app.py

import os
import torch
import gc
import shutil
import streamlit as st
from PIL import Image
from diffusers import DiffusionPipeline, AutoencoderKL


vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae, torch_dtype=torch.float16, variant="fp16",
    use_safetensors=True
)
pipe.load_lora_weights("./lora-trained-thumbs-up-pw-0.5-steps-800")
pipe.fuse_lora(lora_scale=1.0)
pipe.save_pretrained("temp_model")

del pipe
gc.collect()
torch.cuda.empty_cache()

pipe = DiffusionPipeline.from_pretrained(
  "temp_model",
  vae=vae, torch_dtype=torch.float16, variant="fp16",
  use_safetensors=True
)

pipe.load_lora_weights("./lora-trained-thumbs-up-pw-0.5-steps-800-uttam-pw-0.5-steps-1600")
_ = pipe.to("cuda")

prompt = st.text_area("Enter the prompt!")
image = pipe(prompt=prompt, num_inference_steps=25, num_images_per_prompt=1)[:]

shutil.rmtree("temp_model", ignore_errors=True)

st.image(image)