1933 lines
86 KiB
Python
1933 lines
86 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...models import ModelMixin
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from ...models.activations import get_activation
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from ...models.attention import Attention
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from ...models.attention_processor import (
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AttentionProcessor,
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AttnAddedKVProcessor,
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AttnAddedKVProcessor2_0,
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AttnProcessor,
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)
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from ...models.dual_transformer_2d import DualTransformer2DModel
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from ...models.embeddings import (
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GaussianFourierProjection,
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ImageHintTimeEmbedding,
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ImageProjection,
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ImageTimeEmbedding,
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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 ...models.transformer_2d import Transformer2DModel
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from ...models.unet_2d_condition import UNet2DConditionOutput
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from ...utils import is_torch_version, logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def get_down_block(
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down_block_type,
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num_layers,
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in_channels,
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out_channels,
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temb_channels,
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add_downsample,
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resnet_eps,
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resnet_act_fn,
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num_attention_heads,
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resnet_groups=None,
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cross_attention_dim=None,
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downsample_padding=None,
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dual_cross_attention=False,
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use_linear_projection=False,
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only_cross_attention=False,
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upcast_attention=False,
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resnet_time_scale_shift="default",
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resnet_skip_time_act=False,
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resnet_out_scale_factor=1.0,
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cross_attention_norm=None,
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):
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down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
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if down_block_type == "DownBlockFlat":
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return DownBlockFlat(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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add_downsample=add_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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elif down_block_type == "CrossAttnDownBlockFlat":
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if cross_attention_dim is None:
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raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat")
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return CrossAttnDownBlockFlat(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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add_downsample=add_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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cross_attention_dim=cross_attention_dim,
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num_attention_heads=num_attention_heads,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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raise ValueError(f"{down_block_type} is not supported.")
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def get_up_block(
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up_block_type,
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num_layers,
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in_channels,
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out_channels,
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prev_output_channel,
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temb_channels,
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add_upsample,
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resnet_eps,
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resnet_act_fn,
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num_attention_heads,
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resnet_groups=None,
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cross_attention_dim=None,
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dual_cross_attention=False,
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use_linear_projection=False,
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only_cross_attention=False,
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upcast_attention=False,
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resnet_time_scale_shift="default",
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resnet_skip_time_act=False,
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resnet_out_scale_factor=1.0,
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cross_attention_norm=None,
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):
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up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
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if up_block_type == "UpBlockFlat":
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return UpBlockFlat(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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prev_output_channel=prev_output_channel,
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temb_channels=temb_channels,
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add_upsample=add_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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elif up_block_type == "CrossAttnUpBlockFlat":
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if cross_attention_dim is None:
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raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat")
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return CrossAttnUpBlockFlat(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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prev_output_channel=prev_output_channel,
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temb_channels=temb_channels,
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add_upsample=add_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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cross_attention_dim=cross_attention_dim,
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num_attention_heads=num_attention_heads,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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raise ValueError(f"{up_block_type} is not supported.")
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# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat
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class UNetFlatConditionModel(ModelMixin, ConfigMixin):
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r"""
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A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
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shaped output.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
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for all models (such as downloading or saving).
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Parameters:
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sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
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Height and width of input/output sample.
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in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
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out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
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center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
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flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
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down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`):
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The tuple of downsample blocks to use.
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mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`):
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Block type for middle of UNet, it can be either `UNetMidBlockFlatCrossAttn` or
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`UNetMidBlockFlatSimpleCrossAttn`. If `None`, the mid block layer is skipped.
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up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat")`):
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The tuple of upsample blocks to use.
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only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
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Whether to include self-attention in the basic transformer blocks, see
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[`~models.attention.BasicTransformerBlock`].
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block_out_channels (`Tuple[int]`, *optional*, 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`, *optional*, defaults to 2): The number of layers per block.
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downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
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If `None`, normalization and activation layers is skipped in post-processing.
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norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
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cross_attention_dim (`int` or `Tuple[int]`, *optional*, 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.CrossAttnDownBlockFlat`], [`~models.unet_2d_blocks.CrossAttnUpBlockFlat`],
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[`~models.unet_2d_blocks.UNetMidBlockFlatCrossAttn`].
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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 (`int`, *optional*, defaults to 8): The dimension of the attention heads.
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num_attention_heads (`int`, *optional*):
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The number of attention heads. If not defined, defaults to `attention_head_dim`
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resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
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for ResNet blocks (see [`~models.resnet.ResnetBlockFlat`]). Choose from `default` or `scale_shift`.
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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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addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
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Dimension for the timestep embeddings.
