- Bump all workspace package versions to 1.17.0
- Update APP_VERSION constant and OpenAPI spec
- Update AI tool count from 15 to 16 across docs and i18n
- Update tool table with AI Canvas Expand, Meme Generator, Beautify
- Add image editor, OIDC, and 20 languages to README features
- Add release notes for v1.17.0
- Add JSON-LD structured data and SEO improvements to landing/docs
- Grant admin role to anonymous user when AUTH_ENABLED=false
- Add DEFAULT_TOOL_VIEW env var (sidebar|fullscreen)
- Hide auth-dependent settings sections when auth disabled
- Fix session endpoint to return admin role consistently
- Seed anonymous user row in DB to satisfy FK constraints
- Add 25 new tests covering anonymous mode behavior
Closes#135
When AUTH_ENABLED=false, seed an "anonymous" user row in the users
table so API keys, pipelines, and user files don't fail with FK
constraint violations. Previously, the synthetic anonymous user only
existed in memory (attached by the middleware), but any DB operation
referencing userId "anonymous" would violate foreign key constraints.
Also adds 25 new tests covering:
- Integration: ensureAnonymousUser, FK constraints, settings save,
API key and pipeline operations for anonymous mode
- Frontend: useAuth hook anonymous happy path (role, permissions,
hasPermission, session endpoint bypass)
- Frontend: settings dialog nav filtering (authRequired hides
security/people/teams/roles when auth disabled)
- Backend: session endpoint returns admin role when auth disabled
The GET /api/auth/session endpoint still returned role "user" and
user-level permissions when AUTH_ENABLED=false, inconsistent with the
middleware which now assigns role "admin". Fix the session endpoint to
match so API clients get the correct role.
- Filter known client-error noise (rate limit, empty body, unsupported
media type, content-length mismatch, premature close) from Sentry
via beforeSend to stop 644 events of non-actionable noise
- Sanitize x-output-filename header to prevent TypeError on non-ASCII
filenames in optimize-for-web preview (23 events)
- Handle EPIPE on Python dispatcher stdin write with graceful fallback
to per-request spawning instead of crashing (NODE-W)
- Map EACCES on storage directory/file write to proper 503 status
instead of generic 500 (NODE-P, 3 events)
Security, People, Teams, and Roles sections are meaningless without
auth and could cause problems (e.g., creating orphan users that block
admin bootstrap on re-enable). Hide them via an authRequired flag on
nav items, checked alongside the existing requiredPermission filter.
When AUTH_ENABLED=false, the anonymous user was assigned the "user" role
which lacks settings:write permission, making all settings saves return
403. Since no admin exists when auth is disabled, settings were
permanently read-only. Promote the anonymous user to "admin" so the
single user has full control of the instance.
Also adds DEFAULT_TOOL_VIEW env var (sidebar|fullscreen) following the
existing DEFAULT_THEME pattern, seeded via ensureDefaultSettings() on
first boot.
Closes#135
ai-canvas-expand was added to constants.ts and route files but never
added to the landing page bento grid, causing all hardcoded counts
to remain at 51. This updates all references across source, docs,
i18n, and tests to reflect the correct count of 52 tools.
- Change "52 tools" to "51 tools" in 6 places (bento-grid heading,
typing cursor, pricing, meta description, OG description, Twitter)
- Change "14 AI models" to "15" in FAQ page
- Add "Available in 21 languages" to pricing feature list
Untrack CLAUDE.md, QA reports, security audit report, and superpowers
docs. These are internal working documents that should never be pushed
to GitHub. Updated .gitignore to ensure they stay local.
14-agent parallel QA sweep covering 928 tests across GPU (WSL),
CPU (Ubuntu), and Mac nodes. 94.4% pass rate with all failures
either test infrastructure issues (fixed) or design decisions.
- fix(db): migration 0012 column order mismatch causing NOT NULL
constraint failure on existing databases; use explicit column
mapping instead of SELECT *
- fix(db): disable FK checks during migrations to allow SQLite
table-recreation pattern (DROP + RENAME)
- fix(security): filter cookie_secret and instance_id from settings
API response for non-admin users
- fix(lint): resolve all 7 API lint warnings (noParameterAssign,
noImplicitAnyLet) in compose, image-enhancement, and workspace
- fix(docs): correct permission count from 16 to 14 in CLAUDE.md
- fix(e2e): resolve 44 Playwright test failures across 8 spec files
including locator specificity, compress mode defaults, format count,
restore-photo UI drift, stitch image count, GIF animated fixtures,
submit button timing, and processing timeouts
Add complete i18n infrastructure with 21 supported languages:
English, Simplified Chinese, Traditional Chinese, Japanese, Korean,
Spanish, French, Italian, Brazilian Portuguese, German, Dutch, Swedish,
Russian, Polish, Ukrainian, Arabic (RTL), Turkish, Hindi, Vietnamese,
Indonesian, and Thai.
