A Next Version of Diffusion Transformer model for 2D data from Lumina-T2X.
( sample_size: int = 128 patch_size: Optional = 2 in_channels: Optional = 4 hidden_size: Optional = 2304 num_layers: Optional = 32 num_attention_heads: Optional = 32 num_kv_heads: Optional = None multiple_of: Optional = 256 ffn_dim_multiplier: Optional = None norm_eps: Optional = 1e-05 learn_sigma: Optional = True qk_norm: Optional = True cross_attention_dim: Optional = 2048 scaling_factor: Optional = 1.0 )
Parameters
int
) — The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings. 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. int
, optional, defaults to 4) —
The number of input channels for the model. Typically, this matches the number of channels in the input
images. 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. int
, optional, default to 32) —
The number of layers in the model. This defines the depth of the neural network. int
, optional, defaults to 32) —
The number of attention heads in each attention layer. This parameter specifies how many separate attention
mechanisms are used. 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. int
, optional, defaults to 256) —
A factor that the hidden size should be a multiple of. This can help optimize certain hardware
configurations. float
, optional) —
A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on
the model configuration. float
, optional, defaults to 1e-5) —
A small value added to the denominator for numerical stability in normalization layers. bool
, optional, defaults to True) —
Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in
predictions. bool
, optional, defaults to True) —
Indicates if the queries and keys in the attention mechanism should be normalized. int
, optional, defaults to 2048) —
The dimensionality of the text embeddings. This parameter defines the size of the text representations used
in the model. 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. LuminaNextDiT: Diffusion model with a Transformer backbone.
Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
( hidden_states: Tensor timestep: Tensor encoder_hidden_states: Tensor encoder_mask: Tensor image_rotary_emb: Tensor cross_attention_kwargs: Dict = None return_dict = True )
Parameters
Forward pass of LuminaNextDiT.