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 PriorTransformerOutput(BaseOutput):
"""
The output of [`PriorTransformer`].
Args:
predicted_image_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):
The predicted CLIP image embedding conditioned on the CLIP text embedding input.
"""
predicted_image_embedd... | class_definition | 638 | 975 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py | null | 1,100 |
class PriorTransformer(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
"""
A Prior Transformer model.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, def... | class_definition | 978 | 17,324 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/prior_transformer.py | null | 1,101 |
class SD3SingleTransformerBlock(nn.Module):
r"""
A Single Transformer block as part of the MMDiT architecture, used in Stable Diffusion 3 ControlNet.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads... | class_definition | 1,650 | 3,831 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py | null | 1,102 |
class SD3Transformer2DModel(
ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, SD3Transformer2DLoadersMixin
):
"""
The Transformer model introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
sample_size (`int`): The width of the latent i... | class_definition | 3,834 | 20,444 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_sd3.py | null | 1,103 |
class AllegroTransformerBlock(nn.Module):
r"""
Transformer block used in [Allegro](https://github.com/rhymes-ai/Allegro) model.
Args:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head a... | class_definition | 1,284 | 6,282 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py | null | 1,104 |
class AllegroTransformer3DModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
"""
A 3D Transformer model for video-like data.
Args:
patch_size (`int`, defaults to `2`):
The size of spatial patches to use in the patch embedding layer.
patch_size_t (`int`,... | class_definition | 6,285 | 17,562 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_allegro.py | null | 1,105 |
class AdaLayerNormShift(nn.Module):
r"""
Norm layer modified to incorporate timestep embeddings.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, elementwi... | class_definition | 1,386 | 2,167 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py | null | 1,106 |
class HunyuanDiTBlock(nn.Module):
r"""
Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
QKNorm
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The ... | class_definition | 2,192 | 7,784 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py | null | 1,107 |
class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
"""
HunYuanDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16):
... | class_definition | 7,787 | 24,233 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py | null | 1,108 |
class LuminaNextDiTBlock(nn.Module):
"""
A LuminaNextDiTBlock for LuminaNextDiT2DModel.
Parameters:
dim (`int`): Embedding dimension of the input features.
num_attention_heads (`int`): Number of attention heads.
num_kv_heads (`int`):
Number of attention heads in key and ... | class_definition | 1,259 | 6,893 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py | null | 1,109 |
class LuminaNextDiT2DModel(ModelMixin, ConfigMixin):
"""
LuminaNextDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
Parameters:
sample_size (`int`): The width of the latent images. This ... | class_definition | 6,896 | 14,392 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/lumina_nextdit2d.py | null | 1,110 |
class DiTTransformer2DModel(ModelMixin, ConfigMixin):
r"""
A 2D Transformer model as introduced in DiT (https://arxiv.org/abs/2212.09748).
Parameters:
num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (int, optional,... | class_definition | 1,079 | 11,167 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dit_transformer_2d.py | null | 1,111 |
class DualTransformer2DModel(nn.Module):
"""
Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*,... | class_definition | 762 | 7,710 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/dual_transformer_2d.py | null | 1,112 |
class TransformerTemporalModelOutput(BaseOutput):
"""
The output of [`TransformerTemporalModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input.
"""
sample: ... | class_definition | 1,032 | 1,364 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py | null | 1,113 |
class TransformerTemporalModel(ModelMixin, ConfigMixin):
"""
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 ... | class_definition | 1,367 | 9,244 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py | null | 1,114 |
class TransformerSpatioTemporalModel(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 chann... | class_definition | 9,247 | 16,943 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_temporal.py | null | 1,115 |
class LTXVideoAttentionProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the LTX model. It applies a normalization layer and rotary embedding on the query and key vector.
"""
def __init__(self):
if no... | class_definition | 1,402 | 3,691 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py | null | 1,116 |
class LTXVideoRotaryPosEmbed(nn.Module):
def __init__(
self,
dim: int,
base_num_frames: int = 20,
base_height: int = 2048,
base_width: int = 2048,
patch_size: int = 1,
patch_size_t: int = 1,
theta: float = 10000.0,
) -> None:
super().__init... | class_definition | 3,694 | 6,387 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py | null | 1,117 |
class LTXVideoTransformerBlock(nn.Module):
r"""
Transformer block used in [LTX](https://huggingface.co/Lightricks/LTX-Video).
