percent_to_sigma now returns a float instead of a tensor.
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@ -77,9 +77,9 @@ class ModelSamplingDiscrete(torch.nn.Module):
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def percent_to_sigma(self, percent):
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if percent <= 0.0:
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return torch.tensor(999999999.9)
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return 999999999.9
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if percent >= 1.0:
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return torch.tensor(0.0)
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return 0.0
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percent = 1.0 - percent
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return self.sigma(torch.tensor(percent * 999.0))
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return self.sigma(torch.tensor(percent * 999.0)).item()
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@ -67,11 +67,11 @@ class ModelSamplingDiscreteLCM(torch.nn.Module):
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def percent_to_sigma(self, percent):
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if percent <= 0.0:
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return torch.tensor(999999999.9)
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return 999999999.9
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if percent >= 1.0:
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return torch.tensor(0.0)
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return 0.0
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percent = 1.0 - percent
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return self.sigma(torch.tensor(percent * 999.0))
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return self.sigma(torch.tensor(percent * 999.0)).item()
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def rescale_zero_terminal_snr_sigmas(sigmas):
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@ -16,8 +16,8 @@ class PatchModelAddDownscale:
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CATEGORY = "_for_testing"
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def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip):
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sigma_start = model.model.model_sampling.percent_to_sigma(start_percent).item()
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sigma_end = model.model.model_sampling.percent_to_sigma(end_percent).item()
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sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
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sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
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def input_block_patch(h, transformer_options):
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if transformer_options["block"][1] == block_number:
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