes" + } + ], + "https://github.com/fexli/fexli-util-node-comfyui": [ + [ + "FEColor2Image", + "FEColorOut", + "FEImagePadForOutpaint", + "FERandomizedColor2Image" + ], + { + "title_aux": "fexli-util-node-comfyui" + } + ], + "https://github.com/filipemeneses/comfy_pixelization": [ + [ + "Pixelization" + ], + { + "title_aux": "Pixelization" + } + ], + "https://github.com/filliptm/ComfyUI_Fill-Nodes": [ + [ + "FL_ImageRandomizer" + ], + { + "title_aux": "ComfyUI_Fill-Nodes" + } + ], + "https://github.com/fitCorder/fcSuite/raw/main/fcSuite.py": [ + [ + "fcFloat", + "fcFloatMatic", + "fcInteger" + ], + { + "title_aux": "fcSuite" + } + ], + "https://github.com/florestefano1975/comfyui-portrait-master": [ + [ + "PortraitMaster" + ], + { + "title_aux": "comfyui-portrait-master" + } + ], + "https://github.com/florestefano1975/comfyui-prompt-composer": [ + [ + "PromptComposerAssembler", + "PromptComposerEffect", + "PromptComposerStyler", + "PromptComposerTextSingle", + "promptComposerTextMultiple" + ], + { + "title_aux": "comfyui-prompt-composer" + } + ], + "https://github.com/flyingshutter/As_ComfyUI_CustomNodes": [ + [ + "BatchIndex_AS", + "CropImage_AS", + "ImageMixMasked_As", + "ImageToMask_AS", + "Increment_AS", + "Int2Any_AS", + "LatentAdd_AS", + "LatentMixMasked_As", + "LatentMix_AS", + "LatentToImages_AS", + "LoadLatent_AS", + "MapRange_AS", + "MaskToImage_AS", + "Math_AS", + "NoiseImage_AS", + "Number2Float_AS", + "Number2Int_AS", + "Number_AS", + "SaveLatent_AS", + "TextToImage_AS", + "TextWildcardList_AS" + ], + { + "title_aux": "As_ComfyUI_CustomNodes" + } + ], + "https://github.com/gemell1/ComfyUI_GMIC": [ + [ + "GmicCliWrapper" + ], + { + "title_aux": "ComfyUI_GMIC" + } + ], + "https://github.com/giriss/comfy-image-saver": [ + [ + "Cfg Literal", + "Checkpoint Selector", + "Int Literal", + "Sampler Selector", + "Save Image w/Metadata", + "Scheduler Selector", + "Seed Generator", + "String Literal", + "Width/Height Literal" + ], + { + "title_aux": "Save Image with Generation Metadata" + } + ], + "https://github.com/glibsonoran/Plush-for-ComfyUI": [ + [ + "DalleImage", + "Enhancer" + ], + { + "title_aux": "Plush-for-ComfyUI" + } + ], + "https://github.com/glifxyz/ComfyUI-GlifNodes": [ + [ + "GlifConsistencyDecoder", + "GlifPatchConsistencyDecoderTiled" + ], + { + "title_aux": "ComfyUI-GlifNodes" + } + ], + "https://github.com/guoyk93/yk-node-suite-comfyui": [ + [ + "YKImagePadForOutpaint", + "YKMaskToImage" + ], + { + "title_aux": "y.k.'s ComfyUI node suite" + } + ], + "https://github.com/hhhzzyang/Comfyui_Lama": [ + [ + "LamaApply", + "LamaModelLoader", + "YamlConfigLoader" + ], + { + "title_aux": "Comfyui-Lama" + } + ], + "https://github.com/hnmr293/ComfyUI-nodes-hnmr": [ + [ + "CLIPIter", + "Dict2Model", + "GridImage", + "ImageBlend2", + "KSamplerOverrided", + "KSamplerSetting", + "KSamplerXYZ", + "LatentToHist", + "LatentToImage", + "ModelIter", + "RandomLatentImage", + "SaveStateDict", + "SaveText", + "StateDictLoader", + "StateDictMerger", + "StateDictMergerBlockWeighted", + "StateDictMergerBlockWeightedMulti", + "VAEDecodeBatched", + "VAEEncodeBatched", + "VAEIter" + ], + { + "title_aux": "ComfyUI-nodes-hnmr" + } + ], + "https://github.com/hustille/ComfyUI_Fooocus_KSampler": [ + [ + "KSampler With Refiner (Fooocus)" + ], + { + "title_aux": "ComfyUI_Fooocus_KSampler" + } + ], + "https://github.com/hustille/ComfyUI_hus_utils": [ + [ + "3way Prompt Styler", + "Batch State", + "Date Time Format", + "Debug Extra", + "Fetch widget value", + "Text Hash" + ], + { + "title_aux": "hus' utils for ComfyUI" + } + ], + "https://github.com/hylarucoder/ComfyUI-Eagle-PNGInfo": [ + [ + "EagleImageNode", + "SDXLPromptStyler", + "SDXLPromptStylerAdvanced", + "SDXLResolutionPresets" + ], + { + "title_aux": "Eagle PNGInfo" + } + ], + "https://github.com/idrirap/ComfyUI-Lora-Auto-Trigger-Words": [ + [ + "FusionText", + "LoraListNames", + "LoraLoaderAdvanced", + "LoraLoaderStackedAdvanced", + "LoraLoaderStackedVanilla", + "LoraLoaderVanilla", + "LoraTagsOnly", + "Randomizer", + "TagsFormater", + "TagsSelector", + "TextInputBasic" + ], + { + "title_aux": "ComfyUI-Lora-Auto-Trigger-Words" + } + ], + "https://github.com/imb101/ComfyUI-FaceSwap": [ + [ + "FaceSwapNode" + ], + { + "title_aux": "FaceSwap" + } + ], + "https://github.com/jags111/ComfyUI_Jags_Audiotools": [ + [ + "BatchJoinAudio", + "BatchToList", + "BitCrushAudioFX", + "BulkVariation", + "ChorusAudioFX", + "ClippingAudioFX", + "CompressorAudioFX", + "ConcatAudioList", + "ConvolutionAudioFX", + "CutAudio", + "DelayAudioFX", + "DistortionAudioFX", + "DuplicateAudio", + "GainAudioFX", + "GenerateAudioSample", + "GenerateAudioWave", + "GetAudioFromFolderIndex", + "GetSingle", + "GetStringByIndex", + "HighShelfFilter", + "HighpassFilter", + "ImageToSpectral", + "InvertAudioFX", + "JoinAudio", + "LadderFilter", + "LimiterAudioFX", + "ListToBatch", + "LoadAudioDir", + "LoadAudioFile", + "LoadAudioModel (DD)", + "LoadVST3", + "LowShelfFilter", + "LowpassFilter", + "MP3CompressorAudioFX", + "MixAudioTensors", + "NoiseGateAudioFX", + "OTTAudioFX", + "PeakFilter", + "PhaserEffectAudioFX", + "PitchShiftAudioFX", + "PlotSpectrogram", + "PreviewAudioFile", + "PreviewAudioTensor", + "ResampleAudio", + "ReverbAudioFX", + "ReverseAudio", + "SaveAudioTensor", + "SequenceVariation", + "SliceAudio", + "SoundPlayer", + "StretchAudio", + "samplerate" + ], + { + "author": "jags111", + "description": "This extension offers various audio generation tools", + "nickname": "Audiotools", + "title": "Jags_Audiotools", + "title_aux": "ComfyUI_Jags_Audiotools" + } + ], + "https://github.com/jags111/ComfyUI_Jags_VectorMagic": [ + [ + "CircularVAEDecode", + "JagsCLIPSeg", + "JagsClipseg", + "JagsCombineMasks", + "SVG", + "YoloSEGdetectionNode", + "YoloSegNode", + "color_drop", + "my unique name", + "xy_Tiling_KSampler" + ], + { + "author": "jags111", + "description": "This extension offers various vector manipulation and generation tools", + "nickname": "Jags_VectorMagic", + "title": "Jags_VectorMagic", + "title_aux": "ComfyUI_Jags_VectorMagic" + } + ], + "https://github.com/jags111/efficiency-nodes-comfyui": [ + [ + "AnimateDiff Script", + "Apply ControlNet Stack", + "Control Net Stacker", + "Eff. Loader SDXL", + "Efficient Loader", + "HighRes-Fix Script", + "Image Overlay", + "Join XY Inputs of Same Type", + "KSampler (Efficient)", + "KSampler Adv. (Efficient)", + "KSampler SDXL (Eff.)", + "LatentUpscaler", + "LoRA Stacker", + "Manual XY Entry Info", + "NNLatentUpscale", + "Noise Control Script", + "Pack SDXL Tuple", + "Tiled Upscaler Script", + "Unpack SDXL Tuple", + "XY Input: Add/Return Noise", + "XY Input: Aesthetic Score", + "XY Input: CFG Scale", + "XY Input: Checkpoint", + "XY Input: Clip Skip", + "XY Input: Control Net", + "XY Input: Control Net Plot", + "XY Input: Denoise", + "XY Input: LoRA", + "XY Input: LoRA Plot", + "XY Input: LoRA Stacks", + "XY Input: Manual XY Entry", + "XY Input: Prompt S/R", + "XY Input: Refiner On/Off", + "XY Input: Sampler/Scheduler", + "XY Input: Seeds++ Batch", + "XY Input: Steps", + "XY Input: VAE", + "XY Plot" + ], + { + "title_aux": "Efficiency Nodes for ComfyUI Version 2.0+" + } + ], + "https://github.com/jamesWalker55/comfyui-various": [ + [], + { + "nodename_pattern": "^JW", + "title_aux": "Various ComfyUI Nodes by Type" + } + ], + "https://github.com/jesenzhang/ComfyUI_StreamDiffusion": [ + [ + "StreamDiffusion_Loader", + "StreamDiffusion_Sampler" + ], + { + "title_aux": "ComfyUI_StreamDiffusion" + } + ], + "https://github.com/jitcoder/lora-info": [ + [ + "LoraInfo" + ], + { + "title_aux": "LoraInfo" + } + ], + "https://github.com/jjkramhoeft/ComfyUI-Jjk-Nodes": [ + [ + "JjkConcat", + "JjkShowText", + "JjkText", + "SDXLRecommendedImageSize" + ], + { + "title_aux": "ComfyUI-Jjk-Nodes" + } + ], + "https://github.com/jojkaart/ComfyUI-sampler-lcm-alternative": [ + [ + "LCMScheduler", + "SamplerLCMAlternative", + "SamplerLCMCycle" + ], + { + "title_aux": "ComfyUI-sampler-lcm-alternative" + } + ], + "https://github.com/jtrue/ComfyUI-JaRue": [ + [ + "Text2Image_jru", + "YouTube2Prompt_jru" + ], + { + "nodename_pattern": "_jru$", + "title_aux": "ComfyUI-JaRue" + } + ], + "https://github.com/ka-puna/comfyui-yanc": [ + [ + "YANC.ConcatStrings", + "YANC.FormatDatetimeString", + "YANC.GetWidgetValueString", + "YANC.IntegerCaster", + "YANC.MultilineString", + "YANC.TruncateString" + ], + { + "title_aux": "comfyui-yanc" + } + ], + "https://github.com/kenjiqq/qq-nodes-comfyui": [ + [ + "Any List", + "Axis To Float", + "Axis To Int", + "Axis To Model", + "Axis To Number", + "Axis To String", + "Image Accumulator End", + "Image Accumulator Start", + "Load Lines From Text File", + "Slice List", + "XY Grid Helper" + ], + { + "title_aux": "qq-nodes-comfyui" + } + ], + "https://github.com/kijai/ComfyUI-KJNodes": [ + [ + "AddLabel", + "BatchCLIPSeg", + "BatchCropFromMask", + "BatchCropFromMaskAdvanced", + "BatchUncrop", + "BatchUncropAdvanced", + "BboxToInt", + "ColorMatch", + "ColorToMask", + "ConditioningMultiCombine", + "ConditioningSetMaskAndCombine", + "ConditioningSetMaskAndCombine3", + "ConditioningSetMaskAndCombine4", + "ConditioningSetMaskAndCombine5", + "CreateAudioMask", + "CreateFadeMask", + "CreateFadeMaskAdvanced", + "CreateFluidMask", + "CreateGradientMask", + "CreateMagicMask", + "CreateShapeMask", + "CreateTextMask", + "CreateVoronoiMask", + "CrossFadeImages", + "DummyLatentOut", + "EmptyLatentImagePresets", + "FlipSigmasAdjusted", + "FloatConstant", + "GenerateNoise", + "GetImageRangeFromBatch", + "GetImagesFromBatchIndexed", + "GrowMaskWithBlur", + "INTConstant", + "ImageBatchRepeatInterleaving", + "ImageBatchTestPattern", + "ImageConcanate", + "ImageGrabPIL", + "ImageGridComposite2x2", + "ImageGridComposite3x3", + "InjectNoiseToLatent", + "NormalizeLatent", + "OffsetMask", + "ReferenceOnlySimple3", + "ReplaceImagesInBatch", + "ResizeMask", + "ReverseImageBatch", + "RoundMask", + "SaveImageWithAlpha", + "SomethingToString", + "SoundReactive", + "SplitBboxes", + "StableZero123_BatchSchedule", + "VRAM_Debug", + "WidgetToString" + ], + { + "title_aux": "KJNodes for ComfyUI" + } + ], + "https://github.com/kijai/ComfyUI-Marigold": [ + [ + "ColorizeDepthmap", + "MarigoldDepthEstimation", + "RemapDepth", + "SaveImageOpenEXR" + ], + { + "title_aux": "Marigold depth estimation in ComfyUI" + } + ], + "https://github.com/kijai/ComfyUI-SVD": [ + [ + "SVDimg2vid" + ], + { + "title_aux": "ComfyUI-SVD" + } + ], + "https://github.com/kinfolk0117/ComfyUI_GradientDeepShrink": [ + [ + "GradientPatchModelAddDownscale", + "GradientPatchModelAddDownscaleAdvanced" + ], + { + "title_aux": "ComfyUI_GradientDeepShrink" + } + ], + "https://github.com/kinfolk0117/ComfyUI_SimpleTiles": [ + [ + "TileCalc", + "TileMerge", + "TileSplit" + ], + { + "title_aux": "SimpleTiles" + } + ], + "https://github.com/kinfolk0117/ComfyUI_TiledIPAdapter": [ + [ + "TiledIPAdapter" + ], + { + "title_aux": "TiledIPAdapter" + } + ], + "https://github.com/knuknX/ComfyUI-Image-Tools": [ + [ + "BatchImagePathLoader", + "ImageBgRemoveProcessor", + "ImageStandardResizeProcessor", + "SingleImagePathLoader", + "SingleImageUrlLoader" + ], + { + "title_aux": "ComfyUI-Image-Tools" + } + ], + "https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI": [ + [ + "LLLiteLoader" + ], + { + "title_aux": "ControlNet-LLLite-ComfyUI" + } + ], + "https://github.com/komojini/ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes": [ + [ + "S3 Bucket LoRA", + "S3Bucket_Load_LoRA", + "XL DreamBooth LoRA", + "XLDB_LoRA" + ], + { + "title_aux": "ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes" + } + ], + "https://github.com/kwaroran/abg-comfyui": [ + [ + "Remove Image Background (abg)" + ], + { + "title_aux": "abg-comfyui" + } + ], + "https://github.com/laksjdjf/LCMSampler-ComfyUI": [ + [ + "SamplerLCM", + "TAESDLoader" + ], + { + "title_aux": "LCMSampler-ComfyUI" + } + ], + "https://github.com/laksjdjf/LoRA-Merger-ComfyUI": [ + [ + "LoraLoaderFromWeight", + "LoraLoaderWeightOnly", + "LoraMerge", + "LoraSave" + ], + { + "title_aux": "LoRA-Merger-ComfyUI" + } + ], + "https://github.com/laksjdjf/attention-couple-ComfyUI": [ + [ + "Attention couple" + ], + { + "title_aux": "attention-couple-ComfyUI" + } + ], + "https://github.com/laksjdjf/cd-tuner_negpip-ComfyUI": [ + [ + "CDTuner", + "Negapip", + "Negpip" + ], + { + "title_aux": "cd-tuner_negpip-ComfyUI" + } + ], + "https://github.com/laksjdjf/pfg-ComfyUI": [ + [ + "PFG" + ], + { + "title_aux": "pfg-ComfyUI" + } + ], + "https://github.com/lilly1987/ComfyUI_node_Lilly": [ + [ + "CheckpointLoaderSimpleText", + "LoraLoaderText", + "LoraLoaderTextRandom", + "Random_Sampler", + "VAELoaderDecode" + ], + { + "title_aux": "simple wildcard for ComfyUI" + } + ], + "https://github.com/lldacing/comfyui-easyapi-nodes": [ + [ + "Base64ToImage", + "ImageToBase64", + "ImageToBase64Advanced", + "LoadImageToBase64", + "MaskImageToBase64", + "MaskToBase64", + "MaskToBase64Image", + "SamAutoMaskSEGS" + ], + { + "title_aux": "comfyui-easyapi-nodes" + } + ], + "https://github.com/lordgasmic/ComfyUI-Wildcards/raw/master/wildcards.py": [ + [ + "CLIPTextEncodeWithWildcards" + ], + { + "title_aux": "Wildcards" + } + ], + "https://github.com/lrzjason/ComfyUIJasonNode/raw/main/SDXLMixSampler.py": [ + [ + "SDXLMixSampler" + ], + { + "title_aux": "ComfyUIJasonNode" + } + ], + "https://github.com/ltdrdata/ComfyUI-Impact-Pack": [ + [ + "AddMask", + "BasicPipeToDetailerPipe", + "BasicPipeToDetailerPipeSDXL", + "BboxDetectorCombined", + "BboxDetectorCombined_v2", + "BboxDetectorForEach", + "BboxDetectorSEGS", + "BitwiseAndMask", + "BitwiseAndMaskForEach", + "CLIPSegDetectorProvider", + "CfgScheduleHookProvider", + "CombineRegionalPrompts", + "CoreMLDetailerHookProvider", + "DenoiseScheduleHookProvider", + "DenoiseSchedulerDetailerHookProvider", + "DetailerForEach", + "DetailerForEachDebug", + "DetailerForEachDebugPipe", + "DetailerForEachPipe", + "DetailerHookCombine", + "DetailerPipeToBasicPipe", + "EditBasicPipe", + "EditDetailerPipe", + "EditDetailerPipeSDXL", + "EmptySegs", + "FaceDetailer", + "FaceDetailerPipe", + "FromBasicPipe", + "FromBasicPipe_v2", + "FromDetailerPipe", + "FromDetailerPipeSDXL", + "FromDetailerPipe_v2", + "ImageListToImageBatch", + "ImageMaskSwitch", + "ImageReceiver", + "ImageSender", + "ImpactAssembleSEGS", + "ImpactCombineConditionings", + "ImpactCompare", + "ImpactConcatConditionings", + "ImpactConditionalBranch", + "ImpactConditionalStopIteration", + "ImpactControlBridge", + "ImpactControlNetApplySEGS", + "ImpactDecomposeSEGS", + "ImpactDilateMask", + "ImpactDilateMaskInSEGS", + "ImpactDilate_Mask_SEG_ELT", + "ImpactDummyInput", + "ImpactEdit_SEG_ELT", + "ImpactFloat", + "ImpactFrom_SEG_ELT", + "ImpactGaussianBlurMask", + "ImpactGaussianBlurMaskInSEGS", + "ImpactHFTransformersClassifierProvider", + "ImpactImageBatchToImageList", + "ImpactImageInfo", + "ImpactInt", + "ImpactInversedSwitch", + "ImpactIsNotEmptySEGS", + "ImpactKSamplerAdvancedBasicPipe", + "ImpactKSamplerBasicPipe", + "ImpactLatentInfo", + "ImpactLogger", + "ImpactMakeImageBatch", + "ImpactMakeImageList", + "ImpactMinMax", + "ImpactNeg", + "ImpactNodeSetMuteState", + "ImpactQueueTrigger", + "ImpactQueueTriggerCountdown", + "ImpactRemoteBoolean", + "ImpactRemoteInt", + "ImpactSEGSClassify", + "ImpactSEGSConcat", + "ImpactSEGSLabelFilter", + "ImpactSEGSOrderedFilter", + "ImpactSEGSPicker", + "ImpactSEGSRangeFilter", + "ImpactSEGSToMaskBatch", + "ImpactSEGSToMaskList", + "ImpactScaleBy_BBOX_SEG_ELT", + "ImpactSegsAndMask", + "ImpactSegsAndMaskForEach", + "ImpactSetWidgetValue", + "ImpactSimpleDetectorSEGS", + "ImpactSimpleDetectorSEGSPipe", + "ImpactSimpleDetectorSEGS_for_AD", + "ImpactSleep", + "ImpactStringSelector", + "ImpactSwitch", + "ImpactValueReceiver", + "ImpactValueSender", + "ImpactWildcardEncode", + "ImpactWildcardProcessor", + "IterativeImageUpscale", + "IterativeLatentUpscale", + "KSamplerAdvancedProvider", + "KSamplerProvider", + "LatentPixelScale", + "LatentReceiver", + "LatentSender", + "LatentSwitch", + "MMDetDetectorProvider", + "MMDetLoader", + "MaskDetailerPipe", + "MaskListToMaskBatch", + "MaskPainter", + "MaskToSEGS", + "MaskToSEGS_for_AnimateDiff", + "MasksToMaskList", + "MediaPipeFaceMeshToSEGS", + "NoiseInjectionDetailerHookProvider", + "NoiseInjectionHookProvider", + "ONNXDetectorProvider", + "ONNXDetectorSEGS", + "PixelKSampleHookCombine", + "PixelKSampleUpscalerProvider", + "PixelKSampleUpscalerProviderPipe", + "PixelTiledKSampleUpscalerProvider", + "PixelTiledKSampleUpscalerProviderPipe", + "PreviewBridge", + "PreviewBridgeLatent", + "ReencodeLatent", + "ReencodeLatentPipe", + "RegionalPrompt", + "RegionalSampler", + "RegionalSamplerAdvanced", + "RemoveNoiseMask", + "SAMDetectorCombined", + "SAMDetectorSegmented", + "SAMLoader", + "SEGSDetailer", + "SEGSDetailerForAnimateDiff", + "SEGSLabelFilterDetailerHookProvider", + "SEGSOrderedFilterDetailerHookProvider", + "SEGSPaste", + "SEGSPreview", + "SEGSRangeFilterDetailerHookProvider", + "SEGSSwitch", + "SEGSToImageList", + "SegmDetectorCombined", + "SegmDetectorCombined_v2", + "SegmDetectorForEach", + "SegmDetectorSEGS", + "Segs Mask", + "Segs Mask ForEach", + "SegsMaskCombine", + "SegsToCombinedMask", + "SetDefaultImageForSEGS", + "SubtractMask", + "SubtractMaskForEach", + "TiledKSamplerProvider", + "ToBasicPipe", + "ToBinaryMask", + "ToDetailerPipe", + "ToDetailerPipeSDXL", + "TwoAdvancedSamplersForMask", + "TwoSamplersForMask", + "TwoSamplersForMaskUpscalerProvider", + "TwoSamplersForMaskUpscalerProviderPipe", + "UltralyticsDetectorProvider", + "UnsamplerDetailerHookProvider", + "UnsamplerHookProvider" + ], + { + "author": "Dr.Lt.Data", + "description": "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.", + "nickname": "Impact Pack", + "title": "Impact Pack", + "title_aux": "ComfyUI Impact Pack" + } + ], + "https://github.com/ltdrdata/ComfyUI-Inspire-Pack": [ + [ + "AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire", + "ApplyRegionalIPAdapters //Inspire", + "BindImageListPromptList //Inspire", + "CLIPTextEncodeWithWeight //Inspire", + "CacheBackendData //Inspire", + "CacheBackendDataList //Inspire", + "CacheBackendDataNumberKey //Inspire", + "CacheBackendDataNumberKeyList //Inspire", + "Canny_Preprocessor_Provider_for_SEGS //Inspire", + "ChangeImageBatchSize //Inspire", + "CheckpointLoaderSimpleShared //Inspire", + "Color_Preprocessor_Provider_for_SEGS //Inspire", + "ConcatConditioningsWithMultiplier //Inspire", + "DWPreprocessor_Provider_for_SEGS //Inspire", + "FakeScribblePreprocessor_Provider_for_SEGS //Inspire", + "FloatRange //Inspire", + "FromIPAdapterPipe //Inspire", + "GlobalSampler //Inspire", + "GlobalSeed //Inspire", + "HEDPreprocessor_Provider_for_SEGS //Inspire", + "InpaintPreprocessor_Provider_for_SEGS //Inspire", + "KSampler //Inspire", + "KSamplerAdvanced //Inspire", + "KSamplerAdvancedProgress //Inspire", + "KSamplerProgress //Inspire", + "LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire", + "LineArt_Preprocessor_Provider_for_SEGS //Inspire", + "ListCounter //Inspire", + "LoadImage //Inspire", + "LoadImageListFromDir //Inspire", + "LoadImagesFromDir //Inspire", + "LoadPromptsFromDir //Inspire", + "LoadPromptsFromFile //Inspire", + "LoadSinglePromptFromFile //Inspire", + "LoraBlockInfo //Inspire", + "LoraLoaderBlockWeight //Inspire", + "Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire", + "MediaPipeFaceMeshDetectorProvider //Inspire", + "MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire", + "MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire", + "OpenPose_Preprocessor_Provider_for_SEGS //Inspire", + "PromptBuilder //Inspire", + "PromptExtractor //Inspire", + "RegionalConditioningColorMask //Inspire", + "RegionalConditioningSimple //Inspire", + "RegionalIPAdapterColorMask //Inspire", + "RegionalIPAdapterEncodedColorMask //Inspire", + "RegionalIPAdapterEncodedMask //Inspire", + "RegionalIPAdapterMask //Inspire", + "RegionalPromptColorMask //Inspire", + "RegionalPromptSimple //Inspire", + "RegionalSeedExplorerColorMask //Inspire", + "RegionalSeedExplorerMask //Inspire", + "RemoveBackendData //Inspire", + "RemoveBackendDataNumberKey //Inspire", + "RetrieveBackendData //Inspire", + "RetrieveBackendDataNumberKey //Inspire", + "SeedExplorer //Inspire", + "ShowCachedInfo //Inspire", + "TilePreprocessor_Provider_for_SEGS //Inspire", + "ToIPAdapterPipe //Inspire", + "UnzipPrompt //Inspire", + "WildcardEncode //Inspire", + "XY Input: Lora Block Weight //Inspire", + "ZipPrompt //Inspire", + "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire" + ], + { + "author": "Dr.Lt.Data", + "description": "This extension provides various nodes to support Lora Block Weight and the Impact Pack.", + "nickname": "Inspire Pack", + "nodename_pattern": "Inspire$", + "title": "Inspire Pack", + "title_aux": "ComfyUI Inspire Pack" + } + ], + "https://github.com/m-sokes/ComfyUI-Sokes-Nodes": [ + [ + "Custom Date Format | sokes \ud83e\uddac", + "Latent Switch x9 | sokes \ud83e\uddac" + ], + { + "title_aux": "ComfyUI Sokes Nodes" + } + ], + "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes/raw/main/clip-text-encode-split/clip_text_encode_split.py": [ + [ + "RawText", + "RawTextCombine", + "RawTextEncode", + "RawTextReplace" + ], + { + "title_aux": "m957ymj75urz/ComfyUI-Custom-Nodes" + } + ], + "https://github.com/marhensa/sdxl-recommended-res-calc": [ + [ + "RecommendedResCalc" + ], + { + "title_aux": "Recommended Resolution Calculator" + } + ], + "https://github.com/martijnat/comfyui-previewlatent": [ + [ + "PreviewLatent", + "PreviewLatentAdvanced" + ], + { + "title_aux": "comfyui-previewlatent" + } + ], + "https://github.com/matan1905/ComfyUI-Serving-Toolkit": [ + [ + "DiscordServing", + "ServingInputNumber", + "ServingInputText", + "ServingOutput", + "WebSocketServing" + ], + { + "title_aux": "ComfyUI Serving toolkit" + } + ], + "https://github.com/mav-rik/facerestore_cf": [ + [ + "CropFace", + "FaceRestoreCFWithModel", + "FaceRestoreModelLoader" + ], + { + "title_aux": "Facerestore CF (Code Former)" + } + ], + "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding": [ + [ + "DynamicThresholdingFull", + "DynamicThresholdingSimple" + ], + { + "title_aux": "Stable Diffusion Dynamic Thresholding (CFG Scale Fix)" + } + ], + "https://github.com/meap158/ComfyUI-Background-Replacement": [ + [ + "BackgroundReplacement", + "ImageComposite" + ], + { + "title_aux": "ComfyUI-Background-Replacement" + } + ], + "https://github.com/meap158/ComfyUI-GPU-temperature-protection": [ + [ + "GPUTemperatureProtection" + ], + { + "title_aux": "GPU temperature protection" + } + ], + "https://github.com/meap158/ComfyUI-Prompt-Expansion": [ + [ + "PromptExpansion" + ], + { + "title_aux": "ComfyUI-Prompt-Expansion" + } + ], + "https://github.com/melMass/comfy_mtb": [ + [ + "Animation Builder (mtb)", + "Any To String (mtb)", + "Batch Float (mtb)", + "Batch Float Assemble (mtb)", + "Batch Float Fill (mtb)", + "Batch Make (mtb)", + "Batch Merge (mtb)", + "Batch Shake (mtb)", + "Batch Shape (mtb)", + "Batch Transform (mtb)", + "Bbox (mtb)", + "Bbox From Mask (mtb)", + "Blur (mtb)", + "Color Correct (mtb)", + "Colored Image (mtb)", + "Concat Images (mtb)", + "Crop (mtb)", + "Debug (mtb)", + "Deep Bump (mtb)", + "Export With Ffmpeg (mtb)", + "Face Swap (mtb)", + "Film Interpolation (mtb)", + "Fit Number (mtb)", + "Float To Number (mtb)", + "Get Batch From History (mtb)", + "Image Compare (mtb)", + "Image Premultiply (mtb)", + "Image Remove Background Rembg (mtb)", + "Image Resize Factor (mtb)", + "Image Tile Offset (mtb)", + "Int To Bool (mtb)", + "Int To Number (mtb)", + "Interpolate Clip Sequential (mtb)", + "Latent Lerp (mtb)", + "Load Face Analysis Model (mtb)", + "Load Face Enhance Model (mtb)", + "Load Face Swap Model (mtb)", + "Load Film Model (mtb)", + "Load Image From Url (mtb)", + "Load Image Sequence (mtb)", + "Mask To Image (mtb)", + "Math Expression (mtb)", + "Model Patch Seamless (mtb)", + "Pick From Batch (mtb)", + "Qr Code (mtb)", + "Restore Face (mtb)", + "Save Gif (mtb)", + "Save Image Grid (mtb)", + "Save Image Sequence (mtb)", + "Save Tensors (mtb)", + "Sharpen (mtb)", + "Smart Step (mtb)", + "Stack Images (mtb)", + "String Replace (mtb)", + "Styles Loader (mtb)", + "Text To Image (mtb)", + "Transform Image (mtb)", + "Uncrop (mtb)", + "Unsplash Image (mtb)", + "Vae Decode (mtb)" + ], + { + "nodename_pattern": "\\(mtb\\)$", + "title_aux": "MTB Nodes" + } + ], + "https://github.com/mihaiiancu/ComfyUI_Inpaint": [ + [ + "InpaintMediapipe" + ], + { + "title_aux": "mihaiiancu/Inpaint" + } + ], + "https://github.com/mikkel/ComfyUI-text-overlay": [ + [ + "Image Text Overlay" + ], + { + "title_aux": "ComfyUI - Text Overlay Plugin" + } + ], + "https://github.com/mikkel/comfyui-mask-boundingbox": [ + [ + "Mask Bounding Box" + ], + { + "title_aux": "ComfyUI - Mask Bounding Box" + } + ], + "https://github.com/mlinmg/ComfyUI-LaMA-Preprocessor": [ + [ + "LaMaPreprocessor", + "lamaPreprocessor" + ], + { + "title_aux": "LaMa Preprocessor [WIP]" + } + ], + "https://github.com/modusCell/ComfyUI-dimension-node-modusCell": [ + [ + "DimensionProviderFree modusCell", + "DimensionProviderRatio modusCell", + "String Concat modusCell" + ], + { + "title_aux": "Preset Dimensions" + } + ], + "https://github.com/mpiquero7164/ComfyUI-SaveImgPrompt": [ + [ + "Save IMG Prompt" + ], + { + "title_aux": "SaveImgPrompt" + } + ], + "https://github.com/nagolinc/ComfyUI_FastVAEDecorder_SDXL": [ + [ + "FastLatentToImage" + ], + { + "title_aux": "ComfyUI_FastVAEDecorder_SDXL" + } + ], + "https://github.com/natto-maki/ComfyUI-NegiTools": [ + [ + "NegiTools_CompositeImages", + "NegiTools_DepthEstimationByMarigold", + "NegiTools_ImageProperties", + "NegiTools_LatentProperties", + "NegiTools_NoiseImageGenerator", + "NegiTools_OpenAiDalle3", + "NegiTools_OpenAiTranslate", + "NegiTools_OpenPoseToPointList", + "NegiTools_PointListToMask", + "NegiTools_RandomImageLoader", + "NegiTools_SaveImageToDirectory", + "NegiTools_SeedGenerator", + "NegiTools_StereoImageGenerator", + "NegiTools_StringFunction" + ], + { + "title_aux": "ComfyUI-NegiTools" + } + ], + "https://github.com/nicolai256/comfyUI_Nodes_nicolai256/raw/main/yugioh-presets.py": [ + [ + "yugioh_Presets" + ], + { + "title_aux": "comfyUI_Nodes_nicolai256" + } + ], + "https://github.com/ningxiaoxiao/comfyui-NDI": [ + [ + "NDI_LoadImage", + "NDI_SendImage" + ], + { + "title_aux": "comfyui-NDI" + } + ], + "https://github.com/noembryo/ComfyUI-noEmbryo": [ + [ + "PromptTermList1", + "PromptTermList2", + "PromptTermList3", + "PromptTermList4", + "PromptTermList5", + "PromptTermList6" + ], + { + "author": "noEmbryo", + "description": "Some useful nodes for ComfyUI", + "nickname": "noEmbryo", + "title": "noEmbryo nodes for ComfyUI", + "title_aux": "noEmbryo nodes" + } + ], + "https://github.com/noxinias/ComfyUI_NoxinNodes": [ + [ + "NoxinChime", + "NoxinPromptLoad", + "NoxinPromptSave", + "NoxinScaledResolution", + "NoxinSimpleMath", + "NoxinSplitPrompt" + ], + { + "title_aux": "ComfyUI_NoxinNodes" + } + ], + "https://github.com/ntdviet/comfyui-ext/raw/main/custom_nodes/gcLatentTunnel/gcLatentTunnel.py": [ + [ + "gcLatentTunnel" + ], + { + "title_aux": "ntdviet/comfyui-ext" + } + ], + "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92": [ + [ + "CLIPStringEncode _O", + "Chat completion _O", + "ChatGPT Simple _O", + "ChatGPT _O", + "ChatGPT compact _O", + "Chat_Completion _O", + "Chat_Message _O", + "Chat_Message_fromString _O", + "Concat Text _O", + "ConcatRandomNSP_O", + "Debug String _O", + "Debug Text _O", + "Debug Text route _O", + "Edit_image _O", + "Equation1param _O", + "Equation2params _O", + "GetImage_(Width&Height) _O", + "GetLatent_(Width&Height) _O", + "ImageScaleFactor _O", + "ImageScaleFactorSimple _O", + "LatentUpscaleFactor _O", + "LatentUpscaleFactorSimple _O", + "LatentUpscaleMultiply", + "Note _O", + "RandomNSP _O", + "Replace Text _O", + "String _O", + "Text _O", + "Text2Image _O", + "Trim Text _O", + "VAEDecodeParallel _O", + "combine_chat_messages _O", + "compine_chat_messages _O", + "concat Strings _O", + "create image _O", + "create_image _O", + "debug Completeion _O", + "debug messages_O", + "float _O", + "floatToInt _O", + "floatToText _O", + "int _O", + "intToFloat _O", + "load_openAI _O", + "replace String _O", + "replace String advanced _O", + "saveTextToFile _O", + "seed _O", + "selectLatentFromBatch _O", + "string2Image _O", + "trim String _O", + "variation_image _O" + ], + { + "title_aux": "Quality of life Suit:V2" + } + ], + "https://github.com/ostris/ostris_nodes_comfyui": [ + [ + "LLM Pipe Loader - Ostris", + "LLM Prompt Upsampling - Ostris", + "One Seed - Ostris", + "Text Box - Ostris" + ], + { + "nodename_pattern": "- Ostris$", + "title_aux": "Ostris Nodes ComfyUI" + } + ], + "https://github.com/oyvindg/ComfyUI-TrollSuite": [ + [ + "BinaryImageMask", + "ImagePadding", + "LoadLastImage", + "RandomMask", + "TransparentImage" + ], + { + "title_aux": "ComfyUI-TrollSuite" + } + ], + "https://github.com/palant/extended-saveimage-comfyui": [ + [ + "SaveImageExtended" + ], + { + "title_aux": "Extended Save Image for ComfyUI" + } + ], + "https://github.com/palant/image-resize-comfyui": [ + [ + "ImageResize" + ], + { + "title_aux": "Image Resize for ComfyUI" + } + ], + "https://github.com/pants007/comfy-pants": [ + [ + "CLIPTextEncodeAIO", + "Image Make Square" + ], + { + "title_aux": "pants" + } + ], + "https://github.com/paulo-coronado/comfy_clip_blip_node": [ + [ + "CLIPTextEncodeBLIP", + "CLIPTextEncodeBLIP-2", + "Example" + ], + { + "title_aux": "comfy_clip_blip_node" + } + ], + "https://github.com/picturesonpictures/comfy_PoP": [ + [ + "AdaptiveCannyDetector_PoP", + "AnyAspectRatio", + "ConditioningMultiplier_PoP", + "ConditioningNormalizer_PoP", + "LoadImageResizer_PoP", + "LoraStackLoader10_PoP", + "LoraStackLoader_PoP", + "VAEDecoderPoP", + "VAEEncoderPoP" + ], + { + "title_aux": "comfy_PoP" + } + ], + "https://github.com/pkpkTech/ComfyUI-SaveAVIF": [ + [ + "SaveAvif" + ], + { + "title_aux": "ComfyUI-SaveAVIF" + } + ], + "https://github.com/pythongosssss/ComfyUI-Custom-Scripts": [ + [ + "CheckpointLoader|pysssss", + "ConstrainImage|pysssss", + "LoadText|pysssss", + "LoraLoader|pysssss", + "MathExpression|pysssss", + "MultiPrimitive|pysssss", + "PlaySound|pysssss", + "Repeater|pysssss", + "ReroutePrimitive|pysssss", + "SaveText|pysssss", + "ShowText|pysssss", + "StringFunction|pysssss" + ], + { + "title_aux": "pythongosssss/ComfyUI-Custom-Scripts" + } + ], + "https://github.com/pythongosssss/ComfyUI-WD14-Tagger": [ + [ + "WD14Tagger|pysssss" + ], + { + "title_aux": "ComfyUI WD 1.4 Tagger" + } + ], + "https://github.com/ramyma/A8R8_ComfyUI_nodes": [ + [ + "Base64ImageInput", + "Base64ImageOutput" + ], + { + "title_aux": "A8R8 ComfyUI Nodes" + } + ], + "https://github.com/rcfcu2000/zhihuige-nodes-comfyui": [ + [ + "Combine ZHGMasks", + "Cover ZHGMasks", + "ZHG FaceIndex", + "ZHG GetMaskArea", + "ZHG SaveImage", + "ZHG SmoothEdge" + ], + { + "title_aux": "zhihuige-nodes-comfyui" + } + ], + "https://github.com/rcsaquino/comfyui-custom-nodes": [ + [ + "BackgroundRemover | rcsaquino", + "VAELoader | rcsaquino", + "VAEProcessor | rcsaquino" + ], + { + "title_aux": "rcsaquino/comfyui-custom-nodes" + } + ], + "https://github.com/receyuki/comfyui-prompt-reader-node": [ + [ + "SDBatchLoader", + "SDParameterExtractor", + "SDParameterGenerator", + "SDPromptMerger", + "SDPromptReader", + "SDPromptSaver", + "SDTypeConverter" + ], + { + "author": "receyuki", + "description": "ComfyUI node version of the SD Prompt Reader", + "nickname": "SD Prompt Reader", + "title": "SD Prompt Reader", + "title_aux": "comfyui-prompt-reader-node" + } + ], + "https://github.com/rgthree/rgthree-comfy": [ + [], + { + "author": "rgthree", + "description": "A bunch of nodes I created that I also find useful.", + "nickname": "rgthree", + "nodename_pattern": " \\(rgthree\\)$", + "title": "Comfy Nodes", + "title_aux": "rgthree's ComfyUI Nodes" + } + ], + "https://github.com/richinsley/Comfy-LFO": [ + [ + "LFO_Pulse", + "LFO_Sawtooth", + "LFO_Sine", + "LFO_Square", + "LFO_Triangle" + ], + { + "title_aux": "Comfy-LFO" + } + ], + "https://github.com/rklaffehn/rk-comfy-nodes": [ + [ + "RK_CivitAIAddHashes", + "RK_CivitAIMetaChecker" + ], + { + "title_aux": "rk-comfy-nodes" + } + ], + "https://github.com/romeobuilderotti/ComfyUI-PNG-Metadata": [ + [ + "SetMetadataAll", + "SetMetadataString" + ], + { + "title_aux": "ComfyUI PNG Metadata" + } + ], + "https://github.com/rui40000/RUI-Nodes": [ + [ + "ABCondition", + "CharacterCount" + ], + { + "title_aux": "RUI-Nodes" + } + ], + "https://github.com/s1dlx/comfy_meh/raw/main/meh.py": [ + [ + "MergingExecutionHelper" + ], + { + "title_aux": "comfy_meh" + } + ], + "https://github.com/seanlynch/comfyui-optical-flow": [ + [ + "Apply optical flow", + "Compute optical flow", + "Visualize optical flow" + ], + { + "title_aux": "ComfyUI Optical Flow" + } + ], + "https://github.com/seanlynch/srl-nodes": [ + [ + "SRL Conditional Interrrupt", + "SRL Eval", + "SRL Filter Image List", + "SRL Format String" + ], + { + "title_aux": "SRL's nodes" + } + ], + "https://github.com/sergekatzmann/ComfyUI_Nimbus-Pack": [ + [ + "ImageResizeAndCropNode", + "ImageSquareAdapterNode" + ], + { + "title_aux": "ComfyUI_Nimbus-Pack" + } + ], + "https://github.com/shadowcz007/comfyui-mixlab-nodes": [ + [ + "3DImage", + "AppInfo", + "AreaToMask", + "CLIPSeg", + "CLIPSeg_", + "CharacterInText", + "ChatGPTOpenAI", + "Color", + "CombineMasks_", + "CombineSegMasks", + "DynamicDelayProcessor", + "EnhanceImage", + "FaceToMask", + "FeatheredMask", + "FloatSlider", + "FloatingVideo", + "Font", + "GamePal", + "GetImageSize_", + "ImageCropByAlpha", + "IntNumber", + "LimitNumber", + "LoadImagesFromPath", + "LoadImagesFromURL", + "MergeLayers", + "MultiplicationNode", + "NewLayer", + "NoiseImage", + "RandomPrompt", + "ResizeImageMixlab", + "ScreenShare", + "ShowLayer", + "ShowTextForGPT", + "SmoothMask", + "SpeechRecognition", + "SpeechSynthesis", + "SplitLongMask", + "SvgImage", + "SwitchByIndex", + "TextImage", + "TextInput_", + "TextToNumber", + "TransparentImage", + "VAEDecodeConsistencyDecoder", + "VAELoaderConsistencyDecoder" + ], + { + "title_aux": "comfyui-mixlab-nodes" + } + ], + "https://github.com/shiimizu/ComfyUI_smZNodes": [ + [ + "smZ CLIPTextEncode", + "smZ Settings" + ], + { + "title_aux": "smZNodes" + } + ], + "https://github.com/shingo1228/ComfyUI-SDXL-EmptyLatentImage": [ + [ + "SDXL Empty Latent Image" + ], + { + "title_aux": "ComfyUI-SDXL-EmptyLatentImage" + } + ], + "https://github.com/shingo1228/ComfyUI-send-eagle-slim": [ + [ + "Send Webp Image to Eagle" + ], + { + "title_aux": "ComfyUI-send-Eagle(slim)" + } + ], + "https://github.com/shockz0rz/ComfyUI_InterpolateEverything": [ + [ + "OpenposePreprocessorInterpolate" + ], + { + "title_aux": "InterpolateEverything" + } + ], + "https://github.com/shockz0rz/comfy-easy-grids": [ + [ + "FloatToText", + "GridFloatList", + "GridFloats", + "GridIntList", + "GridInts", + "GridStringList", + "GridStrings", + "ImageGridCommander", + "IntToText", + "SaveImageGrid", + "TextConcatenator" + ], + { + "title_aux": "comfy-easy-grids" + } + ], + "https://github.com/sipherxyz/comfyui-art-venture": [ + [ + "AV_CheckpointMerge", + "AV_CheckpointModelsToParametersPipe", + "AV_CheckpointSave", + "AV_ControlNetEfficientLoader", + "AV_ControlNetEfficientLoaderAdvanced", + "AV_ControlNetEfficientStacker", + "AV_ControlNetEfficientStackerSimple", + "AV_ControlNetLoader", + "AV_ControlNetPreprocessor", + "AV_LoraListLoader", + "AV_LoraListStacker", + "AV_LoraLoader", + "AV_ParametersPipeToCheckpointModels", + "AV_ParametersPipeToPrompts", + "AV_PromptsToParametersPipe", + "AV_SAMLoader", + "AV_VAELoader", + "AspectRatioSelector", + "BLIPCaption", + "BLIPLoader", + "BooleanPrimitive", + "ColorBlend", + "ColorCorrect", + "DeepDanbooruCaption", + "DependenciesEdit", + "Fooocus_KSampler", + "Fooocus_KSamplerAdvanced", + "GetBoolFromJson", + "GetFloatFromJson", + "GetIntFromJson", + "GetObjectFromJson", + "GetSAMEmbedding", + "GetTextFromJson", + "ISNetLoader", + "ISNetSegment", + "ImageAlphaComposite", + "ImageApplyChannel", + "ImageExtractChannel", + "ImageGaussianBlur", + "ImageMuxer", + "ImageRepeat", + "ImageScaleDown", + "ImageScaleDownBy", + "ImageScaleDownToSize", + "ImageScaleToMegapixels", + "LaMaInpaint", + "LoadImageAsMaskFromUrl", + "LoadImageFromUrl", + "LoadJsonFromUrl", + "MergeModels", + "NumberScaler", + "OverlayInpaintedImage", + "OverlayInpaintedLatent", + "PrepareImageAndMaskForInpaint", + "QRCodeGenerator", + "RandomFloat", + "RandomInt", + "SAMEmbeddingToImage", + "SDXLAspectRatioSelector", + "SDXLPromptStyler", + "SeedSelector", + "StringToInt", + "StringToNumber" + ], + { + "title_aux": "comfyui-art-venture" + } + ], + "https://github.com/skfoo/ComfyUI-Coziness": [ + [ + "LoraTextExtractor-b1f83aa2", + "MultiLoraLoader-70bf3d77" + ], + { + "title_aux": "ComfyUI-Coziness" + } + ], + "https://github.com/space-nuko/ComfyUI-Disco-Diffusion": [ + [ + "DiscoDiffusion_DiscoDiffusion", + "DiscoDiffusion_DiscoDiffusionExtraSettings", + "DiscoDiffusion_GuidedDiffusionLoader", + "DiscoDiffusion_OpenAICLIPLoader" + ], + { + "title_aux": "Disco Diffusion" + } + ], + "https://github.com/space-nuko/ComfyUI-OpenPose-Editor": [ + [ + "Nui.OpenPoseEditor" + ], + { + "title_aux": "OpenPose Editor" + } + ], + "https://github.com/space-nuko/nui-suite": [ + [ + "Nui.DynamicPromptsTextGen", + "Nui.FeelingLuckyTextGen", + "Nui.OutputString" + ], + { + "title_aux": "nui suite" + } + ], + "https://github.com/spacepxl/ComfyUI-HQ-Image-Save": [ + [ + "LoadLatentEXR", + "SaveEXR", + "SaveLatentEXR", + "SaveTiff" + ], + { + "title_aux": "ComfyUI-HQ-Image-Save" + } + ], + "https://github.com/spacepxl/ComfyUI-Image-Filters": [ + [ + "AlphaClean", + "AlphaMatte", + "BlurImageFast", + "BlurMaskFast", + "DilateErodeMask", + "EnhanceDetail", + "GuidedFilterAlpha", + "RemapRange" + ], + { + "title_aux": "ComfyUI-Image-Filters" + } + ], + "https://github.com/spinagon/ComfyUI-seam-carving": [ + [ + "SeamCarving" + ], + { + "title_aux": "ComfyUI-seam-carving" + } + ], + "https://github.com/spinagon/ComfyUI-seamless-tiling": [ + [ + "CircularVAEDecode", + "MakeCircularVAE", + "OffsetImage", + "SeamlessTile" + ], + { + "title_aux": "Seamless tiling Node for ComfyUI" + } + ], + "https://github.com/spro/comfyui-mirror": [ + [ + "LatentMirror" + ], + { + "title_aux": "Latent Mirror node for ComfyUI" + } + ], + "https://github.com/ssitu/ComfyUI_UltimateSDUpscale": [ + [ + "UltimateSDUpscale", + "UltimateSDUpscaleNoUpscale" + ], + { + "title_aux": "UltimateSDUpscale" + } + ], + "https://github.com/ssitu/ComfyUI_fabric": [ + [ + "FABRICPatchModel", + "FABRICPatchModelAdv", + "KSamplerAdvFABRICAdv", + "KSamplerFABRIC", + "KSamplerFABRICAdv" + ], + { + "title_aux": "ComfyUI fabric" + } + ], + "https://github.com/ssitu/ComfyUI_restart_sampling": [ + [ + "KRestartSampler", + "KRestartSamplerAdv", + "KRestartSamplerSimple" + ], + { + "title_aux": "Restart Sampling" + } + ], + "https://github.com/ssitu/ComfyUI_roop": [ + [ + "RoopImproved", + "roop" + ], + { + "title_aux": "ComfyUI roop" + } + ], + "https://github.com/storyicon/comfyui_segment_anything": [ + [ + "GroundingDinoModelLoader (segment anything)", + "GroundingDinoSAMSegment (segment anything)", + "InvertMask (segment anything)", + "SAMModelLoader (segment anything)" + ], + { + "title_aux": "segment anything" + } + ], + "https://github.com/strimmlarn/ComfyUI_Strimmlarns_aesthetic_score": [ + [ + "AesthetlcScoreSorter", + "CalculateAestheticScore", + "LoadAesteticModel", + "ScoreToNumber" + ], + { + "title_aux": "ComfyUI_Strimmlarns_aesthetic_score" + } + ], + "https://github.com/styler00dollar/ComfyUI-deepcache": [ + [ + "DeepCache" + ], + { + "title_aux": "ComfyUI-deepcache" + } + ], + "https://github.com/styler00dollar/ComfyUI-sudo-latent-upscale": [ + [ + "SudoLatentUpscale" + ], + { + "title_aux": "ComfyUI-sudo-latent-upscale" + } + ], + "https://github.com/syllebra/bilbox-comfyui": [ + [ + "BilboXLut", + "BilboXPhotoPrompt", + "BilboXVignette" + ], + { + "title_aux": "BilboX's ComfyUI Custom Nodes" + } + ], + "https://github.com/sylym/comfy_vid2vid": [ + [ + "CheckpointLoaderSimpleSequence", + "DdimInversionSequence", + "KSamplerSequence", + "LoadImageMaskSequence", + "LoadImageSequence", + "LoraLoaderSequence", + "SetLatentNoiseSequence", + "TrainUnetSequence", + "VAEEncodeForInpaintSequence" + ], + { + "title_aux": "Vid2vid" + } + ], + "https://github.com/szhublox/ambw_comfyui": [ + [ + "Auto Merge Block Weighted", + "CLIPMergeSimple", + "CheckpointSave", + "ModelMergeBlocks", + "ModelMergeSimple" + ], + { + "title_aux": "Auto-MBW" + } + ], + "https://github.com/taabata/Comfy_Syrian_Falcon_Nodes/raw/main/SyrianFalconNodes.py": [ + [ + "CompositeImage", + "KSamplerAlternate", + "KSamplerPromptEdit", + "KSamplerPromptEditAndAlternate", + "LoopBack", + "QRGenerate", + "WordAsImage" + ], + { + "title_aux": "Syrian Falcon Nodes" + } + ], + "https://github.com/taabata/LCM_Inpaint-Outpaint_Comfy": [ + [ + "FreeU_LCM", + "ImageOutputToComfyNodes", + "ImageShuffle", + "LCMGenerate", + "LCMGenerate_ReferenceOnly", + "LCMGenerate_SDTurbo", + "LCMGenerate_img2img", + "LCMGenerate_img2img_IPAdapter", + "LCMGenerate_img2img_controlnet", + "LCMGenerate_inpaintv2", + "LCMGenerate_inpaintv3", + "LCMLoader", + "LCMLoader_RefInpaint", + "LCMLoader_ReferenceOnly", + "LCMLoader_SDTurbo", + "LCMLoader_controlnet", + "LCMLoader_controlnet_inpaint", + "LCMLoader_img2img", + "LCMLoraLoader_inpaint", + "LCMLora_inpaint", + "LCMT2IAdapter", + "LCM_IPAdapter", + "LCM_IPAdapter_inpaint", + "LCM_outpaint_prep", + "LoadImageNode_LCM", + "Loader_SegmindVega", + "OutpaintCanvasTool", + "SaveImage_LCM", + "SaveImage_Puzzle", + "SaveImage_PuzzleV2", + "SegmindVega", + "stitch" + ], + { + "title_aux": "LCM_Inpaint-Outpaint_Comfy" + } + ], + "https://github.com/theUpsider/ComfyUI-Logic": [ + [ + "Bool", + "Compare", + "DebugPrint", + "Float", + "If ANY execute A else B", + "Int", + "String" + ], + { + "title_aux": "ComfyUI-Logic" + } + ], + "https://github.com/theUpsider/ComfyUI-Styles_CSV_Loader": [ + [ + "Load Styles CSV" + ], + { + "title_aux": "Styles CSV Loader Extension for ComfyUI" + } + ], + "https://github.com/thecooltechguy/ComfyUI-MagicAnimate": [ + [ + "MagicAnimate", + "MagicAnimateModelLoader" + ], + { + "title_aux": "ComfyUI-MagicAnimate" + } + ], + "https://github.com/thecooltechguy/ComfyUI-Stable-Video-Diffusion": [ + [ + "SVDDecoder", + "SVDModelLoader", + "SVDSampler", + "SVDSimpleImg2Vid" + ], + { + "title_aux": "ComfyUI Stable Video Diffusion" + } + ], + "https://github.com/thedyze/save-image-extended-comfyui": [ + [ + "SaveImageExtended" + ], + { + "title_aux": "Save Image Extended for ComfyUI" + } + ], + "https://github.com/toyxyz/ComfyUI_toyxyz_test_nodes": [ + [ + "CaptureWebcam", + "LatentDelay", + "LoadWebcamImage", + "SaveImagetoPath" + ], + { + "title_aux": "ComfyUI_toyxyz_test_nodes" + } + ], + "https://github.com/trojblue/trNodes": [ + [ + "JpgConvertNode", + "trColorCorrection", + "trLayering", + "trRouter", + "trRouterLonger" + ], + { + "title_aux": "trNodes" + } + ], + "https://github.com/ttulttul/ComfyUI-Iterative-Mixer": [ + [ + "Batch Unsampler", + "Iterative Mixing KSampler", + "Iterative Mixing KSampler Advanced", + "Latent Batch Comparison Plot", + "Latent Batch Statistics Plot" + ], + { + "title_aux": "ComfyUI Iterative Mixing Nodes" + } + ], + "https://github.com/tudal/Hakkun-ComfyUI-nodes/raw/main/hakkun_nodes.py": [ + [ + "Any Converter", + "Calculate Upscale", + "Image Resize To Height", + "Image Resize To Width", + "Image size to string", + "Load Random Image", + "Load Text", + "Multi Text Merge", + "Prompt Parser", + "Random Line", + "Random Line 4" + ], + { + "title_aux": "Hakkun-ComfyUI-nodes" + } + ], + "https://github.com/tusharbhutt/Endless-Nodes": [ + [ + "ESS Aesthetic Scoring", + "ESS Aesthetic Scoring Auto", + "ESS Combo Parameterizer", + "ESS Combo Parameterizer & Prompts", + "ESS Eight Input Random", + "ESS Eight Input Text Switch", + "ESS Float to Integer", + "ESS Float to Number", + "ESS Float to String", + "ESS Float to X", + "ESS Global Envoy", + "ESS Image Reward", + "ESS Image Reward Auto", + "ESS Image Saver with JSON", + "ESS Integer to Float", + "ESS Integer to Number", + "ESS Integer to String", + "ESS Integer to X", + "ESS Number to Float", + "ESS Number to Integer", + "ESS Number to String", + "ESS Number to X", + "ESS Parameterizer", + "ESS Parameterizer & Prompts", + "ESS Six Float Output", + "ESS Six Input Random", + "ESS Six Input Text Switch", + "ESS Six Integer IO Switch", + "ESS Six Integer IO Widget", + "ESS String to Float", + "ESS String to Integer", + "ESS String to Num", + "ESS String to X", + "\u267e\ufe0f\ud83c\udf0a\u2728 Image Saver with JSON" + ], + { + "author": "BiffMunky", + "description": "A small set of nodes I created for various numerical and text inputs. Features image saver with ability to have JSON saved to separate folder, parameter collection nodes, two aesthetic scoring models, switches for text and numbers, and conversion of string to numeric and vice versa.", + "nickname": "\u267e\ufe0f\ud83c\udf0a\u2728", + "title": "Endless \ufe0f\ud83c\udf0a\u2728 Nodes", + "title_aux": "Endless \ufe0f\ud83c\udf0a\u2728 Nodes" + } + ], + "https://github.com/twri/sdxl_prompt_styler": [ + [ + "SDXLPromptStyler", + "SDXLPromptStylerAdvanced" + ], + { + "title_aux": "SDXL Prompt Styler" + } + ], + "https://github.com/uarefans/ComfyUI-Fans": [ + [ + "Fans Prompt Styler Negative", + "Fans Prompt Styler Positive", + "Fans Styler", + "Fans Text Concatenate" + ], + { + "title_aux": "ComfyUI-Fans" + } + ], + "https://github.com/vanillacode314/SimpleWildcardsComfyUI": [ + [ + "SimpleConcat", + "SimpleWildcard" + ], + { + "author": "VanillaCode314", + "description": "A simple wildcard node for ComfyUI. Can also be used a style prompt node.", + "nickname": "Simple Wildcard", + "title": "Simple Wildcard", + "title_aux": "Simple Wildcard" + } + ], + "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration": [ + [ + "ChatGptPrompt", + "ChatGptTextConcat" + ], + { + "title_aux": "ComfyUI-Chat-GPT-Integration" + } + ], + "https://github.com/violet-chen/comfyui-psd2png": [ + [ + "Psd2Png" + ], + { + "title_aux": "comfyui-psd2png" + } + ], + "https://github.com/wallish77/wlsh_nodes": [ + [ + "Alternating KSampler (WLSH)", + "Build Filename String (WLSH)", + "CLIP +/- w/Text Unified (WLSH)", + "CLIP Positive-Negative (WLSH)", + "CLIP Positive-Negative XL (WLSH)", + "CLIP Positive-Negative XL w/Text (WLSH)", + "CLIP Positive-Negative w/Text (WLSH)", + "Checkpoint Loader w/Name (WLSH)", + "Empty Latent by Pixels (WLSH)", + "Empty Latent by Ratio (WLSH)", + "Empty Latent by Size (WLSH)", + "Generate Border Mask (WLSH)", + "Grayscale Image (WLSH)", + "Image Load with Metadata (WLSH)", + "Image Save with Prompt (WLSH)", + "Image Save with Prompt File (WLSH)", + "Image Save with Prompt/Info (WLSH)", + "Image Save with Prompt/Info File (WLSH)", + "Image Scale By Factor (WLSH)", + "Image Scale by Shortside (WLSH)", + "KSamplerAdvanced (WLSH)", + "Multiply Integer (WLSH)", + "Outpaint to Image (WLSH)", + "Prompt Weight (WLSH)", + "Quick Resolution Multiply (WLSH)", + "Resolutions by Ratio (WLSH)", + "SDXL Quick Empty Latent (WLSH)", + "SDXL Quick Image Scale (WLSH)", + "SDXL Resolutions (WLSH)", + "SDXL Steps (WLSH)", + "Save Positive Prompt(WLSH)", + "Save Prompt (WLSH)", + "Save Prompt/Info (WLSH)", + "Seed and Int (WLSH)", + "Seed to Number (WLSH)", + "Simple Pattern Replace (WLSH)", + "Simple String Combine (WLSH)", + "Time String (WLSH)", + "Upscale by Factor with Model (WLSH)", + "VAE Encode for Inpaint w/Padding (WLSH)" + ], + { + "title_aux": "wlsh_nodes" + } + ], + "https://github.com/whatbirdisthat/cyberdolphin": [ + [ + "\ud83d\udc2c Gradio ChatInterface", + "\ud83d\udc2c OpenAI Advanced", + "\ud83d\udc2c OpenAI Compatible", + "\ud83d\udc2c OpenAI DALL\u00b7E", + "\ud83d\udc2c OpenAI Simple" + ], + { + "title_aux": "cyberdolphin" + } + ], + "https://github.com/whmc76/ComfyUI-Openpose-Editor-Plus": [ + [ + "CDL.OpenPoseEditorPlus" + ], + { + "title_aux": "ComfyUI-Openpose-Editor-Plus" + } + ], + "https://github.com/wmatson/easy-comfy-nodes": [ + [ + "EZAssocDictNode", + "EZAssocImgNode", + "EZAssocStrNode", + "EZEmptyDictNode", + "EZHttpPostNode", + "EZLoadImgBatchFromUrlsNode", + "EZLoadImgFromUrlNode", + "EZRemoveImgBackground", + "EZVideoCombiner" + ], + { + "title_aux": "easy-comfy-nodes" + } + ], + "https://github.com/wolfden/ComfyUi_PromptStylers": [ + [ + "SDXLPromptStylerAll", + "SDXLPromptStylerHorror", + "SDXLPromptStylerMisc", + "SDXLPromptStylerbyArtist", + "SDXLPromptStylerbyCamera", + "SDXLPromptStylerbyComposition", + "SDXLPromptStylerbyCyberpunkSurrealism", + "SDXLPromptStylerbyDepth", + "SDXLPromptStylerbyEnvironment", + "SDXLPromptStylerbyFantasySetting", + "SDXLPromptStylerbyFilter", + "SDXLPromptStylerbyFocus", + "SDXLPromptStylerbyImpressionism", + "SDXLPromptStylerbyLighting", + "SDXLPromptStylerbyMileHigh", + "SDXLPromptStylerbyMood", + "SDXLPromptStylerbyMythicalCreature", + "SDXLPromptStylerbyOriginal", + "SDXLPromptStylerbyQuantumRealism", + "SDXLPromptStylerbySteamPunkRealism", + "SDXLPromptStylerbySubject", + "SDXLPromptStylerbySurrealism", + "SDXLPromptStylerbyTheme", + "SDXLPromptStylerbyTimeofDay", + "SDXLPromptStylerbyWyvern", + "SDXLPromptbyCelticArt", + "SDXLPromptbyContemporaryNordicArt", + "SDXLPromptbyFashionArt", + "SDXLPromptbyGothicRevival", + "SDXLPromptbyIrishFolkArt", + "SDXLPromptbyRomanticNationalismArt", + "SDXLPromptbySportsArt", + "SDXLPromptbyStreetArt", + "SDXLPromptbyVikingArt", + "SDXLPromptbyWildlifeArt" + ], + { + "title_aux": "SDXL Prompt Styler (customized version by wolfden)" + } + ], + "https://github.com/wolfden/ComfyUi_String_Function_Tree": [ + [ + "StringFunction" + ], + { + "title_aux": "ComfyUi_String_Function_Tree" + } + ], + "https://github.com/wsippel/comfyui_ws/raw/main/sdxl_utility.py": [ + [ + "SDXLResolutionPresets" + ], + { + "title_aux": "SDXLResolutionPresets" + } + ], + "https://github.com/wutipong/ComfyUI-TextUtils": [ + [ + "Text Utils - Join N-Elements of String List", + "Text Utils - Join String List", + "Text Utils - Join Strings", + "Text Utils - Split String to List" + ], + { + "title_aux": "ComfyUI-TextUtils" + } + ], + "https://github.com/xXAdonesXx/NodeGPT": [ + [ + "AppendAgent", + "Assistant", + "Chat", + "ChatGPT", + "CombineInput", + "Conditioning", + "CostumeAgent_1", + "CostumeAgent_2", + "CostumeMaster_1", + "Critic", + "DisplayString", + "DisplayTextAsImage", + "EVAL", + "Engineer", + "Executor", + "GroupChat", + "Image_generation_Conditioning", + "LM_Studio", + "LoadAPIconfig", + "LoadTXT", + "MemGPT", + "Memory_Excel", + "Model_1", + "Ollama", + "Output2String", + "Planner", + "Scientist", + "TextCombine", + "TextGeneration", + "TextGenerator", + "TextInput", + "TextOutput", + "UserProxy", + "llama-cpp", + "llava", + "oobaboogaOpenAI" + ], + { + "title_aux": "NodeGPT" + } + ], + "https://github.com/yolain/ComfyUI-Easy-Use": [ + [ + "dynamicThresholdingFull", + "easy LLLiteLoader", + "easy XYPlot", + "easy a1111Loader", + "easy comfyLoader", + "easy controlnetLoader", + "easy controlnetLoaderADV", + "easy detailerFix", + "easy fullLoader", + "easy fullkSampler", + "easy globalSeed", + "easy hiresFix", + "easy imageInsetCrop", + "easy imagePixelPerfect", + "easy imageRemoveBG", + "easy imageSize", + "easy imageSizeByLongerSide", + "easy imageSizeBySide", + "easy kSampler", + "easy kSamplerSDTurbo", + "easy kSamplerTiled", + "easy loraStack", + "easy negative", + "easy pipeIn", + "easy pipeOut", + "easy portraitMaster", + "easy poseEditor", + "easy positive", + "easy preDetailerFix", + "easy preSampling", + "easy preSamplingAdvanced", + "easy preSamplingDynamicCFG", + "easy preSamplingSdTurbo", + "easy samLoaderPipe", + "easy seed", + "easy showSpentTime", + "easy svdLoader", + "easy ultralyticsDetectorPipe", + "easy wildcards", + "easy zero123Loader" + ], + { + "title_aux": "ComfyUI Easy Use" + } + ], + "https://github.com/yolanother/DTAIComfyImageSubmit": [ + [ + "DTSimpleSubmitImage", + "DTSubmitImage" + ], + { + "title_aux": "Comfy AI DoubTech.ai Image Sumission Node" + } + ], + "https://github.com/yolanother/DTAIComfyLoaders": [ + [ + "DTCLIPLoader", + "DTCLIPVisionLoader", + "DTCheckpointLoader", + "DTCheckpointLoaderSimple", + "DTControlNetLoader", + "DTDiffControlNetLoader", + "DTDiffusersLoader", + "DTGLIGENLoader", + "DTLoadImage", + "DTLoadImageMask", + "DTLoadLatent", + "DTLoraLoader", + "DTLorasLoader", + "DTStyleModelLoader", + "DTUpscaleModelLoader", + "DTVAELoader", + "DTunCLIPCheckpointLoader" + ], + { + "title_aux": "Comfy UI Online Loaders" + } + ], + "https://github.com/yolanother/DTAIComfyPromptAgent": [ + [ + "DTPromptAgent", + "DTPromptAgentString" + ], + { + "title_aux": "Comfy UI Prompt Agent" + } + ], + "https://github.com/yolanother/DTAIComfyQRCodes": [ + [ + "QRCode" + ], + { + "title_aux": "Comfy UI QR Codes" + } + ], + "https://github.com/yolanother/DTAIComfyVariables": [ + [ + "DTCLIPTextEncode", + "DTSingleLineStringVariable", + "DTSingleLineStringVariableNoClip", + "FloatVariable", + "IntVariable", + "StringFormat", + "StringFormatSingleLine", + "StringVariable" + ], + { + "title_aux": "Variables for Comfy UI" + } + ], + "https://github.com/yolanother/DTAIImageToTextNode": [ + [ + "DTAIImageToTextNode", + "DTAIImageUrlToTextNode" + ], + { + "title_aux": "Image to Text Node" + } + ], + "https://github.com/youyegit/tdxh_node_comfyui": [ + [ + "TdxhBoolNumber", + "TdxhClipVison", + "TdxhControlNetApply", + "TdxhControlNetProcessor", + "TdxhFloatInput", + "TdxhImageToSize", + "TdxhImageToSizeAdvanced", + "TdxhImg2ImgLatent", + "TdxhIntInput", + "TdxhLoraLoader", + "TdxhOnOrOff", + "TdxhReference", + "TdxhStringInput", + "TdxhStringInputTranslator" + ], + { + "title_aux": "tdxh_node_comfyui" + } + ], + "https://github.com/zcfrank1st/Comfyui-Toolbox": [ + [ + "PreviewJson", + "PreviewVideo", + "SaveJson", + "TestJsonPreview" + ], + { + "title_aux": "Comfyui-Toolbox" + } + ], + "https://github.com/zcfrank1st/Comfyui-Yolov8": [ + [ + "Yolov8Detection", + "Yolov8Segmentation" + ], + { + "title_aux": "ComfyUI Yolov8" + } + ], + "https://github.com/zcfrank1st/comfyui_visual_anagrams": [ + [ + "VisualAnagramsAnimate", + "VisualAnagramsSample" + ], + { + "title_aux": "comfyui_visual_anagram" + } + ], + "https://github.com/zer0TF/cute-comfy": [ + [ + "Cute.Placeholder" + ], + { + "title_aux": "Cute Comfy" + } + ], + "https://github.com/zfkun/ComfyUI_zfkun": [ + [ + "ZFLoadImagePath", + "ZFPreviewText", + "ZFPreviewTextMultiline", + "ZFShareScreen", + "ZFTextTranslation" + ], + { + "title_aux": "ComfyUI_zfkun" + } + ], + "https://github.com/zhuanqianfish/ComfyUI-EasyNode": [ + [ + "EasyCaptureNode", + "EasyVideoOutputNode", + "SendImageWebSocket" + ], + { + "title_aux": "EasyCaptureNode for ComfyUI" + } + ], + "https://raw.githubusercontent.com/throttlekitty/SDXLCustomAspectRatio/main/SDXLAspectRatio.py": [ + [ + "SDXLAspectRatio" + ], + { + "title_aux": "SDXLCustomAspectRatio" + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..aa6e85514fdc8f050675e672fae2c4e01de699d3 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/new/model-list.json @@ -0,0 +1,754 @@ +{ + "models": [ + { + "name": "ip-adapter-faceid_sd15.bin", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "IP-Adapter-FaceID Model (SD1.5)", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "filename": "ip-adapter-faceid_sd15.bin", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin" + }, + { + "name": "ip-adapter-faceid_sd15_lora.safetensors", + "type": "lora", + "base": "SD1.5", + "save_path": "loras/ipadapter", + "description": "IP-Adapter-FaceID LoRA Model (SD1.5)", + "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", + "filename": "ip-adapter-faceid_sd15_lora.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors" + }, + + + { + "name": "LongAnimatediff/lt_long_mm_16_64_frames_v1.1.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_16_64_frames_v1.1.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames_v1.1.ckpt" + }, + + { + "name": "animatediff/v3_sd15_sparsectrl_rgb.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "controlnet", + "base": "SD1.x", + "save_path": "controlnet/SD1.5/animatediff", + "description": "AnimateDiff SparseCtrl RGB ControlNet model", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_sparsectrl_rgb.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_rgb.ckpt" + }, + { + "name": "animatediff/v3_sd15_sparsectrl_scribble.ckpt", + "type": "controlnet", + "base": "SD1.x", + "save_path": "controlnet/SD1.5/animatediff", + "description": "AnimateDiff SparseCtrl Scribble ControlNet model", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_sparsectrl_scribble.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_scribble.ckpt" + }, + { + "name": "animatediff/v3_sd15_mm.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_mm.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_mm.ckpt" + }, + { + "name": "animatediff/v3_sd15_adapter.ckpt", + "type": "lora", + "base": "SD1.x", + "save_path": "loras/SD1.5/animatediff", + "description": "AnimateDiff Adapter LoRA (SD1.5)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v3_sd15_adapter.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_adapter.ckpt" + }, + + { + "name": "Segmind-Vega", + "type": "checkpoint", + "base": "segmind-vega", + "save_path": "checkpoints/segmind-vega", + "description": "The Segmind-Vega Model is a distilled version of the Stable Diffusion XL (SDXL), offering a remarkable 70% reduction in size and an impressive 100% speedup while retaining high-quality text-to-image generation capabilities.", + "reference": "https://huggingface.co/segmind/Segmind-Vega", + "filename": "segmind-vega.safetensors", + "url": "https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors" + }, + { + "name": "Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega", + "type": "lora", + "base": "segmind-vega", + "save_path": "loras/segmind-vega", + "description": "Segmind-VegaRT a distilled consistency adapter for Segmind-Vega that allows to reduce the number of inference steps to only between 2 - 8 steps.", + "reference": "https://huggingface.co/segmind/Segmind-VegaRT", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors" + }, + + { + "name": "stabilityai/Stable Zero123", + "type": "zero123", + "base": "zero123", + "save_path": "checkpoints/zero123", + "description": "Stable Zero123 is a model for view-conditioned image generation based on [a/Zero123](https://github.com/cvlab-columbia/zero123).", + "reference": "https://huggingface.co/stabilityai/stable-zero123", + "filename": "stable_zero123.ckpt", + "url": "https://huggingface.co/stabilityai/stable-zero123/resolve/main/stable_zero123.ckpt" + }, + { + "name": "LongAnimatediff/lt_long_mm_32_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_32_frames.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_32_frames.ckpt" + }, + { + "name": "LongAnimatediff/lt_long_mm_16_64_frames.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", + "filename": "lt_long_mm_16_64_frames.ckpt", + "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames.ckpt" + }, + { + "name": "ip-adapter_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors" + }, + { + "name": "ip-adapter_sd15_light.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15_light.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors" + }, + { + "name": "ip-adapter_sd15_vit-G.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sd15_vit-G.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors" + }, + { + "name": "ip-adapter-plus_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors" + }, + { + "name": "ip-adapter-plus-face_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus-face_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors" + }, + { + "name": "ip-adapter-full-face_sd15.safetensors", + "type": "IP-Adapter", + "base": "SD1.5", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-full-face_sd15.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors" + }, + { + "name": "ip-adapter_sdxl.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sdxl.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors" + }, + { + "name": "ip-adapter_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors" + }, + { + "name": "ip-adapter-plus_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors" + }, + { + "name": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "type": "IP-Adapter", + "base": "SDXL", + "save_path": "ipadapter", + "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "ip-adapter-plus-face_sdxl_vit-h.safetensors", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors" + }, + + { + "name": "SDXL-Turbo 1.0 (fp16)", + "type": "checkpoints", + "base": "SDXL", + "save_path": "checkpoints/SDXL-TURBO", + "description": "[6.9GB] SDXL-Turbo 1.0 fp16", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "filename": "sd_xl_turbo_1.0_fp16.safetensors", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors" + }, + { + "name": "SDXL-Turbo 1.0", + "type": "checkpoints", + "base": "SDXL", + "save_path": "checkpoints/SDXL-TURBO", + "description": "[13.9GB] SDXL-Turbo 1.0", + "reference": "https://huggingface.co/stabilityai/sdxl-turbo", + "filename": "sd_xl_turbo_1.0.safetensors", + "url": "https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0.safetensors" + }, + { + "name": "Stable Video Diffusion Image-to-Video", + "type": "checkpoints", + "base": "SVD", + "save_path": "checkpoints/SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.NOTE: 14 frames @ 576x1024", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid", + "filename": "svd.safetensors", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd.safetensors" + }, + { + "name": "Stable Video Diffusion Image-to-Video (XT)", + "type": "checkpoints", + "base": "SVD", + "save_path": "checkpoints/SVD", + "description": "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it.