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num_class_embeds (`int`, *optional*, defaults to `None`):
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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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time_embedding_type (`str`, *optional*, defaults to `positional`):
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The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
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time_embedding_dim (`int`, *optional*, defaults to `None`):
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An optional override for the dimension of the projected time embedding.
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time_embedding_act_fn (`str`, *optional*, defaults to `None`):
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Optional activation function to use only once on the time embeddings before they are passed to the rest of
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the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
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timestep_post_act (`str`, *optional*, defaults to `None`):
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The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
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time_cond_proj_dim (`int`, *optional*, defaults to `None`):
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The dimension of `cond_proj` layer in the timestep embedding.
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conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
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conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
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projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
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`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
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class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
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embeddings with the class embeddings.
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mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
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Whether to use cross attention with the mid block when using the `UNetMidBlockFlatSimpleCrossAttn`. If
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`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
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`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
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otherwise.
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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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sample_size: Optional[int] = None,
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in_channels: int = 4,
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out_channels: int = 4,
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center_input_sample: bool = False,
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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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"CrossAttnDownBlockFlat",
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"CrossAttnDownBlockFlat",
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"CrossAttnDownBlockFlat",
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"DownBlockFlat",
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),
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mid_block_type: Optional[str] = "UNetMidBlockFlatCrossAttn",
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up_block_types: Tuple[str] = (
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"UpBlockFlat",
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"CrossAttnUpBlockFlat",
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"CrossAttnUpBlockFlat",
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"CrossAttnUpBlockFlat",
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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: Union[int, Tuple[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: Union[int, Tuple[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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dual_cross_attention: bool = False,
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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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resnet_skip_time_act: bool = False,
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resnet_out_scale_factor: int = 1.0,
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time_embedding_type: str = "positional",
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time_embedding_dim: Optional[int] = None,
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time_embedding_act_fn: Optional[str] = None,
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timestep_post_act: Optional[str] = None,
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time_cond_proj_dim: Optional[int] = None,
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conv_in_kernel: int = 3,
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conv_out_kernel: int = 3,
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projection_class_embeddings_input_dim: Optional[int] = None,
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class_embeddings_concat: bool = False,
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mid_block_only_cross_attention: Optional[bool] = None,
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cross_attention_norm: Optional[str] = None,
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addition_embed_type_num_heads=64,
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):
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super().__init__()
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self.sample_size = sample_size
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if num_attention_heads is not None:
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raise ValueError(
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"At the moment it is not possible to define the number of attention heads via `num_attention_heads`"
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" because of a naming issue as described in"
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" https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing"
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" `num_attention_heads` will only be supported in diffusers v0.19."
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)
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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(down_block_types) != len(up_block_types):
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raise ValueError(
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"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`:"
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f" {down_block_types}. `up_block_types`: {up_block_types}."
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)
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if len(block_out_channels) != len(down_block_types):
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raise ValueError(
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"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:"
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f" {block_out_channels}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
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raise ValueError(
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"Must provide the same number of `only_cross_attention` as `down_block_types`."
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f" `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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"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`:"
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f" {num_attention_heads}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
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raise ValueError(
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"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`:"
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f" {attention_head_dim}. `down_block_types`: {down_block_types}."
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)
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if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
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raise ValueError(
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"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`:"
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f" {cross_attention_dim}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
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raise ValueError(
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"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`:"
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f" {layers_per_block}. `down_block_types`: {down_block_types}."
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)
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# input
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conv_in_padding = (conv_in_kernel - 1) // 2
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self.conv_in = LinearMultiDim(
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in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
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)
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# time
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if time_embedding_type == "fourier":
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time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
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if time_embed_dim % 2 != 0:
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raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
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self.time_proj = GaussianFourierProjection(
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time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
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)
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timestep_input_dim = time_embed_dim
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elif time_embedding_type == "positional":
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time_embed_dim = time_embedding_dim or 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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else:
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raise ValueError(
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f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
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)
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self.time_embedding = TimestepEmbedding(
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timestep_input_dim,
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time_embed_dim,
|
|
act_fn=act_fn,
|
|
post_act_fn=timestep_post_act,
|
|
cond_proj_dim=time_cond_proj_dim,
|
|
)
|
|
|
|
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
|
encoder_hid_dim_type = "text_proj"
|
|
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
|
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
|
|
|
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
|
raise ValueError(
|
|
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
|
)
|
|
|
|
if encoder_hid_dim_type == "text_proj":
|
|
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
|
elif encoder_hid_dim_type == "text_image_proj":
|
|
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
|
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
|
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
|
|
self.encoder_hid_proj = TextImageProjection(
|
|
text_embed_dim=encoder_hid_dim,
|
|
image_embed_dim=cross_attention_dim,
|
|
cross_attention_dim=cross_attention_dim,
|
|
)
|
|
elif encoder_hid_dim_type == "image_proj":
|
|
# Kandinsky 2.2
|
|
self.encoder_hid_proj = ImageProjection(
|
|
image_embed_dim=encoder_hid_dim,
|
|
cross_attention_dim=cross_attention_dim,
|
|
)
|
|
elif encoder_hid_dim_type is not None:
|
|
raise ValueError(
|
|
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
|
)
|
|
else:
|
|
self.encoder_hid_proj = None
|
|
|
|
# class embedding
|
|
if class_embed_type is None and num_class_embeds is not None:
|
|
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
|
elif class_embed_type == "timestep":
|
|
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
|
|
elif class_embed_type == "identity":
|
|
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
|
elif class_embed_type == "projection":
|
|
if projection_class_embeddings_input_dim is None:
|
|
raise ValueError(
|
|
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
|
)
