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5 Commits
Author SHA1 Message Date
Claude a4a9dbc33a Auto-download U2Net model on startup; remove dead PyTorch U2Net module 
U2Net only ever downloaded lazily on the first Remove Background click,
unlike SAM which retries on every container start. If that one attempt
failed (DNS/firewall) the model was never fetched again, surfacing as
"No background removal method available. Install u2net or rembg."

Mirrors the existing SAM auto-download/AUTO_DOWNLOAD_SAM pattern for
U2Net, and documents manual host-side recovery in the README.

Also deletes backend/app/services/u2net_model.py (hand-written
U2NET/U2NETP PyTorch classes) — unused since tools.py switched to
cv2.dnn.readNetFromONNX for background removal.
 2026-06-18 13:10:07 +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 d7683b836a Try Cloudflare DNS first in GPU container; add troubleshooting note 
1.1.1.1 uses a different anycast route than 8.8.8.8 so may reach the
container even when Google's servers don't. Falls back to Google if
Cloudflare is also unreachable. If all fail (Errno -3), port 53 UDP is
blocked at the Docker bridge level — comment has the iptables fix.

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
 2026-06-13 23:35:12 +00:00
Claude 46b9066bba Handle old/low-VRAM GPUs and document nvidia-container-toolkit requirement 
GPU tier table extended:
  ultra   ≥16 GB → SDXL (unchanged)
  high    8-16 GB → SDXL (unchanged)
  medium  4-8 GB → SD 2.x (unchanged)
  legacy  2-4 GB → SD 1.5 (~1.7 GB fp16)  ← new: GTX 970/1060/RX 580 etc.
  minimal <2 GB  → SD 1.5 + sequential CPU offload  ← new: very old/integrated GPUs

gpu_detect.py:
- Detects CUDA compute capability (CC); fp16 disabled for CC < 6.0 (pre-Pascal)
- GpuInfo gains compute_capability and warnings fields
- _make_warnings() emits human-readable warnings for low VRAM and old CC
- model tier fallback updated from 'low' to 'legacy'

local_diffusion.py:
- minimal/legacy tiers use enable_sequential_cpu_offload() + enable_attention_slicing(1)
- target resolution per tier: ultra/high=1024, medium=768, legacy/minimal=512
- .to(device) skipped when sequential CPU offload is active

gpu_status.py:
- Response now includes compute_capability and warnings

docker-compose.gpu.yml:
- Full nvidia-container-toolkit install instructions in header comment
- nvidia-docker2 (legacy) fallback documented as comment block inline
- AMD ROCm swap-in instructions added
- GPU tier table documented in header

scripts/gpu_setup.py:
- Prints compute capability, fp16 status, tier, and model selection at startup
- Prints per-tier warnings (old CC, low VRAM)

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
 2026-06-13 15:19:01 +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