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first run sucessfull with text encoder mask bug not fix;

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junsong
2024-11-30 11:50:15 -08:00
parent 861a378edf
commit 9ec31c864f
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Sana/models/act.py
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
import copy
import torch.nn as nn
__all__ = ["build_act", "get_act_name"]
# register activation function here
# name: module, kwargs with default values
REGISTERED_ACT_DICT: dict[str, tuple[type, dict[str, any]]] = {
"relu": (nn.ReLU, {"inplace": True}),
"relu6": (nn.ReLU6, {"inplace": True}),
"hswish": (nn.Hardswish, {"inplace": True}),
"hsigmoid": (nn.Hardsigmoid, {"inplace": True}),
"swish": (nn.SiLU, {"inplace": True}),
"silu": (nn.SiLU, {"inplace": True}),
"tanh": (nn.Tanh, {}),
"sigmoid": (nn.Sigmoid, {}),
"gelu": (nn.GELU, {"approximate": "tanh"}),
"mish": (nn.Mish, {"inplace": True}),
"identity": (nn.Identity, {}),
}
def build_act(name: str or None, **kwargs) -> nn.Module or None:
if name in REGISTERED_ACT_DICT:
act_cls, default_args = copy.deepcopy(REGISTERED_ACT_DICT[name])
for key in default_args:
if key in kwargs:
default_args[key] = kwargs[key]
return act_cls(**default_args)
elif name is None or name.lower() == "none":
return None
else:
raise ValueError(f"do not support: {name}")
def get_act_name(act: nn.Module or None) -> str or None:
if act is None:
return None
module2name = {}
for key, config in REGISTERED_ACT_DICT.items():
module2name[config[0].__name__] = key
return module2name.get(type(act).__name__, "unknown")
Sana/models/basic_modules.py
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import torch
import torch.nn as nn
from timm.models.vision_transformer import Mlp
from .act import build_act, get_act_name
from .norms import build_norm, get_norm_name
from .utils import get_same_padding, val2tuple
class ConvLayer(nn.Module):
def __init__(
self,
in_dim: int,
out_dim: int,
kernel_size=3,
stride=1,
dilation=1,
groups=1,
padding: int or None = None,
use_bias=False,
dropout=0.0,
norm="bn2d",
act="relu",
):
super().__init__()
if padding is None:
padding = get_same_padding(kernel_size)
padding *= dilation
self.in_dim = in_dim
self.out_dim = out_dim
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.groups = groups
self.padding = padding
self.use_bias = use_bias
self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
self.conv = nn.Conv2d(
in_dim,
out_dim,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=padding,
dilation=(dilation, dilation),
groups=groups,
bias=use_bias,
)
self.norm = build_norm(norm, num_features=out_dim)
self.act = build_act(act)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.dropout is not None:
x = self.dropout(x)
x = self.conv(x)
if self.norm:
x = self.norm(x)
if self.act:
x = self.act(x)
return x
class GLUMBConv(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int,
out_feature=None,
kernel_size=3,
stride=1,
padding: int or None = None,
use_bias=False,
norm=(None, None, None),
act=("silu", "silu", None),
dilation=1,
):
out_feature = out_feature or in_features
super().__init__()
use_bias = val2tuple(use_bias, 3)
norm = val2tuple(norm, 3)
act = val2tuple(act, 3)
self.glu_act = build_act(act[1], inplace=False)
self.inverted_conv = ConvLayer(
in_features,
hidden_features * 2,
1,
use_bias=use_bias[0],
norm=norm[0],
act=act[0],
)
self.depth_conv = ConvLayer(
hidden_features * 2,
hidden_features * 2,
kernel_size,
stride=stride,
groups=hidden_features * 2,
padding=padding,
use_bias=use_bias[1],
norm=norm[1],
act=None,
dilation=dilation,
)
self.point_conv = ConvLayer(
hidden_features,
out_feature,
1,
use_bias=use_bias[2],
norm=norm[2],
act=act[2],
)
# from IPython import embed; embed(header='debug dilate conv')
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
x = self.inverted_conv(x)
x = self.depth_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.point_conv(x)
x = x.reshape(B, C, N).permute(0, 2, 1)
return x
class SlimGLUMBConv(GLUMBConv):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# 移除 self.inverted_conv 层
del self.inverted_conv
self.out_dim = self.point_conv.out_dim
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
# 直接使用 x，跳过 self.inverted_conv 层的调用
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
# x = self.inverted_conv(x)
x = self.depth_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.point_conv(x)
x = x.reshape(B, self.out_dim, N).permute(0, 2, 1)
return x
class MBConvPreGLU(nn.Module):
def __init__(
self,
in_dim: int,
out_dim: int,
kernel_size=3,
stride=1,
mid_dim=None,
expand=6,
padding: int or None = None,
use_bias=False,
norm=(None, None, "ln2d"),
act=("silu", "silu", None),
):
super().__init__()
use_bias = val2tuple(use_bias, 3)
norm = val2tuple(norm, 3)
act = val2tuple(act, 3)
mid_dim = mid_dim or round(in_dim * expand)
self.inverted_conv = ConvLayer(
in_dim,
mid_dim * 2,
1,
use_bias=use_bias[0],
norm=norm[0],
act=None,
)
self.glu_act = build_act(act[0], inplace=False)
self.depth_conv = ConvLayer(
mid_dim,
mid_dim,
kernel_size,
stride=stride,
groups=mid_dim,
padding=padding,
use_bias=use_bias[1],
norm=norm[1],
act=act[1],
)
self.point_conv = ConvLayer(
mid_dim,
out_dim,
1,
use_bias=use_bias[2],
norm=norm[2],
act=act[2],
)
def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
x = self.inverted_conv(x)
x, gate = torch.chunk(x, 2, dim=1)
gate = self.glu_act(gate)
x = x * gate
x = self.depth_conv(x)
x = self.point_conv(x)
x = x.reshape(B, C, N).permute(0, 2, 1)
return x
@property
def module_str(self) -> str:
_str = f"{self.depth_conv.kernel_size}{type(self).__name__}("
_str += f"in={self.inverted_conv.in_dim},mid={self.depth_conv.in_dim},out={self.point_conv.out_dim},s={self.depth_conv.stride}"
_str += (
f",norm={get_norm_name(self.inverted_conv.norm)}"
f"+{get_norm_name(self.depth_conv.norm)}"
f"+{get_norm_name(self.point_conv.norm)}"
)
_str += (
f",act={get_act_name(self.inverted_conv.act)}"
f"+{get_act_name(self.depth_conv.act)}"
f"+{get_act_name(self.point_conv.act)}"
)
_str += f",glu_act={get_act_name(self.glu_act)})"
return _str
class DWMlp(Mlp):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.0,
kernel_size=3,
stride=1,
dilation=1,
padding=None,
):
super().__init__(
in_features=in_features,
hidden_features=hidden_features,
out_features=out_features,
act_layer=act_layer,
bias=bias,
drop=drop,
)
hidden_features = hidden_features or in_features
self.hidden_features = hidden_features
if padding is None:
padding = get_same_padding(kernel_size)
padding *= dilation
self.conv = nn.Conv2d(
hidden_features,
hidden_features,
kernel_size=(kernel_size, kernel_size),
stride=(stride, stride),
padding=padding,
