Detection priority (probed once, cached):
1. Real-ESRGAN PyTorch + CUDA GPU → fastest, best quality
2. Real-ESRGAN PyTorch + Apple MPS → fast on Apple Silicon
3. Real-ESRGAN NCNN Vulkan binary → fast on any GPU via Vulkan (no CUDA needed)
4. Real-ESRGAN PyTorch CPU → works, slow (warned in UI)
5. Lanczos → always available, instant fallback
Backend:
- services/upscale.py: full capability probe (probe_upscale_capabilities),
implementations for PyTorch (CUDA/MPS/CPU auto-device) and NCNN binary,
upscale_sync() resolves method with fallback chain,
async upscale_image() runs in thread pool
- print_tools.py: /api/print/upscale uses new service; method="auto" by default;
GET /api/print/upscale/available returns full capability map with device info
and recommended_label; POST /api/print/upscale/refresh-caps busts cache
without restart (useful after installing NCNN binary into container)
Frontend:
- upscale.js: fetches capability map on first open; builds method selector showing
only available options; labels recommended method with ★; shows device info
(CUDA/MPS/CPU/NCNN) in dialog; maps display label back to method key on submit;
shows actual method used in success toast and undo history entry
Scripts:
- scripts/download_realesrgan.py: downloads NCNN Vulkan binary for current platform
(Linux/macOS/Windows) to /app/data/models/realesrgan/; makes executable;
run inside container or locally
https://claude.ai/code/session_01B58MaJCU1R6KwBDJCp8AfN
