Instructions to use mlx-community/Real-ESRGAN-x2plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Real-ESRGAN-x2plus with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Real-ESRGAN-x2plus mlx-community/Real-ESRGAN-x2plus
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,135 Bytes
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license: bsd-3-clause
library_name: mlx
pipeline_tag: image-to-image
tags:
- mlx
- super-resolution
- real-esrgan
- image-to-image
- apple-silicon
---
# Real-ESRGAN-x2plus (MLX)
Apple **MLX** fp16 port of Real-ESRGAN **RealESRGAN_x2plus** (RRDBNet, ×2),
for super-resolution on Apple Silicon. Converted from the official
[xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) release checkpoint
(BSD-3-Clause).
## Usage
```bash
pip install realesrgan-mlx # https://github.com/xocialize/realesrgan-mlx
realesrgan-mlx -i input.png -o out/ -n RealESRGAN_x2plus
```
```python
from realesrgan_mlx.pipeline_mlx import make_upsampler, upscale_image
up = make_upsampler("RealESRGAN_x2plus", tile=256) # tile>0 caps memory on large images
out = upscale_image("input.png", up)
```
`make_upsampler` downloads these weights automatically.
## Details
- **Architecture**: RRDBNet (`num_feat=64`, `num_block=23`, `num_grow_ch=32`)
- **Scale**: ×2 · **Precision**: fp16
- **Parity vs PyTorch**: full-forward 2.4e-6 (CPU fp32); fp16 vs fp32 golden 5.5e-4.
## License
BSD-3-Clause (upstream Real-ESRGAN, Xintao Wang et al.).
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