Spaces:
Running
on
L40S
Running
on
L40S
File size: 15,344 Bytes
4450790 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 |
import torch
class LatentFormat:
scale_factor = 1.0
latent_channels = 4
latent_rgb_factors = None
latent_rgb_factors_bias = None
taesd_decoder_name = None
def process_in(self, latent):
return latent * self.scale_factor
def process_out(self, latent):
return latent / self.scale_factor
class SD15(LatentFormat):
def __init__(self, scale_factor=0.18215):
self.scale_factor = scale_factor
self.latent_rgb_factors = [
# R G B
[ 0.3512, 0.2297, 0.3227],
[ 0.3250, 0.4974, 0.2350],
[-0.2829, 0.1762, 0.2721],
[-0.2120, -0.2616, -0.7177]
]
self.taesd_decoder_name = "taesd_decoder"
class SDXL(LatentFormat):
scale_factor = 0.13025
def __init__(self):
self.latent_rgb_factors = [
# R G B
[ 0.3651, 0.4232, 0.4341],
[-0.2533, -0.0042, 0.1068],
[ 0.1076, 0.1111, -0.0362],
[-0.3165, -0.2492, -0.2188]
]
self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]
self.taesd_decoder_name = "taesdxl_decoder"
class SDXL_Playground_2_5(LatentFormat):
def __init__(self):
self.scale_factor = 0.5
self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
self.latent_rgb_factors = [
# R G B
[ 0.3920, 0.4054, 0.4549],
[-0.2634, -0.0196, 0.0653],
[ 0.0568, 0.1687, -0.0755],
[-0.3112, -0.2359, -0.2076]
]
self.taesd_decoder_name = "taesdxl_decoder"
def process_in(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return (latent - latents_mean) * self.scale_factor / latents_std
def process_out(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return latent * latents_std / self.scale_factor + latents_mean
class SD_X4(LatentFormat):
def __init__(self):
self.scale_factor = 0.08333
self.latent_rgb_factors = [
[-0.2340, -0.3863, -0.3257],
[ 0.0994, 0.0885, -0.0908],
[-0.2833, -0.2349, -0.3741],
[ 0.2523, -0.0055, -0.1651]
]
class SC_Prior(LatentFormat):
latent_channels = 16
def __init__(self):
self.scale_factor = 1.0
self.latent_rgb_factors = [
[-0.0326, -0.0204, -0.0127],
[-0.1592, -0.0427, 0.0216],
[ 0.0873, 0.0638, -0.0020],
[-0.0602, 0.0442, 0.1304],
[ 0.0800, -0.0313, -0.1796],
[-0.0810, -0.0638, -0.1581],
[ 0.1791, 0.1180, 0.0967],
[ 0.0740, 0.1416, 0.0432],
[-0.1745, -0.1888, -0.1373],
[ 0.2412, 0.1577, 0.0928],
[ 0.1908, 0.0998, 0.0682],
[ 0.0209, 0.0365, -0.0092],
[ 0.0448, -0.0650, -0.1728],
[-0.1658, -0.1045, -0.1308],
[ 0.0542, 0.1545, 0.1325],
[-0.0352, -0.1672, -0.2541]
]
class SC_B(LatentFormat):
def __init__(self):
self.scale_factor = 1.0 / 0.43
self.latent_rgb_factors = [
[ 0.1121, 0.2006, 0.1023],
[-0.2093, -0.0222, -0.0195],
[-0.3087, -0.1535, 0.0366],
[ 0.0290, -0.1574, -0.4078]
]
class SD3(LatentFormat):
latent_channels = 16
def __init__(self):
