nodes/banodoco-steerable-motion
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banodoco-steerable-motion/SteerableMotion.py
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# Standard library imports
from ast import literal_eval
from io import BytesIO
import numpy as np
# Third-party library imports
import torch
import torchvision.transforms as transforms
from PIL import Image
import matplotlib.pyplot as plt
# Local application/library specific imports
from .imports.ComfyUI_IPAdapter_plus.IPAdapterPlus import IPAdapterBatchImport, IPAdapterTiledBatchImport, IPAdapterTiledImport, PrepImageForClipVisionImport, IPAdapterAdvancedImport, IPAdapterNoiseImport
from .imports.AdvancedControlNet.nodes_sparsectrl import SparseIndexMethodNodeImport
import matplotlib
class BatchCreativeInterpolationNode:
@classmethod
def IS_CHANGED(cls, **kwargs):
return float("NaN")
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"images": ("IMAGE", ),
"model": ("MODEL", ),
"ipadapter": ("IPADAPTER", ),
"clip_vision": ("CLIP_VISION",),
"type_of_frame_distribution": (["linear", "dynamic"],),
"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": ("STRING", {"multiline": False, "default": "(1.0,1.0)"}),
"dynamic_key_frame_influence_values": ("STRING", {"multiline": True, "default": "(1.0,1.0),(1.0,1.5)(1.0,0.5)"}),
"type_of_strength_distribution": (["linear", "dynamic"],),
"linear_strength_value": ("STRING", {"multiline": False, "default": "(0.3,0.4)"}),
"dynamic_strength_values": ("STRING", {"multiline": True, "default": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)"}),
"buffer": ("INT", {"default": 4, "min": 1, "max": 16, "step": 1}),
"high_detail_mode": ("BOOLEAN", {"default": True}),
},
"optional": {
"base_ipa_advanced_settings": ("ADVANCED_IPA_SETTINGS",),
"detail_ipa_advanced_settings": ("ADVANCED_IPA_SETTINGS",),
}
}
RETURN_TYPES = ("IMAGE","CONDITIONING","CONDITIONING","MODEL","SPARSE_METHOD","INT", "INT")
RETURN_NAMES = ("GRAPH","POSITIVE","NEGATIVE","MODEL","KEYFRAME_POSITIONS","BATCH_SIZE", "BUFFER")
FUNCTION = "combined_function"
CATEGORY = "Steerable-Motion"
def combined_function(self,positive,negative,images,model,ipadapter,clip_vision,
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_strength_distribution,
linear_strength_value,dynamic_strength_values,
buffer, high_detail_mode,base_ipa_advanced_settings=None,
detail_ipa_advanced_settings=None):
# set the matplotlib backend to 'Agg' to prevent crash on macOS
# 'Agg' is a non-interactive backend that can be used in a non-main thread
matplotlib.use('Agg')
def get_keyframe_positions(type_of_frame_distribution, dynamic_frame_distribution_values, images, linear_frame_distribution_value):
if type_of_frame_distribution == "dynamic":
# Check if the input is a string or a list
if isinstance(dynamic_frame_distribution_values, str):
# Parse the keyframe positions, sort them, and then increase each by 1 except the first
keyframes = sorted([int(kf.strip()) for kf in dynamic_frame_distribution_values.split(',')])
elif isinstance(dynamic_frame_distribution_values, list):
# Sort the list and then increase each by 1 except the first
keyframes = sorted(dynamic_frame_distribution_values)
else:
# Calculate the number of keyframes based on the total duration and linear_frames_per_keyframe
# Increase each by 1 except the first
keyframes = [(i * linear_frame_distribution_value) for i in range(len(images))]
# Increase all values by 1 except the first
if len(keyframes) > 1:
return [keyframes[0]] + [kf + 1 for kf in keyframes[1:]]
else:
return keyframes
def create_mask_batch(last_key_frame_position, weights, frames):
# Hardcoded dimensions
width, height = 512, 512
