58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import os
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import base64
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from io import BytesIO
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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import torch
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from diffusers import FluxPipeline
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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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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print("Initializing Flux-Schnell on RTX 5060 Ti (sm_120)...")
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hf_token = os.environ.get("HF_TOKEN")
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-schnell",
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torch_dtype=torch.bfloat16,
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token=hf_token
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)
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# Offload execution stages sequentially to maintain 16GB headroom
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pipe.enable_sequential_cpu_offload()
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pipe.vae.enable_tiling()
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class ImageRequest(BaseModel):
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prompt: str
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model: str = "flux-schnell"
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@app.post("/v1/images/generations")
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async def generate(request: ImageRequest):
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try:
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result = pipe(
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request.prompt,
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guidance_scale=0.0,
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num_inference_steps=4,
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max_sequence_length=256,
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).images[0]
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buffered = BytesIO()
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result.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return {"data": [{"b64_json": img_str}]}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=5005)
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