The published amd64-gpu set carried huggingface_hub at four versions, the tokenizers 0.23.1 breaker, background-removal's scipy strand, and a live #490 flavor mix from transcription's CPU onnxruntime. Six bundles rebuilt at r2 paths under the constrained closure; gpu pins move to 1.20.2 (PyPI dropped 1.20.1); verify-bundle-compatibility.sh gains the any-package multi-version scan. Fixes#692.
The worst-order install test caught tokenizers 0.23.1 against transformers' <0.21 requirement, and the r2 rebuilds drifted onnxruntime. Both are constrained now; the obsolete protobuf<5 arm64 pins are gone (arm64 resolves mediapipe 1.0.0, no python protobuf dependency); transcription and object-eraser-colorize point at r3 archives. Full worst-order install plus the 18-tool installed AI matrix passed against this manifest. Completes #669.
The published arm64 set disagreed on huggingface_hub (four bundles baked 1.22.0), scipy, and scikit-image. All six affected bundles are rebuilt under the constrained closure and published at new -r2 paths so baked manifests keep working; no rebuilt bundle carries hub and the layered compatibility check passes. verify-bundle.sh drops its stale onnxruntime expectation for upscale-enhance. Completes #669.
The arm64 transcription bundle baked huggingface_hub 1.22.0 while inpaint-hq's transformers needs hub <1.0; last-writer-wins in the shared venv made the hq install fail on arm64. Constrain hub at build time, pin it in transcription's package list, and lock both invariants with manifest unit tests. The arm64 transcription bundle still needs a rebuild and republish to ship the fix. Fixes#669.
A release-readiness QA pass over the whole product. The commits split into
defects a user would hit and gates that were reporting green while measuring
nothing.
## Fixes that change behaviour
Rate limiting was bypassable on every install: TRUST_PROXY defaulted to true, so
request.ip came from a client-set header and a forged X-Forwarded-For got past
the login limiter. The default is now a private-network trust list.
A transient Postgres outage stranded in-flight jobs, leaving finished output on
disk with no row pointing at it. A reconciler now resolves those rows and adopts
the bytes rather than dropping the work.
A Redis connection that moved to a new address wedged every read-blocked
consumer, so completions stopped signalling while health still answered 200.
Socket timeouts plus subscriber pings recover it.
Installing more than one AI bundle left the shared venv multi-versioned and
silently broke three tools. The installer now reconciles distributions to one
version each.
Converting an image to JXL at quality 1 through 4 returned a 500, because
libjxl 0.7 rejects the distance those values compute. The quality is floored at
what the encoder honours. A missing ffmpeg was also reported to the user as a
corrupt upload; it now says the engine is unavailable.
RAW uploads reached an unpatched LibRaw on arm64, so it is built from source at
0.22.2, and the release scan was split so it can fail on an unfixed critical
instead of hiding it behind ignore-unfixed.
## Gates that could not fail
Two mutation lanes ran zero mutants because Stryker crawled the gitignored docs
build; coverage discarded its whole report on any failing test; the lint gate
skipped root tests, scripts, and two workspaces; and several generated matrices
counted a host missing ffmpeg as a passing tool. Each now measures what it
claims.
Full evidence and the outstanding release items are tracked locally and are not
part of this branch.
Adds an opt-in High Quality mode to the Object Eraser, backed by a new inpaint-hq feature bundle (Stable Diffusion 1.5 inpainting via diffusers). The default fast LaMa path is unchanged. Both arch archives are published to deepsafe/feature-bundles and the manifest carries their real sha256/sizes.
Verified end to end: a fresh container pulls the bundle from HuggingFace, checksum-verifies it, extracts torch/diffusers plus the fp16 model, and the HQ sidecar erases a large object with a plausible fill.
Refs #141
Adds a dedicated remove-gif-background AI tool: removes the background from an animated GIF, WebP, or APNG frame by frame and reassembles a transparent (or composited) animation in WebP, APNG, or GIF, with full per-frame effects. Reuses the background-removal bundle. Verified end-to-end with the real rembg model.
Closes#496.
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.
* fix(api): prevent a crash when an over-limit upload stream has no consumer yet
busboy's "limit" handler destroyed the file stream with an error but never
attached its own error listener, relying entirely on whatever consumes
part.file downstream to do so. On a fast enough connection (or a fully
buffered body, e.g. Fastify inject()), busboy can process enough bytes to
hit the size limit before the route handler's receiveUpload() call has
attached its own stream listener, leaving the resulting "error" event with
zero listeners -- which crashes the whole process by default in Node.
