4 Commits
Author SHA1 Message Date
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
Claude d01c11f948 Add per-operation AI provider routing
Each operation (inpaint, txt2img, img2img, outpaint) can now use a different
provider. Resolution order: per-op override → global AI_PROVIDER default.

Example: txt2img→openai, inpaint→invokeai, everything else→invokeai default.

Backend:
- config.py: add AI_PROVIDER_INPAINT / TXT2IMG / IMG2IMG / OUTPAINT settings
- remote_provider.py: get_remote_provider(operation) resolves override then default;
  _build_provider() extracted as shared factory; _OP_FIELD maps op→setting name
- ai_tools.py: each endpoint passes its operation to _require_remote();
  GET /api/config runs per-op health checks concurrently, returns operations map
  and overrides; POST /api/config accepts and applies per-op override fields

Frontend:
- ai_provider_settings.js: four new selects (inpaint/txt2img/img2img/outpaint);
  persists to localStorage and sends per-op fields to POST /api/config
- provider-badge.js: shows override summary (e.g. "invokeai · txt2img→openai")
  and per-op health in tooltip
- .env.example: document per-op override env vars with examples

https://claude.ai/code/session_01B58MaJCU1R6KwBDJCp8AfN
2026-06-09 18:14:12 +00:00
Claude 27261c4ef4 Add LaMa magic eraser, remote provider abstraction, and AI tool infrastructure
Backend:
- requirements.txt: add simple-lama-inpainting, rembg[gpu]; upgrade opencv to 4.10+
- app/config.py: add InvokeAI (url, model) and ComfyUI (url, model) settings; OPENAI_MODEL
- app/services/local_inpaint.py: LaMa, OpenCV, rembg wrappers (auto GPU/CPU)
- app/services/remote_provider.py: abstract RemoteAIProvider + OpenAI, InvokeAI, ComfyUI drivers
- app/routers/ai_tools.py: new /api/* endpoints — /erase, /inpaint/lama, /inpaint/fast,
  /background/remove, /inpaint/remote, /generate/txt2img, /generate/img2img,
  /generate/outpaint, GET /config (capability flags)
- app/main.py: register ai_tools router

Frontend:
- services/api.js: add erase(), textToImage(), imageToImage(), remoteInpaint(), getConfig()
- api/capabilities.js: lazy-fetch /api/config singleton; hasRemote() helper
- tools/ai_lama_erase.js: brush-paint mask → LaMa erase → apply to layer
- tools/ai_smart_inpaint.js: brush mask + dialog (Fast/Quality mode + prompt) → inpaint
- core/components/provider-badge.js: shows active provider + health in toolbar
- config.js: register ai_lama_erase and ai_smart_inpaint tools
- main.js: mount provider badge on load
- .env.example: document InvokeAI, ComfyUI, OpenAI provider settings

https://claude.ai/code/session_01B58MaJCU1R6KwBDJCp8AfN
2026-06-09 17:42:48 +00:00