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* [Lora] first upload * add first lora version * upload * more * first training * up * correct * improve * finish loaders and inference * up * up * fix more * up * finish more * finish more * up * up * change year * revert year change * Change lines * Add cloneofsimo as co-author. Co-authored-by: Simo Ryu <cloneofsimo@gmail.com> * finish * fix docs * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com> * upload * finish Co-authored-by: Simo Ryu <cloneofsimo@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com>
31 lines
1.3 KiB
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31 lines
1.3 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Loaders
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There are many weights 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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