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/0039.png from peter168/ddpm-floorplans_tutorial-128: direct link, hf CLI and curl.
- Browser
- Download file 657 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/ca5c72e739c9b385a723b96b0f8fa112e3409349/samples/0039.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128@ca5c72e739c9b385a723b96b0f8fa112e3409349/samples/0039.png
-
curl -L -o 0039.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/ca5c72e739c9b385a723b96b0f8fa112e3409349/samples/0039.png
657 kB

- Xet hash:
- a3225ea4af9e6cc79638736e65078ec54506606f18106593ab9681e3fcabbbad
- Size of remote file:
- 657 kB
- SHA256:
- 0acbb20730960a0c40222bec40ca02e1df0ce9cdb7779dcfd9cc8809639c351d
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