Batch progress was broken because JobProgress events lacked a `type`
field. The frontend checks `data.type === "batch"` to distinguish batch
from single-file SSE events, so batch progress was silently discarded
and multi-file processing appeared stuck at 15%.
Also improves the processing UX for non-AI (Sharp-based) tools: the
progress bar now pulses during the server processing phase and shows
a "This may take a moment" hint after 10 seconds.
Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
AI routes (remove-background, erase-object, ocr, blur-faces, upscale)
were silently swallowing errors - failures returned HTTP 422 to the
client but never appeared in server logs. This made it impossible for
self-hosters to diagnose issues like 504 timeouts from reverse proxies.
Adds request.log.info() at processing start (tool name, image size, key
settings) and request.log.error() in catch blocks, matching the existing
tool-factory pattern.
Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
* feat: add resolveOutputFormat utility for input format preservation
* fix: preserve file order in batch processing with X-File-Results header
Collect all results before streaming the ZIP to guarantee upload order.
Replace X-File-Order with index-based X-File-Results header that maps
each upload index to its processed filename, handling failures and
duplicate filenames correctly.
Closes#13
* fix: use X-File-Results for index-based batch file matching
The frontend now matches processed files to entries by upload index
instead of fragile name/position matching.
* feat: preserve input format in smart-crop with quality control
Smart crop now outputs in the same format as the input (JPG in, JPG out)
instead of always converting to PNG. Adds an optional quality setting
(default 95) for lossy formats.
Closes#14
* feat: add output quality slider to smart crop settings UI
* feat: preserve input format in crop tool
* feat: preserve input format in color adjustment tools
Applies to brightness-contrast, saturation, color-channels, and
color-effects tool routes.
* refactor: avoid double encode in smart-crop content mode
For the simple trim path (no pad-to-square), chain .toFormat() on the
trim pipeline directly instead of creating a second Sharp instance.
This eliminates a redundant intermediate encode that degraded quality
for lossy formats. Also use trimmed.info dimensions instead of a
separate metadata() call for the pad-to-square path.
---------
Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
Clickable localhost:1349 links are misleading on the GitHub Pages site
since users may not have Stirling Image running locally. Use plain path
references instead so it is clear these live on their own instance.
The links used /Stirling-Image/ (wrong casing) which VitePress then
prefixed with the base /stirling-image/, producing a double-prefixed
404 path. Remove the manual base so VitePress prepends it automatically.
Keep the main docker run command front and center. Lite and CUDA
variants are in a collapsible details block so the quick start
section stays scannable.
onnxruntime-gpu reports CUDAExecutionProvider as "available" just
because the library was compiled with CUDA support, even on machines
with no GPU. This made gpu_available() return True incorrectly,
causing upscale.py to try torch.device("cuda") and fall back to
Lanczos instead of running Real-ESRGAN on CPU.
torch.cuda.is_available() actually probes the hardware. Use it as
the single source of truth for GPU detection.
Verified: CUDA image on Apple Silicon (no GPU) now correctly reports
gpu: false and all AI tools run on CPU without crashes.
The STIRLING_GPU=true env var was baked into the :cuda Dockerfile,
which made gpu_available() return True without checking actual
hardware. On machines without a GPU, this would crash upscale.py
(torch.device("cuda") fails) and ocr.py (PaddleOCR use_gpu=True).
Fix: the env var can only disable GPU (set to false/0), never
force-enable it. Hardware detection always runs. Removed the
baked env var from the Dockerfile since it adds no value now.
- README: add CUDA docker run example alongside full and lite
- Getting started: add GPU acceleration tip with speedup numbers
- Deployment: add CUDA row to variants table
- Docker tags: expand benchmarks with warm + cold start tables
- Add :cuda tag to Docker Tags docs with setup, benchmarks, compose example
- Add GPU acceleration tip to AI engine docs
- Include benchmark table from RTX 4070 testing
Add a :cuda Docker image tag that auto-detects NVIDIA GPU at runtime
and falls back gracefully to CPU. Same pattern as Immich.
- New gpu.py shared utility for cached CUDA detection
- Background removal (rembg): pass CUDAExecutionProvider to ONNX Runtime
- Upscaling (Real-ESRGAN): use CUDA device + FP16 when GPU available
- OCR (PaddleOCR): enable use_gpu when CUDA detected
- Dispatcher reports GPU status at startup via readiness signal
- Admin health endpoint exposes GPU availability
- Dockerfile uses ARG GPU=false with conditional NVIDIA CUDA base image
- docker-compose.gpu.yml override for GPU users
- CI/CD workflows build and publish :cuda tag (amd64 only)
Three tags: :latest (CPU), :lite (no AI), :cuda (GPU with CPU fallback)
Centralize duplicated getToken() + Bearer header logic into a single
formatHeaders() helper in lib/api.ts. When no token exists, the
Authorization header is omitted entirely instead of sending an empty
Bearer token, which breaks forward-auth proxies like Authelia behind
Caddy.
Changes:
- Add formatHeaders() with try-catch around localStorage access
- Replace 20+ duplicated getToken() definitions across tool components
- Migrate all call sites including file-details, settings, change-password
- Update tests to verify header omission on empty token
Based on the fix proposed by @jules2689 in #6, with improvements:
file placement (lib/api.ts vs components), localStorage error handling,
simplified truthiness check, and complete call-site coverage.
Co-Authored-By: Julian Nadeau <julian@jnadeau.ca>
Both llms.txt and llms-full.txt incorrectly said MIT. Also added lite
variant mention to the description and a warning callout on the AI
engine docs page noting AI tools are unavailable in the lite image.
README Quick Start now shows both :latest and :lite commands.
Getting Started adds a tip callout about the lite image.
Deployment page lists both variants with a comparison table and
updates the CI/CD description to mention both are built.
Developer guide adds the lite build command.
Linux libheif packages provide heif-convert instead of heif-dec (which
is macOS-only). The decoder now tries heif-convert first, then falls
back to heif-dec. Both accept the same argument syntax.
Ubuntu 24.04 uses plugin-based libheif codecs. Added libheif-plugin-x265
(HEVC encoder) and libheif-plugin-libde265 (HEVC decoder) to the CI test
job. Debian bookworm (Docker) bundles these in libheif1 directly.
Adds a new mode that trims uniform-color borders around the subject,
like GIMP's "Crop to Content." Includes configurable tolerance threshold
and optional pad-to-square with target size for e-commerce workflows.
The original attention-based crop is preserved as "Focus Crop" mode.
Closes#7
New users on dark-mode systems were seeing dark theme on first visit.
The default is now explicitly light, matching the API's DEFAULT_THEME.
Users can still switch to dark or system in settings.
- Health endpoint returns "healthy" instead of "ok" for consistency
- MAX_USERS now configurable via env var (default 5)
- People API returns team names instead of UUIDs in register/list
- PUT user update accepts team names (name-first lookup, fallback to ID)
- Login rate limit follows global rate limit when RATE_LIMIT_PER_MIN > 1000
- Strip-metadata preserves original format encoding instead of always PNG
- Fix e2e tests: rotate/crop/border button selectors match actual UI
- Fix e2e tests: create Engineering/Design teams in people test setup
- Fix e2e tests: people UI uses select for team field, not text input
- Update visual regression baseline for tablet home page
