Create app.py

app.py

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+# Import necessary libraries
+from diffusers import StableDiffusionPipeline  # Import StableDiffusionPipeline for image generation
+import torch  # Import PyTorch for deep learning operations
+import gradio as gr  # Import Gradio for creating a web interface
+
+# Define configuration parameters
+class CFG:
+    image_gen_steps = 35  # Number of steps for image generation
+    image_gen_model_id = "stabilityai/stable-diffusion-2"  # ID of the StableDiffusion model
+    image_gen_size = (400, 400)  # Size of the generated image
+    image_gen_guidance_scale = 9  # Guidance scale for image generation
+
+# Load the StableDiffusion model
+image_gen_model = StableDiffusionPipeline.from_pretrained(
+    CFG.image_gen_model_id,     
+    revision="fp16", 
+    guidance_scale=9
+)
+
+# Define a function for image generation
+def generate_image(prompt):
+    # Generate an image from a text prompt using the loaded model
+    image = image_gen_model(
+        prompt, 
+        num_inference_steps=CFG.image_gen_steps,
+        guidance_scale=CFG.image_gen_guidance_scale
+    ).images[0]
+
+    # Resize the generated image to the specified size
+    image = image.resize(CFG.image_gen_size)
+    return image  # Return the generated image as the result
+
+# Define a Gradio interface
+iface = gr.Interface(
+    fn=generate_image,  # Use the generate_image function for processing input
+    inputs="text",  # Accept text input from the user
+    outputs="image",  # Display the generated image as output
+    title="StableDiffusion Image Generation",  # Set the title of the web interface
+    description="Generate images from text prompts using StableDiffusion model.",  # Provide a description
+    live=False  # Set to False if you don't want real-time updates (for beginner-friendly interaction)
+)
+
+# Start the Gradio interface
+iface.launch(debug=True)