786 lines
38 KiB
Python
786 lines
38 KiB
Python
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# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import functional as F
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...loaders import FromOriginalModelMixin
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from ...utils import BaseOutput, logging
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from ..attention_processor import (
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ADDED_KV_ATTENTION_PROCESSORS,
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CROSS_ATTENTION_PROCESSORS,
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AttentionProcessor,
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AttnAddedKVProcessor,
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AttnProcessor,
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)
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from ..embeddings import TimestepEmbedding, Timesteps
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from ..modeling_utils import ModelMixin
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from ..unets.unet_2d_blocks import UNetMidBlock2DCrossAttn
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from ..unets.unet_2d_condition import UNet2DConditionModel
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from ..unets.unet_motion_model import CrossAttnDownBlockMotion, DownBlockMotion
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class SparseControlNetOutput(BaseOutput):
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"""
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The output of [`SparseControlNetModel`].
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Args:
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down_block_res_samples (`tuple[torch.Tensor]`):
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A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
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be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
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used to condition the original UNet's downsampling activations.
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mid_down_block_re_sample (`torch.Tensor`):
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The activation of the middle block (the lowest sample resolution). Each tensor should be of shape
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`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
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Output can be used to condition the original UNet's middle block activation.
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"""
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down_block_res_samples: Tuple[torch.Tensor]
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mid_block_res_sample: torch.Tensor
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class SparseControlNetConditioningEmbedding(nn.Module):
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def __init__(
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self,
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conditioning_embedding_channels: int,
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conditioning_channels: int = 3,
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block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
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):
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super().__init__()
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self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
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self.blocks = nn.ModuleList([])
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for i in range(len(block_out_channels) - 1):
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channel_in = block_out_channels[i]
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channel_out = block_out_channels[i + 1]
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self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
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self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
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self.conv_out = zero_module(
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nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
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)
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def forward(self, conditioning: torch.Tensor) -> torch.Tensor:
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embedding = self.conv_in(conditioning)
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embedding = F.silu(embedding)
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for block in self.blocks:
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embedding = block(embedding)
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embedding = F.silu(embedding)
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embedding = self.conv_out(embedding)
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return embedding
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class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
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"""
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A SparseControlNet model as described in [SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion
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Models](https://huggingface.co/papers/2311.16933).
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Args:
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in_channels (`int`, defaults to 4):
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The number of channels in the input sample.
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conditioning_channels (`int`, defaults to 4):
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The number of input channels in the controlnet conditional embedding module. If
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`concat_condition_embedding` is True, the value provided here is incremented by 1.
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flip_sin_to_cos (`bool`, defaults to `True`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, defaults to 0):
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The frequency shift to apply to the time embedding.
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down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
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The tuple of downsample blocks to use.
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only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
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block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
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The tuple of output channels for each block.
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layers_per_block (`int`, defaults to 2):
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The number of layers per block.
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downsample_padding (`int`, defaults to 1):
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The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, defaults to 1):
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The scale factor to use for the mid block.
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act_fn (`str`, defaults to "silu"):
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The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups to use for the normalization. If None, normalization and activation layers is skipped
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in post-processing.
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norm_eps (`float`, defaults to 1e-5):
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The epsilon to use for the normalization.
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cross_attention_dim (`int`, defaults to 1280):
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The dimension of the cross attention features.
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transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
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The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
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[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
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[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
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transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
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The number of transformer layers to use in each layer in the middle block.
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attention_head_dim (`int` or `Tuple[int]`, defaults to 8):
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The dimension of the attention heads.
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num_attention_heads (`int` or `Tuple[int]`, *optional*):
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The number of heads to use for multi-head attention.
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use_linear_projection (`bool`, defaults to `False`):
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upcast_attention (`bool`, defaults to `False`):
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resnet_time_scale_shift (`str`, defaults to `"default"`):
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Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
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conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`):
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The tuple of output channel for each block in the `conditioning_embedding` layer.
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global_pool_conditions (`bool`, defaults to `False`):
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TODO(Patrick) - unused parameter
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controlnet_conditioning_channel_order (`str`, defaults to `rgb`):
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motion_max_seq_length (`int`, defaults to `32`):
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The maximum sequence length to use in the motion module.
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motion_num_attention_heads (`int` or `Tuple[int]`, defaults to `8`):
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The number of heads to use in each attention layer of the motion module.
