Instructions to use fusing/ddpm_dummy_update with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fusing/ddpm_dummy_update with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fusing/ddpm_dummy_update", 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:
- 9ec7d2874698961691dbd9551a1fab8c82befcb0c5bd53195eccce087c721455
- Size of remote file:
- 4.08 MB
- SHA256:
- 9238823545915bb4f98a33c093ab53d1db191a9b996941c1e12f12d7726aaeee
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