Instructions to use BackTo2014/DDPM-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BackTo2014/DDPM-test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BackTo2014/DDPM-test", 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:
- 4072bbbadce012ab80766d3cd7f16a9586e306cbefb83bf2f2eaf855c939a746
- Size of remote file:
- 332 MB
- SHA256:
- c4805a9b0d2fd2810c01819f495618f0f37ac395fc53d348904cf57db69b1fa2
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