├── .gitattributes ├── .github └── workflows │ └── publish.yml ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── examples ├── workflow-gaffer-grayscaler-compare.json ├── workflow-gaffer-iclight-all.json ├── workflow-gaffer-iclight-fbc.json ├── workflow-gaffer-iclight-fc-animated.json ├── workflow-gaffer-iclight-fc-normal.json ├── workflow-gaffer-iclight-fc-text.json └── workflow-gaffer-iclight-fc.json ├── iclight.py ├── image ├── all.png ├── node_apply-iclight.png ├── node_calculate_normal_map.png ├── node_gray-scaler.png ├── node_load-iclight-model.png └── node_simple-light-source.png ├── pyproject.toml ├── requirements.txt └── utils ├── image.py └── patches.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | paths: 8 | - "pyproject.toml" 9 | 10 | jobs: 11 | publish-node: 12 | name: Publish Custom Node to registry 13 | runs-on: ubuntu-latest 14 | steps: 15 | - name: Check out code 16 | uses: actions/checkout@v4 17 | - name: Publish Custom Node 18 | uses: Comfy-Org/publish-node-action@main 19 | with: 20 | ## Add your own personal access token to your Github Repository secrets and reference it here. 21 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | /venv 3 | .vscode 4 | *.ckpt 5 | *.safetensors 6 | *.pth 7 | types 8 | *.pyc 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI Gaffer 2 | 3 | comfyui's gaffer 4 | 5 | ## Important updates 6 | 7 | * 2024-05-12: ComfyUI native implementation of [IC-Light](https://github.com/lllyasviel/IC-Light). 8 | 9 | ## Install 10 | 11 | * Recommended use [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager) for installation. 12 | 13 | * Or open the cmd window in the plugin directory of ComfyUI, like ```ComfyUI\custom_nodes```，type 14 | ``` 15 | git clone https://github.com/huagetai/ComfyUI-Gaffer.git 16 | ``` 17 | * Or download the zip file and extracted, copy the resulting folder to ```ComfyUI\custom_ Nodes``` 18 | * Download models 19 | 20 | You can download models here: https://huggingface.co/lllyasviel/ic-light/tree/main 21 | There are 3 models: 22 | * iclight_sd15_fc.safetensors: Use this in FG workflows 23 | - iclight_sd15_fcon.safetensors: Use this in FG workflows 24 | - iclight_sd15_fbc.safetensors: Use this in BG workflows 25 | 26 | After you download these models, please put them under ComfyUI/models/iclight. 27 | 28 | * Restart ComfyUI. 29 | 30 | ## Usage 31 | 32 | ![ALL Model](image/all.png) 33 | 34 | 35 | * Text-Conditioned Model 36 | 37 | * refer to: [Text + foreground Conditioned](examples/workflow-gaffer-iclight-fc-text.json) 38 | 39 | * refer to: [Text + foreground + Lighting Preference Conditioned](examples/workflow-gaffer-iclight-fc.json) 40 | 41 | * Background-Conditioned Model 42 | 43 | * refer to: [Text + foreground + background Conditioned](examples/workflow-gaffer-iclight-fbc.json) 44 | 45 | * Calculate Normal 46 | 47 | * refer to: [Calculate Normal](examples/workflow-gaffer-iclight-fc-normal.json) 48 | 49 | * animated 50 | 51 | * refer to: [Animated](examples/workflow-gaffer-iclight-fc-animated.json) 52 | 53 | * tips: Lighting Preference Requires [ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes) 54 | 55 | * tips 56 | 57 | * Model description and input foreground and background images can be obtained [IC-Light](https://github.com/lllyasviel/IC-Light) 58 | * The overall brightness can be controlled through the grayscale. refer to [grayscaler-compare](examples/workflow-gaffer-grayscaler-compare.json) 59 | 60 | ## Nodes 61 | 62 | 63 | * **Load ICLight Model Node** 64 | 65 | Load ICLight Model 66 | 67 | ![Load ICLight Model](image/node_load-iclight-model.png) 68 | 69 | input: 70 | * iclight_name: The name of the ICLight model to load. 71 | 72 | output: 73 | * iclight: ICLight model information. 74 | 75 | 76 | * **Apply ICLight Node** 77 | 78 | Apply ICLight 79 | 80 | ![Apply ICLight](image/node_apply-iclight.png) 81 | 82 | input: 83 | * model: base model 84 | * vae: VAE 85 | * iclight: ICLight model information. 86 | * position: position prompts 87 | * negative: negative prompts 88 | * fg_pixels: foreground image.The background of the foreground image needs to be removed, and it is recommended to set it to a grayscale background. 