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/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | The abstract from the paper is: | 216_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | *There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contra... | 216_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmit... | 216_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u... | 216_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | 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... | 216_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
1)`.
in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
center_input_sample (`bool`, *optiona... | 216_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip sin to cos for Fourier time embedding.
down_block_types... | 216_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | Tuple of downsample block types.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
Block type for middle of UNet, it can be either `UNetMidBlock2D` or `None`.
up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
Tuple of upsample bl... | 216_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
Tuple of block output channels.
layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
downsample_padding ... | 216_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | downsample_type (`str`, *optional*, defaults to `conv`):
The downsample type for downsampling layers. Choose between "conv" and "resnet"
upsample_type (`str`, *optional*, defaults to `conv`):
The upsample type for upsampling layers. Choose between "conv" and "resnet"
dropout (`float`, *optional*, defaults to 0.0): The ... | 216_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
attn_norm_num_groups (`int`, *optional*, defaults to `No... | 216_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the
given number of groups. If left as `None`, the group norm layer will only be created if
`resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.
norm_eps (`float`, *optional*... | 216_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the... | 216_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2dmodel | .md | `"timestep"`, or `"identity"`.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
conditioning with `class_embed_type` equal to `None`. | 216_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d/#unet2doutput | .md | 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. | 216_3_0 |
/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 | <!--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... | 217_0_0 |
/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 | 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.
--> | 217_0_1 |
/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 | .md | A Transformer model for video-like data. | 217_1_0 |
/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 | .md | 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... | 217_2_0 |
/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 | .md | num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
attention_bias (`bool`, *optional*):
Configu... | 217_2_1 |
/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 | .md | sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
This is fixed during training since it is used to learn a number of position embeddings.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward. See `diffusers.models.act... | 217_2_2 |
/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 | .md | activation functions.
norm_elementwise_affine (`bool`, *optional*):
Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
double_self_attention (`bool`, *optional*):
Configure if each `TransformerBlock` should contain two self-attention layers.
positional_embeddings: ... | 217_2_3 |
/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 | .md | num_positional_embeddings: (`int`, *optional*):
The maximum length of the sequence over which to apply positional embeddings. | 217_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | .md | 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... | 217_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | .md | num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
attention_bias (`bool`, *optional*):
Configu... | 217_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | .md | sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
This is fixed during training since it is used to learn a number of position embeddings.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward. See `diffusers.models.act... | 217_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | .md | activation functions.
norm_elementwise_affine (`bool`, *optional*):
Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
double_self_attention (`bool`, *optional*):
Configure if each `TransformerBlock` should contain two self-attention layers.
positional_embeddings: ... | 217_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/transformer_temporal.md | https://huggingface.co/docs/diffusers/en/api/models/transformer_temporal/#transformertemporalmodeloutput | .md | num_positional_embeddings: (`int`, *optional*):
The maximum length of the sequence over which to apply positional embeddings.
Output | 217_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/ | .md | <!--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... | 218_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/ | .md | 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.
--> | 218_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/#stablecascadeunet | .md | A UNet model from the [Stable Cascade pipeline](../pipelines/stable_cascade.md). | 218_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/stable_cascade_unet.md | https://huggingface.co/docs/diffusers/en/api/models/stable_cascade_unet/#stablecascadeunet | .md | StableCascadeUNet | 218_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/ | .md | <!--Copyright 2024 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required... | 219_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/ | .md | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | 219_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .md | HunyuanDiT2DControlNetModel is an implementation of ControlNet for [Hunyuan-DiT](https://arxiv.org/abs/2405.08748).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. | 219_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .md | With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image ... | 219_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .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 ... | 219_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .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 ... | 219_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .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.* | 219_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .md | This code is implemented by Tencent Hunyuan Team. You can find pre-trained checkpoints for Hunyuan-DiT ControlNets on [Tencent Hunyuan](https://huggingface.co/Tencent-Hunyuan). | 219_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#example-for-loading-hunyuandit2dcontrolnetmodel | .md | ```py
from diffusers import HunyuanDiT2DControlNetModel
import torch
controlnet = HunyuanDiT2DControlNetModel.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", torch_dtype=torch.float16)
``` | 219_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_hunyuandit.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_hunyuandit/#hunyuandit2dcontrolnetmodel | .md | HunyuanDiT2DControlNetModel | 219_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/ | .md | <!--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... | 220_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/ | .md | 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.
--> | 220_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistency-decoder | .md | Consistency decoder can be used to decode the latents from the denoising UNet in the [`StableDiffusionPipeline`]. This decoder was introduced in the [DALL-E 3 technical report](https://openai.com/dall-e-3).
The original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder)... | 220_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistency-decoder | .md | <Tip warning={true}>
Inference is only supported for 2 iterations as of now.
</Tip>
The pipeline could not have been contributed without the help of [madebyollin](https://github.com/madebyollin) and [mrsteyk](https://github.com/mrsteyk) from [this issue](https://github.com/openai/consistencydecoder/issues/1). | 220_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistencydecodervae | .md | ConsistencyDecoderVAE
The consistency decoder used with DALL-E 3.
