# Module Name: ZeroscopeTTV

## Introduction
The ZeroscopeTTV module is a versatile zero-shot video generation model designed to create videos based on textual descriptions. This comprehensive documentation will provide you with an in-depth understanding of the ZeroscopeTTV module, its architecture, purpose, arguments, and detailed usage examples.

## Purpose
The ZeroscopeTTV module serves as a powerful tool for generating videos from text descriptions. Whether you need to create video content for various applications, visualize textual data, or explore the capabilities of ZeroscopeTTV, this module offers a flexible and efficient solution. With its easy-to-use interface, you can quickly generate videos based on your textual input.

## Architecture
The ZeroscopeTTV module is built on top of the Diffusers library, leveraging the power of diffusion models for video generation. It allows you to specify various parameters such as model name, data type, chunk size, dimensions, and more to customize the video generation process. The model performs multiple inference steps and utilizes a diffusion pipeline to generate high-quality videos.

## Class Definition
### `ZeroscopeTTV(model_name: str = "cerspense/zeroscope_v2_576w", torch_dtype=torch.float16, chunk_size: int = 1, dim: int = 1, num_inference_steps: int = 40, height: int = 320, width: int = 576, num_frames: int = 36)`

#### Parameters
- `model_name` (str, optional): The name of the pre-trained model to use. Default is "cerspense/zeroscope_v2_576w".
- `torch_dtype` (torch.dtype, optional): The torch data type to use for computations. Default is torch.float16.
- `chunk_size` (int, optional): The size of chunks for forward chunking. Default is 1.
- `dim` (int, optional): The dimension along which the input is split for forward chunking. Default is 1.
- `num_inference_steps` (int, optional): The number of inference steps to perform. Default is 40.
- `height` (int, optional): The height of the video frames. Default is 320.
- `width` (int, optional): The width of the video frames. Default is 576.
- `num_frames` (int, optional): The number of frames in the video. Default is 36.

## Functionality and Usage
The ZeroscopeTTV module offers a straightforward interface for video generation. It accepts a textual task or description as input and returns the path to the generated video.

### `run(task: str = None, *args, **kwargs) -> str`

#### Parameters
- `task` (str, optional): The input task or description for video generation.

#### Returns
- `str`: The path to the generated video.

## Usage Examples
### Example 1: Basic Usage

```python
from swarms.models import ZeroscopeTTV

# Initialize the ZeroscopeTTV model
zeroscope = ZeroscopeTTV()

# Generate a video based on a textual description
task = "A bird flying in the sky."
video_path = zeroscope.run(task)
print(f"Generated video path: {video_path}")
```

### Example 2: Custom Model and Parameters

You can specify a custom pre-trained model and adjust various parameters for video generation.

```python
custom_model_name = "your_custom_model_path"
custom_dtype = torch.float32
custom_chunk_size = 2
custom_dim = 2
custom_num_inference_steps = 50
custom_height = 480
custom_width = 720
custom_num_frames = 48

custom_zeroscope = ZeroscopeTTV(
    model_name=custom_model_name,
    torch_dtype=custom_dtype,
    chunk_size=custom_chunk_size,
    dim=custom_dim,
    num_inference_steps=custom_num_inference_steps,
    height=custom_height,
    width=custom_width,
    num_frames=custom_num_frames,
)

task = "A car driving on the road."
video_path = custom_zeroscope.run(task)
print(f"Generated video path: {video_path}")
```

### Example 3: Exporting Video Frames

You can also export individual video frames if needed.

```python
from swarms.models import export_to_video

# Generate video frames
video_frames = zeroscope.run("A boat sailing on the water.")

# Export video frames to a video file
video_path = export_to_video(video_frames)
print(f"Generated video path: {video_path}")
```

## Additional Information and Tips
- Ensure that the input textual task or description is clear and descriptive to achieve the desired video output.
- Experiment with different parameter settings to control video resolution, frame count, and inference steps.
- Use the `export_to_video` function to export individual video frames as needed.
- Monitor the progress and output paths to access the generated videos.

## Conclusion
The ZeroscopeTTV module is a powerful solution for zero-shot video generation based on textual descriptions. Whether you are creating videos for storytelling, data visualization, or other applications, ZeroscopeTTV offers a versatile and efficient way to bring your text to life. With a flexible interface and customizable parameters, it empowers you to generate high-quality videos with ease.

If you encounter any issues or have questions about using ZeroscopeTTV, please refer to the Diffusers library documentation or reach out to their support team for further assistance. Enjoy creating videos with ZeroscopeTTV!