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# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from .modeling_utils import ModelMixin
from .resnet import Downsample2D
class MultiAdapter(ModelMixin):
r"""
MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to
user-assigned weighting.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
adapters (`List[T2IAdapter]`, *optional*, defaults to None):
A list of `T2IAdapter` model instances.
"""
def __init__(self, adapters: List["T2IAdapter"]):
super(MultiAdapter, self).__init__()
self.num_adapter = len(adapters)
self.adapters = nn.ModuleList(adapters)
def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = None) -> List[torch.Tensor]:
r"""
Args:
xs (`torch.Tensor`):
(batch, channel, height, width) input images for multiple adapter models concated along dimension 1,
`channel` should equal to `num_adapter` * "number of channel of image".
adapter_weights (`List[float]`, *optional*, defaults to None):
List of floats representing the weight which will be multiply to each adapter's output before adding
them together.
"""
if adapter_weights is None:
adapter_weights = torch.tensor([1 / self.num_adapter] * self.num_adapter)
else:
adapter_weights = torch.tensor(adapter_weights)
if xs.shape[1] % self.num_adapter != 0:
raise ValueError(
f"Expecting multi-adapter's input have number of channel that cab be evenly divisible "
f"by num_adapter: {xs.shape[1]} % {self.num_adapter} != 0"
)
x_list = torch.chunk(xs, self.num_adapter, dim=1)
accume_state = None
for x, w, adapter in zip(x_list, adapter_weights, self.adapters):
features = adapter(x)
if accume_state is None:
accume_state = features
else:
for i in range(len(features)):
accume_state[i] += w * features[i]
return accume_state
class T2IAdapter(ModelMixin, ConfigMixin):
r"""
A simple ResNet-like model that accepts images containing control signals such as keyposes and depth. The model
generates multiple feature maps that are used as additional conditioning in [`UNet2DConditionModel`]. The model's
architecture follows the original implementation of
[Adapter](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L97)
and
[AdapterLight](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L235).
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
in_channels (`int`, *optional*, defaults to 3):
Number of channels of Aapter's input(*control image*). Set this parameter to 1 if you're using gray scale
image as *control image*.
channels (`List[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The number of channel of each downsample block's output hidden state. The `len(block_out_channels)` will
also determine the number of downsample blocks in the Adapter.
num_res_blocks (`int`, *optional*, defaults to 2):
Number of ResNet blocks in each downsample block
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280, 1280],
num_res_blocks: int = 2,
downscale_factor: int = 8,
adapter_type: str = "full_adapter",
):
super().__init__()
if adapter_type == "full_adapter":
self.adapter = FullAdapter(in_channels, channels, num_res_blocks, downscale_factor)
elif adapter_type == "light_adapter":
self.adapter = LightAdapter(in_channels, channels, num_res_blocks, downscale_factor)
else:
raise ValueError(f"unknown adapter_type: {type}. Choose either 'full_adapter' or 'simple_adapter'")
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
return self.adapter(x)
@property
def total_downscale_factor(self):
return self.adapter.total_downscale_factor
# full adapter
class FullAdapter(nn.Module):
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280, 1280],
num_res_blocks: int = 2,
downscale_factor: int = 8,
):
super().__init__()
in_channels = in_channels * downscale_factor**2
self.unshuffle = nn.PixelUnshuffle(downscale_factor)
self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)
self.body = nn.ModuleList(
[
AdapterBlock(channels[0], channels[0], num_res_blocks),
*[
AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True)
for i in range(1, len(channels))
],
]
)
self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 1)
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
x = self.unshuffle(x)
x = self.conv_in(x)
features = []
for block in self.body:
x = block(x)
features.append(x)
return features
class AdapterBlock(nn.Module):
def __init__(self, in_channels, out_channels, num_res_blocks, down=False):
super().__init__()
self.downsample = None
if down:
self.downsample = Downsample2D(in_channels)
self.in_conv = None
if in_channels != out_channels:
self.in_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.resnets = nn.Sequential(
*[AdapterResnetBlock(out_channels) for _ in range(num_res_blocks)],
)
def forward(self, x):
if self.downsample is not None:
x = self.downsample(x)
if self.in_conv is not None:
x = self.in_conv(x)
x = self.resnets(x)
return x
class AdapterResnetBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(channels, channels, kernel_size=1)
def forward(self, x):
h = x
h = self.block1(h)
h = self.act(h)
h = self.block2(h)
return h + x
# light adapter
class LightAdapter(nn.Module):
def __init__(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280],
num_res_blocks: int = 4,
downscale_factor: int = 8,
):
super().__init__()
in_channels = in_channels * downscale_factor**2
self.unshuffle = nn.PixelUnshuffle(downscale_factor)
self.body = nn.ModuleList(
[
LightAdapterBlock(in_channels, channels[0], num_res_blocks),
*[
LightAdapterBlock(channels[i], channels[i + 1], num_res_blocks, down=True)
for i in range(len(channels) - 1)
],
LightAdapterBlock(channels[-1], channels[-1], num_res_blocks, down=True),
]
)
self.total_downscale_factor = downscale_factor * (2 ** len(channels))
def forward(self, x):
x = self.unshuffle(x)
features = []
for block in self.body:
x = block(x)
features.append(x)
return features
class LightAdapterBlock(nn.Module):
def __init__(self, in_channels, out_channels, num_res_blocks, down=False):
super().__init__()
mid_channels = out_channels // 4
self.downsample = None
if down:
self.downsample = Downsample2D(in_channels)
self.in_conv = nn.Conv2d(in_channels, mid_channels, kernel_size=1)
self.resnets = nn.Sequential(*[LightAdapterResnetBlock(mid_channels) for _ in range(num_res_blocks)])
self.out_conv = nn.Conv2d(mid_channels, out_channels, kernel_size=1)
def forward(self, x):
if self.downsample is not None:
x = self.downsample(x)
x = self.in_conv(x)
x = self.resnets(x)
x = self.out_conv(x)
return x
class LightAdapterResnetBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
def forward(self, x):
h = x
h = self.block1(h)
h = self.act(h)
h = self.block2(h)
return h + x
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