facefusion-api/CLAUDE.md

# CLAUDE.md

## Overview

FaceFusion API - A Python REST API wrapping FaceFusion v3.5.3 for face swapping, enhancement, lip sync, and other face/frame processing. Containerized for CPU-only execution, deployed via Gitea CI/CD at `dev.pivoine.art`.

## Architecture

- **FastAPI** async web framework with single uvicorn worker (mandatory due to FaceFusion's global state)
- **FaceFusion** included as git submodule at `facefusion/` pinned to tag 3.5.3
- All FaceFusion processing serialized through `threading.Lock` in `facefusion_bridge.py`
- Background job queue in `worker.py` for async processing

### Key Integration Points

FaceFusion uses global mutable state (`state_manager`) and module-level ONNX inference pools. The bridge (`app/services/facefusion_bridge.py`) replicates the initialization side-effects of `program.py`:
1. Registers all job_keys and step_keys in `job_store`
2. Initializes state defaults via `state_manager.init_item()`
3. Initializes job filesystem via `job_manager.init_jobs()`
4. Symlinks `facefusion/.assets/models/` -> `/data/models/` for model persistence

### Project Structure

```
app/
  main.py              # FastAPI app, lifespan, sys.path setup
  config.py            # Pydantic BaseSettings (FF_ env prefix)
  routers/             # API endpoint handlers
  schemas/             # Pydantic request/response models
  services/
    facefusion_bridge.py  # Core FaceFusion integration
    worker.py             # Background job queue
    file_manager.py       # File upload/output handling
```

## Common Commands

```bash
# Development
docker compose build
docker compose up

# Production (CPU VPS)
docker compose -f docker-compose.prod.yml up -d

# Test endpoints
curl http://localhost:8000/api/v1/health
curl http://localhost:8000/api/v1/system
curl -X POST http://localhost:8000/api/v1/models/download -H 'Content-Type: application/json' -d '["face_swapper"]'

# Sync face swap
curl -X POST http://localhost:8000/api/v1/process \
  -F source=@source.jpg \
  -F target=@target.jpg \
  -o result.jpg
```

## API Endpoints

- `POST /api/v1/process` - Synchronous processing (returns file)
- `POST /api/v1/jobs` - Create async job
- `GET /api/v1/jobs/{id}` - Job status
- `GET /api/v1/jobs/{id}/result` - Download result
- `DELETE /api/v1/jobs/{id}` - Cancel/delete job
- `GET /api/v1/processors` - List processors
- `GET /api/v1/models` - List downloaded models
- `POST /api/v1/models/download` - Download models
- `GET /api/v1/health` - Health check
- `GET /api/v1/system` - System info (CPU, memory)

## Docker

- Base image: `python:3.12-slim` + `onnxruntime`
- Models persisted in `/data/models` Docker volume (not baked into image)
- Single worker mandatory (`--workers 1`)

## Environment Variables

All prefixed with `FF_`:
- `FF_EXECUTION_PROVIDERS` - JSON array, e.g. `["cpu"]`
- `FF_EXECUTION_THREAD_COUNT` - Default 4
- `FF_VIDEO_MEMORY_STRATEGY` - strict/moderate/tolerant
- `FF_MODELS_DIR` - Model storage path
- `FF_MAX_UPLOAD_SIZE_MB` - Upload limit (default 500)

## Important Notes

- Never run with multiple uvicorn workers - FaceFusion global state will corrupt
- Models are 100MB-1GB each; pre-download via `/api/v1/models/download` before processing
- The `facefusion/` submodule should not be modified directly if possible - use symlinks for model paths
- Git operations: always push with the valknarthing ssh key