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---
library_name: diffusers
license: mit
pipeline_tag: text-to-video
tags:
- video-generation
---
# DCM: Dual-Expert Consistency Model for Efficient and High-Quality Video Generation
This repository hosts the Dual-Expert Consistency Model (DCM) as presented in the paper [Dual-Expert Consistency Model for Efficient and High-Quality Video Generation](https://huggingface.co/papers/2506.03123). DCM addresses the challenge of applying Consistency Models to video diffusion, which often leads to temporal inconsistency and loss of detail. By using a dual-expert approach, DCM achieves state-of-the-art visual quality with significantly reduced sampling steps.
For more information, please refer to the project's [Github repository](https://github.com/Vchitect/DCM).
## Usage
You can use this model with the `diffusers` library. Make sure you have `diffusers`, `transformers`, `torch`, `accelerate`, and `imageio` (with `imageio-ffmpeg` for MP4/GIF saving) installed.
```bash
pip install diffusers transformers torch accelerate imageio[ffmpeg]
```
Here is a quick example to generate a video:
```python
from diffusers import DiffusionPipeline
import torch
import imageio
# Load the pipeline
# The custom_pipeline argument is necessary because the pipeline class (WanPipeline)
# is defined within the repository and not part of the standard diffusers library.
pipe = DiffusionPipeline.from_pretrained("Vchitect/DCM", torch_dtype=torch.float16, custom_pipeline="Vchitect/DCM", trust_remote_code=True)
pipe.to("cuda")
# Define the prompt and generation parameters
prompt = "A futuristic car driving through a neon-lit city at night"
generator = torch.Generator(device="cuda").manual_seed(0) # for reproducibility
# Generate video frames
video_frames = pipe(
prompt=prompt,
num_frames=16, # number of frames to generate
num_inference_steps=4, # DCM excels at efficient generation in few steps
guidance_scale=7.5, # Classifier-free guidance scale
generator=generator,
).frames[0] # Assuming the output is a list containing one video (list of frames)
# Save the generated video
output_path = "generated_video.gif" # You can change this to .mp4 if imageio[ffmpeg] is properly set up
imageio.mimsave(output_path, video_frames, fps=8) # frames per second
print(f"Video saved to {output_path}")
``` |