Dilate the mask, crop a padded box around it, run LaMa on the crop at 512, and composite back cleanly. Fixes the ghost remnants (#491) and sharpens small/medium-object fills in high-res images (#141 core). Same model, still offline, no new bundle. Closes#491.
Make on-demand AI feature-bundle installs reliable and self-healing, closing
the failure modes behind most "some tool doesn't work" reports.
Multi-bundle installs: tools needing more than one bundle (Passport Photo,
Enhance Faces) install every required bundle from one action and stay
not-installed until all are present. Verified across all 19 AI tools.
Downloads: self-heal the accelerated Hugging Face (Xet) client so an upgraded
venv no longer silently falls back to slow urllib; restart instead of
corrupting a resumed partial when a proxy ignores Range and returns 200;
verify the completed size; fail fast on disk-full and HTTP 4xx; retry
transient errors five times; add hf_transfer fallback and document Xet egress.
Install integrity: crash-atomic venv writes so a killed or out-of-space
install can no longer tear the shared venv and break other tools; a boot
breadcrumb reseeds a torn venv to a clean state automatically; a post-install
smoke import test refuses to record a bundle whose libraries cannot load; an
install watchdog stops a wedged installer that would otherwise hold the venv
writer lock forever.
Adds unit and end-to-end tests for every failure mode above.
gpu_available() answers "can ANY framework use a GPU" (torch, then ONNX, then
paddle). But torch tools consumed that shared boolean directly as
device = torch.device("cuda" if gpu_available() else "cpu"). On a GPU host where
gpu_available() is True via paddle or ONNX while torch is a CPU-only build, those
tools would route to a CUDA torch cannot use and crash. Transcription had the
mirror problem: it runs on CTranslate2 (not torch), so on a transcription-only
GPU box gpu_available() returned False and Whisper ran on CPU despite a GPU.
Add per-framework helpers to gpu.py:
- torch_gpu_available(): torch.cuda.is_available(), honoring SNAPOTTER_GPU.
- ctranslate2_gpu_available(): ctranslate2.get_cuda_device_count() > 0.
Point each tool at the helper for its own framework: upscale, noise_removal,
enhance_faces and restore use torch_gpu_available(); transcribe uses
ctranslate2_gpu_available(). ocr.py keeps gpu_available() (paddle-aware) and the
dispatcher keeps it for its startup GPU-status line. The SNAPOTTER_GPU override
check is factored into a shared _override_disables_gpu() helper.
TDD: 7 new tests in tests/test_gpu_detection.py cover both helpers (override,
CPU-only, absent framework), including the crux that torch_gpu_available() stays
False on a CPU-only torch build even when a GPU exists for another framework.
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
gpu_available() probed torch, then ONNX Runtime, then nvidia-smi, but never
paddle. The OCR bundle ships paddlepaddle-gpu with no torch or ONNX, so on an
OCR-only GPU host every probe missed the GPU: nvidia-smi saw it but returned
False by design, and OCR silently fell back to Tesseract (CPU, lower quality)
with no signal why.
Add a paddle probe as the last resort in gpu_available(). It runs only after
nvidia-smi confirms a GPU is physically present, and in an isolated subprocess,
because importing paddlepaddle-gpu on a GPU-less host segfaults and would wedge
the shared AI dispatcher. It returns True only when paddle reports both a CUDA
build and a visible device, signalling the result through the exit code so
paddle's own import chatter on stdout cannot corrupt the reading.
CPU-only and torch/ONNX GPU hosts are unaffected: the probe never runs on the
former (nvidia-smi finds nothing) and is never reached on the latter (the torch
step already returns True first).
Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
* fix(docker): pin CUDA base to 12.6 so the GPU image starts on R560+ drivers
The amd64 base nvidia/cuda:12.9.2-cudnn-runtime bakes a cuda>=12.9 driver gate enforced by nvidia-container-toolkit at container start, so the image fails to launch on common production drivers (e.g. 570.x / CUDA 12.8). The AI bundles are all cu126 wheels and the image installs libcublas-12-6, so 12.9 was misaligned with the workload. Pin to nvidia/cuda:12.6.3-cudnn-runtime-ubuntu24.04 to match the wheels and lower the driver floor to R560+.
* fix(ai): broaden OOM detection so the rembg lighter-model fallback fires
onnxruntime/CUDA allocation failures surface as 'Failed to allocate memory for requested buffer', CUBLAS_STATUS_ALLOC_FAILED, or bad_alloc, not just 'out of memory'. The background-removal and transparency-fixer fallback-to-lighter-model paths only matched the literal 'out of memory', so the fallback was dead code and transparency-fixer (default birefnet-hr-matting) always failed with an allocation error. Add isMemoryAllocError() and use it in both checks.
* fix(ai): use bundled PaddleOCR models so OCR runs offline
ocr.py passed no model dirs to PaddleOCR, so PaddleX resolved models from ~/.paddlex and downloaded them from HuggingFace at runtime (slow first use, broken air-gapped), ignoring the models the OCR bundle ships in MODELS_PATH; it also pulled doc-orientation/unwarping models that are not bundled. Pin detection, recognition and textline models to the bundled dirs in MODELS_PATH (per language) and disable use_doc_orientation_classify / use_doc_unwarping, with per-component fallback when a model is absent. Verified: OCR runs with zero HuggingFace requests.
