Update README.md

README.md

@@ -1,12 +1,91 @@
 ---
-title: Fs666
-emoji: 🏃
-colorFrom: green
-colorTo: red
+title: Face Swap Image To Video
+emoji: 🎭
+colorFrom: purple
+colorTo: pink
 sdk: docker
+app_port: 7860
 pinned: false
-license: apache-2.0
-short_description: Swap
+license: other
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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
+
+```bash
+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.)