|
|
---
|
|
|
license: apache-2.0
|
|
|
language: en
|
|
|
library_name: tensorflowjs
|
|
|
tags:
|
|
|
- real-cugan
|
|
|
- super-resolution
|
|
|
- image-upscaling
|
|
|
- anime
|
|
|
- tensorflowjs
|
|
|
- image-to-image
|
|
|
---
|
|
|
|
|
|
# Real-CUGAN Models for TensorFlow.js
|
|
|
|
|
|
[](https://huggingface.co/shammisw/real-cugan-tensorflowjs)
|
|
|
[](https://opensource.org/licenses/Apache-2.0)
|
|
|
|
|
|
This repository provides pre-converted models of **Real-CUGAN** (Real-World-Oriented Cascaded U-Net for Anime Image Super-Resolution) in the **TensorFlow.js GraphModel format**, ready for use in web browsers and Node.js environments.
|
|
|
|
|
|
These models are optimized for upscaling anime-style images and illustrations with high fidelity, speed, and reduced noise.
|
|
|
|
|
|
## ✨ Features
|
|
|
|
|
|
* **High-Quality Anime Upscaling:** Specifically trained for cartoons and anime, preserving sharp lines and details.
|
|
|
* **Web Ready:** Run directly in the browser with TensorFlow.js for client-side image processing.
|
|
|
* **Multiple Scales & Models:** Includes various models for different upscaling factors and noise reduction levels.
|
|
|
* **Lightweight & Fast:** CUGAN is designed to be more efficient than many larger GAN-based upscalers.
|
|
|
|
|
|
---
|
|
|
|
|
|
## 🚀 Usage Example
|
|
|
|
|
|
To use these models, you will need to have TensorFlow.js set up in your project.
|
|
|
|
|
|
```bash
|
|
|
# Using npm
|
|
|
npm install @tensorflow/tfjs
|
|
|
|
|
|
# Using yarn
|
|
|
yarn add @tensorflow/tfjs
|
|
|
```
|
|
|
|
|
|
Here is a basic example of how to load and run a model in JavaScript:
|
|
|
|
|
|
```javascript
|
|
|
import * as tf from '@tensorflow/tfjs';
|
|
|
|
|
|
// The URL to the model.json file in this repository
|
|
|
const MODEL_URL = '[https://huggingface.co/shammisw/real-cugan-tensorflowjs/resolve/main/real-cugan-models/realcugan/4x-conservative-64/model.json](https://huggingface.co/shammisw/real-cugan-tensorflowjs/resolve/main/real-cugan-models/realcugan/4x-conservative-64/model.json)';
|
|
|
|
|
|
async function upscaleImage(imageElement) {
|
|
|
try {
|
|
|
// 1. Load the model
|
|
|
console.log('Loading model...');
|
|
|
const model = await tf.loadGraphModel(MODEL_URL);
|
|
|
console.log('Model loaded.');
|
|
|
|
|
|
// 2. Prepare the input tensor from an HTMLImageElement
|
|
|
// Models are trained on float32 tensors, normalized to the [0, 1] range.
|
|
|
const inputTensor = tf.browser.fromPixels(imageElement)
|
|
|
.toFloat()
|
|
|
.div(255.0)
|
|
|
.expandDims(0); // Add batch dimension: [h, w, c] -> [1, h, w, c]
|
|
|
|
|
|
// 3. Run inference
|
|
|
console.log('Running inference...');
|
|
|
const outputTensor = model.execute(inputTensor);
|
|
|
|
|
|
// 4. Process the output and display it on a canvas
|
|
|
const outputCanvas = document.getElementById('output-canvas');
|
|
|
await tf.browser.toPixels(outputTensor.squeeze(), outputCanvas);
|
|
|
console.log('Upscaling complete!');
|
|
|
|
|
|
// 5. Clean up tensors
|
|
|
tf.dispose([inputTensor, outputTensor]);
|
|
|
|
|
|
} catch (error) {
|
|
|
console.error('Failed to upscale image:', error);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// Find your input image element and pass it to the function
|
|
|
const myImage = document.getElementById('my-input-image');
|
|
|
upscaleImage(myImage);
|
|
|
```
|
|
|
|
|
|
---
|
|
|
|
|
|
## 📂 Available Models
|
|
|
|
|
|
This repository contains the following converted models. The number in the model name (e.g., `-64`) refers to the tile size used during conversion, which can affect performance and memory usage.
|
|
|
|
|
|
| Model Type | Scale | Denoise Level | Path |
|
|
|
| :--------------- | :---: | :-----------: | :------------------------------------------------- |
|
|
|
| **Conservative** | 2x | - | `real-cugan-models/realcugan/2x-conservative-64/` |
|
|
|
| **Conservative** | 4x | - | `real-cugan-models/realcugan/4x-conservative-64/` |
|
|
|
| *More models can be added here as they are converted.* | | | |
|
|
|
|
|
|
---
|
|
|
|
|
|
## 🙏 Acknowledgements & Credits
|
|
|
|
|
|
This repository only contains the converted models. All credit for the research and training of the original models goes to their respective creators.
|
|
|
|
|
|
* **Original Real-CUGAN Models:** The foundational research and PyTorch models were developed by **Bilibili AI Lab**. Their incredible work made this possible.
|
|
|
* **GitHub Repository:** [bilibili/ailab/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)
|
|
|
|
|
|
* **TensorFlow.js Conversion:** The methodology for converting these models to TensorFlow.js format was adapted from the excellent **[web-realesrgan](https://github.com/ts-ai/web-realesrgan)** project, which provided a clear path for on-device super-resolution in the browser.
|
|
|
|
|
|
---
|
|
|
|
|
|
## 📜 License
|
|
|
|
|
|
The code and configuration in this repository are released under the **Apache-2.0**.
|
|
|
|
|
|
The original Real-CUGAN models are subject to their own license terms as specified in the [official Real-CUGAN repository](https://github.com/bilibili/ailab/tree/main/Real-CUGAN). Please ensure compliance with their license if you use these models.
|
|
|
|