dilation=(dilation, dilation),
groups=hidden_features,
bias=bias,
)
def forward(self, x, HW=None):
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = x.reshape(B, H, W, self.hidden_features).permute(0, 3, 1, 2)
x = self.conv(x)
x = x.reshape(B, self.hidden_features, N).permute(0, 2, 1)
x = self.fc2(x)
x = self.drop2(x)
return x
class Mlp(Mlp):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0):
super().__init__(
in_features=in_features,
hidden_features=hidden_features,
out_features=out_features,
act_layer=act_layer,
bias=bias,
drop=drop,
)
def forward(self, x, HW=None):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
if __name__ == "__main__":
model = GLUMBConv(
1152,
1152 * 4,
1152,
use_bias=(True, True, False),
norm=(None, None, None),
act=("silu", "silu", None),
).cuda()
input = torch.randn(4, 256, 1152).cuda()
output = model(input)
Sana/models/norms.py
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
import copy
import warnings
import torch
import torch.nn as nn
from torch.nn.modules.batchnorm import _BatchNorm
__all__ = ["LayerNorm2d", "build_norm", "get_norm_name", "reset_bn", "remove_bn", "set_norm_eps"]
class LayerNorm2d(nn.LayerNorm):
rmsnorm = False
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = x if LayerNorm2d.rmsnorm else x - torch.mean(x, dim=1, keepdim=True)
out = out / torch.sqrt(torch.square(out).mean(dim=1, keepdim=True) + self.eps)
if self.elementwise_affine:
out = out * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)
return out
def extra_repr(self) -> str:
return f"{self.normalized_shape}, eps={self.eps}, elementwise_affine={self.elementwise_affine}, rmsnorm={self.rmsnorm}"
# register normalization function here
# name: module, kwargs with default values
REGISTERED_NORMALIZATION_DICT: dict[str, tuple[type, dict[str, any]]] = {
"bn2d": (nn.BatchNorm2d, {"num_features": None, "eps": 1e-5, "momentum": 0.1, "affine": True}),
"syncbn": (nn.SyncBatchNorm, {"num_features": None, "eps": 1e-5, "momentum": 0.1, "affine": True}),
"ln": (nn.LayerNorm, {"normalized_shape": None, "eps": 1e-5, "elementwise_affine": True}),
"ln2d": (LayerNorm2d, {"normalized_shape": None, "eps": 1e-5, "elementwise_affine": True}),
}
def build_norm(name="bn2d", num_features=None, affine=True, **kwargs) -> nn.Module or None:
if name in ["ln", "ln2d"]:
kwargs["normalized_shape"] = num_features
kwargs["elementwise_affine"] = affine
else:
kwargs["num_features"] = num_features
kwargs["affine"] = affine
if name in REGISTERED_NORMALIZATION_DICT:
norm_cls, default_args = copy.deepcopy(REGISTERED_NORMALIZATION_DICT[name])
for key in default_args:
if key in kwargs:
default_args[key] = kwargs[key]
return norm_cls(**default_args)
elif name is None or name.lower() == "none":
return None
else:
raise ValueError("do not support: %s" % name)
def get_norm_name(norm: nn.Module or None) -> str or None:
if norm is None:
return None
module2name = {}
for key, config in REGISTERED_NORMALIZATION_DICT.items():
module2name[config[0].__name__] = key
return module2name.get(type(norm).__name__, "unknown")
def reset_bn(
model: nn.Module,
data_loader: list,
sync=True,
progress_bar=False,
) -> None:
import copy
import torch.nn.functional as F
from packages.apps.utils import AverageMeter, is_master, sync_tensor
from packages.models.utils import get_device, list_join
from tqdm import tqdm
bn_mean = {}
bn_var = {}
tmp_model = copy.deepcopy(model)
for name, m in tmp_model.named_modules():
if isinstance(m, _BatchNorm):
bn_mean[name] = AverageMeter(is_distributed=False)
bn_var[name] = AverageMeter(is_distributed=False)
def new_forward(bn, mean_est, var_est):
def lambda_forward(x):
x = x.contiguous()
if sync:
batch_mean = x.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True) # 1, C, 1, 1
batch_mean = sync_tensor(batch_mean, reduce="cat")
batch_mean = torch.mean(batch_mean, dim=0, keepdim=True)
batch_var = (x - batch_mean) * (x - batch_mean)
batch_var = batch_var.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
batch_var = sync_tensor(batch_var, reduce="cat")
batch_var = torch.mean(batch_var, dim=0, keepdim=True)
else:
batch_mean = x.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True) # 1, C, 1, 1
batch_var = (x - batch_mean) * (x - batch_mean)
batch_var = batch_var.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
batch_mean = torch.squeeze(batch_mean)
batch_var = torch.squeeze(batch_var)
mean_est.update(batch_mean.data, x.size(0))
var_est.update(batch_var.data, x.size(0))
# bn forward using calculated mean & var
_feature_dim = batch_mean.shape[0]
return F.batch_norm(
x,
batch_mean,
batch_var,
bn.weight[:_feature_dim],
bn.bias[:_feature_dim],
False,
0.0,
bn.eps,
)
return lambda_forward
m.forward = new_forward(m, bn_mean[name], bn_var[name])
# skip if there is no batch normalization layers in the network
if len(bn_mean) == 0:
return
tmp_model.eval()
with torch.inference_mode():
with tqdm(total=len(data_loader), desc="reset bn", disable=not progress_bar or not is_master()) as t:
for images in data_loader:
images = images.to(get_device(tmp_model))
tmp_model(images)
t.set_postfix(
{
"bs": images.size(0),
"res": list_join(images.shape[-2:], "x"),
}
)
t.update()
for name, m in model.named_modules():
if name in bn_mean and bn_mean[name].count > 0:
feature_dim = bn_mean[name].avg.size(0)
assert isinstance(m, _BatchNorm)
m.running_mean.data[:feature_dim].copy_(bn_mean[name].avg)
m.running_var.data[:feature_dim].copy_(bn_var[name].avg)
def remove_bn(model: nn.Module) -> None:
for m in model.modules():
if isinstance(m, _BatchNorm):
m.weight = m.bias = None
m.forward = lambda x: x
def set_norm_eps(model: nn.Module, eps: float or None = None, momentum: float or None = None) -> None:
for m in model.modules():
if isinstance(m, (nn.GroupNorm, nn.LayerNorm, _BatchNorm)):
if eps is not None:
m.eps = eps
if momentum is not None:
m.momentum = momentum
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, scale_factor=1.0, eps: float = 1e-6):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim) * scale_factor)
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
return (self.weight * self._norm(x.float())).type_as(x)
Sana/models/sana.py
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import os
import numpy as np
import torch
import torch.nn as nn
from timm.models.layers import DropPath
from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
from .sana_blocks import (
Attention,
CaptionEmbedder,
FlashAttention,
LiteLA,
MultiHeadCrossAttention,
PatchEmbed,
T2IFinalLayer,
TimestepEmbedder,
t2i_modulate,
)
from .norms import RMSNorm
from .utils import auto_grad_checkpoint, to_2tuple
class SanaBlock(nn.Module):
"""
A Sana block with global shared adaptive layer norm (adaLN-single) conditioning.
