Instructions to use mlx-community/EfRLFN-x4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/EfRLFN-x4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir EfRLFN-x4 mlx-community/EfRLFN-x4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
license: mit
library_name: mlx
pipeline_tag: image-to-image
tags:
- mlx
- super-resolution
- efrlfn
base_model: EvgeneyBogatyrev/EfRLFN
EfRLFN x4 (MLX)
Apple MLX port of EfRLFN (ICLR 2026), x4 super-resolution. PT-vs-MLX parity ~1e-6. Realtime on Apple Silicon (270x480 -> 1080x1920 ~0.06s for x4).
from efrlfn_mlx import EfRLFNConfig
from efrlfn_mlx.pipeline import load_model, upscale_to_file
m = load_model("model.safetensors", EfRLFNConfig.x4())
upscale_to_file(m, "lr.png", "sr.png")
MIT, derived from EvgeneyBogatyrev/EfRLFN.