1012 lines
45 KiB
Python
1012 lines
45 KiB
Python
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 diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.loaders import FromOriginalControlNetMixin
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from diffusers.utils import BaseOutput, logging
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from diffusers.models.embeddings import (
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TextImageProjection,
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TextImageTimeEmbedding,
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TextTimeEmbedding,
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TimestepEmbedding,
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Timesteps,
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)
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from diffusers.models.modeling_utils import ModelMixin
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from diffusers.models.unet_2d_blocks import (
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CrossAttnDownBlock2D,
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DownBlock2D,
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UNetMidBlock2DCrossAttn,
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get_down_block,
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)
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from diffusers.models.unet_2d_condition import UNet2DConditionModel
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from diffusers.models.controlnet import (
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ControlNetConditioningEmbedding,
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ControlNetOutput,
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ControlNetModel,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class UNetDec_ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
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"""
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A ControlNet model.
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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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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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encoder_hid_dim (`int`, *optional*, defaults to None):
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If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
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dimension to `cross_attention_dim`.
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encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
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If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
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embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
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attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
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The dimension of the attention heads.
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use_linear_projection (`bool`, defaults to `False`):
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class_embed_type (`str`, *optional*, defaults to `None`):
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The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
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`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
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addition_embed_type (`str`, *optional*, defaults to `None`):
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Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
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"text". "text" will use the `TextTimeEmbedding` layer.
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num_class_embeds (`int`, *optional*, defaults to 0):
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Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
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class conditioning with `class_embed_type` equal to `None`.
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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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projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
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The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
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`class_embed_type="projection"`.
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controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
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The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
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conditioning_embedding_out_channels (`tuple[int]`, *optional*, 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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"""
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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 = 3,
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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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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D",
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),
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up_block_types: Tuple[str] = (
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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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 = 1280,
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transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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encoder_hid_dim: Optional[int] = None,
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encoder_hid_dim_type: Optional[str] = None,
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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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class_embed_type: Optional[str] = None,
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addition_embed_type: Optional[str] = None,
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addition_time_embed_dim: Optional[int] = None,
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num_class_embeds: Optional[int] = None,
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upcast_attention: bool = False,
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resnet_time_scale_shift: str = "default",
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projection_class_embeddings_input_dim: Optional[int] = None,
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controlnet_conditioning_channel_order: str = "rgb",
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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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addition_embed_type_num_heads=64,
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):
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super().__init__()
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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(
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only_cross_attention
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) != 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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# # 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,
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# block_out_channels[0],
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# kernel_size=conv_in_kernel,
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# padding=conv_in_padding,
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# )
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self.conv_in = None
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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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if encoder_hid_dim_type is None and encoder_hid_dim is not None:
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encoder_hid_dim_type = "text_proj"
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self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
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logger.info(
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"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined."
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)
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if encoder_hid_dim is None and encoder_hid_dim_type is not None:
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raise ValueError(
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f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
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)
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if encoder_hid_dim_type == "text_proj":
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self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
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elif encoder_hid_dim_type == "text_image_proj":
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# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
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# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
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# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
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self.encoder_hid_proj = TextImageProjection(
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text_embed_dim=encoder_hid_dim,
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image_embed_dim=cross_attention_dim,
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cross_attention_dim=cross_attention_dim,
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)
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elif encoder_hid_dim_type is not None:
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raise ValueError(
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f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
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)
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else:
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self.encoder_hid_proj = None
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# class embedding
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if class_embed_type is None and num_class_embeds is not None:
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self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
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elif class_embed_type == "timestep":
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self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
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elif class_embed_type == "identity":
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self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
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elif class_embed_type == "projection":
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if projection_class_embeddings_input_dim is None:
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raise ValueError(
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"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
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)
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# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
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# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
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# 2. it projects from an arbitrary input dimension.
