Fix DreamBooth LoRA fp16 training crash after validation

[Ricardo-M-L]

What does this PR do?

Fixes a regression that aborts `examples/dreambooth/train_dreambooth_lora.py` on the first training step after the first validation, whenever the user combines `--mixed_precision=fp16` and `--validation_prompt`:

```
ValueError: Attempting to unscale FP16 gradients.
```

Root cause

1. LoRA trainable params are upcast to fp32 once, before the training loop, via `cast_training_params(models, dtype=torch.float32)` (the pre-existing mitigation from [Training] fix training resuming problem when using FP16 (SDXL LoRA DreamBooth) #6514 / [training] fix training resuming problem for fp16 (SD LoRA DreamBooth) #6554).
2. Validation builds a pipeline with `unet=unwrap_model(unet)` (i.e. the same module object) and `torch_dtype=weight_dtype`.
3. `log_validation` then does `pipeline = pipeline.to(accelerator.device, dtype=torch_dtype)` (line 150 of the script), which casts every component in-place — including the shared `unet` — back down to fp16. The LoRA adapter weights are nested inside that module and get cast along with the rest.
4. On the next training step the backward pass produces fp16 grads, which `accelerate`'s `GradScaler.unscale_` refuses to process.

Fix

Mirror the pre-training upcast immediately after `log_validation` returns: if `args.mixed_precision == "fp16"`, re-run `cast_training_params(..., dtype=torch.float32)` on the trainable modules. bf16 mixed-precision is unaffected because no grad scaler is involved there.

Only one file is touched; 10 lines added, 0 removed. No behavioral change when `--validation_prompt` is not set, or when mixed-precision is anything other than fp16.

Fixes #13124

Before submitting

• Did you read the contributor guideline?
• This PR fixes a bug (linked above).
• Minimal, targeted change — no refactor.

Who can review?

cc @sayakpaul