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hmwl
2025-01-16 09:50:39 +08:00
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Example.json
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{
"last_node_id": 15,
"last_link_id": 14,
"nodes": [
{
"id": 15,
"type": "CleanFolders",
"pos": [
1179.648193359375,
803.1049194335938
],
"size": [
210,
54
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "output_path",
"type": "STRING",
"link": 13,
"widget": {
"name": "output_path"
},
"label": "output_path"
},
{
"name": "input_path",
"type": "STRING",
"link": 14,
"widget": {
"name": "input_path"
},
"label": "input_path"
}
],
"outputs": [],
"properties": {
"Node name for S&R": "CleanFolders"
},
"widgets_values": [
"",
""
]
},
{
"id": 11,
"type": "CompressImages",
"pos": [
894.0198974609375,
745.897705078125
],
"size": [
210,
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],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9,
"label": "images"
}
],
"outputs": [
{
"name": "temp_dir",
"type": "STRING",
"links": [
13
],
"slot_index": 0,
"label": "temp_dir"
}
],
"properties": {
"Node name for S&R": "CompressImages"
},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 13,
"type": "ImageInvert",
"pos": [
698.370849609375,
745.5828247070312
],
"size": [
140,
26
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 8,
"label": "image"
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
9
],
"slot_index": 0,
"label": "IMAGE"
}
],
"properties": {
"Node name for S&R": "ImageInvert"
},
"widgets_values": []
},
{
"id": 9,
"type": "UnzipToInput",
"pos": [
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798.1468505859375
],
"size": [
210,
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],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "images",
"type": "IMAGE",
"links": [
8
],
"slot_index": 0,
"shape": 6,
"label": "images"
},
{
"name": "folder",
"type": "STRING",
"links": [
14
],
"slot_index": 1,
"label": "folder"
}
],
"properties": {
"Node name for S&R": "UnzipToInput"
},
"widgets_values": [
"input/file.zip"
]
}
],
"links": [
[
8,
9,
0,
13,
0,
"IMAGE"
],
[
9,
13,
0,
11,
0,
"IMAGE"
],
[
13,
11,
0,
15,
0,
"STRING"
],
[
14,
9,
1,
15,
1,
"STRING"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 0.8769226950000008,
"offset": [
-24.799331551459204,
-332.5597278000348
]
},
"groupNodes": {},
"node_versions": {
"comfy-core": "0.3.10"
}
},
"version": 0.4
}
__init__.py
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from .unzip import UnzipToInput
from .compress import CompressImages
from .clean import CleanFolders
NODE_CLASS_MAPPINGS = {
"UnzipToInput": UnzipToInput,
"CompressImages": CompressImages,
"CleanFolders": CleanFolders
}
NODE_DISPLAY_NAME_MAPPINGS = {
"UnzipToInput": "Unzip To Input",
"CompressImages": "Zip Compress",
"CleanFolders": "Clean Folders"
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
clean.py
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import os
import shutil
import folder_paths
class CleanFolders:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"input_path": ("STRING", {"default": ""}),
"output_path": ("STRING", {"default": ""})
}}
RETURN_TYPES = ()
FUNCTION = "clean"
CATEGORY = "comfyui_zip"
OUTPUT_NODE = True
def clean(self, input_path, output_path):
result_msg = []
# 获取基础目录
input_base = folder_paths.get_input_directory()
output_base = folder_paths.get_output_directory()
# 确保路径是字符串类型，如果是元组则取第一个元素
input_path = input_path[0] if isinstance(input_path, tuple) else str(input_path) if input_path else ""
output_path = output_path[0] if isinstance(output_path, tuple) else str(output_path) if output_path else ""
# 只有当提供了路径时才进行删除操作
if input_path:
input_full_path = os.path.join(input_base, input_path)
if os.path.exists(input_full_path):
try:
shutil.rmtree(input_full_path)
result_msg.append(f"已清理输入目录: {input_full_path}")
except Exception as e:
