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Update nodes.py

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kijai 2023-12-03 01:38:04 +02:00
parent 8a8e4db2df
commit 248fcd0cd3
1 changed files with 43 additions and 11 deletions
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54
nodes.py
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@ -44,7 +44,7 @@ class FloatConstant:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { return {"required": {
"value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01}), "value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.001}),
}, },
} }
@ -2487,7 +2487,7 @@ class NormalizeLatent:
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "normalize" FUNCTION = "normalize"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/noise"
OUTPUT_NODE = True OUTPUT_NODE = True
def normalize(self, latent): def normalize(self, latent):
@ -2505,26 +2505,56 @@ class FlipSigmasAdjusted:
} }
} }
RETURN_TYPES = ("SIGMAS",) RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/sigmas" CATEGORY = "KJNodes/noise"
FUNCTION = "get_sigmas_adjusted" FUNCTION = "get_sigmas_adjusted"
def get_sigmas_adjusted(self, sigmas): def get_sigmas_adjusted(self, sigmas):
print(sigmas)
sigmas = sigmas.flip(0) sigmas = sigmas.flip(0)
if sigmas[0] == 0: if sigmas[0] == 0:
sigmas[0] = 0.0001 sigmas[0] = 0.0001
adjusted_sigmas = sigmas.clone() # Create a copy to hold the adjusted values adjusted_sigmas = sigmas.clone()
#offset sigma
# Apply the special adjustment: use the current index except for the first element
for i in range(1, len(sigmas)): for i in range(1, len(sigmas)):
adjusted_sigmas[i] = sigmas[i - 1] adjusted_sigmas[i] = sigmas[i - 1]
if adjusted_sigmas[0] == 0: if adjusted_sigmas[0] == 0:
adjusted_sigmas[0] = 0.0001 # Apply the zero adjustment if necessary adjusted_sigmas[0] = 0.0001
print(adjusted_sigmas)
return (adjusted_sigmas,) return (adjusted_sigmas,)
class InjectNoiseToLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latents":("LATENT",),
"strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.001}),
"noise": ("LATENT",),
"normalize": ("BOOLEAN", {"default": False}),
"average": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "injectnoise"
CATEGORY = "KJNodes/noise"
def injectnoise(self, latents, strength, noise, normalize, average):
samples = latents.copy()
if latents["samples"].shape != noise["samples"].shape:
raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape")
if average:
noised = (samples["samples"].clone() + noise["samples"].clone()) / 2
else:
noised = samples["samples"].clone() + noise["samples"].clone() * strength
if normalize:
noised = noised / noised.std()
samples["samples"] = noised
return (samples,)
NODE_CLASS_MAPPINGS = { NODE_CLASS_MAPPINGS = {
"INTConstant": INTConstant, "INTConstant": INTConstant,
"FloatConstant": FloatConstant, "FloatConstant": FloatConstant,
@ -2571,7 +2601,8 @@ NODE_CLASS_MAPPINGS = {
"ImageGrabPIL": ImageGrabPIL, "ImageGrabPIL": ImageGrabPIL,
"DummyLatentOut": DummyLatentOut, "DummyLatentOut": DummyLatentOut,
"NormalizeLatent": NormalizeLatent, "NormalizeLatent": NormalizeLatent,
"FlipSigmasAdjusted": FlipSigmasAdjusted "FlipSigmasAdjusted": FlipSigmasAdjusted,
"InjectNoiseToLatent": InjectNoiseToLatent
} }
NODE_DISPLAY_NAME_MAPPINGS = { NODE_DISPLAY_NAME_MAPPINGS = {
"INTConstant": "INT Constant", "INTConstant": "INT Constant",
@ -2618,6 +2649,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ImageGrabPIL": "ImageGrabPIL", "ImageGrabPIL": "ImageGrabPIL",
"DummyLatentOut": "DummyLatentOut", "DummyLatentOut": "DummyLatentOut",
"NormalizeLatent": "NormalizeLatent", "NormalizeLatent": "NormalizeLatent",
"FlipSigmasAdjusted": "FlipSigmasAdjusted" "FlipSigmasAdjusted": "FlipSigmasAdjusted",
"InjectNoiseToLatent": "InjectNoiseToLatent"
} }