Instructions to use peter168/ddpm-floorplans_tutorial-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peter168/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("peter168/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
Download samples/0014.png from peter168/ddpm-floorplans_tutorial-128: direct link, hf CLI and curl.
- Browser
- Download file 655 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0014.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128/samples/0014.png
-
curl -L -o 0014.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0014.png
655 kB

- Xet hash:
- c76a8b93f09195f166199d39998794f0d6ede616fa1e363261eb18217534b4f9
- Size of remote file:
- 655 kB
- SHA256:
- 405c0bf5ad1ed42d5121352a76452584a64a80b84786d63a8d2f7a0120d8102e
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