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Code Issues Actions 4 Packages Projects Releases Wiki Activity

add edm schedulers in doc (#7319)

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* add edm schedulers in doc

* add in toctree

* address reviewe comments
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Suraj Patil
2024-03-14 11:52:25 +01:00
committed by GitHub
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docs/source/en/_toctree.yml
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@@ -404,6 +404,10 @@
title: EulerAncestralDiscreteScheduler
- local: api/schedulers/euler
title: EulerDiscreteScheduler
- local: api/schedulers/edm_euler
title: EDMEulerScheduler
- local: api/schedulers/edm_multistep_dpm_solver
title: EDMDPMSolverMultistepScheduler
- local: api/schedulers/heun
title: HeunDiscreteScheduler
- local: api/schedulers/ipndm

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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# EDMEulerScheduler
The Karras formulation of the Euler scheduler (Algorithm 2) from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/).
## EDMEulerScheduler
[[autodoc]] EDMEulerScheduler
## EDMEulerSchedulerOutput
[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput

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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# EDMDPMSolverMultistepScheduler
`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistep`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in 10 steps.
## EDMDPMSolverMultistepScheduler
[[autodoc]] EDMDPMSolverMultistepScheduler
## SchedulerOutput
[[autodoc]] schedulers.scheduling_utils.SchedulerOutput