* fix(enterprise): ship enterprise pkg in prod image, full license features, tracing key fallback
docker/Dockerfile: COPY packages/enterprise manifest+src into the production stage.
Without it, apps/api's workspace link to @snapotter/enterprise dangles and every
import() throws (silently caught), so all 19 enterprise features failed closed
(enterprise.active=false) regardless of a valid license.
scripts/generate-license.mjs: sync PLAN_FEATURES with packages/enterprise/src/license.ts
so a --plan enterprise license unlocks all 19 features (was 8) and team unlocks 8.
apps/api/src/tracing.ts: accept SNAPOTTER_LICENSE_KEY as a fallback to LICENSE_KEY so
distributed_tracing activates with the same key as the rest of the app.
* fix(docker): keep scripts/bake-analytics.mjs in build context
.dockerignore excluded the whole scripts/ dir (PR #82, V1 hardening), but
docker/Dockerfile later added 'COPY scripts/bake-analytics.mjs' for the analytics
bake step. A clean production image build therefore fails with
'scripts/bake-analytics.mjs: not found'. The published image build is gated off in
CI so this latent break went unnoticed. Exclude scripts/* but re-include the one
file the Dockerfile needs.
* fix: S3 upload stream, analytics bake reaches API, dedupe retention field, reconcile orphan jobs
storage-s3.ts: wrap the upload AsyncIterable in Readable.from() so @aws-sdk/lib-storage
accepts it. STORAGE_MODE=s3 file uploads failed with 'Body Data is unsupported format'
for every tool because a bare async generator is not a Readable.
docker/Dockerfile: COPY the builder-baked analytics baked.ts into the API runtime stage.
The API re-copied the committed (off) baked.ts from the build context, so the
SNAPOTTER_ANALYTICS build arg had no effect on the API -- and since the SPA reads
/api/v1/config/analytics, analytics was off everywhere regardless of the arg.
settings-dialog.tsx: remove the duplicate tempFileMaxAgeHours control under Data
Retention; it bound the same setting key as the File Management control with a different
default, so editing either silently overwrote the other.
apps/api/src/index.ts: reconcile orphaned job rows (empty tool_id, never enqueued to
BullMQ) at boot so they don't sit in processing/queued forever and inflate the per-user
concurrent-job count and the upgrade-check in-flight gate.
* fix(web): style the SSO login buttons (they referenced undefined theme tokens)
The OIDC/SAML 'Sign in with <provider>' buttons used bg-secondary /
text-secondary-foreground, which the web theme never defines (it has primary,
background, foreground, muted, border, card, primary-subtle). Those classes resolved
to nothing, so the buttons rendered as bare unstyled text on the login page.
Restyle: the optional (non-enforced) buttons become white-card outline buttons with a
key icon and an orange hover tint, secondary to the primary Login button; the
SSO-enforced buttons become solid primary with the icon.
* fix: gate S3 behind license, custom-role enterprise perms, wire retention UI, cleanup
S3 is a licensed feature, but shipping packages/enterprise in every image removed the
implicit gate, so STORAGE_MODE=s3 worked without a license. Enforce
isFeatureEnabled('s3_storage') at boot and fail fast if unlicensed.
Custom roles can now be granted security:manage / compliance:manage / webhooks:manage
(roles.ts ALL_PERMISSIONS + the Roles UI) so admins can build least-privilege
compliance/security roles instead of only the built-in admin role.
retentionSweep now reads the jobsRetentionDays / auditRetentionDays DB settings the
System Settings UI writes (env vars become the fallback default), mirroring how the
temp-file sweep reads tempFileMaxAgeHours. Previously those two UI controls were no-ops.
Cleanup: drop the never-set snapotter_storage_bytes gauge and the unused
MAX_WORKSPACE_SIZE_GB env var; emit tool_client_error to PostHog from the web
ErrorBoundary (client crashes were not reaching analytics); add the Python
OpenTelemetry packages so the innermost sidecar.<script> span exports; fix the stale
'only local storage' line in the docs; delete two e2e-analytics specs that tested the
removed consent UI.
