5 Commits

Include renames

Author	SHA1	Message	Date
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
Claude	53225e9e05	Improve SAM selection with mask contour and layer operations ... - SAM selection now shows actual mask contour instead of bounding box - Added marching ants animation on the actual mask edge - Added keyboard shortcuts: - Ctrl+C: Copy selection to new layer - Ctrl+X: Cut selection to new layer (removes from original) - Delete: Delete selected area - Escape: Clear selection - Removed eye catalog download from startup (was failing with 429)	2026-01-27 00:00:16 +00:00
Claude	df4ddc2d8c	Automate SAM download and fix database path issues ... - Fix database path mismatch: download_sample_eyes.py now uses ai_photo_edit.db instead of photoedit.db - Add init_database.py script to initialize DB before eye import - Add AUTO_DOWNLOAD_SAM=true environment variable (default: enabled) - Update entrypoint.sh to: 1. Initialize database first 2. Auto-download SAM model (~375MB) on first startup 3. Then import eyes (now works since DB exists) - Update path detection to work in both Docker and local environments	2026-01-25 20:28:57 +00:00
Claude	8b96c86a94	Add local SAM model support for offline Smart Select ... - Add torch, torchvision, segment-anything to requirements - Create download_sam_model.py script to fetch SAM checkpoint - Update tools.py to use local SAM with Replicate API fallback - Add SAM model check to entrypoint.sh with helpful instructions - Model persists in /app/data/models via Docker volume mount	2026-01-25 18:15:07 +00:00
Claude	28e842e190	Add auto-populate eyes on startup and comprehensive .env docs ... - Create entrypoint.sh that auto-downloads sample eyes on first run - Update Dockerfile to use entrypoint script - Rewrite .env.example with step-by-step setup instructions - Add detailed troubleshooting section - Clarify which models work for inpainting vs text-to-image	2026-01-25 18:06:09 +00:00