latents. MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, but stay within the domain of the training data. The result is generated music that is more diverse while staying faithful to the corresponding style. The abstract of the paper is the following: Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music. This pipeline was contributed by sanchit-gandhi.  Tips When constructing a prompt, keep in mind: Descriptive prompt inputs work best; use adjectives to describe the sound (for example, “high quality” or “clear”) and make the prompt context specific where possible (e.g. “melodic techno with a fast beat and synths” works better than “techno”). Using a negative prompt can significantly improve the quality of the generated audio. Try using a negative prompt of “low quality, average quality”. During inference: The quality of the generated audio sample can be controlled by the num_inference_steps argument; higher steps give higher quality audio at the expense of slower inference. Multiple waveforms can be generated in one go: set num_waveforms_per_prompt to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly. The length of the generated audio sample can be controlled by varying the audio_length_in_s argument. 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.  MusicLDMPipeline  class diffusers.MusicLDMPipeline  < source > ( vae: AutoencoderKL text_encoder: Union tokenizer: Union feature_extractor: Optional unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vocoder: SpeechT5HifiGan  )   Parameters   vae (AutoencoderKL) —