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* Update EasyAnimate V5.1 * Add docs && add tests && Fix comments problems in transformer3d and vae * delete comments and remove useless import * delete process * Update EXAMPLE_DOC_STRING * rename transformer file * make fix-copies * make style * refactor pt. 1 * update toctree.yml * add model tests * Update layer_norm for norm_added_q and norm_added_k in Attention * Fix processor problem * refactor vae * Fix problem in comments * refactor tiling; remove einops dependency * fix docs path * make fix-copies * Update src/diffusers/pipelines/easyanimate/pipeline_easyanimate_control.py * update _toctree.yml * fix test * update * update * update * make fix-copies * fix tests --------- Co-authored-by: Aryan <aryan@huggingface.co> Co-authored-by: Aryan <contact.aryanvs@gmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
38 lines
1.2 KiB
Markdown
38 lines
1.2 KiB
Markdown
<!--Copyright 2025 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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# AutoencoderKLMagvit
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The 3D variational autoencoder (VAE) model with KL loss used in [EasyAnimate](https://github.com/aigc-apps/EasyAnimate) was introduced by Alibaba PAI.
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The model can be loaded with the following code snippet.
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```python
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from diffusers import AutoencoderKLMagvit
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vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", torch_dtype=torch.float16).to("cuda")
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```
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## AutoencoderKLMagvit
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[[autodoc]] AutoencoderKLMagvit
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- decode
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- encode
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- all
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## AutoencoderKLOutput
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[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
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## DecoderOutput
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[[autodoc]] models.autoencoders.vae.DecoderOutput
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