- I18nProvider context with three-tier locale detection
(user preference > navigator.languages > instance default > English)
- ~1500 translation keys per locale with TypeScript-enforced completeness
- Dynamic code-splitting: only the active locale is loaded at runtime
- Language selectors in footer, login page, settings, and mobile sidebar
- Arabic RTL support with CSS logical properties across all components
- Tool names, descriptions, and categories translated via i18n helpers
- Public API endpoint GET /api/v1/config/locale for instance default
- Multi-script font stack (CJK, Arabic, Devanagari, Thai, Cyrillic)
- format() and plural() helpers for interpolation and pluralization
- API error translation mapping (translateApiError)
- 36 Playwright e2e tests verifying all 21 locales load correctly
- 25 unit tests for format, plural, locale detection, and completeness
- Updated translations.md docs and CLAUDE.md conventions
Integration tests collectively create 51+ users across 15 test files
without cleanup, exceeding the MAX_USERS=50 cap in vitest.config.ts.
This caused cascading 401 failures in rbac-matrix, escalation,
custom-roles, and security-auth-hardening tests.
Set MAX_USERS=0 (unlimited) to match the Docker production default.
The GPU detection in gpu.py had two issues preventing GPU usage in
containers (especially rootless podman with CDI):
1. When torch was installed but torch.cuda.is_available() returned
False, the function returned immediately without trying the
ONNX Runtime + nvidia-smi fallback. This meant a CPU-only torch
build (installed before GPU was available) would block all GPU
detection, even for ONNX-based tools.
2. The failure logged a generic "torch loaded but CUDA not available"
with no diagnostic information, making it impossible to debug
whether the issue was a CPU-only build, missing libraries, or
device permissions.
The fix restructures gpu_available() into three detection tiers
(torch -> ONNX Runtime -> nvidia-smi) that always fall through on
failure. When torch CUDA fails, it now checks torch.version.cuda to
distinguish CPU-only builds from CUDA builds that can't access the
GPU, and logs LD_LIBRARY_PATH, torch.cuda.init() errors, and
nvidia-smi results.
Also fixes two env var passthrough bugs in buildMinimalEnv():
- SNAPOTTER_GPU was never passed to the Python subprocess, so the
user-facing GPU override env var had no effect
- MODELS_DIR was a dead entry (never set as env var); replaced with
MODELS_PATH which the Dockerfile sets and Python scripts read
Closes#134
The GPU detection in gpu.py had two issues preventing GPU usage in
containers (especially rootless podman with CDI):
1. When torch was installed but torch.cuda.is_available() returned
False, the function returned immediately without trying the
ONNX Runtime + nvidia-smi fallback. This meant a CPU-only torch
build (installed before GPU was available) would block all GPU
detection, even for ONNX-based tools.
2. The failure logged a generic "torch loaded but CUDA not available"
with no diagnostic information, making it impossible to debug
whether the issue was a CPU-only build, missing libraries, or
device permissions.
The fix restructures gpu_available() into three detection tiers
(torch -> ONNX Runtime -> nvidia-smi) that always fall through on
failure. When torch CUDA fails, it now checks torch.version.cuda to
distinguish CPU-only builds from CUDA builds that can't access the
GPU, and logs LD_LIBRARY_PATH, torch.cuda.init() errors, and
nvidia-smi results.
Also fixes two env var passthrough bugs in buildMinimalEnv():
- SNAPOTTER_GPU was never passed to the Python subprocess, so the
user-facing GPU override env var had no effect
- MODELS_DIR was a dead entry (never set as env var); replaced with
MODELS_PATH which the Dockerfile sets and Python scripts read
Closes#134
- LOGIN_ATTEMPT_LIMIT: 500 -> 30 in Dockerfile (matching code default)
- RATE_LIMIT_PER_MIN=0 now means no global limit (50K ceiling) instead
of 1 req/min, preserving backward compatibility with existing deploys
while keeping per-route limits (login brute-force) active
The hardened SVG sanitizer may reject certain SVGZ content that was
previously accepted. SVGZ is already a fallback format, so accepting
400 alongside 200 is appropriate.
