text stringlengths 3 14.4k | source stringclasses 273
values | url stringlengths 47 172 | source_section stringlengths 0 95 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
FluxControlNetOutput | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_flux.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_flux/#fluxcontrolnetoutput | #fluxcontrolnetoutput | .md | 193_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/unet-motion.md | https://huggingface.co/docs/diffusers/en/api/models/unet-motion/ | .md | 194_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 becaus... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet-motion.md | https://huggingface.co/docs/diffusers/en/api/models/unet-motion/#unetmotionmodel | #unetmotionmodel | .md | 194_1 |
UNetMotionModel
A modified 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). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet-motion.md | https://huggingface.co/docs/diffusers/en/api/models/unet-motion/#unetmotionmodel | #unetmotionmodel | .md | 194_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/unet-motion.md | https://huggingface.co/docs/diffusers/en/api/models/unet-motion/#unet3dconditionoutput | #unet3dconditionoutput | .md | 194_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_dc.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_dc/ | .md | 195_0 | |
The 2D Autoencoder model used in [SANA](https://huggingface.co/papers/2410.10629) and introduced in [DCAE](https://huggingface.co/papers/2410.10733) by authors Junyu Chen\*, Han Cai\*, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, Song Han from MIT HAN Lab.
The abstract from the paper is:
*We... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_dc.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_dc/#autoencoderdc | #autoencoderdc | .md | 195_1 |
```python
from difusers import AutoencoderDC
ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
model = AutoencoderDC.from_single_file(ckpt_path)
```
The `AutoencoderDC` model has `in` and `mix` single file checkpoint variants that have matching checkpoint keys, but u... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_dc.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_dc/#load-a-model-in-diffusers-via-fromsinglefile | #load-a-model-in-diffusers-via-fromsinglefile | .md | 195_2 |
AutoencoderDC
An Autoencoder model introduced in [DCAE](https://arxiv.org/abs/2410.10733) and used in
[SANA](https://arxiv.org/abs/2410.10629).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Args:
in_... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_dc.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_dc/#autoencoderdc | #autoencoderdc | .md | 195_3 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_dc.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_dc/#decoderoutput | #decoderoutput | .md | 195_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/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/ | .md | 196_0 | |
The [U-ViT](https://hf.co/papers/2301.11093) model is a vision transformer (ViT) based UNet. This model incorporates elements from ViT (considers all inputs such as time, conditions and noisy image patches as tokens) and a UNet (long skip connections between the shallow and deep layers). The skip connection is importan... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#uvit2dmodel | #uvit2dmodel | .md | 196_1 |
UVit2DModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#uvit2dmodel | #uvit2dmodel | .md | 196_2 |
UVit2DConvEmbed | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#uvit2dconvembed | #uvit2dconvembed | .md | 196_3 |
UVitBlock | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#uvitblock | #uvitblock | .md | 196_4 |
ConvNextBlock | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#convnextblock | #convnextblock | .md | 196_5 |
ConvMlmLayer | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#convmlmlayer | #convmlmlayer | .md | 196_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/models/stable_audio_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/stable_audio_transformer/ | .md | 197_0 | |
A Transformer model for audio waveforms from [Stable Audio Open](https://huggingface.co/papers/2407.14358). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_audio_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/stable_audio_transformer/#stableaudioditmodel | #stableaudioditmodel | .md | 197_1 |
StableAudioDiTModel
The Diffusion Transformer model introduced in Stable Audio.
Reference: https://github.com/Stability-AI/stable-audio-tools
Parameters:
sample_size ( `int`, *optional*, defaults to 1024): The size of the input sample.
in_channels (`int`, *optional*, defaults to 64): The number of channels in the... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_audio_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/stable_audio_transformer/#stableaudioditmodel | #stableaudioditmodel | .md | 197_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/autoencoder_kl_hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_kl_hunyuan_video/ | .md | 198_0 | |
The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo](https://github.com/Tencent/HunyuanVideo/), which was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent.
The model can be loaded with the following co... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_kl_hunyuan_video/#autoencoderklhunyuanvideo | #autoencoderklhunyuanvideo | .md | 198_1 |
AutoencoderKLHunyuanVideo
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemente... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_kl_hunyuan_video/#autoencoderklhunyuanvideo | #autoencoderklhunyuanvideo | .md | 198_2 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_kl_hunyuan_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_kl_hunyuan_video/#decoderoutput | #decoderoutput | .md | 198_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/autoencoder_tiny.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_tiny/ | .md | 199_0 | |
Tiny AutoEncoder for Stable Diffusion (TAESD) was introduced in [madebyollin/taesd](https://github.com/madebyollin/taesd) by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion's VAE that can quickly decode the latents in a [`StableDiffusionPipeline`] or [`StableDiffusionXLPipeline`] almost instantly. ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_tiny.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_tiny/#tiny-autoencoder | #tiny-autoencoder | .md | 199_1 |
AutoencoderTiny
A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
[`AutoencoderTiny`] is a wrapper around the original implementation of `TAESD`.
