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
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.
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.
- 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)
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).
- 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
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
The BiRefNetHRMattingSession.predict normalization crashes when all
pixels share the same value (ma == mi). Use a guarded denominator so
uniform-alpha inputs produce a zero mask instead of a NaN explosion.
Also adds _register_birefnet_hr_matting() to install_feature.py so the
HR-matting model can be downloaded during feature installation, matching
the existing registration in remove_bg.py.
The upscale function called runPythonWithProgress without a timeout parameter,
defaulting to the bridge's 10-minute hard limit. On CPU-only systems like
Synology NAS devices, Real-ESRGAN 4x upscaling easily exceeds this for modest
images. Additionally, when the timeout fired on the dispatcher path, the Python
process was left running and blocked all subsequent AI operations.
This fix adds an adaptive timeout based on input megapixels, scale factor, and
GPU availability (180s/effective-MP on CPU, 30s/effective-MP on GPU, floor of
10 minutes). It also kills the dispatcher on timeout so subsequent requests can
proceed via a fresh restart.
Closes#119
The dispatcher was lazy-initialized on first AI request, but a race
condition meant the first call always missed it (dispatcherReady still
false) and fell through to cold per-request Python. initDispatcher()
starts the dispatcher eagerly and returns a Promise that resolves with
GPU status once ready (or after a timeout).
The close handler called recordCrash() unconditionally, even for exit
code 0 (normal MAX_REQUESTS restart). After 5 normal cycles within 60s
the dispatcher was permanently disabled. Now only non-zero exits count.
gpu.onnx_providers() trusted gpu_available() which returns True via
torch.cuda without checking whether onnxruntime actually has
CUDAExecutionProvider compiled in. When onnxruntime (CPU-only) is
installed, this caused silent fallback to CPU in every ONNX-based tool.
Now verifies onnxruntime.get_available_providers() directly and emits a
diagnostic warning when torch sees CUDA but onnxruntime does not.
Closes#104
Security:
- Apply sanitizeSvg() to all file upload routes (files.ts, user-files.ts)
preventing SSRF and script injection via SVG uploads to file library
Functional:
- Handle PaddleOCR-VL 1.5 markdown_texts output format in ocr.py
- Add empty-text fallback in OCR tier chain (ocr.ts) so higher tiers
that return empty text fall back to the next tier automatically
- Fix SVG->PNG filename extension mismatch in tool-factory.ts so
download endpoint serves correct Content-Type
- Report original upload size (not decoded size) in API response
Test infrastructure:
- Move Playwright auth state from test-results/ to .playwright/ to
prevent mid-run cleanup deleting auth files
- Fix auth.setup.ts navigation race with waitForURL
- Fix gui-batch.spec.ts regex matching "Presets" instead of "reset"
- Fix pipeline-advanced.spec.ts crop bounds and resize assertions
- Broaden pipeline cleanup to include all E2E-prefixed pipelines
- Add 55 unit tests for feature-status.ts (installed.json CRUD, cache
behavior, install lock, model verification, crash recovery, composite
state) using real temp directories
- Add 36 integration tests for full install/uninstall lifecycle against
Docker containers (face-detection bundle, SSE progress, tool gates,
shared model protection, concurrent install prevention, auth guards,
container restart recovery)
- Fix noise-removal CPU timeout by adding megapixel-based timeout
calculation (120s/MP, min 5 minutes)
- Fix Playwright auth storage state race condition (mkdirSync before
saving analytics-user.json)
- Fix 2 skipped tests in fixes-verification.spec.ts by replacing
external ~/Downloads/sample dependency with existing test fixtures
- Enable skipped analytics-consent settings toggle test
- Restructure features.spec.ts to manage bundle state (uninstall/
reinstall OCR) so 501 guard tests run instead of skipping
- Update noise-removal test mock to include sharp metadata() method
Skip alpha matting on CPU (pymatting's sparse matrices are the main
memory hog), auto-downscale images above 2048px before sending to
rembg, and retry with the lighter u2net model when OOM is detected.
Register custom BiRefNet-matting ONNX session in install_feature.py so
rembg.new_session("birefnet-matting") no longer raises ValueError during
on-demand installs. The session was already registered in remove_bg.py
(runtime) and download_models.py (build-time) but was missed in the
install path, causing background-removal bundle installs to always fail.
Send JSON body on install/uninstall POST requests to avoid Fastify 5's
strict content-type parser rejecting body-less POSTs with 415.
Fix error message extraction to preserve structured {"error": ...} JSON
from the Python script and filter out pthread_setaffinity_np noise.