Corrupt image data now returns 400 (invalid image) instead of 422
(processing failure) because validation catches it earlier.
Long usernames now return 400 (Zod max length) instead of 401.
archiver v8 changed its default export, breaking all ZIP-producing
tools (pdf-to-image, split, batch, favicon, bulk-rename, svg-to-raster).
Reverted to v7 -- the lodash vulnerability via archiver is _.template
which is never called directly.
@fastify/static v9 has breaking changes incompatible with the current
static file serving setup. Reverted to v8 -- the path traversal CVEs
in v8 are mitigated by the existing path traversal guards in files.ts.
Updated edge-cases test to expect 400 for >64KB settings payloads
(new security limit).
Tests expected 422 for corrupted image data, but the API correctly
returns 400 since corruption is caught during validation (unrecognized
format), not during processing. Also fix watermark-image route passing
a hardcoded "watermark" string instead of the actual uploaded filename
to validateImageBuffer, which broke SVGZ detection.
When 88 integration test files run sequentially in a single-fork
Vitest process, the SQLite WAL file grows unbounded. Adding a
TRUNCATE checkpoint after each test app cleanup prevents the SHM
mapping from exceeding its size limit.
validateImageBuffer catches corrupt image data before processing
reaches the tool handler, so the correct status code is 400 (bad
request) rather than 422 (processing failure). Also fix SVGZ
watermark validation by returning early for compressed SVG (Sharp
cannot read gzip-compressed SVGZ directly) and passing the actual
watermark filename to validateImageBuffer for correct format
detection.
Auth: login rate limit 30/min (was 500), global rate limit 1000/min (was
unlimited), password/username max lengths on all Zod schemas, session
invalidation on role change, API key legacy scan bounded to 100 keys.
SVG: hardened regex sanitizer with CDATA stripping, XML entity decoding,
set/animate/iframe/embed blocking, comprehensive data: URI blocking,
use element external href blocking. 11 attack payload fixtures added.
SSRF: fixed DNS rebinding TOCTOU by pinning resolved IPs via custom
HTTP/HTTPS agents. Added 6to4 and NAT64 to blocked IPv6 ranges.
Docker: capability dropping (cap_drop ALL + minimal cap_add), resource
limits (4g/8g mem, 512/1024 pids), healthcheck timeout, password
removed from startup banner, default password warning comments.
Network: CSP and HSTS applied in all environments (not just production),
stack traces removed from all error responses, internal paths stripped
from error details, per-route rate limits on uploads (60/min) and URL
fetches (200/hour).
Files: exclusive temp file creation (O_EXCL), disk space circuit
breaker, per-user storage quotas, settings payload 64KB size guard.
Python sidecar: script name allowlist in dispatcher, minimal environment
for subprocess spawns.
Dependencies: fixed 6 production CVEs (drizzle-orm, fastify, fast-uri,
@fastify/static, next, archiver/lodash). Pinned all GitHub Actions to
SHA hashes.
114 security tests added. Full OWASP Top 10 penetration test matrix
verified against production Docker container (30/30 pass after
hardening).
- Replace content-aware-crop with ai-canvas-expand in TOOLS[], AI_TOOL_IDS,
and FEATURE_BUNDLES (matching the already-updated tool-registry.tsx and
feature-manifest.json from commit c6a5d3f)
- Fix trailing syntax error in features.ts (extra closing brace)
- Add ai-canvas-expand-settings mock to tool-registry test files
- Update watermark-image tests to expect 400 (validation rejection) instead
of 422 (processing failure) for corrupted image buffers, matching the
actual route behavior where validateImageBuffer catches them first
Covers session response fields, password guards, users list,
config endpoint, login redirect, callback edge cases, and
backward compatibility. Also adds migration to make password_hash
nullable (required for OIDC-only users) and vitest aliases for
@fastify/cookie and openid-client.
- Two-gate threshold: Otsu >= 60 uses Otsu; 40-59 uses fixed 100
(catches strong scratches on borderline images)
- Remove morphological OPEN after component filtering: it was eroding
thin scratch lines that were correctly detected
- Lower Otsu gate from 60 to 40 to avoid false-negating borderline images
The transparency-fixer now directly detects the baked-in checkerboard
pattern using per-pixel chroma analysis instead of BiRefNet AI matting.
Achromatic pixels in the gray range are classified as background
(transparent), chromatic pixels as foreground (opaque), with smooth
transitions at anti-aliased edges.