89 | * bg_pixels: background image(optional).Required for Background-Conditioned Model.Consistent with the width and height of the foreground image 90 | * multiplier: strength of ic-light model 91 | 92 | output: 93 | * model: model with ICLight 94 | * position: position prompts 95 | * negative: negative prompts 96 | * empty_latent: Empty Latent Image.The width and height are consistent with the foreground image. 97 | 98 | 99 | * **Simple Light Source Node** 100 | 101 | Simple Light Source for Lighting Preference 102 | ![Simple Light Source Node](image/node_simple-light-source.png) 103 | 104 | input: 105 | * light_position: Left Light, Right Light, Top Light, Bottom Light, Top Left Light, Top Right Light, Bottom Left Light, Bottom Right Light 106 | * multiplier: strength of Lighting Preference 107 | * start_color: start color of Lighting Preference 108 | * end_color: end color of Lighting Preference 109 | * width: The width of Lighting Preference 110 | * height: The height of Lighting Preference 111 | tips:Consistent with the width and height of the foreground image 112 | 113 | output: 114 | * image: Lighting Preference. 115 | 116 | tips: 117 | * different effects can be achieved by setting the start color and end color. 118 | 119 | * **Calculate Normal Map Node** 120 | 121 | Calculate Normal Map 122 | 123 | ![Calculate Normal Map](image/node_calculate_normal_map.png) 124 | 125 | input: 126 | * images: A sequence of input images 127 | * mask: Optional, a mask image to specify the computation region. 128 | * sigma: The standard deviation for Gaussian blur, controlling the smoothness of the normal calculation. 129 | * center_input_range: The range used to center the input images. 130 | 131 | output: 132 | * normal: normal map 133 | 134 | **tips**:The node from [kijai-ComfyUI-IC-Light](https://github.com/kijai/ComfyUI-IC-Light) project 135 | 136 | * **Gray Scaler Node** 137 | 138 | Scales the image area to gray according to the provided mask. 139 | 140 | ![Gray Scaler Node](image/node_gray-scaler.png) 141 | * 142 | input: 143 | * image: transparent background image. 144 | * mask: mask indicating areas to be converted to grey. 145 | * multiplier: A value to control the intensity of the grey conversion. 146 | 147 | output: 148 | * image: gray background image. 149 | 150 | 151 | ## Credits 152 | 153 | * [IC-Light](https://github.com/lllyasviel/IC-Light) 154 | * [ComfyUI](https://github.com/comfyanonymous/ComfyUI) 155 | * [kijai-ComfyUI-IC-Light](https://github.com/kijai/ComfyUI-IC-Light) 156 | * [huchenlei-ComfyUI-IC-Light-Native](https://github.com/huchenlei/ComfyUI-IC-Light-Native) -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .iclight import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] -------------------------------------------------------------------------------- /examples/workflow-gaffer-iclight-fbc.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 88, 3 | "last_link_id": 226, 4 | "nodes": [ 5 | { 6 | "id": 28, 7 | "type": "ImageResize+", 8 | "pos": [ 9 | -266.61503629140856, 10 | 966.8616135979687 11 | ], 12 | "size": { 13 | "0": 315, 14 | "1": 218 15 | }, 16 | "flags": {}, 17 | "order": 8, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "image", 22 | "type": "IMAGE", 23 | "link": 123 24 | } 25 | ], 26 | "outputs": [ 27 | { 28 | "name": "IMAGE", 29 | "type": "IMAGE", 30 | "links": [ 31 | 158 32 | ], 33 | "shape": 3, 34 | "slot_index": 0 35 | }, 36 | { 37 | "name": "width", 38 | "type": "INT", 39 | "links": [], 40 | "shape": 3, 41 | "slot_index": 1 42 | }, 43 | { 44 | "name": "height", 45 | "type": "INT", 46 | "links": [], 47 | 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64, 948 | 1, 949 | 88, 950 | 1, 951 | "MASK" 952 | ], 953 | [ 954 | 230, 955 | 88, 956 | 0, 957 | 68, 958 | 0, 959 | "IMAGE" 960 | ], 961 | [ 962 | 231, 963 | 88, 964 | 0, 965 | 62, 966 | 5, 967 | "IMAGE" 968 | ] 969 | ], 970 | "groups": [ 971 | { 972 | "title": "foreground", 973 | "bounding": [ 974 | -754, 975 | 737, 976 | 1247, 977 | 757 978 | ], 979 | "color": "#3f789e", 980 | "font_size": 24, 981 | "locked": false 982 | } 983 | ], 984 | "config": {}, 985 | "extra": { 986 | "ds": { 987 | "scale": 0.5131581182307067, 988 | "offset": { 989 | "0": 1988.018452815201, 990 | "1": 247.8858309778409 991 | } 992 | } 993 | }, 994 | "version": 0.4 995 | } -------------------------------------------------------------------------------- /iclight.