Update README.md

README.md

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-# 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 
+# DEPRECATED - moved to https://github.com/livepeer/ComfyUI-Stream-Pack