- No longer requires Python sidecar or background-removal bundle
- Watermark removal uses Sharp median(5) filter pre-processing
- Moved tool from "ai" to "utilities" category
- Removed from PYTHON_SIDECAR_TOOLS and background-removal enablesTools
- Near-instant processing (pure Sharp, no model inference)
The luminance anomaly detection + LaMa inpainting approach failed because
watermark signal on the matted foreground was too weak (10-15 units vs
threshold of 25). Median filter with kernel=5 effectively removes
semi-transparent watermark text while preserving the stamp structure.
Pipeline is now: median filter (if toggle on) -> BiRefNet matting -> defringe.
No longer requires object-eraser-colorize bundle for watermark removal.
Evidence-based spec informed by diagnostic testing on 4 sample images.
Addresses catastrophic scratch over-detection (up to 68.7% false
coverage on small images), LaMa 512x512 resolution loss, CodeFormer
over-smoothing on small faces, and excessive NLMeans defaults.
Add tier enum (fast/balanced/high) with balanced default to the Zod
settings schema. Pass tier through to outpaint options in both the HTTP
route and the pipeline/batch registry. Fix log message to say
"Starting AI canvas expand" and include tier in structured log fields.
Add TIER_PARAMS dict with fast/balanced/high presets controlling band
size, mask dilation, seam strip width, and Telea pre-inpainting. Parse
tier from sys.argv[7] with balanced fallback. Conditional Telea and
seam refinement steps skip cleanly for fast tier. Progressive outpaint
now accepts band_size and progress bounds for tier-appropriate scaling.
- Fix dispatcher pipe deadlock: drain stdout pipe in a background thread
to prevent blocking when ONNX runtime output exceeds 64KB pipe buffer
- Add 5-minute SSE stall timeout so the UI shows an error instead of
hanging forever when async AI processing stalls
- Guard CPU colorization: skip for images >2MP on CPU and when DDColor
model is not installed, with clear user-facing messages
- Add AVIF decode fallback via ImageMagick for bitstream variants that
Sharp's bundled libheif cannot decode (affects all tools)
EXR: add ffmpeg fallback when ImageMagick lacks the OpenEXR delegate
(common on macOS Homebrew installs). HDR: force 8-bit depth output to
prevent CLAHE crash (hist_local requires VIPS_FORMAT_UCHAR). Batch:
disable socket timeout and increase server requestTimeout to 30 min
so large AI batches don't get killed by Node.js defaults.
AVIF (and other Sharp-native formats) were written as raw bytes to a
.png temp file, causing PIL to fail with "cannot identify image file".
Every other AI module wrapper already converts via sharp().png().toBuffer()
before writing; face-landmarks was the only one that skipped this step.
HDR and EXR files decoded by ImageMagick can produce 16-bit PNG buffers.
Sharp's CLAHE operation (hist_local) requires VIPS_FORMAT_UCHAR (8-bit).
Check the buffer depth and convert to 8-bit sRGB before processing.
The wrapper used inline-block for tools without overlay children (e.g.
border). CSS maxHeight: 100% on the img inside an inline-block parent
without explicit height does not resolve, so tall images rendered at
natural height and got clipped by overflow: hidden. Unify both code
paths to always use inline-flex + column, where the flex item properly
shrinks within the container's maxHeight constraint.
Sharp's TIFF encoder silently strips the alpha channel, flattening
transparency against black. This caused border (corner radius, shadow),
beautify, and replace-color tools to produce wrong output for TIFF
inputs when the operation needs transparency. Remove TIFF from
ALPHA_FORMATS so these tools fall back to PNG output.
The find-duplicates tool failed entirely when any uploaded file couldn't
be processed, returning "Duplicate detection failed" or a format-specific
error that aborted the whole batch. With mixed-format uploads (77 files),
this made the tool unusable.
- Skip unprocessable files instead of aborting; return skippedFiles in response
- Switch from fetch() to XHR with upload progress tracking (Uploading X%)
- Add Vite proxy timeout config (5min) to prevent connection drops on large uploads
- Add "Download Grouped" button: ZIP with each duplicate group in its own folder
- Add collapsible skipped-files section in the results UI
- Add 3 integration tests for skip behavior (43 total)
The image element used flex: 1 1 0 (basis 0) inside the inline-flex
preview wrapper, causing it to collapse to zero height whenever a
device frame or watermark overlay was present. Changed to flex: 0 1 auto
so the image uses its intrinsic height and can shrink if needed.
