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xororz
2024-10-16 21:39:05 +08:00
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@@ -32,6 +32,8 @@ npm run dev
The first run will download the models and cache them in the browser indexedDB. No need to download them again.
Unfortunately, since the transformed model must determine the input size, you need to download the model separately for each different tile size.
I've converted 4 models to tensorflow.js format, you can find the original pytorch models in [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)
- anime_fast `RealESRGAN-animevideov3`
@@ -39,7 +41,7 @@ I've converted 4 models to tensorflow.js format, you can find the original pytor
- general_fast `RealESRGAN-general-x4v3`
- general_plus `RealESRGAN_x4plus`
Now supports [bilibili/ailab/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)🎉🎉🎉, which offers nearly the same restoration quality as Real-ESRGAN but is **5x-10x faster**. 🥳🥳🥳
Now supports [bilibili/ailab/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)🎉🎉🎉, which offers nearly the same restoration quality as Real-ESRGAN but is **up to 5x-10x faster**. 🥳🥳🥳
### Details
@@ -58,7 +60,7 @@ There is something wrong with Real-CUGAN 3x series models. 3x models are not ava
### Model Selection Guide
With nearly the same restoration quality, Real-CUGAN is **5x-10x faster** than Real-ESRGAN. **Real-CUGAN is a better choice in most cases.**
With nearly the same restoration quality, Real-CUGAN is **much faster** than Real-ESRGAN. **Real-CUGAN is a better choice in most cases.**
But **Real-ESRGAN may be better when the input image is very small.** Here, input is a 120x120 image.
@@ -78,12 +80,14 @@ But **Real-ESRGAN may be better when the input image is very small.** Here, inpu
- Denoise3x: This is a strong noise reduction, which will remove most or all of the noise, but it might also cause some loss of fine details, making the image smoother.
- **Tile Size**: The default tile size is 64. 32, 48, 64, and 128 tile sizes are provided for RealCUGAN. The image is not enlarged by the model as a whole; instead, it is split into tiles. The model enlarges each tile sequentially, and then all the tiles are stitched together to form the entire image.
- **Tile Size**: The image is not enlarged by the model as a whole; instead, it is split into tiles. The model enlarges each tile sequentially, and then all the tiles are stitched together to form the entire image.
- On WebGL, larger tile sizes can make your device appear to lag during execution. If your device becomes laggy, you can reduce the tile size.
- On WebGPU, larger tile sizes can speed up the entire process.
Example: 120x120 image cut to 64x64 tile
- If your GPU can handle it, the larger the tile size, the faster the overall computation. Otherwise, smaller slices would be faster.
**Example**: 120x120 image cut to 64x64 tiles
![example2](./src/assets/example2.jpg)