AuraFlowTransformer2DModel  
A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).  
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.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/aura_flow_transformer2d.md