Args:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head att... | class_definition | 6,412 | 10,188 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py | null | 1,118 |
class LTXVideoTransformer3DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
r"""
A Transformer model for video-like data used in [LTX](https://huggingface.co/Lightricks/LTX-Video).
Args:
in_channels (`int`, defaults to `128`):
The number of channels in the input... | class_definition | 10,213 | 18,114 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_ltx.py | null | 1,119 |
class LatteTransformer3DModel(ModelMixin, ConfigMixin):
_supports_gradient_checkpointing = True
"""
A 3D Transformer model for video-like data, paper: https://arxiv.org/abs/2401.03048, offical code:
https://github.com/Vchitect/Latte
Parameters:
num_attention_heads (`int`, *optional*, defau... | class_definition | 1,077 | 15,504 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/latte_transformer_3d.py | null | 1,120 |
class GLUMBConv(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
expand_ratio: float = 4,
norm_type: Optional[str] = None,
residual_connection: bool = True,
) -> None:
super().__init__()
hidden_channels = int(expand_ratio * in_... | class_definition | 1,328 | 3,067 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py | null | 1,121 |
class SanaTransformerBlock(nn.Module):
r"""
Transformer block introduced in [Sana](https://huggingface.co/papers/2410.10629).
"""
def __init__(
self,
dim: int = 2240,
num_attention_heads: int = 70,
attention_head_dim: int = 32,
dropout: float = 0.0,
num_c... | class_definition | 3,070 | 6,698 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py | null | 1,122 |
class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
r"""
A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models.
Args:
in_channels (`int`, defaults to `32`):
The number of channels in the input.
out_channels (`... | class_definition | 6,701 | 20,078 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/sana_transformer.py | null | 1,123 |
class AuraFlowPatchEmbed(nn.Module):
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
pos_embed_max_size=None,
):
super().__init__()
self.num_patches = (height // patch_size) * (width // patch_size)
... | class_definition | 1,644 | 3,774 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,124 |
class AuraFlowFeedForward(nn.Module):
def __init__(self, dim, hidden_dim=None) -> None:
super().__init__()
if hidden_dim is None:
hidden_dim = 4 * dim
final_hidden_dim = int(2 * hidden_dim / 3)
final_hidden_dim = find_multiple(final_hidden_dim, 256)
self.linear_... | class_definition | 3,881 | 4,558 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,125 |
class AuraFlowPreFinalBlock(nn.Module):
def __init__(self, embedding_dim: int, conditioning_embedding_dim: int):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=False)
def forward(self, x: torch.Tensor, conditioning_e... | class_definition | 4,561 | 5,121 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,126 |
class AuraFlowSingleTransformerBlock(nn.Module):
"""Similar to `AuraFlowJointTransformerBlock` with a single DiT instead of an MMDiT."""
def __init__(self, dim, num_attention_heads, attention_head_dim):
super().__init__()
self.norm1 = AdaLayerNormZero(dim, bias=False, norm_type="fp32_layer_nor... | class_definition | 5,146 | 6,735 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,127 |
class AuraFlowJointTransformerBlock(nn.Module):
r"""
Transformer block for Aura Flow. Similar to SD3 MMDiT. Differences (non-exhaustive):
* QK Norm in the attention blocks
* No bias in the attention blocks
* Most LayerNorms are in FP32
Parameters:
dim (`int`): The number of... | class_definition | 6,760 | 9,980 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,128 |
class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).
Parameters:
sample_size (`int`): The width of the latent images. This is fixed during training since
it is used to le... | class_definition | 9,983 | 22,957 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/auraflow_transformer_2d.py | null | 1,129 |
class T5FilmDecoder(ModelMixin, ConfigMixin):
r"""
T5 style decoder with FiLM conditioning.
Args:
input_dims (`int`, *optional*, defaults to `128`):
The number of input dimensions.
targets_length (`int`, *optional*, defaults to `256`):
The length of the targets.
