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 Settings
- Draw Things
- DiffusionBee
- Xet hash:
- d7fd2ea6b4a8945cbb34985f6028a3aa76d70142f61e71ffc0c572f8f75d45b2
- Size of remote file:
- 335 MB
- SHA256:
- 77662edea9184f46e10c37c307360633394ee375b93a3e5b69414d325c278272
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