NOTE: 25 frames @ 576x1024 ", + "reference": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt", + "filename": "svd_xt.safetensors", + "url": "https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors" + }, + + { + "name": "animatediff/mm_sdxl_v10_beta.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SDXL", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sdxl_v10_beta.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt" + }, + { + "name": "animatediff/v2_lora_PanLeft.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_PanLeft.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanLeft.ckpt" + }, + { + "name": "animatediff/v2_lora_PanRight.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_PanRight.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanRight.ckpt" + }, + { + "name": "animatediff/v2_lora_RollingAnticlockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_RollingAnticlockwise.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingAnticlockwise.ckpt" + }, + { + "name": "animatediff/v2_lora_RollingClockwise.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_RollingClockwise.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingClockwise.ckpt" + }, + { + "name": "animatediff/v2_lora_TiltDown.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_TiltDown.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltDown.ckpt" + }, + { + "name": "animatediff/v2_lora_TiltUp.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_TiltUp.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltUp.ckpt" + }, + { + "name": "animatediff/v2_lora_ZoomIn.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_ZoomIn.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomIn.ckpt" + }, + { + "name": "animatediff/v2_lora_ZoomOut.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "motion lora", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/motion_lora", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "v2_lora_ZoomOut.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomOut.ckpt" + }, + + { + "name": "CiaraRowles/TemporalNet1XL (1.0)", + "type": "controlnet", + "base": "SD1.5", + "save_path": "controlnet/TemporalNet1XL", + "description": "This is TemporalNet1XL, it is a re-train of the controlnet TemporalNet1 with Stable Diffusion XL.", + "reference": "https://huggingface.co/CiaraRowles/controlnet-temporalnet-sdxl-1.0", + "filename": "diffusion_pytorch_model.safetensors", + "url": "https://huggingface.co/CiaraRowles/controlnet-temporalnet-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors" + }, + + { + "name": "LCM LoRA SD1.5", + "type": "lora", + "base": "SD1.5", + "save_path": "loras/lcm/SD1.5", + "description": "Latent Consistency LoRA for SD1.5", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "LCM LoRA SSD-1B", + "type": "lora", + "base": "SSD-1B", + "save_path": "loras/lcm/SSD-1B", + "description": "Latent Consistency LoRA for SSD-1B", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-ssd-1b/resolve/main/pytorch_lora_weights.safetensors" + }, + { + "name": "LCM LoRA SDXL", + "type": "lora", + "base": "SSD-1B", + "save_path": "loras/lcm/SDXL", + "description": "Latent Consistency LoRA for SDXL", + "reference": "https://huggingface.co/latent-consistency/lcm-lora-sdxl", + "filename": "pytorch_lora_weights.safetensors", + "url": "https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors" + }, + + { + "name": "face_yolov8m-seg_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "face_yolov8m-seg_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8m-seg_60.pt" + }, + { + "name": "face_yolov8n-seg2_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "face_yolov8n-seg2_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8n-seg2_60.pt" + }, + { + "name": "hair_yolov8n-seg_60.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "hair_yolov8n-seg_60.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/hair_yolov8n-seg_60.pt" + }, + { + "name": "skin_yolov8m-seg_400.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8m-seg_400.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8m-seg_400.pt" + }, + { + "name": "skin_yolov8n-seg_400.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8n-seg_400.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_400.pt" + }, + { + "name": "skin_yolov8n-seg_800.pt (segm)", + "type": "Ultralytics", + "base": "Ultralytics", + "save_path": "ultralytics/segm", + "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", + "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", + "filename": "skin_yolov8n-seg_800.pt", + "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_800.pt" + }, + + { + "name": "CiaraRowles/temporaldiff-v1-animatediff.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/CiaraRowles/TemporalDiff", + "filename": "temporaldiff-v1-animatediff.ckpt", + "url": "https://huggingface.co/CiaraRowles/TemporalDiff/resolve/main/temporaldiff-v1-animatediff.ckpt" + }, + { + "name": "animatediff/mm_sd_v15_v2.ckpt (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/guoyww/animatediff", + "filename": "mm_sd_v15_v2.ckpt", + "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt" + }, + { + "name": "AD_Stabilized_Motion/mm-Stabilized_high.pth (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "filename": "mm-Stabilized_high.pth", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_high.pth" + }, + { + "name": "AD_Stabilized_Motion/mm-Stabilized_mid.pth (ComfyUI-AnimateDiff-Evolved)", + "type": "animatediff", + "base": "SD1.x", + "save_path": "custom_nodes/ComfyUI-AnimateDiff-Evolved/models", + "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node. (Note: Requires ComfyUI-Manager V0.24 or above)", + "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", + "filename": "mm-Stabilized_mid.pth", + "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_mid.pth" + }, + + { + "name": "GFPGANv1.4.pth", + "type": "GFPGAN", + "base": "GFPGAN", + "save_path": "facerestore_models", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/TencentARC/GFPGAN/releases", + "filename": "GFPGANv1.4.pth", + "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" + }, + { + "name": "codeformer.pth", + "type": "CodeFormer", + "base": "CodeFormer", + "save_path": "facerestore_models", + "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/sczhou/CodeFormer/releases", + "filename": "codeformer.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth" + }, + { + "name": "detection_Resnet50_Final.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facerestore_models", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "detection_Resnet50_Final.pth", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" + }, + { + "name": "detection_mobilenet0.25_Final.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facerestore_models", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "detection_mobilenet0.25_Final.pth", + "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth" + }, + { + "name": "yolov5l-face.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facedetection", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "yolov5l-face.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth" + }, + { + "name": "yolov5n-face.pth", + "type": "facexlib", + "base": "facexlib", + "save_path": "facedetection", + "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", + "reference": "https://github.com/xinntao/facexlib", + "filename": "yolov5n-face.pth", + "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth" + }, + + { + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET/fp16)", + "type": "unet", + "base": "SDXL", + "save_path": "unet/xl-inpaint-0.1", + "description": "[5.14GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "filename": "diffusion_pytorch_model.fp16.safetensors", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors" + }, + { + "name": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1 (UNET)", + "type": "unet", + "base": "SDXL", + "save_path": "unet/xl-inpaint-0.1", + "description": "[10.3GB] Stable Diffusion XL inpainting model 0.1. You need UNETLoader instead of CheckpointLoader.", + "reference": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + "filename": "diffusion_pytorch_model.safetensors", + "url": "https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/resolve/main/unet/diffusion_pytorch_model.safetensors" + }, + + { + "name": "Inswapper (face swap)", + "type": "insightface", + "base" : "inswapper", + "save_path": "insightface", + "description": "Checkpoint of the insightface swapper model (used by Comfy-Roop and comfy_mtb)", + "reference": "https://huggingface.co/deepinsight/inswapper/", + "filename": "inswapper_128.onnx", + "url": "https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx" + }, + + { + "name": "CLIPVision model (stabilityai/clip_vision_g)", + "type": "clip_vision", + "base": "SDXL", + "save_path": "clip_vision/SDXL", + "description": "[3.69GB] clip_g vision model", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "clip_vision_g.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/revision/clip_vision_g.safetensors" + }, + + { + "name": "CLIPVision model (IP-Adapter)", + "type": "clip_vision", + "base": "SD1.5", + "save_path": "clip_vision/SD1.5", + "description": "[2.5GB] CLIPVision model (needed for IP-Adapter)", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "pytorch_model.bin", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin" + }, + { + "name": "CLIPVision model (IP-Adapter)", + "type": "clip_vision", + "base": "SDXL", + "save_path": "clip_vision/SDXL", + "description": "[3.69GB] CLIPVision model (needed for IP-Adapter)", + "reference": "https://huggingface.co/h94/IP-Adapter", + "filename": "pytorch_model.bin", + "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/pytorch_model.bin" + }, + + { + "name": "stabilityai/control-lora-canny-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: canny rank128", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-canny-rank128.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-canny-rank128.safetensors" + }, + { + "name": "stabilityai/control-lora-depth-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: depth rank128", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-depth-rank128.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-depth-rank128.safetensors" + }, + { + "name": "stabilityai/control-lora-recolor-rank128.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: recolor rank128", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-recolor-rank128.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-recolor-rank128.safetensors" + }, + { + "name": "stabilityai/control-lora-sketch-rank128-metadata.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: sketch rank128 metadata", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-sketch-rank128-metadata.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank128/control-lora-sketch-rank128-metadata.safetensors" + }, + + { + "name": "stabilityai/control-lora-canny-rank256.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: canny rank256", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-canny-rank256.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors" + }, + { + "name": "stabilityai/control-lora-depth-rank256.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: depth rank256", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-depth-rank256.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors" + }, + { + "name": "stabilityai/control-lora-recolor-rank256.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: recolor rank256", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-recolor-rank256.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors" + }, + { + "name": "stabilityai/control-lora-sketch-rank256.safetensors", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "Control-LoRA: sketch rank256", + "reference": "https://huggingface.co/stabilityai/control-lora", + "filename": "control-lora-sketch-rank256.safetensors", + "url": "https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors" + }, + + { + "name": "kohya-ss/ControlNet-LLLite: SDXL Canny Anime", + "type": "controlnet", + "base": "SDXL", + "save_path": "custom_nodes/ControlNet-LLLite-ComfyUI/models", + "description": "[46.2MB] An extremely compactly designed controlnet model (a.k.a. ControlNet-LLLite). Note: The model structure is highly experimental and may be subject to change in the future.", + "reference": "https://huggingface.co/kohya-ss/controlnet-lllite", + "filename": "controllllite_v01032064e_sdxl_canny_anime.safetensors", + "url": "https://huggingface.co/kohya-ss/controlnet-lllite/resolve/main/controllllite_v01032064e_sdxl_canny_anime.safetensors" + }, + + { + "name": "SDXL-controlnet: OpenPose (v2)", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "ControlNet openpose model for SDXL", + "reference": "https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0", + "filename": "OpenPoseXL2.safetensors", + "url": "https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/resolve/main/OpenPoseXL2.safetensors" + }, + { + "name": "controlnet-SargeZT/controlnet-sd-xl-1.0-softedge-dexined", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "ControlNet softedge model for SDXL", + "reference": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined", + "filename": "controlnet-sd-xl-1.0-softedge-dexined.safetensors", + "url": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined/resolve/main/controlnet-sd-xl-1.0-softedge-dexined.safetensors" + }, + { + "name": "controlnet-SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe", + "type": "controlnet", + "base": "SDXL", + "save_path": "default", + "description": "ControlNet depth-zoe model for SDXL", + "reference": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe", + "filename": "depth-zoe-xl-v1.0-controlnet.safetensors", + "url": "https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe/resolve/main/depth-zoe-xl-v1.0-controlnet.safetensors" + } + ] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/custom-node-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/custom-node-list.json new file mode 100644 index 0000000000000000000000000000000000000000..ee843043402ff7d1e19ca10690d46b21afd841f1 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/custom-node-list.json @@ -0,0 +1,34 @@ +{ + "custom_nodes": [ + { + "author": "Suzie1", + "title": "Guide To Making Custom Nodes in ComfyUI", + "reference": "https://github.com/Suzie1/ComfyUI_Guide_To_Making_Custom_Nodes", + "files": [ + "https://github.com/Suzie1/ComfyUI_Guide_To_Making_Custom_Nodes" + ], + "install_type": "git-clone", + "description": "There is a small node pack attached to this guide. This includes the init file and 3 nodes associated with the tutorials." + }, + { + "author": "dynamixar", + "title": "Atluris", + "reference": "https://github.com/dynamixar/Atluris", + "files": [ + "https://github.com/dynamixar/Atluris" + ], + "install_type": "git-clone", + "description": "Nodes:Random Line" + }, + { + "author": "et118", + "title": "ComfyUI-ElGogh-Nodes", + "reference": "https://github.com/et118/ComfyUI-ElGogh-Nodes", + "files": [ + "https://github.com/et118/ComfyUI-ElGogh-Nodes" + ], + "install_type": "git-clone", + "description": "Nodes:ElGogh Positive Prompt, ElGogh NEGATIVE Prompt, ElGogh Empty Latent Image, ElGogh Checkpoint Loader Simple" + } + ] +} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/extension-node-map.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/extension-node-map.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/extension-node-map.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/model-list.json b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/model-list.json new file mode 100644 index 0000000000000000000000000000000000000000..8e3e1dc4858a08aa46190aa53ba320d565206cf4 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/model-list.json @@ -0,0 +1,3 @@ +{ + "models": [] +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh new file mode 100644 index 0000000000000000000000000000000000000000..5d8d8c48b6e3f48dc1491738c1226f574909c05d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/tutorial/scan.sh @@ -0,0 +1,4 @@ +#!/bin/bash +source ../../../../venv/bin/activate +rm .tmp/*.py > /dev/null +python ../../scanner.py diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb b/ComfyUI/custom_nodes/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..36bab4fe83f42901cb136c273806537e6b6baa0d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/notebooks/comfyui_colab_with_manager.ipynb @@ -0,0 +1,353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "# #@title Environment Setup\n", + "\n", + "from pathlib import Path\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = True #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "USE_COMFYUI_MANAGER = True #@param {type:\"boolean\"}\n", + "INSTALL_CUSTOM_NODES_DEPENDENCIES = True #@param {type:\"boolean\"}\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "OPTIONS['USE_COMFYUI_MANAGER'] = USE_COMFYUI_MANAGER\n", + "OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES'] = INSTALL_CUSTOM_NODES_DEPENDENCIES\n", + "\n", + "current_dir = !pwd\n", + "WORKSPACE = f\"{current_dir[0]}/ComfyUI\"\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + "\n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "#Remove cu121 as it causes issues in Colab.\n", + "#!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117\n", + "!pip3 install accelerate\n", + "!pip3 install einops transformers>=4.25.1 safetensors>=0.3.0 aiohttp pyyaml Pillow scipy tqdm psutil\n", + "!pip3 install xformers!=0.0.18 torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121\n", + "!pip3 install torchsde\n", + "\n", + "if OPTIONS['USE_COMFYUI_MANAGER']:\n", + " %cd custom_nodes\n", + " ![ ! -d ComfyUI-Manager ] && echo -= Initial setup ComfyUI-Manager =- && git clone https://github.com/ltdrdata/ComfyUI-Manager\n", + " %cd ComfyUI-Manager\n", + " !git pull\n", + "\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['INSTALL_CUSTOM_NODES_DEPENDENCIES']:\n", + " !pwd\n", + " !echo -= Install custom nodes dependencies =-\n", + " ![ -f \"custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py\" ] && python \"custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/prestartup_script.py b/ComfyUI/custom_nodes/ComfyUI-Manager/prestartup_script.py new file mode 100644 index 0000000000000000000000000000000000000000..474744fab18630f178c7870529ac515c37b6cd7d --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/prestartup_script.py @@ -0,0 +1,445 @@ +import datetime +import os +import subprocess +import sys +import atexit +import threading +import re +import locale +import platform + + +sys.CM_api = {} + + +message_collapses = [] +import_failed_extensions = set() + + +def register_message_collapse(f): + global message_collapses + message_collapses.append(f) + + +def is_import_failed_extension(x): + global import_failed_extensions + return x in import_failed_extensions + + +sys.__comfyui_manager_register_message_collapse = register_message_collapse +sys.__comfyui_manager_is_import_failed_extension = is_import_failed_extension + +comfyui_manager_path = os.path.dirname(__file__) +custom_nodes_path = os.path.abspath(os.path.join(comfyui_manager_path, "..")) +startup_script_path = os.path.join(comfyui_manager_path, "startup-scripts") +restore_snapshot_path = os.path.join(startup_script_path, "restore-snapshot.json") +git_script_path = os.path.join(comfyui_manager_path, "git_helper.py") + +std_log_lock = threading.Lock() + + +class TerminalHook: + def __init__(self): + self.hooks = {} + + def add_hook(self, k, v): + self.hooks[k] = v + + def remove_hook(self, k): + if k in self.hooks: + del self.hooks[k] + + def write_stderr(self, msg): + for v in self.hooks.values(): + try: + v.write_stderr(msg) + except Exception: + pass + + def write_stdout(self, msg): + for v in self.hooks.values(): + try: + v.write_stdout(msg) + except Exception: + pass + + +terminal_hook = TerminalHook() +sys.__comfyui_manager_terminal_hook = terminal_hook + + +def handle_stream(stream, prefix): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + for msg in stream: + if prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg): + if msg.startswith('100%'): + print('\r' + msg, end="", file=sys.stderr), + else: + print('\r' + msg[:-1], end="", file=sys.stderr), + else: + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + +def process_wrap(cmd_str, cwd_path, handler=None): + process = subprocess.Popen(cmd_str, cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1) + + if handler is None: + handler = handle_stream + + stdout_thread = threading.Thread(target=handler, args=(process.stdout, "")) + stderr_thread = threading.Thread(target=handler, args=(process.stderr, "[!]")) + + stdout_thread.start() + stderr_thread.start() + + stdout_thread.join() + stderr_thread.join() + + return process.wait() + + +try: + if '--port' in sys.argv: + port_index = sys.argv.index('--port') + if port_index + 1 < len(sys.argv): + port = int(sys.argv[port_index + 1]) + postfix = f"_{port}" + else: + postfix = "" + + # Logger setup + if os.path.exists(f"comfyui{postfix}.log"): + if os.path.exists(f"comfyui{postfix}.prev.log"): + if os.path.exists(f"comfyui{postfix}.prev2.log"): + os.remove(f"comfyui{postfix}.prev2.log") + os.rename(f"comfyui{postfix}.prev.log", f"comfyui{postfix}.prev2.log") + os.rename(f"comfyui{postfix}.log", f"comfyui{postfix}.prev.log") + + original_stdout = sys.stdout + original_stderr = sys.stderr + + pat_tqdm = r'\d+%.*\[(.*?)\]' + pat_import_fail = r'seconds \(IMPORT FAILED\):' + pat_custom_node = r'[/\\]custom_nodes[/\\](.*)$' + + is_start_mode = True + is_import_fail_mode = False + + log_file = open(f"comfyui{postfix}.log", "w", encoding="utf-8") + log_lock = threading.Lock() + + class ComfyUIManagerLogger: + def __init__(self, is_stdout): + self.is_stdout = is_stdout + self.encoding = "utf-8" + + def fileno(self): + try: + if self.is_stdout: + return original_stdout.fileno() + else: + return original_stderr.fileno() + except AttributeError: + # Handle error + raise ValueError("The object does not have a fileno method") + + def write(self, message): + global is_start_mode + global is_import_fail_mode + + if any(f(message) for f in message_collapses): + return + + if is_start_mode: + if is_import_fail_mode: + match = re.search(pat_custom_node, message) + if match: + import_failed_extensions.add(match.group(1)) + is_import_fail_mode = False + else: + match = re.search(pat_import_fail, message) + if match: + is_import_fail_mode = True + else: + is_import_fail_mode = False + + if 'Starting server' in message: + is_start_mode = False + + if not self.is_stdout: + match = re.search(pat_tqdm, message) + if match: + message = re.sub(r'([#|])\d', r'\1▌', message) + message = re.sub('#', '█', message) + if '100%' in message: + self.sync_write(message) + else: + original_stderr.write(message) + original_stderr.flush() + else: + self.sync_write(message) + else: + self.sync_write(message) + + def sync_write(self, message): + with log_lock: + log_file.write(message) + log_file.flush() + + with std_log_lock: + if self.is_stdout: + original_stdout.write(message) + original_stdout.flush() + terminal_hook.write_stderr(message) + else: + original_stderr.write(message) + original_stderr.flush() + terminal_hook.write_stdout(message) + + def flush(self): + log_file.flush() + + with std_log_lock: + if self.is_stdout: + original_stdout.flush() + else: + original_stderr.flush() + + def close(self): + self.flush() + + def reconfigure(self, *args, **kwargs): + pass + + # You can close through sys.stderr.close_log() + def close_log(self): + sys.stderr = original_stderr + sys.stdout = original_stdout + log_file.close() + + def close_log(): + sys.stderr = original_stderr + sys.stdout = original_stdout + log_file.close() + + sys.stdout = ComfyUIManagerLogger(True) + sys.stderr = ComfyUIManagerLogger(False) + + atexit.register(close_log) +except Exception as e: + print(f"[ComfyUI-Manager] Logging failed: {e}") + + +print("** ComfyUI startup time:", datetime.datetime.now()) +print("** Platform:", platform.system()) +print("** Python version:", sys.version) +print("** Python executable:", sys.executable) +print("** Log path:", os.path.abspath('comfyui.log')) + + +def check_bypass_ssl(): + try: + import configparser + import ssl + config_path = os.path.join(os.path.dirname(__file__), "config.ini") + config = configparser.ConfigParser() + config.read(config_path) + default_conf = config['default'] + + if 'bypass_ssl' in default_conf and default_conf['bypass_ssl'].lower() == 'true': + print(f"[ComfyUI-Manager] WARN: Unsafe - SSL verification bypass option is Enabled. (see ComfyUI-Manager/config.ini)") + ssl._create_default_https_context = ssl._create_unverified_context # SSL certificate error fix. + except Exception: + pass + + +check_bypass_ssl() + + +# Perform install +processed_install = set() +script_list_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "startup-scripts", "install-scripts.txt") +pip_list = None + + +def get_installed_packages(): + global pip_list + + if pip_list is None: + try: + result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True) + pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()]) + except subprocess.CalledProcessError as e: + print(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.") + return set() + + return pip_list + + +def is_installed(name): + name = name.strip() + + if name.startswith('#'): + return True + + pattern = r'([^<>!=]+)([<>!=]=?)' + match = re.search(pattern, name) + + if match: + name = match.group(1) + + return name.lower() in get_installed_packages() + + +if os.path.exists(restore_snapshot_path): + try: + import json + + cloned_repos = [] + + def msg_capture(stream, prefix): + stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace') + for msg in stream: + if msg.startswith("CLONE: "): + cloned_repos.append(msg[7:]) + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + elif prefix == '[!]' and ('it/s]' in msg or 's/it]' in msg) and ('%|' in msg or 'it [' in msg): + if msg.startswith('100%'): + print('\r' + msg, end="", file=sys.stderr), + else: + print('\r'+msg[:-1], end="", file=sys.stderr), + else: + if prefix == '[!]': + print(prefix, msg, end="", file=sys.stderr) + else: + print(prefix, msg, end="") + + print(f"[ComfyUI-Manager] Restore snapshot.") + cmd_str = [sys.executable, git_script_path, '--apply-snapshot', restore_snapshot_path] + exit_code = process_wrap(cmd_str, custom_nodes_path, handler=msg_capture) + + with open(restore_snapshot_path, 'r', encoding="UTF-8") as json_file: + info = json.load(json_file) + for url in cloned_repos: + try: + repository_name = url.split("/")[-1].strip() + repo_path = os.path.join(custom_nodes_path, repository_name) + repo_path = os.path.abspath(repo_path) + + requirements_path = os.path.join(repo_path, 'requirements.txt') + install_script_path = os.path.join(repo_path, 'install.py') + + this_exit_code = 0 + + if os.path.exists(requirements_path): + with open(requirements_path, 'r', encoding="UTF-8") as file: + for line in file: + package_name = line.strip() + if package_name and not is_installed(package_name): + install_cmd = [sys.executable, "-m", "pip", "install", package_name] + this_exit_code += process_wrap(install_cmd, repo_path) + + if os.path.exists(install_script_path) and f'{repo_path}/install.py' not in processed_install: + processed_install.add(f'{repo_path}/install.py') + install_cmd = [sys.executable, install_script_path] + print(f">>> {install_cmd} / {repo_path}") + this_exit_code += process_wrap(install_cmd, repo_path) + + if this_exit_code != 0: + print(f"[ComfyUI-Manager] Restoring '{repository_name}' is failed.") + + except Exception as e: + print(e) + print(f"[ComfyUI-Manager] Restoring '{repository_name}' is failed.") + + if exit_code != 0: + print(f"[ComfyUI-Manager] Restore snapshot failed.") + else: + print(f"[ComfyUI-Manager] Restore snapshot done.") + + except Exception as e: + print(e) + print(f"[ComfyUI-Manager] Restore snapshot failed.") + + os.remove(restore_snapshot_path) + + +def execute_lazy_install_script(repo_path, executable): + global processed_install + + install_script_path = os.path.join(repo_path, "install.py") + requirements_path = os.path.join(repo_path, "requirements.txt") + + if os.path.exists(requirements_path): + print(f"Install: pip packages for '{repo_path}'") + with open(requirements_path, "r") as requirements_file: + for line in requirements_file: + package_name = line.strip() + if package_name and not is_installed(package_name): + install_cmd = [executable, "-m", "pip", "install", package_name] + process_wrap(install_cmd, repo_path) + + if os.path.exists(install_script_path) and f'{repo_path}/install.py' not in processed_install: + processed_install.add(f'{repo_path}/install.py') + print(f"Install: install script for '{repo_path}'") + install_cmd = [executable, "install.py"] + process_wrap(install_cmd, repo_path) + + +# Check if script_list_path exists +if os.path.exists(script_list_path): + print("\n#######################################################################") + print("[ComfyUI-Manager] Starting dependency installation/(de)activation for the extension\n") + + executed = set() + # Read each line from the file and convert it to a list using eval + with open(script_list_path, 'r', encoding="UTF-8") as file: + for line in file: + if line in executed: + continue + + executed.add(line) + + try: + script = eval(line) + + if script[1].startswith('#') and script[1] != '#FORCE': + if script[1] == "#LAZY-INSTALL-SCRIPT": + execute_lazy_install_script(script[0], script[2]) + + elif os.path.exists(script[0]): + if script[1] == "#FORCE": + del script[1] + else: + if 'pip' in script[1:] and 'install' in script[1:] and is_installed(script[-1]): + continue + + print(f"\n## ComfyUI-Manager: EXECUTE => {script[1:]}") + print(f"\n## Execute install/(de)activation script for '{script[0]}'") + + exit_code = process_wrap(script[1:], script[0]) + + if exit_code != 0: + print(f"install/(de)activation script failed: {script[0]}") + else: + print(f"\n## ComfyUI-Manager: CANCELED => {script[1:]}") + + except Exception as e: + print(f"[ERROR] Failed to execute install/(de)activation script: {line} / {e}") + + # Remove the script_list_path file + if os.path.exists(script_list_path): + os.remove(script_list_path) + + print("\n[ComfyUI-Manager] Startup script completed.") + print("#######################################################################\n") + +del processed_install +del pip_list \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/requirements.txt b/ComfyUI/custom_nodes/ComfyUI-Manager/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3467b3e51c131ba4bbf1db1ae8317aa45cc3ef53 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/requirements.txt @@ -0,0 +1,2 @@ +GitPython +matrix-client==0.4.0 \ No newline at end of file diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scan.sh b/ComfyUI/custom_nodes/ComfyUI-Manager/scan.sh new file mode 100644 index 0000000000000000000000000000000000000000..1b3cc3771790ebedf0c538ff3464125d52e8668c --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scan.sh @@ -0,0 +1,7 @@ +#!/bin/bash +rm ~/.tmp/default/*.py > /dev/null 2>&1 +python scanner.py ~/.tmp/default +cp extension-node-map.json node_db/new/. + +echo Integrity check +./check.sh diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scanner.py b/ComfyUI/custom_nodes/ComfyUI-Manager/scanner.py new file mode 100644 index 0000000000000000000000000000000000000000..07809aef928d2b7e77cf731b394fd1182d2f6830 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scanner.py @@ -0,0 +1,311 @@ +import re +import os +import json +from git import Repo +from torchvision.datasets.utils import download_url +import concurrent + +builtin_nodes = set() + +import sys + + +# prepare temp dir +if len(sys.argv) > 1: + temp_dir = sys.argv[1] +else: + temp_dir = os.path.join(os.getcwd(), ".tmp") + +if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + +print(f"TEMP DIR: {temp_dir}") + + +# scan +def scan_in_file(filename, is_builtin=False): + global builtin_nodes + + try: + with open(filename, encoding='utf-8') as file: + code = file.read() + except UnicodeDecodeError: + with open(filename, encoding='cp949') as file: + code = file.read() + + pattern = r"_CLASS_MAPPINGS\s*=\s*{([^}]*)}" + regex = re.compile(pattern, re.MULTILINE | re.DOTALL) + + nodes = set() + class_dict = {} + + pattern2 = r'^[^=]*_CLASS_MAPPINGS\["(.*?)"\]' + keys = re.findall(pattern2, code) + for key in keys: + nodes.add(key.strip()) + + pattern3 = r'^[^=]*_CLASS_MAPPINGS\[\'(.*?)\'\]' + keys = re.findall(pattern3, code) + for key in keys: + nodes.add(key.strip()) + + matches = regex.findall(code) + for match in matches: + dict_text = match + + key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", dict_text) + for key, value in key_value_pairs: + class_dict[key.strip()] = value.strip() + + key_value_pairs = re.findall(r"'([^']*)'\s*:\s*([^,\n]*)", dict_text) + for key, value in key_value_pairs: + class_dict[key.strip()] = value.strip() + + for key, value in class_dict.items(): + nodes.add(key.strip()) + + update_pattern = r"_CLASS_MAPPINGS.update\s*\({([^}]*)}\)" + update_match = re.search(update_pattern, code) + if update_match: + update_dict_text = update_match.group(1) + update_key_value_pairs = re.findall(r"\"([^\"]*)\"\s*:\s*([^,\n]*)", update_dict_text) + for key, value in update_key_value_pairs: + class_dict[key.strip()] = value.strip() + nodes.add(key.strip()) + + metadata = {} + lines = code.strip().split('\n') + for line in lines: + if line.startswith('@'): + if line.startswith("@author:") or line.startswith("@title:") or line.startswith("@nickname:") or line.startswith("@description:"): + key, value = line[1:].strip().split(':', 1) + metadata[key.strip()] = value.strip() + + if is_builtin: + builtin_nodes += set(nodes) + else: + for x in builtin_nodes: + if x in nodes: + nodes.remove(x) + + return nodes, metadata + + +def get_py_file_paths(dirname): + file_paths = [] + + for root, dirs, files in os.walk(dirname): + if ".git" in root or "__pycache__" in root: + continue + + for file in files: + if file.endswith(".py"): + file_path = os.path.join(root, file) + file_paths.append(file_path) + + return file_paths + + +def get_nodes(target_dir): + py_files = [] + directories = [] + + for item in os.listdir(target_dir): + if ".git" in item or "__pycache__" in item: + continue + + path = os.path.abspath(os.path.join(target_dir, item)) + + if os.path.isfile(path) and item.endswith(".py"): + py_files.append(path) + elif os.path.isdir(path): + directories.append(path) + + return py_files, directories + + +def get_git_urls_from_json(json_file): + with open(json_file, encoding='utf-8') as file: + data = json.load(file) + + custom_nodes = data.get('custom_nodes', []) + git_clone_files = [] + for node in custom_nodes: + if node.get('install_type') == 'git-clone': + files = node.get('files', []) + if files: + git_clone_files.append((files[0], node.get('title'), node.get('nodename_pattern'))) + + git_clone_files.append(("https://github.com/comfyanonymous/ComfyUI", "ComfyUI", None)) + + return git_clone_files + + +def get_py_urls_from_json(json_file): + with open(json_file, encoding='utf-8') as file: + data = json.load(file) + + custom_nodes = data.get('custom_nodes', []) + py_files = [] + for node in custom_nodes: + if node.get('install_type') == 'copy': + files = node.get('files', []) + if files: + py_files.append((files[0], node.get('title'), node.get('nodename_pattern'))) + + return py_files + + +def clone_or_pull_git_repository(git_url): + repo_name = git_url.split("/")[-1].split(".")[0] + repo_dir = os.path.join(temp_dir, repo_name) + + if os.path.exists(repo_dir): + try: + repo = Repo(repo_dir) + origin = repo.remote(name="origin") + origin.pull(rebase=True) + repo.git.submodule('update', '--init', '--recursive') + print(f"Pulling {repo_name}...") + except Exception as e: + print(f"Pulling {repo_name} failed: {e}") + else: + try: + Repo.clone_from(git_url, repo_dir, recursive=True) + print(f"Cloning {repo_name}...") + except Exception as e: + print(f"Cloning {repo_name} failed: {e}") + + +def update_custom_nodes(): + if not os.path.exists(temp_dir): + os.makedirs(temp_dir) + + node_info = {} + + git_url_titles = get_git_urls_from_json('custom-node-list.json') + + def process_git_url_title(url, title, node_pattern): + name = os.path.basename(url) + if name.endswith(".git"): + name = name[:-4] + + node_info[name] = (url, title, node_pattern) + clone_or_pull_git_repository(url) + + with concurrent.futures.ThreadPoolExecutor(10) as executor: + for url, title, node_pattern in git_url_titles: + executor.submit(process_git_url_title, url, title, node_pattern) + + py_url_titles_and_pattern = get_py_urls_from_json('custom-node-list.json') + + def download_and_store_info(url_title_and_pattern): + url, title, node_pattern = url_title_and_pattern + name = os.path.basename(url) + if name.endswith(".py"): + node_info[name] = (url, title, node_pattern) + + try: + download_url(url, temp_dir) + except: + print(f"[ERROR] Cannot download '{url}'") + + with concurrent.futures.ThreadPoolExecutor(10) as executor: + executor.map(download_and_store_info, py_url_titles_and_pattern) + + return node_info + + +def gen_json(node_info): + # scan from .py file + node_files, node_dirs = get_nodes(temp_dir) + + comfyui_path = os.path.abspath(os.path.join(temp_dir, "ComfyUI")) + node_dirs.remove(comfyui_path) + node_dirs = [comfyui_path] + node_dirs + + data = {} + for dirname in node_dirs: + py_files = get_py_file_paths(dirname) + metadata = {} + + nodes = set() + for py in py_files: + nodes_in_file, metadata_in_file = scan_in_file(py, dirname == "ComfyUI") + nodes.update(nodes_in_file) + metadata.update(metadata_in_file) + + dirname = os.path.basename(dirname) + + if len(nodes) > 0 or (dirname in node_info and node_info[dirname][2] is not None): + nodes = list(nodes) + nodes.sort() + + if dirname in node_info: + git_url, title, node_pattern = node_info[dirname] + metadata['title_aux'] = title + if node_pattern is not None: + metadata['nodename_pattern'] = node_pattern + data[git_url] = (nodes, metadata) + else: + print(f"WARN: {dirname} is removed from custom-node-list.json") + + for file in node_files: + nodes, metadata = scan_in_file(file) + + if len(nodes) > 0 or (dirname in node_info and node_info[dirname][2] is not None): + nodes = list(nodes) + nodes.sort() + + file = os.path.basename(file) + + if file in node_info: + url, title, node_pattern = node_info[file] + metadata['title_aux'] = title + if node_pattern is not None: + metadata['nodename_pattern'] = node_pattern + data[url] = (nodes, metadata) + else: + print(f"Missing info: {file}") + + # scan from node_list.json file + extensions = [name for name in os.listdir(temp_dir) if os.path.isdir(os.path.join(temp_dir, name))] + + for extension in extensions: + node_list_json_path = os.path.join(temp_dir, extension, 'node_list.json') + if os.path.exists(node_list_json_path): + git_url, title, node_pattern = node_info[extension] + + with open(node_list_json_path, 'r', encoding='utf-8') as f: + node_list_json = json.load(f) + + metadata_in_url = {} + if git_url not in data: + nodes = set() + else: + nodes_in_url, metadata_in_url = data[git_url] + nodes = set(nodes_in_url) + + for x, desc in node_list_json.items(): + nodes.add(x.strip()) + + metadata_in_url['title_aux'] = title + if node_pattern is not None: + metadata_in_url['nodename_pattern'] = node_pattern + nodes = list(nodes) + nodes.sort() + data[git_url] = (nodes, metadata_in_url) + + json_path = f"extension-node-map.json" + with open(json_path, "w", encoding='utf-8') as file: + json.dump(data, file, indent=4, sort_keys=True) + + +print("### ComfyUI Manager Node Scanner ###") + +print("\n# Updating extensions\n") +updated_node_info = update_custom_nodes() + +print("\n# 'extension-node-map.json' file is generated.\n") +gen_json(updated_node_info) + diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py new file mode 100644 index 0000000000000000000000000000000000000000..d5a70ed6dd92ba90e8084e07fbb9097fe3096ea5 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/colab-dependencies.py @@ -0,0 +1,39 @@ +import os +import subprocess + + +def get_enabled_subdirectories_with_files(base_directory): + subdirs_with_files = [] + for subdir in os.listdir(base_directory): + try: + full_path = os.path.join(base_directory, subdir) + if os.path.isdir(full_path) and not subdir.endswith(".disabled") and not subdir.startswith('.') and subdir != '__pycache__': + print(f"## Install dependencies for '{subdir}'") + requirements_file = os.path.join(full_path, "requirements.txt") + install_script = os.path.join(full_path, "install.py") + + if os.path.exists(requirements_file) or os.path.exists(install_script): + subdirs_with_files.append((full_path, requirements_file, install_script)) + except Exception as e: + print(f"EXCEPTION During Dependencies INSTALL on '{subdir}':\n{e}") + + return subdirs_with_files + + +def install_requirements(requirements_file_path): + if os.path.exists(requirements_file_path): + subprocess.run(["pip", "install", "-r", requirements_file_path]) + + +def run_install_script(install_script_path): + if os.path.exists(install_script_path): + subprocess.run(["python", install_script_path]) + + +custom_nodes_directory = "custom_nodes" +subdirs_with_files = get_enabled_subdirectories_with_files(custom_nodes_directory) + + +for subdir, requirements_file, install_script in subdirs_with_files: + install_requirements(requirements_file) + run_install_script(install_script) diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh new file mode 100644 index 0000000000000000000000000000000000000000..be473dc66f8eeb36c48d409945eb5ae83a030171 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-linux.sh @@ -0,0 +1,21 @@ +git clone https://github.com/comfyanonymous/ComfyUI +cd ComfyUI/custom_nodes +git clone https://github.com/ltdrdata/ComfyUI-Manager +cd .. +python -m venv venv +source venv/bin/activate +python -m pip install -r requirements.txt +python -m pip install -r custom_nodes/ComfyUI-Manager/requirements.txt +python -m pip install torchvision +cd .. +echo "#!/bin/bash" > run_gpu.sh +echo "cd ComfyUI" >> run_gpu.sh +echo "source venv/bin/activate" >> run_gpu.sh +echo "python main.py --preview-method auto" >> run_gpu.sh +chmod +x run_gpu.sh + +echo "#!/bin/bash" > run_cpu.sh +echo "cd ComfyUI" >> run_cpu.sh +echo "source venv/bin/activate" >> run_cpu.sh +echo "python main.py --preview-method auto --cpu" >> run_cpu.sh +chmod +x run_cpu.sh diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-win.bat b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-win.bat new file mode 100644 index 0000000000000000000000000000000000000000..6bb0e8364b5170530c2a85341ad754764c6788ae --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-comfyui-venv-win.bat @@ -0,0 +1,20 @@ +git clone https://github.com/comfyanonymous/ComfyUI +cd ComfyUI/custom_nodes +git clone https://github.com/ltdrdata/ComfyUI-Manager +cd .. +python -m venv venv +call venv/Scripts/activate +python -m pip install -r requirements.txt +python -m pip install -r custom_nodes/ComfyUI-Manager/requirements.txt +python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers +cd .. +echo "cd ComfyUI" >> run_gpu.sh +echo "call venv/Scripts/activate" >> run_gpu.sh +echo "python main.py" >> run_gpu.sh +chmod +x run_gpu.sh + +echo "#!/bin/bash" > run_cpu.sh +echo "cd ComfyUI" >> run_cpu.sh +echo "call venv/Scripts/activate" >> run_cpu.sh +echo "python main.py --cpu" >> run_cpu.sh +chmod +x run_cpu.sh diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat new file mode 100644 index 0000000000000000000000000000000000000000..7b067dfd770d197ccd68e760087536552223f260 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/install-manager-for-portable-version.bat @@ -0,0 +1,2 @@ +.\python_embeded\python.exe -s -m pip install gitpython +.\python_embeded\python.exe -c "import git; git.Repo.clone_from('https://github.com/ltdrdata/ComfyUI-Manager', './ComfyUI/custom_nodes/ComfyUI-Manager')" diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/update-fix.py b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/update-fix.py new file mode 100644 index 0000000000000000000000000000000000000000..d2ac10074607544d0b9cdaf4372e43c7f62bb8d0 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-Manager/scripts/update-fix.py @@ -0,0 +1,12 @@ +import git + +commit_hash = "a361cc1" + +repo = git.Repo('.') + +if repo.is_dirty(): + repo.git.stash() + +repo.git.update_ref("refs/remotes/origin/main", commit_hash) +repo.remotes.origin.fetch() +repo.git.pull("origin", "main") diff --git a/ComfyUI/custom_nodes/ComfyUI-Manager/snapshots/the_snapshot_files_are_located_here b/ComfyUI/custom_nodes/ComfyUI-Manager/snapshots/the_snapshot_files_are_located_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore b/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..68bc17f9ff2104a9d7b6777058bb4c343ca72609 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/LICENSE b/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7 --- /dev/null +++ b/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc.
Adds 'Fullscreen (ttN)' to the node right-click context menu
Opens a Fullscreen image viewer - containing all images generated by the selected node during the current comfy session. ++Adds 'Set Default Fullscreen Node (ttN)' to the node right-click context menu
Sets the currently selected node as the default Fullscreen node ++Adds 'Clear Default Fullscreen Node (ttN)' to the node right-click context menu
Clears the assigned default Fullscreen node +Adds 'Node Dimensions (ttN)' to the node right-click context menu
Allows setting specific node Width and Height values as long as they are above the minimum size for the given node. ++Adds 'Default BG Color (ttN)' to the node right-click context menu
Allows setting specific default background color for every node added. ++Adds 'Show Execution Order (ttN)' to the node right-click context menu
Toggles execution order flags on node corners. ++Adds support for 'ctrl + arrow key' Node movement
This aligns the node(s) to the set ComfyUI grid spacing size and move the node in the direction of the arrow key by the grid spacing value. Holding shift in addition will move the node by the grid spacing size * 10. ++Adds 'Reload Node (ttN)' to the node right-click context menu
Creates a new instance of the node with the same position, size, color and title (will disconnect any IO wires). It attempts to retain set widget values which is useful for replacing nodes when a node/widget update occursAdds 'Slot Type Color (ttN)' to the Link right-click context menu
Opens a color picker dialog menu to update the color of the selected link type.Adds 'Link Border (ttN)' to the Link right-click context menu
Toggles link line border.Adds 'Link Shadow (ttN)' to the Link right-click context menu
Toggles link line shadow.Adds 'Link Style (ttN)' to the Link right-click context menu
Sets the default link line type.+or %[input_name]>[input_name]>[widget_name]% (for inputting nodes)
+
Example: +
+ +  ++
+
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__init__.py b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7210fecdc26a52a06ff902f6541158b747b87ca3
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__init__.py
@@ -0,0 +1,209 @@
+from .tinyterraNodes import TTN_VERSIONS
+import configparser
+import folder_paths
+import subprocess
+import shutil
+import sys
+import os
+
+# ------- CONFIG -------- #
+cwd_path = os.path.dirname(os.path.realpath(__file__))
+comfy_path = folder_paths.base_path
+
+config_path = os.path.join(cwd_path, "config.ini")
+
+optionValues = {
+ "auto_update": ('true', 'false'),
+ "install_rembg": ('true', 'false'),
+ "enable_embed_autocomplete": ('true', 'false'),
+ "enable_interface": ('true', 'false'),
+ "enable_fullscreen": ('true', 'false'),
+ "enable_dynamic_widgets": ('true', 'false'),
+ "enable_dev_nodes": ('true', 'false'),
+ }
+
+def get_config():
+ """Return a configparser.ConfigParser object."""
+ config = configparser.ConfigParser()
+ config.read(config_path)
+ return config
+
+def update_config():
+ #section > option > value
+ for node, version in TTN_VERSIONS.items():
+ config_write("Versions", node, version)
+
+ for option, value in optionValues.items():
+ config_write("Option Values", option, value)
+
+ section_data = {
+ "ttNodes": {
+ "auto_update": False,
+ "install_rembg": False,
+ "enable_interface": True,
+ "enable_fullscreen": True,
+ "enable_embed_autocomplete": True,
+ "enable_dynamic_widgets": True,
+ "enable_dev_nodes": False,
+ }
+ }
+
+ for section, data in section_data.items():
+ for option, value in data.items():
+ if config_read(section, option) is None:
+ config_write(section, option, value)
+
+ # Load the configuration data into a dictionary.
+ config_data = config_load()
+
+ # Iterate through the configuration data.
+ for section, options in config_data.items():
+ if section == "Versions":
+ continue
+ for option in options:
+ # If the option is not in `optionValues` or in `section_data`, remove it.
+ if (option not in optionValues and
+ (section not in section_data or option not in section_data[section])):
+ config_remove(section, option)
+
+def config_load():
+ """Load the entire configuration into a dictionary."""
+ config = get_config()
+ return {section: dict(config.items(section)) for section in config.sections()}
+
+def config_read(section, option):
+ """Read a configuration option."""
+ config = get_config()
+ return config.get(section, option, fallback=None)
+
+def config_write(section, option, value):
+ """Write a configuration option."""
+ config = get_config()
+ if not config.has_section(section):
+ config.add_section(section)
+ config.set(section, str(option), str(value))
+
+ with open(config_path, 'w') as f:
+ config.write(f)
+
+def config_remove(section, option):
+ """Remove an option from a section."""
+ config = get_config()
+ if config.has_section(section):
+ config.remove_option(section, option)
+ with open(config_path, 'w') as f:
+ config.write(f)
+
+def copy_to_web(file):
+ """Copy a file to the web extension path."""
+ shutil.copy(file, web_extension_path)
+
+def config_value_validator(section, option, default):
+ value = str(config_read(section, option)).lower()
+ if value not in optionValues[option]:
+ print(f'\033[92m[{section} Config]\033[91m {option} - \'{value}\' not in {optionValues[option]}, reverting to default.\033[0m')
+ config_write(section, option, default)
+ return default
+ else:
+ return value
+
+# Create a config file if not exists
+if not os.path.isfile(config_path):
+ with open(config_path, 'w') as f:
+ pass
+
+update_config()
+
+# Autoupdate if True
+if config_value_validator("ttNodes", "auto_update", 'false') == 'true':
+ try:
+ with subprocess.Popen(["git", "pull"], cwd=cwd_path, stdout=subprocess.PIPE) as p:
+ p.wait()
+ result = p.communicate()[0].decode()
+ if result != "Already up to date.\n":
+ print("\033[92m[t ttNodes Updated t]\033[0m")
+ except:
+ pass
+
+# Install RemBG if True
+
+Install = config_read("ttNodes", "install_rembg")
+if Install in [True, 'true', 'True']:
+ try:
+ from rembg import remove
+ config_write("ttNodes", "install_rembg", 'Already Installed')
+ except ImportError:
+ if Install not in ('Failed to install', 'Installed successfully'):
+ try:
+ print("\033[92m[ttNodes] \033[0;31mREMBG is not installed. Attempting to install...\033[0m")
+ p = subprocess.Popen([sys.executable, "-m", "pip", "install", "rembg[gpu]"])
+ p.wait()
+ print("\033[92m[ttNodes] REMBG[GPU] Installed!\033[0m")
+
+ config_write("ttNodes", "install_rembg", 'Installed successfully')
+ except:
+ try:
+ print("\033[92m[ttNodes] \033[0;31mREMBG[GPU] failed to install. Attempting to install REMBG...\033[0m")
+ p = subprocess.Popen([sys.executable, "-m", "pip", "install", "rembg"])
+ p.wait()
+ print("\033[92m[ttNodes] REMBG Installed!\033[0m")
+ config_write("ttNodes", "install_rembg", 'Installed successfully')
+ except:
+ config_write("ttNodes", "install_rembg", 'Failed to install')
+ print("\033[92m[ttNodes] \033[0;31mFailed to install REMBG.\033[0m")
+
+# --------- WEB ---------- #
+web_extension_path = os.path.join(comfy_path, "web", "extensions", "tinyterraNodes")
+
+ttNstyles_JS_file_web = os.path.join(web_extension_path, "ttNstyles.js")
+
+ttN_JS_file = os.path.join(cwd_path, "js", "ttN.js")
+ttNxyPlot_JS_file = os.path.join(cwd_path, "js", "ttNxyPlot.js")
+ttNembedAC_JS_file = os.path.join(cwd_path, "js", "ttNembedAC.js")
+ttNwidgets_JS_file = os.path.join(cwd_path, "js", "ttNwidgets.js")
+ttNinterface_JS_file = os.path.join(cwd_path, "js", "ttNinterface.js")
+ttNdynamicWidgets_JS_file = os.path.join(cwd_path, "js", "ttNdynamicWidgets.js")
+ttNfullscreen_JS_file = os.path.join(cwd_path, "js", "ttNfullscreen.js")
+
+if not os.path.exists(web_extension_path):
+ os.makedirs(web_extension_path)
+else:
+ shutil.rmtree(web_extension_path)
+ os.makedirs(web_extension_path)
+
+copy_to_web(ttN_JS_file)
+copy_to_web(ttNwidgets_JS_file)
+copy_to_web(ttNxyPlot_JS_file)
+
+# Enable Custom Styles if True
+if config_value_validator("ttNodes", "enable_interface", 'true') == 'true':
+ copy_to_web(ttNinterface_JS_file)
+
+if config_value_validator("ttNodes", "enable_fullscreen", 'true') == 'true':
+ copy_to_web(ttNfullscreen_JS_file)
+
+# Enable Embed Autocomplete if True
+if config_value_validator("ttNodes", "enable_embed_autocomplete", "true") == 'true':
+ copy_to_web(ttNembedAC_JS_file)
+
+# Enable Dynamic Widgets if True
+if config_value_validator("ttNodes", "enable_dynamic_widgets", "true") == 'true':
+ copy_to_web(ttNdynamicWidgets_JS_file)
+
+# Enable Dev Nodes if True
+if config_value_validator("ttNodes", "enable_dev_nodes", 'true') == 'true':
+ ttNbusJSfile = os.path.join(cwd_path, "dev", "ttNbus.js")
+ ttNdebugJSfile = os.path.join(cwd_path, "dev", "ttNdebug.js")
+
+ from .ttNdev import NODE_CLASS_MAPPINGS as ttNdev_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as ttNdev_DISPLAY_NAME_MAPPINGS
+else:
+ ttNdev_CLASS_MAPPINGS = {}
+ ttNdev_DISPLAY_NAME_MAPPINGS = {}
+
+# ------- MAPPING ------- #
+from .tinyterraNodes import NODE_CLASS_MAPPINGS as ttN_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as ttN_DISPLAY_NAME_MAPPINGS
+
+NODE_CLASS_MAPPINGS = {**ttN_CLASS_MAPPINGS, **ttNdev_CLASS_MAPPINGS}
+NODE_DISPLAY_NAME_MAPPINGS = {**ttN_DISPLAY_NAME_MAPPINGS, **ttNdev_DISPLAY_NAME_MAPPINGS}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/__init__.cpython-310.pyc b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a276ab463dd6f8e19174bc8c2fbe9e20ad4e9590
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diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/adv_encode.cpython-310.pyc b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/adv_encode.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1fea596fe19dd0f2f7ca9b0b6ed19762421d741e
Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/adv_encode.cpython-310.pyc differ
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/tinyterraNodes.cpython-310.pyc b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/tinyterraNodes.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1336df7aeb9d88ec72cd9d345fdd2e78b9f09d14
Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/__pycache__/tinyterraNodes.cpython-310.pyc differ
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/adv_encode.py b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/adv_encode.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6ad2e904e2d15424154ad9ad88775910071a29c
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/adv_encode.py
@@ -0,0 +1,290 @@
+import torch
+import numpy as np
+import itertools
+from math import gcd
+
+from comfy import model_management
+from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
+
+def _grouper(n, iterable):
+ it = iter(iterable)
+ while True:
+ chunk = list(itertools.islice(it, n))
+ if not chunk:
+ return
+ yield chunk
+
+def _norm_mag(w, n):
+ d = w - 1
+ return 1 + np.sign(d) * np.sqrt(np.abs(d)**2 / n)
+ #return np.sign(w) * np.sqrt(np.abs(w)**2 / n)
+
+def divide_length(word_ids, weights):
+ sums = dict(zip(*np.unique(word_ids, return_counts=True)))
+ sums[0] = 1
+ weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def shift_mean_weight(word_ids, weights):
+ delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x,y) if id != 0])
+ weights = [[w if id == 0 else w+delta
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def scale_to_norm(weights, word_ids, w_max):
+ top = np.max(weights)
+ w_max = min(top, w_max)
+ weights = [[w_max if id == 0 else (w/top) * w_max
+ for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
+ return weights
+
+def from_zero(weights, base_emb):
+ weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
+ weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape)
+ return base_emb * weight_tensor
+
+def mask_word_id(tokens, word_ids, target_id, mask_token):
+ new_tokens = [[mask_token if wid == target_id else t
+ for t, wid in zip(x,y)] for x,y in zip(tokens, word_ids)]
+ mask = np.array(word_ids) == target_id
+ return (new_tokens, mask)
+
+def batched_clip_encode(tokens, length, encode_func, num_chunks):
+ embs = []
+ for e in _grouper(32, tokens):
+ enc, pooled = encode_func(e)
+ enc = enc.reshape((len(e), length, -1))
+ embs.append(enc)
+ embs = torch.cat(embs)
+ embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1))
+ return embs
+
+def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
+ pooled_base = base_emb[0,length-1:length,:]
+ wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True)
+ weight_dict = dict((id,w)
+ for id,w in zip(wids ,np.array(weights).reshape(-1)[inds])
+ if w != 1.0)
+
+ if len(weight_dict) == 0:
+ return torch.zeros_like(base_emb), base_emb[0,length-1:length,:]
+
+ weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
+ weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape)
+
+ #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
+ #TODO: find most suitable masking token here
+ m_token = (m_token, 1.0)
+
+ ws = []
+ masked_tokens = []
+ masks = []
+
+ #create prompts
+ for id, w in weight_dict.items():
+ masked, m = mask_word_id(tokens, word_ids, id, m_token)
+ masked_tokens.extend(masked)
+
+ m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device)
+ m = m.reshape(1,-1,1).expand(base_emb.shape)
+ masks.append(m)
+
+ ws.append(w)
+
+ #batch process prompts
+ embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
+ masks = torch.cat(masks)
+
+ embs = (base_emb.expand(embs.shape) - embs)
+ pooled = embs[0,length-1:length,:]
+
+ embs *= masks
+ embs = embs.sum(axis=0, keepdim=True)
+
+ pooled_start = pooled_base.expand(len(ws), -1)
+ ws = torch.tensor(ws).reshape(-1,1).expand(pooled_start.shape)
+ pooled = (pooled - pooled_start) * (ws - 1)
+ pooled = pooled.mean(axis=0, keepdim=True)
+
+ return ((weight_tensor - 1) * embs), pooled_base + pooled
+
+def mask_inds(tokens, inds, mask_token):
+ clip_len = len(tokens[0])
+ inds_set = set(inds)
+ new_tokens = [[mask_token if i*clip_len + j in inds_set else t
+ for j, t in enumerate(x)] for i, x in enumerate(tokens)]
+ return new_tokens
+
+def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
+ w, w_inv = np.unique(weights,return_inverse=True)
+
+ if np.sum(w < 1) == 0:
+ return base_emb, tokens, base_emb[0,length-1:length,:]
+ #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
+ #using the comma token as a masking token seems to work better than aos tokens for SD 1.x
+ m_token = (m_token, 1.0)
+
+ masked_tokens = []
+
+ masked_current = tokens
+ for i in range(len(w)):
+ if w[i] >= 1:
+ continue
+ masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token)
+ masked_tokens.extend(masked_current)
+
+ embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
+ embs = torch.cat([base_emb, embs])
+ w = w[w<=1.0]
+ w_mix = np.diff([0] + w.tolist())
+ w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1,1,1))
+
+ weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True)
+ return weighted_emb, masked_current, weighted_emb[0,length-1:length,:]
+
+def scale_emb_to_mag(base_emb, weighted_emb):
+ norm_base = torch.linalg.norm(base_emb)
+ norm_weighted = torch.linalg.norm(weighted_emb)
+ embeddings_final = (norm_base / norm_weighted) * weighted_emb
+ return embeddings_final
+
+def recover_dist(base_emb, weighted_emb):
+ fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean())
+ embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean())
+ return embeddings_final
+
+def A1111_renorm(base_emb, weighted_emb):
+ embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb
+ return embeddings_final
+
+def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266, length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False):
+ tokens = [[t for t,_,_ in x] for x in tokenized]
+ weights = [[w for _,w,_ in x] for x in tokenized]
+ word_ids = [[wid for _,_,wid in x] for x in tokenized]
+
+ #weight normalization
+ #====================
+
+ #distribute down/up weights over word lengths
+ if token_normalization.startswith("length"):
+ weights = divide_length(word_ids, weights)
+
+ #make mean of word tokens 1
+ if token_normalization.endswith("mean"):
+ weights = shift_mean_weight(word_ids, weights)
+
+ #weight interpretation
+ #=====================
+ pooled = None
+
+ if weight_interpretation == "comfy":
+ weighted_tokens = [[(t,w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
+ weighted_emb, pooled_base = encode_func(weighted_tokens)
+ pooled = pooled_base
+ else:
+ unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokenized]
+ base_emb, pooled_base = encode_func(unweighted_tokens)
+
+ if weight_interpretation == "A1111":
+ weighted_emb = from_zero(weights, base_emb)
+ weighted_emb = A1111_renorm(base_emb, weighted_emb)
+ pooled = pooled_base
+
+ if weight_interpretation == "compel":
+ pos_tokens = [[(t,w) if w >= 1.0 else (t,1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
+ weighted_emb, _ = encode_func(pos_tokens)
+ weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func)
+
+ if weight_interpretation == "comfy++":
+ weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+ weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights]
+ #unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down]
+ embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+ weighted_emb += embs
+
+ if weight_interpretation == "down_weight":
+ weights = scale_to_norm(weights, word_ids, w_max)
+ weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
+
+ if return_pooled:
+ if apply_to_pooled:
+ return weighted_emb, pooled
+ else:
+ return weighted_emb, pooled_base
+ return weighted_emb, None
+
+def encode_token_weights_g(model, token_weight_pairs):
+ return model.clip_g.encode_token_weights(token_weight_pairs)
+
+def encode_token_weights_l(model, token_weight_pairs):
+ l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs)
+ return l_out, None
+
+def encode_token_weights(model, token_weight_pairs, encode_func):
+ if model.layer_idx is not None:
+ model.cond_stage_model.clip_layer(model.layer_idx)
+
+ model_management.load_model_gpu(model.patcher)
+ return encode_func(model.cond_stage_model, token_weight_pairs)
+
+def prepareXL(embs_l, embs_g, pooled, clip_balance):
+ l_w = 1 - max(0, clip_balance - .5) * 2
+ g_w = 1 - max(0, .5 - clip_balance) * 2
+ if embs_l is not None:
+ return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled
+ else:
+ return embs_g, pooled
+
+def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
+ tokenized = clip.tokenize(text, return_word_ids=True)
+ if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
+ embs_l = None
+ embs_g = None
+ pooled = None
+ if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel):
+ embs_l, _ = advanced_encode_from_tokens(tokenized['l'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+ w_max=w_max,
+ return_pooled=False)
+ if 'g' in tokenized:
+ embs_g, pooled = advanced_encode_from_tokens(tokenized['g'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+ w_max=w_max,
+ return_pooled=True,
+ apply_to_pooled=apply_to_pooled)
+ return prepareXL(embs_l, embs_g, pooled, clip_balance)
+ else:
+ return advanced_encode_from_tokens(tokenized['l'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: (clip.encode_from_tokens({'l': x}), None),
+ w_max=w_max)
+def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
+ tokenized1 = clip.tokenize(text1, return_word_ids=True)
+ tokenized2 = clip.tokenize(text2, return_word_ids=True)
+
+ embs_l, _ = advanced_encode_from_tokens(tokenized1['l'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_l),
+ w_max=w_max,
+ return_pooled=False)
+
+ embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'],
+ token_normalization,
+ weight_interpretation,
+ lambda x: encode_token_weights(clip, x, encode_token_weights_g),
+ w_max=w_max,
+ return_pooled=True,
+ apply_to_pooled=apply_to_pooled)
+
+ gcd_num = gcd(embs_l.shape[1], embs_g.shape[1])
+ repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1])
+ repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1])
+
+ return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance)
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/arial.ttf b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/arial.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..ff0815cd8c64b0a245ec780eb8d21867509155b5
Binary files /dev/null and b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/arial.ttf differ
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/config.ini b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/config.ini
new file mode 100644
index 0000000000000000000000000000000000000000..5e1f3095a3f84065830fb5ea7c3e386176077777
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/config.ini
@@ -0,0 +1,46 @@
+[Versions]
+tinyterranodes = 1.2.0
+pipeloader = 1.1.2
+pipeksampler = 1.0.5
+pipeksampleradvanced = 1.0.5
+pipeloadersdxl = 1.1.2
+pipeksamplersdxl = 1.0.2
+pipein = 1.1.0
+pipeout = 1.1.0
+pipeedit = 1.1.1
+pipe2basic = 1.1.0
+pipe2detailer = 1.2.0
+xyplot = 1.2.0
+pipeencodeconcat = 1.0.2
+multilorastack = 1.1.1
+multimodelmerge = 1.0.1
+text = 1.0.0
+textdebug = 1.0.
+concat = 1.0.0
+text3box_3wayconcat = 1.0.0
+text7box_concat = 1.0.0
+imageoutput = 1.1.0
+imagerembg = 0.0.0
+hiresfixscale = 1.0.3
+int = 1.0.0
+float = 1.0.0
+seed = 1.0.0
+
+[Option Values]
+auto_update = ('true', 'false')
+install_rembg = ('true', 'false')
+enable_embed_autocomplete = ('true', 'false')
+enable_interface = ('true', 'false')
+enable_fullscreen = ('true', 'false')
+enable_dynamic_widgets = ('true', 'false')
+enable_dev_nodes = ('true', 'false')
+
+[ttNodes]
+auto_update = False
+install_rembg = False
+enable_interface = True
+enable_fullscreen = True
+enable_embed_autocomplete = True
+enable_dynamic_widgets = True
+enable_dev_nodes = False
+
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttN.js b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttN.js
new file mode 100644
index 0000000000000000000000000000000000000000..660b8e89878b5f7159233ba68c63e7cd52ff414f
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttN.js
@@ -0,0 +1,571 @@
+import { app } from "../../scripts/app.js";
+import { ComfyWidgets } from "../../scripts/widgets.js";
+
+const CONVERTED_TYPE = "converted-widget";
+const GET_CONFIG = Symbol();
+
+function hideWidget(node, widget, suffix = "") {
+ widget.origType = widget.type;
+ widget.origComputeSize = widget.computeSize;
+ widget.origSerializeValue = widget.serializeValue;
+ widget.computeSize = () => [0, -4]; // -4 is due to the gap litegraph adds between widgets automatically
+ widget.type = CONVERTED_TYPE + suffix;
+ widget.serializeValue = () => {
+ // Prevent serializing the widget if we have no input linked
+ if (!node.inputs) {
+ return undefined;
+ }
+ let node_input = node.inputs.find((i) => i.widget?.name === widget.name);
+
+ if (!node_input || !node_input.link) {
+ return undefined;
+ }
+ return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
+ };
+
+ // Hide any linked widgets, e.g. seed+seedControl
+ if (widget.linkedWidgets) {
+ for (const w of widget.linkedWidgets) {
+ hideWidget(node, w, ":" + widget.name);
+ }
+ }
+}
+
+function convertToInput(node, widget, config) {
+ console.log('config:', config)
+ hideWidget(node, widget);
+
+ const { type } = getWidgetType(config);
+
+ // Add input and store widget config for creating on primitive node
+ const sz = node.size;
+ node.addInput(widget.name, type, {
+ widget: { name: widget.name, [GET_CONFIG]: () => config },
+ });
+
+ for (const widget of node.widgets) {
+ widget.last_y += LiteGraph.NODE_SLOT_HEIGHT;
+ }
+
+ // Restore original size but grow if needed
+ node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]);
+}
+
+function getWidgetType(config) {
+ // Special handling for COMBO so we restrict links based on the entries
+ let type = config[0];
+ if (type instanceof Array) {
+ type = "COMBO";
+ }
+ return { type };
+}
+
+app.registerExtension({
+ name: "comfy.ttN",
+ init() {
+ const ttNreloadNode = function (node) {
+ const nodeType = node.constructor.type;
+ const origVals = node.properties.origVals || {};
+
+ const nodeTitle = origVals.title || node.title;
+ const nodeColor = origVals.color || node.color;
+ const bgColor = origVals.bgcolor || node.bgcolor;
+ const oldNode = node
+ const options = {
+ 'size': [...node.size],
+ 'color': nodeColor,
+ 'bgcolor': bgColor,
+ 'pos': [...node.pos]
+ }
+
+ let inputLinks = []
+ let outputLinks = []
+ for (const input of node.inputs) {
+ if (input.link) {
+ const input_name = input.name
+ const input_slot = node.findInputSlot(input_name)
+ const input_node = node.getInputNode(input_slot)
+ const input_link = node.getInputLink(input_slot)
+
+ inputLinks.push([input_link.origin_slot, input_node, input_name])
+ }
+ }
+ for (const output of node.outputs) {
+ if (output.links) {
+ const output_name = output.name
+
+ for (const linkID of output.links) {
+ const output_link = graph.links[linkID]
+ const output_node = graph._nodes_by_id[output_link.target_id]
+ outputLinks.push([output_name, output_node, output_link.target_slot])
+ }
+ }
+ }
+
+ app.graph.remove(node)
+ const newNode = app.graph.add(LiteGraph.createNode(nodeType, nodeTitle, options));
+ if (newNode?.constructor?.hasOwnProperty('ttNnodeVersion')) {
+ newNode.properties.ttNnodeVersion = newNode.constructor.ttNnodeVersion;
+ }
+
+ function handleLinks() {
+ // re-convert inputs
+ for (let w of oldNode.widgets) {
+ if (w.type === 'converted-widget') {
+ const WidgetToConvert = newNode.widgets.find((nw) => nw.name === w.name);
+ for (let i of oldNode.inputs) {
+ if (i.name === w.name) {
+ convertToInput(newNode, WidgetToConvert, i.widget);
+ }
+ }
+ }
+ }
+ // replace input and output links
+ for (let input of inputLinks) {
+ const [output_slot, output_node, input_name] = input;
+ output_node.connect(output_slot, newNode.id, input_name)
+ }
+ for (let output of outputLinks) {
+ const [output_name, input_node, input_slot] = output;
+ newNode.connect(output_name, input_node, input_slot)
+ }
+ }
+
+ // fix widget values
+ let values = oldNode.widgets_values;
+ if (!values) {
+ newNode.widgets.forEach((newWidget, index) => {
+ const oldWidget = oldNode.widgets[index];
+ if (newWidget.name === oldWidget.name && newWidget.type === oldWidget.type) {
+ newWidget.value = oldWidget.value;
+ }
+ });
+ handleLinks();
+ return;
+ }
+ let pass = false
+ const isIterateForwards = values.length <= newNode.widgets.length;
+ let vi = isIterateForwards ? 0 : values.length - 1;
+ function evalWidgetValues(testValue, newWidg) {
+ if (testValue === true || testValue === false) {
+ if (newWidg.options?.on && newWidg.options?.off) {
+ return { value: testValue, pass: true };
+ }
+ } else if (typeof testValue === "number") {
+ if (newWidg.options?.min <= testValue && testValue <= newWidg.options?.max) {
+ return { value: testValue, pass: true };
+ }
+ } else if (newWidg.options?.values?.includes(testValue)) {
+ return { value: testValue, pass: true };
+ } else if (newWidg.inputEl && typeof testValue === "string") {
+ return { value: testValue, pass: true };
+ }
+ return { value: newWidg.value, pass: false };
+ }
+ const updateValue = (wi) => {
+ const oldWidget = oldNode.widgets[wi];
+ let newWidget = newNode.widgets[wi];
+ if (newWidget.name === oldWidget.name && newWidget.type === oldWidget.type) {
+ while ((isIterateForwards ? vi < values.length : vi >= 0) && !pass) {
+ let { value, pass } = evalWidgetValues(values[vi], newWidget);
+ if (pass && value !== null) {
+ newWidget.value = value;
+ break;
+ }
+ vi += isIterateForwards ? 1 : -1;
+ }
+ vi++
+ if (!isIterateForwards) {
+ vi = values.length - (newNode.widgets.length - 1 - wi);
+ }
+ }
+ };
+ if (isIterateForwards) {
+ for (let wi = 0; wi < newNode.widgets.length; wi++) {
+ updateValue(wi);
+ }
+ } else {
+ for (let wi = newNode.widgets.length - 1; wi >= 0; wi--) {
+ updateValue(wi);
+ }
+ }
+ handleLinks();
+ };
+
+ const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions;
+ LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
+ const options = getNodeMenuOptions.apply(this, arguments);
+ node.setDirtyCanvas(true, true);
+
+ options.splice(options.length - 1, 0,
+ {
+ content: "Reload Node (ttN)",
+ callback: () => {
+ var graphcanvas = LGraphCanvas.active_canvas;
+ if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {
+ ttNreloadNode(node);
+ } else {
+ for (var i in graphcanvas.selected_nodes) {
+ ttNreloadNode(graphcanvas.selected_nodes[i]);
+ }
+ }
+ }
+ },
+ );
+ return options;
+ };
+ },
+ beforeRegisterNodeDef(nodeType, nodeData, app) {
+ if (nodeData.name.startsWith("ttN")) {
+ const origOnConfigure = nodeType.prototype.onConfigure;
+ nodeType.prototype.onConfigure = function () {
+ const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : undefined;
+ let nodeVersion = nodeData.input.hidden?.ttNnodeVersion ? nodeData.input.hidden.ttNnodeVersion : null;
+ nodeType.ttNnodeVersion = nodeVersion;
+ this.properties['ttNnodeVersion'] = this.properties['ttNnodeVersion'] ? this.properties['ttNnodeVersion'] : nodeVersion;
+ if (this.properties['ttNnodeVersion'] !== nodeVersion) {
+ if (!this.properties['origVals']) {
+ this.properties['origVals'] = { bgcolor: this.bgcolor, color: this.color, title: this.title }
+ }
+ this.bgcolor = "#d82129";
+ this.color = "#bd000f";
+ this.title = this.title.includes("Node Version Mismatch") ? this.title : this.title + " - Node Version Mismatch"
+ } else if (this.properties['origVals']) {
+ this.bgcolor = this.properties.origVals.bgcolor;
+ this.color = this.properties.origVals.color;
+ this.title = this.properties.origVals.title;
+ delete this.properties['origVals']
+ }
+ return r;
+ };
+ }
+ if (nodeData.name === "ttN textDebug") {
+ const onNodeCreated = nodeType.prototype.onNodeCreated;
+ nodeType.prototype.onNodeCreated = function () {
+ const r = onNodeCreated?.apply(this, arguments);
+ const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
+ w.inputEl.readOnly = true;
+ w.inputEl.style.opacity = 0.7;
+ return r;
+ };
+
+ const onExecuted = nodeType.prototype.onExecuted;
+ nodeType.prototype.onExecuted = function (message) {
+ onExecuted?.apply(this, arguments);
+
+ for (const widget of this.widgets) {
+ if (widget.type === "customtext"){
+ widget.value = message.text.join('');
+ }
+ }
+
+ this.onResize?.(this.size);
+ };
+ }
+ },
+ nodeCreated(node) {
+ if (node.getTitle() === "pipeLoader") {
+ for (let widget of node.widgets) {
+ if (widget.name === "control_after_generate") {
+ widget.value = "fixed"
+ }
+ }
+ }
+ },
+});
+
+
+// ttN Dropdown
+var styleElement = document.createElement("style");
+const cssCode = `
+.ttN-dropdown, .ttN-nested-dropdown {
+ position: relative;
+ box-sizing: border-box;
+ background-color: #171717;
+ box-shadow: 0 4px 4px rgba(255, 255, 255, .25);
+ padding: 0;
+ margin: 0;
+ list-style: none;
+ z-index: 1000;
+ overflow: visible;
+ max-height: fit-content;
+ max-width: fit-content;
+}
+
+.ttN-dropdown {
+ position: absolute;
+ border-radius: 0;
+}
+
+/* Style for final items */
+.ttN-dropdown li.item, .ttN-nested-dropdown li.item {
+ font-weight: normal;
+ min-width: max-content;
+}
+
+/* Style for folders (parent items) */
+.ttN-dropdown li.folder, .ttN-nested-dropdown li.folder {
+ cursor: default;
+ position: relative;
+ border-right: 3px solid cyan;
+}
+
+.ttN-dropdown li.folder::after, .ttN-nested-dropdown li.folder::after {
+ content: ">";
+ position: absolute;
+ right: 2px;
+ font-weight: normal;
+}
+
+.ttN-dropdown li, .ttN-nested-dropdown li {
+ padding: 4px 10px;
+ cursor: pointer;
+ font-family: system-ui;
+ font-size: 0.7rem;
+ position: relative;
+}
+
+/* Style for nested dropdowns */
+.ttN-nested-dropdown {
+ position: absolute;
+ top: 0;
+ left: 100%;
+ margin: 0;
+ border: none;
+ display: none;
+}
+
+.ttN-dropdown li.selected > .ttN-nested-dropdown,
+.ttN-nested-dropdown li.selected > .ttN-nested-dropdown {
+ display: block;
+ border: none;
+}
+
+.ttN-dropdown li.selected,
+.ttN-nested-dropdown li.selected {
+ background-color: #e5e5e5;
+ border: none;
+}
+`
+styleElement.innerHTML = cssCode
+document.head.appendChild(styleElement);
+
+let activeDropdown = null;
+
+export function ttN_RemoveDropdown() {
+ if (activeDropdown) {
+ activeDropdown.removeEventListeners();
+ activeDropdown.dropdown.remove();
+ activeDropdown = null;
+ }
+}
+
+class Dropdown {
+ constructor(inputEl, suggestions, onSelect, isDict = false) {
+ this.dropdown = document.createElement('ul');
+ this.dropdown.setAttribute('role', 'listbox');
+ this.dropdown.classList.add('ttN-dropdown');
+ this.selectedIndex = -1;
+ this.inputEl = inputEl;
+ this.suggestions = suggestions;
+ this.onSelect = onSelect;
+ this.isDict = isDict;
+
+ this.focusedDropdown = this.dropdown;
+
+ this.buildDropdown();
+
+ this.onKeyDownBound = this.onKeyDown.bind(this);
+ this.onWheelBound = this.onWheel.bind(this);
+ this.onClickBound = this.onClick.bind(this);
+
+ this.addEventListeners();
+ }
+
+ buildDropdown() {
+ if (this.isDict) {
+ this.buildNestedDropdown(this.suggestions, this.dropdown);
+ } else {
+ this.suggestions.forEach((suggestion, index) => {
+ this.addListItem(suggestion, index, this.dropdown);
+ });
+ }
+
+ const inputRect = this.inputEl.getBoundingClientRect();
+ this.dropdown.style.top = (inputRect.top + inputRect.height - 10) + 'px';
+ this.dropdown.style.left = inputRect.left + 'px';
+
+ document.body.appendChild(this.dropdown);
+ activeDropdown = this;
+ }
+
+ buildNestedDropdown(dictionary, parentElement) {
+ let index = 0;
+ Object.keys(dictionary).forEach((key) => {
+ const item = dictionary[key];
+ if (typeof item === "object" && item !== null) {
+ const nestedDropdown = document.createElement('ul');
+ nestedDropdown.setAttribute('role', 'listbox');
+ nestedDropdown.classList.add('ttN-nested-dropdown');
+ const parentListItem = document.createElement('li');
+ parentListItem.classList.add('folder');
+ parentListItem.textContent = key;
+ parentListItem.appendChild(nestedDropdown);
+ parentListItem.addEventListener('mouseover', this.onMouseOver.bind(this, index, parentElement));
+ parentElement.appendChild(parentListItem);
+ this.buildNestedDropdown(item, nestedDropdown);
+ index = index + 1;
+ } else {
+ const listItem = document.createElement('li');
+ listItem.classList.add('item');
+ listItem.setAttribute('role', 'option');
+ listItem.textContent = key;
+ listItem.addEventListener('mouseover', this.onMouseOver.bind(this, index, parentElement));
+ listItem.addEventListener('mousedown', this.onMouseDown.bind(this, key));
+ parentElement.appendChild(listItem);
+ index = index + 1;
+ }
+ });
+ }
+
+ addListItem(item, index, parentElement) {
+ const listItem = document.createElement('li');
+ listItem.setAttribute('role', 'option');
+ listItem.textContent = item;
+ listItem.addEventListener('mouseover', this.onMouseOver.bind(this, index));
+ listItem.addEventListener('mousedown', this.onMouseDown.bind(this, item));
+ parentElement.appendChild(listItem);
+ }
+
+ addEventListeners() {
+ document.addEventListener('keydown', this.onKeyDownBound);
+ this.dropdown.addEventListener('wheel', this.onWheelBound);
+ document.addEventListener('click', this.onClickBound);
+ }
+
+ removeEventListeners() {
+ document.removeEventListener('keydown', this.onKeyDownBound);
+ this.dropdown.removeEventListener('wheel', this.onWheelBound);
+ document.removeEventListener('click', this.onClickBound);
+ }
+
+ onMouseOver(index, parentElement) {
+ if (parentElement) {
+ this.focusedDropdown = parentElement;
+ }
+ this.selectedIndex = index;
+ this.updateSelection();
+ }
+
+ onMouseOut() {
+ this.selectedIndex = -1;
+ this.updateSelection();
+ }
+
+ onMouseDown(suggestion, event) {
+ event.preventDefault();
+ this.onSelect(suggestion);
+ this.dropdown.remove();
+ this.removeEventListeners();
+ }
+
+ onKeyDown(event) {
+ const enterKeyCode = 13;
+ const escKeyCode = 27;
+ const arrowUpKeyCode = 38;
+ const arrowDownKeyCode = 40;
+ const arrowRightKeyCode = 39;
+ const arrowLeftKeyCode = 37;
+ const tabKeyCode = 9;
+
+ const items = Array.from(this.focusedDropdown.children);
+ const selectedItem = items[this.selectedIndex];
+
+ if (activeDropdown) {
+ if (event.keyCode === arrowUpKeyCode) {
+ event.preventDefault();
+ this.selectedIndex = Math.max(0, this.selectedIndex - 1);
+ this.updateSelection();
+ }
+
+ else if (event.keyCode === arrowDownKeyCode) {
+ event.preventDefault();
+ this.selectedIndex = Math.min(items.length - 1, this.selectedIndex + 1);
+ this.updateSelection();
+ }
+
+ else if (event.keyCode === arrowRightKeyCode) {
+ event.preventDefault();
+ if (selectedItem && selectedItem.classList.contains('folder')) {
+ const nestedDropdown = selectedItem.querySelector('.ttN-nested-dropdown');
+ if (nestedDropdown) {
+ this.focusedDropdown = nestedDropdown;
+ this.selectedIndex = 0;
+ this.updateSelection();
+ }
+ }
+ }
+
+ else if (event.keyCode === arrowLeftKeyCode && this.focusedDropdown !== this.dropdown) {
+ const parentDropdown = this.focusedDropdown.closest('.ttN-dropdown, .ttN-nested-dropdown').parentNode.closest('.ttN-dropdown, .ttN-nested-dropdown');
+ if (parentDropdown) {
+ this.focusedDropdown = parentDropdown;
+ this.selectedIndex = Array.from(parentDropdown.children).indexOf(this.focusedDropdown.parentNode);
+ this.updateSelection();
+ }
+ }
+
+ else if ((event.keyCode === enterKeyCode || event.keyCode === tabKeyCode) && this.selectedIndex >= 0) {
+ event.preventDefault();
+ if (selectedItem.classList.contains('item')) {
+ this.onSelect(items[this.selectedIndex].textContent);
+ this.dropdown.remove();
+ this.removeEventListeners();
+ }
+
+ const nestedDropdown = selectedItem.querySelector('.ttN-nested-dropdown');
+ if (nestedDropdown) {
+ this.focusedDropdown = nestedDropdown;
+ this.selectedIndex = 0;
+ this.updateSelection();
+ }
+ }
+
+ else if (event.keyCode === escKeyCode) {
+ this.dropdown.remove();
+ this.removeEventListeners();
+ }
+ }
+ }
+
+ onWheel(event) {
+ const top = parseInt(this.dropdown.style.top);
+ if (localStorage.getItem("Comfy.Settings.Comfy.InvertMenuScrolling")) {
+ this.dropdown.style.top = (top + (event.deltaY < 0 ? 10 : -10)) + "px";
+ } else {
+ this.dropdown.style.top = (top + (event.deltaY < 0 ? -10 : 10)) + "px";
+ }
+ }
+
+ onClick(event) {
+ if (!this.dropdown.contains(event.target) && event.target !== this.inputEl) {
+ this.dropdown.remove();
+ this.removeEventListeners();
+ }
+ }
+
+ updateSelection() {
+ Array.from(this.focusedDropdown.children).forEach((li, index) => {
+ if (index === this.selectedIndex) {
+ li.classList.add('selected');
+ } else {
+ li.classList.remove('selected');
+ }
+ });
+ }
+}
+
+export function ttN_CreateDropdown(inputEl, suggestions, onSelect, isDict = false) {
+ ttN_RemoveDropdown();
+ new Dropdown(inputEl, suggestions, onSelect, isDict);
+}
\ No newline at end of file
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNdynamicWidgets.js b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNdynamicWidgets.js
new file mode 100644
index 0000000000000000000000000000000000000000..08412f5599b8b59499030f906090be926c081785
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNdynamicWidgets.js
@@ -0,0 +1,304 @@
+import { app } from "../../scripts/app.js";
+
+let origProps = {};
+
+const findWidgetByName = (node, name) => node.widgets.find((w) => w.name === name);
+
+const doesInputWithNameExist = (node, name) => node.inputs ? node.inputs.some((input) => input.name === name) : false;
+
+function updateNodeHeight(node) {
+ node.setSize([node.size[0], node.computeSize()[1]]);
+}
+
+function toggleWidget(node, widget, show = false, suffix = "") {
+ if (!widget || doesInputWithNameExist(node, widget.name)) return;
+ if (!origProps[widget.name]) {
+ origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize };
+ }
+ const origSize = node.size;
+
+ widget.type = show ? origProps[widget.name].origType : "ttNhidden" + suffix;
+ widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4];
+
+ widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show));
+
+ const height = show ? Math.max(node.computeSize()[1], origSize[1]) : node.size[1];
+ node.setSize([node.size[0], height]);
+
+}
+
+function widgetLogic(node, widget) {
+ if (widget.name === 'lora_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'lora_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'lora1_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'lora1_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora1_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora1_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora1_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'lora2_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'lora2_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora2_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora2_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora2_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'lora3_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'lora3_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora3_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora3_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora3_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'refiner_ckpt_name') {
+ let refiner_lora1 = findWidgetByName(node, 'refiner_lora1_name').value
+ let refiner_lora2 = findWidgetByName(node, 'refiner_lora2_name').value
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'refiner_vae_name'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_name'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_name'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'refiner_vae_name'), true)
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_name'), true)
+ if (refiner_lora1 !== "None") {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'), true)
+ }
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_name'), true)
+ if (refiner_lora2 !== "None") {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'), true)
+ }
+ }
+ }
+ if (widget.name === 'refiner_lora1_name') {
+ let refiner_ckpt = findWidgetByName(node, 'refiner_ckpt_name').value
+
+ if (widget.value === "None" || refiner_ckpt === "None") {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'refiner_lora2_name') {
+ let refiner_ckpt = findWidgetByName(node, 'refiner_ckpt_name').value
+
+ if (widget.value === "None" || refiner_ckpt === "None") {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'), true)
+ }
+ }
+ if (widget.name === 'rescale_after_model') {
+ if (widget.value === false) {
+ toggleWidget(node, findWidgetByName(node, 'rescale_method'))
+ toggleWidget(node, findWidgetByName(node, 'rescale'))
+ toggleWidget(node, findWidgetByName(node, 'percent'))
+ toggleWidget(node, findWidgetByName(node, 'width'))
+ toggleWidget(node, findWidgetByName(node, 'height'))
+ toggleWidget(node, findWidgetByName(node, 'longer_side'))
+ toggleWidget(node, findWidgetByName(node, 'crop'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'rescale_method'), true)
+ toggleWidget(node, findWidgetByName(node, 'rescale'), true)
+
+ let rescale_value = findWidgetByName(node, 'rescale').value
+
+ if (rescale_value === 'by percentage') {
+ toggleWidget(node, findWidgetByName(node, 'percent'), true)
+ } else if (rescale_value === 'to Width/Height') {
+ toggleWidget(node, findWidgetByName(node, 'width'), true)
+ toggleWidget(node, findWidgetByName(node, 'height'), true)
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'longer_side'), true)
+ }
+ toggleWidget(node, findWidgetByName(node, 'crop'), true)
+ }
+ }
+ if (widget.name === 'rescale') {
+ let rescale_after_model = findWidgetByName(node, 'rescale_after_model').value
+ if (widget.value === 'by percentage' && rescale_after_model) {
+ toggleWidget(node, findWidgetByName(node, 'width'))
+ toggleWidget(node, findWidgetByName(node, 'height'))
+ toggleWidget(node, findWidgetByName(node, 'longer_side'))
+ toggleWidget(node, findWidgetByName(node, 'percent'), true)
+ } else if (widget.value === 'to Width/Height' && rescale_after_model) {
+ toggleWidget(node, findWidgetByName(node, 'width'), true)
+ toggleWidget(node, findWidgetByName(node, 'height'), true)
+ toggleWidget(node, findWidgetByName(node, 'percent'))
+ toggleWidget(node, findWidgetByName(node, 'longer_side'))
+ } else if (rescale_after_model) {
+ toggleWidget(node, findWidgetByName(node, 'longer_side'), true)
+ toggleWidget(node, findWidgetByName(node, 'width'))
+ toggleWidget(node, findWidgetByName(node, 'height'))
+ toggleWidget(node, findWidgetByName(node, 'percent'))
+ }
+ }
+ if (widget.name === 'upscale_method') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'factor'))
+ toggleWidget(node, findWidgetByName(node, 'crop'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'factor'), true)
+ toggleWidget(node, findWidgetByName(node, 'crop'), true)
+ }
+ }
+ if (widget.name === 'image_output') {
+ if (widget.value === 'Hide' || widget.value === 'Preview') {
+ toggleWidget(node, findWidgetByName(node, 'save_prefix'))
+ toggleWidget(node, findWidgetByName(node, 'output_path'))
+ toggleWidget(node, findWidgetByName(node, 'embed_workflow'))
+ toggleWidget(node, findWidgetByName(node, 'number_padding'))
+ toggleWidget(node, findWidgetByName(node, 'overwrite_existing'))
+ } else if (widget.value === 'Save' || widget.value === 'Hide/Save') {
+ toggleWidget(node, findWidgetByName(node, 'save_prefix'), true)
+ toggleWidget(node, findWidgetByName(node, 'output_path'), true)
+ toggleWidget(node, findWidgetByName(node, 'embed_workflow'), true)
+ toggleWidget(node, findWidgetByName(node, 'number_padding'), true)
+ toggleWidget(node, findWidgetByName(node, 'overwrite_existing'), true)
+ }
+ }
+ if (widget.name === 'add_noise') {
+ if (widget.value === "disable") {
+ toggleWidget(node, findWidgetByName(node, 'noise_seed'))
+ toggleWidget(node, findWidgetByName(node, 'control_after_generate'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'noise_seed'), true)
+ toggleWidget(node, findWidgetByName(node, 'control_after_generate'), true)
+ }
+ }
+ if (widget.name === 'ckpt_B_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'config_B_name'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'config_B_name'), true)
+ }
+ }
+ if (widget.name === 'ckpt_C_name') {
+ if (widget.value === "None") {
+ toggleWidget(node, findWidgetByName(node, 'config_C_name'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'config_C_name'), true)
+ }
+ }
+ if (widget.name === 'save_model') {
+ if (widget.value === "True") {
+ toggleWidget(node, findWidgetByName(node, 'save_prefix'), true)
+
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'save_prefix'))
+ }
+ }
+ if (widget.name === 'num_loras') {
+ let number_to_show = widget.value + 1
+ for (let i = 0; i < number_to_show; i++) {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name'), true)
+ if (findWidgetByName(node, 'mode').value === "simple") {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)
+ }
+ }
+ for (let i = number_to_show; i < 21; i++) {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
+ }
+ updateNodeHeight(node)
+ }
+ if (widget.name === 'mode') {
+ let number_to_show = findWidgetByName(node, 'num_loras').value + 1
+ for (let i = 0; i < number_to_show; i++) {
+ if (widget.value === "simple") {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
+ } else {
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
+ toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)}
+ }
+ updateNodeHeight(node)
+ }
+ if (widget.name === 'toggle') {
+ widget.type = 'toggle'
+ widget.options = {on: 'Enabled', off: 'Disabled'}
+ }
+}
+
+const getSetWidgets = ['rescale_after_model', 'rescale', 'image_output',
+ 'lora_name', 'lora1_name', 'lora2_name', 'lora3_name',
+ 'refiner_lora1_name', 'refiner_lora2_name', 'upscale_method',
+ 'image_output', 'add_noise',
+ 'ckpt_B_name', 'ckpt_C_name', 'save_model', 'refiner_ckpt_name',
+ 'num_loras', 'mode', 'toggle']
+
+function getSetters(node) {
+ if (node.widgets)
+ for (const w of node.widgets) {
+ if (getSetWidgets.includes(w.name)) {
+ widgetLogic(node, w);
+ let widgetValue = w.value;
+
+ // Define getters and setters for widget values
+ Object.defineProperty(w, 'value', {
+ get() {
+ return widgetValue;
+ },
+ set(newVal) {
+ if (newVal !== widgetValue) {
+ widgetValue = newVal;
+ widgetLogic(node, w);
+ }
+ }
+ });
+ }
+ }
+}
+
+app.registerExtension({
+ name: "comfy.ttN.dynamicWidgets",
+
+ nodeCreated(node) {
+ if (node.getTitle() == "hiresfixScale" ||
+ node.getTitle() == "pipeLoader" ||
+ node.getTitle() == "pipeLoaderSDXL" ||
+ node.getTitle() == "pipeKSampler" ||
+ node.getTitle() == "pipeKSamplerAdvanced" ||
+ node.getTitle() == "pipeKSamplerSDXL" ||
+ node.getTitle() == "imageRemBG" ||
+ node.getTitle() == "imageOutput"||
+ node.getTitle() == "multiModelMerge" ||
+ node.getTitle() == "pipeLoraStack" ||
+ node.getTitle() == "pipeEncodeConcat") {
+ getSetters(node)
+ }
+ }
+});
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNembedAC.js b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNembedAC.js
new file mode 100644
index 0000000000000000000000000000000000000000..0e673c0c4e98e0e6b84f7b58dca13729b3892d31
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNembedAC.js
@@ -0,0 +1,170 @@
+// Imports specific objects from other modules.
