dioLDM 2 is unique in the sense that it takes two cross-attention embeddings, as opposed to one cross-attention conditioning, as in most other LDMs. The abstract of the paper is the following: Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from th