|
|
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
|
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
|
# 2. it projects from an arbitrary input dimension.
|
|
#
|
|
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
|
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
|
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
|
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
|
elif class_embed_type == "simple_projection":
|
|
if projection_class_embeddings_input_dim is None:
|
|
raise ValueError(
|
|
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
|
|
)
|
|
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
|
|
else:
|
|
self.class_embedding = None
|
|
|
|
if addition_embed_type == "text":
|
|
if encoder_hid_dim is not None:
|
|
text_time_embedding_from_dim = encoder_hid_dim
|
|
else:
|
|
text_time_embedding_from_dim = cross_attention_dim
|
|
|
|
self.add_embedding = TextTimeEmbedding(
|
|
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
|
)
|
|
elif addition_embed_type == "text_image":
|
|
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
|
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
|
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
|
|
self.add_embedding = TextImageTimeEmbedding(
|
|
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
|
)
|
|
elif addition_embed_type == "text_time":
|
|
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
|
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
|
elif addition_embed_type == "image":
|
|
# Kandinsky 2.2
|
|
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
|
elif addition_embed_type == "image_hint":
|
|
# Kandinsky 2.2 ControlNet
|
|
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
|
|
elif addition_embed_type is not None:
|
|
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
|
|
|
if time_embedding_act_fn is None:
|
|
self.time_embed_act = None
|
|
else:
|
|
self.time_embed_act = get_activation(time_embedding_act_fn)
|
|
|
|
self.down_blocks = nn.ModuleList([])
|
|
self.up_blocks = nn.ModuleList([])
|
|
|
|
if isinstance(only_cross_attention, bool):
|
|
if mid_block_only_cross_attention is None:
|
|
mid_block_only_cross_attention = only_cross_attention
|
|
|
|
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
|
|
|
if mid_block_only_cross_attention is None:
|
|
mid_block_only_cross_attention = False
|
|
|
|
if isinstance(num_attention_heads, int):
|
|
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
|
|
|
if isinstance(attention_head_dim, int):
|
|
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
|
|
|
if isinstance(layers_per_block, int):
|
|
layers_per_block = [layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
|
|
|
if class_embeddings_concat:
|
|
# The time embeddings are concatenated with the class embeddings. The dimension of the
|
|
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
|
|
# regular time embeddings
|
|
blocks_time_embed_dim = time_embed_dim * 2
|
|
else:
|
|
blocks_time_embed_dim = time_embed_dim
|
|
|
|
# down
|
|
output_channel = block_out_channels[0]
|
|
for i, down_block_type in enumerate(down_block_types):
|
|
input_channel = output_channel
|
|
output_channel = block_out_channels[i]
|
|
is_final_block = i == len(block_out_channels) - 1
|
|
|
|
down_block = get_down_block(
|
|
down_block_type,
|
|
num_layers=layers_per_block[i],
|
|
transformer_layers_per_block=transformer_layers_per_block[i],
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
temb_channels=blocks_time_embed_dim,
|
|
add_downsample=not is_final_block,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
cross_attention_dim=cross_attention_dim[i],
|
|
num_attention_heads=num_attention_heads[i],
|
|
downsample_padding=downsample_padding,
|
|
dual_cross_attention=dual_cross_attention,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention[i],
|
|
upcast_attention=upcast_attention,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
resnet_skip_time_act=resnet_skip_time_act,
|
|
resnet_out_scale_factor=resnet_out_scale_factor,
|
|
cross_attention_norm=cross_attention_norm,
|
|
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
|
)
|
|
self.down_blocks.append(down_block)
|
|
|
|
# mid
|
|
if mid_block_type == "UNetMidBlockFlatCrossAttn":
|
|
self.mid_block = UNetMidBlockFlatCrossAttn(
|
|
transformer_layers_per_block=transformer_layers_per_block[-1],
|
|
in_channels=block_out_channels[-1],
|
|