* fix(env): restore MAX_WORKSPACE_SIZE_GB default
security-auth-hardening.test.ts asserts env.MAX_WORKSPACE_SIZE_GB defaults to 10, so
the var is an intentional (tested) default, not dead code. Removing it in the cleanup
commit broke that unit test. Keep the declaration.
* fix(deps): patch gray-matter onto js-yaml 4.2.0 (close js-yaml DoS alert)
js-yaml 3.14.2 (quadratic-complexity DoS in merge-key handling, GHSA
patched only in 4.2.0) was kept in the tree by a scoped pnpm override
"gray-matter>js-yaml": "^3.14.1" that exempted gray-matter from the
global js-yaml>=4.2.0 override. gray-matter is a build-time-only
transitive dep of the docs site (vitepress-plugin-llms,
@sugarat/theme-shared) and pinned 3.x because it calls the removed
yaml.safeLoad / yaml.safeDump APIs.
Remove the exemption so gray-matter resolves js-yaml 4.2.0, and add a
pnpm patch renaming safeLoad->load / safeDump->dump (the 4.x
equivalents; load is safe by default). js-yaml 3.x is now gone from the
lockfile.
Verified: gray-matter parse+stringify smoke test passes on 4.2.0; full
VitePress docs build green (177 pages, llms plugin parses all tool
frontmatter with no safeLoad/safeDump error).
* docs(ai): document rembg 2.0.69 pin and advisory non-reachability
The patched rembg 2.0.75 pulls a numpy 2.x closure (numpy>=2.3,
scipy>=1.16, scikit-image>=0.26) that is incompatible with the
numpy==1.26.4-locked AI stack (realesrgan 0.3.0 and codeformer-pip 0.0.4
break on numpy 2.x). Both open rembg advisories are unreachable in this
codebase: rembg is used purely as a library (never the `rembg s`
server), and new_session() only receives allowlisted model names
(remove_bg.py ALLOWED_MODELS), never user-controlled paths. Record this
rationale next to the pin; the Dependabot alerts are dismissed as
not_used.
rembg 2.0.75 requires a numpy incompatible with the pinned numpy==1.26.4
that the rest of the ML stack (onnxruntime etc.) depends on, making
pip-audit's resolution impossible. The rembg <2.0.75 advisory (medium) is
accepted as a residual: it only affects the on-demand background-removal AI
bundle (publishing currently paused) and can't be patched without a numpy
2.x migration across the whole Python sidecar.
rembg 2.0.70+ requires numpy>=2.3.0, but the AI bundle pins numpy==1.26.4
(mediapipe, realesrgan/basicsr, codeformer, paddle all need numpy<2). The
unresolvable rembg==2.0.75 + numpy==1.26.4 combination broke pip-audit's
dependency resolution (CI red) and the background-removal bundle build. 2.0.69
is the newest rembg with an unconstrained numpy requirement. Verified: pip-audit
resolves with no unignored vulnerabilities on Python 3.11.
Resolve the actionable Dependabot alerts via pnpm overrides (for transitive deps) and a Python pin bump.
- pnpm overrides: esbuild >=0.28.1 (the lone high-severity alert), @babel/core >=7.29.6, @opentelemetry/core >=2.8.0, js-yaml >=4.2.0, qs >=6.15.2, uuid >=11.1.1, yaml >=2.8.3
- rembg 2.0.62 -> 2.0.75 in requirements.txt and requirements-gpu.txt
Verified: pnpm install, typecheck, lint, and full build all pass.
NOT included: the astro advisory requires Astro 5 -> 6 (a major, breaking framework upgrade), which warrants its own migration PR rather than a security bump.
rembg 2.0.75 pulls in numpy>=2.3.0 which conflicts with our pinned
numpy==1.26.4 and would break the entire AI dependency chain. The two
rembg CVEs (SSRF + path traversal) are in its server/CLI components
which we don't use; they're already in the pip-audit ignore list.
Path resolution for the feature manifest and install script was hardcoded
to /app/..., which only works inside the Docker container. Native installs
(e.g. Proxmox at /opt/snapotter) hit "No such file or directory" errors.
Resolve both paths relative to the source file location via import.meta.url
so they work regardless of where the project is installed.
