Instructions to use BackTo2014/mnist-demo3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BackTo2014/mnist-demo3 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/mnist-demo3", 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:
- d749534e2bd3714631b623b9887d803d42cbd61c1e378015853861abdaccd29b
- Size of remote file:
- 74.2 MB
- SHA256:
- b65881e00b4e83b2ce8236b014a94f2387241a9e71e1060ce342f147b2da659a
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