diffusers/pr_14333/en/api/models/aura_flow_transformer2d.md

AuraFlowTransformer2DModel

A Transformer model for image-like data from AuraFlow.

AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

diffusers.AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

diffusers.AuraFlowTransformer2DModel(sample_size: int = 64, patch_size: int = 2, in_channels: int = 4, num_mmdit_layers: int = 4, num_single_dit_layers: int = 32, attention_head_dim: int = 256, num_attention_heads: int = 12, joint_attention_dim: int = 2048, caption_projection_dim: int = 3072, out_channels: int = 4, pos_embed_max_size: int = 1024)

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Parameters:

sample_size (int) : The width of the latent images. This is fixed during training since it is used to learn a number of position embeddings.

patch_size (int) : Patch size to turn the input data into small patches.

in_channels (int, optional, defaults to 4) : The number of channels in the input.

num_mmdit_layers (int, optional, defaults to 4) : The number of layers of MMDiT Transformer blocks to use.

num_single_dit_layers (int, optional, defaults to 32) : The number of layers of Transformer blocks to use. These blocks use concatenated image and text representations.

attention_head_dim (int, optional, defaults to 256) : The number of channels in each head.

num_attention_heads (int, optional, defaults to 12) : The number of heads to use for multi-head attention.

joint_attention_dim (int, optional) : The number of encoder_hidden_states dimensions to use.

caption_projection_dim (int) : Number of dimensions to use when projecting the encoder_hidden_states.

out_channels (int, defaults to 4) : Number of output channels.

pos_embed_max_size (int, defaults to 1024) : Maximum positions to embed from the image latents.

A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).

forward[[diffusers.AuraFlowTransformer2DModel.forward]]

forward(hidden_states: FloatTensor, encoder_hidden_states: FloatTensor = None, timestep: LongTensor = None, attention_kwargs: dict[str, typing.Any] | None = 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)) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

timestep (torch.LongTensor) : Used to indicate denoising step.

attention_kwargs (dict, optional) : A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput 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 AuraFlowTransformer2DModel forward method.

fuse_qkv_projections[[diffusers.AuraFlowTransformer2DModel.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.

unfuse_qkv_projections[[diffusers.AuraFlowTransformer2DModel.unfuse_qkv_projections]]

unfuse_qkv_projections()

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.