app.py

import streamlit as st
from pathlib import Path
import torch
import pandas as pd
import numpy as np
from diffusers import StableDiffusionPipeline
from transformers import pipeline, set_seed
import matplotlib.pyplot as plt
import cv2

class CFG:
    device = "cuda"
    seed = 42
    generator = torch.Generator(device).manual_seed(seed)
    image_gen_steps = 35
    image_gen_model_id = "stabilityai/stable-diffusion-2"
    image_gen_size = (400, 400)
    image_gen_guidance_scale = 9
    prompt_gen_model_id = "gpt2"
    prompt_dataset_size = 6
    prompt_max_length = 12

# Initialize the Stable Diffusion model
image_gen_model = StableDiffusionPipeline.from_pretrained(
    CFG.image_gen_model_id, torch_dtype=torch.float16,
    revision="fp16", use_auth_token='YOUR_HF_TOKEN', guidance_scale=9
)
image_gen_model = image_gen_model.to(CFG.device)

def generate_image(prompt, model):
    image = model(
        prompt, num_inference_steps=CFG.image_gen_steps,
        generator=CFG.generator,
        guidance_scale=CFG.image_gen_guidance_scale
    ).images[0]

    image = image.resize(CFG.image_gen_size)
    return image
 st.title("Text to Image Generator")

user_input = st.text_input("Enter text to generate image:")

    if st.button("Generate Image"):
        if user_input:
            with st.spinner('Generating image...'):
                generated_image = generate_image(user_input, image_gen_model)
                st.image(generated_image, caption='Generated Image')
        else:
            st.warning("Please enter some text to generate an image.")