Multi-objects' masks from SAM predictor

Thanks for the wonderful work. I have a question, apologies if I have missed the information.

As I understand, SAM is class-agnostic. Is it correct to assume that SAM does not support multi-object segmentation, but rather binary segmentation and the "different" masks generated by the SAM predictor for different objects (I am not talking about the multi-masks for confidence scores) in an image are essentially generated sequentially? In other words, does the SAM predictor loop over the different objects' prompts (such as point prompts, bbox prompts, etc.), treating each one as a separate entity, and generate a binary mask (foreground vs. background) for each of these objects individually?