running out of memory on introduction_to_stablediffusion.ipynb

Thanks for this notebook @rkdan ! This is great!

In `introduction_to_stablediffusion.ipynb` running on Google Colab with an instance of a T4 GPU, I am running out of memory.

https://github.com/acceleratescience/diffusion-models/blob/main/notebooks/introduction_to_stablediffusion.ipynb

My code is here:

https://github.com/neelsoumya/diffusion-models/blob/main/notebooks/introduction_to_stablediffusion.ipynb

I can run until here (where I get an error):

```py
# run both experts
image_base = base(
prompt=prompt,
num_inference_steps=n_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images

image_final = refiner(
prompt=prompt,
num_inference_steps=n_steps,
denoising_start=high_noise_frac,
image=image_base,
).images[0]
```

Error message is

```py
---------------------------------------------------------------------------
OutOfMemoryError Traceback (most recent call last)
in ()
7 ).images
8
----> 9 image_final = refiner(
10 prompt=prompt,
11 num_inference_steps=n_steps,

17 frames
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
547 self.groups,
548 )
--> 549 return F.conv2d(
550 input, weight, bias, self.stride, self.padding, self.dilation, self.groups
551 )

OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 306.12 MiB is free. Process 6006 has 14.44 GiB memory in use. Of the allocated memory 14.17 GiB is allocated by PyTorch, and 145.94 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
```

My code is here:

https://github.com/neelsoumya/diffusion-models/blob/main/notebooks/introduction_to_stablediffusion.ipynb