Let's make `UNet2DConditionModel` take in not only class labels, but also continuous labels

The current UNet2DConditoinModel can take in class labels as described here.

However, in my case and also some other people are using it with continuous labels and in that case there needs to be nn.Linear-like layer in addition to nn.Embedding in about this place

As I mentioned, there seems to be some needs for it, and even if the paper does not address the issue in the way I described, this paper (Continuous Conditional Diffusion Models) is one of the trials to use continuous labels in diffusion models.

To be clearer, I'm trying to generate images based on conditions that are regression-like values, such as heights and widths

Inizia in src/diffusers/models/unets/unet_2d_condition.py, intorno alla riga indicata, esaminando la gestione dei class-label della classe UNet2DConditionModel. Esamina il percorso esistente relativo a nn.Embedding e la documentazione del modello collegata nell’issue prima di determinare l’API necessaria per label continui simili a quelli di una regressione. Il lavoro è completato quando il modello supporta label continui mantenendo il comportamento esistente per i class-label.