diffusers/pr_14385/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]]

diffusers.SD3Transformer2DModel[[diffusers.SD3Transformer2DModel]]

diffusers.SD3Transformer2DModel(sample_size: int = 128, patch_size: int = 2, in_channels: int = 16, num_layers: int = 18, attention_head_dim: int = 64, num_attention_heads: int = 18, joint_attention_dim: int = 4096, caption_projection_dim: int = 1152, pooled_projection_dim: int = 2048, out_channels: int = 16, pos_embed_max_size: int = 96, dual_attention_layers: tuple = (), qk_norm: str | None = None)

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Parameters:

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.

enable_forward_chunking[[diffusers.SD3Transformer2DModel.enable_forward_chunking]]

enable_forward_chunking(chunk_size: int | None = None, dim: int = 0)

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Parameters:

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.

forward[[diffusers.SD3Transformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: Tensor = None, pooled_projections: Tensor = None, timestep: LongTensor = None, block_controlnet_hidden_states: list = None, joint_attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True, skip_layers: list[int] | None = None)

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Parameters:

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.

Returns:

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.

fuse_qkv_projections[[diffusers.SD3Transformer2DModel.fuse_qkv_projections]]

fuse_qkv_projections()

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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.

unfuse_qkv_projections[[diffusers.SD3Transformer2DModel.unfuse_qkv_projections]]

unfuse_qkv_projections()

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.