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import streamlit as st
import os
import random
import spaces
import numpy as np
import torch
from typing import Tuple
from datetime import datetime
from diffusers import PixArtAlphaPipeline, LCMScheduler
# Check if CUDA is available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Define Hugging Face API details
API_URL = "https://api-inference.huggingface.co/models/Huzaifa367/chat-summarizer"
API_TOKEN = os.getenv("AUTH_TOKEN")
HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
# Initialize PixArtAlphaPipeline
pipe = PixArtAlphaPipeline.from_pretrained(
"PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
torch_dtype=torch.float16,
use_safetensors=True,
device=device
)
# Function to generate image based on prompt
def generate_image(prompt: str) -> Tuple[str, int]:
seed = random.randint(0, np.iinfo(np.int32).max)
images = pipe(
prompt=prompt,
width=1024,
height=1024,
num_inference_steps=4,
generator=torch.Generator().manual_seed(seed),
num_images_per_prompt=1,
use_resolution_binning=True,
output_type="pil",
).images
# Save image and return path and seed
image_path = save_image(images[0])
return image_path, seed
# Function to save image and return path
def save_image(img):
unique_name = str(uuid.uuid4()) + ".png"
img.save(unique_name)
return unique_name
# Streamlit app
def main():
st.set_page_config(layout="wide")
st.title("Instant Image Generator")
# Prompt input
prompt = st.text_area("Prompt", "Enter your prompt here...")
# Generate button
if st.button("Generate Image"):
if prompt:
# Generate image based on prompt
image_path, seed = generate_image(prompt)
# Display the generated image
st.image(image_path, use_column_width=True, caption=f"Seed: {seed}")
if __name__ == "__main__":
main()
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