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/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1dmodel | .md | in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 2): Number of channels in the output.
extra_in_channels (`int`, *optional*, defaults to 0):
Number of additional channels to be added to the input of the first down block. Useful for case... | 225_2_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 | time_embedding_type (`str`, *optional*, defaults to `"fourier"`): Type of time embedding to use.
freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for Fourier time embedding.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
Whether to flip sin to cos for Fourier time embedding.
down_block_typ... | 225_2_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 | Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(32, 32, 64)`):
Tuple of block output channels.
mid_block_type (`str`, *optional*, defaul... | 225_2_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 | out_block_type (`str`, *optional*, defaults to `None`): Optional output processing block of UNet.
act_fn (`str`, *optional*, defaults to `None`): Optional activation function in UNet blocks.
norm_num_groups (`int`, *optional*, defaults to 8): The number of groups for normalization.
layers_per_block (`int`, *optional*, ... | 225_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet.md | https://huggingface.co/docs/diffusers/en/api/models/unet/#unet1doutput | .md | UNet1DOutput
The output of [`UNet1DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, sample_size)`):
The hidden states output from the last layer of the model. | 225_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/ | .md | <!--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... | 226_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/ | .md | 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.
--> | 226_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .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... | 226_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .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 3D UNet conditional model. | 226_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | The abstract from the paper is: | 226_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .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... | 226_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .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... | 226_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .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... | 226_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | UNet3DConditionModel
A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Paramet... | 226_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
down_block_typ... | 226_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output c... | 226_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optio... | 226_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | 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 in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int`, *optional*, defaul... | 226_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionmodel | .md | attention_head_dim (`int`, *optional*, defaults to 64): The dimension of the attention heads.
num_attention_heads (`int`, *optional*): The number of attention heads.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding. | 226_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet3d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet3d-cond/#unet3dconditionoutput | .md | UNet3DConditionOutput
The output of [`UNet3DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. | 226_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/ | .md | <!--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... | 227_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/ | .md | 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.
--> | 227_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .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... | 227_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .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 2D UNet conditional model. | 227_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | The abstract from the paper is: | 227_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .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... | 227_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .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... | 227_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .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... | 227_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | UNet2DConditionModel
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Paramet... | 227_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
center_input_sample (`... | 227_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "Cr... | 227_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
up_block_types (`Tuple[str]`, *o... | 227_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | The tuple of upsample blocks to use.
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The... | 227_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
dropout (`float`, *op... | 227_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | act_fn (`str`, *optional*, 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 in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The ep... | 227_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant... | 227_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | [`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is... | 227_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | [`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unets.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_d... | 227_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`int`, *optional*, defaul... | 227_2_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | num_attention_heads (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
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_e... | 227_2_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"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. C... | 227_2_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
Dimension for the timestep embeddings.
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 `N... | 227_2_13 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
Optional activation function to u... | 227_2_14 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the ... | 227_2_15 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Req... | 227_2_16 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionmodel | .md | embeddings with the class embeddings.
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
`onl... | 227_2_17 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#unet2dconditionoutput | .md | UNet2DConditionOutput
The output of [`UNet2DConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. | 227_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#flaxunet2dconditionmodel | .md | [[autodoc]] FlaxUNet2DConditionModel: No module named 'flax' | 227_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/unet2d-cond.md | https://huggingface.co/docs/diffusers/en/api/models/unet2d-cond/#flaxunet2dconditionoutput | .md | [[autodoc]] FlaxUNet2DConditionOutput: No module named 'flax' | 227_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/ | .md | <!--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... | 228_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/ | .md | 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.
--> | 228_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ip-adapter | .md | [IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
<Tip>
Learn how to load an IP-Adapter checkpoint and... | 228_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ipadaptermixin | .md | IPAdapterMixin
Mixin for handling IP Adapters. | 228_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#sd3ipadaptermixin | .md | SD3IPAdapterMixin
Mixin for handling StableDiffusion 3 IP Adapters.
- all
- is_ip_adapter_active | 228_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ipadaptermaskprocessor | .md | IPAdapterMaskProcessor
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `Tru... | 228_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/ip_adapter.md | https://huggingface.co/docs/diffusers/en/api/loaders/ip_adapter/#ipadaptermaskprocessor | .md | resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `False`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `True`):
Whether to binarize the image to 0/1.
do_convert_grayscale (`bool... | 228_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/ | .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... | 229_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/ | .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.
--> | 229_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | .md | LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text ... | 229_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | .md | for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights: | 229_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | .md | - [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/... | 229_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | .md | - [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](http... | 229_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lora | .md | - [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
<Tip>
To l... | 229_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#stablediffusionloraloadermixin | .md | StableDiffusionLoraLoaderMixin
Load LoRA layers into Stable Diffusion [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel). | 229_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#stablediffusionxlloraloadermixin | .md | StableDiffusionXLLoraLoaderMixin
Load LoRA layers into Stable Diffusion XL [`UNet2DConditionModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextM... | 229_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#sd3loraloadermixin | .md | SD3LoraLoaderMixin
Load LoRA layers into [`SD3Transformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection).
