Instructions to use cerspense/zeroscope_v1_320s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cerspense/zeroscope_v1_320s with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v1_320s", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Modelscope without the watermark, trained in 320x320 from the original weights, with no skipped frames for less flicker. See comparison here: https://www.youtube.com/watch?v=r4tOc30Zu0w Model was trained on a subset of the vimeo90k dataset + a selection of music videos
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