nodes/ryanontheinside-ComfyUI_RyanOnTheInside
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add invert , flex mask bug fix

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ryanontheinstide
2024-08-29 12:22:36 -04:00
parent 683cbc3dc5
commit d4e00c3e75
2 changed files with 38 additions and 18 deletions
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nodes/flex/feature_modulation.py
+34 -14
View File
@@ -9,6 +9,14 @@ class FeatureModulationBase(RyanOnTheInside):
CATEGORY = "RyanOnTheInside/FlexFeatures"
FUNCTION = "modulate"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"invert_output": ("BOOLEAN", {"default": False}),
}
}
def visualize(self, feature, width=1920, height=1080):
values = [feature.get_value_at_frame(i) for i in range(feature.frame_count)]
frames = len(values)
@@ -58,14 +66,21 @@ class FeatureModulationBase(RyanOnTheInside):
return img_tensor
def create_processed_feature(self, original_feature, processed_values, name_prefix="Processed"):
def create_processed_feature(self, original_feature, processed_values, name_prefix="Processed", invert_output=False):
class ProcessedFeature(type(original_feature)):
def __init__(self, original_feature, processed_values):
def __init__(self, original_feature, processed_values, invert_output):
self.__dict__.update(original_feature.__dict__)
self.name = f"{name_prefix}_{original_feature.name}"
if invert_output:
self.name = f"Inverted_{self.name}"
self.frame_rate = original_feature.frame_rate
self.frame_count = len(processed_values)
self.data = processed_values
if invert_output:
min_val, max_val = min(processed_values), max(processed_values)
self.data = [max_val - v + min_val for v in processed_values]
else:
self.data = processed_values
def extract(self):
return self
@@ -73,7 +88,7 @@ class FeatureModulationBase(RyanOnTheInside):
def get_value_at_frame(self, frame_index):
return self.data[frame_index]
return ProcessedFeature(original_feature, processed_values)
return ProcessedFeature(original_feature, processed_values, invert_output)
class FeatureMixer(FeatureModulationBase):
@classmethod
@@ -92,6 +107,7 @@ class FeatureMixer(FeatureModulationBase):
"feature_threshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"rise_detection_threshold": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.05}),
"rise_smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 2.0, "step": 0.05}),
**super().INPUT_TYPES()["required"], # Include the invert_output option
}
}
@@ -99,7 +115,7 @@ class FeatureMixer(FeatureModulationBase):
RETURN_NAMES = ("FEATURE", "FEATURE_VISUALIZATION")
FUNCTION = "modulate"
def modulate(self, feature, base_gain, floor, ceiling, peak_sharpness, valley_sharpness, attack, release, smoothing, feature_threshold, rise_detection_threshold, rise_smoothing_factor):
def modulate(self, feature, base_gain, floor, ceiling, peak_sharpness, valley_sharpness, attack, release, smoothing, feature_threshold, rise_detection_threshold, rise_smoothing_factor, invert_output):
values = [feature.get_value_at_frame(i) for i in range(feature.frame_count)]
values = [v if v >= feature_threshold else 0 for v in values]
@@ -144,7 +160,7 @@ class FeatureMixer(FeatureModulationBase):
# chop
final_values = [max(floor, min(ceiling, v)) for v in adjusted]
processed_feature = self.create_processed_feature(feature, final_values, "Processed")
processed_feature = self.create_processed_feature(feature, final_values, "Processed", invert_output)
return (processed_feature, self.visualize(processed_feature))
def apply_rise_time_adjustment(self, values, rise_detection_threshold, rise_smoothing_factor):
@@ -180,13 +196,14 @@ class FeatureScaler(FeatureModulationBase):
"min_output": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"max_output": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"exponent": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 10.0, "step": 0.1}),
**super().INPUT_TYPES()["required"],
}
}
RETURN_TYPES = ("FEATURE", "IMAGE")
RETURN_NAMES = ("FEATURE", "FEATURE_VISUALIZATION")
def modulate(self, feature, scale_type, min_output, max_output, exponent):
def modulate(self, feature, scale_type, min_output, max_output, exponent, invert_output):
values = [feature.get_value_at_frame(i) for i in range(feature.frame_count)]
min_val, max_val = min(values), max(values)
@@ -203,7 +220,7 @@ class FeatureScaler(FeatureModulationBase):
final_values = [min_output + v * (max_output - min_output) for v in scaled]
processed_feature = self.create_processed_feature(feature, final_values, "Scaled")
processed_feature = self.create_processed_feature(feature, final_values, "Scaled", invert_output)
