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Fixed typos in dosctrings of __init__() and in forward() of Unet3DConditionModel (#6663)

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* Fixed typos in __init__ and in forward of Unet3DConditionModel

* Resolving conflicts

---------

Co-authored-by: YiYi Xu <yixu310@gmail.com>
This commit is contained in:
Mikhail Koltakov
2024-02-18 07:56:16 +00:00
committed by GitHub
parent 2938d5a672
commit c18058b405
1 changed files with 6 additions and 6 deletions
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src/diffusers/models/unets/unet_3d_condition.py
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@@ -54,7 +54,7 @@ class UNet3DConditionOutput(BaseOutput):
The output of [`UNet3DConditionModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
sample (`torch.FloatTensor` 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.
"""
@@ -74,9 +74,9 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
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_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
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 channels for each block.
@@ -87,8 +87,8 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
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*, defaults to 1280): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
cross_attention_dim (`int`, *optional*, defaults to 1024): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 64): The dimension of the attention heads.
num_attention_heads (`int`, *optional*): The number of attention heads.
"""
@@ -533,7 +533,7 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
The noisy input tensor with the following shape `(batch, num_channels, num_frames, height, width`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.