diffusers/pr_13881/en/api/models/flux2_transformer.md

Flux2Transformer2DModel

A Transformer model for image-like data from Flux2.

Flux2Transformer2DModel[[diffusers.Flux2Transformer2DModel]]

• 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/

• 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").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]]

• 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.