# 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.