# BriaTransformer2DModel

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

## BriaTransformer2DModel[[diffusers.BriaTransformer2DModel]]

- **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.

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/

- **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.
- **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.
- **controlnet_block_samples** (`list` of `torch.Tensor`, *optional*) --
  A list of tensors that if specified are added to the residuals of transformer blocks.
- **controlnet_single_block_samples** (`list` of `torch.Tensor`, *optional*) --
  A list of tensors that if specified are added to the residuals of single 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.If `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.