| | --- |
| | 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}") |
| | ``` |
