diffusers/pr_14407/en/api/models/hunyuan_transformer2d.md

HunyuanDiT2DModel

A Diffusion Transformer model for 2D data from Hunyuan-DiT.

HunyuanDiT2DModel[[diffusers.HunyuanDiT2DModel]]

diffusers.HunyuanDiT2DModel[[diffusers.HunyuanDiT2DModel]]

diffusers.HunyuanDiT2DModel(num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: int | None = None, patch_size: int | None = None, activation_fn: str = 'gelu-approximate', sample_size = 32, hidden_size = 1152, num_layers: int = 28, mlp_ratio: float = 4.0, learn_sigma: bool = True, cross_attention_dim: int = 1024, norm_type: str = 'layer_norm', cross_attention_dim_t5: int = 2048, pooled_projection_dim: int = 1024, text_len: int = 77, text_len_t5: int = 256, use_style_cond_and_image_meta_size: bool = True)

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Parameters:

num_attention_heads (int, optional, defaults to 16) : The number of heads to use for multi-head attention.

attention_head_dim (int, optional, defaults to 88) : The number of channels in each head.

in_channels (int, optional) : The number of channels in the input and output (specify if the input is continuous).

patch_size (int, optional) : The size of the patch to use for the input.

activation_fn (str, optional, defaults to "geglu") : Activation function to use in feed-forward.

sample_size (int, optional) : The width of the latent images. This is fixed during training since it is used to learn a number of position embeddings.

dropout (float, optional, defaults to 0.0) : The dropout probability to use.

cross_attention_dim (int, optional) : The number of dimension in the clip text embedding.

hidden_size (int, optional) : The size of hidden layer in the conditioning embedding layers.

num_layers (int, optional, defaults to 1) : The number of layers of Transformer blocks to use.

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

learn_sigma (bool, optional, defaults to True) : Whether to predict variance.

cross_attention_dim_t5 (int, optional) : The number dimensions in t5 text embedding.

pooled_projection_dim (int, optional) : The size of the pooled projection.

text_len (int, optional) : The length of the clip text embedding.

text_len_t5 (int, optional) : The length of the T5 text embedding.

use_style_cond_and_image_meta_size (bool, optional) : Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2

HunYuanDiT: Diffusion model with a Transformer backbone.

Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.

enable_forward_chunking[[diffusers.HunyuanDiT2DModel.enable_forward_chunking]]

enable_forward_chunking(chunk_size: int | None = None, dim: int = 0)

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Parameters:

chunk_size (int, optional) : The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=dim.

dim (int, optional, defaults to 0) : The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length).

Sets the attention processor to use feed forward chunking.

forward[[diffusers.HunyuanDiT2DModel.forward]]

forward(hidden_states, timestep, encoder_hidden_states = None, text_embedding_mask = None, encoder_hidden_states_t5 = None, text_embedding_mask_t5 = None, image_meta_size = None, style = None, image_rotary_emb = None, controlnet_block_samples = None, return_dict = True)

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Parameters:

hidden_states (torch.Tensor of shape (batch size, dim, height, width)) : The input tensor.

timestep ( torch.LongTensor, optional) : Used to indicate denoising step.

encoder_hidden_states ( torch.Tensor of shape (batch size, sequence len, embed dims), optional) : Conditional embeddings for cross attention layer. This is the output of BertModel.

text_embedding_mask : torch.Tensor An attention mask of shape (batch, key_tokens) is applied to encoder_hidden_states. This is the output of BertModel.

encoder_hidden_states_t5 ( torch.Tensor of shape (batch size, sequence len, embed dims), optional) : Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.

text_embedding_mask_t5 : torch.Tensor An attention mask of shape (batch, key_tokens) is applied to encoder_hidden_states. This is the output of T5 Text Encoder.

image_meta_size (torch.Tensor) : Conditional embedding indicate the image sizes

style : torch.Tensor: Conditional embedding indicate the style

image_rotary_emb (torch.Tensor) : The image rotary embeddings to apply on query and key tensors during attention calculation.

controlnet_block_samples (list of torch.Tensor, optional) : A list of tensors that if specified are added to the residuals of transformer blocks.

return_dict : bool Whether to return a dictionary.

The HunyuanDiT2DModel forward method.

fuse_qkv_projections[[diffusers.HunyuanDiT2DModel.fuse_qkv_projections]]

fuse_qkv_projections()

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Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-attention modules, key and value projection matrices are fused.

> This API is 🧪 experimental.

set_default_attn_processor[[diffusers.HunyuanDiT2DModel.set_default_attn_processor]]

set_default_attn_processor()

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Disables custom attention processors and sets the default attention implementation.

unfuse_qkv_projections[[diffusers.HunyuanDiT2DModel.unfuse_qkv_projections]]

unfuse_qkv_projections()

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.