Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import DiffusionModel, DiffusionImageProcessor, AutoTokenizer
+from threading import Thread
+
+print("Starting to load the model to memory")
+# Load the diffusion model and image processor
+model = DiffusionModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+processor = DiffusionImageProcessor.from_model(model)
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+
+print("Successfully loaded the model to memory")
+
+def generate_image(text):
+    # Generate an image from the given text prompt
+    inputs = tokenizer(text, return_tensors="pt")
+    # Run generation on GPU if available
+    inputs.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+
+    # Run diffusion model for image generation
+    with torch.no_grad():
+        result = processor.generate(**inputs)
+
+    # Return the generated image
+    return result[0]
+
+# Define a function to handle user input and generate images
+def image_generator(text):
+    generated_image = generate_image(text)
+    return generated_image
+
+# Create a Gradio interface for the image generation
+interface = gr.Interface(
+    fn=image_generator,
+    inputs="text",
+    outputs="image",
+    title="Image Generation from Text",
+    description="Enter a text prompt to generate an image.",
+    examples=["a cat sitting on a couch"]
+)
+
+# Launch the interface
+interface.launch(share=True)