diffusers/pr_13966/en/api/models/krea2_transformer2d.md

Krea2Transformer2DModel

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

Krea2Transformer2DModel[[diffusers.Krea2Transformer2DModel]]

• 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.

• 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.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.