app.py

'''
sudo apt-get update && sudo apt-get install git-lfs ffmpeg cbm
# Clone this repository
git clone https://huggingface.co/spaces/svjack/illustrious-xl-v1.0-demo
# Go into the repository
cd illustrious-xl-v1.0-demo
### Install dependencies ###
conda create --name py310 python=3.10
conda activate py310
# Install ipykernel and add the environment to Jupyter
pip install ipykernel
python -m ipykernel install --user --name py310 --display-name "py310"
pip install -r requirements.txt
python app.py
'''

# Import necessary libraries
import gradio as gr
import diffusers
import torch

# Automatically detect if CUDA is available and set the device accordingly
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the model from the specified file outside the function to ensure better performance
model = diffusers.StableDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/Liberata/illustrious-xl-v1.0/blob/main/Illustrious-XL-v1.0.safetensors",
    torch_dtype=torch.float16
).to(device)  # Move the model to the detected device

# Define the function to generate an image from text using the pre-loaded model
def generate_image(prompt, guidance_scale, num_inference_steps):
    # Generate the image from the text prompt with the specified parameters
    image = model(prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
    return image

# Create a Gradio interface for the text-to-image generation
with gr.Blocks() as demo:
        # Add some documentation using HTML
    gr.HTML("""
    <h2> 😊 Text-to-Image Generation with Liberata/illustrious-xl-v1.0</h2>
    <p>This app allows you to generate images from text prompts using the Liberata/illustrious-xl-v1.0 model.</p>
    <p>Simply enter a text prompt, adjust the parameters, and click the 'Generate Image' button to see the generated image.</p>
    """)
    
    # Create a two-column layout
    with gr.Row():
        with gr.Column():
            # Create a textbox for the user to input the text prompt
            prompt_input = gr.Textbox(label="Text Prompt")
            # Create sliders for the guidance scale and number of inference steps
            guidance_scale_slider = gr.Slider(1, 20, value=7.5, step=0.1, label="Guidance Scale")
            num_inference_steps_slider = gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps")
            # Add a resolution input
            #resolution_input = gr.Textbox(value="1536x1536", label="Resolution")
        with gr.Column():
            # Create an image output to display the generated image
            image_output = gr.Image(label="Generated Image")
    
    # Create a button to trigger the image generation
    generate_button = gr.Button("Generate Image")
    
    # Define the event listener for the button click
    generate_button.click(fn=generate_image, inputs=[prompt_input, guidance_scale_slider, num_inference_steps_slider], outputs=image_output)
    
    # Add examples
    examples = [
        ["1boy, Digital anime-style ALBEDO has light gray, messy hair, blue eyes. Shy look, in white & blue hoodie, at cozy café table with food. Shelf background"],
        ["1boy ,Digital anime-style CHONGYUN has shiny silver hair, green eyes. Cheerful expression, in bright blue T-shirt, at luxurious restaurant with Belgian chocolates. Elegant crystal chandelier background."],
        ["A fox drinking tea under a cherry blossom tree, anime style, 4K resolution"],
        ["Night view of a futuristic city, cyberpunk style, neon lights"],
        ["Medieval knight battling a dragon, epic scene, oil painting texture"]
    ]
    gr.Examples(examples, [prompt_input, guidance_scale_slider, num_inference_steps_slider])

# Launch the Gradio app with share=True
if __name__ == "__main__":
    demo.launch(share=True, show_error=True)