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#
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# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
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# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
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# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
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self.class_embedding = TimestepEmbedding(
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projection_class_embeddings_input_dim, time_embed_dim
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)
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else:
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self.class_embedding = None
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if addition_embed_type == "text":
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if encoder_hid_dim is not None:
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text_time_embedding_from_dim = encoder_hid_dim
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else:
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text_time_embedding_from_dim = cross_attention_dim
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self.add_embedding = TextTimeEmbedding(
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text_time_embedding_from_dim,
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time_embed_dim,
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num_heads=addition_embed_type_num_heads,
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)
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elif addition_embed_type == "text_image":
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# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
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# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
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# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
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self.add_embedding = TextImageTimeEmbedding(
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text_embed_dim=cross_attention_dim,
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image_embed_dim=cross_attention_dim,
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time_embed_dim=time_embed_dim,
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)
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elif addition_embed_type == "text_time":
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self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
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self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
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elif addition_embed_type is not None:
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raise ValueError(
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f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'."
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)
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# control net conditioning embedding
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self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
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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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self.down_blocks = nn.ModuleList([])
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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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# # 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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# for _ in range(layers_per_block):
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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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# if not is_final_block:
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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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# mid
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mid_block_channel = block_out_channels[-1]
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controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
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controlnet_block = zero_module(controlnet_block)
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self.controlnet_mid_block = controlnet_block
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self.mid_block = UNetMidBlock2DCrossAttn(
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transformer_layers_per_block=transformer_layers_per_block[-1],
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in_channels=mid_block_channel,
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temb_channels=time_embed_dim,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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output_scale_factor=mid_block_scale_factor,
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resnet_time_scale_shift=resnet_time_scale_shift,
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cross_attention_dim=cross_attention_dim,
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num_attention_heads=num_attention_heads[-1],
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resnet_groups=norm_num_groups,
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use_linear_projection=use_linear_projection,
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upcast_attention=upcast_attention,
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)
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# up
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self.controlnet_up_blocks = nn.ModuleList([])
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self.num_upsamplers = 0
