ComfyUI Loop Image

Introduction

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.

Differences between Batch and Single Processing

Batch Image Processing

Suitable for scenarios requiring simultaneous processing of multiple different regions
Uses Mask Segmentation node to divide images into multiple parts
Processes one segmented region per iteration
Merges results through Mask Merge after all regions are processed

Single Image Processing

Suitable for scenarios requiring multiple processing passes on the same image
Uses the result of the previous iteration as input for the next
Enables progressive image modification
Ideal for iterative optimization tasks

Node Documentation

1. Batch Processing Nodes

Mask Segmentation🐰

Functionality
- Automatically segments a mask containing multiple independent regions into separate mask sequences
- Each segmented mask corresponds to an independent region in the original image
- Segmentation based on connected component analysis
Segmentation Rules
- Independent regions are identified as separate parts
- Regions with holes are properly processed, maintaining hole structure
Sequence Rules
- Masks are arranged from left to right, then top to bottom
- Sorting based on leftmost pixel position, then topmost pixel position
- This order determines subsequent processing sequence
- Example: In a mask with three regions, leftmost region is iteration 0, middle is 1, rightmost is 2

Batch Image Loop Open🐰

Input/Output Details
- Inputs:
  - segmented_images: Image sequence from Mask Segmentation
  - segmented_masks: Mask sequence from Mask Segmentation
- Outputs:
  - current_image: Currently processed image portion
  - current_mask: Current iteration mask
  - max_iterations: Total iteration count (equals number of segmented regions)
  - iteration_count: Current iteration number (starts from 0)
Usage Notes
- current_image and current_mask can be used directly for subsequent processing
- iteration_count can connect to Loop Index Switch for different processing parameters
- max_iterations used for loop control, usually doesn't need manual handling

Batch Image Loop Close🐰

Input/Output Details
- Inputs:
  - flow_control: Control signal from Loop Open
  - current_image: Currently processed image
  - current_mask: Current processed mask
  - max_iterations: Total iteration count from Loop Open
- Outputs:
  - result_images: All processed image sequences
  - result_masks: All processed mask sequences

Mask Merge🐰

Functionality
- Merges multiple processed image regions back into the original image
- Uses masks to ensure each processed region is correctly placed
- Maintains original content in unprocessed areas
Usage Tips
- original_image: Use original input image
- processed_images: Connect to result_images output from Loop Close
- masks: Connect to result_masks output from Loop Close

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.

2. Single Image Processing Nodes

Single Image Loop Open🐰

Functionality
- Performs multiple iterations of processing on a single image
- Uses the result of each iteration as input for the next
- Suitable for progressive enhancement or multiple optimization scenarios
Input Parameters
- Required Inputs:
  - image: Original image to process
  - max_iterations: Maximum iteration count (1-100)
- Optional Inputs:
  - mask: Optional processing area mask
Output Parameters
- current_image: Current iteration image (original image for first iteration, previous result for subsequent iterations)
- current_mask: Current mask (if provided)
- max_iterations: Set maximum iterations
- iteration_count: Current iteration number (starts from 0)

Single Image Loop Close🐰

Input Parameters
- Required Inputs:
  - flow_control: Control signal from Loop Open
  - current_image: Currently processed image
  - max_iterations: Maximum iterations from Loop Open
- Optional Inputs:
  - current_mask: Processed mask (if using mask)
Output Parameters
- final_image: Final image after all iterations
- final_mask: Final mask (if using mask)

Single Image Processing Features and Applications

Progressive Processing
- Each iteration builds on previous results
- Enables cumulative effects
- Suitable for scenarios requiring fine-tuning
Use Case Examples
- Progressive image enhancement
- Iterative style transfer
- Multiple denoising passes
- Gradual detail optimization

3. Special Function Node

Loop Index Switch🐰
- Function: Select different inputs based on current iteration count
- Usage:
  1. Right-click node and select "Add Loop Input"
  2. Enter desired iteration number (0-99)
  3. Connect corresponding inputs
  4. Use "Remove Loop Input" to delete unwanted inputs
- Note: Only inputs corresponding to current iteration are computed, others are skipped for efficiency

Usage Recommendations

Use batch processing for scenarios requiring different processing in different image regions
Use single image processing for scenarios requiring multiple optimization iterations
Utilize Loop Index Switch to implement different parameters for different iterations
Control iteration count to avoid over-processing

Example Workflows

Batch Processing Workflow

Batch Processing Workflow

Single Processing Workflow

Single Processing Workflow

Acknowledgments

This project references the following excellent open source projects:

ComfyUI-Easy-Use - Provided excellent node design ideas and implementation references
execution-inversion-demo-comfyui - Provided core implementation ideas for loop control
cozy_ex_dynamic - Provided implementation reference for dynamic input nodes

Special thanks to the authors of these projects for their contributions to the ComfyUI community!

