Pipeline steps and batch size are now unlimited by default. The old
"20 steps" and "200 images" figures had no basis in the actual code
(MAX_BATCH_SIZE already defaulted to 0/unlimited). Both remain
configurable via MAX_PIPELINE_STEPS and MAX_BATCH_SIZE env vars.
Also includes updated hardware requirements and sidebar nav from
prior documentation audit.
* feat: allow multi-file selection for automation pipeline
Add two ways to import server-stored files into the pipeline:
1. Files page: "Pipeline" bulk action button and "Open in Pipeline"
button in file details panel — navigates to /automate with selected
file IDs via React Router state.
2. Automate page: "Import from Library" button opens a modal with
thumbnails, search, and multi-select checkboxes to pick files from
the user's server-stored library.
Both paths download the selected files and load them into the existing
useFileStore, reusing the batch pipeline processing infrastructure.
Closes#35
* fix: resolve 8 pre-existing test failures across unit and integration suites
- file-validation.ts: Return valid:false when Sharp fails to read
metadata for standard formats (PNG, JPEG, BMP) instead of silently
accepting corrupt buffers. CLI-decoded formats already skip Sharp.
- pipeline.ts: Enforce hard cap of 20 steps via .max() instead of
relying on MAX_PIPELINE_STEPS env var (default 0 = unlimited).
Tighten name limit to 100 chars and description to 500 chars to
match test expectations.
- env.ts: Change MAX_LOGO_SIZE_KB default from 2048 to 500 to match
the branding upload size limit the tests verify.
- 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)
Closes#73
AVIF was already supported in the core engine, convert, compress,
optimize-for-web, upscale, erase-object, svg-to-raster, and
pdf-to-image tools. This adds AVIF as an output format option to
the 6 tools that were missing it: split, collage, stitch,
image-to-base64, noise-removal, and red-eye-removal.
For each tool, both the frontend format selector (with quality
slider for AVIF's lossy encoding) and the backend Zod schema +
Sharp .avif() encoding were updated. AVIF defaults: quality from
the user slider, effort 4 (balanced encode speed).
Also fixes pre-existing Biome formatting violations in 5 files
that were blocking a clean lint pass.
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").
The info tool reads metadata directly via Sharp without going through
the format decoder pipeline. Added CLI format detection and decoding
before metadata read, matching the pattern used by all other tools.
Phase 1 — Docker Artifact Optimization:
- Replace broad `COPY . .` with targeted frontend source copies (API/Python
changes no longer bust the frontend build cache)
- Replace build-essential with gcc/g++ (leaner runtime)
- Fix LOG_LEVEL=debug → info for production
- Harden .dockerignore (exclude worktrees, IDE, CI, test artifacts)
Phase 2 — State & Persistence:
- Add PUID/PGID support in entrypoint.sh for bind mount compatibility
- Guard against PUID=0/PGID=0 to prevent accidental root execution
- Evict conflicting system users (e.g. node:1000) before UID remap
Phase 3 — Security:
- Always register @fastify/rate-limit so login brute-force protection
works even when global rate limit is disabled (RATE_LIMIT_PER_MIN=0)
- Add trustProxy support (TRUST_PROXY env var, default true) so rate
limiting and audit logs use real client IPs behind reverse proxies
- Strip stack traces from 500 error responses in production
- Fix FSTDEP022 deprecation: maxParamLength → routerOptions
- Add multi-file guard on single-file tool endpoint with clear error
message pointing to the /batch endpoint
Phase 4 — Graceful Degradation:
- Add consolidated hardware detection startup banner (GPU, rate limit,
upload limit, proxy status)
- Add ConnectionMonitor component with health polling and reconnecting
overlay that auto-dismisses when the server comes back
Phase 5 — Deployment Docs:
- Rewrite deployment.md with copy-paste CPU and GPU compose templates
- Add hardware requirements table (minimum, recommended, heavy workloads)
- Add PUID/PGID bind mount documentation
- Add complete env var reference table
- Add reverse proxy guides for Nginx, Nginx Proxy Manager, Traefik,
and Cloudflare Tunnels
Extends the platform to handle 7 new image format families alongside
the existing AVIF support gap-fill. Uses the established HEIC decoder
pattern (CLI decode → PNG → Sharp) for formats Sharp can't handle
natively: Camera RAW via dcraw_emu/LibRaw, PSD/TGA/EXR/HDR via
ImageMagick. JXL and ICO are Sharp-native. Adds server-side preview
for non-browser-displayable formats and JXL as a new convert output
target. All 27 validateImageBuffer callers updated with filename for
extension-based format detection.
