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Update nodes.py

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kijai 2024-04-20 12:33:51 +03:00
parent f8888f5ea6
commit aebdb88df8
1 changed files with 21 additions and 2 deletions
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nodes.py
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@ -3130,6 +3130,7 @@ class CustomSigmas:
return {"required":
{
"sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}),
"interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
}
}
RETURN_TYPES = ("SIGMAS",)
@ -3147,12 +3148,30 @@ SDXL:
SVD:
700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002
"""
def customsigmas(self, sigmas_string):
def customsigmas(self, sigmas_string, interpolate_to_steps):
sigmas_list = sigmas_string.split(', ')
sigmas_float_list = [float(sigma) for sigma in sigmas_list]
sigmas_tensor = torch.tensor(sigmas_float_list)
if len(sigmas_tensor) < interpolate_to_steps:
sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps)
return (sigmas_tensor,)
def loglinear_interp(self, t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
t_steps_np = t_steps.numpy()
xs = np.linspace(0, 1, len(t_steps_np))
ys = np.log(t_steps_np[::-1])
return (sigmas_tensor,)
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
interped_ys_tensor = torch.tensor(interped_ys)
return interped_ys_tensor
class InjectNoiseToLatent:
@classmethod