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

BriaFiboTransformer2DModel

A modified flux Transformer model from Bria

BriaFiboTransformer2DModel[[diffusers.BriaFiboTransformer2DModel]]

• 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.

• ... --

• 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.

• text_encoder_layers (list of torch.Tensor) -- Per-block text encoder hidden states, one tensor per transformer block.

• 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.

• joint_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.