diffusers/pr_14282/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", dtype=torch.bfloat16)

Lumina2Transformer2DModel[[diffusers.Lumina2Transformer2DModel]]

diffusers.Lumina2Transformer2DModel[[diffusers.Lumina2Transformer2DModel]]

diffusers.Lumina2Transformer2DModel(sample_size: int = 128, patch_size: int = 2, in_channels: int = 16, out_channels: int | None = 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: float | None = None, norm_eps: float = 1e-05, scaling_factor: float = 1.0, axes_dim_rope: tuple = (32, 32, 32), axes_lens: tuple = (300, 512, 512), cap_feat_dim: int = 1024)

Source

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.

forward[[diffusers.Lumina2Transformer2DModel.forward]]

forward(hidden_states: Tensor, timestep: Tensor, encoder_hidden_states: Tensor, encoder_attention_mask: Tensor, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)

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Parameters:

hidden_states (torch.Tensor of shape (batch_size, in_channels, height, width)) : Input hidden_states.

timestep (torch.LongTensor) : Used to indicate denoising step.

encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

encoder_attention_mask (torch.Tensor) : Mask applied to encoder_hidden_states during attention.

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:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The Lumina2Transformer2DModel forward method.

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

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

diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)

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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.