WainWong
abe0004331
Features: - Implemented batch image processing nodes (Loop Open/Close) - Implemented single image processing nodes - Added Loop Index Switch for dynamic input control - Added mask segmentation and merging functionality Core components: - flow_control.py: Main loop control implementation - mask_split.py: Mask processing utilities - web/node/dynamicnode.js: Dynamic input UI implementation Documentation: - Added detailed README in both English and Chinese - Included usage instructions and node documentation - Added acknowledgments and references"
361 lines
14 KiB
Markdown
361 lines
14 KiB
Markdown
# ComfyUI Loop Image
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[English](#english) | [简体中文](#简体中文)
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<a name="english"></a>
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# ComfyUI Loop Image
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## Introduction
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ComfyUI Loop Image is a node package specifically designed for image loop processing. It provides two main processing modes: Batch Image Processing and Single Image Processing, along with supporting image segmentation and merging functions.
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## Differences between Batch and Single Processing
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### Batch Image Processing
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- Suitable for scenarios requiring simultaneous processing of multiple different regions
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- Uses Mask Segmentation node to divide images into multiple parts
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- Processes one segmented region per iteration
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- Merges results through Mask Merge after all regions are processed
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### Single Image Processing
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- Suitable for scenarios requiring multiple processing passes on the same image
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- Uses the result of the previous iteration as input for the next
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- Enables progressive image modification
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- Ideal for iterative optimization tasks
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## Node Documentation
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### 1. Batch Processing Nodes
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#### Mask Segmentation🐰
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- **Functionality**
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- Automatically segments a mask containing multiple independent regions into separate mask sequences
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- Each segmented mask corresponds to an independent region in the original image
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- Segmentation based on connected component analysis
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- **Segmentation Rules**
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- Independent regions are identified as separate parts
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- Regions with holes are properly processed, maintaining hole structure
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- **Sequence Rules**
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- Masks are arranged from left to right, then top to bottom
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- Sorting based on leftmost pixel position, then topmost pixel position
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- This order determines subsequent processing sequence
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- Example: In a mask with three regions, leftmost region is iteration 0, middle is 1, rightmost is 2
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#### Batch Image Loop Open🐰
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- **Input/Output Details**
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- Inputs:
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- segmented_images: Image sequence from Mask Segmentation
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- segmented_masks: Mask sequence from Mask Segmentation
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- Outputs:
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- current_image: Currently processed image portion
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- current_mask: Current iteration mask
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- max_iterations: Total iteration count (equals number of segmented regions)
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- iteration_count: Current iteration number (starts from 0)
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- **Usage Notes**
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- current_image and current_mask can be used directly for subsequent processing
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- iteration_count can connect to Loop Index Switch for different processing parameters
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- max_iterations used for loop control, usually doesn't need manual handling
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#### Batch Image Loop Close🐰
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- **Input/Output Details**
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- Inputs:
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- flow_control: Control signal from Loop Open
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- current_image: Currently processed image
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- current_mask: Current processed mask
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- max_iterations: Total iteration count from Loop Open
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- Outputs:
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- result_images: All processed image sequences
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- result_masks: All processed mask sequences
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#### Mask Merge🐰
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- **Functionality**
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- Merges multiple processed image regions back into the original image
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- Uses masks to ensure each processed region is correctly placed
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- Maintains original content in unprocessed areas
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- **Usage Tips**
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- original_image: Use original input image
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- processed_images: Connect to result_images output from Loop Close
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- masks: Connect to result_masks output from Loop Close
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This batch processing system allows you to apply different processing methods to different regions of an image, particularly suitable for scenarios requiring differentiated processing of various image parts.
