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/0149.png from peter168/ddpm-floorplans_tutorial-128: direct link, hf CLI and curl.
- Browser
- Download file 573 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0149.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128/samples/0149.png
-
curl -L -o 0149.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/main/samples/0149.png
573 kB

- Xet hash:
- 521ac87dc641004687ee2b05281ca90bfa50d6e7a173ed1590a8d7b5c3479d45
- Size of remote file:
- 573 kB
- SHA256:
- caa4aec952c34fae9628f97458a477c88437f79aa780666ebcb637ed7c735749
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