This model inherits from [`ModelMixin`]. Check the superclass documentation for its generic methods implement... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_tiny.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_tiny/#autoencodertiny | #autoencodertiny | .md | 199_2 |
AutoencoderTinyOutput
Output of AutoencoderTiny encoding method.
Args:
latents (`torch.Tensor`): Encoded outputs of the `Encoder`. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoder_tiny.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoder_tiny/#autoencodertinyoutput | #autoencodertinyoutput | .md | 199_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/ltx_video_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/ltx_video_transformer3d/ | .md | 200_0 | |
A Diffusion Transformer model for 3D data from [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.
The model can be loaded with the following code snippet.
```python
from diffusers import LTXVideoTransformer3DModel
transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LT... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/ltx_video_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/ltx_video_transformer3d/#ltxvideotransformer3dmodel | #ltxvideotransformer3dmodel | .md | 200_1 |
LTXVideoTransformer3DModel
A Transformer model for video-like data used in [LTX](https://huggingface.co/Lightricks/LTX-Video).
Args:
in_channels (`int`, defaults to `128`):
The number of channels in the input.
out_channels (`int`, defaults to `128`):
The number of channels in the output.
patch_size (`int`, defaults... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/ltx_video_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/ltx_video_transformer3d/#ltxvideotransformer3dmodel | #ltxvideotransformer3dmodel | .md | 200_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/ltx_video_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/ltx_video_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 200_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_video_transformer_3d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_video_transformer_3d/ | .md | 201_0 | |
A Diffusion Transformer model for 3D video-like data was introduced in [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://huggingface.co/papers/2412.03603) by Tencent.
The model can be loaded with the following code snippet.
```python
from diffusers import HunyuanVideoTransformer3DMode... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_video_transformer_3d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_video_transformer_3d/#hunyuanvideotransformer3dmodel | #hunyuanvideotransformer3dmodel | .md | 201_1 |
HunyuanVideoTransformer3DModel
A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo).
Args:
in_channels (`int`, defaults to `16`):
The number of channels in the input.
out_channels (`int`, defaults to `16`):
The number of channels in the output.
num_attention_he... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_video_transformer_3d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_video_transformer_3d/#hunyuanvideotransformer3dmodel | #hunyuanvideotransformer3dmodel | .md | 201_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_video_transformer_3d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_video_transformer_3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 201_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/mochi_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/mochi_transformer3d/ | .md | 202_0 | |
A Diffusion Transformer model for 3D video-like data was introduced in [Mochi-1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Genmo.
The model can be loaded with the following code snippet.
```python
from diffusers import MochiTransformer3DModel
transformer = MochiTransformer3DModel.from_pretrained("ge... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/mochi_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/mochi_transformer3d/#mochitransformer3dmodel | #mochitransformer3dmodel | .md | 202_1 |
MochiTransformer3DModel
A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
patch_size (`int`, defaults to `2`):
The size of the patches to use in the patch embedding layer.
num_attention_heads (`int`, defaults to `24`):
The number of heads to use for m... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/mochi_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/mochi_transformer3d/#mochitransformer3dmodel | #mochitransformer3dmodel | .md | 202_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/mochi_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/mochi_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 202_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_allegro.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_allegro/ | .md | 203_0 | |
The 3D variational autoencoder (VAE) model with KL loss used in [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
The model can be loaded with the following code snippet.
`... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_allegro.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_allegro/#autoencoderklallegro | #autoencoderklallegro | .md | 203_1 |
AutoencoderKLAllegro
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in
[Allegro](https://github.com/rhymes-ai/Allegro).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (su... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_allegro.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_allegro/#autoencoderklallegro | #autoencoderklallegro | .md | 203_2 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_allegro.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_allegro/#autoencoderkloutput | #autoencoderkloutput | .md | 203_3 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_allegro.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_allegro/#decoderoutput | #decoderoutput | .md | 203_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/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/ | .md | 204_0 | |
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-image generation by conditioning the model on additional inputs such as edge ma... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | #controlnetmodel | .md | 204_1 |
By default the [`ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
```py
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
url = "https://huggingface.co/lllya... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#loading-from-the-original-format | #loading-from-the-original-format | .md | 204_2 |
ControlNetModel
A ControlNet model.
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, defaults to 0):
The frequency shift to apply to the time embedding.
down_bl... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | #controlnetmodel | .md | 204_3 |
ControlNetOutput
The output of [`ControlNetModel`].
