diffusers/pr_13966/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]]

• 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.

• 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.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]]

• 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.