#
FluxTransformer2DModel

A Transformer model for image-like data from Flux.

##
FluxTransformer2DModel

###
class diffusers.FluxTransformer2DModel

< source >

( patch_size: int = 1 in_channels: int = 64 out_channels: typing.Optional[int] = 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 pooled_projection_dim: int = 768 guidance_embeds: bool = False axes_dims_rope: typing.Tuple[int, int, int] = (16, 56, 56) )

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`).
- pooled_projection_dim (`int`, defaults to `768`) — The number of dimensions to use for the pooled projection.
- guidance_embeds (`bool`, defaults to `False`) — Whether to use guidance embeddings for guidance-distilled variant of the model.
- 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.

Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

####
forward

< source >

( hidden_states: Tensor encoder_hidden_states: Tensor = None pooled_projections: Tensor = None timestep: LongTensor = None img_ids: Tensor = None txt_ids: Tensor = None guidance: Tensor = None joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None controlnet_block_samples = None controlnet_single_block_samples = None return_dict: bool = True controlnet_blocks_repeat: bool = False )

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.
- 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.

The FluxTransformer2DModel forward method.

Update on GitHub