# ZImageTransformer2DModel

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

## ZImageTransformer2DModel[[diffusers.ZImageTransformer2DModel]]

#### diffusers.ZImageTransformer2DModel[[diffusers.ZImageTransformer2DModel]]

```python
diffusers.ZImageTransformer2DModel(all_patch_size = (2,), all_f_patch_size = (1,), in_channels = 16, dim = 3840, n_layers = 30, n_refiner_layers = 2, n_heads = 30, n_kv_heads = 30, norm_eps = 1e-05, qk_norm = True, cap_feat_dim = 2560, siglip_feat_dim = None, rope_theta = 256.0, t_scale = 1000.0, axes_dims = [32, 48, 48], axes_lens = [1024, 512, 512])
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_z_image.py#L359)

#### forward[[diffusers.ZImageTransformer2DModel.forward]]

```python
forward(x: list, t, cap_feats: list, return_dict: bool = True, controlnet_block_samples: dict[int, torch.Tensor] | None = None, siglip_feats: list[list[torch.Tensor]] | None = None, image_noise_mask: list[list[int]] | None = None, patch_size: int = 2, f_patch_size: int = 1)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_z_image.py#L894)

**Parameters:**

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_and_embed[[diffusers.ZImageTransformer2DModel.patchify_and_embed]]

```python
patchify_and_embed(all_image: list, all_cap_feats: list, patch_size: int, f_patch_size: int)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_z_image.py#L588)

Patchify for basic mode: single image per batch item.

#### patchify_and_embed_omni[[diffusers.ZImageTransformer2DModel.patchify_and_embed_omni]]

```python
patchify_and_embed_omni(all_x: list, all_cap_feats: list, all_siglip_feats: list, patch_size: int, f_patch_size: int, images_noise_mask: list)
```

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_z_image.py#L625)

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