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concat_conditioning_mask (`bool`, defaults to `True`):
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use_simplified_condition_embedding (`bool`, defaults to `True`):
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"""
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(
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self,
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in_channels: int = 4,
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conditioning_channels: int = 4,
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flip_sin_to_cos: bool = True,
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freq_shift: int = 0,
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down_block_types: Tuple[str, ...] = (
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"CrossAttnDownBlockMotion",
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"CrossAttnDownBlockMotion",
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"CrossAttnDownBlockMotion",
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"DownBlockMotion",
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),
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only_cross_attention: Union[bool, Tuple[bool]] = False,
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block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
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layers_per_block: int = 2,
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downsample_padding: int = 1,
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mid_block_scale_factor: float = 1,
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act_fn: str = "silu",
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norm_num_groups: Optional[int] = 32,
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norm_eps: float = 1e-5,
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cross_attention_dim: int = 768,
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transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
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transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
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temporal_transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
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attention_head_dim: Union[int, Tuple[int, ...]] = 8,
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num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
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use_linear_projection: bool = False,
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upcast_attention: bool = False,
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resnet_time_scale_shift: str = "default",
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conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
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global_pool_conditions: bool = False,
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controlnet_conditioning_channel_order: str = "rgb",
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motion_max_seq_length: int = 32,
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motion_num_attention_heads: int = 8,
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concat_conditioning_mask: bool = True,
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use_simplified_condition_embedding: bool = True,
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):
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super().__init__()
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self.use_simplified_condition_embedding = use_simplified_condition_embedding
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# If `num_attention_heads` is not defined (which is the case for most models)
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# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
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# The reason for this behavior is to correct for incorrectly named variables that were introduced
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# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
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# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
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# which is why we correct for the naming here.
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num_attention_heads = num_attention_heads or attention_head_dim
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# Check inputs
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if len(block_out_channels) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
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)
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if isinstance(transformer_layers_per_block, int):
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transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
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if isinstance(temporal_transformer_layers_per_block, int):
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temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
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# input
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conv_in_kernel = 3
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conv_in_padding = (conv_in_kernel - 1) // 2
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self.conv_in = nn.Conv2d(
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in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
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)
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if concat_conditioning_mask:
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conditioning_channels = conditioning_channels + 1
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self.concat_conditioning_mask = concat_conditioning_mask
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# control net conditioning embedding
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if use_simplified_condition_embedding:
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self.controlnet_cond_embedding = zero_module(
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nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
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)
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else:
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self.controlnet_cond_embedding = SparseControlNetConditioningEmbedding(
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conditioning_embedding_channels=block_out_channels[0],
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block_out_channels=conditioning_embedding_out_channels,
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conditioning_channels=conditioning_channels,
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)
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# time
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time_embed_dim = block_out_channels[0] * 4
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self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
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timestep_input_dim = block_out_channels[0]
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self.time_embedding = TimestepEmbedding(