Surfaced by tonight's FULL_MATRIX+FUZZ integration run (880 uncaught
exceptions, all the same root cause). Reproduces deterministically in
isolation; unrelated to this release's actual code delta (file untouched
since PR #413, well before the baseline QA pass).
Fix: attach a baseline no-op error listener the moment the stream is
created, guaranteeing at least one listener always exists. EventEmitter
delivers "error" to every registered listener, so the real consumer's own
error handling is unaffected.
* fix(ai-bundles): rebuild upscale-enhance and photo-restoration to reconcile scipy ABI
upscale-enhance and photo-restoration both depend on codeformer-pip, whose
transitive closure (basicsr -> realesrgan -> gfpgan) pulls in an unpinned
scipy. Both bundles were last built ~June 18-19, before PR #437 added the
manifest's `constraints` array (numpy==1.26.4, scipy==1.12.0, etc.) to pin
exactly this kind of dependency during bundle builds. Only the ocr bundle
was rebuilt after that fix landed.
install_feature.py has no pip install step -- it's a raw tarfile extraction
with no cross-bundle conflict resolution, so installing OCR alongside either
stale bundle left three incompatible scipy versions' files mixed in the same
site-packages directory (a compiled _rotation.*.so from one release next to
Python files expecting a different release's API), breaking the `upscale`
tool and OCR's higher-quality tiers with an ImportError.
Rebuilt both bundles for amd64-gpu and arm64-cpu from the current manifest,
verified scipy/scikit-learn/scikit-image/pandas all resolve to the pinned
versions in the tarballs themselves, then verified end-to-end on real
hardware (Mac arm64 CPU and ubuntu_gpu .248 RTX 4070): installing all
affected bundles together now yields exactly one version of each constrained
package, `upscale` produces correct output, and OCR's balanced/best tiers
correctly use PaddleOCR-GPU instead of erroring out.
Published the rebuilt tarballs to the public deepsafe/feature-bundles
HuggingFace repo and updated this manifest's sha256/sizes to match.
Also adds verify-bundle-compatibility.sh: verify-bundle.sh checks each
bundle in isolation (a fresh venv per bundle), which is exactly why this
shipped twice -- nothing ever checked that bundles built at different times
agree once layered into the one shared venv real installs use. The new
script installs every bundle for an arch into one venv and asserts each
constrained package has exactly one, correct version.
Known follow-up (not fixed here, needs separate discussion): uninstalling a
bundle only removes its downloaded model weights, never the site-packages
it added, so existing installations that already hit this bug have no clean
self-service fix via uninstall+reinstall -- they need a full AI-venv wipe.
* fix(docker): bake a real rate limit default for the all-in-one one-liner
The documented single-container `docker run` install had RATE_LIMIT_PER_MIN=0
(effectively unlimited, ~50k/min) baked in, since only docker-compose.yml
carried a hardened override. A self-hoster following the one-liner path got
no meaningful throttling anywhere, including auth-adjacent routes with no
dedicated per-route limit. Bakes a generous-but-real 1000/min default into
the Dockerfile, raises both compose files' fallback to match so the two
documented install paths converge on the same posture, and updates the Zod
schema default plus docs that quoted the old value.
* fix(api): boot log undercounted tool routes by the conversion-preset total
The "Tool routes: N active" line logged before registerConversionPresets(app)
ran, so it only ever reported the base 158 tools, 83 short of the real
241-tool total. Presets have to register after the base loop (they delegate
to each base tool's own processV2), so the fix moves the log line to after
that call and has registerConversionPresets return its count instead of
reordering the dependency.
* fix(ai): forward {info}/{warning} stderr JSON instead of dropping it
The dispatcher stderr parser only recognized {ready} and {progress,stage}
shaped JSON lines; anything else that parsed as valid JSON (like ocr.py's
GPU-to-tesseract downgrade notice, an {"info": ...} line) matched neither
branch and fell through silently, never reaching docker logs. Adds explicit
{info}/{warning} handling that forwards to console.log/console.warn, same as
the existing [prefix]-tagged non-JSON path.
* fix(api): fall back to a lower OCR tier when PaddleOCR itself is unusable
ocr.ts already retries lower quality tiers on a crashed dispatcher, but the
condition only matched crash-style messages (segfault, exited unexpectedly).
ocr.py's own ImportError/exception handlers already produce messages telling
the caller to use a lower tier (e.g. on the scipy ABI conflict class of bug),
but nothing ever acted on them, so a broken PaddleOCR hard-failed with 422
instead of degrading to Tesseract like ocr-pdf effectively does. Broadens the
retry condition to also catch PaddleOCR-engine-unusable messages.
