Instructions to use harveymannering/mnist-ddpm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use harveymannering/mnist-ddpm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("harveymannering/mnist-ddpm", 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
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ax.imshow(samples[i,0].cpu().numpy(), cmap="gray")
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```
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ax.imshow(samples[i,0].cpu().numpy(), cmap="gray")
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plt.show()
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```
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The full training and inference code can be found at https://github.com/harveymannering/boilerplate_code/blob/main/ddpm.ipynb.
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