153 lines
5.3 KiB
Python
153 lines
5.3 KiB
Python
import os
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import json
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import numpy as np
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from PIL import Image
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import torch
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import folder_paths
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from comfy.utils import common_upscale
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import comfy.model_management
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class MultiSaveImage:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images_1": ("IMAGE",),
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"filename_prefix": ("STRING", {"default": "ComfyUI"}),
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},
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"optional": {
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"images_2": ("IMAGE",),
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"images_3": ("IMAGE",),
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"images_4": ("IMAGE",),
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"images_5": ("IMAGE",),
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"images_6": ("IMAGE",),
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"images_7": ("IMAGE",),
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"images_8": ("IMAGE",),
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"save_individually": ("BOOLEAN", {"default": False}),
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"resize_to_same": ("BOOLEAN", {"default": False}),
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"target_width": ("INT", {"default": 512, "min": 64, "max": 8192}),
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"target_height": ("INT", {"default": 512, "min": 64, "max": 8192}),
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}
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}
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# RETURN_TYPES = ("IMAGE",)
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# RETURN_NAMES = ("images",)
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RETURN_TYPES = ()
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RETURN_NAMES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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CATEGORY = "image"
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def save_images(self, images_1, filename_prefix="ComfyUI", images_2=None, images_3=None,
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images_4=None, images_5=None, images_6=None, images_7=None, images_8=None,
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save_individually=False, resize_to_same=False, target_width=512, target_height=512):
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# 收集所有非空的图像输入
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all_images = []
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image_inputs = [images_1, images_2, images_3, images_4, images_5, images_6, images_7, images_8]
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for img_batch in image_inputs:
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if img_batch is not None:
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# 如果是批量图像，逐个添加
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if len(img_batch.shape) == 4: # 批量图像 [B, H, W, C]
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for i in range(img_batch.shape[0]):
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all_images.append(img_batch[i])
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else: # 单张图像 [H, W, C]
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all_images.append(img_batch)
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if not all_images:
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raise ValueError("至少需要提供一个图像输入")
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# 如果需要调整到相同尺寸
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if resize_to_same and len(all_images) > 1:
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resized_images = []
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for img in all_images:
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# 添加batch维度进行resize
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img_batch = img.unsqueeze(0) # [1, H, W, C]
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# 使用ComfyUI的upscale函数进行尺寸调整
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resized = common_upscale(img_batch, target_width, target_height, "lanczos", "center")
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resized_images.append(resized.squeeze(0)) # 移除batch维度 [H, W, C]
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all_images = resized_images
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# 保存图像的结果信息
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results = []
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# 保存所有图像
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for i, img in enumerate(all_images):
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filename = f"{filename_prefix}_{i+1:03d}"
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result = self._save_single_image(img, filename)
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results.append(result)
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# 准备返回的图像数据
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# 如果所有图像尺寸相同，可以stack；否则返回第一张图像
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try:
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# 检查所有图像是否具有相同尺寸
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shapes = [img.shape for img in all_images]
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if all(shape == shapes[0] for shape in shapes):
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# 所有图像尺寸相同，可以stack
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output_batch = torch.stack(all_images, dim=0)
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else:
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# 尺寸不同，返回第一张图像（保持与SaveImage兼容）
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output_batch = all_images[0].unsqueeze(0)
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except:
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# 如果出现任何问题，返回第一张图像
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output_batch = all_images[0].unsqueeze(0)
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# return {"ui": {"images": results}, "result": (output_batch,)}
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return {"ui": {"images": results}}
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def _save_single_image(self, image, filename_prefix):
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"""保存单张图像"""
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# 确保图像格式正确 [H, W, C]
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if len(image.shape) == 4:
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image = image.squeeze(0)
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# 确保值在0-1范围内
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image = torch.clamp(image, 0.0, 1.0)
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# 转换为numpy并调整到0-255范围
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img_array = (image.cpu().numpy() * 255).astype(np.uint8)
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# 创建PIL Image
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if img_array.shape[2] == 4:
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pil_image = Image.fromarray(img_array, 'RGBA')
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elif img_array.shape[2] == 3:
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pil_image = Image.fromarray(img_array, 'RGB')
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else:
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# 处理灰度图像
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if img_array.shape[2] == 1:
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img_array = img_array.squeeze(2)
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pil_image = Image.fromarray(img_array, 'L')
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# 生成唯一文件名
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counter = 1
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while True:
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filename = f"{filename_prefix}_{counter:05d}.png"
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filepath = os.path.join(self.output_dir, filename)
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if not os.path.exists(filepath):
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break
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counter += 1
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# 保存图像
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pil_image.save(filepath, compress_level=self.compress_level)
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return {
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"filename": filename,
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"subfolder": "",
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"type": self.type
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}
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# # 节点映射
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# NODE_CLASS_MAPPINGS = {
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# "MultiSaveImage": MultiSaveImage
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# }
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# NODE_DISPLAY_NAME_MAPPINGS = {
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# "MultiSaveImage": "Multi Save Image"
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# } |