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

AuraFlowTransformer2DModel

A Transformer model for image-like data from AuraFlow.

AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

diffusers.AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

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

forwarddiffusers.AuraFlowTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/auraflow_transformer_2d.py#L400[{"name": "hidden_states", "val": ": FloatTensor"}, {"name": "encoder_hidden_states", "val": ": FloatTensor = None"}, {"name": "timestep", "val": ": LongTensor = None"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- 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.0If 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.

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.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

fuse_qkv_projections[[diffusers.AuraFlowTransformer2DModel.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]]

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.