Note: ocr-pdf's tesseract-only behavior turned out to be an unrelated,
pre-existing, deliberate design choice (PaddleOCR segfaults on rasterized PDF
pages on arm64), not a graceful-fallback mechanism to copy -- the two tools
weren't actually solving the same problem, so this fixes ocr.ts's own gap
rather than trying to mirror ocr-pdf.
* fix(ai-bundles): lock the numpy-1.x ABI closure so the OCR bundle can't strand scipy
The OCR bundle installs paddleocr[doc-parser] 3.4, whose dependency closure drags
numpy 1.26.4 up to 2.5.1 and pulls scipy/scikit-learn/pandas wheels built against
the numpy 2.x ABI. build-bundle.sh re-pinned only numpy (basePackages), so those
numpy-2.x wheels stayed behind; the by-dir-name site-packages diff then shipped
them, and once merged onto the numpy==1.26.4 base they raise "numpy.dtype size
changed" on import.
Because the dispatcher pre-imports every ML library at startup and disables all AI
after 5 crashes in 60s, one stranded scipy takes down every AI tool, not just OCR
(observed on a CPU host: remove-background worked before the OCR bundle and broke
after). All-7 installs escaped it through last-writer-wins ordering; a subset
install did not, which is why it surfaced only intermittently.
Fix: add a manifest "constraints" list (numpy, scipy, scikit-learn, scikit-image,
pandas pinned to numpy-1.x-ABI versions) and apply it via PIP_CONSTRAINT to every
bundle pip install, so no bundle can pull a numpy-2.x wheel. paddleocr 3.4.1 still
resolves cleanly under the lock and the pinned stack imports without ABI error on
numpy 1.26.4 (validated on py3.12). Also import scipy/sklearn in the OCR path of
verify-bundle.sh so CI catches this class in isolation, and add a manifest
regression test.
Note: the published bundles must be rebuilt and republished (ai-bundles.yml) for
this to reach already-installed bases.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* chore(ai-bundles): sync OCR manifest sha256 to the rebuilt numpy-1.x bundles
Rebuilt the OCR bundle for both arches with the numpy-1.x-ABI constraints from
this PR and republished the tars to deepsafe/feature-bundles/v2.0.0, then updated
the baked manifest sha256 and sizes so installs verify against the fixed archives:
amd64-gpu 5.93 GB sha 2a00a3184f6a635f1fa9ae2a6517ad740a11f9e5ff58c098d2fd369a2bb1e16b
arm64-cpu 1.98 GB sha 6868c264069dcb74c6675c0b1f58dc1c9f60d9aa4459725e3dbde07a99a6a09a
Both tars ship scipy 1.12.0 / scikit-learn 1.4.2 / pandas 2.2.2 (numpy-1.x-ABI)
and zero numpy-2.x wheels, verified by listing the archive contents.
Stopgap note: these tars were built against the ghcr.io latest base (the 2.0.0
image is not published to GHCR), so they are not byte-identical to what the CI
build will produce. When ai-bundles.yml rebuilds at the 2.0.0 release, it will
mint fresh sha256 values and this manifest must be re-synced to them.
Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav
* fix: ship RealESRGAN_x2plus.pth in the upscale-enhance bundle for offline CodeFormer
codeformer-pip 0.0.4 downloads RealESRGAN_x2plus.pth at import of
codeformer.app, unconditionally, even though enhance_faces calls
inference_app with background_enhance=False and never uses the background
upsampler. The weight was not bundled, so explicit CodeFormer face-enhance
(enhance-faces model=codeformer) failed in strict offline mode
(SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0) on a host that had never cached it -- the
guard raised before the import could complete.
Add RealESRGAN_x2plus.pth to the upscale-enhance bundle manifest (only that
bundle uses codeformer-pip; photo-restoration uses the CodeFormer ONNX path)
and link it in prepare_codeformer_weights alongside the other three weights,
replacing the download-or-error guard. Once the bundle ships it, the import
resolves offline and strict mode works.
Archive SHA256s updated in a follow-up once the bundle is rebuilt.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
* fix: require face-detection bundle for enhance-faces + point manifest at the x2plus archives
enhance-faces runs MediaPipe face detection (blaze_face_short_range.tflite)
before CodeFormer/GFPGAN. That model ships in the face-detection bundle, not
the tool's primary upscale-enhance bundle, so a standalone upscale-enhance
install failed face detection (offline: hard error; online: a surprise
download) before reaching the codeformer path. Declare the dependency in
TOOL_EXTRA_BUNDLES like passport-photo does.