result_msg.append(f"清理输入目录失败: {str(e)}")
else:
result_msg.append(f"输入目录不存在: {input_full_path}")
else:
result_msg.append("未提供输入路径，跳过清理")
# 只有当提供了路径时才进行删除操作
if output_path:
output_full_path = os.path.join(output_base, output_path)
if os.path.exists(output_full_path):
try:
shutil.rmtree(output_full_path)
except Exception as e:
pass
return ("",)
# 注册节点
NODE_CLASS_MAPPINGS = {
"CleanFolders": CleanFolders
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CleanFolders": "清理文件夹"
}
compress.py
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import os
import zipfile
import torch
import numpy as np
from PIL import Image
import folder_paths
import tempfile
import shutil
import matplotlib.pyplot as plt
import time
import random
class CompressImages:
_current_id = None
_temp_dirs = {}
_image_counts = {}
_zip_filename = {} # 新增：记录每个工作流的zip文件名
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"filename_prefix": ("STRING", {"default": "ComfyUI"})
},
"hidden": {
"prompt": "PROMPT",
"extra_pnginfo": "EXTRA_PNGINFO",
"unique_id": "UNIQUE_ID",
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("temp_dir",)
FUNCTION = "compress_images"
OUTPUT_NODE = True
CATEGORY = "comfyui_zip"
def compress_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, unique_id=None):
try:
# 确保临时目录存在
if unique_id not in CompressImages._temp_dirs or not os.path.exists(CompressImages._temp_dirs[unique_id]):
folder_name = f"{filename_prefix}{self.prefix_append}_{unique_id}"
CompressImages._temp_dirs[unique_id] = os.path.join(self.output_dir, folder_name)
CompressImages._image_counts[unique_id] = 0
timestamp = int(time.time())
CompressImages._zip_filename[unique_id] = f'{filename_prefix}_{timestamp}.zip'
# 确保目录存在
os.makedirs(CompressImages._temp_dirs[unique_id], exist_ok=True)
temp_dir = CompressImages._temp_dirs[unique_id]
zip_path = os.path.abspath(os.path.join(self.output_dir, CompressImages._zip_filename[unique_id]))
# 追加模式打开zip文件
zip_mode = 'a' if os.path.exists(zip_path) else 'w'
with zipfile.ZipFile(zip_path, zip_mode) as zipf:
for image in images:
if isinstance(image, torch.Tensor):
img_array = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8))
elif isinstance(image, Image.Image):
img = image
else:
raise ValueError(f"不支持的图片格式: {type(image)}")
temp_image_path = os.path.join(temp_dir, f'temp_image_{CompressImages._image_counts[unique_id]:05d}.png')
img.save(temp_image_path)
arcname = f'image_{CompressImages._image_counts[unique_id]:05d}.png'
zipf.write(temp_image_path, arcname)
os.remove(temp_image_path)
CompressImages._image_counts[unique_id] += 1
folder_name = os.path.basename(CompressImages._temp_dirs[unique_id])
# 正确的返回格式
results = (folder_name,) # 这是 RETURN_TYPES 定义的返回值
return {"ui": {"file": [CompressImages._zip_filename[unique_id]]}, "result": (results,)}
except Exception as e:
print(f"发生错误: {str(e)}")
self._cleanup(unique_id)
raise e
class ZipFiles:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
"zip_name": ("STRING", {"default": "images.zip"}),
"save_path": ("STRING", {"default": "ComfyUI/output"}),
},
"hidden": {"unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("STRING", "STRING",)
RETURN_NAMES = ("zip_path", "temp_dir",)
FUNCTION = "zip_files"
def zip_files(self, images, zip_name, save_path, unique_id):
temp_dir = tempfile.gettempdir()
return (output_path, temp_dir)
NODE_CLASS_MAPPINGS = {
"CompressImages": CompressImages
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CompressImages": "Zip Compress"
}
readme.md
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## ComfyUI 压缩包图片处理
## ComfyUI zip package image processing
### 节点介绍
### Node Introduction
#### 1. Zip Compress (压缩图片)
- 功能：将生成的图片打包成zip压缩文件
- Function: Package generated images into a zip file
- 输入参数 (Input Parameters):
- images: 图片输入 (Image input)
- filename_prefix: 文件名前缀，默认为"ComfyUI" (Filename prefix, default is "ComfyUI")
- 输出 (Output):
