#
DiTTransformer2DModel

A Transformer model for image-like data from DiT.

##
DiTTransformer2DModel

###
class diffusers.DiTTransformer2DModel

< source >

( num_attention_heads: int = 16 attention_head_dim: int = 72 in_channels: int = 4 out_channels: typing.Optional[int] = None num_layers: int = 28 dropout: float = 0.0 norm_num_groups: int = 32 attention_bias: bool = True sample_size: int = 32 patch_size: int = 2 activation_fn: str = 'gelu-approximate' num_embeds_ada_norm: typing.Optional[int] = 1000 upcast_attention: bool = False norm_type: str = 'ada_norm_zero' norm_elementwise_affine: bool = False norm_eps: float = 1e-05 )

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 72) — The number of channels in each head.
- in_channels (int, defaults to 4) — The number of channels in the input.
- out_channels (int, optional) — The number of channels in the output. Specify this parameter if the output channel number differs from the input.
- num_layers (int, optional, defaults to 28) — The number of layers of Transformer blocks to use.
- dropout (float, optional, defaults to 0.0) — The dropout probability to use within the Transformer blocks.
- norm_num_groups (int, optional, defaults to 32) — Number of groups for group normalization within Transformer blocks.
- attention_bias (bool, optional, defaults to True) — Configure if the Transformer blocks’ attention should contain a bias parameter.
- sample_size (int, defaults to 32) — The width of the latent images. This parameter is fixed during training.
- patch_size (int, defaults to 2) — Size of the patches the model processes, relevant for architectures working on non-sequential data.
- activation_fn (str, optional, defaults to “gelu-approximate”) — Activation function to use in feed-forward networks within Transformer blocks.
- num_embeds_ada_norm (int, optional, defaults to 1000) — Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during inference.
- upcast_attention (bool, optional, defaults to False) — If true, upcasts the attention mechanism dimensions for potentially improved performance.
- norm_type (str, optional, defaults to “ada_norm_zero”) — Specifies the type of normalization used, can be ‘ada_norm_zero’.
- norm_elementwise_affine (bool, optional, defaults to False) — If true, enables element-wise affine parameters in the normalization layers.
- norm_eps (float, optional, defaults to 1e-5) — A small constant added to the denominator in normalization layers to prevent division by zero.

A 2D Transformer model as introduced in DiT (https://huggingface.co/papers/2212.09748).

####
forward

< source >

( hidden_states: Tensor timestep: typing.Optional[torch.LongTensor] = None class_labels: typing.Optional[torch.LongTensor] = None cross_attention_kwargs: typing.Dict[str, typing.Any] = None return_dict: bool = True )

Parameters

- hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous) — Input `hidden_states`.
- timestep ( `torch.LongTensor`, optional) — Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
- class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, optional) — Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`.
- cross_attention_kwargs ( `Dict[str, Any]`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in diffusers.models.attention_processor.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a UNet2DConditionOutput instead of a plain tuple.

The DiTTransformer2DModel forward method.

Update on GitHub