# Map frames to their corresponding reversed weights for easy lookup
frame_to_weight = {frame: weights[i] for i, frame in enumerate(frames)}
# Create masks for each frame up to last_key_frame_position
masks = []
for frame_number in range(last_key_frame_position):
# Determine the strength of the mask
strength = frame_to_weight.get(frame_number, 0.0)
# Create the mask with the determined strength
mask = torch.full((height, width), strength)
masks.append(mask)
# Convert list of masks to a single tensor
masks_tensor = torch.stack(masks, dim=0)
return masks_tensor
def create_weight_batch(last_key_frame_position, weights, frames):
# Map frames to their corresponding reversed weights for easy lookup
frame_to_weight = {frame: weights[i] for i, frame in enumerate(frames)}
# Create weights for each frame up to last_key_frame_position
weights = []
for frame_number in range(last_key_frame_position):
# Determine the strength of the weight
strength = frame_to_weight.get(frame_number, 0.0)
weights.append(strength)
return weights
def plot_weight_comparison(cn_frame_numbers, cn_weights, ipadapter_frame_numbers, ipadapter_weights, buffer):
plt.figure(figsize=(12, 8))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
# Handle None values for frame numbers and weights
cn_frame_numbers = cn_frame_numbers if cn_frame_numbers is not None else []
cn_weights = cn_weights if cn_weights is not None else []
ipadapter_frame_numbers = ipadapter_frame_numbers if ipadapter_frame_numbers is not None else []
ipadapter_weights = ipadapter_weights if ipadapter_weights is not None else []
max_length = max(len(cn_frame_numbers), len(ipadapter_frame_numbers))
label_counter = 1 if buffer < 0 else 0
for i in range(max_length):
if i < len(cn_frame_numbers):
label = 'cn_strength_buffer' if (i == 0 and buffer > 0) else f'cn_strength_{label_counter}'
plt.plot(cn_frame_numbers[i], cn_weights[i], marker='o', color=colors[i % len(colors)], label=label)
if i < len(ipadapter_frame_numbers):
label = 'ipa_strength_buffer' if (i == 0 and buffer > 0) else f'ipa_strength_{label_counter}'
plt.plot(ipadapter_frame_numbers[i], ipadapter_weights[i], marker='x', linestyle='--', color=colors[i % len(colors)], label=label)
if label_counter == 0 or buffer < 0 or i > 0:
label_counter += 1
plt.legend()
# Adjusted generator expression for max_weight
all_weights = cn_weights + ipadapter_weights
max_weight = max(max(sublist) for sublist in all_weights if sublist) * 1.5
plt.ylim(0, max_weight)
buffer_io = BytesIO()
plt.savefig(buffer_io, format='png', bbox_inches='tight')
plt.close()
buffer_io.seek(0)
img = Image.open(buffer_io)
img_tensor = transforms.ToTensor()(img)
img_tensor = img_tensor.unsqueeze(0)
img_tensor = img_tensor.permute([0, 2, 3, 1])
return img_tensor,
def extract_strength_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":
# Process the dynamic_key_frame_influence_values depending on its format
if isinstance(dynamic_key_frame_influence_values, str):
dynamic_values = eval(dynamic_key_frame_influence_values)
else:
dynamic_values = dynamic_key_frame_influence_values
# Iterate through the dynamic values and convert tuples with two values to three values
dynamic_values_corrected = []
for value in dynamic_values:
if len(value) == 2:
value = (value[0], value[1], value[0])
dynamic_values_corrected.append(value)
return dynamic_values_corrected
else:
# Process for linear or other types
if len(linear_key_frame_influence_value) == 2:
linear_key_frame_influence_value = (linear_key_frame_influence_value[0], linear_key_frame_influence_value[1], linear_key_frame_influence_value[0])
return [linear_key_frame_influence_value for _ in range(len(keyframe_positions) - 1)]
def extract_influence_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value):