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
drop_path=0,
input_size=None,
qk_norm=False,
attn_type="flash",
ffn_type="mlp",
mlp_acts=("silu", "silu", None),
linear_head_dim=32,
**block_kwargs,
):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
if attn_type == "flash":
# flash self attention
self.attn = FlashAttention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
qk_norm=qk_norm,
**block_kwargs,
)
elif attn_type == "linear":
# linear self attention
# TODO: Here the num_heads set to 36 for tmp used
self_num_heads = hidden_size // linear_head_dim
self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
elif attn_type == "vanilla":
# vanilla self attention
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
else:
raise ValueError(f"{attn_type} type is not defined.")
self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
# to be compatible with lower version pytorch
if ffn_type == "dwmlp":
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = DWMlp(
in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
)
elif ffn_type == "glumbconv":
self.mlp = GLUMBConv(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=(None, None, None),
act=mlp_acts,
)
elif ffn_type == "glumbconv_dilate":
self.mlp = GLUMBConv(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=(None, None, None),
act=mlp_acts,
dilation=2,
)
elif ffn_type == "mbconvpreglu":
self.mlp = MBConvPreGLU(
in_dim=hidden_size,
out_dim=hidden_size,
mid_dim=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=None,
act=("silu", "silu", None),
)
elif ffn_type == "mlp":
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(
in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
)
else:
raise ValueError(f"{ffn_type} type is not defined.")
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
def forward(self, x, y, t, mask=None, **kwargs):
B, N, C = x.shape
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C))
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
return x
#############################################################################
# Core Sana Model #
#################################################################################
class Sana(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=1,
in_channels=32,
hidden_size=1152,
depth=28,
num_heads=36,
mlp_ratio=2.5,
class_dropout_prob=0.1,
pred_sigma=False,
drop_path: float = 0.0,
caption_channels=2304,
pe_interpolation=1.0,
config=None,
model_max_length=120,
qk_norm=False,
y_norm=False,
norm_eps=1e-5,
attn_type="flash",
ffn_type="mlp",
use_pe=False,
y_norm_scale_factor=1.0,
patch_embed_kernel=None,
mlp_acts=("silu", "silu", None),
linear_head_dim=32,
**kwargs,
):
super().__init__()
self.pred_sigma = pred_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if pred_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.pe_interpolation = pe_interpolation
self.depth = depth
self.use_pe = use_pe
self.y_norm = y_norm
self.fp32_attention = kwargs.get("use_fp32_attention", False)
kernel_size = patch_embed_kernel or patch_size
self.x_embedder = PatchEmbed(
input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True
)
self.t_embedder = TimestepEmbedder(hidden_size)
num_patches = self.x_embedder.num_patches
self.base_size = input_size // self.patch_size
# Will use fixed sin-cos embedding:
self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
self.y_embedder = CaptionEmbedder(
in_channels=caption_channels,
hidden_size=hidden_size,
uncond_prob=class_dropout_prob,
act_layer=approx_gelu,
token_num=model_max_length,
)
if self.y_norm:
self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps)
drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList(
[
SanaBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path[i],
input_size=(input_size // patch_size, input_size // patch_size),
qk_norm=qk_norm,
attn_type=attn_type,
ffn_type=ffn_type,
mlp_acts=mlp_acts,
linear_head_dim=linear_head_dim,
)
for i in range(depth)
]
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs):
"""
Forward pass of Sana.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N, 1, 120, C) tensor of class labels
"""
x = x.to(self.dtype)
timestep = timestep.to(self.dtype)
y = y.to(self.dtype)
pos_embed = self.pos_embed.to(self.dtype)
self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
if self.use_pe:
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
else:
x = self.x_embedder(x)
t = self.t_embedder(timestep.to(x.dtype)) # (N, D)
t0 = self.t_block(t)
y = self.y_embedder(y, self.training) # (N, 1, L, D)
if self.y_norm:
y = self.attention_y_norm(y)
if mask is not None:
if mask.shape[0] != y.shape[0]:
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
mask = mask.squeeze(1).squeeze(1)
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
else:
y_lens = [y.shape[2]] * y.shape[0]
y = y.squeeze(1).view(1, -1, x.shape[-1])
for block in self.blocks:
x = auto_grad_checkpoint(block, x, y, t0, y_lens) # (N, T, D) #support grad checkpoint
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
return x
def __call__(self, *args, **kwargs):
"""
This method allows the object to be called like a function.
It simply calls the forward method.
"""
return self.forward(*args, **kwargs)
def forward_with_dpmsolver(self, x, timestep, y, mask=None, **kwargs):
"""
dpm solver donnot need variance prediction
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
model_out = self.forward(x, timestep, y, mask)
return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
if self.use_pe:
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1],
int(self.x_embedder.num_patches**0.5),
pe_interpolation=self.pe_interpolation,
base_size=self.base_size,
)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.normal_(self.t_block[1].weight, std=0.02)
# Initialize caption embedding MLP:
nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
if isinstance(grid_size, int):
grid_size = to_2tuple(grid_size)
grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / pe_interpolation
grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / pe_interpolation
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
Sana/models/sana_blocks.py
+798
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@@ -0,0 +1,798 @@
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import math
import os
from typing import Optional
import xformers.ops
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from timm.models.vision_transformer import Attention as Attention_
from timm.models.vision_transformer import Mlp
from transformers import AutoModelForCausalLM
from .norms import RMSNorm
from .utils import get_same_padding, to_2tuple
sdpa_32b = None
Q_4GB_LIMIT = 32000000
"""If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround."""
