Add a :cuda Docker image tag that auto-detects NVIDIA GPU at runtime
and falls back gracefully to CPU. Same pattern as Immich.
- New gpu.py shared utility for cached CUDA detection
- Background removal (rembg): pass CUDAExecutionProvider to ONNX Runtime
- Upscaling (Real-ESRGAN): use CUDA device + FP16 when GPU available
- OCR (PaddleOCR): enable use_gpu when CUDA detected
- Dispatcher reports GPU status at startup via readiness signal
- Admin health endpoint exposes GPU availability
- Dockerfile uses ARG GPU=false with conditional NVIDIA CUDA base image
- docker-compose.gpu.yml override for GPU users
- CI/CD workflows build and publish :cuda tag (amd64 only)
Three tags: :latest (CPU), :lite (no AI), :cuda (GPU with CPU fallback)
- Set up semantic-release with zero-touch CI pipeline on push to main
- Add version sync script to keep all package.json files and APP_VERSION
constant in sync automatically
- Consolidate Docker publishing into single tag-triggered workflow that
pushes to both Docker Hub and ghcr.io with semver tags
- Add help dialog with keyboard shortcuts, getting started guide, and
resource links
- Sync all versions to 0.2.1 to match Docker Hub latest
BiRefNet-Lite times out on first load (~60s+ for 973MB model).
U2-Net works in 2 seconds. Users can still select BiRefNet for
higher quality when they're willing to wait. Added timing hints
in model descriptions and increased timeout for BiRefNet models.
The bridge.ts catch block was catching ALL errors from the venv Python
and falling back to system python3. This masked real script errors
(like rembg model loading failures) by reporting "rembg not installed"
from the system python3 fallback. Now only falls back on ENOENT (venv
binary not found).
Siddharth Kumar Sah