+import { app } from "../../scripts/app.js";
+import { ttN_CreateDropdown, ttN_RemoveDropdown } from "./ttN.js";
+
+// Initialize some global lists and objects.
+let embeddingsList = [];
+let embeddingFiles = [];
+let embeddingsHierarchy = {};
+
+// Convert a list of strings into a hierarchical structure.
+function convertListToHierarchy(list) {
+ const hierarchy = {}; // Initialize an empty hierarchy object.
+
+ // Iterate over each item in the list.
+ for (var item of list) {
+ item = item.replace("embedding:", ""); // Remove any "embedding:" prefix from the item.
+ const parts = item.split(/:\\|\\/); // Split the item by either ':\' or '\'.
+ let currentNode = hierarchy; // Start at the root of the hierarchy.
+
+ // For each part of the split item...
+ parts.forEach((part, index) => {
+ // If it's the last part, set its value to null in the hierarchy.
+ if (index === parts.length - 1) {
+ currentNode[part] = null;
+ } else {
+ // Otherwise, initialize the node if it doesn't exist yet and move deeper into the hierarchy.
+ currentNode[part] = currentNode[part] || {};
+ currentNode = currentNode[part];
+ }
+ });
+ }
+
+ return hierarchy; // Return the filled hierarchy.
+}
+
+// Register an extension to the app.
+app.registerExtension({
+ name: "comfy.ttN.embeddingAC",
+ // Before a node definition is registered...
+ async beforeRegisterNodeDef(nodeType, nodeData, app) {
+ // If the node name matches a specific type...
+ if (nodeData.name === "ttN pipeKSampler") {
+ initializeEmbeddingData(nodeData.input.hidden.embeddingsList[0]);
+ }
+ },
+ // When a node is created...
+ nodeCreated(node) {
+ // If the node has widgets and its title isn't "xyPlot"...
+ if (node.widgets && node.getTitle() !== "xyPlot") {
+ const relevantWidgets = filterRelevantWidgets(node.widgets); // Filter out the relevant widgets.
+ addInputListenersToWidgets(relevantWidgets); // Add input listeners to these widgets.
+ }
+ }
+});
+
+// Returns a list of widgets that either have type "customtext" with dynamic prompts or just have dynamic prompts.
+function filterRelevantWidgets(widgets) {
+ return widgets.filter(widget => (widget.type === "customtext" && widget.dynamicPrompts !== false) || widget.dynamicPrompts);
+}
+
+// Adds input listeners to the given widgets.
+function addInputListenersToWidgets(widgets) {
+ widgets.forEach(widget => {
+ const inputHandler = createWidgetInputHandler(widget); // Create an input handler specific for this widget.
+ setWidgetInputHandler(widget, inputHandler); // Set this handler to the widget.
+ });
+}
+
+// Returns a function that will handle the widget's input.
+function createWidgetInputHandler(widget) {
+ return function handleInput() {
+ const currentWord = getCurrentWordFromInput(widget); // Get the word at the current cursor position in the widget's input.
+ // Check if the current word should trigger embedding suggestions...
+ if (shouldProvideEmbeddingSuggestion(currentWord)) {
+ const suggestions = filterEmbeddingsForInput(currentWord); // Get suggestions for the current word.
+ if (suggestions.length > 0) { // If there are suggestions...
+ // Convert the suggestions to a hierarchy and create a dropdown with these suggestions.
+ embeddingsHierarchy = convertListToHierarchy(suggestions);
+ ttN_CreateDropdown(widget.inputEl, embeddingsHierarchy, selectedSuggestion => {
+ // Update the widget's input value with the selected suggestion when one is chosen.
+ widget.inputEl.value = updateInputWithSuggestion(widget.inputEl.value, selectedSuggestion, widget);
+ }, true);
+ return;
+ }
+ }
+ // If no suggestions, remove any existing dropdown.
+ ttN_RemoveDropdown();
+ };
+}
+
+// Adds or replaces event listeners for the widget's input.
+function setWidgetInputHandler(widget, handler) {
+ ['input', 'mousedown'].forEach(event => {
+ // Remove any existing listeners and then add the new handler.
+ widget.inputEl.removeEventListener(event, handler);
+ widget.inputEl.addEventListener(event, handler);
+ });
+}
+
+// Returns the word at the current cursor position from the widget's input.
+function getCurrentWordFromInput(widget) {
+ const cursorPosition = widget.inputEl.selectionStart;
+ const segments = widget.inputEl.value.split(' ');
+ return segments[widget.inputEl.value.substring(0, cursorPosition).split(' ').length - 1].toLowerCase();
+}
+
+// Determines if the current word should trigger embedding suggestions.
+function shouldProvideEmbeddingSuggestion(word) {
+ const suggestionPrefix = 'embedding:';
+ return suggestionPrefix.startsWith(word) && word.length > 2 || word.startsWith(suggestionPrefix);
+}
+
+// Filters embeddings based on a specific word.
+function filterEmbeddingsForInput(input) {
+ const prefixes = ['embedding', 'embeddin', 'embeddi', 'embedd', 'embed', 'embe', 'emb']
+
+ let inputLowered = input.toLowerCase();
+ let cleanedInput = inputLowered.replace('embedding:', '');
+
+ prefixes.forEach(prefix => {
+ if (inputLowered.startsWith(prefix)) {
+ cleanedInput = cleanedInput.replace(prefix, '');
+ }
+ })
+
+ cleanedInput = cleanedInput.replace(/\//g, "\\");
+
+ return embeddingsList.filter(embedding => {
+ const embeddingName = getFileName(embedding).toLowerCase();
+ embedding = embedding.replace('embedding:', '').toLowerCase();
+ if (embeddingName.startsWith(cleanedInput) || embedding.startsWith(cleanedInput) || prefixes.includes(cleanedInput)) {
+ return true;
+ }
+ return false
+ });
+}
+
+function getFileName(path) {
+ const parts = path.split(/[\/:\\]/); // Split the path by '/' or ':'
+ const fileName = parts[parts.length - 1]; // Get the last part (filename with extension)
+ return fileName;
+}
+
+// Updates the widget's input text with a selected suggestion.
+function updateInputWithSuggestion(inputText, selectedSuggestion, widget) {
+ const cursorPosition = widget.inputEl.selectionStart;
+ const inputSegments = inputText.split(' ');
+ const cursorSegmentIndex = inputText.substring(0, cursorPosition).split(' ').length - 1;
+
+ if (inputSegments[cursorSegmentIndex].startsWith('emb')) {
+ inputSegments[cursorSegmentIndex] = 'embedding:' + selectedSuggestion;
+ }
+
+ return inputSegments.join(' ');
+}
+
+// Initializes data related to embeddings.
+function initializeEmbeddingData(initialEmbeddingsList) {
+ embeddingsList = initialEmbeddingsList;
+
+ embeddingsList.forEach(embedding => {
+ const fileName = embedding.split('\\').slice(-1)[0];
+ embeddingFiles.push(fileName);
+ });
+
+ embeddingsList = embeddingsList.map(embedding => {
+ const segments = embedding.split('/');
+ return segments.map((segment, index) => "embedding:" + segments.slice(0, index + 1).join('/'));
+ }).flat();
+}
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNfullscreen.js b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNfullscreen.js
new file mode 100644
index 0000000000000000000000000000000000000000..1333e605b222e2eac91a448eb2074ffa3714c46d
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNfullscreen.js
@@ -0,0 +1,540 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+const FULLSCREEN_WRAPPER_ID = "ttN-FullscreenWrapper";
+const FULLSCREEN_IMAGE_ID = "ttN-FullscreenImage";
+const IMAGE_PREVIEWS_WRAPPER_ID = "ttN-imagePreviewsWrapper";
+
+let ttN_isFullscreen = false;
+let ttN_FullscreenImage = new Image();
+let ttN_FullscreenImageIndex = 0;
+let ttN_FullscreenNode = null;
+let ttN_Slideshow = true;
+
+let ttN_srcDict = {};
+let ttN_imageElementsDict = {};
+
+loadSrcDict()
+
+const ARROW_KEYS = ['ArrowUp', 'ArrowDown', 'ArrowLeft', 'ArrowRight'];
+
+function saveSrcDict() {
+ sessionStorage.setItem('ttN_srcDict', JSON.stringify(ttN_srcDict));
+}
+
+function loadSrcDict() {
+ const savedData = sessionStorage.getItem('ttN_srcDict');
+ if (savedData) {
+ ttN_srcDict = JSON.parse(savedData);
+ }
+}
+
+function clearSrcDict() {
+ ttN_srcDict = {};
+ sessionStorage.removeItem('ttN_srcDict');
+}
+
+function _getSelectedNode() {
+ const graphcanvas = LGraphCanvas.active_canvas;
+ if (graphcanvas.selected_nodes && Object.keys(graphcanvas.selected_nodes).length === 1) {
+ return Object.values(graphcanvas.selected_nodes)[0];
+ }
+ return null;
+}
+
+function _findFullImageSRC(node) {
+ if (node.imgs) {
+ let img = node.imgs.find(imgElement => imgElement.src.includes("filename"));
+ return img ? img.src : null;
+ }
+ return null;
+}
+
+function _findLatentPreviewImageSRC(node) {
+ if (!node.imgs) return null;
+
+ if (node.imageIndex !== null && node.imageIndex < node.imgs.length) {
+ return node.imgs[node.imageIndex].src;
+ } else if (node.overIndex !== null && node.overIndex < node.imgs.length) {
+ return node.imgs[node.overIndex].src;
+ }
+ return null;
+}
+
+function _initiateFullscreen(Element) {
+ if (Element.requestFullscreen) {
+ return Element.requestFullscreen();
+ } else if (Element.mozRequestFullScreen) {
+ return Element.mozRequestFullScreen();
+ } else if (Element.webkitRequestFullscreen) {
+ return Element.webkitRequestFullscreen();
+ } else if (Element.msRequestFullscreen) {
+ return Element.msRequestFullscreen();
+ }
+}
+
+function _applyTranslation(Element, Index, List) {
+ let translationConst = (Index / (List.length - 1)) * 100;
+
+ translationConst = translationConst - (0.5 / (List.length - 1)) * 100
+
+ if (Index === 0) { translationConst = 0; }
+ Element.style.transform = 'translateX(-' + translationConst + '%)';
+}
+
+function _handleArrowKeys(e) {
+ e.stopPropagation();
+
+ const FullscreenWrapper = document.getElementById(FULLSCREEN_WRAPPER_ID);
+ const imagePreviewsWrapper = document.getElementById(IMAGE_PREVIEWS_WRAPPER_ID);
+ const imageList = ttN_srcDict[ttN_FullscreenNode.id] || [];
+ const comfyMenu = document.getElementsByClassName("comfy-menu")[0]
+ const litegraph = document.getElementsByClassName("litegraph")[0]
+
+
+ switch (e.code) {
+ case 'ArrowLeft':
+ if (e.ctrlKey) {
+ ttN_FullscreenImageIndex = 0;
+ break;
+ }
+
+ if (e.shiftKey) {
+ ttN_FullscreenImageIndex -= 5;
+ if (ttN_FullscreenImageIndex < 0) ttN_FullscreenImageIndex = 0
+ break;
+ }
+
+ ttN_FullscreenImageIndex -= 1;
+ if (ttN_FullscreenImageIndex < 0) ttN_FullscreenImageIndex = 0
+ break;
+
+ case 'ArrowRight':
+ if (e.ctrlKey) {
+ ttN_FullscreenImageIndex = imageList.length - 1;
+ break;
+ }
+
+ if (e.shiftKey) {
+ ttN_FullscreenImageIndex += 5;
+ if (ttN_FullscreenImageIndex > imageList.length - 1) ttN_FullscreenImageIndex = imageList.length - 1
+ break;
+ }
+
+ ttN_FullscreenImageIndex += 1;
+ if (ttN_FullscreenImageIndex > imageList.length - 1) ttN_FullscreenImageIndex = imageList.length - 1
+ break;
+
+ case 'ArrowUp':
+ if (imagePreviewsWrapper) {
+ if (imagePreviewsWrapper.style.display === 'none') {
+ FullscreenWrapper.append(comfyMenu)
+ imagePreviewsWrapper.style.display = 'flex'
+ } else {
+ litegraph.append(comfyMenu)
+ imagePreviewsWrapper.style.display = 'none'
+ }
+ }
+ break;
+
+ case 'ArrowDown':
+ ttN_Slideshow = !ttN_Slideshow;
+
+ if (ttN_Slideshow) {
+ ttN_FullscreenImageIndex = -1;
+ imagePreviewsWrapper.style.display = 'none'
+ litegraph.append(comfyMenu)
+ } else {
+ imagePreviewsWrapper.style.display = 'flex'
+ FullscreenWrapper.append(comfyMenu)
+ }
+ break;
+ }
+ _applyTranslation(imagePreviewsWrapper, ttN_FullscreenImageIndex, imageList);
+ updateImageElements()
+}
+
+function _handleWheelEvent(e) {
+ e.stopPropagation();
+
+ var InvertMenuScrolling = localStorage.getItem('Comfy.Settings.Comfy.InvertMenuScrolling')
+ console.log("InvertMenuScrolling", InvertMenuScrolling)
+
+ if (InvertMenuScrolling === "true") {
+ console.log('yes')
+ if (e.deltaY > 0) {
+ // Scrolling down
+ ttN_FullscreenImageIndex += 1;
+ } else if (e.deltaY < 0) {
+ // Scrolling up
+ ttN_FullscreenImageIndex -= 1;
+ if (ttN_FullscreenImageIndex < 0) ttN_FullscreenImageIndex = 0;
+ }
+ } else {
+ console.log('no')
+ if (e.deltaY < 0) {
+ // Scrolling down
+ ttN_FullscreenImageIndex += 1;
+ } else if (e.deltaY > 0) {
+ // Scrolling up
+ ttN_FullscreenImageIndex -= 1;
+ if (ttN_FullscreenImageIndex < 0) ttN_FullscreenImageIndex = 0;
+ }
+ }
+ updateImageElements()
+}
+
+function _handleEscapeKey(e) {
+ e.stopPropagation();
+ LGraphCanvas.prototype.ttNcloseFullscreen();
+}
+
+function _handleExecutedEvent(event) {
+ setTimeout(updateImageTLDE, 500);
+}
+
+function _handleReconnectingEvent(e) {
+ clearSrcDict();
+ sessionStorage.removeItem('Comfy.Settings.ttN.default_fullscreen_node');
+ ttN_imageElementsDict = {};
+}
+
+function _triggerFullscreen(node) {
+ updateImageTLDE();
+ ttN_FullscreenImageIndex = -1;
+ LGraphCanvas.prototype.ttNcreateFullscreen(node);
+ ttN_FullscreenNode = node;
+}
+
+function ttNfullscreenEventListener(e) {
+ if (!ttN_isFullscreen) {
+ if ((e.code === 'ArrowUp' && e.shiftKey) && !e.ctrlKey) {
+ e.stopPropagation();
+
+ let selected_node = _getSelectedNode();
+ if (selected_node) {
+ _triggerFullscreen(selected_node);
+ return
+ }
+
+ let defaultNodeID = JSON.parse(sessionStorage.getItem('Comfy.Settings.ttN.default_fullscreen_node'));
+ if (defaultNodeID) {
+ let defaultNode = app.graph._nodes_by_id[defaultNodeID];
+ if (defaultNode) {
+ _triggerFullscreen(defaultNode);
+ return
+ }
+ }
+ }
+
+ return;
+ }
+
+ e.stopPropagation();
+
+ updateImageTLDE();
+
+ const imagePreviewsWrapper = document.getElementById(IMAGE_PREVIEWS_WRAPPER_ID);
+ const imageList = ttN_srcDict[ttN_FullscreenNode.id] || [];
+
+ switch (e.type) {
+ case 'wheel':
+ _handleWheelEvent(e);
+ _applyTranslation(imagePreviewsWrapper, ttN_FullscreenImageIndex, imageList);
+ break;
+ case 'keydown':
+ if (ARROW_KEYS.includes(e.code)) {
+ _handleArrowKeys(e);
+ _applyTranslation(imagePreviewsWrapper, ttN_FullscreenImageIndex, imageList);
+ } else if (e.code === 'Escape') {
+ _handleEscapeKey(e);
+ _applyTranslation(imagePreviewsWrapper, ttN_FullscreenImageIndex, imageList);
+ }
+ break;
+ }
+}
+
+function updateImageTLDE() {
+ for (let node of app.graph._nodes) {
+ if (!node.imgs) continue
+
+ let imgSrc = _findFullImageSRC(node);
+ if (!imgSrc) continue;
+
+ _removeLatentPreviewImageSRC(node, imgSrc)
+
+ ttN_srcDict[node.id] = ttN_srcDict[node.id] || [];
+
+ let index = ttN_srcDict[node.id].length;
+
+ if (!ttN_srcDict[node.id].includes((index, imgSrc))) {
+ ttN_srcDict[node.id].push((index, imgSrc));
+ if (ttN_Slideshow) {
+ updateImageElements(index);
+ }
+ }
+
+ }
+ saveSrcDict();
+ updateImageElements();
+};
+
+function _getImageDivFromSrc(imgSrc, index) {
+ // If image element doesn't exist, create it
+ if (!ttN_imageElementsDict[imgSrc]) {
+ const imgWrapper = document.createElement('div');
+ imgWrapper.classList.add('ttN-imgWrapper');
+
+ const imgElement = document.createElement('img');
+ imgElement.src = imgSrc;
+ imgElement.classList.add('ttN-img');
+ imgWrapper.appendChild(imgElement);
+
+ imgElement.addEventListener('click', () => {
+ ttN_FullscreenImageIndex = index;
+ updateImageElements(index);
+ });
+
+ ttN_imageElementsDict[imgSrc] = imgWrapper;
+ }
+ return ttN_imageElementsDict[imgSrc];
+}
+
+function _removeLatentPreviewImageSRC(node, imgSrc) {
+ let latentPreviewSrc = _findLatentPreviewImageSRC(node);
+ if (imgSrc && latentPreviewSrc) {
+ for (let i in node.imgs) {
+ if(!node.imgs[i].src.includes("filename")) {
+ node.imgs.splice(i, 1);
+ }
+ }
+ }
+}
+
+function _handleLatentPreview(imgDivList) {
+ let latentPreview = _findLatentPreviewImageSRC(ttN_FullscreenNode);
+ if (latentPreview && !latentPreview.includes("filename") && ttN_FullscreenImageIndex === imgDivList.length - 1) {
+ ttN_FullscreenImage.src = latentPreview
+ }
+}
+
+function updateImageElements(indexOverride = null) {
+ if (!ttN_isFullscreen) return;
+
+ const srcList = ttN_srcDict[ttN_FullscreenNode.id] || null;
+ if (!srcList) return;
+
+ const fullscreenWrapper = document.getElementById(FULLSCREEN_WRAPPER_ID);
+ if (!fullscreenWrapper) return
+
+ const imgDivList = srcList.map((src, index) => _getImageDivFromSrc(src, index));
+
+ ttN_FullscreenImageIndex = indexOverride || ttN_FullscreenImageIndex
+ if ((ttN_FullscreenImageIndex > imgDivList.length - 1) || (ttN_FullscreenImageIndex === -1)) {
+ ttN_FullscreenImageIndex = imgDivList.length - 1
+ }
+ if (ttN_FullscreenImageIndex < -1) ttN_FullscreenImageIndex = 0
+
+ ttN_FullscreenImage.src = imgDivList[ttN_FullscreenImageIndex].children[0].src;
+
+ const previewsWrapper = document.getElementById(IMAGE_PREVIEWS_WRAPPER_ID);
+ if (!previewsWrapper) return
+
+ if (ttN_Slideshow) {
+ fullscreenWrapper.classList.add('ttN-slideshow')
+ _handleLatentPreview(imgDivList);
+ } else {
+ fullscreenWrapper.classList.remove('ttN-slideshow')
+ }
+
+ if (ttN_FullscreenImageIndex > imgDivList.length - 1) ttN_FullscreenImageIndex = imgDivList.length - 1;
+ if (ttN_FullscreenImageIndex < 0) ttN_FullscreenImageIndex = 0;
+
+ imgDivList.forEach((imgDiv, index) => {
+ if (previewsWrapper.children[index] != imgDiv) previewsWrapper.appendChild(imgDiv);
+
+ const orderValue = index - ttN_FullscreenImageIndex;
+ imgDiv.style.order = orderValue;
+
+ if (index < ttN_FullscreenImageIndex) {
+ // For images before the selected image
+ imgDiv.classList.remove('ttN-divSelected', 'ttN-divAfter');
+ imgDiv.children[0].classList.remove('ttN-imgSelected', 'ttN-imgAfter');
+
+ imgDiv.classList.add('ttN-divBefore');
+ //imgDiv.children[0].classList.add('ttN-imgBefore');
+ }
+ else if (index === ttN_FullscreenImageIndex) {
+ // For the selected image
+ imgDiv.classList.remove('ttN-divBefore', 'ttN-divAfter');
+ imgDiv.children[0].classList.remove('ttN-imgBefore', 'ttN-imgAfter');
+
+ imgDiv.classList.add('ttN-divSelected');
+ imgDiv.children[0].classList.add('ttN-imgSelected');
+ }
+ else if (index > ttN_FullscreenImageIndex) {
+ // For images after the selected image
+ imgDiv.classList.remove('ttN-divSelected', 'ttN-divBefore');
+ imgDiv.children[0].classList.remove('ttN-imgSelected', 'ttN-imgBefore');
+
+ imgDiv.classList.add('ttN-divAfter');
+ //imgDiv.children[0].classList.add('ttN-imgAfter');
+ }
+ });
+
+ _applyTranslation(previewsWrapper, ttN_FullscreenImageIndex, imgDivList);
+}
+
+api.addEventListener("status", _handleExecutedEvent);
+api.addEventListener("progress", _handleExecutedEvent);
+api.addEventListener("execution_cached", _handleExecutedEvent);
+api.addEventListener("reconnecting", _handleReconnectingEvent);
+
+app.registerExtension({
+ name: "comfy.ttN.fullscreen",
+ init() {
+ document.addEventListener("keydown", ttNfullscreenEventListener, true);
+ },
+ setup() {
+ LGraphCanvas.prototype.ttNcreateFullscreen = function (node) {
+ if (!node || ttN_isFullscreen) return;
+
+ document.addEventListener("wheel", ttNfullscreenEventListener, true);
+
+ const fullscreenWrapper = document.createElement('div');
+ fullscreenWrapper.id = FULLSCREEN_WRAPPER_ID;
+ fullscreenWrapper.classList.add('ttN-slideshow');
+ document.body.appendChild(fullscreenWrapper);
+
+ const fullscreenImage = new Image();
+ fullscreenImage.src = _findFullImageSRC(node) || _findLatentPreviewImageSRC(node) || '';
+ fullscreenImage.id = FULLSCREEN_IMAGE_ID;
+ fullscreenWrapper.appendChild(fullscreenImage);
+
+ const previewsWrapper = document.createElement('div');
+ previewsWrapper.id = IMAGE_PREVIEWS_WRAPPER_ID;
+ previewsWrapper.style.display = 'none';
+
+ fullscreenWrapper.appendChild(previewsWrapper);
+
+ _initiateFullscreen(fullscreenWrapper).then(() => {
+ ttN_isFullscreen = true;
+ ttN_FullscreenImage = fullscreenImage;
+ updateImageElements();
+ }).catch(err => {
+ console.error("Error attempting to enable full-screen mode:", err.message, err.name);
+ });
+
+ fullscreenWrapper.onfullscreenchange = function (event) {
+ if (!document.fullscreenElement) {
+ LGraphCanvas.prototype.ttNcloseFullscreen();
+ }
+ };
+ }
+
+ LGraphCanvas.prototype.ttNcloseFullscreen = function () {
+ if (!ttN_isFullscreen) return;
+
+ const comfyMenu = document.getElementsByClassName("comfy-menu")[0]
+ const litegraph = document.getElementsByClassName("litegraph")[0]
+ litegraph.append(comfyMenu);
+
+ const fullscreenWrapper = document.getElementById(FULLSCREEN_WRAPPER_ID);
+ document.body.removeChild(fullscreenWrapper);
+
+ document.removeEventListener("wheel", ttNfullscreenEventListener, true);
+
+ ttN_isFullscreen = false;
+ }
+
+ const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions;
+ LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
+ const options = getNodeMenuOptions.apply(this, arguments);
+ node.setDirtyCanvas(true, true);
+
+ options.splice(options.length - 1, 0,
+ {
+ content: "Fullscreen (ttN)",
+ callback: () => { LGraphCanvas.prototype.ttNcreateFullscreen(node) }
+ },
+ {
+ content: "Set Default Fullscreen Node (ttN)",
+ callback: function () {
+ let selectedNode = _getSelectedNode();
+ if (selectedNode) {
+ sessionStorage.setItem('Comfy.Settings.ttN.default_fullscreen_node', JSON.stringify(selectedNode.id));
+ }
+ }
+ },
+ {
+ content: "Clear Default Fullscreen Node (ttN)",
+ callback: function () {
+ sessionStorage.removeItem('Comfy.Settings.ttN.default_fullscreen_node');
+ }
+ },
+ null
+ );
+
+ return options;
+ };
+ }
+});
+
+var styleElement = document.createElement("style");
+const cssCode = `
+
+#ttN-FullscreenWrapper {
+ display: flex;
+ justify-content: center;
+ align-items: end;
+ background-color: #1f1f1f;
+}
+
+#ttN-FullscreenImage {
+ height: inherit;
+ position: absolute;
+}
+
+#ttN-imagePreviewsWrapper {
+ position: absolute;
+ width: max-content;
+ z-index: 1;
+ height: 14vh;
+ left: 50vw;
+ transition: transform 0.2s ease;
+}
+
+.ttN-imgWrapper {
+ position: sticky;
+ transition: transform 0.2s ease;
+ align-self: end;
+}
+
+.ttN-img {
+ height: 121px;
+ margin: 7px;
+ cursor: pointer;
+ display: block;
+ border: 3px solid rgba(255,255,255);
+ box-shadow: 0px 0px 0px 10px;
+ transition: all 0.4s ease;
+}
+
+.ttN-img:hover {
+ transform: scale(1.1);
+ z-index: 1;
+}
+
+.ttN-imgSelected {
+ height: 200px!important;
+ z-index: 1;
+ transition: 0.1s;
+}
+.ttN-slideshow {
+ background: black!important;
+}
+
+`;
+
+styleElement.innerHTML = cssCode
+document.head.appendChild(styleElement);
\ No newline at end of file
diff --git a/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNinterface.js b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNinterface.js
new file mode 100644
index 0000000000000000000000000000000000000000..d30cf02d2c48a3faee5aa7596fc9dcd9acf8b5dd
--- /dev/null
+++ b/ComfyUI/custom_nodes/ComfyUI_tinyterraNodes/js/ttNinterface.js
@@ -0,0 +1,587 @@
+import { app } from "../../scripts/app.js";
+
+const customPipeLineLink = "#7737AA"
+const customPipeLineSDXLLink = "#0DC52B"
+const customIntLink = "#29699C"
+const customXYPlotLink = "#74DA5D"
+
+var customLinkColors = JSON.parse(localStorage.getItem('Comfy.Settings.ttN.customLinkColors')) || {};
+if (!customLinkColors["PIPE_LINE"] || !LGraphCanvas.link_type_colors["PIPE_LINE"]) {customLinkColors["PIPE_LINE"] = customPipeLineLink;}
+if (!customLinkColors["PIPE_LINE_SDXL"] || !LGraphCanvas.link_type_colors["PIPE_LINE_SDXL"]) {customLinkColors["PIPE_LINE_SDXL"] = customPipeLineSDXLLink;}
+if (!customLinkColors["INT"] || !LGraphCanvas.link_type_colors["INT"]) {customLinkColors["INT"] = customIntLink;}
+if (!customLinkColors["XYPLOT"] || !LGraphCanvas.link_type_colors["XYPLOT"]) {customLinkColors["XYPLOT"] = customXYPlotLink;}
+
+localStorage.setItem('Comfy.Settings.ttN.customLinkColors', JSON.stringify(customLinkColors));
+
+let ttNisFullscreen = false;
+let ttNcurrentFullscreenImage = null;
+let ttNcurrentNode = null;
+
+app.registerExtension({
+ name: "comfy.ttN.interface",
+ init() {
+ function adjustToGrid(val, gridSize) {
+ return Math.round(val / gridSize) * gridSize;
+ }
+
+ function moveNodeBasedOnKey(e, node, gridSize, shiftMult) {
+ switch (e.code) {
+ case 'ArrowUp':
+ node.pos[1] -= gridSize * shiftMult;
+ break;
+ case 'ArrowDown':
+ node.pos[1] += gridSize * shiftMult;
+ break;
+ case 'ArrowLeft':
+ node.pos[0] -= gridSize * shiftMult;
+ break;
+ case 'ArrowRight':
+ node.pos[0] += gridSize * shiftMult;
+ break;
+ }
+ node.setDirtyCanvas(true, true);
+ }
+
+ function keyMoveNode(e, node) {
+ let gridSize = JSON.parse(localStorage.getItem('Comfy.Settings.Comfy.SnapToGrid.GridSize'));
+ gridSize = gridSize ? parseInt(gridSize) : 1;
+ let shiftMult = e.shiftKey ? 10 : 1;
+
+ node.pos[0] = adjustToGrid(node.pos[0], gridSize);
+ node.pos[1] = adjustToGrid(node.pos[1], gridSize);
+
+ moveNodeBasedOnKey(e, node, gridSize, shiftMult);
+ }
+
+ function getSelectedNodes(e) {
+ const inputField = e.composedPath()[0];
+ if (inputField.tagName === "TEXTAREA") return;
+ if (e.ctrlKey && ['ArrowUp', 'ArrowDown', 'ArrowLeft', 'ArrowRight'].includes(e.code)) {
+ let graphcanvas = LGraphCanvas.active_canvas;
+ for (let node in graphcanvas.selected_nodes) {
+ keyMoveNode(e, graphcanvas.selected_nodes[node]);
+ }
+ }
+ }
+
+ window.addEventListener("keydown", getSelectedNodes, true);
+
+ LGraphCanvas.prototype.ttNcreateDialog = function (htmlContent, onOK, onCancel) {
+ var dialog = document.createElement("div");
+ dialog.is_modified = false;
+ dialog.className = "ttN-dialog";
+ dialog.innerHTML = htmlContent + "";
+
+ dialog.close = function() {
+ if (dialog.parentNode) {
+ dialog.parentNode.removeChild(dialog);
+ }
+ };
+
+ var inputs = Array.from(dialog.querySelectorAll("input, select"));
+
+ inputs.forEach(input => {
+ input.addEventListener("keydown", function(e) {
+ dialog.is_modified = true;
+ if (e.keyCode == 27) { // ESC
+ onCancel && onCancel();
+ dialog.close();
+ } else if (e.keyCode == 13) { // Enter
+ onOK && onOK(dialog, inputs.map(input => input.value));
+ dialog.close();
+ } else if (e.keyCode != 13 && e.target.localName != "textarea") {
+ return;
+ }
+ e.preventDefault();
+ e.stopPropagation();
+ });
+ });
+
+ var graphcanvas = LGraphCanvas.active_canvas;
+ var canvas = graphcanvas.canvas;
+
+ var rect = canvas.getBoundingClientRect();
+ var offsetx = -20;
+ var offsety = -20;
+ if (rect) {
+ offsetx -= rect.left;
+ offsety -= rect.top;
+ }
+
+ if (event) {
+ dialog.style.left = event.clientX + offsetx + "px";
+ dialog.style.top = event.clientY + offsety + "px";
+ } else {
+ dialog.style.left = canvas.width * 0.5 + offsetx + "px";
+ dialog.style.top = canvas.height * 0.5 + offsety + "px";
+ }
+
+ var button = dialog.querySelector("#ok");
+ button.addEventListener("click", function() {
+ onOK && onOK(dialog, inputs.map(input => input.value));
+ dialog.close();
+ });
+
+ canvas.parentNode.appendChild(dialog);
+
+ if(inputs) inputs[0].focus();
+
+ var dialogCloseTimer = null;
+ dialog.addEventListener("mouseleave", function(e) {
+ if(LiteGraph.dialog_close_on_mouse_leave)
+ if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave)
+ dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close();
+ });
+ dialog.addEventListener("mouseenter", function(e) {
+ if(LiteGraph.dialog_close_on_mouse_leave)
+ if(dialogCloseTimer) clearTimeout(dialogCloseTimer);
+ });
+
+ return dialog;
+ };
+
+ LGraphCanvas.prototype.ttNsetNodeDimension = function (node) {
+ const nodeWidth = node.size[0];
+ const nodeHeight = node.size[1];
+
+ let input_html = "";
+ input_html += "";
+
+ LGraphCanvas.prototype.ttNcreateDialog("Width/Height" + input_html,
+ function(dialog, values) {
+ var widthValue = Number(values[0]) ? values[0] : nodeWidth;
+ var heightValue = Number(values[1]) ? values[1] : nodeHeight;
+ let sz = node.computeSize();
+ node.setSize([Math.max(sz[0], widthValue), Math.max(sz[1], heightValue)]);
+ if (dialog.parentNode) {
+ dialog.parentNode.removeChild(dialog);
+ }
+ node.setDirtyCanvas(true, true);
+ },
+ null
+ );
+ };
+
+ LGraphCanvas.prototype.ttNsetSlotTypeColor = function(slot){
+ var slotColor = LGraphCanvas.link_type_colors[slot.output.type].toUpperCase();
+ var slotType = slot.output.type;
+ // Check if the color is in the correct format
+ if (!/^#([0-9A-F]{3}){1,2}$/i.test(slotColor)) {
+ slotColor = "#FFFFFF";
+ }
+
+ // Check if browser supports color input type
+ var inputType = "color";
+ var inputID = " id='colorPicker'";
+ var inputElem = document.createElement("input");
+ inputElem.setAttribute("type", inputType);
+ if (inputElem.type !== "color") {
+ // If it doesn't, fall back to text input
+ inputType = "text";
+ inputID = " ";
+ }
+
+ let input_html = "$\Large\color{white}{Nodes}$
+ +## ttN/pipe + +
+
+
+pipeLoader
+ +(Modified from [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui) and [ADV_CLIP_emb](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb)) + +Combination of Efficiency Loader and Advanced CLIP Text Encode with an additional pipe output ++ _**Inputs -** model, vae, clip skip, (lora1, modelstrength clipstrength), (Lora2, modelstrength clipstrength), (Lora3, modelstrength clipstrength), (positive prompt, token normalization, weight interpretation), (negative prompt, token normalization, weight interpretation), (latent width, height), batch size, seed_ ++ _**Outputs -** pipe, model, conditioning, conditioning, samples, vae, clip, seed_ +
+ 
+
+With pipeLoader and pipeKSampler:
+
+
+
+
+pipeKSampler
+ +(Modified from [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui) and [QOLS_Omar92](https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92)) + +Combination of Efficiency Loader and Advanced CLIP Text Encode with an additional pipe output ++ _**Inputs -** pipe, (optional pipe overrides), xyplot, (Lora, model strength, clip strength), (upscale method, factor, crop), sampler state, steps, cfg, sampler name, scheduler, denoise, (image output [None, Preview, Save]), Save_Prefix, seed_ ++ _**Outputs -** pipe, model, conditioning, conditioning, samples, vae, clip, image, seed_ + +Old node layout: + +
+
+
+ pipeKSamplerAdvanced
+ +Combination of Efficiency Loader and Advanced CLIP Text Encode with an additional pipe output ++ _**Inputs -** pipe, (optional pipe overrides), xyplot, (Lora, model strength, clip strength), (upscale method, factor, crop), sampler state, steps, cfg, sampler name, scheduler, starts_at_step, return_with_leftover_noise, (image output [None, Preview, Save]), Save_Prefix_ ++ _**Outputs -** pipe, model, conditioning, conditioning, samples, vae, clip, image, seed_ + +
+
+
+pipeLoaderSDXL
+ +SDXL Loader and Advanced CLIP Text Encode with an additional pipe output ++ _**Inputs -** model, vae, clip skip, (lora1, modelstrength clipstrength), (Lora2, modelstrength clipstrength), model, vae, clip skip, (lora1, modelstrength clipstrength), (Lora2, modelstrength clipstrength), (positive prompt, token normalization, weight interpretation), (negative prompt, token normalization, weight interpretation), (latent width, height), batch size, seed_ ++ _**Outputs -** sdxlpipe, model, conditioning, conditioning, vae, model, conditioning, conditioning, vae, samples, clip, seed_ +
+ 
+
+With pipeLoaderSDXL and pipeKSamplerSDXL:
+
+
+
+
+pipeKSamplerSDXL
+ +SDXL Sampler (base and refiner in one) and Advanced CLIP Text Encode with an additional pipe output ++ _**Inputs -** sdxlpipe, (optional pipe overrides), (upscale method, factor, crop), sampler state, base_steps, refiner_steps cfg, sampler name, scheduler, (image output [None, Preview, Save]), Save_Prefix, seed_ ++ _**Outputs -** pipe, model, conditioning, conditioning, vae, model, conditioning, conditioning, vae, samples, clip, image, seed_ + +Old node layout: + +
+
+
+pipeIN
+ +Encode up to 8 frequently used inputs into a single Pipe line. ++ _**Inputs -** model, conditioning, conditioning, samples, vae, clip, image, seed_ ++ _**Outputs -** pipe_ +
+
+
+pipeOUT
+ +Decode single Pipe line into the 8 original outputs, AND a Pipe throughput. ++ _**Inputs -** pipe_ ++ _**Outputs -** model, conditioning, conditioning, samples, vae, clip, image, seed, pipe_ +
+
+
+pipeEDIT
+ +Update/Overwrite any of the 8 original inputs in a Pipe line with new information. ++ _**Inputs -** pipe, model, conditioning, conditioning, samples, vae, clip, image, seed_ ++ _**Outputs -** pipe_ +
+
+
+pipe > basic_pipe
+ +Convert ttN pipe line to basic pipe (to be compatible with [ImpactPack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)), WITH original pipe throughput ++ _**Inputs -** pipe[model, conditioning, conditioning, samples, vae, clip, image, seed]_ ++ _**Outputs -** basic_pipe[model, clip, vae, conditioning, conditioning], pipe_ +
+
+
+pipe > Detailer Pipe
+ +Convert ttN pipe line to detailer pipe (to be compatible with [ImpactPack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)), WITH original pipe throughput ++ _**Inputs -** pipe[model, conditioning, conditioning, samples, vae, clip, image, seed], bbox_detector, sam_model_opt_ ++ _**Outputs -** detailer_pipe[model, vae, conditioning, conditioning, bbox_detector, sam_model_opt], pipe_ +
+
+
+## ttN/image
+
+pipe > xyPlot
+ +pipeKSampler input to generate xy plots using sampler and loader values. (Any values not set by xyPlot will be taken from the corresponding pipeKSampler or pipeLoader) ++ _**Inputs -** grid_spacing, latent_id, flip_xy, x_axis, x_values, y_axis, y_values_ ++ _**Outputs -** xyPlot_ +
+
+
+imageOutput
+ +Preview or Save an image with one node, with image throughput. ++ _**Inputs -** image, image output[Hide, Preview, Save, Hide/Save], output path, save prefix, number padding[None, 2-9], file type[PNG, JPG, JPEG, BMP, TIFF, TIF] overwrite existing[True, False], embed workflow[True, False]_ ++ _**Outputs -** image_ + +
+
+
+imageRemBG
+ +(Using [RemBG](https://github.com/danielgatis/rembg)) + +Background Removal node with optional image preview & save. ++ _**Inputs -** image, image output[Disabled, Preview, Save], save prefix_ ++ _**Outputs -** image, mask_ + +Example of a photobashing workflow using pipeNodes, imageRemBG, imageOutput and nodes from [ADV_CLIP_emb](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb) and [ImpactPack](https://github.com/ltdrdata/ComfyUI-Impact-Pack/tree/Main): + + +
+
+
+## ttN/text
+hiresFix
+ +Upscale image by model, optional rescale of result image. ++ _**Inputs -** image, vae, upscale_model, rescale_after_model[true, false], rescale[by_percentage, to Width/Height], rescale method[nearest-exact, bilinear, area], factor, width, height, crop, image_output[Hide, Preview, Save], save prefix, output_latent[true, false]_ ++ _**Outputs -** image, latent_ +
+
+
+text
+ +Basic TextBox Loader. ++ _**Outputs -** text (STRING)_ +
+
+
+textDebug
+ +Text input, to display text inside the node, with optional print to console. ++ _**inputs -** text, print_to_console_ ++ _**Outputs -** text (STRING)_ +
+
+
+textConcat
+ +3 TextBOX inputs with a single concatenated output. ++ _**inputs -** text1, text2, text3 (STRING's), delimiter_ ++ _**Outputs -** text (STRING)_ +
+
+
+7x TXT Loader Concat
+ +7 TextBOX inputs concatenated with spaces into a single output, AND seperate text outputs. ++ _**inputs -** text1, text2, text3, text4, text5, text6, text7 (STRING's), delimiter_ ++ _**Outputs -** text1, text2, text3, text4, text5, text6, text7, concat (STRING's)_ +
+
+
+## ttN/util
+3x TXT Loader MultiConcat
+ +3 TextBOX inputs with seperate text outputs AND multiple concatenation variations (concatenated with spaces). ++ _**inputs -** text1, text2, text3 (STRING's), delimiter_ ++ _**Outputs -** text1, text2, text3, 1 & 2, 1 & 3, 2 & 3, concat (STRING's)_ +
+
+
+seed
+ +Basic Seed Loader. ++ _**Outputs -** seed (INT)_ +
+
+
+float
+ +float loader and converter ++ _**inputs -** float (FLOAT)_ ++ _**Outputs -** float, int, text (FLOAT, INT, STRING)_ +
+
+
+ int
+ +int loader and converter ++ _**inputs -** int (INT)_ ++ _**Outputs -** int, float, text (INT, FLOAT, STRING)_ ++[Efficiency Nodes Wiki](https://github.com/jags111/efficiency-nodes-comfyui/wiki)
+ +### Nodes: + +
+
+
+Efficient Loader & Eff. Loader SDXL
+-
+
- Nodes that can load & cache Checkpoint, VAE, & LoRA type models. (cache settings found in config file 'node_settings.json') +
- Able to apply LoRA & Control Net stacks via their
lora_stackandcnet_stackinputs.
+ - Come with positive and negative prompt text boxes. You can also set the way you want the prompt to be encoded via the
token_normalizationandweight_interpretationwidgets.
+ - These node's also feature a variety of custom menu options as shown below.
+
+ note: "🔍 View model info..." requires ComfyUI-Custom-Scripts to be installed to function.
+ - These loaders are used by the XY Plot node for many of its plot type dependencies. +
+ 
+ 
+
+
+
+KSampler (Efficient), KSampler Adv. (Efficient), KSampler SDXL (Eff.)
+ +- Modded KSamplers with the ability to live preview generations and/or vae decode images. +- Feature a special seed box that allows for a clearer management of seeds. (-1 seed to apply the selected seed behavior) +- Can execute a variety of scripts, such as the XY Plot script. To activate thescript, simply connect the input connection.
+
+
+ .png)
+
+ .png)
+
+ .png)
+
+
+
+
+Script Nodes
+ +- A group of node's that are used in conjuction with the Efficient KSamplers to execute a variety of 'pre-wired' set of actions. +- Script nodes can be chained if their input/outputs allow it. Multiple instances of the same Script Node in a chain does nothing. +
+ 
+
+
+
+ XY Plot
+-
+
- Node that allows users to specify parameters for the Efficiency KSamplers to plot on a grid. +
+ 
+
+
+
+ HighRes-Fix
+-
+
- Node that the gives user the ability to upscale KSampler results through variety of different methods. +
- Comes out of the box with popular Neural Network Latent Upscalers such as Ttl's ComfyUi_NNLatentUpscale and City96's SD-Latent-Upscaler. +
- Supports ControlNet guided latent upscaling. (You must have Fannovel's comfyui_controlnet_aux installed to unlock this feature) +
- Local models---The node pulls the required files from huggingface hub by default. You can create a models folder and place the modules there if you have a flaky connection or prefer to use it completely offline, it will load them locally instead. The path should be: ComfyUI/custom_nodes/efficiency-nodes-comfyui/models; Alternatively, just clone the entire HF repo to it: (git clone https://huggingface.co/city96/SD-Latent-Upscaler) to ComfyUI/custom_nodes/efficiency-nodes-comfyui/models +
+ 
+
+
+
+ Noise Control
+-
+
- This node gives the user the ability to manipulate noise sources in a variety of ways, such as the sampling's RNG source. +
- The CFG Denoiser noise hijack was developed by smZ, it allows you to get closer recreating Automatic1111 results. + Note: The CFG Denoiser does not work with a variety of conditioning types such as ControlNet & GLIGEN +
- This node also allows you to add noise Seed Variations to your generations. +
- For trying to replicate Automatic1111 images, this node will help you achieve it. Encode your prompt using "length+mean"
token_normalizationwith "A1111"weight_interpretation, set the Noise Control Script node'srng_sourceto "gpu", and turn thecfg_denoiserto true.
+
+ 
+
+
+
+ Tiled Upscaler
+-
+
- The Tiled Upscaler script attempts to encompas BlenderNeko's ComfyUI_TiledKSampler workflow into 1 node. +
- Script supports Tiled ControlNet help via the options. +
- Strongly recommend the
preview_methodbe "vae_decoded_only" when running the script.
+
+ 
+
+
+AnimateDiff
+-
+
- To unlock the AnimateDiff script it is required you have installed Kosinkadink's ComfyUI-AnimateDiff-Evolved. +
- The latent
batch_sizewhen running this script becomes your frame count.
+
+ 
+
+
+
+Image Overlay
+-
+
- Node that allows for flexible image overlaying. Works also with image batches. +
+ 
+
+
+
+
+SimpleEval Nodes
+-
+
- A collection of nodes that allows users to write simple Python expressions for a variety of data types using the simpleeval library. +
- To activate you must have installed the simpleeval library in your Python workspace. +
pip install simpleeval+ +
+ 
+
+ 
+
+ 
+
+
+
+## Workflow Examples:
+
+Kindly load all PNG files in same name in the (workflow driectory) to comfyUI to get all this workflows. The PNG files have the json embedded into them and are easy to drag and drop !Latent Upscale nodes
+-
+
- Forked from NN latent this node provides some remarkable neural enhancement to the latents making scaling a cool task +
- Both NN latent upscale and Latent upscaler does the Latent improvemnet in remarkable ways. If you face any issue regarding same please install the nodes from this link([SD-Latent-Upscaler](https://github.com/city96/SD-Latent-Upscaler) and the NN latent upscale from [ComfyUI_NNlatentUpscale](https://github.com/Ttl/ComfyUi_NNLatentUpscale) + +
+ 
+
+ 
+
+
+
+ +1. HiRes-Fixing
+ [
](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/HiResfix_workflow.png)+ +2. SDXL Refining & **Noise Control Script**
+ [
](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/SDXL_base_refine_noise_workflow.png)+ +3. **XY Plot**: LoRA
model_strength vs clip_strength+ [
](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/Eff_XYPlot%20-%20LoRA%20Model%20vs%20Clip%20Strengths01.png)+ +4. Stacking Scripts: **XY Plot** + **Noise Control** + **HiRes-Fix**
+ [
](https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/v2.0/workflows/XYPlot%20-%20Seeds%20vs%20Checkpoints%20%26%20Stacked%20Scripts.png)+ +5. Stacking Scripts: **AnimateDiff** + **HiRes-Fix** (with ControlNet)
+ [
](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/eff_animatescriptWF001.gif)+ +6. SVD workflow: **Stable Video Diffusion** + *Kohya Hires** (with latent control)
+
+ + +### Dependencies +The python library simpleeval is required to be installed if you wish to use the **Simpleeval Nodes**. +
pip install simpleeval+Also can be installed with a simple pip command
+'pip install simpleeval' + +A single file library for easily adding evaluatable expressions into python projects. Say you want to allow a user to set an alarm volume, which could depend on the time of day, alarm level, how many previous alarms had gone off, and if there is music playing at the time. + +check Notes for more information. + +## **Install:** +To install, drop the "_**efficiency-nodes-comfyui**_" folder into the "_**...\ComfyUI\ComfyUI\custom_nodes**_" directory and restart UI. + +## Todo + +[ ] Add guidance to notebook + + +# Comfy Resources + +**Efficiency Linked Repos** +- [BlenderNeko ComfyUI_ADV_CLIP_emb](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb) by@BlenderNeko +- [Chrisgoringe cg-noise](https://github.com/chrisgoringe/cg-noise) by@Chrisgoringe +- [pythongosssss ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by@pythongosssss +- [shiimizu ComfyUI_smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes) by@shiimizu +- [LEv145_images-grid-comfyUI-plugin](https://github.com/LEv145/images-grid-comfy-plugin)) by@LEv145 +- [ltdrdata-ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) by@ltdrdata +- [pythongosssss-ComfyUI-custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by@pythongosssss +- [RockOfFire-ComfyUI_Comfyroll_CustomNodes](https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes) by@RockOfFire + +**Guides**: +- [Official Examples (eng)](https://comfyanonymous.github.io/ComfyUI_examples/)- +- [ComfyUI Community Manual (eng)](https://blenderneko.github.io/ComfyUI-docs/) by @BlenderNeko + +- **Extensions and Custom Nodes**: +- [Plugins for Comfy List (eng)](https://github.com/WASasquatch/comfyui-plugins) by @WASasquatch +- [ComfyUI tag on CivitAI (eng)](https://civitai.com/tag/comfyui)- +- [Tomoaki's personal Wiki (jap)](https://comfyui.creamlab.net/guides/) by @tjhayasaka + + ## Support +If you create a cool image with our nodes, please show your result and message us on twitter at @jags111 or @NeuralismAI . + +You can join the NEURALISM AI DISCORD or JAGS AI DISCORD +Share your work created with this model. Exchange experiences and parameters. And see more interesting custom workflows. + +Support us in Patreon for more future models and new versions of AI notebooks. +- tip me on [patreon] + + My buymeacoffee.com pages and links are here and if you feel you are happy with my work just buy me a coffee ! + + coffee for JAGS AI + +Thank you for being awesome! + +
+
+
+
diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__init__.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a73a32cee3bfc5a6015a3da0510126cc117b48d7
--- /dev/null
+++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__init__.py
@@ -0,0 +1,18 @@
+import os
+import subprocess
+import importlib.util
+import folder_paths
+import shutil
+import sys
+import traceback
+
+from .efficiency_nodes import NODE_CLASS_MAPPINGS
+#from .py.ttl_nn_latent_upscaler import NODE_CLASS_MAPPINGS
+#from .py.city96_latent_upscaler import NODE_CLASS_MAPPINGS
+
+
+WEB_DIRECTORY = "js"
+
+CC_VERSION = 2.0
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'CC_VERSION']
diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/__init__.cpython-310.pyc b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/__init__.cpython-310.pyc
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diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/tsc_utils.cpython-310.pyc b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/tsc_utils.cpython-310.pyc
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diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/arial.ttf b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/arial.ttf
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diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/efficiency_nodes.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/efficiency_nodes.py
new file mode 100644
index 0000000000000000000000000000000000000000..39e014f55543424a57d5d86360ba1e16c3892f35
--- /dev/null
+++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/efficiency_nodes.py
@@ -0,0 +1,4350 @@
+# Efficiency Nodes - A collection of my ComfyUI custom nodes to help streamline workflows and reduce total node count.
+# by Luciano Cirino (Discord: TSC#9184) - April 2023 - October 2023
+# https://github.com/LucianoCirino/efficiency-nodes-comfyui
+
+from torch import Tensor
+from PIL import Image, ImageOps, ImageDraw, ImageFont
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+import torch
+
+import ast
+from pathlib import Path
+from importlib import import_module
+import os
+import sys
+import copy
+import subprocess
+import json
+import psutil
+
+# Get the absolute path of various directories
+my_dir = os.path.dirname(os.path.abspath(__file__))
+custom_nodes_dir = os.path.abspath(os.path.join(my_dir, '..'))
+comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
+
+# Construct the path to the font file
+font_path = os.path.join(my_dir, 'arial.ttf')
+
+# Append comfy_dir to sys.path & import files
+sys.path.append(comfy_dir)
+from nodes import LatentUpscaleBy, KSampler, KSamplerAdvanced, VAEDecode, VAEDecodeTiled, VAEEncode, VAEEncodeTiled, \
+ ImageScaleBy, CLIPSetLastLayer, CLIPTextEncode, ControlNetLoader, ControlNetApply, ControlNetApplyAdvanced, \
+ PreviewImage, MAX_RESOLUTION
+from comfy_extras.nodes_upscale_model import UpscaleModelLoader, ImageUpscaleWithModel
+from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL, CLIPTextEncodeSDXLRefiner
+import comfy.sample
+import comfy.samplers
+import comfy.sd
+import comfy.utils
+import comfy.latent_formats
+sys.path.remove(comfy_dir)
+
+# Append my_dir to sys.path & import files
+sys.path.append(my_dir)
+from tsc_utils import *
+from .py import smZ_cfg_denoiser
+from .py import smZ_rng_source
+from .py import cg_mixed_seed_noise
+from .py import city96_latent_upscaler
+from .py import ttl_nn_latent_upscaler
+from .py import bnk_tiled_samplers
+from .py import bnk_adv_encode
+sys.path.remove(my_dir)
+
+# Append custom_nodes_dir to sys.path
+sys.path.append(custom_nodes_dir)
+
+# GLOBALS
+REFINER_CFG_OFFSET = 0 #Refiner CFG Offset
+
+########################################################################################################################
+# Common function for encoding prompts
+def encode_prompts(positive_prompt, negative_prompt, token_normalization, weight_interpretation, clip, clip_skip,
+ refiner_clip, refiner_clip_skip, ascore, is_sdxl, empty_latent_width, empty_latent_height,
+ return_type="both"):
+
+ positive_encoded = negative_encoded = refiner_positive_encoded = refiner_negative_encoded = None
+
+ # Process base encodings if needed
+ if return_type in ["base", "both"]:
+ clip = CLIPSetLastLayer().set_last_layer(clip, clip_skip)[0]
+
+ positive_encoded = bnk_adv_encode.AdvancedCLIPTextEncode().encode(clip, positive_prompt, token_normalization, weight_interpretation)[0]
+ negative_encoded = bnk_adv_encode.AdvancedCLIPTextEncode().encode(clip, negative_prompt, token_normalization, weight_interpretation)[0]
+
+ # Process refiner encodings if needed
+ if return_type in ["refiner", "both"] and is_sdxl and refiner_clip and refiner_clip_skip and ascore:
+ refiner_clip = CLIPSetLastLayer().set_last_layer(refiner_clip, refiner_clip_skip)[0]
+
+ refiner_positive_encoded = bnk_adv_encode.AdvancedCLIPTextEncode().encode(refiner_clip, positive_prompt, token_normalization, weight_interpretation)[0]
+ refiner_positive_encoded = bnk_adv_encode.AddCLIPSDXLRParams().encode(refiner_positive_encoded, empty_latent_width, empty_latent_height, ascore[0])[0]
+
+ refiner_negative_encoded = bnk_adv_encode.AdvancedCLIPTextEncode().encode(refiner_clip, negative_prompt, token_normalization, weight_interpretation)[0]
+ refiner_negative_encoded = bnk_adv_encode.AddCLIPSDXLRParams().encode(refiner_negative_encoded, empty_latent_width, empty_latent_height, ascore[1])[0]
+
+ # Return results based on return_type
+ if return_type == "base":
+ return positive_encoded, negative_encoded, clip
+ elif return_type == "refiner":
+ return refiner_positive_encoded, refiner_negative_encoded, refiner_clip
+ elif return_type == "both":
+ return positive_encoded, negative_encoded, clip, refiner_positive_encoded, refiner_negative_encoded, refiner_clip
+
+########################################################################################################################
+# TSC Efficient Loader
+class TSC_EfficientLoader:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
+ "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
+ "clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+ "lora_name": (["None"] + folder_paths.get_filename_list("loras"),),
+ "lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "positive": ("STRING", {"default": "CLIP_POSITIVE","multiline": True}),
+ "negative": ("STRING", {"default": "CLIP_NEGATIVE", "multiline": True}),
+ "token_normalization": (["none", "mean", "length", "length+mean"],),
+ "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
+ "empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
+ "empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 262144})},
+ "optional": {"lora_stack": ("LORA_STACK", ),
+ "cnet_stack": ("CONTROL_NET_STACK",)},
+ "hidden": { "prompt": "PROMPT",
+ "my_unique_id": "UNIQUE_ID",},
+ }
+
+ RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "DEPENDENCIES",)
+ RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "CLIP", "DEPENDENCIES", )
+ FUNCTION = "efficientloader"
+ CATEGORY = "Efficiency Nodes/Loaders"
+
+ def efficientloader(self, ckpt_name, vae_name, clip_skip, lora_name, lora_model_strength, lora_clip_strength,
+ positive, negative, token_normalization, weight_interpretation, empty_latent_width,
+ empty_latent_height, batch_size, lora_stack=None, cnet_stack=None, refiner_name="None",
+ ascore=None, prompt=None, my_unique_id=None, loader_type="regular"):
+
+ # Clean globally stored objects
+ globals_cleanup(prompt)
+
+ # Create Empty Latent
+ latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
+
+ # Retrieve cache numbers
+ vae_cache, ckpt_cache, lora_cache, refn_cache = get_cache_numbers("Efficient Loader")
+
+ if lora_name != "None" or lora_stack:
+ # Initialize an empty list to store LoRa parameters.
+ lora_params = []
+
+ # Check if lora_name is not the string "None" and if so, add its parameters.
+ if lora_name != "None":
+ lora_params.append((lora_name, lora_model_strength, lora_clip_strength))
+
+ # If lora_stack is not None or an empty list, extend lora_params with its items.
+ if lora_stack:
+ lora_params.extend(lora_stack)
+
+ # Load LoRa(s)
+ model, clip = load_lora(lora_params, ckpt_name, my_unique_id, cache=lora_cache, ckpt_cache=ckpt_cache, cache_overwrite=True)
+
+ if vae_name == "Baked VAE":
+ vae = get_bvae_by_ckpt_name(ckpt_name)
+ else:
+ model, clip, vae = load_checkpoint(ckpt_name, my_unique_id, cache=ckpt_cache, cache_overwrite=True)
+ lora_params = None
+
+ # Load Refiner Checkpoint if given
+ if refiner_name != "None":
+ refiner_model, refiner_clip, _ = load_checkpoint(refiner_name, my_unique_id, output_vae=False,
+ cache=refn_cache, cache_overwrite=True, ckpt_type="refn")
+ else:
+ refiner_model = refiner_clip = None
+
+ # Extract clip_skips
+ refiner_clip_skip = clip_skip[1] if loader_type == "sdxl" else None
+ clip_skip = clip_skip[0] if loader_type == "sdxl" else clip_skip
+
+ # Encode prompt based on loader_type
+ positive_encoded, negative_encoded, clip, refiner_positive_encoded, refiner_negative_encoded, refiner_clip = \
+ encode_prompts(positive, negative, token_normalization, weight_interpretation, clip, clip_skip,
+ refiner_clip, refiner_clip_skip, ascore, loader_type == "sdxl",
+ empty_latent_width, empty_latent_height)
+
+ # Apply ControlNet Stack if given
+ if cnet_stack:
+ controlnet_conditioning = TSC_Apply_ControlNet_Stack().apply_cnet_stack(positive_encoded, negative_encoded, cnet_stack)
+ positive_encoded, negative_encoded = controlnet_conditioning[0], controlnet_conditioning[1]
+
+ # Check for custom VAE
+ if vae_name != "Baked VAE":
+ vae = load_vae(vae_name, my_unique_id, cache=vae_cache, cache_overwrite=True)
+
+ # Data for XY Plot
+ dependencies = (vae_name, ckpt_name, clip, clip_skip, refiner_name, refiner_clip, refiner_clip_skip,
+ positive, negative, token_normalization, weight_interpretation, ascore,
+ empty_latent_width, empty_latent_height, lora_params, cnet_stack)
+
+ ### Debugging
+ ###print_loaded_objects_entries()
+ print_loaded_objects_entries(my_unique_id, prompt)
+
+ if loader_type == "regular":
+ return (model, positive_encoded, negative_encoded, {"samples":latent}, vae, clip, dependencies,)
+ elif loader_type == "sdxl":
+ return ((model, clip, positive_encoded, negative_encoded, refiner_model, refiner_clip,
+ refiner_positive_encoded, refiner_negative_encoded), {"samples":latent}, vae, dependencies,)
+
+#=======================================================================================================================
+# TSC Efficient Loader SDXL
+class TSC_EfficientLoaderSDXL(TSC_EfficientLoader):
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": { "base_ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
+ "base_clip_skip": ("INT", {"default": -2, "min": -24, "max": -1, "step": 1}),
+ "refiner_ckpt_name": (["None"] + folder_paths.get_filename_list("checkpoints"),),
+ "refiner_clip_skip": ("INT", {"default": -2, "min": -24, "max": -1, "step": 1}),
+ "positive_ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+ "negative_ascore": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+ "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
+ "positive": ("STRING", {"default": "CLIP_POSITIVE", "multiline": True}),
+ "negative": ("STRING", {"default": "CLIP_NEGATIVE", "multiline": True}),
+ "token_normalization": (["none", "mean", "length", "length+mean"],),
+ "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
+ "empty_latent_width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
+ "empty_latent_height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})},
+ "optional": {"lora_stack": ("LORA_STACK", ), "cnet_stack": ("CONTROL_NET_STACK",),},
+ "hidden": { "prompt": "PROMPT", "my_unique_id": "UNIQUE_ID",},
+ }
+
+ RETURN_TYPES = ("SDXL_TUPLE", "LATENT", "VAE", "DEPENDENCIES",)
+ RETURN_NAMES = ("SDXL_TUPLE", "LATENT", "VAE", "DEPENDENCIES", )
+ FUNCTION = "efficientloaderSDXL"
+ CATEGORY = "Efficiency Nodes/Loaders"
+
+ def efficientloaderSDXL(self, base_ckpt_name, base_clip_skip, refiner_ckpt_name, refiner_clip_skip, positive_ascore,
+ negative_ascore, vae_name, positive, negative, token_normalization, weight_interpretation,
+ empty_latent_width, empty_latent_height, batch_size, lora_stack=None, cnet_stack=None,
+ prompt=None, my_unique_id=None):
+ clip_skip = (base_clip_skip, refiner_clip_skip)
+ lora_name = "None"
+ lora_model_strength = lora_clip_strength = 0
+ return super().efficientloader(base_ckpt_name, vae_name, clip_skip, lora_name, lora_model_strength, lora_clip_strength,
+ positive, negative, token_normalization, weight_interpretation, empty_latent_width, empty_latent_height,
+ batch_size, lora_stack=lora_stack, cnet_stack=cnet_stack, refiner_name=refiner_ckpt_name,
+ ascore=(positive_ascore, negative_ascore), prompt=prompt, my_unique_id=my_unique_id, loader_type="sdxl")
+
+#=======================================================================================================================
+# TSC Unpack SDXL Tuple
+class TSC_Unpack_SDXL_Tuple:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {"sdxl_tuple": ("SDXL_TUPLE",)},}
+
+ RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING","CONDITIONING", "MODEL", "CLIP", "CONDITIONING", "CONDITIONING",)
+ RETURN_NAMES = ("BASE_MODEL", "BASE_CLIP", "BASE_CONDITIONING+", "BASE_CONDITIONING-",
+ "REFINER_MODEL", "REFINER_CLIP","REFINER_CONDITIONING+","REFINER_CONDITIONING-",)
+ FUNCTION = "unpack_sdxl_tuple"
+ CATEGORY = "Efficiency Nodes/Misc"
+
+ def unpack_sdxl_tuple(self, sdxl_tuple):
+ return (sdxl_tuple[0], sdxl_tuple[1],sdxl_tuple[2],sdxl_tuple[3],
+ sdxl_tuple[4],sdxl_tuple[5],sdxl_tuple[6],sdxl_tuple[7],)
+
+# =======================================================================================================================
+# TSC Pack SDXL Tuple
+class TSC_Pack_SDXL_Tuple:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {"base_model": ("MODEL",),
+ "base_clip": ("CLIP",),
+ "base_positive": ("CONDITIONING",),
+ "base_negative": ("CONDITIONING",),
+ "refiner_model": ("MODEL",),
+ "refiner_clip": ("CLIP",),
+ "refiner_positive": ("CONDITIONING",),
+ "refiner_negative": ("CONDITIONING",),},}
+
+ RETURN_TYPES = ("SDXL_TUPLE",)
+ RETURN_NAMES = ("SDXL_TUPLE",)
+ FUNCTION = "pack_sdxl_tuple"
+ CATEGORY = "Efficiency Nodes/Misc"
+
+ def pack_sdxl_tuple(self, base_model, base_clip, base_positive, base_negative,
+ refiner_model, refiner_clip, refiner_positive, refiner_negative):
+ return ((base_model, base_clip, base_positive, base_negative,
+ refiner_model, refiner_clip, refiner_positive, refiner_negative),)
+
+########################################################################################################################
+# TSC LoRA Stacker
+class TSC_LoRA_Stacker:
+ modes = ["simple", "advanced"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ loras = ["None"] + folder_paths.get_filename_list("loras")
+ inputs = {
+ "required": {
+ "input_mode": (cls.modes,),
+ "lora_count": ("INT", {"default": 3, "min": 0, "max": 50, "step": 1}),
+ }
+ }
+
+ for i in range(1, 50):
+ inputs["required"][f"lora_name_{i}"] = (loras,)
+ inputs["required"][f"lora_wt_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+ inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+ inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+
+ inputs["optional"] = {
+ "lora_stack": ("LORA_STACK",)
+ }
+ return inputs
+
+ RETURN_TYPES = ("LORA_STACK",)
+ RETURN_NAMES = ("LORA_STACK",)
+ FUNCTION = "lora_stacker"
+ CATEGORY = "Efficiency Nodes/Stackers"
+
+ def lora_stacker(self, input_mode, lora_count, lora_stack=None, **kwargs):
+
+ # Extract values from kwargs
+ loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)]
+
+ # Create a list of tuples using provided parameters, exclude tuples with lora_name as "None"
+ if input_mode == "simple":
+ weights = [kwargs.get(f"lora_wt_{i}") for i in range(1, lora_count + 1)]
+ loras = [(lora_name, lora_weight, lora_weight) for lora_name, lora_weight in zip(loras, weights) if
+ lora_name != "None"]
+ else:
+ model_strs = [kwargs.get(f"model_str_{i}") for i in range(1, lora_count + 1)]
+ clip_strs = [kwargs.get(f"clip_str_{i}") for i in range(1, lora_count + 1)]
+ loras = [(lora_name, model_str, clip_str) for lora_name, model_str, clip_str in
+ zip(loras, model_strs, clip_strs) if lora_name != "None"]
+
+ # If lora_stack is not None, extend the loras list with lora_stack
+ if lora_stack is not None:
+ loras.extend([l for l in lora_stack if l[0] != "None"])
+
+ return (loras,)
+
+#=======================================================================================================================
+# TSC Control Net Stacker
+class TSC_Control_Net_Stacker:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {"control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+ "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})},
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("CONTROL_NET_STACK",)
+ RETURN_NAMES = ("CNET_STACK",)
+ FUNCTION = "control_net_stacker"
+ CATEGORY = "Efficiency Nodes/Stackers"
+
+ def control_net_stacker(self, control_net, image, strength, start_percent, end_percent, cnet_stack=None):
+ # If control_net_stack is None, initialize as an empty list
+ cnet_stack = [] if cnet_stack is None else cnet_stack
+
+ # Extend the control_net_stack with the new tuple
+ cnet_stack.extend([(control_net, image, strength, start_percent, end_percent)])
+
+ return (cnet_stack,)
+
+#=======================================================================================================================
+# TSC Apply ControlNet Stack
+class TSC_Apply_ControlNet_Stack:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {"positive": ("CONDITIONING",),
+ "negative": ("CONDITIONING",)},
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)}
+ }
+
+ RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+ RETURN_NAMES = ("CONDITIONING+","CONDITIONING-",)
+ FUNCTION = "apply_cnet_stack"
+ CATEGORY = "Efficiency Nodes/Stackers"
+
+ def apply_cnet_stack(self, positive, negative, cnet_stack=None):
+ if cnet_stack is None:
+ return (positive, negative)
+
+ for control_net_tuple in cnet_stack:
+ control_net, image, strength, start_percent, end_percent = control_net_tuple
+ controlnet_conditioning = ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image,
+ strength, start_percent, end_percent)
+ positive, negative = controlnet_conditioning[0], controlnet_conditioning[1]
+
+ return (positive, negative, )
+
+
+########################################################################################################################
+# TSC KSampler (Efficient)
+class TSC_KSampler:
+
+ empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required":
+ {"model": ("MODEL",),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+ "positive": ("CONDITIONING",),
+ "negative": ("CONDITIONING",),
+ "latent_image": ("LATENT",),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "preview_method": (["auto", "latent2rgb", "taesd", "vae_decoded_only", "none"],),
+ "vae_decode": (["true", "true (tiled)", "false"],),
+ },
+ "optional": { "optional_vae": ("VAE",),
+ "script": ("SCRIPT",),},
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
+ }
+
+ RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "IMAGE", )
+ RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "IMAGE", )
+ OUTPUT_NODE = True
+ FUNCTION = "sample"
+ CATEGORY = "Efficiency Nodes/Sampling"
+
+ def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+ preview_method, vae_decode, denoise=1.0, prompt=None, extra_pnginfo=None, my_unique_id=None,
+ optional_vae=(None,), script=None, add_noise=None, start_at_step=None, end_at_step=None,
+ return_with_leftover_noise=None, sampler_type="regular"):
+
+ # Rename the vae variable
+ vae = optional_vae
+
+ # If vae is not connected, disable vae decoding
+ if vae == (None,) and vae_decode != "false":
+ print(f"{warning('KSampler(Efficient) Warning:')} No vae input detected, proceeding as if vae_decode was false.\n")
+ vae_decode = "false"
+
+ #---------------------------------------------------------------------------------------------------------------
+ # Unpack SDXL Tuple embedded in the 'model' channel
+ if sampler_type == "sdxl":
+ sdxl_tuple = model
+ model, _, positive, negative, refiner_model, _, refiner_positive, refiner_negative = sdxl_tuple
+ else:
+ refiner_model = refiner_positive = refiner_negative = None
+
+ #---------------------------------------------------------------------------------------------------------------
+ def keys_exist_in_script(*keys):
+ return any(key in script for key in keys) if script else False
+
+ #---------------------------------------------------------------------------------------------------------------
+ def vae_decode_latent(vae, samples, vae_decode):
+ return VAEDecodeTiled().decode(vae,samples,320)[0] if "tiled" in vae_decode else VAEDecode().decode(vae,samples)[0]
+
+ def vae_encode_image(vae, pixels, vae_decode):
+ return VAEEncodeTiled().encode(vae,pixels,320)[0] if "tiled" in vae_decode else VAEEncode().encode(vae,pixels)[0]
+
+ # ---------------------------------------------------------------------------------------------------------------
+ def process_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+ denoise, sampler_type, add_noise, start_at_step, end_at_step, return_with_leftover_noise,
+ refiner_model, refiner_positive, refiner_negative, vae, vae_decode, preview_method):
+
+ # Store originals
+ previous_preview_method = global_preview_method()
+ original_prepare_noise = comfy.sample.prepare_noise
+ original_KSampler = comfy.samplers.KSampler
+ original_model_str = str(model)
+
+ # Initialize output variables
+ samples = images = gifs = preview = cnet_imgs = None
+
+ try:
+ # Change the global preview method (temporarily)
+ set_preview_method(preview_method)
+
+ # ------------------------------------------------------------------------------------------------------
+ # Check if "noise" exists in the script before main sampling has taken place
+ if keys_exist_in_script("noise"):
+ rng_source, cfg_denoiser, add_seed_noise, m_seed, m_weight = script["noise"]
+ smZ_rng_source.rng_rand_source(rng_source) # this function monkey patches comfy.sample.prepare_noise
+ if cfg_denoiser:
+ comfy.samplers.KSampler = smZ_cfg_denoiser.SDKSampler
+ if add_seed_noise:
+ comfy.sample.prepare_noise = cg_mixed_seed_noise.get_mixed_noise_function(comfy.sample.prepare_noise, m_seed, m_weight)
+ else:
+ m_seed = m_weight = None
+ else:
+ rng_source = cfg_denoiser = add_seed_noise = m_seed = m_weight = None
+
+ # ------------------------------------------------------------------------------------------------------
+ # Check if "anim" exists in the script before main sampling has taken place
+ if keys_exist_in_script("anim"):
+ if preview_method != "none":
+ set_preview_method("none") # disable preview method
+ print(f"{warning('KSampler(Efficient) Warning:')} Live preview disabled for animatediff generations.")