Also loosen mediapipe==0.10.21 to >=0.10.21 in requirements.txt and
requirements-gpu.txt to match the feature manifest. The exact pin has no
cp313 wheel, so it fails on Python 3.13 (Debian 13 default). mediapipe
0.10.35 ships py3-none universal wheels that resolve cleanly.
Reported-by: MickLesk (community-scripts/ProxmoxVE#14720)
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
* feat(shared): add enhance-faces tool definition and i18n strings
* feat(ai): add face enhancement script with GFPGAN and CodeFormer support
Detects faces via MediaPipe dual-model approach, then enhances using
GFPGAN (proven) or CodeFormer (via codeformer-pip) with auto fallback.
Supports strength-based alpha blending with original image.
* feat(ai): add TypeScript bridge for face enhancement
* feat(api): add enhance-faces route with GFPGAN/CodeFormer support
* feat(web): add enhance-faces settings component and register in tool registry
* feat(docker): add CodeFormer dependency and model download
- Add codeformer-pip to both CPU and GPU requirements
- Download CodeFormer model (~375MB) at Docker build time
- Add CodeFormer to smoke test verification
* fix(enhance-faces): address code review findings
- Skip alpha blend for CodeFormer (strength already applied via fidelity weight)
- Hide "only enhance main face" checkbox when Best (CodeFormer) is selected
- Fix sensitivity slider labels (swap More/Fewer faces to match actual behavior)
- Register EnhanceFacesControls in pipeline step settings
- Remove model names from user-facing descriptions
* fix(enhance-faces): fix CodeFormer integration and Docker setup
- Add codeformer-pip install to Dockerfile with --no-deps to avoid numpy 2.x conflict
- Re-pin numpy==1.26.4 after codeformer-pip install
- Pin codeformer-pip==0.0.4 in requirements files
- Broaden auto-mode fallback to catch any Exception from CodeFormer
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
- Pin PaddlePaddle to 3.0.0 on ARM64 to fix segfault in PIR inference
engine (3.1+ crashes on aarch64 Debian Bookworm)
- Fix text extraction for PaddleOCR 3.4.x result format (rec_texts)
- Add Node.js-level fallback chain (best -> balanced -> fast) when
Python subprocess crashes
- Add multi-image OCR: processes all uploaded files sequentially with
per-file progress and filename headers in combined output
- Convert input images to PNG via Sharp before OCR so HEIC, AVIF, WebP,
TIFF all work transparently
- Implement real auto-detect language using Tesseract multi-lang script
detection (analyzes Unicode ranges for Hangul, CJK, Kana, Latin)
- Default enhance to off (hurts clean digital images)
Replace the Python seam-carving library with caire (esimov/caire v1.5.0),
a Go-based content-aware resize engine that is faster and supports both
shrinking and enlarging via seam insertion.
- Add Go builder stage in Dockerfile to compile caire from source
- Rewrite seam-carving.ts to call caire via execFile (no Python sidecar)
- Remove content-aware-resize from PYTHON_SIDECAR_TOOLS (60s timeout)
- Add new options: blur radius, edge sensitivity, square mode, face detection
- Move content-aware toggle below standard resize in UI (subtler placement)
- Rename "Don't enlarge" to "Limit to original size" with hover tooltip
- Add smooth progress bar for medium-duration tools
- Delete seam_carve.py and remove seam-carving pip dependency
- Update integration tests and visual regression screenshots
Merge CPU, CUDA, and lite Docker images into a single unified image.
One tag (latest) works on all platforms: amd64 (NVIDIA CUDA) and arm64 (CPU).
GPU auto-detected at runtime. All ML models and packages baked in.
Key changes:
- Platform-conditional Dockerfile (nvidia/cuda on amd64, node on arm64)
- tini as PID 1 for proper signal handling
- Fix FILES_STORAGE_PATH data loss bug
- Fix RealESRGAN upscaler (was broken, always fell back to Lanczos)
- Fix PaddleOCR language codes and stdout corruption
- Simplified CI/CD (single build, single tag)
- Expanded model pre-download with verification
- Shutdown timeout, improved health endpoint
- Remove unused lama-cleaner
