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import torch |
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class LatentFormat: |
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scale_factor = 1.0 |
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latent_channels = 4 |
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latent_rgb_factors = None |
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latent_rgb_factors_bias = None |
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taesd_decoder_name = None |
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def process_in(self, latent): |
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return latent * self.scale_factor |
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def process_out(self, latent): |
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return latent / self.scale_factor |
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class SD15(LatentFormat): |
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def __init__(self, scale_factor=0.18215): |
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self.scale_factor = scale_factor |
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self.latent_rgb_factors = [ |
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[ 0.3512, 0.2297, 0.3227], |
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[ 0.3250, 0.4974, 0.2350], |
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[-0.2829, 0.1762, 0.2721], |
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[-0.2120, -0.2616, -0.7177] |
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] |
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self.taesd_decoder_name = "taesd_decoder" |
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class SDXL(LatentFormat): |
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scale_factor = 0.13025 |
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def __init__(self): |
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self.latent_rgb_factors = [ |
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[ 0.3651, 0.4232, 0.4341], |
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[-0.2533, -0.0042, 0.1068], |
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[ 0.1076, 0.1111, -0.0362], |
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[-0.3165, -0.2492, -0.2188] |
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] |
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self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011] |
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self.taesd_decoder_name = "taesdxl_decoder" |
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class SDXL_Playground_2_5(LatentFormat): |
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def __init__(self): |
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self.scale_factor = 0.5 |
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self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) |
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self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) |
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self.latent_rgb_factors = [ |
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[ 0.3920, 0.4054, 0.4549], |
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[-0.2634, -0.0196, 0.0653], |
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[ 0.0568, 0.1687, -0.0755], |
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[-0.3112, -0.2359, -0.2076] |
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] |
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self.taesd_decoder_name = "taesdxl_decoder" |
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def process_in(self, latent): |
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latents_mean = self.latents_mean.to(latent.device, latent.dtype) |
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latents_std = self.latents_std.to(latent.device, latent.dtype) |
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return (latent - latents_mean) * self.scale_factor / latents_std |
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def process_out(self, latent): |
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latents_mean = self.latents_mean.to(latent.device, latent.dtype) |
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latents_std = self.latents_std.to(latent.device, latent.dtype) |
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return latent * latents_std / self.scale_factor + latents_mean |
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class SD_X4(LatentFormat): |
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def __init__(self): |
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self.scale_factor = 0.08333 |
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self.latent_rgb_factors = [ |
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[-0.2340, -0.3863, -0.3257], |
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[ 0.0994, 0.0885, -0.0908], |
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[-0.2833, -0.2349, -0.3741], |
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[ 0.2523, -0.0055, -0.1651] |
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] |
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class SC_Prior(LatentFormat): |
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latent_channels = 16 |
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def __init__(self): |
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self.scale_factor = 1.0 |
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self.latent_rgb_factors = [ |
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[-0.0326, -0.0204, -0.0127], |
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[-0.1592, -0.0427, 0.0216], |
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[ 0.0873, 0.0638, -0.0020], |
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[-0.0602, 0.0442, 0.1304], |
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[ 0.0800, -0.0313, -0.1796], |
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[-0.0810, -0.0638, -0.1581], |
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[ 0.1791, 0.1180, 0.0967], |
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[ 0.0740, 0.1416, 0.0432], |
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[-0.1745, -0.1888, -0.1373], |
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[ 0.2412, 0.1577, 0.0928], |
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[ 0.1908, 0.0998, 0.0682], |
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[ 0.0209, 0.0365, -0.0092], |
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[ 0.0448, -0.0650, -0.1728], |
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[-0.1658, -0.1045, -0.1308], |
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[ 0.0542, 0.1545, 0.1325], |
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[-0.0352, -0.1672, -0.2541] |
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] |
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class SC_B(LatentFormat): |
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def __init__(self): |
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self.scale_factor = 1.0 / 0.43 |
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self.latent_rgb_factors = [ |
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[ 0.1121, 0.2006, 0.1023], |
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[-0.2093, -0.0222, -0.0195], |
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[-0.3087, -0.1535, 0.0366], |
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[ 0.0290, -0.1574, -0.4078] |
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] |
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class SD3(LatentFormat): |
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latent_channels = 16 |
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def __init__(self): |
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self.scale_factor = 1.5305 |
