app.py

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

# Define the function to generate an image from text using the Liberata/illustrious-xl-v1.0 model
def generate_image(prompt, guidance_scale, num_inference_steps):
    # Load the model from the specified file
    model = diffusers.StableDiffusionXLPipeline.from_pretrained(
        "Liberata/illustrious-xl-v1.0",
        torch_dtype=torch.float16
    )
    # 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:
    # 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")
    # 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")
    
    # 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>
    """)
    
    # 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)

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