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It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `predict_x0=True` and `thresholding=True` to use dynamic t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#tips | #tips | .md | 240_2 |
UniPCMultistepScheduler
`UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | #unipcmultistepscheduler | .md | 240_3 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#scheduleroutput | #scheduleroutput | .md | 240_4 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/ | .md | 241_0 | |
`RePaintScheduler` is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the [`RePaintPipeline`], and it is based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2201.09865) by Andreas Lugmayr e... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | #repaintscheduler | .md | 241_1 |
RePaintScheduler
`RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | #repaintscheduler | .md | 241_2 |
RePaintSchedulerOutput
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_samp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduleroutput | #repaintscheduleroutput | .md | 241_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver/ | .md | 242_0 | |
`DPMSolverMultistepScheduler` is 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/2211.010... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver/#dpmsolvermultistepscheduler | #dpmsolvermultistepscheduler | .md | 242_1 |
It is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver/#tips | #tips | .md | 242_2 |
DPMSolverMultistepScheduler
`DPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver/#dpmsolvermultistepscheduler | #dpmsolvermultistepscheduler | .md | 242_3 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver/#scheduleroutput | #scheduleroutput | .md | 242_4 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lcm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lcm/ | .md | 243_0 | |
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
This scheduler should be ab... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lcm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lcm/#overview | #overview | .md | 243_1 |
LCMScheduler
`LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config
attributes that are passed in the scheduler's `__... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lcm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lcm/#lcmscheduler | #lcmscheduler | .md | 243_2 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_euler/ | .md | 244_0 | |
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 ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_euler/#edmeulerscheduler | #edmeulerscheduler | .md | 244_1 |
EDMEulerScheduler
Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
https://arxiv.org/abs/2206.00364
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the supe... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_euler/#edmeulerscheduler | #edmeulerscheduler | .md | 244_2 |
EDMEulerSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_euler/#edmeulerscheduleroutput | #edmeulerscheduleroutput | .md | 244_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/stochastic_karras_ve.md | https://huggingface.co/docs/diffusers/en/api/schedulers/stochastic_karras_ve/ | .md | 245_0 | |
`KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) and [Score-based generative modeling through stochastic differential equations](https://huggingface.co/... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/stochastic_karras_ve.md | https://huggingface.co/docs/diffusers/en/api/schedulers/stochastic_karras_ve/#karrasvescheduler | #karrasvescheduler | .md | 245_1 |
KarrasVeScheduler
A stochastic scheduler tailored to variance-expanding models.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
<Tip>
For more details on the paramete... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/stochastic_karras_ve.md | https://huggingface.co/docs/diffusers/en/api/schedulers/stochastic_karras_ve/#karrasvescheduler | #karrasvescheduler | .md | 245_2 |
KarrasVeOutput
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
derivative (`torch.Tensor`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/stochastic_karras_ve.md | https://huggingface.co/docs/diffusers/en/api/schedulers/stochastic_karras_ve/#karrasveoutput | #karrasveoutput | .md | 245_3 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/vq_diffusion.md | https://huggingface.co/docs/diffusers/en/api/schedulers/vq_diffusion/ | .md | 246_0 | |
`VQDiffusionScheduler` converts the transformer model's output into a sample for the unnoised image at the previous diffusion timestep. It was introduced in [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://huggingface.co/papers/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang,... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/vq_diffusion.md | https://huggingface.co/docs/diffusers/en/api/schedulers/vq_diffusion/#vqdiffusionscheduler | #vqdiffusionscheduler | .md | 246_1 |
VQDiffusionScheduler
A scheduler for vector quantized diffusion.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_vec_classes (`int`):
The number of classes of... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/vq_diffusion.md | https://huggingface.co/docs/diffusers/en/api/schedulers/vq_diffusion/#vqdiffusionscheduler | #vqdiffusionscheduler | .md | 246_2 |
VQDiffusionSchedulerOutput
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
Computed sample x_{t-1} of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/vq_diffusion.md | https://huggingface.co/docs/diffusers/en/api/schedulers/vq_diffusion/#vqdiffusionscheduleroutput | #vqdiffusionscheduleroutput | .md | 246_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddpm/ | .md | 247_0 | |
[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline. ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddpm/#ddpmscheduler | #ddpmscheduler | .md | 247_1 |
DDPMScheduler
`DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddpm/#ddpmscheduler | #ddpmscheduler | .md | 247_2 |
DDPMSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sam... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddpm/#ddpmscheduleroutput | #ddpmscheduleroutput | .md | 247_3 |
<!--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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete/ | .md | 248_0 | |
The `KDPM2DiscreteScheduler` is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).
