diffusers/pr_14333/en/api/models/ideogram4_transformer2d.md

Ideogram4Transformer2DModel

A transformer for image-like data from Ideogram 4.

Ideogram4Transformer2DModel[[diffusers.Ideogram4Transformer2DModel]]

diffusers.Ideogram4Transformer2DModel[[diffusers.Ideogram4Transformer2DModel]]

diffusers.Ideogram4Transformer2DModel(in_channels: int = 128, num_layers: int = 34, attention_head_dim: int = 256, num_attention_heads: int = 18, intermediate_size: int = 12288, adaln_dim: int = 512, llm_features_dim: int = 53248, rope_theta: int = 5000000, mrope_section: tuple = (24, 20, 20), norm_eps: float = 1e-05)

Source

Parameters:

in_channels (int, defaults to 128) : Latent channel count after patchification (ae_channels * patch_size ** 2).

num_layers (int, defaults to 34) : Number of transformer blocks.

attention_head_dim (int, defaults to 256) : Dimension of each attention head; the total hidden size is attention_head_dim * num_attention_heads.

num_attention_heads (int, defaults to 18) : Number of attention heads.

intermediate_size (int, defaults to 12288) : Feed-forward hidden size used by the SwiGLU MLP inside each block.

adaln_dim (int, defaults to 512) : Dimensionality of the conditioning vector consumed by the AdaLN modulations.

llm_features_dim (int, defaults to 53248) : Dimensionality of the per-token text features fed into the model (typically a concatenation of hidden states from several layers of the text encoder).

rope_theta (int, defaults to 5_000_000) : Base used by the multi-axis rotary position embedding.

mrope_section (tuple[int, int, int], defaults to (24, 20, 20)) : Number of frequencies allocated to each of the (t, h, w) axes of MRoPE.

norm_eps (float, defaults to 1e-5) : Epsilon used by the RMSNorm modules inside the transformer blocks.

The flow-matching transformer backbone used by the Ideogram 4 pipeline.

The transformer operates on a single packed sequence containing both text-conditioning tokens (produced by a multimodal text encoder) and the patchified image latents. Per-token indicators distinguish the two roles, and a block-diagonal attention mask derived from segment_ids restricts each sample to attend only to itself within a packed batch.

forward[[diffusers.Ideogram4Transformer2DModel.forward]]

forward(hidden_states: Tensor, timestep: Tensor, encoder_hidden_states: Tensor, position_ids: Tensor, segment_ids: Tensor, indicator: Tensor, attention_kwargs: dict | None = None, return_dict: bool = True)

Source

Parameters:

hidden_states (torch.Tensor of shape (batch_size, sequence_length, in_channels)) : Packed sequence of patchified noisy image tokens. Non-image positions are masked out internally.

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

encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_length, llm_features_dim)) : Per-token text conditioning features. Non-text positions are masked out internally.

position_ids (torch.Tensor of shape (batch_size, sequence_length, 3)) : (t, h, w) coordinates consumed by the multi-axis RoPE.

segment_ids (torch.Tensor of shape (batch_size, sequence_length)) : Per-token sample id within a packed batch. Positions sharing a segment_id attend to each other.

indicator (torch.Tensor of shape (batch_size, sequence_length)) : Per-token role: LLM_TOKEN_INDICATOR (text) or OUTPUT_IMAGE_INDICATOR (image).

attention_kwargs (dict, optional) : A kwargs dictionary passed along to the attention processor. A "scale" entry scales the LoRA weights (when the PEFT backend is active).

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 a tensor of shape (batch_size, sequence_length, in_channels) in the model's compute dtype. Only positions tagged with OUTPUT_IMAGE_INDICATOR carry meaningful velocity predictions.

Predict the flow-matching velocity for the image-token positions of the packed sequence.