PostHog and Sentry were silently disabled in production Docker builds.
The app runs as ESM ("type": "module") via tsx, where require() is not
defined. The catch blocks swallowed the ReferenceError, leaving both
clients as null. Switch to await import() and store the Sentry module
reference for use in the error handler.
Pipeline steps and batch size are now unlimited by default. The old
"20 steps" and "200 images" figures had no basis in the actual code
(MAX_BATCH_SIZE already defaulted to 0/unlimited). Both remain
configurable via MAX_PIPELINE_STEPS and MAX_BATCH_SIZE env vars.
Also includes updated hardware requirements and sidebar nav from
prior documentation audit.
When auth was disabled, users could log out, reach the login page,
and authenticate with the default admin/admin credentials to gain
full admin privileges — defeating the purpose of AUTH_ENABLED=false.
Defense-in-depth fix across five layers:
- Skip ensureDefaultAdmin() when auth is disabled (no admin user seeded)
- Return 403 from POST /api/auth/login when auth is disabled
- Return synthetic anonymous user from GET /api/auth/session when auth is disabled
- Hide logout button in settings when auth is disabled
- Redirect /login and /change-password to / via AuthGuard when auth is disabled
Closes#90
* feat: allow multi-file selection for automation pipeline
Add two ways to import server-stored files into the pipeline:
1. Files page: "Pipeline" bulk action button and "Open in Pipeline"
button in file details panel — navigates to /automate with selected
file IDs via React Router state.
2. Automate page: "Import from Library" button opens a modal with
thumbnails, search, and multi-select checkboxes to pick files from
the user's server-stored library.
Both paths download the selected files and load them into the existing
useFileStore, reusing the batch pipeline processing infrastructure.
Closes#35
* fix: resolve 8 pre-existing test failures across unit and integration suites
- file-validation.ts: Return valid:false when Sharp fails to read
metadata for standard formats (PNG, JPEG, BMP) instead of silently
accepting corrupt buffers. CLI-decoded formats already skip Sharp.
- pipeline.ts: Enforce hard cap of 20 steps via .max() instead of
relying on MAX_PIPELINE_STEPS env var (default 0 = unlimited).
Tighten name limit to 100 chars and description to 500 chars to
match test expectations.
- env.ts: Change MAX_LOGO_SIZE_KB default from 2048 to 500 to match
the branding upload size limit the tests verify.
- Convert all AI bridge inputs to PNG before writing to disk so PIL can
read AVIF/WebP/TIFF (7 bridge files; face-detection and OCR already
had this pattern)
- Add title/author aliases to edit-metadata schema so common field names
actually write EXIF tags instead of being silently stripped by Zod
- Port extend/pad crop logic from passport-photo single endpoint to the
batch pipeline so crop regions extending beyond the image get filled
with background color instead of producing all-white output
- Clamp quantized color channels to 255 in color-palette to prevent
Math.round(255/16)*16=256 from producing invalid hex like #100100100
- Compare OCR fallback warning against expected engine name per tier
instead of comparing engine name against tier name (always mismatch)
When auth was disabled, the backend middleware attached the first admin
user from the database to every request, and the frontend granted all 12
permissions. This gave every unauthenticated visitor full admin access
to user management, settings, teams, branding, and feature installation.
Now both layers use role "user" with user-level permissions so tools,
files, and pipelines still work without login while admin-only routes
correctly return 403.
Closes#72
Closes#73
AVIF was already supported in the core engine, convert, compress,
optimize-for-web, upscale, erase-object, svg-to-raster, and
pdf-to-image tools. This adds AVIF as an output format option to
the 6 tools that were missing it: split, collage, stitch,
image-to-base64, noise-removal, and red-eye-removal.
For each tool, both the frontend format selector (with quality
slider for AVIF's lossy encoding) and the backend Zod schema +
Sharp .avif() encoding were updated. AVIF defaults: quality from
the user slider, effort 4 (balanced encode speed).
Also fixes pre-existing Biome formatting violations in 5 files
that were blocking a clean lint pass.
1. split batch 404: register split tool in batch registry via
registerToolProcessFn() so /api/v1/tools/split/batch works
2. CodeFormer crash: inference_app() expects a file path, not a numpy
array. Save to temp file before calling, read result back.
3. OCR fallback chain: fix case-sensitive "Segmentation fault" match
that prevented PaddleOCR crash from triggering Tesseract fallback.
Also add "process crashed" check. Upgrade ARM paddlepaddle to >=3.2.1.
4. blur-faces large images: downscale to 1920px max before MediaPipe
detection, scale coordinates back. Also add rotation retry for
portrait-oriented images where BlazeFace misses faces. Applied to
detect_faces.py, enhance_faces.py, and restore.py.
5. color-adjustments tool ID: fix mismatch in index.ts registration
array (was "color-adjustments", should be "adjust-colors").
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.
Phase 1 — Docker Artifact Optimization:
- Replace broad `COPY . .` with targeted frontend source copies (API/Python
changes no longer bust the frontend build cache)
- Replace build-essential with gcc/g++ (leaner runtime)
- Fix LOG_LEVEL=debug → info for production
- Harden .dockerignore (exclude worktrees, IDE, CI, test artifacts)
Phase 2 — State & Persistence:
- Add PUID/PGID support in entrypoint.sh for bind mount compatibility
- Guard against PUID=0/PGID=0 to prevent accidental root execution
- Evict conflicting system users (e.g. node:1000) before UID remap
Phase 3 — Security:
- Always register @fastify/rate-limit so login brute-force protection
works even when global rate limit is disabled (RATE_LIMIT_PER_MIN=0)
- Add trustProxy support (TRUST_PROXY env var, default true) so rate
limiting and audit logs use real client IPs behind reverse proxies
- Strip stack traces from 500 error responses in production
- Fix FSTDEP022 deprecation: maxParamLength → routerOptions
- Add multi-file guard on single-file tool endpoint with clear error
message pointing to the /batch endpoint
Phase 4 — Graceful Degradation:
- Add consolidated hardware detection startup banner (GPU, rate limit,
upload limit, proxy status)
- Add ConnectionMonitor component with health polling and reconnecting
overlay that auto-dismisses when the server comes back
Phase 5 — Deployment Docs:
- Rewrite deployment.md with copy-paste CPU and GPU compose templates
- Add hardware requirements table (minimum, recommended, heavy workloads)
- Add PUID/PGID bind mount documentation
- Add complete env var reference table
- Add reverse proxy guides for Nginx, Nginx Proxy Manager, Traefik,
and Cloudflare Tunnels
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.
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.
- 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 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>
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.
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.
- 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.
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.
Tracks which AI feature bundles are installed via /data/ai/installed.json
with atomic writes, in-memory caching, file-based install locks, and
startup recovery for interrupted installs.
ashim-hq