diffusers/pr_14404/en/api/models/flux2_transformer.md

Flux2Transformer2DModel

A Transformer model for image-like data from Flux2.

Flux2Transformer2DModel[[diffusers.Flux2Transformer2DModel]]

diffusers.Flux2Transformer2DModel[[diffusers.Flux2Transformer2DModel]]

diffusers.Flux2Transformer2DModel(patch_size: int = 1, in_channels: int = 128, out_channels: int | None = 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: tuple = (32, 32, 32, 32), rope_theta: int = 2000, eps: float = 1e-06, guidance_embeds: bool = True)

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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[[diffusers.Flux2Transformer2DModel.forward]]

forward(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: dict[str, typing.Any] | None = None, return_dict: bool = True, kv_cache: Flux2KVCache | None = None, kv_cache_mode: str | None = None, num_ref_tokens: int = 0, ref_fixed_timestep: float = 0.0)

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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.

guidance (torch.Tensor, optional) : Guidance scale embedding used for guidance-distilled variants of the model.

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.

kv_cache (Flux2KVCache, optional) : KV cache for reference image tokens. When kv_cache_mode is "extract", a new cache is created and returned. When "cached", the provided cache is used to inject ref K/V during attention.

kv_cache_mode (str, optional) : One of "extract" (first step with ref tokens) or "cached" (subsequent steps using cached ref K/V). When None, standard forward pass without KV caching.

num_ref_tokens (int, defaults to 0) : Number of reference image tokens prepended to hidden_states (only used when kv_cache_mode="extract").

ref_fixed_timestep (float, defaults to 0.0) : Fixed timestep for reference token modulation (only used when kv_cache_mode="extract").

Returns:

If return_dict is True, an ~models.transformer_2d.Transformer2DModelOutput is returned, otherwise a tuple where the first element is the sample tensor. When kv_cache_mode="extract", also returns the populated Flux2KVCache.

The Flux2Transformer2DModel forward method.

Flux2Transformer2DModelOutput[[diffusers.models.transformers.transformer_flux2.Flux2Transformer2DModelOutput]]

diffusers.models.transformers.transformer_flux2.Flux2Transformer2DModelOutput[[diffusers.models.transformers.transformer_flux2.Flux2Transformer2DModelOutput]]

diffusers.models.transformers.transformer_flux2.Flux2Transformer2DModelOutput(sample: torch.Tensor, kv_cache: Flux2KVCache | None = None)

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Parameters:

sample (torch.Tensor of shape (batch_size, num_channels, height, width)) : The hidden states output conditioned on the encoder_hidden_states input.

kv_cache (Flux2KVCache, optional) : The populated KV cache for reference image tokens. Only returned when kv_cache_mode="extract".

The output of Flux2Transformer2DModel.