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b701455 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | import torch
import torch.nn as nn
import logging
from src.Utilities import util
from src.Attention import AttentionMethods
from src.Device import Device
from src.cond import cast
def Normalize(
in_channels: int, dtype: torch.dtype = None, device: torch.device = None
) -> torch.nn.GroupNorm:
"""#### Normalize the input channels.
#### Args:
- `in_channels` (int): The input channels.
- `dtype` (torch.dtype, optional): The data type. Defaults to `None`.
- `device` (torch.device, optional): The device. Defaults to `None`.
#### Returns:
- `torch.nn.GroupNorm`: The normalized input channels
"""
return torch.nn.GroupNorm(
num_groups=32,
num_channels=in_channels,
eps=1e-6,
affine=True,
dtype=dtype,
device=device,
)
if Device.spargeattn_enabled():
logging.info("Using SpargeAttn (Sparse + SageAttention) cross attention")
optimized_attention = AttentionMethods.attention_sparge
elif Device.sageattention_enabled():
logging.info("Using SageAttention cross attention")
optimized_attention = AttentionMethods.attention_sage
elif Device.xformers_enabled():
logging.info("Using xformers cross attention")
optimized_attention = AttentionMethods.attention_xformers
else:
logging.info("Using pytorch cross attention")
optimized_attention = AttentionMethods.attention_pytorch
optimized_attention_masked = optimized_attention
def optimized_attention_for_device() -> AttentionMethods.attention_pytorch:
"""#### Get the optimized attention for a device.
#### Returns:
- `function`: The optimized attention function.
"""
return AttentionMethods.attention_pytorch
class CrossAttention(nn.Module):
"""#### Cross attention module, which applies attention across the query and context.
#### Args:
- `query_dim` (int): The query dimension.
- `context_dim` (int, optional): The context dimension. Defaults to `None`.
- `heads` (int, optional): The number of heads. Defaults to `8`.
- `dim_head` (int, optional): The head dimension. Defaults to `64`.
- `dropout` (float, optional): The dropout rate. Defaults to `0.0`.
- `dtype` (torch.dtype, optional): The data type. Defaults to `None`.
- `device` (torch.device, optional): The device. Defaults to `None`.
- `operations` (cast.disable_weight_init, optional): The operations. Defaults to `cast.disable_weight_init`.
"""
def __init__(
self,
query_dim: int,
context_dim: int = None,
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
dtype: torch.dtype = None,
device: torch.device = None,
operations: cast.disable_weight_init = cast.disable_weight_init,
):
super().__init__()
inner_dim = dim_head * heads
context_dim = util.default(context_dim, query_dim)
self.heads = heads
self.dim_head = dim_head
self.to_q = operations.Linear(
query_dim, inner_dim, bias=False, dtype=dtype, device=device
)
self.to_k = operations.Linear(
context_dim, inner_dim, bias=False, dtype=dtype, device=device
)
self.to_v = operations.Linear(
context_dim, inner_dim, bias=False, dtype=dtype, device=device
)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout),
)
# Optimization: Cache for static context projections
self._context_cache = {}
def forward(
self,
x: torch.Tensor,
context: torch.Tensor = None,
value: torch.Tensor = None,
mask: torch.Tensor = None,
) -> torch.Tensor:
"""#### Forward pass of the cross attention module.
#### Args:
- `x` (torch.Tensor): The input tensor.
- `context` (torch.Tensor, optional): The context tensor. Defaults to `None`.
- `value` (torch.Tensor, optional): The value tensor. Defaults to `None`.
- `mask` (torch.Tensor, optional): The mask tensor. Defaults to `None`.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
q = self.to_q(x)
context = util.default(context, x)
# Optimization: Cache K and V if context is static (e.g. prompt embeddings)
# We use id(context) as key since it's typically the same object across steps
if context is not x:
cache_key = id(context)
if cache_key in self._context_cache:
k, v = self._context_cache[cache_key]
else:
k = self.to_k(context)
v = self.to_v(context)
# Keep cache size minimal
if len(self._context_cache) > 2:
self._context_cache.clear()
self._context_cache[cache_key] = (k, v)
else:
k = self.to_k(context)
v = self.to_v(context)
out = optimized_attention(q, k, v, self.heads)
return self.to_out(out)
class AttnBlock(nn.Module):
"""#### Attention block, which applies attention to the input tensor.
#### Args:
- `in_channels` (int): The input channels.
"""
def __init__(self, in_channels: int):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = cast.disable_weight_init.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.k = cast.disable_weight_init.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.v = cast.disable_weight_init.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.proj_out = cast.disable_weight_init.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
if Device.spargeattn_enabled_vae():
logging.info("Using SpargeAttn (Sparse + SageAttention) in VAE")
self.optimized_attention = AttentionMethods.sparge_attention
elif Device.sageattention_enabled_vae():
logging.info("Using SageAttention in VAE")
self.optimized_attention = AttentionMethods.sage_attention
elif Device.xformers_enabled_vae():
logging.info("Using xformers attention in VAE")
self.optimized_attention = AttentionMethods.xformers_attention
else:
logging.info("Using pytorch attention in VAE")
self.optimized_attention = AttentionMethods.pytorch_attention
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""#### Forward pass of the attention block.
#### Args:
- `x` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
h_ = self.optimized_attention(q, k, v)
h_ = self.proj_out(h_)
return x + h_
def make_attn(in_channels: int, attn_type: str = "vanilla") -> AttnBlock:
"""#### Make an attention block.
#### Args:
- `in_channels` (int): The input channels.
- `attn_type` (str, optional): The attention type. Defaults to "vanilla".
#### Returns:
- `AttnBlock`: A class instance of the attention block.
"""
return AttnBlock(in_channels)
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