Update the upscale-enhance archive SHA256/sizes to the rebuilt bundles that
include RealESRGAN_x2plus.pth (amd64-gpu + arm64-cpu), verified to install and
run enhance-faces model=codeformer in strict offline mode with zero downloads.
Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
Rebuilt the object-eraser-colorize, ocr, and transcription arm64-cpu
bundles with the protobuf<5 pin (PR #417) and republished them to
deepsafe/feature-bundles/v2.0.0. Update the manifest archive checksums,
compressed sizes, and (previously 0) extracted sizes to match the new
tarballs so install_feature.py's sha256 verification passes.
All three rebuilt bundles bake protobuf 4.25.9; verified gzip-clean and
that paddle 3.2.2 / onnxruntime coexist with protobuf 4.25.9 on aarch64.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* fix(ai): pin protobuf<5 on arm64 so mediapipe face landmarks work
aarch64 has no mediapipe wheel above 0.10.18, and 0.10.18 calls
MessageFactory.GetPrototype (removed in protobuf 5+). With protobuf
unpinned, the paddle/onnxruntime deps pull protobuf 7.x into the shared
AI venv and break mediapipe FaceLandmarker, so red-eye-removal fails on
every input (blur-faces and smart-crop keep working via a prebuilt graph).
Split mediapipe by platform and pin protobuf>=4.25.3,<5 for aarch64 only.
x86_64 keeps mediapipe 0.10.35, which works with protobuf 7, so
requirements-gpu.txt (amd64 only) stays unpinned. Also fixes a latent
issue where mediapipe>=0.10.21 was unsatisfiable on aarch64.
Verified live on the arm64 container: red-eye-removal completes on real
jpg and heic faces; OCR (tesseract) and paddle import unaffected.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* fix(ai): pin protobuf<5 in arm64 bundles lacking a mediapipe constraint
Bundles are built from docker/feature-manifest.json, not requirements.txt, so
this is the change that actually fixes the shipped arm64 bundles. On arm64,
object-eraser-colorize (onnxruntime), ocr (paddle) and transcription
(faster-whisper pulls onnxruntime) install a protobuf-dependent package with no
mediapipe to cap protobuf, so they bake protobuf 7.x. All bundles share one
/data/ai/venv at install time, so whichever of those installs last overwrites
protobuf to 7.x and breaks mediapipe FaceLandmarker (red-eye-removal). Pin
protobuf>=4.25.3,<5 in those three arm64 lists (appended last so it downgrades
after the puller installs). The four mediapipe bundles already resolve <5.
Dry-run on aarch64 confirmed paddle + protobuf 4.25.9 resolve with no conflict.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
* refactor(ai): keep protobuf fix in feature-manifest.json only
requirements.txt is not consumed by the Docker image build (the base
/opt/venv is installed from a hardcoded package list, and the ML libs
ship via bundles), so the requirements changes had no effect on shipped
artifacts and only tripped the dependency-review scanner on the protobuf
range. Revert them; the operative arm64 bundle fix lives entirely in
docker/feature-manifest.json.
Claude-Session: https://claude.ai/code/session_01VtvE6K8iEr5jGFJJpHEaPA
Found and fixed during a full local Docker build validation (amd64/arm64, all
four fleet targets, AI bundle installs, QA harness) and the follow-up bug
sweep requested afterward. None of the affected scripts run in CI, so these
had been silently broken indefinitely.
- docker/feature-manifest.json: pythonVersion was a flat "3.11", but the
amd64 base (Ubuntu 24.04) ships Python 3.12 while arm64 (Debian bookworm)
ships 3.11. Changed to a per-arch object matching the file's existing
convention.
- tests/qa/api-sweep.mts and verify-ai.mts: bare "@snapotter/shared" import
can't resolve since tests/ is not a pnpm workspace member, making both
silently unrunnable via their own documented command on any fresh
checkout. Switched to a relative import.
- tests/qa/generate-ledger.mts: wrote to docs/qa/ without creating the
directory first; docs/ is gitignored except COMMUNITY_GUIDE.md, so a fresh
checkout threw ENOENT.
- Seven QA Playwright spec files (input-preview, settings,
settings-extended, multifile, output-preview, pipeline-ui, smoke) had
~115 fixture() calls using directory names that don't exist. Resolved
every call programmatically against the real fixture tree.
- packages/ai/src/bridge.ts: AI dispatcher restart (happens on every bundle
install) was falsely counted as a crash, risking permanent dispatcher
disable after enough legitimate restarts within the crash window. Added a
shuttingDown flag checked at all three recordCrash() call sites.
