diffusers/main/en/api/models/sd3_transformer2d.md

# 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.