138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
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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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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# R G B
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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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# R G B
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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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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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# R G B
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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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[-0.0645, 0.0177, 0.1052],
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[ 0.0028, 0.0312, 0.0650],
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[ 0.1848, 0.0762, 0.0360],
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[ 0.0944, 0.0360, 0.0889],
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[ 0.0897, 0.0506, -0.0364],
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[-0.0020, 0.1203, 0.0284],
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[ 0.0855, 0.0118, 0.0283],
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[-0.0539, 0.0658, 0.1047],
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[-0.0057, 0.0116, 0.0700],
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[-0.0412, 0.0281, -0.0039],
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[ 0.1106, 0.1171, 0.1220],
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[-0.0248, 0.0682, -0.0481],
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[ 0.0815, 0.0846, 0.1207],
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[-0.0120, -0.0055, -0.0867],
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[-0.0749, -0.0634, -0.0456],
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[-0.1418, -0.1457, -0.1259]
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]
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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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