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timestep_input_dim,
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time_embed_dim,
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act_fn=act_fn,
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)
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self.down_blocks = nn.ModuleList([])
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self.controlnet_down_blocks = nn.ModuleList([])
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if isinstance(cross_attention_dim, int):
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cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
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if isinstance(only_cross_attention, bool):
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only_cross_attention = [only_cross_attention] * len(down_block_types)
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if isinstance(attention_head_dim, int):
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attention_head_dim = (attention_head_dim,) * len(down_block_types)
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if isinstance(num_attention_heads, int):
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num_attention_heads = (num_attention_heads,) * len(down_block_types)
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if isinstance(motion_num_attention_heads, int):
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motion_num_attention_heads = (motion_num_attention_heads,) * len(down_block_types)
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# down
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output_channel = block_out_channels[0]
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controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
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controlnet_block = zero_module(controlnet_block)
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self.controlnet_down_blocks.append(controlnet_block)
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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if down_block_type == "CrossAttnDownBlockMotion":
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down_block = CrossAttnDownBlockMotion(
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in_channels=input_channel,
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out_channels=output_channel,
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temb_channels=time_embed_dim,
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dropout=0,
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num_layers=layers_per_block,
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transformer_layers_per_block=transformer_layers_per_block[i],
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resnet_eps=norm_eps,
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resnet_time_scale_shift=resnet_time_scale_shift,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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resnet_pre_norm=True,
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num_attention_heads=num_attention_heads[i],
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cross_attention_dim=cross_attention_dim[i],
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add_downsample=not is_final_block,
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dual_cross_attention=False,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention[i],
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upcast_attention=upcast_attention,
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temporal_num_attention_heads=motion_num_attention_heads[i],
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temporal_max_seq_length=motion_max_seq_length,
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temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
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temporal_double_self_attention=False,
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)
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|
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elif down_block_type == "DownBlockMotion":
|
||
|
|
down_block = DownBlockMotion(
|
||
|
|
in_channels=input_channel,
|
||
|
|
out_channels=output_channel,
|
||
|
|
temb_channels=time_embed_dim,
|
||
|
|
dropout=0,
|
||
|
|
num_layers=layers_per_block,
|
||
|
|
resnet_eps=norm_eps,
|
||
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
||
|
|
resnet_act_fn=act_fn,
|
||
|
|
resnet_groups=norm_num_groups,
|
||
|
|
resnet_pre_norm=True,
|
||
|
|
add_downsample=not is_final_block,
|
||
|
|
temporal_num_attention_heads=motion_num_attention_heads[i],
|
||
|
|
temporal_max_seq_length=motion_max_seq_length,
|
||
|
|
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
||
|
|
temporal_double_self_attention=False,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Invalid `block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
|
||
|
|
)
|
||
|
|
|
||
|
|
self.down_blocks.append(down_block)
|
||
|
|
|
||
|
|
for _ in range(layers_per_block):
|
||
|
|
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||
|
|
controlnet_block = zero_module(controlnet_block)
|
||
|
|
self.controlnet_down_blocks.append(controlnet_block)
|
||
|
|
|
||
|
|
if not is_final_block:
|
||
|
|
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||
|
|
controlnet_block = zero_module(controlnet_block)
|
||
|
|
self.controlnet_down_blocks.append(controlnet_block)
|
||
|
|
|
||
|
|
# mid
|
||
|
|
mid_block_channels = block_out_channels[-1]
|
||
|
|
|
||
|
|
controlnet_block = nn.Conv2d(mid_block_channels, mid_block_channels, kernel_size=1)
|
||
|
|
controlnet_block = zero_module(controlnet_block)
|
||
|
|
self.controlnet_mid_block = controlnet_block
|
||
|
|
|
||
|
|
if transformer_layers_per_mid_block is None:
|
||
|
|
transformer_layers_per_mid_block = (
|
||
|
|
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
|
||
|
|
)
|
||
|
|
|
||
|
|
self.mid_block = UNetMidBlock2DCrossAttn(
|
||
|
|
in_channels=mid_block_channels,
|
||
|
|
temb_channels=time_embed_dim,
|
||
|
|
dropout=0,
|
||
|
|
num_layers=1,
|
||
|
|
transformer_layers_per_block=transformer_layers_per_mid_block,
|
||
|
|
resnet_eps=norm_eps,
|
||
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
||
|
|
resnet_act_fn=act_fn,
|
||
|
|
resnet_groups=norm_num_groups,
|
||
|
|
resnet_pre_norm=True,
|
||
|
|
num_attention_heads=num_attention_heads[-1],
|
||
|
|
output_scale_factor=mid_block_scale_factor,
|
||
|
|
cross_attention_dim=cross_attention_dim[-1],
|
||
|
|
dual_cross_attention=False,
|
||
|
|
use_linear_projection=use_linear_projection,
|
||
|
|
upcast_attention=upcast_attention,
|
||
|
|
attention_type="default",
|
||
|
|
)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_unet(
|
||
|
|
cls,
|
||
|
|
unet: UNet2DConditionModel,
|
||
|
|
controlnet_conditioning_channel_order: str = "rgb",
|
||
|
|
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||
|
|
load_weights_from_unet: bool = True,
|
||
|
|
conditioning_channels: int = 3,
|
||
|
|
) -> "SparseControlNetModel":
|
||
|
|
r"""
|
||
|
|
Instantiate a [`SparseControlNetModel`] from [`UNet2DConditionModel`].
|
||
|
|
|
||
|
|
Parameters:
|
||
|
|
unet (`UNet2DConditionModel`):
|
||
|
|
The UNet model weights to copy to the [`SparseControlNetModel`]. All configuration options are also
|
||
|
|
copied where applicable.