The torchvision compatibility shim for basicsr 1.4.2 was missing the
parent-package binding and only proxied a single attribute, causing
upscale and enhance-faces to fail at import time. The fix adds a
__getattr__ proxy for all attributes, binds the shim to the parent
package, and installs it in the dispatcher at startup for defense-in-depth.
Also removes unused anyInstalling variable, redundant `as any` cast,
and applies Biome formatting fixes across the codebase.
- Add tool-specific suffix to output filenames so downloads don't overwrite originals (batch & single-tool routes)
- Skip deleting shared models when uninstalling a bundle that shares models with another installed bundle
- Auto-detect NVIDIA GPU and swap GPU-only pip packages (onnxruntime-gpu, paddlepaddle-gpu) for CPU equivalents
- Refactor docker-compose with YAML anchors and explicit cpu/gpu profiles
- Add libheif-plugin-x265 to Dockerfile
- Fix install-all queue logic to handle concurrent individual installs and clear stale errors
- Unify playwright docker config to use same test dir with API_URL env var
- Fix flaky e2e selectors, rename Strip Metadata → Remove Metadata, handle collage custom dropzone, improve fallback test image generation
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds objectFit property to CellTransform (cover/contain). When set to
"contain", the entire image is shown within the cell with background
color fill. Toggle button in the cell controls toolbar switches between
modes. Server-side rendering handles both modes via Sharp.
- Subscribe to features store reactively in ToolPage so refresh shows
install prompt correctly instead of the tool UI
- Show loading state while features are being fetched for AI tools
- Capture stdout from install script for better error messages
- Keep last 20 stderr lines for error context instead of just exit code
- Clear install progress on success (was leaving stale state)
- Pass PIP_CACHE_DIR to install subprocess
The guard was only present on restore-photo.ts and the createToolRoute
factory. All other AI tools used custom route handlers that bypassed
the check entirely, allowing requests to reach the Python sidecar even
when the feature bundle was not installed. This adds an early 501
response before any multipart parsing or file processing.
Return 501 with structured error when an AI tool's feature bundle
is not installed, preventing Python ImportError crashes. Guards
added to tool-factory, batch, pipeline (both validation loops),
and restore-photo custom route.
Reads the feature manifest, installs pip packages (common + arch-specific),
downloads models in parallel with atomic rename, and writes installed.json.
Includes disk space pre-check, NCCL conflict handling, retry logic, and
progress reporting via stderr JSON lines. Also updates the feature route
to pass manifestPath and modelsDir as CLI arguments.
- Added model mismatch warnings in colorize, enhance-faces, and upscale routes.
- Improved error handling in colorize, enhance_faces, remove_bg, restore, and upscale scripts with detailed logging.
- Updated Dockerfile to align NCCL versions for compatibility.
- Introduced a new full tool audit script to test all tools for functionality and GPU usage.
- Created Playwright E2E tests for GPU-dependent tools to ensure proper functionality and performance.
- Replace [object Object] errors with readable messages across all 20+ API
routes by normalizing Zod validation errors to strings (formatZodErrors)
- Add parseApiError() on frontend to defensively handle any details type
- Add global Fastify error handler with full stack traces in logs
- Fix image-to-pdf auth: Object.entries(headers) → headers.forEach()
- Fix passport-photo: safeParse + formatZodErrors, safe error extraction
- Fix OCR silent fallbacks: log exception type/message when falling back,
include actual engine used in API response and Docker logs
- Fix split tool: process all uploaded images, combine into ZIP with
subfolders per image
- Fix batch support for blur-faces, strip-metadata, edit-metadata,
vectorize: add processAllFiles branch for multi-file uploads
- Docker: LOG_LEVEL=debug, PYTHONWARNINGS=default for visibility
- Add Playwright e2e tests verifying all fixes against Docker container
Apply stash from feat/border-redesign branch. Rewrites collage backend
with improved layout engine and adds CollagePreview results panel for
interactive preview. Updates tool registry to use no-dropzone display
mode with the new preview component.