# 2k = 37 748 736
# 1024 = 9 437 184
# 2k model goes very slightly over 4GB
from comfy import model_management
if model_management.xformers_enabled():
import xformers
import xformers.ops
else:
if model_management.xpu_available:
import intel_extension_for_pytorch as ipex
import os
if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None):
from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit
sdpa_32b = scaled_dot_product_attention_32_bit
print("Using IPEX 4GB SDPA workaround")
else:
print("No IPEX 4GB workaround")
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def t2i_modulate(x, shift, scale):
return x * (1 + scale) + shift
class MultiHeadCrossAttention(nn.Module):
def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, **block_kwargs):
super().__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads
self.q_linear = nn.Linear(d_model, d_model)
self.kv_linear = nn.Linear(d_model, d_model * 2)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(d_model, d_model)
self.proj_drop = nn.Dropout(proj_drop)
if qk_norm:
# not used for now
self.q_norm = RMSNorm(d_model, scale_factor=1.0, eps=1e-6)
self.k_norm = RMSNorm(d_model, scale_factor=1.0, eps=1e-6)
else:
self.q_norm = nn.Identity()
self.k_norm = nn.Identity()
def forward(self, x, cond, mask=None):
# query/value: img tokens; key: condition; mask: if padding tokens
B, N, C = x.shape
q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)
kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
k, v = kv.unbind(2)
if model_management.xformers_enabled():
attn_bias = None
if mask is not None:
attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask)
x = xformers.ops.memory_efficient_attention(
q, k, v,
p=self.attn_drop.p,
attn_bias=attn_bias
)
else:
q, k, v = map(lambda t: t.permute(0, 2, 1, 3),(q, k, v),)
attn_mask = None
if mask is not None and len(mask) > 1:
# Create equivalent of xformer diagonal block mask, still only correct for square masks
# But depth doesn't matter as tensors can expand in that dimension
attn_mask_template = torch.ones(
[q.shape[2] // B, mask[0]],
dtype=torch.bool,
device=q.device
)
attn_mask = torch.block_diag(attn_mask_template)
# create a mask on the diagonal for each mask in the batch
for n in range(B - 1):
attn_mask = torch.block_diag(attn_mask, attn_mask_template)
p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity()
if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT:
sdpa = sdpa_32b
else:
sdpa = torch.nn.functional.scaled_dot_product_attention
x = sdpa(
q, k, v,
attn_mask=attn_mask,
dropout_p=p
).permute(0, 2, 1, 3).contiguous()
x = x.view(B, -1, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LiteLA(Attention_):
r"""Lightweight linear attention"""
PAD_VAL = 1
def __init__(
self,
in_dim: int,
out_dim: int,
heads: Optional[int] = None,
heads_ratio: float = 1.0,
dim=32,
eps=1e-15,
use_bias=False,
qk_norm=False,
norm_eps=1e-5,
):
heads = heads or int(out_dim // dim * heads_ratio)
super().__init__(in_dim, num_heads=heads, qkv_bias=use_bias)
self.in_dim = in_dim
self.out_dim = out_dim
self.heads = heads
self.dim = out_dim // heads # TODO: need some change
self.eps = eps
self.kernel_func = nn.ReLU(inplace=False)
if qk_norm:
self.q_norm = RMSNorm(in_dim, scale_factor=1.0, eps=norm_eps)
self.k_norm = RMSNorm(in_dim, scale_factor=1.0, eps=norm_eps)
else:
self.q_norm = nn.Identity()
self.k_norm = nn.Identity()
def attn_matmul(self, q, k, v: torch.Tensor) -> torch.Tensor:
# lightweight linear attention
q = self.kernel_func(q) # B, h, h_d, N
k = self.kernel_func(k)
q, k, v = q.float(), k.float(), v.float()
v = F.pad(v, (0, 0, 0, 1), mode="constant", value=LiteLA.PAD_VAL)
vk = torch.matmul(v, k)
out = torch.matmul(vk, q)
if out.dtype in [torch.float16, torch.bfloat16]:
out = out.float()
out = out[:, :, :-1] / (out[:, :, -1:] + self.eps)
return out
def forward(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, C)
q, k, v = qkv.unbind(2) # B, N, 3, C --> B, N, C
dtype = q.dtype
q = self.q_norm(q).transpose(-1, -2) # (B, N, C) -> (B, C, N)
k = self.k_norm(k).transpose(-1, -2) # (B, N, C) -> (B, C, N)
v = v.transpose(-1, -2)
q = q.reshape(B, C // self.dim, self.dim, N) # (B, h, h_d, N)
k = k.reshape(B, C // self.dim, self.dim, N).transpose(-1, -2) # (B, h, N, h_d)
v = v.reshape(B, C // self.dim, self.dim, N) # (B, h, h_d, N)
out = self.attn_matmul(q, k, v).to(dtype)
out = out.view(B, C, N).permute(0, 2, 1) # B, N, C
out = self.proj(out)
if torch.get_autocast_gpu_dtype() == torch.float16:
out = out.clip(-65504, 65504)
return out
@property
def module_str(self) -> str:
_str = type(self).__name__ + "("
eps = f"{self.eps:.1E}"
_str += f"i={self.in_dim},o={self.out_dim},h={self.heads},d={self.dim},eps={eps}"
return _str
def __repr__(self):
return f"EPS{self.eps}-" + super().__repr__()
class PAGCFGIdentitySelfAttnProcessorLiteLA:
r"""Self Attention with Perturbed Attention & CFG Guidance"""
def __init__(self, attn):
self.attn = attn
def __call__(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch.Tensor:
x_uncond, x_org, x_ptb = x.chunk(3)
x_org = torch.cat([x_uncond, x_org])
B, N, C = x_org.shape
qkv = self.attn.qkv(x_org).reshape(B, N, 3, C)
# B, N, 3, C --> B, N, C
q, k, v = qkv.unbind(2)
dtype = q.dtype
q = self.attn.q_norm(q).transpose(-1, -2) # (B, N, C) -> (B, C, N)
k = self.attn.k_norm(k).transpose(-1, -2) # (B, N, C) -> (B, C, N)
v = v.transpose(-1, -2)
q = q.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
k = k.reshape(B, C // self.attn.dim, self.attn.dim, N).transpose(-1, -2) # (B, h, N, h_d)
v = v.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
out = self.attn.attn_matmul(q, k, v).to(dtype)
out = out.view(B, C, N).permute(0, 2, 1) # B, N, C
out = self.attn.proj(out)
# perturbed path (identity attention)
v_weight = self.attn.qkv.weight[C * 2 : C * 3, :] # Shape: (dim, dim)
if self.attn.qkv.bias:
v_bias = self.attn.qkv.bias[C * 2 : C * 3] # Shape: (dim,)
x_ptb = (torch.matmul(x_ptb, v_weight.t()) + v_bias).to(dtype)
else:
x_ptb = torch.matmul(x_ptb, v_weight.t()).to(dtype)
x_ptb = self.attn.proj(x_ptb)
out = torch.cat([out, x_ptb])
if torch.get_autocast_gpu_dtype() == torch.float16:
out = out.clip(-65504, 65504)
return out
class PAGIdentitySelfAttnProcessorLiteLA:
r"""Self Attention with Perturbed Attention Guidance"""
def __init__(self, attn):
self.attn = attn
def __call__(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch.Tensor:
x_org, x_ptb = x.chunk(2)
B, N, C = x_org.shape
qkv = self.attn.qkv(x_org).reshape(B, N, 3, C)
# B, N, 3, C --> B, N, C
q, k, v = qkv.unbind(2)
dtype = q.dtype
q = self.attn.q_norm(q).transpose(-1, -2) # (B, N, C) -> (B, C, N)
k = self.attn.k_norm(k).transpose(-1, -2) # (B, N, C) -> (B, C, N)
v = v.transpose(-1, -2)
q = q.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
k = k.reshape(B, C // self.attn.dim, self.attn.dim, N).transpose(-1, -2) # (B, h, N, h_d)
v = v.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
out = self.attn.attn_matmul(q, k, v).to(dtype)
out = out.view(B, C, N).permute(0, 2, 1) # B, N, C
out = self.attn.proj(out)
# perturbed path (identity attention)
v_weight = self.attn.qkv.weight[C * 2 : C * 3, :] # Shape: (dim, dim)
if self.attn.qkv.bias:
v_bias = self.attn.qkv.bias[C * 2 : C * 3] # Shape: (dim,)
x_ptb = (torch.matmul(x_ptb, v_weight.t()) + v_bias).to(dtype)
else:
x_ptb = torch.matmul(x_ptb, v_weight.t()).to(dtype)
x_ptb = self.attn.proj(x_ptb)
out = torch.cat([out, x_ptb])
if torch.get_autocast_gpu_dtype() == torch.float16:
out = out.clip(-65504, 65504)
return out
class SelfAttnProcessorLiteLA:
r"""Self Attention with Lite Linear Attention"""
def __init__(self, attn):
self.attn = attn
def __call__(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch.Tensor:
B, N, C = x.shape
if HW is None:
H = W = int(N**0.5)
else:
H, W = HW
qkv = self.attn.qkv(x).reshape(B, N, 3, C)
# B, N, 3, C --> B, N, C
q, k, v = qkv.unbind(2)
dtype = q.dtype
q = self.attn.q_norm(q).transpose(-1, -2) # (B, N, C) -> (B, C, N)
k = self.attn.k_norm(k).transpose(-1, -2) # (B, N, C) -> (B, C, N)
v = v.transpose(-1, -2)
q = q.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
k = k.reshape(B, C // self.attn.dim, self.attn.dim, N).transpose(-1, -2) # (B, h, N, h_d)
v = v.reshape(B, C // self.attn.dim, self.attn.dim, N) # (B, h, h_d, N)
out = self.attn.attn_matmul(q, k, v).to(dtype)
out = out.view(B, C, N).permute(0, 2, 1) # B, N, C
out = self.attn.proj(out)
if torch.get_autocast_gpu_dtype() == torch.float16:
out = out.clip(-65504, 65504)
return out
class FlashAttention(Attention_):
"""Multi-head Flash Attention block with qk norm."""
