text stringlengths 41 89.8k | type stringclasses 1
value | start int64 79 258k | end int64 342 260k | depth int64 0 0 | filepath stringlengths 81 164 | parent_class null | class_index int64 0 1.38k |
|---|---|---|---|---|---|---|---|
class AnimateDiffTransformer3D(nn.Module):
"""
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... | class_definition | 2,241 | 9,464 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,000 |
class DownBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "... | class_definition | 9,467 | 15,402 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,001 |
class CrossAttnDownBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
... | class_definition | 15,405 | 23,816 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,002 |
class CrossAttnUpBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_... | class_definition | 23,819 | 32,658 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,003 |
class UpBlockMotion(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
... | class_definition | 32,661 | 38,727 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,004 |
class UNetMidBlockCrossAttnMotion(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shi... | class_definition | 38,730 | 46,615 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,005 |
class MotionModules(nn.Module):
def __init__(
self,
in_channels: int,
layers_per_block: int = 2,
transformer_layers_per_block: Union[int, Tuple[int]] = 8,
num_attention_heads: Union[int, Tuple[int]] = 8,
attention_bias: bool = False,
cross_attention_dim: Optio... | class_definition | 46,618 | 48,330 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,006 |
class MotionAdapter(ModelMixin, ConfigMixin, FromOriginalModelMixin):
@register_to_config
def __init__(
self,
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
motion_layers_per_block: Union[int, Tuple[int]] = 2,
motion_transformer_layers_per_block: Union[int, Tuple[i... | class_definition | 48,333 | 55,325 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,007 |
class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
r"""
A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documen... | class_definition | 55,328 | 102,674 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_motion_model.py | null | 1,008 |
class FlaxUNet2DConditionOutput(BaseOutput):
"""
The output of [`FlaxUNet2DConditionModel`].
Args:
sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
... | class_definition | 1,185 | 1,528 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py | null | 1,009 |
class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
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 [`FlaxModelMixin`]. Check the superclass documentation for it's generic meth... | class_definition | 1,556 | 22,280 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py | null | 1,010 |
class I2VGenXLTransformerTemporalEncoder(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
activation_fn: str = "geglu",
upcast_attention: bool = False,
ff_inner_dim: Optional[int] = None,
dropout: int = 0.0,
... | class_definition | 1,661 | 3,166 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py | null | 1,011 |
class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
I2VGenXL UNet. It is 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 fo... | class_definition | 3,169 | 32,774 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py | null | 1,012 |
class UNet1DOutput(BaseOutput):
"""
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.
"""
sample: torch.Tensor | class_definition | 1,040 | 1,314 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py | null | 1,013 |
class UNet1DModel(ModelMixin, ConfigMixin):
r"""
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).
... | class_definition | 1,317 | 10,786 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d.py | null | 1,014 |
class UNet2DOutput(BaseOutput):
"""
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.
"""
sample: torch.Tensor | class_definition | 1,025 | 1,301 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py | null | 1,015 |
class UNet2DModel(ModelMixin, ConfigMixin):
r"""
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).
... | class_definition | 1,304 | 17,074 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d.py | null | 1,016 |
class FlaxCrossAttnDownBlock2D(nn.Module):
r"""
Cross Attention 2D Downsizing block - original architecture from Unet transformers:
https://arxiv.org/abs/2103.06104
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
... | class_definition | 788 | 4,406 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py | null | 1,017 |
class FlaxDownBlock2D(nn.Module):
r"""
Flax 2D downsizing block
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
Dropout rate
num_layers (:... | class_definition | 4,409 | 6,247 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py | null | 1,018 |
class FlaxCrossAttnUpBlock2D(nn.Module):
r"""
Cross Attention 2D Upsampling block - original architecture from Unet transformers:
https://arxiv.org/abs/2103.06104
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
... | class_definition | 6,250 | 10,166 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py | null | 1,019 |
class FlaxUpBlock2D(nn.Module):
r"""
Flax 2D upsampling block
Parameters:
in_channels (:obj:`int`):
Input channels
out_channels (:obj:`int`):
Output channels
prev_output_channel (:obj:`int`):
Output channels from the previous block
dropout... | class_definition | 10,169 | 12,404 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py | null | 1,020 |
class FlaxUNetMidBlock2DCrossAttn(nn.Module):
r"""
Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
Parameters:
in_channels (:obj:`int`):
Input channels
dropout (:obj:`float`, *optional*, defaults to 0.0):
... | class_definition | 12,407 | 15,571 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py | null | 1,021 |
class UNetSpatioTemporalConditionOutput(BaseOutput):
"""
The output of [`UNetSpatioTemporalConditionModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of l... | class_definition | 638 | 1,018 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py | null | 1,022 |
class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and
returns a sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclas... | class_definition | 1,021 | 23,236 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py | null | 1,023 |
class UNet3DConditionOutput(BaseOutput):
"""
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.
