# AuraFlowTransformer2DModel

A Transformer model for image-like data from [AuraFlow](https://blog.fal.ai/auraflow/).

## AuraFlowTransformer2DModel[[diffusers.AuraFlowTransformer2DModel]]

- **sample_size** (`int`) -- The width of the latent images. This is fixed during training since
  it is used to learn a number of position embeddings.
- **patch_size** (`int`) -- Patch size to turn the input data into small patches.
- **in_channels** (`int`, *optional*, defaults to 4) -- The number of channels in the input.
- **num_mmdit_layers** (`int`, *optional*, defaults to 4) -- The number of layers of MMDiT Transformer blocks to use.
- **num_single_dit_layers** (`int`, *optional*, defaults to 32) --
  The number of layers of Transformer blocks to use. These blocks use concatenated image and text
  representations.
- **attention_head_dim** (`int`, *optional*, defaults to 256) -- The number of channels in each head.
- **num_attention_heads** (`int`, *optional*, defaults to 12) -- The number of heads to use for multi-head attention.
- **joint_attention_dim** (`int`, *optional*) -- The number of `encoder_hidden_states` dimensions to use.
- **caption_projection_dim** (`int`) -- Number of dimensions to use when projecting the `encoder_hidden_states`.
- **out_channels** (`int`, defaults to 4) -- Number of output channels.
- **pos_embed_max_size** (`int`, defaults to 1024) -- Maximum positions to embed from the image latents.

A 2D Transformer model as introduced in AuraFlow (https://blog.fal.ai/auraflow/).

- **hidden_states** (`torch.FloatTensor` of shape `(batch size, channel, height, width)`) --
  Input `hidden_states`.
- **encoder_hidden_states** (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`) --
  Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
- **timestep** (`torch.LongTensor`) --
  Used to indicate denoising step.
- **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.If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [AuraFlowTransformer2DModel](/docs/diffusers/main/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel) forward method.

Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.

> [!WARNING] > This API is 🧪 experimental.

Disables the fused QKV projection if enabled.

> [!WARNING] > This API is 🧪 experimental.