Instructions to use VanessaHu/26CVPR-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VanessaHu/26CVPR-baseline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("VanessaHu/26CVPR-baseline", 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:
- 1deb2bf3d7d90e4694db23aa244aa8b58c3922e497569df9c4871e4f58b2cd2f
- Size of remote file:
- 2.25 MB
- SHA256:
- ae3d15b2b21c9bd9d8ba4c19c6a32cdb08ac29c7a251757fa20574e95760768c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.