Hugging Face's logo

Diffusers documentation

ControlNet

You are viewing v0.24.0 version. A newer version v0.31.0 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

ControlNet

The ControlNet model was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, 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 ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with “zero convolutions” (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

Loading from the original format

By default the ControlNetModel should be loaded with from_pretrained(), but it can also be loaded from the original format using FromOriginalControlnetMixin.from_single_file as follows:

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
controlnet = ControlNetModel.from_single_file(url)

url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)

ControlNetModel

class diffusers.ControlNetModel

< >

( 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') mid_block_type: typing.Optional[str] = 'UNetMidBlock2DCrossAttn' 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.Union[typing.Tuple[int, ...], NoneType] = (16, 32, 96, 256) global_pool_conditions: bool = False addition_embed_type_num_heads: int = 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) — TODO(Patrick) - unused parameter.
  • addition_embed_type_num_heads (int, defaults to 64) — The number of heads to use for the TextTimeEmbedding layer.

A ControlNet model.

forward

< >

( sample: FloatTensor timestep: typing.Union[torch.Tensor, float, int] encoder_hidden_states: Tensor controlnet_cond: FloatTensor 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.Union[typing.Dict[str, torch.Tensor], NoneType] = None cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None guess_mode: bool = False return_dict: bool = True ) → ControlNetOutput or tuple

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) — 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 ControlNetModel forward method.

from_unet

< >

( unet: UNet2DConditionModel controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: typing.Union[typing.Tuple[int, ...], NoneType] = (16, 32, 96, 256) load_weights_from_unet: bool = True conditioning_channels: int = 3 )

Parameters

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

Instantiate a ControlNetModel from UNet2DConditionModel.

set_attention_slice

< >

( 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

< >

( 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.CustomDiffusionAttnProcessor2_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.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.CustomDiffusionAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor]]] _remove_lora = False )

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.

ControlNetOutput

class diffusers.models.controlnet.ControlNetOutput

< >

( 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.

The output of ControlNetModel.

FlaxControlNetModel

class diffusers.FlaxControlNetModel

< >

( sample_size: int = 32 in_channels: int = 4 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 attention_head_dim: typing.Union[int, typing.Tuple[int, ...]] = 8 num_attention_heads: typing.Union[int, typing.Tuple[int, ...], NoneType] = None cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: dtype = <class 'jax.numpy.float32'> flip_sin_to_cos: bool = True freq_shift: int = 0 controlnet_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 0x7f3c3541f580> 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.

This model inherits from FlaxModelMixin. 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 flax.linen.Module 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.

Inherent JAX features such as the following are supported:

FlaxControlNetOutput

class diffusers.models.controlnet_flax.FlaxControlNetOutput

< >

( down_block_res_samples: Array mid_block_res_sample: Array )

Parameters

  • down_block_res_samples (jnp.ndarray) —
  • mid_block_res_sample (jnp.ndarray) —

The output of FlaxControlNetModel.

replace

< >

( **updates )

“Returns a new object replacing the specified fields with new values.

←Prior Transformer Overview→