diffusers/pr_13881/en/api/models/dit_transformer2d.md

DiTTransformer2DModel

A Transformer model for image-like data from DiT.

DiTTransformer2DModel[[diffusers.DiTTransformer2DModel]]

• 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).

• 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.If 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.