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from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download, login
from safetensors.torch import load_file
from io import BytesIO
import os
app = FastAPI()
# Get Hugging Face token from environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
def load_model():
try:
# Login to Hugging Face if token is provided
if HF_TOKEN:
login(token=HF_TOKEN)
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors"
# Load model with explicit error handling
unet = UNet2DConditionModel.from_config(
base,
subfolder="unet"
).to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
pipe = StableDiffusionXLPipeline.from_pretrained(
base,
unet=unet,
torch_dtype=torch.float16,
variant="fp16"
).to("cuda")
# Configure scheduler
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config,
timestep_spacing="trailing"
)
return pipe
except Exception as e:
raise Exception(f"Failed to load model: {str(e)}")
# Load model at startup with error handling
try:
pipe = load_model()
except Exception as e:
print(f"Model initialization failed: {str(e)}")
raise
@app.get("/generate")
async def generate_image(prompt: str):
try:
# Generate image
image = pipe(
prompt,
num_inference_steps=4,
guidance_scale=0
).images[0]
# Save image to buffer
buffer = BytesIO()
image.save(buffer, format="PNG")
buffer.seek(0)
return Response(content=buffer.getvalue(), media_type="image/png")
except Exception as e:
return {"error": str(e)}
@app.get("/health")
async def health_check():
return {"status": "healthy"}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860) |