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self.up_blocks = nn.ModuleList([])
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reversed_block_out_channels = list(reversed(block_out_channels))
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reversed_num_attention_heads = list(reversed(num_attention_heads))
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reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
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only_cross_attention = list(reversed(only_cross_attention))
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output_channel = reversed_block_out_channels[0]
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for i, up_block_type in enumerate(up_block_types):
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is_final_block = i == len(block_out_channels) - 1
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
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# add upsample block for all BUT final layer
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if not is_final_block:
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add_upsample = True
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self.num_upsamplers += 1
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else:
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add_upsample = False
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up_block = get_up_block(
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up_block_type,
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num_layers=layers_per_block + 1,
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transformer_layers_per_block=reversed_transformer_layers_per_block[i],
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in_channels=input_channel,
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out_channels=output_channel,
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prev_output_channel=prev_output_channel,
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temb_channels=time_embed_dim,
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add_upsample=add_upsample,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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cross_attention_dim=cross_attention_dim,
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num_attention_heads=reversed_num_attention_heads[i],
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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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resnet_time_scale_shift=resnet_time_scale_shift,
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attention_head_dim=(
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attention_head_dim[i]
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if attention_head_dim[i] is not None
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else output_channel
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),
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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# if i>0: # 因为我们只输出transformer相关的，而第一级的upblock是纯conv
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for _ in range(layers_per_block + 1):
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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_up_blocks.append(controlnet_block)
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@classmethod
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def from_unet(
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cls,
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unet: UNet2DConditionModel,
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controlnet_conditioning_channel_order: str = "rgb",
|
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conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
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load_weights_from_unet: bool = True,
|
|
):
|
|
r"""
|
|
Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
|
|
|
|
Parameters:
|
|
unet (`UNet2DConditionModel`):
|
|
The UNet model weights to copy to the [`ControlNetModel`]. 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
|
|
)
|
|
encoder_hid_dim = (
|
|
unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
|
|
)
|
|
encoder_hid_dim_type = (
|
|
unet.config.encoder_hid_dim_type
|
|
if "encoder_hid_dim_type" in unet.config
|
|
else None
|
|
)
|
|
addition_embed_type = (
|
|
unet.config.addition_embed_type
|
|
if "addition_embed_type" in unet.config
|
|
else None
|
|
)
|
|
addition_time_embed_dim = (
|
|
unet.config.addition_time_embed_dim
|
|
if "addition_time_embed_dim" in unet.config
|
|
else None
|
|
)
|
|
|
|
controlnet = cls(
|
|
encoder_hid_dim=encoder_hid_dim,
|
|
encoder_hid_dim_type=encoder_hid_dim_type,
|
|
addition_embed_type=addition_embed_type,
|
|
addition_time_embed_dim=addition_time_embed_dim,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
in_channels=unet.config.in_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,
|
|
attention_head_dim=unet.config.attention_head_dim,
|
|
num_attention_heads=unet.config.num_attention_heads,
|
|
use_linear_projection=unet.config.use_linear_projection,
|
|
class_embed_type=unet.config.class_embed_type,
|
|
num_class_embeds=unet.config.num_class_embeds,
|
|
upcast_attention=unet.config.upcast_attention,
|
|
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
|
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
|
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
|
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
|
).requires_grad_(False)
|
|
|
|
if load_weights_from_unet:
|
|
# controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
|
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
|
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
|
|
|
if controlnet.class_embedding:
|
|
