Instructions to use peter168/ddpm-floorplans_tutorial-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peter168/ddpm-floorplans_tutorial-256 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("peter168/ddpm-floorplans_tutorial-256", 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:
- 73bca9c5f2e85c87bbd2f697a833de8b56eef1b31dbfa4bae7528521e1a1eb20
- Size of remote file:
- 455 MB
- SHA256:
- 8c22f6cfa8c1887176a28eec98958a28f26b7f367e842ac1e8bba731d09e1bad
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