import torch import numpy as np import cv2 import comfy.utils from typing import List, Dict, Any, Tuple class DDImageUniformSize: """ DD 图像统一尺寸 - 将输入的图像或视频统一调整为指定分辨率 支持多输入端口，多种缩放方法和尺寸适配策略 只有接入内容的输入端口才会生成对应的输出 """ @classmethod def INPUT_TYPES(cls): # 基本配置 inputs = { "required": { "缩放方法": (["邻近-精确", "双线性插值", "区域", "双三次插值", "lanczos"], {"default": "双线性插值"}), "宽度": ("INT", {"default": 512, "min": 8, "max": 8192, "step": 8}), "高度": ("INT", {"default": 512, "min": 8, "max": 8192, "step": 8}), "尺寸适配": (["自适应", "拉伸", "裁剪", "填充"], {"default": "自适应"}), }, "optional": { # 四个固定的可选输入端口 "图片A": ("IMAGE",), "图片B": ("IMAGE",), "图片C": ("IMAGE",), "图片D": ("IMAGE",), } } return inputs # 默认输出端口设置 - 所有可能的输出 RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE") RETURN_NAMES = ("图片A", "图片B", "图片C", "图片D") FUNCTION = "resize_images" CATEGORY = "🍺DD系列节点" # 常量：插值方法映射 INTERPOLATION_MAP = { "邻近-精确": cv2.INTER_NEAREST_EXACT, "双线性插值": cv2.INTER_LINEAR, "区域": cv2.INTER_AREA, "双三次插值": cv2.INTER_CUBIC, "lanczos": cv2.INTER_LANCZOS4 } def _resize_batch(self, image_batch, target_size, interpolation_mode, size_adapt): """调整一批图像的大小""" if image_batch is None: return None # 确保输入为张量 if not isinstance(image_batch, torch.Tensor): return None # 获取图像尺寸 if len(image_batch.shape) == 3: # 单张图片 [H, W, C] image_batch = image_batch.unsqueeze(0) # [1, H, W, C] batch_size, height, width, channels = image_batch.shape target_height, target_width = target_size result = None # 获取插值方法 interpolation = self.INTERPOLATION_MAP.get(interpolation_mode, cv2.INTER_LINEAR) # 根据尺寸适配方法调整图像 if size_adapt == "拉伸": # 直接调整到目标尺寸 result = self._batch_resize(image_batch, target_size, interpolation) elif size_adapt == "自适应": # 保持宽高比缩放 result = self._batch_adaptive_resize(image_batch, target_size, interpolation) elif size_adapt == "裁剪": # 保持宽高比缩放后居中裁剪 result = self._batch_center_crop(image_batch, target_size, interpolation) elif size_adapt == "填充": # 保持宽高比缩放后填充 result = self._batch_pad(image_batch, target_size, interpolation) else: # 默认使用自适应 result = self._batch_adaptive_resize(image_batch, target_size, interpolation) return result def _batch_resize(self, batch, target_size, interpolation): """批量调整尺寸 - 直接拉伸""" # 使用PyTorch内置的resize函数 target_height, target_width = target_size # 将图像从BHWC转换为BCHW batch_bchw = batch.permute(0, 3, 1, 2) torch_mode = self._get_torch_mode(interpolation) # 只在适当的模式下使用align_corners参数 if torch_mode in ['bilinear', 'bicubic']: resized = torch.nn.functional.interpolate( batch_bchw, size=(target_height, target_width), mode=torch_mode, align_corners=False ) else: # 对于'nearest'和'area'模式不使用align_corners参数 resized = torch.nn.functional.interpolate( batch_bchw, size=(target_height, target_width), mode=torch_mode ) # 转回BHWC return resized.permute(0, 2, 3, 1) def _get_torch_mode(self, cv2_interpolation): """将OpenCV插值模式转换为PyTorch模式""" if cv2_interpolation in [cv2.INTER_NEAREST, cv2.INTER_NEAREST_EXACT]: return 'nearest' elif cv2_interpolation == cv2.INTER_LINEAR: return 'bilinear' elif cv2_interpolation == cv2.INTER_CUBIC: return 'bicubic' elif cv2_interpolation == cv2.INTER_AREA: return 'area' else: return 'bilinear' # 默认返回双线性 def _batch_adaptive_resize(self, batch, target_size, interpolation): """批量自适应调整尺寸 - 保持宽高比""" batch_size, height, width, channels = batch.shape target_height, target_width = target_size # 计算缩放比例 ratio = min(target_width / width, target_height / height) new_width = int(width * ratio) new_height = int(height * ratio) # 先调整大小 batch_bchw = batch.permute(0, 3, 1, 2) torch_mode = self._get_torch_mode(interpolation) # 只在适当的模式下使用align_corners参数 if torch_mode in ['bilinear', 'bicubic']: resized = torch.nn.functional.interpolate( batch_bchw, size=(new_height, new_width), mode=torch_mode, align_corners=False ) else: # 对于'nearest'和'area'模式不使用align_corners参数 resized = torch.nn.functional.interpolate( batch_bchw, size=(new_height, new_width), mode=torch_mode ) # 创建目标大小的空张量 result = torch.zeros(batch_size, channels, target_height, target_width, device=batch.device) # 计算偏移量 y_offset = (target_height - new_height) // 2 x_offset = (target_width - new_width) // 2 # 将调整后的图像放在中心 result[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized # 转回BHWC return result.permute(0, 2, 3, 1) def _batch_center_crop(self, batch, target_size, interpolation): """批量中心裁剪 - 先调整大小然后裁剪""" batch_size, height, width, channels = batch.shape target_height, target_width = target_size # 计算缩放比例 - 以较大的比例为准，确保裁剪 ratio = max(target_width / width, target_height / height) new_width = int(width * ratio) new_height = int(height * ratio) # 调整大小 batch_bchw = batch.permute(0, 3, 1, 2) torch_mode = self._get_torch_mode(interpolation) # 只在适当的模式下使用align_corners参数 if torch_mode in ['bilinear', 'bicubic']: resized = torch.nn.functional.interpolate( batch_bchw, size=(new_height, new_width), mode=torch_mode, align_corners=False ) else: # 对于'nearest'和'area'模式不使用align_corners参数 resized = torch.nn.functional.interpolate( batch_bchw, size=(new_height, new_width), mode=torch_mode ) # 计算裁剪区域 y_start = (new_height - target_height) // 2 x_start = (new_width - target_width) // 2 # 裁剪中心区域 cropped = resized[:, :, y_start:y_start + target_height, x_start:x_start + target_width] # 转回BHWC return cropped.permute(0, 2, 3, 1) def _batch_pad(self, batch, target_size, interpolation): """批量填充 - 保持宽高比并填充""" # 这与自适应调整相同，因为我们已经创建了全零背景并居中放置调整后的图像 return self._batch_adaptive_resize(batch, target_size, interpolation) def resize_images(self, 缩放方法, 宽度, 高度, 尺寸适配, 图片A=None, 图片B=None, 图片C=None, 图片D=None): """根据指定参数统一调整所有输入图像的大小""" target_size = (高度, 宽度) # (H, W) results = [] # 创建输入图像和名称的映射 input_images = { "图片A": 图片A, "图片B": 图片B, "图片C": 图片C, "图片D": 图片D } # 过滤出有内容的输入 valid_inputs = {name: img for name, img in input_images.items() if img is not None} # 动态设置输出类型和名称 if len(valid_inputs) > 0: self.RETURN_TYPES = tuple(["IMAGE"] * len(valid_inputs)) self.RETURN_NAMES = tuple(valid_inputs.keys()) else: # 如果没有有效输入，提供一个默认输出 self.RETURN_TYPES = ("IMAGE",) self.RETURN_NAMES = ("图片A",) # 创建默认空图像 empty_image = torch.zeros(1, 高度, 宽度, 3) return (empty_image,) # 处理每个有效输入 for img in valid_inputs.values(): # 调整图像大小 resized = self._resize_batch(img, target_size, 缩放方法, 尺寸适配) results.append(resized) return tuple(results) # 节点类映射 NODE_CLASS_MAPPINGS = { "DD-ImageUniformSize": DDImageUniformSize } # 节点显示名称映射 - 使用英文（中文通过locales提供） NODE_DISPLAY_NAME_MAPPINGS = { "DD-ImageUniformSize": "DD Image Uniform Size" }