diffusers/pr_14358/en/api/models/bria_transformer.md

BriaTransformer2DModel

A modified flux Transformer model from Bria

BriaTransformer2DModel[[diffusers.BriaTransformer2DModel]]

diffusers.BriaTransformer2DModel[[diffusers.BriaTransformer2DModel]]

diffusers.BriaTransformer2DModel(patch_size: int = 1, in_channels: int = 64, num_layers: int = 19, num_single_layers: int = 38, attention_head_dim: int = 128, num_attention_heads: int = 24, joint_attention_dim: int = 4096, pooled_projection_dim: int = None, guidance_embeds: bool = False, axes_dims_rope: list = [16, 56, 56], rope_theta = 10000, time_theta = 10000)

Source

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.

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/

forward[[diffusers.BriaTransformer2DModel.forward]]

forward(hidden_states: Tensor, encoder_hidden_states: Tensor = None, pooled_projections: Tensor = None, timestep: LongTensor = None, img_ids: Tensor = None, txt_ids: Tensor = None, guidance: Tensor = None, attention_kwargs: dict[str, typing.Any] | None = None, return_dict: bool = True, controlnet_block_samples = None, controlnet_single_block_samples = None)

Source

Parameters:

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.

return_dict (bool, optional, defaults to True) : Whether or not to return a ~models.transformer_2d.Transformer2DModelOutput instead of a plain tuple.

Returns:

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 forward method.