diffusers/pr_13921/en/api/models/hunyuanimage_transformer_2d.md

HunyuanImageTransformer2DModel

A Diffusion Transformer model for HunyuanImage2.1.

The model can be loaded with the following code snippet.

from diffusers import HunyuanImageTransformer2DModel

transformer = HunyuanImageTransformer2DModel.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

HunyuanImageTransformer2DModel[[diffusers.HunyuanImageTransformer2DModel]]

diffusers.HunyuanImageTransformer2DModel[[diffusers.HunyuanImageTransformer2DModel]]

Source

The Transformer model used in HunyuanImage-2.1.

forwarddiffusers.HunyuanImageTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/models/transformers/transformer_hunyuanimage.py#L743[{"name": "hidden_states", "val": ": Tensor"}, {"name": "timestep", "val": ": LongTensor"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "encoder_attention_mask", "val": ": Tensor"}, {"name": "timestep_r", "val": ": torch.LongTensor | None = None"}, {"name": "encoder_hidden_states_2", "val": ": torch.Tensor | None = None"}, {"name": "encoder_attention_mask_2", "val": ": torch.Tensor | None = None"}, {"name": "guidance", "val": ": torch.Tensor | None = None"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- hidden_states (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch_size, num_channels, num_frames, height, width)) -- Input hidden_states.

• timestep (torch.LongTensor) -- Used to indicate denoising step.
• encoder_hidden_states (torch.Tensor of shape (batch_size, sequence_len, embed_dims)) -- Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
• encoder_attention_mask (torch.Tensor) -- Mask applied to encoder_hidden_states during attention.
• timestep_r (torch.LongTensor, optional) -- Refiner timestep conditioning.
• encoder_hidden_states_2 (torch.Tensor, optional) -- Additional conditional embeddings computed from a second text encoder.
• encoder_attention_mask_2 (torch.Tensor, optional) -- Mask applied to encoder_hidden_states_2 during attention.
• guidance (torch.Tensor, optional) -- Guidance scale embedding used for guidance-distilled variants of the model.
• attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.
• return_dict (bool, optional, defaults to True) -- Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.0If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

The HunyuanImageTransformer2DModel forward method.

Parameters:

in_channels (int, defaults to 16) : The number of channels in the input.

out_channels (int, defaults to 16) : The number of channels in the output.

num_attention_heads (int, defaults to 24) : The number of heads to use for multi-head attention.

attention_head_dim (int, defaults to 128) : The number of channels in each head.

num_layers (int, defaults to 20) : The number of layers of dual-stream blocks to use.

num_single_layers (int, defaults to 40) : The number of layers of single-stream blocks to use.

num_refiner_layers (int, defaults to 2) : The number of layers of refiner blocks to use.

mlp_ratio (float, defaults to 4.0) : The ratio of the hidden layer size to the input size in the feedforward network.

patch_size (int, defaults to 2) : The size of the spatial patches to use in the patch embedding layer.

patch_size_t (int, defaults to 1) : The size of the tmeporal patches to use in the patch embedding layer.

qk_norm (str, defaults to rms_norm) : The normalization to use for the query and key projections in the attention layers.

guidance_embeds (bool, defaults to True) : Whether to use guidance embeddings in the model.

text_embed_dim (int, defaults to 4096) : Input dimension of text embeddings from the text encoder.

pooled_projection_dim (int, defaults to 768) : The dimension of the pooled projection of the text embeddings.

rope_theta (float, defaults to 256.0) : The value of theta to use in the RoPE layer.

rope_axes_dim (tuple[int], defaults to (16, 56, 56)) : The dimensions of the axes to use in the RoPE layer.

image_condition_type (str, optional, defaults to None) : The type of image conditioning to use. If None, no image conditioning is used. If latent_concat, the image is concatenated to the latent stream. If token_replace, the image is used to replace first-frame tokens in the latent stream and apply conditioning.

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

diffusers.models.modeling_outputs.Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

Source

The output of Transformer2DModel.

Parameters:

sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) : The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.