Zeroscope v2 576w

--- pipeline_tag: text-to-video license: cc-by-nc-4.0 ---  # 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=...