Add the enterprise package with Ed25519 license key validation and
feature gating. Enterprise code lives in the public repo under a
proprietary license (Cal.com/PostHog model), protected legally, not
by code hiding.
Implement S3-compatible storage backend as the first enterprise
feature. The file-storage module now delegates to either local
filesystem or S3 based on STORAGE_MODE env var. Works with AWS S3,
Cloudflare R2, DigitalOcean Spaces, MinIO, and any S3-compatible
provider. Workspace files remain local (ephemeral processing).
New env vars: STORAGE_MODE, S3_BUCKET, S3_REGION, S3_ENDPOINT,
S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY, S3_FORCE_PATH_STYLE,
S3_PREFIX, SNAPOTTER_LICENSE_KEY.
Tested against MinIO: 10 S3 integration tests + 82 existing tests
pass with zero regressions.
Phase 1 quick fixes:
- Add isInputFocused() guard to Cmd+A/D/T/J shortcuts (P1-7)
- Add Go Home button to tool-not-found page (P2-30)
- Fix hardcoded "Import from Library" string in file library modal (P2-28)
- Fix TeamEntry.id type from number to string to match API (P2-6)
- Add eye toggle to confirm password field (P2-10)
- Add Apply/Cancel buttons to Free Transform options bar (P2-14)
Phase 2 state fixes:
- Add sessionStorage persistence to pipeline store (P1-26)
- Fix Free Transform 0 dimensions by falling back to selection bounds (P1-6)
Phase 3 editor features:
- Constrain brush/eraser drawing within active selection bounds (P1-5)
- Add feather radius control to selection options (P2-21)
- Add flow control slider to brush options (P2-22)
- Add brush/block mode selector to eraser options (P2-23)
- Add estimated file size display to export dialog (P2-18)
Phase 4:
- Add Playwright e2e tests for key fixes (404 page, routing, pipeline persistence, export dialog)
- Expose birefnet-hr-matting in UI (People/Ultra) and fix model defaults
(People/Max now uses birefnet-matting for true alpha matting)
- Add output format selector (PNG/WebP/AVIF) with lossless alpha support
- Add edge smoothing post-processing (Off/Light/Medium/Strong) via
morphological mask refinement to reduce gray halo artifacts
- Add color decontamination to remove background color spill from
semi-transparent edge pixels
- Thread new settings through full stack: frontend -> API schema ->
Python sidecar -> Sharp effects pipeline
- Add i18n keys for all 21 locales
- Add unit tests for new option serialization (3 tests)
- Add integration tests for new settings validation (4 tests)
HuggingFace snapshot_download had no retry logic, causing lama-onnx and
codeformer-onnx installs to fail on transient network errors. Direct URL
downloads already had 3 retries with exponential backoff -- this adds
the same pattern to HF downloads (3 attempts, 10s/20s backoff).
Process images in 512px tiles instead of all at once, drastically
reducing peak VRAM usage. If OOM still occurs, retry with 256px tiles
after clearing the CUDA cache. Covers both upscale and face enhance.
Closes#191
Closes#193. Shapes now support no-fill and no-stroke toggles for drawing
outlines or fill-only shapes. Adds RGBA color picker with opacity control,
stroke dash styles (solid/dashed/dotted), and i18n for all shape labels.
Add support for models defined via downloadFn/args (rembg_session,
hf_snapshot) in bundle verification, recovery, and uninstall paths.
Previously only path-based models were tracked, so bundles using
rembg or HF snapshot downloads appeared broken after install.
Also improve pip install error messages with user-friendly hints for
common failures (basicsr build issues, OOM, disk full) and add better
error context for rembg session download failures.
In Italian, "Artificial Intelligence" is "Intelligenza Artificiale",
abbreviated as "IA". Updates all 17 occurrences across tool names,
descriptions, and UI strings.
Closes#192
Lucide v0.577 dropped Wand2 and Columns from the icons object used
for dynamic lookup. Named exports still exist as aliases, but the
landing site and tools index use icons[name] which fails silently.
Updates shared constants and web app icon-map to use the current names.
Handle OOM kills (exit code 137) with actionable memory guidance,
filter ANSI/progress noise from error output, add --no-cache-dir to
pip installs, reduce download concurrency to 2, and bump default
container memory from 4g to 6g.
Add a "Generate strong password" button with Sparkles icon to the
Add Members form in Settings > People. Generated passwords are shown
in plain text with a copy button and an amber warning to copy before
creating the user. Also upgraded the button style on the change
password page to match. Translated copy/warning strings for all 21
locales.
Closes#139
- 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
- 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)
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.
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
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
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
- 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)
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)
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.
- Refactor use-tool-processor and use-pipeline-processor hooks
- Enhance dropzone component with improved UX
- Improve seam carving with better error handling and tests
- Add JXL format encoding support to format-encoders
- Update tool routes for consistent format handling
- Add dropzone unit tests
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