Add Conditional Diffusion Distillation

### Model/Pipeline/Scheduler description

Conditional Diffusion Distillation (CoDi) is a new diffusion generation method recently proposed by Google Research and Johns Hopkins University. Accepted by CVPR24, CoDi is based on consistency models and offers a significant advancement in accelerating latent diffusion models. This method enables faster generation in just 1-4 steps.

Key Features:

- Parameter-Efficient Distillation: CoDi is the first method that allows users to accelerate any diffusion model by simply loading a pre-trained acceleration ControlNet.
- No Architectural Changes Required: The process does not require modifications to the diffusion scheduler or model architecture, ensuring seamless integration.
- Enhanced Performance: For example, models like `stablediffusionapi/juggernaut-reborn` can be accelerated to generate results in 4 steps without the need for distillation of the juggernaut-reborn model.


The difference between Conditional Diffusion Distillation and recent LCM-LORA is listed below

| | Conditional Diffusion Distillation (CoDi) | LCM-LORA |
|---|---|---|
| Scheduler | Anything (Euler is tested) | LCM |
| Adapter | ControlNet | LORA |
| Full-training | Available | None |
| Backbone| SD1.5 (including its variant like juggernaut-reborn) | SD and SXL |

### 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

project page: https://fast-codi.github.io
paper: https://arxiv.org/abs/2310.01407

@MKFMIKU will submit a PR for providing the trainng code in PyTorch and a rough pretrained model.

Bắt đầu với trang dự án và paper của CoDi, sau đó xem xét các tích hợp hiện có của diffusion model, pipeline, ControlNet và scheduler trong diffusers. Thêm hỗ trợ cho ControlNet tăng tốc được pretrained như mô tả mà không yêu cầu thay đổi scheduler hoặc kiến trúc, và xác minh quá trình tạo trong 1–4 bước đã nêu.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.