Instructions to use fusing/ddpm-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fusing/ddpm-cifar10 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fusing/ddpm-cifar10", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 8682899
Update config.json
Browse files- config.json +1 -1
config.json
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"up_blocks": ["UNetResUpBlock2D", "UNetResUpBlock2D", "UNetResAttnUpBlock2D", "UNetResUpBlock2D"],
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"conv_resample": true,
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"downsample_padding": 0,
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"num_head_channels":
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"ch": 128,
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"ch_mult": [
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1,
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"up_blocks": ["UNetResUpBlock2D", "UNetResUpBlock2D", "UNetResAttnUpBlock2D", "UNetResUpBlock2D"],
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"conv_resample": true,
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"downsample_padding": 0,
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"num_head_channels": null,
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"ch": 128,
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"ch_mult": [
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1,
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