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

# Lumina2Transformer2DModel

A Diffusion Transformer model for 3D video-like data was introduced in [Lumina Image 2.0](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0) by Alpha-VLLM.

The model can be loaded with the following code snippet.

```python
from diffusers import Lumina2Transformer2DModel

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

## Lumina2Transformer2DModel[[diffusers.Lumina2Transformer2DModel]]

- **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.

- **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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.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](/docs/diffusers/main/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel) forward method.

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

- **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.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](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).