diffusers/pr_12249/en/api/models/dit_transformer2d.md

DiTTransformer2DModel

A Transformer model for image-like data from DiT.

DiTTransformer2DModel[[diffusers.DiTTransformer2DModel]]

diffusers.DiTTransformer2DModel[[diffusers.DiTTransformer2DModel]]

Source

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

forwarddiffusers.DiTTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_12249/src/diffusers/models/transformers/dit_transformer_2d.py#L148[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "class_labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "cross_attention_kwargs", "val": ": typing.Dict[str, typing.Any] = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The DiTTransformer2DModel forward method.

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.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.