diff --git a/.DS_Store b/.DS_Store index f433ff2..0d2bc53 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/control/nodes.py b/control/nodes.py index 9dcd8de..8a3b547 100644 --- a/control/nodes.py +++ b/control/nodes.py @@ -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(