source stringclasses 273
values | url stringlengths 47 172 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#uvitblock | .md | UVitBlock | 196_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#convnextblock | .md | ConvNextBlock | 196_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/uvit2d.md | https://huggingface.co/docs/diffusers/en/api/models/uvit2d/#convmlmlayer | .md | ConvMlmLayer | 196_6_0 |
/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 | <!--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... | 197_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 197_0_1 |
/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 | .md | A Transformer model for audio waveforms from [Stable Audio Open](https://huggingface.co/papers/2407.14358). | 197_1_0 |
/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 | .md | 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... | 197_2_0 |
/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 | .md | num_layers (`int`, *optional*, defaults to 24): The number of layers of Transformer blocks to use.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 24): The number of heads to use for the query states.
num_key_value_attentio... | 197_2_1 |
/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 | .md | The number of heads to use for the key and value states.
out_channels (`int`, defaults to 64): Number of output channels.
cross_attention_dim ( `int`, *optional*, defaults to 768): Dimension of the cross-attention projection.
time_proj_dim ( `int`, *optional*, defaults to 256): Dimension of the timestep inner projectio... | 197_2_2 |
/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 | .md | Input dimension of the global hidden states projection.
cross_attention_input_dim ( `int`, *optional*, defaults to 768):
Input dimension of the cross-attention projection | 197_2_3 |
/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 | <!-- 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... | 198_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 198_0_1 |
/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 | .md | 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... | 198_1_0 |
/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 | .md | vae = AutoencoderKLHunyuanVideo.from_pretrained("hunyuanvideo-community/HunyuanVideo", subfolder="vae", torch_dtype=torch.float16)
``` | 198_1_1 |
/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 | .md | 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... | 198_2_0 |
/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 | .md | 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. | 198_3_0 |
/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 | <!--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... | 199_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 199_0_1 |
/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 | .md | 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. ... | 199_1_0 |
/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 | .md | pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(prompt, num_inferen... | 199_1_1 |
/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 | .md | pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(prompt, num_in... | 199_1_2 |
/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 | .md | 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... | 199_2_0 |
/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 | .md | Parameters:
in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.
out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
encoder_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64, 64, 64, 64)`):
Tuple of integers representing the number of outpu... | 199_2_1 |
/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 | .md | tuple should be equal to the number of encoder blocks.
decoder_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64, 64, 64, 64)`):
Tuple of integers representing the number of output channels for each decoder block. The length of the
tuple should be equal to the number of decoder blocks.
act_fn (`str`, *opti... | 199_2_2 |
/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 | .md | Activation function to be used throughout the model.
latent_channels (`int`, *optional*, defaults to 4):
Number of channels in the latent representation. The latent space acts as a compressed representation of
the input image.
upsampling_scaling_factor (`int`, *optional*, defaults to 2):
Scaling factor for upsampling i... | 199_2_3 |
/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 | .md | upsampling process.
num_encoder_blocks (`Tuple[int]`, *optional*, defaults to `(1, 3, 3, 3)`):
Tuple of integers representing the number of encoder blocks at each stage of the encoding process. The
length of the tuple should be equal to the number of stages in the encoder. Each stage has a different
number of encoder b... | 199_2_4 |
/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 | .md | Tuple of integers representing the number of decoder blocks at each stage of the decoding process. The
length of the tuple should be equal to the number of stages in the decoder. Each stage has a different
number of decoder blocks.
latent_magnitude (`float`, *optional*, defaults to 3.0):
Magnitude of the latent represe... | 199_2_5 |
/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 | .md | the extent of information preservation.
latent_shift (float, *optional*, defaults to 0.5):
Shift applied to the latent representation. This parameter controls the center of the latent space.
scaling_factor (`float`, *optional*, defaults to 1.0):
The component-wise standard deviation of the trained latent space computed... | 199_2_6 |
/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 | .md | training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_f... | 199_2_7 |
/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 | .md | / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. For this Autoencoder,
however, no such scaling factor was used, hence the value of 1.0 as the default.
force_upcast (`bool`, *optional*,... | 199_2_8 |
/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 | .md | If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without losing too much precision, in which case
`force_upcast` can be set to `False` (see this fp16-friendly
[AutoEncoder](https://huggingface.co/madebyollin/sdxl-vae-... | 199_2_9 |
/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 | .md | AutoencoderTinyOutput
Output of AutoencoderTiny encoding method.