temb_channels=blocks_time_embed_dim,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
output_scale_factor=mid_block_scale_factor,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
cross_attention_dim=cross_attention_dim[-1],
|
|
num_attention_heads=num_attention_heads[-1],
|
|
resnet_groups=norm_num_groups,
|
|
dual_cross_attention=dual_cross_attention,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn":
|
|
self.mid_block = UNetMidBlockFlatSimpleCrossAttn(
|
|
in_channels=block_out_channels[-1],
|
|
temb_channels=blocks_time_embed_dim,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
output_scale_factor=mid_block_scale_factor,
|
|
cross_attention_dim=cross_attention_dim[-1],
|
|
attention_head_dim=attention_head_dim[-1],
|
|
resnet_groups=norm_num_groups,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
skip_time_act=resnet_skip_time_act,
|
|
only_cross_attention=mid_block_only_cross_attention,
|
|
cross_attention_norm=cross_attention_norm,
|
|
)
|
|
elif mid_block_type is None:
|
|
self.mid_block = None
|
|
else:
|
|
raise ValueError(f"unknown mid_block_type : {mid_block_type}")
|
|
|
|
# count how many layers upsample the images
|
|
self.num_upsamplers = 0
|
|
|
|
# up
|
|
reversed_block_out_channels = list(reversed(block_out_channels))
|
|
reversed_num_attention_heads = list(reversed(num_attention_heads))
|
|
reversed_layers_per_block = list(reversed(layers_per_block))
|
|
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
|
|
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
|
|
only_cross_attention = list(reversed(only_cross_attention))
|
|
|
|
output_channel = reversed_block_out_channels[0]
|
|
for i, up_block_type in enumerate(up_block_types):
|
|
is_final_block = i == len(block_out_channels) - 1
|
|
|
|
prev_output_channel = output_channel
|
|
output_channel = reversed_block_out_channels[i]
|
|
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
|
|
|
# add upsample block for all BUT final layer
|
|
if not is_final_block:
|
|
add_upsample = True
|
|
self.num_upsamplers += 1
|
|
else:
|
|
add_upsample = False
|
|
|
|
up_block = get_up_block(
|
|
up_block_type,
|
|
num_layers=reversed_layers_per_block[i] + 1,
|
|
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=blocks_time_embed_dim,
|
|
add_upsample=add_upsample,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
cross_attention_dim=reversed_cross_attention_dim[i],
|
|
num_attention_heads=reversed_num_attention_heads[i],
|
|
dual_cross_attention=dual_cross_attention,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention[i],
|
|
upcast_attention=upcast_attention,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
resnet_skip_time_act=resnet_skip_time_act,
|
|
resnet_out_scale_factor=resnet_out_scale_factor,
|
|
cross_attention_norm=cross_attention_norm,
|
|
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
|
)
|
|
self.up_blocks.append(up_block)
|
|
prev_output_channel = output_channel
|
|
|
|
# out
|
|
if norm_num_groups is not None:
|
|
self.conv_norm_out = nn.GroupNorm(
|
|
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
|
|
)
|
|
|
|
self.conv_act = get_activation(act_fn)
|
|
|
|
else:
|
|
self.conv_norm_out = None
|
|
self.conv_act = None
|
|
|
|
conv_out_padding = (conv_out_kernel - 1) // 2
|
|
self.conv_out = LinearMultiDim(
|
|
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
|
|
)
|
|
|
|
@property
|
|
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
|
r"""
|
|
Returns:
|
|
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
|
indexed by its weight name.
|
|
"""
|
|
# set recursively
|
|
processors = {}
|
|
|
|
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
|
if hasattr(module, "set_processor"):
|
|
processors[f"{name}.processor"] = module.processor
|
|
|
|
for sub_name, child in module.named_children():
|
|
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
|
|
|
return processors
|
|
|
|
for name, module in self.named_children():
|
|
fn_recursive_add_processors(name, module, processors)
|
|
|
|
return processors
|
|
|
|
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
|
r"""
|
|
Sets the attention processor to use to compute attention.
|
|
|
|
Parameters:
|
|
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
|
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
|
for **all** `Attention` layers.
|
|
|
|
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
|
processor. This is strongly recommended when setting trainable attention processors.