+ motion_model, beta_schedule, context_options, frame_rate, loop_count, format, pingpong, save_image = script["anim"]
+ model = AnimateDiffLoaderWithContext().load_mm_and_inject_params(model, motion_model, beta_schedule, context_options)[0]
+
+ # ------------------------------------------------------------------------------------------------------
+ # Store run parameters as strings. Load previous stored samples if all parameters match.
+ latent_image_hash = tensor_to_hash(latent_image["samples"])
+ positive_hash = tensor_to_hash(positive[0][0])
+ negative_hash = tensor_to_hash(negative[0][0])
+ refiner_positive_hash = tensor_to_hash(refiner_positive[0][0]) if refiner_positive is not None else None
+ refiner_negative_hash = tensor_to_hash(refiner_negative[0][0]) if refiner_negative is not None else None
+
+ # Include motion_model, beta_schedule, and context_options as unique identifiers if they exist.
+ model_identifier = [original_model_str, motion_model, beta_schedule, context_options] if keys_exist_in_script("anim")\
+ else [original_model_str]
+
+ parameters = [model_identifier] + [seed, steps, cfg, sampler_name, scheduler, positive_hash, negative_hash,
+ latent_image_hash, denoise, sampler_type, add_noise, start_at_step,
+ end_at_step, return_with_leftover_noise, refiner_model, refiner_positive_hash,
+ refiner_negative_hash, rng_source, cfg_denoiser, add_seed_noise, m_seed, m_weight]
+
+ # Convert all elements in parameters to strings, except for the hash variable checks
+ parameters = [str(item) if not isinstance(item, type(latent_image_hash)) else item for item in parameters]
+
+ # Load previous latent if all parameters match, else returns 'None'
+ samples = load_ksampler_results("latent", my_unique_id, parameters)
+
+ if samples is None: # clear stored images
+ store_ksampler_results("image", my_unique_id, None)
+ store_ksampler_results("cnet_img", my_unique_id, None)
+
+ if samples is not None: # do not re-sample
+ images = load_ksampler_results("image", my_unique_id)
+ cnet_imgs = True # "True" will denote that it can be loaded provided the preprocessor matches
+
+ # Sample the latent_image(s) using the Comfy KSampler nodes
+ elif sampler_type == "regular":
+ samples = KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
+ latent_image, denoise=denoise)[0] if denoise>0 else latent_image
+
+ elif sampler_type == "advanced":
+ samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
+ positive, negative, latent_image, start_at_step, end_at_step,
+ return_with_leftover_noise, denoise=1.0)[0]
+
+ elif sampler_type == "sdxl":
+ # Disable refiner if refine_at_step is -1
+ if end_at_step == -1:
+ end_at_step = steps
+
+ # Perform base model sampling
+ add_noise = return_with_leftover_noise = True
+ samples = KSamplerAdvanced().sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
+ positive, negative, latent_image, start_at_step, end_at_step,
+ return_with_leftover_noise, denoise=1.0)[0]
+
+ # Perform refiner model sampling
+ if refiner_model and end_at_step < steps:
+ add_noise = return_with_leftover_noise = False
+ samples = KSamplerAdvanced().sample(refiner_model, add_noise, seed, steps, cfg + REFINER_CFG_OFFSET,
+ sampler_name, scheduler, refiner_positive, refiner_negative,
+ samples, end_at_step, steps,
+ return_with_leftover_noise, denoise=1.0)[0]
+
+ # Cache the first pass samples in the 'last_helds' dictionary "latent" if not xyplot
+ if not any(keys_exist_in_script(key) for key in ["xyplot"]):
+ store_ksampler_results("latent", my_unique_id, samples, parameters)
+
+ # ------------------------------------------------------------------------------------------------------
+ # Check if "hiresfix" exists in the script after main sampling has taken place
+ if keys_exist_in_script("hiresfix"):
+ # Unpack the tuple from the script's "hiresfix" key
+ upscale_type, latent_upscaler, upscale_by, use_same_seed, hires_seed, hires_steps, hires_denoise,\
+ iterations, hires_control_net, hires_cnet_strength, preprocessor, preprocessor_imgs, \
+ latent_upscale_function, latent_upscale_model, pixel_upscale_model = script["hiresfix"]
+
+ # Define hires_seed
+ hires_seed = seed if use_same_seed else hires_seed
+
+ # Define latent_upscale_model
+ if latent_upscale_model is None:
+ latent_upscale_model = model
+ elif keys_exist_in_script("anim"):
+ latent_upscale_model = \
+ AnimateDiffLoaderWithContext().load_mm_and_inject_params(latent_upscale_model, motion_model,
+ beta_schedule, context_options)[0]
+
+ # Generate Preprocessor images and Apply Control Net
+ if hires_control_net is not None:
+ # Attempt to load previous "cnet_imgs" if previous images were loaded and preprocessor is same
+ if cnet_imgs is True:
+ cnet_imgs = load_ksampler_results("cnet_img", my_unique_id, [preprocessor])
+ # If cnet_imgs is None, generate new ones
+ if cnet_imgs is None:
+ if images is None:
+ images = vae_decode_latent(vae, samples, vae_decode)
+ store_ksampler_results("image", my_unique_id, images)
+ cnet_imgs = AIO_Preprocessor().execute(preprocessor, images)[0]
+ store_ksampler_results("cnet_img", my_unique_id, cnet_imgs, [preprocessor])
+ positive = ControlNetApply().apply_controlnet(positive, hires_control_net, cnet_imgs, hires_cnet_strength)[0]
+
+ # Iterate for the given number of iterations
+ if upscale_type == "latent":
+ for _ in range(iterations):
+ upscaled_latent_image = latent_upscale_function().upscale(samples, latent_upscaler, upscale_by)[0]
+ samples = KSampler().sample(latent_upscale_model, hires_seed, hires_steps, cfg, sampler_name, scheduler,
+ positive, negative, upscaled_latent_image, denoise=hires_denoise)[0]
+ images = None # set to None when samples is updated
+ elif upscale_type == "pixel":
+ if images is None:
+ images = vae_decode_latent(vae, samples, vae_decode)
+ store_ksampler_results("image", my_unique_id, images)
+ images = ImageUpscaleWithModel().upscale(pixel_upscale_model, images)[0]
+ images = ImageScaleBy().upscale(images, "nearest-exact", upscale_by/4)[0]
+
+ # ------------------------------------------------------------------------------------------------------
+ # Check if "tile" exists in the script after main sampling has taken place
+ if keys_exist_in_script("tile"):
+ # Unpack the tuple from the script's "tile" key
+ upscale_by, tile_size, tiling_strategy, tiling_steps, tile_seed, tiled_denoise,\
+ tile_controlnet, strength = script["tile"]
+
+ # Decode image, store if first decode
+ if images is None:
+ images = vae_decode_latent(vae, samples, vae_decode)
+ if not any(keys_exist_in_script(key) for key in ["xyplot", "hiresfix"]):
+ store_ksampler_results("image", my_unique_id, images)
+
+ # Upscale image
+ upscaled_image = ImageScaleBy().upscale(images, "nearest-exact", upscale_by)[0]
+ upscaled_latent = vae_encode_image(vae, upscaled_image, vae_decode)
+
+ # If using Control Net, Apply Control Net using upscaled_image and loaded control_net
+ if tile_controlnet is not None:
+ positive = ControlNetApply().apply_controlnet(positive, tile_controlnet, upscaled_image, 1)[0]
+
+ # Sample latent
+ TSampler = bnk_tiled_samplers.TiledKSampler
+ samples = TSampler().sample(model, tile_seed, tile_size, tile_size, tiling_strategy, tiling_steps, cfg,
+ sampler_name, scheduler, positive, negative, upscaled_latent,
+ denoise=tiled_denoise)[0]
+ images = None # set to None when samples is updated
+
+ # ------------------------------------------------------------------------------------------------------
+ # Check if "anim" exists in the script after the main sampling has taken place
+ if keys_exist_in_script("anim"):
+ if images is None:
+ images = vae_decode_latent(vae, samples, vae_decode)
+ if not any(keys_exist_in_script(key) for key in ["xyplot", "hiresfix", "tile"]):
+ store_ksampler_results("image", my_unique_id, images)
+ gifs = AnimateDiffCombine().generate_gif(images, frame_rate, loop_count, format=format,
+ pingpong=pingpong, save_image=save_image, prompt=prompt, extra_pnginfo=extra_pnginfo)["ui"]["gifs"]
+
+ # ------------------------------------------------------------------------------------------------------
+
+ # Decode image if not yet decoded
+ if "true" in vae_decode:
+ if images is None:
+ images = vae_decode_latent(vae, samples, vae_decode)
+ # Store decoded image as base image of no script is detected
+ if all(not keys_exist_in_script(key) for key in ["xyplot", "hiresfix", "tile", "anim"]):
+ store_ksampler_results("image", my_unique_id, images)
+
+ # Append Control Net Images (if exist)
+ if cnet_imgs is not None and not True:
+ if preprocessor_imgs and upscale_type == "latent":
+ if keys_exist_in_script("xyplot"):
+ print(
+ f"{warning('HighRes-Fix Warning:')} Preprocessor images auto-disabled when XY Plotting.")
+ else:
+ # Resize cnet_imgs if necessary and stack
+ if images.shape[1:3] != cnet_imgs.shape[1:3]: # comparing height and width
+ cnet_imgs = quick_resize(cnet_imgs, images.shape)
+ images = torch.cat([images, cnet_imgs], dim=0)
+
+ # Define preview images
+ if keys_exist_in_script("anim"):
+ preview = {"gifs": gifs, "images": list()}
+ elif preview_method == "none" or (preview_method == "vae_decoded_only" and vae_decode == "false"):
+ preview = {"images": list()}
+ elif images is not None:
+ preview = PreviewImage().save_images(images, prompt=prompt, extra_pnginfo=extra_pnginfo)["ui"]
+
+ # Define a dummy output image
+ if images is None and vae_decode == "false":
+ images = TSC_KSampler.empty_image
+
+ finally:
+ # Restore global changes
+ set_preview_method(previous_preview_method)
+ comfy.samplers.KSampler = original_KSampler
+ comfy.sample.prepare_noise = original_prepare_noise
+
+ return samples, images, gifs, preview
+
+ # ---------------------------------------------------------------------------------------------------------------
+ # Clean globally stored objects of non-existant nodes
+ globals_cleanup(prompt)
+
+ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ # If not XY Plotting
+ if not keys_exist_in_script("xyplot"):
+
+ # Process latent image
+ samples, images, gifs, preview = process_latent_image(model, seed, steps, cfg, sampler_name, scheduler,
+ positive, negative, latent_image, denoise, sampler_type, add_noise,
+ start_at_step, end_at_step, return_with_leftover_noise, refiner_model,
+ refiner_positive, refiner_negative, vae, vae_decode, preview_method)
+
+ if sampler_type == "sdxl":
+ result = (sdxl_tuple, samples, vae, images,)
+ else:
+ result = (model, positive, negative, samples, vae, images,)
+
+ if preview is None:
+ return {"result": result}
+ else:
+ return {"ui": preview, "result": result}
+
+ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ # If XY Plot
+ elif keys_exist_in_script("xyplot"):
+
+ # If no vae connected, throw errors
+ if vae == (None,):
+ print(f"{error('KSampler(Efficient) Error:')} VAE input must be connected in order to use the XY Plot script.")
+
+ return {"ui": {"images": list()},
+ "result": (model, positive, negative, latent_image, vae, TSC_KSampler.empty_image,)}
+
+ # If vae_decode is not set to true, print message that changing it to true
+ if "true" not in vae_decode:
+ print(f"{warning('KSampler(Efficient) Warning:')} VAE decoding must be set to \'true\'"
+ " for the XY Plot script, proceeding as if \'true\'.\n")
+
+ #___________________________________________________________________________________________________________
+ # Initialize, unpack, and clean variables for the XY Plot script
+ vae_name = None
+ ckpt_name = None
+ clip = None
+ clip_skip = None
+ refiner_name = None
+ refiner_clip = None
+ refiner_clip_skip = None
+ positive_prompt = None
+ negative_prompt = None
+ ascore = None
+ empty_latent_width = None
+ empty_latent_height = None
+ lora_stack = None
+ cnet_stack = None
+
+ # Split the 'samples' tensor
+ samples_tensors = torch.split(latent_image['samples'], 1, dim=0)
+
+ # Check if 'noise_mask' exists and split if it does
+ if 'noise_mask' in latent_image:
+ noise_mask_tensors = torch.split(latent_image['noise_mask'], 1, dim=0)
+ latent_tensors = [{'samples': img, 'noise_mask': mask} for img, mask in
+ zip(samples_tensors, noise_mask_tensors)]
+ else:
+ latent_tensors = [{'samples': img} for img in samples_tensors]
+
+ # Set latent only to the first of the batch
+ latent_image = latent_tensors[0]
+
+ # Unpack script Tuple (X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, dependencies)
+ X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models, xyplot_as_output_image,\
+ xyplot_id, dependencies = script["xyplot"]
+
+ #_______________________________________________________________________________________________________
+ # The below section is used to check wether the XY_type is allowed for the Ksampler instance being used.
+ # If not the correct type, this section will abort the xy plot script.
+
+ samplers = {
+ "regular": {
+ "disallowed": ["AddNoise", "ReturnNoise", "StartStep", "EndStep", "RefineStep",
+ "Refiner", "Refiner On/Off", "AScore+", "AScore-"],
+ "name": "KSampler (Efficient)"
+ },
+ "advanced": {
+ "disallowed": ["RefineStep", "Denoise", "RefineStep", "Refiner", "Refiner On/Off",
+ "AScore+", "AScore-"],
+ "name": "KSampler Adv. (Efficient)"
+ },
+ "sdxl": {
+ "disallowed": ["AddNoise", "EndStep", "Denoise"],
+ "name": "KSampler SDXL (Eff.)"
+ }
+ }
+
+ # Define disallowed XY_types for each ksampler type
+ def get_ksampler_details(sampler_type):
+ return samplers.get(sampler_type, {"disallowed": [], "name": ""})
+
+ def suggest_ksampler(X_type, Y_type, current_sampler):
+ for sampler, details in samplers.items():
+ if sampler != current_sampler and X_type not in details["disallowed"] and Y_type not in details["disallowed"]:
+ return details["name"]
+ return "a different KSampler"
+
+ # In your main function or code segment:
+ details = get_ksampler_details(sampler_type)
+ disallowed_XY_types = details["disallowed"]
+ ksampler_name = details["name"]
+
+ if X_type in disallowed_XY_types or Y_type in disallowed_XY_types:
+ error_prefix = f"{error(f'{ksampler_name} Error:')}"
+
+ failed_type = []
+ if X_type in disallowed_XY_types:
+ failed_type.append(f"X_type: '{X_type}'")
+ if Y_type in disallowed_XY_types:
+ failed_type.append(f"Y_type: '{Y_type}'")
+
+ suggested_ksampler = suggest_ksampler(X_type, Y_type, sampler_type)
+
+ print(f"{error_prefix} Invalid value for {' and '.join(failed_type)}. "
+ f"Use {suggested_ksampler} for this XY Plot type."
+ f"\nDisallowed XY_types for this KSampler are: {', '.join(disallowed_XY_types)}.")
+
+ return {"ui": {"images": list()},
+ "result": (model, positive, negative, latent_image, vae, TSC_KSampler.empty_image,)}
+
+ #_______________________________________________________________________________________________________
+ # Unpack Effficient Loader dependencies
+ if dependencies is not None:
+ vae_name, ckpt_name, clip, clip_skip, refiner_name, refiner_clip, refiner_clip_skip,\
+ positive_prompt, negative_prompt, token_normalization, weight_interpretation, ascore,\
+ empty_latent_width, empty_latent_height, lora_stack, cnet_stack = dependencies
+
+ #_______________________________________________________________________________________________________
+ # Printout XY Plot values to be processed
+ def process_xy_for_print(value, replacement, type_):
+
+ if type_ == "Seeds++ Batch" and isinstance(value, list):
+ return [v + seed for v in value] # Add seed to every entry in the list
+
+ elif type_ == "Scheduler" and isinstance(value, tuple):
+ return value[0] # Return only the first entry of the tuple
+
+ elif type_ == "VAE" and isinstance(value, list):
+ # For each string in the list, extract the filename from the path
+ return [os.path.basename(v) for v in value]
+
+ elif (type_ == "Checkpoint" or type_ == "Refiner") and isinstance(value, list):
+ # For each tuple in the list, return only the first value if the second or third value is None
+ return [(os.path.basename(v[0]),) + v[1:] if v[1] is None or v[2] is None
+ else (os.path.basename(v[0]), v[1]) if v[2] is None
+ else (os.path.basename(v[0]),) + v[1:] for v in value]
+
+ elif type_ == "LoRA" and isinstance(value, list):
+ # Return only the first Tuple of each inner array
+ return [[(os.path.basename(v[0][0]),) + v[0][1:], "..."] if len(v) > 1
+ else [(os.path.basename(v[0][0]),) + v[0][1:]] for v in value]
+
+ elif type_ == "LoRA Batch" and isinstance(value, list):
+ # Extract the basename of the first value of the first tuple from each sublist
+ return [os.path.basename(v[0][0]) for v in value if v and isinstance(v[0], tuple) and v[0][0]]
+
+ elif (type_ == "LoRA Wt" or type_ == "LoRA MStr") and isinstance(value, list):
+ # Extract the first value of the first tuple from each sublist
+ return [v[0][1] for v in value if v and isinstance(v[0], tuple)]
+
+ elif type_ == "LoRA CStr" and isinstance(value, list):
+ # Extract the first value of the first tuple from each sublist
+ return [v[0][2] for v in value if v and isinstance(v[0], tuple)]
+
+ elif type_ == "ControlNetStrength" and isinstance(value, list):
+ # Extract the third entry of the first tuple from each inner list
+ return [round(inner_list[0][2], 3) for inner_list in value]
+
+ elif type_ == "ControlNetStart%" and isinstance(value, list):
+ # Extract the third entry of the first tuple from each inner list
+ return [round(inner_list[0][3], 3) for inner_list in value]
+
+ elif type_ == "ControlNetEnd%" and isinstance(value, list):
+ # Extract the third entry of the first tuple from each inner list
+ return [round(inner_list[0][4], 3) for inner_list in value]
+
+ elif isinstance(value, tuple):
+ return tuple(replacement if v is None else v for v in value)
+
+ else:
+ return replacement if value is None else value
+
+ # Determine the replacements based on X_type and Y_type
+ replacement_X = scheduler if X_type == 'Sampler' else clip_skip if X_type == 'Checkpoint' else None
+ replacement_Y = scheduler if Y_type == 'Sampler' else clip_skip if Y_type == 'Checkpoint' else None
+
+ # Process X_value and Y_value
+ X_value_processed = process_xy_for_print(X_value, replacement_X, X_type)
+ Y_value_processed = process_xy_for_print(Y_value, replacement_Y, Y_type)
+
+ print(info("-" * 40))
+ print(info('XY Plot Script Inputs:'))
+ print(info(f"(X) {X_type}:"))
+ for item in X_value_processed:
+ print(info(f" {item}"))
+ print(info(f"(Y) {Y_type}:"))
+ for item in Y_value_processed:
+ print(info(f" {item}"))
+ print(info("-" * 40))
+
+ #_______________________________________________________________________________________________________
+ # Perform various initializations in this section
+
+ # If not caching models, set to 1.
+ if cache_models == "False":
+ vae_cache = ckpt_cache = lora_cache = refn_cache = 1
+ else:
+ # Retrieve cache numbers
+ vae_cache, ckpt_cache, lora_cache, refn_cache = get_cache_numbers("XY Plot")
+ # Pack cache numbers in a tuple
+ cache = (vae_cache, ckpt_cache, lora_cache, refn_cache)
+
+ # Add seed to every entry in the list
+ X_value = [v + seed for v in X_value] if "Seeds++ Batch" == X_type else X_value
+ Y_value = [v + seed for v in Y_value] if "Seeds++ Batch" == Y_type else Y_value
+
+ # Embedd original prompts into prompt variables
+ positive_prompt = (positive_prompt, positive_prompt)
+ negative_prompt = (negative_prompt, negative_prompt)
+
+ # Set lora_stack to None if one of types are LoRA
+ if "LoRA" in X_type or "LoRA" in Y_type:
+ lora_stack = None
+
+ # Define the manipulated and static Control Net Variables with a tuple with shape (cn_1, cn_2, cn_3).
+ # The information in this tuple will be used by the plotter to properly plot Control Net XY input types.
+ cn_1, cn_2, cn_3 = None, None, None
+ # If X_type has "ControlNet" or both X_type and Y_type have "ControlNet"
+ if "ControlNet" in X_type:
+ cn_1, cn_2, cn_3 = X_value[0][0][2], X_value[0][0][3], X_value[0][0][4]
+ # If only Y_type has "ControlNet" and not X_type
+ elif "ControlNet" in Y_type:
+ cn_1, cn_2, cn_3 = Y_value[0][0][2], Y_value[0][0][3], Y_value[0][0][4]
+ # Additional checks for other substrings
+ if "ControlNetStrength" in X_type or "ControlNetStrength" in Y_type:
+ cn_1 = None
+ if "ControlNetStart%" in X_type or "ControlNetStart%" in Y_type:
+ cn_2 = None
+ if "ControlNetEnd%" in X_type or "ControlNetEnd%" in Y_type:
+ cn_3 = None
+ # Embed the information in cnet_stack
+ cnet_stack = (cnet_stack, (cn_1, cn_2, cn_3))
+
+ # Optimize image generation by prioritization:
+ priority = [
+ "Checkpoint",
+ "Refiner",
+ "LoRA",
+ "VAE",
+ ]
+ conditioners = {
+ "Positive Prompt S/R",
+ "Negative Prompt S/R",
+ "AScore+",
+ "AScore-",
+ "Clip Skip",
+ "Clip Skip (Refiner)",
+ "ControlNetStrength",
+ "ControlNetStart%",
+ "ControlNetEnd%"
+ }
+ # Get priority values; return a high number if the type is not in priority list
+ x_priority = priority.index(X_type) if X_type in priority else 999
+ y_priority = priority.index(Y_type) if Y_type in priority else 999
+
+ # Check if both are conditioners
+ are_both_conditioners = X_type in conditioners and Y_type in conditioners
+
+ # Special cases
+ is_special_case = (
+ (X_type == "Refiner On/Off" and Y_type in ["RefineStep", "Steps"]) or
+ (X_type == "Nothing" and Y_type != "Nothing")
+ )
+
+ # Determine whether to flip
+ flip_xy = (y_priority < x_priority and not are_both_conditioners) or is_special_case
+
+ # Perform the flip if necessary
+ if flip_xy:
+ X_type, Y_type = Y_type, X_type
+ X_value, Y_value = Y_value, X_value
+
+ #_______________________________________________________________________________________________________
+ # The below code will clean from the cache any ckpt/vae/lora models it will not be reusing.
+ # Note: Special LoRA types will not trigger cache: "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"
+
+ # Map the type names to the dictionaries
+ dict_map = {"VAE": [], "Checkpoint": [], "LoRA": [], "Refiner": []}
+
+ # Create a list of tuples with types and values
+ type_value_pairs = [(X_type, X_value.copy()), (Y_type, Y_value.copy())]
+
+ # Iterate over type-value pairs
+ for t, v in type_value_pairs:
+ if t in dict_map:
+ # Flatten the list of lists of tuples if the type is "LoRA"
+ if t == "LoRA":
+ dict_map[t] = [item for sublist in v for item in sublist]
+ else:
+ dict_map[t] = v
+
+ vae_dict = dict_map.get("VAE", [])
+
+ # Construct ckpt_dict and also update vae_dict based on the third entry of the tuples in dict_map["Checkpoint"]
+ if dict_map.get("Checkpoint", []):
+ ckpt_dict = [t[0] for t in dict_map["Checkpoint"]]
+ for t in dict_map["Checkpoint"]:
+ if t[2] is not None and t[2] != "Baked VAE":
+ vae_dict.append(t[2])
+ else:
+ ckpt_dict = []
+
+ lora_dict = [[t,] for t in dict_map.get("LoRA", [])] if dict_map.get("LoRA", []) else []
+
+ # Construct refn_dict
+ if dict_map.get("Refiner", []):
+ refn_dict = [t[0] for t in dict_map["Refiner"]]
+ else:
+ refn_dict = []
+
+ # If both ckpt_dict and lora_dict are not empty, manipulate lora_dict as described
+ if ckpt_dict and lora_dict:
+ lora_dict = [(lora_stack, ckpt) for ckpt in ckpt_dict for lora_stack in lora_dict]
+ # If lora_dict is not empty and ckpt_dict is empty, insert ckpt_name into each tuple in lora_dict
+ elif lora_dict:
+ lora_dict = [(lora_stack, ckpt_name) for lora_stack in lora_dict]
+
+ # Avoid caching models accross both X and Y
+ if X_type == "Checkpoint":
+ lora_dict = []
+ refn_dict = []
+ elif X_type == "Refiner":
+ ckpt_dict = []
+ lora_dict = []
+ elif X_type == "LoRA":
+ ckpt_dict = []
+ refn_dict = []
+
+ ### Print dict_arrays for debugging
+ ###print(f"vae_dict={vae_dict}\nckpt_dict={ckpt_dict}\nlora_dict={lora_dict}\nrefn_dict={refn_dict}")
+
+ # Clean values that won't be reused
+ clear_cache_by_exception(xyplot_id, vae_dict=vae_dict, ckpt_dict=ckpt_dict, lora_dict=lora_dict, refn_dict=refn_dict)
+
+ ### Print loaded_objects for debugging
+ ###print_loaded_objects_entries()
+
+ #_______________________________________________________________________________________________________
+ # Function that changes appropiate variables for next processed generations (also generates XY_labels)
+ def define_variable(var_type, var, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
+ clip_skip, refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,
+ lora_stack, cnet_stack, var_label, num_label):
+
+ # Define default max label size limit
+ max_label_len = 42
+
+ # If var_type is "AddNoise", update 'add_noise' with 'var', and generate text label
+ if var_type == "AddNoise":
+ add_noise = var
+ text = f"AddNoise: {add_noise}"
+
+ # If var_type is "Seeds++ Batch", generate text label
+ elif var_type == "Seeds++ Batch":
+ seed = var
+ text = f"Seed: {seed}"
+
+ # If var_type is "Steps", update 'steps' with 'var' and generate text label
+ elif var_type == "Steps":
+ steps = var
+ text = f"Steps: {steps}"
+
+ # If var_type is "StartStep", update 'start_at_step' with 'var' and generate text label
+ elif var_type == "StartStep":
+ start_at_step = var
+ text = f"StartStep: {start_at_step}"
+
+ # If var_type is "EndStep", update 'end_at_step' with 'var' and generate text label
+ elif var_type == "EndStep":
+ end_at_step = var
+ text = f"EndStep: {end_at_step}"
+
+ # If var_type is "RefineStep", update 'end_at_step' with 'var' and generate text label
+ elif var_type == "RefineStep":
+ end_at_step = var
+ text = f"RefineStep: {end_at_step}"
+
+ # If var_type is "ReturnNoise", update 'return_with_leftover_noise' with 'var', and generate text label
+ elif var_type == "ReturnNoise":
+ return_with_leftover_noise = var
+ text = f"ReturnNoise: {return_with_leftover_noise}"
+
+ # If var_type is "CFG Scale", update cfg with var and generate text label
+ elif var_type == "CFG Scale":
+ cfg = var
+ text = f"CFG: {round(cfg,2)}"
+
+ # If var_type is "Sampler", update sampler_name and scheduler with var, and generate text label
+ elif var_type == "Sampler":
+ sampler_name = var[0]
+ if var[1] == "":
+ text = f"{sampler_name}"
+ else:
+ if var[1] != None:
+ scheduler = (var[1], scheduler[1])
+ else:
+ scheduler = (scheduler[1], scheduler[1])
+ text = f"{sampler_name} ({scheduler[0]})"
+ text = text.replace("ancestral", "a").replace("uniform", "u").replace("exponential","exp")
+
+ # If var_type is "Scheduler", update scheduler and generate labels
+ elif var_type == "Scheduler":
+ if len(var) == 2:
+ scheduler = (var[0], scheduler[1])
+ text = f"{sampler_name} ({scheduler[0]})"
+ else:
+ scheduler = (var, scheduler[1])
+ text = f"{scheduler[0]}"
+ text = text.replace("ancestral", "a").replace("uniform", "u").replace("exponential","exp")
+
+ # If var_type is "Denoise", update denoise and generate labels
+ elif var_type == "Denoise":
+ denoise = var
+ text = f"Denoise: {round(denoise, 2)}"
+
+ # If var_type is "VAE", update vae_name and generate labels
+ elif var_type == "VAE":
+ vae_name = var
+ vae_filename = os.path.splitext(os.path.basename(vae_name))[0]
+ text = f"VAE: {vae_filename}"
+
+ # If var_type is "Positive Prompt S/R", update positive_prompt and generate labels
+ elif var_type == "Positive Prompt S/R":
+ search_txt, replace_txt = var
+ if replace_txt != None:
+ positive_prompt = (positive_prompt[1].replace(search_txt, replace_txt, 1), positive_prompt[1])
+ else:
+ positive_prompt = (positive_prompt[1], positive_prompt[1])
+ replace_txt = search_txt
+ text = f"{replace_txt}"
+
+ # If var_type is "Negative Prompt S/R", update negative_prompt and generate labels
+ elif var_type == "Negative Prompt S/R":
+ search_txt, replace_txt = var
+ if replace_txt:
+ negative_prompt = (negative_prompt[1].replace(search_txt, replace_txt, 1), negative_prompt[1])
+ else:
+ negative_prompt = (negative_prompt[1], negative_prompt[1])
+ replace_txt = search_txt
+ text = f"(-) {replace_txt}"
+
+ # If var_type is "AScore+", update positive ascore and generate labels
+ elif var_type == "AScore+":
+ ascore = (var,ascore[1])
+ text = f"+AScore: {ascore[0]}"
+
+ # If var_type is "AScore-", update negative ascore and generate labels
+ elif var_type == "AScore-":
+ ascore = (ascore[0],var)
+ text = f"-AScore: {ascore[1]}"
+
+ # If var_type is "Checkpoint", update model and clip (if needed) and generate labels
+ elif var_type == "Checkpoint":
+ ckpt_name = var[0]
+ if var[1] == None:
+ clip_skip = (clip_skip[1],clip_skip[1])
+ else:
+ clip_skip = (var[1],clip_skip[1])
+ if var[2] != None:
+ vae_name = var[2]
+ ckpt_filename = os.path.splitext(os.path.basename(ckpt_name))[0]
+ text = f"{ckpt_filename}"
+
+ # If var_type is "Refiner", update model and clip (if needed) and generate labels
+ elif var_type == "Refiner":
+ refiner_name = var[0]
+ if var[1] == None:
+ refiner_clip_skip = (refiner_clip_skip[1],refiner_clip_skip[1])
+ else:
+ refiner_clip_skip = (var[1],refiner_clip_skip[1])
+ ckpt_filename = os.path.splitext(os.path.basename(refiner_name))[0]
+ text = f"{ckpt_filename}"
+
+ # If var_type is "Refiner On/Off", set end_at_step = max steps and generate labels
+ elif var_type == "Refiner On/Off":
+ end_at_step = int(var * steps)
+ text = f"Refiner: {'On' if var < 1 else 'Off'}"
+
+ elif var_type == "Clip Skip":
+ clip_skip = (var, clip_skip[1])
+ text = f"ClipSkip ({clip_skip[0]})"
+
+ elif var_type == "Clip Skip (Refiner)":
+ refiner_clip_skip = (var, refiner_clip_skip[1])
+ text = f"RefClipSkip ({refiner_clip_skip[0]})"
+
+ elif "LoRA" in var_type:
+ if not lora_stack:
+ lora_stack = var.copy()
+ else:
+ # Updating the first tuple of lora_stack
+ lora_stack[0] = tuple(v if v is not None else lora_stack[0][i] for i, v in enumerate(var[0]))
+
+ max_label_len = 50 + (12 * (len(lora_stack) - 1))
+ lora_name, lora_model_wt, lora_clip_wt = lora_stack[0]
+ lora_filename = os.path.splitext(os.path.basename(lora_name))[0]
+
+ if var_type == "LoRA":
+ if len(lora_stack) == 1:
+ lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
+ lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')
+ lora_filename = lora_filename[:max_label_len - len(f"LoRA: ({lora_model_wt})")]
+ if lora_model_wt == lora_clip_wt:
+ text = f"LoRA: {lora_filename}({lora_model_wt})"
+ else:
+ text = f"LoRA: {lora_filename}({lora_model_wt},{lora_clip_wt})"
+ elif len(lora_stack) > 1:
+ lora_filenames = [os.path.splitext(os.path.basename(lora_name))[0] for lora_name, _, _ in
+ lora_stack]
+ lora_details = [(format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.'),
+ format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')) for
+ _, lora_model_wt, lora_clip_wt in lora_stack]
+ non_name_length = sum(
+ len(f"({lora_details[i][0]},{lora_details[i][1]})") + 2 for i in range(len(lora_stack)))
+ available_space = max_label_len - non_name_length
+ max_name_length = available_space // len(lora_stack)
+ lora_filenames = [filename[:max_name_length] for filename in lora_filenames]
+ text_elements = [
+ f"{lora_filename}({lora_details[i][0]})" if lora_details[i][0] == lora_details[i][1]
+ else f"{lora_filename}({lora_details[i][0]},{lora_details[i][1]})" for i, lora_filename in
+ enumerate(lora_filenames)]
+ text = " ".join(text_elements)
+
+ elif var_type == "LoRA Batch":
+ text = f"LoRA: {lora_filename}"
+
+ elif var_type == "LoRA Wt":
+ lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
+ text = f"LoRA Wt: {lora_model_wt}"
+
+ elif var_type == "LoRA MStr":
+ lora_model_wt = format(float(lora_model_wt), ".2f").rstrip('0').rstrip('.')
+ text = f"LoRA Mstr: {lora_model_wt}"
+
+ elif var_type == "LoRA CStr":
+ lora_clip_wt = format(float(lora_clip_wt), ".2f").rstrip('0').rstrip('.')
+ text = f"LoRA Cstr: {lora_clip_wt}"
+
+ elif var_type in ["ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"]:
+ if "Strength" in var_type:
+ entry_index = 2
+ elif "Start%" in var_type:
+ entry_index = 3
+ elif "End%" in var_type:
+ entry_index = 4
+
+ # If the first entry of cnet_stack is None, set it to var
+ if cnet_stack[0] is None:
+ cnet_stack = (var, cnet_stack[1])
+ else:
+ # Extract the desired entry from var's first tuple
+ entry_from_var = var[0][entry_index]
+
+ # Extract the first tuple from cnet_stack[0][0] and make it mutable
+ first_cn_entry = list(cnet_stack[0][0])
+
+ # Replace the appropriate entry
+ first_cn_entry[entry_index] = entry_from_var
+
+ # Further update first_cn_entry based on cnet_stack[1]
+ for i, value in enumerate(cnet_stack[1][-3:]): # Considering last 3 entries
+ if value is not None:
+ first_cn_entry[i + 2] = value # "+2" to offset for the first 2 entries of the tuple
+
+ # Convert back to tuple for the updated values
+ updated_first_entry = tuple(first_cn_entry)
+
+ # Construct the updated cnet_stack[0] using the updated_first_entry and the rest of the values from cnet_stack[0]
+ updated_cnet_stack_0 = [updated_first_entry] + list(cnet_stack[0][1:])
+
+ # Update cnet_stack
+ cnet_stack = (updated_cnet_stack_0, cnet_stack[1])
+
+ # Print the desired value
+ text = f'{var_type}: {round(cnet_stack[0][0][entry_index], 3)}'
+
+ elif var_type == "XY_Capsule":
+ text = var.getLabel()
+
+ else: # No matching type found
+ text=""
+
+ def truncate_texts(texts, num_label, max_label_len):
+ truncate_length = max(min(max(len(text) for text in texts), max_label_len), 24)
+
+ return [text if len(text) <= truncate_length else text[:truncate_length] + "..." for text in
+ texts]
+
+ # Add the generated text to var_label if it's not full
+ if len(var_label) < num_label:
+ var_label.append(text)
+
+ # If var_type VAE , truncate entries in the var_label list when it's full
+ if len(var_label) == num_label and (var_type == "VAE" or var_type == "Checkpoint"
+ or var_type == "Refiner" or "LoRA" in var_type):
+ var_label = truncate_texts(var_label, num_label, max_label_len)
+
+ # Return the modified variables
+ return add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
+ sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip, \
+ refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
+ lora_stack, cnet_stack, var_label
+
+ #_______________________________________________________________________________________________________
+ # The function below is used to optimally load Checkpoint/LoRA/VAE models between generations.
+ def define_model(model, clip, clip_skip, refiner_model, refiner_clip, refiner_clip_skip,
+ ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
+ positive_prompt, negative_prompt, ascore, vae, vae_name, lora_stack, cnet_stack, index,
+ types, xyplot_id, cache, sampler_type, empty_latent_width, empty_latent_height):
+
+ # Variable to track wether to encode prompt or not
+ encode = False
+ encode_refiner = False
+
+ # Unpack types tuple
+ X_type, Y_type = types
+
+ # Note: Index is held at 0 when Y_type == "Nothing"
+
+ # Load Checkpoint if required. If Y_type is LoRA, required models will be loaded by load_lora func.
+ if (X_type == "Checkpoint" and index == 0 and Y_type != "LoRA"):
+ if lora_stack is None:
+ model, clip, _ = load_checkpoint(ckpt_name, xyplot_id, cache=cache[1])
+ else: # Load Efficient Loader LoRA
+ model, clip = load_lora(lora_stack, ckpt_name, xyplot_id,
+ cache=None, ckpt_cache=cache[1])
+ encode = True
+
+ # Load LoRA if required
+ elif (X_type == "LoRA" and index == 0):
+ # Don't cache Checkpoints
+ model, clip = load_lora(lora_stack, ckpt_name, xyplot_id, cache=cache[2])
+ encode = True
+ elif Y_type == "LoRA": # X_type must be Checkpoint, so cache those as defined
+ model, clip = load_lora(lora_stack, ckpt_name, xyplot_id,
+ cache=None, ckpt_cache=cache[1])
+ encode = True
+ elif X_type == "LoRA Batch" or X_type == "LoRA Wt" or X_type == "LoRA MStr" or X_type == "LoRA CStr":
+ # Don't cache Checkpoints or LoRAs
+ model, clip = load_lora(lora_stack, ckpt_name, xyplot_id, cache=0)
+ encode = True
+
+ if (X_type == "Refiner" and index == 0) or Y_type == "Refiner":
+ refiner_model, refiner_clip, _ = \
+ load_checkpoint(refiner_name, xyplot_id, output_vae=False, cache=cache[3], ckpt_type="refn")
+ encode_refiner = True
+
+ # Encode base prompt if required
+ encode_types = ["Positive Prompt S/R", "Negative Prompt S/R", "Clip Skip", "ControlNetStrength",
+ "ControlNetStart%", "ControlNetEnd%"]
+ if (X_type in encode_types and index == 0) or Y_type in encode_types:
+ encode = True
+
+ # Encode refiner prompt if required
+ encode_refiner_types = ["Positive Prompt S/R", "Negative Prompt S/R", "AScore+", "AScore-",
+ "Clip Skip (Refiner)"]
+ if (X_type in encode_refiner_types and index == 0) or Y_type in encode_refiner_types:
+ encode_refiner = True
+
+ # Encode base prompt
+ if encode == True:
+ positive, negative, clip = \
+ encode_prompts(positive_prompt, negative_prompt, token_normalization, weight_interpretation,
+ clip, clip_skip, refiner_clip, refiner_clip_skip, ascore, sampler_type == "sdxl",
+ empty_latent_width, empty_latent_height, return_type="base")
+ # Apply ControlNet Stack if given
+ if cnet_stack:
+ controlnet_conditioning = TSC_Apply_ControlNet_Stack().apply_cnet_stack(positive, negative, cnet_stack)
+ positive, negative = controlnet_conditioning[0], controlnet_conditioning[1]
+
+ if encode_refiner == True:
+ refiner_positive, refiner_negative, refiner_clip = \
+ encode_prompts(positive_prompt, negative_prompt, token_normalization, weight_interpretation,
+ clip, clip_skip, refiner_clip, refiner_clip_skip, ascore, sampler_type == "sdxl",
+ empty_latent_width, empty_latent_height, return_type="refiner")
+
+ # Load VAE if required
+ if (X_type == "VAE" and index == 0) or Y_type == "VAE":
+ #vae = load_vae(vae_name, xyplot_id, cache=cache[0])
+ vae = get_bvae_by_ckpt_name(ckpt_name) if vae_name == "Baked VAE" \
+ else load_vae(vae_name, xyplot_id, cache=cache[0])
+ elif X_type == "Checkpoint" and index == 0 and vae_name:
+ vae = get_bvae_by_ckpt_name(ckpt_name) if vae_name == "Baked VAE" \
+ else load_vae(vae_name, xyplot_id, cache=cache[0])
+
+ return model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae
+
+ # ______________________________________________________________________________________________________
+ # The below function is used to generate the results based on all the processed variables
+ def process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, positive, negative,
+ refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode,
+ sampler_type, latent_list=[], image_tensor_list=[], image_pil_list=[], xy_capsule=None):
+
+ capsule_result = None
+ if xy_capsule is not None:
+ capsule_result = xy_capsule.get_result(model, clip, vae)
+ if capsule_result is not None:
+ image, latent = capsule_result
+ latent_list.append(latent)
+
+ if capsule_result is None:
+
+ samples, images, _, _ = process_latent_image(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
+ latent_image, denoise, sampler_type, add_noise, start_at_step,
+ end_at_step, return_with_leftover_noise, refiner_model,
+ refiner_positive, refiner_negative, vae, vae_decode, preview_method)
+
+ # Add the latent tensor to the tensors list
+ latent_list.append(samples)
+
+ # Decode the latent tensor if required
+ image = images if images is not None else vae_decode_latent(vae, samples, vae_decode)
+
+ if xy_capsule is not None:
+ xy_capsule.set_result(image, samples)
+
+ # Add the resulting image tensor to image_tensor_list
+ image_tensor_list.append(image)
+
+ # Convert the image from tensor to PIL Image and add it to the image_pil_list
+ image_pil_list.append(tensor2pil(image))
+
+ # Return the touched variables
+ return latent_list, image_tensor_list, image_pil_list
+
+ # ______________________________________________________________________________________________________
+ # The below section is the heart of the XY Plot image generation
+
+ # Initiate Plot label text variables X/Y_label
+ X_label = []
+ Y_label = []
+
+ # Store the KSamplers original scheduler inside the same scheduler variable
+ scheduler = (scheduler, scheduler)
+
+ # Store the Eff Loaders original clip_skips inside the same clip_skip variables
+ clip_skip = (clip_skip, clip_skip)
+ refiner_clip_skip = (refiner_clip_skip, refiner_clip_skip)
+
+ # Store types in a Tuple for easy function passing
+ types = (X_type, Y_type)
+
+ # Clone original model parameters
+ def clone_or_none(*originals):
+ cloned_items = []
+ for original in originals:
+ try:
+ cloned_items.append(original.clone())
+ except (AttributeError, TypeError):
+ # If not clonable, just append the original item
+ cloned_items.append(original)
+ return cloned_items
+ original_model, original_clip, original_positive, original_negative,\
+ original_refiner_model, original_refiner_clip, original_refiner_positive, original_refiner_negative =\
+ clone_or_none(model, clip, positive, negative, refiner_model, refiner_clip, refiner_positive, refiner_negative)
+
+ # Fill Plot Rows (X)
+ for X_index, X in enumerate(X_value):
+
+ # Define X parameters and generate labels
+ add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
+ sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip,\
+ refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
+ lora_stack, cnet_stack, X_label = \
+ define_variable(X_type, X, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name,
+ ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
+ negative_prompt, ascore, lora_stack, cnet_stack, X_label, len(X_value))
+
+ if X_type != "Nothing" and Y_type == "Nothing":
+ if X_type == "XY_Capsule":
+ model, clip, refiner_model, refiner_clip = \
+ clone_or_none(original_model, original_clip, original_refiner_model, original_refiner_clip)
+ model, clip, vae = X.pre_define_model(model, clip, vae)
+
+ # Models & Conditionings
+ model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
+ define_model(model, clip, clip_skip[0], refiner_model, refiner_clip, refiner_clip_skip[0],
+ ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
+ positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],
+ 0, types, xyplot_id, cache, sampler_type, empty_latent_width, empty_latent_height)
+
+ xy_capsule = None
+ if X_type == "XY_Capsule":
+ xy_capsule = X
+
+ # Generate Results
+ latent_list, image_tensor_list, image_pil_list = \
+ process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler[0], positive, negative,
+ refiner_positive, refiner_negative, latent_image, denoise, vae, vae_decode, sampler_type, xy_capsule=xy_capsule)
+
+ elif X_type != "Nothing" and Y_type != "Nothing":
+ for Y_index, Y in enumerate(Y_value):
+
+ if Y_type == "XY_Capsule" or X_type == "XY_Capsule":
+ model, clip, refiner_model, refiner_clip = \
+ clone_or_none(original_model, original_clip, original_refiner_model, original_refiner_clip)
+
+ if Y_type == "XY_Capsule" and X_type == "XY_Capsule":
+ Y.set_x_capsule(X)
+
+ # Define Y parameters and generate labels
+ add_noise, seed, steps, start_at_step, end_at_step, return_with_leftover_noise, cfg,\
+ sampler_name, scheduler, denoise, vae_name, ckpt_name, clip_skip,\
+ refiner_name, refiner_clip_skip, positive_prompt, negative_prompt, ascore,\
+ lora_stack, cnet_stack, Y_label = \
+ define_variable(Y_type, Y, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name,
+ ckpt_name, clip_skip, refiner_name, refiner_clip_skip, positive_prompt,
+ negative_prompt, ascore, lora_stack, cnet_stack, Y_label, len(Y_value))
+
+ if Y_type == "XY_Capsule":
+ model, clip, vae = Y.pre_define_model(model, clip, vae)
+ elif X_type == "XY_Capsule":
+ model, clip, vae = X.pre_define_model(model, clip, vae)
+
+ # Models & Conditionings
+ model, positive, negative, refiner_model, refiner_positive, refiner_negative, vae = \
+ define_model(model, clip, clip_skip[0], refiner_model, refiner_clip, refiner_clip_skip[0],
+ ckpt_name, refiner_name, positive, negative, refiner_positive, refiner_negative,
+ positive_prompt[0], negative_prompt[0], ascore, vae, vae_name, lora_stack, cnet_stack[0],
+ Y_index, types, xyplot_id, cache, sampler_type, empty_latent_width,
+ empty_latent_height)
+
+ # Generate Results
+ xy_capsule = None
+ if Y_type == "XY_Capsule":
+ xy_capsule = Y
+
+ latent_list, image_tensor_list, image_pil_list = \
+ process_values(model, refiner_model, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler[0],
+ positive, negative, refiner_positive, refiner_negative, latent_image,
+ denoise, vae, vae_decode, sampler_type, xy_capsule=xy_capsule)
+
+ # Clean up cache
+ if cache_models == "False":
+ clear_cache_by_exception(xyplot_id, vae_dict=[], ckpt_dict=[], lora_dict=[], refn_dict=[])
+ else:
+ # Avoid caching models accross both X and Y
+ if X_type == "Checkpoint":
+ clear_cache_by_exception(xyplot_id, lora_dict=[], refn_dict=[])
+ elif X_type == "Refiner":
+ clear_cache_by_exception(xyplot_id, ckpt_dict=[], lora_dict=[])
+ elif X_type == "LoRA":
+ clear_cache_by_exception(xyplot_id, ckpt_dict=[], refn_dict=[])
+
+ # __________________________________________________________________________________________________________
+ # Function for printing all plot variables (WARNING: This function is an absolute mess)
+ def print_plot_variables(X_type, Y_type, X_value, Y_value, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
+ clip_skip, refiner_name, refiner_clip_skip, ascore, lora_stack, cnet_stack, sampler_type,
+ num_rows, num_cols, i_height, i_width):
+
+ print("-" * 40) # Print an empty line followed by a separator line
+ print(f"{xyplot_message('XY Plot Results:')}")
+
+ def get_vae_name(X_type, Y_type, X_value, Y_value, vae_name):
+ if X_type == "VAE":
+ vae_name = "\n ".join(map(lambda x: os.path.splitext(os.path.basename(str(x)))[0], X_value))
+ elif Y_type == "VAE":
+ vae_name = "\n ".join(map(lambda y: os.path.splitext(os.path.basename(str(y)))[0], Y_value))
+ elif vae_name:
+ vae_name = os.path.splitext(os.path.basename(str(vae_name)))[0]
+ else:
+ vae_name = ""
+ return vae_name
+
+ def get_clip_skip(X_type, Y_type, X_value, Y_value, cskip, mode):
+ clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)"
+ if X_type == clip_type:
+ cskip = ", ".join(map(str, X_value))
+ elif Y_type == clip_type:
+ cskip = ", ".join(map(str, Y_value))
+ elif cskip[1] != None:
+ cskip = cskip[1]
+ else:
+ cskip = ""
+ return cskip
+
+ def get_checkpoint_name(X_type, Y_type, X_value, Y_value, ckpt_name, clip_skip, mode, vae_name=None):
+
+ # If ckpt_name is None, return it as is
+ if ckpt_name is not None:
+ ckpt_name = os.path.basename(ckpt_name)
+
+ # Define types based on mode
+ primary_type = "Checkpoint" if mode == "ckpt" else "Refiner"
+ clip_type = "Clip Skip" if mode == "ckpt" else "Clip Skip (Refiner)"
+
+ # Determine ckpt and othr based on primary type
+ if X_type == primary_type:
+ ckpt_type, ckpt_value = X_type, X_value.copy()
+ othr_type, othr_value = Y_type, Y_value.copy()
+ elif Y_type == primary_type:
+ ckpt_type, ckpt_value = Y_type, Y_value.copy()
+ othr_type, othr_value = X_type, X_value.copy()
+ else:
+ # Process as per original function if mode is "ckpt"
+ clip_skip = get_clip_skip(X_type, Y_type, X_value, Y_value, clip_skip, mode)
+ if mode == "ckpt":
+ if vae_name:
+ vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
+ return ckpt_name, clip_skip, vae_name
+ else:
+ # For refn mode
+ return ckpt_name, clip_skip
+
+ # Process clip skip based on mode
+ if othr_type == clip_type:
+ clip_skip = ", ".join(map(str, othr_value))
+ elif ckpt_value[0][1] != None:
+ clip_skip = None
+
+ # Process vae_name based on mode
+ if mode == "ckpt":
+ if othr_type == "VAE":
+ vae_name = get_vae_name(X_type, Y_type, X_value, Y_value, vae_name)
+ elif ckpt_value[0][2] != None:
+ vae_name = None
+
+ def format_name(v, _type):
+ base = os.path.basename(v[0])
+ if _type == clip_type and v[1] is not None:
+ return base
+ elif _type == "VAE" and v[1] is not None and v[2] is not None:
+ return f"{base}({v[1]})"
+ elif v[1] is not None and v[2] is not None:
+ return f"{base}({v[1]}) + vae:{v[2]}"
+ elif v[1] is not None:
+ return f"{base}({v[1]})"
+ else:
+ return base
+
+ ckpt_name = "\n ".join([format_name(v, othr_type) for v in ckpt_value])
+ if mode == "ckpt":
+ return ckpt_name, clip_skip, vae_name
+ else:
+ return ckpt_name, clip_skip
+
+ def get_lora_name(X_type, Y_type, X_value, Y_value, lora_stack=None):
+ lora_name = lora_wt = lora_model_str = lora_clip_str = None
+
+ # Check for all possible LoRA types
+ lora_types = ["LoRA", "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"]
+
+ if X_type not in lora_types and Y_type not in lora_types:
+ if lora_stack:
+ names_list = []
+ for name, model_wt, clip_wt in lora_stack:
+ base_name = os.path.splitext(os.path.basename(name))[0]
+ formatted_str = f"{base_name}({round(model_wt, 3)},{round(clip_wt, 3)})"
+ names_list.append(formatted_str)
+ lora_name = f"[{', '.join(names_list)}]"
+ else:
+ if X_type in lora_types:
+ value = get_lora_sublist_name(X_type, X_value)
+ if X_type == "LoRA":
+ lora_name = value
+ lora_model_str = None
+ lora_clip_str = None
+ if X_type == "LoRA Batch":
+ lora_name = value
+ lora_model_str = X_value[0][0][1] if lora_model_str is None else lora_model_str
+ lora_clip_str = X_value[0][0][2] if lora_clip_str is None else lora_clip_str
+ elif X_type == "LoRA MStr":
+ lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
+ lora_model_str = value
+ lora_clip_str = X_value[0][0][2] if lora_clip_str is None else lora_clip_str
+ elif X_type == "LoRA CStr":
+ lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
+ lora_model_str = X_value[0][0][1] if lora_model_str is None else lora_model_str
+ lora_clip_str = value
+ elif X_type == "LoRA Wt":
+ lora_name = os.path.basename(X_value[0][0][0]) if lora_name is None else lora_name
+ lora_wt = value
+
+ if Y_type in lora_types:
+ value = get_lora_sublist_name(Y_type, Y_value)
+ if Y_type == "LoRA":
+ lora_name = value
+ lora_model_str = None
+ lora_clip_str = None
+ if Y_type == "LoRA Batch":
+ lora_name = value
+ lora_model_str = Y_value[0][0][1] if lora_model_str is None else lora_model_str
+ lora_clip_str = Y_value[0][0][2] if lora_clip_str is None else lora_clip_str
+ elif Y_type == "LoRA MStr":
+ lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
+ lora_model_str = value
+ lora_clip_str = Y_value[0][0][2] if lora_clip_str is None else lora_clip_str
+ elif Y_type == "LoRA CStr":
+ lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
+ lora_model_str = Y_value[0][0][1] if lora_model_str is None else lora_model_str
+ lora_clip_str = value
+ elif Y_type == "LoRA Wt":
+ lora_name = os.path.basename(Y_value[0][0][0]) if lora_name is None else lora_name
+ lora_wt = value
+
+ return lora_name, lora_wt, lora_model_str, lora_clip_str
+
+ def get_lora_sublist_name(lora_type, lora_value):
+ if lora_type == "LoRA" or lora_type == "LoRA Batch":
+ formatted_sublists = []
+ for sublist in lora_value:
+ formatted_entries = []
+ for x in sublist:
+ base_name = os.path.splitext(os.path.basename(str(x[0])))[0]
+ formatted_str = f"{base_name}({round(x[1], 3)},{round(x[2], 3)})" if lora_type == "LoRA" else f"{base_name}"
+ formatted_entries.append(formatted_str)
+ formatted_sublists.append(f"{', '.join(formatted_entries)}")
+ return "\n ".join(formatted_sublists)
+ elif lora_type == "LoRA MStr":
+ return ", ".join([str(round(x[0][1], 3)) for x in lora_value])
+ elif lora_type == "LoRA CStr":
+ return ", ".join([str(round(x[0][2], 3)) for x in lora_value])
+ elif lora_type == "LoRA Wt":
+ return ", ".join([str(round(x[0][1], 3)) for x in lora_value]) # assuming LoRA Wt uses the second value
+ else:
+ return ""
+
+ # VAE, Checkpoint, Clip Skip, LoRA
+ ckpt_name, clip_skip, vae_name = get_checkpoint_name(X_type, Y_type, X_value, Y_value, ckpt_name, clip_skip, "ckpt", vae_name)
+ lora_name, lora_wt, lora_model_str, lora_clip_str = get_lora_name(X_type, Y_type, X_value, Y_value, lora_stack)
+ refiner_name, refiner_clip_skip = get_checkpoint_name(X_type, Y_type, X_value, Y_value, refiner_name, refiner_clip_skip, "refn")
+
+ # AddNoise
+ add_noise = ", ".join(map(str, X_value)) if X_type == "AddNoise" else ", ".join(
+ map(str, Y_value)) if Y_type == "AddNoise" else add_noise
+
+ # Seeds++ Batch
+ seed = "\n ".join(map(str, X_value)) if X_type == "Seeds++ Batch" else "\n ".join(
+ map(str, Y_value)) if Y_type == "Seeds++ Batch" else seed
+
+ # Steps
+ steps = ", ".join(map(str, X_value)) if X_type == "Steps" else ", ".join(
+ map(str, Y_value)) if Y_type == "Steps" else steps
+
+ # StartStep
+ start_at_step = ", ".join(map(str, X_value)) if X_type == "StartStep" else ", ".join(
+ map(str, Y_value)) if Y_type == "StartStep" else start_at_step
+
+ # EndStep/RefineStep
+ end_at_step = ", ".join(map(str, X_value)) if X_type in ["EndStep", "RefineStep"] else ", ".join(
+ map(str, Y_value)) if Y_type in ["EndStep", "RefineStep"] else end_at_step
+
+ # ReturnNoise
+ return_with_leftover_noise = ", ".join(map(str, X_value)) if X_type == "ReturnNoise" else ", ".join(
+ map(str, Y_value)) if Y_type == "ReturnNoise" else return_with_leftover_noise
+
+ # CFG
+ cfg = ", ".join(map(str, X_value)) if X_type == "CFG Scale" else ", ".join(
+ map(str, Y_value)) if Y_type == "CFG Scale" else round(cfg,3)
+
+ # Sampler/Scheduler
+ if X_type == "Sampler":
+ if Y_type == "Scheduler":
+ sampler_name = ", ".join([f"{x[0]}" for x in X_value])
+ scheduler = ", ".join([f"{y}" for y in Y_value])
+ else:
+ sampler_name = ", ".join([f"{x[0]}({x[1] if x[1] != '' and x[1] is not None else scheduler[1]})" for x in X_value])
+ scheduler = "_"
+ elif Y_type == "Sampler":
+ if X_type == "Scheduler":
+ sampler_name = ", ".join([f"{y[0]}" for y in Y_value])
+ scheduler = ", ".join([f"{x}" for x in X_value])
+ else:
+ sampler_name = ", ".join([f"{y[0]}({y[1] if y[1] != '' and y[1] is not None else scheduler[1]})" for y in Y_value])
+ scheduler = "_"
+ else:
+ scheduler = ", ".join([str(x[0]) if isinstance(x, tuple) else str(x) for x in X_value]) if X_type == "Scheduler" else \
+ ", ".join([str(y[0]) if isinstance(y, tuple) else str(y) for y in Y_value]) if Y_type == "Scheduler" else scheduler[0]
+
+ # Denoise
+ denoise = ", ".join(map(str, X_value)) if X_type == "Denoise" else ", ".join(
+ map(str, Y_value)) if Y_type == "Denoise" else round(denoise,3)
+
+ # Check if ascore is None
+ if ascore is None:
+ pos_ascore = neg_ascore = None
+ else:
+ # Ascore+
+ pos_ascore = (", ".join(map(str, X_value)) if X_type == "Ascore+"
+ else ", ".join(map(str, Y_value)) if Y_type == "Ascore+" else round(ascore[0],3))
+ # Ascore-
+ neg_ascore = (", ".join(map(str, X_value)) if X_type == "Ascore-"
+ else ", ".join(map(str, Y_value)) if Y_type == "Ascore-" else round(ascore[1],3))
+
+ #..........................................PRINTOUTS....................................................
+ print(f"(X) {X_type}")
+ print(f"(Y) {Y_type}")
+ print(f"img_count: {len(X_value)*len(Y_value)}")
+ print(f"img_dims: {i_height} x {i_width}")
+ print(f"plot_dim: {num_cols} x {num_rows}")
+ print(f"ckpt: {ckpt_name if ckpt_name is not None else ''}")
+ if clip_skip:
+ print(f"clip_skip: {clip_skip}")
+ if sampler_type == "sdxl":
+ if refiner_clip_skip == "_":
+ print(f"refiner(clipskip): {refiner_name if refiner_name is not None else ''}")
+ else:
+ print(f"refiner: {refiner_name if refiner_name is not None else ''}")
+ print(f"refiner_clip_skip: {refiner_clip_skip if refiner_clip_skip is not None else ''}")
+ print(f"+ascore: {pos_ascore if pos_ascore is not None else ''}")
+ print(f"-ascore: {neg_ascore if neg_ascore is not None else ''}")
+ if lora_name:
+ print(f"lora: {lora_name}")
+ if lora_wt:
+ print(f"lora_wt: {lora_wt}")
+ if lora_model_str:
+ print(f"lora_mstr: {lora_model_str}")
+ if lora_clip_str:
+ print(f"lora_cstr: {lora_clip_str}")
+ if vae_name:
+ print(f"vae: {vae_name}")
+ if sampler_type == "advanced":
+ print(f"add_noise: {add_noise}")
+ print(f"seed: {seed}")
+ print(f"steps: {steps}")
+ if sampler_type == "advanced":
+ print(f"start_at_step: {start_at_step}")
+ print(f"end_at_step: {end_at_step}")
+ print(f"return_noise: {return_with_leftover_noise}")
+ if sampler_type == "sdxl":
+ print(f"start_at_step: {start_at_step}")
+ if X_type == "Refiner On/Off":
+ print(f"refine_at_percent: {X_value[0]}")
+ elif Y_type == "Refiner On/Off":
+ print(f"refine_at_percent: {Y_value[0]}")
+ else:
+ print(f"refine_at_step: {end_at_step}")
+ print(f"cfg: {cfg}")
+ if scheduler == "_":
+ print(f"sampler(scheduler): {sampler_name}")
+ else:
+ print(f"sampler: {sampler_name}")
+ print(f"scheduler: {scheduler}")
+ if sampler_type == "regular":
+ print(f"denoise: {denoise}")
+
+ if X_type == "Positive Prompt S/R" or Y_type == "Positive Prompt S/R":
+ positive_prompt = ", ".join([str(x[0]) if i == 0 else str(x[1]) for i, x in enumerate(
+ X_value)]) if X_type == "Positive Prompt S/R" else ", ".join(
+ [str(y[0]) if i == 0 else str(y[1]) for i, y in
+ enumerate(Y_value)]) if Y_type == "Positive Prompt S/R" else positive_prompt
+ print(f"+prompt_s/r: {positive_prompt}")
+
+ if X_type == "Negative Prompt S/R" or Y_type == "Negative Prompt S/R":
+ negative_prompt = ", ".join([str(x[0]) if i == 0 else str(x[1]) for i, x in enumerate(
+ X_value)]) if X_type == "Negative Prompt S/R" else ", ".join(
+ [str(y[0]) if i == 0 else str(y[1]) for i, y in
+ enumerate(Y_value)]) if Y_type == "Negative Prompt S/R" else negative_prompt
+ print(f"-prompt_s/r: {negative_prompt}")
+
+ if "ControlNet" in X_type or "ControlNet" in Y_type:
+ cnet_strength, cnet_start_pct, cnet_end_pct = cnet_stack[1]
+
+ if "ControlNet" in X_type:
+ if "Strength" in X_type:
+ cnet_strength = [str(round(inner_list[0][2], 3)) for inner_list in X_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
+ if "Start%" in X_type:
+ cnet_start_pct = [str(round(inner_list[0][3], 3)) for inner_list in X_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
+ if "End%" in X_type:
+ cnet_end_pct = [str(round(inner_list[0][4], 3)) for inner_list in X_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(inner_list[0]) >= 3]
+ if "ControlNet" in Y_type:
+ if "Strength" in Y_type:
+ cnet_strength = [str(round(inner_list[0][2], 3)) for inner_list in Y_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(
+ inner_list[0]) >= 3]
+ if "Start%" in Y_type:
+ cnet_start_pct = [str(round(inner_list[0][3], 3)) for inner_list in Y_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(
+ inner_list[0]) >= 3]
+ if "End%" in Y_type:
+ cnet_end_pct = [str(round(inner_list[0][4], 3)) for inner_list in Y_value if
+ isinstance(inner_list, list) and
+ inner_list and isinstance(inner_list[0], tuple) and len(
+ inner_list[0]) >= 3]
+
+ if "ControlNet" in X_type or "ControlNet" in Y_type:
+ print(f"cnet_strength: {', '.join(cnet_strength) if isinstance(cnet_strength, list) else cnet_strength}")
+ print(f"cnet_start%: {', '.join(cnet_start_pct) if isinstance(cnet_start_pct, list) else cnet_start_pct}")
+ print(f"cnet_end%: {', '.join(cnet_end_pct) if isinstance(cnet_end_pct, list) else cnet_end_pct}")
+
+ # ______________________________________________________________________________________________________
+ def adjusted_font_size(text, initial_font_size, i_width):
+ font = ImageFont.truetype(str(Path(font_path)), initial_font_size)
+ text_width = font.getlength(text)
+
+ if text_width > (i_width * 0.9):
+ scaling_factor = 0.9 # A value less than 1 to shrink the font size more aggressively
+ new_font_size = int(initial_font_size * (i_width / text_width) * scaling_factor)
+ else:
+ new_font_size = initial_font_size
+
+ return new_font_size
+
+ # ______________________________________________________________________________________________________
+
+ def rearrange_list_A(arr, num_cols, num_rows):
+ new_list = []
+ for i in range(num_rows):
+ for j in range(num_cols):
+ index = j * num_rows + i
+ new_list.append(arr[index])
+ return new_list
+
+ def rearrange_list_B(arr, num_rows, num_cols):
+ new_list = []
+ for i in range(num_rows):
+ for j in range(num_cols):
+ index = i * num_cols + j
+ new_list.append(arr[index])
+ return new_list
+
+ # Extract plot dimensions
+ num_rows = max(len(Y_value) if Y_value is not None else 0, 1)
+ num_cols = max(len(X_value) if X_value is not None else 0, 1)
+
+ # Flip X & Y results back if flipped earlier (for Checkpoint/LoRA For loop optimizations)
+ if flip_xy == True:
+ X_type, Y_type = Y_type, X_type
+ X_value, Y_value = Y_value, X_value
+ X_label, Y_label = Y_label, X_label
+ num_rows, num_cols = num_cols, num_rows
+ image_pil_list = rearrange_list_A(image_pil_list, num_rows, num_cols)
+ else:
+ image_pil_list = rearrange_list_B(image_pil_list, num_rows, num_cols)
+ image_tensor_list = rearrange_list_A(image_tensor_list, num_cols, num_rows)
+ latent_list = rearrange_list_A(latent_list, num_cols, num_rows)
+
+ # Extract final image dimensions
+ i_height, i_width = image_tensor_list[0].shape[1], image_tensor_list[0].shape[2]
+
+ # Print XY Plot Results
+ print_plot_variables(X_type, Y_type, X_value, Y_value, add_noise, seed, steps, start_at_step, end_at_step,
+ return_with_leftover_noise, cfg, sampler_name, scheduler, denoise, vae_name, ckpt_name,
+ clip_skip, refiner_name, refiner_clip_skip, ascore, lora_stack, cnet_stack,
+ sampler_type, num_rows, num_cols, i_height, i_width)
+
+ # Concatenate the 'samples' and 'noise_mask' tensors along the first dimension (dim=0)
+ keys = latent_list[0].keys()
+ result = {}
+ for key in keys:
+ tensors = [d[key] for d in latent_list]
+ result[key] = torch.cat(tensors, dim=0)
+ latent_list = result
+
+ # Store latent_list as last latent
+ ###update_value_by_id("latent", my_unique_id, latent_list)
+
+ # Calculate the dimensions of the white background image
+ border_size_top = i_width // 15
+
+ # Longest Y-label length
+ if len(Y_label) > 0:
+ Y_label_longest = max(len(s) for s in Y_label)
+ else:
+ # Handle the case when the sequence is empty
+ Y_label_longest = 0 # or any other appropriate value
+
+ Y_label_scale = min(Y_label_longest + 4,24) / 24
+
+ if Y_label_orientation == "Vertical":
+ border_size_left = border_size_top
+ else: # Assuming Y_label_orientation is "Horizontal"
+ # border_size_left is now min(i_width, i_height) plus 20% of the difference between the two
+ border_size_left = min(i_width, i_height) + int(0.2 * abs(i_width - i_height))
+ border_size_left = int(border_size_left * Y_label_scale)
+
+ # Modify the border size, background width and x_offset initialization based on Y_type and Y_label_orientation
+ if Y_type == "Nothing":
+ bg_width = num_cols * i_width + (num_cols - 1) * grid_spacing
+ x_offset_initial = 0
+ else:
+ if Y_label_orientation == "Vertical":
+ bg_width = num_cols * i_width + (num_cols - 1) * grid_spacing + 3 * border_size_left
+ x_offset_initial = border_size_left * 3
+ else: # Assuming Y_label_orientation is "Horizontal"
+ bg_width = num_cols * i_width + (num_cols - 1) * grid_spacing + border_size_left
+ x_offset_initial = border_size_left
+
+ # Modify the background height based on X_type
+ if X_type == "Nothing":
+ bg_height = num_rows * i_height + (num_rows - 1) * grid_spacing
+ y_offset = 0
+ else:
+ bg_height = num_rows * i_height + (num_rows - 1) * grid_spacing + 3 * border_size_top
+ y_offset = border_size_top * 3
+
+ # Create the white background image
+ background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
+
+ for row in range(num_rows):
+
+ # Initialize the X_offset
+ x_offset = x_offset_initial
+
+ for col in range(num_cols):
+ # Calculate the index for image_pil_list
+ index = col * num_rows + row
+ img = image_pil_list[index]
+
+ # Paste the image
+ background.paste(img, (x_offset, y_offset))
+
+ if row == 0 and X_type != "Nothing":
+ # Assign text
+ text = X_label[col]
+
+ # Add the corresponding X_value as a label above the image
+ initial_font_size = int(48 * img.width / 512)
+ font_size = adjusted_font_size(text, initial_font_size, img.width)
+ label_height = int(font_size*1.5)
+
+ # Create a white background label image
+ label_bg = Image.new('RGBA', (img.width, label_height), color=(255, 255, 255, 0))
+ d = ImageDraw.Draw(label_bg)
+
+ # Create the font object
+ font = ImageFont.truetype(str(Path(font_path)), font_size)
+
+ # Calculate the text size and the starting position
+ _, _, text_width, text_height = d.textbbox([0,0], text, font=font)
+ text_x = (img.width - text_width) // 2
+ text_y = (label_height - text_height) // 2
+
+ # Add the text to the label image
+ d.text((text_x, text_y), text, fill='black', font=font)
+
+ # Calculate the available space between the top of the background and the top of the image
+ available_space = y_offset - label_height
+
+ # Calculate the new Y position for the label image
+ label_y = available_space // 2
+
+ # Paste the label image above the image on the background using alpha_composite()
+ background.alpha_composite(label_bg, (x_offset, label_y))
+
+ if col == 0 and Y_type != "Nothing":
+ # Assign text
+ text = Y_label[row]
+
+ # Add the corresponding Y_value as a label to the left of the image
+ if Y_label_orientation == "Vertical":
+ initial_font_size = int(48 * i_width / 512) # Adjusting this to be same as X_label size
+ font_size = adjusted_font_size(text, initial_font_size, i_width)
+ else: # Assuming Y_label_orientation is "Horizontal"
+ initial_font_size = int(48 * (border_size_left/Y_label_scale) / 512) # Adjusting this to be same as X_label size
+ font_size = adjusted_font_size(text, initial_font_size, int(border_size_left/Y_label_scale))
+
+ # Create a white background label image
+ label_bg = Image.new('RGBA', (img.height, int(font_size*1.2)), color=(255, 255, 255, 0))
+ d = ImageDraw.Draw(label_bg)
+
+ # Create the font object
+ font = ImageFont.truetype(str(Path(font_path)), font_size)
+
+ # Calculate the text size and the starting position
+ _, _, text_width, text_height = d.textbbox([0,0], text, font=font)
+ text_x = (img.height - text_width) // 2
+ text_y = (font_size - text_height) // 2
+
+ # Add the text to the label image
+ d.text((text_x, text_y), text, fill='black', font=font)
+
+ # Rotate the label_bg 90 degrees counter-clockwise only if Y_label_orientation is "Vertical"
+ if Y_label_orientation == "Vertical":
+ label_bg = label_bg.rotate(90, expand=True)
+
+ # Calculate the available space between the left of the background and the left of the image
+ available_space = x_offset - label_bg.width
+
+ # Calculate the new X position for the label image
+ label_x = available_space // 2
+
+ # Calculate the Y position for the label image based on its orientation
+ if Y_label_orientation == "Vertical":
+ label_y = y_offset + (img.height - label_bg.height) // 2
+ else: # Assuming Y_label_orientation is "Horizontal"
+ label_y = y_offset + img.height - (img.height - label_bg.height) // 2
+
+ # Paste the label image to the left of the image on the background using alpha_composite()
+ background.alpha_composite(label_bg, (label_x, label_y))
+
+ # Update the x_offset
+ x_offset += img.width + grid_spacing
+
+ # Update the y_offset
+ y_offset += img.height + grid_spacing
+
+ xy_plot_image = pil2tensor(background)
+
+ # Generate the preview_images
+ preview_images = PreviewImage().save_images(xy_plot_image)["ui"]["images"]
+
+ # Generate output_images
+ output_images = torch.stack([tensor.squeeze() for tensor in image_tensor_list])
+
+ # Set the output_image the same as plot image defined by 'xyplot_as_output_image'
+ if xyplot_as_output_image == True:
+ output_images = xy_plot_image
+
+ # Print cache if set to true
+ if cache_models == "True":
+ print_loaded_objects_entries(xyplot_id, prompt)
+
+ print("-" * 40) # Print an empty line followed by a separator line
+
+ if sampler_type == "sdxl":
+ sdxl_tuple = original_model, original_clip, original_positive, original_negative,\
+ original_refiner_model, original_refiner_clip, original_refiner_positive, original_refiner_negative
+ result = (sdxl_tuple, latent_list, optional_vae, output_images,)
+ else:
+ result = (original_model, original_positive, original_negative, latent_list, optional_vae, output_images,)
+ return {"ui": {"images": preview_images}, "result": result}
+
+#=======================================================================================================================
+# TSC KSampler Adv (Efficient)
+class TSC_KSamplerAdvanced(TSC_KSampler):
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": (["enable", "disable"],),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+ "positive": ("CONDITIONING",),
+ "negative": ("CONDITIONING",),
+ "latent_image": ("LATENT",),
+ "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+ "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
+ "return_with_leftover_noise": (["disable", "enable"],),
+ "preview_method": (["auto", "latent2rgb", "taesd", "none"],),
+ "vae_decode": (["true", "true (tiled)", "false", "output only", "output only (tiled)"],),
+ },
+ "optional": {"optional_vae": ("VAE",),
+ "script": ("SCRIPT",), },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", },
+ }
+
+ RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "IMAGE",)
+ RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "IMAGE",)
+ OUTPUT_NODE = True
+ FUNCTION = "sample_adv"
+ CATEGORY = "Efficiency Nodes/Sampling"
+
+ def sample_adv(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
+ latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview_method, vae_decode,
+ prompt=None, extra_pnginfo=None, my_unique_id=None, optional_vae=(None,), script=None):
+
+ return super().sample(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative,
+ latent_image, preview_method, vae_decode, denoise=1.0, prompt=prompt, extra_pnginfo=extra_pnginfo, my_unique_id=my_unique_id,
+ optional_vae=optional_vae, script=script, add_noise=add_noise, start_at_step=start_at_step,end_at_step=end_at_step,
+ return_with_leftover_noise=return_with_leftover_noise,sampler_type="advanced")
+
+#=======================================================================================================================
+# TSC KSampler SDXL (Efficient)
+class TSC_KSamplerSDXL(TSC_KSampler):
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required":
+ {"sdxl_tuple": ("SDXL_TUPLE",),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
+ "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+ "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+ "latent_image": ("LATENT",),
+ "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+ "refine_at_step": ("INT", {"default": -1, "min": -1, "max": 10000}),
+ "preview_method": (["auto", "latent2rgb", "taesd", "none"],),
+ "vae_decode": (["true", "true (tiled)", "false", "output only", "output only (tiled)"],),
+ },
+ "optional": {"optional_vae": ("VAE",),
+ "script": ("SCRIPT",),},
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
+ }
+
+ RETURN_TYPES = ("SDXL_TUPLE", "LATENT", "VAE", "IMAGE",)
+ RETURN_NAMES = ("SDXL_TUPLE", "LATENT", "VAE", "IMAGE",)
+ OUTPUT_NODE = True
+ FUNCTION = "sample_sdxl"
+ CATEGORY = "Efficiency Nodes/Sampling"
+
+ def sample_sdxl(self, sdxl_tuple, noise_seed, steps, cfg, sampler_name, scheduler, latent_image,
+ start_at_step, refine_at_step, preview_method, vae_decode, prompt=None, extra_pnginfo=None,
+ my_unique_id=None, optional_vae=(None,), refiner_extras=None, script=None):
+ # sdxl_tuple sent through the 'model' channel
+ negative = None
+ return super().sample(sdxl_tuple, noise_seed, steps, cfg, sampler_name, scheduler,
+ refiner_extras, negative, latent_image, preview_method, vae_decode, denoise=1.0,
+ prompt=prompt, extra_pnginfo=extra_pnginfo, my_unique_id=my_unique_id, optional_vae=optional_vae,
+ script=script, add_noise=None, start_at_step=start_at_step, end_at_step=refine_at_step,
+ return_with_leftover_noise=None,sampler_type="sdxl")
+
+########################################################################################################################
+# Common XY Plot Functions/Variables
+XYPLOT_LIM = 50 #XY Plot default axis size limit
+XYPLOT_DEF = 3 #XY Plot default batch count
+CKPT_EXTENSIONS = LORA_EXTENSIONS = ['.safetensors', '.ckpt']
+VAE_EXTENSIONS = ['.safetensors', '.ckpt', '.pt']
+try:
+ xy_batch_default_path = os.path.abspath(os.sep) + "example_folder"
+except Exception:
+ xy_batch_default_path = ""
+
+def generate_floats(batch_count, first_float, last_float):
+ if batch_count > 1:
+ interval = (last_float - first_float) / (batch_count - 1)
+ return [round(first_float + i * interval, 3) for i in range(batch_count)]
+ else:
+ return [first_float] if batch_count == 1 else []
+
+def generate_ints(batch_count, first_int, last_int):
+ if batch_count > 1:
+ interval = (last_int - first_int) / (batch_count - 1)
+ values = [int(first_int + i * interval) for i in range(batch_count)]
+ else:
+ values = [first_int] if batch_count == 1 else []
+ values = list(set(values)) # Remove duplicates
+ values.sort() # Sort in ascending order
+ return values
+
+def get_batch_files(directory_path, valid_extensions, include_subdirs=False):
+ batch_files = []
+
+ try:
+ if include_subdirs:
+ # Using os.walk to get files from subdirectories
+ for dirpath, dirnames, filenames in os.walk(directory_path):
+ for file in filenames:
+ if any(file.endswith(ext) for ext in valid_extensions):
+ batch_files.append(os.path.join(dirpath, file))
+ else:
+ # Previous code for just the given directory
+ batch_files = [os.path.join(directory_path, f) for f in os.listdir(directory_path) if
+ os.path.isfile(os.path.join(directory_path, f)) and any(
+ f.endswith(ext) for ext in valid_extensions)]
+ except Exception as e:
+ print(f"Error while listing files in {directory_path}: {e}")
+
+ return batch_files
+
+def print_xy_values(xy_type, xy_value, xy_name):
+ print("===== XY Value Returns =====")
+ print(f"{xy_name} Values:")
+ print("- Type:", xy_type)
+ print("- Entries:", xy_value)
+ print("============================")
+
+#=======================================================================================================================
+# TSC XY Plot
+class TSC_XYplot:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "grid_spacing": ("INT", {"default": 0, "min": 0, "max": 500, "step": 5}),
+ "XY_flip": (["False","True"],),
+ "Y_label_orientation": (["Horizontal", "Vertical"],),
+ "cache_models": (["True", "False"],),
+ "ksampler_output_image": (["Images","Plot"],),},
+ "optional": {
+ "dependencies": ("DEPENDENCIES", ),
+ "X": ("XY", ),
+ "Y": ("XY", ),},
+ "hidden": {"my_unique_id": "UNIQUE_ID"},
+ }
+
+ RETURN_TYPES = ("SCRIPT",)
+ RETURN_NAMES = ("SCRIPT",)
+ FUNCTION = "XYplot"
+ CATEGORY = "Efficiency Nodes/Scripts"
+
+ def XYplot(self, grid_spacing, XY_flip, Y_label_orientation, cache_models, ksampler_output_image, my_unique_id,
+ dependencies=None, X=None, Y=None):
+
+ # Unpack X & Y Tuples if connected
+ if X != None:
+ X_type, X_value = X
+ else:
+ X_type = "Nothing"
+ X_value = [""]
+ if Y != None:
+ Y_type, Y_value = Y
+ else:
+ Y_type = "Nothing"
+ Y_value = [""]
+
+ # If types are the same exit. If one isn't "Nothing", print error
+ if X_type != "XY_Capsule" and (X_type == Y_type):
+ if X_type != "Nothing":
+ print(f"{error('XY Plot Error:')} X and Y input types must be different.")