Args:
latents (`torch.Tensor`): Encoded outputs of the `Encoder`. | 199_3_0 |
/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 | <!-- 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... | 200_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 200_0_1 |
/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 | .md | 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... | 200_1_0 |
/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 | .md | 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... | 200_2_0 |
/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 | .md | patch_size_t (`int`, defaults to `1`):
The size of the tmeporal patches to use in the patch embedding layer.
num_attention_heads (`int`, defaults to `32`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, defaults to `64`):
The number of channels in each head.
cross_attention_dim (`int`, ... | 200_2_1 |
/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 | .md | num_layers (`int`, defaults to `28`):
The number of layers of Transformer blocks to use.
activation_fn (`str`, defaults to `"gelu-approximate"`):
Activation function to use in feed-forward.
qk_norm (`str`, defaults to `"rms_norm_across_heads"`):
The normalization layer to use. | 200_2_2 |
/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 | .md | 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... | 200_3_0 |
/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 | <!-- 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... | 201_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 201_0_1 |
/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 | .md | 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... | 201_1_0 |
/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 | .md | 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... | 201_2_0 |
/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 | .md | attention_head_dim (`int`, defaults to `128`):
The number of channels in each head.
num_layers (`int`, defaults to `20`):
The number of layers of dual-stream blocks to use.
num_single_layers (`int`, defaults to `40`):
The number of layers of single-stream blocks to use.
num_refiner_layers (`int`, defaults to `2`):
The ... | 201_2_1 |
/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 | .md | mlp_ratio (`float`, defaults to `4.0`):
The ratio of the hidden layer size to the input size in the feedforward network.
patch_size (`int`, defaults to `2`):
The size of the spatial patches to use in the patch embedding layer.
patch_size_t (`int`, defaults to `1`):
The size of the tmeporal patches to use in the patch e... | 201_2_2 |
/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 | .md | guidance_embeds (`bool`, defaults to `True`):
Whether to use guidance embeddings in the model.
text_embed_dim (`int`, defaults to `4096`):
Input dimension of text embeddings from the text encoder.
pooled_projection_dim (`int`, defaults to `768`):
The dimension of the pooled projection of the text embeddings.
rope_theta... | 201_2_3 |
/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 | .md | 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... | 201_3_0 |
/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 | <!-- 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... | 202_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 202_0_1 |
/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 | .md | 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... | 202_1_0 |
/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 | .md | 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... | 202_2_0 |
/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 | .md | attention_head_dim (`int`, defaults to `128`):
The number of channels in each head.
num_layers (`int`, defaults to `48`):
The number of layers of Transformer blocks to use.
in_channels (`int`, defaults to `12`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `None`):
The number of cha... | 202_2_1 |
/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 | .md | qk_norm (`str`, defaults to `"rms_norm"`):
The normalization layer to use.
text_embed_dim (`int`, defaults to `4096`):
Input dimension of text embeddings from the text encoder.
time_embed_dim (`int`, defaults to `256`):
Output dimension of timestep embeddings.
activation_fn (`str`, defaults to `"swiglu"`):
Activation f... | 202_2_2 |
/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 | .md | 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... | 202_3_0 |
/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 | <!-- 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... | 203_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 203_0_1 |
/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 | .md | 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.
`... | 203_1_0 |
/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 | .md | 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... | 203_2_0 |
/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 | .md | Number of channels in the input image.
out_channels (int, defaults to `3`):
Number of channels in the output.
down_block_types (`Tuple[str, ...]`, defaults to `("AllegroDownBlock3D", "AllegroDownBlock3D", "AllegroDownBlock3D", "AllegroDownBlock3D")`):
Tuple of strings denoting which types of down blocks to use.
up_bloc... | 203_2_1 |
/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 | .md | Tuple of strings denoting which types of up blocks to use.
block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`):
Tuple of integers denoting number of output channels in each block.
temporal_downsample_blocks (`Tuple[bool, ...]`, defaults to `(True, True, False, False)`):
Tuple of booleans denoting... | 203_2_2 |
/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 | .md | latent_channels (`int`, defaults to `4`):
Number of channels in latents.
