Add a denoise parameter to the SDTurboScheduler.
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@ -87,6 +87,7 @@ class SDTurboScheduler:
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return {"required":
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{"model": ("MODEL",),
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"steps": ("INT", {"default": 1, "min": 1, "max": 10}),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("SIGMAS",)
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@ -94,8 +95,9 @@ class SDTurboScheduler:
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FUNCTION = "get_sigmas"
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def get_sigmas(self, model, steps):
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timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[:steps]
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def get_sigmas(self, model, steps, denoise):
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start_step = 10 - int(10 * denoise)
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timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
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sigmas = model.model.model_sampling.sigma(timesteps)
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sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
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return (sigmas, )
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