StableDiffusion3 pipeline RuntimeError when using prompt_embeds

### Describe the bug

**StableDiffusion3** pipeline throws a RuntimeError when using `prompt_embeds` in lieu of `prompt` when using `num_images_per_prompt > 1`.

I am attempting to generate images using the StableDiffusion3 pipeline with some precomputed prompt embeddings. The prompt embeddings using the `.encode_prompt(...)` method of the pipeline and are passed to the call of the pipeline. Passing these encoded prompts to the pipeline leads to a Runtime error when:
- `num_images_per_prompt >1` for both the `.encode_prompt(...)` and the `__call__(...)`.
- `num_images_per_prompt=1` for `.encode_prompt(...)` and `num_images_per_prompt >1` for the `__call__(...)`.

The **StableDiffusionXL** pipeline does not have these errors.

### Reproduction

# StableDiffusion3 Failing Cases

## encode_prompt `num_images_per_prompt>1` and call `num_images_per_prompt>1`
The code for this failing case is below:
```python
import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/stable-diffusion-3.5-medium"
pipe = DiffusionPipeline.from_pretrained(
model_name, torch_dtype=torch.float16
).to("cuda")

# encode the prompts
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
prompt="A painting of a cat",
prompt_2=None,
prompt_3=None,
device="cuda",
do_classifier_free_guidance=True,
num_images_per_prompt=2, # NOTE
)

# sample (generate) from the diffusion model.
with torch.inference_mode():
out = pipe(
height=64, # this is set small for speeding up testing
width=64, # this is set small for speeding up testing
num_images_per_prompt=2, # NOTE
num_inference_steps=1,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=torch.Generator(0)
)
```

## encode_prompt `num_images_per_prompt=1` and call `num_images_per_prompt>1`

```python
import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/stable-diffusion-3.5-medium"
pipe = DiffusionPipeline.from_pretrained(
model_name, torch_dtype=torch.float16
).to("cuda")

# encode the prompts
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
prompt="A painting of a cat",
prompt_2=None,
prompt_3=None,
device="cuda",
do_classifier_free_guidance=True,
num_images_per_prompt=1, # NOTE
)

# sample (generate) from the diffusion model.
with torch.inference_mode():
out = pipe(
height=64, # this is set small for speeding up testing
width=64, # this is set small for speeding up testing
num_images_per_prompt=2, # NOTE
num_inference_steps=1,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=torch.Generator(0)
)
```

# Expected Behaviour (StableDiffusionXL pipeline)

## encode_prompt `num_images_per_prompt=1` and call `num_images_per_prompt>1`

```python
import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(
model_name, torch_dtype=torch.float16
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
prompt="A painting of a cat",
device="cuda",
do_classifier_free_guidance=True,
num_images_per_prompt=1,
)

with torch.inference_mode():
out = pipe(
# prompt="Cat",
height=64,
width=64,
num_images_per_prompt=2,
num_inference_steps=1,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=torch.Generator(0)
)
print(len(out.images)) # this returns 2.
```

## encode_prompt `num_images_per_prompt>1` and call `num_images_per_prompt>1`

```python
import torch
from diffusers import DiffusionPipeline

model_name = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(
model_name, torch_dtype=torch.float16
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
prompt="A painting of a cat",
device="cuda",
do_classifier_free_guidance=True,
num_images_per_prompt=2,
)

with torch.inference_mode():
out = pipe(
# prompt="Cat",
height=64,
width=64,
num_images_per_prompt=2,
num_inference_steps=1,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=torch.Generator(0)
)
print(len(out.images)) # this returns 4.
```

### Logs

```shell
Traceback (most recent call last):
File "/home/jajal/research/diffusion-trajectory/sd3.py", line 26, in
out = pipe(
^^^^^
File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/research/diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py", line 1060, in __call__
noise_pred = self.transformer(
^^^^^^^^^^^^^^^^^
File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/research/diffusers/src/diffusers/models/transformers/transformer_sd3.py", line 389, in forward
temb = self.time_text_embed(timestep, pooled_projections)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/mambaforge/envs/diff-traf/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jajal/research/diffusers/src/diffusers/models/embeddings.py", line 1606, in forward
conditioning = timesteps_emb + pooled_projections
~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~
RuntimeError: The size of tensor a (8) must match the size of tensor b (4) at non-singleton dimension 0
```

### System Info

- 🤗 Diffusers version: 0.33.0.dev0
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Running on Google Colab?: No
- Python version: 3.12.8
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.28.1
- Transformers version: 4.48.2
- Accelerate version: 1.3.0
- PEFT version: not installed
- Bitsandbytes version: not installed
- Safetensors version: 0.5.2
- xFormers version: 0.0.29.post2
- Accelerator: NVIDIA GeForce RTX 4070 Ti, 12282 MiB
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no

### Who can help?

@yiyixuxu @sayakpaul