layers_per_block (`int`, defaults to `2`):
Number of resnet or attention or temporal convolution layers per down/up block.
act_fn (`str`, defaults to `"silu"`):
The activation function to use.
norm_num_groups (`int`, defaults to `32`):
Number of g... | 203_2_3 |
/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 | .md | Ratio by which temporal dimension of samples are compressed.
sample_size (`int`, defaults to `320`):
Default latent size.
scaling_factor (`float`, defaults to `0.13235`):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the laten... | 203_2_4 |
/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 | .md | model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis w... | 203_2_5 |
/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 | .md | Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
force_upcast (`bool`, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which c... | 203_2_6 |
/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 | .md | 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. | 203_3_0 |
/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 | .md | 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. | 203_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 204_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 204_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | 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... | 204_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | *We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn ... | 204_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple ... | 204_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.* | 204_1_3 |
/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 | .md | 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... | 204_2_0 |
/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 | .md | url = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors" # can also be a local path
pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
``` | 204_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | 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... | 204_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | The tuple of downsample blocks to use.
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, defaults to 2):
The number of layers per block.
downsample_padding... | 204_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | mid_block_scale_factor (`float`, defaults to 1):
The scale factor to use for the mid block.
act_fn (`str`, defaults to "silu"):
The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
... | 204_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | norm_eps (`float`, defaults to 1e-5):
The epsilon to use for the normalization.
cross_attention_dim (`int`, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.Basi... | 204_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, th... | 204_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
The dimension of the attention heads.
use_linear_projection (`bool`, defaults to `False`):
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to... | 204_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
addition_embed_type (`str`, *optional*, defaults to `None`):
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
num_class_embeds (`int`, *option... | 204_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | class conditioning with `class_embed_type` equal to `None`.
upcast_attention (`bool`, defaults to `False`):
resnet_time_scale_shift (`str`, defaults to `"default"`):
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
projection_class_embeddings_input_dim (`int`, *op... | 204_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
`class_embed_type="projection"`.
controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
conditioning_embedding_out_channels (`tuple[int]... | 204_3_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetmodel | .md | global_pool_conditions (`bool`, defaults to `False`):
TODO(Patrick) - unused parameter.
addition_embed_type_num_heads (`int`, defaults to 64):
The number of heads to use for the `TextTimeEmbedding` layer. | 204_3_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetoutput | .md | 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 ... | 204_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#controlnetoutput | .md | used to condition the original UNet's downsampling activations.
mid_down_block_re_sample (`torch.Tensor`):
The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
Output can be... | 204_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#flaxcontrolnetmodel | .md | FlaxControlNetModel | 204_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet/#flaxcontrolnetoutput | .md | [[autodoc]] FlaxControlNetOutput: No module named 'flax' | 204_6_0 |
/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 | <!-- 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... | 205_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License. --> | 205_0_1 |
/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 | .md | 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... | 205_1_0 |
/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 | .md | 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... | 205_2_0 |
/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 | .md | Number of input channels.
out_channels (`int`, defaults to `3`):
Number of output channels.
latent_channels (`int`, defaults to `128`):
Number of latent channels.
block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`):
The number of output channels for each block.
spatio_temporal_scaling (`Tuple[boo... | 205_2_1 |
/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 | .md | layers_per_block (`Tuple[int, ...]`, defaults to `(4, 3, 3, 3, 4)`):
The number of layers per block.
patch_size (`int`, defaults to `4`):
The size of spatial patches.
patch_size_t (`int`, defaults to `1`):
The size of temporal patches.
resnet_norm_eps (`float`, defaults to `1e-6`):
Epsilon value for ResNet normalizatio... | 205_2_2 |
/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 | .md | The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion mode... | 205_2_3 |
/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 | .md | diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
encoder_causal (`bool`, defau... | 205_2_4 |
/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 | .md | decoder_causal (`bool`, defaults to `False`):
Whether the decoder should behave causally (future frames depend only on past frames) or not.
- decode
- encode
- all | 205_2_5 |
/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 | .md | 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. | 205_3_0 |
/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 | .md | 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. | 205_4_0 |
/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 | <!--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... | 206_0_0 |
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