Files
PaintPlus/backend/app/main.py
T
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

149 lines
4.8 KiB
Python

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, HTMLResponse
from contextlib import asynccontextmanager
from pathlib import Path
import asyncio
import os
from app.config import settings
from app.database import init_db
from app.routers import projects, edits, images, patches, generate, tools, ai_tools, print_tools
from app.routers import gpu_status
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Initialize database on startup; auto-install Real-ESRGAN NCNN in background."""
init_db()
# Kick off NCNN install in background if no AI upscaler detected
from app.services.upscale import probe_upscale_capabilities, ensure_ncnn_installed
caps = probe_upscale_capabilities()
if not caps["realesrgan_pytorch"] and not caps["realesrgan_ncnn"]:
asyncio.create_task(ensure_ncnn_installed())
# Pre-download SAM model in background so first click is fast
from app.services.sam_service import ensure_sam_installed
asyncio.create_task(ensure_sam_installed())
# If local GPU provider is active, log GPU info at startup
if settings.ai_provider.lower() == "local_gpu" or any(
v.lower() == "local_gpu"
for v in [
settings.ai_provider_inpaint,
settings.ai_provider_txt2img,
settings.ai_provider_img2img,
settings.ai_provider_outpaint,
]
if v
):
from app.services.gpu_detect import get_cached_gpu_info
info = get_cached_gpu_info()
print(
f"[gpu] {info.device_name} | {info.vram_gb:.1f} GB | tier={info.tier} | "
f"backend={info.backend}"
)
if settings.auto_download_models:
# Download model weight files to disk cache in background so first
# user request loads from local disk instead of the internet.
from app.services.local_diffusion import prefetch_model_files
asyncio.create_task(prefetch_model_files())
yield
app = FastAPI(
title="AI Photo Edit API",
description="API for AI-powered photo editing with mask-scoped regeneration",
version="1.0.0",
lifespan=lifespan
)
# Configure CORS
app.add_middleware(
CORSMiddleware,
allow_origins=settings.cors_origins_list,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Include routers
app.include_router(projects.router)
app.include_router(edits.router)
app.include_router(images.router)
app.include_router(patches.router)
app.include_router(generate.router)
app.include_router(tools.router)
app.include_router(ai_tools.router)
app.include_router(print_tools.router)
app.include_router(gpu_status.router)
@app.get("/api")
def api_root():
"""API info endpoint"""
return {
"name": "AI Photo Edit API",
"version": "1.0.0",
"status": "running"
}
@app.get("/health")
def health():
"""Health check endpoint"""
return {"status": "healthy"}
# Static files directory
STATIC_DIR = Path("/app/static")
# Serve static assets - mount subdirectories if they exist
if STATIC_DIR.exists():
# React-style assets folder
if (STATIC_DIR / "assets").exists():
app.mount("/assets", StaticFiles(directory=STATIC_DIR / "assets"), name="assets")
# miniPaint dist folder (webpack bundle)
if (STATIC_DIR / "dist").exists():
app.mount("/dist", StaticFiles(directory=STATIC_DIR / "dist"), name="dist")
# miniPaint images folder
if (STATIC_DIR / "images").exists():
app.mount("/images", StaticFiles(directory=STATIC_DIR / "images"), name="images")
# miniPaint CSS folder
if (STATIC_DIR / "src").exists():
app.mount("/src", StaticFiles(directory=STATIC_DIR / "src"), name="src")
@app.get("/", response_class=HTMLResponse)
async def serve_spa():
"""Serve miniPaint index.html"""
index_path = STATIC_DIR / "index.html"
if index_path.exists():
return FileResponse(index_path)
return HTMLResponse("<h1>Frontend not built. Run npm build in frontend/</h1>")
@app.get("/{full_path:path}")
async def serve_spa_routes(request: Request, full_path: str):
"""
Catch-all route for serving static files.
Serves static files if they exist, otherwise returns index.html.
"""
# Don't catch API routes
if full_path.startswith(("projects", "edits", "patches", "tools", "generate", "health", "docs", "openapi.json", "api")):
return {"detail": "Not Found"}
# Check if it's a static file
static_file = STATIC_DIR / full_path
if static_file.exists() and static_file.is_file():
return FileResponse(static_file)
# Otherwise serve index.html
index_path = STATIC_DIR / "index.html"
if index_path.exists():
return FileResponse(index_path)
return HTMLResponse("<h1>Frontend not built</h1>", status_code=404)