ComfyUI-SwinIR

ComfyUI-SwinIR

A ComfyUI custom node for SwinIR (Swin Transformer for Image Restoration) supporting image super-resolution and denoising.

Features

• Multiple Model Types: Support for classical SR, lightweight SR, real-world SR, and denoising
• Flexible Configuration: Customizable model parameters (window size, embed dim, depths, etc.)
• Memory Efficient: Tiled processing for large images
• Batch Processing: Process multiple images at once

Installation

1. Clone this repository into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/alexcong/ComfyUI-SwinIR.git

2. Install dependencies:

cd ComfyUI-SwinIR
pip install -r requirements.txt

3. Download SwinIR models from the official repository and place them in ComfyUI/models/upscale_models/

Usage

Nodes

SwinIR Model Loader

Loads a SwinIR model with specified configuration.

Parameters:

• model_name: Select from available models in upscale_models folder
• model_type: Choose model type (classicalSR, lightweightSR, realSR, denoising)
• upscale: Upscale factor (1-8)
• window_size: Window size for attention (default: 8)
• embed_dim: Embedding dimension (default: 180)
• depths: Comma-separated depths for each layer (e.g., "6, 6, 6, 6, 6, 6")
• num_heads: Comma-separated number of attention heads (e.g., "6, 6, 6, 6, 6, 6")
• mlp_ratio: MLP ratio (default: 2.0)
• img_size: Training image size (default: 128) - must match model's training size

SwinIR Upscale/Denoise

Processes images using the loaded SwinIR model.

Parameters:

• swinir_model: Model from SwinIR Model Loader
• images: Input images
• tile_size: Tile size for processing (default: 512)
• overlap: Overlap between tiles (default: 32)

Example Workflow

1. Add SwinIR Model Loader node
2. Configure model parameters to match your downloaded model
3. Add SwinIR Upscale/Denoise node
4. Connect model output to the upscale node
5. Connect your image input
6. Run!

Common Model Configurations

Classical SR (x2)

• Model: 001_classicalSR_DF2K_s64w8_SwinIR-M_x2.pth
• Type: classicalSR
• Upscale: 2
• Window Size: 8
• Embed Dim: 180
• Depths: "6, 6, 6, 6, 6, 6"
• Num Heads: "6, 6, 6, 6, 6, 6"
• MLP Ratio: 2.0
• Img Size: 64 (from s64 in filename)

Lightweight SR (x2)

• Model: 002_lightweightSR_DIV2K_s64w8_SwinIR-S_x2.pth
• Type: lightweightSR
• Upscale: 2
• Window Size: 8
• Embed Dim: 60
• Depths: "6, 6, 6, 6"
• Num Heads: "6, 6, 6, 6"
• MLP Ratio: 2.0
• Img Size: 64

Real-World SR (x4)

• Model: 003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth
• Type: realSR
• Upscale: 4
• Window Size: 8
• Embed Dim: 180
• Depths: "6, 6, 6, 6, 6, 6"
• Num Heads: "6, 6, 6, 6, 6, 6"
• MLP Ratio: 2.0
• Img Size: 64

Color Denoising (Noise 25)

• Model: 005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth
• Type: denoising
• Upscale: 1
• Window Size: 8
• Embed Dim: 180
• Depths: "6, 6, 6, 6, 6, 6"
• Num Heads: "6, 6, 6, 6, 6, 6"
• MLP Ratio: 2.0
• Img Size: 128 (from s128 in filename)

Testing

Run the test suite:

python test_nodes.py

Model Requirements for Testing

The test suite includes a real model loading test that requires downloading a pre-trained model:

1. Download 005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth from:

   • SwinIR GitHub Releases

2. Place the model in the same directory as test_nodes.py

Without the model, the real model test will be skipped. Other tests run using synthetic models and don't require downloads.

Test Coverage

• Model loading test
• Basic upscaling test
• Tiled processing test
• Batch processing test
• Real model loading test (validates the attention mask fix)

Credits

• SwinIR: JingyunLiang/SwinIR
• Paper: SwinIR: Image Restoration Using Swin Transformer

License

This project follows the same license as the original SwinIR repository.