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Dynamic Strength

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peter942
2023-11-17 20:38:06 +01:00
parent 731614589e
commit 4279245a07
2 changed files with 45 additions and 26 deletions
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control/nodes.py
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@@ -166,9 +166,11 @@ class BatchCreativeInterpolationNode:
"linear_frame_distribution_value": ("INT", {"default": 16, "min": 4, "max": 64, "step": 1}),
"dynamic_frame_distribution_values": ("STRING", {"multiline": True, "default": "0,10,26,40"}),
"type_of_key_frame_influence": (["linear", "dynamic"],),
"linear_key_frame_influence_value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.001}),
"linear_key_frame_influence_value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
"dynamic_key_frame_influence_values": ("STRING", {"multiline": True, "default": "1.0,1.0,1.0,0.5"}),
"cn_strength": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
"type_of_cn_strength_distribution": (["linear", "dynamic"],),
"linear_cn_strength_value": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
"dynamic_cn_strength_values": ("STRING", {"multiline": True, "default": "0.9,0.9,0.9,0.5"}),
"soft_scaled_cn_weights_multiplier": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 10.0, "step": 0.01}),
"interpolation": (["ease-in", "ease-out", "ease-in-out"],),
"buffer": ("INT", {"default": 4, "min": 0, "max": 16, "step": 1}),
@@ -183,28 +185,31 @@ class BatchCreativeInterpolationNode:
CATEGORY = "ComfyUI-Creative-Interpolation 🎞️🅟🅞🅜/Interpolation"
def combined_function(self, positive, negative, control_net_name, images,type_of_frame_distribution,linear_frame_distribution_value,dynamic_frame_distribution_values,type_of_key_frame_influence,linear_key_frame_influence_value,dynamic_key_frame_influence_values,cn_strength,soft_scaled_cn_weights_multiplier,interpolation,buffer):
def combined_function(self, positive, negative, control_net_name, images,type_of_frame_distribution,linear_frame_distribution_value,dynamic_frame_distribution_values,type_of_key_frame_influence,linear_key_frame_influence_value,dynamic_key_frame_influence_values,type_of_cn_strength_distribution,linear_cn_strength_value,dynamic_cn_strength_values,soft_scaled_cn_weights_multiplier,interpolation,buffer):
def calculate_dynamic_influence_ranges(keyframe_positions, lengths_of_influence):
if len(keyframe_positions) < 2 or len(keyframe_positions) != len(lengths_of_influence):
def calculate_dynamic_influence_ranges(keyframe_positions, key_frame_influence_values):
if len(keyframe_positions) < 2 or len(keyframe_positions) != len(key_frame_influence_values):
return []
influence_ranges = []
for i, position in enumerate(keyframe_positions):
length_of_influence = lengths_of_influence[i]
prev_position = keyframe_positions[i - 1] if i > 0 else position
next_position = keyframe_positions[i + 1] if i < len(keyframe_positions) - 1 else position
influence_factor = key_frame_influence_values[i]
half_prev_distance = (position - prev_position) * length_of_influence / 2
half_next_distance = (next_position - position) * length_of_influence / 2
# Calculate the base range size
range_size = influence_factor * (keyframe_positions[-1] - keyframe_positions[0]) / (len(keyframe_positions) - 1) / 2
start_influence = max(0, int(position - half_prev_distance))
end_influence = min(keyframe_positions[-1], int(position + half_next_distance))
# Calculate symmetric start and end influence
start_influence = position - range_size
end_influence = position + range_size
influence_ranges.append((start_influence, end_influence))
# Adjust start and end influence to not exceed previous and next keyframes
start_influence = max(start_influence, keyframe_positions[i - 1] if i > 0 else 0)
end_influence = min(end_influence, keyframe_positions[i + 1] if i < len(keyframe_positions) - 1 else keyframe_positions[-1])
influence_ranges.append((round(start_influence), round(end_influence)))
return influence_ranges
def add_starting_buffer(influence_ranges, buffer=4):
shifted_ranges = [(0, buffer)]
for start, end in influence_ranges:
@@ -213,27 +218,37 @@ class BatchCreativeInterpolationNode:
def get_keyframe_positions(type_of_frame_distribution, dynamic_frame_distribution_values, images, linear_frame_distribution_value):
if type_of_frame_distribution == "dynamic":
# Sort the keyframe positions in numerical order
return sorted([int(kf.strip()) for kf in dynamic_frame_distribution_values.split(',')])
# Check if the input is a string or a list
if isinstance(dynamic_frame_distribution_values, str):
# Sort the keyframe positions in numerical order
return sorted([int(kf.strip()) for kf in dynamic_frame_distribution_values.split(',')])
elif isinstance(dynamic_frame_distribution_values, list):
return sorted(dynamic_frame_distribution_values)
else:
# Calculate the number of keyframes based on the total duration and linear_frames_per_keyframe
return [i * linear_frame_distribution_value for i in range(len(images))]
def get_keyframe_influence_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value):
def extract_keyframe_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value):
if type_of_key_frame_influence == "dynamic":
# Parse the dynamic key frame influence values without sorting
return [float(influence.strip()) for influence in dynamic_key_frame_influence_values.split(',')]
# Check if the input is a string or a list
if isinstance(dynamic_key_frame_influence_values, str):
# Parse the dynamic key frame influence values without sorting
return [float(influence.strip()) for influence in dynamic_key_frame_influence_values.split(',')]
elif isinstance(dynamic_key_frame_influence_values, list):
return dynamic_key_frame_influence_values
else:
# Create a list with the linear_key_frame_influence_value for each keyframe
return [linear_key_frame_influence_value for _ in keyframe_positions]
keyframe_positions = get_keyframe_positions(type_of_frame_distribution, dynamic_frame_distribution_values, images, linear_frame_distribution_value)
key_frame_influence_values = get_keyframe_influence_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value)
inluence_ranges = calculate_dynamic_influence_ranges(keyframe_positions,key_frame_influence_values)
cn_strength_values = extract_keyframe_values(type_of_cn_strength_distribution, dynamic_cn_strength_values, keyframe_positions, linear_cn_strength_value)
influence_ranges = add_starting_buffer(inluence_ranges, buffer)
key_frame_influence_values = extract_keyframe_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value)
influence_ranges = calculate_dynamic_influence_ranges(keyframe_positions,key_frame_influence_values)
influence_ranges = add_starting_buffer(influence_ranges, buffer)
for i, (start, end) in enumerate(influence_ranges):
@@ -244,21 +259,25 @@ class BatchCreativeInterpolationNode:
strength_from = 1.0
strength_to = 1.0
return_at_midpoint = False
cn_strength = cn_strength_values[0]
elif i == 1: # First image
image = images[0]
strength_from = 1.0
strength_to = 0.0
return_at_midpoint = False
return_at_midpoint = False
cn_strength = cn_strength_values[0]
elif i == len(images): # Last image
image = images[i-1]
strength_from = 0.0
strength_to = 1.0
return_at_midpoint = False
return_at_midpoint = False
cn_strength = cn_strength_values[i-1]
else: # Middle images
image = images[i-1]
strength_from = 0.0
strength_to = 1.0
return_at_midpoint = True
cn_strength = cn_strength_values[i-1]
latent_keyframe_interpolation_node = LatentKeyframeInterpolationNodeImport()
latent_keyframe, = latent_keyframe_interpolation_node.load_keyframe(