+ return (None,)
+
+ # Check that dependencies are connected for specific plot types
+ encode_types = {
+ "Checkpoint", "Refiner",
+ "LoRA", "LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr",
+ "Positive Prompt S/R", "Negative Prompt S/R",
+ "AScore+", "AScore-",
+ "Clip Skip", "Clip Skip (Refiner)",
+ "ControlNetStrength", "ControlNetStart%", "ControlNetEnd%"
+ }
+
+ if X_type in encode_types or Y_type in encode_types:
+ if dependencies is None: # Not connected
+ print(f"{error('XY Plot Error:')} The dependencies input must be connected for certain plot types.")
+ # Return None
+ return (None,)
+
+ # Check if both X_type and Y_type are special lora_types
+ lora_types = {"LoRA Batch", "LoRA Wt", "LoRA MStr", "LoRA CStr"}
+ if (X_type in lora_types and Y_type not in lora_types) or (Y_type in lora_types and X_type not in lora_types):
+ print(
+ f"{error('XY Plot Error:')} Both X and Y must be connected to use the 'LoRA Plot' node.")
+ return (None,)
+
+ # Clean Schedulers from Sampler data (if other type is Scheduler)
+ if X_type == "Sampler" and Y_type == "Scheduler":
+ # Clear X_value Scheduler's
+ X_value = [(x[0], "") for x in X_value]
+ elif Y_type == "Sampler" and X_type == "Scheduler":
+ # Clear Y_value Scheduler's
+ Y_value = [(y[0], "") for y in Y_value]
+
+ # Embed information into "Scheduler" X/Y_values for text label
+ if X_type == "Scheduler" and Y_type != "Sampler":
+ # X_value second tuple value of each array entry = None
+ X_value = [(x, None) for x in X_value]
+
+ if Y_type == "Scheduler" and X_type != "Sampler":
+ # Y_value second tuple value of each array entry = None
+ Y_value = [(y, None) for y in Y_value]
+
+ # Clean VAEs from Checkpoint data if other type is VAE
+ if X_type == "Checkpoint" and Y_type == "VAE":
+ # Clear X_value VAE's
+ X_value = [(t[0], t[1], None) for t in X_value]
+ elif Y_type == "VAE" and X_type == "Checkpoint":
+ # Clear Y_value VAE's
+ Y_value = [(t[0], t[1], None) for t in Y_value]
+
+ # Flip X and Y
+ if XY_flip == "True":
+ X_type, Y_type = Y_type, X_type
+ X_value, Y_value = Y_value, X_value
+
+ # Define Ksampler output image behavior
+ xyplot_as_output_image = ksampler_output_image == "Plot"
+
+ # Pack xyplot tuple into its dictionary item under script
+ script = {"xyplot": (X_type, X_value, Y_type, Y_value, grid_spacing, Y_label_orientation, cache_models,
+ xyplot_as_output_image, my_unique_id, dependencies)}
+
+ return (script,)
+
+#=======================================================================================================================
+# TSC XY Plot: Seeds Values
+class TSC_XYplot_SeedsBatch:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, batch_count):
+ if batch_count == 0:
+ return (None,)
+ xy_type = "Seeds++ Batch"
+ xy_value = list(range(batch_count))
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Add/Return Noise
+class TSC_XYplot_AddReturnNoise:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "XY_type": (["add_noise", "return_with_leftover_noise"],)}
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, XY_type):
+ type_mapping = {
+ "add_noise": "AddNoise",
+ "return_with_leftover_noise": "ReturnNoise"
+ }
+ xy_type = type_mapping[XY_type]
+ xy_value = ["enable", "disable"]
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Step Values
+class TSC_XYplot_Steps:
+ parameters = ["steps","start_at_step", "end_at_step", "refine_at_step"]
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "target_parameter": (cls.parameters,),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_step": ("INT", {"default": 10, "min": 1, "max": 10000}),
+ "last_step": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "first_start_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+ "last_start_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
+ "first_end_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
+ "last_end_step": ("INT", {"default": 20, "min": 0, "max": 10000}),
+ "first_refine_step": ("INT", {"default": 10, "min": 0, "max": 10000}),
+ "last_refine_step": ("INT", {"default": 20, "min": 0, "max": 10000}),
+ }
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_parameter, batch_count, first_step, last_step, first_start_step, last_start_step,
+ first_end_step, last_end_step, first_refine_step, last_refine_step):
+
+ if target_parameter == "steps":
+ xy_type = "Steps"
+ xy_first = first_step
+ xy_last = last_step
+ elif target_parameter == "start_at_step":
+ xy_type = "StartStep"
+ xy_first = first_start_step
+ xy_last = last_start_step
+ elif target_parameter == "end_at_step":
+ xy_type = "EndStep"
+ xy_first = first_end_step
+ xy_last = last_end_step
+ elif target_parameter == "refine_at_step":
+ xy_type = "RefineStep"
+ xy_first = first_refine_step
+ xy_last = last_refine_step
+
+ xy_value = generate_ints(batch_count, xy_first, xy_last)
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: CFG Values
+class TSC_XYplot_CFG:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0}),
+ "last_cfg": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 100.0}),
+ }
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, batch_count, first_cfg, last_cfg):
+ xy_type = "CFG Scale"
+ xy_value = generate_floats(batch_count, first_cfg, last_cfg)
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: Sampler & Scheduler Values
+class TSC_XYplot_Sampler_Scheduler:
+ parameters = ["sampler", "scheduler", "sampler & scheduler"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ samplers = ["None"] + comfy.samplers.KSampler.SAMPLERS
+ schedulers = ["None"] + comfy.samplers.KSampler.SCHEDULERS
+ inputs = {
+ "required": {
+ "target_parameter": (cls.parameters,),
+ "input_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
+ }
+ }
+ for i in range(1, XYPLOT_LIM+1):
+ inputs["required"][f"sampler_{i}"] = (samplers,)
+ inputs["required"][f"scheduler_{i}"] = (schedulers,)
+
+ return inputs
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_parameter, input_count, **kwargs):
+ if target_parameter == "scheduler":
+ xy_type = "Scheduler"
+ schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
+ xy_value = [scheduler for scheduler in schedulers if scheduler != "None"]
+ elif target_parameter == "sampler":
+ xy_type = "Sampler"
+ samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
+ xy_value = [(sampler, None) for sampler in samplers if sampler != "None"]
+ else:
+ xy_type = "Sampler"
+ samplers = [kwargs.get(f"sampler_{i}") for i in range(1, input_count + 1)]
+ schedulers = [kwargs.get(f"scheduler_{i}") for i in range(1, input_count + 1)]
+ xy_value = [(sampler, scheduler if scheduler != "None" else None) for sampler,
+ scheduler in zip(samplers, schedulers) if sampler != "None"]
+
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: Denoise Values
+class TSC_XYplot_Denoise:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_denoise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "last_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, batch_count, first_denoise, last_denoise):
+ xy_type = "Denoise"
+ xy_value = generate_floats(batch_count, first_denoise, last_denoise)
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: VAE Values
+class TSC_XYplot_VAE:
+
+ modes = ["VAE Names", "VAE Batch"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+
+ vaes = ["None", "Baked VAE"] + folder_paths.get_filename_list("vae")
+
+ inputs = {
+ "required": {
+ "input_mode": (cls.modes,),
+ "batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
+ "subdirectories": ("BOOLEAN", {"default": False}),
+ "batch_sort": (["ascending", "descending"],),
+ "batch_max": ("INT", {"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
+ "vae_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
+ }
+ }
+
+ for i in range(1, XYPLOT_LIM+1):
+ inputs["required"][f"vae_name_{i}"] = (vaes,)
+
+ return inputs
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, input_mode, batch_path, subdirectories, batch_sort, batch_max, vae_count, **kwargs):
+
+ xy_type = "VAE"
+
+ if "Batch" not in input_mode:
+ # Extract values from kwargs
+ vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, vae_count + 1)]
+ xy_value = [vae for vae in vaes if vae != "None"]
+ else:
+ if batch_max == 0:
+ return (None,)
+
+ try:
+ vaes = get_batch_files(batch_path, VAE_EXTENSIONS, include_subdirs=subdirectories)
+
+ if not vaes:
+ print(f"{error('XY Plot Error:')} No VAE files found.")
+ return (None,)
+
+ if batch_sort == "ascending":
+ vaes.sort()
+ elif batch_sort == "descending":
+ vaes.sort(reverse=True)
+
+ # Construct the xy_value using the obtained vaes
+ xy_value = [vae for vae in vaes]
+
+ if batch_max != -1: # If there's a limit
+ xy_value = xy_value[:batch_max]
+
+ except Exception as e:
+ print(f"{error('XY Plot Error:')} {e}")
+ return (None,)
+
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: Prompt S/R
+class TSC_XYplot_PromptSR:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ inputs = {
+ "required": {
+ "target_prompt": (["positive", "negative"],),
+ "search_txt": ("STRING", {"default": "", "multiline": False}),
+ "replace_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM-1}),
+ }
+ }
+
+ # Dynamically add replace_X inputs
+ for i in range(1, XYPLOT_LIM):
+ replace_key = f"replace_{i}"
+ inputs["required"][replace_key] = ("STRING", {"default": "", "multiline": False})
+
+ return inputs
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_prompt, search_txt, replace_count, **kwargs):
+ if search_txt == "":
+ return (None,)
+
+ if target_prompt == "positive":
+ xy_type = "Positive Prompt S/R"
+ elif target_prompt == "negative":
+ xy_type = "Negative Prompt S/R"
+
+ # Create base entry
+ xy_values = [(search_txt, None)]
+
+ if replace_count > 0:
+ # Append additional entries based on replace_count
+ xy_values.extend([(search_txt, kwargs.get(f"replace_{i+1}")) for i in range(replace_count)])
+
+ return ((xy_type, xy_values),)
+
+#=======================================================================================================================
+# TSC XY Plot: Aesthetic Score
+class TSC_XYplot_AScore:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "target_ascore": (["positive", "negative"],),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_ascore": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+ "last_ascore": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_ascore, batch_count, first_ascore, last_ascore):
+ if target_ascore == "positive":
+ xy_type = "AScore+"
+ else:
+ xy_type = "AScore-"
+ xy_value = generate_floats(batch_count, first_ascore, last_ascore)
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: Refiner On/Off
+class TSC_XYplot_Refiner_OnOff:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "refine_at_percent": ("FLOAT",{"default": 0.80, "min": 0.00, "max": 1.00, "step": 0.01})},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, refine_at_percent):
+ xy_type = "Refiner On/Off"
+ xy_value = [refine_at_percent, 1]
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Clip Skip
+class TSC_XYplot_ClipSkip:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "target_ckpt": (["Base","Refiner"],),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
+ "last_clip_skip": ("INT", {"default": -3, "min": -24, "max": -1, "step": 1}),
+ },
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_ckpt, batch_count, first_clip_skip, last_clip_skip):
+ if target_ckpt == "Base":
+ xy_type = "Clip Skip"
+ else:
+ xy_type = "Clip Skip (Refiner)"
+ xy_value = generate_ints(batch_count, first_clip_skip, last_clip_skip)
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: Checkpoint Values
+class TSC_XYplot_Checkpoint:
+ modes = ["Ckpt Names", "Ckpt Names+ClipSkip", "Ckpt Names+ClipSkip+VAE", "Checkpoint Batch"]
+ @classmethod
+ def INPUT_TYPES(cls):
+ checkpoints = ["None"] + folder_paths.get_filename_list("checkpoints")
+ vaes = ["Baked VAE"] + folder_paths.get_filename_list("vae")
+
+ inputs = {
+ "required": {
+ "target_ckpt": (["Base", "Refiner"],),
+ "input_mode": (cls.modes,),
+ "batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
+ "subdirectories": ("BOOLEAN", {"default": False}),
+ "batch_sort": (["ascending", "descending"],),
+ "batch_max": ("INT", {"default": -1, "min": -1, "max": 50, "step": 1}),
+ "ckpt_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1})
+ }
+ }
+
+ for i in range(1, XYPLOT_LIM+1):
+ inputs["required"][f"ckpt_name_{i}"] = (checkpoints,)
+ inputs["required"][f"clip_skip_{i}"] = ("INT", {"default": -1, "min": -24, "max": -1, "step": 1})
+ inputs["required"][f"vae_name_{i}"] = (vaes,)
+
+ return inputs
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, target_ckpt, input_mode, batch_path, subdirectories, batch_sort, batch_max, ckpt_count, **kwargs):
+
+ # Define XY type
+ xy_type = "Checkpoint" if target_ckpt == "Base" else "Refiner"
+
+ if "Batch" not in input_mode:
+ # Extract values from kwargs
+ checkpoints = [kwargs.get(f"ckpt_name_{i}") for i in range(1, ckpt_count + 1)]
+ clip_skips = [kwargs.get(f"clip_skip_{i}") for i in range(1, ckpt_count + 1)]
+ vaes = [kwargs.get(f"vae_name_{i}") for i in range(1, ckpt_count + 1)]
+
+ # Set None for Clip Skip and/or VAE if not correct modes
+ for i in range(ckpt_count):
+ if "ClipSkip" not in input_mode:
+ clip_skips[i] = None
+ if "VAE" not in input_mode:
+ vaes[i] = None
+
+ xy_value = [(checkpoint, clip_skip, vae) for checkpoint, clip_skip, vae in zip(checkpoints, clip_skips, vaes) if
+ checkpoint != "None"]
+ else:
+ if batch_max == 0:
+ return (None,)
+
+ try:
+ ckpts = get_batch_files(batch_path, CKPT_EXTENSIONS, include_subdirs=subdirectories)
+
+ if not ckpts:
+ print(f"{error('XY Plot Error:')} No Checkpoint files found.")
+ return (None,)
+
+ if batch_sort == "ascending":
+ ckpts.sort()
+ elif batch_sort == "descending":
+ ckpts.sort(reverse=True)
+
+ # Construct the xy_value using the obtained ckpts
+ xy_value = [(ckpt, None, None) for ckpt in ckpts]
+
+ if batch_max != -1: # If there's a limit
+ xy_value = xy_value[:batch_max]
+
+ except Exception as e:
+ print(f"{error('XY Plot Error:')} {e}")
+ return (None,)
+
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: LoRA Batch (DISABLED)
+class TSC_XYplot_LoRA_Batch:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+
+ return {"required": {
+ "batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
+ "subdirectories": ("BOOLEAN", {"default": False}),
+ "batch_sort": (["ascending", "descending"],),
+ "batch_max": ("INT",{"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
+ "model_strength": ("FLOAT", {"default": 1.0, "min": -10.00, "max": 10.0, "step": 0.01}),
+ "clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})},
+ "optional": {"lora_stack": ("LORA_STACK",)}
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, batch_path, subdirectories, batch_sort, model_strength, clip_strength, batch_max, lora_stack=None):
+ if batch_max == 0:
+ return (None,)
+
+ xy_type = "LoRA"
+
+ loras = get_batch_files(batch_path, LORA_EXTENSIONS, include_subdirs=subdirectories)
+
+ if not loras:
+ print(f"{error('XY Plot Error:')} No LoRA files found.")
+ return (None,)
+
+ if batch_sort == "ascending":
+ loras.sort()
+ elif batch_sort == "descending":
+ loras.sort(reverse=True)
+
+ # Construct the xy_value using the obtained loras
+ xy_value = [[(lora, model_strength, clip_strength)] + (lora_stack if lora_stack else []) for lora in loras]
+
+ if batch_max != -1: # If there's a limit
+ xy_value = xy_value[:batch_max]
+
+ return ((xy_type, xy_value),) if xy_value else (None,)
+
+#=======================================================================================================================
+# TSC XY Plot: LoRA Values
+class TSC_XYplot_LoRA:
+ modes = ["LoRA Names", "LoRA Names+Weights", "LoRA Batch"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ loras = ["None"] + folder_paths.get_filename_list("loras")
+
+ inputs = {
+ "required": {
+ "input_mode": (cls.modes,),
+ "batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
+ "subdirectories": ("BOOLEAN", {"default": False}),
+ "batch_sort": (["ascending", "descending"],),
+ "batch_max": ("INT", {"default": -1, "min": -1, "max": XYPLOT_LIM, "step": 1}),
+ "lora_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM, "step": 1}),
+ "model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ }
+ }
+
+ for i in range(1, XYPLOT_LIM+1):
+ inputs["required"][f"lora_name_{i}"] = (loras,)
+ inputs["required"][f"model_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+ inputs["required"][f"clip_str_{i}"] = ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01})
+
+ inputs["optional"] = {
+ "lora_stack": ("LORA_STACK",)
+ }
+ return inputs
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def __init__(self):
+ self.lora_batch = TSC_XYplot_LoRA_Batch()
+
+ def xy_value(self, input_mode, batch_path, subdirectories, batch_sort, batch_max, lora_count, model_strength,
+ clip_strength, lora_stack=None, **kwargs):
+
+ xy_type = "LoRA"
+ result = (None,)
+ lora_stack = lora_stack if lora_stack else []
+
+ if "Batch" not in input_mode:
+ # Extract values from kwargs
+ loras = [kwargs.get(f"lora_name_{i}") for i in range(1, lora_count + 1)]
+ model_strs = [kwargs.get(f"model_str_{i}", model_strength) for i in range(1, lora_count + 1)]
+ clip_strs = [kwargs.get(f"clip_str_{i}", clip_strength) for i in range(1, lora_count + 1)]
+
+ # Use model_strength and clip_strength for the loras where values are not provided
+ if "Weights" not in input_mode:
+ for i in range(lora_count):
+ model_strs[i] = model_strength
+ clip_strs[i] = clip_strength
+
+ # Extend each sub-array with lora_stack if it's not None
+ xy_value = [[(lora, model_str, clip_str)] + lora_stack for lora, model_str, clip_str
+ in zip(loras, model_strs, clip_strs) if lora != "None"]
+
+ result = ((xy_type, xy_value),)
+ else:
+ try:
+ result = self.lora_batch.xy_value(batch_path, subdirectories, batch_sort, model_strength,
+ clip_strength, batch_max, lora_stack)
+ except Exception as e:
+ print(f"{error('XY Plot Error:')} {e}")
+
+ return result
+
+#=======================================================================================================================
+# TSC XY Plot: LoRA Plot
+class TSC_XYplot_LoRA_Plot:
+
+ modes = ["X: LoRA Batch, Y: LoRA Weight",
+ "X: LoRA Batch, Y: Model Strength",
+ "X: LoRA Batch, Y: Clip Strength",
+ "X: Model Strength, Y: Clip Strength",
+ ]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ loras = ["None"] + folder_paths.get_filename_list("loras")
+ return {"required": {
+ "input_mode": (cls.modes,),
+ "lora_name": (loras,),
+ "model_strength": ("FLOAT", {"default": 1.0, "min": -10.00, "max": 10.0, "step": 0.01}),
+ "clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+ "X_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "X_batch_path": ("STRING", {"default": xy_batch_default_path, "multiline": False}),
+ "X_subdirectories": ("BOOLEAN", {"default": False}),
+ "X_batch_sort": (["ascending", "descending"],),
+ "X_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "X_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "Y_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "Y_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "Y_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),},
+ "optional": {"lora_stack": ("LORA_STACK",)}
+ }
+
+ RETURN_TYPES = ("XY","XY",)
+ RETURN_NAMES = ("X","Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def __init__(self):
+ self.lora_batch = TSC_XYplot_LoRA_Batch()
+
+ def generate_values(self, mode, X_or_Y, *args, **kwargs):
+ result = self.lora_batch.xy_value(*args, **kwargs)
+
+ if result and result[0]:
+ xy_type, xy_value_list = result[0]
+
+ # Adjust type based on the mode
+ if "LoRA Weight" in mode:
+ xy_type = "LoRA Wt"
+ elif "Model Strength" in mode:
+ xy_type = "LoRA MStr"
+ elif "Clip Strength" in mode:
+ xy_type = "LoRA CStr"
+
+ # Check whether the value is for X or Y
+ if "LoRA Batch" in mode: # Changed condition
+ return self.generate_batch_values(*args, **kwargs)
+ else:
+ return ((xy_type, xy_value_list),)
+
+ return (None,)
+
+ def xy_value(self, input_mode, lora_name, model_strength, clip_strength, X_batch_count, X_batch_path, X_subdirectories,
+ X_batch_sort, X_first_value, X_last_value, Y_batch_count, Y_first_value, Y_last_value, lora_stack=None):
+
+ x_value, y_value = [], []
+ lora_stack = lora_stack if lora_stack else []
+
+ if "Model Strength" in input_mode and "Clip Strength" in input_mode:
+ if lora_name == 'None':
+ return (None,None,)
+ if "LoRA Batch" in input_mode:
+ lora_name = None
+ if "LoRA Weight" in input_mode:
+ model_strength = None
+ clip_strength = None
+ if "Model Strength" in input_mode:
+ model_strength = None
+ if "Clip Strength" in input_mode:
+ clip_strength = None
+
+ # Handling X values
+ if "X: LoRA Batch" in input_mode:
+ try:
+ x_value = self.lora_batch.xy_value(X_batch_path, X_subdirectories, X_batch_sort,
+ model_strength, clip_strength, X_batch_count, lora_stack)[0][1]
+ except Exception as e:
+ print(f"{error('XY Plot Error:')} {e}")
+ return (None,)
+ x_type = "LoRA Batch"
+ elif "X: Model Strength" in input_mode:
+ x_floats = generate_floats(X_batch_count, X_first_value, X_last_value)
+ x_type = "LoRA MStr"
+ x_value = [[(lora_name, x, clip_strength)] + lora_stack for x in x_floats]
+
+ # Handling Y values
+ y_floats = generate_floats(Y_batch_count, Y_first_value, Y_last_value)
+ if "Y: LoRA Weight" in input_mode:
+ y_type = "LoRA Wt"
+ y_value = [[(lora_name, y, y)] + lora_stack for y in y_floats]
+ elif "Y: Model Strength" in input_mode:
+ y_type = "LoRA MStr"
+ y_value = [[(lora_name, y, clip_strength)] + lora_stack for y in y_floats]
+ elif "Y: Clip Strength" in input_mode:
+ y_type = "LoRA CStr"
+ y_value = [[(lora_name, model_strength, y)] + lora_stack for y in y_floats]
+
+ return ((x_type, x_value), (y_type, y_value))
+
+#=======================================================================================================================
+# TSC XY Plot: LoRA Stacks
+class TSC_XYplot_LoRA_Stacks:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "node_state": (["Enabled"],)},
+ "optional": {
+ "lora_stack_1": ("LORA_STACK",),
+ "lora_stack_2": ("LORA_STACK",),
+ "lora_stack_3": ("LORA_STACK",),
+ "lora_stack_4": ("LORA_STACK",),
+ "lora_stack_5": ("LORA_STACK",),},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, node_state, lora_stack_1=None, lora_stack_2=None, lora_stack_3=None, lora_stack_4=None, lora_stack_5=None):
+ xy_type = "LoRA"
+ xy_value = [stack for stack in [lora_stack_1, lora_stack_2, lora_stack_3, lora_stack_4, lora_stack_5] if stack is not None]
+ if not xy_value or not any(xy_value) or node_state == "Disabled":
+ return (None,)
+ else:
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Control Net Strength (DISABLED)
+class TSC_XYplot_Control_Net_Strength:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ },
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, control_net, image, batch_count, first_strength, last_strength,
+ start_percent, end_percent, cnet_stack=None):
+
+ if batch_count == 0:
+ return (None,)
+
+ xy_type = "ControlNetStrength"
+ strength_increment = (last_strength - first_strength) / (batch_count - 1) if batch_count > 1 else 0
+
+ xy_value = []
+
+ # Always add the first strength.
+ xy_value.append([(control_net, image, first_strength, start_percent, end_percent)])
+
+ # Add intermediate strengths only if batch_count is more than 2.
+ for i in range(1, batch_count - 1):
+ xy_value.append([(control_net, image, first_strength + i * strength_increment, start_percent,
+ end_percent)])
+
+ # Always add the last strength if batch_count is more than 1.
+ if batch_count > 1:
+ xy_value.append([(control_net, image, last_strength, start_percent, end_percent)])
+
+ # If cnet_stack is provided, extend each inner array with its content
+ if cnet_stack:
+ for inner_list in xy_value:
+ inner_list.extend(cnet_stack)
+
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Control Net Start % (DISABLED)
+class TSC_XYplot_Control_Net_Start:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ },
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, control_net, image, batch_count, first_start_percent, last_start_percent,
+ strength, end_percent, cnet_stack=None):
+
+ if batch_count == 0:
+ return (None,)
+
+ xy_type = "ControlNetStart%"
+ percent_increment = (last_start_percent - first_start_percent) / (batch_count - 1) if batch_count > 1 else 0
+
+ xy_value = []
+
+ # Always add the first start_percent.
+ xy_value.append([(control_net, image, strength, first_start_percent, end_percent)])
+
+ # Add intermediate start percents only if batch_count is more than 2.
+ for i in range(1, batch_count - 1):
+ xy_value.append([(control_net, image, strength, first_start_percent + i * percent_increment,
+ end_percent)])
+
+ # Always add the last start_percent if batch_count is more than 1.
+ if batch_count > 1:
+ xy_value.append([(control_net, image, strength, last_start_percent, end_percent)])
+
+ # If cnet_stack is provided, extend each inner array with its content
+ if cnet_stack:
+ for inner_list in xy_value:
+ inner_list.extend(cnet_stack)
+
+ return ((xy_type, xy_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Control Net End % (DISABLED)
+class TSC_XYplot_Control_Net_End:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ },
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, control_net, image, batch_count, first_end_percent, last_end_percent,
+ strength, start_percent, cnet_stack=None):
+
+ if batch_count == 0:
+ return (None,)
+
+ xy_type = "ControlNetEnd%"
+ percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0
+
+ xy_value = []
+
+ # Always add the first end_percent.
+ xy_value.append([(control_net, image, strength, start_percent, first_end_percent)])
+
+ # Add intermediate end percents only if batch_count is more than 2.
+ for i in range(1, batch_count - 1):
+ xy_value.append([(control_net, image, strength, start_percent,
+ first_end_percent + i * percent_increment)])
+
+ # Always add the last end_percent if batch_count is more than 1.
+ if batch_count > 1:
+ xy_value.append([(control_net, image, strength, start_percent, last_end_percent)])
+
+ # If cnet_stack is provided, extend each inner array with its content
+ if cnet_stack:
+ for inner_list in xy_value:
+ inner_list.extend(cnet_stack)
+
+ return ((xy_type, xy_value),)
+
+
+# =======================================================================================================================
+# TSC XY Plot: Control Net
+class TSC_XYplot_Control_Net(TSC_XYplot_Control_Net_Strength, TSC_XYplot_Control_Net_Start, TSC_XYplot_Control_Net_End):
+ parameters = ["strength", "start_percent", "end_percent"]
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "target_parameter": (cls.parameters,),
+ "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ },
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, control_net, image, target_parameter, batch_count, first_strength, last_strength, first_start_percent,
+ last_start_percent, first_end_percent, last_end_percent, strength, start_percent, end_percent, cnet_stack=None):
+
+ if target_parameter == "strength":
+ return TSC_XYplot_Control_Net_Strength.xy_value(self, control_net, image, batch_count, first_strength,
+ last_strength, start_percent, end_percent, cnet_stack=cnet_stack)
+ elif target_parameter == "start_percent":
+ return TSC_XYplot_Control_Net_Start.xy_value(self, control_net, image, batch_count, first_start_percent,
+ last_start_percent, strength, end_percent, cnet_stack=cnet_stack)
+ elif target_parameter == "end_percent":
+ return TSC_XYplot_Control_Net_End.xy_value(self, control_net, image, batch_count, first_end_percent,
+ last_end_percent, strength, start_percent, cnet_stack=cnet_stack)
+
+#=======================================================================================================================
+# TSC XY Plot: Control Net Plot
+class TSC_XYplot_Control_Net_Plot:
+
+ plot_types = ["X: Strength, Y: Start%",
+ "X: Strength, Y: End%",
+ "X: Start%, Y: Strength",
+ "X: Start%, Y: End%",
+ "X: End%, Y: Strength",
+ "X: End%, Y: Start%"]
+
+ @classmethod
+ def INPUT_TYPES(cls):
+
+ return {
+ "required": {
+ "control_net": ("CONTROL_NET",),
+ "image": ("IMAGE",),
+ "plot_type": (cls.plot_types,),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
+ "X_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "X_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "X_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "Y_batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
+ "Y_first_value": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
+ "Y_last_value": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),},
+ "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
+ }
+
+ RETURN_TYPES = ("XY","XY",)
+ RETURN_NAMES = ("X","Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def get_value(self, axis, control_net, image, strength, start_percent, end_percent,
+ batch_count, first_value, last_value):
+
+ # Adjust upper bound for Start% and End% type
+ if axis in ["Start%", "End%"]:
+ first_value = min(1, first_value)
+ last_value = min(1, last_value)
+
+ increment = (last_value - first_value) / (batch_count - 1) if batch_count > 1 else 0
+
+ values = []
+
+ # Always add the first value.
+ if axis == "Strength":
+ values.append([(control_net, image, first_value, start_percent, end_percent)])
+ elif axis == "Start%":
+ values.append([(control_net, image, strength, first_value, end_percent)])
+ elif axis == "End%":
+ values.append([(control_net, image, strength, start_percent, first_value)])
+
+ # Add intermediate values only if batch_count is more than 2.
+ for i in range(1, batch_count - 1):
+ if axis == "Strength":
+ values.append(
+ [(control_net, image, first_value + i * increment, start_percent, end_percent)])
+ elif axis == "Start%":
+ values.append(
+ [(control_net, image, strength, first_value + i * increment, end_percent)])
+ elif axis == "End%":
+ values.append(
+ [(control_net, image, strength, start_percent, first_value + i * increment)])
+
+ # Always add the last value if batch_count is more than 1.
+ if batch_count > 1:
+ if axis == "Strength":
+ values.append([(control_net, image, last_value, start_percent, end_percent)])
+ elif axis == "Start%":
+ values.append([(control_net, image, strength, last_value, end_percent)])
+ elif axis == "End%":
+ values.append([(control_net, image, strength, start_percent, last_value)])
+
+ return values
+
+ def xy_value(self, control_net, image, strength, start_percent, end_percent, plot_type,
+ X_batch_count, X_first_value, X_last_value, Y_batch_count, Y_first_value, Y_last_value,
+ cnet_stack=None):
+
+ x_type, y_type = plot_type.split(", ")
+
+ # Now split each type by ": "
+ x_type = x_type.split(": ")[1].strip()
+ y_type = y_type.split(": ")[1].strip()
+
+ x_entry = None
+ y_entry = None
+
+ if X_batch_count > 0:
+ x_value = self.get_value(x_type, control_net, image, strength, start_percent,
+ end_percent, X_batch_count, X_first_value, X_last_value)
+ # If cnet_stack is provided, extend each inner array with its content
+ if cnet_stack:
+ for inner_list in x_value:
+ inner_list.extend(cnet_stack)
+
+ x_entry = ("ControlNet" + x_type, x_value)
+
+ if Y_batch_count > 0:
+ y_value = self.get_value(y_type, control_net, image, strength, start_percent,
+ end_percent, Y_batch_count, Y_first_value, Y_last_value)
+ # If cnet_stack is provided, extend each inner array with its content
+ if cnet_stack:
+ for inner_list in y_value:
+ inner_list.extend(cnet_stack)
+
+ y_entry = ("ControlNet" + y_type, y_value)
+
+ return (x_entry, y_entry,)
+
+#=======================================================================================================================
+# TSC XY Plot: Manual Entry Notes
+class TSC_XYplot_Manual_XY_Entry_Info:
+
+ syntax = "(X/Y_types) (X/Y_values)\n" \
+ "Seeds++ Batch batch_count\n" \
+ "Steps steps_1;steps_2;...\n" \
+ "StartStep start_step_1;start_step_2;...\n" \
+ "EndStep end_step_1;end_step_2;...\n" \
+ "CFG Scale cfg_1;cfg_2;...\n" \
+ "Sampler(1) sampler_1;sampler_2;...\n" \
+ "Sampler(2) sampler_1,scheduler_1;...\n" \
+ "Sampler(3) sampler_1;...;,default_scheduler\n" \
+ "Scheduler scheduler_1;scheduler_2;...\n" \
+ "Denoise denoise_1;denoise_2;...\n" \
+ "VAE vae_1;vae_2;vae_3;...\n" \
+ "+Prompt S/R search_txt;replace_1;replace_2;...\n" \
+ "-Prompt S/R search_txt;replace_1;replace_2;...\n" \
+ "Checkpoint(1) ckpt_1;ckpt_2;ckpt_3;...\n" \
+ "Checkpoint(2) ckpt_1,clip_skip_1;...\n" \
+ "Checkpoint(3) ckpt_1;ckpt_2;...;,default_clip_skip\n" \
+ "Clip Skip clip_skip_1;clip_skip_2;...\n" \
+ "LoRA(1) lora_1;lora_2;lora_3;...\n" \
+ "LoRA(2) lora_1;...;,default_model_str,default_clip_str\n" \
+ "LoRA(3) lora_1,model_str_1,clip_str_1;..."
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ samplers = ";\n".join(comfy.samplers.KSampler.SAMPLERS)
+ schedulers = ";\n".join(comfy.samplers.KSampler.SCHEDULERS)
+ vaes = ";\n".join(folder_paths.get_filename_list("vae"))
+ ckpts = ";\n".join(folder_paths.get_filename_list("checkpoints"))
+ loras = ";\n".join(folder_paths.get_filename_list("loras"))
+ return {"required": {
+ "notes": ("STRING", {"default":
+ f"_____________SYNTAX_____________\n{cls.syntax}\n\n"
+ f"____________SAMPLERS____________\n{samplers}\n\n"
+ f"___________SCHEDULERS___________\n{schedulers}\n\n"
+ f"_____________VAES_______________\n{vaes}\n\n"
+ f"___________CHECKPOINTS__________\n{ckpts}\n\n"
+ f"_____________LORAS______________\n{loras}\n","multiline": True}),},}
+
+ RETURN_TYPES = ()
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+
+#=======================================================================================================================
+# TSC XY Plot: Manual Entry
+class TSC_XYplot_Manual_XY_Entry:
+
+ plot_types = ["Nothing", "Seeds++ Batch", "Steps", "StartStep", "EndStep", "CFG Scale", "Sampler", "Scheduler",
+ "Denoise", "VAE", "Positive Prompt S/R", "Negative Prompt S/R", "Checkpoint", "Clip Skip", "LoRA"]
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "plot_type": (cls.plot_types,),
+ "plot_value": ("STRING", {"default": "", "multiline": True}),}
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, plot_type, plot_value):
+
+ # Store X values as arrays
+ if plot_type not in {"Positive Prompt S/R", "Negative Prompt S/R", "VAE", "Checkpoint", "LoRA"}:
+ plot_value = plot_value.replace(" ", "") # Remove spaces
+ plot_value = plot_value.replace("\n", "") # Remove newline characters
+ plot_value = plot_value.rstrip(";") # Remove trailing semicolon
+ plot_value = plot_value.split(";") # Turn to array
+
+ # Define the valid bounds for each type
+ bounds = {
+ "Seeds++ Batch": {"min": 1, "max": 50},
+ "Steps": {"min": 1, "max": 10000},
+ "StartStep": {"min": 0, "max": 10000},
+ "EndStep": {"min": 0, "max": 10000},
+ "CFG Scale": {"min": 0, "max": 100},
+ "Sampler": {"options": comfy.samplers.KSampler.SAMPLERS},
+ "Scheduler": {"options": comfy.samplers.KSampler.SCHEDULERS},
+ "Denoise": {"min": 0, "max": 1},
+ "VAE": {"options": folder_paths.get_filename_list("vae")},
+ "Checkpoint": {"options": folder_paths.get_filename_list("checkpoints")},
+ "Clip Skip": {"min": -24, "max": -1},
+ "LoRA": {"options": folder_paths.get_filename_list("loras"),
+ "model_str": {"min": -10, "max": 10},"clip_str": {"min": -10, "max": 10},},
+ }
+
+ # Validates a value based on its corresponding value_type and bounds.
+ def validate_value(value, value_type, bounds):
+ # ________________________________________________________________________
+ # Seeds++ Batch
+ if value_type == "Seeds++ Batch":
+ try:
+ x = int(float(value))
+ if x < bounds["Seeds++ Batch"]["min"]:
+ x = bounds["Seeds++ Batch"]["min"]
+ elif x > bounds["Seeds++ Batch"]["max"]:
+ x = bounds["Seeds++ Batch"]["max"]
+ except ValueError:
+ print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid batch count.")
+ return None
+ if float(value) != x:
+ print(f"\033[31mmXY Plot Error:\033[0m '{value}' is not a valid batch count.")
+ return None
+ return x
+ # ________________________________________________________________________
+ # Steps
+ elif value_type == "Steps":
+ try:
+ x = int(value)
+ if x < bounds["Steps"]["min"]:
+ x = bounds["Steps"]["min"]
+ elif x > bounds["Steps"]["max"]:
+ x = bounds["Steps"]["max"]
+ return x
+ except ValueError:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Step count.")
+ return None
+ # __________________________________________________________________________________________________________
+ # Start at Step
+ elif value_type == "StartStep":
+ try:
+ x = int(value)
+ if x < bounds["StartStep"]["min"]:
+ x = bounds["StartStep"]["min"]
+ elif x > bounds["StartStep"]["max"]:
+ x = bounds["StartStep"]["max"]
+ return x
+ except ValueError:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Start Step.")
+ return None
+ # __________________________________________________________________________________________________________
+ # End at Step
+ elif value_type == "EndStep":
+ try:
+ x = int(value)
+ if x < bounds["EndStep"]["min"]:
+ x = bounds["EndStep"]["min"]
+ elif x > bounds["EndStep"]["max"]:
+ x = bounds["EndStep"]["max"]
+ return x
+ except ValueError:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid End Step.")
+ return None
+ # __________________________________________________________________________________________________________
+ # CFG Scale
+ elif value_type == "CFG Scale":
+ try:
+ x = float(value)
+ if x < bounds["CFG Scale"]["min"]:
+ x = bounds["CFG Scale"]["min"]
+ elif x > bounds["CFG Scale"]["max"]:
+ x = bounds["CFG Scale"]["max"]
+ return x
+ except ValueError:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a number between {bounds['CFG Scale']['min']}"
+ f" and {bounds['CFG Scale']['max']} for CFG Scale.")
+ return None
+ # __________________________________________________________________________________________________________
+ # Sampler
+ elif value_type == "Sampler":
+ if isinstance(value, str) and ',' in value:
+ value = tuple(map(str.strip, value.split(',')))
+ if isinstance(value, tuple):
+ if len(value) >= 2:
+ value = value[:2] # Slice the value tuple to keep only the first two elements
+ sampler, scheduler = value
+ scheduler = scheduler.lower() # Convert the scheduler name to lowercase
+ if sampler not in bounds["Sampler"]["options"]:
+ valid_samplers = '\n'.join(bounds["Sampler"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{sampler}' is not a valid sampler. Valid samplers are:\n{valid_samplers}")
+ sampler = None
+ if scheduler not in bounds["Scheduler"]["options"]:
+ valid_schedulers = '\n'.join(bounds["Scheduler"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{scheduler}' is not a valid scheduler. Valid schedulers are:\n{valid_schedulers}")
+ scheduler = None
+ if sampler is None or scheduler is None:
+ return None
+ else:
+ return sampler, scheduler
+ else:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid sampler.'")
+ return None
+ else:
+ if value not in bounds["Sampler"]["options"]:
+ valid_samplers = '\n'.join(bounds["Sampler"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid sampler. Valid samplers are:\n{valid_samplers}")
+ return None
+ else:
+ return value, None
+ # __________________________________________________________________________________________________________
+ # Scheduler
+ elif value_type == "Scheduler":
+ if value not in bounds["Scheduler"]["options"]:
+ valid_schedulers = '\n'.join(bounds["Scheduler"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Scheduler. Valid Schedulers are:\n{valid_schedulers}")
+ return None
+ else:
+ return value
+ # __________________________________________________________________________________________________________
+ # Denoise
+ elif value_type == "Denoise":
+ try:
+ x = float(value)
+ if x < bounds["Denoise"]["min"]:
+ x = bounds["Denoise"]["min"]
+ elif x > bounds["Denoise"]["max"]:
+ x = bounds["Denoise"]["max"]
+ return x
+ except ValueError:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a number between {bounds['Denoise']['min']} "
+ f"and {bounds['Denoise']['max']} for Denoise.")
+ return None
+ # __________________________________________________________________________________________________________
+ # VAE
+ elif value_type == "VAE":
+ if value not in bounds["VAE"]["options"]:
+ valid_vaes = '\n'.join(bounds["VAE"]["options"])
+ print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid VAE. Valid VAEs are:\n{valid_vaes}")
+ return None
+ else:
+ return value
+ # __________________________________________________________________________________________________________
+ # Checkpoint
+ elif value_type == "Checkpoint":
+ if isinstance(value, str) and ',' in value:
+ value = tuple(map(str.strip, value.split(',')))
+ if isinstance(value, tuple):
+ if len(value) >= 2:
+ value = value[:2] # Slice the value tuple to keep only the first two elements
+ checkpoint, clip_skip = value
+ try:
+ clip_skip = int(clip_skip) # Convert the clip_skip to integer
+ except ValueError:
+ print(f"\033[31mXY Plot Error:\033[0m '{clip_skip}' is not a valid clip_skip. "
+ f"Valid clip skip values are integers between {bounds['Clip Skip']['min']} and {bounds['Clip Skip']['max']}.")
+ return None
+ if checkpoint not in bounds["Checkpoint"]["options"]:
+ valid_checkpoints = '\n'.join(bounds["Checkpoint"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{checkpoint}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
+ checkpoint = None
+ if clip_skip < bounds["Clip Skip"]["min"] or clip_skip > bounds["Clip Skip"]["max"]:
+ print(f"\033[31mXY Plot Error:\033[0m '{clip_skip}' is not a valid clip skip. "
+ f"Valid clip skip values are integers between {bounds['Clip Skip']['min']} and {bounds['Clip Skip']['max']}.")
+ clip_skip = None
+ if checkpoint is None or clip_skip is None:
+ return None
+ else:
+ return checkpoint, clip_skip, None
+ else:
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint.'")
+ return None
+ else:
+ if value not in bounds["Checkpoint"]["options"]:
+ valid_checkpoints = '\n'.join(bounds["Checkpoint"]["options"])
+ print(
+ f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid checkpoint. Valid checkpoints are:\n{valid_checkpoints}")
+ return None
+ else:
+ return value, None, None
+ # __________________________________________________________________________________________________________
+ # Clip Skip
+ elif value_type == "Clip Skip":
+ try:
+ x = int(value)
+ if x < bounds["Clip Skip"]["min"]:
+ x = bounds["Clip Skip"]["min"]
+ elif x > bounds["Clip Skip"]["max"]:
+ x = bounds["Clip Skip"]["max"]
+ return x
+ except ValueError:
+ print(f"\033[31mXY Plot Error:\033[0m '{value}' is not a valid Clip Skip.")
+ return None
+ # __________________________________________________________________________________________________________
+ # LoRA
+ elif value_type == "LoRA":
+ if isinstance(value, str) and ',' in value:
+ value = tuple(map(str.strip, value.split(',')))
+
+ if isinstance(value, tuple):
+ lora_name, model_str, clip_str = (value + (1.0, 1.0))[:3] # Defaults model_str and clip_str to 1 if not provided
+
+ if lora_name not in bounds["LoRA"]["options"]:
+ valid_loras = '\n'.join(bounds["LoRA"]["options"])
+ print(f"{error('XY Plot Error:')} '{lora_name}' is not a valid LoRA. Valid LoRAs are:\n{valid_loras}")
+ lora_name = None
+
+ try:
+ model_str = float(model_str)
+ clip_str = float(clip_str)
+ except ValueError:
+ print(f"{error('XY Plot Error:')} The LoRA model strength and clip strength values should be numbers"
+ f" between {bounds['LoRA']['model_str']['min']} and {bounds['LoRA']['model_str']['max']}.")
+ return None
+
+ if model_str < bounds["LoRA"]["model_str"]["min"] or model_str > bounds["LoRA"]["model_str"]["max"]:
+ print(f"{error('XY Plot Error:')} '{model_str}' is not a valid LoRA model strength value. "
+ f"Valid lora model strength values are between {bounds['LoRA']['model_str']['min']} and {bounds['LoRA']['model_str']['max']}.")
+ model_str = None
+
+ if clip_str < bounds["LoRA"]["clip_str"]["min"] or clip_str > bounds["LoRA"]["clip_str"]["max"]:
+ print(f"{error('XY Plot Error:')} '{clip_str}' is not a valid LoRA clip strength value. "
+ f"Valid lora clip strength values are between {bounds['LoRA']['clip_str']['min']} and {bounds['LoRA']['clip_str']['max']}.")
+ clip_str = None
+
+ if lora_name is None or model_str is None or clip_str is None:
+ return None
+ else:
+ return lora_name, model_str, clip_str
+ else:
+ if value not in bounds["LoRA"]["options"]:
+ valid_loras = '\n'.join(bounds["LoRA"]["options"])
+ print(f"{error('XY Plot Error:')} '{value}' is not a valid LoRA. Valid LoRAs are:\n{valid_loras}")
+ return None
+ else:
+ return value, 1.0, 1.0
+
+ # __________________________________________________________________________________________________________
+ else:
+ return None
+
+ # Validate plot_value array length is 1 if doing a "Seeds++ Batch"
+ if len(plot_value) != 1 and plot_type == "Seeds++ Batch":
+ print(f"{error('XY Plot Error:')} '{';'.join(plot_value)}' is not a valid batch count.")
+ return (None,)
+
+ # Apply allowed shortcut syntax to certain input types
+ if plot_type in ["Sampler", "Checkpoint", "LoRA"]:
+ if plot_value[-1].startswith(','):
+ # Remove the leading comma from the last entry and store it as suffixes
+ suffixes = plot_value.pop().lstrip(',').split(',')
+ # Split all preceding entries into subentries
+ plot_value = [entry.split(',') for entry in plot_value]
+ # Make all entries the same length as suffixes by appending missing elements
+ for entry in plot_value:
+ entry += suffixes[len(entry) - 1:]
+ # Join subentries back into strings
+ plot_value = [','.join(entry) for entry in plot_value]
+
+ # Prompt S/R X Cleanup
+ if plot_type in {"Positive Prompt S/R", "Negative Prompt S/R"}:
+ if plot_value[0] == '':
+ print(f"{error('XY Plot Error:')} Prompt S/R value can not be empty.")
+ return (None,)
+ else:
+ plot_value = [(plot_value[0], None) if i == 0 else (plot_value[0], x) for i, x in enumerate(plot_value)]
+
+ # Loop over each entry in plot_value and check if it's valid
+ if plot_type not in {"Nothing", "Positive Prompt S/R", "Negative Prompt S/R"}:
+ for i in range(len(plot_value)):
+ plot_value[i] = validate_value(plot_value[i], plot_type, bounds)
+ if plot_value[i] == None:
+ return (None,)
+
+ # Set up Seeds++ Batch structure
+ if plot_type == "Seeds++ Batch":
+ plot_value = list(range(plot_value[0]))
+
+ # Nest LoRA value in another array to reflect LoRA stack changes
+ if plot_type == "LoRA":
+ plot_value = [[x] for x in plot_value]
+
+ # Clean X/Y_values
+ if plot_type == "Nothing":
+ plot_value = [""]
+
+ return ((plot_type, plot_value),)
+
+#=======================================================================================================================
+# TSC XY Plot: Join Inputs
+class TSC_XYplot_JoinInputs:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "XY_1": ("XY",),
+ "XY_2": ("XY",),},
+ }
+
+ RETURN_TYPES = ("XY",)
+ RETURN_NAMES = ("X or Y",)
+ FUNCTION = "xy_value"
+ CATEGORY = "Efficiency Nodes/XY Inputs"
+
+ def xy_value(self, XY_1, XY_2):
+ xy_type_1, xy_value_1 = XY_1
+ xy_type_2, xy_value_2 = XY_2
+
+ if xy_type_1 != xy_type_2:
+ print(f"{error('Join XY Inputs Error:')} Input types must match")
+ return (None,)
+ elif xy_type_1 == "Seeds++ Batch":
+ xy_type = xy_type_1
+ combined_length = len(xy_value_1) + len(xy_value_2)
+ xy_value = list(range(combined_length))
+ elif xy_type_1 == "Positive Prompt S/R" or xy_type_1 == "Negative Prompt S/R":
+ xy_type = xy_type_1
+ xy_value = xy_value_1 + [(xy_value_1[0][0], t[1]) for t in xy_value_2[1:]]
+ else:
+ xy_type = xy_type_1
+ xy_value = xy_value_1 + xy_value_2
+ return ((xy_type, xy_value),)
+
+########################################################################################################################
+# TSC Image Overlay
+class TSC_ImageOverlay:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "base_image": ("IMAGE",),
+ "overlay_image": ("IMAGE",),
+ "overlay_resize": (["None", "Fit", "Resize by rescale_factor", "Resize to width & heigth"],),
+ "resize_method": (["nearest-exact", "bilinear", "area"],),
+ "rescale_factor": ("FLOAT", {"default": 1, "min": 0.01, "max": 16.0, "step": 0.1}),
+ "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
+ "x_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
+ "y_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 10}),
+ "rotation": ("INT", {"default": 0, "min": -180, "max": 180, "step": 5}),
+ "opacity": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 5}),
+ },
+ "optional": {"optional_mask": ("MASK",),}
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "apply_overlay_image"
+ CATEGORY = "Efficiency Nodes/Image"
+
+ def apply_overlay_image(self, base_image, overlay_image, overlay_resize, resize_method, rescale_factor,
+ width, height, x_offset, y_offset, rotation, opacity, optional_mask=None):
+
+ # Pack tuples and assign variables
+ size = width, height
+ location = x_offset, y_offset
+ mask = optional_mask
+
+ # Check for different sizing options
+ if overlay_resize != "None":
+ #Extract overlay_image size and store in Tuple "overlay_image_size" (WxH)
+ overlay_image_size = overlay_image.size()
+ overlay_image_size = (overlay_image_size[2], overlay_image_size[1])
+ if overlay_resize == "Fit":
+ h_ratio = base_image.size()[1] / overlay_image_size[1]
+ w_ratio = base_image.size()[2] / overlay_image_size[0]
+ ratio = min(h_ratio, w_ratio)
+ overlay_image_size = tuple(round(dimension * ratio) for dimension in overlay_image_size)
+ elif overlay_resize == "Resize by rescale_factor":
+ overlay_image_size = tuple(int(dimension * rescale_factor) for dimension in overlay_image_size)
+ elif overlay_resize == "Resize to width & heigth":
+ overlay_image_size = (size[0], size[1])
+
+ samples = overlay_image.movedim(-1, 1)
+ overlay_image = comfy.utils.common_upscale(samples, overlay_image_size[0], overlay_image_size[1], resize_method, False)
+ overlay_image = overlay_image.movedim(1, -1)
+
+ overlay_image = tensor2pil(overlay_image)
+
+ # Add Alpha channel to overlay
+ overlay_image = overlay_image.convert('RGBA')
+ overlay_image.putalpha(Image.new("L", overlay_image.size, 255))
+
+ # If mask connected, check if the overlay_image image has an alpha channel
+ if mask is not None:
+ # Convert mask to pil and resize
+ mask = tensor2pil(mask)
+ mask = mask.resize(overlay_image.size)
+ # Apply mask as overlay's alpha
+ overlay_image.putalpha(ImageOps.invert(mask))
+
+ # Rotate the overlay image
+ overlay_image = overlay_image.rotate(rotation, expand=True)
+
+ # Apply opacity on overlay image
+ r, g, b, a = overlay_image.split()
+ a = a.point(lambda x: max(0, int(x * (1 - opacity / 100))))
+ overlay_image.putalpha(a)
+
+ # Split the base_image tensor along the first dimension to get a list of tensors
+ base_image_list = torch.unbind(base_image, dim=0)
+
+ # Convert each tensor to a PIL image, apply the overlay, and then convert it back to a tensor
+ processed_base_image_list = []
+ for tensor in base_image_list:
+ # Convert tensor to PIL Image
+ image = tensor2pil(tensor)
+
+ # Paste the overlay image onto the base image
+ if mask is None:
+ image.paste(overlay_image, location)
+ else:
+ image.paste(overlay_image, location, overlay_image)
+
+ # Convert PIL Image back to tensor
+ processed_tensor = pil2tensor(image)
+
+ # Append to list
+ processed_base_image_list.append(processed_tensor)
+
+ # Combine the processed images back into a single tensor
+ base_image = torch.stack([tensor.squeeze() for tensor in processed_base_image_list])
+
+ # Return the edited base image
+ return (base_image,)
+
+########################################################################################################################
+# Noise Sources & Seed Variations
+# https://github.com/shiimizu/ComfyUI_smZNodes
+# https://github.com/chrisgoringe/cg-noise
+
+# TSC Noise Sources & Variations Script
+class TSC_Noise_Control_Script:
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "rng_source": (["cpu", "gpu", "nv"],),
+ "cfg_denoiser": ("BOOLEAN", {"default": False}),
+ "add_seed_noise": ("BOOLEAN", {"default": False}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "weight": ("FLOAT", {"default": 0.015, "min": 0, "max": 1, "step": 0.001})},
+ "optional": {"script": ("SCRIPT",)}
+ }
+
+ RETURN_TYPES = ("SCRIPT",)
+ RETURN_NAMES = ("SCRIPT",)
+ FUNCTION = "noise_control"
+ CATEGORY = "Efficiency Nodes/Scripts"
+
+ def noise_control(self, rng_source, cfg_denoiser, add_seed_noise, seed, weight, script=None):
+ script = script or {}
+ script["noise"] = (rng_source, cfg_denoiser, add_seed_noise, seed, weight)
+ return (script,)
+
+########################################################################################################################
+# Add controlnet options if have controlnet_aux installed (https://github.com/Fannovel16/comfyui_controlnet_aux)
+use_controlnet_widget = preprocessor_widget = (["_"],)
+if os.path.exists(os.path.join(custom_nodes_dir, "comfyui_controlnet_aux")):
+ printout = "Attempting to add Control Net options to the 'HiRes-Fix Script' Node (comfyui_controlnet_aux add-on)..."
+ #print(f"{message('Efficiency Nodes:')} {printout}", end="", flush=True)
+
+ try:
+ with suppress_output():
+ AIO_Preprocessor = getattr(import_module("comfyui_controlnet_aux.__init__"), 'AIO_Preprocessor')
+ use_controlnet_widget = ("BOOLEAN", {"default": False})
+ preprocessor_widget = AIO_Preprocessor.INPUT_TYPES()["optional"]["preprocessor"]
+ print(f"\r{message('Efficiency Nodes:')} {printout}{success('Success!')}")
+ except Exception:
+ print(f"\r{message('Efficiency Nodes:')} {printout}{error('Failed!')}")
+
+# TSC HighRes-Fix with model latent upscalers (https://github.com/city96/SD-Latent-Upscaler)
+class TSC_HighRes_Fix:
+
+ default_latent_upscalers = LatentUpscaleBy.INPUT_TYPES()["required"]["upscale_method"][0]
+
+ city96_upscale_methods =\
+ ["city96." + ver for ver in city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["latent_ver"][0]]
+ city96_scalings_raw = city96_latent_upscaler.LatentUpscaler.INPUT_TYPES()["required"]["scale_factor"][0]
+ city96_scalings_float = [float(scale) for scale in city96_scalings_raw]
+
+ ttl_nn_upscale_methods = \
+ ["ttl_nn." + ver for ver in
+ ttl_nn_latent_upscaler.NNLatentUpscale.INPUT_TYPES()["required"]["version"][0]]
+
+ latent_upscalers = default_latent_upscalers + city96_upscale_methods + ttl_nn_upscale_methods
+ pixel_upscalers = folder_paths.get_filename_list("upscale_models")
+
+ @classmethod
+ def INPUT_TYPES(cls):
+
+ return {"required": {"upscale_type": (["latent","pixel"],),
+ "hires_ckpt_name": (["(use same)"] + folder_paths.get_filename_list("checkpoints"),),
+ "latent_upscaler": (cls.latent_upscalers,),
+ "pixel_upscaler": (cls.pixel_upscalers,),
+ "upscale_by": ("FLOAT", {"default": 1.25, "min": 0.01, "max": 8.0, "step": 0.05}),
+ "use_same_seed": ("BOOLEAN", {"default": True}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "hires_steps": ("INT", {"default": 12, "min": 1, "max": 10000}),
+ "denoise": ("FLOAT", {"default": .56, "min": 0.00, "max": 1.00, "step": 0.01}),
+ "iterations": ("INT", {"default": 1, "min": 0, "max": 5, "step": 1}),
+ "use_controlnet": use_controlnet_widget,
+ "control_net_name": (folder_paths.get_filename_list("controlnet"),),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ "preprocessor": preprocessor_widget,
+ "preprocessor_imgs": ("BOOLEAN", {"default": False})
+ },
+ "optional": {"script": ("SCRIPT",)},
+ "hidden": {"my_unique_id": "UNIQUE_ID"}
+ }
+
+ RETURN_TYPES = ("SCRIPT",)
+ FUNCTION = "hires_fix_script"
+ CATEGORY = "Efficiency Nodes/Scripts"
+
+ def hires_fix_script(self, upscale_type, hires_ckpt_name, latent_upscaler, pixel_upscaler, upscale_by,
+ use_same_seed, seed, hires_steps, denoise, iterations, use_controlnet, control_net_name,
+ strength, preprocessor, preprocessor_imgs, script=None, my_unique_id=None):
+ latent_upscale_function = None
+ latent_upscale_model = None
+ pixel_upscale_model = None
+
+ def float_to_string(num):
+ if num == int(num):
+ return "{:.1f}".format(num)
+ else:
+ return str(num)
+
+ if iterations > 0 and upscale_by > 0:
+ if upscale_type == "latent":
+ # For latent methods from city96
+ if latent_upscaler in self.city96_upscale_methods:
+ # Remove extra characters added
+ latent_upscaler = latent_upscaler.replace("city96.", "")
+
+ # Set function to city96_latent_upscaler.LatentUpscaler
+ latent_upscale_function = city96_latent_upscaler.LatentUpscaler
+
+ # Find the nearest valid scaling in city96_scalings_float
+ nearest_scaling = min(self.city96_scalings_float, key=lambda x: abs(x - upscale_by))
+
+ # Retrieve the index of the nearest scaling
+ nearest_scaling_index = self.city96_scalings_float.index(nearest_scaling)
+
+ # Use the index to get the raw string representation
+ nearest_scaling_raw = self.city96_scalings_raw[nearest_scaling_index]
+
+ upscale_by = float_to_string(upscale_by)
+
+ # Check if the input upscale_by value was different from the nearest valid value
+ if upscale_by != nearest_scaling_raw:
+ print(f"{warning('HighRes-Fix Warning:')} "
+ f"When using 'city96.{latent_upscaler}', 'upscale_by' must be one of {self.city96_scalings_raw}.\n"
+ f"Rounding to the nearest valid value ({nearest_scaling_raw}).\033[0m")
+ upscale_by = nearest_scaling_raw
+
+ # For ttl upscale methods
+ elif latent_upscaler in self.ttl_nn_upscale_methods:
+ # Remove extra characters added
+ latent_upscaler = latent_upscaler.replace("ttl_nn.", "")
+
+ # Bound to min/max limits
+ upscale_by_clamped = min(max(upscale_by, 1), 2)
+ if upscale_by != upscale_by_clamped:
+ print(f"{warning('HighRes-Fix Warning:')} "
+ f"When using 'ttl_nn.{latent_upscaler}', 'upscale_by' must be between 1 and 2.\n"
+ f"Rounding to the nearest valid value ({upscale_by_clamped}).\033[0m")
+ upscale_by = upscale_by_clamped
+
+ latent_upscale_function = ttl_nn_latent_upscaler.NNLatentUpscale
+
+ # For default upscale methods
+ elif latent_upscaler in self.default_latent_upscalers:
+ latent_upscale_function = LatentUpscaleBy
+
+ else: # Default
+ latent_upscale_function = LatentUpscaleBy
+ latent_upscaler = self.default_latent_upscalers[0]
+ print(f"{warning('HiResFix Script Warning:')} Chosen latent upscale method not found! "
+ f"defaulting to '{latent_upscaler}'.\n")
+
+ # Load Checkpoint if defined
+ if hires_ckpt_name == "(use same)":
+ clear_cache(my_unique_id, 0, "ckpt")
+ else:
+ latent_upscale_model, _, _ = \
+ load_checkpoint(hires_ckpt_name, my_unique_id, output_vae=False, cache=1, cache_overwrite=True)
+
+ elif upscale_type == "pixel":
+ pixel_upscale_model = UpscaleModelLoader().load_model(pixel_upscaler)[0]
+
+ control_net = ControlNetLoader().load_controlnet(control_net_name)[0] if use_controlnet is True else None
+
+ # Construct the script output
+ script = script or {}
+ script["hiresfix"] = (upscale_type, latent_upscaler, upscale_by, use_same_seed, seed, hires_steps,
+ denoise, iterations, control_net, strength, preprocessor, preprocessor_imgs,
+ latent_upscale_function, latent_upscale_model, pixel_upscale_model)
+
+ return (script,)
+
+########################################################################################################################
+# TSC Tiled Upscaler (https://github.com/BlenderNeko/ComfyUI_TiledKSampler)
+class TSC_Tiled_Upscaler:
+ @classmethod
+ def INPUT_TYPES(cls):
+ # Split the list based on the keyword "tile"
+ cnet_filenames = [name for name in folder_paths.get_filename_list("controlnet")]
+
+ return {"required": {"upscale_by": ("FLOAT", {"default": 1.25, "min": 0.01, "max": 8.0, "step": 0.05}),
+ "tile_size": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
+ "tiling_strategy": (["random", "random strict", "padded", 'simple', 'none'],),
+ "tiling_steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
+ "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+ "denoise": ("FLOAT", {"default": .4, "min": 0.0, "max": 1.0, "step": 0.01}),
+ "use_controlnet": ("BOOLEAN", {"default": False}),
+ "tile_controlnet": (cnet_filenames,),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ },
+ "optional": {"script": ("SCRIPT",)}}
+
+ RETURN_TYPES = ("SCRIPT",)
+ FUNCTION = "tiled_sampling"
+ CATEGORY = "Efficiency Nodes/Scripts"
+
+ def tiled_sampling(self, upscale_by, tile_size, tiling_strategy, tiling_steps, seed, denoise,
+ use_controlnet, tile_controlnet, strength, script=None):
+ if tiling_strategy != 'none':
+ script = script or {}
+ tile_controlnet = ControlNetLoader().load_controlnet(tile_controlnet)[0] if use_controlnet else None
+
+ script["tile"] = (upscale_by, tile_size, tiling_strategy, tiling_steps, seed, denoise, tile_controlnet, strength)
+ return (script,)
+
+########################################################################################################################
+# NODE MAPPING
+NODE_CLASS_MAPPINGS = {
+ "KSampler (Efficient)": TSC_KSampler,
+ "KSampler Adv. (Efficient)":TSC_KSamplerAdvanced,
+ "KSampler SDXL (Eff.)": TSC_KSamplerSDXL,
+ "Efficient Loader": TSC_EfficientLoader,
+ "Eff. Loader SDXL": TSC_EfficientLoaderSDXL,
+ "LoRA Stacker": TSC_LoRA_Stacker,
+ "Control Net Stacker": TSC_Control_Net_Stacker,
+ "Apply ControlNet Stack": TSC_Apply_ControlNet_Stack,
+ "Unpack SDXL Tuple": TSC_Unpack_SDXL_Tuple,
+ "Pack SDXL Tuple": TSC_Pack_SDXL_Tuple,
+ "XY Plot": TSC_XYplot,
+ "XY Input: Seeds++ Batch": TSC_XYplot_SeedsBatch,
+ "XY Input: Add/Return Noise": TSC_XYplot_AddReturnNoise,
+ "XY Input: Steps": TSC_XYplot_Steps,
+ "XY Input: CFG Scale": TSC_XYplot_CFG,
+ "XY Input: Sampler/Scheduler": TSC_XYplot_Sampler_Scheduler,
+ "XY Input: Denoise": TSC_XYplot_Denoise,
+ "XY Input: VAE": TSC_XYplot_VAE,
+ "XY Input: Prompt S/R": TSC_XYplot_PromptSR,
+ "XY Input: Aesthetic Score": TSC_XYplot_AScore,
+ "XY Input: Refiner On/Off": TSC_XYplot_Refiner_OnOff,
+ "XY Input: Checkpoint": TSC_XYplot_Checkpoint,
+ "XY Input: Clip Skip": TSC_XYplot_ClipSkip,
+ "XY Input: LoRA": TSC_XYplot_LoRA,
+ "XY Input: LoRA Plot": TSC_XYplot_LoRA_Plot,
+ "XY Input: LoRA Stacks": TSC_XYplot_LoRA_Stacks,
+ "XY Input: Control Net": TSC_XYplot_Control_Net,
+ "XY Input: Control Net Plot": TSC_XYplot_Control_Net_Plot,
+ "XY Input: Manual XY Entry": TSC_XYplot_Manual_XY_Entry,
+ "Manual XY Entry Info": TSC_XYplot_Manual_XY_Entry_Info,
+ "Join XY Inputs of Same Type": TSC_XYplot_JoinInputs,
+ "Image Overlay": TSC_ImageOverlay,
+ "Noise Control Script": TSC_Noise_Control_Script,
+ "HighRes-Fix Script": TSC_HighRes_Fix,
+ "Tiled Upscaler Script": TSC_Tiled_Upscaler
+}
+
+########################################################################################################################
+# Add AnimateDiff Script based off Kosinkadink's Nodes (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
+if os.path.exists(os.path.join(custom_nodes_dir, "ComfyUI-AnimateDiff-Evolved")):
+ printout = "Attempting to add 'AnimatedDiff Script' Node (ComfyUI-AnimateDiff-Evolved add-on)..."
