[🌟 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.

まず、リンク先の upstream リポジトリにある ConsistencyTTA の実装とその easy_inference バージョンを読み、次に Hugging Face で利用可能なチェックポイントを調べてください。モデルの推論フローを、diffusers に既存する音声生成の統合と比較してください。ConsistencyTTA がモデル実装とともに diffusers に統合され、プロジェクトの API を通じてチェックポイントを利用できるようになれば完了です。

索引モデルが issue の本文から書いたものです。