Instructions to use tayy786/raos-virtual-try-on-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tayy786/raos-virtual-try-on-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tayy786/raos-virtual-try-on-model", 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
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 281fd27c0d5f421596891d37c4411ce1f46b106e16462e0f703af510b4dbe030
- Size of remote file:
- 246 MB
- SHA256:
- 3d57efda2d001f378b87212e23aa2cb2d7d5589586b93ddfab136cdb4504ef3f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.