diffusers/pr_13881/en/api/models/bria_transformer.md

BriaTransformer2DModel

A modified flux Transformer model from Bria

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.

• 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 forward method.