app.py

from typing import Dict, List
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from diffusers import StableDiffusionXLPipeline
import torch
import base64
from io import BytesIO

app = FastAPI()

# Load the model
model_name = "colt12/maxcushion"
pipe = StableDiffusionXLPipeline.from_pretrained(model_name, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

class Item(BaseModel):
    prompt: str
    negative_prompt: str = ""
    num_inference_steps: int = 30
    guidance_scale: float = 7.5

@app.post("/generate")
async def generate(item: Item) -> Dict[str, str]:
    try:
        # Generate the image
        image = pipe(
            prompt=item.prompt,
            negative_prompt=item.negative_prompt,
            num_inference_steps=item.num_inference_steps,
            guidance_scale=item.guidance_scale
        ).images[0]

        # Convert to base64
        buffered = BytesIO()
        image.save(buffered, format="PNG")
        image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")

        return {"image": image_base64}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    return {"message": "SDXL Image Generation API"}