Add sample usage and video-generation tag

This PR enhances the model card for the Dual-Expert Consistency Model (DCM). It includes a practical sample usage snippet that demonstrates how to generate videos using the `diffusers` library, making it easier for users to get started. Additionally, the `video-generation` tag has been added to the metadata to improve discoverability on the Hugging Face Hub.

README.md

@@ -1,11 +1,53 @@
 ---
-license: mit
 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 [DCM: 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.
+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)
 
-For more information, please refer to the project's [Github repository](https://github.com/Vchitect/DCM).
+# 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}")
+```