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.

Start with the CoDi project page and paper, then review the existing diffusion model, pipeline, ControlNet, and scheduler integrations in diffusers. Add support for the described pretrained acceleration ControlNet without requiring scheduler or architecture changes, and verify generation in the stated 1–4 steps.

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