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)
( 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
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. 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.
( sample: torch.Tensor )
Parameters
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.