def __init__(
self,
dim,
num_heads=8,
qkv_bias=True,
qk_norm=False,
**block_kwargs,
):
"""
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
qkv_bias (bool: If True, add a learnable bias to query, key, value.
"""
super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
if qk_norm:
self.q_norm = nn.LayerNorm(dim)
self.k_norm = nn.LayerNorm(dim)
else:
self.q_norm = nn.Identity()
self.k_norm = nn.Identity()
def forward(self, x, mask=None, HW=None, block_id=None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, C)
q, k, v = qkv.unbind(2)
dtype = q.dtype
q = self.q_norm(q)
k = self.k_norm(k)
q = q.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype)
k = k.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype)
v = v.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype)
use_fp32_attention = getattr(self, "fp32_attention", False) # necessary for NAN loss
if use_fp32_attention:
q, k, v = q.float(), k.float(), v.float()
attn_bias = None
if mask is not None:
attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)
attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float("-inf"))
if _xformers_available:
x = xformers.ops.memory_efficient_attention(q, k, v, p=self.attn_drop.p, attn_bias=attn_bias)
else:
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None and mask.ndim == 2:
mask = (1 - mask.to(x.dtype)) * -10000.0
mask = mask[:, None, None].repeat(1, self.num_heads, 1, 1)
x = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
x = x.transpose(1, 2)
x = x.view(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
if torch.get_autocast_gpu_dtype() == torch.float16:
x = x.clip(-65504, 65504)
return x
#################################################################################
# AMP attention with fp32 softmax to fix loss NaN problem during training #
#################################################################################
class Attention(Attention_):
def forward(self, x, HW=None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
# B,N,3,H,C -> B,H,N,C
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
use_fp32_attention = getattr(self, "fp32_attention", False)
if use_fp32_attention:
q, k = q.float(), k.float()
with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class FinalLayer(nn.Module):
"""
The final layer of Sana.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class T2IFinalLayer(nn.Module):
"""
The final layer of Sana.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size**0.5)
self.out_channels = out_channels
def forward(self, x, t):
shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
x = t2i_modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class MaskFinalLayer(nn.Module):
"""
The final layer of Sana.
"""
def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True))
def forward(self, x, t):
shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DecoderLayer(nn.Module):
"""
The final layer of Sana.
"""
def __init__(self, hidden_size, decoder_hidden_size):
super().__init__()
self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
def forward(self, x, t):
shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
x = modulate(self.norm_decoder(x), shift, scale)
x = self.linear(x)
return x
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(self.dtype)
t_emb = self.mlp(t_freq)
return t_emb
@property
def dtype(self):
try:
return next(self.parameters()).dtype
except StopIteration:
return torch.float32
class SizeEmbedder(TimestepEmbedder):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
self.outdim = hidden_size
def forward(self, s, bs):
if s.ndim == 1:
s = s[:, None]
assert s.ndim == 2
if s.shape[0] != bs:
s = s.repeat(bs // s.shape[0], 1)
assert s.shape[0] == bs
b, dims = s.shape[0], s.shape[1]
s = rearrange(s, "b d -> (b d)")
s_freq = self.timestep_embedding(s, self.frequency_embedding_size).to(self.dtype)
s_emb = self.mlp(s_freq)
s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
return s_emb
@property
def dtype(self):
try:
return next(self.parameters()).dtype
except StopIteration:
return torch.float32
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
class CaptionEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(
self,
in_channels,
hidden_size,
uncond_prob,
act_layer=nn.GELU(approximate="tanh"),
token_num=120,
):
super().__init__()
self.y_proj = Mlp(
in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0
)
self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5))
self.uncond_prob = uncond_prob
def initialize_gemma_params(self, model_name="google/gemma-2b-it"):
num_layers = len(self.custom_gemma_layers)
text_encoder = AutoModelForCausalLM.from_pretrained(model_name).get_decoder()
pretrained_layers = text_encoder.layers[-num_layers:]
for custom_layer, pretrained_layer in zip(self.custom_gemma_layers, pretrained_layers):
info = custom_layer.load_state_dict(pretrained_layer.state_dict(), strict=False)
print(f"**** {info} ****")
print(f"**** Initialized {num_layers} Gemma layers from pretrained model: {model_name} ****")
def token_drop(self, caption, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
else:
drop_ids = force_drop_ids == 1
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
return caption
def forward(self, caption, train, force_drop_ids=None, mask=None):
if train:
assert caption.shape[2:] == self.y_embedding.shape
use_dropout = self.uncond_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
caption = self.token_drop(caption, force_drop_ids)
caption = self.y_proj(caption)
return caption
class CaptionEmbedderDoubleBr(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate="tanh"), token_num=120):
super().__init__()
self.proj = Mlp(
in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0
)
self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10**0.5)
self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10**0.5)
self.uncond_prob = uncond_prob
def token_drop(self, global_caption, caption, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
else:
drop_ids = force_drop_ids == 1
global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
return global_caption, caption
def forward(self, caption, train, force_drop_ids=None):
assert caption.shape[2:] == self.y_embedding.shape
global_caption = caption.mean(dim=2).squeeze()
use_dropout = self.uncond_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids)
y_embed = self.proj(global_caption)
return y_embed, caption
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
kernel_size=None,
padding=0,
norm_layer=None,
flatten=True,
bias=True,
):
super().__init__()
kernel_size = kernel_size or patch_size
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
if not padding and kernel_size % 2 > 0:
padding = get_same_padding(kernel_size)
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias
)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
B, C, H, W = x.shape
assert (H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
assert (W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class PatchEmbedMS(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
patch_size=16,
in_chans=3,
embed_dim=768,
kernel_size=None,
padding=0,
norm_layer=None,
flatten=True,
bias=True,
):
super().__init__()
kernel_size = kernel_size or patch_size
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.flatten = flatten
if not padding and kernel_size % 2 > 0:
padding = get_same_padding(kernel_size)
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias
)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
Sana/models/sana_multi_scale.py
+374
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@@ -0,0 +1,374 @@
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import torch
import torch.nn as nn
from timm.models.layers import DropPath
from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
from .sana import Sana, get_2d_sincos_pos_embed
from .sana_blocks import (
Attention,
CaptionEmbedder,
FlashAttention,
LiteLA,
MultiHeadCrossAttention,
PatchEmbedMS,
T2IFinalLayer,
t2i_modulate,
)
from .utils import auto_grad_checkpoint
class SanaMSBlock(nn.Module):
"""
A Sana block with global shared adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
drop_path=0.0,
input_size=None,
qk_norm=False,
attn_type="flash",
ffn_type="mlp",
mlp_acts=("silu", "silu", None),
linear_head_dim=32,
cross_norm=False,
**block_kwargs,
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
if attn_type == "flash":
# flash self attention
self.attn = FlashAttention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
qk_norm=qk_norm,
**block_kwargs,
)
elif attn_type == "linear":
# linear self attention
# TODO: Here the num_heads set to 36 for tmp used
self_num_heads = hidden_size // linear_head_dim
self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
elif attn_type == "vanilla":
# vanilla self attention
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
else:
raise ValueError(f"{attn_type} type is not defined.")
