diffusers/pr_13966/en/api/models/sd3_transformer2d.md

SD3 Transformer Model

The Transformer model introduced in Stable Diffusion 3. 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.

• 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.

• 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.
• 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 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.

> This API is 🧪 experimental.

Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.