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TransformerTemporalModel
A Transformer model for video-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
Th... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | #transformertemporalmodeloutput | .md | 217_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/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/ | .md | 218_0 | |
A UNet model from the [Stable Cascade pipeline](../pipelines/stable_cascade.md). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/#stablecascadeunet | #stablecascadeunet | .md | 218_1 |
StableCascadeUNet | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/#stablecascadeunet | #stablecascadeunet | .md | 218_2 |
<!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/ | .md | 219_0 | |
HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
With a ControlNet ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | #hunyuandit2dcontrolnetmodel | .md | 219_1 |
```py
from diffusers import HunyuanDiT2DControlNetModel
import torch
controlnet = HunyuanDiT2DControlNetModel.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", torch_dtype=torch.float16)
``` | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#example-for-loading-hunyuandit2dcontrolnetmodel | #example-for-loading-hunyuandit2dcontrolnetmodel | .md | 219_2 |
HunyuanDiT2DControlNetModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | #hunyuandit2dcontrolnetmodel | .md | 219_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/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/ | .md | 220_0 | |
Consistency decoder can be used to decode the latents from the denoising UNet in the [`StableDiffusionPipeline`]. This decoder was introduced in the [DALL-E 3 technical report](https://openai.com/dall-e-3).
The original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder)... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistency-decoder | #consistency-decoder | .md | 220_1 |
ConsistencyDecoderVAE
The consistency decoder used with DALL-E 3.
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
>>> pipe = StableDiffusionPipeline.fro... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistencydecodervae | #consistencydecodervae | .md | 220_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/ | .md | 221_0 | |
SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://arxiv.org/abs/2307.04725).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
The SparseCtrl version o... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | #sparsecontrolnetmodel | .md | 221_1 |
```python
import torch
from diffusers import SparseControlNetModel
# fp32 variant in float16
# 1. Scribble checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16)
# 2. RGB checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/a... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#example-for-loading-sparsecontrolnetmodel | #example-for-loading-sparsecontrolnetmodel | .md | 221_2 |
SparseControlNetModel
A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
Models](https://arxiv.org/abs/2311.16933).
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
conditioning_channels (`int`, defaults to 4):
The number of i... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | #sparsecontrolnetmodel | .md | 221_3 |
SparseControlNetOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetoutput | #sparsecontrolnetoutput | .md | 221_4 |
<!--Copyright 2024 The HuggingFace Team and The InstantX 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/ | .md | 222_0 | |
SD3ControlNetModel is an implementation of ControlNet for Stable Diffusion 3.
The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | #sd3controlnetmodel | .md | 222_1 |
By default the [`SD3ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
pipe = ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#loading-from-the-original-format | #loading-from-the-original-format | .md | 222_2 |
SD3ControlNetModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | #sd3controlnetmodel | .md | 222_3 |
SD3ControlNetOutput
SD3ControlNetOutput(controlnet_block_samples: Tuple[torch.Tensor]) | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetoutput | #sd3controlnetoutput | .md | 222_4 |
<!--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/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/ | .md | 223_0 | |
The VQ-VAE model was introduced in [Neural Discrete Representation Learning](https://huggingface.co/papers/1711.00937) by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. The model is used in 🤗 Diffusers to decode latent representations into images. Unlike [`AutoencoderKL`], the [`VQModel`] works in a quantize... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | #vqmodel | .md | 223_1 |
VQModel
A VQ-VAE model for decoding latent representations.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input ima... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | #vqmodel | .md | 223_2 |
VQEncoderOutput
Output of VQModel encoding method.
Args:
latents (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The encoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqencoderoutput | #vqencoderoutput | .md | 223_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/models/latte_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/latte_transformer3d/ | .md | 224_0 | |
A Diffusion Transformer model for 3D data from [Latte](https://github.com/Vchitect/Latte).