+ print(f"{message('Efficiency Nodes:')} {printout}", end="")
+ try:
+ module = import_module("ComfyUI-AnimateDiff-Evolved.animatediff.nodes")
+ AnimateDiffLoaderWithContext = getattr(module, 'AnimateDiffLoaderWithContext')
+ AnimateDiffCombine = getattr(module, 'AnimateDiffCombine_Deprecated')
+ print(f"\r{message('Efficiency Nodes:')} {printout}{success('Success!')}")
+
+ # TSC AnimatedDiff Script (https://github.com/BlenderNeko/ComfyUI_TiledKSampler)
+ class TSC_AnimateDiff_Script:
+ @classmethod
+ def INPUT_TYPES(cls):
+
+ return {"required": {
+ "motion_model": AnimateDiffLoaderWithContext.INPUT_TYPES()["required"]["model_name"],
+ "beta_schedule": AnimateDiffLoaderWithContext.INPUT_TYPES()["required"]["beta_schedule"],
+ "frame_rate": AnimateDiffCombine.INPUT_TYPES()["required"]["frame_rate"],
+ "loop_count": AnimateDiffCombine.INPUT_TYPES()["required"]["loop_count"],
+ "format": AnimateDiffCombine.INPUT_TYPES()["required"]["format"],
+ "pingpong": AnimateDiffCombine.INPUT_TYPES()["required"]["pingpong"],
+ "save_image": AnimateDiffCombine.INPUT_TYPES()["required"]["save_image"]},
+ "optional": {"context_options": ("CONTEXT_OPTIONS",)}
+ }
+
+ RETURN_TYPES = ("SCRIPT",)
+ FUNCTION = "animatediff"
+ CATEGORY = "Efficiency Nodes/Scripts"
+
+ def animatediff(self, motion_model, beta_schedule, frame_rate, loop_count, format, pingpong, save_image,
+ script=None, context_options=None):
+ script = script or {}
+ script["anim"] = (motion_model, beta_schedule, context_options, frame_rate, loop_count, format, pingpong, save_image)
+ return (script,)
+
+ NODE_CLASS_MAPPINGS.update({"AnimateDiff Script": TSC_AnimateDiff_Script})
+
+ except Exception:
+ print(f"\r{message('Efficiency Nodes:')} {printout}{error('Failed!')}")
+
+########################################################################################################################
+# Simpleeval Nodes (https://github.com/danthedeckie/simpleeval)
+try:
+ import simpleeval
+
+ # TSC Evaluate Integers
+ class TSC_EvaluateInts:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "python_expression": ("STRING", {"default": "((a + b) - c) / 2", "multiline": False}),
+ "print_to_console": (["False", "True"],), },
+ "optional": {
+ "a": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
+ "b": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
+ "c": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}), },
+ }
+
+ RETURN_TYPES = ("INT", "FLOAT", "STRING",)
+ OUTPUT_NODE = True
+ FUNCTION = "evaluate"
+ CATEGORY = "Efficiency Nodes/Simple Eval"
+
+ def evaluate(self, python_expression, print_to_console, a=0, b=0, c=0):
+ # simple_eval doesn't require the result to be converted to a string
+ result = simpleeval.simple_eval(python_expression, names={'a': a, 'b': b, 'c': c})
+ int_result = int(result)
+ float_result = float(result)
+ string_result = str(result)
+ if print_to_console == "True":
+ print(f"\n{error('Evaluate Integers:')}")
+ print(f"\033[90m{{a = {a} , b = {b} , c = {c}}} \033[0m")
+ print(f"{python_expression} = \033[92m INT: " + str(int_result) + " , FLOAT: " + str(
+ float_result) + ", STRING: " + string_result + "\033[0m")
+ return (int_result, float_result, string_result,)
+
+
+ # ==================================================================================================================
+ # TSC Evaluate Floats
+ class TSC_EvaluateFloats:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "python_expression": ("STRING", {"default": "((a + b) - c) / 2", "multiline": False}),
+ "print_to_console": (["False", "True"],), },
+ "optional": {
+ "a": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}),
+ "b": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}),
+ "c": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 1}), },
+ }
+
+ RETURN_TYPES = ("INT", "FLOAT", "STRING",)
+ OUTPUT_NODE = True
+ FUNCTION = "evaluate"
+ CATEGORY = "Efficiency Nodes/Simple Eval"
+
+ def evaluate(self, python_expression, print_to_console, a=0, b=0, c=0):
+ # simple_eval doesn't require the result to be converted to a string
+ result = simpleeval.simple_eval(python_expression, names={'a': a, 'b': b, 'c': c})
+ int_result = int(result)
+ float_result = float(result)
+ string_result = str(result)
+ if print_to_console == "True":
+ print(f"\n{error('Evaluate Floats:')}")
+ print(f"\033[90m{{a = {a} , b = {b} , c = {c}}} \033[0m")
+ print(f"{python_expression} = \033[92m INT: " + str(int_result) + " , FLOAT: " + str(
+ float_result) + ", STRING: " + string_result + "\033[0m")
+ return (int_result, float_result, string_result,)
+
+
+ # ==================================================================================================================
+ # TSC Evaluate Strings
+ class TSC_EvaluateStrs:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {"required": {
+ "python_expression": ("STRING", {"default": "a + b + c", "multiline": False}),
+ "print_to_console": (["False", "True"],)},
+ "optional": {
+ "a": ("STRING", {"default": "Hello", "multiline": False}),
+ "b": ("STRING", {"default": " World", "multiline": False}),
+ "c": ("STRING", {"default": "!", "multiline": False}), }
+ }
+
+ RETURN_TYPES = ("STRING",)
+ OUTPUT_NODE = True
+ FUNCTION = "evaluate"
+ CATEGORY = "Efficiency Nodes/Simple Eval"
+
+ def evaluate(self, python_expression, print_to_console, a="", b="", c=""):
+ variables = {'a': a, 'b': b, 'c': c} # Define the variables for the expression
+
+ functions = simpleeval.DEFAULT_FUNCTIONS.copy()
+ functions.update({"len": len}) # Add the functions for the expression
+
+ result = simpleeval.simple_eval(python_expression, names=variables, functions=functions)
+ if print_to_console == "True":
+ print(f"\n{error('Evaluate Strings:')}")
+ print(f"\033[90ma = {a} \nb = {b} \nc = {c}\033[0m")
+ print(f"{python_expression} = \033[92m" + str(result) + "\033[0m")
+ return (str(result),) # Convert result to a string before returning
+
+
+ # ==================================================================================================================
+ # TSC Simple Eval Examples
+ class TSC_EvalExamples:
+ @classmethod
+ def INPUT_TYPES(cls):
+ filepath = os.path.join(my_dir, 'workflows', 'SimpleEval_Node_Examples.txt')
+ with open(filepath, 'r') as file:
+ examples = file.read()
+ return {"required": {"models_text": ("STRING", {"default": examples, "multiline": True}), }, }
+
+ RETURN_TYPES = ()
+ CATEGORY = "Efficiency Nodes/Simple Eval"
+
+ # ==================================================================================================================
+ NODE_CLASS_MAPPINGS.update({"Evaluate Integers": TSC_EvaluateInts})
+ NODE_CLASS_MAPPINGS.update({"Evaluate Floats": TSC_EvaluateFloats})
+ NODE_CLASS_MAPPINGS.update({"Evaluate Strings": TSC_EvaluateStrs})
+ NODE_CLASS_MAPPINGS.update({"Simple Eval Examples": TSC_EvalExamples})
+
+except ImportError:
+ print(f"{warning('Efficiency Nodes Warning:')} Failed to import python package 'simpleeval'; related nodes disabled.\n")
diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/funding.yml b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/funding.yml
new file mode 100644
index 0000000000000000000000000000000000000000..648a4a8da4c39b70d4775182e4548a914f44b06d
--- /dev/null
+++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/funding.yml
@@ -0,0 +1,3 @@
+github: ["https://www.buymeacoffee.com/jagsai", jags111]
+patreon: jags111
+custom: ["https://www.paypal.me/revsmart", orchidsasia.com]
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+Images in this group+
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+ |
+ |
+ |
+ |
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+ |
+ |
+ |
+ |
+
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+ |
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+ |
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+ |
+
"); + notes.push( + $el("span", { + innerHTML: pre, + }) + ); + } + if (m) { + notes.push( + $el("a", { + href: m[0], + textContent: m[0], + target: "_blank", + }) + ); + } + + end = fin; + } while (m); + return notes; + } + + let textarea; + let notesContainer; + const editText = "✏️ Edit"; + const edit = $el("a", { + textContent: editText, + href: "#", + style: { + float: "right", + color: "greenyellow", + textDecoration: "none", + }, + onclick: async (e) => { + e.preventDefault(); + + if (textarea) { + this.customNotes = textarea.value; + + const resp = await api.fetchApi( + "/pysssss/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`), + { + method: "POST", + body: this.customNotes, + } + ); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving notes (${req.status}) ${req.statusText}`); + return; + } + + e.target.textContent = editText; + textarea.remove(); + textarea = null; + + notesContainer.replaceChildren(...parseNote.call(this)); + } else { + e.target.textContent = "💾 Save"; + textarea = $el("textarea", { + style: { + width: "100%", + minWidth: "200px", + minHeight: "50px", + }, + textContent: this.customNotes, + }); + e.target.after(textarea); + notesContainer.replaceChildren(); + textarea.style.height = Math.min(textarea.scrollHeight, 300) + "px"; + } + }, + }); + + notesContainer = $el("div.pysssss-model-notes", parseNote.call(this)); + return $el( + "div", + { + style: { display: "contents" }, + }, + [edit, notesContainer] + ); + } + + addInfo() { + this.addInfoEntry("Notes", this.getNoteInfo()); + } + + addInfoEntry(name, value) { + return $el( + "p", + { + parent: this.info, + }, + [ + typeof name === "string" ? $el("label", { textContent: name + ": " }) : name, + typeof value === "string" ? $el("span", { textContent: value }) : value, + ] + ); + } + + async getCivitaiDetails() { + const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash); + if (req.status === 200) { + return await req.json(); + } else if (req.status === 404) { + throw new Error("Model not found"); + } else { + throw new Error(`Error loading info (${req.status}) ${req.statusText}`); + } + } + + addCivitaiInfo() { + const promise = this.getCivitaiDetails(); + const content = $el("span", { textContent: "ℹ️ Loading..." }); + + this.addInfoEntry( + $el("label", [ + $el("img", { + style: { + width: "18px", + position: "relative", + top: "3px", + margin: "0 5px 0 0", + }, + src: "https://civitai.com/favicon.ico", + }), + $el("span", { textContent: "Civitai: " }), + ]), + content + ); + + return promise + .then((info) => { + content.replaceChildren( + $el("a", { + href: "https://civitai.com/models/" + info.modelId, + textContent: "View " + info.model.name, + target: "_blank", + }) + ); + + if (info.images?.length) { + this.img.src = info.images[0].url; + this.img.style.display = ""; + + this.imgSave = $el("button", { + textContent: "Use as preview", + parent: this.imgWrapper, + onclick: async () => { + // Convert the preview to a blob + const blob = await (await fetch(this.img.src)).blob(); + + // Store it in temp + const name = "temp_preview." + new URL(this.img.src).pathname.split(".")[1]; + const body = new FormData(); + body.append("image", new File([blob], name)); + body.append("overwrite", "true"); + body.append("type", "temp"); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving preview (${req.status}) ${req.statusText}`); + return; + } + + // Use as preview + await api.fetchApi("/pysssss/save/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + filename: name, + type: "temp", + }), + headers: { + "content-type": "application/json", + }, + }); + app.refreshComboInNodes(); + }, + }); + } + + return info; + }) + .catch((err) => { + content.textContent = "⚠️ " + err.message; + }); + } +} diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/utils.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/utils.js new file mode 100644 index 0000000000000000000000000000000000000000..1ea55b1461022da1bed8ecca8145ced19703c4ad --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/utils.js @@ -0,0 +1,94 @@ +import { app } from '../../../../scripts/app.js' +import { $el } from "../../../../scripts/ui.js"; + +export function addStylesheet(url) { + if (url.endsWith(".js")) { + url = url.substr(0, url.length - 2) + "css"; + } + $el("link", { + parent: document.head, + rel: "stylesheet", + type: "text/css", + href: url.startsWith("http") ? url : getUrl(url), + }); +} + +export function getUrl(path, baseUrl) { + if (baseUrl) { + return new URL(path, baseUrl).toString(); + } else { + return new URL("../" + path, import.meta.url).toString(); + } +} + +export async function loadImage(url) { + return new Promise((res, rej) => { + const img = new Image(); + img.onload = res; + img.onerror = rej; + img.src = url; + }); +} + +export function addMenuHandler(nodeType, cb) { + + const GROUPED_MENU_ORDER = { + "🔄 Swap with...": 0, + "⛓ Add link...": 1, + "📜 Add script...": 2, + "🔍 View model info...": 3, + "🌱 Seed behavior...": 4, + "📐 Set Resolution...": 5, + "✏️ Add 𝚇 input...": 6, + "✏️ Add 𝚈 input...": 7 + }; + + const originalGetOpts = nodeType.prototype.getExtraMenuOptions; + + nodeType.prototype.getExtraMenuOptions = function () { + let r = originalGetOpts ? originalGetOpts.apply(this, arguments) || [] : []; + + const insertOption = (option) => { + if (GROUPED_MENU_ORDER.hasOwnProperty(option.content)) { + // Find the right position for the option + let targetPos = r.length; // default to the end + + for (let i = 0; i < r.length; i++) { + if (GROUPED_MENU_ORDER.hasOwnProperty(r[i].content) && + GROUPED_MENU_ORDER[option.content] < GROUPED_MENU_ORDER[r[i].content]) { + targetPos = i; + break; + } + } + // Insert the option at the determined position + r.splice(targetPos, 0, option); + } else { + // If the option is not in the GROUPED_MENU_ORDER, simply add it to the end + r.push(option); + } + }; + + cb.call(this, insertOption); + + return r; + }; +} + +export function findWidgetByName(node, widgetName) { + return node.widgets.find(widget => widget.name === widgetName); +} + +// Utility functions +export function addNode(name, nextTo, options) { + options = { select: true, shiftX: 0, shiftY: 0, before: false, ...(options || {}) }; + const node = LiteGraph.createNode(name); + app.graph.add(node); + node.pos = [ + nextTo.pos[0] + options.shiftX, + nextTo.pos[1] + options.shiftY, + ]; + if (options.select) { + app.canvas.selectNode(node, false); + } + return node; +} diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/modelInfo.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/modelInfo.js new file mode 100644 index 0000000000000000000000000000000000000000..de167e92172a5f0318126a427e872cb820a30c1e --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/modelInfo.js @@ -0,0 +1,336 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; +import { ModelInfoDialog } from "./common/modelInfoDialog.js"; +import { addMenuHandler } from "./common/utils.js"; + +const MAX_TAGS = 500; + +class LoraInfoDialog extends ModelInfoDialog { + getTagFrequency() { + if (!this.metadata.ss_tag_frequency) return []; + + const datasets = JSON.parse(this.metadata.ss_tag_frequency); + const tags = {}; + for (const setName in datasets) { + const set = datasets[setName]; + for (const t in set) { + if (t in tags) { + tags[t] += set[t]; + } else { + tags[t] = set[t]; + } + } + } + + return Object.entries(tags).sort((a, b) => b[1] - a[1]); + } + + getResolutions() { + let res = []; + if (this.metadata.ss_bucket_info) { + const parsed = JSON.parse(this.metadata.ss_bucket_info); + if (parsed?.buckets) { + for (const { resolution, count } of Object.values(parsed.buckets)) { + res.push([count, `${resolution.join("x")} * ${count}`]); + } + } + } + res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]); + let r = this.metadata.ss_resolution; + if (r) { + const s = r.split(","); + const w = s[0].replace("(", ""); + const h = s[1].replace(")", ""); + res.push(`${w.trim()}x${h.trim()} (Base res)`); + } else if ((r = this.metadata["modelspec.resolution"])) { + res.push(r + " (Base res"); + } + if (!res.length) { + res.push("⚠️ Unknown"); + } + return res; + } + + getTagList(tags) { + return tags.map((t) => + $el( + "li.pysssss-model-tag", + { + dataset: { + tag: t[0], + }, + $: (el) => { + el.onclick = () => { + el.classList.toggle("pysssss-model-tag--selected"); + }; + }, + }, + [ + $el("p", { + textContent: t[0], + }), + $el("span", { + textContent: t[1], + }), + ] + ) + ); + } + + addTags() { + let tags = this.getTagFrequency(); + let hasMore; + if (tags?.length) { + const c = tags.length; + let list; + if (c > MAX_TAGS) { + tags = tags.slice(0, MAX_TAGS); + hasMore = $el("p", [ + $el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }), + $el("a", { + href: "#", + textContent: `Show all ${c}`, + onclick: () => { + list.replaceChildren(...this.getTagList(this.getTagFrequency())); + hasMore.remove(); + }, + }), + ]); + } + list = $el("ol.pysssss-model-tags-list", this.getTagList(tags)); + this.tags = $el("div", [list]); + } else { + this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" }); + } + + this.content.append(this.tags); + + if (hasMore) { + this.content.append(hasMore); + } + } + + async addInfo() { + this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown"); + this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown"); + this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown"); + + this.addInfoEntry( + "Resolution", + $el( + "select", + this.getResolutions().map((r) => $el("option", { textContent: r })) + ) + ); + + super.addInfo(); + const p = this.addCivitaiInfo(); + this.addTags(); + + const info = await p; + if (info) { + $el( + "p", + { + parent: this.content, + textContent: "Trained Words: ", + }, + [ + $el("pre", { + textContent: info.trainedWords.join(", "), + style: { + whiteSpace: "pre-wrap", + margin: "10px 0", + background: "#222", + padding: "5px", + borderRadius: "5px", + maxHeight: "250px", + overflow: "auto", + }, + }), + ] + ); + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } + + createButtons() { + const btns = super.createButtons(); + + function copyTags(e, tags) { + const textarea = $el("textarea", { + parent: document.body, + style: { + position: "fixed", + }, + textContent: tags.map((el) => el.dataset.tag).join(", "), + }); + textarea.select(); + try { + document.execCommand("copy"); + if (!e.target.dataset.text) { + e.target.dataset.text = e.target.textContent; + } + e.target.textContent = "Copied " + tags.length + " tags"; + setTimeout(() => { + e.target.textContent = e.target.dataset.text; + }, 1000); + } catch (ex) { + prompt("Copy to clipboard: Ctrl+C, Enter", text); + } finally { + document.body.removeChild(textarea); + } + } + + btns.unshift( + $el("button", { + type: "button", + textContent: "Copy Selected", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag--selected")]); + }, + }), + $el("button", { + type: "button", + textContent: "Copy All", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag")]); + }, + }) + ); + + return btns; + } +} + +class CheckpointInfoDialog extends ModelInfoDialog { + async addInfo() { + super.addInfo(); + const info = await this.addCivitaiInfo(); + if (info) { + this.addInfoEntry("Base Model", info.baseModel || "⚠️ Unknown"); + + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } +} + +const generateNames = (prefix, start, end) => { + const result = []; + if (start < end) { + for (let i = start; i <= end; i++) { + result.push(`${prefix}${i}`); + } + } else { + for (let i = start; i >= end; i--) { + result.push(`${prefix}${i}`); + } + } + return result +} + +// NOTE: Orders reversed so they appear in ascending order +const infoHandler = { + "Efficient Loader": { + "loras": ["lora_name"], + "checkpoints": ["ckpt_name"] + }, + "Eff. Loader SDXL": { + "checkpoints": ["refiner_ckpt_name", "base_ckpt_name"] + }, + "LoRA Stacker": { + "loras": generateNames("lora_name_", 50, 1) + }, + "XY Input: LoRA": { + "loras": generateNames("lora_name_", 50, 1) + }, + "HighRes-Fix Script": { + "checkpoints": ["hires_ckpt_name"] + } +}; + +// Utility functions and other parts of your code remain unchanged + +app.registerExtension({ + name: "efficiency.ModelInfo", + beforeRegisterNodeDef(nodeType) { + const types = infoHandler[nodeType.comfyClass]; + + if (types) { + addMenuHandler(nodeType, function (insertOption) { // Here, we are calling addMenuHandler + let submenuItems = []; // to store submenu items + + const addSubMenuOption = (type, widgetNames) => { + widgetNames.forEach(widgetName => { + const widgetValue = this.widgets.find(w => w.name === widgetName)?.value; + + // Check if widgetValue is "None" + if (!widgetValue || widgetValue === "None") { + return; + } + + let value = widgetValue; + if (value.content) { + value = value.content; + } + const cls = type === "loras" ? LoraInfoDialog : CheckpointInfoDialog; + + const label = widgetName; + + // Push to submenuItems + submenuItems.push({ + content: label, + callback: async () => { + new cls(value).show(type, value); + }, + }); + }); + }; + + if (typeof types === 'object') { + Object.keys(types).forEach(type => { + addSubMenuOption(type, types[type]); + }); + } + + // If we have submenu items, use insertOption + if (submenuItems.length) { + insertOption({ // Using insertOption here + content: "🔍 View model info...", + has_submenu: true, + callback: (value, options, e, menu, node) => { + new LiteGraph.ContextMenu(submenuItems, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed + } + }); + } + }); + } + }, +}); + + + + + + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/setResolution.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/setResolution.js new file mode 100644 index 0000000000000000000000000000000000000000..c65ee8b4af6555b66017d28f4b641b180d10e033 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/setResolution.js @@ -0,0 +1,88 @@ +// Additional functions and imports +import { app } from "../../../scripts/app.js"; +import { addMenuHandler, findWidgetByName } from "./common/utils.js"; + +// A mapping for resolutions based on the type of the loader +const RESOLUTIONS = { + "Efficient Loader": [ + {width: 512, height: 512}, + {width: 512, height: 768}, + {width: 512, height: 640}, + {width: 640, height: 512}, + {width: 640, height: 768}, + {width: 640, height: 640}, + {width: 768, height: 512}, + {width: 768, height: 768}, + {width: 768, height: 640}, + ], + "Eff. Loader SDXL": [ + {width: 1024, height: 1024}, + {width: 1152, height: 896}, + {width: 896, height: 1152}, + {width: 1216, height: 832}, + {width: 832, height: 1216}, + {width: 1344, height: 768}, + {width: 768, height: 1344}, + {width: 1536, height: 640}, + {width: 640, height: 1536} + ] +}; + +// Function to set the resolution of a node +function setNodeResolution(node, width, height) { + let widthWidget = findWidgetByName(node, "empty_latent_width"); + let heightWidget = findWidgetByName(node, "empty_latent_height"); + + if (widthWidget) { + widthWidget.value = width; + } + + if (heightWidget) { + heightWidget.value = height; + } +} + +// The callback for the resolution submenu +function resolutionMenuCallback(node, width, height) { + return function() { + setNodeResolution(node, width, height); + }; +} + +// Show the set resolution submenu +function showResolutionMenu(value, options, e, menu, node) { + const resolutions = RESOLUTIONS[node.type]; + if (!resolutions) { + return false; + } + + const resolutionOptions = resolutions.map(res => ({ + content: `${res.width} x ${res.height}`, + callback: resolutionMenuCallback(node, res.width, res.height) + })); + + new LiteGraph.ContextMenu(resolutionOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.SetResolution", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["Efficient Loader", "Eff. Loader SDXL"].includes(nodeData.name)) { + addMenuHandler(nodeType, function (insertOption) { + insertOption({ + content: "📐 Set Resolution...", + has_submenu: true, + callback: showResolutionMenu + }); + }); + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapLoaders.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapLoaders.js new file mode 100644 index 0000000000000000000000000000000000000000..6e051e67185d6c7a1eae53525353e59fa7eaf20a --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapLoaders.js @@ -0,0 +1,135 @@ +import { app } from "../../../scripts/app.js"; +import { addMenuHandler } from "./common/utils.js"; +import { findWidgetByName } from "./common/utils.js"; + +function replaceNode(oldNode, newNodeName) { + const newNode = LiteGraph.createNode(newNodeName); + if (!newNode) { + return; + } + app.graph.add(newNode); + + newNode.pos = oldNode.pos.slice(); + newNode.size = oldNode.size.slice(); + + // Transfer widget values + const widgetMapping = { + "ckpt_name": "base_ckpt_name", + "vae_name": "vae_name", + "clip_skip": "base_clip_skip", + "positive": "positive", + "negative": "negative", + "prompt_style": "prompt_style", + "empty_latent_width": "empty_latent_width", + "empty_latent_height": "empty_latent_height", + "batch_size": "batch_size" + }; + + let effectiveWidgetMapping = widgetMapping; + + // Invert the mapping when going from "Eff. Loader SDXL" to "Efficient Loader" + if (oldNode.type === "Eff. Loader SDXL" && newNodeName === "Efficient Loader") { + effectiveWidgetMapping = {}; + for (const [key, value] of Object.entries(widgetMapping)) { + effectiveWidgetMapping[value] = key; + } + } + + oldNode.widgets.forEach(widget => { + const newName = effectiveWidgetMapping[widget.name]; + if (newName) { + const newWidget = findWidgetByName(newNode, newName); + if (newWidget) { + newWidget.value = widget.value; + } + } + }); + + // Hardcoded transfer for specific outputs based on the output names from the nodes in the image + const outputMapping = { + "MODEL": null, // Not present in "Eff. Loader SDXL" + "CONDITIONING+": null, // Not present in "Eff. Loader SDXL" + "CONDITIONING-": null, // Not present in "Eff. Loader SDXL" + "LATENT": "LATENT", + "VAE": "VAE", + "CLIP": null, // Not present in "Eff. Loader SDXL" + "DEPENDENCIES": "DEPENDENCIES" + }; + + // Transfer connections from old node outputs to new node outputs based on the outputMapping + oldNode.outputs.forEach((output, index) => { + if (output && output.links && outputMapping[output.name]) { + const newOutputName = outputMapping[output.name]; + + // If the new node does not have this output, skip + if (newOutputName === null) { + return; + } + + const newOutputIndex = newNode.findOutputSlot(newOutputName); + if (newOutputIndex !== -1) { + output.links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(newOutputIndex, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + } + }); + + // Remove old node + app.graph.remove(oldNode); +} + +function replaceNodeMenuCallback(currentNode, targetNodeName) { + return function() { + replaceNode(currentNode, targetNodeName); + }; +} + +function showSwapMenu(value, options, e, menu, node) { + const swapOptions = []; + + if (node.type !== "Efficient Loader") { + swapOptions.push({ + content: "Efficient Loader", + callback: replaceNodeMenuCallback(node, "Efficient Loader") + }); + } + + if (node.type !== "Eff. Loader SDXL") { + swapOptions.push({ + content: "Eff. Loader SDXL", + callback: replaceNodeMenuCallback(node, "Eff. Loader SDXL") + }); + } + + new LiteGraph.ContextMenu(swapOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.SwapLoaders", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["Efficient Loader", "Eff. Loader SDXL"].includes(nodeData.name)) { + addMenuHandler(nodeType, function (insertOption) { + insertOption({ + content: "🔄 Swap with...", + has_submenu: true, + callback: showSwapMenu + }); + }); + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapSamplers.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapSamplers.js new file mode 100644 index 0000000000000000000000000000000000000000..4d28113cf05a5713eb27fe3537a4119d7cf3fbd7 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapSamplers.js @@ -0,0 +1,191 @@ +import { app } from "../../../scripts/app.js"; +import { addMenuHandler } from "./common/utils.js"; +import { findWidgetByName } from "./common/utils.js"; + +function replaceNode(oldNode, newNodeName) { + // Create new node + const newNode = LiteGraph.createNode(newNodeName); + if (!newNode) { + return; + } + app.graph.add(newNode); + + // Position new node at the same position as the old node + newNode.pos = oldNode.pos.slice(); + + // Define widget mappings + const mappings = { + "KSampler (Efficient) <-> KSampler Adv. (Efficient)": { + seed: "noise_seed", + cfg: "cfg", + sampler_name: "sampler_name", + scheduler: "scheduler", + preview_method: "preview_method", + vae_decode: "vae_decode" + }, + "KSampler (Efficient) <-> KSampler SDXL (Eff.)": { + seed: "noise_seed", + cfg: "cfg", + sampler_name: "sampler_name", + scheduler: "scheduler", + preview_method: "preview_method", + vae_decode: "vae_decode" + }, + "KSampler Adv. (Efficient) <-> KSampler SDXL (Eff.)": { + noise_seed: "noise_seed", + steps: "steps", + cfg: "cfg", + sampler_name: "sampler_name", + scheduler: "scheduler", + start_at_step: "start_at_step", + preview_method: "preview_method", + vae_decode: "vae_decode"} + }; + + const swapKey = `${oldNode.type} <-> ${newNodeName}`; + + let widgetMapping = {}; + + // Check if a reverse mapping is needed + if (!mappings[swapKey]) { + const reverseKey = `${newNodeName} <-> ${oldNode.type}`; + const reverseMapping = mappings[reverseKey]; + if (reverseMapping) { + widgetMapping = Object.entries(reverseMapping).reduce((acc, [key, value]) => { + acc[value] = key; + return acc; + }, {}); + } + } else { + widgetMapping = mappings[swapKey]; + } + + if (oldNode.type === "KSampler (Efficient)" && (newNodeName === "KSampler Adv. (Efficient)" || newNodeName === "KSampler SDXL (Eff.)")) { + const denoise = Math.min(Math.max(findWidgetByName(oldNode, "denoise").value, 0), 1); // Ensure denoise is between 0 and 1 + const steps = Math.min(Math.max(findWidgetByName(oldNode, "steps").value, 0), 10000); // Ensure steps is between 0 and 10000 + + const total_steps = Math.floor(steps / denoise); + const start_at_step = total_steps - steps; + + findWidgetByName(newNode, "steps").value = Math.min(Math.max(total_steps, 0), 10000); // Ensure total_steps is between 0 and 10000 + findWidgetByName(newNode, "start_at_step").value = Math.min(Math.max(start_at_step, 0), 10000); // Ensure start_at_step is between 0 and 10000 + } + else if ((oldNode.type === "KSampler Adv. (Efficient)" || oldNode.type === "KSampler SDXL (Eff.)") && newNodeName === "KSampler (Efficient)") { + const stepsAdv = Math.min(Math.max(findWidgetByName(oldNode, "steps").value, 0), 10000); // Ensure stepsAdv is between 0 and 10000 + const start_at_step = Math.min(Math.max(findWidgetByName(oldNode, "start_at_step").value, 0), 10000); // Ensure start_at_step is between 0 and 10000 + + const denoise = Math.min(Math.max((stepsAdv - start_at_step) / stepsAdv, 0), 1); // Ensure denoise is between 0 and 1 + const stepsTotal = stepsAdv - start_at_step; + + findWidgetByName(newNode, "denoise").value = denoise; + findWidgetByName(newNode, "steps").value = Math.min(Math.max(stepsTotal, 0), 10000); // Ensure stepsTotal is between 0 and 10000 + } + + // Transfer widget values from old node to new node + oldNode.widgets.forEach(widget => { + const newName = widgetMapping[widget.name]; + if (newName) { + const newWidget = findWidgetByName(newNode, newName); + if (newWidget) { + newWidget.value = widget.value; + } + } + }); + + // Determine the starting indices based on the node types + let oldNodeInputStartIndex = 0; + let newNodeInputStartIndex = 0; + let oldNodeOutputStartIndex = 0; + let newNodeOutputStartIndex = 0; + + if (oldNode.type === "KSampler SDXL (Eff.)" || newNodeName === "KSampler SDXL (Eff.)") { + oldNodeInputStartIndex = (oldNode.type === "KSampler SDXL (Eff.)") ? 1 : 3; + newNodeInputStartIndex = (newNodeName === "KSampler SDXL (Eff.)") ? 1 : 3; + oldNodeOutputStartIndex = (oldNode.type === "KSampler SDXL (Eff.)") ? 1 : 3; + newNodeOutputStartIndex = (newNodeName === "KSampler SDXL (Eff.)") ? 1 : 3; + } + + // Transfer connections from old node to new node + oldNode.inputs.slice(oldNodeInputStartIndex).forEach((input, index) => { + if (input && input.link !== null) { + const originLinkInfo = oldNode.graph.links[input.link]; + if (originLinkInfo) { + const originNode = oldNode.graph.getNodeById(originLinkInfo.origin_id); + if (originNode) { + originNode.connect(originLinkInfo.origin_slot, newNode, index + newNodeInputStartIndex); + } + } + } + }); + + oldNode.outputs.slice(oldNodeOutputStartIndex).forEach((output, index) => { + if (output && output.links) { + output.links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(index + newNodeOutputStartIndex, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + }); + + // Remove old node + app.graph.remove(oldNode); +} + +function replaceNodeMenuCallback(currentNode, targetNodeName) { + return function() { + replaceNode(currentNode, targetNodeName); + }; +} + +function showSwapMenu(value, options, e, menu, node) { + const swapOptions = []; + + if (node.type !== "KSampler (Efficient)") { + swapOptions.push({ + content: "KSampler (Efficient)", + callback: replaceNodeMenuCallback(node, "KSampler (Efficient)") + }); + } + if (node.type !== "KSampler Adv. (Efficient)") { + swapOptions.push({ + content: "KSampler Adv. (Efficient)", + callback: replaceNodeMenuCallback(node, "KSampler Adv. (Efficient)") + }); + } + if (node.type !== "KSampler SDXL (Eff.)") { + swapOptions.push({ + content: "KSampler SDXL (Eff.)", + callback: replaceNodeMenuCallback(node, "KSampler SDXL (Eff.)") + }); + } + + new LiteGraph.ContextMenu(swapOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.SwapSamplers", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["KSampler (Efficient)", "KSampler Adv. (Efficient)", "KSampler SDXL (Eff.)"].includes(nodeData.name)) { + addMenuHandler(nodeType, function (insertOption) { + insertOption({ + content: "🔄 Swap with...", + has_submenu: true, + callback: showSwapMenu + }); + }); + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapScripts.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapScripts.js new file mode 100644 index 0000000000000000000000000000000000000000..a0aee9bb4168732480c75eccaf6bfa73b80a1a9c --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapScripts.js @@ -0,0 +1,100 @@ +import { app } from "../../../scripts/app.js"; +import { addMenuHandler } from "./common/utils.js"; + +function replaceNode(oldNode, newNodeName) { + const newNode = LiteGraph.createNode(newNodeName); + if (!newNode) { + return; + } + app.graph.add(newNode); + + newNode.pos = oldNode.pos.slice(); + + // Transfer connections from old node to new node + // XY Plot and AnimateDiff have only one output + if(["XY Plot", "AnimateDiff Script"].includes(oldNode.type)) { + if (oldNode.outputs[0] && oldNode.outputs[0].links) { + oldNode.outputs[0].links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(0, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + } else { + // Noise Control Script, HighRes-Fix Script, and Tiled Upscaler Script have 1 input and 1 output at index 0 + if (oldNode.inputs[0] && oldNode.inputs[0].link !== null) { + const originLinkInfo = oldNode.graph.links[oldNode.inputs[0].link]; + if (originLinkInfo) { + const originNode = oldNode.graph.getNodeById(originLinkInfo.origin_id); + if (originNode) { + originNode.connect(originLinkInfo.origin_slot, newNode, 0); + } + } + } + + if (oldNode.outputs[0] && oldNode.outputs[0].links) { + oldNode.outputs[0].links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(0, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + } + + // Remove old node + app.graph.remove(oldNode); +} + +function replaceNodeMenuCallback(currentNode, targetNodeName) { + return function() { + replaceNode(currentNode, targetNodeName); + }; +} + +function showSwapMenu(value, options, e, menu, node) { + const scriptNodes = [ + "XY Plot", + "Noise Control Script", + "HighRes-Fix Script", + "Tiled Upscaler Script", + "AnimateDiff Script" + ]; + + const swapOptions = scriptNodes.filter(n => n !== node.type).map(n => ({ + content: n, + callback: replaceNodeMenuCallback(node, n) + })); + + new LiteGraph.ContextMenu(swapOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.SwapScripts", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["XY Plot", "Noise Control Script", "HighRes-Fix Script", "Tiled Upscaler Script", "AnimateDiff Script"].includes(nodeData.name)) { + addMenuHandler(nodeType, function (insertOption) { + insertOption({ + content: "🔄 Swap with...", + has_submenu: true, + callback: showSwapMenu + }); + }); + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapXYinputs.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapXYinputs.js new file mode 100644 index 0000000000000000000000000000000000000000..218ba61fce86ad1c363621f5274db417a26e0be9 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapXYinputs.js @@ -0,0 +1,98 @@ +import { app } from "../../../scripts/app.js"; +import { addMenuHandler } from "./common/utils.js"; + +function replaceNode(oldNode, newNodeName) { + const newNode = LiteGraph.createNode(newNodeName); + if (!newNode) { + return; + } + app.graph.add(newNode); + + newNode.pos = oldNode.pos.slice(); + + // Handle the special nodes with two outputs + const nodesWithTwoOutputs = ["XY Input: LoRA Plot", "XY Input: Control Net Plot", "XY Input: Manual XY Entry"]; + let outputCount = nodesWithTwoOutputs.includes(oldNode.type) ? 2 : 1; + + // Transfer output connections from old node to new node + oldNode.outputs.slice(0, outputCount).forEach((output, index) => { + if (output && output.links) { + output.links.forEach(link => { + const targetLinkInfo = oldNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(index, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + }); + + // Remove old node + app.graph.remove(oldNode); +} + +function replaceNodeMenuCallback(currentNode, targetNodeName) { + return function() { + replaceNode(currentNode, targetNodeName); + }; +} + +function showSwapMenu(value, options, e, menu, node) { + const swapOptions = []; + const xyInputNodes = [ + "XY Input: Seeds++ Batch", + "XY Input: Add/Return Noise", + "XY Input: Steps", + "XY Input: CFG Scale", + "XY Input: Sampler/Scheduler", + "XY Input: Denoise", + "XY Input: VAE", + "XY Input: Prompt S/R", + "XY Input: Aesthetic Score", + "XY Input: Refiner On/Off", + "XY Input: Checkpoint", + "XY Input: Clip Skip", + "XY Input: LoRA", + "XY Input: LoRA Plot", + "XY Input: LoRA Stacks", + "XY Input: Control Net", + "XY Input: Control Net Plot", + "XY Input: Manual XY Entry" + ]; + + for (const nodeType of xyInputNodes) { + if (node.type !== nodeType) { + swapOptions.push({ + content: nodeType, + callback: replaceNodeMenuCallback(node, nodeType) + }); + } + } + + new LiteGraph.ContextMenu(swapOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.swapXYinputs", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name.startsWith("XY Input:")) { + addMenuHandler(nodeType, function (insertOption) { + insertOption({ + content: "🔄 Swap with...", + has_submenu: true, + callback: showSwapMenu + }); + }); + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/previewfix.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/previewfix.js new file mode 100644 index 0000000000000000000000000000000000000000..fa2b9421645dfa59bb74dbf93fe7aa99d849267d --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/previewfix.js @@ -0,0 +1,67 @@ +import { app } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js"; + +app.registerExtension({ + name: "efficiency.previewfix", + lastExecutedNodeId: null, + blobsToRevoke: [], // Array to accumulate blob URLs for revocation + debug: false, + + log(...args) { + if (this.debug) console.log(...args); + }, + + error(...args) { + if (this.debug) console.error(...args); + }, + + shouldRevokeBlobForNode(nodeId) { + const node = app.graph.getNodeById(nodeId); + + const validTitles = [ + "KSampler (Efficient)", + "KSampler Adv. (Efficient)", + "KSampler SDXL (Eff.)" + ]; + + if (!node || !validTitles.includes(node.title)) { + return false; + } + + const getValue = name => ((node.widgets || []).find(w => w.name === name) || {}).value; + return getValue("preview_method") !== "none" && getValue("vae_decode").includes("true"); + }, + + setup() { + // Intercepting blob creation to store and immediately revoke the last blob URL + const originalCreateObjectURL = URL.createObjectURL; + URL.createObjectURL = (object) => { + const blobURL = originalCreateObjectURL(object); + if (blobURL.startsWith('blob:')) { + this.log("[BlobURLLogger] Blob URL created:", blobURL); + + // If the current node meets the criteria, add the blob URL to the revocation list + if (this.shouldRevokeBlobForNode(this.lastExecutedNodeId)) { + this.blobsToRevoke.push(blobURL); + } + } + return blobURL; + }; + + // Listen to the start of the node execution to revoke all accumulated blob URLs + api.addEventListener("executing", ({ detail }) => { + if (this.lastExecutedNodeId !== detail || detail === null) { + this.blobsToRevoke.forEach(blob => { + this.log("[BlobURLLogger] Revoking Blob URL:", blob); + URL.revokeObjectURL(blob); + }); + this.blobsToRevoke = []; // Clear the list after revoking all blobs + } + + // Update the last executed node ID + this.lastExecutedNodeId = detail; + }); + + this.log("[BlobURLLogger] Hook attached."); + }, +}); \ No newline at end of file diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/seedcontrol.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/seedcontrol.js new file mode 100644 index 0000000000000000000000000000000000000000..59657fa5dcad8b033d6adaee233f342038a1ca8a --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/seedcontrol.js @@ -0,0 +1,251 @@ +import { app } from "../../scripts/app.js"; +import { addMenuHandler } from "./node_options/common/utils.js"; + +const LAST_SEED_BUTTON_LABEL = '🎲 Randomize / ♻️ Last Queued Seed'; +const SEED_BEHAVIOR_RANDOMIZE = 'Randomize'; +const SEED_BEHAVIOR_INCREMENT = 'Increment'; +const SEED_BEHAVIOR_DECREMENT = 'Decrement'; + +const NODE_WIDGET_MAP = { + "KSampler (Efficient)": "seed", + "KSampler Adv. (Efficient)": "noise_seed", + "KSampler SDXL (Eff.)": "noise_seed", + "Noise Control Script": "seed", + "HighRes-Fix Script": "seed", + "Tiled Upscaler Script": "seed" +}; + +const SPECIFIC_WIDTH = 325; // Set to desired width + +function setNodeWidthForMappedTitles(node) { + if (NODE_WIDGET_MAP[node.comfyClass]) { + node.setSize([SPECIFIC_WIDTH, node.size[1]]); + } +} + +class SeedControl { + constructor(node, seedName) { + this.lastSeed = -1; + this.serializedCtx = {}; + this.node = node; + this.seedBehavior = 'randomize'; // Default behavior + + let controlAfterGenerateIndex; + + for (const [i, w] of this.node.widgets.entries()) { + if (w.name === seedName) { + this.seedWidget = w; + } else if (w.name === 'control_after_generate') { + controlAfterGenerateIndex = i; + this.node.widgets.splice(i, 1); + } + } + + if (!this.seedWidget) { + throw new Error('Something\'s wrong; expected seed widget'); + } + + this.lastSeedButton = this.node.addWidget("button", LAST_SEED_BUTTON_LABEL, null, () => { + const isValidValue = Number.isInteger(this.seedWidget.value) && this.seedWidget.value >= min && this.seedWidget.value <= max; + + // Special case: if the current label is the default and seed value is -1 + if (this.lastSeedButton.name === LAST_SEED_BUTTON_LABEL && this.seedWidget.value == -1) { + return; // Do nothing and return early + } + + if (isValidValue && this.seedWidget.value != -1) { + this.lastSeed = this.seedWidget.value; + this.seedWidget.value = -1; + } else if (this.lastSeed !== -1) { + this.seedWidget.value = this.lastSeed; + } else { + this.seedWidget.value = -1; // Set to -1 if the label didn't update due to a seed value issue + } + + if (isValidValue) { + this.updateButtonLabel(); // Update the button label to reflect the change + } + }, { width: 50, serialize: false }); + + setNodeWidthForMappedTitles(node); + if (controlAfterGenerateIndex !== undefined) { + const addedWidget = this.node.widgets.pop(); + this.node.widgets.splice(controlAfterGenerateIndex, 0, addedWidget); + setNodeWidthForMappedTitles(node); + } + + const max = Math.min(1125899906842624, this.seedWidget.options.max); + const min = Math.max(-1125899906842624, this.seedWidget.options.min); + const range = (max - min) / (this.seedWidget.options.step / 10); + + this.seedWidget.serializeValue = async (node, index) => { + // Check if the button is disabled + if (this.lastSeedButton.disabled) { + return this.seedWidget.value; + } + + const currentSeed = this.seedWidget.value; + this.serializedCtx = { + wasSpecial: currentSeed == -1, + }; + + if (this.serializedCtx.wasSpecial) { + switch (this.seedBehavior) { + case 'increment': + this.serializedCtx.seedUsed = this.lastSeed + 1; + break; + case 'decrement': + this.serializedCtx.seedUsed = this.lastSeed - 1; + break; + default: + this.serializedCtx.seedUsed = Math.floor(Math.random() * range) * (this.seedWidget.options.step / 10) + min; + break; + } + + // Ensure the seed value is an integer and remains within the accepted range + this.serializedCtx.seedUsed = Number.isInteger(this.serializedCtx.seedUsed) ? Math.min(Math.max(this.serializedCtx.seedUsed, min), max) : this.seedWidget.value; + + } else { + this.serializedCtx.seedUsed = this.seedWidget.value; + } + + if (node && node.widgets_values) { + node.widgets_values[index] = this.serializedCtx.seedUsed; + } else { + // Update the last seed value and the button's label to show the current seed value + this.lastSeed = this.serializedCtx.seedUsed; + this.updateButtonLabel(); + } + + this.seedWidget.value = this.serializedCtx.seedUsed; + + if (this.serializedCtx.wasSpecial) { + this.lastSeed = this.serializedCtx.seedUsed; + this.updateButtonLabel(); + } + + return this.serializedCtx.seedUsed; + }; + + this.seedWidget.afterQueued = () => { + // Check if the button is disabled + if (this.lastSeedButton.disabled) { + return; // Exit the function immediately + } + + if (this.serializedCtx.wasSpecial) { + this.seedWidget.value = -1; + } + + // Check if seed has changed to a non -1 value, and if so, update lastSeed + if (this.seedWidget.value !== -1) { + this.lastSeed = this.seedWidget.value; + } + + this.updateButtonLabel(); + this.serializedCtx = {}; + }; + } + + setBehavior(behavior) { + this.seedBehavior = behavior; + + // Capture the current seed value as lastSeed and then set the seed widget value to -1 + if (this.seedWidget.value != -1) { + this.lastSeed = this.seedWidget.value; + this.seedWidget.value = -1; + } + + this.updateButtonLabel(); + } + + updateButtonLabel() { + + switch (this.seedBehavior) { + case 'increment': + this.lastSeedButton.name = `➕ Increment / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`; + break; + case 'decrement': + this.lastSeedButton.name = `➖ Decrement / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`; + break; + default: + this.lastSeedButton.name = `🎲 Randomize / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`; + break; + } + } + +} + +function showSeedBehaviorMenu(value, options, e, menu, node) { + const behaviorOptions = [ + { + content: "🎲 Randomize", + callback: () => { + node.seedControl.setBehavior('randomize'); + } + }, + { + content: "➕ Increment", + callback: () => { + node.seedControl.setBehavior('increment'); + } + }, + { + content: "➖ Decrement", + callback: () => { + node.seedControl.setBehavior('decrement'); + } + } + ]; + + new LiteGraph.ContextMenu(behaviorOptions, { + event: e, + callback: null, + parentMenu: menu, + node: node + }); + + return false; // This ensures the original context menu doesn't proceed +} + +// Extension Definition +app.registerExtension({ + name: "efficiency.seedcontrol", + async beforeRegisterNodeDef(nodeType, nodeData, _app) { + if (NODE_WIDGET_MAP[nodeData.name]) { + addMenuHandler(nodeType, function (insertOption) { + // Check conditions before showing the seed behavior option + let showSeedOption = true; + + if (nodeData.name === "Noise Control Script") { + // Check for 'add_seed_noise' widget being false + const addSeedNoiseWidget = this.widgets.find(w => w.name === 'add_seed_noise'); + if (addSeedNoiseWidget && !addSeedNoiseWidget.value) { + showSeedOption = false; + } + } else if (nodeData.name === "HighRes-Fix Script") { + // Check for 'use_same_seed' widget being true + const useSameSeedWidget = this.widgets.find(w => w.name === 'use_same_seed'); + if (useSameSeedWidget && useSameSeedWidget.value) { + showSeedOption = false; + } + } + + if (showSeedOption) { + insertOption({ + content: "🌱 Seed behavior...", + has_submenu: true, + callback: showSeedBehaviorMenu + }); + } + }); + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated ? onNodeCreated.apply(this, []) : undefined; + this.seedControl = new SeedControl(this, NODE_WIDGET_MAP[nodeData.name]); + this.seedControl.seedWidget.value = -1; + }; + } + }, +}); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/widgethider.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/widgethider.js new file mode 100644 index 0000000000000000000000000000000000000000..25878aa2316aab056d0a7c94a30318d3475ded1f --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/widgethider.js @@ -0,0 +1,596 @@ +import { app } from "../../scripts/app.js"; + +let origProps = {}; +let initialized = false; + +const findWidgetByName = (node, name) => { + return node.widgets ? node.widgets.find((w) => w.name === name) : null; +}; + +const doesInputWithNameExist = (node, name) => { + return node.inputs ? node.inputs.some((input) => input.name === name) : false; +}; + +const HIDDEN_TAG = "tschide"; +// Toggle Widget + change size +function toggleWidget(node, widget, show = false, suffix = "") { + if (!widget || doesInputWithNameExist(node, widget.name)) return; + + // Store the original properties of the widget if not already stored + if (!origProps[widget.name]) { + origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize }; + } + + const origSize = node.size; + + // Set the widget type and computeSize based on the show flag + widget.type = show ? origProps[widget.name].origType : HIDDEN_TAG + suffix; + widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4]; + + // Recursively handle linked widgets if they exist + widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show)); + + // Calculate the new height for the node based on its computeSize method + const newHeight = node.computeSize()[1]; + node.setSize([node.size[0], newHeight]); +} + +const WIDGET_HEIGHT = 24; +// Use for Multiline Widget Nodes (aka Efficient Loaders) +function toggleWidget_2(node, widget, show = false, suffix = "") { + if (!widget || doesInputWithNameExist(node, widget.name)) return; + + const isCurrentlyVisible = widget.type !== HIDDEN_TAG + suffix; + if (isCurrentlyVisible === show) return; // Early exit if widget is already in the desired state + + if (!origProps[widget.name]) { + origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize }; + } + + widget.type = show ? origProps[widget.name].origType : HIDDEN_TAG + suffix; + widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4]; + + if (initialized){ + const adjustment = show ? WIDGET_HEIGHT : -WIDGET_HEIGHT; + node.setSize([node.size[0], node.size[1] + adjustment]); + } +} + +// New function to handle widget visibility based on input_mode +function handleInputModeWidgetsVisibility(node, inputModeValue) { + // Utility function to generate widget names up to a certain count + function generateWidgetNames(baseName, count) { + return Array.from({ length: count }, (_, i) => `${baseName}_${i + 1}`); + } + + // Common widget groups + const batchWidgets = ["batch_path", "subdirectories", "batch_sort", "batch_max"]; + const xbatchWidgets = ["X_batch_path", "X_subdirectories", "X_batch_sort", "X_batch_max"]; + const ckptWidgets = [...generateWidgetNames("ckpt_name", 50)]; + const clipSkipWidgets = [...generateWidgetNames("clip_skip", 50)]; + const vaeNameWidgets = [...generateWidgetNames("vae_name", 50)]; + const loraNameWidgets = [...generateWidgetNames("lora_name", 50)]; + const loraWtWidgets = [...generateWidgetNames("lora_wt", 50)]; + const modelStrWidgets = [...generateWidgetNames("model_str", 50)]; + const clipStrWidgets = [...generateWidgetNames("clip_str", 50)]; + const xWidgets = ["X_batch_count", "X_first_value", "X_last_value"] + const yWidgets = ["Y_batch_count", "Y_first_value", "Y_last_value"] + + const nodeVisibilityMap = { + "LoRA Stacker": { + "simple": [...modelStrWidgets, ...clipStrWidgets], + "advanced": [...loraWtWidgets] + }, + "XY Input: Steps": { + "steps": ["first_start_step", "last_start_step", "first_end_step", "last_end_step", "first_refine_step", "last_refine_step"], + "start_at_step": ["first_step", "last_step", "first_end_step", "last_end_step", "first_refine_step", "last_refine_step"], + "end_at_step": ["first_step", "last_step", "first_start_step", "last_start_step", "first_refine_step", "last_refine_step"], + "refine_at_step": ["first_step", "last_step", "first_start_step", "last_start_step", "first_end_step", "last_end_step"] + }, + "XY Input: VAE": { + "VAE Names": [...batchWidgets], + "VAE Batch": [...vaeNameWidgets, "vae_count"] + }, + "XY Input: Checkpoint": { + "Ckpt Names": [...clipSkipWidgets, ...vaeNameWidgets, ...batchWidgets], + "Ckpt Names+ClipSkip": [...vaeNameWidgets, ...batchWidgets], + "Ckpt Names+ClipSkip+VAE": [...batchWidgets], + "Checkpoint Batch": [...ckptWidgets, ...clipSkipWidgets, ...vaeNameWidgets, "ckpt_count"] + }, + "XY Input: LoRA": { + "LoRA Names": [...modelStrWidgets, ...clipStrWidgets, ...batchWidgets], + "LoRA Names+Weights": [...batchWidgets, "model_strength", "clip_strength"], + "LoRA Batch": [...loraNameWidgets, ...modelStrWidgets, ...clipStrWidgets, "lora_count"] + }, + "XY Input: LoRA Plot": { + "X: LoRA Batch, Y: LoRA Weight": ["lora_name", "model_strength", "clip_strength", "X_first_value", "X_last_value"], + "X: LoRA Batch, Y: Model Strength": ["lora_name", "model_strength", "model_strength", "X_first_value", "X_last_value"], + "X: LoRA Batch, Y: Clip Strength": ["lora_name", "clip_strength", "X_first_value", "X_last_value"], + "X: Model Strength, Y: Clip Strength": [...xbatchWidgets, "model_strength", "clip_strength"], + }, + "XY Input: Control Net": { + "strength": ["first_start_percent", "last_start_percent", "first_end_percent", "last_end_percent", "strength"], + "start_percent": ["first_strength", "last_strength", "first_end_percent", "last_end_percent", "start_percent"], + "end_percent": ["first_strength", "last_strength", "first_start_percent", "last_start_percent", "end_percent"] + }, + "XY Input: Control Net Plot": { + "X: Strength, Y: Start%": ["strength", "start_percent"], + "X: Strength, Y: End%": ["strength","end_percent"], + "X: Start%, Y: Strength": ["start_percent", "strength"], + "X: Start%, Y: End%": ["start_percent", "end_percent"], + "X: End%, Y: Strength": ["end_percent", "strength"], + "X: End%, Y: Start%": ["end_percent", "start_percent"], + } + }; + + const inputModeVisibilityMap = nodeVisibilityMap[node.comfyClass]; + + if (!inputModeVisibilityMap || !inputModeVisibilityMap[inputModeValue]) return; + + // Reset all widgets to visible + for (const key in inputModeVisibilityMap) { + for (const widgetName of inputModeVisibilityMap[key]) { + const widget = findWidgetByName(node, widgetName); + toggleWidget(node, widget, true); + } + } + + // Hide the specific widgets for the current input_mode value + for (const widgetName of inputModeVisibilityMap[inputModeValue]) { + const widget = findWidgetByName(node, widgetName); + toggleWidget(node, widget, false); + } +} + +// Handle multi-widget visibilities +function handleVisibility(node, countValue, node_type) { + const inputModeValue = findWidgetByName(node, "input_mode").value; + const baseNamesMap = { + "LoRA": ["lora_name", "model_str", "clip_str"], + "Checkpoint": ["ckpt_name", "clip_skip", "vae_name"], + "LoRA Stacker": ["lora_name", "model_str", "clip_str", "lora_wt"] + }; + + const baseNames = baseNamesMap[node_type]; + + const isBatchMode = inputModeValue.includes("Batch"); + if (isBatchMode) {countValue = 0;} + + for (let i = 1; i <= 50; i++) { + const nameWidget = findWidgetByName(node, `${baseNames[0]}_${i}`); + const firstWidget = findWidgetByName(node, `${baseNames[1]}_${i}`); + const secondWidget = findWidgetByName(node, `${baseNames[2]}_${i}`); + const thirdWidget = node_type === "LoRA Stacker" ? findWidgetByName(node, `${baseNames[3]}_${i}`) : null; + + if (i <= countValue) { + toggleWidget(node, nameWidget, true); + + if (node_type === "LoRA Stacker") { + if (inputModeValue === "simple") { + toggleWidget(node, firstWidget, false); // model_str + toggleWidget(node, secondWidget, false); // clip_str + toggleWidget(node, thirdWidget, true); // lora_wt + } else if (inputModeValue === "advanced") { + toggleWidget(node, firstWidget, true); // model_str + toggleWidget(node, secondWidget, true); // clip_str + toggleWidget(node, thirdWidget, false); // lora_wt + } + } else if (node_type === "Checkpoint") { + if (inputModeValue.includes("ClipSkip")){toggleWidget(node, firstWidget, true);} + if (inputModeValue.includes("VAE")){toggleWidget(node, secondWidget, true);} + } else if (node_type === "LoRA") { + if (inputModeValue.includes("Weights")){ + toggleWidget(node, firstWidget, true); + toggleWidget(node, secondWidget, true); + } + } + } + else { + toggleWidget(node, nameWidget, false); + toggleWidget(node, firstWidget, false); + toggleWidget(node, secondWidget, false); + if (thirdWidget) {toggleWidget(node, thirdWidget, false);} + } + } +} + +// Sampler & Scheduler XY input visibility logic +function handleSamplerSchedulerVisibility(node, countValue, targetParameter) { + for (let i = 1; i <= 50; i++) { + const samplerWidget = findWidgetByName(node, `sampler_${i}`); + const schedulerWidget = findWidgetByName(node, `scheduler_${i}`); + + if (i <= countValue) { + if (targetParameter === "sampler") { + toggleWidget(node, samplerWidget, true); + toggleWidget(node, schedulerWidget, false); + } else if (targetParameter === "scheduler") { + toggleWidget(node, samplerWidget, false); + toggleWidget(node, schedulerWidget, true); + } else { // targetParameter is "sampler & scheduler" + toggleWidget(node, samplerWidget, true); + toggleWidget(node, schedulerWidget, true); + } + } else { + toggleWidget(node, samplerWidget, false); + toggleWidget(node, schedulerWidget, false); + } + } +} + +// Handle simple widget visibility based on a count +function handleWidgetVisibility(node, thresholdValue, widgetNamePrefix, maxCount) { + for (let i = 1; i <= maxCount; i++) { + const widget = findWidgetByName(node, `${widgetNamePrefix}${i}`); + if (widget) { + toggleWidget(node, widget, i <= thresholdValue); + } + } +} + +// Disable the 'Ckpt Name+ClipSkip+VAE' option if 'target_ckpt' is "Refiner" +let last_ckpt_input_mode; +let last_target_ckpt; +function xyCkptRefinerOptionsRemove(widget, node) { + + let target_ckpt = findWidgetByName(node, "target_ckpt").value + let input_mode = widget.value + + if ((input_mode === "Ckpt Names+ClipSkip+VAE") && (target_ckpt === "Refiner")) { + if (last_ckpt_input_mode === "Ckpt Names+ClipSkip") { + if (last_target_ckpt === "Refiner"){ + widget.value = "Checkpoint Batch"; + } else {widget.value = "Ckpt Names+ClipSkip";} + } else if (last_ckpt_input_mode === "Checkpoint Batch") { + if (last_target_ckpt === "Refiner"){ + widget.value = "Ckpt Names+ClipSkip"; + } else {widget.value = "Checkpoint Batch";} + } else if (last_ckpt_input_mode !== 'undefined') { + widget.value = last_ckpt_input_mode; + } else { + widget.value = "Ckpt Names"; + } + } else if (input_mode !== "Ckpt Names+ClipSkip+VAE"){ + last_ckpt_input_mode = input_mode; + } + last_target_ckpt = target_ckpt +} + +// Create a map of node titles to their respective widget handlers +const nodeWidgetHandlers = { + "Efficient Loader": { + 'lora_name': handleEfficientLoaderLoraName + }, + "Eff. Loader SDXL": { + 'refiner_ckpt_name': handleEffLoaderSDXLRefinerCkptName + }, + "LoRA Stacker": { + 'input_mode': handleLoRAStackerInputMode, + 'lora_count': handleLoRAStackerLoraCount + }, + "XY Input: Steps": { + 'target_parameter': handleXYInputStepsTargetParameter + }, + "XY Input: Sampler/Scheduler": { + 'target_parameter': handleXYInputSamplerSchedulerTargetParameter, + 'input_count': handleXYInputSamplerSchedulerInputCount + }, + "XY Input: VAE": { + 'input_mode': handleXYInputVAEInputMode, + 'vae_count': handleXYInputVAEVaeCount + }, + "XY Input: Prompt S/R": { + 'replace_count': handleXYInputPromptSRReplaceCount + }, + "XY Input: Checkpoint": { + 'input_mode': handleXYInputCheckpointInputMode, + 'ckpt_count': handleXYInputCheckpointCkptCount, + 'target_ckpt': handleXYInputCheckpointTargetCkpt + }, + "XY Input: LoRA": { + 'input_mode': handleXYInputLoRAInputMode, + 'lora_count': handleXYInputLoRALoraCount + }, + "XY Input: LoRA Plot": { + 'input_mode': handleXYInputLoRAPlotInputMode + }, + "XY Input: LoRA Stacks": { + 'node_state': handleXYInputLoRAStacksNodeState + }, + "XY Input: Control Net": { + 'target_parameter': handleXYInputControlNetTargetParameter + }, + "XY Input: Control Net Plot": { + 'plot_type': handleXYInputControlNetPlotPlotType + }, + "Noise Control Script": { + 'add_seed_noise': handleNoiseControlScript + }, + "HighRes-Fix Script": { + 'upscale_type': handleHiResFixScript, + 'use_same_seed': handleHiResFixScript, + 'use_controlnet':handleHiResFixScript + }, + "Tiled Upscaler Script": { + 'use_controlnet':handleTiledUpscalerScript + }, +}; + +// In the main function where widgetLogic is called +function widgetLogic(node, widget) { + // Retrieve the handler for the current node title and widget name + const handler = nodeWidgetHandlers[node.comfyClass]?.[widget.name]; + if (handler) { + handler(node, widget); + } +} + +// Efficient Loader Handlers +function handleEfficientLoaderLoraName(node, widget) { + if (widget.value === 'None') { + toggleWidget_2(node, findWidgetByName(node, 'lora_model_strength')); + toggleWidget_2(node, findWidgetByName(node, 'lora_clip_strength')); + } else { + toggleWidget_2(node, findWidgetByName(node, 'lora_model_strength'), true); + toggleWidget_2(node, findWidgetByName(node, 'lora_clip_strength'), true); + } +} + +// Eff. Loader SDXL Handlers +function handleEffLoaderSDXLRefinerCkptName(node, widget) { + if (widget.value === 'None') { + toggleWidget_2(node, findWidgetByName(node, 'refiner_clip_skip')); + toggleWidget_2(node, findWidgetByName(node, 'positive_ascore')); + toggleWidget_2(node, findWidgetByName(node, 'negative_ascore')); + } else { + toggleWidget_2(node, findWidgetByName(node, 'refiner_clip_skip'), true); + toggleWidget_2(node, findWidgetByName(node, 'positive_ascore'), true); + toggleWidget_2(node, findWidgetByName(node, 'negative_ascore'), true); + } +} + +// Noise Control Script Seed Handler +function handleNoiseControlScript(node, widget) { + + function ensureSeedControlExists(callback) { + if (node.seedControl && node.seedControl.lastSeedButton) { + callback(); + } else { + setTimeout(() => ensureSeedControlExists(callback), 0); + } + } + + ensureSeedControlExists(() => { + if (widget.value === false) { + toggleWidget(node, findWidgetByName(node, 'seed')); + toggleWidget(node, findWidgetByName(node, 'weight')); + toggleWidget(node, node.seedControl.lastSeedButton); + node.seedControl.lastSeedButton.disabled = true; // Disable the button + } else { + toggleWidget(node, findWidgetByName(node, 'seed'), true); + toggleWidget(node, findWidgetByName(node, 'weight'), true); + node.seedControl.lastSeedButton.disabled = false; // Enable the button + toggleWidget(node, node.seedControl.lastSeedButton, true); + } + }); + +} + +/// HighRes-Fix Script Handlers +function handleHiResFixScript(node, widget) { + + function ensureSeedControlExists(callback) { + if (node.seedControl && node.seedControl.lastSeedButton) { + callback(); + } else { + setTimeout(() => ensureSeedControlExists(callback), 0); + } + } + + if (findWidgetByName(node, 'upscale_type').value === "latent") { + toggleWidget(node, findWidgetByName(node, 'pixel_upscaler')); + + toggleWidget(node, findWidgetByName(node, 'hires_ckpt_name'), true); + toggleWidget(node, findWidgetByName(node, 'latent_upscaler'), true); + toggleWidget(node, findWidgetByName(node, 'use_same_seed'), true); + toggleWidget(node, findWidgetByName(node, 'hires_steps'), true); + toggleWidget(node, findWidgetByName(node, 'denoise'), true); + toggleWidget(node, findWidgetByName(node, 'iterations'), true); + + ensureSeedControlExists(() => { + if (findWidgetByName(node, 'use_same_seed').value == true) { + toggleWidget(node, findWidgetByName(node, 'seed')); + toggleWidget(node, node.seedControl.lastSeedButton); + node.seedControl.lastSeedButton.disabled = true; // Disable the button + } else { + toggleWidget(node, findWidgetByName(node, 'seed'), true); + node.seedControl.lastSeedButton.disabled = false; // Enable the button + toggleWidget(node, node.seedControl.lastSeedButton, true); + } + }); + + if (findWidgetByName(node, 'use_controlnet').value == '_'){ + toggleWidget(node, findWidgetByName(node, 'use_controlnet')); + toggleWidget(node, findWidgetByName(node, 'control_net_name')); + toggleWidget(node, findWidgetByName(node, 'strength')); + toggleWidget(node, findWidgetByName(node, 'preprocessor')); + toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs')); + } + else{ + toggleWidget(node, findWidgetByName(node, 'use_controlnet'), true); + + if (findWidgetByName(node, 'use_controlnet').value == true){ + toggleWidget(node, findWidgetByName(node, 'control_net_name'), true); + toggleWidget(node, findWidgetByName(node, 'strength'), true); + toggleWidget(node, findWidgetByName(node, 'preprocessor'), true); + toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs'), true); + } + else{ + toggleWidget(node, findWidgetByName(node, 'control_net_name')); + toggleWidget(node, findWidgetByName(node, 'strength')); + toggleWidget(node, findWidgetByName(node, 'preprocessor')); + toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs')); + } + } + + } else if (findWidgetByName(node, 'upscale_type').value === "pixel") { + toggleWidget(node, findWidgetByName(node, 'hires_ckpt_name')); + toggleWidget(node, findWidgetByName(node, 'latent_upscaler')); + toggleWidget(node, findWidgetByName(node, 'use_same_seed')); + toggleWidget(node, findWidgetByName(node, 'hires_steps')); + toggleWidget(node, findWidgetByName(node, 'denoise')); + toggleWidget(node, findWidgetByName(node, 'iterations')); + toggleWidget(node, findWidgetByName(node, 'seed')); + ensureSeedControlExists(() => { + toggleWidget(node, node.seedControl.lastSeedButton); + node.seedControl.lastSeedButton.disabled = true; // Disable the button + }); + toggleWidget(node, findWidgetByName(node, 'use_controlnet')); + toggleWidget(node, findWidgetByName(node, 'control_net_name')); + toggleWidget(node, findWidgetByName(node, 'strength')); + toggleWidget(node, findWidgetByName(node, 'preprocessor')); + toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs')); + + toggleWidget(node, findWidgetByName(node, 'pixel_upscaler'), true); + } +} + +/// Tiled Upscaler Script Handler +function handleTiledUpscalerScript(node, widget) { + if (findWidgetByName(node, 'use_controlnet').value == true){ + toggleWidget(node, findWidgetByName(node, 'tile_controlnet'), true); + toggleWidget(node, findWidgetByName(node, 'strength'), true); + } + else{ + toggleWidget(node, findWidgetByName(node, 'tile_controlnet')); + toggleWidget(node, findWidgetByName(node, 'strength')); + } +} + +// LoRA Stacker Handlers +function handleLoRAStackerInputMode(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); + handleVisibility(node, findWidgetByName(node, "lora_count").value, "LoRA Stacker"); +} + +function handleLoRAStackerLoraCount(node, widget) { + handleVisibility(node, widget.value, "LoRA Stacker"); +} + +// XY Input: Steps Handlers +function handleXYInputStepsTargetParameter(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); +} + +// XY Input: Sampler/Scheduler Handlers +function handleXYInputSamplerSchedulerTargetParameter(node, widget) { + handleSamplerSchedulerVisibility(node, findWidgetByName(node, 'input_count').value, widget.value); +} + +function handleXYInputSamplerSchedulerInputCount(node, widget) { + handleSamplerSchedulerVisibility(node, widget.value, findWidgetByName(node, 'target_parameter').value); +} + +// XY Input: VAE Handlers +function handleXYInputVAEInputMode(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); + if (widget.value === "VAE Names") { + handleWidgetVisibility(node, findWidgetByName(node, "vae_count").value, "vae_name_", 50); + } else { + handleWidgetVisibility(node, 0, "vae_name_", 50); + } +} + +function handleXYInputVAEVaeCount(node, widget) { + if (findWidgetByName(node, "input_mode").value === "VAE Names") { + handleWidgetVisibility(node, widget.value, "vae_name_", 50); + } +} + +// XY Input: Prompt S/R Handlers +function handleXYInputPromptSRReplaceCount(node, widget) { + handleWidgetVisibility(node, widget.value, "replace_", 49); +} + +// XY Input: Checkpoint Handlers +function handleXYInputCheckpointInputMode(node, widget) { + xyCkptRefinerOptionsRemove(widget, node); + handleInputModeWidgetsVisibility(node, widget.value); + handleVisibility(node, findWidgetByName(node, "ckpt_count").value, "Checkpoint"); +} + +function handleXYInputCheckpointCkptCount(node, widget) { + handleVisibility(node, widget.value, "Checkpoint"); +} + +function handleXYInputCheckpointTargetCkpt(node, widget) { + xyCkptRefinerOptionsRemove(findWidgetByName(node, "input_mode"), node); +} + +// XY Input: LoRA Handlers +function handleXYInputLoRAInputMode(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); + handleVisibility(node, findWidgetByName(node, "lora_count").value, "LoRA"); +} + +function handleXYInputLoRALoraCount(node, widget) { + handleVisibility(node, widget.value, "LoRA"); +} + +// XY Input: LoRA Plot Handlers +function handleXYInputLoRAPlotInputMode(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); +} + +// XY Input: LoRA Stacks Handlers +function handleXYInputLoRAStacksNodeState(node, widget) { + toggleWidget(node, findWidgetByName(node, "node_state"), false); +} + +// XY Input: Control Net Handlers +function handleXYInputControlNetTargetParameter(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); +} + +// XY Input: Control Net Plot Handlers +function handleXYInputControlNetPlotPlotType(node, widget) { + handleInputModeWidgetsVisibility(node, widget.value); +} + +app.registerExtension({ + name: "efficiency.widgethider", + nodeCreated(node) { + for (const w of node.widgets || []) { + let widgetValue = w.value; + + // Store the original descriptor if it exists + let originalDescriptor = Object.getOwnPropertyDescriptor(w, 'value'); + + widgetLogic(node, w); + + Object.defineProperty(w, 'value', { + get() { + // If there's an original getter, use it. Otherwise, return widgetValue. + let valueToReturn = originalDescriptor && originalDescriptor.get + ? originalDescriptor.get.call(w) + : widgetValue; + + return valueToReturn; + }, + set(newVal) { + + // If there's an original setter, use it. Otherwise, set widgetValue. + if (originalDescriptor && originalDescriptor.set) { + originalDescriptor.set.call(w, newVal); + } else { + widgetValue = newVal; + } + + widgetLogic(node, w); + } + }); + } + setTimeout(() => {initialized = true;}, 500); + } +}); + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/workflowfix.js b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/workflowfix.js new file mode 100644 index 0000000000000000000000000000000000000000..82c51421caccd7caca517dce32447ba9f90c2833 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/workflowfix.js @@ -0,0 +1,142 @@ +// Detect and update Efficiency Nodes from v1.92 to v2.00 changes (Final update?) +import { app } from '../../scripts/app.js' +import { addNode } from "./node_options/common/utils.js"; + +const ext = { + name: "efficiency.WorkflowFix", +}; + +function reloadHiResFixNode(originalNode) { + + // Safeguard against missing 'pos' property + const position = originalNode.pos && originalNode.pos.length === 2 ? { x: originalNode.pos[0], y: originalNode.pos[1] } : { x: 0, y: 0 }; + + // Recreate the node + const newNode = addNode("HighRes-Fix Script", originalNode, position); + + // Transfer input connections from old node to new node + originalNode.inputs.forEach((input, index) => { + if (input && input.link !== null) { + const originLinkInfo = originalNode.graph.links[input.link]; + if (originLinkInfo) { + const originNode = originalNode.graph.getNodeById(originLinkInfo.origin_id); + if (originNode) { + originNode.connect(originLinkInfo.origin_slot, newNode, index); + } + } + } + }); + + // Transfer output connections from old node to new node + originalNode.outputs.forEach((output, index) => { + if (output && output.links) { + output.links.forEach(link => { + const targetLinkInfo = originalNode.graph.links[link]; + if (targetLinkInfo) { + const targetNode = originalNode.graph.getNodeById(targetLinkInfo.target_id); + if (targetNode) { + newNode.connect(index, targetNode, targetLinkInfo.target_slot); + } + } + }); + } + }); + + // Remove the original node after all connections are transferred + originalNode.graph.remove(originalNode); + + return newNode; +} + +ext.loadedGraphNode = function(node, app) { + const originalNode = node; // This line ensures that originalNode refers to the provided node + const kSamplerTypes = [ + "KSampler (Efficient)", + "KSampler Adv. (Efficient)", + "KSampler SDXL (Eff.)" + ]; + + // EFFICIENT LOADER & EFF. LOADER SDXL + /* Changes: + Added "token_normalization" & "weight_interpretation" widget below prompt text boxes, + below code fixes the widget values for empty_latent_width, empty_latent_height, and batch_size + by shifting down by 2 widget values starting from the "token_normalization" widget. + Logic triggers when "token_normalization" is a number instead of a string. + */ + if (node.comfyClass === "Efficient Loader" || node.comfyClass === "Eff. Loader SDXL") { + const tokenWidget = node.widgets.find(w => w.name === "token_normalization"); + const weightWidget = node.widgets.find(w => w.name === "weight_interpretation"); + + if (typeof tokenWidget.value === 'number') { + console.log("[EfficiencyUpdate]", `Fixing '${node.comfyClass}' token and weight widgets:`, node); + const index = node.widgets.indexOf(tokenWidget); + if (index !== -1) { + for (let i = node.widgets.length - 1; i > index + 1; i--) { + node.widgets[i].value = node.widgets[i - 2].value; + } + } + tokenWidget.value = "none"; + weightWidget.value = "comfy"; + } + } + + // KSAMPLER (EFFICIENT), KSAMPLER ADV. (EFFICIENT), & KSAMPLER SDXL (EFF.) + /* Changes: + Removed the "sampler_state" widget which cause all widget values to shift down by a factor of 1. + Fix involves moving all widget values by -1. "vae_decode" value is lost in this process, so in + below fix I manually set it to its default value of "true". + */ + else if (kSamplerTypes.includes(node.comfyClass)) { + + const seedWidgetName = (node.comfyClass === "KSampler (Efficient)") ? "seed" : "noise_seed"; + const stepsWidgetName = (node.comfyClass === "KSampler (Efficient)") ? "steps" : "start_at_step"; + + const seedWidget = node.widgets.find(w => w.name === seedWidgetName); + const stepsWidget = node.widgets.find(w => w.name === stepsWidgetName); + + if (isNaN(seedWidget.value) && isNaN(stepsWidget.value)) { + console.log("[EfficiencyUpdate]", `Fixing '${node.comfyClass}' node widgets:`, node); + for (let i = 0; i < node.widgets.length - 1; i++) { + node.widgets[i].value = node.widgets[i + 1].value; + } + node.widgets[node.widgets.length - 1].value = "true"; + } + } + + // HIGHRES-FIX SCRIPT + /* Changes: + Many new changes where added, so in order to properly update, aquired the values of the original + widgets, reload a new node, transffer the known original values, and transffer connection. + This fix is triggered when the upscale_type widget is neither "latent" or "pixel". + */ + // Check if the current node is "HighRes-Fix Script" and if any of the above fixes were applied + else if (node.comfyClass === "HighRes-Fix Script") { + const upscaleTypeWidget = node.widgets.find(w => w.name === "upscale_type"); + + if (upscaleTypeWidget && upscaleTypeWidget.value !== "latent" && upscaleTypeWidget.value !== "pixel") { + console.log("[EfficiencyUpdate]", "Reloading 'HighRes-Fix Script' node:", node); + + // Reload the node and get the new node instance + const newNode = reloadHiResFixNode(node); + + // Update the widgets of the new node + const targetWidgetNames = ["latent_upscaler", "upscale_by", "hires_steps", "denoise", "iterations"]; + + // Extract the first five values of the original node + const originalValues = originalNode.widgets.slice(0, 5).map(w => w.value); + + targetWidgetNames.forEach((name, index) => { + const widget = newNode.widgets.find(w => w.name === name); + if (widget && originalValues[index] !== undefined) { + if (name === "latent_upscaler" && typeof originalValues[index] === 'string') { + widget.value = originalValues[index].replace("SD-Latent-Upscaler", "city96"); + } else { + widget.value = originalValues[index]; + } + } + }); + } + } +} + +app.registerExtension(ext); diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/models/readme.md b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/models/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/models/readme.md @@ -0,0 +1 @@ + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/node_settings.json b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/node_settings.json new file mode 100644 index 0000000000000000000000000000000000000000..66b7f9e2ae35a5b43adc1ddff521f07d3884f5c9 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/node_settings.json @@ -0,0 +1,19 @@ +{ + "Efficient Loader": { + "model_cache": { + "vae": 1, + "ckpt": 1, + "lora": 1, + "refn": 1 + } + }, + "XY Plot": { + "model_cache": { + "vae": 5, + "ckpt": 5, + "lora": 5, + "refn": 1 + } + } +} + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__init__.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4287ca8617970fa8fc025b75cb319c7032706910 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__init__.py @@ -0,0 +1 @@ +# \ No newline at end of file diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/__init__.cpython-310.pyc b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..46205039b3a35c4e4d46728865395ec390334386 Binary files /dev/null and b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/__init__.cpython-310.pyc differ diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/bnk_adv_encode.cpython-310.pyc b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/bnk_adv_encode.cpython-310.pyc new file mode 100644 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a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/ttl_nn_latent_upscaler.cpython-310.pyc b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/ttl_nn_latent_upscaler.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..51412ce286c61e5831aa0b1eff3862562c5eab85 Binary files /dev/null and b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/ttl_nn_latent_upscaler.cpython-310.pyc differ diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_adv_encode.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_adv_encode.py new file mode 100644 index 0000000000000000000000000000000000000000..d095746cb553653974272e2e96cb68b275723ff5 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_adv_encode.py @@ -0,0 +1,341 @@ +import torch +import numpy as np +import itertools +from math import gcd + +from comfy import model_management +from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG + +def _grouper(n, iterable): + it = iter(iterable) + while True: + chunk = list(itertools.islice(it, n)) + if not chunk: + return + yield chunk + +def _norm_mag(w, n): + d = w - 1 + return 1 + np.sign(d) * np.sqrt(np.abs(d)**2 / n) + #return np.sign(w) * np.sqrt(np.abs(w)**2 / n) + +def divide_length(word_ids, weights): + sums = dict(zip(*np.unique(word_ids, return_counts=True))) + sums[0] = 1 + weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0 + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + +def shift_mean_weight(word_ids, weights): + delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x,y) if id != 0]) + weights = [[w if id == 0 else w+delta + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + +def scale_to_norm(weights, word_ids, w_max): + top = np.max(weights) + w_max = min(top, w_max) + weights = [[w_max if id == 0 else (w/top) * w_max + for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] + return weights + +def from_zero(weights, base_emb): + weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) + weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape) + return base_emb * weight_tensor + +def mask_word_id(tokens, word_ids, target_id, mask_token): + new_tokens = [[mask_token if wid == target_id else t + for t, wid in zip(x,y)] for x,y in zip(tokens, word_ids)] + mask = np.array(word_ids) == target_id + return (new_tokens, mask) + +def batched_clip_encode(tokens, length, encode_func, num_chunks): + embs = [] + for e in _grouper(32, tokens): + enc, pooled = encode_func(e) + enc = enc.reshape((len(e), length, -1)) + embs.append(enc) + embs = torch.cat(embs) + embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1)) + return embs + +def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): + pooled_base = base_emb[0,length-1:length,:] + wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True) + weight_dict = dict((id,w) + for id,w in zip(wids ,np.array(weights).reshape(-1)[inds]) + if w != 1.0) + + if len(weight_dict) == 0: + return torch.zeros_like(base_emb), base_emb[0,length-1:length,:] + + weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) + weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape) + + #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) + #TODO: find most suitable masking token here + m_token = (m_token, 1.0) + + ws = [] + masked_tokens = [] + masks = [] + + #create prompts + for id, w in weight_dict.items(): + masked, m = mask_word_id(tokens, word_ids, id, m_token) + masked_tokens.extend(masked) + + m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device) + m = m.reshape(1,-1,1).expand(base_emb.shape) + masks.append(m) + + ws.append(w) + + #batch process prompts + embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) + masks = torch.cat(masks) + + embs = (base_emb.expand(embs.shape) - embs) + pooled = embs[0,length-1:length,:] + + embs *= masks + embs = embs.sum(axis=0, keepdim=True) + + pooled_start = pooled_base.expand(len(ws), -1) + ws = torch.tensor(ws).reshape(-1,1).expand(pooled_start.shape) + pooled = (pooled - pooled_start) * (ws - 1) + pooled = pooled.mean(axis=0, keepdim=True) + + return ((weight_tensor - 1) * embs), pooled_base + pooled + +def mask_inds(tokens, inds, mask_token): + clip_len = len(tokens[0]) + inds_set = set(inds) + new_tokens = [[mask_token if i*clip_len + j in inds_set else t + for j, t in enumerate(x)] for i, x in enumerate(tokens)] + return new_tokens + +def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): + w, w_inv = np.unique(weights,return_inverse=True) + + if np.sum(w < 1) == 0: + return base_emb, tokens, base_emb[0,length-1:length,:] + #m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) + #using the comma token as a masking token seems to work better than aos tokens for SD 1.x + m_token = (m_token, 1.0) + + masked_tokens = [] + + masked_current = tokens + for i in range(len(w)): + if w[i] >= 1: + continue + masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token) + masked_tokens.extend(masked_current) + + embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) + embs = torch.cat([base_emb, embs]) + w = w[w<=1.0] + w_mix = np.diff([0] + w.tolist()) + w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1,1,1)) + + weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True) + return weighted_emb, masked_current, weighted_emb[0,length-1:length,:] + +def scale_emb_to_mag(base_emb, weighted_emb): + norm_base = torch.linalg.norm(base_emb) + norm_weighted = torch.linalg.norm(weighted_emb) + embeddings_final = (norm_base / norm_weighted) * weighted_emb + return embeddings_final + +def recover_dist(base_emb, weighted_emb): + fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean()) + embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean()) + return embeddings_final + +def A1111_renorm(base_emb, weighted_emb): + embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb + return embeddings_final + +def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266, length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False): + tokens = [[t for t,_,_ in x] for x in tokenized] + weights = [[w for _,w,_ in x] for x in tokenized] + word_ids = [[wid for _,_,wid in x] for x in tokenized] + + #weight normalization + #==================== + + #distribute down/up weights over word lengths + if token_normalization.startswith("length"): + weights = divide_length(word_ids, weights) + + #make mean of word tokens 1 + if token_normalization.endswith("mean"): + weights = shift_mean_weight(word_ids, weights) + + #weight interpretation + #===================== + pooled = None + + if weight_interpretation == "comfy": + weighted_tokens = [[(t,w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] + weighted_emb, pooled_base = encode_func(weighted_tokens) + pooled = pooled_base + else: + unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokenized] + base_emb, pooled_base = encode_func(unweighted_tokens) + + if weight_interpretation == "A1111": + weighted_emb = from_zero(weights, base_emb) + weighted_emb = A1111_renorm(base_emb, weighted_emb) + pooled = pooled_base + + if weight_interpretation == "compel": + pos_tokens = [[(t,w) if w >= 1.0 else (t,1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] + weighted_emb, _ = encode_func(pos_tokens) + weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func) + + if weight_interpretation == "comfy++": + weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights] + #unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down] + embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + weighted_emb += embs + + if weight_interpretation == "down_weight": + weights = scale_to_norm(weights, word_ids, w_max) + weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) + + if return_pooled: + if apply_to_pooled: + return weighted_emb, pooled + else: + return weighted_emb, pooled_base + return weighted_emb, None + +def encode_token_weights_g(model, token_weight_pairs): + return model.clip_g.encode_token_weights(token_weight_pairs) + +def encode_token_weights_l(model, token_weight_pairs): + l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs) + return l_out, None + +def encode_token_weights(model, token_weight_pairs, encode_func): + if model.layer_idx is not None: + model.cond_stage_model.clip_layer(model.layer_idx) + + model_management.load_model_gpu(model.patcher) + return encode_func(model.cond_stage_model, token_weight_pairs) + +def prepareXL(embs_l, embs_g, pooled, clip_balance): + l_w = 1 - max(0, clip_balance - .5) * 2 + g_w = 1 - max(0, .5 - clip_balance) * 2 + if embs_l is not None: + return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled + else: + return embs_g, pooled + +def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True): + tokenized = clip.tokenize(text, return_word_ids=True) + if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)): + embs_l = None + embs_g = None + pooled = None + if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel): + embs_l, _ = advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_l), + w_max=w_max, + return_pooled=False) + if 'g' in tokenized: + embs_g, pooled = advanced_encode_from_tokens(tokenized['g'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_g), + w_max=w_max, + return_pooled=True, + apply_to_pooled=apply_to_pooled) + return prepareXL(embs_l, embs_g, pooled, clip_balance) + else: + return advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: (clip.encode_from_tokens({'l': x}), None), + w_max=w_max) +def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True): + tokenized1 = clip.tokenize(text1, return_word_ids=True) + tokenized2 = clip.tokenize(text2, return_word_ids=True) + + embs_l, _ = advanced_encode_from_tokens(tokenized1['l'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_l), + w_max=w_max, + return_pooled=False) + + embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'], + token_normalization, + weight_interpretation, + lambda x: encode_token_weights(clip, x, encode_token_weights_g), + w_max=w_max, + return_pooled=True, + apply_to_pooled=apply_to_pooled) + + gcd_num = gcd(embs_l.shape[1], embs_g.shape[1]) + repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1]) + repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1]) + + return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance) + +######################################################################################################################## +from nodes import MAX_RESOLUTION + +class AdvancedCLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "text": ("STRING", {"multiline": True}), + "clip": ("CLIP",), + "token_normalization": (["none", "mean", "length", "length+mean"],), + "weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],), + # "affect_pooled": (["disable", "enable"],), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning/advanced" + + def encode(self, clip, text, token_normalization, weight_interpretation, affect_pooled='disable'): + embeddings_final, pooled = advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, + apply_to_pooled=affect_pooled == 'enable') + return ([[embeddings_final, {"pooled_output": pooled}]],) + + +class AddCLIPSDXLRParams: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning": ("CONDITIONING",), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning/advanced" + + def encode(self, conditioning, width, height, ascore): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['width'] = width + n[1]['height'] = height + n[1]['aesthetic_score'] = ascore + c.append(n) + return (c,) + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiled_samplers.