* fix(docker): add CAP_KILL so container shutdown is graceful
cap_drop: ALL without re-adding KILL meant tini (PID 1, root) could not forward SIGTERM to the gosu-dropped snapotter process (root minus CAP_KILL cannot signal a different UID). docker stop logged '[FATAL tini] forwarding signal: Operation not permitted', never delivered the signal, and fell back to SIGKILL after the 10s timeout. Add KILL to cap_add in both compose files. Verified: docker stop completes in 0s with SIGTERM delivered (exit 143) and no FATAL tini.
* fix(ai): serialize bundle installs against AI jobs to prevent sidecar segfault
A feature bundle install rewrites the shared Python venv (pip + copytree of site-packages/*.so) as a background subprocess, with no coordination against AI tool jobs that dlopen native libs (torch / onnxruntime CUDA) from the same venv; a job loading a shared object while it is overwritten segfaults the sidecar. Add a process-wide async mutex (venv-lock.ts): bridge.run() acquires it before every AI script and the install route holds it across the installer subprocess. Both run in the same Node process so a module-level lock suffices. Verified: concurrent install + AI job produces zero segfaults and the job serializes behind the install.
* fix(ai): make the venv lock read/write so concurrent AI jobs are not serialized
The first cut used an exclusive mutex, which (a) deferred the dispatcher spawn by a microtask and broke unit tests that synchronously drive the mocked spawn, and (b) serialized AI jobs against each other, removing the dispatcher's by-id request multiplexing. Make it a writer-preferring read/write lock: AI jobs are shared readers (with a synchronous fast path so spawn still happens in-tick) and a bundle install is the exclusive writer. Verified: all 764 AI unit tests pass.
* fix(ai): degrade OCR to Tesseract on CPU-only hosts instead of segfaulting
The amd64 AI bundle ships paddlepaddle-gpu, whose native libs dlopen
libcuda.so.1 at import and segfault on a host without a GPU (libcuda is the
driver lib, injected only by nvidia-container-toolkit on GPU hosts). The
segfault crashed the shared long-lived AI dispatcher and, after a few attempts,
tripped the bridge crash-recovery permanent-disable, wedging all AI until a
container restart. The standalone ocr tool defaults to quality=balanced
(PaddleOCR), so it hit this on every CPU-only deployment; ocr-pdf already
hardcoded Tesseract and was unaffected.
ocr.py now gates the PaddleOCR tiers on gpu_available(): balanced/best
transparently fall back to fast (Tesseract, CPU-capable) when no usable GPU is
present, and run_paddleocr_v5/run_paddleocr_vl refuse before importing paddle so
the GPU build is never dlopen'd on CPU. GPU hosts are unchanged.
Verified on a CPU-only Windows/WSL2 box: ocr returns Tesseract text across
repeated runs with the dispatcher staying healthy (no wedge).
* feat(tracing): add OpenTelemetry dependencies and --import preload flag
* feat(enterprise): add distributed_tracing feature gate
* feat(tracing): add SDK bootstrap with enterprise gating
* fix(tracing): correct test coverage for enterprise-unavailable path and prevent double-init
Test 2 now mocks @snapotter/enterprise to throw an import error, exercising
the catch block in the preload. Test 3 imports with no endpoint so the preload
is a no-op, avoiding leaked SDK from double-initialization. Added idempotency
guard to initTracing() as a safety net.
* feat(tracing): add Pino trace mixin and shared logger
When OTel tracing is active, every Pino log line now includes traceId,
spanId, and traceFlags fields for log-to-trace correlation. The mixin
is a no-op when no SDK is registered (community users).
* feat(tracing): add _otel to ToolJobData and inject trace context at enqueue
Add optional _otel carrier field to ToolJobData for W3C trace context
propagation across BullMQ job boundaries. When an active OTel span exists,
propagation.inject() writes traceparent/tracestate into the job data before
queue.add(). When no SDK is registered (community edition), the carrier
stays empty and _otel remains undefined -- zero overhead.
* feat(tracing): extract trace context and create spans in BullMQ worker
* feat(tracing): inject trace context into Python sidecar calls
* feat(tracing): add trace context extraction to Python sidecar
* feat(tracing): add shutdownTracing to graceful shutdown sequence
* feat(tracing): enrich HTTP spans with tool_id and user_id attributes
* docs: add OpenTelemetry env var documentation to .env.example
* test(tracing): add lifecycle integration tests for trace propagation
* fix(tracing): inject trace context into pipeline and batch flow jobs
* fix(tracing): add sidecar.execute Node-side span and remove unnecessary comment
Wraps PythonDispatcher.run() with a sidecar.execute span on the Node
side so traces show the full round-trip (Node span -> Python span).
Also removes an obvious comment from logger.ts.
