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parser.add_argument("--wavelet_loss_level", type=int, default=2, help="Wavelet loss level 1 (main), 2 (details), or 3. Higher levels are available for DWT for higher resolution training. Default: 2")
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#parser.add_argument("--wavelet_loss_rectified_flow", default=True, help="Use rectified flow to estimate clean latents before wavelet loss")
parser.add_argument("--wavelet_loss_band_level_weights", type=str, default=None, help="Wavelet loss band level weights, uses band weights if not defined for a given level. Input example: {'ll1': 0.1, 'lh1': 0.01, 'hl1': 0.01, 'hh1': 0.05, 'll2': 0.1, 'lh2': 0.01, 'hl2': 0.01, 'hh2': 0.05} E.x. Default: none.")
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parser.add_argument("--wavelet_loss_band_weights", type=str, default=r"{ 'll': 0.1, 'lh': 0.01, 'hl': 0.01, 'hh': 0.05}", help="Wavelet loss band weights.")
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parser.add_argument("--wavelet_loss_ll_level_threshold", default=None, help="Wavelet loss which level to calculate the loss for the low frequency (ll). -1 means last n level. Default: None")
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parser.add_argument(
@@ -1075,16 +1075,7 @@ def __init__(
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# Default weights from paper:
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# "Training Generative Image Super-Resolution Models by Wavelet-Domain Losses"
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