... | class_definition | 1,677 | 2,026 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py | null | 1,024 |
class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
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 generi... | class_definition | 2,029 | 34,441 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_3d_condition.py | null | 1,025 |
class Kandinsky3UNetOutput(BaseOutput):
sample: torch.Tensor = None | class_definition | 1,111 | 1,182 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,026 |
class Kandinsky3EncoderProj(nn.Module):
def __init__(self, encoder_hid_dim, cross_attention_dim):
super().__init__()
self.projection_linear = nn.Linear(encoder_hid_dim, cross_attention_dim, bias=False)
self.projection_norm = nn.LayerNorm(cross_attention_dim)
def forward(self, x):
... | class_definition | 1,185 | 1,589 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,027 |
class Kandinsky3UNet(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
in_channels: int = 4,
time_embedding_dim: int = 1536,
groups: int = 32,
attention_head_dim: int = 64,
layers_per_block: Union[int, Tuple[int]] = 3,
block_out_channels: T... | class_definition | 1,592 | 10,105 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,028 |
class Kandinsky3UpSampleBlock(nn.Module):
def __init__(
self,
in_channels,
cat_dim,
out_channels,
time_embed_dim,
context_dim=None,
num_blocks=3,
groups=32,
head_dim=64,
expansion_ratio=4,
compression_ratio=2,
up_sample=... | class_definition | 10,108 | 12,631 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,029 |
class Kandinsky3DownSampleBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
time_embed_dim,
context_dim=None,
num_blocks=3,
groups=32,
head_dim=64,
expansion_ratio=4,
compression_ratio=2,
down_sample=True,
... | class_definition | 12,634 | 15,100 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,030 |
class Kandinsky3ConditionalGroupNorm(nn.Module):
def __init__(self, groups, normalized_shape, context_dim):
super().__init__()
self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)
self.context_mlp = nn.Sequential(nn.SiLU(), nn.Linear(context_dim, 2 * normalized_shape))
se... | class_definition | 15,103 | 15,787 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,031 |
class Kandinsky3Block(nn.Module):
def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):
super().__init__()
self.group_norm = Kandinsky3ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)
self.activation = nn.SiLU()
... | class_definition | 15,790 | 16,897 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,032 |
class Kandinsky3ResNetBlock(nn.Module):
def __init__(
self, in_channels, out_channels, time_embed_dim, norm_groups=32, compression_ratio=2, up_resolutions=4 * [None]
):
super().__init__()
kernel_sizes = [1, 3, 3, 1]
hidden_channel = max(in_channels, out_channels) // compression_r... | class_definition | 16,900 | 18,604 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,033 |
class Kandinsky3AttentionPooling(nn.Module):
def __init__(self, num_channels, context_dim, head_dim=64):
super().__init__()
self.attention = Attention(
context_dim,
context_dim,
dim_head=head_dim,
out_dim=num_channels,
out_bias=False,
... | class_definition | 18,607 | 19,175 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,034 |
class Kandinsky3AttentionBlock(nn.Module):
def __init__(self, num_channels, time_embed_dim, context_dim=None, norm_groups=32, head_dim=64, expansion_ratio=4):
super().__init__()
self.in_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)
self.attention = Attentio... | class_definition | 19,178 | 20,752 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_kandinsky3.py | null | 1,035 |
class AutoencoderTinyBlock(nn.Module):
"""
Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
blocks.
