Claude
46b9066bba
Handle old/low-VRAM GPUs and document nvidia-container-toolkit requirement
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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
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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