Spaces:
Running
Running
File size: 7,803 Bytes
8aa9c9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
# *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/guoyww/AnimateDiff
# Copyright 2023 The HuggingFace Team. All rights reserved.
# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and 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.
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class InflatedConv3d(nn.Conv2d):
def forward(self, x):
video_length = x.shape[2]
x = rearrange(x, "b c f h w -> (b f) c h w")
x = super().forward(x)
x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
return x
class Upsample3D(nn.Module):
def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
conv = None
if use_conv_transpose:
raise NotImplementedError
elif use_conv:
self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
def forward(self, hidden_states, output_size=None):
assert hidden_states.shape[1] == self.channels
if self.use_conv_transpose:
raise NotImplementedError
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
dtype = hidden_states.dtype
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(torch.float32)
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
hidden_states = hidden_states.contiguous()
# if `output_size` is passed we force the interpolation output
# size and do not make use of `scale_factor=2`
if output_size is None:
hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
else:
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
# If the input is bfloat16, we cast back to bfloat16
if dtype == torch.bfloat16:
hidden_states = hidden_states.to(dtype)
hidden_states = self.conv(hidden_states)
return hidden_states
class Downsample3D(nn.Module):
def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if use_conv:
self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
raise NotImplementedError
def forward(self, hidden_states):
assert hidden_states.shape[1] == self.channels
if self.use_conv and self.padding == 0:
raise NotImplementedError
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states)
return hidden_states
class ResnetBlock3D(nn.Module):
def __init__(
self,
*,
in_channels,
out_channels=None,
conv_shortcut=False,
dropout=0.0,
temb_channels=512,
groups=32,
groups_out=None,
pre_norm=True,
eps=1e-6,
non_linearity="swish",
time_embedding_norm="default",
output_scale_factor=1.0,
use_in_shortcut=None,
):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
if temb_channels is not None:
if self.time_embedding_norm == "default":
time_emb_proj_out_channels = out_channels
elif self.time_embedding_norm == "scale_shift":
time_emb_proj_out_channels = out_channels * 2
else:
raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
else:
self.time_emb_proj = None
self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "mish":
self.nonlinearity = Mish()
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
self.conv_shortcut = None
if self.use_in_shortcut:
self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, input_tensor, temb):
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, None]
if temb is not None and self.time_embedding_norm == "default":
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
if temb is not None and self.time_embedding_norm == "scale_shift":
scale, shift = torch.chunk(temb, 2, dim=1)
hidden_states = hidden_states * (1 + scale) + shift
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) / self.output_scale_factor
return output_tensor
class Mish(torch.nn.Module):
def forward(self, hidden_states):
return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) |