diffusers/pr_14404/en/api/models/krea2_transformer2d.md

Krea2Transformer2DModel

The single-stream MMDiT flow-matching transformer used by Krea 2.

Krea2Transformer2DModel[[diffusers.Krea2Transformer2DModel]]

diffusers.Krea2Transformer2DModel[[diffusers.Krea2Transformer2DModel]]

diffusers.Krea2Transformer2DModel(in_channels: int = 64, num_layers: int = 28, attention_head_dim: int = 128, num_attention_heads: int = 48, num_key_value_heads: int = 12, intermediate_size: int = 16384, timestep_embed_dim: int = 256, text_hidden_dim: int = 2560, num_text_layers: int = 12, text_num_attention_heads: int = 20, text_num_key_value_heads: int = 20, text_intermediate_size: int = 6912, num_layerwise_text_blocks: int = 2, num_refiner_text_blocks: int = 2, axes_dims_rope: tuple = (32, 48, 48), rope_theta: float = 1000.0, norm_eps: float = 1e-05)

Source

Parameters:

in_channels (int, defaults to 64) : Latent channel count after patchification (vae_channels * patch_size ** 2).

num_layers (int, defaults to 28) : Number of transformer blocks.

attention_head_dim (int, defaults to 128) : Dimension of each attention head; the total hidden size is attention_head_dim * num_attention_heads.

num_attention_heads (int, defaults to 48) : Number of query heads.

num_key_value_heads (int, defaults to 12) : Number of key/value heads for grouped-query attention.

intermediate_size (int, defaults to 16384) : Feed-forward hidden size of the SwiGLU MLP inside each block.

timestep_embed_dim (int, defaults to 256) : Width of the sinusoidal timestep embedding before its MLP.

text_hidden_dim (int, defaults to 2560) : Hidden size of the text encoder whose hidden states are consumed.

num_text_layers (int, defaults to 12) : Number of tapped text-encoder hidden states stacked per token.

text_num_attention_heads (int, defaults to 20) : Number of query heads in the text fusion blocks.

text_num_key_value_heads (int, defaults to 20) : Number of key/value heads in the text fusion blocks.

text_intermediate_size (int, defaults to 6912) : Feed-forward hidden size of the SwiGLU MLP inside the text fusion blocks.

num_layerwise_text_blocks (int, defaults to 2) : Number of text fusion blocks applied across the tapped-layer axis (per token).

num_refiner_text_blocks (int, defaults to 2) : Number of text fusion blocks applied across the token sequence.

axes_dims_rope (tuple[int, int, int], defaults to (32, 48, 48)) : Head-dim split across the (t, h, w) rotary position axes.

rope_theta (float, defaults to 1000.0) : Base used by the rotary position embedding.

norm_eps (float, defaults to 1e-5) : Epsilon used by all RMSNorm modules.

The single-stream MMDiT flow-matching backbone used by the Krea 2 pipeline.

Text conditioning enters as a stack of hidden states tapped from several layers of a multimodal text encoder. A small text-fusion transformer collapses the layer axis and refines the token sequence; the result is concatenated with the patchified image latents into a single [text, image] sequence processed by the transformer blocks. The timestep conditions every block through one shared modulation vector plus per-block learned tables.

forward[[diffusers.Krea2Transformer2DModel.forward]]

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

Source

Parameters:

hidden_states (torch.Tensor of shape (batch_size, image_seq_len, in_channels)) : Packed (patchified) noisy image latents.

encoder_hidden_states (torch.Tensor of shape (batch_size, text_seq_len, num_text_layers, text_hidden_dim)) : Stack of tapped text-encoder hidden states per token.

timestep (torch.Tensor of shape (batch_size,)) : Flow-matching time in [0, 1] (1 is pure noise, 0 is clean data).

position_ids (torch.Tensor of shape (text_seq_len + image_seq_len, 3)) : (t, h, w) rotary coordinates for the combined sequence. Text rows are all-zero; image rows hold the latent-grid coordinates.

encoder_attention_mask (torch.Tensor of shape (batch_size, text_seq_len), optional) : Boolean mask marking valid text tokens. Pass None when every text token is valid.

attention_kwargs (dict, optional) : A kwargs dictionary that, when it contains a scale entry, sets the LoRA scale applied to this transformer's adapters for the duration of the forward pass.

return_dict (bool, optional, defaults to True) : Whether to return a Transformer2DModelOutput instead of a plain tuple.

Returns:

Transformer2DModelOutput or a tuple whose first element is the velocity tensor of shape (batch_size, image_seq_len, in_channels).

Predict the flow-matching velocity for the image tokens.