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* start * add more logic * Update src/diffusers/models/unet_2d_condition_flax.py * match weights * up * make model work * making class more general, fixing missed file rename * small fix * make new conversion work * up * finalize conversion * up * first batch of variable renamings * remove c and c_prev var names * add mid and out block structure * add pipeline * up * finish conversion * finish * upload * more fixes * Apply suggestions from code review * add attr * up * uP * up * finish tests * finish * uP * finish * fix test * up * naming consistency in tests * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Nathan Lambert <nathan@huggingface.co> Co-authored-by: Anton Lozhkov <anton@huggingface.co> * remove hardcoded 16 * Remove bogus * fix some stuff * finish * improve logging * docs * upload Co-authored-by: Nathan Lambert <nol@berkeley.edu> Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Nathan Lambert <nathan@huggingface.co> Co-authored-by: Anton Lozhkov <anton@huggingface.co>
69 lines
2.0 KiB
Plaintext
69 lines
2.0 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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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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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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## UNet1DModel
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[[autodoc]] UNet1DModel
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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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## FlaxModelMixin
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[[autodoc]] FlaxModelMixin
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## FlaxUNet2DConditionOutput
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[[autodoc]] models.unet_2d_condition_flax.FlaxUNet2DConditionOutput
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## FlaxUNet2DConditionModel
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[[autodoc]] FlaxUNet2DConditionModel
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## FlaxDecoderOutput
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[[autodoc]] models.vae_flax.FlaxDecoderOutput
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## FlaxAutoencoderKLOutput
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[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
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## FlaxAutoencoderKL
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[[autodoc]] FlaxAutoencoderKL
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