|
||
|
|
"""
|
||
|
|
transformer_layers_per_block = (
|
||
|
|
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||
|
|
)
|
||
|
|
down_block_types = unet.config.down_block_types
|
||
|
|
|
||
|
|
for i in range(len(down_block_types)):
|
||
|
|
if "CrossAttn" in down_block_types[i]:
|
||
|
|
down_block_types[i] = "CrossAttnDownBlockMotion"
|
||
|
|
elif "Down" in down_block_types[i]:
|
||
|
|
down_block_types[i] = "DownBlockMotion"
|
||
|
|
else:
|
||
|
|
raise ValueError("Invalid `block_type` encountered. Must be a cross-attention or down block")
|
||
|
|
|
||
|
|
controlnet = cls(
|
||
|
|
in_channels=unet.config.in_channels,
|
||
|
|
conditioning_channels=conditioning_channels,
|
||
|
|
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||
|
|
freq_shift=unet.config.freq_shift,
|
||
|
|
down_block_types=unet.config.down_block_types,
|
||
|
|
only_cross_attention=unet.config.only_cross_attention,
|
||
|
|
block_out_channels=unet.config.block_out_channels,
|
||
|
|
layers_per_block=unet.config.layers_per_block,
|
||
|
|
downsample_padding=unet.config.downsample_padding,
|
||
|
|
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||
|
|
act_fn=unet.config.act_fn,
|
||
|
|
norm_num_groups=unet.config.norm_num_groups,
|
||
|
|
norm_eps=unet.config.norm_eps,
|
||
|
|
cross_attention_dim=unet.config.cross_attention_dim,
|
||
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
||
|
|
attention_head_dim=unet.config.attention_head_dim,
|
||
|
|
num_attention_heads=unet.config.num_attention_heads,
|
||
|
|
use_linear_projection=unet.config.use_linear_projection,
|
||
|
|
upcast_attention=unet.config.upcast_attention,
|
||
|
|
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||
|
|
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||
|
|
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||
|
|
)
|
||
|
|
|
||
|
|
if load_weights_from_unet:
|
||
|
|
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict(), strict=False)
|
||
|
|
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict(), strict=False)
|
||
|
|
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict(), strict=False)
|
||
|
|
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
|
||
|
|
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
|
||
|
|
|
||
|
|
return controlnet
|
||
|
|
|
||
|
|
@property
|
||
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||
|
|
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||
|
|
r"""
|
||
|
|
Returns:
|
||
|
|
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||
|
|
indexed by its weight name.
|
||
|
|
"""
|
||
|
|
# set recursively
|
||
|
|
processors = {}
|
||
|
|
|
||
|
|
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||
|
|
if hasattr(module, "get_processor"):
|
||
|
|
processors[f"{name}.processor"] = module.get_processor()
|
||
|
|
|
||
|
|
for sub_name, child in module.named_children():
|
||
|
|
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||
|
|
|
||
|
|
return processors
|
||
|
|
|
||
|
|
for name, module in self.named_children():
|
||
|
|
fn_recursive_add_processors(name, module, processors)
|
||
|
|
|
||
|
|
return processors
|
||
|
|
|
||
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||
|
|
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||
|
|
r"""
|
||
|
|
Sets the attention processor to use to compute attention.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
"""
|
||
|
|
count = len(self.attn_processors.keys())
|
||
|
|
|
||
|
|
if isinstance(processor, dict) and len(processor) != count:
|
||
|
|
raise ValueError(
|
||
|
|
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||
|
|
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||
|
|
)
|
||
|
|
|
||
|
|
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||
|
|
if hasattr(module, "set_processor"):
|
||
|
|
if not isinstance(processor, dict):
|
||
|
|
module.set_processor(processor)
|
||
|
|
else:
|
||
|
|
module.set_processor(processor.pop(f"{name}.processor"))
|
||
|
|
|
||
|
|
for sub_name, child in module.named_children():
|
||
|
|
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||
|
|
|
||
|
|
for name, module in self.named_children():
|
||
|
|
fn_recursive_attn_processor(name, module, processor)
|
||
|
|
|
||
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||
|
|
def set_default_attn_processor(self):
|
||
|
|
"""
|
||
|
|
Disables custom attention processors and sets the default attention implementation.
|
||
|
|
"""
|
||
|
|
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||
|
|
processor = AttnAddedKVProcessor()
|
||
|
|
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||
|
|
processor = AttnProcessor()
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||
|
|
)
|
||
|
|
|
||
|
|
self.set_attn_processor(processor)
|
||
|
|
|
||
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||
|
|
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||
|
|
r"""
|
||
|
|
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.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
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`.