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
|
|
|
# controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
|
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
|
# controlnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False) # TODO:将各个upblock第一层融合层使用weight进行部分初始化
|
|
|
|
src_tensors = dict(named_params_and_buffers(unet.up_blocks))
|
|
for name, tensor in named_params_and_buffers(controlnet.up_blocks):
|
|
assert (name in src_tensors), name
|
|
try:
|
|
# print('Successfully initializing ControlNet!', name, tensor.shape, src_tensors[name].shape)
|
|
tensor.copy_(src_tensors[name].detach())
|
|
except:
|
|
# print('Mismatch occured in initializing ControlNet!', name, tensor.shape, src_tensors[name].shape)
|
|
# TODO: 确保所有upblock参数有初始化
|
|
if tensor.dim() == 1:
|
|
tensor.copy_(src_tensors[name].detach()[:tensor.shape[0]])
|
|
else:
|
|
tensor.copy_(src_tensors[name].detach()[:, :tensor.shape[1]])
|
|
|
|
return controlnet
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if hasattr(module, "gradient_checkpointing"):
|
|
module.gradient_checkpointing = value
|
|
|
|
def forward(
|
|
self,
|
|
sample: torch.FloatTensor,
|
|
timestep: Union[torch.Tensor, float, int],
|
|
encoder_hidden_states: torch.Tensor,
|
|
# controlnet_cond: torch.FloatTensor,
|
|
conditioning_scale: float = 1.0,
|
|
class_labels: Optional[torch.Tensor] = None,
|
|
timestep_cond: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
guess_mode: bool = False,
|
|
return_dict: bool = True,
|
|
only_return_transformer_layers_out: bool = False,
|
|
) -> Union[ControlNetOutput, Tuple]:
|
|
"""
|
|
The [`ControlNetModel`] forward method.
|
|
|
|
Args:
|
|
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.
|
|
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.
|
|
"""
|
|
|
|
# prepare attention_mask
|
|
if attention_mask is not None:
|
|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
# 1. time
|
|
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"
|
|
if isinstance(timestep, float):
|
|
dtype = torch.float32 if is_mps else torch.float64
|
|
else:
|
|
dtype = torch.int32 if is_mps 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)
|
|
aug_emb = None
|
|
|
|
if self.class_embedding is not None:
|
|
if class_labels is None:
|
|
raise ValueError(
|
|
"class_labels should be provided when num_class_embeds > 0"
|
|
)
|
|
|
|
if self.config.class_embed_type == "timestep":
|
|
class_labels = self.time_proj(class_labels)
|
|
|
|
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
|
emb = emb + class_emb
|
|
|
|
if self.config.addition_embed_type is not None:
|
|
if self.config.addition_embed_type == "text":
|
|
aug_emb = self.add_embedding(encoder_hidden_states)
|
|
|
|
elif self.config.addition_embed_type == "text_time":
|
|
if "text_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
|
)
|
|
text_embeds = added_cond_kwargs.get("text_embeds")
|
|
if "time_ids" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
|
)
|
|
time_ids = added_cond_kwargs.get("time_ids")
|
|
time_embeds = self.add_time_proj(time_ids.flatten())
|
|
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
|
|
|
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
|
add_embeds = add_embeds.to(emb.dtype)
|
|
aug_emb = self.add_embedding(add_embeds)
|
|
|
|
emb = emb + aug_emb if aug_emb is not None else emb
|
|
|
|
if self.conv_in is not None:
|
|
sample = self.conv_in(sample)
|
|
|
|
# 4. mid
|
|
if self.mid_block is not None:
|
|
sample = self.mid_block(
|
|
sample,
|
|
emb,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=attention_mask,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
)
|
|
mid_block_res_sample = self.controlnet_mid_block(sample)
|
|
|
|
# 5. up
|
|
|
|
out_block_res_samples = []
|
|
for i, upsample_block in enumerate(self.up_blocks):
|
|
if (hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention):
|
|
sample, res_samples = upsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
# res_hidden_states_tuple=res_samples,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
)
|
|
else:
|
|
sample, res_samples = upsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
# res_hidden_states_tuple=res_samples,
|
|
)
|
|
# print('Out', i, [res_sample.shape for res_sample in res_samples])
|
|
# if i in range(1, len(self.up_blocks)-1):
|
|
# res_samples[-1] = res_samples[-1][:, :sample.shape[1]//2] # 为了适配controlNet输出的shape
|
|
|
|
out_block_res_samples += res_samples
|
|
|
|
# 5. Control net blocks
|
|
|
|
controlnet_up_block_res_samples = ()
|
|
assert len(out_block_res_samples) == len(self.controlnet_up_blocks), (len(out_block_res_samples), len(self.controlnet_up_blocks))
|
|
for i, controlnet_block in enumerate(self.controlnet_up_blocks): # zero proj out
|
|
# if only_return_transformer_layers_out and i < 3: # 第一个upblock是纯conv的
|
|
# continue
|
|
up_block_res_sample = controlnet_block(out_block_res_samples[i])
|
|
controlnet_up_block_res_samples = controlnet_up_block_res_samples + (up_block_res_sample,)
|
|
|
|
up_block_res_samples = controlnet_up_block_res_samples
|
|
|
|
# 6. scaling
|
|
if guess_mode and not self.config.global_pool_conditions:
|
|
raise NotImplementedError
|
|
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:
|
|
up_block_res_samples = [sample * conditioning_scale for sample in up_block_res_samples]
|
|
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
|
|
|
if self.config.global_pool_conditions:
|
|
up_block_res_samples = [torch.mean(sample, dim=(2, 3), keepdim=True) for sample in up_block_res_samples]
|
|
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
|
|
|
if only_return_transformer_layers_out:
|
|
down_block_res_samples = [sample for i, sample in enumerate(up_block_res_samples) if not i % 3== 2][::-1]
|
|
else:
|
|
down_block_res_samples = list(reversed(up_block_res_samples))
|
|
|
|
if not return_dict:
|
|
return (down_block_res_samples, mid_block_res_sample, up_block_res_samples)