The original codebase can be found at [crowsonkb/k-di... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete/#kdpm2discretescheduler | #kdpm2discretescheduler | .md | 248_1 |
KDPM2DiscreteScheduler
KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating the Design Space of
Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentatio... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete/#kdpm2discretescheduler | #kdpm2discretescheduler | .md | 248_2 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete/#scheduleroutput | #scheduleroutput | .md | 248_3 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/score_sde_vp.md | https://huggingface.co/docs/diffusers/en/api/schedulers/score_sde_vp/ | .md | 249_0 | |
`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/score_sde_vp.md | https://huggingface.co/docs/diffusers/en/api/schedulers/score_sde_vp/#scoresdevpscheduler | #scoresdevpscheduler | .md | 249_1 |
ScoreSdeVpScheduler
`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Arg... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/score_sde_vp.md | https://huggingface.co/docs/diffusers/en/api/schedulers/score_sde_vp/#scoresdevpscheduler | #scoresdevpscheduler | .md | 249_2 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler/ | .md | 250_0 | |
The Euler scheduler (Algorithm 2) is 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://g... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler/#eulerdiscretescheduler | #eulerdiscretescheduler | .md | 250_1 |
EulerDiscreteScheduler
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion ste... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler/#eulerdiscretescheduler | #eulerdiscretescheduler | .md | 250_2 |
EulerDiscreteSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_ori... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler/#eulerdiscretescheduleroutput | #eulerdiscretescheduleroutput | .md | 250_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/heun.md | https://huggingface.co/docs/diffusers/en/api/schedulers/heun/ | .md | 251_0 | |
The Heun scheduler (Algorithm 1) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. The scheduler is ported from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library and created by [Katherine Crowson](https://gi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/heun.md | https://huggingface.co/docs/diffusers/en/api/schedulers/heun/#heundiscretescheduler | #heundiscretescheduler | .md | 251_1 |
HeunDiscreteScheduler
Scheduler with Heun steps for discrete beta schedules.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/heun.md | https://huggingface.co/docs/diffusers/en/api/schedulers/heun/#heundiscretescheduler | #heundiscretescheduler | .md | 251_2 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/heun.md | https://huggingface.co/docs/diffusers/en/api/schedulers/heun/#scheduleroutput | #scheduleroutput | .md | 251_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/deis.md | https://huggingface.co/docs/diffusers/en/api/schedulers/deis/ | .md | 252_0 | |
Diffusion Exponential Integrator Sampler (DEIS) is proposed in [Fast Sampling of Diffusion Models with Exponential Integrator](https://huggingface.co/papers/2204.13902) by Qinsheng Zhang and Yongxin Chen. `DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs).
This i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/deis.md | https://huggingface.co/docs/diffusers/en/api/schedulers/deis/#deismultistepscheduler | #deismultistepscheduler | .md | 252_1 |
It is recommended to set `solver_order` to 2 or 3, while `solver_order=1` is equivalent to [`DDIMScheduler`].
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set `thresholding=True` to use the dynamic thresholding. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/deis.md | https://huggingface.co/docs/diffusers/en/api/schedulers/deis/#tips | #tips | .md | 252_2 |
DEISMultistepScheduler
`DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs).
