Hawk Lee
e21a5c9eba
The eff537d commit changed all files from 755 to 644.
Restores original executable permissions.
1012 lines
45 KiB
Python
Executable File
1012 lines
45 KiB
Python
Executable File
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",
|
|
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
|
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.")
|