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 StableAudioAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the Stable Audio model. It applies rotary embedding on query and key vector, and allows MHA, GQA or MQA.
"""
def __init__(self):
... | class_definition | 155,848 | 161,016 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 800 |
class HunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
"""
def __init__(self):
if not... | class_definition | 161,019 | 164,910 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 801 |
class FusedHunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused
projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
query and key vector.
"""
... | class_definition | 164,913 | 169,073 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 802 |
class PAGHunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This
variant of the processor employs [Per... | class_definition | 169,076 | 173,976 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 803 |
class PAGCFGHunyuanAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This
variant of the processor employs [... | class_definition | 173,979 | 178,988 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 804 |
class LuminaAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector.
"""
def __init__(self):
if n... | class_definition | 178,991 | 182,665 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 805 |
class FusedAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses
fused projection layers. For self-attention modules, all projection matrices (i.e., query, key, value) are fused.
For cross-attention modules, key and value... | class_definition | 182,668 | 187,083 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 806 |
class CustomDiffusionXFormersAttnProcessor(nn.Module):
r"""
Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.
Args:
train_kv (`bool`, defaults to `True`):
Whether to newly train the key and value matrices corresponding to the text features.
... | class_definition | 187,086 | 192,112 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 807 |
class CustomDiffusionAttnProcessor2_0(nn.Module):
r"""
Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled
dot-product attention.
Args:
train_kv (`bool`, defaults to `True`):
Whether to newly train the key and value matric... | class_definition | 192,115 | 196,983 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 808 |
class SlicedAttnProcessor:
r"""
Processor for implementing sliced attention.
Args:
slice_size (`int`, *optional*):
The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
`attention_head_dim` must be a multiple of the `slice_s... | class_definition | 196,986 | 200,151 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 809 |
class SlicedAttnAddedKVProcessor:
r"""
Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.
Args:
slice_size (`int`, *optional*):
The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`,... | class_definition | 200,154 | 203,793 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 810 |
class SpatialNorm(nn.Module):
"""
Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002.
Args:
f_channels (`int`):
The number of channels for input to group normalization layer, and output of the spatial norm layer.
zq_channels (`int`):
T... | class_definition | 203,796 | 204,884 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 811 |
class IPAdapterAttnProcessor(nn.Module):
r"""
Attention processor for Multiple IP-Adapters.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
num_tokens (`int... | class_definition | 204,887 | 214,003 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 812 |
class IPAdapterAttnProcessor2_0(torch.nn.Module):
r"""
Attention processor for IP-Adapter for PyTorch 2.0.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
n... | class_definition | 214,006 | 224,945 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 813 |
class IPAdapterXFormersAttnProcessor(torch.nn.Module):
r"""
Attention processor for IP-Adapter using xFormers.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
... | class_definition | 224,948 | 236,018 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 814 |
class SD3IPAdapterJointAttnProcessor2_0(torch.nn.Module):
"""
Attention processor for IP-Adapter used typically in processing the SD3-like self-attention projections, with
additional image-based information and timestep embeddings.
Args:
hidden_size (`int`):
The number of hidden cha... | class_definition | 236,021 | 242,929 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 815 |
class PAGIdentitySelfAttnProcessor2_0:
r"""
Processor for implementing PAG using scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
PAG reference: https://arxiv.org/abs/2403.17377
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
... | class_definition | 242,932 | 246,962 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 816 |
class PAGCFGIdentitySelfAttnProcessor2_0:
r"""
Processor for implementing PAG using scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
PAG reference: https://arxiv.org/abs/2403.17377
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
... | class_definition | 246,965 | 251,128 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 817 |
class SanaMultiscaleAttnProcessor2_0:
r"""
Processor for implementing multiscale quadratic attention.
"""
def __call__(self, attn: SanaMultiscaleLinearAttention, hidden_states: torch.Tensor) -> torch.Tensor:
height, width = hidden_states.shape[-2:]
if height * width > attn.attention_hea... | class_definition | 251,131 | 253,361 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 818 |
class LoRAAttnProcessor:
r"""
Processor for implementing attention with LoRA.
"""
def __init__(self):
pass | class_definition | 253,364 | 253,495 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 819 |
class LoRAAttnProcessor2_0:
r"""
Processor for implementing attention with LoRA (enabled by default if you're using PyTorch 2.0).
