Add a SplitSigmasDenoise node as an alternative to SplitSigmas.
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@ -139,6 +139,7 @@ class SplitSigmas:
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}
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}
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RETURN_TYPES = ("SIGMAS","SIGMAS")
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RETURN_NAMES = ("high_sigmas", "low_sigmas")
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CATEGORY = "sampling/custom_sampling/sigmas"
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FUNCTION = "get_sigmas"
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@ -148,6 +149,27 @@ class SplitSigmas:
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sigmas2 = sigmas[step:]
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return (sigmas1, sigmas2)
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class SplitSigmasDenoise:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"sigmas": ("SIGMAS", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("SIGMAS","SIGMAS")
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RETURN_NAMES = ("high_sigmas", "low_sigmas")
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CATEGORY = "sampling/custom_sampling/sigmas"
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FUNCTION = "get_sigmas"
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def get_sigmas(self, sigmas, denoise):
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steps = max(sigmas.shape[-1] - 1, 0)
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total_steps = round(steps * denoise)
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sigmas1 = sigmas[:-(total_steps)]
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sigmas2 = sigmas[-(total_steps + 1):]
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return (sigmas1, sigmas2)
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class FlipSigmas:
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@classmethod
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def INPUT_TYPES(s):
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@ -599,6 +621,7 @@ NODE_CLASS_MAPPINGS = {
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"SamplerDPMPP_SDE": SamplerDPMPP_SDE,
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"SamplerDPMAdaptative": SamplerDPMAdaptative,
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"SplitSigmas": SplitSigmas,
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"SplitSigmasDenoise": SplitSigmasDenoise,
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"FlipSigmas": FlipSigmas,
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"CFGGuider": CFGGuider,
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