README.md

metadata

title: Face Swap Image To Video
emoji: 🎭
colorFrom: purple
colorTo: pink
sdk: docker
app_port: 7860
pinned: false
license: other

Face Swap: Image → Video

Upload a face photo and a target video; the app swaps the face from your photo onto every face it detects in the video, frame by frame, and re-attaches the original audio.

It uses:

• insightface (buffalo_l) for face detection/analysis
• inswapper_128.onnx for the actual face swap
• onnxruntime-gpu to run on a GPU
• ffmpeg to remux the original audio back onto the output

This Space builds from a custom Dockerfile (lean python:3.10-slim base) rather than the Gradio SDK's auto-build, which avoids compiling Python from source and pulling in a huge C toolchain — the auto-build path was timing out during apt-get on this dependency stack. Build should now take a few minutes instead of 40+.

Setup on Hugging Face

1. Create a new Space → SDK: Docker (not Gradio).
2. Upload all files in this folder (Dockerfile, app.py, requirements.txt, README.md) to the Space repo root.
3. In Settings → Hardware, select a rented GPU tier (e.g. T4 small, T4 medium, or A10G) — CPU Basic will work but will be very slow for video.
4. Build/restart the Space. On first launch it downloads:
   • the buffalo_l face analysis model (auto, via insightface)
   • inswapper_128.onnx (auto, via a Hugging Face Hub mirror — see below)

If the download is slow

• The app already enables hf_transfer (parallel chunked downloads), which is usually a big speedup over a plain single-stream download.
• Turn on Persistent Storage in Settings → this is the biggest win: without it, the ~530 MB model gets re-downloaded every time the Space restarts or wakes from sleep. With it, you only pay for the slow download once.
• Check the Space logs — the app prints which mirror it's trying ([model-download] trying ...). If it's stuck on one mirror, that mirror may be throttled; you can reorder/edit INSWAPPER_MIRRORS in app.py to try a different one first, or download the file yourself and upload it directly to the Space root as inswapper_128.onnx (skips the download entirely).

If the automatic model download fails

inswapper_128.onnx (~530 MB) isn't included in this zip because it's a large binary model file, and its original hosting has moved around over time. app.py tries a few known public mirrors on the Hub automatically. If all of them fail (mirrors do occasionally disappear), just download the file yourself from wherever you can find a trusted copy and drop it into the Space's root folder as inswapper_128.onnx — the app checks for a local copy first before trying to download anything.

Files

File	Purpose
Dockerfile	Lean build: python:3.10-slim + ffmpeg + pip deps
app.py	Gradio UI + face-swap pipeline
requirements.txt	Python packages
README.md	This file / Space metadata header

Responsible use

Only upload media you have the rights and consent to modify. Don't use this to impersonate real people without their permission, create non-consensual explicit content, or spread misinformation. You are responsible for how you use the output.

Local testing

pip install -r requirements.txt
python app.py

(You'll need ffmpeg installed locally too, and a CUDA-capable GPU + matching drivers for onnxruntime-gpu to actually use the GPU — otherwise it'll fall back to CPU.)