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | from typing import TypedDict, Callable 4 | import torch 5 | import numpy as np 6 | import folder_paths 7 | import comfy.model_management as model_management 8 | from comfy.sd import VAE 9 | from comfy.utils import load_torch_file 10 | from comfy.diffusers_convert import convert_unet_state_dict 11 | from comfy.ldm.models.autoencoder import AutoencoderKL 12 | from comfy.model_base import BaseModel 13 | from comfy.model_patcher import ModelPatcher 14 | from nodes import MAX_RESOLUTION 15 | from .utils.image import generate_gradient_image, LightPosition, resize_and_center_crop 16 | from .utils.patches import calculate_weight_adjust_channel 17 | 18 | # logger 19 | logging.basicConfig(level=logging.INFO) 20 | LOGGER = logging.getLogger(__name__) 21 | 22 | # contents 23 | MODEL_TYPE_ICLIGHT = "iclight" 24 | 25 | # set the models directory 26 | if MODEL_TYPE_ICLIGHT not in folder_paths.folder_names_and_paths: 27 | current_paths = [os.path.join(folder_paths.models_dir, MODEL_TYPE_ICLIGHT)] 28 | else: 29 | current_paths, _ = folder_paths.folder_names_and_paths[MODEL_TYPE_ICLIGHT] 30 | folder_paths.folder_names_and_paths[MODEL_TYPE_ICLIGHT] = (current_paths, folder_paths.supported_pt_extensions) 31 | 32 | 33 | class UnetParams(TypedDict): 34 | input: torch.Tensor 35 | timestep: torch.Tensor 36 | c: dict 37 | cond_or_uncond: torch.Tensor 38 | 39 | class ICLightPatch(): 40 | def __init__(self, model: ModelPatcher, vae: VAE, iclight): 41 | model_type = str(type(model.model.model_config).__name__) 42 | if "SD15" not in model_type: 43 | raise Exception(f"Attempted to load {model_type} model, IC-Light is only compatible with SD 1.5 models.") 44 | 45 | assert isinstance(vae.first_stage_model, AutoencoderKL), "vae only supported for AutoencoderKL" 46 | 47 | self.device = model_management.get_torch_device() 48 | dtype = model_management.unet_dtype() 49 | if dtype not in [torch.float32, torch.float16, torch.bfloat16]: 50 | dtype = torch.float16 if model_management.should_use_fp16() else torch.float32 51 | self.dtype = dtype 52 | 53 | self.work_model = model.clone() 54 | self.vae = vae 55 | 56 | self.fbc = iclight.get("fbc", False) 57 | self.sd_dict = iclight.get("sd_dict", {}) 58 | 59 | self.empty_latent = None 60 | 61 | def encode(self, pixels): 62 | original_sample_mode = self.vae.first_stage_model.regularization.sample 63 | self.vae.first_stage_model.regularization.sample = False 64 | out_samples = self.vae.encode(pixels) 65 | self.vae.first_stage_model.regularization.sample = original_sample_mode 66 | return out_samples 67 | 68 | def get_concat_conds(self, fg_pixels, bg_pixels=None): 69 | fg_samples = self.encode(fg_pixels) 70 | self.empty_latent = torch.zeros_like(fg_samples) 71 | if self.fbc: 72 | if bg_pixels is None: 73 | raise ValueError("When using background-conditioned Model, 'bg_pixel' is required") 74 | 75 | bg_pixels = resize_and_center_crop(bg_pixels, fg_pixels.shape[2], fg_pixels.shape[1]) 76 | bg_samples = self.encode(bg_pixels) 77 | 78 | fg_batch_size = bg_samples.size(0) // fg_samples.size(0) 79 | bg_batch_size = fg_samples.size(0) // bg_samples.size(0) 80 | 81 | if fg_batch_size > 1: 82 | fg_samples = fg_samples.repeat(fg_batch_size, 1, 1, 1) 83 | if bg_batch_size > 1: 84 | bg_samples = bg_samples.repeat(bg_batch_size, 1, 1, 1) 85 | 86 | concat_samples = torch.cat((fg_samples, bg_samples), dim=1) 87 | else: 88 | concat_samples = fg_samples 89 | 90 | 91 | 92 | base_model: BaseModel = self.work_model.model 93 | scale_factor = base_model.model_config.latent_format.scale_factor 94 | concat_conds = concat_samples * scale_factor 95 | # concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1) 96 | 97 | self.empty_latent = torch.zeros_like(fg_samples) 98 | 99 | return concat_conds 100 | 101 | def patch(self, strength_patch: float): 102 | # Patch ComfyUI's LoRA weight application to accept multi-channel inputs. 