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

```

Start in train_dreambooth_lora_sdxl.py by tracing collate_fn, the prompt embedding construction, and the repeat calls under prior preservation. Compare the batch image order with the resulting embedding order and verify whether the model inputs remain aligned. Done means the ordering question is resolved and any required correction is covered by an appropriate test or reproducible check.

Scritto dal modello di indicizzazione a partire dal testo della issue.