# Flux2Transformer2DModel

A Transformer model for image-like data from [Flux2](https://hf.co/black-forest-labs/FLUX.2-dev).

## 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](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
- **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](/docs/diffusers/main/en/api/models/flux2_transformer#diffusers.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](/docs/diffusers/main/en/api/models/flux2_transformer#diffusers.Flux2Transformer2DModel).