- temp_dir: 临时目录路径 (Temporary directory path)
- 生成的zip文件将保存在ComfyUI的输出目录中 (Generated zip file will be saved in ComfyUI's output directory)
#### 2. UnzipToInput (解压到输入)
- 功能：解压zip文件并将图片加载为可处理的格式
- Function: Extract zip file and load images into processable format
- 输入参数 (Input Parameters):
- zip_path: zip文件路径，支持本地路径或URL (Zip file path, supports local path or URL)
- 输出 (Output):
- images: 解压出的图片列表 (List of extracted images)
- folder: 解压目录名称 (Extraction directory name)
> **注意**: 压缩包仅支持图片文件的zip，不支持其他类型文件和文件夹
> **Note**: Only supports zip files containing images, other file types and folders are not supported
#### 3. 清理文件夹 (Clean Folders)
- 功能：清理临时文件夹和输出文件夹
- Function: Clean temporary and output folders
- 输入参数 (Input Parameters):
- input_path: 需要清理的输入路径 (Input path to clean)
- output_path: 需要清理的输出路径 (Output path to clean)
- 输出 (Output):
- 无返回值，直接执行清理操作 (No return value, directly performs cleaning operation)
### 使用说明
### Usage Instructions
1. 压缩图片 (Compress Images):
- 将图片节点连接到Zip Compress节点
- 设置文件名前缀
- 运行后将在输出目录生成zip文件
2. 解压图片 (Extract Images):
- 在UnzipToInput节点中输入zip文件路径
- 支持本地zip文件或在线zip文件URL
- 解压后的图片可直接用于后续处理
3. 清理文件夹 (Clean Folders):
- 用于清理工作流程中产生的临时文件
- 可选择性地清理输入或输出目录
- 建议在工作流程结束时使用
### 测试JSON文件
项目中有一个Example.json文件，可以作为使用的参考
There is an Example.json file in the project that you can refer to for usage.
unzip.py
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import os
import time
import torch
import urllib.request
from urllib.parse import urlparse
import folder_paths
import numpy as np
from PIL import Image
import shutil
class UnzipToInput:
def __init__(self):
self.output_dir = folder_paths.get_input_directory()
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"zip_path": ("STRING", {"default": ""}),
}
}
RETURN_TYPES = ("IMAGE", "STRING",)
RETURN_NAMES = ("images", "folder",)
OUTPUT_IS_LIST = (True, False,)
FUNCTION = "unzip_process"
CATEGORY = "comfyui_zip"
def download_file(self, url, save_path):
urllib.request.urlretrieve(url, save_path)
def is_image_file(self, filename):
image_extensions = {'.png', '.jpg', '.jpeg', '.webp', '.bmp'}
return os.path.splitext(filename.lower())[1] in image_extensions
def unzip_process(self, zip_path):
timestamp = str(int(time.time()))
extract_dir = os.path.join(self.output_dir, f"unzip_{timestamp}")
os.makedirs(extract_dir, exist_ok=True)
# 处理URL或本地路径
if zip_path.startswith(('http://', 'https://')):
parsed_url = urlparse(zip_path)
zip_filename = os.path.basename(parsed_url.path)
temp_zip = os.path.join(extract_dir, zip_filename)
self.download_file(zip_path, temp_zip)
zip_path = temp_zip
# 解压文件
import zipfile
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
# 获取所有文件列表
name_list = zip_ref.namelist()
# 过滤掉 __MACOSX 文件夹
filtered_names = [name for name in name_list
if not name.startswith('__MACOSX/')
and not name.startswith('._')]
# 解压文件，处理中文编码
for name in filtered_names:
try:
# 尝试使用 cp437 解码，然后用 utf-8 编码
unicode_name = name.encode('cp437').decode('utf-8')
except:
# 如果失败，直接使用原名称
unicode_name = name
# 获取解压后的完整路径
extracted_path = os.path.join(extract_dir, unicode_name)
# 确保目标目录存在
os.makedirs(os.path.dirname(extracted_path), exist_ok=True)
# 提取文件
with zip_ref.open(name) as source, open(extracted_path, 'wb') as target:
shutil.copyfileobj(source, target)
# 收集所有图片文件
image_files = []
for root, _, files in os.walk(extract_dir):
for file in files:
if self.is_image_file(file):
image_files.append(os.path.join(root, file))
if not image_files:
raise ValueError("No image files found in ZIP archive")
# 读取所有图片并转换为tensor
images_list = []
for img_path in image_files:
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img_tensor = np.array(img).astype(np.float32) / 255.0
img_tensor = torch.from_numpy(img_tensor)
img_tensor = img_tensor.unsqueeze(0)
images_list.append(img_tensor)
# 返回图片列表和文件夹名称
folder_name = os.path.basename(extract_dir)
return (images_list, folder_name)