source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/#peftadaptermixin | .md | - Attach multiple adapters and iteratively activate/deactivate them.
- Activate/deactivate all adapters from the model.
- Get a list of the active adapters. | 234_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/ | .md | <!--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... | 235_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/ | .md | 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.
--> | 235_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/#cosinedpmsolvermultistepscheduler | .md | The [`CosineDPMSolverMultistepScheduler`] is a variant of [`DPMSolverMultistepScheduler`] with cosine schedule, proposed by Nichol and Dhariwal (2021).
It is being used in the [Stable Audio Open](https://arxiv.org/abs/2407.14358) paper and the [Stability-AI/stable-audio-tool](https://github.com/Stability-AI/stable-audi... | 235_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/#cosinedpmsolvermultistepscheduler | .md | CosineDPMSolverMultistepScheduler | 235_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/#scheduleroutput | .md | 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. | 235_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/ | .md | <!--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... | 236_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/ | .md | 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.
--> | 236_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | `DPMSolverSinglestepScheduler` is a single step 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.... | 236_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | 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.
The original implementation can be ... | 236_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#tips | .md | 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 ... | 236_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | DPMSolverSinglestepScheduler
`DPMSolverSinglestepScheduler` 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.
Ar... | 236_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linea... | 236_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
solver_order (`int`, defaults to 2):
The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
sampling, and `solver_order=3` for unconditional sampling.
prediction_type (`str`, defau... | 236_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to us... | 236_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | as Stable Diffusion.
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
sample_max_value (`float`, defaults to 1.0):
The threshold value for dynamic thresholding. Valid only when `thresholding=True` and
`algorithm_type="dpmsolver+... | 236_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | Algorithm type for the solver; can be `dpmsolver` or `dpmsolver++` or `sde-dpmsolver++`. The `dpmsolver`
type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) paper, and the
`dpmsolver++` type implements the algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095)
... | 236_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | sampling like in Stable Diffusion.
solver_type (`str`, defaults to `midpoint`):
Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
lower_order_final (`bool`, defau... | 236_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the s... | 236_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
use_beta_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Re... | 236_3_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | final_sigmas_type (`str`, *optional*, defaults to `"zero"`):
The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
lambda_min_clipped (`float`, defaults to `-inf`):
Cli... | 236_3_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#dpmsolversinglestepscheduler | .md | cosine (`squaredcos_cap_v2`) noise schedule.
variance_type (`str`, *optional*):
Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output
contains the predicted Gaussian variance. | 236_3_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#scheduleroutput | .md | 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. | 236_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/ | .md | <!--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... | 237_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/ | .md | 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.
--> | 237_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#ipndmscheduler | .md | `IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at [crowsonkb/v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296). | 237_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#ipndmscheduler | .md | IPNDMScheduler
A fourth-order Improved Pseudo Linear Multistep 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 ... | 237_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#scheduleroutput | .md | 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. | 237_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/ | .md | <!--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... | 238_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/ | .md | 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.
--> | 238_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | `EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, 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++: Fas... | 238_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | 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. | 238_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | EDMDPMSolverMultistepScheduler
Implements DPMSolverMultistepScheduler in EDM formulation as presented in Karras et al. 2022 [1].
`EDMDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
ht... | 238_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | 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:
sigma_min (`float`, *optional*, defaults to 0.002):
Minimum noise magnitude in the sigma schedule. This was set to 0.0... | 238_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | range is [0, 10].
sigma_max (`float`, *optional*, defaults to 80.0):
Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable
range is [0.2, 80.0].
sigma_data (`float`, *optional*, defaults to 0.5):
The standard deviation of the data distribution. This is set to 0.5 in the ... | 238_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper
(https://arxiv.org/abs/2206.00364). Other acceptable value is "exponential". The exponential schedule was
incorporated in this model: https://huggingface.co/stabilityai/cosxl.
num_train_timesteps (`int`, defaults to 1000):
T... | 238_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
sampling, and `solver_order=3` for unconditional sampling.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion... | 238_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic t... | 238_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | The threshold value for dynamic thresholding. Valid only when `thresholding=True` and
`algorithm_type="dpmsolver++"`.
algorithm_type (`str`, defaults to `dpmsolver++`):
Algorithm type for the solver; can be `dpmsolver++` or `sde-dpmsolver++`. The `dpmsolver++` type implements
the algorithms in the [DPMSolver++](https:/... | 238_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.
solver_type (`str`, defaults to `midpoint`):
Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
sample quality, especially for a small number of steps. It is... | 238_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final (`bool`, defaults to `False`):
Whether to use Euler's method in the final step. It is a trade-off between numerical stabilit... | 238_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#edmdpmsolvermultistepscheduler | .md | steps, but sometimes may result in blurring.
final_sigmas_type (`str`, defaults to `"zero"`):
The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. | 238_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#scheduleroutput | .md | 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. | 238_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/ | .md | <!--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... | 239_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/ | .md | 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.