Specific t... | 229_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#fluxloraloadermixin | .md | FluxLoraLoaderMixin
Load LoRA layers into [`FluxTransformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
Specific to [`StableDiffusion3Pipeline`]. | 229_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#cogvideoxloraloadermixin | .md | CogVideoXLoraLoaderMixin
Load LoRA layers into [`CogVideoXTransformer3DModel`]. Specific to [`CogVideoXPipeline`]. | 229_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#mochi1loraloadermixin | .md | Mochi1LoraLoaderMixin
Load LoRA layers into [`MochiTransformer3DModel`]. Specific to [`MochiPipeline`]. | 229_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#amusedloraloadermixin | .md | AmusedLoraLoaderMixin | 229_8_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/lora.md | https://huggingface.co/docs/diffusers/en/api/loaders/lora/#lorabasemixin | .md | LoraBaseMixin
Utility class for handling LoRAs. | 229_9_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/ | .md | <!--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... | 230_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/ | .md | 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.
--> | 230_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#single-files | .md | The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
* a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models
* a model stored in their originally d... | 230_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#single-files | .md | > [!TIP]
> Read the [Model files and layouts](../../using-diffusers/other-formats) guide to learn more about the Diffusers-multifolder layout versus the single-file layout, and how to load models stored in these different layouts. | 230_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#supported-pipelines | .md | - [`StableDiffusionPipeline`]
- [`StableDiffusionImg2ImgPipeline`]
- [`StableDiffusionInpaintPipeline`]
- [`StableDiffusionControlNetPipeline`]
- [`StableDiffusionControlNetImg2ImgPipeline`]
- [`StableDiffusionControlNetInpaintPipeline`]
- [`StableDiffusionUpscalePipeline`]
- [`StableDiffusionXLPipeline`]
- [`StableDif... | 230_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#supported-pipelines | .md | - [`StableDiffusionXLInpaintPipeline`]
- [`StableDiffusionXLInstructPix2PixPipeline`]
- [`StableDiffusionXLControlNetPipeline`]
- [`StableDiffusionXLKDiffusionPipeline`]
- [`StableDiffusion3Pipeline`]
- [`LatentConsistencyModelPipeline`]
- [`LatentConsistencyModelImg2ImgPipeline`]
- [`StableDiffusionControlNetXSPipelin... | 230_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#supported-models | .md | - [`UNet2DConditionModel`]
- [`StableCascadeUNet`]
- [`AutoencoderKL`]
- [`ControlNetModel`]
- [`SD3Transformer2DModel`]
- [`FluxTransformer2DModel`] | 230_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#fromsinglefilemixin | .md | FromSingleFileMixin
Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`]. | 230_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/single_file.md | https://huggingface.co/docs/diffusers/en/api/loaders/single_file/#fromoriginalmodelmixin | .md | FromOriginalModelMixin
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model. | 230_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/ | .md | <!--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... | 231_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/ | .md | 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.
--> | 231_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/#sd3transformer2d | .md | This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
The [`SD3Transformer2DLoadersMixin`] class currently ... | 231_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/#sd3transformer2d | .md | <Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</Tip> | 231_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/transformer_sd3.md | https://huggingface.co/docs/diffusers/en/api/loaders/transformer_sd3/#sd3transformer2dloadersmixin | .md | SD3Transformer2DLoadersMixin
Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`.
- all
- _load_ip_adapter_weights | 231_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/ | .md | <!--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... | 232_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/ | .md | 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.
--> | 232_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/#textual-inversion | .md | Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
[`TextualInversionLoaderMixin`] provides a function for loading Textual ... | 232_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/#textual-inversion | .md | <Tip>
To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
</Tip> | 232_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/textual_inversion.md | https://huggingface.co/docs/diffusers/en/api/loaders/textual_inversion/#textualinversionloadermixin | .md | TextualInversionLoaderMixin
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder. | 232_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/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... | 233_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/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.
--> | 233_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet | .md | Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is us... | 233_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet | .md | *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] function instead. | 233_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet | .md | The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
<Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</... | 233_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/unet.md | https://huggingface.co/docs/diffusers/en/api/loaders/unet/#unet2dconditionloadersmixin | .md | UNet2DConditionLoadersMixin
Load LoRA layers into a [`UNet2DCondtionModel`]. | 233_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/ | .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... | 234_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/ | .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.
--> | 234_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/#peft | .md | Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with... | 234_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/loaders/peft.md | https://huggingface.co/docs/diffusers/en/api/loaders/peft/#peftadaptermixin | .md | PeftAdapterMixin
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them in a base model, check out the PEFT
[documentation](https://huggingface.co/docs/peft/index).
Install the latest version of PEFT, and use thi... | 234_2_0 |
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