return (processed_feature, self.visualize(processed_feature))
class FeatureMath(FeatureModulationBase):
@@ -216,13 +233,14 @@ class FeatureMath(FeatureModulationBase):
"operation": (["add", "subtract", "multiply", "divide", "max", "min"],),
"weight1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"weight2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
**super().INPUT_TYPES()["required"],
}
}
RETURN_TYPES = ("FEATURE", "IMAGE")
RETURN_NAMES = ("FEATURE", "FEATURE_VISUALIZATION")
def modulate(self, feature1, feature2, operation, weight1, weight2):
def modulate(self, feature1, feature2, operation, weight1, weight2, invert_output):
values1 = [feature1.get_value_at_frame(i) for i in range(feature1.frame_count)]
values2 = [feature2.get_value_at_frame(i) for i in range(feature2.frame_count)]
@@ -244,7 +262,7 @@ class FeatureMath(FeatureModulationBase):
elif operation == "min":
combined = [min(weight1 * v1, weight2 * v2) for v1, v2 in zip(values1, values2)]
processed_feature = self.create_processed_feature(feature1, combined, "Combined")
processed_feature = self.create_processed_feature(feature1, combined, "Combined", invert_output)
return (processed_feature, self.visualize(processed_feature))
class FeatureSmoothing(FeatureModulationBase):
@@ -257,13 +275,14 @@ class FeatureSmoothing(FeatureModulationBase):
"window_size": ("INT", {"default": 5, "min": 3, "max": 21, "step": 2}),
"alpha": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"sigma": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 5.0, "step": 0.1}),
**super().INPUT_TYPES()["required"],
}
}
RETURN_TYPES = ("FEATURE", "IMAGE")
RETURN_NAMES = ("FEATURE", "FEATURE_VISUALIZATION")
def modulate(self, feature, smoothing_type, window_size, alpha, sigma):
def modulate(self, feature, smoothing_type, window_size, alpha, sigma, invert_output):
values = [feature.get_value_at_frame(i) for i in range(feature.frame_count)]
if smoothing_type == "moving_average":
@@ -281,7 +300,7 @@ class FeatureSmoothing(FeatureModulationBase):
kernel = kernel / np.sum(kernel)
smoothed = np.convolve(values, kernel, mode='same')
processed_feature = self.create_processed_feature(feature, smoothed, "Smoothed")
processed_feature = self.create_processed_feature(feature, smoothed, "Smoothed", invert_output)
return (processed_feature, self.visualize(processed_feature))
class FeatureOscillator(FeatureModulationBase):
@@ -295,13 +314,14 @@ class FeatureOscillator(FeatureModulationBase):
"amplitude": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"phase_shift": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 2*np.pi, "step": 0.1}),
"blend": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
**super().INPUT_TYPES()["required"],
}
}
RETURN_TYPES = ("FEATURE", "IMAGE")
RETURN_NAMES = ("FEATURE", "FEATURE_VISUALIZATION")
def modulate(self, feature, oscillator_type, frequency, amplitude, phase_shift, blend):
def modulate(self, feature, oscillator_type, frequency, amplitude, phase_shift, blend, invert_output):
values = [feature.get_value_at_frame(i) for i in range(feature.frame_count)]
t = np.linspace(0, 2*np.pi, len(values))
@@ -316,5 +336,5 @@ class FeatureOscillator(FeatureModulationBase):
blended = [v * (1 - blend) + osc * blend for v, osc in zip(values, oscillation)]
processed_feature = self.create_processed_feature(feature, blended, "Oscillated")
processed_feature = self.create_processed_feature(feature, blended, "Oscillated", invert_output)
return (processed_feature, self.visualize(processed_feature))
nodes/masks/flex_masks.py
+4 -4
View File
@@ -141,8 +141,8 @@ class FlexMaskVoronoiScheduled(FlexMaskBase):
"y_offset": ("FLOAT", {"default": 0.0, "min": -1000.0, "max": 1000.0, "step": 0.1}),
"feature_param": (["scale", "detail", "randomness", "seed", "x_offset", "y_offset"],),
"formula": (list(cls.formulas.keys()),),
"a": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
"b": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
"x_offset": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
"y_offset": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}),
}
}
@@ -170,9 +170,9 @@ class FlexMaskVoronoiScheduled(FlexMaskBase):
elif feature_param == "seed":
seed = int(seed + (schedule_value * strength * 1000000))
elif feature_param == "x_offset":
x_offset *= (1 + schedule_value * strength)
x_offset += width * schedule_value * strength
elif feature_param == "y_offset":
y_offset *= (1 + schedule_value * strength)
y_offset += height * schedule_value * strength
# Create VoronoiNoise instance
voronoi = VoronoiNoise(