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/deis.md | https://huggingface.co/docs/diffusers/en/api/schedulers/deis/#deismultistepscheduler | #deismultistepscheduler | .md | 252_3 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/deis.md | https://huggingface.co/docs/diffusers/en/api/schedulers/deis/#scheduleroutput | #scheduleroutput | .md | 252_4 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver_inverse/ | .md | 253_0 | |
`DPMSolverMultistepInverse` is the inverted 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/2211.0109... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver_inverse/#dpmsolvermultistepinverse | #dpmsolvermultistepinverse | .md | 253_1 |
Dynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space
diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stabl... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver_inverse/#tips | #tips | .md | 253_2 |
DPMSolverMultistepInverseScheduler
`DPMSolverMultistepInverseScheduler` is the reverse scheduler of [`DPMSolverMultistepScheduler`].
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading a... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver_inverse/#dpmsolvermultistepinversescheduler | #dpmsolvermultistepinversescheduler | .md | 253_3 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver_inverse/#scheduleroutput | #scheduleroutput | .md | 253_4 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_euler_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_euler_discrete/ | .md | 254_0 | |
`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://arxiv.org/abs/2403.03206). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_euler_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_euler_discrete/#flowmatcheulerdiscretescheduler | #flowmatcheulerdiscretescheduler | .md | 254_1 |
FlowMatchEulerDiscreteScheduler
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diff... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_euler_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_euler_discrete/#flowmatcheulerdiscretescheduler | #flowmatcheulerdiscretescheduler | .md | 254_2 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete_ancestral/ | .md | 255_0 | |
The `KDPM2DiscreteScheduler` with ancestral sampling is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).
The original codebase can be ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete_ancestral/#kdpm2ancestraldiscretescheduler | #kdpm2ancestraldiscretescheduler | .md | 255_1 |
KDPM2AncestralDiscreteScheduler
KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating
the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete_ancestral/#kdpm2ancestraldiscretescheduler | #kdpm2ancestraldiscretescheduler | .md | 255_2 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_discrete_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_discrete_ancestral/#scheduleroutput | #scheduleroutput | .md | 255_3 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_heun_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_heun_discrete/ | .md | 256_0 | |
`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://arxiv.org/abs/2403.03206). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_heun_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_heun_discrete/#flowmatchheundiscretescheduler | #flowmatchheundiscretescheduler | .md | 256_1 |
FlowMatchHeunDiscreteScheduler
Heun scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffus... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/flow_match_heun_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/flow_match_heun_discrete/#flowmatchheundiscretescheduler | #flowmatchheundiscretescheduler | .md | 256_2 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/pndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/pndm/ | .md | 257_0 | |
`PNDMScheduler`, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/pndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/pndm/#pndmscheduler | #pndmscheduler | .md | 257_1 |
PNDMScheduler
`PNDMScheduler` uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step
method.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/pndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/pndm/#pndmscheduler | #pndmscheduler | .md | 257_2 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/pndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/pndm/#scheduleroutput | #scheduleroutput | .md | 257_3 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/tcd.md | https://huggingface.co/docs/diffusers/en/api/schedulers/tcd/ | .md | 258_0 | |
[Trajectory Consistency Distillation](https://huggingface.co/papers/2402.19159) by Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao and Tat-Jen Cham introduced a Strategic Stochastic Sampling (Algorithm 4) that is capable of generating good samples in a small number of steps. Distinguis... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/tcd.md | https://huggingface.co/docs/diffusers/en/api/schedulers/tcd/#tcdscheduler | #tcdscheduler | .md | 258_1 |
TCDScheduler
`TCDScheduler` incorporates the `Strategic Stochastic Sampling` introduced by the paper `Trajectory Consistency
Distillation`, extending the original Multistep Consistency Sampling to enable unrestricted trajectory traversal.