103 | try: 104 | ModelPatcher.calculate_weight = calculate_weight_adjust_channel(ModelPatcher.calculate_weight) 105 | except: 106 | raise Exception("Could not patch calculate_weight") 107 | 108 | ic_model_state_dict = self.sd_dict 109 | 110 | in_channels = ic_model_state_dict["input_blocks.0.0.weight"].shape[1] 111 | self.work_model.model.model_config.unet_config["in_channels"] = in_channels 112 | 113 | self.work_model.add_patches( 114 | patches={ 115 | ("diffusion_model." + key): (value.to(dtype=self.dtype, device=self.device),) 116 | for key, value in ic_model_state_dict.items() 117 | }, strength_patch=strength_patch 118 | ) 119 | 120 | def set_unet_function_wrapper(self, concat_conds): 121 | def apply_c_concat(params: UnetParams) -> UnetParams: 122 | sample = params["input"] 123 | params["c"]["c_concat"] = torch.cat( 124 | ([concat_conds.to(sample.device)] * (sample.shape[0] // concat_conds.shape[0])), 125 | dim=0, 126 | ) 127 | return params 128 | 129 | def dummy_unet_apply(unet_apply: Callable, params: UnetParams): 130 | return unet_apply(x=params["input"], t=params["timestep"], **params["c"]) 131 | 132 | model_function_wrapper = self.work_model.model_options.get("model_function_wrapper", dummy_unet_apply) 133 | 134 | def hook_model_function_wrapper(unet_apply: Callable, params: UnetParams): 135 | return model_function_wrapper(unet_apply, params=apply_c_concat(params)) 136 | 137 | self.work_model.set_model_unet_function_wrapper(hook_model_function_wrapper) 138 | 139 | 140 | class ICLightModelLoader: 141 | """ 142 | Class for loading and managing the weights of ICLight models. 143 | """ 144 | 145 | def __init__(self): 146 | """ 147 | Initializes the ICLight model loader with default state. 148 | """ 149 | self.iclight = {"model": None, "sd_dict": None, "fbc": False} 150 | 151 | @classmethod 152 | def INPUT_TYPES(cls): 153 | """ 154 | Class method defining the required input types for model loading. 155 | 156 | Returns: 157 | dict: A dictionary specifying the required input types for the model load operation. 158 | """ 159 | return {"required": {"iclight_name": (folder_paths.get_filename_list(MODEL_TYPE_ICLIGHT),)}} 160 | 161 | RETURN_TYPES = ("ICLIGHT",) 162 | RETURN_NAMES = ("iclight",) 163 | FUNCTION = "load_iclight_model" 164 | CATEGORY = "gaffer" 165 | 166 | def load_iclight_model(self, iclight_name): 167 | """ 168 | Loads the weights of an ICLight model. 169 | 170 | Parameters: 171 | iclight_name (str): The name of the ICLight model to load. 172 | 173 | Returns: 174 | tuple: A tuple containing the loaded ICLight model information. 175 | 176 | Raises: 177 | Exception: If the specified ICLight model file is not found. 178 | """ 179 | LOGGER.info("Loading ICLight Model weights") 180 | 181 | # Safely retrieves the full path of the model and checks for its existence 182 | ckpt_path = folder_paths.get_full_path(MODEL_TYPE_ICLIGHT, iclight_name) 183 | if not os.path.exists(ckpt_path): 184 | error_message = f"Invalid ICLight model: {iclight_name}. File not found." 185 | LOGGER.error(error_message) 186 | raise Exception(error_message) 187 | 188 | # Loads the model securely 189 | model = load_torch_file(ckpt_path, safe_load=True) 190 | 191 | # converts it to a hardware-optimized precision 192 | sd_dict = convert_unet_state_dict(model) 193 | 194 | # Optimizes performance by converting the model's precision on demand 195 | sd_dict = {key: sd_dict[key].half() for key in sd_dict.keys()} 196 | 197 | # Updates the internal state to reflect the loaded model details 198 | self.iclight['model'] = model 199 | self.iclight['sd_dict'] = sd_dict 200 | self.iclight['fbc'] = 'fbc' in iclight_name.lower() 201 | 202 | LOGGER.info(f"ICLight model loaded from {iclight_name}") 203 | return (self.iclight,) 204 | 205 | 206 | class ApplyICLight: 207 | @classmethod 208 | def