"""
def __init__(self):
pass | class_definition | 253,498 | 253,681 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 820 |
class LoRAXFormersAttnProcessor:
r"""
Processor for implementing attention with LoRA using xFormers.
"""
def __init__(self):
pass | class_definition | 253,684 | 253,838 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 821 |
class LoRAAttnAddedKVProcessor:
r"""
Processor for implementing attention with LoRA with extra learnable key and value matrices for the text encoder.
"""
def __init__(self):
pass | class_definition | 253,841 | 254,044 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 822 |
class FluxSingleAttnProcessor2_0(FluxAttnProcessor2_0):
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
def __init__(self):
deprecation_message = "`FluxSingleAttnProcessor2_0` is deprecated and will be removed in a future versio... | class_definition | 254,047 | 254,519 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 823 |
class SanaLinearAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product linear attention.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tenso... | class_definition | 254,522 | 256,123 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 824 |
class PAGCFGSanaLinearAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product linear attention.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch... | class_definition | 256,126 | 258,181 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 825 |
class PAGIdentitySanaLinearAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product linear attention.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[... | class_definition | 258,184 | 260,271 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 826 |
class ControlNetOutput(ControlNetOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `ControlNetOutput` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet import ControlNetOutput`,... | class_definition | 826 | 1,293 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet.py | null | 827 |
class ControlNetModel(ControlNetModel):
def __init__(
self,
in_channels: int = 4,
conditioning_channels: int = 3,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlo... | class_definition | 1,296 | 5,228 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet.py | null | 828 |
class ControlNetConditioningEmbedding(ControlNetConditioningEmbedding):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `ControlNetConditioningEmbedding` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from diffusers.models.con... | class_definition | 5,231 | 5,773 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet.py | null | 829 |
class FlaxModelMixin(PushToHubMixin):
r"""
Base class for all Flax models.
[`FlaxModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and
saving models.
- **config_name** ([`str`]) -- Filename to save a model to when calling [`~FlaxModelMixin... | class_definition | 1,466 | 26,953 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_flax_utils.py | null | 830 |
class SD3ControlNetOutput(SD3ControlNetOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `SD3ControlNetOutput` from `diffusers.models.controlnet_sd3` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_sd3 import S... | class_definition | 862 | 1,356 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sd3.py | null | 831 |
class SD3ControlNetModel(SD3ControlNetModel):
def __init__(
self,
sample_size: int = 128,
patch_size: int = 2,
in_channels: int = 16,
num_layers: int = 18,
attention_head_dim: int = 64,
num_attention_heads: int = 18,
joint_attention_dim: int = 4096,
... | class_definition | 1,359 | 2,859 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sd3.py | null | 832 |
class SD3MultiControlNetModel(SD3MultiControlNetModel):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `SD3MultiControlNetModel` from `diffusers.models.controlnet_sd3` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.controlnet_... | class_definition | 2,862 | 3,376 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sd3.py | null | 833 |
class FP32SiLU(nn.Module):
r"""
SiLU activation function with input upcasted to torch.float32.
"""
def __init__(self):
super().__init__()
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return F.silu(inputs.float(), inplace=False).to(inputs.dtype) | class_definition | 1,393 | 1,687 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 834 |
class GELU(nn.Module):
r"""
GELU activation function with tanh approximation support with `approximate="tanh"`.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
approximate (`str`, *optional*, defaults to `"none"... | class_definition | 1,690 | 2,887 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 835 |
class GEGLU(nn.Module):
r"""
A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channels in the output.
bias (`bool`, defaults to True): Whether t... | class_definition | 2,890 | 4,544 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 836 |
class SwiGLU(nn.Module):
r"""
A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU`
but uses SiLU / Swish instead of GeLU.
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channe... | class_definition | 4,547 | 5,375 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 837 |
class ApproximateGELU(nn.Module):
r"""
The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
[paper](https://arxiv.org/abs/1606.08415).
Parameters:
dim_in (`int`): The number of channels in the input.
dim_out (`int`): The number of channe... | class_definition | 5,378 | 6,089 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 838 |
class LinearActivation(nn.Module):
def __init__(self, dim_in: int, dim_out: int, bias: bool = True, activation: str = "silu"):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out, bias=bias)
self.activation = get_activation(activation)
def forward(self, hidden_states):
hid... | class_definition | 6,092 | 6,495 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/activations.py | null | 839 |
class MultiAdapter(ModelMixin):
r"""
MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to
user-assigned weighting.