# Check and convert linear_key_frame_influence_value if it's a float or string float
# if it's a string that starts with a parenthesis, convert it to a tuple
if isinstance(linear_key_frame_influence_value, str) and linear_key_frame_influence_value[0] == "(":
linear_key_frame_influence_value = eval(linear_key_frame_influence_value)
if not isinstance(linear_key_frame_influence_value, tuple):
if isinstance(linear_key_frame_influence_value, (float, str)):
try:
value = float(linear_key_frame_influence_value)
linear_key_frame_influence_value = (value, value)
except ValueError:
raise ValueError("linear_key_frame_influence_value must be a float or a string representing a float")
number_of_outputs = len(keyframe_positions) - 1
if type_of_key_frame_influence == "dynamic":
# Convert list of individual float values into tuples
if all(isinstance(x, float) for x in dynamic_key_frame_influence_values):
dynamic_values = [(value, value) for value in dynamic_key_frame_influence_values]
elif isinstance(dynamic_key_frame_influence_values[0], str) and dynamic_key_frame_influence_values[0] == "(":
string_representation = ''.join(dynamic_key_frame_influence_values)
dynamic_values = eval(f'[{string_representation}]')
else:
dynamic_values = dynamic_key_frame_influence_values if isinstance(dynamic_key_frame_influence_values, list) else [dynamic_key_frame_influence_values]
return dynamic_values[:number_of_outputs]
else:
return [linear_key_frame_influence_value for _ in range(number_of_outputs)]
def calculate_weights(batch_index_from, batch_index_to, strength_from, strength_to, interpolation,revert_direction_at_midpoint, last_key_frame_position,i, number_of_items,buffer):
# Initialize variables based on the position of the keyframe
range_start = batch_index_from
range_end = batch_index_to
# if it's the first value, set influence range from 1.0 to 0.0
if i == number_of_items - 1:
range_end = last_key_frame_position
steps = range_end - range_start
diff = strength_to - strength_from
# Calculate index for interpolation
index = np.linspace(0, 1, steps // 2 + 1) if revert_direction_at_midpoint else np.linspace(0, 1, steps)
# Calculate weights based on interpolation type
if interpolation == "linear":
weights = np.linspace(strength_from, strength_to, len(index))
elif interpolation == "ease-in":
weights = diff * np.power(index, 2) + strength_from
elif interpolation == "ease-out":
weights = diff * (1 - np.power(1 - index, 2)) + strength_from
elif interpolation == "ease-in-out":
weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
if revert_direction_at_midpoint:
weights = np.concatenate([weights, weights[::-1]])
# Generate frame numbers
frame_numbers = np.arange(range_start, range_start + len(weights))
# "Dropper" component: For keyframes with negative start, drop the weights
if range_start < 0 and i > 0:
drop_count = abs(range_start)
weights = weights[drop_count:]
frame_numbers = frame_numbers[drop_count:]
# Dropper component: for keyframes a range_End is greater than last_key_frame_position, drop the weights
if range_end > last_key_frame_position and i < number_of_items - 1:
drop_count = range_end - last_key_frame_position
weights = weights[:-drop_count]
frame_numbers = frame_numbers[:-drop_count]
return weights, frame_numbers
def process_weights(frame_numbers, weights, multiplier):
# Multiply weights by the multiplier and apply the bounds of 0.0 and 1.0
adjusted_weights = [min(max(weight * multiplier, 0.0), 1.0) for weight in weights]
# Filter out frame numbers and weights where the weight is 0.0
filtered_frames_and_weights = [(frame, weight) for frame, weight in zip(frame_numbers, adjusted_weights) if weight > 0.0]
