Update app.py

app.py

@@ -2,6 +2,16 @@ from huggingface_hub import from_pretrained_keras
 import keras_cv
 import gradio as gr
 
+
+## load model
+resolution = 512
+dreambooth_model = keras_cv.models.StableDiffusion(
+        img_width=resolution, img_height=resolution, jit_compile=True, 
+    )
+loaded_diffusion_model = from_pretrained_keras("shivi/dreambooth_diffusion_model")
+dreambooth_model._diffusion_model = loaded_diffusion_model
+
+
 def generate_images(prompt: str, num_imgs_to_gen: int):
     """
     This function is used to generate images using our fine-tuned keras dreambooth stable diffusion model.
@@ -11,13 +21,6 @@ def generate_images(prompt: str, num_imgs_to_gen: int):
     Returns:
         all_images (List): List of images that were generated using the model
     """
-    resolution = 512
-    dreambooth_model = keras_cv.models.StableDiffusion(
-        img_width=resolution, img_height=resolution, jit_compile=True, 
-    )
-    loaded_diffusion_model = from_pretrained_keras("shivi/dreambooth_diffusion_model")
-    dreambooth_model._diffusion_model = loaded_diffusion_model
-    
     generated_img = dreambooth_model.text_to_image(
         prompt, batch_size=num_imgs_to_gen
     )
@@ -46,6 +49,6 @@ with gr.Blocks() as demo:
                 [prompt,samples], gallery, generate_images, cache_examples=False)
     
     gr.Markdown("Some fun examples I created while playing with the model:")
-    gr.Makdown("![Example 1](sample_outputs/1.png)")
+    gr.Markdown("![Example 1](sample_outputs/1.png)")
 
-demo.launch(debug=True)
+demo.launch(debug=True)