#
AuraFlowTransformer2DModel

A Transformer model for image-like data from AuraFlow.

##
AuraFlowTransformer2DModel

###
class diffusers.AuraFlowTransformer2DModel

< source >

( 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 )

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 16) — 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 4) — The number of layers of Transformer blocks to use. These blocks use concatenated image and text representations.
- attention_head_dim (`int`, optional, defaults to 64) — The number of channels in each head.
- num_attention_heads (`int`, optional, defaults to 18) — 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 16) — Number of output channels.
- pos_embed_max_size (`int`, defaults to 4096) — Maximum positions to embed from the image latents.

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

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