This code is based on the official repo of TCD(https://github.com/jabir-zheng... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/tcd.md | https://huggingface.co/docs/diffusers/en/api/schedulers/tcd/#tcdscheduler | #tcdscheduler | .md | 258_2 |
TCDSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_noised_sample... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/tcd.md | https://huggingface.co/docs/diffusers/en/api/schedulers/tcd/#tcdscheduleroutput | #tcdscheduleroutput | .md | 258_3 |
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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
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_sde.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_sde/ | .md | 259_0 | |
The `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_sde.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_sde/#dpmsolversdescheduler | #dpmsolversdescheduler | .md | 259_1 |
DPMSolverSDEScheduler | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_sde.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_sde/#dpmsolversdescheduler | #dpmsolversdescheduler | .md | 259_2 |
SchedulerOutput
Base class for the output of a scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/dpm_sde.md | https://huggingface.co/docs/diffusers/en/api/schedulers/dpm_sde/#scheduleroutput | #scheduleroutput | .md | 259_3 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim/ | .md | 260_0 | |
[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The abstract from the paper is:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulat... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim/#ddimscheduler | #ddimscheduler | .md | 260_1 |
The paper [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose:
<Tip warning={true}>
🧪 This ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim/#tips | #tips | .md | 260_2 |
DDIMScheduler
`DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedul... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim/#ddimscheduler | #ddimscheduler | .md | 260_3 |
DDIMSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sam... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim/#ddimscheduleroutput | #ddimscheduleroutput | .md | 260_4 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lms_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lms_discrete/ | .md | 261_0 | |
`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lms_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lms_discrete/#lmsdiscretescheduler | #lmsdiscretescheduler | .md | 261_1 |
LMSDiscreteScheduler
A linear multistep scheduler for discrete beta schedules.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, default... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lms_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lms_discrete/#lmsdiscretescheduler | #lmsdiscretescheduler | .md | 261_2 |
LMSDiscreteSchedulerOutput
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_origi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/lms_discrete.md | https://huggingface.co/docs/diffusers/en/api/schedulers/lms_discrete/#lmsdiscretescheduleroutput | #lmsdiscretescheduleroutput | .md | 261_3 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cm_stochastic_iterative/ | .md | 262_0 | |
[Consistency Models](https://huggingface.co/papers/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever introduced a multistep and onestep scheduler (Algorithm 1) that is capable of generating good samples in one or a small number of steps.
The abstract from the paper is:
*Diffusion models hav... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cm_stochastic_iterative/#cmstochasticiterativescheduler | #cmstochasticiterativescheduler | .md | 262_1 |
CMStochasticIterativeScheduler
Multistep and onestep sampling for consistency models.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cm_stochastic_iterative/#cmstochasticiterativescheduler | #cmstochasticiterativescheduler | .md | 262_2 |
CMStochasticIterativeSchedulerOutput
Output class for the scheduler's `step` function.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cm_stochastic_iterative/#cmstochasticiterativescheduleroutput | #cmstochasticiterativescheduleroutput | .md | 262_3 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/consistency_decoder.md | https://huggingface.co/docs/diffusers/en/api/schedulers/consistency_decoder/ | .md | 263_0 | |
This scheduler is a part of the [`ConsistencyDecoderPipeline`] and was introduced in [DALL-E 3](https://openai.com/dall-e-3).
The original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/consistency_decoder.md | https://huggingface.co/docs/diffusers/en/api/schedulers/consistency_decoder/#consistencydecoderscheduler | #consistencydecoderscheduler | .md | 263_1 |
ConsistencyDecoderScheduler | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/consistency_decoder.md | https://huggingface.co/docs/diffusers/en/api/schedulers/consistency_decoder/#consistencydecoderscheduler | #consistencydecoderscheduler | .md | 263_2 |
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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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim_inverse/ | .md | 264_0 | |
`DDIMInverseScheduler` is the inverted scheduler from [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The implementation is mostly based on the DDIM inversion definition from [Null-text Inversion for Editing Real Images using Guided... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim_inverse/#ddiminversescheduler | #ddiminversescheduler | .md | 264_1 |
DDIMInverseScheduler
`DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`].
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ddim_inverse.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ddim_inverse/#ddiminversescheduler | #ddiminversescheduler | .md | 264_2 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler_ancestral/ | .md | 265_0 | |
A scheduler that uses ancestral sampling with Euler method steps. 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#L72) implem... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler_ancestral/#eulerancestraldiscretescheduler | #eulerancestraldiscretescheduler | .md | 265_1 |
EulerAncestralDiscreteScheduler
Ancestral sampling with Euler method steps.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/euler_ancestral.md | https://huggingface.co/docs/diffusers/en/api/schedulers/euler_ancestral/#eulerancestraldiscretescheduler | #eulerancestraldiscretescheduler | .md | 265_2 |
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