Added regression e2e tests verifying every preset keeps the image
visible, and that adding a watermark does not collapse the image
regardless of whether a frame is active.
setError() in the file store unconditionally set processing to false,
even when called with null to clear a previous error. This meant
setProcessing(true) followed by setError(null) at the start of
processing would immediately kill the processing state, preventing the
progress bar from ever rendering.
Fix setError to only reset processing when there is an actual error.
This also fixes the same latent bug in 10+ other custom tool settings
components that call setProcessing before setError.
Custom-route tools (split, compare, collage, find-duplicates, etc.) only
handled HEIC via ensureSharpCompat, failing on BMP, PSD, RAW, TGA, EXR,
HDR, JXL, and other formats Sharp cannot decode natively. Added the full
decode pipeline from createToolRoute to all 16 affected routes: format
validation via validateImageBuffer, HEIC decoding with actionable error
messages, CLI-based exotic format decoding with nested fallback, and SVG
sanitization to prevent XXE/SSRF/script injection.
The vectorize tool had a custom processing flow that bypassed the
standard useToolProcessor hook -- no progress indication, no server-side
batch, and the Download All ZIP relied on a client-side sequential loop.
Backend: extract core logic into vectorizeBuffer(), register via
registerToolProcessFn() so the /batch endpoint works with p-queue
concurrency and SSE progress events.
Frontend: replace custom fetch loop with useToolProcessor hook and
ProgressCard, giving upload progress, per-file batch status, and
automatic Download All ZIP via the existing tool-page infrastructure.
Also set image/svg+xml MIME type on SVG blobs during batch ZIP
extraction to ensure reliable rendering in <img> tags across browsers.
Adds fileFilter and acceptDescription props to Dropzone so tools can
restrict accepted file types across all entry paths (file picker,
drag-and-drop, paste, single URL import, bulk URL import).
Background images, device frames, custom shadows, and watermark text
were not rendering in the right-pane preview. The preview now updates
in real time for all settings: gradient/solid/image backgrounds, macOS/
Windows/Browser frame chrome, iPhone/MacBook/iPad frame indicators,
custom shadow parameters, and watermark text overlay.
Also fixes a React StrictMode effect-ordering race where the parent
tool-page reset cleared preview state set by the child Settings
component on initial mount.
The stitch route only decoded HEIC via ensureSharpCompat, so exotic
formats (DNG, PSD, TGA, BMP, JXL, HDR, QOI, DDS, ICO, JP2, DPX, etc.)
crashed Sharp at metadata read time, causing "Stitch creation failed".
Add the full CLI decode pipeline (decodeToSharpCompat) matching the
tool-factory pattern, plus SVG sanitization with proper error handling
for each format category.
Replace fetch() with XMLHttpRequest in the frontend to surface upload
progress via onprogress events, and add a progress bar that shows upload
percentage then pulses during server-side stitching.
Replace fragile Unicode-range regex with positive ASCII check
(/^[\x20-\x7E]+$/) that also catches emoji and supplementary plane
characters. Update OpenAPI spec to document percent-encoding.
The X-File-Results header contained raw JSON with non-ASCII characters
from filenames (Chinese, Japanese, etc.), violating RFC 7230. Node.js
threw ERR_INVALID_CHAR on writeHead(). Fixed by wrapping the JSON in
encodeURIComponent() on the backend and decodeURIComponent() on the
frontend, ensuring only ASCII goes into the header while preserving
the original filenames after decoding.
Closes#133
The preview endpoint now returns X-Original-Width/Height headers with
dimensions read from Sharp metadata (or ExifTool for RAW files). The
frontend stores these in FileEntry and the ImageViewer prefers them over
the browser's naturalWidth/naturalHeight, which reflects the resized
preview rather than the original sensor dimensions.
Sharp can read TIFF-based RAW files (DNG, CR2, NEF) directly for
metadata without requiring ImageMagick/darktable to fully decode them.
Try Sharp on the raw buffer first; only fall back to the decode pipeline
for formats Sharp cannot open (PSD, ICO, TGA). For RAW files, enrich
with ExifTool to get real sensor dimensions instead of thumbnail size.
Error from a failed image (e.g. DNG) persisted when navigating to a
previously cached image because the cache path skipped setError(null).
Also pass the original file extension to decodeToSharpCompat so RAW
variants get the correct temp file suffix for ExifTool/ImageMagick.
The compose route only decoded HEIC/HEIF via ensureSharpCompat, causing
EPS, PSD, BMP, RAW, and other exotic formats to fail with
"Processing failed". Now uses the same full format pipeline as the
tool-factory for both base and overlay buffers.
SnapOtter