Instructions to use Wjjjh/Recursive-VAM-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wjjjh/Recursive-VAM-models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wjjjh/Recursive-VAM-models", torch_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:
- 0af41f3e6024c5f2d134a47c1c62c53d7c9a5d8d460d87acf95760b96995414e
- Size of remote file:
- 1.33 MB
- SHA256:
- a335860ad83919337d2c51bfd45f43c9dbbb4b44e576a475b616f65b641d2bfc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.