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pipeline_tag: text-to-video |
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--- |
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# zeroscope_v2 567w |
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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.<br /> |
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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 /> |
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zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320 |
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### Using it with the 1111 text2video extension |
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1. Download files in the zs2_576w folder. |
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2. Replace the respective files in the 'stable-diffusion-webui\models\ModelScope\t2v' directory. |
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### Upscaling recommendations |
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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 /> |
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### Usage in 🧨 Diffusers |
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Let's first install the libraries required: |
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```bash |
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$ pip install diffusers transformers accelerate torch |
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``` |
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Now, generate a video: |
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```py |
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import torch |
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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from diffusers.utils import export_to_video |
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pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_model_cpu_offload() |
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prompt = "Darth Vader is surfing on waves" |
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video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames |
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video_path = export_to_video(video_frames) |
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``` |
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Here are some results: |
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<table> |
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<tr> |
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Darth vader is surfing on waves. |
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<br> |
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif" |
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alt="Darth vader surfing in waves." |
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style="width: 576;" /> |
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</center></td> |
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</tr> |
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</table> |
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### Known issues |
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Lower resolutions or fewer frames could lead to suboptimal output. <br /> |
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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 /> |