self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
if ffn_type == "dwmlp":
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = DWMlp(
in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
)
elif ffn_type == "glumbconv":
self.mlp = GLUMBConv(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=(None, None, None),
act=mlp_acts,
)
elif ffn_type == "glumbconv_dilate":
self.mlp = GLUMBConv(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=(None, None, None),
act=mlp_acts,
dilation=2,
)
elif ffn_type == "mlp":
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(
in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
)
elif ffn_type == "mbconvpreglu":
self.mlp = MBConvPreGLU(
in_dim=hidden_size,
out_dim=hidden_size,
mid_dim=int(hidden_size * mlp_ratio),
use_bias=(True, True, False),
norm=None,
act=mlp_acts,
)
else:
raise ValueError(f"{ffn_type} type is not defined.")
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
def forward(self, x, y, t, mask=None, HW=None, **kwargs):
B, N, C = x.shape
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp), HW=HW))
return x
#############################################################################
# Core Sana Model #
#################################################################################
class SanaMS(Sana):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=32,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
learn_sigma=False,
pred_sigma=False,
drop_path: float = 0.0,
caption_channels=2304,
pe_interpolation=1.0,
config=None,
model_max_length=300,
qk_norm=False,
y_norm=False,
norm_eps=1e-5,
attn_type="linear",
ffn_type="glumbconv",
use_pe=False,
y_norm_scale_factor=1.0,
patch_embed_kernel=None,
mlp_acts=("silu", "silu", None),
linear_head_dim=32,
cross_norm=False,
**kwargs,
):
super().__init__(
input_size=input_size,
patch_size=patch_size,
in_channels=in_channels,
hidden_size=hidden_size,
depth=depth,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
class_dropout_prob=class_dropout_prob,
learn_sigma=learn_sigma,
pred_sigma=pred_sigma,
drop_path=drop_path,
caption_channels=caption_channels,
pe_interpolation=pe_interpolation,
config=config,
model_max_length=model_max_length,
qk_norm=qk_norm,
y_norm=y_norm,
norm_eps=norm_eps,
attn_type=attn_type,
ffn_type=ffn_type,
use_pe=use_pe,
y_norm_scale_factor=y_norm_scale_factor,
patch_embed_kernel=patch_embed_kernel,
mlp_acts=mlp_acts,
linear_head_dim=linear_head_dim,
**kwargs,
)
self.dtype = torch.get_default_dtype()
self.h = self.w = 0
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
self.pos_embed_ms = None
kernel_size = patch_embed_kernel or patch_size
self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True)
self.y_embedder = CaptionEmbedder(
in_channels=caption_channels,
hidden_size=hidden_size,
uncond_prob=class_dropout_prob,
act_layer=approx_gelu,
token_num=model_max_length,
)
drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList(
[
SanaMSBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path[i],
input_size=(input_size // patch_size, input_size // patch_size),
qk_norm=qk_norm,
attn_type=attn_type,
ffn_type=ffn_type,
mlp_acts=mlp_acts,
linear_head_dim=linear_head_dim,
cross_norm=cross_norm,
)
for i in range(depth)
]
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize()
def forward(self, x, timesteps, context, **kwargs):
"""
Forward pass that adapts comfy input to original forward function
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
timesteps: (N,) tensor of diffusion timesteps
context: (N, 1, 120, C) conditioning
"""
## size/ar from cond with fallback based on the latent image shape.
bs = x.shape[0]
## Still accepts the input w/o that dim but returns garbage
if len(context.shape) == 3:
context = context.unsqueeze(1)
## run original forward pass
out = self.forward_raw(
x = x.to(self.dtype),
timestep = timesteps.to(self.dtype),
y = context.to(self.dtype),
)
## only return EPS
out = out.to(torch.float)
return out
def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs):
"""
Forward pass of Sana.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N, 1, 120, C) tensor of class labels
"""
bs = x.shape[0]
x = x.to(self.dtype)
timestep = timestep.to(self.dtype)
y = y.to(self.dtype)
self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
if self.use_pe:
x = self.x_embedder(x)
if self.pos_embed_ms is None or self.pos_embed_ms.shape[1:] != x.shape[1:]:
self.pos_embed_ms = (
torch.from_numpy(
get_2d_sincos_pos_embed(
self.pos_embed.shape[-1],
(self.h, self.w),
pe_interpolation=self.pe_interpolation,
base_size=self.base_size,
)
)
.unsqueeze(0)
.to(x.device)
.to(self.dtype)
)
x += self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2
else:
x = self.x_embedder(x)
t = self.t_embedder(timestep) # (N, D)
t0 = self.t_block(t)
y = self.y_embedder(y, self.training, mask=mask) # (N, D)
if self.y_norm:
y = self.attention_y_norm(y)
if mask is not None:
if mask.shape[0] != y.shape[0]:
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
mask = mask.squeeze(1).squeeze(1)
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
else:
y_lens = [y.shape[2]] * y.shape[0]
y = y.squeeze(1).view(1, -1, x.shape[-1])
for block in self.blocks:
x = auto_grad_checkpoint(
block, x, y, t0, y_lens, (self.h, self.w), **kwargs
) # (N, T, D) #support grad checkpoint
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
return x
def __call__(self, *args, **kwargs):
"""
This method allows the object to be called like a function.