Args:
in_channels (`int`): The number of input channels.
out_channels (`int`): The number of output channels.
act_f... | class_definition | 21,709 | 23,092 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,036 |
class UNetMidBlock2D(nn.Module):
"""
A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
Args:
in_channels (`int`): The number of input channels.
temb_channels (`int`): The number of temporal embedding channels.
dropout (`float`, *opti... | class_definition | 23,095 | 30,870 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,037 |
class UNetMidBlock2DCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
out_channels: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: flo... | class_definition | 30,873 | 37,261 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,038 |
class UNetMidBlock2DSimpleCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
... | class_definition | 37,264 | 42,284 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,039 |
class AttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "... | class_definition | 42,287 | 47,923 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,040 |
class CrossAttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
resnet_eps: float = 1e-6,
r... | class_definition | 47,926 | 54,543 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,041 |
class DownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swis... | class_definition | 54,546 | 57,959 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,042 |
class DownEncoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_gr... | class_definition | 57,962 | 61,022 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,043 |
class AttnDownEncoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resne... | class_definition | 61,025 | 65,103 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,044 |
class AttnSkipDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str... | class_definition | 65,106 | 69,546 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,045 |
class SkipDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "... | class_definition | 69,549 | 73,046 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,046 |
class ResnetDownsampleBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn:... | class_definition | 73,049 | 77,018 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,047 |
class SimpleCrossAttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_... | class_definition | 77,021 | 83,479 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,048 |
class KDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 4,
resnet_eps: float = 1e-5,
resnet_act_fn: str = "gelu",
resnet_group_size: int = 32,
a... | class_definition | 83,482 | 86,628 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,049 |
class KCrossAttnDownBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
cross_attention_dim: int,
dropout: float = 0.0,
num_layers: int = 4,
resnet_group_size: int = 32,
add_downsample: bool = True,
... | class_definition | 86,631 | 91,556 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,050 |
class AttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: int = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resn... | class_definition | 91,559 | 97,307 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,051 |
class CrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_... | class_definition | 97,310 | 104,404 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,052 |
class UpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
... | class_definition | 104,407 | 108,684 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,053 |
class UpDecoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default,... | class_definition | 108,687 | 111,336 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,054 |
class AttnUpDecoderBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
... | class_definition | 111,339 | 115,151 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,055 |
class AttnSkipUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6... | class_definition | 115,154 | 120,389 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,056 |
class SkipUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
... | class_definition | 120,392 | 124,755 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,057 |
class ResnetUpsampleBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
prev_output_channel: int,
out_channels: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = ... | class_definition | 124,758 | 129,144 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,058 |
class SimpleCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
resolution_idx: Optional[int] = None,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float... | class_definition | 129,147 | 136,014 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,059 |
class KUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
resolution_idx: int,
dropout: float = 0.0,
num_layers: int = 5,
resnet_eps: float = 1e-5,
resnet_act_fn: str = "gelu",
resnet_grou... | class_definition | 136,017 | 139,506 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,060 |
class KCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
resolution_idx: int,
dropout: float = 0.0,
num_layers: int = 4,
resnet_eps: float = 1e-5,
resnet_act_fn: str = "gelu",
re... | class_definition | 139,509 | 145,157 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,061 |
class KAttentionBlock(nn.Module):
r"""
A basic Transformer block.
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each hea... | class_definition | 145,226 | 150,573 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_2d_blocks.py | null | 1,062 |
class MultiControlNetModel(ModelMixin):
r"""
Multiple `ControlNetModel` wrapper class for Multi-ControlNet
This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
compatible with `ControlNetModel`.
Args:
controlnets (`List[ControlNetM... | class_definition | 313 | 9,460 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/multicontrolnet.py | null | 1,063 |
class HunyuanControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor] | class_definition | 1,274 | 1,366 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py | null | 1,064 |
class HunyuanDiT2DControlNetModel(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
conditioning_channels: int = 3,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
patch_size: Optional[int] = None,
... | class_definition | 1,369 | 12,929 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py | null | 1,065 |
class HunyuanDiT2DMultiControlNetModel(ModelMixin):
r"""
`HunyuanDiT2DMultiControlNetModel` wrapper class for Multi-HunyuanDiT2DControlNetModel
This module is a wrapper for multiple instances of the `HunyuanDiT2DControlNetModel`. The `forward()` API is
designed to be compatible with `HunyuanDiT2DContro... | class_definition | 12,932 | 16,918 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_hunyuan.py | null | 1,066 |
class ControlNetOutput(BaseOutput):
"""
The output of [`ControlNetModel`].
Args:
down_block_res_samples (`tuple[torch.Tensor]`):
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape `(batch_size, channel * res... | class_definition | 1,571 | 2,493 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py | null | 1,067 |
class ControlNetConditioningEmbedding(nn.Module):
"""
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
training. This requires Control... | class_definition | 2,496 | 4,392 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py | null | 1,068 |
class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
A ControlNet model.
