mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
267 lines
12 KiB
Python
267 lines
12 KiB
Python
# thanks Comfyui for the rgb factors (https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/latent_formats.py)
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def get_rgb_factors(model_family, model_type = None):
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if model_family in ["wan", "qwen"]:
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if model_type =="ti2v_2_2":
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latent_channels = 48
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latent_dimensions = 3
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latent_rgb_factors = [
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[ 0.0119, 0.0103, 0.0046],
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[-0.1062, -0.0504, 0.0165],
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[ 0.0140, 0.0409, 0.0491],
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[-0.0813, -0.0677, 0.0607],
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[ 0.0656, 0.0851, 0.0808],
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[ 0.0264, 0.0463, 0.0912],
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[ 0.0295, 0.0326, 0.0590],
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[-0.0244, -0.0270, 0.0025],
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[ 0.0443, -0.0102, 0.0288],
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[-0.0465, -0.0090, -0.0205],
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[ 0.0359, 0.0236, 0.0082],
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[-0.0776, 0.0854, 0.1048],
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[ 0.0564, 0.0264, 0.0561],
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[ 0.0006, 0.0594, 0.0418],
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[-0.0319, -0.0542, -0.0637],
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[-0.0268, 0.0024, 0.0260],
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[ 0.0539, 0.0265, 0.0358],
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[-0.0359, -0.0312, -0.0287],
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[-0.0285, -0.1032, -0.1237],
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[ 0.1041, 0.0537, 0.0622],
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[-0.0086, -0.0374, -0.0051],
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[ 0.0390, 0.0670, 0.2863],
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[ 0.0069, 0.0144, 0.0082],
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[ 0.0006, -0.0167, 0.0079],
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[ 0.0313, -0.0574, -0.0232],
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[-0.1454, -0.0902, -0.0481],
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[ 0.0714, 0.0827, 0.0447],
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[-0.0304, -0.0574, -0.0196],
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[ 0.0401, 0.0384, 0.0204],
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[-0.0758, -0.0297, -0.0014],
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[ 0.0568, 0.1307, 0.1372],
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[-0.0055, -0.0310, -0.0380],
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[ 0.0239, -0.0305, 0.0325],
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[-0.0663, -0.0673, -0.0140],
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[-0.0416, -0.0047, -0.0023],
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[ 0.0166, 0.0112, -0.0093],
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[-0.0211, 0.0011, 0.0331],
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[ 0.1833, 0.1466, 0.2250],
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[-0.0368, 0.0370, 0.0295],
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[-0.3441, -0.3543, -0.2008],
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[-0.0479, -0.0489, -0.0420],
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[-0.0660, -0.0153, 0.0800],
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[-0.0101, 0.0068, 0.0156],
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[-0.0690, -0.0452, -0.0927],
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[-0.0145, 0.0041, 0.0015],
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[ 0.0421, 0.0451, 0.0373],
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[ 0.0504, -0.0483, -0.0356],
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[-0.0837, 0.0168, 0.0055]
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]
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else:
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latent_channels = 16
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latent_dimensions = 3
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latent_rgb_factors = [
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[-0.1299, -0.1692, 0.2932],
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[ 0.0671, 0.0406, 0.0442],
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[ 0.3568, 0.2548, 0.1747],
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[ 0.0372, 0.2344, 0.1420],
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[ 0.0313, 0.0189, -0.0328],
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[ 0.0296, -0.0956, -0.0665],
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[-0.3477, -0.4059, -0.2925],
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[ 0.0166, 0.1902, 0.1975],
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[-0.0412, 0.0267, -0.1364],
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[-0.1293, 0.0740, 0.1636],
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[ 0.0680, 0.3019, 0.1128],
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[ 0.0032, 0.0581, 0.0639],
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[-0.1251, 0.0927, 0.1699],
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[ 0.0060, -0.0633, 0.0005],
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[ 0.3477, 0.2275, 0.2950],
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[ 0.1984, 0.0913, 0.1861]
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]
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latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360]
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# latent_rgb_factors_bias = [0.0259, -0.0192, -0.0761]
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elif model_family =="flux":
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scale_factor = 0.3611
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shift_factor = 0.1159
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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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latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
