- Process files one at a time for real per-file progress bar
- Sync right panel with left panel file navigation (arrows work)
- Show image preview before conversion
- Add "Download All as JSON" and "Download All as Text" batch buttons
- Unify batch action button styles
Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
- Add z-30 to country dropdown container to prevent overlap with settings below
- Add mouse wheel zoom on preview canvas (0.5x to 3x)
- Add zoom in/out buttons and reset with percentage display
- Update hint text to "Drag / Scroll to zoom"
* 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>
* 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>
Switch from manual fetch to useToolProcessor hook for consistency and
batch support. Add scale/custom sizing modes, DPI presets, 7-format
button grid, quality slider, transparent/color background toggle with
preset swatches, and ProgressCard during processing.
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
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
Users running lite mode had no way to tell why AI tools were greyed out.
Now the public health endpoint reports the variant, and a visible banner
appears in the tool panel when running in lite mode.
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>
* 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>
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>
stirling-image