Three fixes to ensure zero network access after docker pull:
1. rembg model allowlist: validate model parameter against the 7
pre-downloaded models, preventing rembg from attempting to download
unknown models via a raw API call.
2. GFPGAN/CodeFormer auxiliary models: pre-download facexlib's
detection_Resnet50_Final.pth and parsing_parsenet.pth at build time.
These were previously downloaded on first use via basicsr. Symlinks
in /app/gfpgan/weights/ ensure codeformer-pip also finds them.
3. OpenCV colorize models: pre-download the prototxt, caffemodel, and
pts_in_hull.npy so the lightweight OpenCV colorizer fallback works
in addition to the primary DDColor method.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
MediaPipe >= 0.10.30 removed the mp.solutions namespace. This broke
face blur, face enhance, red-eye removal, and photo restoration for
users running newer mediapipe versions (closes#43).
All 5 Python scripts that use mediapipe now try the legacy mp.solutions
API first and fall back to the new mp.tasks API on AttributeError.
Model files (blaze_face_short_range.task, face_landmarker.task) are
pre-downloaded during Docker build into /opt/models/mediapipe/ so the
image works fully airgapped. Local dev auto-downloads to .models/.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
- Old API (mp.solutions.face_mesh) for Docker with mediapipe < 0.10.30
- New API (mp.tasks.vision.FaceLandmarker) for newer mediapipe >= 0.10.30
- Auto-downloads face_landmarker.task model on first use with new API
- Extracted shared landmark index constants and key point extraction
* 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>
enhance_faces.py relied on implicit PyTorch auto-detection for both
GFPGAN and CodeFormer, bypassing the centralized gpu.py module.
inpaint.py queried ort.get_available_providers() directly, which
reports compiled-in backends rather than actual hardware.
Both tools now go through gpu.py so STIRLING_GPU=false correctly
forces CPU across every AI tool.
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>
* feat(image-enhancement): add analysis and correction types
* feat(image-enhancement): implement auto-enhance analysis and correction engine
* test(image-enhancement): add unit tests for auto-enhance engine
* feat(image-enhancement): add API route with analyze endpoint and register in constants/i18n
* feat(image-enhancement): add UI component with mode selector, intensity slider, and analysis badges
* test(image-enhancement): add integration and e2e tests
* fix(image-enhancement): use modulate instead of gamma for exposure correction
Sharp's gamma() only accepts values between 1.0 and 3.0, but brightening
underexposed images computed gamma < 1.0. Switch to modulate({ brightness })
which handles both brightening and darkening correctly.
---------
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
Add 12 previously undocumented routes to the OpenAPI 3.1 specification:
content-aware-resize, edit-metadata (+ inspect), stitch, pdf-to-image
(+ info, preview), gif-tools/info, remove-background/effects, preview,
pipeline/tools, and pipeline/batch. Fix license from MIT to AGPL-3.0,
correct DELETE /files response from 204 to 200 with body, and update
VitePress API docs (rest.md tool table, ai.md model parameters). Also
register the sharpen operation in the image-engine OPERATION_MAP so it
can be used as a standalone pipeline step.
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
- Multi-file sequential processing with per-file progress
- Structured results table with type badges and copy per-result
- Copy All and Export CSV functionality
- Thorough scan toggle (maps to tryHarder in zxing-wasm)
- Before/after view shows annotated image with bounding boxes
- Updated tool description in constants and i18n
Update erase-object pipeline, eraser canvas, and inpainting Python script.
Add LaMa model download script and update Dockerfile for model support.
Update multi-file tool routes for consistency.
Replace the confusing 2-mode smart crop with a clear 3-mode system:
- Subject Focus: Sharp attention/entropy saliency crop with social media presets
- Face Focus: MediaPipe face detection with headshot framing presets
- Auto Trim: Border removal with optional pad-to-square
Adds detectFaces() to AI package, face preset constants, backward
compatibility for old mode names, and comprehensive integration tests.
