3 Commits

Include renames

Author	SHA1	Message	Date
Claude	38e80f8fb8	Add BEN2 and BiRefNet-HR as selectable Remove Background models ... BEN2 becomes the new default local backend (clean cutouts, strong on hair/fur edges), with BiRefNet-HR available as a high-res/print alternate and U2Net kept as the lightweight fallback. Both are MIT-licensed and download weights from HuggingFace on first use (cached via the existing hf_cache bind mount), unlike U2Net/SAM which need an explicit download script. - config: new BG_REMOVAL_MODEL setting (default "ben2") - tools.py: remove-background-base64 now tries local backends in order (request.model override > BG_REMOVAL_MODEL > ben2/u2net), falling back to rembg's birefnet-general session as a last resort - requirements.gpu.txt / Dockerfile.gpu: add ben2 + transformers deps needed for the new backends, with a build-time smoke test for ben2 - frontend: model dropdown in the Remove Background dialog, threaded through api.js to the new request field Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ro4PwQKvSc3CH19LSN21Ht	2026-06-18 14:20:24 +00:00
Claude	11772e620c	Fix build cache, DNS/model download, and AI Edit error handling ... Build / pip layer fixes: - Add BUILDID ARG to Dockerfile.gpu; pass from docker-compose.gpu.yml build args so pip layers can be force-busted without --no-cache: BUILDID=$(date +%s) docker compose -f docker-compose.gpu.yml up --build Model download (DNS-blocked environments): - Change HF model cache from named volume to ./data/hf_cache bind mount so models can be pre-downloaded on the host (no rebuild needed) - Remove now-unused hf_model_cache named volume - README: add iptables fix + huggingface-cli offline download instructions Error handling improvements: - ai_edit_region: catch ConnectError/Errno-3 → return 503 with exact fix commands - _require_remote: give actionable message when local_gpu provider fails to load - _build_provider: catch AttributeError (torch.xpu from wrong diffusers) not just ImportError - local_diffusion.py: fix docstring to reflect <0.29.0 pin https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM	2026-06-14 00:14:34 +00:00
Claude	8fe8498df2	Add local GPU inference: auto-detect GPU, auto-download best diffusion models ... Adds AI_PROVIDER=local_gpu — a fully self-contained GPU inference engine using HuggingFace Diffusers that requires zero InvokeAI/ComfyUI setup. All existing providers (InvokeAI, ComfyUI, OpenAI, Replicate) remain intact and can be mixed with local GPU via per-operation overrides. New features: - GPU auto-detection (CUDA/NVIDIA, MPS/Apple Silicon, CPU fallback) - VRAM-tiered model selection: ultra ≥16 GB → SDXL inpaint + SDXL base high 8-16 GB → SDXL inpaint + SDXL base medium 4-8 GB → SD 2.x inpaint + SD 2.1 low <4 GB → SD 2.x (small) - Auto-download model weights to HuggingFace disk cache at startup (background task; first request loads from local disk, not internet) - LRU pipeline cache evicts oldest GPU pipeline when VRAM limit reached - Per-operation model overrides via HF_MODEL_INPAINT / HF_MODEL_TXT2IMG etc. - Optional HF_TOKEN for gated/private HuggingFace models New files: - backend/app/services/gpu_detect.py — GPU detection + tier/model mapping - backend/app/services/local_diffusion.py — Diffusers provider + LRU cache - backend/app/routers/gpu_status.py — GET /api/gpu/status, POST /api/gpu/prefetch - backend/requirements.gpu.txt — Diffusers ecosystem deps (GPU only) - docker-compose.gpu.yml — NVIDIA GPU compose (one-command startup) - Dockerfile.gpu — pytorch/pytorch:2.1.0-cuda12.1 base image - scripts/gpu_setup.py — Startup GPU info logger Modified: - backend/app/config.py — local_gpu settings added - backend/app/services/remote_provider.py — local_gpu registered as provider - backend/app/routers/ai_tools.py — /api/config exposes GPU tier + caps - backend/app/main.py — GPU router + background prefetch task - backend/entrypoint.sh — runs gpu_setup.py at container start - .env.example — local_gpu documented as first option Quick start with GPU: docker compose -f docker-compose.gpu.yml up --build https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM	2026-06-13 15:08:41 +00:00