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* add mixin class for pipeline from original sd ckpt * Improve * make style * merge main into * Improve more * fix more * up * Apply suggestions from code review * finish docs * rename * make style --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
43 lines
1.5 KiB
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43 lines
1.5 KiB
Plaintext
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Loaders
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There are many ways to train adapter neural networks for diffusion models, such as
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- [Textual Inversion](./training/text_inversion.mdx)
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- [LoRA](https://github.com/cloneofsimo/lora)
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- [Hypernetworks](https://arxiv.org/abs/1609.09106)
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Such adapter neural networks often only consist of a fraction of the number of weights compared
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to the pretrained model and as such are very portable. The Diffusers library offers an easy-to-use
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API to load such adapter neural networks via the [`loaders.py` module](https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders.py).
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**Note**: This module is still highly experimental and prone to future changes.
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## LoaderMixins
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### UNet2DConditionLoadersMixin
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[[autodoc]] loaders.UNet2DConditionLoadersMixin
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### TextualInversionLoaderMixin
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[[autodoc]] loaders.TextualInversionLoaderMixin
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### LoraLoaderMixin
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[[autodoc]] loaders.LoraLoaderMixin
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### FromCkptMixin
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[[autodoc]] loaders.FromCkptMixin
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