It simply calls the forward method.
"""
return self.forward(*args, **kwargs)
def forward_with_dpmsolver(self, x, timestep, y, data_info, **kwargs):
"""
dpm solver donnot need variance prediction
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
model_out = self.forward(x, timestep, y, data_info=data_info, **kwargs)
return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
assert self.h * self.w == x.shape[1]
x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
return imgs
def initialize(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.normal_(self.t_block[1].weight, std=0.02)
# Initialize caption embedding MLP:
nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
Sana/models/utils.py
+591
View File
@@ -0,0 +1,591 @@
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
import math
import os
import random
import re
import sys
from collections.abc import Iterable
from itertools import repeat
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torch.utils.checkpoint import checkpoint, checkpoint_sequential
from torchvision import transforms as T
def _ntuple(n):
def parse(x):
if isinstance(x, Iterable) and not isinstance(x, str):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
def set_grad_checkpoint(model, gc_step=1):
assert isinstance(model, nn.Module)
def set_attr(module):
module.grad_checkpointing = True
module.grad_checkpointing_step = gc_step
model.apply(set_attr)
def set_fp32_attention(model):
assert isinstance(model, nn.Module)
def set_attr(module):
module.fp32_attention = True
model.apply(set_attr)
def auto_grad_checkpoint(module, *args, **kwargs):
if getattr(module, "grad_checkpointing", False):
if isinstance(module, Iterable):
gc_step = module[0].grad_checkpointing_step
return checkpoint_sequential(module, gc_step, *args, **kwargs)
else:
return checkpoint(module, *args, **kwargs)
return module(*args, **kwargs)
def checkpoint_sequential(functions, step, input, *args, **kwargs):
# Hack for keyword-only parameter in a python 2.7-compliant way
preserve = kwargs.pop("preserve_rng_state", True)
if kwargs:
raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
def run_function(start, end, functions):
def forward(input):
for j in range(start, end + 1):
input = functions[j](input, *args)
return input
return forward
if isinstance(functions, torch.nn.Sequential):
functions = list(functions.children())
# the last chunk has to be non-volatile
end = -1
segment = len(functions) // step
for start in range(0, step * (segment - 1), step):
end = start + step - 1
input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve)
return run_function(end + 1, len(functions) - 1, functions)(input)
def window_partition(x, window_size):
"""
Partition into non-overlapping windows with padding if needed.
Args:
x (tensor): input tokens with [B, H, W, C].
window_size (int): window size.
Returns:
windows: windows after partition with [B * num_windows, window_size, window_size, C].
(Hp, Wp): padded height and width before partition
"""
B, H, W, C = x.shape
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows, (Hp, Wp)
def window_unpartition(windows, window_size, pad_hw, hw):
"""
Window unpartition into original sequences and removing padding.
Args:
x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
window_size (int): window size.
pad_hw (Tuple): padded height and width (Hp, Wp).
hw (Tuple): original height and width (H, W) before padding.
Returns:
x: unpartitioned sequences with [B, H, W, C].
"""
Hp, Wp = pad_hw
H, W = hw
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
if Hp > H or Wp > W:
x = x[:, :H, :W, :].contiguous()
return x
def get_rel_pos(q_size, k_size, rel_pos):
"""
Get relative positional embeddings according to the relative positions of
query and key sizes.
Args:
q_size (int): size of query q.
k_size (int): size of key k.
rel_pos (Tensor): relative position embeddings (L, C).
Returns:
Extracted positional embeddings according to relative positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos if needed.
if rel_pos.shape[0] != max_rel_dist:
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_rel_dist,
mode="linear",
)
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
else:
rel_pos_resized = rel_pos
# Scale the coords with short length if shapes for q and k are different.
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
return rel_pos_resized[relative_coords.long()]
def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
"""
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
Args:
attn (Tensor): attention map.
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
Returns:
attn (Tensor): attention map with added relative positional embeddings.
"""
q_h, q_w = q_size
k_h, k_w = k_size
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
B, _, dim = q.shape
r_q = q.reshape(B, q_h, q_w, dim)
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
B, q_h * q_w, k_h * k_w
)
return attn
def mean_flat(tensor):
return tensor.mean(dim=list(range(1, tensor.ndim)))
#################################################################################
# Token Masking and Unmasking #
#################################################################################
def get_mask(batch, length, mask_ratio, device, mask_type=None, data_info=None, extra_len=0):
"""
Get the binary mask for the input sequence.
Args:
- batch: batch size
- length: sequence length
- mask_ratio: ratio of tokens to mask
- data_info: dictionary with info for reconstruction
return:
mask_dict with following keys:
- mask: binary mask, 0 is keep, 1 is remove
- ids_keep: indices of tokens to keep
- ids_restore: indices to restore the original order
"""
assert mask_type in ["random", "fft", "laplacian", "group"]
mask = torch.ones([batch, length], device=device)
len_keep = int(length * (1 - mask_ratio)) - extra_len
if mask_type == "random" or mask_type == "group":
noise = torch.rand(batch, length, device=device) # noise in [0, 1]
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
ids_removed = ids_shuffle[:, len_keep:]
elif mask_type in ["fft", "laplacian"]:
if "strength" in data_info:
strength = data_info["strength"]
else:
N = data_info["N"][0]
img = data_info["ori_img"]
# 获取原图的尺寸信息
_, C, H, W = img.shape
if mask_type == "fft":
# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
reshaped_image = img.reshape((batch, -1, H // N, N, W // N, N))
fft_image = torch.fft.fftn(reshaped_image, dim=(3, 5))
# 取绝对值并求和获取频率强度
strength = torch.sum(torch.abs(fft_image), dim=(1, 3, 5)).reshape(
(
batch,
-1,
)
)
elif type == "laplacian":
laplacian_kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32).reshape(
1, 1, 3, 3
)
laplacian_kernel = laplacian_kernel.repeat(C, 1, 1, 1)
# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
reshaped_image = img.reshape(-1, C, H // N, N, W // N, N).permute(0, 2, 4, 1, 3, 5).reshape(-1, C, N, N)
laplacian_response = F.conv2d(reshaped_image, laplacian_kernel, padding=1, groups=C)
strength = laplacian_response.sum(dim=[1, 2, 3]).reshape(
(
batch,
-1,
)
)
# 对频率强度进行归一化，然后使用torch.multinomial进行采样
probabilities = strength / (strength.max(dim=1)[0][:, None] + 1e-5)
ids_shuffle = torch.multinomial(probabilities.clip(1e-5, 1), length, replacement=False)
ids_keep = ids_shuffle[:, :len_keep]
ids_restore = torch.argsort(ids_shuffle, dim=1)
ids_removed = ids_shuffle[:, len_keep:]
mask[:, :len_keep] = 0
mask = torch.gather(mask, dim=1, index=ids_restore)
return {"mask": mask, "ids_keep": ids_keep, "ids_restore": ids_restore, "ids_removed": ids_removed}
def mask_out_token(x, ids_keep, ids_removed=None):
"""
Mask out the tokens specified by ids_keep.