LatteTransformer3DModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/latte_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/latte_transformer3d/#lattetransformer3dmodel | #lattetransformer3dmodel | .md | 224_1 |
<!--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/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/ | .md | 225_0 | |
The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system becau... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | #unet1dmodel | .md | 225_1 |
UNet1DModel
A 1D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int`, *optional*): Def... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | #unet1dmodel | .md | 225_2 |
UNet1DOutput
The output of [`UNet1DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, sample_size)`):
The hidden states output from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1doutput | #unet1doutput | .md | 225_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/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/ | .md | 226_0 | |
The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system becau... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | #unet3dconditionmodel | .md | 226_1 |
UNet3DConditionModel
A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Paramet... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | #unet3dconditionmodel | .md | 226_2 |
UNet3DConditionOutput
The output of [`UNet3DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionoutput | #unet3dconditionoutput | .md | 226_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/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/ | .md | 227_0 | |
The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system becau... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | #unet2dconditionmodel | .md | 227_1 |
UNet2DConditionModel
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Paramet... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | #unet2dconditionmodel | .md | 227_2 |
UNet2DConditionOutput
The output of [`UNet2DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionoutput | #unet2dconditionoutput | .md | 227_3 |
[[autodoc]] FlaxUNet2DConditionModel: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#flaxunet2dconditionmodel | #flaxunet2dconditionmodel | .md | 227_4 |
[[autodoc]] FlaxUNet2DConditionOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#flaxunet2dconditionoutput | #flaxunet2dconditionoutput | .md | 227_5 |
<!--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/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/ | .md | 228_0 | |
[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
<Tip>
Learn how to load an IP-Adapter checkpoint and... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ip-adapter | #ip-adapter | .md | 228_1 |
IPAdapterMixin
Mixin for handling IP Adapters. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ipadaptermixin | #ipadaptermixin | .md | 228_2 |
SD3IPAdapterMixin
Mixin for handling StableDiffusion 3 IP Adapters.
- all
- is_ip_adapter_active | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#sd3ipadaptermixin | #sd3ipadaptermixin | .md | 228_3 |
IPAdapterMaskProcessor
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `Tru... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ipadaptermaskprocessor | #ipadaptermaskprocessor | .md | 228_4 |
<!--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/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/ | .md | 229_0 | |
LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | #lora | .md | 229_1 |
StableDiffusionLoraLoaderMixin
Load LoRA layers into Stable Diffusion [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#stablediffusionloraloadermixin | #stablediffusionloraloadermixin | .md | 229_2 |
StableDiffusionXLLoraLoaderMixin
Load LoRA layers into Stable Diffusion XL [`UNet2DConditionModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextM... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#stablediffusionxlloraloadermixin | #stablediffusionxlloraloadermixin | .md | 229_3 |
SD3LoraLoaderMixin
Load LoRA layers into [`SD3Transformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection).
Specific t... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#sd3loraloadermixin | #sd3loraloadermixin | .md | 229_4 |
FluxLoraLoaderMixin
Load LoRA layers into [`FluxTransformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
Specific to [`StableDiffusion3Pipeline`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#fluxloraloadermixin | #fluxloraloadermixin | .md | 229_5 |
CogVideoXLoraLoaderMixin
Load LoRA layers into [`CogVideoXTransformer3DModel`]. Specific to [`CogVideoXPipeline`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#cogvideoxloraloadermixin | #cogvideoxloraloadermixin | .md | 229_6 |
Mochi1LoraLoaderMixin
Load LoRA layers into [`MochiTransformer3DModel`]. Specific to [`MochiPipeline`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#mochi1loraloadermixin | #mochi1loraloadermixin | .md | 229_7 |
AmusedLoraLoaderMixin | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#amusedloraloadermixin | #amusedloraloadermixin | .md | 229_8 |
LoraBaseMixin
Utility class for handling LoRAs. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lorabasemixin | #lorabasemixin | .md | 229_9 |
<!--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/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/ | .md | 230_0 | |
The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
* a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models
* a model stored in their originally d... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#single-files | #single-files | .md | 230_1 |
- [`StableDiffusionPipeline`]
- [`StableDiffusionImg2ImgPipeline`]
- [`StableDiffusionInpaintPipeline`]
- [`StableDiffusionControlNetPipeline`]
- [`StableDiffusionControlNetImg2ImgPipeline`]
- [`StableDiffusionControlNetInpaintPipeline`]
- [`StableDiffusionUpscalePipeline`]
- [`StableDiffusionXLPipeline`]
- [`StableDif... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#supported-pipelines | #supported-pipelines | .md | 230_2 |
- [`UNet2DConditionModel`]
- [`StableCascadeUNet`]
- [`AutoencoderKL`]
- [`ControlNetModel`]
- [`SD3Transformer2DModel`]
- [`FluxTransformer2DModel`] | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#supported-models | #supported-models | .md | 230_3 |
FromSingleFileMixin
Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#fromsinglefilemixin | #fromsinglefilemixin | .md | 230_4 |
FromOriginalModelMixin
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#fromoriginalmodelmixin | #fromoriginalmodelmixin | .md | 230_5 |
<!--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/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/ | .md | 231_0 | |
This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
The [`SD3Transformer2DLoadersMixin`] class currently ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/#sd3transformer2d | #sd3transformer2d | .md | 231_1 |
SD3Transformer2DLoadersMixin
Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`.
