#
ControlNetUnionModel

ControlNetUnionModel is an implementation of ControlNet for Stable Diffusion XL.

The ControlNet model was introduced in ControlNetPlus by xinsir6. It supports multiple conditioning inputs without increasing computation.

We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters.

##
Loading

By default the ControlNetUnionModel should be loaded with from_pretrained().

    from diffusers import StableDiffusionXLControlNetUnionPipeline, ControlNetUnionModel

    controlnet = ControlNetUnionModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0")
    pipe = StableDiffusionXLControlNetUnionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet)

##
ControlNetUnionModel

###
class diffusers.ControlNetUnionModel

< source >

( in_channels: int = 4 conditioning_channels: int = 3 flip_sin_to_cos: bool = True freq_shift: int = 0 down_block_types: typing.Tuple[str, ...] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = False block_out_channels: typing.Tuple[int, ...] = (320, 640, 1280, 1280) layers_per_block: int = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 act_fn: str = 'silu' norm_num_groups: typing.Optional[int] = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 1280 transformer_layers_per_block: typing.Union[int, typing.Tuple[int, ...]] = 1 encoder_hid_dim: typing.Optional[int] = None encoder_hid_dim_type: typing.Optional[str] = None attention_head_dim: typing.Union[int, typing.Tuple[int, ...]] = 8 num_attention_heads: typing.Union[int, typing.Tuple[int, ...], NoneType] = None use_linear_projection: bool = False class_embed_type: typing.Optional[str] = None addition_embed_type: typing.Optional[str] = None addition_time_embed_dim: typing.Optional[int] = None num_class_embeds: typing.Optional[int] = None upcast_attention: bool = False resnet_time_scale_shift: str = 'default' projection_class_embeddings_input_dim: typing.Optional[int] = None controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: typing.Optional[typing.Tuple[int, ...]] = (48, 96, 192, 384) global_pool_conditions: bool = False addition_embed_type_num_heads: int = 64 num_control_type: int = 6 num_trans_channel: int = 320 num_trans_head: int = 8 num_trans_layer: int = 1 num_proj_channel: int = 320 )

Parameters

- in_channels (`int`, defaults to 4) — The number of channels in the input sample.
- flip_sin_to_cos (`bool`, defaults to `True`) — Whether to flip the sin to cos in the time embedding.
- freq_shift (`int`, defaults to 0) — The frequency shift to apply to the time embedding.
- down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`) — The tuple of downsample blocks to use.
- only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`) —
- block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`) — The tuple of output channels for each block.
- layers_per_block (`int`, defaults to 2) — The number of layers per block.
- downsample_padding (`int`, defaults to 1) — The padding to use for the downsampling convolution.
- mid_block_scale_factor (`float`, defaults to 1) — The scale factor to use for the mid block.
- act_fn (`str`, defaults to “silu”) — The activation function to use.
- norm_num_groups (`int`, optional, defaults to 32) — The number of groups to use for the normalization. If None, normalization and activation layers is skipped in post-processing.
- norm_eps (`float`, defaults to 1e-5) — The epsilon to use for the normalization.
- cross_attention_dim (`int`, defaults to 1280) — The dimension of the cross attention features.
- transformer_layers_per_block (`int` or `Tuple[int]`, optional, defaults to 1) — The number of transformer blocks of type `BasicTransformerBlock`. Only relevant for `~models.unet_2d_blocks.CrossAttnDownBlock2D`, `~models.unet_2d_blocks.CrossAttnUpBlock2D`, `~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`.
- encoder_hid_dim (`int`, optional, defaults to None) — If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` dimension to `cross_attention_dim`.
- encoder_hid_dim_type (`str`, optional, defaults to `None`) — If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
- attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8) — The dimension of the attention heads.
- use_linear_projection (`bool`, defaults to `False`) —
- class_embed_type (`str`, optional, defaults to `None`) — The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None, `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
- addition_embed_type (`str`, optional, defaults to `None`) — Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or “text”. “text” will use the `TextTimeEmbedding` layer.
- num_class_embeds (`int`, optional, defaults to 0) — Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`.
- upcast_attention (`bool`, defaults to `False`) —
- resnet_time_scale_shift (`str`, defaults to `"default"`) — Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
- projection_class_embeddings_input_dim (`int`, optional, defaults to `None`) — The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
- controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`) — The channel order of conditional image. Will convert to `rgb` if it’s `bgr`.
- conditioning_embedding_out_channels (`tuple[int]`, optional, defaults to `(48, 96, 192, 384)`) — The tuple of output channel for each block in the `conditioning_embedding` layer.
- global_pool_conditions (`bool`, defaults to `False`) —

A ControlNetUnion model.

