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for comfyui image proprocessor 2 | Adapt for Hunyuan now 3 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] -------------------------------------------------------------------------------- /node.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | from PIL import Image 4 | import torch 5 | 6 | def pil2tensor(image: Image) -> torch.Tensor: 7 | return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) 8 | 9 | def tensor2pil(t_image: torch.Tensor) -> Image: 10 | return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) 11 | 12 | def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0): 13 | if ksize % 2 == 0: 14 | ksize += 1 # ksize must be odd 15 | blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX) 16 | return blurred_image 17 | 18 | def apply_guided_filter(image_np, radius, eps): 19 | # Convert image to float32 for the guided filter 20 | image_np_float = np.float32(image_np) / 255.0 21 | # Apply the guided filter 22 | filtered_image = cv2.ximgproc.guidedFilter(image_np_float, image_np_float, radius, eps) 23 | # Scale back to uint8 24 | filtered_image = np.clip(filtered_image * 255, 0, 255).astype(np.uint8) 25 | return filtered_image 26 | 27 | class TTPlanet_Tile_Preprocessor_GF: 28 | def __init__(self, blur_strength=3.0, radius=7, eps=0.01): 29 | self.blur_strength = blur_strength 30 | self.radius = radius 31 | self.eps = eps 32 | 33 | @classmethod 34 | def INPUT_TYPES(cls): 35 | return { 36 | "required": { 37 | "image": ("IMAGE",), 38 | "scale_factor": ("FLOAT", {"default": 1.00, "min": 1.00, "max": 8.00, "step": 0.05}), 39 | "blur_strength": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 10.0, "step": 0.1}), 40 | "radius": ("INT", {"default": 7, "min": 1, "max": 20, "step": 1}), 41 | "eps": ("FLOAT", {"default": 0.01, "min": 0.001, "max": 0.1, "step": 0.001}), 42 | }, 43 | "optional": {} 44 | } 45 | 46 | RETURN_TYPES = ("IMAGE",) 47 | RETURN_NAMES = ("image_output",) 48 | FUNCTION = 'process_image' 49 | CATEGORY = 'TTP_TILE' 50 | 51 | def process_image(self, image, scale_factor, blur_strength, radius, eps): 52 | ret_images = [] 53 | 54 | for i in image: 55 | # Convert tensor to PIL for processing 56 | _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') 57 | img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR 58 | 59 | # Apply Gaussian blur 60 | img_np = apply_gaussian_blur(img_np, ksize=int(blur_strength), sigmaX=blur_strength / 2) 61 | 62 | # Apply Guided Filter 63 | img_np = apply_guided_filter(img_np, radius, eps) 64 | 65 | 66 | # Resize image 67 | height, width = img_np.shape[:2] 68 | new_width = int(width / scale_factor) 69 | new_height = int(height / scale_factor) 70 | resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) 71 | resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LINEAR) 72 | 73 | 74 | 75 | # Convert OpenCV back to PIL and then to tensor 76 | pil_img = Image.fromarray(resized_img[:, :, ::-1]) # BGR to RGB 77 | tensor_img = pil2tensor(pil_img) 78 | ret_images.append(tensor_img) 79 | 80 | return (torch.cat(ret_images, dim=0),) 81 | 82 | class TTPlanet_Tile_Preprocessor_Simple: 83 | def __init__(self, blur_strength=3.0): 84 | self.blur_strength = blur_strength 85 | 86 | @classmethod 87 | def INPUT_TYPES(cls): 88 | return { 89 | "required": { 90 | "image": ("IMAGE",), 91 | "scale_factor": ("FLOAT", {"default": 2.00, "min": 1.00, "max": 8.00, "step": 0.05}), 92 | "blur_strength": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 20.0, "step": 0.1}), 93 | }, 94 | "optional": {} 95 | } 96 | 97 | RETURN_TYPES = ("IMAGE",) 98 | RETURN_NAMES = ("image_output",) 99 | FUNCTION = 'process_image' 100 | CATEGORY = 'TTP_TILE' 101 | 102 | def process_image(self, image, scale_factor, blur_strength): 103 | ret_images = [] 104 | 105 | for i in image: 106 | # Convert tensor to PIL for processing 107 | _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') 108 | 109 | # Convert PIL image to OpenCV format 110 | img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR 111 | 112 | # Resize image first if you want blur to apply after resizing 113 | height, width = img_np.shape[:2] 114 | new_width = int(width / scale_factor) 115 | new_height = int(height / scale_factor) 116 | resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) 117 | resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LINEAR) 118 | 119 | # Apply Gaussian blur after resizing 120 | img_np = apply_gaussian_blur(resized_img, ksize=int(blur_strength), sigmaX=blur_strength / 2) 121 | 122 | # Convert