# 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.