- 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 Auto/AI/Fast buttons with Fast/Balanced/Best (consistent with other tools)
- Rename "Denoise" to "Noise Reduction" with explanatory subtitle
- Change output format from 3 buttons to dropdown with all formats (PNG, JPG, WebP, AVIF, TIFF, GIF, HEIC, HEIF)
- Add HEIC/HEIF input decoding (was missing unlike other tools)
- Add HEIC/HEIF/AVIF output conversion via Sharp and heif-enc
- Generate browser-compatible WebP preview for non-previewable output formats
- Fix torchvision compatibility shim so Real-ESRGAN actually loads (was silently falling back to Lanczos)
- Fix denoise crash: Image.fromarray() instead of type(img).fromarray()
- Redirect stdout for entire AI pipeline to prevent library messages corrupting JSON output
- Add GFPGAN model download for face enhancement
- Use batch endpoint for multi-file uploads (enables Download All ZIP)
- Fix multi-image: process selected file, not always first
- Fix progress bar: asymptotic fill prevents visual stalling
- Fix slider: write results to captured index, not current selection
- Add model selection (Auto/AI/Fast), face enhancement, denoise
- Add output format (PNG/JPEG/WebP) with quality control
- Add Upscale All for sequential batch processing with queue
- More granular Python progress stages for smoother UX
- Fix temp file leak: clean up preprocessed image in finally block
- Log warning instead of silently swallowing preprocessing failures
- Simplify auto_detect_language to honest default (was a stub that
wasted time loading a model but always returned "en")
Ultra quality (People only):
- BiRefNet-matting ONNX (928MB) for true alpha matting with per-pixel
transparency on hair wisps and fine edges
- Custom rembg session class, zero new Python dependencies
- Model pre-downloaded in Docker build for immediate availability
Quality tier labels: Fast / HD / Max / Ultra (shorter, fits 4-col grid)
Adds a new "Ultra" quality tier for People subject type that uses
BiRefNet-matting (ONNX, 928MB) for true alpha matting instead of
binary segmentation. Produces per-pixel transparency for hair wisps
and fine edges that standard models miss.
- Custom rembg session class loads BiRefNet-matting ONNX from GitHub releases
- Zero new Python dependencies (reuses existing onnxruntime)
- Model pre-downloaded in Docker build alongside existing models
- Ultra option only visible when subject is People
- Falls back to Best when switching to Products/General
Remove Background:
- Two-phase flow: AI removes bg once, then effects adjust instantly
- Blur background effect with real-time CSS preview (portrait mode)
- Drop shadow effect with opacity control
- Gradient backgrounds with presets, custom colors, and angle
- Custom background image upload (including HEIC/HEIF)
- Solid color backgrounds moved from Python to Node.js/Sharp
- Effects-only API endpoint for instant re-renders without AI re-run
- HEIC/HEIF input support (decoded before passing to Python/rembg)
- Passport/ID photo checkbox defaults ON for People subject
- Before/after slider preserved when no effects active
- 15 comprehensive Playwright e2e tests
Color Tools:
- Consolidated 4 tools (brightness-contrast, saturation, color-channels,
color-effects) into single "Adjust Colors" tool
- Added exposure, temperature, tint, hue, sharpness controls
- SVG filter-based live preview for all adjustments
- Backward-compatible URL redirects from old tool paths
Other fixes:
- Favicon tool: download button instead of auto-download
- Batch processing: HEIC filename extension fix
- File store: processedFilename field for proper batch downloads
- Add bidirectional HEIF support: decode (input) and encode (output) via system heif-convert/heif-enc
- Add server-side WebP preview generation for non-browser-previewable formats (HEIC, TIFF)
- Fix content-aware resize failing on HEIF input (decode before passing to caire)
- Fix content-aware resize timeout on large images by downscaling to max 1200px and using JPEG intermediate
- Add HEIF as target format in convert tool
- Add loading spinner for HEIF preview decode in file store
- Fix file picker not accepting HEIF files (explicit .heic,.heif,.hif extensions)
- Extend frontend timeout for medium tools to 180s with 45s progress animation
- Redesign rotate controls with preset buttons and compact flip section
- Remove misleading savings percentage from convert tool
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
- Replace Rule of Thirds button with checkbox for clearer toggle
- Show side-by-side comparison after crop instead of overlay slider
- Add custom aspect ratio option with W:H number inputs
- Fix batch crop failing on files with different dimensions by
sending percentage-based coordinates instead of absolute pixels
- Add failed-file error state display in tool page
- Remove all lite/full variant logic from frontend, API, shared constants,
docs, and tests (single unified Docker image only)
- Replace single QEMU multi-arch Docker build with per-architecture native
builds (amd64 + arm64) and manifest merge to fix disk space exhaustion
- Add disk cleanup step and per-platform build cache scopes
- Switch release trigger from push to workflow_dispatch
- Add GitHub issue templates and PR template
