Why use torch.repeat instead of torch.repeat_interleave in train_dreambooth_lora_sdxl

### Describe the bug

### in `train_dreambooth_lora_sdxl.py`
### you can see those codes:
```python
if not args.train_text_encoder:
unet_added_conditions = {
"time_ids": add_time_ids,
"text_embeds": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),
}
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
inp_noisy_latents if args.do_edm_style_training else noisy_model_input,
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
return_dict=False,
)[0]
else:
unet_added_conditions = {"time_ids": add_time_ids}
prompt_embeds, pooled_prompt_embeds = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=None,
prompt=None,
text_input_ids_list=[tokens_one, tokens_two],
)
unet_added_conditions.update(
{"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}
)
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
inp_noisy_latents if args.do_edm_style_training else noisy_model_input,
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
return_dict=False,
)[0]
```
### it means we should repeat prompt_embeds many time for every picture.
### but in collect_fn:

```python
def collate_fn(examples, with_prior_preservation=False):
pixel_values = [example["instance_images"] for example in examples]
prompts = [example["instance_prompt"] for example in examples]
original_sizes = [example["original_size"] for example in examples]
crop_top_lefts = [example["crop_top_left"] for example in examples]

# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
pixel_values += [example["class_images"] for example in examples]
prompts += [example["class_prompt"] for example in examples]
original_sizes += [example["original_size"] for example in examples]
crop_top_lefts += [example["crop_top_left"] for example in examples]

pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()

batch = {
"pixel_values": pixel_values,
"prompts": prompts,
"original_sizes": original_sizes,
"crop_top_lefts": crop_top_lefts,
}
return batch

```
### you can see class_images are directly append to the batch.
### but when we have no train_dataset.custom_instance_prompts provided, the prompt_embeds like:
```python
if not train_dataset.custom_instance_prompts:
if not args.train_text_encoder:
prompt_embeds = instance_prompt_hidden_states
unet_add_text_embeds = instance_pooled_prompt_embeds
if args.with_prior_preservation:
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
# if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
# batch prompts on all training steps
else:
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)
tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)
if args.with_prior_preservation:
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)
class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
```
---
### they seems to be[ins_token, cls_token] or [ins_embed, cls_embed]
### so back to the code like
### `prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)`
### are they wrong? because if you use `repeat` , the embed will like: prompt_embeds_input = [ins_embed, cls_embed, ins_embed, cls_embed, ....]
### but i think it should be [ins_embed, ins_embed, ....cls_embed, cls_embed]

### Reproduction

None, just the question.

### Logs

```shell

```

Beginne in train_dreambooth_lora_sdxl.py, indem du collate_fn, die Erstellung der Prompt-Embeddings und die repeat-Aufrufe unter prior preservation nachverfolgst. Vergleiche die Reihenfolge der Batch-Bilder mit der daraus resultierenden Reihenfolge der Embeddings und überprüfe, ob die Modelleingaben weiterhin ausgerichtet sind. Erledigt ist die Aufgabe, wenn die Frage der Reihenfolge geklärt ist und jede erforderliche Korrektur durch einen geeigneten Test oder eine reproduzierbare Überprüfung abgedeckt ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.