#
Lumina2Transformer2DModel

A Diffusion Transformer model for 3D video-like data was introduced in Lumina Image 2.0 by Alpha-VLLM.

The model can be loaded with the following code snippet.

    from diffusers import Lumina2Transformer2DModel

    transformer = Lumina2Transformer2DModel.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", subfolder="transformer", torch_dtype=torch.bfloat16)

##
Lumina2Transformer2DModel

###
class diffusers.Lumina2Transformer2DModel

< source >

( sample_size: int = 128 patch_size: int = 2 in_channels: int = 16 out_channels: typing.Optional[int] = None hidden_size: int = 2304 num_layers: int = 26 num_refiner_layers: int = 2 num_attention_heads: int = 24 num_kv_heads: int = 8 multiple_of: int = 256 ffn_dim_multiplier: typing.Optional[float] = None norm_eps: float = 1e-05 scaling_factor: float = 1.0 axes_dim_rope: typing.Tuple[int, int, int] = (32, 32, 32) axes_lens: typing.Tuple[int, int, int] = (300, 512, 512) cap_feat_dim: int = 1024 )

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.
- 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.

Lumina2NextDiT: Diffusion model with a Transformer backbone.

##
Transformer2DModelOutput

###
class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

Parameters

- sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if Transformer2DModel is discrete) — The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.

Update on GitHub