Instructions to use dylanebert/multi-view-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dylanebert/multi-view-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dylanebert/multi-view-diffusion", 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
- Xet hash:
- c2d9a7532b5a075eeef74b5e09b2ac0302c24c2f0c51a76e44f615e5129e3858
- Size of remote file:
- 1.26 GB
- SHA256:
- 2a56cfd4ffcf40be097c430324ec184cc37187f6dafef128ef9225438a3c03c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.