SanaTransformer2DModel  
A 2D Transformer model introduced in [Sana](https://huggingface.co/papers/2410.10629) family of models.  
Args:
in_channels (`int`, defaults to `32`):
The number of channels in the input.
out_channels (`int`, *optional*, defaults to `32`):
The number of channels in the output.
num_attention_heads (`int`, defaults to `70`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, defaults to `32`):
The number of channels in each head.
num_layers (`int`, defaults to `20`):
The number of layers of Transformer blocks to use.
num_cross_attention_heads (`int`, *optional*, defaults to `20`):
The number of heads to use for cross-attention.
cross_attention_head_dim (`int`, *optional*, defaults to `112`):
The number of channels in each head for cross-attention.
cross_attention_dim (`int`, *optional*, defaults to `2240`):
The number of channels in the cross-attention output.
caption_channels (`int`, defaults to `2304`):
The number of channels in the caption embeddings.
mlp_ratio (`float`, defaults to `2.5`):
The expansion ratio to use in the GLUMBConv layer.
dropout (`float`, defaults to `0.0`):
The dropout probability.
attention_bias (`bool`, defaults to `False`):
Whether to use bias in the attention layer.
sample_size (`int`, defaults to `32`):
The base size of the input latent.
patch_size (`int`, defaults to `1`):
The size of the patches to use in the patch embedding layer.
norm_elementwise_affine (`bool`, defaults to `False`):
Whether to use elementwise affinity in the normalization layer.
norm_eps (`float`, defaults to `1e-6`):
The epsilon value for the normalization layer.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/models/sana_transformer2d.md