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self.shift_factor = 0.0609 |
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self.latent_rgb_factors = [ |
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[-0.0922, -0.0175, 0.0749], |
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[ 0.0311, 0.0633, 0.0954], |
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[ 0.1994, 0.0927, 0.0458], |
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[ 0.0856, 0.0339, 0.0902], |
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[ 0.0587, 0.0272, -0.0496], |
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[-0.0006, 0.1104, 0.0309], |
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[ 0.0978, 0.0306, 0.0427], |
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[-0.0042, 0.1038, 0.1358], |
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[-0.0194, 0.0020, 0.0669], |
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[-0.0488, 0.0130, -0.0268], |
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[ 0.0922, 0.0988, 0.0951], |
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[-0.0278, 0.0524, -0.0542], |
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[ 0.0332, 0.0456, 0.0895], |
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[-0.0069, -0.0030, -0.0810], |
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[-0.0596, -0.0465, -0.0293], |
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[-0.1448, -0.1463, -0.1189] |
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] |
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self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925] |
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self.taesd_decoder_name = "taesd3_decoder" |
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def process_in(self, latent): |
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return (latent - self.shift_factor) * self.scale_factor |
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def process_out(self, latent): |
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return (latent / self.scale_factor) + self.shift_factor |
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class StableAudio1(LatentFormat): |
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latent_channels = 64 |
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class Flux(SD3): |
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latent_channels = 16 |
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def __init__(self): |
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self.scale_factor = 0.3611 |
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self.shift_factor = 0.1159 |
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self.latent_rgb_factors =[ |
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[-0.0346, 0.0244, 0.0681], |
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[ 0.0034, 0.0210, 0.0687], |
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[ 0.0275, -0.0668, -0.0433], |
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[-0.0174, 0.0160, 0.0617], |
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[ 0.0859, 0.0721, 0.0329], |
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[ 0.0004, 0.0383, 0.0115], |
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[ 0.0405, 0.0861, 0.0915], |
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[-0.0236, -0.0185, -0.0259], |
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[-0.0245, 0.0250, 0.1180], |
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[ 0.1008, 0.0755, -0.0421], |
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[-0.0515, 0.0201, 0.0011], |
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[ 0.0428, -0.0012, -0.0036], |
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[ 0.0817, 0.0765, 0.0749], |
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[-0.1264, -0.0522, -0.1103], |
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[-0.0280, -0.0881, -0.0499], |
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[-0.1262, -0.0982, -0.0778] |
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] |
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self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] |
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self.taesd_decoder_name = "taef1_decoder" |
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def process_in(self, latent): |
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return (latent - self.shift_factor) * self.scale_factor |
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def process_out(self, latent): |
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return (latent / self.scale_factor) + self.shift_factor |
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class Mochi(LatentFormat): |
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latent_channels = 12 |
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def __init__(self): |
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self.scale_factor = 1.0 |
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self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141, |
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-0.05565264470995561, 0.012767231469026969, -0.04703542746246419, |
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0.043896967884726704, -0.09346305707025976, -0.09918314763016893, |
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-0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1) |
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self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617, |
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0.959253732819592, 0.8244560132752793, 0.917259975397747, |
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0.9294154431013696, 1.3720942357788521, 0.881393668867029, |
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0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1) |
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self.latent_rgb_factors =[ |
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[-0.0069, -0.0045, 0.0018], |
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[ 0.0154, -0.0692, -0.0274], |
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[ 0.0333, 0.0019, 0.0206], |
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[-0.1390, 0.0628, 0.1678], |
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[-0.0725, 0.0134, -0.1898], |
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[ 0.0074, -0.0270, -0.0209], |
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[-0.0176, -0.0277, -0.0221], |
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[ 0.5294, 0.5204, 0.3852], |
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[-0.0326, -0.0446, -0.0143], |
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[-0.0659, 0.0153, -0.0153], |
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[ 0.0185, -0.0217, 0.0014], |
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[-0.0396, -0.0495, -0.0281] |
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] |
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self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453] |
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self.taesd_decoder_name = None |
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def process_in(self, latent): |
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latents_mean = self.latents_mean.to(latent.device, latent.dtype) |
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latents_std = self.latents_std.to(latent.device, latent.dtype) |
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return (latent - latents_mean) * self.scale_factor / latents_std |
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def process_out(self, latent): |
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latents_mean = self.latents_mean.to(latent.device, latent.dtype) |
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latents_std = self.latents_std.to(latent.device, latent.dtype) |
