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:
- 1baefef995b2613e32d2dc7f9cade2ffe395c665a71b9f498ab4be574973d06b
- Size of remote file:
- 167 MB
- SHA256:
- 9e614d1d936ceffff2f80b9ca4db9d72ecc30f4e576b338c8d20cac4c05d6679
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