diffusers/pr_11739/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.

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.

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.