cerspense / zeroscope_v2_576w

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 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 using vid2vid in the 1111 text2video extension by 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 that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of 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 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.

Usage in 🧨 Diffusers

Let's first install the libraries required:

$ pip install diffusers transformers accelerate torch

Now, generate a video:

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:

Known issues

Lower resolutions or fewer frames could lead to suboptimal output.

Thanks to camenduru , kabachuha , ExponentialML , dotsimulate , VANYA , polyware , tin2tin