88 lines
3.8 KiB
Markdown
88 lines
3.8 KiB
Markdown
# ComfyUI Super Resolution
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A collection of high-performance neural network-based Super Resolution models for ComfyUI.
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## Features
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- **Multiple SR Models**: Choose from FSRCNN, ESPCN, LapSRN, and EDSR with different quality/speed tradeoffs
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- **Modular Design**: Load models once and reuse them across multiple upscaling operations
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- **CUDA Acceleration**: Automatic GPU acceleration when available
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- **Multiple Scale Factors**: Support for 2x, 3x, and 4x upscaling
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## Installation
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1. Clone this repository to your ComfyUI custom_nodes directory:
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```
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cd ComfyUI/custom_nodes
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git clone https://github.com/yourusername/ComfyUI_SuperResolution
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```
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2. Restart ComfyUI - the necessary models will be automatically downloaded on first use
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## Usage
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1. Add a **SR Model Loader** node to your workflow
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2. Select your desired model type and scale factor
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3. Connect the model output to a **SR Upscale** node
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4. Connect an image to the upscale node
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5. Run your workflow to get high-quality upscaled images!
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## Model Comparison
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| Model | Architecture | Features | Best For | Speed | Quality |
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|-------|-------------|----------|----------|-------|---------|
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| **FSRCNN-small** | Lightweight CNN | Fast, minimal memory use | Real-time processing, mobile | ★★★★★ | ★★ |
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| **FSRCNN** | CNN with larger features | Good balance of speed/quality | General purpose | ★★★★ | ★★★ |
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| **ESPCN** | Sub-pixel convolutions | Efficient upscaling at end | Text/line drawings | ★★★★ | ★★★ |
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| **VDSR** | Very deep CNN | Better edge reconstruction | Detailed images with edges | ★★★ | ★★★★ |
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| **LapSRN** | Laplacian pyramid | Progressive upscaling | Sharp edges, details | ★★★ | ★★★★ |
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| **EDSR** | Deep residual network | Most parameters, best quality | Maximum detail | ★★ | ★★★★★ |
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## Technical Details
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Each model has unique architectural characteristics:
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1. **FSRCNN / FSRCNN-small**: Direct mapping from low to high resolution, lightweight with fewer parameters.
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2. **ESPCN**: Uses the efficient sub-pixel convolution technique that processes at low resolution and only expands dimensions at the final layer.
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3. **VDSR**: Very Deep Super Resolution network with 20 convolutional layers, uses global residual learning.
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4. **LapSRN**: Uses a Laplacian pyramid structure to progressively upscale images, which preserves edges better than the others.
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5. **EDSR**: Enhanced Deep Super-Resolution, a residual network with significantly more parameters, offering the highest quality but slower processing.
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## Performance Expectations (RTX 4090)
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| Model | 2x Upscaling | 4x Upscaling | Memory Usage |
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|-------|--------------|--------------|--------------|
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| FSRCNN-small | 200+ FPS | 150+ FPS | < 100MB |
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| FSRCNN | 100+ FPS | 80+ FPS | < 150MB |
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| ESPCN | 90+ FPS | 70+ FPS | < 150MB |
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| LapSRN | 60+ FPS | 40+ FPS | < 200MB |
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| EDSR | 40+ FPS | 25+ FPS | < 500MB |
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## When to Use Each Model
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- **FSRCNN-small**: When maximum speed is required
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- **FSRCNN**: For a good balance of quality and performance
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- **ESPCN**: For text and line art upscaling
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- **LapSRN**: When you need better edge preservation than FSRCNN
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- **EDSR**: When maximum quality is desired and performance is secondary
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## Comparison to Traditional Methods
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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.
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## Credits
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This nodepack implements models originally created by:
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- FSRCNN: Dong et al. (https://github.com/ryanontheinside/FSRCNN_Tensorflow)
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- EDSR: Lim et al. (https://github.com/ryanontheinside/EDSR_Tensorflow)
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- ESPCN: Shi et al. (https://github.com/ryanontheinside/TF-ESPCN)
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- LapSRN: Lai et al. (https://github.com/ryanontheinside/TF-LapSRN)
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## License
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MIT |