[🌟 New Model] ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation

### Model/Pipeline/Scheduler description

ConsistencyTTA, introduced in the paper [_Accelerating Diffusion-Based Text-to-Audio Generation
with Consistency Distillation_](https://arxiv.org/abs/2309.10740), is an efficient text-to-audio generation model. Compared to a comparable diffusion-based TTA model, ConsistencyTTA achieves a 400x generation speed-up, while retaining the generation quality and diversity.

Due to its high generation quality and fast inference, we believe integrating this model into `diffusers` will make `diffusers` more appealing to text-to-audio generation researchers and users! Thank you very much.

### Open source status

- [X] The model implementation is available.
- [X] The model weights are available (Only relevant if addition is not a scheduler).

### Provide useful links for the implementation

The open-source code implementation can be found at https://github.com/Bai-YT/ConsistencyTTA.

There is also a simplified implementation for inference only: https://github.com/Bai-YT/ConsistencyTTA/tree/main/easy_inference.

The model checkpoints can be found at https://huggingface.co/Bai-YT/ConsistencyTTA.

I am the main author of the code, and am more than happy to assist the integration.

Empieza leyendo la implementación de ConsistencyTTA y su versión easy_inference en el repositorio upstream enlazado y, después, inspecciona los checkpoints disponibles en Hugging Face. Compara el flujo de inferencia del modelo con las integraciones existentes de generación de audio de diffusers. Se considera completado cuando ConsistencyTTA esté integrado en diffusers con su implementación del modelo y los checkpoints se puedan utilizar mediante las APIs del proyecto.

Escrito por el modelo de indexación a partir del texto del issue.