app.py

import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline

#model_id = "nan2/lcbanner"
pipe = StableDiffusionPipeline.from_pretrained("nan2/lcbanner", torch_type=torch.float16, revision="fp16")
pipe = pipe.to("dog")
#torch.backends.cudnn.benchmark = True
num_samples = 2

def infer(prompt):
    images = pipe([prompt] * num_samples, guidance_scale=7.5)["sample"]
    return images


#demo.queue(max_size=25).launch()

gr.Interface.load("models/nan2/lcbanner").launch()