# LuminaNextDiT2DModel

A Next Version of Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X).

## LuminaNextDiT2DModel[[diffusers.LuminaNextDiT2DModel]]

#### diffusers.LuminaNextDiT2DModel[[diffusers.LuminaNextDiT2DModel]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/lumina_nextdit2d.py#L178)

LuminaNextDiT: Diffusion model with a Transformer backbone.

Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.

forwarddiffusers.LuminaNextDiT2DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/lumina_nextdit2d.py#L291[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "encoder_mask", "val": ": Tensor"}, {"name": "image_rotary_emb", "val": ": Tensor"}, {"name": "cross_attention_kwargs", "val": ": dict = None"}, {"name": "return_dict", "val": " = True"}]- **hidden_states** (torch.Tensor) -- Input tensor of shape (N, C, H, W).
- **timestep** (torch.Tensor) -- Tensor of diffusion timesteps of shape (N,).
- **encoder_hidden_states** (torch.Tensor) -- Tensor of caption features of shape (N, D).
- **encoder_mask** (torch.Tensor) -- Tensor of caption masks of shape (N, L).0

Forward pass of LuminaNextDiT.

**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.

learn_sigma (`bool`, *optional*, defaults to True) : Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in predictions.

qk_norm (`bool`, *optional*, defaults to True) : Indicates if the queries and keys in the attention mechanism should be normalized.

cross_attention_dim (`int`, *optional*, defaults to 2048) : The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model.

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.