Instructions to use danbrown/testing-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use danbrown/testing-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("danbrown/testing-1", 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:
- fedba1da0693c2ae0989363f1290a5bd0fd15d6f9e0ac8e7e4e2cf05cb34e530
- Size of remote file:
- 7.7 GB
- SHA256:
- ca42a7a0094e112fc442de6ad6738f1ce7ebdf064436e1e862a7f28c3586be88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.