# SD3 Transformer Model The Transformer model introduced in [Stable Diffusion 3](https://hf.co/papers/2403.03206). Its novelty lies in the MMDiT transformer block. ## SD3Transformer2DModel[[diffusers.SD3Transformer2DModel]] - **sample_size** (`int`, defaults to `128`) -- The width/height of the latents. This is fixed during training since it is used to learn a number of position embeddings. - **patch_size** (`int`, defaults to `2`) -- Patch size to turn the input data into small patches. - **in_channels** (`int`, defaults to `16`) -- The number of latent channels in the input. - **num_layers** (`int`, defaults to `18`) -- The number of layers of transformer blocks to use. - **attention_head_dim** (`int`, defaults to `64`) -- The number of channels in each head. - **num_attention_heads** (`int`, defaults to `18`) -- The number of heads to use for multi-head attention. - **joint_attention_dim** (`int`, defaults to `4096`) -- The embedding dimension to use for joint text-image attention. - **caption_projection_dim** (`int`, defaults to `1152`) -- The embedding dimension of caption embeddings. - **pooled_projection_dim** (`int`, defaults to `2048`) -- The embedding dimension of pooled text projections. - **out_channels** (`int`, defaults to `16`) -- The number of latent channels in the output. - **pos_embed_max_size** (`int`, defaults to `96`) -- The maximum latent height/width of positional embeddings. - **dual_attention_layers** (`tuple[int, ...]`, defaults to `()`) -- The number of dual-stream transformer blocks to use. - **qk_norm** (`str`, *optional*, defaults to `None`) -- The normalization to use for query and key in the attention layer. If `None`, no normalization is used. The Transformer model introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206). - **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, channel, height, width)`) -- Input `hidden_states`. - **encoder_hidden_states** (`torch.Tensor` of shape `(batch size, sequence_len, embed_dims)`) -- Conditional embeddings (embeddings computed from the input conditions such as prompts) to use. - **pooled_projections** (`torch.Tensor` of shape `(batch_size, projection_dim)`) -- Embeddings projected from the embeddings of input conditions. - **timestep** (`torch.LongTensor`) -- Used to indicate denoising step. - **block_controlnet_hidden_states** (`list` of `torch.Tensor`) -- A list of tensors that if specified are added to the residuals of transformer blocks. - **joint_attention_kwargs** (`dict`, *optional*) -- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether or not to return a `~models.transformer_2d.Transformer2DModelOutput` instead of a plain tuple. - **skip_layers** (`list` of `int`, *optional*) -- A list of layer indices to skip during the forward pass.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a `tuple` where the first element is the sample tensor. The [SD3Transformer2DModel](/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel) 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 the fused QKV projection if enabled. > [!WARNING] > This API is 🧪 experimental.