diffusers/main/en/api/models/bria_transformer.md

# BriaTransformer2DModel

A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)

## BriaTransformer2DModel[[diffusers.BriaTransformer2DModel]]

#### diffusers.BriaTransformer2DModel[[diffusers.BriaTransformer2DModel]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_bria.py#L506)

The Transformer model introduced in Flux. Based on FluxPipeline with several changes:
- no pooled embeddings
- We use zero padding for prompts
- No guidance embedding since this is not a distilled version
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

forwarddiffusers.BriaTransformer2DModel.forwardhttps://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_bria.py#L584[{"name": "hidden_states", "val": ": Tensor"}, {"name": "encoder_hidden_states", "val": ": Tensor = None"}, {"name": "pooled_projections", "val": ": Tensor = None"}, {"name": "timestep", "val": ": LongTensor = None"}, {"name": "img_ids", "val": ": Tensor = None"}, {"name": "txt_ids", "val": ": Tensor = None"}, {"name": "guidance", "val": ": Tensor = None"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "controlnet_block_samples", "val": " = None"}, {"name": "controlnet_single_block_samples", "val": " = None"}]- **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.
- **pooled_projections** (`torch.FloatTensor` of shape `(batch_size, projection_dim)`) -- Embeddings projected
  from the embeddings of input conditions.
- **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.
- **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.0If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.

The [BriaTransformer2DModel](/docs/diffusers/main/en/api/models/bria_transformer#diffusers.BriaTransformer2DModel) forward method.

**Parameters:**

patch_size (`int`) : Patch size to turn the input data into small patches.

in_channels (`int`, *optional*, defaults to 16) : The number of channels in the input.

num_layers (`int`, *optional*, defaults to 18) : The number of layers of MMDiT blocks to use.

num_single_layers (`int`, *optional*, defaults to 18) : The number of layers of single DiT blocks to use.

attention_head_dim (`int`, *optional*, defaults to 64) : The number of channels in each head.

num_attention_heads (`int`, *optional*, defaults to 18) : The number of heads to use for multi-head attention.

joint_attention_dim (`int`, *optional*) : The number of `encoder_hidden_states` dimensions to use.

pooled_projection_dim (`int`) : Number of dimensions to use when projecting the `pooled_projections`.

guidance_embeds (`bool`, defaults to False) : Whether to use guidance embeddings.

**Returns:**

If `return_dict` is True, an `~models.transformer_2d.Transformer2DModelOutput` is returned, otherwise a
`tuple` where the first element is the sample tensor.