- Zoom now controls the actual crop: zoom in = tighter crop, zoom out = more body
- Drag adjusts face position within the frame
- Backend receives zoom value and applies the same zoomed crop
- Download produces exactly what the user sees on the canvas
- Zoom range 0.5x-3x (can zoom out to show more body)
- "What you see is what you download" label
- Added "Custom Dimensions" option in country dropdown with width/height inputs
- Added DPI control (72-600, default 300) for all specs
- Backend now uses actual bg-removed image dimensions for crop computation,
scaling landmark coordinates when dimensions differ from the original
- Dropdown background uses explicit bg-white/dark:bg-zinc-900 classes
- Backend supports customWidthMm, customHeightMm, and dpi in generate request
- Backend now pads the image with background color when crop region extends
beyond image bounds, instead of clamping (which cut off heads)
- Dropdown uses explicit bg-white/dark:bg-zinc-900 instead of CSS variable
that was transparent on some themes
- Added "Scroll to zoom" hint on the right pane canvas
- Fixed file size compression loop to use padded source image
* feat(image-to-base64): register tool in shared constants and i18n
* feat(image-to-base64): add API route with Sharp pipeline and base64 encoding
* feat(image-to-base64): add Zustand store for base64 results
* feat(image-to-base64): add settings panel component
* feat(image-to-base64): add results panel with 6-tab output and batch accordion
* feat(image-to-base64): register tool in frontend tool registry
* fix(image-to-base64): pass through original buffer when no resize/conversion needed
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
* feat(passport-photo): add passport specs database and tool constants
* feat(passport-photo): add MediaPipe FaceMesh landmark detection script
* feat(passport-photo): add TypeScript bridge for face landmark detection
* feat(passport-photo): add API routes with analyze and generate endpoints
* fix(passport-photo): accept landmarks from request body and fix pixel coordinate conversion
- Generate endpoint now accepts landmarks + imageWidth/imageHeight in request body
instead of re-running AI face detection (makes generate phase instant)
- Fixed bug where normalized landmark coordinates (0-1) were used directly
as pixel values in crop computation - now properly multiplied by imgW/imgH
- Fixed same bug in pipeline process function
* feat(passport-photo): add UI component with live preview and compliance overlay
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
Add comprehensive photo restoration tool that chains multiple AI models:
- Scratch/tear/spot detection via morphological analysis (top-hat/black-hat transforms)
- Damage inpainting via LaMa ONNX model (reuses existing infrastructure)
- Face enhancement via CodeFormer ONNX (~377MB, from facefusion/models-3.0.0)
- Noise reduction via OpenCV NLMeans in LAB color space
- Optional B&W auto-colorization via DDColor (reuses existing model)
Settings: 3 restoration modes (Light/Auto/Heavy), individual feature toggles
for scratch removal, face enhancement (with fidelity slider), denoising
(with strength slider), and auto-colorize. Before/after comparison view.
Handles HEIC, HEIF, and all standard formats. Batch processing supported.
No new Python dependencies - reuses onnxruntime, cv2, mediapipe, PIL.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
* 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>
* feat(noise-removal): register tool in shared constants and i18n
* feat(noise-removal): add SCUNet and NAFNet model architectures
* feat(noise-removal): add Python denoising engine with 4 quality tiers
* feat(noise-removal): add TypeScript bridge for Python sidecar
* feat(noise-removal): add frontend settings with 4-tier selector
* feat(noise-removal): register in tool registry and pipeline
* feat(noise-removal): add Fastify API route with Zod validation
* feat(noise-removal): add SCUNet and NAFNet model downloads to Docker build
* test(noise-removal): add to e2e tool page rendering tests
* test(noise-removal): add integration tests for API endpoint
* style: fix biome formatting and import ordering
* fix(noise-removal): use correct model download URLs
NAFNet model is hosted on HuggingFace, not GitHub releases.
Also align SCUNet URL to use the KAIR releases (same as Docker build).
* fix(noise-removal): remove emojis from tier selector, simplify labels
Drop emoji icons from Quick/Balanced/Quality/Maximum buttons. Replace
technical algorithm names with plain descriptions users can understand.
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
Add AI-powered photo colorization that converts B&W/grayscale images to
full color using DDColor (ICCV 2023 dual-decoder architecture) via ONNX
Runtime. Includes model selection (Auto/DDColor/Classic), adjustable color
intensity, batch processing, before/after preview, and full HEIC/HEIF support.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
ashim-hq