# Separate the filtered frame numbers and weights
filtered_frame_numbers, filtered_weights = zip(*filtered_frames_and_weights) if filtered_frames_and_weights else ([], [])
return list(filtered_frame_numbers), list(filtered_weights)
def calculate_influence_frame_number(key_frame_position, next_key_frame_position, distance):
# Calculate the absolute distance between key frames
key_frame_distance = abs(next_key_frame_position - key_frame_position)
# Apply the distance multiplier
extended_distance = key_frame_distance * distance
# Determine the direction of influence based on the positions of the key frames
if key_frame_position < next_key_frame_position:
# Normal case: influence extends forward
influence_frame_number = key_frame_position + extended_distance
else:
# Reverse case: influence extends backward
influence_frame_number = key_frame_position - extended_distance
# Return the result rounded to the nearest integer
return round(influence_frame_number)
# GET KEYFRAME POSITIONS
keyframe_positions = get_keyframe_positions(type_of_frame_distribution, dynamic_frame_distribution_values, images, linear_frame_distribution_value)
shifted_keyframes_position = [position + buffer - 2 for position in keyframe_positions]
shifted_keyframe_positions_string = ','.join(str(pos) for pos in shifted_keyframes_position)
# GET SPARSE INDEXES
sparseindexmethod = SparseIndexMethodNodeImport()
sparse_indexes, = sparseindexmethod.get_method(shifted_keyframe_positions_string)
# ADD BUFFER TO KEYFRAME POSITIONS
if buffer > 0:
# add front buffer
keyframe_positions = [position + buffer - 1 for position in keyframe_positions]
keyframe_positions.insert(0, 0)
# add end buffer
last_position_with_buffer = keyframe_positions[-1] + buffer + 1
keyframe_positions.append(last_position_with_buffer)
# GET BASE ADVANCED SETTINGS OR SET DEFAULTS
if base_ipa_advanced_settings is None:
if high_detail_mode:
base_ipa_advanced_settings = {
"ipa_starts_at": 0.0,
"ipa_ends_at": 0.3,
"ipa_weight_type": "ease in-out",
"ipa_weight": 1.0,
"ipa_embeds_scaling": "V only",
"ipa_noise_strength": 0.0,
"use_image_for_noise": False,
"type_of_noise": "fade",
"noise_blur": 0,
}
else:
base_ipa_advanced_settings = {
"ipa_starts_at": 0.0,
"ipa_ends_at": 0.75,
"ipa_weight_type": "ease in-out",
"ipa_weight": 1.0,
"ipa_embeds_scaling": "V only",
"ipa_noise_strength": 0.0,
"use_image_for_noise": False,
"type_of_noise": "fade",
"noise_blur": 0,
}
# GET DETAILED ADVANCED SETTINGS OR SET DEFAULTS
if detail_ipa_advanced_settings is None:
if high_detail_mode:
detail_ipa_advanced_settings = {
"ipa_starts_at": 0.25,
"ipa_ends_at": 0.75,
"ipa_weight_type": "ease in-out",
"ipa_weight": 1.0,
"ipa_embeds_scaling": "V only",
"ipa_noise_strength": 0.0,
"use_image_for_noise": False,
"type_of_noise": "fade",
"noise_blur": 0,
}
strength_values = extract_strength_values(type_of_strength_distribution, dynamic_strength_values, keyframe_positions, linear_strength_value)
strength_values = [literal_eval(val) if isinstance(val, str) else val for val in strength_values]
corrected_strength_values = []
for val in strength_values:
if len(val) == 2:
val = (val[0], val[1], val[0])
corrected_strength_values.append(val)
strength_values = corrected_strength_values
# GET KEYFRAME INFLUENCE VALUES
key_frame_influence_values = extract_influence_values(type_of_key_frame_influence, dynamic_key_frame_influence_values, keyframe_positions, linear_key_frame_influence_value)
key_frame_influence_values = [literal_eval(val) if isinstance(val, str) else val for val in key_frame_influence_values]
# CALCULATE LAST KEYFRAME POSITION
if len(keyframe_positions) == 4:
last_key_frame_position = (keyframe_positions[-1]) - 1
else:
last_key_frame_position = (keyframe_positions[-1])
