The info tool reads metadata directly via Sharp without going through
the format decoder pipeline. Added CLI format detection and decoding
before metadata read, matching the pattern used by all other tools.
Sharp's metadata() returns format:"heif" for AVIF files. The compress
function was using this raw value without normalizing through FORMAT_MAP,
so toFormat("heif",...) was called which requires a compression option.
Now both explicit and detected formats go through FORMAT_MAP, mapping
heif→avif correctly.
dcraw_emu is not included in libraw-dev (only libraries). ImageMagick
already delegates to LibRaw for all major RAW formats (DNG, CR2, NEF,
ARW, ORF, RW2). Moved ImageMagick helpers above RAW decoder to fix
declaration order.
Extends the platform to handle 7 new image format families alongside
the existing AVIF support gap-fill. Uses the established HEIC decoder
pattern (CLI decode → PNG → Sharp) for formats Sharp can't handle
natively: Camera RAW via dcraw_emu/LibRaw, PSD/TGA/EXR/HDR via
ImageMagick. JXL and ICO are Sharp-native. Adds server-side preview
for non-browser-displayable formats and JXL as a new convert output
target. All 27 validateImageBuffer callers updated with filename for
extension-based format detection.
.worktrees/ and .claude/ contain git worktrees from other developers
which have their own test files. Without exclusion, vitest picks them
up and fails due to mismatched React/module resolution.
Guard startPolling in handleOnline to only fire when transitioning from
offline state. Previously, a spurious browser online event while already
connected would start a polling interval that never gets cleared.
When lazyWithRetry exhausts all retry attempts on a chunk error, also
call setDisconnected() so the reconnecting banner appears alongside the
ErrorBoundary's "Update Available" card.
When the Python dispatcher crashes and bridge.ts retries via per-request
spawning, the shim from dispatcher.py isn't loaded. basicsr then fails
importing torchvision.transforms.functional_tensor (removed in v0.17).
Adding the shim directly to both scripts ensures they work regardless
of whether they run through the dispatcher or standalone.
- Add "unable to preload" pattern to isChunkError for Vite CSS preload failures
- Move ConnectionMonitor and ConnectionBanner outside ErrorBoundary so they
remain visible when the error boundary catches a render crash
- Add test for CSS preload error retry
Fastify's find-my-way router defaults maxParamLength to 100 chars.
Files with long names (e.g., stock photo filenames >100 chars) would
return 404 on download because the :filename param exceeded the limit.
The checkHealth method handles non-ok responses (e.g. 503) but only
network-level errors had test coverage. Add nonOkHealth helper and
test to verify connected → disconnected transition on HTTP 503.
checkHealth previously ignored non-ok responses (e.g. 503), silently
doing nothing when the server reported unhealthy. Add else branch to
transition connected → disconnected on non-ok status. Add tests for
connected → disconnected on fetch failure and offline → reconnected
on health check success.
- Add safe_onnx_session() to gpu.py with graceful CUDA EP → CPU fallback
- Replace bare ort.InferenceSession() calls across colorize, restore, inpaint, remove_bg
- Add libcublas-12-6 to production Dockerfile for ONNX Runtime CUDA EP
- Add skipIfFeatureNotInstalled guards to remove-bg, blur-faces, smart-crop, ocr, noise-removal e2e specs
- Add AI tool install prompt detection in tools-all.spec.ts
- Add smart-crop to PYTHON_SIDECAR_TOOLS so frontend shows install prompt correctly
- Create Dockerfile.test.dockerignore to include tests/ in test image builds
- Add libheif-examples and exiftool to Dockerfile.test for HEIC and metadata tests
- Regenerate visual regression baselines for Docker/Linux and skip on non-Docker platforms
- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize,
restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal)
closing all HIGH/MEDIUM coverage gaps from the test matrix audit
- Fix ensureAiDirs() crash on non-Docker environments by gating on
isDockerEnvironment() — prevents ENOENT when /data doesn't exist
- Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature
manifest for broader Python version compatibility
- Add Python 3.14 version guard warning in install_feature.py
- Remove duplicate torchvision shims from upscale.py and enhance_faces.py
(dispatcher.py already handles this at startup)
- Remove orphaned tools.batch i18n key and dead pipeline-builder filter
- Regenerate 4 visual regression baselines for current UI state
- Add data-testid to passport-photo generate button for E2E testability
The torchvision compatibility shim for basicsr 1.4.2 was missing the
parent-package binding and only proxied a single attribute, causing
upscale and enhance-faces to fail at import time. The fix adds a
__getattr__ proxy for all attributes, binds the shim to the parent
package, and installs it in the dispatcher at startup for defense-in-depth.
Also removes unused anyInstalling variable, redundant `as any` cast,
and applies Biome formatting fixes across the codebase.
- Fix NameError in restore.py: face enhancement loop used undefined
variable `i`, now uses enumerate()
- Fix gpu.py ONNX fallback: previous smoke-test with empty bytes
always raised, making GPU detection unreachable via the ONNX path.
Now uses nvidia-smi hardware check after confirming CUDA EP is
compiled in — works on Linux, Windows, and gracefully fails on macOS
- Fix cpu_fallback_packages stripping CUDA-specific index URLs when
replacing paddlepaddle-gpu with paddlepaddle for CPU-only systems
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Pin torch==2.6.0+cu126 and torchvision==0.21.0+cu126 in feature
manifest to prevent NCCL symbol mismatch on CUDA 12.6 base images
- Move lpips after torch in install order to prevent wrong version
resolution from PyPI
- Add einops to upscale-enhance common deps (required by SCUNet)
- Update cpu_fallback_packages to handle multi-package CUDA torch
entries on amd64 without GPU
- Fix gpu.py ONNX CUDA detection: replace hardcoded .so path with
cross-platform session smoke-test
- Fix os.dup(1) crashes on Windows in upscale, enhance_faces, and
noise_removal by wrapping in try/except with sys.stderr fallback
- Guard top-level numpy/cv2 imports in colorize.py and restore.py
with helpful error messages
- Add weights_only=False fallback for torch.load in noise_removal
- Fix integration tests to accept 501 for uninstalled AI features
and 422 for missing system tools (exiftool, libheif)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix SKIP_MUST_CHANGE_PASSWORD not affecting login/session API responses,
causing frontend redirect even when the env var was set after user creation
- Increase Docker Playwright timeouts (test: 600s, expect: 60s, AI processing: 300s)
to support CPU-only self-hosted environments
- Increase default rate limit from 100 to 50000 req/min for self-hosted deployments
- Fix OCR tests: use filechooser pattern (Dropzone has no static file input),
correct enhance checkbox default, rewrite for actual fixture behavior
- Fix remove-bg tests: update quality labels (Balanced→HD, Best→Max)
- Fix noise-removal skip guard: use waitFor() instead of instant isVisible()
- Fix automate pipeline save test: clean up stale E2E pipelines before assertion
- Add tool-specific suffix to output filenames so downloads don't overwrite originals (batch & single-tool routes)
- Skip deleting shared models when uninstalling a bundle that shares models with another installed bundle
- Auto-detect NVIDIA GPU and swap GPU-only pip packages (onnxruntime-gpu, paddlepaddle-gpu) for CPU equivalents
- Refactor docker-compose with YAML anchors and explicit cpu/gpu profiles
- Add libheif-plugin-x265 to Dockerfile
- Fix install-all queue logic to handle concurrent individual installs and clear stale errors
- Unify playwright docker config to use same test dir with API_URL env var
- Fix flaky e2e selectors, rename Strip Metadata → Remove Metadata, handle collage custom dropzone, improve fallback test image generation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ashim-hq