Diffusers documentation
Lumina2Transformer2DModel
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.Tensorof 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 theencoder_hidden_statesinput. If discrete, returns probability distributions for the unnoised latent pixels.
The output of Transformer2DModel.