Instructions to use peter168/ddpm_floorplans_128_multidimCond_mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peter168/ddpm_floorplans_128_multidimCond_mix 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_128_multidimCond_mix", 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/0079.png from peter168/ddpm_floorplans_128_multidimCond_mix: direct link, hf CLI and curl.
- Browser
- Download file 90.8 kB
-
https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0079.png
- Command line
-
hf download hf://peter168/ddpm_floorplans_128_multidimCond_mix/samples/0079.png
-
curl -L -o 0079.png https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0079.png
90.8 kB

- Xet hash:
- ef089f3864e929a5fe93e947f70a8714af959c2ee016ec57dcb4f8a84afeedc9
- Size of remote file:
- 90.8 kB
- SHA256:
- f98f5528eaa1a5b60ed7a57e73c80a9be2740ac8c5f9c2baa0fdd83434d183ce
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