Model and input data type is not same

**Is your feature request related to a problem? Please describe.**
Hi, when I trained sdv1.5 model with fp16 mode by using the `examples/text_to_image/train_text_to_image.py` file, I found there is a mismatch between unet model and input data. Specificaly, In this [line](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py#L993) , the `unet` model has float32 dtype, but the `noisy_latents` has the float16 dtype. Although it will not raise an error in cuda , I use my custom device it will raise an error, I wonder how can I change this code to use float16.

**Describe the solution you'd like.**
To avoid get a wrong model, I would like you give a right code to match model and input.

**Describe alternatives you've considered.**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context.**
Add any other context or screenshots about the feature request here.

Read examples/text_to_image/train_text_to_image.py around line 993, focusing on the dtype of the unet model and noisy_latents in fp16 mode. Reproduce the mismatch on the reported custom device and verify that the training path passes matching dtypes without breaking the existing CUDA behavior.

索引モデルが issue の本文から書いたものです。