|
|
|
|
"""
|
|
count = len(self.attn_processors.keys())
|
|
|
|
if isinstance(processor, dict) and len(processor) != count:
|
|
raise ValueError(
|
|
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
|
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
|
)
|
|
|
|
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
|
if hasattr(module, "set_processor"):
|
|
if not isinstance(processor, dict):
|
|
module.set_processor(processor)
|
|
else:
|
|
module.set_processor(processor.pop(f"{name}.processor"))
|
|
|
|
for sub_name, child in module.named_children():
|
|
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
|
|
|
for name, module in self.named_children():
|
|
fn_recursive_attn_processor(name, module, processor)
|
|
|
|
def set_default_attn_processor(self):
|
|
"""
|
|
Disables custom attention processors and sets the default attention implementation.
|
|
"""
|
|
self.set_attn_processor(AttnProcessor())
|
|
|
|
def set_attention_slice(self, slice_size):
|
|
r"""
|
|
Enable sliced attention computation.
|
|
|
|
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
|
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
|
|
|
Args:
|
|
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
|
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
|
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
|
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
|
must be a multiple of `slice_size`.
|
|
"""
|
|
sliceable_head_dims = []
|
|
|
|
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
|
if hasattr(module, "set_attention_slice"):
|
|
sliceable_head_dims.append(module.sliceable_head_dim)
|
|
|
|
for child in module.children():
|
|
fn_recursive_retrieve_sliceable_dims(child)
|
|
|
|
# retrieve number of attention layers
|
|
for module in self.children():
|
|
fn_recursive_retrieve_sliceable_dims(module)
|
|
|
|
num_sliceable_layers = len(sliceable_head_dims)
|
|
|
|
if slice_size == "auto":
|
|
# half the attention head size is usually a good trade-off between
|
|
# speed and memory
|
|
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
|
elif slice_size == "max":
|
|
# make smallest slice possible
|
|
slice_size = num_sliceable_layers * [1]
|
|
|
|
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
|
|
|
if len(slice_size) != len(sliceable_head_dims):
|
|
raise ValueError(
|
|
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
|
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
|
)
|
|
|
|
for i in range(len(slice_size)):
|
|
size = slice_size[i]
|
|
dim = sliceable_head_dims[i]
|
|
if size is not None and size > dim:
|
|
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
|
|
|
# Recursively walk through all the children.
|
|
# Any children which exposes the set_attention_slice method
|
|
# gets the message
|
|
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
|
if hasattr(module, "set_attention_slice"):
|
|
module.set_attention_slice(slice_size.pop())
|
|
|
|
for child in module.children():
|
|
fn_recursive_set_attention_slice(child, slice_size)
|
|
|
|
reversed_slice_size = list(reversed(slice_size))
|
|
for module in self.children():
|
|
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)):
|
|
module.gradient_checkpointing = value
|
|
|
|
def forward(
|
|
self,
|
|
sample: torch.FloatTensor,
|
|
timestep: Union[torch.Tensor, float, int],
|
|
encoder_hidden_states: torch.Tensor,
|
|
class_labels: Optional[torch.Tensor] = None,
|
|
timestep_cond: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
|
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
|
|
mid_block_additional_residual: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
return_dict: bool = True,
|
|
) -> Union[UNet2DConditionOutput, Tuple]:
|
|
r"""
|
|
The [`UNetFlatConditionModel`] forward method.
|
|
|
|
Args:
|
|
sample (`torch.FloatTensor`):
|
|
The noisy input tensor with the following shape `(batch, channel, height, width)`.
|
|
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
|
|
encoder_hidden_states (`torch.FloatTensor`):
|
|
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
|
|
encoder_attention_mask (`torch.Tensor`):
|
|
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
|
|
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
|
|
which adds large negative values to the attention scores corresponding to "discard" tokens.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
|
|
tuple.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
|
|
added_cond_kwargs: (`dict`, *optional*):
|
|
A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
|
|
are passed along to the UNet blocks.
|
|
|
|
Returns:
|
|
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
|
If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
|
|
a `tuple` is returned where the first element is the sample tensor.
|
|
"""
|
|
# By default samples have to be AT least a multiple of the overall upsampling factor.
|
|
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
|
|
# However, the upsampling interpolation output size can be forced to fit any upsampling size
|
|
# on the fly if necessary.
|
|
default_overall_up_factor = 2**self.num_upsamplers
|
|
|
|
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
|
|
forward_upsample_size = False
|
|
upsample_size = None
|
|
|
|
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
|
logger.info("Forward upsample size to force interpolation output size.")