INPUT_TYPES(s): 209 | return { 210 | "required": { 211 | "model": ("MODEL",), 212 | "vae": ("VAE",), 213 | "iclight": ("ICLIGHT",), 214 | "positive": ("CONDITIONING",), 215 | "negative": ("CONDITIONING",), 216 | "fg_pixels": ("IMAGE",), 217 | "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), 218 | }, 219 | "optional": { 220 | "bg_pixels": ("IMAGE",), 221 | }, 222 | } 223 | 224 | RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT") 225 | RETURN_NAMES = ("model", "positive", "negative", "empty_latent") 226 | FUNCTION = "apply" 227 | CATEGORY = "gaffer" 228 | DESCRIPTION = """""" 229 | 230 | def apply(self, model: ModelPatcher, vae: VAE, iclight, positive, negative, fg_pixels, multiplier, bg_pixels=None): 231 | iclight_patch = ICLightPatch(model, vae, iclight) 232 | # add iclight patches 233 | iclight_patch.patch(multiplier) 234 | concat_conds = iclight_patch.get_concat_conds(fg_pixels, bg_pixels) 235 | iclight_patch.set_unet_function_wrapper(concat_conds) 236 | return (iclight_patch.work_model, positive, negative, {"samples": iclight_patch.empty_latent}) 237 | 238 | 239 | class LightSource: 240 | @classmethod 241 | def INPUT_TYPES(s): 242 | return { 243 | "required": { 244 | "light_position": ([member.value for member in LightPosition],), 245 | "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.001}), 246 | "start_color": ("STRING", {"default": "#FFFFFF"}), 247 | "end_color": ("STRING", {"default": "#000000"}), 248 | "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), 249 | "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }) 250 | } 251 | } 252 | 253 | RETURN_TYPES = ("IMAGE",) 254 | RETURN_NAMES = ("IMAGE",) 255 | FUNCTION = "execute" 256 | CATEGORY = "gaffer" 257 | DESCRIPTION = """ 258 | Generates a gradient image that can be used 259 | as a simple light source. The color can be 260 | specified in RGB or hex format. 261 | """ 262 | 263 | def execute(self, light_position, multiplier, start_color, end_color, width, height): 264 | def toRgb(color): 265 | if color.startswith('#') and len(color) == 7: # e.g. "#RRGGBB" 266 | color_rgb = tuple(int(color[i:i + 2], 16) for i in (1, 3, 5)) 267 | else: # e.g. "255,255,255" 268 | color_rgb = tuple(int(i) for i in color.split(',')) 269 | return color_rgb 270 | 271 | lightPosition = LightPosition(light_position) 272 | start_color_rgb = toRgb(start_color) 273 | end_color_rgb = toRgb(end_color) 274 | image = generate_gradient_image(width, height, start_color_rgb, end_color_rgb, multiplier, lightPosition) 275 | 276 | image = image.astype(np.float32) / 255.0 277 | image = torch.from_numpy(image)[None,] 278 | return (image,) 279 | 280 | 281 | class CalculateNormalMap: 282 | @classmethod 283 | def INPUT_TYPES(cls): 284 | return { 285 | "required": { 286 | "images": ("IMAGE",), 287 | "sigma": ("FLOAT", {"default": 10.0, "min": 0.01, "max": 100.0, "step": 0.01, }), 288 | "center_input_range": ("BOOLEAN", {"default": False, }), 289 | }, 290 | "optional": { 291 | "mask": ("MASK",), 292 | } 293 | } 294 | 295 | RETURN_TYPES = ("IMAGE",) 296 | RETURN_NAMES = ("normal",) 297 | FUNCTION = "execute" 298 | CATEGORY = "gaffer" 299 | DESCRIPTION = """ 300 | Calculates normal map from different directional exposures. 301 | Takes in 4 images as a batch: 302 | left, right, bottom, top 303 | """ 304 | 305 | def execute(self, images, sigma, center_input_range, mask=None): 306 | """ 307 | Executes an image processing algorithm to calculate and return a normal map. 308 | 309 | Parameters: 310 | images - A sequence of input images. 311 | sigma - The standard deviation for Gaussian blur, controlling the smoothness of the normal calculation. 312 | center_input_range - The range used to center the input images. 313 | mask - Optional, a mask image to specify the computation region. 