ComfyUI Super Resolution

A collection of high-performance neural network-based Super Resolution models for ComfyUI.

Features

• Multiple SR Models: Choose from FSRCNN, ESPCN, LapSRN, and EDSR with different quality/speed tradeoffs
• Modular Design: Load models once and reuse them across multiple upscaling operations
• CUDA Acceleration: Automatic GPU acceleration when available
• Multiple Scale Factors: Support for 2x, 3x, and 4x upscaling

Installation

1. Clone this repository to your ComfyUI custom_nodes directory:

   cd ComfyUI/custom_nodes
   git clone https://github.com/yourusername/ComfyUI_SuperResolution
   

2. Restart ComfyUI - the necessary models will be automatically downloaded on first use

Usage

1. Add a SR Model Loader node to your workflow
2. Select your desired model type and scale factor
3. Connect the model output to a SR Upscale node
4. Connect an image to the upscale node
5. Run your workflow to get high-quality upscaled images!

Model Comparison

| Model | Architecture | Features | Best For | Speed | Quality | |-------|-------------|----------|----------|-------|---------| | FSRCNN-small | Lightweight CNN | Fast, minimal memory use | Real-time processing, mobile | ★★★★★ | ★★ | | FSRCNN | CNN with larger features | Good balance of speed/quality | General purpose | ★★★★ | ★★★ | | ESPCN | Sub-pixel convolutions | Efficient upscaling at end | Text/line drawings | ★★★★ | ★★★ | | VDSR | Very deep CNN | Better edge reconstruction | Detailed images with edges | ★★★ | ★★★★ | | LapSRN | Laplacian pyramid | Progressive upscaling | Sharp edges, details | ★★★ | ★★★★ | | EDSR | Deep residual network | Most parameters, best quality | Maximum detail | ★★ | ★★★★★ |

Technical Details

Each model has unique architectural characteristics:

1. FSRCNN / FSRCNN-small: Direct mapping from low to high resolution, lightweight with fewer parameters.

2. ESPCN: Uses the efficient sub-pixel convolution technique that processes at low resolution and only expands dimensions at the final layer.

3. VDSR: Very Deep Super Resolution network with 20 convolutional layers, uses global residual learning.

4. LapSRN: Uses a Laplacian pyramid structure to progressively upscale images, which preserves edges better than the others.

5. EDSR: Enhanced Deep Super-Resolution, a residual network with significantly more parameters, offering the highest quality but slower processing.

Performance Expectations (RTX 4090)

| Model | 2x Upscaling | 4x Upscaling | Memory Usage | |-------|--------------|--------------|--------------| | FSRCNN-small | 200+ FPS | 150+ FPS | < 100MB | | FSRCNN | 100+ FPS | 80+ FPS | < 150MB | | ESPCN | 90+ FPS | 70+ FPS | < 150MB | | LapSRN | 60+ FPS | 40+ FPS | < 200MB | | EDSR | 40+ FPS | 25+ FPS | < 500MB |

When to Use Each Model

• FSRCNN-small: When maximum speed is required
• FSRCNN: For a good balance of quality and performance
• ESPCN: For text and line art upscaling
• LapSRN: When you need better edge preservation than FSRCNN
• EDSR: When maximum quality is desired and performance is secondary

Comparison to Traditional Methods

These neural network-based upscaling methods offer significantly better quality than traditional algorithms like Lanczos, Bicubic, or Bilinear interpolation, while often being just as fast thanks to CUDA acceleration.

Credits

This nodepack implements models originally created by:

• FSRCNN: Dong et al. (https://github.com/ryanontheinside/FSRCNN_Tensorflow)
• EDSR: Lim et al. (https://github.com/ryanontheinside/EDSR_Tensorflow)
• ESPCN: Shi et al. (https://github.com/ryanontheinside/TF-ESPCN)
• LapSRN: Lai et al. (https://github.com/ryanontheinside/TF-LapSRN)

License

MIT