Read kdiffusion sigma params from opts
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@ -345,25 +345,25 @@ class KDiffusionSampler:
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extra_params_kwargs['eta'] = self.eta
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if len(self.extra_params) > 0:
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s_churn = p.override_settings.get('s_churn', p.s_churn)
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s_tmin = p.override_settings.get('s_tmin', p.s_tmin)
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s_tmax = p.override_settings.get('s_tmax', p.s_tmax)
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s_noise = p.override_settings.get('s_noise', p.s_noise)
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s_churn = getattr(opts, 's_churn', p.s_churn)
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s_tmin = getattr(opts, 's_tmin', p.s_tmin)
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s_tmax = getattr(opts, 's_tmax', p.s_tmax)
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s_noise = getattr(opts, 's_noise', p.s_noise)
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if s_churn != self.s_churn:
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extra_params_kwargs['s_churn'] = s_churn
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p.s_churn = s_churn
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p.extra_generation_params['Sigma churn'] = s_churn
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if s_tmin != self.s_tmin:
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extra_params_kwargs['s_tmin'] = s_churn
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extra_params_kwargs['s_tmin'] = s_tmin
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p.s_tmin = s_tmin
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p.extra_generation_params['Sigma tmin'] = s_tmin
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if s_tmax != self.s_tmax:
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extra_params_kwargs['s_tmax'] = s_churn
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extra_params_kwargs['s_tmax'] = s_tmax
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p.s_tmax = s_tmax
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p.extra_generation_params['Sigma tmax'] = s_tmax
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if s_noise != self.s_noise:
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extra_params_kwargs['s_noise'] = s_churn
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extra_params_kwargs['s_noise'] = s_noise
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p.s_noise = s_noise
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p.extra_generation_params['Sigma noise'] = s_noise
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