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

# HunyuanDiT2DModel

A Diffusion Transformer model for 2D data from [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT).

## HunyuanDiT2DModel[[diffusers.HunyuanDiT2DModel]]

- **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.

- **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](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

- **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](/docs/diffusers/main/en/api/models/hunyuan_transformer2d#diffusers.HunyuanDiT2DModel) forward method.

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.

> [!WARNING] > This API is 🧪 experimental.

Disables custom attention processors and sets the default attention implementation.

Disables the fused QKV projection if enabled.

> [!WARNING] > This API is 🧪 experimental.