# DiTTransformer2DModel

A Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748).

## DiTTransformer2DModel[[diffusers.DiTTransformer2DModel]]

#### diffusers.DiTTransformer2DModel[[diffusers.DiTTransformer2DModel]]

[Source](https://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/models/transformers/dit_transformer_2d.py#L31)

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

forwarddiffusers.DiTTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/v0.38.0/src/diffusers/models/transformers/dit_transformer_2d.py#L148[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": torch.LongTensor | None = None"}, {"name": "class_labels", "val": ": torch.LongTensor | None = None"}, {"name": "cross_attention_kwargs", "val": ": dict = 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](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/v0.38.0/en/api/models/unet2d-cond#diffusers.models.unets.unet_2d_condition.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](/docs/diffusers/v0.38.0/en/api/models/dit_transformer2d#diffusers.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.