diffusers/pr_13751/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]]

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The Transformer model introduced in Stable Diffusion 3.

enable_forward_chunkingdiffusers.SD3Transformer2DModel.enable_forward_chunkinghttps://github.com/huggingface/diffusers/blob/vr_13751/src/diffusers/models/transformers/transformer_sd3.py#L175[{"name": "chunk_size", "val": ": int | None = None"}, {"name": "dim", "val": ": int = 0"}]- 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).0

Sets the attention processor to use feed forward chunking.

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.

forward[[diffusers.SD3Transformer2DModel.forward]]

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The SD3Transformer2DModel forward method.

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.

fuse_qkv_projections[[diffusers.SD3Transformer2DModel.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]]

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Disables the fused QKV projection if enabled.

> This API is 🧪 experimental.