####
forward

< source >

( sample: Tensor timestep: typing.Union[torch.Tensor, float, int] encoder_hidden_states: Tensor controlnet_cond: typing.Union[diffusers.models.controlnets.controlnet_union.ControlNetUnionInput, diffusers.models.controlnets.controlnet_union.ControlNetUnionInputProMax] control_type: Tensor conditioning_scale: float = 1.0 class_labels: typing.Optional[torch.Tensor] = None timestep_cond: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None added_cond_kwargs: typing.Optional[typing.Dict[str, torch.Tensor]] = None cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None guess_mode: bool = False return_dict: bool = True ) →
`ControlNetOutput` or `tuple`

Parameters

- sample (`torch.Tensor`) — The noisy input tensor.
- timestep (`Union[torch.Tensor, float, int]`) — The number of timesteps to denoise an input.
- encoder_hidden_states (`torch.Tensor`) — The encoder hidden states.
- controlnet_cond (`Union[ControlNetUnionInput, ControlNetUnionInputProMax]`) — The conditional input tensors.
- control_type (`torch.Tensor`) — A tensor of shape `(batch, num_control_type)` with values `0` or `1` depending on whether the control type is used.
- conditioning_scale (`float`, defaults to `1.0`) — The scale factor for ControlNet outputs.
- class_labels (`torch.Tensor`, optional, defaults to `None`) — Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
- timestep_cond (`torch.Tensor`, optional, defaults to `None`) — Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep embeddings.
- attention_mask (`torch.Tensor`, optional, defaults to `None`) — An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to “discard” tokens.
- added_cond_kwargs (`dict`) — Additional conditions for the Stable Diffusion XL UNet.
- cross_attention_kwargs (`dict[str]`, optional, defaults to `None`) — A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
- guess_mode (`bool`, defaults to `False`) — In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
- return_dict (`bool`, defaults to `True`) — Whether or not to return a `ControlNetOutput` instead of a plain tuple.

Returns

`ControlNetOutput` or `tuple`

If `return_dict` is `True`, a `ControlNetOutput` is returned, otherwise a tuple is returned where the first element is the sample tensor.

The ControlNetUnionModel forward method.

####
from_unet

< source >

( unet: UNet2DConditionModel controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: typing.Optional[typing.Tuple[int, ...]] = (16, 32, 96, 256) load_weights_from_unet: bool = True )

Parameters

- unet (`UNet2DConditionModel`) — The UNet model weights to copy to the ControlNetUnionModel. All configuration options are also copied where applicable.

Instantiate a ControlNetUnionModel from UNet2DConditionModel.

####
set_attention_slice

< source >

( slice_size: typing.Union[str, int, typing.List[int]] )

Parameters

- slice_size (`str` or `int` or `list(int)`, optional, defaults to `"auto"`) — When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`.

Enable sliced attention computation.

When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed.

####
set_attn_processor

< source >

( processor: typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.JointAttnProcessor2_0, diffusers.models.attention_processor.PAGJointAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGJointAttnProcessor2_0, diffusers.models.attention_processor.FusedJointAttnProcessor2_0, diffusers.models.attention_processor.AllegroAttnProcessor2_0, diffusers.models.attention_processor.AuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.CogVideoXAttnProcessor2_0, diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.StableAudioAttnProcessor2_0, diffusers.models.attention_processor.HunyuanAttnProcessor2_0, diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.LuminaAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_0, diffusers.models.attention_processor.FusedAttnProcessor2_0, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, typing.Dict[str, typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.JointAttnProcessor2_0, diffusers.models.attention_processor.PAGJointAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGJointAttnProcessor2_0, diffusers.models.attention_processor.FusedJointAttnProcessor2_0, diffusers.models.attention_processor.AllegroAttnProcessor2_0, diffusers.models.attention_processor.AuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.CogVideoXAttnProcessor2_0, diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.StableAudioAttnProcessor2_0, diffusers.models.attention_processor.HunyuanAttnProcessor2_0, diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.LuminaAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_0, diffusers.models.attention_processor.FusedAttnProcessor2_0, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor]]] )

Parameters

- processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`) — The instantiated processor class or a dictionary of processor classes that will be set as the processor for all `Attention` layers.

  If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.

Sets the attention processor to use to compute attention.

####
set_default_attn_processor

< source >

( )

Disables custom attention processors and sets the default attention implementation.

< > Update on GitHub