diffusers/pr_14409/en/api/models/lumina_nextdit2d.md

LuminaNextDiT2DModel

A Next Version of Diffusion Transformer model for 2D data from Lumina-T2X.

LuminaNextDiT2DModel[[diffusers.LuminaNextDiT2DModel]]

diffusers.LuminaNextDiT2DModel[[diffusers.LuminaNextDiT2DModel]]

diffusers.LuminaNextDiT2DModel(sample_size: int = 128, patch_size: int | None = 2, in_channels: int | None = 4, hidden_size: int | None = 2304, num_layers: int | None = 32, num_attention_heads: int | None = 32, num_kv_heads: int | None = None, multiple_of: int | None = 256, ffn_dim_multiplier: float | None = None, norm_eps: float | None = 1e-05, learn_sigma: bool | None = True, qk_norm: bool | None = True, cross_attention_dim: int | None = 2048, scaling_factor: float | None = 1.0)

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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.

LuminaNextDiT: Diffusion model with a Transformer backbone.

Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.

forward[[diffusers.LuminaNextDiT2DModel.forward]]

forward(hidden_states: Tensor, timestep: Tensor, encoder_hidden_states: Tensor, encoder_mask: Tensor, image_rotary_emb: Tensor, cross_attention_kwargs: dict = None, return_dict = True)

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Parameters:

hidden_states (torch.Tensor) : Input tensor of shape (N, C, H, W).

timestep (torch.Tensor) : Tensor of diffusion timesteps of shape (N,).

encoder_hidden_states (torch.Tensor) : Tensor of caption features of shape (N, D).

encoder_mask (torch.Tensor) : Tensor of caption masks of shape (N, L).

image_rotary_emb (torch.Tensor) : Pre-computed rotary positional embeddings.

cross_attention_kwargs (dict, optional) : A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

Returns: ~models.transformer_2d.Transformer2DModelOutput or tuple

If return_dict is True, a ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a plain tuple is returned.

Forward pass of LuminaNextDiT.