... | class_definition | 890 | 5,641 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,130 |
class DecoderLayer(nn.Module):
r"""
T5 decoder layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention heads.
d_ff (`int`):
S... | class_definition | 5,644 | 8,271 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,131 |
class T5LayerSelfAttentionCond(nn.Module):
r"""
T5 style self-attention layer with conditioning.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention... | class_definition | 8,274 | 9,721 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,132 |
class T5LayerCrossAttention(nn.Module):
r"""
T5 style cross-attention layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_kv (`int`):
Size of the key-value projection vectors.
num_heads (`int`):
Number of attention heads.
drop... | class_definition | 9,724 | 11,159 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,133 |
class T5LayerFFCond(nn.Module):
r"""
T5 style feed-forward conditional layer.
Args:
d_model (`int`):
Size of the input hidden states.
d_ff (`int`):
Size of the intermediate feed-forward layer.
dropout_rate (`float`):
Dropout probability.
l... | class_definition | 11,162 | 12,489 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,134 |
class T5DenseGatedActDense(nn.Module):
r"""
T5 style feed-forward layer with gated activations and dropout.
Args:
d_model (`int`):
Size of the input hidden states.
d_ff (`int`):
Size of the intermediate feed-forward layer.
dropout_rate (`float`):
... | class_definition | 12,492 | 13,550 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,135 |
class T5LayerNorm(nn.Module):
r"""
T5 style layer normalization module.
Args:
hidden_size (`int`):
Size of the input hidden states.
eps (`float`, `optional`, defaults to `1e-6`):
A small value used for numerical stability to avoid dividing by zero.
"""
def _... | class_definition | 13,553 | 14,971 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,136 |
class NewGELUActivation(nn.Module):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
return... | class_definition | 14,974 | 15,398 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,137 |
class T5FiLMLayer(nn.Module):
"""
T5 style FiLM Layer.
Args:
in_features (`int`):
Number of input features.
out_features (`int`):
Number of output features.
"""
def __init__(self, in_features: int, out_features: int):
super().__init__()
self.... | class_definition | 15,401 | 16,023 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/t5_film_transformer.py | null | 1,138 |
class FluxSingleTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): Th... | class_definition | 1,803 | 4,332 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py | null | 1,139 |
class FluxTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Args:
dim (`int`):
The embedding dimension of the block.
num_attention_heads (`int`):
... | class_definition | 4,357 | 8,926 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py | null | 1,140 |
class FluxTransformer2DModel(
ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, FluxTransformer2DLoadersMixin
):
"""
The Transformer model introduced in Flux.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
Args:
patch_size (`int`, defaults to `1`):
... | class_definition | 8,929 | 25,988 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_flux.py | null | 1,141 |
class Transformer2DModelOutput(Transformer2DModelOutput):
def __init__(self, *args, **kwargs):
deprecation_message = "Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs imp... | class_definition | 1,203 | 1,681 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py | null | 1,142 |
class Transformer2DModel(LegacyModelMixin, LegacyConfigMixin):
"""
A 2D Transformer model for image-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): Th... | class_definition | 1,684 | 28,962 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_2d.py | null | 1,143 |
class PixArtTransformer2DModel(ModelMixin, ConfigMixin):
r"""
A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
https://arxiv.org/abs/2403.04692).
Parameters:
num_attention_heads (int, optional, defaults to 16): The number of heads to use for mul... | class_definition | 1,230 | 21,578 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py | null | 1,144 |
class HunyuanVideoAttnProcessor2_0:
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"HunyuanVideoAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn... | class_definition | 1,531 | 5,748 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,145 |
class HunyuanVideoPatchEmbed(nn.Module):
def __init__(
self,
patch_size: Union[int, Tuple[int, int, int]] = 16,
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
super().__init__()
patch_size = (patch_size, patch_size, patch_size) if isinstance(patch_size, int)... | class_definition | 5,751 | 6,409 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,146 |
class HunyuanVideoAdaNorm(nn.Module):
def __init__(self, in_features: int, out_features: Optional[int] = None) -> None:
super().__init__()
out_features = out_features or 2 * in_features
self.linear = nn.Linear(in_features, out_features)
self.nonlinearity = nn.SiLU()
def forward... | class_definition | 6,412 | 7,062 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,147 |
class HunyuanVideoIndividualTokenRefinerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_width_ratio: str = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
