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/0164.png from peter168/ddpm-floorplans_tutorial-128: direct link, hf CLI and curl.
- Browser
- Download file 564 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/4043b4105ecadab72e2d1a84a730297dbd90d4aa/samples/0164.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128@4043b4105ecadab72e2d1a84a730297dbd90d4aa/samples/0164.png
-
curl -L -o 0164.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/4043b4105ecadab72e2d1a84a730297dbd90d4aa/samples/0164.png
564 kB

- Xet hash:
- bdaad88d0d599725dcc6baf7d3e11b88a69af65d9cc65e7034ced54198b5a6e3
- Size of remote file:
- 564 kB
- SHA256:
- 1b4d7fed5c2b2a3ae28cc083199378015cdc10d8a792c4ce2d66cc634067fed3
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