Hugging Face's logo

Diffusers documentation

SparseControlNetModel

You are viewing v0.30.2 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

SparseControlNetModel

SparseControlNetModel is an implementation of ControlNet for AnimateDiff.

ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.

The SparseCtrl version of ControlNet was introduced in SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models for achieving controlled generation in text-to-video diffusion models by Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, and Bo Dai.

The abstract from the paper is:

The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at this https URL.

Example for loading SparseControlNetModel

import torch
from diffusers import SparseControlNetModel

# fp32 variant in float16
# 1. Scribble checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16)

# 2. RGB checkpoint
controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-rgb", torch_dtype=torch.float16)

# For loading fp16 variant, pass `variant="fp16"` as an additional parameter

SparseControlNetModel

class diffusers.SparseControlNetModel

< >

( in_channels: int = 4 conditioning_channels: int = 4 flip_sin_to_cos: bool = True freq_shift: int = 0 down_block_types: Tuple = ('CrossAttnDownBlockMotion', 'CrossAttnDownBlockMotion', 'CrossAttnDownBlockMotion', 'DownBlockMotion') only_cross_attention: Union = False block_out_channels: Tuple = (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: Optional = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 768 transformer_layers_per_block: Union = 1 transformer_layers_per_mid_block: Union = None temporal_transformer_layers_per_block: Union = 1 attention_head_dim: Union = 8 num_attention_heads: Union = None use_linear_projection: bool = False upcast_attention: bool = False resnet_time_scale_shift: str = 'default' conditioning_embedding_out_channels: Optional = (16, 32, 96, 256) global_pool_conditions: bool = False controlnet_conditioning_channel_order: str = 'rgb' motion_max_seq_length: int = 32 motion_num_attention_heads: int = 8 concat_conditioning_mask: bool = True use_simplified_condition_embedding: bool = True )

Parameters

  • in_channels (int, defaults to 4) — The number of channels in the input sample.
  • conditioning_channels (int, defaults to 4) — The number of input channels in the controlnet conditional embedding module. If concat_condition_embedding is True, the value provided here is incremented by 1.
  • 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.
  • transformer_layers_per_mid_block (int or Tuple[int], optional, defaults to 1) — The number of transformer layers to use in each layer in the middle block.
  • attention_head_dim (int or Tuple[int], defaults to 8) — The dimension of the attention heads.
  • num_attention_heads (int or Tuple[int], optional) — The number of heads to use for multi-head attention.
  • use_linear_projection (bool, defaults to False) —
  • 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.
  • conditioning_embedding_out_channels (Tuple[int], 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
  • controlnet_conditioning_channel_order (str, defaults to rgb) —
  • motion_max_seq_length (int, defaults to 32) — The maximum sequence length to use in the motion module.
  • motion_num_attention_heads (int or Tuple[int], defaults to 8) — The number of heads to use in each attention layer of the motion module.
  • concat_conditioning_mask (bool, defaults to True) —
  • use_simplified_condition_embedding (bool, defaults to True) —

A SparseControlNet model as described in SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models.

forward

< >

( sample: Tensor timestep: Union encoder_hidden_states: Tensor controlnet_cond: Tensor conditioning_scale: float = 1.0 timestep_cond: Optional = None attention_mask: Optional = None cross_attention_kwargs: Optional = None conditioning_mask: Optional = 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 (torch.Tensor) — 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 SparseControlNetModel forward method.

from_unet

< >

( unet: UNet2DConditionModel controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: Optional = (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 SparseControlNetModel. All configuration options are also copied where applicable.

Instantiate a SparseControlNetModel from UNet2DConditionModel.

set_attention_slice

< >

( slice_size: Union )

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: Union )

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.

SparseControlNetOutput

class diffusers.models.controlnet_sparsectrl.SparseControlNetOutput

< >

( down_block_res_samples: Tuple 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 middle 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 SparseControlNetModel.

< > Update on GitHub

←SD3ControlNetModel Overview→