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://arxiv.org/abs/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.LongTensorof shape(batch size, num latent pixels)if discrete,torch.FloatTensorof shape(batch size, channel, height, width)if continuous) — Inputhidden_states. - timestep (
torch.LongTensor, optional) — Used to indicate denoising step. Optional timestep to be applied as an embedding inAdaLayerNorm. - class_labels (
torch.LongTensorof shape(batch size, num classes), optional) — Used to indicate class labels conditioning. Optional class labels to be applied as an embedding inAdaLayerZeroNorm. - cross_attention_kwargs (
Dict[str, Any], optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessoras defined underself.processorin diffusers.models.attention_processor. - return_dict (
bool, optional, defaults toTrue) — Whether or not to return a UNet2DConditionOutput instead of a plain tuple.
The DiTTransformer2DModel forward method.