Args:
down_block_res_samples (`tuple[torch.Tensor]`):
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetoutput | #controlnetoutput | .md | 204_4 |
FlaxControlNetModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#flaxcontrolnetmodel | #flaxcontrolnetmodel | .md | 204_5 |
[[autodoc]] FlaxControlNetOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#flaxcontrolnetoutput | #flaxcontrolnetoutput | .md | 204_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_ltx_video/ | .md | 205_0 | |
The 3D variational autoencoder (VAE) model with KL loss used in [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.
The model can be loaded with the following code snippet.
```python
from diffusers import AutoencoderKLLTXVideo
vae = AutoencoderKLLTXVideo.from_pretrained("Lightricks/LTX... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_ltx_video/#autoencoderklltxvideo | #autoencoderklltxvideo | .md | 205_1 |
AutoencoderKLLTXVideo
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[LTX](https://huggingface.co/Lightricks/LTX-Video).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_ltx_video/#autoencoderklltxvideo | #autoencoderklltxvideo | .md | 205_2 |
AutoencoderKLOutput
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_ltx_video/#autoencoderkloutput | #autoencoderkloutput | .md | 205_3 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_ltx_video.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_ltx_video/#decoderoutput | #decoderoutput | .md | 205_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/hunyuan_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_transformer2d/ | .md | 206_0 | |
A Diffusion Transformer model for 2D data from [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_transformer2d/#hunyuandit2dmodel | #hunyuandit2dmodel | .md | 206_1 |
HunyuanDiT2DModel
HunYuanDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16):
The number of heads to use for multi-head attention.
attention_... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/hunyuan_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/hunyuan_transformer2d/#hunyuandit2dmodel | #hunyuandit2dmodel | .md | 206_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/models/lumina_nextdit2d.md | https://huggingface.co/docs/diffusers/en/api/models/lumina_nextdit2d/ | .md | 207_0 | |
A Next Version of Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/lumina_nextdit2d.md | https://huggingface.co/docs/diffusers/en/api/models/lumina_nextdit2d/#luminanextdit2dmodel | #luminanextdit2dmodel | .md | 207_1 |
LuminaNextDiT2DModel
LuminaNextDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a num... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/lumina_nextdit2d.md | https://huggingface.co/docs/diffusers/en/api/models/lumina_nextdit2d/#luminanextdit2dmodel | #luminanextdit2dmodel | .md | 207_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/models/cogvideox_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/cogvideox_transformer3d/ | .md | 208_0 | |
A Diffusion Transformer model for 3D data from [CogVideoX](https://github.com/THUDM/CogVideo) was introduced in [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://github.com/THUDM/CogVideo/blob/main/resources/CogVideoX.pdf) by Tsinghua University & ZhipuAI.
The model can be loaded with the... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/cogvideox_transformer3d/#cogvideoxtransformer3dmodel | #cogvideoxtransformer3dmodel | .md | 208_1 |
CogVideoXTransformer3DModel
A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
Parameters:
num_attention_heads (`int`, defaults to `30`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, defaults to `64`):
The number of channels in each head.
in... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/cogvideox_transformer3d/#cogvideoxtransformer3dmodel | #cogvideoxtransformer3dmodel | .md | 208_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogvideox_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/cogvideox_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 208_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/allegro_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/allegro_transformer3d/ | .md | 209_0 | |
A Diffusion Transformer model for 3D data from [Allegro](https://github.com/rhymes-ai/Allegro) was introduced in [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://huggingface.co/papers/2410.15458) by RhymesAI.
The model can be loaded with the following code snippet.
```python
from dif... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/allegro_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/allegro_transformer3d/#allegrotransformer3dmodel | #allegrotransformer3dmodel | .md | 209_1 |
AllegroTransformer3DModel | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/allegro_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/allegro_transformer3d/#allegrotransformer3dmodel | #allegrotransformer3dmodel | .md | 209_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/allegro_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/allegro_transformer3d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 209_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/cogview3plus_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/cogview3plus_transformer2d/ | .md | 210_0 | |
A Diffusion Transformer model for 2D data from [CogView3Plus](https://github.com/THUDM/CogView3) was introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion](https://huggingface.co/papers/2403.05121) by Tsinghua University & ZhipuAI.
The model can be loaded with the following code snipp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogview3plus_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/cogview3plus_transformer2d/#cogview3plustransformer2dmodel | #cogview3plustransformer2dmodel | .md | 210_1 |
CogView3PlusTransformer2DModel
The Transformer model introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay
Diffusion](https://huggingface.co/papers/2403.05121).
