ComfyUI Extension: ComfyUI-SwinIR

Authored by alexcong

Created

Updated

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

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    README

    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
    1. Install dependencies:
    cd ComfyUI-SwinIR
pip install -r requirements.txt
    1. 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:

    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

    License

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