super().__init__()
hid... | class_definition | 7,065 | 8,684 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,148 |
class HunyuanVideoIndividualTokenRefiner(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
num_layers: int,
mlp_width_ratio: float = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
super()._... | class_definition | 8,687 | 10,329 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,149 |
class HunyuanVideoTokenRefiner(nn.Module):
def __init__(
self,
in_channels: int,
num_attention_heads: int,
attention_head_dim: int,
num_layers: int,
mlp_ratio: float = 4.0,
mlp_drop_rate: float = 0.0,
attention_bias: bool = True,
) -> None:
... | class_definition | 10,332 | 12,096 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,150 |
class HunyuanVideoRotaryPosEmbed(nn.Module):
def __init__(self, patch_size: int, patch_size_t: int, rope_dim: List[int], theta: float = 256.0) -> None:
super().__init__()
self.patch_size = patch_size
self.patch_size_t = patch_size_t
self.rope_dim = rope_dim
self.theta = thet... | class_definition | 12,099 | 13,695 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,151 |
class HunyuanVideoSingleTransformerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_ratio: float = 4.0,
qk_norm: str = "rms_norm",
) -> None:
super().__init__()
hidden_size = num_attention_heads * attention_head... | class_definition | 13,698 | 16,367 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,152 |
class HunyuanVideoTransformerBlock(nn.Module):
def __init__(
self,
num_attention_heads: int,
attention_head_dim: int,
mlp_ratio: float,
qk_norm: str = "rms_norm",
) -> None:
super().__init__()
hidden_size = num_attention_heads * attention_head_dim
... | class_definition | 16,370 | 19,494 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,153 |
class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
r"""
A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo).
Args:
in_channels (`int`, defaults to `16`):
The number of channels ... | class_definition | 19,497 | 32,230 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_hunyuan_video.py | null | 1,154 |
class CogVideoXBlock(nn.Module):
r"""
Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) 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 atten... | class_definition | 1,509 | 6,331 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py | null | 1,155 |
class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
"""
A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
Parameters:
num_attention_heads (`int`, defaults to `30`):
The number of heads to use for multi-head attention.
... | class_definition | 6,334 | 23,546 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py | null | 1,156 |
class CogView3PlusTransformerBlock(nn.Module):
r"""
Transformer block used in [CogView](https://github.com/THUDM/CogView3) model.
Args:
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 | 1,398 | 4,825 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py | null | 1,157 |
class CogView3PlusTransformer2DModel(ModelMixin, ConfigMixin):
r"""
The Transformer model introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay
Diffusion](https://huggingface.co/papers/2403.05121).
Args:
patch_size (`int`, defaults to `2`):
The size of the pat... | class_definition | 4,828 | 16,380 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_cogview3plus.py | null | 1,158 |
class StableAudioGaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
# Copied from diffusers.models.embeddings.GaussianFourierProjection.__init__
def __init__(
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=... | class_definition | 1,284 | 2,401 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py | null | 1,159 |
class StableAudioDiTBlock(nn.Module):
r"""
Transformer block used in Stable Audio model (https://github.com/Stability-AI/stable-audio-tools). Allow skip
connection and QKNorm
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number... | class_definition | 2,426 | 6,635 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py | null | 1,160 |
class StableAudioDiTModel(ModelMixin, ConfigMixin):
"""
The Diffusion Transformer model introduced in Stable Audio.
Reference: https://github.com/Stability-AI/stable-audio-tools
Parameters:
sample_size ( `int`, *optional*, defaults to 1024): The size of the input sample.
in_channels (`... | class_definition | 6,638 | 19,324 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/stable_audio_transformer.py | null | 1,161 |
class MochiModulatedRMSNorm(nn.Module):
def __init__(self, eps: float):
super().__init__()
self.eps = eps
self.norm = RMSNorm(0, eps, False)
def forward(self, hidden_states, scale=None):
hidden_states_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.fl... | class_definition | 1,454 | 2,004 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,162 |
class MochiLayerNormContinuous(nn.Module):
def __init__(
self,
embedding_dim: int,
conditioning_embedding_dim: int,
eps=1e-5,
bias=True,
):
super().__init__()
# AdaLN
self.silu = nn.SiLU()
self.linear_1 = nn.Linear(conditioning_embedding_d... | class_definition | 2,007 | 2,870 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,163 |
class MochiRMSNormZero(nn.Module):
r"""
Adaptive RMS Norm used in Mochi.
Parameters:
embedding_dim (`int`): The size of each embedding vector.