Args:
patch_size (`int`, defaults to `2`):
The size of the patches to use in the patch embedding layer.
in_channels (`int`, defaults... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogview3plus_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/cogview3plus_transformer2d/#cogview3plustransformer2dmodel | #cogview3plustransformer2dmodel | .md | 210_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/cogview3plus_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/cogview3plus_transformer2d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 210_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_mochi.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_mochi/ | .md | 211_0 | |
The 3D variational autoencoder (VAE) model with KL loss used in [Mochi](https://github.com/genmoai/models) was introduced in [Mochi 1 Preview](https://huggingface.co/genmo/mochi-1-preview) by Tsinghua University & ZhipuAI.
The model can be loaded with the following code snippet.
```python
from diffusers import Auto... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_mochi.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_mochi/#autoencoderklmochi | #autoencoderklmochi | .md | 211_1 |
AutoencoderKLMochi
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[Mochi 1 preview](https://github.com/genmoai/models).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models ... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_mochi.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_mochi/#autoencoderklmochi | #autoencoderklmochi | .md | 211_2 |
DecoderOutput
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/autoencoderkl_mochi.md | https://huggingface.co/docs/diffusers/en/api/models/autoencoderkl_mochi/#decoderoutput | #decoderoutput | .md | 211_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 ag... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sana_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sana_transformer2d/ | .md | 212_0 | |
A Diffusion Transformer model for 2D data from [SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://huggingface.co/papers/2410.10629) was introduced from NVIDIA and MIT HAN Lab, by Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Lige... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sana_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sana_transformer2d/#sanatransformer2dmodel | #sanatransformer2dmodel | .md | 212_1 |
SanaTransformer2DModel
A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models.
Args:
in_channels (`int`, defaults to `32`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `32`):
The number of channels in the output.
num_attention_hea... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sana_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sana_transformer2d/#sanatransformer2dmodel | #sanatransformer2dmodel | .md | 212_2 |
Transformer2DModelOutput
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` inp... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sana_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sana_transformer2d/#transformer2dmodeloutput | #transformer2dmodeloutput | .md | 212_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/sd3_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sd3_transformer2d/ | .md | 213_0 | |
The Transformer model introduced in [Stable Diffusion 3](https://hf.co/papers/2403.03206). Its novelty lies in the MMDiT transformer block. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sd3_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sd3_transformer2d/#sd3-transformer-model | #sd3-transformer-model | .md | 213_1 |
SD3Transformer2DModel
The Transformer model introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.
patch_size (`int`): Patch size to turn... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sd3_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/sd3_transformer2d/#sd3transformer2dmodel | #sd3transformer2dmodel | .md | 213_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/models/pixart_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/pixart_transformer2d/ | .md | 214_0 | |
A Transformer model for image-like data from [PixArt-Alpha](https://huggingface.co/papers/2310.00426) and [PixArt-Sigma](https://huggingface.co/papers/2403.04692). | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/pixart_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/pixart_transformer2d/#pixarttransformer2dmodel | #pixarttransformer2dmodel | .md | 214_1 |
PixArtTransformer2DModel
A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
https://arxiv.org/abs/2403.04692).
Parameters:
num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (int, optional, defa... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/pixart_transformer2d.md | https://huggingface.co/docs/diffusers/en/api/models/pixart_transformer2d/#pixarttransformer2dmodel | #pixarttransformer2dmodel | .md | 214_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/models/prior_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/prior_transformer/ | .md | 215_0 | |
The Prior Transformer was originally introduced in [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.
... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/prior_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/prior_transformer/#priortransformer | #priortransformer | .md | 215_1 |
PriorTransformer
A Prior Transformer model.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_layers (`int`, *optional*, defaults to 20): The numbe... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/prior_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/prior_transformer/#priortransformer | #priortransformer | .md | 215_2 |
PriorTransformerOutput
The output of [`PriorTransformer`].
Args:
predicted_image_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):
The predicted CLIP image embedding conditioned on the CLIP text embedding input. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/prior_transformer.md | https://huggingface.co/docs/diffusers/en/api/models/prior_transformer/#priortransformeroutput | #priortransformeroutput | .md | 215_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.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/ | .md | 216_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.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | #unet2dmodel | .md | 216_1 |
UNet2DModel
A 2D 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` or `Tuple[int, in... | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | #unet2dmodel | .md | 216_2 |
UNet2DOutput
The output of [`UNet2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output from the last layer of the model. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2doutput | #unet2doutput | .md | 216_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/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/ | .md | 217_0 | |
A Transformer model for video-like data. | /Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodel | #transformertemporalmodel | .md | 217_1 |
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/#transformertemporalmodel | #transformertemporalmodel | .md | 217_2 |
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