import torch from .imagefunc import log, tensor2pil, pil2tensor, gaussian_blur class GaussianBlur: def __init__(self): self.NODE_NAME = 'GaussianBlur' @classmethod def INPUT_TYPES(self): return { "required": { "image": ("IMAGE", ), # "blur": ("INT", {"default": 20, "min": 1, "max": 999, "step": 1}), # 模糊 }, "optional": { } } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image",) FUNCTION = 'gaussian_blur' CATEGORY = '😺dzNodes/LayerFilter' def gaussian_blur(self, image, blur): ret_images = [] for i in image: _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') ret_images.append(pil2tensor(gaussian_blur(_canvas, blur))) log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') return (torch.cat(ret_images, dim=0),) class LS_GaussianBlurV2: def __init__(self): self.NODE_NAME = 'GaussianBlurV2' @classmethod def INPUT_TYPES(self): return { "required": { "image": ("IMAGE", ), # "blur": ("FLOAT", {"default": 20, "min": 0, "max": 1000, "step": 0.05}), # 模糊 }, "optional": { } } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image",) FUNCTION = 'gaussian_blur_v2' CATEGORY = '😺dzNodes/LayerFilter' def gaussian_blur_v2(self, image, blur): ret_images = [] if blur: for i in image: _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') ret_images.append(pil2tensor(gaussian_blur(_canvas, blur))) else: return (image,) log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') return (torch.cat(ret_images, dim=0),) NODE_CLASS_MAPPINGS = { "LayerFilter: GaussianBlur": GaussianBlur, "LayerFilter: GaussianBlurV2": LS_GaussianBlurV2 } NODE_DISPLAY_NAME_MAPPINGS = { "LayerFilter: GaussianBlur": "LayerFilter: GaussianBlur", "LayerFilter: GaussianBlurV2": "LayerFilter: Gaussian Blur V2" }