Instructions to use Jiajun36/ddpm-floorplans_tutorial-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jiajun36/ddpm-floorplans_tutorial-128 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jiajun36/ddpm-floorplans_tutorial-128", 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:
- 80f9e9b94cfbad3530097c7f875030f10964c4caa017ae2aaf37ceaa029f01c0
- Size of remote file:
- 455 MB
- SHA256:
- 4856cc96a32f6a01cc8f69eba42d311908102d056cf75b4515897d881aee84d0
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