About

For more ComfyUI tutorials and updates, visit:

Bilibili: CyberEve
Content includes:
- ComfyUI node development tutorials
- Workflow usage tutorials
- Latest feature updates
- AI drawing tips

If you find this project helpful, please follow the author's Bilibili account for more resources!

简介

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。

批量处理与单图处理的区别

批量图像处理

适用于需要同时处理多个不同区域的场景
通过Mask Segmentation节点将图像分割成多个部分
每次循环处理一个分割区域
所有区域处理完成后通过Mask Merge合并结果

单图像处理

适用于需要对同一图像进行多次处理的场景
每次循环使用上一次的处理结果作为输入
可以实现渐进式的图像修改
适合迭代优化类的任务

节点说明

1. 批量处理节点详解

Mask Segmentation🐰 (遮罩分割)

功能说明
- 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
- 每个分割后的遮罩对应原图中的一个独立区域
- 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
分割规则
- 相互独立的区域会被识别为不同的部分
- 包含孔洞的区域会被正确处理，保持孔洞结构
顺序规则
- 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
- 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
- 这个顺序决定了后续循环处理的顺序
- 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

Batch Image Loop Open🐰 (批量循环开始)

输入输出详解
- 输入：
  - segmented_images: 来自Mask Segmentation的图像序列
  - segmented_masks: 来自Mask Segmentation的遮罩序列
- 输出：
  - current_image: 当前迭代处理的图像部分
  - current_mask: 当前迭代的遮罩
  - max_iterations: 总迭代次数（等于分割区域的数量）
  - iteration_count: 当前迭代次数（从0开始）
使用说明
- current_image和current_mask可以直接用于后续处理
- iteration_count可以连接到Loop Index Switch来选择不同的处理参数
- max_iterations用于循环控制，一般不需要手动使用

Batch Image Loop Close🐰 (批量循环结束)

输入输出详解
- 输入：
  - flow_control: 来自Loop Open的控制信号
  - current_image: 处理后的当前图像
  - current_mask: 处理后的当前遮罩
  - max_iterations: 来自Loop Open的总迭代次数
- 输出：
  - result_images: 所有处理完成的图像序列
  - result_masks: 所有处理完成的遮罩序列

Mask Merge🐰 (遮罩合并)

功能说明
- 将循环处理后的多个图像区域合并回原始图像
- 使用遮罩确保每个处理过的区域正确放回原位
- 保持未处理区域的原始内容不变
使用技巧
- original_image: 使用原始输入图像
- processed_images: 连接Loop Close的result_images输出
- masks: 连接Loop Close的result_masks输出

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。

2. 单图处理节点详解

Single Image Loop Open🐰 (单图循环开始)

功能说明
- 对同一张图像进行多次迭代处理
- 每次迭代都使用上一次的处理结果作为输入
- 适合需要渐进式改善或多次优化的场景
输入参数详解
- 必需输入：
  - image: 需要处理的原始图像
  - max_iterations: 最大迭代次数（1-100）
- 可选输入：
  - mask: 可选的处理区域遮罩
输出参数详解
- 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）
- current_mask: 当前使用的遮罩（如果提供了遮罩）
- max_iterations: 设定的最大迭代次数
- iteration_count: 当前迭代次数（从0开始）

Single Image Loop Close🐰 (单图循环结束)

输入参数详解
- 必需输入：
  - flow_control: 来自Loop Open的控制信号
  - current_image: 当前迭代处理后的图像
  - max_iterations: 来自Loop Open的最大迭代次数
- 可选输入：
  - current_mask: 处理后的遮罩（如果使用了遮罩）
输出参数详解
- final_image: 所有迭代完成后的最终图像
- final_mask: 最终的遮罩（如果使用了遮罩）

单图处理的特点和应用场景

渐进式处理
- 每次迭代都基于上一次的结果
- 可以实现累积效果
- 适合需要多次微调的场景
使用场景示例
- 图像渐进式增强
- 迭代式风格转换
- 多次降噪处理
- 逐步细节优化

与Loop Index Switch的配合使用

可以使用Loop Index Switch根据iteration_count选择不同的处理参数

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。

3. 特殊功能节点

Loop Index Switch🐰
- 功能：根据当前循环次数选择不同的输入
- 使用方法：
  1. 右键点击节点选择"Add Loop Input"
  2. 输入想要添加的循环序号(0-99)
  3. 连接对应的输入
  4. 可以通过"Remove Loop Input"删除不需要的输入
- 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

使用建议

批量处理适合需要在图像不同区域应用不同处理的场景
单图处理适合需要多次迭代优化的场景
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
注意控制循环次数，避免过度处理

示例工作流

批量图片工作流