Hahihula-ComfyUI-batching-nodes/EXAMPLES.md

ComfyUI Batching Nodes - Examples & Tutorials

This document provides detailed examples and tutorials for using the ComfyUI Batching Nodes.

Table of Contents

• Prompt Batching Examples
• Image Batching Examples
• Combined Batching Examples
• Advanced Techniques

Prompt Batching Examples

Example 1: Generate Character Variations

Goal: Create 4 different character portraits

Workflow:

1. Add Batch Text node
2. Enter prompts:

a warrior in plate armor, fantasy art, detailed
a mage with staff, mystical aura, fantasy art, detailed
a rogue in leather armor, fantasy art, detailed
a cleric with holy symbol, fantasy art, detailed

3. Connect to CLIP Text Encode → Checkpoint → KSampler → VAE Decode → Save Image
4. Queue once → Get 4 images automatically!

Tip: Use consistent formatting across prompts for style consistency.

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Example 2: Test Different Art Styles

Goal: See the same subject in different artistic styles

Workflow:

1. Add Batch Text node with delimiter ,
2. Enter prompts:

oil painting style, portrait of a cat, classical art
watercolor style, portrait of a cat, soft colors
digital art style, portrait of a cat, vibrant
pencil sketch style, portrait of a cat, detailed lines

3. Connect to your standard workflow
4. Use same seed for all to compare styles fairly

Result: 4 images of the same subject in different styles

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Example 3: Product Description Variations

Goal: Generate product images with different descriptions

Workflow:

1. Add Batch Text node
2. Enter prompts:

professional product photography, studio lighting, white background
product in natural environment, outdoor setting
lifestyle product shot, in use, realistic
product detail close-up, macro photography

3. Process through your workflow

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Image Batching Examples

Example 4: Upscale Entire Photo Collection

Goal: Upscale 50 vacation photos at once

Workflow:

1. Add Batch Images node
2. Set folder_path: /path/to/vacation_photos
3. Set image_extensions: jpg,jpeg
4. Set sort_by: name
5. Set max_images: 0 (all images)
6. Connect: Batch Images → VAE Encode → Upscale Model → VAE Decode → Save Image
7. Queue once → All 50 photos processed!

Time saved: Instead of 50 manual loads, just one click!

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Example 5: Apply Face Restoration to Generated Images

Goal: Fix faces in a batch of AI-generated images

Workflow:

1. Place generated images in folder: /input/faces_to_fix
2. Add Batch Images node
3. Set folder_path: /input/faces_to_fix
4. Connect: Batch Images → Face Restore Node → Save Image
5. All faces fixed automatically

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Example 6: Convert Image Format in Batch

Goal: Convert 100 WebP images to PNG

Workflow:

1. Add Batch Images node
2. Set folder_path: /input/webp_images
3. Set image_extensions: webp
4. Connect: Batch Images → Save Image (with PNG format)
5. All converted in one go!

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Example 7: Process Only Recent Photos

Goal: Process photos from last week only

Workflow:

1. Add Batch Images node
2. Set folder_path: /input/all_photos
3. Set sort_by: modified
4. Set max_images: 20 (or however many you expect)
5. Process newest first

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Combined Batching Examples

Example 8: Style Transfer Matrix

Goal: Apply 3 different styles to 5 different images (15 total outputs)

Workflow:

1. Add Batch Text node with 3 style prompts:

in the style of Van Gogh, impressionist
in the style of Picasso, cubist
in the style of Monet, post-impressionist

2. Add Batch Images node pointing to folder with 5 images

3. Connect both to your style transfer workflow

4. Result: 3 styles × 5 images = 15 unique combinations!

Matrix visualization:

              Image1   Image2   Image3   Image4   Image5
Van Gogh        ✓        ✓        ✓        ✓        ✓
Picasso         ✓        ✓        ✓        ✓        ✓
Monet           ✓        ✓        ✓        ✓        ✓

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Example 9: A/B Testing Prompts on Same Images

Goal: Test which prompt works better on your test images

Workflow:

1. Batch Text with 2 prompt variations:

high quality, detailed, professional photography
ultra realistic, 8k, photorealistic, award winning

2. Batch Images with 10 test images

3. Connect both to KSampler with SAME SEED

4. Result: 20 images (2 prompts × 10 images) to compare

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Example 10: Product Mockup Generator

Goal: Place product in different environments

Workflow:

1. Batch Images: Product photos from different angles (3 images)
2. Batch Text: Different environment descriptions (4 prompts)

in modern kitchen, bright lighting
in cozy living room, warm atmosphere
in professional office, clean design
in outdoor garden, natural light

3. Connect both to ControlNet workflow
4. Result: 12 product mockups (3 angles × 4 environments)

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Advanced Techniques

Technique 1: Resume Interrupted Batches

If your batch processing was interrupted:

Workflow:

1. Check how many images were already processed
2. Use start_index to skip processed images
3. Continue from where you left off

Example:

First run: max_images: 50, start_index: 0   → Process images 0-49
Interrupted at image 30!
Resume run: max_images: 0, start_index: 30  → Process images 30-end

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Technique 2: Process in Chunks for Memory Management

For very large batches:

Workflow:

1. First chunk: start_index: 0, max_images: 100
2. Second chunk: start_index: 100, max_images: 100
3. Third chunk: start_index: 200, max_images: 100
4. Continue until done

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Technique 3: Organized File Naming

Use the filenames output for smart saving:

Workflow:

1. Batch Images → outputs both images and filenames
2. Use a String manipulation node to modify filename
3. Pass modified filename to Save Image node
4. Result: Organized output with meaningful names

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Technique 4: Conditional Processing

Process only certain images based on properties:

Pseudo-workflow:

1. Load images with Batch Images
2. Use image dimensions checker (custom node)
3. Filter to only portrait orientation
4. Process filtered batch

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Technique 5: Delimiter Tricks

Comma-separated for CSV-like input:

Delimiter: ","
Input: "cat, sitting, indoors, cute, cat, playing, outdoors, energetic"
Result: 4 prompts (assuming word pairs)

Multi-line for complex prompts:

Delimiter: "\n\n"  (double newline)
Input: "A detailed description
that spans multiple lines
for one prompt.

A second detailed description
also spanning lines
for the second prompt."
Result: 2 prompts

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Tips for Best Results

For Prompt Batching:

• Keep prompts similar in structure for consistency
• Use the same seed to isolate prompt variations
• Test with 3-5 prompts first before large batches
• Include quality tokens in all prompts if needed

For Image Batching:

• Name files with leading zeros for proper sorting (001, 002, etc.)
• Test with max_images: 5 first to verify workflow
• Use absolute paths for folder_path
• Check console for loading confirmation messages

For Combined Batching:

• Understand multiplication: N prompts × M images = N×M outputs
• Start small (2×2 = 4) to test before large matrices
• Monitor memory usage
• Use consistent seeds for fair comparisons

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Common Workflows Summarized

Goal	Nodes Used	Key Settings
Multiple prompt variations	Batch Text	Different prompts, same settings
Upscale folder of images	Batch Images	max_images: 0
Test styles on one subject	Batch Text	Same subject, different styles
Process recent photos	Batch Images	sort_by: modified, max_images: N
Style transfer matrix	Both nodes	Multiple styles × multiple images
Resume interrupted batch	Batch Images	start_index: N
Convert image formats	Batch Images	Load one format, save another
Character sheet generation	Batch Text	Different angles/views

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Getting Help

If you need help with any of these examples:

1. Check the console for error messages
2. Verify your node connections
3. Start with simple examples first
4. Open an issue on GitHub with your workflow JSON

Happy batching! 🚀