Instructions to use diffusers/ddpm_dummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffusers/ddpm_dummy with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("diffusers/ddpm_dummy", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
c9de2e1
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Parent(s): a09a880
Update README.md
Browse files
README.md
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@@ -9,7 +9,7 @@ import torch
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model = UNetModel.from_pretrained("fusing/ddpm_dummy")
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batch_size, num_channels, height, width = 1, 3, 32, 32
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dummy_noise = torch.
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time_step = torch.tensor([10])
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image = model(dummy_noise, time_step)
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model = UNetModel.from_pretrained("fusing/ddpm_dummy")
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batch_size, num_channels, height, width = 1, 3, 32, 32
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dummy_noise = torch.ones((batch_size, num_channels, height, width))
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time_step = torch.tensor([10])
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image = model(dummy_noise, time_step)
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