These are the modifications of to include the possibility of training text2image models with Scheduled Pseudo Huber loss, introduced in https://huggingface.co/papers/2403.16728. (https://github.com/kabachuha/SPHL-for-stable-diffusion)
If you suspect that the part of the training dataset might be corrupted, and you don’t want these outliers to distort the model’s supposed output
If you want to improve the aesthetic quality of pictures by helping the model disentangle concepts and be less influenced by another sorts of pictures.
See https://github.com/huggingface/diffusers/issues/7488 for the detailed description.
The same usage as in the case of the corresponding vanilla Diffusers scripts https://github.com/huggingface/diffusers/tree/main/examples