diffusers/pr_12631/en/api/models/lumina2_transformer2d.md

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[[diffusers.Lumina2Transformer2DModel]]

diffusers.Lumina2Transformer2DModel[[diffusers.Lumina2Transformer2DModel]]

Source

Lumina2NextDiT: Diffusion model with a Transformer backbone.

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.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

Source

The output of Transformer2DModel.

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.