self.scale_factor = 1.5305
self.shift_factor = 0.0609
self.latent_rgb_factors = [
[-0.0922, -0.0175, 0.0749],
[ 0.0311, 0.0633, 0.0954],
[ 0.1994, 0.0927, 0.0458],
[ 0.0856, 0.0339, 0.0902],
[ 0.0587, 0.0272, -0.0496],
[-0.0006, 0.1104, 0.0309],
[ 0.0978, 0.0306, 0.0427],
[-0.0042, 0.1038, 0.1358],
[-0.0194, 0.0020, 0.0669],
[-0.0488, 0.0130, -0.0268],
[ 0.0922, 0.0988, 0.0951],
[-0.0278, 0.0524, -0.0542],
[ 0.0332, 0.0456, 0.0895],
[-0.0069, -0.0030, -0.0810],
[-0.0596, -0.0465, -0.0293],
[-0.1448, -0.1463, -0.1189]
]
self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]
self.taesd_decoder_name = "taesd3_decoder"
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor
class StableAudio1(LatentFormat):
latent_channels = 64
class Flux(SD3):
latent_channels = 16
def __init__(self):
self.scale_factor = 0.3611
self.shift_factor = 0.1159
self.latent_rgb_factors =[
[-0.0346, 0.0244, 0.0681],
[ 0.0034, 0.0210, 0.0687],
[ 0.0275, -0.0668, -0.0433],
[-0.0174, 0.0160, 0.0617],
[ 0.0859, 0.0721, 0.0329],
[ 0.0004, 0.0383, 0.0115],
[ 0.0405, 0.0861, 0.0915],
[-0.0236, -0.0185, -0.0259],
[-0.0245, 0.0250, 0.1180],
[ 0.1008, 0.0755, -0.0421],
[-0.0515, 0.0201, 0.0011],
[ 0.0428, -0.0012, -0.0036],
[ 0.0817, 0.0765, 0.0749],
[-0.1264, -0.0522, -0.1103],
[-0.0280, -0.0881, -0.0499],
[-0.1262, -0.0982, -0.0778]
]
self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
self.taesd_decoder_name = "taef1_decoder"
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor
class Mochi(LatentFormat):
latent_channels = 12
def __init__(self):
self.scale_factor = 1.0
self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141,
-0.05565264470995561, 0.012767231469026969, -0.04703542746246419,
0.043896967884726704, -0.09346305707025976, -0.09918314763016893,
-0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1)
self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617,
0.959253732819592, 0.8244560132752793, 0.917259975397747,
0.9294154431013696, 1.3720942357788521, 0.881393668867029,
0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)
self.latent_rgb_factors =[
[-0.0069, -0.0045, 0.0018],
[ 0.0154, -0.0692, -0.0274],
[ 0.0333, 0.0019, 0.0206],
[-0.1390, 0.0628, 0.1678],
[-0.0725, 0.0134, -0.1898],
[ 0.0074, -0.0270, -0.0209],
[-0.0176, -0.0277, -0.0221],
[ 0.5294, 0.5204, 0.3852],
[-0.0326, -0.0446, -0.0143],
[-0.0659, 0.0153, -0.0153],
[ 0.0185, -0.0217, 0.0014],
[-0.0396, -0.0495, -0.0281]
]
self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]
self.taesd_decoder_name = None #TODO
def process_in(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return (latent - latents_mean) * self.scale_factor / latents_std
