Update README.md

README.md

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 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}")
-```