Instructions to use Amazingldl/VisualBox with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Amazingldl/VisualBox with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Amazingldl/VisualBox", 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:
- c9d0f8f1d429de26c06d80d9f3952a407d963543970f6c8532917e6f6c99cffb
- Size of remote file:
- 1.45 GB
- SHA256:
- e5220c53e942eb3bbe08a92e58e7ea789be44590172103d757dba29d732772b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.