|
|
forward_upsample_size = True
|
|
|
|
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
|
|
# expects mask of shape:
|
|
# [batch, key_tokens]
|
|
# adds singleton query_tokens dimension:
|
|
# [batch, 1, key_tokens]
|
|
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
|
|
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
|
|
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
|
|
if attention_mask is not None:
|
|
# assume that mask is expressed as:
|
|
# (1 = keep, 0 = discard)
|
|
# convert mask into a bias that can be added to attention scores:
|
|
# (keep = +0, discard = -10000.0)
|
|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
|
if encoder_attention_mask is not None:
|
|
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
|
|
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
|
|
|
# 0. center input if necessary
|
|
if self.config.center_input_sample:
|
|
sample = 2 * sample - 1.0
|
|
|
|
# 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)
|
|
|
|
# `Timesteps` does not contain any weights and will always return f32 tensors
|
|
# there might be better ways to encapsulate this.
|
|
class_labels = class_labels.to(dtype=sample.dtype)
|
|
|
|
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
|
|
|
|
if self.config.class_embeddings_concat:
|
|
emb = torch.cat([emb, class_emb], dim=-1)
|
|
else:
|
|
emb = emb + class_emb
|
|
|
|
if self.config.addition_embed_type == "text":
|
|
aug_emb = self.add_embedding(encoder_hidden_states)
|
|
elif self.config.addition_embed_type == "text_image":
|
|
# Kandinsky 2.1 - style
|
|
if "image_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires"
|
|
" the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
|
)
|
|
|
|
image_embs = added_cond_kwargs.get("image_embeds")
|
|
text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
|
|
aug_emb = self.add_embedding(text_embs, image_embs)
|
|
elif self.config.addition_embed_type == "text_time":
|
|
# SDXL - style
|
|
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)
|
|
elif self.config.addition_embed_type == "image":
|
|
# Kandinsky 2.2 - style
|
|
if "image_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the"
|
|
" keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
|
|
)
|
|
image_embs = added_cond_kwargs.get("image_embeds")
|
|
aug_emb = self.add_embedding(image_embs)
|
|
elif self.config.addition_embed_type == "image_hint":
|
|
# Kandinsky 2.2 - style
|
|
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires"
|
|
" the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
|
|
)
|
|
image_embs = added_cond_kwargs.get("image_embeds")
|
|
hint = added_cond_kwargs.get("hint")
|
|
aug_emb, hint = self.add_embedding(image_embs, hint)
|
|
sample = torch.cat([sample, hint], dim=1)
|
|
|
|
emb = emb + aug_emb if aug_emb is not None else emb
|
|
|
|
if self.time_embed_act is not None:
|
|
emb = self.time_embed_act(emb)
|
|
|
|
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
|
|
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
|
|
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
|
|
# Kadinsky 2.1 - style
|
|
if "image_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which"
|
|
" requires the keyword argument `image_embeds` to be passed in `added_conditions`"
|
|
)
|
|
|
|
image_embeds = added_cond_kwargs.get("image_embeds")
|
|
encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
|
|
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
|
|
# Kandinsky 2.2 - style
|
|
if "image_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires"
|
|
" the keyword argument `image_embeds` to be passed in `added_conditions`"
|
|
)
|
|
image_embeds = added_cond_kwargs.get("image_embeds")
|
|
encoder_hidden_states = self.encoder_hid_proj(image_embeds)
|
|
# 2. pre-process
|
|
sample = self.conv_in(sample)
|
|
|
|
# 3. down
|
|
|
|
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
|
|
is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
|
|
|
|
down_block_res_samples = (sample,)
|
|
for downsample_block in self.down_blocks:
|
|
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
|
# For t2i-adapter CrossAttnDownBlockFlat
|
|
additional_residuals = {}
|
|
if is_adapter and len(down_block_additional_residuals) > 0:
|
|
additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
|
|
|
|
sample, res_samples = downsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=attention_mask,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
**additional_residuals,
|
|
)
|
|
else:
|
|
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
|
|
|
if is_adapter and len(down_block_additional_residuals) > 0:
|
|
sample += down_block_additional_residuals.pop(0)
|
|
|
|
down_block_res_samples += res_samples
|
|
|
|
if is_controlnet:
|
|
new_down_block_res_samples = ()
|
|
|
|
for down_block_res_sample, down_block_additional_residual in zip(
|
|
down_block_res_samples, down_block_additional_residuals
|
|
):
|
|
down_block_res_sample = down_block_res_sample + down_block_additional_residual
|
|
new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
|
|
|
|
down_block_res_samples = new_down_block_res_samples
|
|
|
|
# 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,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
)
|
|
|
|
if is_controlnet:
|
|
sample = sample + mid_block_additional_residual
|
|
|
|
# 5. up
|
|
for i, upsample_block in enumerate(self.up_blocks):
|
|
is_final_block = i == len(self.up_blocks) - 1
|
|
|
|
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
|
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
|
|
|
# if we have not reached the final block and need to forward the
|
|
# upsample size, we do it here
|
|
if not is_final_block and forward_upsample_size:
|
|
upsample_size = down_block_res_samples[-1].shape[2:]
|
|
|
|
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
|
sample = 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,
|
|
