From f205eb704eed49d5696012849351724fd25c2a7d Mon Sep 17 00:00:00 2001 From: doctorpangloss <2229300+doctorpangloss@users.noreply.github.com> Date: Mon, 6 Oct 2025 15:50:41 -0700 Subject: [PATCH] Sherlock a node from EasyInpaint --- comfy_extras/nodes/nodes_inpainting.py | 165 ++++++++++++++++++++++++- 1 file changed, 163 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes/nodes_inpainting.py b/comfy_extras/nodes/nodes_inpainting.py index d1e1abe77..ec81acd82 100644 --- a/comfy_extras/nodes/nodes_inpainting.py +++ b/comfy_extras/nodes/nodes_inpainting.py @@ -2,6 +2,8 @@ from typing import NamedTuple, Optional import torch import torch.nn.functional as F +import comfy.utils + from jaxtyping import Float from torch import Tensor @@ -78,8 +80,8 @@ def composite( return destination padded_source = torch.zeros_like(destination) padded_source[:, :, dest_y_start:dest_y_end, dest_x_start:dest_x_end] = source[ - :, :, src_y_start:src_y_end, src_x_start:src_x_end - ] + :, :, src_y_start:src_y_end, src_x_start:src_x_end + ] if mask is None: final_mask = torch.zeros(dest_b, 1, dest_h, dest_w, device=destination.device) final_mask[:, :, dest_y_start:dest_y_end, dest_x_start:dest_x_end] = 1.0 @@ -233,11 +235,170 @@ class CompositeCroppedAndFittedInpaintResult(CustomNode): return final_image.permute(0, 2, 3, 1), +class ImageAndMaskResizeNode: + """ + Sherlocked from https://github.com/CY-CHENYUE/ComfyUI-InpaintEasy + + MIT License + + Copyright (c) 2024 CYCHENYUE + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + """ + DESCRIPTION = "Resize the image and mask simultaneously (from InpaintEasy- 同时调整图片和蒙版的大小)" + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center", "top_left", "top_right", "bottom_left", "bottom_right"] + + def __init__(self): + self.type = "ImageMaskResize" + self.output_node = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "width": ("INT", { + "default": 512, + "min": 64, + "max": 8192, + "step": 8 + }), + "height": ("INT", { + "default": 512, + "min": 64, + "max": 8192, + "step": 8 + }), + "resize_method": (s.upscale_methods, {"default": "lanczos"}), + "crop": (s.crop_methods, {"default": "disabled"}), + "mask_blur_radius": ("INT", { + "default": 10, + "min": 0, + "max": 64, + "step": 1 + }), + } + } + + RETURN_TYPES = ("IMAGE", "MASK",) + RETURN_NAMES = ("image", "mask",) + FUNCTION = "resize_image_and_mask" + + CATEGORY = "inpaint" + + def resize_image_and_mask(self, image, mask, width, height, resize_method="lanczos", crop="disabled", mask_blur_radius=0): + # 处理宽高为0的情况 + if width == 0 and height == 0: + return (image, mask) + + # 对于图像的处理 + samples = image.movedim(-1, 1) # NHWC -> NCHW + 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])) + + # 使用 torch.nn.functional 直接进行缩放和裁剪 + if crop != "disabled": + old_width = samples.shape[3] + old_height = samples.shape[2] + + # 计算缩放比例 + scale = max(width / old_width, height / old_height) + scaled_width = int(old_width * scale) + scaled_height = int(old_height * scale) + + # 使用 common_upscale 进行缩放 + samples = comfy.utils.common_upscale(samples, scaled_width, scaled_height, resize_method, crop="disabled") + + # 蒙版始终使用bilinear插值 + mask = F.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(scaled_height, scaled_width), mode='bilinear', align_corners=True) + + # 计算裁剪位置 + crop_x = 0 + crop_y = 0 + + if crop == "center": + crop_x = (scaled_width - width) // 2 + crop_y = (scaled_height - height) // 2 + elif crop == "top_left": + crop_x = 0 + crop_y = 0 + elif crop == "top_right": + crop_x = scaled_width - width + crop_y = 0 + elif crop == "bottom_left": + crop_x = 0 + crop_y = scaled_height - height + elif crop == "bottom_right": + crop_x = scaled_width - width + crop_y = scaled_height - height + elif crop == "random": + crop_x = torch.randint(0, max(1, scaled_width - width), (1,)).item() + crop_y = torch.randint(0, max(1, scaled_height - height), (1,)).item() + + # 执行裁剪 + samples = samples[:, :, crop_y:crop_y + height, crop_x:crop_x + width] + mask = mask[:, :, crop_y:crop_y + height, crop_x:crop_x + width] + else: + # 直接使用 common_upscale 调整大小 + samples = comfy.utils.common_upscale(samples, width, height, resize_method, crop="disabled") + mask = F.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(height, width), mode='bilinear', align_corners=True) + + image_resized = samples.movedim(1, -1) # NCHW -> NHWC + mask_resized = mask.squeeze(1) # NCHW -> NHW + + # 在返回之前添加高斯模糊处理 + if mask_blur_radius > 0: + # 创建高斯核 + kernel_size = mask_blur_radius * 2 + 1 + x = torch.arange(kernel_size, dtype=torch.float32, device=mask_resized.device) + x = x - (kernel_size - 1) / 2 + gaussian = torch.exp(-(x ** 2) / (2 * (mask_blur_radius / 3) ** 2)) + gaussian = gaussian / gaussian.sum() + + # 将kernel转换为2D + gaussian_2d = gaussian.view(1, -1) * gaussian.view(-1, 1) + gaussian_2d = gaussian_2d.view(1, 1, kernel_size, kernel_size) + + # 应用高斯模糊 + mask_for_blur = mask_resized.unsqueeze(1) # Add channel dimension + # 对边界进行padding，使用reflect模式避免边缘问题 + padding = kernel_size // 2 + mask_padded = F.pad(mask_for_blur, (padding, padding, padding, padding), mode='reflect') + mask_resized = F.conv2d(mask_padded, gaussian_2d.to(mask_resized.device), padding=0).squeeze(1) + + # 确保值在0-1范围内 + mask_resized = torch.clamp(mask_resized, 0, 1) + + return (image_resized, mask_resized) + + NODE_CLASS_MAPPINGS = { "CropAndFitInpaintToDiffusionSize": CropAndFitInpaintToDiffusionSize, "CompositeCroppedAndFittedInpaintResult": CompositeCroppedAndFittedInpaintResult, + "ImageAndMaskResizeNode": ImageAndMaskResizeNode } NODE_DISPLAY_NAME_MAPPINGS = { "CropAndFitInpaintToDiffusionSize": "Crop & Fit Inpaint Region", "CompositeCroppedAndFittedInpaintResult": "Composite Inpaint Result", + "ImageAndMaskResizeNode": "Image and Mask Resize" }