def process_out(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return latent * latents_std / self.scale_factor + latents_mean
class LTXV(LatentFormat):
latent_channels = 128
def __init__(self):
self.latent_rgb_factors = [
[ 1.1202e-02, -6.3815e-04, -1.0021e-02],
[ 8.6031e-02, 6.5813e-02, 9.5409e-04],
[-1.2576e-02, -7.5734e-03, -4.0528e-03],
[ 9.4063e-03, -2.1688e-03, 2.6093e-03],
[ 3.7636e-03, 1.2765e-02, 9.1548e-03],
[ 2.1024e-02, -5.2973e-03, 3.4373e-03],
[-8.8896e-03, -1.9703e-02, -1.8761e-02],
[-1.3160e-02, -1.0523e-02, 1.9709e-03],
[-1.5152e-03, -6.9891e-03, -7.5810e-03],
[-1.7247e-03, 4.6560e-04, -3.3839e-03],
[ 1.3617e-02, 4.7077e-03, -2.0045e-03],
[ 1.0256e-02, 7.7318e-03, 1.3948e-02],
[-1.6108e-02, -6.2151e-03, 1.1561e-03],
[ 7.3407e-03, 1.5628e-02, 4.4865e-04],
[ 9.5357e-04, -2.9518e-03, -1.4760e-02],
[ 1.9143e-02, 1.0868e-02, 1.2264e-02],
[ 4.4575e-03, 3.6682e-05, -6.8508e-03],
[-4.5681e-04, 3.2570e-03, 7.7929e-03],
[ 3.3902e-02, 3.3405e-02, 3.7454e-02],
[-2.3001e-02, -2.4877e-03, -3.1033e-03],
[ 5.0265e-02, 3.8841e-02, 3.3539e-02],
[-4.1018e-03, -1.1095e-03, 1.5859e-03],
[-1.2689e-01, -1.3107e-01, -2.1005e-01],
[ 2.6276e-02, 1.4189e-02, -3.5963e-03],
[-4.8679e-03, 8.8486e-03, 7.8029e-03],
[-1.6610e-03, -4.8597e-03, -5.2060e-03],
[-2.1010e-03, 2.3610e-03, 9.3796e-03],
[-2.2482e-02, -2.1305e-02, -1.5087e-02],
[-1.5753e-02, -1.0646e-02, -6.5083e-03],
[-4.6975e-03, 5.0288e-03, -6.7390e-03],
[ 1.1951e-02, 2.0712e-02, 1.6191e-02],
[-6.3704e-03, -8.4827e-03, -9.5483e-03],
[ 7.2610e-03, -9.9326e-03, -2.2978e-02],
[-9.1904e-04, 6.2882e-03, 9.5720e-03],
[-3.7178e-02, -3.7123e-02, -5.6713e-02],
[-1.3373e-01, -1.0720e-01, -5.3801e-02],
[-5.3702e-03, 8.1256e-03, 8.8397e-03],
[-1.5247e-01, -2.1437e-01, -2.1843e-01],
[ 3.1441e-02, 7.0335e-03, -9.7541e-03],
[ 2.1528e-03, -8.9817e-03, -2.1023e-02],
[ 3.8461e-03, -5.8957e-03, -1.5014e-02],
[-4.3470e-03, -1.2940e-02, -1.5972e-02],
[-5.4781e-03, -1.0842e-02, -3.0204e-03],
[-6.5347e-03, 3.0806e-03, -1.0163e-02],
[-5.0414e-03, -7.1503e-03, -8.9686e-04],
[-8.5851e-03, -2.4351e-03, 1.0674e-03],
[-9.0016e-03, -9.6493e-03, 1.5692e-03],
[ 5.0914e-03, 1.2099e-02, 1.9968e-02],
[ 1.3758e-02, 1.1669e-02, 8.1958e-03],
[-1.0518e-02, -1.1575e-02, -4.1307e-03],
[-2.8410e-02, -3.1266e-02, -2.2149e-02],
[ 2.9336e-03, 3.6511e-02, 1.8717e-02],
[-1.6703e-02, -1.6696e-02, -4.4529e-03],
[ 4.8818e-02, 4.0063e-02, 8.7410e-03],
[-1.5066e-02, -5.7328e-04, 2.9785e-03],
[-1.7613e-02, -8.1034e-03, 1.3086e-02],
[-9.2633e-03, 1.0803e-02, -6.3489e-03],
[ 3.0851e-03, 4.7750e-04, 1.2347e-02],
[-2.2785e-02, -2.3043e-02, -2.6005e-02],
[-2.4787e-02, -1.5389e-02, -2.2104e-02],
[-2.3572e-02, 1.0544e-03, 1.2361e-02],
[-7.8915e-03, -1.2271e-03, -6.0968e-03],
