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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

class diffusers.HunyuanImageTransformer2DModel

< source >

( 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: typing.Tuple[int, int] = (1, 1) qk_norm: str = 'rms_norm' guidance_embeds: bool = False text_embed_dim: int = 3584 text_embed_2_dim: typing.Optional[int] = None rope_theta: float = 256.0 rope_axes_dim: typing.Tuple[int, ...] = (64, 64) use_meanflow: bool = False )

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.

Transformer2DModelOutput

class diffusers.models.modeling_outputs.Transformer2DModelOutput

< source >

( sample: torch.Tensor )

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.

Update on GitHub

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