Redundant reinitialization of text encoders in train_dreambooth_lora_flux

### Describe the bug

In the `train_dreambooth_lora_flux.py` script, during each call to `log_validation`, the text encoders `text_encoder_one` and `text_encoder_two` are reinitialized. https://github.com/huggingface/diffusers/blob/8ba90aa706a733f45d83508a5b221da3c59fe4cd/examples/dreambooth/train_dreambooth_lora_flux.py#L1768
This occurs even when the text encoders do not need to be trained (`if not args.train_text_encoder`). This unnecessary reinitialization can lead to inefficient use of resources and may cause CUDA out-of-memory errors, especially in scenarios where VRAM is less than 48 GiB.

Since the validation prompt is fixed (only one prompt is used), we can optimize the process by precomputing the text embeddings during the instance prompt preprocessing. This would allow the model to fit within 40 GiB of VRAM, preventing CUDA OOM issues.

### Proposed Fix

To address this issue, add the following code snippet to precompute the validation prompt embeddings only once when the text encoders do not need to be trained and custom instance prompts are not used:

```python
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
instance_prompt_hidden_states, instance_pooled_prompt_embeds, instance_text_ids = compute_text_embeddings(
args.instance_prompt, text_encoders, tokenizers
)

if args.validation_prompt is not None:
validation_prompt_hidden_states, validation_pooled_prompt_embeds, _ = compute_text_embeddings(
args.validation_prompt, text_encoders, tokenizers
)
```

This change will prevent the unnecessary reinitialization of text encoders and reduce the VRAM usage during training.

### Reproduction

``` python
from huggingface_hub import snapshot_download

local_dir = "./dog"
snapshot_download(
"diffusers/dog-example",
local_dir=local_dir, repo_type="dataset",
ignore_patterns=".gitattributes",
)

export MODEL_NAME="black-forest-labs/FLUX.1-dev"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="trained-flux-lora"

accelerate launch train_dreambooth_lora_flux.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--mixed_precision="bf16" \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--learning_rate=1e-5 \
--report_to="wandb" \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=500 \
--validation_prompt="A photo of sks dog in a bucket" \
--validation_epochs=25 \
--seed="0" \
--push_to_hub
```

### Logs

_No response_

### System Info

- 🤗 Diffusers version: 0.31.0.dev0
- Platform: Linux-5.15.0-47-generic-x86_64-with-glibc2.35
- Running on Google Colab?: No
- Python version: 3.10.8
- PyTorch version (GPU?): 2.4.0+cu121 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.24.5
- Transformers version: 4.43.4
- Accelerate version: 0.30.1
- PEFT version: 0.12.0
- Bitsandbytes version: not installed
- Safetensors version: 0.4.3
- xFormers version: not installed
- Accelerator: NVIDIA A40, 49140 MiB
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help?

@sayakpaul

Start in examples/dreambooth/train_dreambooth_lora_flux.py at the log_validation code around line 1768, then trace instance prompt preprocessing and compute_text_embeddings. Precompute the specified embeddings only for the stated conditions and ensure validation no longer reinitializes unused text encoders. Re-run the provided accelerate training command and verify reduced VRAM use without CUDA OOM.

Vom Indexierungsmodell aus dem Issue-Text verfasst.