[-1.1478e-02, -1.2543e-03, 6.2679e-03],
[-5.4229e-02, 2.6644e-02, 6.3394e-03],
[ 4.4216e-03, -7.3338e-03, -1.0464e-02],
[-4.5013e-03, 1.6082e-03, 1.4420e-02],
[ 1.3673e-02, 8.8877e-03, 4.1253e-03],
[-1.0145e-02, 9.0072e-03, 1.5695e-02],
[-5.6234e-03, 1.1847e-03, 8.1261e-03],
[-3.7171e-03, -5.3538e-03, 1.2590e-03],
[ 2.9476e-02, 2.1424e-02, 3.0424e-02],
[-3.4925e-02, -2.4340e-02, -2.5316e-02],
[-3.4127e-02, -2.2406e-02, -1.0589e-02],
[-1.7342e-02, -1.3249e-02, -1.0719e-02],
[-2.1478e-03, -8.6051e-03, -2.9878e-03],
[ 1.2089e-03, -4.2391e-03, -6.8569e-03],
[ 9.0411e-04, -6.6886e-03, -6.7547e-05],
[ 1.6048e-02, -1.0057e-02, -2.8929e-02],
[ 1.2290e-03, 1.0163e-02, 1.8861e-02],
[ 1.7264e-02, 2.7257e-04, 1.3785e-02],
[-1.3482e-02, -3.6427e-03, 6.7481e-04],
[ 4.6782e-03, -5.2423e-03, 2.4467e-03],
[-5.9113e-03, -6.2244e-03, -1.8162e-03],
[ 1.5496e-02, 1.4582e-02, 1.9514e-03],
[ 7.4958e-03, 1.5886e-03, -8.2305e-03],
[ 1.9086e-02, 1.6360e-03, -3.9674e-03],
[-5.7021e-03, -2.7307e-03, -4.1066e-03],
[ 1.7450e-03, 1.4602e-02, 2.5794e-02],
[-8.2788e-04, 2.2902e-03, 4.5161e-03],
[ 1.1632e-02, 8.9193e-03, -7.2813e-03],
[ 7.5721e-03, 2.6784e-03, 1.1393e-02],
[ 5.1939e-03, 3.6903e-03, 1.4049e-02],
[-1.8383e-02, -2.2529e-02, -2.4477e-02],
[ 5.8842e-04, -5.7874e-03, -1.4770e-02],
[-1.6125e-02, -8.6101e-03, -1.4533e-02],
[ 2.0540e-02, 2.0729e-02, 6.4338e-03],
[ 3.3587e-03, -1.1226e-02, -1.6444e-02],
[-1.4742e-03, -1.0489e-02, 1.7097e-03],
[ 2.8130e-02, 2.3546e-02, 3.2791e-02],
[-1.8532e-02, -1.2842e-02, -8.7756e-03],
[-8.0533e-03, -1.0771e-02, -1.7536e-02],
[-3.9009e-03, 1.6150e-02, 3.3359e-02],
[-7.4554e-03, -1.4154e-02, -6.1910e-03],
[ 3.4734e-03, -1.1370e-02, -1.0581e-02],
[ 1.1476e-02, 3.9281e-03, 2.8231e-03],
[ 7.1639e-03, -1.4741e-03, -3.8066e-03],
[ 2.2250e-03, -8.7552e-03, -9.5719e-03],
[ 2.4146e-02, 2.1696e-02, 2.8056e-02],
[-5.4365e-03, -2.4291e-02, -1.7802e-02],
[ 7.4263e-03, 1.0510e-02, 1.2705e-02],
[ 6.2669e-03, 6.2658e-03, 1.9211e-02],
[ 1.6378e-02, 9.4933e-03, 6.6971e-03],
[ 1.7173e-02, 2.3601e-02, 2.3296e-02],
[-1.4568e-02, -9.8279e-03, -1.1556e-02],
[ 1.4431e-02, 1.4430e-02, 6.6362e-03],
[-6.8230e-03, 1.8863e-02, 1.4555e-02],
[ 6.1156e-03, 3.4700e-03, -2.6662e-03],
[-2.6983e-03, -5.9402e-03, -9.2276e-03],
[ 1.0235e-02, 7.4173e-03, -7.6243e-03],
[-1.3255e-02, 1.9322e-02, -9.2153e-04],
[ 2.4222e-03, -4.8039e-03, -1.5759e-02],
[ 2.6244e-02, 2.5951e-02, 2.0249e-02],
[ 1.5711e-02, 1.8498e-02, 2.7407e-03],
[-2.1714e-03, 4.7214e-03, -2.2443e-02],
[-7.4747e-03, 7.4166e-03, 1.4430e-02],
[-8.3906e-03, -7.9776e-03, 9.7927e-03],
[ 3.8321e-02, 9.6622e-03, -1.9268e-02],
[-1.4605e-02, -6.7032e-03, 3.9675e-03]
]
self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
|