diffusers/pr_12507/en/api/models/controlnet_union.md

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[[diffusers.ControlNetUnionModel]]

diffusers.ControlNetUnionModel[[diffusers.ControlNetUnionModel]]

Source

A ControlNetUnion model.

forwarddiffusers.ControlNetUnionModel.forwardhttps://github.com/huggingface/diffusers/blob/vr_12507/src/diffusers/models/controlnets/controlnet_union.py#L540[{"name": "sample", "val": ": Tensor"}, {"name": "timestep", "val": ": typing.Union[torch.Tensor, float, int]"}, {"name": "encoder_hidden_states", "val": ": Tensor"}, {"name": "controlnet_cond", "val": ": typing.List[torch.Tensor]"}, {"name": "control_type", "val": ": Tensor"}, {"name": "control_type_idx", "val": ": typing.List[int]"}, {"name": "conditioning_scale", "val": ": typing.Union[float, typing.List[float]] = 1.0"}, {"name": "class_labels", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "timestep_cond", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "added_cond_kwargs", "val": ": typing.Optional[typing.Dict[str, torch.Tensor]] = None"}, {"name": "cross_attention_kwargs", "val": ": typing.Optional[typing.Dict[str, typing.Any]] = None"}, {"name": "from_multi", "val": ": bool = False"}, {"name": "guess_mode", "val": ": bool = False"}, {"name": "return_dict", "val": ": bool = True"}]- 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 (List[torch.Tensor]) -- 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.
• control_type_idx (List[int]) -- The indices of control_type.
• 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.
• from_multi (bool, defaults to False) -- Use standard scaling when called from MultiControlNetUnionModel.
• 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 ~models.controlnet.ControlNetOutput instead of a plain tuple.0~models.controlnet.ControlNetOutput or tupleIf return_dict is True, a ~models.controlnet.ControlNetOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

The ControlNetUnionModel forward method.

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) --

Returns:

~models.controlnet.ControlNetOutput` **or** `tuple

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

from_unet[[diffusers.ControlNetUnionModel.from_unet]]

Source

Instantiate a ControlNetUnionModel from UNet2DConditionModel.

Parameters:

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

set_attention_slice[[diffusers.ControlNetUnionModel.set_attention_slice]]

Source

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.

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.

set_default_attn_processor[[diffusers.ControlNetUnionModel.set_default_attn_processor]]

Source

Disables custom attention processors and sets the default attention implementation.