450 lines
26 KiB
Python
450 lines
26 KiB
Python
# 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
|
|
import folder_paths
|
|
from .imports.IPAdapterPlus import IPAdapterApplyImport, prep_image, IPAdapterEncoderImport
|
|
from .imports.AdvancedControlNet.latent_keyframe_nodes import LatentKeyframeInterpolationNodeImport
|
|
from .imports.AdvancedControlNet.weight_nodes import ScaledSoftUniversalWeightsImport
|
|
from .imports.AdvancedControlNet.nodes_sparsectrl import SparseIndexMethodNodeImport
|
|
from .imports.AdvancedControlNet.nodes import ControlNetLoaderAdvancedImport, AdvancedControlNetApplyImport,TimestepKeyframeNodeImport
|
|
|
|
|
|
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",),
|
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
|
|
"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)"}),
|
|
"soft_scaled_cn_weights_multiplier": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 10.0, "step": 0.1}),
|
|
"buffer": ("INT", {"default": 4, "min": 0, "max": 16, "step": 1}),
|
|
"relative_cn_strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
"relative_ipadapter_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
"ipadapter_noise": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"ipadapter_start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"ipadapter_end_at": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"cn_start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"cn_end_at": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE","CONDITIONING","CONDITIONING","MODEL","SPARSE_METHOD","INT")
|
|
# "comparison_diagram, positive, negative, model, sparse_indexes, last_key_frame_position"
|
|
RETURN_NAMES = ("GRAPH","POSITIVE","NEGATIVE","MODEL","KEYFRAME_POSITIONS","BATCH_SIZE")
|
|
FUNCTION = "combined_function"
|
|
|
|
CATEGORY = "Steerable-Motion"
|
|
|
|
def combined_function(self,positive,negative,images,model,ipadapter,clip_vision,control_net_name,
|
|
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, soft_scaled_cn_weights_multiplier,
|
|
buffer, relative_cn_strength,relative_ipadapter_strength,ipadapter_noise,
|
|
ipadapter_start_at=0.0,ipadapter_end_at=0.75, cn_start_at=0.0, cn_end_at=0.75):
|
|
|
|
|
|
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):
|
|
# 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 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 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 buffer > 0:
|
|
if i == 0:
|
|
range_start = 0
|
|
elif i == 1:
|
|
range_start = buffer
|
|
else:
|
|
if i == 1:
|
|
range_start = 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:
|
|
keyframe_positions = [position + buffer - 1 for position in keyframe_positions]
|
|
keyframe_positions.insert(0, 0)
|
|
|
|
# GET STRENGTH VALUES
|
|
|
|
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
|
|
last_key_frame_position = (keyframe_positions[-1] + 1)
|
|
|
|
# CREATE LISTS FOR WEIGHTS AND FRAME NUMBERS
|
|
all_cn_frame_numbers = []
|
|
all_cn_weights = []
|
|
all_ipa_weights = []
|
|
all_ipa_frame_numbers = []
|
|
|
|
for i in range(len(keyframe_positions)):
|
|
|
|
keyframe_position = keyframe_positions[i]
|
|
interpolation = "ease-in-out"
|
|
# strength_from = strength_to = 1.0
|
|
|
|
if i == 0: # buffer
|
|
|
|
if buffer > 0: # First image with buffer
|
|
image = images[0]
|
|
strength_from = strength_to = strength_values[0][1]
|
|
else:
|
|
continue # Skip first image without buffer
|
|
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[0]
|
|
key_frame_influence_from, key_frame_influence_to = key_frame_influence_values[0]
|
|
start_strength, mid_strength, end_strength = strength_values[0]
|
|
|
|
keyframe_position = keyframe_positions[i]
|
|
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)
|
|
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(images): # last image
|
|
|
|
# GET IMAGE AND KEYFRAME INFLUENCE VALUES
|
|
image = images[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]
|
|
# strength_from, strength_to = cn_strength_values[i-1]
|
|
|
|
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
|
|
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"
|
|
|
|
else: # middle images
|
|
|
|
# GET IMAGE AND KEYFRAME INFLUENCE VALUES
|
|
image = images[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
|
|
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)
|
|
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])
|
|
|
|
# IMPORT REQUIRED NODES
|
|
latent_keyframe_interpolation_node = LatentKeyframeInterpolationNodeImport()
|
|
scaled_soft_control_net_weights = ScaledSoftUniversalWeightsImport()
|
|
timestep_keyframe_node = TimestepKeyframeNodeImport()
|
|
control_net_loader = ControlNetLoaderAdvancedImport()
|
|
apply_advanced_control_net = AdvancedControlNetApplyImport()
|
|
ipadapter_application = IPAdapterApplyImport()
|
|
ipadapter_encoder = IPAdapterEncoderImport()
|
|
|
|
# IF CONTROL NET IS USED, APPLY IT
|
|
if relative_cn_strength > 0.0:
|
|
cn_frame_numbers, cn_weights = process_weights(frame_numbers, weights, relative_cn_strength)
|
|
latent_keyframe, = latent_keyframe_interpolation_node.load_keyframe(cn_weights, cn_frame_numbers)
|
|
control_net_weights, _ = scaled_soft_control_net_weights.load_weights(soft_scaled_cn_weights_multiplier, False)
|
|
timestep_keyframe = timestep_keyframe_node.load_keyframe(start_percent=0.0, control_net_weights=control_net_weights, latent_keyframe=latent_keyframe, prev_timestep_keyframe=None)[0]
|
|
control_net = control_net_loader.load_controlnet(control_net_name, timestep_keyframe)[0]
|
|
positive, negative = apply_advanced_control_net.apply_controlnet(positive, negative, control_net, image.unsqueeze(0), 1.0, cn_start_at, cn_end_at)
|
|
all_cn_frame_numbers.append(cn_frame_numbers)
|
|
all_cn_weights.append(cn_weights)
|
|
else:
|
|
all_cn_frame_numbers = None
|
|
all_cn_weights = None
|
|
|
|
# IF IP ADAPTER IS USED, APPLY IT
|
|
if relative_ipadapter_strength > 0.0:
|
|
ipa_frame_numbers, ipa_weights = process_weights(frame_numbers, weights, relative_ipadapter_strength)
|
|
prepped_image = prep_image(image=image.unsqueeze(0), interpolation="LANCZOS", crop_position="pad", sharpening=0.0)[0]
|
|
mask = create_mask_batch(last_key_frame_position, ipa_weights, ipa_frame_numbers)
|
|
embed, = ipadapter_encoder.preprocess(clip_vision, prepped_image, True, 0.0, 1.0)
|
|
model, = ipadapter_application.apply_ipadapter(ipadapter=ipadapter, model=model, weight=1.0, image=None, weight_type="original",
|
|
noise=ipadapter_noise, embeds=embed, attn_mask=mask, start_at=ipadapter_start_at, end_at=ipadapter_end_at, unfold_batch=True)
|
|
all_ipa_frame_numbers.append(ipa_frame_numbers)
|
|
all_ipa_weights.append(ipa_weights)
|
|
else:
|
|
all_ipa_frame_numbers = None
|
|
all_ipa_weights = None
|
|
|
|
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
|
|
|
|
# NODE MAPPING
|
|
NODE_CLASS_MAPPINGS = {
|
|
"BatchCreativeInterpolation": BatchCreativeInterpolationNode
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"BatchCreativeInterpolation": "Batch Creative Interpolation 🎞️🅢🅜"
|
|
}
|