ControlNet
Last updated
Last updated
The ControlNet model was introduced in by Lvmin Zhang and Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
The abstract from the paper is:
We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.
By default the should be loaded with , but it can also be loaded from the original format using FromOriginalControlnetMixin.from_single_file as follows:
Copied
( in_channels: int = 4conditioning_channels: int = 3flip_sin_to_cos: bool = Truefreq_shift: int = 0down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D')only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = Falseblock_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280)layers_per_block: int = 2downsample_padding: int = 1mid_block_scale_factor: float = 1act_fn: str = 'silu'norm_num_groups: typing.Optional[int] = 32norm_eps: float = 1e-05cross_attention_dim: int = 1280transformer_layers_per_block: typing.Union[int, typing.Tuple[int]] = 1encoder_hid_dim: typing.Optional[int] = Noneencoder_hid_dim_type: typing.Optional[str] = Noneattention_head_dim: typing.Union[int, typing.Tuple[int]] = 8num_attention_heads: typing.Union[int, typing.Tuple[int], NoneType] = Noneuse_linear_projection: bool = Falseclass_embed_type: typing.Optional[str] = Noneaddition_embed_type: typing.Optional[str] = Noneaddition_time_embed_dim: typing.Optional[int] = Nonenum_class_embeds: typing.Optional[int] = Noneupcast_attention: bool = Falseresnet_time_scale_shift: str = 'default'projection_class_embeddings_input_dim: typing.Optional[int] = Nonecontrolnet_conditioning_channel_order: str = 'rgb'conditioning_embedding_out_channels: typing.Optional[typing.Tuple[int]] = (16, 32, 96, 256)global_pool_conditions: bool = Falseaddition_embed_type_num_heads = 64 )
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 CrossAttnDownBlock2D, CrossAttnUpBlock2D, 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 (16, 32, 96, 256)) — The tuple of output channel for each block in the conditioning_embedding layer.
global_pool_conditions (bool, defaults to False) —
A ControlNet model.
forward
Parameters
sample (torch.FloatTensor) — 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 (torch.FloatTensor) — The conditional input tensor of shape (batch_size, sequence_length, hidden_size).
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) —
attention_mask (torch.Tensor, optional, defaults to None) —
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.
Returns
from_unet
( unet: UNet2DConditionModelcontrolnet_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
set_attention_slice
( slice_size )
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
( processor: typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, 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.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, 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
( )
Disables custom attention processors and sets the default attention implementation.
( down_block_res_samples: typing.Tuple[torch.Tensor]mid_block_res_sample: Tensor )
Parameters
down_block_res_samples (tuple[torch.Tensor]) — A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should be of shape (batch_size, channel * resolution, height //resolution, width // resolution). Output can be used to condition the original UNet’s downsampling activations.
mid_down_block_re_sample (torch.Tensor) — The activation of the midde block (the lowest sample resolution). Each tensor should be of shape (batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution). Output can be used to condition the original UNet’s middle block activation.
( sample_size: int = 32in_channels: int = 4down_block_types: typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D')only_cross_attention: typing.Union[bool, typing.Tuple[bool]] = Falseblock_out_channels: typing.Tuple[int] = (320, 640, 1280, 1280)layers_per_block: int = 2attention_head_dim: typing.Union[int, typing.Tuple[int]] = 8num_attention_heads: typing.Union[int, typing.Tuple[int], NoneType] = Nonecross_attention_dim: int = 1280dropout: float = 0.0use_linear_projection: bool = Falsedtype: dtype = <class 'jax.numpy.float32'>flip_sin_to_cos: bool = Truefreq_shift: int = 0controlnet_conditioning_channel_order: str = 'rgb'conditioning_embedding_out_channels: typing.Tuple[int] = (16, 32, 96, 256)parent: typing.Union[typing.Type[flax.linen.module.Module], typing.Type[flax.core.scope.Scope], typing.Type[flax.linen.module._Sentinel], NoneType] = <flax.linen.module._Sentinel object at 0x7f3306dc42e0>name: typing.Optional[str] = None )
Parameters
sample_size (int, optional) — The size of the input sample.
in_channels (int, optional, defaults to 4) — The number of channels in the input sample.
down_block_types (Tuple[str], optional, defaults to ("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")) — The tuple of downsample blocks to use.
block_out_channels (Tuple[int], optional, defaults to (320, 640, 1280, 1280)) — The tuple of output channels for each block.
layers_per_block (int, optional, defaults to 2) — The number of layers per block.
attention_head_dim (int or Tuple[int], optional, defaults to 8) — The dimension of the attention heads.
num_attention_heads (int or Tuple[int], optional) — The number of attention heads.
cross_attention_dim (int, optional, defaults to 768) — The dimension of the cross attention features.
dropout (float, optional, defaults to 0) — Dropout probability for down, up and bottleneck blocks.
flip_sin_to_cos (bool, optional, defaults to True) — Whether to flip the sin to cos in the time embedding.
freq_shift (int, optional, defaults to 0) — The frequency shift to apply to the time embedding.
controlnet_conditioning_channel_order (str, optional, defaults to rgb) — The channel order of conditional image. Will convert to rgb if it’s bgr.
conditioning_embedding_out_channels (tuple, optional, defaults to (16, 32, 96, 256)) — The tuple of output channel for each block in the conditioning_embedding layer.
A ControlNet model.
Inherent JAX features such as the following are supported:
( down_block_res_samples: Arraymid_block_res_sample: Array )
Parameters
down_block_res_samples (jnp.ndarray) —
mid_block_res_sample (jnp.ndarray) —
replace
( **updates )
“Returns a new object replacing the specified fields with new values.
( sample: FloatTensortimestep: typing.Union[torch.Tensor, float, int]encoder_hidden_states: Tensorcontrolnet_cond: FloatTensorconditioning_scale: float = 1.0class_labels: typing.Optional[torch.Tensor] = Nonetimestep_cond: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneadded_cond_kwargs: typing.Union[typing.Dict[str, torch.Tensor], NoneType] = Nonecross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = Noneguess_mode: bool = Falsereturn_dict: bool = True ) → or tuple
return_dict (bool, defaults to True) — Whether or not to return a instead of a plain tuple.
or tuple
If return_dict is True, a is returned, otherwise a tuple is returned where the first element is the sample tensor.
The forward method.
unet (UNet2DConditionModel) — The UNet model weights to copy to the . All configuration options are also copied where applicable.
Instantiate a from .
The output of .
This model inherits from . Check the superclass documentation for it’s generic methods implemented for all models (such as downloading or saving).
This model is also a Flax Linen subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its general usage and behavior.
The output of .