|
||
|
|
"""
|
||
|
|
sliceable_head_dims = []
|
||
|
|
|
||
|
|
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||
|
|
if hasattr(module, "set_attention_slice"):
|
||
|
|
sliceable_head_dims.append(module.sliceable_head_dim)
|
||
|
|
|
||
|
|
for child in module.children():
|
||
|
|
fn_recursive_retrieve_sliceable_dims(child)
|
||
|
|
|
||
|
|
# retrieve number of attention layers
|
||
|
|
for module in self.children():
|
||
|
|
fn_recursive_retrieve_sliceable_dims(module)
|
||
|
|
|
||
|
|
num_sliceable_layers = len(sliceable_head_dims)
|
||
|
|
|
||
|
|
if slice_size == "auto":
|
||
|
|
# half the attention head size is usually a good trade-off between
|
||
|
|
# speed and memory
|
||
|
|
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||
|
|
elif slice_size == "max":
|
||
|
|
# make smallest slice possible
|
||
|
|
slice_size = num_sliceable_layers * [1]
|
||
|
|
|
||
|
|
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||
|
|
|
||
|
|
if len(slice_size) != len(sliceable_head_dims):
|
||
|
|
raise ValueError(
|
||
|
|
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||
|
|
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||
|
|
)
|
||
|
|
|
||
|
|
for i in range(len(slice_size)):
|
||
|
|
size = slice_size[i]
|
||
|
|
dim = sliceable_head_dims[i]
|
||
|
|
if size is not None and size > dim:
|
||
|
|
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||
|
|
|
||
|
|
# Recursively walk through all the children.
|
||
|
|
# Any children which exposes the set_attention_slice method
|
||
|
|
# gets the message
|
||
|
|
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||
|
|
if hasattr(module, "set_attention_slice"):
|
||
|
|
module.set_attention_slice(slice_size.pop())
|
||
|
|
|
||
|
|
for child in module.children():
|
||
|
|
fn_recursive_set_attention_slice(child, slice_size)
|
||
|
|
|
||
|
|
reversed_slice_size = list(reversed(slice_size))
|
||
|
|
for module in self.children():
|
||
|
|
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
sample: torch.Tensor,
|
||
|
|
timestep: Union[torch.Tensor, float, int],
|
||
|
|
encoder_hidden_states: torch.Tensor,
|
||
|
|
controlnet_cond: torch.Tensor,
|
||
|
|
conditioning_scale: float = 1.0,
|
||
|
|
timestep_cond: Optional[torch.Tensor] = None,
|
||
|
|
attention_mask: Optional[torch.Tensor] = None,
|
||
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||
|
|
conditioning_mask: Optional[torch.Tensor] = None,
|
||
|
|
guess_mode: bool = False,
|
||
|
|
return_dict: bool = True,
|
||
|
|
) -> Union[SparseControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
|
||
|
|
"""
|
||
|
|
The [`SparseControlNetModel`] forward method.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
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 [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||
|
|
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.
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||
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|
"""
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||
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|
sample_batch_size, sample_channels, sample_num_frames, sample_height, sample_width = sample.shape
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|
sample = torch.zeros_like(sample)
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|
|
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||
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|
# check channel order
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|
channel_order = self.config.controlnet_conditioning_channel_order
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|
|
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||
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|
if channel_order == "rgb":
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|
# in rgb order by default
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||
|
|
...
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||
|
|
elif channel_order == "bgr":
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|
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
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||
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|
else:
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|
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
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||
|
|
|
||
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|
# prepare attention_mask
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||
|
|
if attention_mask is not None:
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|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
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|
attention_mask = attention_mask.unsqueeze(1)
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||
|
|
|
||
|
|
# 1. time
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||
|
|
timesteps = timestep
|
||
|
|
if not torch.is_tensor(timesteps):
|
||
|
|
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||
|
|
# This would be a good case for the `match` statement (Python 3.10+)
|
||
|
|
is_mps = sample.device.type == "mps"
|
||
|
|
is_npu = sample.device.type == "npu"
|
||
|
|
if isinstance(timestep, float):
|
||
|
|
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
|
||
|
|
else:
|
||
|
|
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
|
||
|
|
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||
|
|
elif len(timesteps.shape) == 0:
|
||
|
|
timesteps = timesteps[None].to(sample.device)
|
||
|
|
|
||
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||
|
|
timesteps = timesteps.expand(sample.shape[0])
|
||
|
|
|
||
|
|
t_emb = self.time_proj(timesteps)
