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:
- 71ccbb470837cab936bcfa92e7b1babfd018a8a2174776a098ca528843e166a2
- Size of remote file:
- 335 MB
- SHA256:
- 378595b8ec40d387d360ed68021d9a34c25d1109286b05d348b2c2956d226df3
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