---
library_name: diffusers
---
  
  
# MISHANM/video_generation
  
The MISHANM/video_generation model is a diffusion-based video generation model . It is designed to generate high-quality videos from textual prompts using advanced diffusion techniques.  
  
## Model Details  
1. Language: English
2. Tasks: Video Generation
  
### Model Example output  
  
This is the model inference output:   


<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/66851b2c4461866b07738832/833kmbAdDGSxirLft0el7.mp4"></video>


 ## How to Get Started with the Model  

  ## Diffusers

```shell
pip install git+https://github.com/huggingface/diffusers.git
```
Use the code below to get started with the model.  
  
```python  
import imageio  
import imageio_ffmpeg  
import torch  
from diffusers import MochiPipeline  
from diffusers.utils import export_to_video  
  
# Load the pre-trained video generation model  
model = MochiPipeline.from_pretrained(  
    "MISHANM/video_generation",  
 #   variant="bf16",  
    torch_dtype=torch.bfloat16,  
    device_map="balanced"  
)  
  
# Enable memory savings by tiling the VAE  
model.enable_vae_tiling()  
  
# Define the prompt and number of frames  
prompt = "A cow drinking water on the surface of Mars."  
num_frames = 20  
  
frames = model(prompt, num_frames=num_frames).frames[0]  
    
export_to_video(frames, "video.mp4", fps=30)  
  
print("Video generation complete. Saved as 'video.mp4'.")  


```

## Uses  
  
### Direct Use  
  
The model is intended for generating videos from textual descriptions. It can be used in creative applications, content generation, and artistic exploration.  
  
### Out-of-Scope Use  
  
The model is not suitable for generating videos with explicit or harmful content. It may not perform well with highly abstract or nonsensical prompts.  
  
## Bias, Risks, and Limitations  
  
The model may reflect biases present in the training data. It may generate stereotypical or biased videos based on the input prompts.  
  
### Recommendations  
  
Users should be aware of potential biases and limitations. It is recommended to review generated content for appropriateness and accuracy.  

## Citation Information
```
@misc{MISHANM/video_generation,
  author = {Mishan Maurya},
  title = {Introducing Video Generation model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}
```