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* docs for attention * types for embeddings * unet2d docstrings * UNet2DConditionModel docstrings * fix typos * style and vq-vae docstrings * docstrings for VAE * Update src/diffusers/models/unet_2d.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * make style * added inherits from sentence * docstring to forward * make style * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * finish model docs * up Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
48 lines
1.5 KiB
Plaintext
48 lines
1.5 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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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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specific language governing permissions and limitations under the License.
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# Models
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Diffusers contains pretrained models for popular algorithms and modules for creating the next set of diffusion models.
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The primary function of these models is to denoise an input sample, by modeling the distribution $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$.
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The models are built on the base class ['ModelMixin'] that is a `torch.nn.module` with basic functionality for saving and loading models both locally and from the HuggingFace hub.
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## ModelMixin
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[[autodoc]] ModelMixin
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## UNet2DOutput
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[[autodoc]] models.unet_2d.UNet2DOutput
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## UNet2DModel
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[[autodoc]] UNet2DModel
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## UNet2DConditionOutput
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[[autodoc]] models.unet_2d_condition.UNet2DConditionOutput
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## UNet2DConditionModel
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[[autodoc]] UNet2DConditionModel
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## DecoderOutput
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[[autodoc]] models.vae.DecoderOutput
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## VQEncoderOutput
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[[autodoc]] models.vae.VQEncoderOutput
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## VQModel
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[[autodoc]] VQModel
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## AutoencoderKLOutput
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[[autodoc]] models.vae.AutoencoderKLOutput
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## AutoencoderKL
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[[autodoc]] AutoencoderKL
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