Args:
- x: input sequence, [N, L, D]
- ids_keep: indices of tokens to keep
return:
- x_masked: masked sequence
"""
N, L, D = x.shape # batch, length, dim
x_remain = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
if ids_removed is not None:
x_masked = torch.gather(x, dim=1, index=ids_removed.unsqueeze(-1).repeat(1, 1, D))
return x_remain, x_masked
else:
return x_remain
def mask_tokens(x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
mask = torch.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore
def unmask_tokens(x, ids_restore, mask_token):
# x: [N, T, D] if extras == 0 (i.e., no cls token) else x: [N, T+1, D]
mask_tokens = mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1)
x = torch.cat([x, mask_tokens], dim=1)
x = torch.gather(x, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
return x
# Parse 'None' to None and others to float value
def parse_float_none(s):
assert isinstance(s, str)
return None if s == "None" else float(s)
# ----------------------------------------------------------------------------
# Parse a comma separated list of numbers or ranges and return a list of ints.
# Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10]
def parse_int_list(s):
if isinstance(s, list):
return s
ranges = []
range_re = re.compile(r"^(\d+)-(\d+)$")
for p in s.split(","):
m = range_re.match(p)
if m:
ranges.extend(range(int(m.group(1)), int(m.group(2)) + 1))
else:
ranges.append(int(p))
return ranges
def init_processes(fn, args):
"""Initialize the distributed environment."""
os.environ["MASTER_ADDR"] = args.master_address
os.environ["MASTER_PORT"] = str(random.randint(2000, 6000))
print(f'MASTER_ADDR = {os.environ["MASTER_ADDR"]}')
print(f'MASTER_PORT = {os.environ["MASTER_PORT"]}')
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend="nccl", init_method="env://", rank=args.global_rank, world_size=args.global_size)
fn(args)
if args.global_size > 1:
cleanup()
def mprint(*args, **kwargs):
"""
Print only from rank 0.
"""
if dist.get_rank() == 0:
print(*args, **kwargs)
def cleanup():
"""
End DDP training.
"""
dist.barrier()
mprint("Done!")
dist.barrier()
dist.destroy_process_group()
# ----------------------------------------------------------------------------
# logging info.
class Logger:
"""
Redirect stderr to stdout, optionally print stdout to a file,
and optionally force flushing on both stdout and the file.
"""
def __init__(self, file_name=None, file_mode="w", should_flush=True):
self.file = None
if file_name is not None:
self.file = open(file_name, file_mode)
self.should_flush = should_flush
self.stdout = sys.stdout
self.stderr = sys.stderr
sys.stdout = self
sys.stderr = self
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def write(self, text):
"""Write text to stdout (and a file) and optionally flush."""
if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
return
if self.file is not None:
self.file.write(text)
self.stdout.write(text)
if self.should_flush:
self.flush()
def flush(self):
"""Flush written text to both stdout and a file, if open."""
if self.file is not None:
self.file.flush()
self.stdout.flush()
def close(self):
"""Flush, close possible files, and remove stdout/stderr mirroring."""
self.flush()
# if using multiple loggers, prevent closing in wrong order
if sys.stdout is self:
sys.stdout = self.stdout
if sys.stderr is self:
sys.stderr = self.stderr
if self.file is not None:
self.file.close()
class StackedRandomGenerator:
def __init__(self, device, seeds):
super().__init__()
self.generators = [torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds]
def randn(self, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators])
def randn_like(self, input):
return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device)
def randint(self, *args, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randint(*args, size=size[1:], generator=gen, **kwargs) for gen in self.generators])
def prepare_prompt_ar(prompt, ratios, device="cpu", show=True):
# get aspect_ratio or ar
aspect_ratios = re.findall(r"--aspect_ratio\s+(\d+:\d+)", prompt)
ars = re.findall(r"--ar\s+(\d+:\d+)", prompt)
custom_hw = re.findall(r"--hw\s+(\d+:\d+)", prompt)
if show:
print("aspect_ratios:", aspect_ratios, "ars:", ars, "hws:", custom_hw)
prompt_clean = prompt.split("--aspect_ratio")[0].split("--ar")[0].split("--hw")[0]
if len(aspect_ratios) + len(ars) + len(custom_hw) == 0 and show:
print(
"Wrong prompt format. Set to default ar: 1. change your prompt into format '--ar h:w or --hw h:w' for correct generating"
)
if len(aspect_ratios) != 0:
ar = float(aspect_ratios[0].split(":")[0]) / float(aspect_ratios[0].split(":")[1])
elif len(ars) != 0:
ar = float(ars[0].split(":")[0]) / float(ars[0].split(":")[1])
else:
ar = 1.0
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
if len(custom_hw) != 0:
custom_hw = [float(custom_hw[0].split(":")[0]), float(custom_hw[0].split(":")[1])]
else:
custom_hw = ratios[closest_ratio]
default_hw = ratios[closest_ratio]
prompt_show = f"prompt: {prompt_clean.strip()}\nSize: --ar {closest_ratio}, --bin hw {ratios[closest_ratio]}, --custom hw {custom_hw}"
return (
prompt_clean,
prompt_show,
torch.tensor(default_hw, device=device)[None],
torch.tensor([float(closest_ratio)], device=device)[None],
torch.tensor(custom_hw, device=device)[None],
)
def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
orig_height, orig_width = samples.shape[2], samples.shape[3]
# Check if resizing is needed
if orig_height != new_height or orig_width != new_width:
ratio = max(new_height / orig_height, new_width / orig_width)
resized_width = int(orig_width * ratio)
resized_height = int(orig_height * ratio)
# Resize
samples = F.interpolate(samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False)
# Center Crop
start_x = (resized_width - new_width) // 2
end_x = start_x + new_width
start_y = (resized_height - new_height) // 2
end_y = start_y + new_height
samples = samples[:, :, start_y:end_y, start_x:end_x]
return samples
def resize_and_crop_img(img: Image, new_width, new_height):
orig_width, orig_height = img.size
ratio = max(new_width / orig_width, new_height / orig_height)
resized_width = int(orig_width * ratio)
resized_height = int(orig_height * ratio)
img = img.resize((resized_width, resized_height), Image.LANCZOS)
left = (resized_width - new_width) / 2
top = (resized_height - new_height) / 2
right = (resized_width + new_width) / 2
bottom = (resized_height + new_height) / 2
img = img.crop((left, top, right, bottom))
return img
def mask_feature(emb, mask):
if emb.shape[0] == 1:
keep_index = mask.sum().item()
return emb[:, :, :keep_index, :], keep_index
else:
masked_feature = emb * mask[:, None, :, None]
return masked_feature, emb.shape[2]
def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore
"""Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore
"""Return tuple with min_len by repeating element at idx_repeat."""
# convert to list first
x = val2list(x)
# repeat elements if necessary
if len(x) > 0:
x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
return tuple(x)
def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number"
return kernel_size // 2