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
- Xet hash:
- bfaf80ec413cf8268acfa25f316535b40004527f649b1a49dd900bac57c26150
- Size of remote file:
- 455 MB
- SHA256:
- 5b7669aad6842e96b02d13301f01d830292add8890cd0bfea95d8fbb78889dcb
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