class IPBin:
def __init__(self):
self.indicies = []
self.image_schedule = []
self.weight_schedule = []
self.imageBatch = []
self.bigImageBatch = []
self.noiseBatch = []
self.bigNoiseBatch = []
def length(self):
return len(self.image_schedule)
def add(self, image, big_image, noise, big_noise, image_index, frame_numbers, weights):
# Map frames to their corresponding reversed weights for easy lookup
frame_to_weight = {frame: weights[i] for i, frame in enumerate(frame_numbers)}
# Search for image index, if it isn't there add the image
try:
index = self.indicies.index(image_index)
except ValueError:
self.imageBatch.append(image)
self.bigImageBatch.append(big_image)
if noise is not None: self.noiseBatch.append(noise)
if big_noise is not None: self.bigNoiseBatch.append(big_noise)
self.indicies.append(image_index)
index = self.indicies.index(image_index)
self.image_schedule.extend([index] * (frame_numbers[-1] + 1 - len(self.image_schedule)))
self.weight_schedule.extend([0] * (frame_numbers[0] - len(self.weight_schedule)))
self.weight_schedule.extend(frame_to_weight[frame] for frame in range(frame_numbers[0], frame_numbers[-1] + 1))
# CREATE LISTS FOR WEIGHTS AND FRAME NUMBERS
all_cn_frame_numbers = []
all_cn_weights = []
all_ipa_weights = []
all_ipa_frame_numbers = []
# Start with one bin
bins = [IPBin()]
for i in range(len(keyframe_positions)):
keyframe_position = keyframe_positions[i]
interpolation = "ease-in-out"
# strength_from = strength_to = 1.0
image_index = 0
if i == 0: # buffer
image = images[0]
image_index = 0
strength_from = strength_to = strength_values[0][1]
batch_index_from = 0
batch_index_to_excl = buffer
weights, frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, strength_from, strength_to, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
elif i == 1: # first image
# GET IMAGE AND KEYFRAME INFLUENCE VALUES
image = images[i-1]
image_index = i-1
key_frame_influence_from, key_frame_influence_to = key_frame_influence_values[i-1]
start_strength, mid_strength, end_strength = strength_values[i-1]
keyframe_position = keyframe_positions[i] + 1
next_key_frame_position = keyframe_positions[i+1] + 1
batch_index_from = keyframe_position
batch_index_to_excl = calculate_influence_frame_number(keyframe_position, next_key_frame_position, key_frame_influence_to)
weights, frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, mid_strength, end_strength, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
# interpolation = "ease-in"
elif i == len(keyframe_positions) - 2: # last image
# GET IMAGE AND KEYFRAME INFLUENCE VALUES
image = images[i-1]
image_index = i - 1
key_frame_influence_from,key_frame_influence_to = key_frame_influence_values[i-1]
start_strength, mid_strength, end_strength = strength_values[i-1]
if len(keyframe_positions) == 4:
keyframe_position = keyframe_positions[i] - 1
else:
keyframe_position = keyframe_positions[i]
previous_key_frame_position = keyframe_positions[i-1]
batch_index_from = calculate_influence_frame_number(keyframe_position, previous_key_frame_position, key_frame_influence_from)
batch_index_to_excl = keyframe_position + 1
weights, frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, start_strength, mid_strength, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
# interpolation = "ease-out"
elif i == len(keyframe_positions) - 1: # buffer
image = images[i-2]
image_index = i - 2
strength_from = strength_to = strength_values[i-2][1]
if len(keyframe_positions) == 4:
batch_index_from = keyframe_positions[i-1]
batch_index_to_excl = last_key_frame_position - 1
else:
batch_index_from = keyframe_positions[i-1] + 1
batch_index_to_excl = last_key_frame_position
