diffusers/main/en/api/models/pixart_transformer2d.md

# PixArtTransformer2DModel

A Transformer model for image-like data from [PixArt-Alpha](https://huggingface.co/papers/2310.00426) and [PixArt-Sigma](https://huggingface.co/papers/2403.04692).

## PixArtTransformer2DModel[[diffusers.PixArtTransformer2DModel]]

- **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.
- **cross_attention_dim** (int, optional) --
  The dimensionality for cross-attention layers, typically matching the encoder's hidden dimension.
- **attention_bias** (bool, optional, defaults to True) --
  Configure if the Transformer blocks' attention should contain a bias parameter.
- **sample_size** (int, defaults to 128) --
  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-6) --
  A small constant added to the denominator in normalization layers to prevent division by zero.
- **interpolation_scale** (int, optional) -- Scale factor to use during interpolating the position embeddings.
- **use_additional_conditions** (bool, optional) -- If we're using additional conditions as inputs.
- **attention_type** (str, optional, defaults to "default") -- Kind of attention mechanism to be used.
- **caption_channels** (int, optional, defaults to None) --
  Number of channels to use for projecting the caption embeddings.
- **use_linear_projection** (bool, optional, defaults to False) --
  Deprecated argument. Will be removed in a future version.
- **num_vector_embeds** (bool, optional, defaults to False) --
  Deprecated argument. Will be removed in a future version.

A 2D Transformer model as introduced in PixArt family of models (https://huggingface.co/papers/2310.00426,
https://huggingface.co/papers/2403.04692).

- **hidden_states** (`torch.FloatTensor` of shape `(batch size, channel, height, width)`) --
  Input `hidden_states`.
- **encoder_hidden_states** (`torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*) --
  Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
  self-attention.
- **timestep** (`torch.LongTensor`, *optional*) --
  Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
- **added_cond_kwargs** -- (`dict[str, Any]`, *optional*): Additional conditions to be used as inputs.
- **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).
- **attention_mask** ( `torch.Tensor`, *optional*) --
  An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
  is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
  negative values to the attention scores corresponding to "discard" tokens.
- **encoder_attention_mask** ( `torch.Tensor`, *optional*) --
  Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

  * Mask `(batch, sequence_length)` True = keep, False = discard.
  * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

  If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
  above. This bias will be added to the cross-attention scores.
- **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 [PixArtTransformer2DModel](/docs/diffusers/main/en/api/models/pixart_transformer2d#diffusers.PixArtTransformer2DModel) forward method.

Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.

> [!WARNING] > This API is 🧪 experimental.

Disables custom attention processors and sets the default attention implementation.

Safe to just use `AttnProcessor()` as PixArt doesn't have any exotic attention processors in default model.

Disables the fused QKV projection if enabled.

> [!WARNING] > This API is 🧪 experimental.