Create app.py

app.py

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+
+import gradio as gr
+from PIL import Image
+from diffusers import DiffusionPipeline
+
+# Load model and scheduler
+ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
+
+def generate_image(prompt, negative_prompt, width, height):
+    # Run pipeline in inference (sample random noise and denoise)
+    images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6, negative_prompts=[negative_prompt]).images
+    # Resize image to desired width and height
+    resized_images = [image.resize((width, height)) for image in images]
+    # Save images
+    for idx, image in enumerate(resized_images):
+        image.save(f"squirrel-{idx}.png")
+    return "Images generated successfully!"
+
+# Define the interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=["text", "text", "number", "number"],
+    outputs="text",
+    layout="vertical",
+    title="Image Generation",
+    description="Generate images based on prompts.",
+    article="For more information, visit the documentation: [link](https://docs.gradio.app/)",
+)
+
+# Launch the interface
+iface.launch()