#
Flux2Transformer2DModel

A Transformer model for image-like data from Flux2.

##
Flux2Transformer2DModel

###
class diffusers.Flux2Transformer2DModel

< source >

( patch_size: int = 1 in_channels: int = 128 out_channels: typing.Optional[int] = None num_layers: int = 8 num_single_layers: int = 48 attention_head_dim: int = 128 num_attention_heads: int = 48 joint_attention_dim: int = 15360 timestep_guidance_channels: int = 256 mlp_ratio: float = 3.0 axes_dims_rope: typing.Tuple[int, ...] = (32, 32, 32, 32) rope_theta: int = 2000 eps: float = 1e-06 )

Parameters

- patch_size (`int`, defaults to `1`) — Patch size to turn the input data into small patches.
- in_channels (`int`, defaults to `128`) — 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 `8`) — The number of layers of dual stream DiT blocks to use.
- num_single_layers (`int`, defaults to `48`) — 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 `48`) — The number of attention heads to use.
- joint_attention_dim (`int`, defaults to `15360`) — 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 `True`) — Whether to use guidance embeddings for guidance-distilled variant of the model.
- axes_dims_rope (`Tuple[int]`, defaults to `(32, 32, 32, 32)`) — The dimensions to use for the rotary positional embeddings.

The Transformer model introduced in Flux 2.

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

####
forward

< source >

( hidden_states: Tensor encoder_hidden_states: 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 return_dict: bool = True )

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.
- 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