This model inherits from [`ModelMixin`]. Check the superclass documentation for common methods such as downloading
or saving.
... | class_definition | 879 | 11,115 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 840 |
class T2IAdapter(ModelMixin, ConfigMixin):
r"""
A simple ResNet-like model that accepts images containing control signals such as keyposes and depth. The model
generates multiple feature maps that are used as additional conditioning in [`UNet2DConditionModel`]. The model's
architecture follows the origi... | class_definition | 11,118 | 14,674 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 841 |
class FullAdapter(nn.Module):
r"""
See [`T2IAdapter`] for more information.
"""
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280, 1280],
num_res_blocks: int = 2,
downscale_factor: int = 8,
):
super().__init__()
... | class_definition | 14,694 | 16,356 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 842 |
class FullAdapterXL(nn.Module):
r"""
See [`T2IAdapter`] for more information.
"""
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280, 1280],
num_res_blocks: int = 2,
downscale_factor: int = 16,
):
super().__init__()
... | class_definition | 16,359 | 18,092 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 843 |
class AdapterBlock(nn.Module):
r"""
An AdapterBlock is a helper model that contains multiple ResNet-like blocks. It is used in the `FullAdapter` and
`FullAdapterXL` models.
Args:
in_channels (`int`):
Number of channels of AdapterBlock's input.
out_channels (`int`):
... | class_definition | 18,095 | 19,773 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 844 |
class AdapterResnetBlock(nn.Module):
r"""
An `AdapterResnetBlock` is a helper model that implements a ResNet-like block.
Args:
channels (`int`):
Number of channels of AdapterResnetBlock's input and output.
"""
def __init__(self, channels: int):
super().__init__()
... | class_definition | 19,776 | 20,629 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 845 |
class LightAdapter(nn.Module):
r"""
See [`T2IAdapter`] for more information.
"""
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280],
num_res_blocks: int = 4,
downscale_factor: int = 8,
):
super().__init__()
in_ch... | class_definition | 20,650 | 22,049 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 846 |
class LightAdapterBlock(nn.Module):
r"""
A `LightAdapterBlock` is a helper model that contains multiple `LightAdapterResnetBlocks`. It is used in the
`LightAdapter` model.
Args:
in_channels (`int`):
Number of channels of LightAdapterBlock's input.
out_channels (`int`):
... | class_definition | 22,052 | 23,688 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 847 |
class LightAdapterResnetBlock(nn.Module):
"""
A `LightAdapterResnetBlock` is a helper model that implements a ResNet-like block with a slightly different
architecture than `AdapterResnetBlock`.
Args:
channels (`int`):
Number of channels of LightAdapterResnetBlock's input and output.... | class_definition | 23,691 | 24,619 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/adapter.py | null | 848 |
class SparseControlNetOutput(SparseControlNetOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `SparseControlNetOutput` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from diffusers.models.controlnets.control... | class_definition | 938 | 1,468 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sparsectrl.py | null | 849 |
class SparseControlNetConditioningEmbedding(SparseControlNetConditioningEmbedding):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `SparseControlNetConditioningEmbedding` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please u... | class_definition | 1,471 | 2,098 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sparsectrl.py | null | 850 |
class SparseControlNetModel(SparseControlNetModel):
def __init__(
self,
in_channels: int = 4,
conditioning_channels: int = 4,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlockMotion",
"... | class_definition | 2,101 | 5,903 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/controlnet_sparsectrl.py | null | 851 |
class FlaxAttention(nn.Module):
r"""
A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762
Parameters:
query_dim (:obj:`int`):
Input hidden states dimension
heads (:obj:`int`, *optional*, defaults to 8):
Number of heads
dim_head... | class_definition | 4,896 | 10,607 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py | null | 852 |
class FlaxBasicTransformerBlock(nn.Module):
r"""
A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:
https://arxiv.org/abs/1706.03762
Parameters:
dim (:obj:`int`):
Inner hidden states dimension
n_heads (:obj:`int`):
... | class_definition | 10,610 | 13,912 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py | null | 853 |
class FlaxTransformer2DModel(nn.Module):
r"""
A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:
https://arxiv.org/pdf/1506.02025.pdf
Parameters:
in_channels (:obj:`int`):
Input number of channels
n_heads (:obj:`int`):
... | class_definition | 13,915 | 18,016 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py | null | 854 |
class FlaxFeedForward(nn.Module):
r"""
Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's
[`FeedForward`] class, with the following simplifications:
- The activation function is currently hardcoded to a gated linear unit from:
https://arx... | class_definition | 18,019 | 19,316 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py | null | 855 |
class FlaxGEGLU(nn.Module):
r"""
Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from
https://arxiv.org/abs/2002.05202.
