Instructions to use zimhe/SpatialDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zimhe/SpatialDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zimhe/SpatialDiffusion", 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:
- 6fa95bad082ebc13074f749ce5037486e077dcf242b26ca20ed3b3f84c743ab0
- Size of remote file:
- 246 MB
- SHA256:
- 3ece5df6035867a0f018ae4a7df2bf1219c58c11d55d5b60f7bea56d280b93a8
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