upsample_size=upsample_size,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
)
|
|
else:
|
|
sample = upsample_block(
|
|
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
|
)
|
|
|
|
# 6. post-process
|
|
if self.conv_norm_out:
|
|
sample = self.conv_norm_out(sample)
|
|
sample = self.conv_act(sample)
|
|
sample = self.conv_out(sample)
|
|
|
|
if not return_dict:
|
|
return (sample,)
|
|
|
|
return UNet2DConditionOutput(sample=sample)
|
|
|
|
|
|
class LinearMultiDim(nn.Linear):
|
|
def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):
|
|
in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)
|
|
if out_features is None:
|
|
out_features = in_features
|
|
out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)
|
|
self.in_features_multidim = in_features
|
|
self.out_features_multidim = out_features
|
|
super().__init__(np.array(in_features).prod(), np.array(out_features).prod())
|
|
|
|
def forward(self, input_tensor, *args, **kwargs):
|
|
shape = input_tensor.shape
|
|
n_dim = len(self.in_features_multidim)
|
|
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features)
|
|
output_tensor = super().forward(input_tensor)
|
|
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim)
|
|
return output_tensor
|
|
|
|
|
|
class ResnetBlockFlat(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
in_channels,
|
|
out_channels=None,
|
|
dropout=0.0,
|
|
temb_channels=512,
|
|
groups=32,
|
|
groups_out=None,
|
|
pre_norm=True,
|
|
eps=1e-6,
|
|
time_embedding_norm="default",
|
|
use_in_shortcut=None,
|
|
second_dim=4,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
self.pre_norm = pre_norm
|
|
self.pre_norm = True
|
|
|
|
in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)
|
|
self.in_channels_prod = np.array(in_channels).prod()
|
|
self.channels_multidim = in_channels
|
|
|
|
if out_channels is not None:
|
|
out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)
|
|
out_channels_prod = np.array(out_channels).prod()
|
|
self.out_channels_multidim = out_channels
|
|
else:
|
|
out_channels_prod = self.in_channels_prod
|
|
self.out_channels_multidim = self.channels_multidim
|
|
self.time_embedding_norm = time_embedding_norm
|
|
|
|
if groups_out is None:
|
|
groups_out = groups
|
|
|
|
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True)
|
|
self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)
|
|
|
|
if temb_channels is not None:
|
|
self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod)
|
|
else:
|
|
self.time_emb_proj = None
|
|
|
|
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True)
|
|
self.dropout = torch.nn.Dropout(dropout)
|
|
self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)
|
|
|
|
self.nonlinearity = nn.SiLU()
|
|
|
|
self.use_in_shortcut = (
|
|
self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut
|
|
)
|
|
|
|
self.conv_shortcut = None
|
|
if self.use_in_shortcut:
|
|
self.conv_shortcut = torch.nn.Conv2d(
|
|
self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0
|
|
)
|
|
|
|
def forward(self, input_tensor, temb):
|
|
shape = input_tensor.shape
|
|
n_dim = len(self.channels_multidim)
|
|
input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1)
|
|
input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)
|
|
|
|
hidden_states = input_tensor
|
|
|
|
hidden_states = self.norm1(hidden_states)
|
|
hidden_states = self.nonlinearity(hidden_states)
|
|
hidden_states = self.conv1(hidden_states)
|
|
|
|
if temb is not None:
|
|
temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
|
|
hidden_states = hidden_states + temb
|
|
|
|
hidden_states = self.norm2(hidden_states)
|
|
hidden_states = self.nonlinearity(hidden_states)
|
|
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.conv2(hidden_states)
|
|
|
|
if self.conv_shortcut is not None:
|
|
input_tensor = self.conv_shortcut(input_tensor)
|
|
|
|
output_tensor = input_tensor + hidden_states
|
|
|
|
output_tensor = output_tensor.view(*shape[0:-n_dim], -1)
|
|
output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim)
|
|
|
|
return output_tensor
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
|
|
class DownBlockFlat(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: 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_downsample=True,
|
|
downsample_padding=1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=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)
|
|
|
|
if add_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
LinearMultiDim(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(self, hidden_states, temb=None):
|
|
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)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
|
|
class CrossAttnDownBlockFlat(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: 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,
|
|
downsample_padding=1,
|
|
add_downsample=True,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
attentions = []
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=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,
|
|
)
|
|
)
|
|
if not dual_cross_attention:
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
)
|
|
else:
|
|
attentions.append(
|
|
DualTransformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
LinearMultiDim(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
additional_residuals=None,
|
|
):
|
|
output_states = ()
|
|
|
|
blocks = list(zip(self.resnets, self.attentions))
|
|
|
|
for i, (resnet, attn) in enumerate(blocks):
|
|
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 = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(attn, return_dict=False),
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
None, # timestep
|
|
None, # class_labels
|
|
cross_attention_kwargs,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
**ckpt_kwargs,
|
|
)[0]
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
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]
|
|
|
|
