28 lines
1.8 KiB
Plaintext
28 lines
1.8 KiB
Plaintext
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<!--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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# 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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## API
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Models should provide the `def forward` function and initialization of the model.
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All saving, loading, and utilities should be in the base ['ModelMixin'] class.
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## Examples
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- The ['UNetModel'] was proposed in [TODO](https://arxiv.org/) and has been used in paper1, paper2, paper3.
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- Extensions of the ['UNetModel'] include the ['UNetGlideModel'] that uses attention and timestep embeddings for the [GLIDE](https://arxiv.org/abs/2112.10741) paper, the ['UNetGradTTS'] model from this [paper](https://arxiv.org/abs/2105.06337) for text-to-speech, ['UNetLDMModel'] for latent-diffusion models in this [paper](https://arxiv.org/abs/2112.10752), and the ['TemporalUNet'] used for time-series prediciton in this reinforcement learning [paper](https://arxiv.org/abs/2205.09991).
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- TODO: mention VAE / SDE score estimation
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