Create app.py

app.py

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+# Import necessary libraries
+import torch
+from diffusers import StableDiffusionXLPipeline
+import gradio as gr
+import time
+
+# Check if we have a GPU (CUDA) or need to use the CPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the Stable Diffusion XL pipeline
+# We use torch_dtype=torch.float16 for faster generation and less memory usage
+print("Loading Stable Diffusion XL pipeline... This may take a few minutes.")
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16,
+    use_safetensors=True
+)
+# Move the pipeline to the chosen device (GPU or CPU)
+pipe = pipe.to(device)
+print("Model loaded successfully!")
+
+# Define the image generation function
+def generate_image(prompt):
+    """
+    This function takes a text prompt and returns a generated image.
+    """
+    # Add a consistent style to all prompts to get a children's book look
+    enhanced_prompt = f"children's book illustration, watercolor style, cute, whimsical, {prompt}"
+
+    # Generate the image with some default parameters
+    # guidance_scale controls how closely the image follows the prompt
+    image = pipe(
+        prompt=enhanced_prompt,
+        guidance_scale=9.5,
+        num_inference_steps=25  # More steps can mean higher quality, but is slower
+    ).images[0]  # We get the first (and only) image from the result
+
+    return image
+
+# Create the Gradio Interface
+# We are creating a simple one-input (prompt) one-output (image) interface
+demo = gr.Interface(
+    fn=generate_image,  # The function to call
+    inputs=gr.Textbox(  # The input is a text box for the prompt
+        label="Enter your scene description",
+        lines=2,
+        placeholder="A brave little mouse exploring a giant forest..."
+    ),
+    outputs=gr.Image(label="Generated Illustration", type="pil"), # The output is an image
+    title="Children's Book Illustrator 🤖🎨",
+    description="Generate beautiful illustrations for your children's story. Enter a description of a scene."
+)
+
+# This is the key part for making the API work correctly with n8n.
+# We launch the Gradio app with a custom `api_name`.
+# Setting `api_name="generate"` creates an API endpoint at `/api/generate/`
+demo.launch(debug=True, server_name="0.0.0.0", api_name="generate")