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to cos in the t... | class_definition | 4,395 | 43,204 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet.py | null | 1,069 |
class SD3ControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor] | class_definition | 1,518 | 1,606 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py | null | 1,070 |
class SD3ControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 128,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 18,
... | class_definition | 1,609 | 19,643 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py | null | 1,071 |
class SD3MultiControlNetModel(ModelMixin):
r"""
`SD3ControlNetModel` wrapper class for Multi-SD3ControlNet
This module is a wrapper for multiple instances of the `SD3ControlNetModel`. The `forward()` API is designed to be
compatible with `SD3ControlNetModel`.
Args:
controlnets (`List[SD3Co... | class_definition | 19,646 | 21,710 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sd3.py | null | 1,072 |
class SparseControlNetOutput(BaseOutput):
"""
The output of [`SparseControlNetModel`].
Args:
down_block_res_samples (`tuple[torch.Tensor]`):
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape `(batch_size, c... | class_definition | 1,478 | 2,412 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py | null | 1,073 |
class SparseControlNetConditioningEmbedding(nn.Module):
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
):
super().__init__()
self.conv_in = nn.Conv2d(conditioning_... | class_definition | 2,415 | 3,676 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py | null | 1,074 |
class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
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 o... | class_definition | 3,679 | 38,266 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_sparsectrl.py | null | 1,075 |
class ControlNetXSOutput(BaseOutput):
"""
The output of [`UNetControlNetXSModel`].
Args:
sample (`Tensor` of shape `(batch_size, num_channels, height, width)`):
The output of the `UNetControlNetXSModel`. Unlike `ControlNetOutput` this is NOT to be added to the base
model out... | class_definition | 1,670 | 2,062 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,076 |
class DownBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a
`ControlNetXSCrossAttnDownBlock2D`"""
def __init__(
self,
resnets: nn.ModuleList,
base_to_ctrl: nn.ModuleList,
ctrl_to_base: nn.ModuleList... | class_definition | 2,065 | 2,711 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,077 |
class MidBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a
`ControlNetXSCrossAttnMidBlock2D`"""
def __init__(self, midblock: UNetMidBlock2DCrossAttn, base_to_ctrl: nn.ModuleList, ctrl_to_base: nn.ModuleList):
super().__ini... | class_definition | 2,714 | 3,154 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,078 |
class UpBlockControlNetXSAdapter(nn.Module):
"""Components that together with corresponding components from the base model will form a `ControlNetXSCrossAttnUpBlock2D`"""
def __init__(self, ctrl_to_base: nn.ModuleList):
super().__init__()
self.ctrl_to_base = ctrl_to_base | class_definition | 3,157 | 3,453 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,079 |
class ControlNetXSAdapter(ModelMixin, ConfigMixin):
r"""
A `ControlNetXSAdapter` model. To use it, pass it into a `UNetControlNetXSModel` (together with a
`UNet2DConditionModel` base model).
This model inherits from [`ModelMixin`] and [`ConfigMixin`]. Check the superclass documentation for it's generic... | class_definition | 9,146 | 23,073 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,080 |
class UNetControlNetXSModel(ModelMixin, ConfigMixin):
r"""
A UNet fused with a ControlNet-XS adapter model
This model inherits from [`ModelMixin`] and [`ConfigMixin`]. Check the superclass documentation for it's generic
methods implemented for all models (such as downloading or saving).
`UNetContr... | class_definition | 23,076 | 56,035 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,081 |
class ControlNetXSCrossAttnDownBlock2D(nn.Module):
def __init__(
self,
base_in_channels: int,
base_out_channels: int,
ctrl_in_channels: int,
ctrl_out_channels: int,
temb_channels: int,
norm_num_groups: int = 32,
ctrl_max_norm_num_groups: int = 32,
... | class_definition | 56,038 | 70,367 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,082 |
class ControlNetXSCrossAttnMidBlock2D(nn.Module):
def __init__(
self,
base_channels: int,
ctrl_channels: int,
temb_channels: Optional[int] = None,
norm_num_groups: int = 32,
ctrl_max_norm_num_groups: int = 32,
transformer_layers_per_block: int = 1,
bas... | class_definition | 70,370 | 76,810 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,083 |
class ControlNetXSCrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
ctrl_skip_channels: List[int],
temb_channels: int,
norm_num_groups: int = 32,
resolution_idx: Optional[int] = None,
... | class_definition | 76,813 | 86,448 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_xs.py | null | 1,084 |
class QuickGELU(nn.Module):
"""
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
return input * torch.sigmoid(1.702 * input) | class_definition | 1,523 | 1,788 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py | null | 1,085 |
class ResidualAttentionMlp(nn.Module):
def __init__(self, d_model: int):
super().__init__()
self.c_fc = nn.Linear(d_model, d_model * 4)
self.gelu = QuickGELU()
self.c_proj = nn.Linear(d_model * 4, d_model)
def forward(self, x: torch.Tensor):
x = self.c_fc(x)
x = ... | class_definition | 1,791 | 2,167 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py | null | 1,086 |
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = nn.LayerNorm(d_model)
self.mlp = ResidualAttentionMlp(d_model)
self.ln_2 =... | class_definition | 2,170 | 2,930 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py | null | 1,087 |
class ControlNetUnionModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
"""
A ControlNetUnion model.
