morj
/

renaissance

@@ -263,12 +263,12 @@ my_base_model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
 ```
 ### 4. Load Weights from the h5 model which is hosted on Hugging Face:
 ```python
-my_base_model.diffusion_model.load_weights(path/to/file/renaissance_model.h5)
 ```
 ### 5. Create a variable to hold the values of the to-be-generated image such as prompt, batch size, iterations, and seed
 ```python
 img = my_base_model.text_to_image(
-       prompt=A woman with an enigmatic smile against a dark background,
        batch_size=1,  # How many images to generate at once
        num_steps=25,  # Number of iterations (controls image quality)
        seed=123,  # Set this to always get the same image from the same prompt
@@ -279,7 +279,7 @@ img = my_base_model.text_to_image(
 def plot_images(images):
     plt.figure(figsize=(5, 5))
     plt.imshow(images)
-    plt.axis(&#34;off&#34;)
 plot_images(img)
 ```

 ```
 ### 4. Load Weights from the h5 model which is hosted on Hugging Face:
 ```python
+my_base_model.diffusion_model.load_weights('path/to/file/renaissance_model.h5')
 ```
 ### 5. Create a variable to hold the values of the to-be-generated image such as prompt, batch size, iterations, and seed
 ```python
 img = my_base_model.text_to_image(
+       prompt='A woman with an enigmatic smile against a dark background',
        batch_size=1,  # How many images to generate at once
        num_steps=25,  # Number of iterations (controls image quality)
        seed=123,  # Set this to always get the same image from the same prompt
 def plot_images(images):
     plt.figure(figsize=(5, 5))
     plt.imshow(images)
+    plt.axis('off')
 plot_images(img)
 ```