Reduce to one example, add mixed prec + jit compile

app.py

@@ -1,9 +1,12 @@
 from huggingface_hub import from_pretrained_keras
 from keras_cv import models
 import gradio as gr
+from tensorflow import keras
+
+keras.mixed_precision.set_global_policy("mixed_float16")
 
 sd_dreambooth_model = models.StableDiffusion(
-    img_width=512, img_height=512
+    img_width=512, img_height=512, jit_compile=True
 )
 db_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_dosa")
 sd_dreambooth_model._diffusion_model = db_diffusion_model
@@ -17,7 +20,7 @@ def generate_images(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidanc
         num_steps=num_steps,
         unconditional_guidance_scale=guidance_scale
     )
-    return generated_images 
+    return generated_images
 
 with gr.Blocks() as demo:
     gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Keras Dreambooth - The Humble Dosa</h2>")
@@ -35,10 +38,7 @@ with gr.Blocks() as demo:
     
     run.click(fn=generate_images, inputs=[prompt, negative_prompt, samples, num_steps, guidance_scale], outputs=gallery)
 
-    gr.Examples([["realistic picture of a man eating a bhr dosa", "home", 1, 50, 7.5],
-                 ["realistic picture of a bhr dosa on a plate", "chutney", 1, 50, 7.5],
-                 ["realistic picture of a bhr dosa in a restaurant", "sambar", 1, 50, 7.5],
-                ],
+    gr.Examples([["realistic picture of a bhr dosa in a restaurant", "sambar", 1, 50, 7.5]],
                 [prompt, negative_prompt, samples, num_steps, guidance_scale], gallery, generate_images, cache_examples=True)
     gr.Markdown('Demo created by [Bharat Raghunathan](https://huggingface.co/bharat-raghunathan/)')
 # pass function, input type for prompt, the output for multiple images