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elif model_family == "ltxv":
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latent_channels = 128
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latent_dimensions = 3
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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],
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[ 4.6782e-03, -5.2423e-03, 2.4467e-03],
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[-5.9113e-03, -6.2244e-03, -1.8162e-03],
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[ 1.5496e-02, 1.4582e-02, 1.9514e-03],
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[ 7.4958e-03, 1.5886e-03, -8.2305e-03],
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[ 1.9086e-02, 1.6360e-03, -3.9674e-03],
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[-5.7021e-03, -2.7307e-03, -4.1066e-03],
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[ 1.7450e-03, 1.4602e-02, 2.5794e-02],
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[-8.2788e-04, 2.2902e-03, 4.5161e-03],
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[ 1.1632e-02, 8.9193e-03, -7.2813e-03],
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[ 7.5721e-03, 2.6784e-03, 1.1393e-02],
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[ 5.1939e-03, 3.6903e-03, 1.4049e-02],
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[-1.8383e-02, -2.2529e-02, -2.4477e-02],
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[ 5.8842e-04, -5.7874e-03, -1.4770e-02],
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[-1.6125e-02, -8.6101e-03, -1.4533e-02],
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[ 2.0540e-02, 2.0729e-02, 6.4338e-03],
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[ 3.3587e-03, -1.1226e-02, -1.6444e-02],
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[-1.4742e-03, -1.0489e-02, 1.7097e-03],
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[ 2.8130e-02, 2.3546e-02, 3.2791e-02],
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[-1.8532e-02, -1.2842e-02, -8.7756e-03],
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[-8.0533e-03, -1.0771e-02, -1.7536e-02],
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[-3.9009e-03, 1.6150e-02, 3.3359e-02],
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[-7.4554e-03, -1.4154e-02, -6.1910e-03],
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[ 3.4734e-03, -1.1370e-02, -1.0581e-02],
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[ 1.1476e-02, 3.9281e-03, 2.8231e-03],
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[ 7.1639e-03, -1.4741e-03, -3.8066e-03],
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[ 2.2250e-03, -8.7552e-03, -9.5719e-03],
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[ 2.4146e-02, 2.1696e-02, 2.8056e-02],
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[-5.4365e-03, -2.4291e-02, -1.7802e-02],
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[ 7.4263e-03, 1.0510e-02, 1.2705e-02],
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[ 6.2669e-03, 6.2658e-03, 1.9211e-02],
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[ 1.6378e-02, 9.4933e-03, 6.6971e-03],
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[ 1.7173e-02, 2.3601e-02, 2.3296e-02],
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[-1.4568e-02, -9.8279e-03, -1.1556e-02],
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[ 1.4431e-02, 1.4430e-02, 6.6362e-03],
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[-6.8230e-03, 1.8863e-02, 1.4555e-02],
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[ 6.1156e-03, 3.4700e-03, -2.6662e-03],
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[-2.6983e-03, -5.9402e-03, -9.2276e-03],
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[ 1.0235e-02, 7.4173e-03, -7.6243e-03],
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[-1.3255e-02, 1.9322e-02, -9.2153e-04],
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[ 2.4222e-03, -4.8039e-03, -1.5759e-02],
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[ 2.6244e-02, 2.5951e-02, 2.0249e-02],
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[ 1.5711e-02, 1.8498e-02, 2.7407e-03],
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[-2.1714e-03, 4.7214e-03, -2.2443e-02],
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[-7.4747e-03, 7.4166e-03, 1.4430e-02],
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[-8.3906e-03, -7.9776e-03, 9.7927e-03],
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[ 3.8321e-02, 9.6622e-03, -1.9268e-02],
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[-1.4605e-02, -6.7032e-03, 3.9675e-03]
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]
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latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
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elif model_family == "hunyuan":
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latent_channels = 16
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latent_dimensions = 3
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scale_factor = 0.476986
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latent_rgb_factors = [
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[-0.0395, -0.0331, 0.0445],
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[ 0.0696, 0.0795, 0.0518],
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[ 0.0135, -0.0945, -0.0282],
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[ 0.0108, -0.0250, -0.0765],
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[-0.0209, 0.0032, 0.0224],
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[-0.0804, -0.0254, -0.0639],
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[-0.0991, 0.0271, -0.0669],
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[-0.0646, -0.0422, -0.0400],
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[-0.0696, -0.0595, -0.0894],
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[-0.0799, -0.0208, -0.0375],
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[ 0.1166, 0.1627, 0.0962],
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[ 0.1165, 0.0432, 0.0407],
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[-0.2315, -0.1920, -0.1355],
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[-0.0270, 0.0401, -0.0821],
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[-0.0616, -0.0997, -0.0727],
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[ 0.0249, -0.0469, -0.1703]
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]
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latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]
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else:
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latent_rgb_factors_bias = latent_rgb_factors = None
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return latent_rgb_factors, latent_rgb_factors_bias |