diffusers/pr_13966/en/api/models/z_image_transformer2d.md

ZImageTransformer2DModel

A Transformer model for image-like data from Z-Image.

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 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.