# DiTTransformer2DModel A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748). ## 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether or not to return a [UNet2DConditionOutput](/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.models.unets.unet_2d_condition.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](/docs/diffusers/main/en/api/models/dit_transformer2d#diffusers.DiTTransformer2DModel) forward method.