314 | 315 | Returns: 316 | normal - The calculated normal map. 317 | """ 318 | 319 | # Input validation 320 | self.validate_inputs(images, sigma, center_input_range, mask) 321 | 322 | # Centering images to adjust the brightness levels to have a mean closer to zero 323 | images = self.center_images(images, center_input_range) 324 | 325 | # Compute the normal based on image intensities and Gaussian blur 326 | normal = self.calculate_normal(images, sigma, mask) 327 | 328 | # Normalize and clamp the normal values to ensure they stay within valid ranges 329 | normal = self.normalize_and_clamp(normal) 330 | 331 | return (normal,) 332 | 333 | def validate_inputs(self, images, sigma, center_input_range, mask): 334 | if not isinstance(images, torch.Tensor) or images.ndim != 4: 335 | raise ValueError("images must be a 4-dimensional torch.Tensor") 336 | if not (0.01 <= sigma <= 100.0): 337 | raise ValueError("sigma must be between 0.01 and 100.0") 338 | if not isinstance(center_input_range, bool): 339 | raise ValueError("center_input_range must be a boolean") 340 | if mask is not None and (not isinstance(mask, torch.Tensor) or mask.ndim != 3): 341 | raise ValueError("mask must be a 3-dimensional torch.Tensor") 342 | 343 | def center_images(self, images, center_input_range): 344 | if center_input_range: 345 | images = images * 0.5 + 0.5 346 | return images 347 | 348 | def calculate_normal(self, images, sigma, mask): 349 | images_np = images.numpy().astype(np.float32) 350 | left = images_np[0] 351 | right = images_np[1] 352 | bottom = images_np[2] 353 | top = images_np[3] 354 | ambient = (left + right + bottom + top) / 4.0 355 | height, width, _ = ambient.shape 356 | 357 | def safe_divide(a, b): 358 | epsilon = 1e-5 359 | return ((a + epsilon) / (b + epsilon)) - 1.0 360 | 361 | left = safe_divide(left, ambient) 362 | right = safe_divide(right, ambient) 363 | bottom = safe_divide(bottom, ambient) 364 | top = safe_divide(top, ambient) 365 | 366 | u = (right - left) * 0.5 367 | v = (top - bottom) * 0.5 368 | 369 | u = np.mean(u, axis=2) 370 | v = np.mean(v, axis=2) 371 | h = (1.0 - u ** 2.0 - v ** 2.0).clip(0, 1e5) ** (0.5 * sigma) 372 | z = np.zeros_like(h) 373 | 374 | normal = np.stack([u, v, h], axis=2) 375 | normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 376 | 377 | if mask is not None: 378 | matting = mask.numpy().astype(np.float32) 379 | matting = matting[..., np.newaxis] 380 | normal = normal * matting + np.stack([z, z, 1 - z], axis=2) * (1 - matting) 381 | normal = torch.from_numpy(normal) 382 | else: 383 | normal += np.stack([z, z, 1 - z], axis=2) 384 | normal = torch.from_numpy(normal).unsqueeze(0) 385 | 386 | return normal 387 | 388 | def normalize_and_clamp(self, normal): 389 | normal = (normal + 1.0) / 2.0 390 | normal = torch.clamp(normal, 0, 1) 391 | return normal 392 | 393 | 394 | class GrayScaler: 395 | """Class to scale image regions to grey based on provided masks.""" 396 | @classmethod 397 | def INPUT_TYPES(s): 398 | return { 399 | "required": { 400 | "image": ("IMAGE",), 401 | "mask": ("MASK",), 402 | "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), 403 | } 404 | } 405 | 406 | CATEGORY = "gaffer" 407 | RETURN_TYPES = ("IMAGE",) 408 | FUNCTION = "apply" 409 | 410 | def apply(self, image: torch.Tensor, mask: torch.Tensor, multiplier): 411 | """ 412 | Applies grey scaling to image regions as indicated by the mask. 413 | 414 | Args: 415 | image: torch.Tensor: The input image tensor. 416 | mask: torch.Tensor: A mask tensor where 1 indicates areas to be converted to grey. 417 | multiplier: float: A value to control the intensity of the grey conversion. 418 | Returns: 419 | tuple: A tuple containing the image with masked regions turned grey. 420 | """ 421 | # Validate inputs 422 | if not isinstance(image, torch.Tensor) or not isinstance(mask, torch.Tensor): 423 | raise ValueError("image and mask must be torch.Tensor types.") 424 | if image.ndim != 4 or mask.ndim not in [3, 4]: 425 | raise ValueError("image must be a 4D tensor, and mask must be a 3D or 4D tensor.") 