diffusers/pr_14404/en/api/models/chroma_transformer.md

ChromaTransformer2DModel

A modified flux Transformer model from Chroma

ChromaTransformer2DModel[[diffusers.ChromaTransformer2DModel]]

diffusers.ChromaTransformer2DModel[[diffusers.ChromaTransformer2DModel]]

diffusers.ChromaTransformer2DModel(patch_size: int = 1, in_channels: int = 64, out_channels: int | None = None, num_layers: int = 19, num_single_layers: int = 38, attention_head_dim: int = 128, num_attention_heads: int = 24, joint_attention_dim: int = 4096, axes_dims_rope: tuple = (16, 56, 56), approximator_num_channels: int = 64, approximator_hidden_dim: int = 5120, approximator_layers: int = 5)

Source

Parameters:

patch_size (int, defaults to 1) : Patch size to turn the input data into small patches.

in_channels (int, defaults to 64) : The number of channels in the input.

out_channels (int, optional, defaults to None) : The number of channels in the output. If not specified, it defaults to in_channels.

num_layers (int, defaults to 19) : The number of layers of dual stream DiT blocks to use.

num_single_layers (int, defaults to 38) : The number of layers of single stream DiT blocks to use.

attention_head_dim (int, defaults to 128) : The number of dimensions to use for each attention head.

num_attention_heads (int, defaults to 24) : The number of attention heads to use.

joint_attention_dim (int, defaults to 4096) : The number of dimensions to use for the joint attention (embedding/channel dimension of encoder_hidden_states).

axes_dims_rope (tuple[int], defaults to (16, 56, 56)) : The dimensions to use for the rotary positional embeddings.

The Transformer model introduced in Flux, modified for Chroma.

Reference: https://huggingface.co/lodestones/Chroma1-HD

forward[[diffusers.ChromaTransformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: Tensor = None, timestep: LongTensor = None, img_ids: Tensor = None, txt_ids: Tensor = None, attention_mask: Tensor = None, joint_attention_kwargs: dict[str, typing.Any] | None = None, controlnet_block_samples = None, controlnet_single_block_samples = None, return_dict: bool = True, controlnet_blocks_repeat: bool = False)

Source

Parameters:

hidden_states (torch.Tensor of shape (batch_size, image_sequence_length, in_channels)) : Input hidden_states.

encoder_hidden_states (torch.Tensor of shape (batch_size, text_sequence_length, joint_attention_dim)) : Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.

timestep ( torch.LongTensor) : Used to indicate denoising step.

img_ids (torch.Tensor) : Image position ids used to compute the rotary positional embeddings.

txt_ids (torch.Tensor) : Text position ids used to compute the rotary positional embeddings.

attention_mask (torch.Tensor, optional) : Mask applied to encoder_hidden_states during attention.

controlnet_block_samples (list of torch.Tensor, optional) : A list of tensors that if specified are added to the residuals of transformer blocks.

controlnet_single_block_samples (list of torch.Tensor, optional) : A list of tensors that if specified are added to the residuals of single transformer blocks.

controlnet_blocks_repeat (bool, optional, defaults to False) : Whether to repeat the controlnet block samples across all 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.

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 FluxTransformer2DModel forward method.