LuminaNextDiT2DModel  
LuminaNextDiT: Diffusion model with a Transformer backbone.  
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.  
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`, *optional*, (`int`, *optional*, defaults to 2):
The size of each patch in the image. This parameter defines the resolution of patches fed into the model.
in_channels (`int`, *optional*, defaults to 4):
The number of input channels for the model. Typically, this matches the number of channels in the input
images.
hidden_size (`int`, *optional*, defaults to 4096):
The dimensionality of the hidden layers in the model. This parameter determines the width of the model's
hidden representations.
num_layers (`int`, *optional*, default to 32):
The number of layers in the model. This defines the depth of the neural network.
num_attention_heads (`int`, *optional*, defaults to 32):
The number of attention heads in each attention layer. This parameter specifies how many separate attention
mechanisms are used.
num_kv_heads (`int`, *optional*, defaults to 8):
The number of key-value heads in the attention mechanism, if different from the number of attention heads.
If None, it defaults to num_attention_heads.
multiple_of (`int`, *optional*, defaults to 256):
A factor that the hidden size should be a multiple of. This can help optimize certain hardware
configurations.
ffn_dim_multiplier (`float`, *optional*):
A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on
the model configuration.
norm_eps (`float`, *optional*, defaults to 1e-5):
A small value added to the denominator for numerical stability in normalization layers.
learn_sigma (`bool`, *optional*, defaults to True):
Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in
predictions.
qk_norm (`bool`, *optional*, defaults to True):
Indicates if the queries and keys in the attention mechanism should be normalized.
cross_attention_dim (`int`, *optional*, defaults to 2048):
The dimensionality of the text embeddings. This parameter defines the size of the text representations used
in the model.
scaling_factor (`float`, *optional*, defaults to 1.0):
A scaling factor applied to certain parameters or layers in the model. This can be used for adjusting the
overall scale of the model's operations.
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