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return latent * latents_std / self.scale_factor + latents_mean |
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class LTXV(LatentFormat): |
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latent_channels = 128 |
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def __init__(self): |
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self.latent_rgb_factors = [ |
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[ 1.1202e-02, -6.3815e-04, -1.0021e-02], |
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[ 8.6031e-02, 6.5813e-02, 9.5409e-04], |
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[-1.2576e-02, -7.5734e-03, -4.0528e-03], |
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[ 9.4063e-03, -2.1688e-03, 2.6093e-03], |
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[ 3.7636e-03, 1.2765e-02, 9.1548e-03], |
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[ 2.1024e-02, -5.2973e-03, 3.4373e-03], |
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[-8.8896e-03, -1.9703e-02, -1.8761e-02], |
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[-1.3160e-02, -1.0523e-02, 1.9709e-03], |
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[-1.5152e-03, -6.9891e-03, -7.5810e-03], |
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[-1.7247e-03, 4.6560e-04, -3.3839e-03], |
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[ 1.3617e-02, 4.7077e-03, -2.0045e-03], |
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[ 1.0256e-02, 7.7318e-03, 1.3948e-02], |
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[-1.6108e-02, -6.2151e-03, 1.1561e-03], |
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[ 7.3407e-03, 1.5628e-02, 4.4865e-04], |
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[ 9.5357e-04, -2.9518e-03, -1.4760e-02], |
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[ 1.9143e-02, 1.0868e-02, 1.2264e-02], |
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[ 4.4575e-03, 3.6682e-05, -6.8508e-03], |
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[-4.5681e-04, 3.2570e-03, 7.7929e-03], |
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[ 3.3902e-02, 3.3405e-02, 3.7454e-02], |
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[-2.3001e-02, -2.4877e-03, -3.1033e-03], |
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[ 5.0265e-02, 3.8841e-02, 3.3539e-02], |
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[-4.1018e-03, -1.1095e-03, 1.5859e-03], |
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[-1.2689e-01, -1.3107e-01, -2.1005e-01], |
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[ 2.6276e-02, 1.4189e-02, -3.5963e-03], |
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[-4.8679e-03, 8.8486e-03, 7.8029e-03], |
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[-1.6610e-03, -4.8597e-03, -5.2060e-03], |
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[-2.1010e-03, 2.3610e-03, 9.3796e-03], |
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[-2.2482e-02, -2.1305e-02, -1.5087e-02], |
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[-1.5753e-02, -1.0646e-02, -6.5083e-03], |
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[-4.6975e-03, 5.0288e-03, -6.7390e-03], |
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[ 1.1951e-02, 2.0712e-02, 1.6191e-02], |
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[-6.3704e-03, -8.4827e-03, -9.5483e-03], |
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[ 7.2610e-03, -9.9326e-03, -2.2978e-02], |
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[-9.1904e-04, 6.2882e-03, 9.5720e-03], |
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[-3.7178e-02, -3.7123e-02, -5.6713e-02], |
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[-1.3373e-01, -1.0720e-01, -5.3801e-02], |
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[-5.3702e-03, 8.1256e-03, 8.8397e-03], |
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[-1.5247e-01, -2.1437e-01, -2.1843e-01], |
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[ 3.1441e-02, 7.0335e-03, -9.7541e-03], |
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[ 2.1528e-03, -8.9817e-03, -2.1023e-02], |
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[ 3.8461e-03, -5.8957e-03, -1.5014e-02], |
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[-4.3470e-03, -1.2940e-02, -1.5972e-02], |
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[-5.4781e-03, -1.0842e-02, -3.0204e-03], |
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[-6.5347e-03, 3.0806e-03, -1.0163e-02], |
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[-5.0414e-03, -7.1503e-03, -8.9686e-04], |
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[-8.5851e-03, -2.4351e-03, 1.0674e-03], |
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[-9.0016e-03, -9.6493e-03, 1.5692e-03], |
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[ 5.0914e-03, 1.2099e-02, 1.9968e-02], |
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[ 1.3758e-02, 1.1669e-02, 8.1958e-03], |
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[-1.0518e-02, -1.1575e-02, -4.1307e-03], |
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[-2.8410e-02, -3.1266e-02, -2.2149e-02], |
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[ 2.9336e-03, 3.6511e-02, 1.8717e-02], |
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[-1.6703e-02, -1.6696e-02, -4.4529e-03], |
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[ 4.8818e-02, 4.0063e-02, 8.7410e-03], |
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[-1.5066e-02, -5.7328e-04, 2.9785e-03], |
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[-1.7613e-02, -8.1034e-03, 1.3086e-02], |
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[-9.2633e-03, 1.0803e-02, -6.3489e-03], |
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[ 3.0851e-03, 4.7750e-04, 1.2347e-02], |
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[-2.2785e-02, -2.3043e-02, -2.6005e-02], |
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[-2.4787e-02, -1.5389e-02, -2.2104e-02], |
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[-2.3572e-02, 1.0544e-03, 1.2361e-02], |
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[-7.8915e-03, -1.2271e-03, -6.0968e-03], |
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[-1.1478e-02, -1.2543e-03, 6.2679e-03], |
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[-5.4229e-02, 2.6644e-02, 6.3394e-03], |
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[ 4.4216e-03, -7.3338e-03, -1.0464e-02], |
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[-4.5013e-03, 1.6082e-03, 1.4420e-02], |
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[ 1.3673e-02, 8.8877e-03, 4.1253e-03], |
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[-1.0145e-02, 9.0072e-03, 1.5695e-02], |
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[-5.6234e-03, 1.1847e-03, 8.1261e-03], |
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[-3.7171e-03, -5.3538e-03, 1.2590e-03], |
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[ 2.9476e-02, 2.1424e-02, 3.0424e-02], |
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[-3.4925e-02, -2.4340e-02, -2.5316e-02], |
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[-3.4127e-02, -2.2406e-02, -1.0589e-02], |
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[-1.7342e-02, -1.3249e-02, -1.0719e-02], |
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[-2.1478e-03, -8.6051e-03, -2.9878e-03], |
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[ 1.2089e-03, -4.2391e-03, -6.8569e-03], |
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[ 9.0411e-04, -6.6886e-03, -6.7547e-05], |
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[ 1.6048e-02, -1.0057e-02, -2.8929e-02], |
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[ 1.2290e-03, 1.0163e-02, 1.8861e-02], |
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[ 1.7264e-02, 2.7257e-04, 1.3785e-02], |
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[-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] |
|
|