- packages/image-engine/src/operations/auto-enhance.ts: image-enhancement
hung 40+ seconds on large RAW photos (confirmed on a real 20.2MP file) in
Sharp's .clahe() step, whose cost scales with total pixel count regardless
of tile size. Added a 16-megapixel cap above which CLAHE is skipped;
verified against the real file (40+s -> 2.0s) with no regression to other
RAW formats or normal-sized images. Fixing this surfaced a second,
smaller bug where the saturation step's CLAHE compensation boost was
keyed off the raw toggle instead of whether CLAHE actually ran.
- Two QA-harness robustness gaps closed per "fix everything, even the small
bugs": the passport-photo/erase-object input-preview tests now skip
cleanly with a clear reason on a container without their AI bundle
installed, and docker-compose.qa.yml's hardcoded project/container name
(the actual root cause of a mid-validation container swap between two
concurrent sessions) is now parameterized via QA_PROJECT_NAME.
Full validation report is local-only per repo convention.
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.
- fix(ocr): change paddlepaddle-gpu from --extra-index-url to --index-url
for the CUDA 12.6 package index. With --extra-index-url, pip could
resolve from PyPI (CUDA 11 build) instead of the cu126 index, causing
"libcusolver.so.11: undefined symbol" errors on CUDA 12 containers.
- revert version to 1.17.1 (v1.17.2 release was deleted)
Add pre-built release archives (Linux amd64/arm64) to the release
workflow, published as GitHub Release assets. Each archive is a
self-contained tar.gz (~240MB) with built frontend, API source,
and production node_modules. Users extract and run without needing
pnpm build.
Also includes AI install manifest fixes for Proxmox/bare-metal users:
- Pin setuptools<75 for Python 3.13 basicsr compatibility
- Pre-install basicsr with --no-build-isolation before realesrgan
- Loosen mediapipe pins from == to >= for Python 3.13 wheels
- Add retry logic to HuggingFace model downloads
The enhance-faces tool requires MediaPipe for face detection, but the
upscale-enhance feature bundle did not include mediapipe in its pip
packages. Users who installed only the upscale-enhance bundle got
"Face detection requires MediaPipe" errors. Added mediapipe to both
amd64 and arm64 package lists, matching the pattern used by the
face-detection and photo-restoration bundles.
Also added feature-manifest.test.ts with 25 tests validating bundle
dependency completeness to prevent similar missing-dependency bugs.
Closes#129
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").
- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize,
restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal)
closing all HIGH/MEDIUM coverage gaps from the test matrix audit
- Fix ensureAiDirs() crash on non-Docker environments by gating on
isDockerEnvironment() — prevents ENOENT when /data doesn't exist
- Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature
manifest for broader Python version compatibility
- Add Python 3.14 version guard warning in install_feature.py
- Remove duplicate torchvision shims from upscale.py and enhance_faces.py
(dispatcher.py already handles this at startup)
- Remove orphaned tools.batch i18n key and dead pipeline-builder filter
- Regenerate 4 visual regression baselines for current UI state
- Add data-testid to passport-photo generate button for E2E testability
- Pin torch==2.6.0+cu126 and torchvision==0.21.0+cu126 in feature
manifest to prevent NCCL symbol mismatch on CUDA 12.6 base images
- Move lpips after torch in install order to prevent wrong version
resolution from PyPI
- Add einops to upscale-enhance common deps (required by SCUNet)
- Update cpu_fallback_packages to handle multi-package CUDA torch
entries on amd64 without GPU
- Fix gpu.py ONNX CUDA detection: replace hardcoded .so path with
cross-platform session smoke-test
- Fix os.dup(1) crashes on Windows in upscale, enhance_faces, and
noise_removal by wrapping in try/except with sys.stderr fallback
- Guard top-level numpy/cv2 imports in colorize.py and restore.py
with helpful error messages
- Add weights_only=False fallback for torch.load in noise_removal
- Fix integration tests to accept 501 for uninstalled AI features
and 422 for missing system tools (exiftool, libheif)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Authoritative JSON manifest defining all 6 AI feature bundles with:
- Exact pip package versions and platform-specific variants (amd64/arm64)
- pip flags (--no-deps for codeformer, --extra-index-url for torch/paddle)
- postInstall re-pins (numpy==1.26.4 after codeformer)
- Model download entries (direct URL, rembg sessions, HuggingFace snapshots)
- Bundle-to-tool mapping matching shared/features.ts
Used by install_feature.py at runtime to install bundles on demand.
