Instructions to use cerspense/zeroscope_v1-1_320s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cerspense/zeroscope_v1-1_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_v1-1_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. This updated version fixes stretching issues present in v1, but produces different results overall Model was trained on a subset of the vimeo90k dataset + a selection of music videos
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