"""
def __init__(
self, embedding_dim: int, hidden_dim: int, eps: float = 1e-5, elementwise_affine: bool = False
) -> None:
sup... | class_definition | 2,873 | 3,891 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,164 |
class MochiTransformerBlock(nn.Module):
r"""
Transformer block used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head att... | class_definition | 3,916 | 9,097 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,165 |
class MochiRoPE(nn.Module):
r"""
RoPE implementation used in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
base_height (`int`, defaults to `192`):
Base height used to compute interpolation scale for rotary positional embeddings.
base_width (`int`, defaults to `192... | class_definition | 9,100 | 11,447 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,166 |
class MochiTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
r"""
A Transformer model for video-like data introduced in [Mochi](https://huggingface.co/genmo/mochi-1-preview).
Args:
patch_size (`int`, defaults to `2`):
The size of the patches to use i... | class_definition | 11,472 | 18,960 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/transformers/transformer_mochi.py | null | 1,167 |
class TemporalDecoder(nn.Module):
def __init__(
self,
in_channels: int = 4,
out_channels: int = 3,
block_out_channels: Tuple[int] = (128, 256, 512, 512),
layers_per_block: int = 2,
):
super().__init__()
self.layers_per_block = layers_per_block
sel... | class_definition | 1,212 | 6,077 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py | null | 1,168 |
class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (s... | class_definition | 6,080 | 15,986 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py | null | 1,169 |
class CogVideoXSafeConv3d(nn.Conv3d):
r"""
A 3D convolution layer that splits the input tensor into smaller parts to avoid OOM in CogVideoX Model.
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
memory_count = (
(input.shape[0] * input.shape[1] * input.shape[2] * input.s... | class_definition | 1,377 | 2,518 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,170 |
class CogVideoXCausalConv3d(nn.Module):
r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model.
Args:
in_channels (`int`): Number of channels in the input tensor.
out_channels (`int`): Number of output channels produced by the convolution.
ke... | class_definition | 2,521 | 5,678 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,171 |
class CogVideoXSpatialNorm3D(nn.Module):
r"""
Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. This implementation is specific
to 3D-video like data.
CogVideoXSafeConv3d is used instead of nn.Conv3d to avoid OOM in CogVideoX Model.
Args:
f_channels (`int`... | class_definition | 5,681 | 7,791 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,172 |
class CogVideoXResnetBlock3D(nn.Module):
r"""
A 3D ResNet block used in the CogVideoX model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
dropout (`float`... | class_definition | 7,794 | 12,854 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,173 |
class CogVideoXDownBlock3D(nn.Module):
r"""
A downsampling block used in the CogVideoX model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
temb_channels (... | class_definition | 12,857 | 17,031 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,174 |
class CogVideoXMidBlock3D(nn.Module):
r"""
A middle block used in the CogVideoX model.
Args:
in_channels (`int`):
Number of input channels.
temb_channels (`int`, defaults to `512`):
Number of time embedding channels.
dropout (`float`, defaults to `0.0`):
... | class_definition | 17,034 | 20,263 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,175 |
class CogVideoXUpBlock3D(nn.Module):
r"""
An upsampling block used in the CogVideoX model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
temb_channels (`in... | class_definition | 20,266 | 24,600 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,176 |
class CogVideoXEncoder3D(nn.Module):
r"""
The `CogVideoXEncoder3D` layer of a variational autoencoder that encodes its input into a latent representation.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, default... | class_definition | 24,603 | 30,720 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,177 |
class CogVideoXDecoder3D(nn.Module):
r"""
The `CogVideoXDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
sample.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optio... | class_definition | 30,723 | 37,187 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,178 |
class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[CogVideoX](https://github.com/THUDM/CogVideo).
This model inherits from [`ModelMixin`]. Check the super... | class_definition | 37,190 | 61,639 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py | null | 1,179 |
class MochiChunkedGroupNorm3D(nn.Module):
r"""
Applies per-frame group normalization for 5D video inputs. It also supports memory-efficient chunked group
normalization.
Args:
num_channels (int): Number of channels expected in input
num_groups (int, optional): Number of groups to separat... | class_definition | 1,280 | 2,500 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,180 |
class MochiResnetBlock3D(nn.Module):
r"""
A 3D ResNet block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
non_linearity (`str`, de... | class_definition | 2,503 | 4,493 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,181 |
class MochiDownBlock3D(nn.Module):
r"""
An downsampling block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, def... | class_definition | 4,496 | 9,185 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,182 |
class MochiMidBlock3D(nn.Module):
r"""
A middle block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
num_layers (`int`, defaults to `3`):
Number of resnet blocks in the block.