|
|
|
|
return ControlNetOutput(
|
|
down_block_res_samples=down_block_res_samples,
|
|
mid_block_res_sample=mid_block_res_sample,
|
|
up_block_res_samples=up_block_res_samples,
|
|
)
|
|
|
|
|
|
def zero_module(module):
|
|
for p in module.parameters():
|
|
nn.init.zeros_(p)
|
|
return module
|
|
|
|
|
|
def named_params_and_buffers(module):
|
|
assert isinstance(module, torch.nn.Module)
|
|
return list(module.named_parameters()) + list(module.named_buffers())
|
|
|
|
|
|
from ComfyUI_AIIA.personalive.models.unet_2d_blocks import UpBlock2D, CrossAttnUpBlock2D
|
|
from diffusers.models.resnet import ResnetBlock2D
|
|
|
|
from diffusers.utils import is_torch_version
|
|
class UpBlock2D_woskip(UpBlock2D):
|
|
def __init__(
|
|
self,
|
|
prev_output_channel: int,
|
|
out_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor=1.0,
|
|
add_upsample=True,
|
|
):
|
|
super(UpBlock2D_woskip, self).__init__(
|
|
in_channels=0,
|
|
prev_output_channel=prev_output_channel,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
dropout=dropout,
|
|
num_layers=num_layers,
|
|
resnet_eps=resnet_eps,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
resnet_pre_norm=resnet_pre_norm,
|
|
output_scale_factor=output_scale_factor,
|
|
add_upsample=add_upsample,
|
|
)
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
def forward(self, hidden_states, temb=None, upsample_size=None, scale: float = 1.0):
|
|
output_states = []
|
|
for resnet in self.resnets:
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
if is_torch_version(">=", "1.11.0"):
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
|
|
)
|
|
else:
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet), hidden_states, temb
|
|
)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=scale)
|
|
output_states = output_states + [hidden_states,]
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size, scale=scale)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class CrossAttnUpBlock2D_woskip(CrossAttnUpBlock2D):
|
|
def __init__(
|
|
self,
|
|
out_channels: int,
|
|
prev_output_channel: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads=1,
|
|
cross_attention_dim=1280,
|
|
output_scale_factor=1.0,
|
|
add_upsample=True,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
attention_type="default",
|
|
):
|
|
super(CrossAttnUpBlock2D_woskip, self).__init__(
|
|
in_channels=0,
|
|
out_channels=out_channels,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=temb_channels,
|
|
dropout=dropout,
|
|
num_layers=num_layers,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
resnet_eps=resnet_eps,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
resnet_pre_norm=resnet_pre_norm,
|
|
num_attention_heads=num_attention_heads,
|
|
cross_attention_dim=cross_attention_dim,
|
|
output_scale_factor=output_scale_factor,
|
|
add_upsample=add_upsample,
|
|
dual_cross_attention=dual_cross_attention,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
attention_type=attention_type,
|
|
)
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
upsample_size: Optional[int] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
):
|
|
lora_scale = (
|
|
cross_attention_kwargs.get("scale", 1.0)
|
|
if cross_attention_kwargs is not None
|
|
else 1.0
|
|
)
|
|
output_states = []
|
|
for resnet, attn in zip(self.resnets, self.attentions):
|
|
|
|
if self.training and self.gradient_checkpointing:
|
|
|
|
def create_custom_forward(module, return_dict=None):
|
|
def custom_forward(*inputs):
|
|
if return_dict is not None:
|
|
return module(*inputs, return_dict=return_dict)
|
|
else:
|
|
return module(*inputs)
|
|
|
|
return custom_forward
|
|
|
|
ckpt_kwargs: Dict[str, Any] = (
|
|
{"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
|
|
)
|
|
hidden_states = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(resnet),
|
|
hidden_states,
|
|
temb,
|
|
**ckpt_kwargs,
|
|
)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb, scale=lora_scale)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
output_states = output_states + [hidden_states,]
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
def get_up_block(
|
|
up_block_type,
|
|
num_layers,
|
|
in_channels,
|
|
out_channels,
|
|
prev_output_channel,
|
|
temb_channels,
|
|
add_upsample,
|
|
resnet_eps,
|
|
resnet_act_fn,
|
|
transformer_layers_per_block=1,
|
|
num_attention_heads=None,
|
|
resnet_groups=None,
|
|
cross_attention_dim=None,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
resnet_time_scale_shift="default",
|
|
attention_type="default",
|
|
resnet_skip_time_act=False,
|
|
resnet_out_scale_factor=1.0,
|
|
cross_attention_norm=None,
|
|
attention_head_dim=None,
|
|
upsample_type=None,
|
|
dropout=0.0,
|
|
):
|
|
# If attn head dim is not defined, we default it to the number of heads
|
|
if attention_head_dim is None:
|
|
logger.warn(
|
|
f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
|
|
)
|
|
attention_head_dim = num_attention_heads
|
|
|
|
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
|
|
|
if up_block_type == "UpBlock2D":
|
|
return UpBlock2D_woskip(
|
|
num_layers=num_layers,
|
|
# in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=temb_channels,
|
|
dropout=dropout,
|
|
add_upsample=add_upsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
)
|
|
elif up_block_type == "CrossAttnUpBlock2D":
|
|
if cross_attention_dim is None:
|
|
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
|
|
return CrossAttnUpBlock2D_woskip(
|
|
num_layers=num_layers,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
# in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=temb_channels,
|
|
dropout=dropout,
|
|
add_upsample=add_upsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
dual_cross_attention=dual_cross_attention,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
attention_type=attention_type,
|
|
)
|
|
|
|
raise ValueError(f"{up_block_type} does not exist.")
|