Instructions to use cerspense/zeroscope_v2_XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cerspense/zeroscope_v2_XL 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_v2_XL", 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
Add diffusers example
#20
by patrickvonplaten - opened
No description provided.
This PR adds an example of how to use the model with diffusers. With diffusers one can generate up to 36 frames using less just ~12 GB of VRAM. See: https://github.com/huggingface/diffusers/pull/3930
Amazing!!
cerspense changed pull request status to merged