Pillow 12.x conflicts with pinned numpy 1.26.4, rembg, realesrgan,
and mediapipe. Revert to working 11.1.0 pins and ignore the CVEs
in pip-audit instead — they require a coordinated major version
upgrade across the entire ML stack (Pillow, numpy, torch, basicsr).
Ignored CVEs:
- CVE-2024-27763 (basicsr, no fix available)
- CVE-2026-40086 (rembg, fix needs Pillow 12)
- CVE-2026-25990 (Pillow, fix is 12.1.1)
- CVE-2026-40192 (Pillow, fix is 12.2.0)
- Increase QR generate max-size test timeout to 120s (10000x10000
PNG generation exceeds 30s default on CI runners)
- Update Pillow 11.1.0 → >=12.2.0 (CVE-2026-25990, CVE-2026-40192)
- Update rembg 2.0.62 → >=2.0.75 (CVE-2026-40086)
- Update opencv-python-headless to flexible range >=4.10,<4.12
- Ignore CVE-2024-27763 in pip-audit (basicsr transitive dep from
realesrgan, no fix available upstream)
- Align requirements-gpu.txt and Dockerfile with same versions
Closes#17, #18, #19, #31, #32, #33, #34
Format preservation (#17, #18, #19):
- Add resolveOutputFormat to rotate, resize, text-overlay, watermark-text,
border, replace-color, blur-faces, upscale, erase-object, restore-photo
- Alpha-aware fallback: border with corner radius/shadow and replace-color
with makeTransparent fall back to PNG for non-alpha formats (JPEG)
- Python sidecar tools (blur-faces, upscale, erase-object) now convert
PNG output back to input format, matching restore-photo/colorize pattern
- Upscale and erase-object default to "auto" format detection instead of PNG
Dispatcher stability (#31, #32):
- Add gc.collect() and torch.cuda.empty_cache() after each dispatcher request
- Add configurable max_requests (default 50) for periodic dispatcher restart
- Add exponential backoff to dispatcher crash recovery in bridge.ts
- Circuit breaker: 5 crashes within 60s permanently disables dispatcher
- Reset crash counter on successful dispatcher startup
Health & security (#33, #34):
- Export getDispatcherStatus() from @snapotter/ai with running/ready/failed/
gpu/pid/consecutiveCrashes fields
- Admin health endpoint now includes full dispatcher status
- Add pip-audit job to CI workflow for Python dependency scanning
- Bump APP_VERSION to 1.15.11 (was hardcoded at 1.15.9, causing
health endpoint to report wrong version in Docker images)
- Fix cpu_fallback_packages() splitting --index-url into separate
pip install arguments, breaking torch install on CPU-only amd64
Code fixes:
- Sidebar state bleed: reset file store on HomePage mount
- restore-photo: raise error instead of silently skipping colorize
when DDColor model missing
- PaddleOCR OOM: cap input images to 2048px before OCR inference
- Torch CPU optimization: use --index-url .../whl/cpu on CPU nodes
Test fixes:
- upscale: add exact:true to scale factor button locators
- smart-crop: add exact:true to "Pad to square" locator
- colorize: use regex for model button names (Best/Balanced/Fast)
- enhance-faces: use .first() for ambiguous percentage display
- passport-photo: fix DPI locator, .or() compound, generate fallback
- people: update maxUsers assertions for unlimited (0) default
- automate: "Save Pipeline" → "Save" matching actual button text
- tools.test: add resize to Sharp mock chain for OCR tests
- Add enable_mkldnn=False to PaddleOCR constructor to bypass PaddlePaddle
3.3+ OneDNN/PIR crash on CPU-only systems
- Add 25MP and 75% max-reduction guard to seam carving with clear error
messages instead of silent timeout/crash
- Replace barcode/QR AVIF test fixtures with actual scannable codes
(old fixtures did not contain real barcodes)
- Add Cloudflare Pages deployment for landing page (snapotter.com) and
docs (docs.snapotter.com)
- Create deploy-landing.yml and update deploy-docs.yml workflows
- Update CI to ignore apps/landing/** paths
- Fix logo transparency (remove white background) across all apps
- Recreate social-preview.png with SnapOtter branding
- Update all docs URLs from GitHub Pages to docs.snapotter.com
- Update VitePress config: light theme default, fix llms.txt paths
- Add .vitepress/cache/ and .env.* to gitignore
- 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 model is set to "auto", CodeFormer failure previously threw an
error telling users to manually switch to GFPGAN. Now it falls back
to GFPGAN automatically, matching the graceful degradation pattern
already used in OCR.
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").
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.
