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 671 kB
-
https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/394b9f661899b4008f5e2fa5f8ea39db567d1c17/samples/0149.png
- Command line
-
hf download hf://peter168/ddpm-floorplans_tutorial-128@394b9f661899b4008f5e2fa5f8ea39db567d1c17/samples/0149.png
-
curl -L -o 0149.png https://huggingface.co/peter168/ddpm-floorplans_tutorial-128/resolve/394b9f661899b4008f5e2fa5f8ea39db567d1c17/samples/0149.png
671 kB

- Xet hash:
- 46e568ce56a660096778bf95b38094226e96557311652041677588bd5d2fb836
- Size of remote file:
- 671 kB
- SHA256:
- 3f9a46b0d73d0a091a78a296bd815332e2582a2e3ac27f2d9be2692b046fa50c
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