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

# Ideogram4Transformer2DModel

A transformer for image-like data from [Ideogram 4](https://github.com/ideogram-oss/ideogram-4). 

## Ideogram4Transformer2DModel[[diffusers.Ideogram4Transformer2DModel]]

- **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.

- **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](/docs/diffusers/main/en/api/models/sana_video_transformer3d#diffusers.models.modeling_outputs.Transformer2DModelOutput) instead of a plain tuple.[Transformer2DModelOutput](/docs/diffusers/main/en/api/models/sana_video_transformer3d#diffusers.models.modeling_outputs.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.