---
license: mit
library_name: mlx
pipeline_tag: video-to-video
tags:
- mlx
- frame-interpolation
- video-frame-interpolation
- rife
- apple-silicon
---

# RIFE 4.25 (MLX)

Apple **MLX** port of [Practical-RIFE](https://github.com/hzwer/Practical-RIFE)
**4.25** — real-time video **frame interpolation** on Apple Silicon. **MIT.**

First MLX/Apple-Silicon-native RIFE: torch-free inference, arbitrary-timestep
interpolation, `--multi` Nx frame rate, `--scale` pyramid for 4K, audio-preserving
video. Converted from the official RIFE 4.25 `flownet.pkl` (Google-Drive-only
upstream) to fp32 safetensors.

## Usage

```bash
pip install rife-mlx   # https://github.com/xocialize/rife-mlx
rife-mlx -i input.mp4 -o out.mp4 --multi 2          # 2x fps, keep audio
rife-mlx --img0 a.png --img1 b.png -t 0.5 -o mid.png
```

```python
from rife_mlx.utils.weights import build_model
from rife_mlx.pipeline_mlx import interpolate_pair
model = build_model("4.25")                    # auto-downloads this repo
mid = interpolate_pair(model, frame_a, frame_b, 0.5) # HWC uint8
```

## Details

- **Architecture**: IFNet (5 coarse-to-fine IFBlocks c=[192,128,96,64,32],
  LeakyReLU, ResConv+beta, Head encoder, ConvTranspose+PixelShuffle).
- **Precision**: fp32 (~23 MB) — RIFE's coarse-to-fine flow is fp16-sensitive.
- **Parity vs PyTorch (CPU fp32)**: warp 2.2e-6 · interp 1.2e-7 · full IFNet 1.43e-3.

## License

MIT (upstream Practical-RIFE, © hzwer). Weights are the official RIFE 4.25
release, converted to MLX.