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### 2. Single Image Processing Nodes
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#### Single Image Loop Open🐰
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- **Functionality**
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- Performs multiple iterations of processing on a single image
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- Uses the result of each iteration as input for the next
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- Suitable for progressive enhancement or multiple optimization scenarios
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- **Input Parameters**
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- **Required Inputs**:
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- image: Original image to process
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- max_iterations: Maximum iteration count (1-100)
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- **Optional Inputs**:
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- mask: Optional processing area mask
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- **Output Parameters**
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- current_image: Current iteration image (original image for first iteration, previous result for subsequent iterations)
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- current_mask: Current mask (if provided)
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- max_iterations: Set maximum iterations
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- iteration_count: Current iteration number (starts from 0)
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#### Single Image Loop Close🐰
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- **Input Parameters**
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- **Required Inputs**:
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- flow_control: Control signal from Loop Open
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- current_image: Currently processed image
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- max_iterations: Maximum iterations from Loop Open
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- **Optional Inputs**:
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- current_mask: Processed mask (if using mask)
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- **Output Parameters**
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- final_image: Final image after all iterations
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- final_mask: Final mask (if using mask)
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#### Single Image Processing Features and Applications
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1. **Progressive Processing**
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- Each iteration builds on previous results
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- Enables cumulative effects
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- Suitable for scenarios requiring fine-tuning
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2. **Use Case Examples**
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- Progressive image enhancement
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- Iterative style transfer
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- Multiple denoising passes
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- Gradual detail optimization
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### 3. Special Function Node
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- **Loop Index Switch🐰**
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- Function: Select different inputs based on current iteration count
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- Usage:
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1. Right-click node and select "Add Loop Input"
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2. Enter desired iteration number (0-99)
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3. Connect corresponding inputs
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4. Use "Remove Loop Input" to delete unwanted inputs
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- Note: Only inputs corresponding to current iteration are computed, others are skipped for efficiency
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## Usage Recommendations
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1. Use batch processing for scenarios requiring different processing in different image regions
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2. Use single image processing for scenarios requiring multiple optimization iterations
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3. Utilize Loop Index Switch to implement different parameters for different iterations
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4. Control iteration count to avoid over-processing
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## Example Workflows
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TODO
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## Acknowledgments
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This project references the following excellent open source projects:
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- [ComfyUI-Easy-Use](https://github.com/yolain/ComfyUI-Easy-Use/) - Provided excellent node design ideas and implementation references
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- [execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui) - Provided core implementation ideas for loop control
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- [cozy_ex_dynamic](https://github.com/cozy-comfyui/cozy_ex_dynamic) - Provided implementation reference for dynamic input nodes
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Special thanks to the authors of these projects for their contributions to the ComfyUI community!
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## About
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For more ComfyUI tutorials and updates, visit:
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- Bilibili: [CyberEve](https://space.bilibili.com/16993154)
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- Content includes:
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- ComfyUI node development tutorials
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- Workflow usage tutorials
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- Latest feature updates
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- AI drawing tips
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If you find this project helpful, please follow the author's Bilibili account for more resources!
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---
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<a name="简体中文"></a>
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## 简介
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ComfyUI Loop Image是一个专门用于处理图像循环操作的节点包。它提供了两种主要的循环处理模式：批量图像处理(Batch)和单图像重复处理(Single)，以及配套的图像分割与合并功能。
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## 批量处理与单图处理的区别
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### 批量图像处理
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- 适用于需要同时处理多个不同区域的场景
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- 通过Mask Segmentation节点将图像分割成多个部分
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- 每次循环处理一个分割区域
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- 所有区域处理完成后通过Mask Merge合并结果
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### 单图像处理
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- 适用于需要对同一图像进行多次处理的场景
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- 每次循环使用上一次的处理结果作为输入
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- 可以实现渐进式的图像修改
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- 适合迭代优化类的任务
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## 节点说明
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### 1. 批量处理节点详解
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#### Mask Segmentation🐰 (遮罩分割)
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- **功能说明**
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- 将一个包含多个独立区域的遮罩图自动分割成独立的遮罩序列
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- 每个分割后的遮罩对应原图中的一个独立区域
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- 分割基于连通区域分析，即相互不连接的区域会被分为不同部分
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- **分割规则**
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- 相互独立的区域会被识别为不同的部分
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- 包含孔洞的区域会被正确处理，保持孔洞结构
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- **顺序规则**
