app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from diffusers import FluxPipeline
import torch
from io import BytesIO
from fastapi.responses import StreamingResponse

app = FastAPI()

class Prompt(BaseModel):
    text: str

# Load the FLUX model
model_id = "black-forest-labs/FLUX.1-schnell"
pipe = FluxPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

@app.post("/generate-image/")
async def generate_image(prompt: Prompt):
    try:
        # Generate the image
        image = pipe(
            prompt.text,
            guidance_scale=0.0,
            num_inference_steps=4,
            max_sequence_length=256,
            generator=torch.Generator("cpu").manual_seed(0)
        ).images[0]

        # Save image to a BytesIO object
        img_byte_arr = BytesIO()
        image.save(img_byte_arr, format='PNG')
        img_byte_arr.seek(0)

        return StreamingResponse(img_byte_arr, media_type="image/png")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))