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* [MS Text To Video} Add first text to video * upload * make first model example * match unet3d params * make sure weights are correcctly converted * improve * forward pass works, but diff result * make forward work * fix more * finish * refactor video output class. * feat: add support for a video export utility. * fix: opencv availability check. * run make fix-copies. * add: docs for the model components. * add: standalone pipeline doc. * edit docstring of the pipeline. * add: right path to TransformerTempModel * add: first set of tests. * complete fast tests for text to video. * fix bug * up * three fast tests failing. * add: note on slow tests * make work with all schedulers * apply styling. * add slow tests * change file name * update * more correction * more fixes * finish * up * Apply suggestions from code review * up * finish * make copies * fix pipeline tests * fix more tests * Apply suggestions from code review Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * apply suggestions * up * revert --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
102 lines
2.8 KiB
Plaintext
102 lines
2.8 KiB
Plaintext
<!--Copyright 2023 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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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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# 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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## UNet1DOutput
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[[autodoc]] models.unet_1d.UNet1DOutput
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## UNet1DModel
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[[autodoc]] UNet1DModel
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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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## UNet3DConditionOutput
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[[autodoc]] models.unet_3d_condition.UNet3DConditionOutput
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## UNet3DConditionModel
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[[autodoc]] UNet3DConditionModel
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## DecoderOutput
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[[autodoc]] models.vae.DecoderOutput
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## VQEncoderOutput
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[[autodoc]] models.vq_model.VQEncoderOutput
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## VQModel
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[[autodoc]] VQModel
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## AutoencoderKLOutput
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[[autodoc]] models.autoencoder_kl.AutoencoderKLOutput
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## AutoencoderKL
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[[autodoc]] AutoencoderKL
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## Transformer2DModel
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[[autodoc]] Transformer2DModel
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## Transformer2DModelOutput
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[[autodoc]] models.transformer_2d.Transformer2DModelOutput
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## TransformerTemporalModel
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[[autodoc]] models.transformer_temporal.TransformerTemporalModel
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## Transformer2DModelOutput
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[[autodoc]] models.transformer_temporal.TransformerTemporalModelOutput
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## PriorTransformer
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[[autodoc]] models.prior_transformer.PriorTransformer
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## PriorTransformerOutput
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[[autodoc]] models.prior_transformer.PriorTransformerOutput
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## ControlNetOutput
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[[autodoc]] models.controlnet.ControlNetOutput
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## ControlNetModel
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[[autodoc]] ControlNetModel
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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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