import gradio as gr
from PIL import Image  

import torch
import re
import os
import requests

from customization import customize_vae_decoder
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel, DDIMScheduler, EulerDiscreteScheduler
from torchvision import transforms
from attribution import MappingNetwork

import math
from typing import List
from PIL import Image, ImageChops
import numpy as np
import torch

with gr.Blocks() as demo:
    gr.Markdown(
"""<div style="transform: translate(0, 50%);">
        <h1 style="text-align: center;"><b>WOUAF:
Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models</b> <br> <a href="https://wouaf.vercel.app">Project Page</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <a href="https://huggingface.co/spaces/wouaf/WOUAF-Text-to-Image">New Demo</a></h1>
        <br>
        <br>
        <br>
        <br>
        <br>
        <br>
        <h1 style="text-align: center;"> With generous support from Intel, we have <a href="https://huggingface.co/spaces/wouaf/WOUAF-Text-to-Image">transferred the demo</a> to a better and faster GPU. </h1>      
        </div>
"""
)


if __name__ == "__main__":
    demo.launch()