- all
- _load_ip_adapter_weights | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/#sd3transformer2dloadersmixin | #sd3transformer2dloadersmixin | .md | 231_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/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/ | .md | 232_0 | |
Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
[`TextualInversionLoaderMixin`] provides a function for loading Textual ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/#textual-inversion | #textual-inversion | .md | 232_1 |
TextualInversionLoaderMixin
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/#textualinversionloadermixin | #textualinversionloadermixin | .md | 232_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/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/ | .md | 233_0 | |
Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is us... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet | #unet | .md | 233_1 |
UNet2DConditionLoadersMixin
Load LoRA layers into a [`UNet2DCondtionModel`]. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet2dconditionloadersmixin | #unet2dconditionloadersmixin | .md | 233_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/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/ | .md | 234_0 | |
Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/#peft | #peft | .md | 234_1 |
PeftAdapterMixin
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them in a base model, check out the PEFT
[documentation](https://huggingface.co/docs/peft/index).
Install the latest version of PEFT, and use thi... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/#peftadaptermixin | #peftadaptermixin | .md | 234_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/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/ | .md | 235_0 | |
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... | /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 | #cosinedpmsolvermultistepscheduler | .md | 235_1 |
CosineDPMSolverMultistepScheduler | /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 | #cosinedpmsolvermultistepscheduler | .md | 235_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/cosine_dpm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/cosine_dpm/#scheduleroutput | #scheduleroutput | .md | 235_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/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/ | .md | 236_0 | |
`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.... | /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 | #dpmsolversinglestepscheduler | .md | 236_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/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#tips | #tips | .md | 236_2 |
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... | /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 | #dpmsolversinglestepscheduler | .md | 236_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/singlestep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/singlestep_dpm_solver/#scheduleroutput | #scheduleroutput | .md | 236_4 |
<!--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/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/ | .md | 237_0 | |
`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). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#ipndmscheduler | #ipndmscheduler | .md | 237_1 |
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 ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#ipndmscheduler | #ipndmscheduler | .md | 237_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/ipndm.md | https://huggingface.co/docs/diffusers/en/api/schedulers/ipndm/#scheduleroutput | #scheduleroutput | .md | 237_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/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/ | .md | 238_0 | |
`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... | /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 | #edmdpmsolvermultistepscheduler | .md | 238_1 |
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... | /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 | #edmdpmsolvermultistepscheduler | .md | 238_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/edm_multistep_dpm_solver.md | https://huggingface.co/docs/diffusers/en/api/schedulers/edm_multistep_dpm_solver/#scheduleroutput | #scheduleroutput | .md | 238_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/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/ | .md | 239_0 | |
🤗 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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulers | #schedulers | .md | 239_1 |
| A1111/k-diffusion | 🤗 Diffusers |
|--------------------------|----------------------------------------------------------------------------|
| Karras | init with `use_karras_sigmas=True` |
| ... | /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 | #noise-schedules-and-schedule-types | .md | 239_2 |
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... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#schedulermixin | #schedulermixin | .md | 239_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/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#scheduleroutput | #scheduleroutput | .md | 239_4 |
[`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.
The different schedulers in this class, dependin... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#karrasdiffusionschedulers | #karrasdiffusionschedulers | .md | 239_5 |
PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/schedulers/overview.md | https://huggingface.co/docs/diffusers/en/api/schedulers/overview/#pushtohubmixin | #pushtohubmixin | .md | 239_6 |
<!--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/unipc.md | https://huggingface.co/docs/diffusers/en/api/schedulers/unipc/ | .md | 240_0 | |
`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... | /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_1 |
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