weights, frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, strength_from, strength_to, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
else: # middle images
# GET IMAGE AND KEYFRAME INFLUENCE VALUES
image = images[i-1]
image_index = i - 1
key_frame_influence_from,key_frame_influence_to = key_frame_influence_values[i-1]
start_strength, mid_strength, end_strength = strength_values[i-1]
keyframe_position = keyframe_positions[i]
# CALCULATE WEIGHTS FOR FIRST HALF
previous_key_frame_position = keyframe_positions[i-1]
batch_index_from = calculate_influence_frame_number(keyframe_position, previous_key_frame_position, key_frame_influence_from)
batch_index_to_excl = keyframe_position + 1
first_half_weights, first_half_frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, start_strength, mid_strength, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
# CALCULATE WEIGHTS FOR SECOND HALF
next_key_frame_position = keyframe_positions[i+1]
batch_index_from = keyframe_position
batch_index_to_excl = calculate_influence_frame_number(keyframe_position, next_key_frame_position, key_frame_influence_to) + 2
second_half_weights, second_half_frame_numbers = calculate_weights(batch_index_from, batch_index_to_excl, mid_strength, end_strength, interpolation, False, last_key_frame_position, i, len(keyframe_positions), buffer)
# COMBINE FIRST AND SECOND HALF
weights = np.concatenate([first_half_weights, second_half_weights])
frame_numbers = np.concatenate([first_half_frame_numbers, second_half_frame_numbers])
# PROCESS WEIGHTS
ipa_frame_numbers, ipa_weights = process_weights(frame_numbers, weights, 1.0)
# print(f'i {i} image index {image_index} ====')
# # print(f"frame numbers {frame_numbers}")
# # print(f"weights {weights}")
# print(f"frame numbers {ipa_frame_numbers}")
# print(f"weights {ipa_weights}")
# print("------")
# Prepare images and noise
prepare_for_clip_vision = PrepImageForClipVisionImport()
prepped_image, = prepare_for_clip_vision.prep_image(image=image.unsqueeze(0), interpolation="LANCZOS", crop_position="pad", sharpening=0.1)
if base_ipa_advanced_settings["ipa_noise_strength"] > 0:
if base_ipa_advanced_settings["use_image_for_noise"]:
noise_image = prepped_image
else:
noise_image = None
ipa_noise = IPAdapterNoiseImport()
negative_noise, = ipa_noise.make_noise(type=base_ipa_advanced_settings["type_of_noise"], strength=base_ipa_advanced_settings["ipa_noise_strength"], blur=base_ipa_advanced_settings["noise_blur"], image_optional=noise_image)
else:
negative_noise = None
if high_detail_mode and detail_ipa_advanced_settings["ipa_noise_strength"] > 0:
if detail_ipa_advanced_settings["use_image_for_noise"]:
noise_image = image.unsqueeze(0)
else:
noise_image = None
ipa_noise = IPAdapterNoiseImport()
big_negative_noise, = ipa_noise.make_noise(type=detail_ipa_advanced_settings["type_of_noise"], strength=detail_ipa_advanced_settings["ipa_noise_strength"], blur=detail_ipa_advanced_settings["noise_blur"], image_optional=noise_image)
else:
big_negative_noise = None
if len(ipa_frame_numbers) > 0:
# Fill up bins with image frames. Bins will automatically be created when needed but all the frames should be able to be packed into two bins
active_index = -1
# Find a bin that we can fit the next image into
for i, bin in enumerate(bins):
if bin.length() <= ipa_frame_numbers[0]:
active_index = i
break
# If we didn't find a suitable bin, add a new one
if active_index == -1:
bins.append(IPBin())
active_index = len(bins) - 1
# Add the image to the bin
bins[active_index].add(prepped_image, image.unsqueeze(0), negative_noise, big_negative_noise, image_index, ipa_frame_numbers, ipa_weights)
# for i, bin in enumerate(bins):
# print(f"{i} schedule {bin.image_schedule}")
# print(f"{i} weights {bin.weight_schedule}")
# i += 1
all_ipa_frame_numbers.append(ipa_frame_numbers)