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- 分割后的遮罩按照从左到右排列，若左右位置相等，再按照从上到下的顺序
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- 排序依据是每个区域最左边的像素点的位置，再按照最上边的像素点的位置
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- 这个顺序决定了后续循环处理的顺序
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- 例如：如果遮罩中有三个区域，最左边的区域将是第0次迭代，中间的是第1次，最右边的是第2次
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#### Batch Image Loop Open🐰 (批量循环开始)
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- **输入输出详解**
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- 输入：
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- segmented_images: 来自Mask Segmentation的图像序列
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- segmented_masks: 来自Mask Segmentation的遮罩序列
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- 输出：
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- current_image: 当前迭代处理的图像部分
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- current_mask: 当前迭代的遮罩
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- max_iterations: 总迭代次数（等于分割区域的数量）
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- iteration_count: 当前迭代次数（从0开始）
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- **使用说明**
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- current_image和current_mask可以直接用于后续处理
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- iteration_count可以连接到Loop Index Switch来选择不同的处理参数
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- max_iterations用于循环控制，一般不需要手动使用
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#### Batch Image Loop Close🐰 (批量循环结束)
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- **输入输出详解**
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- 输入：
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- flow_control: 来自Loop Open的控制信号
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- current_image: 处理后的当前图像
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- current_mask: 处理后的当前遮罩
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- max_iterations: 来自Loop Open的总迭代次数
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- 输出：
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- result_images: 所有处理完成的图像序列
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- result_masks: 所有处理完成的遮罩序列
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#### Mask Merge🐰 (遮罩合并)
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- **功能说明**
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- 将循环处理后的多个图像区域合并回原始图像
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- 使用遮罩确保每个处理过的区域正确放回原位
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- 保持未处理区域的原始内容不变
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- **使用技巧**
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- original_image: 使用原始输入图像
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- processed_images: 连接Loop Close的result_images输出
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- masks: 连接Loop Close的result_masks输出
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这样的批量处理系统允许你对图像的不同区域应用不同的处理方法，特别适合需要对图像不同部分进行差异化处理的场景。
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### 2. 单图处理节点详解
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#### Single Image Loop Open🐰 (单图循环开始)
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- **功能说明**
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- 对同一张图像进行多次迭代处理
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- 每次迭代都使用上一次的处理结果作为输入
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- 适合需要渐进式改善或多次优化的场景
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- **输入参数详解**
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- **必需输入**：
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- image: 需要处理的原始图像
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- max_iterations: 最大迭代次数（1-100）
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- **可选输入**：
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- mask: 可选的处理区域遮罩
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- **输出参数详解**
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- current_image: 当前迭代的图像（第一次是原始图像，之后是上一次处理的结果）
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- current_mask: 当前使用的遮罩（如果提供了遮罩）
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- max_iterations: 设定的最大迭代次数
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- iteration_count: 当前迭代次数（从0开始）
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#### Single Image Loop Close🐰 (单图循环结束)
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- **输入参数详解**
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- **必需输入**：
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- flow_control: 来自Loop Open的控制信号
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- current_image: 当前迭代处理后的图像
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- max_iterations: 来自Loop Open的最大迭代次数
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- **可选输入**：
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- current_mask: 处理后的遮罩（如果使用了遮罩）
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- **输出参数详解**
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- final_image: 所有迭代完成后的最终图像
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- final_mask: 最终的遮罩（如果使用了遮罩）
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#### 单图处理的特点和应用场景
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1. **渐进式处理**
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- 每次迭代都基于上一次的结果
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- 可以实现累积效果
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- 适合需要多次微调的场景
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2. **使用场景示例**
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- 图像渐进式增强
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- 迭代式风格转换
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- 多次降噪处理
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- 逐步细节优化
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### 与Loop Index Switch的配合使用
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- 可以使用Loop Index Switch根据iteration_count选择不同的处理参数
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这种单图循环处理方式特别适合需要精细调整或渐进式改善的场景，通过多次迭代可以达到更理想的处理效果。配合Loop Index Switch，还可以实现更复杂的参数控制策略。
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### 3. 特殊功能节点
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- **Loop Index Switch🐰**
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- 功能：根据当前循环次数选择不同的输入
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- 使用方法：
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1. 右键点击节点选择"Add Loop Input"
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2. 输入想要添加的循环序号(0-99)
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3. 连接对应的输入
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4. 可以通过"Remove Loop Input"删除不需要的输入
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- 注意：只有当前迭代次数对应的输入会被计算，其他输入会被跳过，提高效率
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## 使用建议
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1. 批量处理适合需要在图像不同区域应用不同处理的场景
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2. 单图处理适合需要多次迭代优化的场景
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3. 合理使用Loop Index Switch节点可以实现在不同迭代次数使用不同参数
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4. 注意控制循环次数，避免过度处理
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## 示例工作流
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TODO
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## 致谢
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本项目在开发过程中参考和借鉴了以下优秀的开源项目：
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- [ComfyUI-Easy-Use](https://github.com/yolain/ComfyUI-Easy-Use/) - 提供了优秀的节点设计思路和实现参考
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- [execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui) - 提供了循环控制的核心实现思路
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- [cozy_ex_dynamic](https://github.com/cozy-comfyui/cozy_ex_dynamic) - 提供了动态输入节点的实现参考
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特别感谢这些项目的作者们为ComfyUI社区做出的贡献！
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## 关于作者
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欢迎访问作者的B站主页，获取更多ComfyUI教程和更新：
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- B站：[CyberEve](https://space.bilibili.com/16993154)
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- 内容包括：
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- ComfyUI节点开发教程
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- 工作流使用教程
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- 最新功能更新介绍
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- AI绘画技巧分享
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如果您觉得这个项目对您有帮助，欢迎关注作者B站账号获取更多资源！
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---
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*Note: 本项目遵循开源协议，欢迎提出建议和改进意见。* |