diffusers/pr_13881/en/api/models/aura_flow_transformer2d.md

AuraFlowTransformer2DModel

A Transformer model for image-like data from AuraFlow.

AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

• 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/).

• 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.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.

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.

Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.