Parameters:
dim (:obj:`int`):
Input hidden states dimension
dropout (:obj:`float`, *optional*, d... | class_definition | 19,319 | 20,323 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_flax.py | null | 856 |
class FlaxUpsample2D(nn.Module):
out_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
self.out_channels,
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
def _... | class_definition | 667 | 1,324 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py | null | 857 |
class FlaxDownsample2D(nn.Module):
out_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
self.out_channels,
kernel_size=(3, 3),
strides=(2, 2),
padding=((1, 1), (1, 1)), # padding="VALID",
dtype=self.dtype... | class_definition | 1,327 | 1,917 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py | null | 858 |
class FlaxResnetBlock2D(nn.Module):
in_channels: int
out_channels: int = None
dropout_prob: float = 0.0
use_nin_shortcut: bool = None
dtype: jnp.dtype = jnp.float32
def setup(self):
out_channels = self.in_channels if self.out_channels is None else self.out_channels
self.norm1 =... | class_definition | 1,920 | 4,020 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet_flax.py | null | 859 |
class PatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for SD3 cropping.
Args:
height (`int`, defaults to `224`): The height of the image.
width (`int`, defaults to `224`): The width of the image.
patch_size (`int`, defaults to `16`): The size of the patches.
... | class_definition | 16,828 | 22,167 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 860 |
class LuminaPatchEmbed(nn.Module):
"""
2D Image to Patch Embedding with support for Lumina-T2X
Args:
patch_size (`int`, defaults to `2`): The size of the patches.
in_channels (`int`, defaults to `4`): The number of input channels.
embed_dim (`int`, defaults to `768`): The output dim... | class_definition | 22,170 | 24,142 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 861 |
class CogVideoXPatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 2,
patch_size_t: Optional[int] = None,
in_channels: int = 16,
embed_dim: int = 1920,
text_embed_dim: int = 4096,
bias: bool = True,
sample_width: int = 90,
sample_heigh... | class_definition | 24,145 | 30,265 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 862 |
class CogView3PlusPatchEmbed(nn.Module):
def __init__(
self,
in_channels: int = 16,
hidden_size: int = 2560,
patch_size: int = 2,
text_hidden_size: int = 4096,
pos_embed_max_size: int = 128,
):
super().__init__()
self.in_channels = in_channels
... | class_definition | 30,268 | 32,737 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 863 |
class FluxPosEmbed(nn.Module):
# modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
def __init__(self, theta: int, axes_dim: List[int]):
super().__init__()
self.theta = theta
self.axes_dim = axes_dim
d... | class_definition | 50,167 | 51,329 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 864 |
class TimestepEmbedding(nn.Module):
def __init__(
self,
in_channels: int,
time_embed_dim: int,
act_fn: str = "silu",
out_dim: int = None,
post_act_fn: Optional[str] = None,
cond_proj_dim=None,
sample_proj_bias=True,
):
super().__init__()
... | class_definition | 51,332 | 52,693 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 865 |
class Timesteps(nn.Module):
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
s... | class_definition | 52,696 | 53,333 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 866 |
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
def __init__(
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
):
super().__init__()
self.weight = nn.Parameter(torch.randn(embed... | class_definition | 53,336 | 54,361 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 867 |
class SinusoidalPositionalEmbedding(nn.Module):
"""Apply positional information to a sequence of embeddings.
Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
them
Args:
embed_dim: (int): Dimension of the positional embedding.
... | class_definition | 54,364 | 55,344 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 868 |
class ImagePositionalEmbeddings(nn.Module):
"""
Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
height and width of the latent space.
