--- tags: - Text-to-Video --- ![model example](https://i.imgur.com/1mrNnh8.png) # zeroscope_v2 567w A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.
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/))
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.
### Known issues Lower resolutions or fewer frames could lead to suboptimal output.
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)