File size: 6,868 Bytes
c31821c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | from dataclasses import dataclass
from enum import Enum
from json import JSONEncoder
import torch
class SDVersion(Enum):
SD1 = 1
SD2 = 2
SDXL = 3
Unknown = -1
def __str__(self):
return self.name
@classmethod
def from_str(cls, str):
try:
return cls[str]
except KeyError:
return cls.Unknown
def match(self, sd_model):
if sd_model.is_sd1 and self == SDVersion.SD1:
return True
elif sd_model.is_sd2 and self == SDVersion.SD2:
return True
elif sd_model.is_sdxl and self == SDVersion.SDXL:
return True
elif self == SDVersion.Unknown:
return True
else:
return False
class ModelType(Enum):
UNET = 0
CONTROLNET = 1
LORA = 2
UNDEFINED = -1
@classmethod
def from_string(cls, s):
return getattr(cls, s.upper(), None)
def __str__(self):
return self.name.lower()
@dataclass
class ModelConfig:
profile: dict
static_shapes: bool
fp32: bool
inpaint: bool
refit: bool
lora: bool
vram: int
unet_hidden_dim: int = 4
def is_compatible_from_dict(self, feed_dict: dict):
distance = 0
for k, v in feed_dict.items():
_min, _opt, _max = self.profile[k]
v_tensor = torch.Tensor(list(v.shape))
r_min = torch.Tensor(_max) - v_tensor
r_opt = (torch.Tensor(_opt) - v_tensor).abs()
r_max = v_tensor - torch.Tensor(_min)
if torch.any(r_min < 0) or torch.any(r_max < 0):
return (False, distance)
distance += r_opt.sum() + 0.5 * (r_max.sum() + 0.5 * r_min.sum())
return (True, distance)
def is_compatible(
self, width: int, height: int, batch_size: int, max_embedding: int
):
distance = 0
sample = self.profile["sample"]
embedding = self.profile["encoder_hidden_states"]
batch_size *= 2
width = width // 8
height = height // 8
_min, _opt, _max = sample
if _min[0] > batch_size or _max[0] < batch_size:
return (False, distance)
if _min[2] > height or _max[2] < height:
return (False, distance)
if _min[3] > width or _max[3] < width:
return (False, distance)
_min_em, _opt_em, _max_em = embedding
if _min_em[1] > max_embedding or _max_em[1] < max_embedding:
return (False, distance)
distance = (
abs(_opt[0] - batch_size)
+ abs(_opt[2] - height)
+ abs(_opt[3] - width)
+ 0.5 * (abs(_max[2] - height) + abs(_max[3] - width))
)
return (True, distance)
class ModelConfigEncoder(JSONEncoder):
def default(self, o: ModelConfig):
return o.__dict__
@dataclass
class ProfileSettings:
bs_min: int
bs_opt: int
bs_max: int
h_min: int
h_opt: int
h_max: int
w_min: int
w_opt: int
w_max: int
t_min: int
t_opt: int
t_max: int
static_shape: bool = False
def __str__(self) -> str:
return "Batch Size: {}-{}-{}\nHeight: {}-{}-{}\nWidth: {}-{}-{}\nToken Count: {}-{}-{}".format(
self.bs_min,
self.bs_opt,
self.bs_max,
self.h_min,
self.h_opt,
self.h_max,
self.w_min,
self.w_opt,
self.w_max,
self.t_min,
self.t_opt,
self.t_max,
)
def out(self):
return (
self.bs_min,
self.bs_opt,
self.bs_max,
self.h_min,
self.h_opt,
self.h_max,
self.w_min,
self.w_opt,
self.w_max,
self.t_min,
self.t_opt,
self.t_max,
)
def token_to_dim(self, static_shapes: bool):
self.t_min = (self.t_min // 75) * 77
self.t_opt = (self.t_opt // 75) * 77
self.t_max = (self.t_max // 75) * 77
if static_shapes:
self.t_min = self.t_max = self.t_opt
self.bs_min = self.bs_max = self.bs_opt
self.h_min = self.h_max = self.h_opt
self.w_min = self.w_max = self.w_opt
self.static_shape = True
def get_latent_dim(self):
return (
self.h_min // 8,
self.h_opt // 8,
self.h_max // 8,
self.w_min // 8,
self.w_opt // 8,
self.w_max // 8,
)
def get_a1111_batch_dim(self):
static_batch = self.bs_min == self.bs_max == self.bs_opt
if self.t_max <= 77:
return (self.bs_min * 2, self.bs_opt * 2, self.bs_max * 2)
elif self.t_max > 77 and static_batch:
return (self.bs_opt, self.bs_opt, self.bs_opt)
elif self.t_max > 77 and not static_batch:
if self.t_opt > 77:
return (self.bs_min, self.bs_opt, self.bs_max * 2)
return (self.bs_min, self.bs_opt * 2, self.bs_max * 2)
else:
raise Exception("Uncovered case in get_batch_dim")
class ProfilePrests:
def __init__(self):
self.profile_presets = {
"512x512 | Batch Size 1 (Static)": ProfileSettings(
1, 1, 1, 512, 512, 512, 512, 512, 512, 75, 75, 75
),
"768x768 | Batch Size 1 (Static)": ProfileSettings(
1, 1, 1, 768, 768, 768, 768, 768, 768, 75, 75, 75
),
"1024x1024 | Batch Size 1 (Static)": ProfileSettings(
1, 1, 1, 1024, 1024, 1024, 1024, 1024, 1024, 75, 75, 75
),
"256x256 - 512x512 | Batch Size 1-4": ProfileSettings(
1, 1, 4, 256, 512, 512, 256, 512, 512, 75, 75, 150
),
"512x512 - 768x768 | Batch Size 1-4": ProfileSettings(
1, 1, 4, 512, 512, 768, 512, 512, 768, 75, 75, 150
),
"768x768 - 1024x1024 | Batch Size 1-4": ProfileSettings(
1, 1, 4, 768, 1024, 1024, 768, 1024, 1024, 75, 75, 150
),
}
self.default = ProfileSettings(
1, 1, 4, 512, 512, 768, 512, 512, 768, 75, 75, 150
)
self.default_xl = ProfileSettings(
1, 1, 1, 1024, 1024, 1024, 1024, 1024, 1024, 75, 75, 75
)
def get_settings_from_version(self, version: str):
static = False
if version == "Default":
return *self.default.out(), static
if "Static" in version:
static = True
return *self.profile_presets[version].out(), static
def get_choices(self):
return list(self.profile_presets.keys()) + ["Default"]
def get_default(self, is_xl: bool):
if is_xl:
return self.default_xl
return self.default
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