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
Add EfRLFN MLX weights
Browse files- NOTICE +3 -0
- README.md +17 -0
- config.json +7 -0
- model.safetensors +3 -0
NOTICE
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efrlfn-mlx — Apple MLX port of EfRLFN. MIT License.
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Derived from EvgeneyBogatyrev/EfRLFN (MIT), "EfRLFN: Efficient Real-time Super-Resolution",
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ICLR 2026. Pretrained x2/x4 weights are the official releases, converted to MLX safetensors.
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README.md
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---
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license: mit
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library_name: mlx
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pipeline_tag: image-to-image
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tags: [mlx, super-resolution, efrlfn]
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base_model: EvgeneyBogatyrev/EfRLFN
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---
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# EfRLFN x4 (MLX)
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Apple MLX port of [EfRLFN](https://github.com/EvgeneyBogatyrev/EfRLFN) (ICLR 2026), x4 super-resolution.
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PT-vs-MLX parity ~1e-6. Realtime on Apple Silicon (270x480 -> 1080x1920 ~0.06s for x4).
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```python
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from efrlfn_mlx import EfRLFNConfig
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from efrlfn_mlx.pipeline import load_model, upscale_to_file
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m = load_model("model.safetensors", EfRLFNConfig.x4())
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upscale_to_file(m, "lr.png", "sr.png")
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```
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MIT, derived from EvgeneyBogatyrev/EfRLFN.
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config.json
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{
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"in_channels": 3,
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"out_channels": 3,
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"feature_channels": 52,
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"num_blocks": 6,
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"upscale": 4
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:742940547cd93bfc8b66627056a08081acbff37c0f5d773ab273f8d2f4ee31ad
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size 2020675
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