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# Text-to-video synthesis

Text-to-video synthesis from [ModelScope](https://modelscope.cn/) can be considered the same as Stable Diffusion structure-wise but it is extended to videos instead of static images. More specifically, this system allows us to generate videos from a natural language text prompt.

From the [model summary](https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis):

*This model is based on a multi-stage text-to-video generation diffusion model, which inputs a description text and returns a video that matches the text description. Only English input is supported.*

Resources:

* [Website](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)
* [GitHub repository](https://github.com/modelscope/modelscope/)
* [Spaces] (TODO)

## Available Pipelines:

| Pipeline | Tasks | Demo
|---|---|:---:|
| [DiffusionPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py) | *Text-to-Video Generation* | [Spaces] (TODO)

## Usage example 

Let's start by generating a short video with the default length of 16 frames (2s at 8 fps):

```python 
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe = pipe.to("cuda")

prompt = "Spiderman is surfing"
video_frames = pipe(prompt).frames
video_path = export_to_video(video_frames)
video_path
```

Diffusers supports different optimization techniques to improve the latency
and memory footprint of a pipeline. Since videos are often more memory-heavy than images,
we can enable CPU offloading and VAE slicing to keep the memory footprint at bay.

Let's generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing:

```python
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.enable_model_cpu_offload()

# memory optimization
pipe.enable_vae_slicing()

prompt = "Darth Vader surfing a wave"
video_frames = pipe(prompt, num_frames=64).frames
video_path = export_to_video(video_frames)
video_path
```

It just takes **7 GBs of GPU memory** to generate the 64 video frames using PyTorch 2.0, "fp16" precision and the techniques mentioned above.

We can also use a different scheduler easily, using the same method we'd use for Stable Diffusion:

```python
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

prompt = "Spiderman is surfing"
video_frames = pipe(prompt, num_inference_steps=25).frames
video_path = export_to_video(video_frames)
video_path
```

Here are some sample outputs: 

<table>
    <tr>
        <td><center>
        An astronaut riding a horse.
        <br>
        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astr.gif"
            alt="An astronaut riding a horse."
            style="width: 300px;" />
        </center></td>
        <td ><center>
        Darth vader surfing in waves.
        <br>
        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vader.gif"
            alt="Darth vader surfing in waves."
            style="width: 300px;" />
        </center></td>
    </tr>
</table>

## Available checkpoints 

* [damo-vilab/text-to-video-ms-1.7b](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b/)
* [damo-vilab/text-to-video-ms-1.7b-legacy](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b-legacy)

## DiffusionPipeline
[[autodoc]] DiffusionPipeline
	- all
	- __call__
