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