diffusers/pr_14407/en/api/models/pixart_transformer2d.md

PixArtTransformer2DModel

A Transformer model for image-like data from PixArt-Alpha and PixArt-Sigma.

PixArtTransformer2DModel[[diffusers.PixArtTransformer2DModel]]

diffusers.PixArtTransformer2DModel[[diffusers.PixArtTransformer2DModel]]

diffusers.PixArtTransformer2DModel(num_attention_heads: int = 16, attention_head_dim: int = 72, in_channels: int = 4, out_channels: int | None = 8, num_layers: int = 28, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: int | None = 1152, attention_bias: bool = True, sample_size: int = 128, patch_size: int = 2, activation_fn: str = 'gelu-approximate', num_embeds_ada_norm: int | None = 1000, upcast_attention: bool = False, norm_type: str = 'ada_norm_single', norm_elementwise_affine: bool = False, norm_eps: float = 1e-06, interpolation_scale: int | None = None, use_additional_conditions: bool | None = None, caption_channels: int | None = None, attention_type: str | None = 'default')

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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.

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).

forward[[diffusers.PixArtTransformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: typing.Optional[torch.Tensor] = None, timestep: typing.Optional[torch.LongTensor] = None, added_cond_kwargs: dict = None, cross_attention_kwargs: dict = None, attention_mask: typing.Optional[torch.Tensor] = None, encoder_attention_mask: typing.Optional[torch.Tensor] = None, return_dict: bool = True)

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Parameters:

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.

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 instead of a plain tuple.

Returns:

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 forward method.

fuse_qkv_projections[[diffusers.PixArtTransformer2DModel.fuse_qkv_projections]]

fuse_qkv_projections()

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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.

> This API is 🧪 experimental.

set_default_attn_processor[[diffusers.PixArtTransformer2DModel.set_default_attn_processor]]

set_default_attn_processor()

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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.

unfuse_qkv_projections[[diffusers.PixArtTransformer2DModel.unfuse_qkv_projections]]

unfuse_qkv_projections()

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.