TF Lite convert error

Hi,
Leaving this here in case someone is also trying to convert to a TF lite model.

From the keras_cv documentation:
```
!pip install --upgrade keras-cv
!pip install --upgrade tensorflow
```
Load the model:

```py
import time
import keras_cv
from tensorflow import keras
import matplotlib.pyplot as plt

model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model.diffusion_model)
tflite_model = converter.convert()
```

It seems a similar error occurs when trying to save the model.
```
model.diffusion_model.save(save_dir)
```

The error in conversion:
```
[/usr/local/lib/python3.7/dist-packages/keras_cv/models/generative/stable_diffusion/__internal__/layers/group_normalization.py](https://localhost:8080/#) in _create_broadcast_shape(self, input_shape)
85
86 def _create_broadcast_shape(self, input_shape):
---> 87 broadcast_shape = [1] * len(input_shape)
88 broadcast_shape[self.axis] = input_shape[self.axis] // self.groups

TypeError: Exception encountered when calling layer 'group_normalization_60' (type GroupNormalization).

len is not well defined for a symbolic Tensor (Shape:0). Please call `x.shape` rather than `len(x)` for shape information.

Call arguments received by layer 'group_normalization_60' (type GroupNormalization):
• args=('tf.Tensor(shape=(None, 64, 64, 320), dtype=float32)',)
• kwargs=
```