Ultra quality (People only):
- BiRefNet-matting ONNX (928MB) for true alpha matting with per-pixel
transparency on hair wisps and fine edges
- Custom rembg session class, zero new Python dependencies
- Model pre-downloaded in Docker build for immediate availability
Quality tier labels: Fast / HD / Max / Ultra (shorter, fits 4-col grid)
Remove Background:
- Two-phase flow: AI removes bg once, then effects adjust instantly
- Blur background effect with real-time CSS preview (portrait mode)
- Drop shadow effect with opacity control
- Gradient backgrounds with presets, custom colors, and angle
- Custom background image upload (including HEIC/HEIF)
- Solid color backgrounds moved from Python to Node.js/Sharp
- Effects-only API endpoint for instant re-renders without AI re-run
- HEIC/HEIF input support (decoded before passing to Python/rembg)
- Passport/ID photo checkbox defaults ON for People subject
- Before/after slider preserved when no effects active
- 15 comprehensive Playwright e2e tests
Color Tools:
- Consolidated 4 tools (brightness-contrast, saturation, color-channels,
color-effects) into single "Adjust Colors" tool
- Added exposure, temperature, tint, hue, sharpness controls
- SVG filter-based live preview for all adjustments
- Backward-compatible URL redirects from old tool paths
Other fixes:
- Favicon tool: download button instead of auto-download
- Batch processing: HEIC filename extension fix
- File store: processedFilename field for proper batch downloads
- Add bidirectional HEIF support: decode (input) and encode (output) via system heif-convert/heif-enc
- Add server-side WebP preview generation for non-browser-previewable formats (HEIC, TIFF)
- Fix content-aware resize failing on HEIF input (decode before passing to caire)
- Fix content-aware resize timeout on large images by downscaling to max 1200px and using JPEG intermediate
- Add HEIF as target format in convert tool
- Add loading spinner for HEIF preview decode in file store
- Fix file picker not accepting HEIF files (explicit .heic,.heif,.hif extensions)
- Extend frontend timeout for medium tools to 180s with 45s progress animation
- Redesign rotate controls with preset buttons and compact flip section
- Remove misleading savings percentage from convert tool
Replace the Python seam-carving library with caire (esimov/caire v1.5.0),
a Go-based content-aware resize engine that is faster and supports both
shrinking and enlarging via seam insertion.
- Add Go builder stage in Dockerfile to compile caire from source
- Rewrite seam-carving.ts to call caire via execFile (no Python sidecar)
- Remove content-aware-resize from PYTHON_SIDECAR_TOOLS (60s timeout)
- Add new options: blur radius, edge sensitivity, square mode, face detection
- Move content-aware toggle below standard resize in UI (subtler placement)
- Rename "Don't enlarge" to "Limit to original size" with hover tooltip
- Add smooth progress bar for medium-duration tools
- Delete seam_carve.py and remove seam-carving pip dependency
- Update integration tests and visual regression screenshots
- Replace Rule of Thirds button with checkbox for clearer toggle
- Show side-by-side comparison after crop instead of overlay slider
- Add custom aspect ratio option with W:H number inputs
- Fix batch crop failing on files with different dimensions by
sending percentage-based coordinates instead of absolute pixels
- Add failed-file error state display in tool page
Several RBAC features from feat/rbac-permissions were silently lost
during the merge into main. This restores and completes them:
- Add permissions and teamName to login/session API responses
- Export Permission and Role types from shared package
- Filter settings tabs by user permissions in frontend
- Extend useAuth hook with role, permissions, and hasPermission
- Restrict teams listing to admin only
- Add admin override for API keys, files, and pipelines listing
- Add ownership scoping to file access, download, and delete routes
- Register userFileRoutes in integration test server
- Mock auth import in unit permissions test to avoid SQLite lock
Replace requireAuth with requirePermission("pipelines:own") on pipeline
save/list/delete routes. Admin users with pipelines:all can see and
delete all pipelines. Unauthorized delete returns 404 to avoid leaking
resource existence.
Migrate settings, teams, branding, and user management routes to use
the new permission-based authorization system. Remove requireAdmin
function entirely.
Create the RBAC permission module that maps roles to permissions and
provides a requirePermission middleware to replace requireAdmin. Update
the test server to use requirePermission for the admin health check.
- 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
GPU-dependent libraries (paddlepaddle-gpu, torch CUDA, realesrgan)
cannot be imported at Docker build time because the CUDA driver is
only available at runtime. Smoke test now verifies CPU-only imports
(rembg, cv2, numpy, mediapipe, seam_carving) and checks that model
files exist on disk. GPU imports are verified at runtime.
paddlepaddle-gpu requires libcuda.so.1 at import time, but no CUDA
driver exists during Docker build. The download script now catches
this ImportError and skips PaddleOCR model pre-download on amd64.
Models will download on first use at runtime when the CUDA driver
is available via nvidia-container-toolkit.
On arm64 (CPU paddlepaddle), models are still pre-downloaded at build
time as before.
Also reverted CI to amd64-only Docker build test for speed. Multi-arch
build is tested on release via the release workflow.
paddlepaddle-gpu needs libcuda.so.1 at import time, but the real NVIDIA
driver is only injected at runtime by the container toolkit. Install
cuda-compat-12-6 which provides forward-compat stubs that satisfy the
dlopen without a real GPU. Also force CPU mode via env vars in the
download script.
paddlepaddle-gpu tries to load libcuda.so.1 on import, but no GPU
driver exists during Docker build. Set PADDLE_DEVICE=cpu, FLAGS_use_cuda=0,
and CUDA_VISIBLE_DEVICES="" before any ML imports to force CPU mode.
-i replaces the entire package index so paddleocr couldn't be found.
Use --extra-index-url to add PaddlePaddle's index alongside PyPI, and
install paddleocr separately so it resolves from PyPI.
paddlepaddle-gpu==3.0.0 is not on PyPI, it is hosted on PaddlePaddle's
own package index. Added -i flag pointing to the cu126 stable index
for the amd64 GPU build.
- Warn on startup if deprecated STIRLING_VARIANT env var is set
- Broaden upscale.py exception handling to catch RuntimeError/OSError
for Lanczos fallback (not just ImportError)
- Add QEMU + multi-arch (amd64+arm64) to CI Docker build test
- Use .get() instead of .all() for single-row health check query
- Restore container_name in docker-compose.yml for backwards compat
PaddleOCR prints download/init messages to stdout which corrupts the
JSON result that the bridge expects. Same risk with basicsr/realesrgan.
Applied the same fd-level stdout redirect pattern already used in
remove_bg.py: redirect fd 1 to stderr during ML work, restore for
the JSON result. Also added show_log=False to PaddleOCR constructor.
Siddharth Kumar Sah