426 | 427 | # Adjust mask dimensions if necessary 428 | if mask.ndim == 3: 429 | # [B, H, W] => [B, H, W, C=1] 430 | mask = mask.unsqueeze(-1) 431 | 432 | grey_value = 0.5 * multiplier 433 | image = image * mask + (1 - mask) * grey_value 434 | 435 | return (image,) 436 | 437 | 438 | NODE_CLASS_MAPPINGS = { 439 | "ICLightModelLoader": ICLightModelLoader, 440 | "ApplyICLight": ApplyICLight, 441 | "LightSource": LightSource, 442 | "CalculateNormalMap": CalculateNormalMap, 443 | "GrayScaler": GrayScaler 444 | } 445 | NODE_DISPLAY_NAME_MAPPINGS = { 446 | "ICLightModelLoader": "Load ICLight Model", 447 | "ApplyICLight": "Apply ICLight", 448 | "LightSource": "Simple Light Source", 449 | "CalculateNormalMap": "Calculate Normal Map", 450 | "GrayScaler": "Gray Scaler", 451 | } 452 | -------------------------------------------------------------------------------- /image/all.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/all.png -------------------------------------------------------------------------------- /image/node_apply-iclight.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/node_apply-iclight.png -------------------------------------------------------------------------------- /image/node_calculate_normal_map.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/node_calculate_normal_map.png -------------------------------------------------------------------------------- /image/node_gray-scaler.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/node_gray-scaler.png -------------------------------------------------------------------------------- /image/node_load-iclight-model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/node_load-iclight-model.png -------------------------------------------------------------------------------- /image/node_simple-light-source.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/image/node_simple-light-source.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui-gaffer" 3 | description = "Nodes:Load ICLight Model,Apply ICLight,Simple Light Source,Calculate Normal Map" 4 | version = "1.0.0" 5 | license = "LICENSE" 6 | dependencies = [] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/huagetai/ComfyUI-Gaffer" 10 | # Used by Comfy Registry https://comfyregistry.org 11 | 12 | [tool.comfy] 13 | PublisherId = "" 14 | DisplayName = "ComfyUI-Gaffer" 15 | Icon = "" 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huagetai/ComfyUI-Gaffer/e2301a5dc9a169057dcd349ea6cd289aac881e9f/requirements.txt -------------------------------------------------------------------------------- /utils/image.py: -------------------------------------------------------------------------------- 1 | ### Light Source 2 | import numpy as np 3 | from enum import Enum 4 | 5 | import torch 6 | import comfy 7 | 8 | 9 | class LightPosition(Enum): 10 | LEFT = "Left Light" 11 | RIGHT = "Right Light" 12 | TOP = "Top Light" 13 | BOTTOM = "Bottom Light" 14 | TOP_LEFT = "Top Left Light" 15 | TOP_RIGHT = "Top Right Light" 16 | BOTTOM_LEFT = "Bottom Left Light" 17 | BOTTOM_RIGHT = "Bottom Right Light" 18 | 19 | 20 | def generate_gradient_image(width: int, height: int, start_color: tuple, end_color: tuple, multiplier: float, 21 | lightPosition: LightPosition): 22 | """ 23 | Generate a gradient image with a light source effect. 24 | 25 | Parameters: 26 | width (int): Width of the image. 27 | height (int): Height of the image. 28 | start_color: Starting color RGB of the gradient. 29 | end_color: Ending color RGB of the gradient. 30 | multiplier: Weight of light. 31 | lightPosition (LightPosition): Position of the light source. 32 | 33 | Returns: 34 | np.array: 2D gradient image array. 35 | """ 36 | # Create a gradient from 0 to 1 and apply multiplier 37 | if lightPosition == LightPosition.LEFT: 38 | gradient = np.tile(np.linspace(0, 1, width) ** multiplier, (height, 1)) 39 | elif lightPosition == LightPosition.RIGHT: 40 | gradient = np.tile(np.linspace(1, 0, width) ** multiplier, (height, 1)) 41 | elif lightPosition == LightPosition.TOP: 42 | gradient = np.tile(np.linspace(0, 1, height) ** multiplier, (width, 1)).T 43 | elif lightPosition == LightPosition.BOTTOM: 44 | gradient = np.tile(np.linspace(1, 0, height) ** multiplier, (width, 1)).T 45 | elif lightPosition == LightPosition.BOTTOM_RIGHT: 46 | x = np.linspace(1, 0, width) ** multiplier 47 | y = np.linspace(1, 0, height) ** multiplier 48 | x_mesh, y_mesh = np.meshgrid(x, y) 49 | gradient = np.sqrt(x_mesh ** 2 + y_mesh ** 2) / np.sqrt(2.0) 50 | elif lightPosition == LightPosition.BOTTOM_LEFT: 51 | x = np.linspace(0, 1, width) ** multiplier 52 | y = np.linspace(1, 0, height) ** multiplier 53 | x_mesh, y_mesh = np.meshgrid(x, y) 54 | gradient = np.sqrt(x_mesh ** 2 + y_mesh ** 2) / np.sqrt(2.0) 55 | elif lightPosition == LightPosition.TOP_RIGHT: 56 | x = np.linspace(1, 0, width) ** multiplier 57 | y = np.linspace(0, 1, height) ** multiplier 58 | x_mesh, y_mesh = np.meshgrid(x, y) 59 | gradient = np.sqrt(x_mesh ** 2 + y_mesh ** 2) / np.sqrt(2.0) 60 | elif lightPosition == LightPosition.TOP_LEFT: 61 | x = np.linspace(0, 1, width) ** multiplier 62 | y = np.linspace(0, 1, height) ** multiplier 63 | x_mesh, y_mesh = np.meshgrid(x, y) 64 | gradient = np.sqrt(x_mesh ** 2 + y_mesh ** 2) / np.sqrt(2.0) 65 | else: 66 | raise ValueError(f"Unsupported position. Choose from {', '.join([member.value for member in LightPosition])}.") 67 | 68 | # Interpolate between start_color and end_color based on the gradient 69 | gradient_img = np.zeros((height, width, 3), dtype=np.float32) 70 | for i in range(3): 71 | gradient_img[..., i] = start_color[i] + (end_color[i] - start_color[i]) * gradient 72 | 73 | gradient_img = np.clip(gradient_img, 0, 255).astype(np.uint8) 74 | return gradient_img 75 | 76 | 77 | def p(image): 78 | return image.permute([0, 3, 1, 2]) 79 | 80 | 81 | def pb(image): 82 | return image.permute([0, 2, 3, 1]) 83 | 84 | 85 | def resize_and_center_crop(image: torch.Tensor, target_width: int, target_height: int): 86 | _, original_height, original_width, _ = image.shape 87 | if original_height == 0 or original_width == 0: 88 | raise ValueError("The width and height of the image cannot be 0") 89 | 90 | if original_width == target_width and original_height == target_height: 91 | return image 92 | 93 | outputs = p(image) 94 | outputs = comfy.utils.common_upscale(outputs, target_width, target_height, "lanczos", "center") 95 | outputs = pb(outputs) 96 | 97 | return outputs 98 | -------------------------------------------------------------------------------- /utils/patches.py: -------------------------------------------------------------------------------- 1 | #credit to huchenlei for this 2 | #from https://github.com/huchenlei/ComfyUI-layerdiffuse/blob/151f7460bbc9d7437d4f0010f21f80178f7a84a6/layered_diffusion.py#L34-L96 3 | 4 | import torch 5 | import functools 6 | from comfy.model_patcher import ModelPatcher 7 | import comfy.model_management 8 | 9 | def calculate_weight_adjust_channel(func): 10 | """Patches ComfyUI's LoRA weight application to accept multi-channel inputs.""" 11 | 12 | @functools.wraps(func) 13 | def calculate_weight( 14 | self: ModelPatcher, patches, weight: torch.Tensor, key: str 15 | ) -> torch.Tensor: 16 | weight = func(self, patches, weight, key) 17 | 18 | for p in patches: 19 | alpha = p[0] 20 | v = p[1] 21 | 22 | # The recursion call should be handled in the main func call. 23 | if isinstance(v, list): 24 | continue 25 | 26 | if len(v) == 1: 27 | patch_type = "diff" 28 | elif len(v) == 2: 29 | patch_type = v[0] 30 | v = v[1] 31 | 32 | if patch_type == "diff": 33 | w1 = v[0] 34 | if all( 35 | ( 36 | alpha != 0.0, 37 | w1.shape != weight.shape, 38 | w1.ndim == weight.ndim == 4, 39 | ) 40 | ): 41 | new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)] 42 | print( 43 | f"IC-Light: Merged with {key} channel changed from {weight.shape} to {new_shape}" 44 | ) 45 | new_diff = alpha * comfy.model_management.cast_to_device( 46 | w1, weight.device, weight.dtype 47 | ) 48 | new_weight = torch.zeros(size=new_shape).to(weight) 49 | new_weight[ 50 | : weight.shape[0], 51 | : weight.shape[1], 52 | : weight.shape[2], 53 | : weight.shape[3], 54 | ] = weight 55 | new_weight[ 56 | : new_diff.shape[0], 57 | : new_diff.shape[1], 58 | : new_diff.shape[2], 59 | : new_diff.shape[3], 60 | ] += new_diff 61 | new_weight = new_weight.contiguous().clone() 62 | weight = new_weight 63 | return weight 64 | 65 | return calculate_weight --------------------------------------------------------------------------------