Instructions to use KORMo-VL/KORMo-VL-Diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KORMo-VL/KORMo-VL-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("KORMo-VL/KORMo-VL-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:
- af024248fe04e8a011b368794e8a8c4bb1b0deb30caa115d9d609f3b31b23eb1
- Size of remote file:
- 6.21 MB
- SHA256:
- adee384828edd810a7b887d2e7198cad05c6b334dfd994ea9a4afbe8763d15f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.