non power of 2 sized images with stable cascade fail

### Describe the bug

I am not sure if this is a bug, but the documentation does not mention a limitation to power of 2 that I can find.

Generating a non power of 2 sized image fails.

### Reproduction

```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline

device = "cuda"
num_images_per_prompt = 1

prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", variant='bf16',
torch_dtype=torch.bfloat16)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", variant='bf16',
torch_dtype=torch.float16)

prompt = "Anthropomorphic cat dressed as a pilot"
negative_prompt = ""

prior.enable_model_cpu_offload()
decoder.enable_model_cpu_offload()

prior_output = prior(
prompt=prompt,
height=1024,
width=680,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=20
)

decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images
```

### Logs

```shell
Traceback (most recent call last):
File "REDACT\test.py", line 28, in
decoder_output = decoder(
^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\diffusers\pipelines\stable_cascade\pipeline_stable_cascade.py", line 443, in __call__
predicted_latents = self.decoder(
^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\accelerate\hooks.py", line 166, in new_forward
output = module._old_forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\diffusers\models\unets\unet_stable_cascade.py", line 605, in forward
x = self._up_decode(level_outputs, timestep_ratio_embed, clip)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\diffusers\models\unets\unet_stable_cascade.py", line 553, in _up_decode
x = block(x, skip)
^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\diffusers\models\unets\unet_stable_cascade.py", line 74, in forward
x = self.norm(self.depthwise(x))
^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\conv.py", line 460, in forward
return self._conv_forward(input, self.weight, self.bias)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "REDACT\venv\Lib\site-packages\torch\nn\modules\conv.py", line 456, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Input type (float) and bias type (struct c10::Half) should be the same

Process finished with exit code 1
```

### System Info

diffusers 0.27.2
torch 2.2.2+cu121

### Who can help?

_No response_

Reproduisez l’exemple Stable Cascade avec height=1024 et width=680 en utilisant les versions indiquées. Commencez par src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py et src/diffusers/models/unets/unet_stable_cascade.py, en particulier l’appel à decoder et le chemin _up_decode. Déterminez si l’échec est dû à des dimensions qui ne sont pas des puissances de deux ou au dtype mismatch signalé, puis ajoutez un test de régression ou clarifiez les dimensions prises en charge dans la documentation.

Rédigé par le modèle d'indexation à partir du texte de l'issue.