Create app.py

app.py

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+# Import necessary libraries
+import gradio as gr
+import transformers_js_py
+
+# 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 = transformers_js_py.StableDiffusionXLPipeline.from_pretrained(
+        "https://huggingface.co/Liberata/illustrious-xl-v1.0/resolve/main/Illustrious-XL-v1.0.safetensors"
+    )
+    # 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)