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/0069.png from peter168/ddpm_floorplans_128_multidimCond_mix: direct link, hf CLI and curl.
- Browser
- Download file 164 kB
-
https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0069.png
- Command line
-
hf download hf://peter168/ddpm_floorplans_128_multidimCond_mix/samples/0069.png
-
curl -L -o 0069.png https://huggingface.co/peter168/ddpm_floorplans_128_multidimCond_mix/resolve/main/samples/0069.png
164 kB

- Xet hash:
- f74ed8040be513d5106ea10febc5d7658c45c7dd2f076598f8612e543d39f874
- Size of remote file:
- 164 kB
- SHA256:
- f649be7335680fa35df828cd2a34a8fdbcb0af862186288c4b05dff0f0a9dd13
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