# ZImageTransformer2DModel

A Transformer model for image-like data from [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo).

## ZImageTransformer2DModel[[diffusers.ZImageTransformer2DModel]]

- **x** (`list` of `torch.Tensor` or nested `list` of `torch.Tensor`) --
  Input latents. A flat list when running in standard mode, or a nested list when running in omni mode.
- **t** (`torch.Tensor`) --
  Used to indicate denoising step.
- **cap_feats** (`list` of `torch.Tensor` or nested `list` of `torch.Tensor`) --
  Conditional caption embeddings (embeddings computed from the input conditions such as prompts) to use.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain
  tuple.
- **controlnet_block_samples** (`dict` of `int` to `torch.Tensor`, *optional*) --
  A mapping from block index to tensor that if specified are added to the residuals of transformer
  blocks.
- **siglip_feats** (`list` of `list` of `torch.Tensor`, *optional*) --
  Optional SigLIP image features used as additional conditioning.
- **image_noise_mask** (`list` of `list` of `int`, *optional*) --
  Per-image noise masks indicating noisy vs. clean tokens in omni mode.
- **patch_size** (`int`, *optional*, defaults to 2) --
  Spatial patch size used to patchify the input latents.
- **f_patch_size** (`int`, *optional*, defaults to 1) --
  Temporal patch size used to patchify the input latents.

The [ZImageTransformer2DModel](/docs/diffusers/main/en/api/models/z_image_transformer2d#diffusers.ZImageTransformer2DModel) forward method.

Flow: patchify -> t_embed -> x_embed -> x_refine -> cap_embed -> cap_refine
-> [siglip_embed -> siglip_refine] -> build_unified -> main_layers -> final_layer -> unpatchify

Patchify for basic mode: single image per batch item.

Patchify for omni mode: multiple images per batch item with noise masks.