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
>>> pipe = StableDiffusionPipeline.fro... | 220_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/consistency_decoder_vae.md | https://huggingface.co/docs/diffusers/en/api/models/consistency_decoder_vae/#consistencydecodervae | .md | >>> image = pipe("horse", generator=torch.manual_seed(0)).images[0]
>>> image
```
- all
- decode | 220_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/ | .md | <!-- 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... | 221_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/ | .md | 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. --> | 221_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | SparseControlNetModel is an implementation of ControlNet for [AnimateDiff](https://arxiv.org/abs/2307.04725).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. | 221_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | The SparseCtrl version of ControlNet was introduced in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models](https://arxiv.org/abs/2311.16933) for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
The abstra... | 221_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | *The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, ... | 221_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving... | 221_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both o... | 221_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#example-for-loading-sparsecontrolnetmodel | .md | ```python
import torch
from diffusers import SparseControlNetModel
# fp32 variant in float16
# 1. Scribble checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16)
# 2. RGB checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/a... | 221_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | SparseControlNetModel
A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
Models](https://arxiv.org/abs/2311.16933).
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
conditioning_channels (`int`, defaults to 4):
The number of i... | 221_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | `concat_condition_embedding` is True, the value provided here is incremented by 1.
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_block_types (`tuple[str]`, defaults to `("Cro... | 221_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .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... | 221_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .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
... | 221_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .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... | 221_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer layers to use in each layer in the middle block.
attention_head_dim (`int` or `Tuple[int]`, defaults to 8):
The dimension of the attention heads.
num_attenti... | 221_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | The number of heads to use for multi-head attention.
use_linear_projection (`bool`, defaults to `False`):
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`.
con... | 221_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | The tuple of output channel for each block in the `conditioning_embedding` layer.
global_pool_conditions (`bool`, defaults to `False`):
TODO(Patrick) - unused parameter
controlnet_conditioning_channel_order (`str`, defaults to `rgb`):
motion_max_seq_length (`int`, defaults to `32`):
The maximum sequence length to use i... | 221_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetmodel | .md | The number of heads to use in each attention layer of the motion module.
concat_conditioning_mask (`bool`, defaults to `True`):
use_simplified_condition_embedding (`bool`, defaults to `True`): | 221_3_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sparsectrl.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sparsectrl/#sparsecontrolnetoutput | .md | SparseControlNetOutput | 221_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/ | .md | <!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by... | 222_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/ | .md | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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.
--> | 222_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | .md | SD3ControlNetModel is an implementation of ControlNet for Stable Diffusion 3.
The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to... | 222_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | .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 ... | 222_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | .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 ... | 222_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | .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.* | 222_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#loading-from-the-original-format | .md | By default the [`SD3ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
pipe = ... | 222_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetmodel | .md | SD3ControlNetModel | 222_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/controlnet_sd3.md | https://huggingface.co/docs/diffusers/en/api/models/controlnet_sd3/#sd3controlnetoutput | .md | SD3ControlNetOutput
SD3ControlNetOutput(controlnet_block_samples: Tuple[torch.Tensor]) | 222_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/ | .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... | 223_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/ | .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.
--> | 223_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | The VQ-VAE model was introduced in [Neural Discrete Representation Learning](https://huggingface.co/papers/1711.00937) by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. The model is used in 🤗 Diffusers to decode latent representations into images. Unlike [`AutoencoderKL`], the [`VQModel`] works in a quantize... | 223_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | *Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder n... | 223_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of "posterior collapse" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -... | 223_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.* | 223_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | VQModel
A VQ-VAE model for decoding latent representations.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input ima... | 223_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output ch... | 223_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebo... | 223_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | norm_num_groups (`int`, *optional*, defaults to `32`): Number of groups for normalization layers.
vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
scaling_factor (`float`, *optional*, defaults to `0.18215`):
The component-wise standard deviation of the trained latent space computed using ... | 223_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .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... | 223_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqmodel | .md | Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
norm_type (`str`, *optional*, defaults to `"group"`):
Type of normalization layer to use. Can be one of `"group"` or `"spatial"`. | 223_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/vq.md | https://huggingface.co/docs/diffusers/en/api/models/vq/#vqencoderoutput | .md | VQEncoderOutput
Output of VQModel encoding method.
Args:
latents (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The encoded output sample from the last layer of the model. | 223_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/latte_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/latte_transformer3d/ | .md | <!--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... | 224_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/latte_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/latte_transformer3d/ | .md | 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.
--> | 224_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/latte_transformer3d.md | https://huggingface.co/docs/diffusers/en/api/models/latte_transformer3d/#lattetransformer3dmodel | .md | A Diffusion Transformer model for 3D data from [Latte](https://github.com/Vchitect/Latte).
LatteTransformer3DModel | 224_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/ | .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... | 225_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/ | .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.
--> | 225_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | 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... | 225_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 1D UNet model. | 225_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | The abstract from the paper is: | 225_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | *There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contra... | 225_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmit... | 225_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u... | 225_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | UNet1DModel
A 1D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int`, *optional*): Def... | 225_2_0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.