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiled_samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..d3e8e0de2773003866073d9790f7b36e01b3b8b1 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiled_samplers.py @@ -0,0 +1,345 @@ +# https://github.com/BlenderNeko/ComfyUI_TiledKSampler +import sys +import os +import itertools +import numpy as np +from tqdm.auto import tqdm +import torch + +#sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) +import comfy.sd +import comfy.controlnet +import comfy.model_management +import comfy.sample +from . import bnk_tiling as tiling +import latent_preview +#import torch +#import itertools +#import numpy as np +MAX_RESOLUTION=8192 + +####################### + +def recursion_to_list(obj, attr): + current = obj + yield current + while True: + current = getattr(current, attr, None) + if current is not None: + yield current + else: + return + +def copy_cond(cond): + return [[c1,c2.copy()] for c1,c2 in cond] + +def slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, cond, area, device): + tile_h_end = tile_h + tile_h_len + tile_w_end = tile_w + tile_w_len + coords = area[0] #h_len, w_len, h, w, + mask = area[1] + if coords is not None: + h_len, w_len, h, w = coords + h_end = h + h_len + w_end = w + w_len + if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w: + new_h = max(0, h - tile_h) + new_w = max(0, w - tile_w) + new_h_end = min(tile_h_end, h_end - tile_h) + new_w_end = min(tile_w_end, w_end - tile_w) + cond[1]['area'] = (new_h_end - new_h, new_w_end - new_w, new_h, new_w) + else: + return (cond, True) + if mask is not None: + new_mask = tiling.get_slice(mask, tile_h,tile_h_len,tile_w,tile_w_len) + if new_mask.sum().to(device) == 0.0 and 'mask' in cond[1]: + return (cond, True) + else: + cond[1]['mask'] = new_mask + return (cond, False) + +def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen): + tile_h_end = tile_h + tile_h_len + tile_w_end = tile_w + tile_w_len + if gligen is None: + return + gligen_type = gligen[0] + gligen_model = gligen[1] + gligen_areas = gligen[2] + + gligen_areas_new = [] + for emb, h_len, w_len, h, w in gligen_areas: + h_end = h + h_len + w_end = w + w_len + if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w: + new_h = max(0, h - tile_h) + new_w = max(0, w - tile_w) + new_h_end = min(tile_h_end, h_end - tile_h) + new_w_end = min(tile_w_end, w_end - tile_w) + gligen_areas_new.append((emb, new_h_end - new_h, new_w_end - new_w, new_h, new_w)) + + if len(gligen_areas_new) == 0: + del cond['gligen'] + else: + cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new) + +def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img): + if img is None: + img = model.cond_hint_original + model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).to(model.control_model.dtype).to(model.device) + +def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img): + model.control_input = None + if img is None: + img = model.cond_hint_original + model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).float().to(model.device) + +# TODO: refactor some of the mess + +from PIL import Image + +def sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview=False): + end_at_step = min(end_at_step, steps) + device = comfy.model_management.get_torch_device() + samples = latent_image["samples"] + noise_mask = latent_image["noise_mask"] if "noise_mask" in latent_image else None + force_full_denoise = return_with_leftover_noise == "enable" + if add_noise == "disable": + noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu") + else: + skip = latent_image["batch_index"] if "batch_index" in latent_image else None + noise = comfy.sample.prepare_noise(samples, noise_seed, skip) + + if noise_mask is not None: + noise_mask = comfy.sample.prepare_mask(noise_mask, noise.shape, device='cpu') + + shape = samples.shape + samples = samples.clone() + + tile_width = min(shape[-1] * 8, tile_width) + tile_height = min(shape[2] * 8, tile_height) + + real_model = None + positive_copy = comfy.sample.convert_cond(positive) + negative_copy = comfy.sample.convert_cond(negative) + modelPatches, inference_memory = comfy.sample.get_additional_models(positive_copy, negative_copy, model.model_dtype()) + comfy.model_management.load_models_gpu([model] + modelPatches, model.memory_required(noise.shape) + inference_memory) + real_model = model.model + + sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + + if tiling_strategy != 'padded': + if noise_mask is not None: + samples += sampler.sigmas[start_at_step].cpu() * noise_mask * model.model.process_latent_out(noise) + else: + samples += sampler.sigmas[start_at_step].cpu() * model.model.process_latent_out(noise) + + #cnets + cnets = [c['control'] for (_, c) in positive + negative if 'control' in c and isinstance(c['control'], comfy.controlnet.ControlNet)] + cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")])) + cnet_imgs = [ + torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu') + if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 else None + for m in cnets] + + #T2I + T2Is = [c['control'] for (_, c) in positive + negative if 'control' in c and isinstance(c['control'], comfy.controlnet.T2IAdapter)] + T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")] + T2I_imgs = [ + torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu') + if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 or (m.channels_in == 1 and m.cond_hint_original.shape[1] != 1) else None + for m in T2Is + ] + T2I_imgs = [ + torch.mean(img, 1, keepdim=True) if img is not None and m.channels_in == 1 and m.cond_hint_original.shape[1] else img + for m, img in zip(T2Is, T2I_imgs) + ] + + #cond area and mask + spatial_conds_pos = [ + (c[1]['area'] if 'area' in c[1] else None, + comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None) + for c in positive + ] + spatial_conds_neg = [ + (c[1]['area'] if 'area' in c[1] else None, + comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None) + for c in negative + ] + + #gligen + gligen_pos = [ + c[1]['gligen'] if 'gligen' in c[1] else None + for c in positive + ] + gligen_neg = [ + c[1]['gligen'] if 'gligen' in c[1] else None + for c in negative + ] + + + gen = torch.manual_seed(noise_seed) + if tiling_strategy == 'random' or tiling_strategy == 'random strict': + tiles = tiling.get_tiles_and_masks_rgrid(end_at_step - start_at_step, samples.shape, tile_height, tile_width, gen) + elif tiling_strategy == 'padded': + tiles = tiling.get_tiles_and_masks_padded(end_at_step - start_at_step, samples.shape, tile_height, tile_width) + else: + tiles = tiling.get_tiles_and_masks_simple(end_at_step - start_at_step, samples.shape, tile_height, tile_width) + + total_steps = sum([num_steps for img_pass in tiles for steps_list in img_pass for _,_,_,_,num_steps,_ in steps_list]) + current_step = [0] + + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + previewer = None + if preview: + previewer = latent_preview.get_previewer(device, model.model.latent_format) + + + with tqdm(total=total_steps) as pbar_tqdm: + pbar = comfy.utils.ProgressBar(total_steps) + + def callback(step, x0, x, total_steps): + current_step[0] += 1 + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(current_step[0], preview=preview_bytes) + pbar_tqdm.update(1) + + if tiling_strategy == "random strict": + samples_next = samples.clone() + for img_pass in tiles: + for i in range(len(img_pass)): + for tile_h, tile_h_len, tile_w, tile_w_len, tile_steps, tile_mask in img_pass[i]: + tiled_mask = None + if noise_mask is not None: + tiled_mask = tiling.get_slice(noise_mask, tile_h, tile_h_len, tile_w, tile_w_len).to(device) + if tile_mask is not None: + if tiled_mask is not None: + tiled_mask *= tile_mask.to(device) + else: + tiled_mask = tile_mask.to(device) + + if tiling_strategy == 'padded' or tiling_strategy == 'random strict': + tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask = tiling.mask_at_boundary( tile_h, tile_h_len, tile_w, tile_w_len, + tile_height, tile_width, samples.shape[-2], samples.shape[-1], + tiled_mask, device) + + + if tiled_mask is not None and tiled_mask.sum().cpu() == 0.0: + continue + + tiled_latent = tiling.get_slice(samples, tile_h, tile_h_len, tile_w, tile_w_len).to(device) + + if tiling_strategy == 'padded': + tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device) + else: + if tiled_mask is None or noise_mask is None: + tiled_noise = torch.zeros_like(tiled_latent) + else: + tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device) * (1 - tiled_mask) + + #TODO: all other condition based stuff like area sets and GLIGEN should also happen here + + #cnets + for m, img in zip(cnets, cnet_imgs): + slice_cnet(tile_h, tile_h_len, tile_w, tile_w_len, m, img) + + #T2I + for m, img in zip(T2Is, T2I_imgs): + slices_T2I(tile_h, tile_h_len, tile_w, tile_w_len, m, img) + + pos = [c.copy() for c in positive_copy]#copy_cond(positive_copy) + neg = [c.copy() for c in negative_copy]#copy_cond(negative_copy) + + #cond areas + pos = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area, device) for c, area in zip(pos, spatial_conds_pos)] + pos = [c for c, ignore in pos if not ignore] + neg = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area, device) for c, area in zip(neg, spatial_conds_neg)] + neg = [c for c, ignore in neg if not ignore] + + #gligen + for cond, gligen in zip(pos, gligen_pos): + slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen) + for cond, gligen in zip(neg, gligen_neg): + slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen) + + tile_result = sampler.sample(tiled_noise, pos, neg, cfg=cfg, latent_image=tiled_latent, start_step=start_at_step + i * tile_steps, last_step=start_at_step + i*tile_steps + tile_steps, force_full_denoise=force_full_denoise and i+1 == end_at_step - start_at_step, denoise_mask=tiled_mask, callback=callback, disable_pbar=True, seed=noise_seed) + tile_result = tile_result.cpu() + if tiled_mask is not None: + tiled_mask = tiled_mask.cpu() + if tiling_strategy == "random strict": + tiling.set_slice(samples_next, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask) + else: + tiling.set_slice(samples, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask) + if tiling_strategy == "random strict": + samples = samples_next.clone() + + + comfy.sample.cleanup_additional_models(modelPatches) + + out = latent_image.copy() + out["samples"] = samples.cpu() + return (out, ) + + +class TiledKSampler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}), + "tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}), + "tiling_strategy": (["random", "random strict", "padded", 'simple'], ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise): + steps_total = int(steps / denoise) + return sample_common(model, 'enable', seed, tile_width, tile_height, tiling_strategy, steps_total, cfg, sampler_name, scheduler, positive, negative, latent_image, steps_total-steps, steps_total, 'disable', denoise=1.0, preview=True) + +class TiledKSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": (["enable", "disable"], ), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}), + "tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}), + "tiling_strategy": (["random", "random strict", "padded", 'simple'], ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "return_with_leftover_noise": (["disable", "enable"], ), + "preview": (["disable", "enable"], ), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview, denoise=1.0): + return sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview= preview == 'enable') diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiling.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiling.py new file mode 100644 index 0000000000000000000000000000000000000000..096f8baa1a1a639e3ed251e415a95aa14bcf8753 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiling.py @@ -0,0 +1,175 @@ +import torch +import itertools +import numpy as np + +def grouper(n, iterable): + it = iter(iterable) + while True: + chunk = list(itertools.islice(it, n)) + if not chunk: + return + yield chunk + +def create_batches(n, iterable): + groups = itertools.groupby(iterable, key= lambda x: (x[1], x[3])) + for _, x in groups: + for y in grouper(n, x): + yield y + + +def get_slice(tensor, h, h_len, w, w_len): + t = tensor.narrow(-2, h, h_len) + t = t.narrow(-1, w, w_len) + return t + +def set_slice(tensor1,tensor2, h, h_len, w, w_len, mask=None): + if mask is not None: + tensor1[:,:,h:h+h_len,w:w+w_len] = tensor1[:,:,h:h+h_len,w:w+w_len] * (1 - mask) + tensor2 * mask + else: + tensor1[:,:,h:h+h_len,w:w+w_len] = tensor2 + +def get_tiles_and_masks_simple(steps, latent_shape, tile_height, tile_width): + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + tile_size_h = int(tile_height // 8) + tile_size_w = int(tile_width // 8) + + h = np.arange(0,latent_size_h, tile_size_h) + w = np.arange(0,latent_size_w, tile_size_w) + + def create_tile(hs, ws, i, j): + h = int(hs[i]) + w = int(ws[j]) + h_len = min(tile_size_h, latent_size_h - h) + w_len = min(tile_size_w, latent_size_w - w) + return (h, h_len, w, w_len, steps, None) + + passes = [ + [[create_tile(h, w, i, j) for i in range(len(h)) for j in range(len(w))]], + ] + return passes + +def get_tiles_and_masks_padded(steps, latent_shape, tile_height, tile_width): + batch_size = latent_shape[0] + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + + tile_size_h = int(tile_height // 8) + tile_size_h = int((tile_size_h // 4) * 4) + tile_size_w = int(tile_width // 8) + tile_size_w = int((tile_size_w // 4) * 4) + + #masks + mask_h = [0,tile_size_h // 4, tile_size_h - tile_size_h // 4, tile_size_h] + mask_w = [0,tile_size_w // 4, tile_size_w - tile_size_w // 4, tile_size_w] + masks = [[] for _ in range(3)] + for i in range(3): + for j in range(3): + mask = torch.zeros((batch_size,1,tile_size_h, tile_size_w), dtype=torch.float32, device='cpu') + mask[:,:,mask_h[i]:mask_h[i+1],mask_w[j]:mask_w[j+1]] = 1.0 + masks[i].append(mask) + + def create_mask(h_ind, w_ind, h_ind_max, w_ind_max, mask_h, mask_w, h_len, w_len): + mask = masks[1][1] + if not (h_ind == 0 or h_ind == h_ind_max or w_ind == 0 or w_ind == w_ind_max): + return get_slice(mask, 0, h_len, 0, w_len) + mask = mask.clone() + if h_ind == 0 and mask_h: + mask += masks[0][1] + if h_ind == h_ind_max and mask_h: + mask += masks[2][1] + if w_ind == 0 and mask_w: + mask += masks[1][0] + if w_ind == w_ind_max and mask_w: + mask += masks[1][2] + if h_ind == 0 and w_ind == 0 and mask_h and mask_w: + mask += masks[0][0] + if h_ind == 0 and w_ind == w_ind_max and mask_h and mask_w: + mask += masks[0][2] + if h_ind == h_ind_max and w_ind == 0 and mask_h and mask_w: + mask += masks[2][0] + if h_ind == h_ind_max and w_ind == w_ind_max and mask_h and mask_w: + mask += masks[2][2] + return get_slice(mask, 0, h_len, 0, w_len) + + h = np.arange(0,latent_size_h, tile_size_h) + h_shift = np.arange(tile_size_h // 2, latent_size_h - tile_size_h // 2, tile_size_h) + w = np.arange(0,latent_size_w, tile_size_w) + w_shift = np.arange(tile_size_w // 2, latent_size_w - tile_size_h // 2, tile_size_w) + + + def create_tile(hs, ws, mask_h, mask_w, i, j): + h = int(hs[i]) + w = int(ws[j]) + h_len = min(tile_size_h, latent_size_h - h) + w_len = min(tile_size_w, latent_size_w - w) + mask = create_mask(i,j,len(hs)-1, len(ws)-1, mask_h, mask_w, h_len, w_len) + return (h, h_len, w, w_len, steps, mask) + + passes = [ + [[create_tile(h, w, True, True, i, j) for i in range(len(h)) for j in range(len(w))]], + [[create_tile(h_shift, w, False, True, i, j) for i in range(len(h_shift)) for j in range(len(w))]], + [[create_tile(h, w_shift, True, False, i, j) for i in range(len(h)) for j in range(len(w_shift))]], + [[create_tile(h_shift, w_shift, False, False, i,j) for i in range(len(h_shift)) for j in range(len(w_shift))]], + ] + + return passes + +def mask_at_boundary(h, h_len, w, w_len, tile_size_h, tile_size_w, latent_size_h, latent_size_w, mask, device='cpu'): + tile_size_h = int(tile_size_h // 8) + tile_size_w = int(tile_size_w // 8) + + if (h_len == tile_size_h or h_len == latent_size_h) and (w_len == tile_size_w or w_len == latent_size_w): + return h, h_len, w, w_len, mask + h_offset = min(0, latent_size_h - (h + tile_size_h)) + w_offset = min(0, latent_size_w - (w + tile_size_w)) + new_mask = torch.zeros((1,1,tile_size_h, tile_size_w), dtype=torch.float32, device=device) + new_mask[:,:,-h_offset:h_len if h_offset == 0 else tile_size_h, -w_offset:w_len if w_offset == 0 else tile_size_w] = 1.0 if mask is None else mask + return h + h_offset, tile_size_h, w + w_offset, tile_size_w, new_mask + +def get_tiles_and_masks_rgrid(steps, latent_shape, tile_height, tile_width, generator): + + def calc_coords(latent_size, tile_size, jitter): + tile_coords = int((latent_size + jitter - 1) // tile_size + 1) + tile_coords = [np.clip(tile_size * c - jitter, 0, latent_size) for c in range(tile_coords + 1)] + tile_coords = [(c1, c2-c1) for c1, c2 in zip(tile_coords, tile_coords[1:])] + return tile_coords + + #calc stuff + batch_size = latent_shape[0] + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + tile_size_h = int(tile_height // 8) + tile_size_w = int(tile_width // 8) + + tiles_all = [] + + for s in range(steps): + rands = torch.rand((2,), dtype=torch.float32, generator=generator, device='cpu').numpy() + + jitter_w1 = int(rands[0] * tile_size_w) + jitter_w2 = int(((rands[0] + .5) % 1.0) * tile_size_w) + jitter_h1 = int(rands[1] * tile_size_h) + jitter_h2 = int(((rands[1] + .5) % 1.0) * tile_size_h) + + #calc number of tiles + tiles_h = [ + calc_coords(latent_size_h, tile_size_h, jitter_h1), + calc_coords(latent_size_h, tile_size_h, jitter_h2) + ] + tiles_w = [ + calc_coords(latent_size_w, tile_size_w, jitter_w1), + calc_coords(latent_size_w, tile_size_w, jitter_w2) + ] + + tiles = [] + if s % 2 == 0: + for i, h in enumerate(tiles_h[0]): + for w in tiles_w[i%2]: + tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None)) + else: + for i, w in enumerate(tiles_w[0]): + for h in tiles_h[i%2]: + tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None)) + tiles_all.append(tiles) + return [tiles_all] \ No newline at end of file diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/cg_mixed_seed_noise.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/cg_mixed_seed_noise.py new file mode 100644 index 0000000000000000000000000000000000000000..b670838039908319db131827b48942d63ed5aaa0 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/cg_mixed_seed_noise.py @@ -0,0 +1,16 @@ +# https://github.com/chrisgoringe/cg-noise +import torch + +def get_mixed_noise_function(original_noise_function, variation_seed, variation_weight): + def prepare_mixed_noise(latent_image:torch.Tensor, seed, batch_inds): + single_image_latent = latent_image[0].unsqueeze_(0) + different_noise = original_noise_function(single_image_latent, variation_seed, batch_inds) + original_noise = original_noise_function(single_image_latent, seed, batch_inds) + if latent_image.shape[0]==1: + mixed_noise = original_noise * (1.0-variation_weight) + different_noise * (variation_weight) + else: + mixed_noise = torch.empty_like(latent_image) + for i in range(latent_image.shape[0]): + mixed_noise[i] = original_noise * (1.0-variation_weight*i) + different_noise * (variation_weight*i) + return mixed_noise + return prepare_mixed_noise diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/city96_latent_upscaler.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/city96_latent_upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..219fd68ae2acd9ee2446971a011f0e018af7747d --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/city96_latent_upscaler.py @@ -0,0 +1,94 @@ +# https://github.com/city96/SD-Latent-Upscaler +# check if any issues on weights and updates and local install for same +import torch +import torch.nn as nn +from safetensors.torch import load_file +from huggingface_hub import hf_hub_download + +class Upscaler(nn.Module): + """ + Basic NN layout, ported from: + https://github.com/city96/SD-Latent-Upscaler/blob/main/upscaler.py + """ + version = 2.1 # network revision + def head(self): + return [ + nn.Conv2d(self.chan, self.size, kernel_size=self.krn, padding=self.pad), + nn.ReLU(), + nn.Upsample(scale_factor=self.fac, mode="nearest"), + nn.ReLU(), + ] + def core(self): + layers = [] + for _ in range(self.depth): + layers += [ + nn.Conv2d(self.size, self.size, kernel_size=self.krn, padding=self.pad), + nn.ReLU(), + ] + return layers + def tail(self): + return [ + nn.Conv2d(self.size, self.chan, kernel_size=self.krn, padding=self.pad), + ] + + def __init__(self, fac, depth=16): + super().__init__() + self.size = 64 # Conv2d size + self.chan = 4 # in/out channels + self.depth = depth # no. of layers + self.fac = fac # scale factor + self.krn = 3 # kernel size + self.pad = 1 # padding + + self.sequential = nn.Sequential( + *self.head(), + *self.core(), + *self.tail(), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.sequential(x) + + +class LatentUpscaler: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "samples": ("LATENT", ), + "latent_ver": (["v1", "xl"],), + "scale_factor": (["1.25", "1.5", "2.0"],), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + CATEGORY = "latent" + + def upscale(self, samples, latent_ver, scale_factor): + model = Upscaler(scale_factor) + weights = str(hf_hub_download( + repo_id="city96/SD-Latent-Upscaler", + filename=f"latent-upscaler-v{model.version}_SD{latent_ver}-x{scale_factor}.safetensors") + ) + # weights = f"./latent-upscaler-v{model.version}_SD{latent_ver}-x{scale_factor}.safetensors" + + model.load_state_dict(load_file(weights)) + lt = samples["samples"] + lt = model(lt) + del model + if "noise_mask" in samples.keys(): + # expand the noise mask to the same shape as the latent + mask = torch.nn.functional.interpolate(samples['noise_mask'], scale_factor=float(scale_factor), mode='bicubic') + return ({"samples": lt, "noise_mask": mask},) + return ({"samples": lt},) +NODE_CLASS_MAPPINGS = { + "LatentUpscaler": LatentUpscaler, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LatentUpscaler": "EFF_C Latent Upscaler" +} diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sd15_resizer.pt b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sd15_resizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..bca5b91b2b15a8b1e5ec2e151252c483da441b80 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sd15_resizer.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fa1cb8168b305d556f2e8178c820b0a6956f4ba84ebc9443fac69be43ffd6fe +size 12628977 diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sdxl_resizer.pt b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sdxl_resizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..b72dac3623a86fb63107fa0fda6d9c05ee6e0dff --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sdxl_resizer.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0bca261c96e136cb9e2f330f40386e6cbcaaf464cd39e9f7752c9e7b32e825e8 +size 12628977 diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_cfg_denoiser.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_cfg_denoiser.py new file mode 100644 index 0000000000000000000000000000000000000000..967712cc02d09f416df77d0892ddfdcc23b5e275 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_cfg_denoiser.py @@ -0,0 +1,321 @@ +# https://github.com/shiimizu/ComfyUI_smZNodes +import comfy +import torch +from typing import List +import comfy.sample +from comfy import model_base, model_management +from comfy.samplers import KSampler, KSamplerX0Inpaint, wrap_model +#from comfy.k_diffusion.external import CompVisDenoiser +import nodes +import inspect +import functools +import importlib +import os +import re +import itertools + +import torch +from comfy import model_management + +def catenate_conds(conds): + if not isinstance(conds[0], dict): + return torch.cat(conds) + + return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()} + + +def subscript_cond(cond, a, b): + if not isinstance(cond, dict): + return cond[a:b] + + return {key: vec[a:b] for key, vec in cond.items()} + + +def pad_cond(tensor, repeats, empty): + if not isinstance(tensor, dict): + return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1)).to(device=tensor.device)], axis=1) + + tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty) + return tensor + + +class CFGDenoiser(torch.nn.Module): + """ + Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet) + that can take a noisy picture and produce a noise-free picture using two guidances (prompts) + instead of one. Originally, the second prompt is just an empty string, but we use non-empty + negative prompt. + """ + + def __init__(self, model): + super().__init__() + self.inner_model = model + self.model_wrap = None + self.mask = None + self.nmask = None + self.init_latent = None + self.steps = None + """number of steps as specified by user in UI""" + + self.total_steps = None + """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler""" + + self.step = 0 + self.image_cfg_scale = None + self.padded_cond_uncond = False + self.sampler = None + self.model_wrap = None + self.p = None + self.mask_before_denoising = False + + + def combine_denoised(self, x_out, conds_list, uncond, cond_scale): + denoised_uncond = x_out[-uncond.shape[0]:] + denoised = torch.clone(denoised_uncond) + + for i, conds in enumerate(conds_list): + for cond_index, weight in conds: + denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale) + + return denoised + + def combine_denoised_for_edit_model(self, x_out, cond_scale): + out_cond, out_img_cond, out_uncond = x_out.chunk(3) + denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond) + + return denoised + + def get_pred_x0(self, x_in, x_out, sigma): + return x_out + + def update_inner_model(self): + self.model_wrap = None + + c, uc = self.p.get_conds() + self.sampler.sampler_extra_args['cond'] = c + self.sampler.sampler_extra_args['uncond'] = uc + + def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond): + model_management.throw_exception_if_processing_interrupted() + + is_edit_model = False + + conds_list, tensor = cond + assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" + + if self.mask_before_denoising and self.mask is not None: + x = self.init_latent * self.mask + self.nmask * x + + batch_size = len(conds_list) + repeats = [len(conds_list[i]) for i in range(batch_size)] + + if False: + image_uncond = torch.zeros_like(image_cond) + make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm, 'transformer_options': {'from_smZ': True}} # pylint: disable=C3001 + else: + image_uncond = image_cond + if isinstance(uncond, dict): + make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": None, "c_adm": x.c_adm, 'transformer_options': {'from_smZ': True}} + else: + make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": None, "c_adm": x.c_adm, 'transformer_options': {'from_smZ': True}} + + if not is_edit_model: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond]) + else: + x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) + sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)]) + + skip_uncond = False + + # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it + if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model: + skip_uncond = True + x_in = x_in[:-batch_size] + sigma_in = sigma_in[:-batch_size] + + if tensor.shape[1] == uncond.shape[1] or skip_uncond: + if is_edit_model: + cond_in = catenate_conds([tensor, uncond, uncond]) + elif skip_uncond: + cond_in = tensor + else: + cond_in = catenate_conds([tensor, uncond]) + + x_out = torch.zeros_like(x_in) + for batch_offset in range(0, x_out.shape[0], batch_size): + a = batch_offset + b = a + batch_size + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], **make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b])) + else: + x_out = torch.zeros_like(x_in) + for batch_offset in range(0, tensor.shape[0], batch_size): + a = batch_offset + b = min(a + batch_size, tensor.shape[0]) + + if not is_edit_model: + c_crossattn = subscript_cond(tensor, a, b) + else: + c_crossattn = torch.cat([tensor[a:b]], uncond) + + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], **make_condition_dict(c_crossattn, image_cond_in[a:b])) + + if not skip_uncond: + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], **make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:])) + + denoised_image_indexes = [x[0][0] for x in conds_list] + if skip_uncond: + fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes]) + x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be + + if is_edit_model: + denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) + elif skip_uncond: + denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0) + else: + denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) + + if not self.mask_before_denoising and self.mask is not None: + denoised = self.init_latent * self.mask + self.nmask * denoised + + self.step += 1 + del x_out + return denoised + +# ======================================================================== + +def expand(tensor1, tensor2): + def adjust_tensor_shape(tensor_small, tensor_big): + # Calculate replication factor + # -(-a // b) is ceiling of division without importing math.ceil + replication_factor = -(-tensor_big.size(1) // tensor_small.size(1)) + + # Use repeat to extend tensor_small + tensor_small_extended = tensor_small.repeat(1, replication_factor, 1) + + # Take the rows of the extended tensor_small to match tensor_big + tensor_small_matched = tensor_small_extended[:, :tensor_big.size(1), :] + + return tensor_small_matched + + # Check if their second dimensions are different + if tensor1.size(1) != tensor2.size(1): + # Check which tensor has the smaller second dimension and adjust its shape + if tensor1.size(1) < tensor2.size(1): + tensor1 = adjust_tensor_shape(tensor1, tensor2) + else: + tensor2 = adjust_tensor_shape(tensor2, tensor1) + return (tensor1, tensor2) + +def _find_outer_instance(target, target_type): + import inspect + frame = inspect.currentframe() + while frame: + if target in frame.f_locals: + found = frame.f_locals[target] + if isinstance(found, target_type) and found != 1: # steps == 1 + return found + frame = frame.f_back + return None + +# ======================================================================== +def bounded_modulo(number, modulo_value): + return number if number < modulo_value else modulo_value + +def calc_cond(c, current_step): + """Group by smZ conds that may do prompt-editing / regular conds / comfy conds.""" + _cond = [] + # Group by conds from smZ + fn=lambda x : x[1].get("from_smZ", None) is not None + an_iterator = itertools.groupby(c, fn ) + for key, group in an_iterator: + ls=list(group) + # Group by prompt-editing conds + fn2=lambda x : x[1].get("smZid", None) + an_iterator2 = itertools.groupby(ls, fn2) + for key2, group2 in an_iterator2: + ls2=list(group2) + if key2 is not None: + orig_len = ls2[0][1].get('orig_len', 1) + i = bounded_modulo(current_step, orig_len - 1) + _cond = _cond + [ls2[i]] + else: + _cond = _cond + ls2 + return _cond + +class CFGNoisePredictor(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.ksampler = _find_outer_instance('self', comfy.samplers.KSampler) + self.step = 0 + self.orig = comfy.samplers.CFGNoisePredictor(model) #CFGNoisePredictorOrig(model) + self.inner_model = model + self.inner_model2 = CFGDenoiser(model.apply_model) + self.inner_model2.num_timesteps = model.num_timesteps + self.inner_model2.device = self.ksampler.device if hasattr(self.ksampler, "device") else None + self.s_min_uncond = 0.0 + self.alphas_cumprod = model.alphas_cumprod + self.c_adm = None + self.init_cond = None + self.init_uncond = None + self.is_prompt_editing_u = False + self.is_prompt_editing_c = False + + def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None): + + cc=calc_cond(cond, self.step) + uu=calc_cond(uncond, self.step) + self.step += 1 + + if (any([p[1].get('from_smZ', False) for p in cc]) or + any([p[1].get('from_smZ', False) for p in uu])): + if model_options.get('transformer_options',None) is None: + model_options['transformer_options'] = {} + model_options['transformer_options']['from_smZ'] = True + + # Only supports one cond + for ix in range(len(cc)): + if cc[ix][1].get('from_smZ', False): + cc = [cc[ix]] + break + for ix in range(len(uu)): + if uu[ix][1].get('from_smZ', False): + uu = [uu[ix]] + break + c=cc[0][1] + u=uu[0][1] + _cc = cc[0][0] + _uu = uu[0][0] + if c.get("adm_encoded", None) is not None: + self.c_adm = torch.cat([c['adm_encoded'], u['adm_encoded']]) + # SDXL. Need to pad with repeats + _cc, _uu = expand(_cc, _uu) + _uu, _cc = expand(_uu, _cc) + x.c_adm = self.c_adm + conds_list = c.get('conds_list', [[(0, 1.0)]]) + image_cond = txt2img_image_conditioning(None, x) + out = self.inner_model2(x, timestep, cond=(conds_list, _cc), uncond=_uu, cond_scale=cond_scale, s_min_uncond=self.s_min_uncond, image_cond=image_cond) + return out + +def txt2img_image_conditioning(sd_model, x, width=None, height=None): + return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) + +# ======================================================================================= + +def set_model_k(self: KSampler): + self.model_denoise = CFGNoisePredictor(self.model) # main change + if ((getattr(self.model, "parameterization", "") == "v") or + (getattr(self.model, "model_type", -1) == model_base.ModelType.V_PREDICTION)): + self.model_wrap = wrap_model(self.model_denoise, quantize=True) + self.model_wrap.parameterization = getattr(self.model, "parameterization", "v") + else: + self.model_wrap = wrap_model(self.model_denoise, quantize=True) + self.model_wrap.parameterization = getattr(self.model, "parameterization", "eps") + self.model_k = KSamplerX0Inpaint(self.model_wrap) + +class SDKSampler(comfy.samplers.KSampler): + def __init__(self, *args, **kwargs): + super(SDKSampler, self).__init__(*args, **kwargs) + set_model_k(self) diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_rng_source.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_rng_source.py new file mode 100644 index 0000000000000000000000000000000000000000..54e33ca6bc9dc1cf3b4030fee18b4fa505d3a53b --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_rng_source.py @@ -0,0 +1,140 @@ +# https://github.com/shiimizu/ComfyUI_smZNodes +import numpy as np + +philox_m = [0xD2511F53, 0xCD9E8D57] +philox_w = [0x9E3779B9, 0xBB67AE85] + +two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32) +two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32) + + +def uint32(x): + """Converts (N,) np.uint64 array into (2, N) np.unit32 array.""" + return x.view(np.uint32).reshape(-1, 2).transpose(1, 0) + + +def philox4_round(counter, key): + """A single round of the Philox 4x32 random number generator.""" + + v1 = uint32(counter[0].astype(np.uint64) * philox_m[0]) + v2 = uint32(counter[2].astype(np.uint64) * philox_m[1]) + + counter[0] = v2[1] ^ counter[1] ^ key[0] + counter[1] = v2[0] + counter[2] = v1[1] ^ counter[3] ^ key[1] + counter[3] = v1[0] + + +def philox4_32(counter, key, rounds=10): + """Generates 32-bit random numbers using the Philox 4x32 random number generator. + + Parameters: + counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation). + key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed). + rounds (int): The number of rounds to perform. + + Returns: + numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers. + """ + + for _ in range(rounds - 1): + philox4_round(counter, key) + + key[0] = key[0] + philox_w[0] + key[1] = key[1] + philox_w[1] + + philox4_round(counter, key) + return counter + + +def box_muller(x, y): + """Returns just the first out of two numbers generated by Box–Muller transform algorithm.""" + u = x * two_pow32_inv + two_pow32_inv / 2 + v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2 + + s = np.sqrt(-2.0 * np.log(u)) + + r1 = s * np.sin(v) + return r1.astype(np.float32) + + +class Generator: + """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU""" + + def __init__(self, seed): + self.seed = seed + self.offset = 0 + + def randn(self, shape): + """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform.""" + + n = 1 + for x in shape: + n *= x + + counter = np.zeros((4, n), dtype=np.uint32) + counter[0] = self.offset + counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3] + self.offset += 1 + + key = np.empty(n, dtype=np.uint64) + key.fill(self.seed) + key = uint32(key) + + g = philox4_32(counter, key) + + return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3] + +#======================================================================================================================= +# Monkey Patch "prepare_noise" function +# https://github.com/shiimizu/ComfyUI_smZNodes +import torch +import functools +from comfy.sample import np +import comfy.model_management + +def rng_rand_source(rand_source='cpu'): + device = comfy.model_management.text_encoder_device() + + def prepare_noise(latent_image, seed, noise_inds=None, device='cpu'): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.Generator(device).manual_seed(seed) + if rand_source == 'nv': + rng = Generator(seed) + if noise_inds is None: + shape = latent_image.size() + if rand_source == 'nv': + return torch.asarray(rng.randn(shape), device=device) + else: + return torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, + device=device) + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1] + 1): + shape = [1] + list(latent_image.size())[1:] + if rand_source == 'nv': + noise = torch.asarray(rng.randn(shape), device=device) + else: + noise = torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, + device=device) + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + + if rand_source == 'cpu': + if hasattr(comfy.sample, 'prepare_noise_orig'): + comfy.sample.prepare_noise = comfy.sample.prepare_noise_orig + else: + if not hasattr(comfy.sample, 'prepare_noise_orig'): + comfy.sample.prepare_noise_orig = comfy.sample.prepare_noise + _prepare_noise = functools.partial(prepare_noise, device=device) + comfy.sample.prepare_noise = _prepare_noise + + + diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/ttl_nn_latent_upscaler.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/ttl_nn_latent_upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..7430228197ef6514244e34c64b981bfdad649791 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/ttl_nn_latent_upscaler.py @@ -0,0 +1,321 @@ +import torch +#from .latent_resizer import LatentResizer +from comfy import model_management +import os + +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +def normalization(channels): + return nn.GroupNorm(32, channels) + + +def zero_module(module): + for p in module.parameters(): + p.detach().zero_() + return module + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalization(in_channels) + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def attention(self, h_: torch.Tensor) -> torch.Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q, k, v = map( + lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v) + ) + h_ = nn.functional.scaled_dot_product_attention( + q, k, v + ) # scale is dim ** -0.5 per default + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x, **kwargs): + h_ = x + h_ = self.attention(h_) + h_ = self.proj_out(h_) + return x + h_ + + +def make_attn(in_channels, attn_kwargs=None): + return AttnBlock(in_channels) + + +class ResBlockEmb(nn.Module): + def __init__( + self, + channels, + emb_channels, + dropout=0, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + nn.Conv2d(channels, self.out_channels, kernel_size, padding=padding), + ) + + self.skip_t_emb = skip_t_emb + self.emb_out_channels = ( + 2 * self.out_channels if use_scale_shift_norm else self.out_channels + ) + if self.skip_t_emb: + print(f"Skipping timestep embedding in {self.__class__.__name__}") + assert not self.use_scale_shift_norm + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + self.emb_out_channels, + ), + ) + + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + nn.Conv2d( + self.out_channels, + self.out_channels, + kernel_size, + padding=padding, + ) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = nn.Conv2d( + channels, self.out_channels, kernel_size, padding=padding + ) + else: + self.skip_connection = nn.Conv2d(channels, self.out_channels, 1) + + def forward(self, x, emb): + h = self.in_layers(x) + + if self.skip_t_emb: + emb_out = torch.zeros_like(h) + else: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + if self.exchange_temb_dims: + emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class LatentResizer(nn.Module): + def __init__(self, in_blocks=10, out_blocks=10, channels=128, dropout=0, attn=True): + super().__init__() + self.conv_in = nn.Conv2d(4, channels, 3, padding=1) + + self.channels = channels + embed_dim = 32 + self.embed = nn.Sequential( + nn.Linear(1, embed_dim), + nn.SiLU(), + nn.Linear(embed_dim, embed_dim), + ) + + self.in_blocks = nn.ModuleList([]) + for b in range(in_blocks): + if (b == 1 or b == in_blocks - 1) and attn: + self.in_blocks.append(make_attn(channels)) + self.in_blocks.append(ResBlockEmb(channels, embed_dim, dropout)) + + self.out_blocks = nn.ModuleList([]) + for b in range(out_blocks): + if (b == 1 or b == out_blocks - 1) and attn: + self.out_blocks.append(make_attn(channels)) + self.out_blocks.append(ResBlockEmb(channels, embed_dim, dropout)) + + self.norm_out = normalization(channels) + self.conv_out = nn.Conv2d(channels, 4, 3, padding=1) + + @classmethod + def load_model(cls, filename, device="cpu", dtype=torch.float32, dropout=0): + if not 'weights_only' in torch.load.__code__.co_varnames: + weights = torch.load(filename, map_location=torch.device("cpu")) + else: + weights = torch.load(filename, map_location=torch.device("cpu"), weights_only=True) + in_blocks = 0 + out_blocks = 0 + in_tfs = 0 + out_tfs = 0 + channels = weights["conv_in.bias"].shape[0] + for k in weights.keys(): + k = k.split(".") + if k[0] == "in_blocks": + in_blocks = max(in_blocks, int(k[1])) + if k[2] == "q" and k[3] == "weight": + in_tfs += 1 + if k[0] == "out_blocks": + out_blocks = max(out_blocks, int(k[1])) + if k[2] == "q" and k[3] == "weight": + out_tfs += 1 + in_blocks = in_blocks + 1 - in_tfs + out_blocks = out_blocks + 1 - out_tfs + resizer = cls( + in_blocks=in_blocks, + out_blocks=out_blocks, + channels=channels, + dropout=dropout, + attn=(out_tfs != 0), + ) + resizer.load_state_dict(weights) + resizer.eval() + resizer.to(device, dtype=dtype) + return resizer + + def forward(self, x, scale=None, size=None): + if scale is None and size is None: + raise ValueError("Either scale or size needs to be not None") + if scale is not None and size is not None: + raise ValueError("Both scale or size can't be not None") + if scale is not None: + size = (x.shape[-2] * scale, x.shape[-1] * scale) + size = tuple([int(round(i)) for i in size]) + else: + scale = size[-1] / x.shape[-1] + + # Output is the same size as input + if size == x.shape[-2:]: + return x + + scale = torch.tensor([scale - 1], dtype=x.dtype).to(x.device).unsqueeze(0) + emb = self.embed(scale) + + x = self.conv_in(x) + + for b in self.in_blocks: + if isinstance(b, ResBlockEmb): + x = b(x, emb) + else: + x = b(x) + x = F.interpolate(x, size=size, mode="bilinear") + for b in self.out_blocks: + if isinstance(b, ResBlockEmb): + x = b(x, emb) + else: + x = b(x) + + x = self.norm_out(x) + x = F.silu(x) + x = self.conv_out(x) + return x + +######################################################## +class NNLatentUpscale: + """ + Upscales SDXL latent using neural network + """ + + def __init__(self): + self.local_dir = os.path.dirname(os.path.realpath(__file__)) + self.scale_factor = 0.13025 + self.dtype = torch.float32 + if model_management.should_use_fp16(): + self.dtype = torch.float16 + self.weight_path = { + "SDXL": os.path.join(self.local_dir, "sdxl_resizer.pt"), + "SD 1.x": os.path.join(self.local_dir, "sd15_resizer.pt"), + } + self.version = "none" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "latent": ("LATENT",), + "version": (["SDXL", "SD 1.x"],), + "upscale": ( + "FLOAT", + { + "default": 1.5, + "min": 1.0, + "max": 2.0, + "step": 0.01, + "display": "number", + }, + ), + }, + } + + RETURN_TYPES = ("LATENT",) + + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, latent, version, upscale): + device = model_management.get_torch_device() + samples = latent["samples"].to(device=device, dtype=self.dtype) + + if version != self.version: + self.model = LatentResizer.load_model(self.weight_path[version], device, self.dtype) + self.version = version + + self.model.to(device=device) + latent_out = (self.model(self.scale_factor * samples, scale=upscale) / self.scale_factor) + + if self.dtype != torch.float32: + latent_out = latent_out.to(dtype=torch.float32) + + latent_out = latent_out.to(device="cpu") + + self.model.to(device=model_management.vae_offload_device()) + return ({"samples": latent_out},) + +NODE_CLASS_MAPPINGS = { + "NNLatentUpscale": NNLatentUpscale +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "NNlLatentUpscale": "EFF Latent Upscale" +} diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/requirements.txt b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..91a003faad5ae84019184079c17fb73b8bba8485 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/requirements.txt @@ -0,0 +1,2 @@ +clip-interrogator +simpleeval diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/tsc_utils.py b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/tsc_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..426e10eebe6ce858f35ec6a873f098e304a74817 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/tsc_utils.py @@ -0,0 +1,555 @@ +# Efficiency Nodes Utility functions +from torch import Tensor +import torch +from PIL import Image +import numpy as np +import os +import sys +import io +from contextlib import contextmanager +import json +import folder_paths + +# Get the absolute path of the parent directory of the current script +my_dir = os.path.dirname(os.path.abspath(__file__)) + +# Add the My directory path to the sys.path list +sys.path.append(my_dir) + +# Construct the absolute path to the ComfyUI directory +comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..')) + +# Add the ComfyUI directory path to the sys.path list +sys.path.append(comfy_dir) + +# Import functions from ComfyUI +import comfy.sd +import comfy.utils +import latent_preview +from comfy.cli_args import args + +# Cache for Efficiency Node models +loaded_objects = { + "ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id]) + "refn": [], # (ckpt_name, ckpt_model, clip, bvae, [id]) + "vae": [], # (vae_name, vae, [id]) + "lora": [] # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id]) +} + +# Cache for Efficient Ksamplers +last_helds = { + "latent": [], # (latent, [parameters], id) # Base sampling latent results + "image": [], # (image, id) # Base sampling image results + "cnet_img": [] # (cnet_img, [parameters], id) # HiRes-Fix control net preprocessor image results +} + +def load_ksampler_results(key: str, my_unique_id, parameters_list=None): + global last_helds + for data in last_helds[key]: + id_ = data[-1] # ID is always the last element in the tuple + if id_ == my_unique_id: + if parameters_list is not None: + # Ensure tuple has at least 3 elements and match with parameters_list + if len(data) >= 3 and data[1] == parameters_list: + return data[0] + else: + return data[0] + return None + +def store_ksampler_results(key: str, my_unique_id, value, parameters_list=None): + global last_helds + + for i, data in enumerate(last_helds[key]): + id_ = data[-1] # ID will always be the last in the tuple + if id_ == my_unique_id: + # Check if parameters_list is provided or not + updated_data = (value, parameters_list, id_) if parameters_list is not None else (value, id_) + last_helds[key][i] = updated_data + return True + + # If parameters_list is given + if parameters_list is not None: + last_helds[key].append((value, parameters_list, my_unique_id)) + else: + last_helds[key].append((value, my_unique_id)) + return True + +# Tensor to PIL (grabbed from WAS Suite) +def tensor2pil(image: torch.Tensor) -> Image.Image: + return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) + +# Convert PIL to Tensor (grabbed from WAS Suite) +def pil2tensor(image: Image.Image) -> torch.Tensor: + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + +# Convert tensor to PIL, resize it, and convert back to tensor +def quick_resize(source_tensor: torch.Tensor, target_shape: tuple) -> torch.Tensor: + resized_images = [] + for img in source_tensor: + resized_pil = tensor2pil(img.squeeze(0)).resize((target_shape[2], target_shape[1]), Image.ANTIALIAS) + resized_images.append(pil2tensor(resized_pil).squeeze(0)) + return torch.stack(resized_images, dim=0) + +# Create a function to compute the hash of a tensor +import hashlib +def tensor_to_hash(tensor): + byte_repr = tensor.cpu().numpy().tobytes() # Convert tensor to bytes + return hashlib.sha256(byte_repr).hexdigest() # Compute hash + +# Color coded messages functions +MESSAGE_COLOR = "\033[36m" # Cyan +XYPLOT_COLOR = "\033[35m" # Purple +SUCCESS_COLOR = "\033[92m" # Green +WARNING_COLOR = "\033[93m" # Yellow +ERROR_COLOR = "\033[91m" # Red +INFO_COLOR = "\033[90m" # Gray +def format_message(text, color_code): + RESET_COLOR = "\033[0m" + return f"{color_code}{text}{RESET_COLOR}" +def message(text): + return format_message(text, MESSAGE_COLOR) +def warning(text): + return format_message(text, WARNING_COLOR) +def error(text): + return format_message(text, ERROR_COLOR) +def success(text): + return format_message(text, SUCCESS_COLOR) +def xyplot_message(text): + return format_message(text, XYPLOT_COLOR) +def info(text): + return format_message(text, INFO_COLOR) + +def extract_node_info(prompt, id, indirect_key=None): + # Convert ID to string + id = str(id) + node_id = None + + # If an indirect_key (like 'script') is provided, perform a two-step lookup + if indirect_key: + # Ensure the id exists in the prompt and has an 'inputs' entry with the indirect_key + if id in prompt and 'inputs' in prompt[id] and indirect_key in prompt[id]['inputs']: + # Extract the indirect_id + indirect_id = prompt[id]['inputs'][indirect_key][0] + + # Ensure the indirect_id exists in the prompt + if indirect_id in prompt: + node_id = indirect_id + return prompt[indirect_id].get('class_type', None), node_id + + # If indirect_key is not found within the prompt + return None, None + + # If no indirect_key is provided, perform a direct lookup + return prompt.get(id, {}).get('class_type', None), node_id + +def extract_node_value(prompt, id, key): + # If ID is in data, return its 'inputs' value for a given key. Otherwise, return None. + return prompt.get(str(id), {}).get('inputs', {}).get(key, None) + +def print_loaded_objects_entries(id=None, prompt=None, show_id=False): + print("-" * 40) # Print an empty line followed by a separator line + if id is not None: + id = str(id) # Convert ID to string + if prompt is not None and id is not None: + node_name, _ = extract_node_info(prompt, id) + if show_id: + print(f"\033[36m{node_name} Models Cache: (node_id:{int(id)})\033[0m") + else: + print(f"\033[36m{node_name} Models Cache:\033[0m") + elif id is None: + print(f"\033[36mGlobal Models Cache:\033[0m") + else: + print(f"\033[36mModels Cache: \nnode_id:{int(id)}\033[0m") + entries_found = False + for key in ["ckpt", "refn", "vae", "lora"]: + entries_with_id = loaded_objects[key] if id is None else [entry for entry in loaded_objects[key] if id in entry[-1]] + if not entries_with_id: # If no entries with the chosen ID, print None and skip this key + continue + entries_found = True + print(f"{key.capitalize()}:") + for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1 + if key == "lora": + base_ckpt_name = os.path.splitext(os.path.basename(entry[1]))[0] # Split logic for base_ckpt + if id is None: + associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids + print(f" [{i}] base_ckpt: {base_ckpt_name} (ids: {associated_ids})") + else: + print(f" [{i}] base_ckpt: {base_ckpt_name}") + for name, strength_model, strength_clip in entry[0]: + lora_model_info = f"{os.path.splitext(os.path.basename(name))[0]}({round(strength_model, 2)},{round(strength_clip, 2)})" + print(f" lora(mod,clip): {lora_model_info}") + else: + name_without_ext = os.path.splitext(os.path.basename(entry[0]))[0] + if id is None: + associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids + print(f" [{i}] {name_without_ext} (ids: {associated_ids})") + else: + print(f" [{i}] {name_without_ext}") + if not entries_found: + print("-") + +# This function cleans global variables associated with nodes that are no longer detected on UI +def globals_cleanup(prompt): + global loaded_objects + global last_helds + + # Step 1: Clean up last_helds + for key in list(last_helds.keys()): + original_length = len(last_helds[key]) + last_helds[key] = [ + (*values, id_) + for *values, id_ in last_helds[key] + if str(id_) in prompt.keys() + ] + + # Step 2: Clean up loaded_objects + for key in list(loaded_objects.keys()): + for i, tup in enumerate(list(loaded_objects[key])): + # Remove ids from id array in each tuple that don't exist in prompt + id_array = [id for id in tup[-1] if str(id) in prompt.keys()] + if len(id_array) != len(tup[-1]): + if id_array: + loaded_objects[key][i] = tup[:-1] + (id_array,) + ###print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}') + else: + # If id array becomes empty, delete the corresponding tuple + loaded_objects[key].remove(tup) + ###print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.') + +def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=True, ckpt_type="ckpt"): + global loaded_objects + + # Create copies of the arguments right at the start + ckpt_name = ckpt_name.copy() if isinstance(ckpt_name, (list, dict, set)) else ckpt_name + + # Check if the type is valid + if ckpt_type not in ["ckpt", "refn"]: + raise ValueError(f"Invalid checkpoint type: {ckpt_type}") + + for entry in loaded_objects[ckpt_type]: + if entry[0] == ckpt_name: + _, model, clip, vae, ids = entry + cache_full = cache and len([entry for entry in loaded_objects[ckpt_type] if id in entry[-1]]) >= cache + + if cache_full: + clear_cache(id, cache, ckpt_type) + elif id not in ids: + ids.append(id) + + return model, clip, vae + + if os.path.isabs(ckpt_name): + ckpt_path = ckpt_name + else: + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + with suppress_output(): + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + model = out[0] + clip = out[1] + vae = out[2] if output_vae else None # Load VAE from the checkpoint path only if output_vae is True + + if cache: + cache_list = [entry for entry in loaded_objects[ckpt_type] if id in entry[-1]] + if len(cache_list) < cache: + loaded_objects[ckpt_type].append((ckpt_name, model, clip, vae, [id])) + else: + clear_cache(id, cache, ckpt_type) + if cache_overwrite: + for e in loaded_objects[ckpt_type]: + if id in e[-1]: + e[-1].remove(id) + # If the id list becomes empty, remove the entry from the ckpt_type list + if not e[-1]: + loaded_objects[ckpt_type].remove(e) + break + loaded_objects[ckpt_type].append((ckpt_name, model, clip, vae, [id])) + + return model, clip, vae + +def get_bvae_by_ckpt_name(ckpt_name): + for ckpt in loaded_objects["ckpt"]: + if ckpt[0] == ckpt_name: + return ckpt[3] # return 'bvae' variable + return None # return None if no match is found + +def load_vae(vae_name, id, cache=None, cache_overwrite=False): + global loaded_objects + + # Create copies of the argument right at the start + vae_name = vae_name.copy() if isinstance(vae_name, (list, dict, set)) else vae_name + + for i, entry in enumerate(loaded_objects["vae"]): + if entry[0] == vae_name: + vae, ids = entry[1], entry[2] + if id not in ids: + if cache and len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) >= cache: + return vae + ids.append(id) + if cache: + clear_cache(id, cache, "vae") + return vae + + if os.path.isabs(vae_name): + vae_path = vae_name + else: + vae_path = folder_paths.get_full_path("vae", vae_name) + + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + + if cache: + if len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) < cache: + loaded_objects["vae"].append((vae_name, vae, [id])) + else: + clear_cache(id, cache, "vae") + if cache_overwrite: + # Find the first entry with the id, remove the id from the entry's id list + for e in loaded_objects["vae"]: + if id in e[-1]: + e[-1].remove(id) + # If the id list becomes empty, remove the entry from the "vae" list + if not e[-1]: + loaded_objects["vae"].remove(e) + break + loaded_objects["vae"].append((vae_name, vae, [id])) + + return vae + +def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_overwrite=False): + global loaded_objects + + # Create copies of the arguments right at the start + lora_params = lora_params.copy() if isinstance(lora_params, (list, dict, set)) else lora_params + ckpt_name = ckpt_name.copy() if isinstance(ckpt_name, (list, dict, set)) else ckpt_name + + for entry in loaded_objects["lora"]: + + # Convert to sets and compare + if set(entry[0]) == set(lora_params) and entry[1] == ckpt_name: + + _, _, lora_model, lora_clip, ids = entry + cache_full = cache and len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) >= cache + + if cache_full: + clear_cache(id, cache, "lora") + elif id not in ids: + ids.append(id) + + # Additional cache handling for 'ckpt' just like in 'load_checkpoint' function + for ckpt_entry in loaded_objects["ckpt"]: + if ckpt_entry[0] == ckpt_name: + _, _, _, _, ckpt_ids = ckpt_entry + ckpt_cache_full = ckpt_cache and len( + [ckpt_entry for ckpt_entry in loaded_objects["ckpt"] if id in ckpt_entry[-1]]) >= ckpt_cache + + if ckpt_cache_full: + clear_cache(id, ckpt_cache, "ckpt") + elif id not in ckpt_ids: + ckpt_ids.append(id) + + return lora_model, lora_clip + + def recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths): + if len(lora_params) == 0: + return ckpt, clip + + lora_name, strength_model, strength_clip = lora_params[0] + if os.path.isabs(lora_name): + lora_path = lora_name + else: + lora_path = folder_paths.get_full_path("loras", lora_name) + + lora_model, lora_clip = comfy.sd.load_lora_for_models(ckpt, clip, comfy.utils.load_torch_file(lora_path), strength_model, strength_clip) + + # Call the function again with the new lora_model and lora_clip and the remaining tuples + return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths) + + # Unpack lora parameters from the first element of the list for now + lora_name, strength_model, strength_clip = lora_params[0] + ckpt, clip, _ = load_checkpoint(ckpt_name, id, cache=ckpt_cache) + + lora_model, lora_clip = recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths) + + if cache: + if len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) < cache: + loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id])) + else: + clear_cache(id, cache, "lora") + if cache_overwrite: + # Find the first entry with the id, remove the id from the entry's id list + for e in loaded_objects["lora"]: + if id in e[-1]: + e[-1].remove(id) + # If the id list becomes empty, remove the entry from the "lora" list + if not e[-1]: + loaded_objects["lora"].remove(e) + break + loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id])) + + return lora_model, lora_clip + +def clear_cache(id, cache, dict_name): + """ + Clear the cache for a specific id in a specific dictionary. + If the cache limit is reached for a specific id, deletes the id from the oldest entry. + If the id array of the entry becomes empty, deletes the entry. + """ + # Get all entries associated with the id_element + id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]] + while len(id_associated_entries) > cache: + # Identify an older entry (but not necessarily the oldest) containing id + older_entry = id_associated_entries[0] + # Remove the id_element from the older entry + older_entry[-1].remove(id) + # If the id array of the older entry becomes empty after this, delete the entry + if not older_entry[-1]: + loaded_objects[dict_name].remove(older_entry) + # Update the id_associated_entries + id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]] + + +def clear_cache_by_exception(node_id, vae_dict=None, ckpt_dict=None, lora_dict=None, refn_dict=None): + global loaded_objects + + dict_mapping = { + "vae_dict": "vae", + "ckpt_dict": "ckpt", + "lora_dict": "lora", + "refn_dict": "refn" + } + + for arg_name, arg_val in {"vae_dict": vae_dict, "ckpt_dict": ckpt_dict, "lora_dict": lora_dict, "refn_dict": refn_dict}.items(): + if arg_val is None: + continue + + dict_name = dict_mapping[arg_name] + + for tuple_idx, tuple_item in enumerate(loaded_objects[dict_name].copy()): + if arg_name == "lora_dict": + # Iterate over the tuples (lora_params, ckpt_name) in arg_val + for lora_params, ckpt_name in arg_val: + # Compare lists of tuples considering order inside tuples, but not order of tuples + if set(lora_params) == set(tuple_item[0]) and ckpt_name == tuple_item[1]: + break + else: # If no match was found in lora_dict, remove the tuple from loaded_objects + if node_id in tuple_item[-1]: + tuple_item[-1].remove(node_id) + if not tuple_item[-1]: + loaded_objects[dict_name].remove(tuple_item) + continue + elif tuple_item[0] not in arg_val: # Only remove the tuple if it's not in arg_val + if node_id in tuple_item[-1]: + tuple_item[-1].remove(node_id) + if not tuple_item[-1]: + loaded_objects[dict_name].remove(tuple_item) + +# Retrieve the cache number from 'node_settings' json file +def get_cache_numbers(node_name): + # Get the directory path of the current file + my_dir = os.path.dirname(os.path.abspath(__file__)) + # Construct the file path for node_settings.json + settings_file = os.path.join(my_dir, 'node_settings.json') + # Load the settings from the JSON file + with open(settings_file, 'r') as file: + node_settings = json.load(file) + # Retrieve the cache numbers for the given node + model_cache_settings = node_settings.get(node_name, {}).get('model_cache', {}) + vae_cache = int(model_cache_settings.get('vae', 1)) + ckpt_cache = int(model_cache_settings.get('ckpt', 1)) + lora_cache = int(model_cache_settings.get('lora', 1)) + refn_cache = int(model_cache_settings.get('ckpt', 1)) + return vae_cache, ckpt_cache, lora_cache, refn_cache, + +def print_last_helds(id=None): + print("\n" + "-" * 40) # Print an empty line followed by a separator line + if id is not None: + id = str(id) # Convert ID to string + print(f"Node-specific Last Helds (node_id:{int(id)})") + else: + print(f"Global Last Helds:") + for key in ["preview_images", "latent", "output_images", "vae_decode"]: + entries_with_id = last_helds[key] if id is None else [entry for entry in last_helds[key] if id == entry[-1]] + if not entries_with_id: # If no entries with the chosen ID, print None and skip this key + continue + print(f"{key.capitalize()}:") + for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1 + if isinstance(entry[0], bool): # Special handling for boolean types + output = entry[0] + else: + output = len(entry[0]) + if id is None: + print(f" [{i}] Output: {output} (id: {entry[-1]})") + else: + print(f" [{i}] Output: {output}") + print("-" * 40) # Print a separator line + print("\n") # Print an empty line + +# For suppressing print outputs from functions +@contextmanager +def suppress_output(): + original_stdout = sys.stdout + original_stderr = sys.stderr + + sys.stdout = io.StringIO() + sys.stderr = io.StringIO() + + try: + yield + finally: + sys.stdout = original_stdout + sys.stderr = original_stderr + +# Set global preview_method +def set_preview_method(method): + if method == 'auto' or method == 'LatentPreviewMethod.Auto': + args.preview_method = latent_preview.LatentPreviewMethod.Auto + elif method == 'latent2rgb' or method == 'LatentPreviewMethod.Latent2RGB': + args.preview_method = latent_preview.LatentPreviewMethod.Latent2RGB + elif method == 'taesd' or method == 'LatentPreviewMethod.TAESD': + args.preview_method = latent_preview.LatentPreviewMethod.TAESD + else: + args.preview_method = latent_preview.LatentPreviewMethod.NoPreviews + +# Extract global preview_method +def global_preview_method(): + return args.preview_method + +#----------------------------------------------------------------------------------------------------------------------- +# Delete efficiency nodes web extensions from 'ComfyUI\web\extensions'. +# Pull https://github.com/comfyanonymous/ComfyUI/pull/1273 now allows defining web extensions through a dir path in init +import shutil + +# Destination directory +destination_dir = os.path.join(comfy_dir, 'web', 'extensions', 'efficiency-nodes-comfyui') + +# Check if the directory exists and delete it +if os.path.exists(destination_dir): + shutil.rmtree(destination_dir) + +#----------------------------------------------------------------------------------------------------------------------- +# Other +class XY_Capsule: + def pre_define_model(self, model, clip, 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sha256:57070784efece2cf03396e4512d7723cf0384fe5d8f3ba25ad7d329f33f5a6d6 +size 1731313 diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/SimpleEval_Node_Examples.txt b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/SimpleEval_Node_Examples.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ac0307880c8f79a67b96c2b642eaf3cf39c25d2 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/SimpleEval_Node_Examples.txt @@ -0,0 +1,56 @@ +The Evaluate Integers, Floats, and Strings nodes +now employ the SimpleEval library, enabling secure +creation and execution of custom Python expressions. + +(https://github.com/danthedeckie/simpleeval) + +Below is a short list of what is possible. +______________________________________________ + +"EVALUATE INTEGERS/FLOATS" NODE EXPRESSION EXAMPLES: + +Addition: a + b + c +Subtraction: a - b - c +Multiplication: a * b * c +Division: a / b / c +Modulo: a % b % c +Exponentiation: a ** b ** c +Floor Division: a // b // c +Absolute Value: abs(a) + abs(b) + abs(c) +Maximum: max(a, b, c) +Minimum: min(a, b, c) +Sum of Squares: a**2 + b**2 + c**2 +Bitwise And: a & b & c +Bitwise Or: a | b | c +Bitwise Xor: a ^ b ^ c +Left Shift: a << 1 + b << 1 + c << 1 +Right Shift: a >> 1 + b >> 1 + c >> 1 +Greater Than Comparison: a > b > c +Less Than Comparison: a < b < c +Equal To Comparison: a == b == c +Not Equal To Comparison: a != b != c +______________________________________________ + +"EVALUATE STRINGS" NODE EXPRESSION EXAMPLES: + +Concatenate: a + b + c +Format: f'{a} {b} {c}' +Length: len(a) + len(b) + len(c) +Uppercase: a.upper() + b.upper() + c.upper() +Lowercase: a.lower() + b.lower() + c.lower() +Capitalize: a.capitalize() + b.capitalize() + c.capitalize() +Title Case: a.title() + b.title() + c.title() +Strip: a.strip() + b.strip() + c.strip() +Find Substring: a.find('sub') + b.find('sub') + c.find('sub') +Replace Substring: a.replace('old', 'new') + b.replace('old', 'new') + c.replace('old', 'new') +Count Substring: a.count('sub') + b.count('sub') + c.count('sub') +Check Numeric: a.isnumeric() + b.isnumeric() + c.isnumeric() +Check Alphabetic: a.isalpha() + b.isalpha() + c.isalpha() +Check Alphanumeric: a.isalnum() + b.isalnum() + c.isalnum() +Check Start: a.startswith('prefix') + b.startswith('prefix') + c.startswith('prefix') +Check End: a.endswith('suffix') + b.endswith('suffix') + c.endswith('suffix') +Split: a.split(' ') + b.split(' ') + c.split(' ') +Zero Fill: a.zfill(5) + b.zfill(5) + c.zfill(5) +Slice: a[:5] + b[:5] + c[:5] +Reverse: a[::-1] + b[::-1] + c[::-1] +______________________________________________ \ No newline at end of file diff --git a/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/Tiled Upscaler Script.png b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/Tiled Upscaler Script.png new file mode 100644 index 0000000000000000000000000000000000000000..51d62203044f83dc32474e024764fcf03e39d100 --- /dev/null +++ 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0000000000000000000000000000000000000000..6f6a0e958593c047d21335949172cb3b3ee84b00 --- /dev/null +++ b/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/eff_animatescriptWF001.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8d9caa168bda753ab909e8a5da766867371d359d7e168f0e97431bf51ba42b0 +size 13015298 diff --git a/ComfyUI/custom_nodes/example_node.py.example b/ComfyUI/custom_nodes/example_node.py.example new file mode 100644 index 0000000000000000000000000000000000000000..733014f3c7d393a81130b41810e3e1d574dd256b --- /dev/null +++ b/ComfyUI/custom_nodes/example_node.py.example @@ -0,0 +1,102 @@ +class Example: + """ + A example node + + Class methods + ------------- + INPUT_TYPES (dict): + Tell the main program input parameters of nodes. + + Attributes + ---------- + RETURN_TYPES (`tuple`): + The type of each element in the output tulple. + RETURN_NAMES (`tuple`): + Optional: The name of each output in the output tulple. + FUNCTION (`str`): + The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() + OUTPUT_NODE ([`bool`]): + If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. + The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. + Assumed to be False if not present. + CATEGORY (`str`): + The category the node should appear in the UI. + execute(s) -> tuple || None: + The entry point method. The name of this method must be the same as the value of property `FUNCTION`. + For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + """ + Return a dictionary which contains config for all input fields. + Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". + Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. + The type can be a list for selection. + + Returns: `dict`: + - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` + - Value input_fields (`dict`): Contains input fields config: + * Key field_name (`string`): Name of a entry-point method's argument + * Value field_config (`tuple`): + + First value is a string indicate the type of field or a list for selection. + + Secound value is a config for type "INT", "STRING" or "FLOAT". + """ + return { + "required": { + "image": ("IMAGE",), + "int_field": ("INT", { + "default": 0, + "min": 0, #Minimum value + "max": 4096, #Maximum value + "step": 64, #Slider's step + "display": "number" # Cosmetic only: display as "number" or "slider" + }), + "float_field": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.01, + "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + "display": "number"}), + "print_to_screen": (["enable", "disable"],), + "string_field": ("STRING", { + "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node + "default": "Hello World!" + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + #RETURN_NAMES = ("image_output_name",) + + FUNCTION = "test" + + #OUTPUT_NODE = False + + CATEGORY = "Example" + + def test(self, image, string_field, int_field, float_field, print_to_screen): + if print_to_screen == "enable": + print(f"""Your input contains: + string_field aka input text: {string_field} + int_field: {int_field} + float_field: {float_field} + """) + #do some processing on the image, in this example I just invert it + image = 1.0 - image + return (image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Example": Example +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Example": "Example Node" +} diff --git a/ComfyUI/execution.py b/ComfyUI/execution.py new file mode 100644 index 0000000000000000000000000000000000000000..53ba2e0f8a3be86d892a18a6f92453bc2f249ed4 --- /dev/null +++ b/ComfyUI/execution.py @@ -0,0 +1,794 @@ +import os +import sys +import copy +import json +import logging +import threading +import heapq +import traceback +import gc +import inspect + +import torch +import nodes + +import comfy.model_management + +def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}): + valid_inputs = class_def.INPUT_TYPES() + input_data_all = {} + for x in inputs: + input_data = inputs[x] + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id not in outputs: + input_data_all[x] = (None,) + continue + obj = outputs[input_unique_id][output_index] + input_data_all[x] = obj + else: + if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]): + input_data_all[x] = [input_data] + + if "hidden" in valid_inputs: + h = valid_inputs["hidden"] + for x in h: + if h[x] == "PROMPT": + input_data_all[x] = [prompt] + if h[x] == "EXTRA_PNGINFO": + if "extra_pnginfo" in extra_data: + input_data_all[x] = [extra_data['extra_pnginfo']] + if h[x] == "UNIQUE_ID": + input_data_all[x] = [unique_id] + return input_data_all + +def map_node_over_list(obj, input_data_all, func, allow_interrupt=False): + # check if node wants the lists + input_is_list = False + if hasattr(obj, "INPUT_IS_LIST"): + input_is_list = obj.INPUT_IS_LIST + + if len(input_data_all) == 0: + max_len_input = 0 + else: + max_len_input = max([len(x) for x in input_data_all.values()]) + + # get a slice of inputs, repeat last input when list isn't long enough + def slice_dict(d, i): + d_new = dict() + for k,v in d.items(): + d_new[k] = v[i if len(v) > i else -1] + return d_new + + results = [] + if input_is_list: + if allow_interrupt: + nodes.before_node_execution() + results.append(getattr(obj, func)(**input_data_all)) + elif max_len_input == 0: + if allow_interrupt: + nodes.before_node_execution() + results.append(getattr(obj, func)()) + else: + for i in range(max_len_input): + if allow_interrupt: + nodes.before_node_execution() + results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) + return results + +def get_output_data(obj, input_data_all): + + results = [] + uis = [] + return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) + + for r in return_values: + if isinstance(r, dict): + if 'ui' in r: + uis.append(r['ui']) + if 'result' in r: + results.append(r['result']) + else: + results.append(r) + + output = [] + if len(results) > 0: + # check which outputs need concatenating + output_is_list = [False] * len(results[0]) + if hasattr(obj, "OUTPUT_IS_LIST"): + output_is_list = obj.OUTPUT_IS_LIST + + # merge node execution results + for i, is_list in zip(range(len(results[0])), output_is_list): + if is_list: + output.append([x for o in results for x in o[i]]) + else: + output.append([o[i] for o in results]) + + ui = dict() + if len(uis) > 0: + ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} + return output, ui + +def format_value(x): + if x is None: + return None + elif isinstance(x, (int, float, bool, str)): + return x + else: + return str(x) + +def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage): + unique_id = current_item + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if unique_id in outputs: + return (True, None, None) + + for x in inputs: + input_data = inputs[x] + + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id not in outputs: + result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage) + if result[0] is not True: + # Another node failed further upstream + return result + + input_data_all = None + try: + input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data) + if server.client_id is not None: + server.last_node_id = unique_id + server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id) + + obj = object_storage.get((unique_id, class_type), None) + if obj is None: + obj = class_def() + object_storage[(unique_id, class_type)] = obj + + output_data, output_ui = get_output_data(obj, input_data_all) + outputs[unique_id] = output_data + if len(output_ui) > 0: + outputs_ui[unique_id] = output_ui + if server.client_id is not None: + server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) + except comfy.model_management.InterruptProcessingException as iex: + logging.info("Processing interrupted") + + # skip formatting inputs/outputs + error_details = { + "node_id": unique_id, + } + + return (False, error_details, iex) + except Exception as ex: + typ, _, tb = sys.exc_info() + exception_type = full_type_name(typ) + input_data_formatted = {} + if input_data_all is not None: + input_data_formatted = {} + for name, inputs in input_data_all.items(): + input_data_formatted[name] = [format_value(x) for x in inputs] + + output_data_formatted = {} + for node_id, node_outputs in outputs.items(): + output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs] + + logging.error("!!! Exception during processing !!!") + logging.error(traceback.format_exc()) + + error_details = { + "node_id": unique_id, + "exception_message": str(ex), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "current_inputs": input_data_formatted, + "current_outputs": output_data_formatted + } + return (False, error_details, ex) + + executed.add(unique_id) + + return (True, None, None) + +def recursive_will_execute(prompt, outputs, current_item): + unique_id = current_item + inputs = prompt[unique_id]['inputs'] + will_execute = [] + if unique_id in outputs: + return [] + + for x in inputs: + input_data = inputs[x] + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id not in outputs: + will_execute += recursive_will_execute(prompt, outputs, input_unique_id) + + return will_execute + [unique_id] + +def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item): + unique_id = current_item + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + + is_changed_old = '' + is_changed = '' + to_delete = False + if hasattr(class_def, 'IS_CHANGED'): + if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]: + is_changed_old = old_prompt[unique_id]['is_changed'] + if 'is_changed' not in prompt[unique_id]: + input_data_all = get_input_data(inputs, class_def, unique_id, outputs) + if input_data_all is not None: + try: + #is_changed = class_def.IS_CHANGED(**input_data_all) + is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED") + prompt[unique_id]['is_changed'] = is_changed + except: + to_delete = True + else: + is_changed = prompt[unique_id]['is_changed'] + + if unique_id not in outputs: + return True + + if not to_delete: + if is_changed != is_changed_old: + to_delete = True + elif unique_id not in old_prompt: + to_delete = True + elif inputs == old_prompt[unique_id]['inputs']: + for x in inputs: + input_data = inputs[x] + + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id in outputs: + to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id) + else: + to_delete = True + if to_delete: + break + else: + to_delete = True + + if to_delete: + d = outputs.pop(unique_id) + del d + return to_delete + +class PromptExecutor: + def __init__(self, server): + self.outputs = {} + self.object_storage = {} + self.outputs_ui = {} + self.old_prompt = {} + self.server = server + + def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): + node_id = error["node_id"] + class_type = prompt[node_id]["class_type"] + + # First, send back the status to the frontend depending + # on the exception type + if isinstance(ex, comfy.model_management.InterruptProcessingException): + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + } + self.server.send_sync("execution_interrupted", mes, self.server.client_id) + else: + if self.server.client_id is not None: + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + + "exception_message": error["exception_message"], + "exception_type": error["exception_type"], + "traceback": error["traceback"], + "current_inputs": error["current_inputs"], + "current_outputs": error["current_outputs"], + } + self.server.send_sync("execution_error", mes, self.server.client_id) + + # Next, remove the subsequent outputs since they will not be executed + to_delete = [] + for o in self.outputs: + if (o not in current_outputs) and (o not in executed): + to_delete += [o] + if o in self.old_prompt: + d = self.old_prompt.pop(o) + del d + for o in to_delete: + d = self.outputs.pop(o) + del d + + def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): + nodes.interrupt_processing(False) + + if "client_id" in extra_data: + self.server.client_id = extra_data["client_id"] + else: + self.server.client_id = None + + if self.server.client_id is not None: + self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id) + + with torch.inference_mode(): + #delete cached outputs if nodes don't exist for them + to_delete = [] + for o in self.outputs: + if o not in prompt: + to_delete += [o] + for o in to_delete: + d = self.outputs.pop(o) + del d + to_delete = [] + for o in self.object_storage: + if o[0] not in prompt: + to_delete += [o] + else: + p = prompt[o[0]] + if o[1] != p['class_type']: + to_delete += [o] + for o in to_delete: + d = self.object_storage.pop(o) + del d + + for x in prompt: + recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x) + + current_outputs = set(self.outputs.keys()) + for x in list(self.outputs_ui.keys()): + if x not in current_outputs: + d = self.outputs_ui.pop(x) + del d + + comfy.model_management.cleanup_models() + if self.server.client_id is not None: + self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id) + executed = set() + output_node_id = None + to_execute = [] + + for node_id in list(execute_outputs): + to_execute += [(0, node_id)] + + while len(to_execute) > 0: + #always execute the output that depends on the least amount of unexecuted nodes first + to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute))) + output_node_id = to_execute.pop(0)[-1] + + # This call shouldn't raise anything if there's an error deep in + # the actual SD code, instead it will report the node where the + # error was raised + success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage) + if success is not True: + self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex) + break + + for x in executed: + self.old_prompt[x] = copy.deepcopy(prompt[x]) + self.server.last_node_id = None + if comfy.model_management.DISABLE_SMART_MEMORY: + comfy.model_management.unload_all_models() + + + +def validate_inputs(prompt, item, validated): + unique_id = item + if unique_id in validated: + return validated[unique_id] + + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] + + class_inputs = obj_class.INPUT_TYPES() + required_inputs = class_inputs['required'] + + errors = [] + valid = True + + validate_function_inputs = [] + if hasattr(obj_class, "VALIDATE_INPUTS"): + validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args + + for x in required_inputs: + if x not in inputs: + error = { + "type": "required_input_missing", + "message": "Required input is missing", + "details": f"{x}", + "extra_info": { + "input_name": x + } + } + errors.append(error) + continue + + val = inputs[x] + info = required_inputs[x] + type_input = info[0] + if isinstance(val, list): + if len(val) != 2: + error = { + "type": "bad_linked_input", + "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val + } + } + errors.append(error) + continue + + o_id = val[0] + o_class_type = prompt[o_id]['class_type'] + r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES + if r[val[1]] != type_input: + received_type = r[val[1]] + details = f"{x}, {received_type} != {type_input}" + error = { + "type": "return_type_mismatch", + "message": "Return type mismatch between linked nodes", + "details": details, + "extra_info": { + "input_name": x, + "input_config": info, + "received_type": received_type, + "linked_node": val + } + } + errors.append(error) + continue + try: + r = validate_inputs(prompt, o_id, validated) + if r[0] is False: + # `r` will be set in `validated[o_id]` already + valid = False + continue + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_inner_validation", + "message": "Exception when validating inner node", + "details": str(ex), + "extra_info": { + "input_name": x, + "input_config": info, + "exception_message": str(ex), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "linked_node": val + } + }] + validated[o_id] = (False, reasons, o_id) + continue + else: + try: + if type_input == "INT": + val = int(val) + inputs[x] = val + if type_input == "FLOAT": + val = float(val) + inputs[x] = val + if type_input == "STRING": + val = str(val) + inputs[x] = val + except Exception as ex: + error = { + "type": "invalid_input_type", + "message": f"Failed to convert an input value to a {type_input} value", + "details": f"{x}, {val}, {ex}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + "exception_message": str(ex) + } + } + errors.append(error) + continue + + if len(info) > 1: + if "min" in info[1] and val < info[1]["min"]: + error = { + "type": "value_smaller_than_min", + "message": "Value {} smaller than min of {}".format(val, info[1]["min"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + if "max" in info[1] and val > info[1]["max"]: + error = { + "type": "value_bigger_than_max", + "message": "Value {} bigger than max of {}".format(val, info[1]["max"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + + if x not in validate_function_inputs: + if isinstance(type_input, list): + if val not in type_input: + input_config = info + list_info = "" + + # Don't send back gigantic lists like if they're lots of + # scanned model filepaths + if len(type_input) > 20: + list_info = f"(list of length {len(type_input)})" + input_config = None + else: + list_info = str(type_input) + + error = { + "type": "value_not_in_list", + "message": "Value not in list", + "details": f"{x}: '{val}' not in {list_info}", + "extra_info": { + "input_name": x, + "input_config": input_config, + "received_value": val, + } + } + errors.append(error) + continue + + if len(validate_function_inputs) > 0: + input_data_all = get_input_data(inputs, obj_class, unique_id) + input_filtered = {} + for x in input_data_all: + if x in validate_function_inputs: + input_filtered[x] = input_data_all[x] + + #ret = obj_class.VALIDATE_INPUTS(**input_filtered) + ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") + for x in input_filtered: + for i, r in enumerate(ret): + if r is not True: + details = f"{x}" + if r is not False: + details += f" - {str(r)}" + + error = { + "type": "custom_validation_failed", + "message": "Custom validation failed for node", + "details": details, + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + + if len(errors) > 0 or valid is not True: + ret = (False, errors, unique_id) + else: + ret = (True, [], unique_id) + + validated[unique_id] = ret + return ret + +def full_type_name(klass): + module = klass.