- 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
- ci.yml: skip lint/test/docker on docs-only and markdown-only pushes
- deploy-docs.yml: only rebuild GitHub Pages when apps/docs/** changes
- README.md: updated key features and content
- images: updated dashboard screenshots, removed stale images
Replace CGO_ENABLED=0 (which fails because gioui.org requires CGO on Linux)
with a proper C cross-compiler approach using Debian multi-arch packages.
Running caire-builder with --platform=\$BUILDPLATFORM avoids QEMU crashes on
Apple Silicon; the C cross-compiler bridges the CGO gap for the target arch.
Also adds SKIP_MODEL_DOWNLOADS=true to the CI docker build job to prevent
HuggingFace CDN 504s in CI (image structure is what matters there).
urlretrieve against the HuggingFace CDN was consistently returning HTTP
504 in GitHub Actions runners for LaMa, NAFNet, and the OpenCV caffemodel.
The huggingface_hub library has built-in retry logic, resumable downloads,
and better CDN routing than bare urlretrieve.
- download_lama_model: urlretrieve → hf_hub_download (Carve/LaMa-ONNX)
- download_nafnet_model: urlretrieve → hf_hub_download (mikestealth/nafnet-models)
- download_opencv_colorize_models: caffemodel → hf_hub_download (space repo_type)
- _urlretrieve: retry count 3→5, flat 10s delay → exponential backoff (10/20/40/80s)
- Also reverts the SKIP_MODEL_DOWNLOADS=true from CI workflow (wrong approach)
The Docker Build Test was consistently failing because HuggingFace CDN
returns 504 Gateway Timeout when downloading the LaMa ONNX model (~200MB)
from GitHub Actions runners. Model availability is an external dependency,
not something CI can control.
Added SKIP_MODEL_DOWNLOADS build arg (default: false). When set to true,
the download_models.py step is skipped entirely. CI only needs to verify
the image structure builds — Python deps install, Node build runs, app
code is copied — not that every ML model CDN is reachable.
Production builds (docker build without the arg) still download all models
as before.
The edit-metadata integration tests require exiftool (libimage-exiftool-perl)
which was missing from the CI test runner, causing 4 tests to fail with 422.
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)
Ubuntu 24.04 uses plugin-based libheif codecs. Added libheif-plugin-x265
(HEVC encoder) and libheif-plugin-libde265 (HEVC decoder) to the CI test
job. Debian bookworm (Docker) bundles these in libheif1 directly.
- Remove @fastify/swagger and @fastify/swagger-ui (API docs live on GitHub Pages)
- Run typecheck, build, and docker CI jobs in parallel instead of sequentially
- 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
- Lint & typecheck on every push/PR
- Build verification
- Docker build test
- Multi-arch Docker publish to GHCR on main (amd64 + arm64)
- Build cache via GitHub Actions cache
- Concurrency control to cancel stale runs
ashim-hq