- Parallelize all 14 model downloads using ThreadPoolExecutor (6 workers)
Downloads were sequential (~30 min), now concurrent (~5-10 min)
- Switch Docker cache from type=gha to type=registry (GHCR)
GHA cache has 10 GB limit causing blob eviction and corrupted builds
Registry cache has no size limit and persists across runner instances
- Add pip download cache mounts to all pip install layers
Prevents re-downloading packages when layers rebuild
Allows re-triggering the release workflow after a Docker push failure
without needing new commits. If semantic-release produces no new version
the workflow now uses the latest existing git tag for the Docker build.
- Remove all lite/full variant logic from frontend, API, shared constants,
docs, and tests (single unified Docker image only)
- Replace single QEMU multi-arch Docker build with per-architecture native
builds (amd64 + arm64) and manifest merge to fix disk space exhaustion
- Add disk cleanup step and per-platform build cache scopes
- Switch release trigger from push to workflow_dispatch
- Add GitHub issue templates and PR template
Merge CPU, CUDA, and lite Docker images into a single unified image.
One tag (latest) works on all platforms: amd64 (NVIDIA CUDA) and arm64 (CPU).
GPU auto-detected at runtime. All ML models and packages baked in.
Key changes:
- Platform-conditional Dockerfile (nvidia/cuda on amd64, node on arm64)
- tini as PID 1 for proper signal handling
- Fix FILES_STORAGE_PATH data loss bug
- Fix RealESRGAN upscaler (was broken, always fell back to Lanczos)
- Fix PaddleOCR language codes and stdout corruption
- Simplified CI/CD (single build, single tag)
- Expanded model pre-download with verification
- Shutdown timeout, improved health endpoint
- Remove unused lama-cleaner
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)
Update all references across docs, workflows, UI components, and config
to point to the new GitHub org (stirling-image/stirling-image) and Docker
Hub account (stirlingimage/stirling-image) ahead of repo transfer.
- 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
ashim-hq