Args:
in_channels (`int`, defaults to 4):
The number of channels in the input sample.
flip_sin_to_cos (`bool`, defaults to `True`):
Whether to flip the sin to co... | class_definition | 2,933 | 41,054 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_union.py | null | 1,088 |
class FluxControlNetOutput(BaseOutput):
controlnet_block_samples: Tuple[torch.Tensor]
controlnet_single_block_samples: Tuple[torch.Tensor] | class_definition | 1,545 | 1,691 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py | null | 1,089 |
class FluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
patch_size: int = 1,
in_channels: int = 64,
num_layers: int = 19,
num_single_layers: int = 38,
attention_head... | class_definition | 1,694 | 18,994 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py | null | 1,090 |
class FluxMultiControlNetModel(ModelMixin):
r"""
`FluxMultiControlNetModel` wrapper class for Multi-FluxControlNetModel
This module is a wrapper for multiple instances of the `FluxControlNetModel`. The `forward()` API is designed to be
compatible with `FluxControlNetModel`.
Args:
controlne... | class_definition | 18,997 | 23,975 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flux.py | null | 1,091 |
class FlaxControlNetOutput(BaseOutput):
"""
The output of [`FlaxControlNetModel`].
Args:
down_block_res_samples (`jnp.ndarray`):
mid_block_res_sample (`jnp.ndarray`):
"""
down_block_res_samples: jnp.ndarray
mid_block_res_sample: jnp.ndarray | class_definition | 1,139 | 1,421 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py | null | 1,092 |
class FlaxControlNetConditioningEmbedding(nn.Module):
conditioning_embedding_channels: int
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256)
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.conv_in = nn.Conv(
self.block_out_channels[0],
kernel_size=(3, ... | class_definition | 1,424 | 3,151 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py | null | 1,093 |
class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
A ControlNet model.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it’s generic methods
implemented for all models (such as downloading or saving).
This model is also a Flax Linen [`flax.li... | class_definition | 3,179 | 16,714 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnets/controlnet_flax.py | null | 1,094 |
class PerceiverAttention(nn.Module):
def __init__(self, dim: int, dim_head: int = 64, heads: int = 8, kv_dim: Optional[int] = None):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = ... | class_definition | 1,438 | 3,340 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py | null | 1,095 |
class LocalFacialExtractor(nn.Module):
def __init__(
self,
id_dim: int = 1280,
vit_dim: int = 1024,
depth: int = 10,
dim_head: int = 64,
heads: int = 16,
num_id_token: int = 5,
num_queries: int = 32,
output_dim: int = 2048,
ff_mult: int... | class_definition | 3,343 | 7,215 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py | null | 1,096 |
class PerceiverCrossAttention(nn.Module):
def __init__(self, dim: int = 3072, dim_head: int = 128, heads: int = 16, kv_dim: int = 2048):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
# Layer n... | class_definition | 7,218 | 9,310 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py | null | 1,097 |
class ConsisIDBlock(nn.Module):
r"""
Transformer block used in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID) model.
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head... | class_definition | 9,335 | 14,022 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py | null | 1,098 |
class ConsisIDTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
"""
A Transformer model for video-like data in [ConsisID](https://github.com/PKU-YuanGroup/ConsisID).
Parameters:
num_attention_heads (`int`, defaults to `30`):
The number of heads to use for multi-head attenti... | class_definition | 14,025 | 36,504 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/consisid_transformer_3d.py | null | 1,099 |
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