# apply additional residuals to the output of the last pair of resnet and attention blocks
|
|
if i == len(blocks) - 1 and additional_residuals is not None:
|
|
hidden_states = hidden_states + additional_residuals
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
|
|
class UpBlockFlat(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
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().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=resnet_in_channels + res_skip_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)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
|
for resnet in self.resnets:
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
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)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim
|
|
class CrossAttnUpBlockFlat(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
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,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
attentions = []
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=resnet_in_channels + res_skip_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,
|
|
)
|
|
)
|
|
if not dual_cross_attention:
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
)
|
|
else:
|
|
attentions.append(
|
|
DualTransformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
res_hidden_states_tuple: Tuple[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,
|
|
):
|
|
for resnet, attn in zip(self.resnets, self.attentions):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
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 = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(attn, return_dict=False),
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
None, # timestep
|
|
None, # class_labels
|
|
cross_attention_kwargs,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
**ckpt_kwargs,
|
|
)[0]
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
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]
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat
|
|
class UNetMidBlockFlatCrossAttn(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: 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,
|
|
output_scale_factor=1.0,
|
|
cross_attention_dim=1280,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=False,
|
|
upcast_attention=False,
|
|
):
|
|
super().__init__()
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
|
|
|
# there is always at least one resnet
|
|
resnets = [
|
|
ResnetBlockFlat(
|
|
in_channels=in_channels,
|
|
out_channels=in_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,
|
|
)
|
|
]
|
|
attentions = []
|
|
|
|
for _ in range(num_layers):
|
|
if not dual_cross_attention:
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
in_channels // num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
)
|
|
else:
|
|
attentions.append(
|
|
DualTransformer2DModel(
|
|
num_attention_heads,
|
|
in_channels // num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=in_channels,
|
|
out_channels=in_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.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
) -> torch.FloatTensor:
|
|
hidden_states = self.resnets[0](hidden_states, temb)
|
|
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
|
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]
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat
|
|
class UNetMidBlockFlatSimpleCrossAttn(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_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,
|
|
attention_head_dim=1,
|
|
output_scale_factor=1.0,
|
|
cross_attention_dim=1280,
|
|
skip_time_act=False,
|
|
only_cross_attention=False,
|
|
cross_attention_norm=None,
|
|
):
|
|
super().__init__()
|
|
|
|
self.has_cross_attention = True
|
|
|
|
self.attention_head_dim = attention_head_dim
|
|
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
|
|
|
self.num_heads = in_channels // self.attention_head_dim
|
|
|
|
# there is always at least one resnet
|
|
resnets = [
|
|
ResnetBlockFlat(
|
|
in_channels=in_channels,
|
|
out_channels=in_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,
|
|
skip_time_act=skip_time_act,
|
|
)
|
|
]
|
|
attentions = []
|
|
|
|
for _ in range(num_layers):
|
|
processor = (
|
|
AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor()
|
|
)
|
|
|
|
attentions.append(
|
|
Attention(
|
|
query_dim=in_channels,
|
|
cross_attention_dim=in_channels,
|
|
heads=self.num_heads,
|
|
dim_head=self.attention_head_dim,
|
|
added_kv_proj_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
bias=True,
|
|
upcast_softmax=True,
|
|
only_cross_attention=only_cross_attention,
|
|
cross_attention_norm=cross_attention_norm,
|
|
processor=processor,
|
|
)
|
|
)
|
|
resnets.append(
|
|
ResnetBlockFlat(
|
|
in_channels=in_channels,
|
|
out_channels=in_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,
|
|
skip_time_act=skip_time_act,
|
|
)
|
|
)
|
|
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.FloatTensor,
|
|
temb: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
):
|
|
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
|
|
|
|
if attention_mask is None:
|
|
# if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.
|
|
mask = None if encoder_hidden_states is None else encoder_attention_mask
|
|
else:
|
|
# when attention_mask is defined: we don't even check for encoder_attention_mask.
|
|
# this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.
|
|
# TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.
|
|
# then we can simplify this whole if/else block to:
|
|
# mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask
|
|
mask = attention_mask
|
|
|
|
hidden_states = self.resnets[0](hidden_states, temb)
|
|
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
|
# attn
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
|
|
# resnet
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
return hidden_states
|