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sigma experimenting

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kijai 2024-03-22 21:16:27 +02:00
parent 52e98cfa15
commit 7cc091c1d0
1 changed files with 18 additions and 6 deletions
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24
nodes.py
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@ -2778,14 +2778,18 @@ class FlipSigmasAdjusted:
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"divide_by_last_sigma": ("BOOLEAN", {"default": False}),
"divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}),
"offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}),
}
}
RETURN_TYPES = ("SIGMAS",)
RETURN_TYPES = ("SIGMAS", "STRING",)
RETURN_NAMES = ("SIGMAS", "sigmas_string",)
CATEGORY = "KJNodes/noise"
FUNCTION = "get_sigmas_adjusted"
def get_sigmas_adjusted(self, sigmas):
def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by):
sigmas = sigmas.flip(0)
if sigmas[0] == 0:
@ -2793,12 +2797,20 @@ class FlipSigmasAdjusted:
adjusted_sigmas = sigmas.clone()
#offset sigma
for i in range(1, len(sigmas)):
adjusted_sigmas[i] = sigmas[i - 1]
offset_index = i - offset_by
if 0 <= offset_index < len(sigmas):
adjusted_sigmas[i] = sigmas[offset_index]
else:
adjusted_sigmas[i] = 0.0001
if adjusted_sigmas[0] == 0:
adjusted_sigmas[0] = 0.0001
return (adjusted_sigmas,)
if divide_by_last_sigma:
adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1]
sigma_np_array = adjusted_sigmas.numpy()
array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf)
adjusted_sigmas = adjusted_sigmas / divide_by
return (adjusted_sigmas, array_string,)
class InjectNoiseToLatent: