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Replaces the stub StepGroundTruth with a full side-by-side Original vs Reconstruction view. Adds VLM-based image region detection (qwen2.5vl), mflux image generation proxy, sync scroll/zoom, manual region drawing, and score/notes persistence. New backend endpoints: detect-images, generate-image, validate, get validation. New standalone mflux-service (scripts/mflux-service.py) for Metal GPU generation. Dockerfile.base: adds fonts-liberation (Apache-2.0). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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mflux-service — Standalone FastAPI wrapper for mflux image generation.
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Runs NATIVELY on Mac Mini (requires Metal GPU, not Docker).
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Generates images using Flux Schnell via the mflux library.
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Setup:
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python3 -m venv ~/mflux-env
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source ~/mflux-env/bin/activate
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pip install mflux fastapi uvicorn
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Run:
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source ~/mflux-env/bin/activate
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python scripts/mflux-service.py
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Or as a background service:
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nohup ~/mflux-env/bin/python scripts/mflux-service.py > /tmp/mflux-service.log 2>&1 &
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License: Apache-2.0
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"""
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import base64
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import io
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import logging
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import os
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import time
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from typing import Optional
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import uvicorn
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from fastapi import FastAPI
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from pydantic import BaseModel
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
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logger = logging.getLogger("mflux-service")
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app = FastAPI(title="mflux Image Generation Service", version="1.0.0")
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# Lazy-loaded generator
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_flux = None
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def _get_flux():
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"""Lazy-load the Flux model on first use."""
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global _flux
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if _flux is None:
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logger.info("Loading Flux Schnell model (first call, may download ~12 GB)...")
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from mflux import Flux1
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_flux = Flux1(
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model_name="schnell",
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quantize=8,
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)
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logger.info("Flux Schnell model loaded.")
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return _flux
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class GenerateRequest(BaseModel):
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prompt: str
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width: int = 512
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height: int = 512
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steps: int = 4
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seed: Optional[int] = None
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class GenerateResponse(BaseModel):
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image_b64: Optional[str] = None
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success: bool = True
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error: Optional[str] = None
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duration_ms: int = 0
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@app.get("/health")
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async def health():
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return {"status": "ok", "model": "flux-schnell", "gpu": "metal"}
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@app.post("/generate", response_model=GenerateResponse)
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async def generate_image(req: GenerateRequest):
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"""Generate an image from a text prompt using Flux Schnell."""
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t0 = time.time()
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# Validate dimensions (must be multiples of 64 for Flux)
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width = max(256, min(1024, (req.width // 64) * 64))
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height = max(256, min(1024, (req.height // 64) * 64))
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try:
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from mflux import Config
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flux = _get_flux()
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image = flux.generate_image(
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seed=req.seed or int(time.time()) % 2**31,
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prompt=req.prompt,
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config=Config(
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num_inference_steps=req.steps,
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height=height,
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width=width,
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),
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)
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# Convert PIL image to base64
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buf = io.BytesIO()
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image.save(buf, format="PNG")
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buf.seek(0)
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img_b64 = "data:image/png;base64," + base64.b64encode(buf.read()).decode("utf-8")
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duration_ms = int((time.time() - t0) * 1000)
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logger.info(f"Generated {width}x{height} image in {duration_ms}ms: {req.prompt[:60]}...")
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return GenerateResponse(image_b64=img_b64, success=True, duration_ms=duration_ms)
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except Exception as e:
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duration_ms = int((time.time() - t0) * 1000)
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logger.error(f"Generation failed: {e}")
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return GenerateResponse(image_b64=None, success=False, error=str(e), duration_ms=duration_ms)
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if __name__ == "__main__":
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port = int(os.getenv("MFLUX_PORT", "8095"))
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logger.info(f"Starting mflux-service on port {port}")
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uvicorn.run(app, host="0.0.0.0", port=port)
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