import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image,DDPMScheduler
from PIL import Image
import numpy as np
import gradio as gr
import gc 


def main(prompt):
   
    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
    adapter_id = "ksyint/teu_lora"

    pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, variant="fp16")
    pipe.scheduler = DDPMScheduler.from_config(pipe.scheduler.config)
    pipe.to("cuda")

    pipe.load_lora_weights(adapter_id)
    pipe.fuse_lora()

    image = pipe(prompt=prompt, num_inference_steps=60, guidance_scale=7.0,strength=5.0).images[0]
    #gc.collect()
    #torch.cuda.empty_cache()
    return image


iface = gr.Interface(fn=main, inputs="text", outputs="image", title="Text to Image Generation", 
             description="Generate images based on textual prompts.")
if __name__ == "__main__":
    
    iface.launch( )
                        


#interface.launch(server_name=“0.0.0.0”, server_port=7860)df