"""
def __init__(
self,
in_channels... | class_definition | 9,188 | 12,244 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,183 |
class MochiUpBlock3D(nn.Module):
r"""
An upsampling block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, default... | class_definition | 12,247 | 15,284 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,184 |
class FourierFeatures(nn.Module):
def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
super().__init__()
self.start = start
self.stop = stop
self.step = step
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
r"""Forward method of the `FourierFeature... | class_definition | 15,287 | 16,364 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,185 |
class MochiEncoder3D(nn.Module):
r"""
The `MochiEncoder3D` layer of a variational autoencoder that encodes input video samples to its latent
representation.
Args:
in_channels (`int`, *optional*):
The number of input channels.
out_channels (`int`, *optional*):
The... | class_definition | 16,367 | 21,395 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,186 |
class MochiDecoder3D(nn.Module):
r"""
The `MochiDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
sample.
Args:
in_channels (`int`, *optional*):
The number of input channels.
out_channels (`int`, *optional*):
The ... | class_definition | 21,398 | 26,009 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,187 |
class AutoencoderKLMochi(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[Mochi 1 preview](https://github.com/genmoai/models).
This model inherits from [`ModelMixin`]. Check the superclass documentation fo... | class_definition | 26,012 | 48,043 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py | null | 1,188 |
class ConsistencyDecoderVAEOutput(BaseOutput):
"""
Output of encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for samp... | class_definition | 1,349 | 1,761 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py | null | 1,189 |
class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
r"""
The consistency decoder used with DALL-E 3.
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consisten... | class_definition | 1,764 | 19,720 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py | null | 1,190 |
class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implem... | class_definition | 1,338 | 25,237 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py | null | 1,191 |
class Snake1d(nn.Module):
"""
A 1-dimensional Snake activation function module.
"""
def __init__(self, hidden_dim, logscale=True):
super().__init__()
self.alpha = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.beta = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.al... | class_definition | 1,033 | 1,934 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,192 |
class OobleckResidualUnit(nn.Module):
"""
A residual unit composed of Snake1d and weight-normalized Conv1d layers with dilations.
"""
def __init__(self, dimension: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.snake1 = Snake1d(dimension)
... | class_definition | 1,937 | 3,320 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,193 |
class OobleckEncoderBlock(nn.Module):
"""Encoder block used in Oobleck encoder."""
def __init__(self, input_dim, output_dim, stride: int = 1):
super().__init__()
self.res_unit1 = OobleckResidualUnit(input_dim, dilation=1)
self.res_unit2 = OobleckResidualUnit(input_dim, dilation=3)
... | class_definition | 3,323 | 4,190 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,194 |
class OobleckDecoderBlock(nn.Module):
"""Decoder block used in Oobleck decoder."""
def __init__(self, input_dim, output_dim, stride: int = 1):
super().__init__()
self.snake1 = Snake1d(input_dim)
self.conv_t1 = weight_norm(
nn.ConvTranspose1d(
input_dim,
... | class_definition | 4,193 | 5,207 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,195 |
class OobleckDiagonalGaussianDistribution(object):
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
self.parameters = parameters
self.mean, self.scale = parameters.chunk(2, dim=1)
self.std = nn.functional.softplus(self.scale) + 1e-4
self.var = self.std * sel... | class_definition | 5,210 | 6,732 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,196 |
class AutoencoderOobleckOutput(BaseOutput):
"""
Output of AutoencoderOobleck encoding method.
Args:
latent_dist (`OobleckDiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and standard deviation of
`OobleckDiagonalGaussianDistribution`. `Oob... | class_definition | 6,746 | 7,240 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,197 |
class OobleckDecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels, sequence_length)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.Tensor | class_definition | 7,254 | 7,540 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,198 |
class OobleckEncoder(nn.Module):
"""Oobleck Encoder"""
def __init__(self, encoder_hidden_size, audio_channels, downsampling_ratios, channel_multiples):
super().__init__()
strides = downsampling_ratios
channel_multiples = [1] + channel_multiples
# Create first convolution
... | class_definition | 7,543 | 8,980 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,199 |
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