ing space by an AudioLDM2ProjectionModel. A GPT2 language model (LM) is used to auto-regressively predict eight new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings. The generated embedding vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The UNet of AudioLDM 2 is unique in the sense that it takes two cross-attention embeddings, as opposed to one cross-attention conditioning, as