HunyuanDiT2DModel  
HunYuanDiT: Diffusion model with a Transformer backbone.  
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.  
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16):
The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88):
The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
patch_size (`int`, *optional*):
The size of the patch to use for the input.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to use in feed-forward.
sample_size (`int`, *optional*):
The width of the latent images. This is fixed during training since it is used to learn a number of
position embeddings.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
cross_attention_dim (`int`, *optional*):
The number of dimension in the clip text embedding.
hidden_size (`int`, *optional*):
The size of hidden layer in the conditioning embedding layers.
num_layers (`int`, *optional*, defaults to 1):
The number of layers of Transformer blocks to use.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of the hidden layer size to the input size.
learn_sigma (`bool`, *optional*, defaults to `True`):
Whether to predict variance.
cross_attention_dim_t5 (`int`, *optional*):
The number dimensions in t5 text embedding.
pooled_projection_dim (`int`, *optional*):
The size of the pooled projection.
text_len (`int`, *optional*):
The length of the clip text embedding.
text_len_t5 (`int`, *optional*):
The length of the T5 text embedding.
use_style_cond_and_image_meta_size (`bool`,  *optional*):
Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
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