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.