219 lines
8.2 KiB
Python
219 lines
8.2 KiB
Python
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
|