all_ipa_weights.append(ipa_weights)
# Go through the bins and create IPAdapters for them
for i, bin in enumerate(bins):
ipadapter_application = IPAdapterBatchImport()
negative_noise = torch.cat(bin.noiseBatch, dim=0) if len(bin.noiseBatch) > 0 else None
model, *_ = ipadapter_application.apply_ipadapter(model=model, ipadapter=ipadapter, image=torch.cat(bin.imageBatch, dim=0), weight=[x * base_ipa_advanced_settings["ipa_weight"] for x in bin.weight_schedule], weight_type=base_ipa_advanced_settings["ipa_weight_type"], start_at=base_ipa_advanced_settings["ipa_starts_at"], end_at=base_ipa_advanced_settings["ipa_ends_at"], clip_vision=clip_vision,image_negative=negative_noise,embeds_scaling=base_ipa_advanced_settings["ipa_embeds_scaling"], encode_batch_size=1, image_schedule=bin.image_schedule)
if high_detail_mode:
tiled_ipa_application = IPAdapterTiledBatchImport()
negative_noise = torch.cat(bin.bigNoiseBatch, dim=0) if len(bin.bigNoiseBatch) > 0 else None
model, *_ = tiled_ipa_application.apply_tiled(model=model, ipadapter=ipadapter, image=torch.cat(bin.bigImageBatch, dim=0), weight=[x * detail_ipa_advanced_settings["ipa_weight"] for x in bin.weight_schedule], weight_type=detail_ipa_advanced_settings["ipa_weight_type"], start_at=detail_ipa_advanced_settings["ipa_starts_at"], end_at=detail_ipa_advanced_settings["ipa_ends_at"], clip_vision=clip_vision,sharpening=0.1,image_negative=negative_noise,embeds_scaling=detail_ipa_advanced_settings["ipa_embeds_scaling"], encode_batch_size=1, image_schedule=bin.image_schedule)
comparison_diagram, = plot_weight_comparison(all_cn_frame_numbers, all_cn_weights, all_ipa_frame_numbers, all_ipa_weights, buffer)
return comparison_diagram, positive, negative, model, sparse_indexes, last_key_frame_position, buffer
class IpaConfigurationNode:
WEIGHT_TYPES = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output', 'weak middle', 'strong middle']
IPA_EMBEDS_SCALING_OPTIONS = ["V only", "K+V", "K+V w/ C penalty", "K+mean(V) w/ C penalty"]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"ipa_starts_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"ipa_ends_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"ipa_weight_type": (cls.WEIGHT_TYPES,),
"ipa_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
"ipa_embeds_scaling": (cls.IPA_EMBEDS_SCALING_OPTIONS,),
"ipa_noise_strength": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
"use_image_for_noise": ("BOOLEAN", {"default": False}),
"type_of_noise": (["fade", "dissolve", "gaussian", "shuffle"], ),
"noise_blur": ("INT", { "default": 0, "min": 0, "max": 32, "step": 1 }),
},
"optional": {}
}
FUNCTION = "process_inputs"
RETURN_TYPES = ("ADVANCED_IPA_SETTINGS",)
RETURN_NAMES = ("configuration",)
CATEGORY = "Steerable-Motion"
@classmethod
def process_inputs(cls, ipa_starts_at, ipa_ends_at, ipa_weight_type, ipa_weight, ipa_embeds_scaling, ipa_noise_strength, use_image_for_noise, type_of_noise, noise_blur):
return {
"ipa_starts_at": ipa_starts_at,
"ipa_ends_at": ipa_ends_at,
"ipa_weight_type": ipa_weight_type,
"ipa_weight": ipa_weight,
"ipa_embeds_scaling": ipa_embeds_scaling,
"ipa_noise_strength": ipa_noise_strength,
"use_image_for_noise": use_image_for_noise,
"type_of_noise": type_of_noise,
"noise_blur": noise_blur,
},
# NODE MAPPING
NODE_CLASS_MAPPINGS = {
"BatchCreativeInterpolation": BatchCreativeInterpolationNode,
"IpaConfiguration": IpaConfigurationNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"BatchCreativeInterpolation": "Batch Creative Interpolation 🎞️🅢🅜",
"IpaConfiguration": "IPA Configuration 🎞️🅢🅜",
}
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