For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
For VQ-dif... | class_definition | 55,347 | 57,426 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 869 |
class LabelEmbedding(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
Args:
num_classes (`int`): The number of classes.
hidden_size (`int`): The size of the vector embeddings.
dropout_prob (`float`): The probab... | class_definition | 57,429 | 58,841 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 870 |
class TextImageProjection(nn.Module):
def __init__(
self,
text_embed_dim: int = 1024,
image_embed_dim: int = 768,
cross_attention_dim: int = 768,
num_image_text_embeds: int = 10,
):
super().__init__()
self.num_image_text_embeds = num_image_text_embeds
... | class_definition | 58,844 | 59,766 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 871 |
class ImageProjection(nn.Module):
def __init__(
self,
image_embed_dim: int = 768,
cross_attention_dim: int = 768,
num_image_text_embeds: int = 32,
):
super().__init__()
self.num_image_text_embeds = num_image_text_embeds
self.image_embeds = nn.Linear(image... | class_definition | 59,769 | 60,569 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 872 |
class IPAdapterFullImageProjection(nn.Module):
def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):
super().__init__()
from .attention import FeedForward
self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn="gelu")
self.norm = nn.LayerNorm... | class_definition | 60,572 | 61,013 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 873 |
class IPAdapterFaceIDImageProjection(nn.Module):
def __init__(self, image_embed_dim=1024, cross_attention_dim=1024, mult=1, num_tokens=1):
super().__init__()
from .attention import FeedForward
self.num_tokens = num_tokens
self.cross_attention_dim = cross_attention_dim
self.f... | class_definition | 61,016 | 61,672 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 874 |
class CombinedTimestepLabelEmbeddings(nn.Module):
def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
self.timestep_embedder = TimestepEmbedding(in_channels=256,... | class_definition | 61,675 | 62,491 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 875 |
class CombinedTimestepTextProjEmbeddings(nn.Module):
def __init__(self, embedding_dim, pooled_projection_dim):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed... | class_definition | 62,494 | 63,307 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 876 |
class CombinedTimestepGuidanceTextProjEmbeddings(nn.Module):
def __init__(self, embedding_dim, pooled_projection_dim):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
self.timestep_embedder = TimestepEmbedding(in_channels=256, ti... | class_definition | 63,310 | 64,455 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 877 |
class CogView3CombinedTimestepSizeEmbeddings(nn.Module):
def __init__(self, embedding_dim: int, condition_dim: int, pooled_projection_dim: int, timesteps_dim: int = 256):
super().__init__()
self.time_proj = Timesteps(num_channels=timesteps_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
... | class_definition | 64,458 | 66,124 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 878 |
class HunyuanDiTAttentionPool(nn.Module):
# Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
super().__init__()
self.positiona... | class_definition | 66,127 | 67,825 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 879 |
class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
def __init__(
self,
embedding_dim,
pooled_projection_dim=1024,
seq_len=256,
cross_attention_dim=2048,
use_style_cond_and_image_meta_size=True,
):
super().__init__()
self.time_proj = T... | class_definition | 67,828 | 70,345 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 880 |
class LuminaCombinedTimestepCaptionEmbedding(nn.Module):
def __init__(self, hidden_size=4096, cross_attention_dim=2048, frequency_embedding_size=256):
super().__init__()
self.time_proj = Timesteps(
num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0
... | class_definition | 70,348 | 71,697 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 881 |
class MochiCombinedTimestepCaptionEmbedding(nn.Module):
def __init__(
self,
embedding_dim: int,
pooled_projection_dim: int,
text_embed_dim: int,
time_embed_dim: int = 256,
num_attention_heads: int = 8,
) -> None:
super().__init__()
self.time_proj ... | class_definition | 71,700 | 73,038 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 882 |
class TextTimeEmbedding(nn.Module):
def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):
super().__init__()
self.norm1 = nn.LayerNorm(encoder_dim)
self.pool = AttentionPooling(num_heads, encoder_dim)
self.proj = nn.Linear(encoder_dim, time_embed_dim)
... | class_definition | 73,041 | 73,670 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 883 |
class TextImageTimeEmbedding(nn.Module):
def __init__(self, text_embed_dim: int = 768, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.text_proj = nn.Linear(text_embed_dim, time_embed_dim)
self.text_norm = nn.LayerNorm(time_embed_dim)
self.image_proj ... | class_definition | 73,673 | 74,374 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 884 |
class ImageTimeEmbedding(nn.Module):
def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
self.image_norm = nn.LayerNorm(time_embed_dim)
def forward(self, image_embeds: torch.Tensor):
... | class_definition | 74,377 | 74,866 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 885 |
class ImageHintTimeEmbedding(nn.Module):
def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):
super().__init__()
self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)
self.image_norm = nn.LayerNorm(time_embed_dim)
self.input_hint_block = nn.Sequential(
... | class_definition | 74,869 | 76,039 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 886 |
class AttentionPooling(nn.Module):
# Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54
def __init__(self, num_heads, embed_dim, dtype=None):
super().__init__()
self.dtype = dtype
self.positional_embedding = nn.Paramet... | class_definition | 76,042 | 78,320 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 887 |
class MochiAttentionPool(nn.Module):
def __init__(
self,
num_attention_heads: int,
embed_dim: int,
output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.output_dim = output_dim or embed_dim
self.num_attention_heads = num_attention_heads
... | class_definition | 78,323 | 81,450 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 888 |
class GLIGENTextBoundingboxProjection(nn.Module):
def __init__(self, positive_len, out_dim, feature_type="text-only", fourier_freqs=8):
super().__init__()
self.positive_len = positive_len
self.out_dim = out_dim
self.fourier_embedder_dim = fourier_freqs
self.position_dim = fo... | class_definition | 82,086 | 85,757 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 889 |
class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
"""
For PixArt-Alpha.
