File size: 5,882 Bytes
31112ad |
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 |
# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
#
# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
# output and results therefrom are provided "AS IS" without any express or implied warranties of
# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
# of rights and permissions under this agreement.
# See the License for the specific language governing permissions and limitations under the License.
from collections.abc import Sequence
from dataclasses import dataclass
from enum import Enum
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import Tensor
from diffusers.models import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
from ..vae.hunyuanvideo_15_vae import (
CausalConv3d,
ResnetBlock,
RMS_norm,
forward_with_checkpointing,
swish,
)
class UpsamplerType(Enum):
LEARNED = "learned"
FIXED = "fixed"
NONE = "none"
LEARNED_FIXED = "learned_fixed"
@dataclass
class UpsamplerConfig:
load_from: str
enable: bool = False
hidden_channels: int = 128
num_blocks: int = 16
model_type: UpsamplerType = UpsamplerType.NONE
version: str = "720p"
class SRResidualCausalBlock3D(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.block = nn.Sequential(
CausalConv3d(channels, channels, kernel_size=3),
nn.SiLU(inplace=True),
CausalConv3d(channels, channels, kernel_size=3),
nn.SiLU(inplace=True),
CausalConv3d(channels, channels, kernel_size=3),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.block(x)
class SRTo720pUpsampler(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
in_channels: int,
out_channels: int,
hidden_channels: int | None = None,
num_blocks: int = 6,
global_residual: bool = False,
):
super().__init__()
if hidden_channels is None:
hidden_channels = 64
self.in_conv = CausalConv3d(in_channels, hidden_channels, kernel_size=3)
self.blocks = nn.ModuleList([SRResidualCausalBlock3D(hidden_channels) for _ in range(num_blocks)])
self.out_conv = CausalConv3d(hidden_channels, out_channels, kernel_size=3)
self.global_residual = bool(global_residual)
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
y = self.in_conv(x)
for blk in self.blocks:
y = blk(y)
y = self.out_conv(y)
if self.global_residual and (y.shape == residual.shape):
y += residual
return y
class SRTo1080pUpsampler(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
z_channels: int,
out_channels: int,
block_out_channels: tuple[int, ...],
num_res_blocks: int = 2,
is_residual: bool = False,
):
super().__init__()
self.num_res_blocks = num_res_blocks
self.block_out_channels = block_out_channels
self.z_channels = z_channels
block_in = block_out_channels[0]
self.conv_in = CausalConv3d(z_channels, block_in, kernel_size=3)
self.up = nn.ModuleList()
for i_level, ch in enumerate(block_out_channels):
block = nn.ModuleList()
block_out = ch
for _ in range(self.num_res_blocks + 1):
block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
block_in = block_out
up = nn.Module()
up.block = block
self.up.append(up)
self.norm_out = RMS_norm(block_in, images=False)
self.conv_out = CausalConv3d(block_in, out_channels, kernel_size=3)
self.gradient_checkpointing = False
self.is_residual = is_residual
def forward(self, z: Tensor, target_shape: Sequence[int] = None) -> Tensor:
"""
Args:
z: (B, C, T, H, W)
target_shape: (H, W)
"""
use_checkpointing = bool(self.training and self.gradient_checkpointing)
if target_shape is not None and z.shape[-2:] != target_shape:
bsz = z.shape[0]
z = rearrange(z, "b c f h w -> (b f) c h w")
z = F.interpolate(z, size=target_shape, mode="bilinear", align_corners=False)
z = rearrange(z, "(b f) c h w -> b c f h w", b=bsz)
# z to block_in
repeats = self.block_out_channels[0] // (self.z_channels)
h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)
# upsampling
for i_level in range(len(self.block_out_channels)):
for i_block in range(self.num_res_blocks + 1):
x_list= [h]
del h
h = self.up[i_level].block[i_block](x_list)
if hasattr(self.up[i_level], "upsample"):
x_list= [h]
del h
h = self.up[i_level].upsample(x_list)
# end
h = self.norm_out(h).to(z.dtype)
h = swish(h)
h = self.conv_out(h)
return h
|