|
||
|
|
|
||
|
|
# timesteps does not contain any weights and will always return f32 tensors
|
||
|
|
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||
|
|
# there might be better ways to encapsulate this.
|
||
|
|
t_emb = t_emb.to(dtype=sample.dtype)
|
||
|
|
|
||
|
|
emb = self.time_embedding(t_emb, timestep_cond)
|
||
|
|
emb = emb.repeat_interleave(sample_num_frames, dim=0, output_size=emb.shape[0] * sample_num_frames)
|
||
|
|
|
||
|
|
# 2. pre-process
|
||
|
|
batch_size, channels, num_frames, height, width = sample.shape
|
||
|
|
|
||
|
|
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
||
|
|
sample = self.conv_in(sample)
|
||
|
|
|
||
|
|
batch_frames, channels, height, width = sample.shape
|
||
|
|
sample = sample[:, None].reshape(sample_batch_size, sample_num_frames, channels, height, width)
|
||
|
|
|
||
|
|
if self.concat_conditioning_mask:
|
||
|
|
controlnet_cond = torch.cat([controlnet_cond, conditioning_mask], dim=1)
|
||
|
|
|
||
|
|
batch_size, channels, num_frames, height, width = controlnet_cond.shape
|
||
|
|
controlnet_cond = controlnet_cond.permute(0, 2, 1, 3, 4).reshape(
|
||
|
|
batch_size * num_frames, channels, height, width
|
||
|
|
)
|
||
|
|
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||
|
|
batch_frames, channels, height, width = controlnet_cond.shape
|
||
|
|
controlnet_cond = controlnet_cond[:, None].reshape(batch_size, num_frames, channels, height, width)
|
||
|
|
|
||
|
|
sample = sample + controlnet_cond
|
||
|
|
|
||
|
|
batch_size, num_frames, channels, height, width = sample.shape
|
||
|
|
sample = sample.reshape(sample_batch_size * sample_num_frames, channels, height, width)
|
||
|
|
|
||
|
|
# 3. down
|
||
|
|
down_block_res_samples = (sample,)
|
||
|
|
for downsample_block in self.down_blocks:
|
||
|
|
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||
|
|
sample, res_samples = downsample_block(
|
||
|
|
hidden_states=sample,
|
||
|
|
temb=emb,
|
||
|
|
encoder_hidden_states=encoder_hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
num_frames=num_frames,
|
||
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
|
||
|
|
|
||
|
|
down_block_res_samples += res_samples
|
||
|
|
|
||
|
|
# 4. mid
|
||
|
|
if self.mid_block is not None:
|
||
|
|
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||
|
|
sample = self.mid_block(
|
||
|
|
sample,
|
||
|
|
emb,
|
||
|
|
encoder_hidden_states=encoder_hidden_states,
|
||
|
|
attention_mask=attention_mask,
|
||
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
sample = self.mid_block(sample, emb)
|
||
|
|
|
||
|
|
# 5. Control net blocks
|
||
|
|
controlnet_down_block_res_samples = ()
|
||
|
|
|
||
|
|
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
||
|
|
down_block_res_sample = controlnet_block(down_block_res_sample)
|
||
|
|
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
|
||
|
|
|
||
|
|
down_block_res_samples = controlnet_down_block_res_samples
|
||
|
|
mid_block_res_sample = self.controlnet_mid_block(sample)
|
||
|
|
|
||
|
|
# 6. scaling
|
||
|
|
if guess_mode and not self.config.global_pool_conditions:
|
||
|
|
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||
|
|
scales = scales * conditioning_scale
|
||
|
|
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||
|
|
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||
|
|
else:
|
||
|
|
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||
|
|
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||
|
|
|
||
|
|
if self.config.global_pool_conditions:
|
||
|
|
down_block_res_samples = [
|
||
|
|
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||
|
|
]
|
||
|
|
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (down_block_res_samples, mid_block_res_sample)
|
||
|
|
|
||
|
|
return SparseControlNetOutput(
|
||
|
|
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from diffusers.models.controlnets.controlnet.zero_module
|
||
|
|
def zero_module(module: nn.Module) -> nn.Module:
|
||
|
|
for p in module.parameters():
|
||
|
|
nn.init.zeros_(p)
|
||
|
|
return module
|