#
BriaTransformer2DModel

A modified flux Transformer model from Bria

##
BriaTransformer2DModel

###
class diffusers.BriaTransformer2DModel

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( 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: typing.List[int] = [16, 56, 56] rope_theta = 10000 time_theta = 10000 )

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

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( 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: typing.Optional[typing.Dict[str, typing.Any]] = None return_dict: bool = True controlnet_block_samples = None controlnet_single_block_samples = None )

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

The BriaTransformer2DModel forward method.

Update on GitHub