- 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>
The store set an initial 5% optimistically, then SSE sent real progress
starting from 0%, causing the bar to visibly drop. Now progress only
moves forward — both SSE and polling paths use Math.max to never
regress below the current value.
The bottom-right install indicator showed "1/6 installed" even when
installing a single bundle, implying all 6 would be installed. Now the
counter only appears during "Install All" operations.
Replace FeatureInstallPrompt's local SSE/polling with useFeaturesStore
so install progress, errors, and recovery are handled globally — works
across navigation, logout/login, and partial downloads. Shows fun
progress messages and ETA matching the settings page.
Add compose healthcheck and optional GPU profile (--profile gpu).
Replace static "Preview not available" and "Cannot preview this image
format" messages with a loading spinner in ImageViewer and SplitCanvas,
since HEIC files get server-side conversion and the preview appears
after a few seconds. Also default showDetails to true on /fullscreen.
The home page's file-uploaded view rendered its own tool list without
checking AI feature install status, so download icons disappeared after
uploading a file. Now both Quick Actions and All Tools sections show the
download badge consistently with the sidebar ToolPanel.
Uses XMLHttpRequest upload progress to track image upload (0-80%),
then shows "Processing collage..." for the server-side compositing
phase (80-100%). Progress bar with percentage shown in the preview area.
Adds objectFit property to CellTransform (cover/contain). When set to
"contain", the entire image is shown within the cell with background
color fill. Toggle button in the cell controls toolbar switches between
modes. Server-side rendering handles both modes via Sharp.
- Fix pan: use `movement` (cumulative) instead of `delta` (per-frame)
in useDrag — delta accumulation against static memo was wrong
- Fix wheel zoom: replace useWheel with direct DOM addEventListener
using { passive: false } so preventDefault() works and page doesn't
scroll while zooming
- Fix controls: remove auto-hide timer, keep zoom slider and reset
button visible while cell is selected
The lock file stored process.pid (Node server PID) which is always
alive. When releaseInstallLock wasn't called (e.g., error path),
the lock persisted forever, making the bundle stuck in "installing"
and blocking all future installs.
Fix: remove PID from lock entirely. Use simple file existence as
mutex. On server startup, unconditionally delete any lock file.
The previous selector returned the isToolInstalled function reference
which never changes, so the component didn't re-render when bundles
loaded. Now subscribes to the bundles array directly via useMemo.
- Subscribe to features store reactively in ToolPage so refresh shows
install prompt correctly instead of the tool UI
- Show loading state while features are being fetched for AI tools
- Capture stdout from install script for better error messages
- Keep last 20 stderr lines for error context instead of just exit code
- Clear install progress on success (was leaving stale state)
- Pass PIP_CACHE_DIR to install subprocess
The guard was only present on restore-photo.ts and the createToolRoute
factory. All other AI tools used custom route handlers that bypassed
the check entirely, allowing requests to reach the Python sidecar even
when the feature bundle was not installed. This adds an early 501
response before any multipart parsing or file processing.
The on-demand feature download system stores models at /data/ai/models/
(set via MODELS_PATH env var), but all Python scripts hardcoded
/opt/models/ as the base path. Each script now reads MODELS_PATH and
falls back to /opt/models for backward compatibility.
Remove all ML pip installs (onnxruntime, rembg, realesrgan, paddlepaddle,
mediapipe, codeformer), model downloads, and post-install fixups from the
Dockerfile. The base image now ships only Node.js + Sharp + Python with
numpy/Pillow/opencv. AI features are installed on-demand at runtime via
the feature manifest and install_feature.py script.
Key changes:
- Remove SKIP_MODEL_DOWNLOADS build arg (no longer needed)
- Remove apt-get purge of build-essential (needed for runtime pip installs)
- Remove PaddleX symlinks and facexlib weight directory setup
- Add COPY of feature-manifest.json and install_feature.py
- Update PYTHON_VENV_PATH to /data/ai/venv, add MODELS_PATH and DATA_DIR
- Entrypoint bootstraps AI venv from /opt/venv on first container start
with crash-safe temp directory pattern
Show an install prompt instead of the normal tool UI when an AI feature
bundle is not installed. Admins see a one-click install button with SSE
progress tracking and polling fallback; non-admins see a message to
contact their administrator.
Show a download icon on AI tools that are not yet installed, and fetch
feature status on mount in both the sidebar tool panel and fullscreen
grid page.
Add Zustand features store for tracking AI feature bundle state with
fetch, refresh, isToolInstalled, and getBundleForTool methods. Extend
parseApiError to return structured FeatureNotInstalledError objects
when the backend returns FEATURE_NOT_INSTALLED, and handle them in
both tool and pipeline processor hooks with user-friendly messages.
Check installed.json before exec()-ing AI scripts so that requests
for uninstalled feature bundles return a structured error instead
of crashing with an ImportError. Also sets U2NET_HOME to the
bundled model directory when present.
Return 501 with structured error when an AI tool's feature bundle
is not installed, preventing Python ImportError crashes. Guards
added to tool-factory, batch, pipeline (both validation loops),
and restore-photo custom route.
Reads the feature manifest, installs pip packages (common + arch-specific),
downloads models in parallel with atomic rename, and writes installed.json.
Includes disk space pre-check, NCCL conflict handling, retry logic, and
progress reporting via stderr JSON lines. Also updates the feature route
to pass manifestPath and modelsDir as CLI arguments.
ashim-hq