- Add safe_onnx_session() to gpu.py with graceful CUDA EP → CPU fallback
- Replace bare ort.InferenceSession() calls across colorize, restore, inpaint, remove_bg
- Add libcublas-12-6 to production Dockerfile for ONNX Runtime CUDA EP
- Add skipIfFeatureNotInstalled guards to remove-bg, blur-faces, smart-crop, ocr, noise-removal e2e specs
- Add AI tool install prompt detection in tools-all.spec.ts
- Add smart-crop to PYTHON_SIDECAR_TOOLS so frontend shows install prompt correctly
- Create Dockerfile.test.dockerignore to include tests/ in test image builds
- Add libheif-examples and exiftool to Dockerfile.test for HEIC and metadata tests
- Regenerate visual regression baselines for Docker/Linux and skip on non-Docker platforms
- Pin torch==2.6.0+cu126 and torchvision==0.21.0+cu126 in feature
manifest to prevent NCCL symbol mismatch on CUDA 12.6 base images
- Move lpips after torch in install order to prevent wrong version
resolution from PyPI
- Add einops to upscale-enhance common deps (required by SCUNet)
- Update cpu_fallback_packages to handle multi-package CUDA torch
entries on amd64 without GPU
- Fix gpu.py ONNX CUDA detection: replace hardcoded .so path with
cross-platform session smoke-test
- Fix os.dup(1) crashes on Windows in upscale, enhance_faces, and
noise_removal by wrapping in try/except with sys.stderr fallback
- Guard top-level numpy/cv2 imports in colorize.py and restore.py
with helpful error messages
- Add weights_only=False fallback for torch.load in noise_removal
- Fix integration tests to accept 501 for uninstalled AI features
and 422 for missing system tools (exiftool, libheif)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The on-demand feature download system stores models at /data/ai/models/
(set via MODELS_PATH env var), but all Python scripts hardcoded
/opt/models/ as the base path. Each script now reads MODELS_PATH and
falls back to /opt/models for backward compatibility.
- 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.
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