pages/textimage.py

import streamlit as st
import torch
import numpy as np
from PIL import Image
import random
import uuid
from diffusers import PixArtAlphaPipeline

# Check for CUDA availability
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# # Load the PixArtAlphaPipeline
# if torch.cuda.is_available():
#     pipe = PixArtAlphaPipeline.from_pretrained(
#         "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
#         torch_dtype=torch.float16,
#         use_safetensors=True,
#     )
#     pipe.to(device)
#     st.write("Model loaded successfully!")
# else:
    # st.error("This demo requires GPU support, which is not available on this system.")

# Load the PixArtAlphaPipeline

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
    torch_dtype=torch.float16,
    use_safetensors=True,
)
pipe.to(device)
st.write("Model loaded successfully!")

# Constants
MAX_SEED = np.iinfo(np.int32).max

# Function to save image and return the path
def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

# Main function for image generation
def generate_image(prompt, style, use_negative_prompt, negative_prompt, seed, width, height, inference_steps):
    generator = torch.Generator().manual_seed(seed)

    # Apply the selected style
    if style == "(No style)":
        prompt_text = prompt
    else:
        prompt_text, _ = apply_style(style, prompt, negative_prompt)

    # Generate the image
    images = pipe(
        prompt=prompt_text,
        negative_prompt=None,
        width=width,
        height=height,
        guidance_scale=0,
        num_inference_steps=inference_steps,
        generator=generator,
        num_images_per_prompt=1,
        use_resolution_binning=True,
        output_type="pil",
    ).images

    # Save the image and display
    if images:
        img_path = save_image(images[0])
        img = Image.open(img_path)
        st.image(img, caption="Generated Image", use_column_width=True)
        st.success("Image generated successfully!")
    else:
        st.error("Failed to generate image. Please try again.")

# Helper function to apply selected style
def apply_style(style_name, positive, negative):
    # Define styles dictionary (similar to your Gradio code)
    styles = {
        "(No style)": (positive, ""),
        "Cinematic": ("cinematic still " + positive, "anime, cartoon, ..."),
        "Realistic": ("Photorealistic " + positive, "drawing, painting, ..."),
        # Add other styles here...
    }
    return styles.get(style_name, styles["(No style)"])

# Streamlit UI
st.title("Instant Image Generator")

prompt = st.text_input("Prompt", "Enter your prompt")

style_names = ["(No style)", "Cinematic", "Realistic"]  # Add other styles here...
style = st.selectbox("Image Style", style_names)

use_negative_prompt = st.checkbox("Use negative prompt")
negative_prompt = st.text_input("Negative prompt", "")

seed = st.slider("Seed", 0, MAX_SEED, 0)
width = st.slider("Width", 256, 4192, 1024, step=32)
height = st.slider("Height", 256, 4192, 1024, step=32)
inference_steps = st.slider("Steps", 4, 20, 4)

if st.button("Generate Image"):
    generate_image(prompt, style, use_negative_prompt, negative_prompt, seed, width, height, inference_steps)