__module__ + if module == 'builtins': + return klass.__qualname__ + return module + '.' + klass.__qualname__ + +def validate_prompt(prompt): + outputs = set() + for x in prompt: + class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] + if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True: + outputs.add(x) + + if len(outputs) == 0: + error = { + "type": "prompt_no_outputs", + "message": "Prompt has no outputs", + "details": "", + "extra_info": {} + } + return (False, error, [], []) + + good_outputs = set() + errors = [] + node_errors = {} + validated = {} + for o in outputs: + valid = False + reasons = [] + try: + m = validate_inputs(prompt, o, validated) + valid = m[0] + reasons = m[1] + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_validation", + "message": "Exception when validating node", + "details": str(ex), + "extra_info": { + "exception_type": exception_type, + "traceback": traceback.format_tb(tb) + } + }] + validated[o] = (False, reasons, o) + + if valid is True: + good_outputs.add(o) + else: + logging.error(f"Failed to validate prompt for output {o}:") + if len(reasons) > 0: + logging.error("* (prompt):") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + errors += [(o, reasons)] + for node_id, result in validated.items(): + valid = result[0] + reasons = result[1] + # If a node upstream has errors, the nodes downstream will also + # be reported as invalid, but there will be no errors attached. + # So don't return those nodes as having errors in the response. + if valid is not True and len(reasons) > 0: + if node_id not in node_errors: + class_type = prompt[node_id]['class_type'] + node_errors[node_id] = { + "errors": reasons, + "dependent_outputs": [], + "class_type": class_type + } + logging.error(f"* {class_type} {node_id}:") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + node_errors[node_id]["dependent_outputs"].append(o) + logging.error("Output will be ignored") + + if len(good_outputs) == 0: + errors_list = [] + for o, errors in errors: + for error in errors: + errors_list.append(f"{error['message']}: {error['details']}") + errors_list = "\n".join(errors_list) + + error = { + "type": "prompt_outputs_failed_validation", + "message": "Prompt outputs failed validation", + "details": errors_list, + "extra_info": {} + } + + return (False, error, list(good_outputs), node_errors) + + return (True, None, list(good_outputs), node_errors) + +MAXIMUM_HISTORY_SIZE = 10000 + +class PromptQueue: + def __init__(self, server): + self.server = server + self.mutex = threading.RLock() + self.not_empty = threading.Condition(self.mutex) + self.task_counter = 0 + self.queue = [] + self.currently_running = {} + self.history = {} + server.prompt_queue = self + + def put(self, item): + with self.mutex: + heapq.heappush(self.queue, item) + self.server.queue_updated() + self.not_empty.notify() + + def get(self, timeout=None): + with self.not_empty: + while len(self.queue) == 0: + self.not_empty.wait(timeout=timeout) + if timeout is not None and len(self.queue) == 0: + return None + item = heapq.heappop(self.queue) + i = self.task_counter + self.currently_running[i] = copy.deepcopy(item) + self.task_counter += 1 + self.server.queue_updated() + return (item, i) + + def task_done(self, item_id, outputs): + with self.mutex: + prompt = self.currently_running.pop(item_id) + if len(self.history) > MAXIMUM_HISTORY_SIZE: + self.history.pop(next(iter(self.history))) + self.history[prompt[1]] = { "prompt": prompt, "outputs": {} } + for o in outputs: + self.history[prompt[1]]["outputs"][o] = outputs[o] + self.server.queue_updated() + + def get_current_queue(self): + with self.mutex: + out = [] + for x in self.currently_running.values(): + out += [x] + return (out, copy.deepcopy(self.queue)) + + def get_tasks_remaining(self): + with self.mutex: + return len(self.queue) + len(self.currently_running) + + def wipe_queue(self): + with self.mutex: + self.queue = [] + self.server.queue_updated() + + def delete_queue_item(self, function): + with self.mutex: + for x in range(len(self.queue)): + if function(self.queue[x]): + if len(self.queue) == 1: + self.wipe_queue() + else: + self.queue.pop(x) + heapq.heapify(self.queue) + self.server.queue_updated() + return True + return False + + def get_history(self, prompt_id=None, max_items=None, offset=-1): + with self.mutex: + if prompt_id is None: + out = {} + i = 0 + if offset < 0 and max_items is not None: + offset = len(self.history) - max_items + for k in self.history: + if i >= offset: + out[k] = self.history[k] + if max_items is not None and len(out) >= max_items: + break + i += 1 + return out + elif prompt_id in self.history: + return {prompt_id: copy.deepcopy(self.history[prompt_id])} + else: + return {} + + def wipe_history(self): + with self.mutex: + self.history = {} + + def delete_history_item(self, id_to_delete): + with self.mutex: + self.history.pop(id_to_delete, None) diff --git a/ComfyUI/extra_model_paths.yaml.example b/ComfyUI/extra_model_paths.yaml.example new file mode 100644 index 0000000000000000000000000000000000000000..846d04dbeb43b00bdb08ccb37680eda41beb343e --- /dev/null +++ b/ComfyUI/extra_model_paths.yaml.example @@ -0,0 +1,42 @@ +#Rename this to extra_model_paths.yaml and ComfyUI will load it + + +#config for a1111 ui +#all you have to do is change the base_path to where yours is installed +a111: + base_path: path/to/stable-diffusion-webui/ + + checkpoints: models/Stable-diffusion + configs: models/Stable-diffusion + vae: models/VAE + loras: | + models/Lora + models/LyCORIS + upscale_models: | + models/ESRGAN + models/RealESRGAN + models/SwinIR + embeddings: embeddings + hypernetworks: models/hypernetworks + controlnet: models/ControlNet + +#config for comfyui +#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc. + +#comfyui: +# base_path: path/to/comfyui/ +# checkpoints: models/checkpoints/ +# clip: models/clip/ +# clip_vision: models/clip_vision/ +# configs: models/configs/ +# controlnet: models/controlnet/ +# embeddings: models/embeddings/ +# loras: models/loras/ +# upscale_models: models/upscale_models/ +# vae: models/vae/ + +#other_ui: +# base_path: path/to/ui +# checkpoints: models/checkpoints +# gligen: models/gligen +# custom_nodes: path/custom_nodes diff --git a/ComfyUI/folder_paths.py b/ComfyUI/folder_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..a8726d8dd041728ead949f8ec2d400571fa5070c --- /dev/null +++ b/ComfyUI/folder_paths.py @@ -0,0 +1,247 @@ +import os +import time + +supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) + +folder_names_and_paths = {} + +base_path = os.path.dirname(os.path.realpath(__file__)) +models_dir = os.path.join(base_path, "models") +folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) +folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) + +folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) +folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) +folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions) +folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions) +folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) +folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) +folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) +folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) +folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) + +folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) +folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) + +folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) + +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) + +folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) + +folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) + +output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") +temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp") +input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") + +filename_list_cache = {} + +if not os.path.exists(input_directory): + try: + os.makedirs(input_directory) + except: + print("Failed to create input directory") + +def set_output_directory(output_dir): + global output_directory + output_directory = output_dir + +def set_temp_directory(temp_dir): + global temp_directory + temp_directory = temp_dir + +def set_input_directory(input_dir): + global input_directory + input_directory = input_dir + +def get_output_directory(): + global output_directory + return output_directory + +def get_temp_directory(): + global temp_directory + return temp_directory + +def get_input_directory(): + global input_directory + return input_directory + + +#NOTE: used in http server so don't put folders that should not be accessed remotely +def get_directory_by_type(type_name): + if type_name == "output": + return get_output_directory() + if type_name == "temp": + return get_temp_directory() + if type_name == "input": + return get_input_directory() + return None + + +# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format +# otherwise use default_path as base_dir +def annotated_filepath(name): + if name.endswith("[output]"): + base_dir = get_output_directory() + name = name[:-9] + elif name.endswith("[input]"): + base_dir = get_input_directory() + name = name[:-8] + elif name.endswith("[temp]"): + base_dir = get_temp_directory() + name = name[:-7] + else: + return name, None + + return name, base_dir + + +def get_annotated_filepath(name, default_dir=None): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + if default_dir is not None: + base_dir = default_dir + else: + base_dir = get_input_directory() # fallback path + + return os.path.join(base_dir, name) + + +def exists_annotated_filepath(name): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + base_dir = get_input_directory() # fallback path + + filepath = os.path.join(base_dir, name) + return os.path.exists(filepath) + + +def add_model_folder_path(folder_name, full_folder_path): + global folder_names_and_paths + if folder_name in folder_names_and_paths: + folder_names_and_paths[folder_name][0].append(full_folder_path) + else: + folder_names_and_paths[folder_name] = ([full_folder_path], set()) + +def get_folder_paths(folder_name): + return folder_names_and_paths[folder_name][0][:] + +def recursive_search(directory, excluded_dir_names=None): + if not os.path.isdir(directory): + return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + + result = [] + dirs = {directory: os.path.getmtime(directory)} + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + for d in subdirs: + path = os.path.join(dirpath, d) + dirs[path] = os.path.getmtime(path) + return result, dirs + +def filter_files_extensions(files, extensions): + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) + + + +def get_full_path(folder_name, filename): + global folder_names_and_paths + if folder_name not in folder_names_and_paths: + return None + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + + return None + +def get_filename_list_(folder_name): + global folder_names_and_paths + output_list = set() + folders = folder_names_and_paths[folder_name] + output_folders = {} + for x in folders[0]: + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) + output_list.update(filter_files_extensions(files, folders[1])) + output_folders = {**output_folders, **folders_all} + + return (sorted(list(output_list)), output_folders, time.perf_counter()) + +def cached_filename_list_(folder_name): + global filename_list_cache + global folder_names_and_paths + if folder_name not in filename_list_cache: + return None + out = filename_list_cache[folder_name] + + for x in out[1]: + time_modified = out[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + folders = folder_names_and_paths[folder_name] + for x in folders[0]: + if os.path.isdir(x): + if x not in out[1]: + return None + + return out + +def get_filename_list(folder_name): + out = cached_filename_list_(folder_name) + if out is None: + out = get_filename_list_(folder_name) + global filename_list_cache + filename_list_cache[folder_name] = out + return list(out[0]) + +def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0): + def map_filename(filename): + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return (digits, prefix) + + def compute_vars(input, image_width, image_height): + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + return input + + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + print(err) + raise Exception(err) + + try: + counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + return full_output_folder, filename, counter, subfolder, filename_prefix diff --git a/ComfyUI/input/Screenshot 2023-12-30 182919.jpg b/ComfyUI/input/Screenshot 2023-12-30 182919.jpg new file mode 100644 index 0000000000000000000000000000000000000000..978ea3ed3fff6e18cfce70bbd853f0db339399e6 Binary files /dev/null and b/ComfyUI/input/Screenshot 2023-12-30 182919.jpg differ diff --git a/ComfyUI/input/example.png b/ComfyUI/input/example.png new file mode 100644 index 0000000000000000000000000000000000000000..7b7f3c9cbbe6d8750c4a9eaf65d6ae4d2f108f79 Binary files /dev/null and b/ComfyUI/input/example.png differ diff --git a/ComfyUI/input/gsro3x7zylwa1_resized.png b/ComfyUI/input/gsro3x7zylwa1_resized.png new file mode 100644 index 0000000000000000000000000000000000000000..c3e6713ec3e20eca4c8a586363be32ddfe050ac0 --- /dev/null +++ b/ComfyUI/input/gsro3x7zylwa1_resized.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78b24a0276389ea7da2217757579716fca8fc7ba3f07afe8840b22cbce2f2c33 +size 1099009 diff --git a/ComfyUI/latent_preview.py b/ComfyUI/latent_preview.py new file mode 100644 index 0000000000000000000000000000000000000000..61754751efee96fb25c18b19ff2032a875ca221a --- /dev/null +++ b/ComfyUI/latent_preview.py @@ -0,0 +1,97 @@ +import torch +from PIL import Image +import struct +import numpy as np +from comfy.cli_args import args, LatentPreviewMethod +from comfy.taesd.taesd import TAESD +import folder_paths +import comfy.utils + +MAX_PREVIEW_RESOLUTION = 512 + +class LatentPreviewer: + def decode_latent_to_preview(self, x0): + pass + + def decode_latent_to_preview_image(self, preview_format, x0): + preview_image = self.decode_latent_to_preview(x0) + return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) + +class TAESDPreviewerImpl(LatentPreviewer): + def __init__(self, taesd): + self.taesd = taesd + + def decode_latent_to_preview(self, x0): + x_sample = self.taesd.decode(x0[:1])[0].detach() + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + + preview_image = Image.fromarray(x_sample) + return preview_image + + +class Latent2RGBPreviewer(LatentPreviewer): + def __init__(self, latent_rgb_factors): + self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") + + def decode_latent_to_preview(self, x0): + latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors + + latents_ubyte = (((latent_image + 1) / 2) + .clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + .byte()).cpu() + + return Image.fromarray(latents_ubyte.numpy()) + + +def get_previewer(device, latent_format): + previewer = None + method = args.preview_method + if method != LatentPreviewMethod.NoPreviews: + # TODO previewer methods + taesd_decoder_path = None + if latent_format.taesd_decoder_name is not None: + taesd_decoder_path = next( + (fn for fn in folder_paths.get_filename_list("vae_approx") + if fn.startswith(latent_format.taesd_decoder_name)), + "" + ) + taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path) + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + if taesd_decoder_path: + method = LatentPreviewMethod.TAESD + + if method == LatentPreviewMethod.TAESD: + if taesd_decoder_path: + taesd = TAESD(None, taesd_decoder_path).to(device) + previewer = TAESDPreviewerImpl(taesd) + else: + print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) + + if previewer is None: + if latent_format.latent_rgb_factors is not None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) + return previewer + +def prepare_callback(model, steps, x0_output_dict=None): + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = get_previewer(model.load_device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + if x0_output_dict is not None: + x0_output_dict["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + return callback + diff --git a/ComfyUI/main.py b/ComfyUI/main.py new file mode 100644 index 0000000000000000000000000000000000000000..f6aeceed2afb1890ca4af6f5ffda8fc4e63d57d1 --- /dev/null +++ b/ComfyUI/main.py @@ -0,0 +1,228 @@ +import comfy.options +comfy.options.enable_args_parsing() + +import os +import importlib.util +import folder_paths +import time + +def execute_prestartup_script(): + def execute_script(script_path): + module_name = os.path.splitext(script_path)[0] + try: + spec = importlib.util.spec_from_file_location(module_name, script_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return True + except Exception as e: + print(f"Failed to execute startup-script: {script_path} / {e}") + return False + + node_paths = folder_paths.get_folder_paths("custom_nodes") + for custom_node_path in node_paths: + possible_modules = os.listdir(custom_node_path) + node_prestartup_times = [] + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__": + continue + + script_path = os.path.join(module_path, "prestartup_script.py") + if os.path.exists(script_path): + time_before = time.perf_counter() + success = execute_script(script_path) + node_prestartup_times.append((time.perf_counter() - time_before, module_path, success)) + if len(node_prestartup_times) > 0: + print("\nPrestartup times for custom nodes:") + for n in sorted(node_prestartup_times): + if n[2]: + import_message = "" + else: + import_message = " (PRESTARTUP FAILED)" + print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) + print() + +execute_prestartup_script() + + +# Main code +import asyncio +import itertools +import shutil +import threading +import gc + +from comfy.cli_args import args + +if os.name == "nt": + import logging + logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + +if __name__ == "__main__": + if args.cuda_device is not None: + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) + print("Set cuda device to:", args.cuda_device) + + if args.deterministic: + if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8" + + import cuda_malloc + +import comfy.utils +import yaml + +import execution +import server +from server import BinaryEventTypes +from nodes import init_custom_nodes +import comfy.model_management + +def cuda_malloc_warning(): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + cuda_malloc_warning = False + if "cudaMallocAsync" in device_name: + for b in cuda_malloc.blacklist: + if b in device_name: + cuda_malloc_warning = True + if cuda_malloc_warning: + print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") + +def prompt_worker(q, server): + e = execution.PromptExecutor(server) + last_gc_collect = 0 + need_gc = False + gc_collect_interval = 10.0 + + while True: + timeout = None + if need_gc: + timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0) + + queue_item = q.get(timeout=timeout) + if queue_item is not None: + item, item_id = queue_item + execution_start_time = time.perf_counter() + prompt_id = item[1] + e.execute(item[2], prompt_id, item[3], item[4]) + need_gc = True + q.task_done(item_id, e.outputs_ui) + if server.client_id is not None: + server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, server.client_id) + + current_time = time.perf_counter() + execution_time = current_time - execution_start_time + print("Prompt executed in {:.2f} seconds".format(execution_time)) + + if need_gc: + current_time = time.perf_counter() + if (current_time - last_gc_collect) > gc_collect_interval: + gc.collect() + comfy.model_management.soft_empty_cache() + last_gc_collect = current_time + need_gc = False + +async def run(server, address='', port=8188, verbose=True, call_on_start=None): + await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop()) + + +def hijack_progress(server): + def hook(value, total, preview_image): + comfy.model_management.throw_exception_if_processing_interrupted() + server.send_sync("progress", {"value": value, "max": total}, server.client_id) + if preview_image is not None: + server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server.client_id) + comfy.utils.set_progress_bar_global_hook(hook) + + +def cleanup_temp(): + temp_dir = folder_paths.get_temp_directory() + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir, ignore_errors=True) + + +def load_extra_path_config(yaml_path): + with open(yaml_path, 'r') as stream: + config = yaml.safe_load(stream) + for c in config: + conf = config[c] + if conf is None: + continue + base_path = None + if "base_path" in conf: + base_path = conf.pop("base_path") + for x in conf: + for y in conf[x].split("\n"): + if len(y) == 0: + continue + full_path = y + if base_path is not None: + full_path = os.path.join(base_path, full_path) + print("Adding extra search path", x, full_path) + folder_paths.add_model_folder_path(x, full_path) + + +if __name__ == "__main__": + if args.temp_directory: + temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") + print(f"Setting temp directory to: {temp_dir}") + folder_paths.set_temp_directory(temp_dir) + cleanup_temp() + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + server = server.PromptServer(loop) + q = execution.PromptQueue(server) + + extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml") + if os.path.isfile(extra_model_paths_config_path): + load_extra_path_config(extra_model_paths_config_path) + + if args.extra_model_paths_config: + for config_path in itertools.chain(*args.extra_model_paths_config): + load_extra_path_config(config_path) + + init_custom_nodes() + + cuda_malloc_warning() + + server.add_routes() + hijack_progress(server) + + threading.Thread(target=prompt_worker, daemon=True, args=(q, server,)).start() + + if args.output_directory: + output_dir = os.path.abspath(args.output_directory) + print(f"Setting output directory to: {output_dir}") + folder_paths.set_output_directory(output_dir) + + #These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes + folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints")) + folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip")) + folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae")) + + if args.input_directory: + input_dir = os.path.abspath(args.input_directory) + print(f"Setting input directory to: {input_dir}") + folder_paths.set_input_directory(input_dir) + + if args.quick_test_for_ci: + exit(0) + + call_on_start = None + if args.auto_launch: + def startup_server(address, port): + import webbrowser + if os.name == 'nt' and address == '0.0.0.0': + address = '127.0.0.1' + webbrowser.open(f"http://{address}:{port}") + call_on_start = startup_server + + try: + loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)) + except KeyboardInterrupt: + print("\nStopped server") + + cleanup_temp() diff --git a/ComfyUI/models/checkpoints/SDv2-768.ckpt b/ComfyUI/models/checkpoints/SDv2-768.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..b07ed31d364d0f4d9e223ce22115f0f61a682109 --- /dev/null +++ b/ComfyUI/models/checkpoints/SDv2-768.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43c90409bd4493ea9f4734a166fc6180913f97cef4f69a2b7883edf700779693 +size 2607544561 diff --git a/ComfyUI/models/checkpoints/darkSushiMixMix_225D.safetensors b/ComfyUI/models/checkpoints/darkSushiMixMix_225D.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..acec6293eca607d0f39da20ef363a7a3b84542b2 --- /dev/null +++ b/ComfyUI/models/checkpoints/darkSushiMixMix_225D.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cca17b08da3254aa2f0242a84ab8641c89d1bc71cc1046fc79f8e906581a97ba +size 2132627182 diff --git a/ComfyUI/models/checkpoints/put_checkpoints_here b/ComfyUI/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/checkpoints/sd_xl_base_1.0.safetensors b/ComfyUI/models/checkpoints/sd_xl_base_1.0.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..a4e26e69370f72c43e5b5f53879919c86bcd6822 --- /dev/null +++ b/ComfyUI/models/checkpoints/sd_xl_base_1.0.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b +size 6938078334 diff --git a/ComfyUI/models/checkpoints/sd_xl_refiner_1.0.safetensors b/ComfyUI/models/checkpoints/sd_xl_refiner_1.0.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..73f461bed1c6ccf74548ebd9da0e62122f7a25bd --- /dev/null +++ b/ComfyUI/models/checkpoints/sd_xl_refiner_1.0.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f +size 6075981930 diff --git a/ComfyUI/models/checkpoints/svd_xt.safetensors b/ComfyUI/models/checkpoints/svd_xt.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..834e6b8c6f74a1a5e530eda031edd5c43993b4e2 --- /dev/null +++ b/ComfyUI/models/checkpoints/svd_xt.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2652c23d64a1da5f14d55011b9b6dce55f2e72e395719f1cd1f8a079b00a451 +size 9559625980 diff --git a/ComfyUI/models/checkpoints/v1-5-pruned-emaonly.ckpt b/ComfyUI/models/checkpoints/v1-5-pruned-emaonly.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..bdc7ea15824c46103a08ba376b47eebf09f36895 --- /dev/null +++ b/ComfyUI/models/checkpoints/v1-5-pruned-emaonly.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c86efd062ea772972dd068c3870773f1fd9a37ba9a288ee79ec848448ce7785 +size 2132791380 diff --git a/ComfyUI/models/clip/put_clip_or_text_encoder_models_here b/ComfyUI/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/clip_vision/put_clip_vision_models_here b/ComfyUI/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/configs/anything_v3.yaml b/ComfyUI/models/configs/anything_v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/ComfyUI/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/ComfyUI/models/configs/v1-inference.yaml b/ComfyUI/models/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/ComfyUI/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/ComfyUI/models/configs/v1-inference_clip_skip_2.yaml b/ComfyUI/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/ComfyUI/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml b/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7eca31c7b5e571c2b1348e94ed9d69978ebd2d52 --- /dev/null +++ b/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/ComfyUI/models/configs/v1-inference_fp16.yaml b/ComfyUI/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..147f42b17b835cc839338156f99e8f971df5c1aa --- /dev/null +++ b/ComfyUI/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/ComfyUI/models/configs/v1-inpainting-inference.yaml b/ComfyUI/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45f3f82d461cd8c6109f26ec3b1da75366eda0b0 --- /dev/null +++ b/ComfyUI/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/ComfyUI/models/configs/v2-inference-v.yaml b/ComfyUI/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ec8dfbfefe94ae8522c93017668fea78d580acf --- /dev/null +++ b/ComfyUI/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/ComfyUI/models/configs/v2-inference-v_fp32.yaml b/ComfyUI/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5c9b9cb29ca162ade44a7c922f59e75d7d57813 --- /dev/null +++ b/ComfyUI/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/ComfyUI/models/configs/v2-inference.yaml b/ComfyUI/models/configs/v2-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c --- /dev/null +++ b/ComfyUI/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/ComfyUI/models/configs/v2-inference_fp32.yaml b/ComfyUI/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d03231f3f2c2e8ef8fbe0d781e5f3d65409ef3a --- /dev/null +++ b/ComfyUI/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/ComfyUI/models/configs/v2-inpainting-inference.yaml b/ComfyUI/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32a9471d71b828c51bcbbabfe34c5f6c8282c803 --- /dev/null +++ b/ComfyUI/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/ComfyUI/models/controlnet/put_controlnets_and_t2i_here b/ComfyUI/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/diffusers/put_diffusers_models_here b/ComfyUI/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/embeddings/bad-artist-anime.pt b/ComfyUI/models/embeddings/bad-artist-anime.pt new file mode 100644 index 0000000000000000000000000000000000000000..41dc1ef4174928ea838448c87d06f5926dfa4ef2 --- /dev/null +++ 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b/ComfyUI/models/vae/orangemix.vae.pt new file mode 100644 index 0000000000000000000000000000000000000000..0f7b497b5546c1ea8f37609be6efee4d2736105d --- /dev/null +++ b/ComfyUI/models/vae/orangemix.vae.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f921fb3f29891d2a77a6571e56b8b5052420d2884129517a333c60b1b4816cdf +size 822802803 diff --git a/ComfyUI/models/vae/put_vae_here b/ComfyUI/models/vae/put_vae_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/ComfyUI/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ComfyUI/nodes.py b/ComfyUI/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..8e3ec947cd4e422075d8c16406c0e1b73da88380 --- /dev/null +++ b/ComfyUI/nodes.py @@ -0,0 +1,1888 @@ +import torch + +import os +import sys +import json +import hashlib +import traceback +import math +import time +import random + +from PIL import Image, ImageOps, ImageSequence +from PIL.PngImagePlugin import PngInfo +import numpy as np +import safetensors.torch + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + + +import comfy.diffusers_load +import comfy.samplers +import comfy.sample +import comfy.sd +import comfy.utils +import comfy.controlnet + +import comfy.clip_vision + +import comfy.model_management +from comfy.cli_args import args + +import importlib + +import folder_paths +import latent_preview + +def before_node_execution(): + comfy.model_management.throw_exception_if_processing_interrupted() + +def interrupt_processing(value=True): + comfy.model_management.interrupt_current_processing(value) + +MAX_RESOLUTION=8192 + +class CLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + +class ConditioningCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "combine" + + CATEGORY = "conditioning" + + def combine(self, conditioning_1, conditioning_2): + return (conditioning_1 + conditioning_2, ) + +class ConditioningAverage : + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), + "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "addWeighted" + + CATEGORY = "conditioning" + + def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): + out = [] + + if len(conditioning_from) > 1: + print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + pooled_output_from = conditioning_from[0][1].get("pooled_output", None) + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) + t0 = cond_from[:,:t1.shape[1]] + if t0.shape[1] < t1.shape[1]: + t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) + + tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) + t_to = conditioning_to[i][1].copy() + if pooled_output_from is not None and pooled_output_to is not None: + t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) + elif pooled_output_from is not None: + t_to["pooled_output"] = pooled_output_from + + n = [tw, t_to] + out.append(n) + return (out, ) + +class ConditioningConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning_to": ("CONDITIONING",), + "conditioning_from": ("CONDITIONING",), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from): + out = [] + + if len(conditioning_from) > 1: + print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from),1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out, ) + +class ConditioningSetArea: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['area'] = (height // 8, width // 8, y // 8, x // 8) + n[1]['strength'] = strength + n[1]['set_area_to_bounds'] = False + c.append(n) + return (c, ) + +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['area'] = ("percentage", height, width, y, x) + n[1]['strength'] = strength + n[1]['set_area_to_bounds'] = False + c.append(n) + return (c, ) + +class ConditioningSetMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, mask, set_cond_area, strength): + c = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + for t in conditioning: + n = [t[0], t[1].copy()] + _, h, w = mask.shape + n[1]['mask'] = mask + n[1]['set_area_to_bounds'] = set_area_to_bounds + n[1]['mask_strength'] = strength + c.append(n) + return (c, ) + +class ConditioningZeroOut: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "zero_out" + + CATEGORY = "advanced/conditioning" + + def zero_out(self, conditioning): + c = [] + for t in conditioning: + d = t[1].copy() + if "pooled_output" in d: + d["pooled_output"] = torch.zeros_like(d["pooled_output"]) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c, ) + +class ConditioningSetTimestepRange: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "set_range" + + CATEGORY = "advanced/conditioning" + + def set_range(self, conditioning, start, end): + c = [] + for t in conditioning: + d = t[1].copy() + d['start_percent'] = start + d['end_percent'] = end + n = [t[0], d] + c.append(n) + return (c, ) + +class VAEDecode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "latent" + + def decode(self, vae, samples): + return (vae.decode(samples["samples"]), ) + +class VAEDecodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "_for_testing" + + def decode(self, vae, samples, tile_size): + return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), ) + +class VAEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent" + + @staticmethod + def vae_encode_crop_pixels(pixels): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + return pixels + + def encode(self, vae, pixels): + pixels = self.vae_encode_crop_pixels(pixels) + t = vae.encode(pixels[:,:,:,:3]) + return ({"samples":t}, ) + +class VAEEncodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing" + + def encode(self, vae, pixels, tile_size): + pixels = VAEEncode.vae_encode_crop_pixels(pixels) + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) + return ({"samples":t}, ) + +class VAEEncodeForInpaint: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/inpaint" + + def encode(self, vae, pixels, mask, grow_mask_by=6): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + pixels = pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + #grow mask by a few pixels to keep things seamless in latent space + if grow_mask_by == 0: + mask_erosion = mask + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + t = vae.encode(pixels) + + return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) + +class SaveLatent: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "save" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for latent sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = None + if not args.disable_metadata: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.latent" + + results = list() + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + output = {} + output["latent_tensor"] = samples["samples"] + output["latent_format_version_0"] = torch.tensor([]) + + comfy.utils.save_torch_file(output, file, metadata=metadata) + return { "ui": { "latents": results } } + + +class LoadLatent: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] + return {"required": {"latent": [sorted(files), ]}, } + + CATEGORY = "_for_testing" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "load" + + def load(self, latent): + latent_path = folder_paths.get_annotated_filepath(latent) + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + return (samples, ) + + @classmethod + def IS_CHANGED(s, latent): + image_path = folder_paths.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent): + if not folder_paths.exists_annotated_filepath(latent): + return "Invalid latent file: {}".format(latent) + return True + + +class CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders" + + def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + config_path = folder_paths.get_full_path("configs", config_name) + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + +class CheckpointLoaderSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out[:3] + +class DiffusersLoader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + for root, subdir, files in os.walk(search_path, followlinks=True): + if "model_index.json" in files: + paths.append(os.path.relpath(root, start=search_path)) + + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders/deprecated" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + path = os.path.join(search_path, model_path) + if os.path.exists(path): + model_path = path + break + + return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + +class unCLIPCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out + +class CLIPSetLastLayer: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP", ), + "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_last_layer" + + CATEGORY = "conditioning" + + def set_last_layer(self, clip, stop_at_clip_layer): + clip = clip.clone() + clip.clip_layer(stop_at_clip_layer) + return (clip,) + +class LoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP", ), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "load_lora" + + CATEGORY = "loaders" + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + +class VAELoader: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + return sd + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (s.vae_list(), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + CATEGORY = "loaders" + + #TODO: scale factor? + def load_vae(self, vae_name): + if vae_name in ["taesd", "taesdxl"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + return (vae,) + +class ControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name): + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) + return (controlnet,) + +class DiffControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, model, control_net_name): + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) + return (controlnet,) + + +class ControlNetApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning" + + def apply_controlnet(self, conditioning, control_net, image, strength): + if strength == 0: + return (conditioning, ) + + c = [] + control_hint = image.movedim(-1,1) + for t in conditioning: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = True + c.append(n) + return (c, ) + + +class ControlNetApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning" + + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): + if strength == 0: + return (positive, negative) + + control_hint = image.movedim(-1,1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1]) + + +class UNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name): + unet_path = folder_paths.get_full_path("unet", unet_name) + model = comfy.sd.load_unet(unet_path) + return (model,) + +class CLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name): + clip_path = folder_paths.get_full_path("clip", clip_name) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class DualCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2): + clip_path1 = folder_paths.get_full_path("clip", clip_name1) + clip_path2 = folder_paths.get_full_path("clip", clip_name2) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class CLIPVisionLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), + }} + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "load_clip" + + CATEGORY = "loaders" + + def load_clip(self, clip_name): + clip_path = folder_paths.get_full_path("clip_vision", clip_name) + clip_vision = comfy.clip_vision.load(clip_path) + return (clip_vision,) + +class CLIPVisionEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "image": ("IMAGE",) + }} + RETURN_TYPES = ("CLIP_VISION_OUTPUT",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip_vision, image): + output = clip_vision.encode_image(image) + return (output,) + +class StyleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} + + RETURN_TYPES = ("STYLE_MODEL",) + FUNCTION = "load_style_model" + + CATEGORY = "loaders" + + def load_style_model(self, style_model_name): + style_model_path = folder_paths.get_full_path("style_models", style_model_name) + style_model = comfy.sd.load_style_model(style_model_path) + return (style_model,) + + +class StyleModelApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + + CATEGORY = "conditioning/style_model" + + def apply_stylemodel(self, clip_vision_output, style_model, conditioning): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class unCLIPConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_adm" + + CATEGORY = "conditioning" + + def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): + if strength == 0: + return (conditioning, ) + + c = [] + for t in conditioning: + o = t[1].copy() + x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} + if "unclip_conditioning" in o: + o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] + else: + o["unclip_conditioning"] = [x] + n = [t[0], o] + c.append(n) + return (c, ) + +class GLIGENLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} + + RETURN_TYPES = ("GLIGEN",) + FUNCTION = "load_gligen" + + CATEGORY = "loaders" + + def load_gligen(self, gligen_name): + gligen_path = folder_paths.get_full_path("gligen", gligen_name) + gligen = comfy.sd.load_gligen(gligen_path) + return (gligen,) + +class GLIGENTextBoxApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "text": ("STRING", {"multiline": True}), + "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning/gligen" + + def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): + c = [] + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) + for t in conditioning_to: + n = [t[0], t[1].copy()] + position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + + n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) + c.append(n) + return (c, ) + +class EmptyLatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent" + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + + +class LatentFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "frombatch" + + CATEGORY = "latent/batch" + + def frombatch(self, samples, batch_index, length): + s = samples.copy() + s_in = samples["samples"] + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s["samples"] = s_in[batch_index:batch_index + length].clone() + if "noise_mask" in samples: + masks = samples["noise_mask"] + if masks.shape[0] == 1: + s["noise_mask"] = masks.clone() + else: + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = masks[batch_index:batch_index + length].clone() + if "batch_index" not in s: + s["batch_index"] = [x for x in range(batch_index, batch_index+length)] + else: + s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] + return (s,) + +class RepeatLatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "repeat" + + CATEGORY = "latent/batch" + + def repeat(self, samples, amount): + s = samples.copy() + s_in = samples["samples"] + + s["samples"] = s_in.repeat((amount, 1,1,1)) + if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: + masks = samples["noise_mask"] + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + if "batch_index" in s: + offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 + s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] + return (s,) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3])) + else: + width = max(64, width) + height = max(64, height) + + s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + return (s,) + +class LatentUpscaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, scale_by): + s = samples.copy() + width = round(samples["samples"].shape[3] * scale_by) + height = round(samples["samples"].shape[2] * scale_by) + s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + return (s,) + +class LatentRotate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "rotate" + + CATEGORY = "latent/transform" + + def rotate(self, samples, rotation): + s = samples.copy() + rotate_by = 0 + if rotation.startswith("90"): + rotate_by = 1 + elif rotation.startswith("180"): + rotate_by = 2 + elif rotation.startswith("270"): + rotate_by = 3 + + s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) + return (s,) + +class LatentFlip: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "flip" + + CATEGORY = "latent/transform" + + def flip(self, samples, flip_method): + s = samples.copy() + if flip_method.startswith("x"): + s["samples"] = torch.flip(samples["samples"], dims=[2]) + elif flip_method.startswith("y"): + s["samples"] = torch.flip(samples["samples"], dims=[3]) + + return (s,) + +class LatentComposite: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples_to": ("LATENT",), + "samples_from": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): + x = x // 8 + y = y // 8 + feather = feather // 8 + samples_out = samples_to.copy() + s = samples_to["samples"].clone() + samples_to = samples_to["samples"] + samples_from = samples_from["samples"] + if feather == 0: + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + else: + samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + mask = torch.ones_like(samples_from) + for t in range(feather): + if y != 0: + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + + if y + samples_from.shape[2] < samples_to.shape[2]: + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + if x != 0: + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + if x + samples_from.shape[3] < samples_to.shape[3]: + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + rev_mask = torch.ones_like(mask) - mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask + samples_out["samples"] = s + return (samples_out,) + +class LatentBlend: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples1": ("LATENT",), + "samples2": ("LATENT",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0, + "max": 1, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "blend" + + CATEGORY = "_for_testing" + + def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): + + samples_out = samples1.copy() + samples1 = samples1["samples"] + samples2 = samples2["samples"] + + if samples1.shape != samples2.shape: + samples2.permute(0, 3, 1, 2) + samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2.permute(0, 2, 3, 1) + + samples_blended = self.blend_mode(samples1, samples2, blend_mode) + samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) + samples_out["samples"] = samples_blended + return (samples_out,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + +class LatentCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "crop" + + CATEGORY = "latent/transform" + + def crop(self, samples, width, height, x, y): + s = samples.copy() + samples = samples['samples'] + x = x // 8 + y = y // 8 + + #enfonce minimum size of 64 + if x > (samples.shape[3] - 8): + x = samples.shape[3] - 8 + if y > (samples.shape[2] - 8): + y = samples.shape[2] - 8 + + new_height = height // 8 + new_width = width // 8 + to_x = new_width + x + to_y = new_height + y + s['samples'] = samples[:,:,y:to_y, x:to_x] + return (s,) + +class SetLatentNoiseMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "mask": ("MASK",), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "set_mask" + + CATEGORY = "latent/inpaint" + + def set_mask(self, samples, mask): + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (s,) + +def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + latent_image = latent["samples"] + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return (out, ) + +class KSampler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) + +class KSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": (["enable", "disable"], ), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "return_with_leftover_noise": (["disable", "enable"], ), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + disable_noise = False + if add_noise == "disable": + disable_noise = True + return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) + +class SaveImage: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image" + + def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + file = f"{filename}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results } } + +class PreviewImage(SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadImage: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True})}, + } + + CATEGORY = "image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + def load_image(self, image): + image_path = folder_paths.get_annotated_filepath(image) + img = Image.open(image_path) + output_images = [] + output_masks = [] + for i in ImageSequence.Iterator(img): + i = ImageOps.exif_transpose(i) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) + + @classmethod + def IS_CHANGED(s, image): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class LoadImageMask: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True}), + "channel": (s._color_channels, ), } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "load_image" + def load_image(self, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + if i.getbands() != ("R", "G", "B", "A"): + i = i.convert("RGBA") + mask = None + c = channel[0].upper() + if c in i.getbands(): + mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + if c == 'A': + mask = 1. - mask + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + return (mask.unsqueeze(0),) + + @classmethod + def IS_CHANGED(s, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class ImageScale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = image + else: + samples = image.movedim(-1,1) + + if width == 0: + width = max(1, round(samples.shape[3] * height / samples.shape[2])) + elif height == 0: + height = max(1, round(samples.shape[2] * width / samples.shape[3])) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) + s = s.movedim(1,-1) + return (s,) + +class ImageScaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, scale_by): + samples = image.movedim(-1,1) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +class ImageInvert: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "invert" + + CATEGORY = "image" + + def invert(self, image): + s = 1.0 - image + return (s,) + +class ImageBatch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "batch" + + CATEGORY = "image" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + s = torch.cat((image1, image2), dim=0) + return (s,) + +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + +class ImagePadForOutpaint: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering): + d1, d2, d3, d4 = image.size() + + new_image = torch.zeros( + (d1, d2 + top + bottom, d3 + left + right, d4), + dtype=torch.float32, + ) + new_image[:, top:top + d2, left:left + d3, :] = image + + mask = torch.ones( + (d2 + top + bottom, d3 + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (d2, d3), + dtype=torch.float32 + ) + + if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: + + for i in range(d2): + for j in range(d3): + dt = i if top != 0 else d2 + db = d2 - i if bottom != 0 else d2 + + dl = j if left != 0 else d3 + dr = d3 - j if right != 0 else d3 + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + t[i, j] = v * v + + mask[top:top + d2, left:left + d3] = t + + return (new_image, mask) + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoaderSimple": CheckpointLoaderSimple, + "CLIPTextEncode": CLIPTextEncode, + "CLIPSetLastLayer": CLIPSetLastLayer, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAEEncodeForInpaint": VAEEncodeForInpaint, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "LatentUpscaleBy": LatentUpscaleBy, + "LatentFromBatch": LatentFromBatch, + "RepeatLatentBatch": RepeatLatentBatch, + "SaveImage": SaveImage, + "PreviewImage": PreviewImage, + "LoadImage": LoadImage, + "LoadImageMask": LoadImageMask, + "ImageScale": ImageScale, + "ImageScaleBy": ImageScaleBy, + "ImageInvert": ImageInvert, + "ImageBatch": ImageBatch, + "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, + "ConditioningAverage": ConditioningAverage , + "ConditioningCombine": ConditioningCombine, + "ConditioningConcat": ConditioningConcat, + "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, + "ConditioningSetMask": ConditioningSetMask, + "KSamplerAdvanced": KSamplerAdvanced, + "SetLatentNoiseMask": SetLatentNoiseMask, + "LatentComposite": LatentComposite, + "LatentBlend": LatentBlend, + "LatentRotate": LatentRotate, + "LatentFlip": LatentFlip, + "LatentCrop": LatentCrop, + "LoraLoader": LoraLoader, + "CLIPLoader": CLIPLoader, + "UNETLoader": UNETLoader, + "DualCLIPLoader": DualCLIPLoader, + "CLIPVisionEncode": CLIPVisionEncode, + "StyleModelApply": StyleModelApply, + "unCLIPConditioning": unCLIPConditioning, + "ControlNetApply": ControlNetApply, + "ControlNetApplyAdvanced": ControlNetApplyAdvanced, + "ControlNetLoader": ControlNetLoader, + "DiffControlNetLoader": DiffControlNetLoader, + "StyleModelLoader": StyleModelLoader, + "CLIPVisionLoader": CLIPVisionLoader, + "VAEDecodeTiled": VAEDecodeTiled, + "VAEEncodeTiled": VAEEncodeTiled, + "unCLIPCheckpointLoader": unCLIPCheckpointLoader, + "GLIGENLoader": GLIGENLoader, + "GLIGENTextBoxApply": GLIGENTextBoxApply, + + "CheckpointLoader": CheckpointLoader, + "DiffusersLoader": DiffusersLoader, + + "LoadLatent": LoadLatent, + "SaveLatent": SaveLatent, + + "ConditioningZeroOut": ConditioningZeroOut, + "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "KSampler": "KSampler", + "KSamplerAdvanced": "KSampler (Advanced)", + # Loaders + "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", + "CheckpointLoaderSimple": "Load Checkpoint", + "VAELoader": "Load VAE", + "LoraLoader": "Load LoRA", + "CLIPLoader": "Load CLIP", + "ControlNetLoader": "Load ControlNet Model", + "DiffControlNetLoader": "Load ControlNet Model (diff)", + "StyleModelLoader": "Load Style Model", + "CLIPVisionLoader": "Load CLIP Vision", + "UpscaleModelLoader": "Load Upscale Model", + # Conditioning + "CLIPVisionEncode": "CLIP Vision Encode", + "StyleModelApply": "Apply Style Model", + "CLIPTextEncode": "CLIP Text Encode (Prompt)", + "CLIPSetLastLayer": "CLIP Set Last Layer", + "ConditioningCombine": "Conditioning (Combine)", + "ConditioningAverage ": "Conditioning (Average)", + "ConditioningConcat": "Conditioning (Concat)", + "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetMask": "Conditioning (Set Mask)", + "ControlNetApply": "Apply ControlNet", + "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", + # Latent + "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", + "SetLatentNoiseMask": "Set Latent Noise Mask", + "VAEDecode": "VAE Decode", + "VAEEncode": "VAE Encode", + "LatentRotate": "Rotate Latent", + "LatentFlip": "Flip Latent", + "LatentCrop": "Crop Latent", + "EmptyLatentImage": "Empty Latent Image", + "LatentUpscale": "Upscale Latent", + "LatentUpscaleBy": "Upscale Latent By", + "LatentComposite": "Latent Composite", + "LatentBlend": "Latent Blend", + "LatentFromBatch" : "Latent From Batch", + "RepeatLatentBatch": "Repeat Latent Batch", + # Image + "SaveImage": "Save Image", + "PreviewImage": "Preview Image", + "LoadImage": "Load Image", + "LoadImageMask": "Load Image (as Mask)", + "ImageScale": "Upscale Image", + "ImageScaleBy": "Upscale Image By", + "ImageUpscaleWithModel": "Upscale Image (using Model)", + "ImageInvert": "Invert Image", + "ImagePadForOutpaint": "Pad Image for Outpainting", + "ImageBatch": "Batch Images", + # _for_testing + "VAEDecodeTiled": "VAE Decode (Tiled)", + "VAEEncodeTiled": "VAE Encode (Tiled)", +} + +EXTENSION_WEB_DIRS = {} + +def load_custom_node(module_path, ignore=set()): + module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + try: + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location(module_name, module_path) + module_dir = os.path.split(module_path)[0] + else: + module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) + module_dir = module_path + + module = importlib.util.module_from_spec(module_spec) + sys.modules[module_name] = module + module_spec.loader.exec_module(module) + + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: + web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) + if os.path.isdir(web_dir): + EXTENSION_WEB_DIRS[module_name] = web_dir + + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + for name in module.NODE_CLASS_MAPPINGS: + if name not in ignore: + NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name] + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + return True + else: + print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + return False + except Exception as e: + print(traceback.format_exc()) + print(f"Cannot import {module_path} module for custom nodes:", e) + return False + +def load_custom_nodes(): + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) + node_paths = folder_paths.get_folder_paths("custom_nodes") + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(os.path.realpath(custom_node_path)) + if "__pycache__" in possible_modules: + possible_modules.remove("__pycache__") + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue + if module_path.endswith(".disabled"): continue + time_before = time.perf_counter() + success = load_custom_node(module_path, base_node_names) + node_import_times.append((time.perf_counter() - time_before, module_path, success)) + + if len(node_import_times) > 0: + print("\nImport times for custom nodes:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) + print() + +def init_custom_nodes(): + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_compositing.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py", + "nodes_hypertile.py", + "nodes_model_advanced.py", + "nodes_model_downscale.py", + "nodes_images.py", + "nodes_video_model.py", + "nodes_sag.py", + "nodes_perpneg.py", + "nodes_stable3d.py", + ] + + for node_file in extras_files: + load_custom_node(os.path.join(extras_dir, node_file)) + + load_custom_nodes() diff --git a/ComfyUI/notebooks/comfyui_colab.ipynb b/ComfyUI/notebooks/comfyui_colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec83265b42cf88125c0c6c646df5986395a96c77 --- /dev/null +++ b/ComfyUI/notebooks/comfyui_colab.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "#@title Environment Setup\n", + "\n", + "from pathlib import Path\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "WORKSPACE = 'ComfyUI'\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + " \n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ComfyUI/output/ComfyUI_00001_.webp b/ComfyUI/output/ComfyUI_00001_.webp new file mode 100644 index 0000000000000000000000000000000000000000..527fc158e8c02a7d9259cf62806497b4da1fd46f --- /dev/null +++ b/ComfyUI/output/ComfyUI_00001_.webp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68720dccb451e5b61d237ccc194a054a5ddc426a4c1a74822bdcbbe9894d3380 +size 3006610 diff --git a/ComfyUI/output/ComfyUI_00002_.webp b/ComfyUI/output/ComfyUI_00002_.webp new file mode 100644 index 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(deselect with '-m "not inference"') +testpaths = tests +addopts = -s \ No newline at end of file diff --git a/ComfyUI/requirements.txt b/ComfyUI/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e804618e7156cf66e1c5437854358d0c652d9f14 --- /dev/null +++ b/ComfyUI/requirements.txt @@ -0,0 +1,12 @@ +torch +torchsde +torchvision +einops +transformers>=4.25.1 +safetensors>=0.3.0 +aiohttp +pyyaml +Pillow +scipy +tqdm +psutil diff --git a/ComfyUI/script_examples/basic_api_example.py b/ComfyUI/script_examples/basic_api_example.py new file mode 100644 index 0000000000000000000000000000000000000000..242d3175f2eecceeae70ed54d7b0d4f02ae5cfb7 --- /dev/null +++ b/ComfyUI/script_examples/basic_api_example.py @@ -0,0 +1,120 @@ +import json +from urllib import request, parse +import random + +#This is the ComfyUI api prompt format. + +#If you want it for a specific workflow you can "enable dev mode options" +#in the settings of the UI (gear beside the "Queue Size: ") this will enable +#a button on the UI to save workflows in api format. + +#keep in mind ComfyUI is pre alpha software so this format will change a bit. + +#this is the one for the default workflow +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.ckpt" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +def queue_prompt(prompt): + p = {"prompt": prompt} + data = json.dumps(p).encode('utf-8') + req = request.Request("http://127.0.0.1:8188/prompt", data=data) + request.urlopen(req) + + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + + +queue_prompt(prompt) + + diff --git a/ComfyUI/script_examples/websockets_api_example.py b/ComfyUI/script_examples/websockets_api_example.py new file mode 100644 index 0000000000000000000000000000000000000000..57a6cbd9bad181e547146e159a7969235dc9a945 --- /dev/null +++ b/ComfyUI/script_examples/websockets_api_example.py @@ -0,0 +1,164 @@ +#This is an example that uses the websockets api to know when a prompt execution is done +#Once the prompt execution is done it downloads the images using the /history endpoint + +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import json +import urllib.request +import urllib.parse + +server_address = "127.0.0.1:8188" +client_id = str(uuid.uuid4()) + +def queue_prompt(prompt): + p = {"prompt": prompt, "client_id": client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + +def get_image(filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: + return response.read() + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +def get_images(ws, prompt): + prompt_id = queue_prompt(prompt)['prompt_id'] + output_images = {} + while True: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['node'] is None and data['prompt_id'] == prompt_id: + break #Execution is done + else: + continue #previews are binary data + + history = get_history(prompt_id)[prompt_id] + for o in history['outputs']: + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + if 'images' in node_output: + images_output = [] + for image in node_output['images']: + image_data = get_image(image['filename'], image['subfolder'], image['type']) + images_output.append(image_data) + output_images[node_id] = images_output + + return output_images + +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.ckpt" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + +ws = websocket.WebSocket() +ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) +images = get_images(ws, prompt) + +#Commented out code to display the output images: + +# for node_id in images: +# for image_data in images[node_id]: +# from PIL import Image +# import io +# image = Image.open(io.BytesIO(image_data)) +# image.show() + diff --git a/ComfyUI/server.py b/ComfyUI/server.py new file mode 100644 index 0000000000000000000000000000000000000000..9b1e3269d7fc8cc6c2ba4781e23d69414c0b84fa --- /dev/null +++ b/ComfyUI/server.py @@ -0,0 +1,638 @@ +import os +import sys +import asyncio +import traceback + +import nodes +import folder_paths +import execution +import uuid +import urllib +import json +import glob +import struct +from PIL import Image, ImageOps +from PIL.PngImagePlugin import PngInfo +from io import BytesIO + +try: + import aiohttp + from aiohttp import web +except ImportError: + print("Module 'aiohttp' not installed. Please install it via:") + print("pip install aiohttp") + print("or") + print("pip install -r requirements.txt") + sys.exit() + +import mimetypes +from comfy.cli_args import args +import comfy.utils +import comfy.model_management + + +class BinaryEventTypes: + PREVIEW_IMAGE = 1 + UNENCODED_PREVIEW_IMAGE = 2 + +async def send_socket_catch_exception(function, message): + try: + await function(message) + except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err: + print("send error:", err) + +@web.middleware +async def cache_control(request: web.Request, handler): + response: web.Response = await handler(request) + if request.path.endswith('.js') or request.path.endswith('.css'): + response.headers.setdefault('Cache-Control', 'no-cache') + return response + +def create_cors_middleware(allowed_origin: str): + @web.middleware + async def cors_middleware(request: web.Request, handler): + if request.method == "OPTIONS": + # Pre-flight request. Reply successfully: + response = web.Response() + else: + response = await handler(request) + + response.headers['Access-Control-Allow-Origin'] = allowed_origin + response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS' + response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' + response.headers['Access-Control-Allow-Credentials'] = 'true' + return response + + return cors_middleware + +class PromptServer(): + def __init__(self, loop): + PromptServer.instance = self + + mimetypes.init() + mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8' + + self.supports = ["custom_nodes_from_web"] + self.prompt_queue = None + self.loop = loop + self.messages = asyncio.Queue() + self.number = 0 + + middlewares = [cache_control] + if args.enable_cors_header: + middlewares.append(create_cors_middleware(args.enable_cors_header)) + + max_upload_size = round(args.max_upload_size * 1024 * 1024) + self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) + self.sockets = dict() + self.web_root = os.path.join(os.path.dirname( + os.path.realpath(__file__)), "web") + routes = web.RouteTableDef() + self.routes = routes + self.last_node_id = None + self.client_id = None + + self.on_prompt_handlers = [] + + @routes.get('/ws') + async def websocket_handler(request): + ws = web.WebSocketResponse() + await ws.prepare(request) + sid = request.rel_url.query.get('clientId', '') + if sid: + # Reusing existing session, remove old + self.sockets.pop(sid, None) + else: + sid = uuid.uuid4().hex + + self.sockets[sid] = ws + + try: + # Send initial state to the new client + await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid) + # On reconnect if we are the currently executing client send the current node + if self.client_id == sid and self.last_node_id is not None: + await self.send("executing", { "node": self.last_node_id }, sid) + + async for msg in ws: + if msg.type == aiohttp.WSMsgType.ERROR: + print('ws connection closed with exception %s' % ws.exception()) + finally: + self.sockets.pop(sid, None) + return ws + + @routes.get("/") + async def get_root(request): + return web.FileResponse(os.path.join(self.web_root, "index.html")) + + @routes.get("/embeddings") + def get_embeddings(self): + embeddings = folder_paths.get_filename_list("embeddings") + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) + + @routes.get("/extensions") + async def get_extensions(request): + files = glob.glob(os.path.join( + glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True) + + extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) + + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) + extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( + name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files))) + + return web.json_response(extensions) + + def get_dir_by_type(dir_type): + if dir_type is None: + dir_type = "input" + + if dir_type == "input": + type_dir = folder_paths.get_input_directory() + elif dir_type == "temp": + type_dir = folder_paths.get_temp_directory() + elif dir_type == "output": + type_dir = folder_paths.get_output_directory() + + return type_dir, dir_type + + def image_upload(post, image_save_function=None): + image = post.get("image") + overwrite = post.get("overwrite") + + image_upload_type = post.get("type") + upload_dir, image_upload_type = get_dir_by_type(image_upload_type) + + if image and image.file: + filename = image.filename + if not filename: + return web.Response(status=400) + + subfolder = post.get("subfolder", "") + full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder)) + filepath = os.path.abspath(os.path.join(full_output_folder, filename)) + + if os.path.commonpath((upload_dir, filepath)) != upload_dir: + return web.Response(status=400) + + if not os.path.exists(full_output_folder): + os.makedirs(full_output_folder) + + split = os.path.splitext(filename) + + if overwrite is not None and (overwrite == "true" or overwrite == "1"): + pass + else: + i = 1 + while os.path.exists(filepath): + filename = f"{split[0]} ({i}){split[1]}" + filepath = os.path.join(full_output_folder, filename) + i += 1 + + if image_save_function is not None: + image_save_function(image, post, filepath) + else: + with open(filepath, "wb") as f: + f.write(image.file.read()) + + return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) + else: + return web.Response(status=400) + + @routes.post("/upload/image") + async def upload_image(request): + post = await request.post() + return image_upload(post) + + + @routes.post("/upload/mask") + async def upload_mask(request): + post = await request.post() + + def image_save_function(image, post, filepath): + original_ref = json.loads(post.get("original_ref")) + filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = original_ref.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if original_ref.get("subfolder", "") != "": + full_output_dir = os.path.join(output_dir, original_ref["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + with Image.open(file) as original_pil: + metadata = PngInfo() + if hasattr(original_pil,'text'): + for key in original_pil.text: + metadata.add_text(key, original_pil.text[key]) + original_pil = original_pil.convert('RGBA') + mask_pil = Image.open(image.file).convert('RGBA') + + # alpha copy + new_alpha = mask_pil.getchannel('A') + original_pil.putalpha(new_alpha) + original_pil.save(filepath, compress_level=4, pnginfo=metadata) + + return image_upload(post, image_save_function) + + @routes.get("/view") + async def view_image(request): + if "filename" in request.rel_url.query: + filename = request.rel_url.query["filename"] + filename,output_dir = folder_paths.annotated_filepath(filename) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = request.rel_url.query.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if "subfolder" in request.rel_url.query: + full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + filename = os.path.basename(filename) + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + if 'preview' in request.rel_url.query: + with Image.open(file) as img: + preview_info = request.rel_url.query['preview'].split(';') + image_format = preview_info[0] + if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''): + image_format = 'webp' + + quality = 90 + if preview_info[-1].isdigit(): + quality = int(preview_info[-1]) + + buffer = BytesIO() + if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb': + img = img.convert("RGB") + img.save(buffer, format=image_format, quality=quality) + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type=f'image/{image_format}', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + if 'channel' not in request.rel_url.query: + channel = 'rgba' + else: + channel = request.rel_url.query["channel"] + + if channel == 'rgb': + with Image.open(file) as img: + if img.mode == "RGBA": + r, g, b, a = img.split() + new_img = Image.merge('RGB', (r, g, b)) + else: + new_img = img.convert("RGB") + + buffer = BytesIO() + new_img.save(buffer, format='PNG') + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + elif channel == 'a': + with Image.open(file) as img: + if img.mode == "RGBA": + _, _, _, a = img.split() + else: + a = Image.new('L', img.size, 255) + + # alpha img + alpha_img = Image.new('RGBA', img.size) + alpha_img.putalpha(a) + alpha_buffer = BytesIO() + alpha_img.save(alpha_buffer, format='PNG') + alpha_buffer.seek(0) + + return web.Response(body=alpha_buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + else: + return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""}) + + return web.Response(status=404) + + @routes.get("/view_metadata/{folder_name}") + async def view_metadata(request): + folder_name = request.match_info.get("folder_name", None) + if folder_name is None: + return web.Response(status=404) + if not "filename" in request.rel_url.query: + return web.Response(status=404) + + filename = request.rel_url.query["filename"] + if not filename.endswith(".safetensors"): + return web.Response(status=404) + + safetensors_path = folder_paths.get_full_path(folder_name, filename) + if safetensors_path is None: + return web.Response(status=404) + out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024) + if out is None: + return web.Response(status=404) + dt = json.loads(out) + if not "__metadata__" in dt: + return web.Response(status=404) + return web.json_response(dt["__metadata__"]) + + @routes.get("/system_stats") + async def get_queue(request): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) + vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) + system_stats = { + "system": { + "os": os.name, + "python_version": sys.version, + "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded" + }, + "devices": [ + { + "name": device_name, + "type": device.type, + "index": device.index, + "vram_total": vram_total, + "vram_free": vram_free, + "torch_vram_total": torch_vram_total, + "torch_vram_free": torch_vram_free, + } + ] + } + return web.json_response(system_stats) + + @routes.get("/prompt") + async def get_prompt(request): + return web.json_response(self.get_queue_info()) + + def node_info(node_class): + obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] + info = {} + info['input'] = obj_class.INPUT_TYPES() + info['output'] = obj_class.RETURN_TYPES + info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) + info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] + info['name'] = node_class + info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class + info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' + info['category'] = 'sd' + if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: + info['output_node'] = True + else: + info['output_node'] = False + + if hasattr(obj_class, 'CATEGORY'): + info['category'] = obj_class.CATEGORY + return info + + @routes.get("/object_info") + async def get_object_info(request): + out = {} + for x in nodes.NODE_CLASS_MAPPINGS: + try: + out[x] = node_info(x) + except Exception as e: + print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr) + traceback.print_exc() + return web.json_response(out) + + @routes.get("/object_info/{node_class}") + async def get_object_info_node(request): + node_class = request.match_info.get("node_class", None) + out = {} + if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS): + out[node_class] = node_info(node_class) + return web.json_response(out) + + @routes.get("/history") + async def get_history(request): + max_items = request.rel_url.query.get("max_items", None) + if max_items is not None: + max_items = int(max_items) + return web.json_response(self.prompt_queue.get_history(max_items=max_items)) + + @routes.get("/history/{prompt_id}") + async def get_history(request): + prompt_id = request.match_info.get("prompt_id", None) + return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id)) + + @routes.get("/queue") + async def get_queue(request): + queue_info = {} + current_queue = self.prompt_queue.get_current_queue() + queue_info['queue_running'] = current_queue[0] + queue_info['queue_pending'] = current_queue[1] + return web.json_response(queue_info) + + @routes.post("/prompt") + async def post_prompt(request): + print("got prompt") + resp_code = 200 + out_string = "" + json_data = await request.json() + json_data = self.trigger_on_prompt(json_data) + + if "number" in json_data: + number = float(json_data['number']) + else: + number = self.number + if "front" in json_data: + if json_data['front']: + number = -number + + self.number += 1 + + if "prompt" in json_data: + prompt = json_data["prompt"] + valid = execution.validate_prompt(prompt) + extra_data = {} + if "extra_data" in json_data: + extra_data = json_data["extra_data"] + + if "client_id" in json_data: + extra_data["client_id"] = json_data["client_id"] + if valid[0]: + prompt_id = str(uuid.uuid4()) + outputs_to_execute = valid[2] + self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) + response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} + return web.json_response(response) + else: + print("invalid prompt:", valid[1]) + return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) + else: + return web.json_response({"error": "no prompt", "node_errors": []}, status=400) + + @routes.post("/queue") + async def post_queue(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_queue() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + delete_func = lambda a: a[1] == id_to_delete + self.prompt_queue.delete_queue_item(delete_func) + + return web.Response(status=200) + + @routes.post("/interrupt") + async def post_interrupt(request): + nodes.interrupt_processing() + return web.Response(status=200) + + @routes.post("/history") + async def post_history(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_history() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + self.prompt_queue.delete_history_item(id_to_delete) + + return web.Response(status=200) + + def add_routes(self): + self.app.add_routes(self.routes) + + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + self.app.add_routes([ + web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True), + ]) + + self.app.add_routes([ + web.static('/', self.web_root, follow_symlinks=True), + ]) + + def get_queue_info(self): + prompt_info = {} + exec_info = {} + exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining() + prompt_info['exec_info'] = exec_info + return prompt_info + + async def send(self, event, data, sid=None): + if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: + await self.send_image(data, sid=sid) + elif isinstance(data, (bytes, bytearray)): + await self.send_bytes(event, data, sid) + else: + await self.send_json(event, data, sid) + + def encode_bytes(self, event, data): + if not isinstance(event, int): + raise RuntimeError(f"Binary event types must be integers, got {event}") + + packed = struct.pack(">I", event) + message = bytearray(packed) + message.extend(data) + return message + + async def send_image(self, image_data, sid=None): + image_type = image_data[0] + image = image_data[1] + max_size = image_data[2] + if max_size is not None: + if hasattr(Image, 'Resampling'): + resampling = Image.Resampling.BILINEAR + else: + resampling = Image.ANTIALIAS + + image = ImageOps.contain(image, (max_size, max_size), resampling) + type_num = 1 + if image_type == "JPEG": + type_num = 1 + elif image_type == "PNG": + type_num = 2 + + bytesIO = BytesIO() + header = struct.pack(">I", type_num) + bytesIO.write(header) + image.save(bytesIO, format=image_type, quality=95, compress_level=1) + preview_bytes = bytesIO.getvalue() + await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) + + async def send_bytes(self, event, data, sid=None): + message = self.encode_bytes(event, data) + + if sid is None: + for ws in self.sockets.values(): + await send_socket_catch_exception(ws.send_bytes, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_bytes, message) + + async def send_json(self, event, data, sid=None): + message = {"type": event, "data": data} + + if sid is None: + for ws in self.sockets.values(): + await send_socket_catch_exception(ws.send_json, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_json, message) + + def send_sync(self, event, data, sid=None): + self.loop.call_soon_threadsafe( + self.messages.put_nowait, (event, data, sid)) + + def queue_updated(self): + self.send_sync("status", { "status": self.get_queue_info() }) + + async def publish_loop(self): + while True: + msg = await self.messages.get() + await self.send(*msg) + + async def start(self, address, port, verbose=True, call_on_start=None): + runner = web.AppRunner(self.app, access_log=None) + await runner.setup() + site = web.TCPSite(runner, address, port) + await site.start() + + if address == '': + address = '0.0.0.0' + if verbose: + print("Starting server\n") + print("To see the GUI go to: http://{}:{}".format(address, port)) + if call_on_start is not None: + call_on_start(address, port) + + def add_on_prompt_handler(self, handler): + self.on_prompt_handlers.append(handler) + + def trigger_on_prompt(self, json_data): + for handler in self.on_prompt_handlers: + try: + json_data = handler(json_data) + except Exception as e: + print(f"[ERROR] An error occurred during the on_prompt_handler processing") + traceback.print_exc() + + return json_data diff --git a/ComfyUI/tests-ui/.gitignore b/ComfyUI/tests-ui/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b512c09d476623ff4bf8d0d63c29b784925dbdf8 --- /dev/null +++ b/ComfyUI/tests-ui/.gitignore @@ -0,0 +1 @@ +node_modules \ No newline at end of file diff --git a/ComfyUI/tests-ui/afterSetup.js b/ComfyUI/tests-ui/afterSetup.js new file mode 100644 index 0000000000000000000000000000000000000000..983f3af643cda02096668e4ac092c3f252214ab8 --- /dev/null +++ b/ComfyUI/tests-ui/afterSetup.js @@ -0,0 +1,9 @@ +const { start } = require("./utils"); +const lg = require("./utils/litegraph"); + +// Load things once per test file before to ensure its all warmed up for the tests +beforeAll(async () => { + lg.setup(global); + await start({ resetEnv: true }); + lg.teardown(global); +}); diff --git a/ComfyUI/tests-ui/babel.config.json b/ComfyUI/tests-ui/babel.config.json new file mode 100644 index 0000000000000000000000000000000000000000..526ddfd8df1146d2750d61dc0793b366c7e81e82 --- /dev/null +++ b/ComfyUI/tests-ui/babel.config.json @@ -0,0 +1,3 @@ +{ + "presets": ["@babel/preset-env"] +} diff --git a/ComfyUI/tests-ui/globalSetup.js b/ComfyUI/tests-ui/globalSetup.js new file mode 100644 index 0000000000000000000000000000000000000000..b9d97f58a96f90b3b7f7fc66b15183edcc0837ab --- /dev/null +++ b/ComfyUI/tests-ui/globalSetup.js @@ -0,0 +1,14 @@ +module.exports = async function () { + global.ResizeObserver = class ResizeObserver { + observe() {} + unobserve() {} + disconnect() {} + }; + + const { nop } = require("./utils/nopProxy"); + global.enableWebGLCanvas = nop; + + HTMLCanvasElement.prototype.getContext = nop; + + localStorage["Comfy.Settings.Comfy.Logging.Enabled"] = "false"; +}; diff --git a/ComfyUI/tests-ui/jest.config.js b/ComfyUI/tests-ui/jest.config.js new file mode 100644 index 0000000000000000000000000000000000000000..86fff50574b099dca3365e19e4e5bb48454ed80d --- /dev/null +++ b/ComfyUI/tests-ui/jest.config.js @@ -0,0 +1,11 @@ +/** @type {import('jest').Config} */ +const config = { + testEnvironment: "jsdom", + setupFiles: 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+ "devDependencies": { + "@babel/preset-env": "^7.22.20", + "@types/jest": "^29.5.5", + "jest": "^29.7.0", + "jest-environment-jsdom": "^29.7.0" + } +} diff --git a/ComfyUI/tests-ui/setup.js b/ComfyUI/tests-ui/setup.js new file mode 100644 index 0000000000000000000000000000000000000000..8bbd9dcdf20cde46f1f2c10591d9c9415a71391a --- /dev/null +++ b/ComfyUI/tests-ui/setup.js @@ -0,0 +1,88 @@ +const { spawn } = require("child_process"); +const { resolve } = require("path"); +const { existsSync, mkdirSync, writeFileSync } = require("fs"); +const http = require("http"); + +async function setup() { + // Wait up to 30s for it to start + let success = false; + let child; + for (let i = 0; i < 30; i++) { + try { + await new Promise((res, rej) => { + http + .get("http://127.0.0.1:8188/object_info", (resp) => { + let data = ""; + resp.on("data", (chunk) => { + data += chunk; + }); + resp.on("end", () => { + // Modify the response data to add some checkpoints + const objectInfo = JSON.parse(data); + objectInfo.CheckpointLoaderSimple.input.required.ckpt_name[0] = ["model1.safetensors", "model2.ckpt"]; + objectInfo.VAELoader.input.required.vae_name[0] = ["vae1.safetensors", "vae2.ckpt"]; + + data = JSON.stringify(objectInfo, undefined, "\t"); + + const outDir = resolve("./data"); + if (!existsSync(outDir)) { + mkdirSync(outDir); + } + + const outPath = resolve(outDir, "object_info.json"); + console.log(`Writing ${Object.keys(objectInfo).length} nodes to ${outPath}`); + writeFileSync(outPath, data, { + encoding: "utf8", + }); + res(); + }); + }) + .on("error", rej); + }); + success = true; + break; + } catch (error) { + console.log(i + "/30", error); + if (i === 0) { + // Start the server on first iteration if it fails to connect + console.log("Starting ComfyUI server..."); + + let python = resolve("../../python_embeded/python.exe"); + let args; + let cwd; + if (existsSync(python)) { + args = ["-s", "ComfyUI/main.py"]; + cwd = "../.."; + } else { + python = "python"; + args = ["main.py"]; + cwd = ".."; + } + args.push("--cpu"); + console.log(python, ...args); + child = spawn(python, args, { cwd }); + child.on("error", (err) => { + console.log(`Server error (${err})`); + i = 30; + }); + child.on("exit", (code) => { + if (!success) { + console.log(`Server exited (${code})`); + i = 30; + } + }); + } + await new Promise((r) => { + setTimeout(r, 1000); + }); + } + } + + child?.kill(); + + if (!success) { + throw new Error("Waiting for server failed..."); + } +} + + setup(); \ No newline at end of file diff --git a/ComfyUI/tests-ui/tests/extensions.test.js b/ComfyUI/tests-ui/tests/extensions.test.js new file mode 100644 index 0000000000000000000000000000000000000000..159e5113a293695532b7a73c1d3add754b364039 --- /dev/null +++ b/ComfyUI/tests-ui/tests/extensions.test.js @@ -0,0 +1,196 @@ +// @ts-check +///