Instructions to use jprve/StableDiff-Vanilla-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jprve/StableDiff-Vanilla-V2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jprve/StableDiff-Vanilla-V2", 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:
- 1cdc7c1f135d30d78bc6c879bd2817f6116ab1cd2c21153d65efc3bda620ec08
- Size of remote file:
- 505 MB
- SHA256:
- a313348c15aaff63afceea515028e982a48d893858fe96541ed79c543609ea2d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.