- 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
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
Merge CPU, CUDA, and lite Docker images into a single unified image.
One tag (latest) works on all platforms: amd64 (NVIDIA CUDA) and arm64 (CPU).
GPU auto-detected at runtime. All ML models and packages baked in.
Key changes:
- Platform-conditional Dockerfile (nvidia/cuda on amd64, node on arm64)
- tini as PID 1 for proper signal handling
- Fix FILES_STORAGE_PATH data loss bug
- Fix RealESRGAN upscaler (was broken, always fell back to Lanczos)
- Fix PaddleOCR language codes and stdout corruption
- Simplified CI/CD (single build, single tag)
- Expanded model pre-download with verification
- Shutdown timeout, improved health endpoint
- Remove unused lama-cleaner
- Replace OpenCV Haar Cascades with MediaPipe for face detection, using
short-range model first with full-range fallback for better accuracy
- Add auto-orient to remove-background route for EXIF-rotated photos
- Change default background removal model from u2net to birefnet-general-lite
- Fix flaky test by setting SQLite busy_timeout before journal_mode pragma
Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
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.
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)
Fix PaddleOCR crash by removing deprecated parameters (use_angle_cls,
show_log, cls) that were removed in PaddleOCR v3. Add graceful
fallback to Tesseract when PaddleOCR fails at runtime, so users
always get OCR results without errors.
Replace the external mask file upload workflow with an interactive
canvas-based brush tool. Users now paint directly on the image to
mark areas for erasure. Adds EraserCanvas component with adjustable
brush size, undo/clear, and mask export. Switch Python inpainting
from broken lama-cleaner to OpenCV cv2.inpaint (Telea algorithm).
Add before/after comparison slider after processing.
Fix face detection failure caused by MediaPipe 0.10.33 removing the
mp.solutions API. Replace with OpenCV Haar cascade which works reliably
in headless Docker. Add autoOrient() call before detection to handle
EXIF-rotated phone photos. Remove technical jargon from UI.
- Set up semantic-release with zero-touch CI pipeline on push to main
- Add version sync script to keep all package.json files and APP_VERSION
constant in sync automatically
- Consolidate Docker publishing into single tag-triggered workflow that
pushes to both Docker Hub and ghcr.io with semver tags
- Add help dialog with keyboard shortcuts, getting started guide, and
resource links
- Sync all versions to 0.2.1 to match Docker Hub latest
BiRefNet-Lite times out on first load (~60s+ for 973MB model).
U2-Net works in 2 seconds. Users can still select BiRefNet for
higher quality when they're willing to wait. Added timing hints
in model descriptions and increased timeout for BiRefNet models.
- Switch default from birefnet-general (973MB, 4min) to
birefnet-general-lite (faster, still SOTA quality)
- Fix Python script stdout pollution — progress messages now go to
stderr so the JSON result parser doesn't break
- Pre-download birefnet-general-lite in Docker build
- Switch default model from U2-Net to BiRefNet (state-of-the-art)
- Add 6 model options: BiRefNet, BiRefNet Lite, BiRefNet Portrait,
BRIA RMBG, IS-Net, U2-Net
- Add animated progress bar with stage indicators (loading model,
analyzing, removing, refining edges) and elapsed timer
- Add intuitive background color presets (Transparent, White, Black,
Red, Green, Blue) as clickable buttons + custom color picker
- Handle background color compositing in Python (PIL alpha composite)
- Add checkerboard pattern to before/after slider for transparency
- Pre-bake BiRefNet model (973MB) in Docker image for instant use
- Add user management endpoints (register, list, delete, change password)
- Add API key management (create, list, delete)
- Add settings persistence endpoints (get, put)
- Wire settings dialog to real backend (People, API Keys, System, Security)
- Fix login auth flow (window.location.href for full reload)
- Fix download URLs returning 401 (make public since UUIDs are unguessable)
- Fix border tool shadowColor validation (accept 6-8 hex digits)
- Fix remove-bg alpha matting fallback (retry without on failure)
- Fix AI tool silent fallbacks (report errors instead of no-ops)
- Add checkerboard background to before/after slider for transparency
- Add progress bars to all AI tool components
- Add Playwright E2E test suite (131 tests across 9 test files)
- Rewrite Dockerfile for production (tsx runtime, pre-baked AI models)
- Add .dockerignore for faster builds
- Add proper accessible labels to login form
Siddharth Kumar Sah