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/0159.png from peter168/ddpm-floorplans_tutorial-128: direct link, hf CLI and curl.
- Browser
- Download file 568 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0159.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128/samples/0159.png
-
curl -L -o 0159.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0159.png
568 kB

- Xet hash:
- 01199d816d8e00cdea249751da95cac3acad7282645a1f66d16041aef5d945f5
- Size of remote file:
- 568 kB
- SHA256:
- 5d5ea4a61b6e8b09e849a99ebded972308046fbab3714a164dab5bb9bd9bd287
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