dog on rain.md

---
pipeline_tag: text-to-video
license: cc-by-nc-4.0
---

![model example](https://i.imgur.com/1mrNnh8.png)

# zeroscope_v2 576w
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained from the [original weights](https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis) using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br />
zeroscope_v2_567w is specifically designed for upscaling with [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) using vid2vid in the [1111 text2video](https://github.com/kabachuha/sd-webui-text2video) extension by [kabachuha](https://github.com/kabachuha). Leveraging this model as a preliminary step allows for superior overall compositions at higher resolutions in zeroscope_v2_XL, permitting faster exploration in 576x320 before transitioning to a high-resolution render. See some [example outputs](https://www.youtube.com/watch?v=HO3APT_0UA4) that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of [dotsimulate](https://www.instagram.com/dotsimulate/))<br />

zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320

### Using it with the 1111 text2video extension

1. Download files in the zs2_576w folder.
2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory.

### Upscaling recommendations

For upscaling, it's recommended to use [zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) via vid2vid in the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip. <br />

### Usage in 🧨 Diffusers

Let's first install the libraries required:

```bash
$ pip install diffusers transformers accelerate torch
```

Now, generate a video:

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

pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

prompt = "Darth Vader is surfing on waves"
video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
video_path = export_to_video(video_frames)
```

Here are some results:

<table>
    <tr>
        Darth vader is surfing on waves.
        <br>
        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif"
            alt="Darth vader surfing in waves."
            style="width: 576;" />
        </center></td>
    </tr>
</table>

### Known issues

Lower resolutions or fewer frames could lead to suboptimal output. <br />

Thanks to [camenduru](https://github.com/camenduru), [kabachuha](https://github.com/kabachuha), [ExponentialML](https://github.com/ExponentialML), [dotsimulate](https://www.instagram.com/dotsimulate/), [VANYA](https://twitter.com/veryVANYA), [polyware](https://twitter.com/polyware_ai), [tin2tin](https://github.com/tin2tin)<br />