- 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)
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)
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
Siddharth Kumar Sah