--> | 239_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | 🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by... | 239_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`. | 239_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | Depending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:
- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model
- during *inference*, a scheduler defines... | 239_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | - during *inference*, a scheduler defines how to update a sample based on a pretrained model's output
Many schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map t... | 239_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | A1111/k-diffusion | 🤗 Diffusers | Usage |
|---------------------|-------------------------------------|-----------------------------------------------------------------------------------... | 239_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM++ 2M | [`DPMSolverMultistepScheduler`] | |
| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` ... | 239_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type="sde-dpmsolver++"` |
| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type="sde-dpmsolver++"` ... | 239_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` |
| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | |
| DPM... | 239_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM2 | [`KDPM2DiscreteScheduler`] | |
| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` ... | 239_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | |
| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` ... | 239_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | DPM adaptive | N/A | |
| DPM fast | N/A | ... | 239_1_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | Euler | [`EulerDiscreteScheduler`] | |
| Euler a | [`EulerAncestralDiscreteScheduler`] | ... | 239_1_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | Heun | [`HeunDiscreteScheduler`] | |
| LMS | [`LMSDiscreteScheduler`] | ... | 239_1_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` |
| N/A | [`DEISMultistepScheduler`] | ... | 239_1_13 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | .md | | N/A | [`UniPCMultistepScheduler`] | | | 239_1_14 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#noise-schedules-and-schedule-types | .md | | A1111/k-diffusion | 🤗 Diffusers |
|--------------------------|----------------------------------------------------------------------------|
| Karras | init with `use_karras_sigmas=True` |
| ... | 239_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#noise-schedules-and-schedule-types | .md | | sgm_uniform | init with `timestep_spacing="trailing"` |
| simple | init with `timestep_spacing="trailing"` |
| exponential | init with `timestep_spacing="linspace"`, `use_exponential_sigmas=True` |
| ... | 239_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#noise-schedules-and-schedule-types | .md | | beta | init with `timestep_spacing="linspace"`, `use_beta_sigmas=True` |
All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. | 239_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulermixin | .md | SchedulerMixin
Base class for all schedulers.
[`SchedulerMixin`] contains common functions shared by all schedulers such as general loading and saving
functionalities.
[`ConfigMixin`] takes care of storing the configuration attributes (like `num_train_timesteps`) that are passed to
the scheduler's `__init__` func... | 239_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulermixin | .md | Class attributes:
- **_compatibles** (`List[str]`) -- A list of scheduler classes that are compatible with the parent scheduler
class. Use [`~ConfigMixin.from_config`] to load a different compatible scheduler class (should be overridden
by parent class). | 239_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#scheduleroutput | .md | 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. | 239_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#karrasdiffusionschedulers | .md | [`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed. | 239_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#karrasdiffusionschedulers | .md | The different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffuser... | 239_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#pushtohubmixin | .md | PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | 239_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/ | .md | <!--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... | 240_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/ | .md | 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.
--> | 240_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | `UniPCMultistepScheduler` is a training-free framework designed for fast sampling of diffusion models. It was introduced in [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://huggingface.co/papers/2302.04867) by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu.
It... | 240_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | It consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
UniPC is by design model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction and data prediction models. The c... | 240_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | *Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent ... | 240_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | cannot generate satisfying images in many applications where fewer steps (e.g., <10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports... | 240_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both ... | 240_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256×256 (conditional) with only 10 function evaluations. Code is available at [this https URL](https://github.com/wl-zhao/UniPC).* | 240_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#tips | .md | 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... | 240_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | 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... | 240_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | The number of diffusion steps to train the model.
beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepp... | 240_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
solver_order (`int`, default `2`):
The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + ... | 240_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | unconditional sampling.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
Video](https://imagen.research.googl... | 240_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion.
dynamic_thresholding_ratio (`float`, defaults to 0.995):
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
sample_max_value (`float`, defaults to 1.0):
The t... | 240_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | predict_x0 (`bool`, defaults to `True`):
Whether to use the updating algorithm on the predicted x0.
solver_type (`str`, default `bh2`):
Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`
otherwise.
lower_order_final (`bool`, default `True`):
Whether to use lower-... | 240_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
disable_corrector (`list`, default `[]`):
Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`
and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Cor... | 240_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | solver_p (`SchedulerMixin`, default `None`):
Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.
use_karras_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
the sigmas are determined ac... | 240_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
use_beta_sigmas (`bool`, *optional*, defaults to `False`):
Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Re... | 240_3_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | timestep_spacing (`str`, defaults to `"linspace"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
steps_offset (`int`, defaults to 0):
An offset added to the inference steps, ... | 240_3_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
rescale_betas_zero_snr (`bool`, defaults to `False`):
Whether to rescale the betas to have zero terminal SNR. This... | 240_3_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#unipcmultistepscheduler | .md | dark samples instead of limiting it to samples with medium brightness. Loosely related to
[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). | 240_3_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/#scheduleroutput | .md | 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. | 240_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/ | .md | <!--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... | 241_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/ | .md | 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.
--> | 241_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | `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... | 241_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | *Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses o... | 241_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the ... | 241_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperf... | 241_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | The original implementation can be found at [andreas128/RePaint](https://github.com/andreas128/). | 241_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | 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 (`... | 241_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/repaint.md | https://huggingface.co/docs/diffusers/en/api/schedulers/repaint/#repaintscheduler | .md | beta_start (`float`, defaults to 0.0001):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
`linear`, `scaled_linea... | 241_2_1 |
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