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/0009.png from peter168/ddpm_floorplans_128_multidimCond_mix: direct link, hf CLI and curl.
- Browser
- Download file 258 kB
-
https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0009.png
- Command line
-
hf download hf://peter168/ddpm_floorplans_128_multidimCond_mix/samples/0009.png
-
curl -L -o 0009.png https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0009.png
258 kB

- Xet hash:
- 3b259dc989a6d4abe5622bd83b022cf1a962bb23afd40e74d6f1883af725a1d3
- Size of remote file:
- 258 kB
- SHA256:
- 3b1f6c9772c27006120488fc7d0b426cbe151559c5cd425c6f9662dcdb40d2b8
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