OpenCV back to PIL and then to tensor 123 | _canvas = Image.fromarray(img_np[:, :, ::-1]) # BGR to RGB 124 | tensor_img = pil2tensor(_canvas) 125 | ret_images.append(tensor_img) 126 | 127 | return (torch.cat(ret_images, dim=0),) 128 | 129 | class TTPlanet_Tile_Preprocessor_cufoff: 130 | def __init__(self, blur_strength=3.0, cutoff_frequency=30, filter_strength=1.0): 131 | self.blur_strength = blur_strength 132 | self.cutoff_frequency = cutoff_frequency 133 | self.filter_strength = filter_strength 134 | 135 | @classmethod 136 | def INPUT_TYPES(cls): 137 | return { 138 | "required": { 139 | "image": ("IMAGE",), 140 | "scale_factor": ("FLOAT", {"default": 1.00, "min": 1.00, "max": 8.00, "step": 0.05}), 141 | "blur_strength": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 10.0, "step": 0.1}), 142 | "cutoff_frequency": ("INT", {"default": 100, "min": 0, "max": 256, "step": 1}), 143 | "filter_strength": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}), 144 | }, 145 | "optional": {} 146 | } 147 | 148 | RETURN_TYPES = ("IMAGE",) 149 | RETURN_NAMES = ("image_output",) 150 | FUNCTION = 'process_image' 151 | CATEGORY = 'TTP_TILE' 152 | 153 | def process_image(self, image, scale_factor, blur_strength, cutoff_frequency, filter_strength): 154 | ret_images = [] 155 | 156 | for i in image: 157 | # Convert tensor to PIL for processing 158 | _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') 159 | img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR 160 | 161 | # Apply low pass filter with new strength parameter 162 | img_np = apply_low_pass_filter(img_np, cutoff_frequency, filter_strength) 163 | 164 | # Resize image 165 | height, width = img_np.shape[:2] 166 | new_width = int(width / scale_factor) 167 | new_height = int(height / scale_factor) 168 | resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA) 169 | resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LINEAR) 170 | 171 | # Apply Gaussian blur 172 | img_np = apply_gaussian_blur(img_np, ksize=int(blur_strength), sigmaX=blur_strength / 2) 173 | 174 | # Convert OpenCV back to PIL and then to tensor 175 | pil_img = Image.fromarray(resized_img[:, :, ::-1]) # BGR to RGB 176 | tensor_img = pil2tensor(pil_img) 177 | ret_images.append(tensor_img) 178 | 179 | return (torch.cat(ret_images, dim=0),) 180 | 181 | 182 | def mask_to_pil(mask) -> Image: 183 | if isinstance(mask, torch.Tensor): 184 | mask_np = mask.squeeze().cpu().numpy() 185 | elif isinstance(mask, np.ndarray): 186 | mask_np = mask 187 | else: 188 | raise TypeError("Unsupported mask type") 189 | mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8)) 190 | return mask_pil 191 | 192 | class MaskBlackener: 193 | def __init__(self): 194 | pass 195 | 196 | @classmethod 197 | def INPUT_TYPES(cls): 198 | return { 199 | "required": { 200 | "image": ("IMAGE",), 201 | "mask": ("MASK",), 202 | }, 203 | } 204 | 205 | RETURN_TYPES = ("IMAGE",) 206 | RETURN_NAMES = ("blackened_image",) 207 | FUNCTION = 'apply_black_mask' 208 | CATEGORY = 'Image Processing' 209 | 210 | def apply_black_mask(self, image, mask): 211 | # Convert image tensor to PIL 212 | image_pil = tensor2pil(image.squeeze(0)).convert('RGB') 213 | 214 | # Convert mask to PIL 215 | mask_pil = mask_to_pil(mask).convert('L') 216 | 217 | # Create a black image of the same size 218 | black_image = Image.new('RGB', image_pil.size, (0, 0, 0)) 219 | 220 | # Apply the mask: use the original image where mask is black, and black image where mask is white 221 | blackened_image = Image.composite(black_image, image_pil, mask_pil) 222 | 223 | # Convert the result back to tensor 224 | blackened_image_tensor = pil2tensor(blackened_image) 225 | 226 | return (blackened_image_tensor,) 227 | 228 | NODE_CLASS_MAPPINGS = { 229 | "TTPlanet_Tile_Preprocessor_GF": TTPlanet_Tile_Preprocessor_GF, 230 | "TTPlanet_Tile_Preprocessor_Simple": TTPlanet_Tile_Preprocessor_Simple, 231 | "TTPlanet_Tile_Preprocessor_cufoff": TTPlanet_Tile_Preprocessor_cufoff, 232 | "TTPlanet_inpainting_Preprecessor": MaskBlackener 233 | } 234 | 235 | NODE_DISPLAY_NAME_MAPPINGS = { 236 | "TTPlanet_Tile_Preprocessor_GF": "🪐TTP Tile Preprocessor HYDiT GF", 237 | "TTPlanet_Tile_Preprocessor_Simple": "🪐TTP Tile Preprocessor HYDiT Simple", 238 | "TTPlanet_Tile_Preprocessor_cufoff": "🪐TTP Tile Preprocessor HYDiT cufoff", 239 | "TTPlanet_inpainting_Preprecessor" : "🪐TTP Inpainting Preprocessor HYDiT" 240 | } -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python-headless 2 | --------------------------------------------------------------------------------