diffusers/main/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", dtype=torch.bfloat16)

HunyuanImageTransformer2DModel[[diffusers.HunyuanImageTransformer2DModel]]

diffusers.HunyuanImageTransformer2DModel[[diffusers.HunyuanImageTransformer2DModel]]

diffusers.HunyuanImageTransformer2DModel(in_channels: int = 64, out_channels: int = 64, num_attention_heads: int = 28, attention_head_dim: int = 128, num_layers: int = 20, num_single_layers: int = 40, num_refiner_layers: int = 2, mlp_ratio: float = 4.0, patch_size: tuple = (1, 1), qk_norm: str = 'rms_norm', guidance_embeds: bool = False, text_embed_dim: int = 3584, text_embed_2_dim: int | None = None, rope_theta: float = 256.0, rope_axes_dim: tuple = (64, 64), use_meanflow: bool = False)

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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.

The Transformer model used in HunyuanImage-2.1.

forward[[diffusers.HunyuanImageTransformer2DModel.forward]]

forward(hidden_states: Tensor, timestep: LongTensor, encoder_hidden_states: Tensor, encoder_attention_mask: Tensor, timestep_r: typing.Optional[torch.LongTensor] = None, encoder_hidden_states_2: typing.Optional[torch.Tensor] = None, encoder_attention_mask_2: typing.Optional[torch.Tensor] = None, guidance: typing.Optional[torch.Tensor] = None, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True)

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Parameters:

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.

Returns:

If 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.

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

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

diffusers.models.modeling_outputs.Transformer2DModelOutput(sample: torch.Tensor)

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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.

The output of Transformer2DModel.