Reference:
https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
"""
def __init__(self, embedding_dim, size_emb_dim, use_additi... | class_definition | 85,760 | 87,656 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 890 |
class PixArtAlphaTextProjection(nn.Module):
"""
Projects caption embeddings. Also handles dropout for classifier-free guidance.
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
"""
def __init__(self, in_features, hidden_size, out_features=... | class_definition | 87,659 | 88,819 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 891 |
class IPAdapterPlusImageProjectionBlock(nn.Module):
def __init__(
self,
embed_dims: int = 768,
dim_head: int = 64,
heads: int = 16,
ffn_ratio: float = 4,
) -> None:
super().__init__()
from .attention import FeedForward
self.ln0 = nn.LayerNorm(embe... | class_definition | 88,822 | 89,881 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 892 |
class IPAdapterPlusImageProjection(nn.Module):
"""Resampler of IP-Adapter Plus.
Args:
embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,
that is the same
number of the channels in the `unet.config.cross_attention_dim`. Default... | class_definition | 89,884 | 91,907 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 893 |
class IPAdapterFaceIDPlusImageProjection(nn.Module):
"""FacePerceiverResampler of IP-Adapter Plus.
Args:
embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,
that is the same
number of the channels in the `unet.config.cross_atte... | class_definition | 91,910 | 94,912 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 894 |
class IPAdapterTimeImageProjectionBlock(nn.Module):
"""Block for IPAdapterTimeImageProjection.
Args:
hidden_dim (`int`, defaults to 1280):
The number of hidden channels.
dim_head (`int`, defaults to 64):
The number of head channels.
heads (`int`, defaults to 20):... | class_definition | 94,915 | 98,351 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 895 |
class IPAdapterTimeImageProjection(nn.Module):
"""Resampler of SD3 IP-Adapter with timestep embedding.
Args:
embed_dim (`int`, defaults to 1152):
The feature dimension.
output_dim (`int`, defaults to 2432):
The number of output channels.
hidden_dim (`int`, defaul... | class_definition | 98,456 | 101,572 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 896 |
class MultiIPAdapterImageProjection(nn.Module):
def __init__(self, IPAdapterImageProjectionLayers: Union[List[nn.Module], Tuple[nn.Module]]):
super().__init__()
self.image_projection_layers = nn.ModuleList(IPAdapterImageProjectionLayers)
def forward(self, image_embeds: List[torch.Tensor]):
... | class_definition | 101,575 | 103,574 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py | null | 897 |
class ResnetBlockCondNorm2D(nn.Module):
r"""
A Resnet block that use normalization layer that incorporate conditioning information.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output ... | class_definition | 1,305 | 8,134 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py | null | 898 |
class ResnetBlock2D(nn.Module):
r"""
A Resnet block.
Parameters:
in_channels (`int`): The number of channels in the input.
out_channels (`int`, *optional*, default to be `None`):
The number of output channels for the first conv2d layer. If None, same as `in_channels`.
dr... | class_definition | 8,137 | 16,939 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/resnet.py | null | 899 |
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