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* Allow `UNet2DModel` to use arbitrary class embeddings.

We can currently use class conditioning in `UNet2DConditionModel`, but
not in `UNet2DModel`. However, `UNet2DConditionModel` requires text
conditioning too, which is unrelated to other types of conditioning.
This commit makes it possible for `UNet2DModel` to be conditioned on
entities other than timesteps. This is useful for training /
research purposes. We can currently train models to perform
unconditional image generation or text-to-image generation, but it's not
straightforward to train a model to perform class-conditioned image
generation, if text conditioning is not required.

We could potentiall use `UNet2DConditionModel` for class-conditioning
without text embeddings by using down/up blocks without
cross-conditioning. However:
- The mid block currently requires cross attention.
- We are required to provide `encoder_hidden_states` to `forward`.

* Style

* Align class conditioning, add docstring for `num_class_embeds`.

* Copy docstring to versatile_diffusion UNetFlatConditionModel