Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+
+# Load the pre-trained model
+pipeline = DiffusionPipeline.from_pretrained("stablediffusionapi/juggernaut-xl-v5")
+
+# Load the LORA weights
+pipeline.load_lora_weights("openskyml/dalle-3-xl")
+
+# Define a function to generate images
+def generate_image(text):
+    # Encode the text using the LORA model
+    input_ids = pipeline.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=512,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+
+    # Generate an image using the encoded input
+    image = pipeline.generate(
+        input_ids,
+        attention_mask=input_ids.attention_mask,
+        max_length=512,
+        padding='max_length',
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+
+    # Decode the image
+    image = pipeline.decode(image, skip_special_tokens=True)
+
+    return image
+
+# Create a Gradio interface
+interface = gr.Interface(
+    title='Text-to-Image App',
+    description='Generate images from text using a pre-trained diffusion model',
+    input_widget=gr.TextInput(
+        label='Enter text',
+        placeholder='Type some text here'
+    ),
+    output_widget=gr.ImageOutput(
+        label='Generated image'
+    ),
+    submit_button=gr.Button(
+        label='Generate image'
+    )
+)
+
+# Define a callback function to handle user input
+def handle_input(text):
+    # Generate an image using the generate_image function
+    image = generate_image(text)
+
+    # Display the generated image
+    interface.output_widget.set_image(image)
+
+    return image
+
+# Set up the Gradio interface
+interface.attach_handlers(
+    input_widget=handle_input,
+    submit_button=handle_input
+)
+
+# Run the Gradio interface
+interface.run()