See table below for details on the three checkpoints: Checkpoint Task UNet Model Size Total Model Size Training Data / h audioldm2 Text-to-audio 350M 1.1B 1150k audioldm2-large Text-to-audio 750M 1.5B 1150k audioldm2-music Text-to-music 350M 1.1B 665k  Constructing a prompt Descriptive prompt inputs work best: use adjectives to describe the sound (e.g. “high quality” or “clear”) and make the prompt context specific (e.g. “water stream in a forest” instead of “stream”). It’s best to use general terms like “cat” or “dog” instead of specific names or abstract objects the model may not be familiar with. Using a negative prompt can significantly improve the quality of the generated waveform, by guiding the generation away from terms that correspond to poor quality audio. Try using a negative prompt of “Low quality.”  Controlling inference The quality of the predicted audio sample can be controlled by the num_inference_steps argument; higher steps give higher quality audio at the expense of slower inference. The length of the predicted audio sample can be controlled by varying the audio_length_in_s argument.  Evaluating generated waveforms: The quality of the generated waveforms can vary significantly based on the seed. Try generating with different seeds until you find a satisfactory generation. Multiple waveforms can be generated in one go: set num_waveforms_per_prompt to a value greater than 1. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly. The following example demonstrates how to construct good music generation using the aforementioned tips: example. Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.  AudioLDM2Pipeline  class diffusers.AudioLDM2Pipeline  < source > ( vae: AutoencoderKL text_encoder: ClapModel text_encoder_2: T5EncoderModel projection_model: AudioLDM2ProjectionModel language_model: GPT2Model tokenizer: Union tokenizer_2: Union feature_extractor: ClapFeatureExtractor unet: AudioLDM2UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vocoder: SpeechT5HifiGan  )   Parameters   vae (AutoencoderKL) —