582 lines
28 KiB
Python
582 lines
28 KiB
Python
import numpy as np
|
|
import torch
|
|
import folder_paths
|
|
from PIL import Image
|
|
from ast import literal_eval
|
|
from .control import ControlNetAdvancedImport, T2IAdapterAdvancedImport, load_controlnet, ControlNetWeightsTypeImport, T2IAdapterWeightsTypeImport,\
|
|
LatentKeyframeGroupImport, TimestepKeyframeImport, TimestepKeyframeGroupImport, is_advanced_controlnet
|
|
import matplotlib.pyplot as plt
|
|
from .IPAdapterPlus import contrast_adaptive_sharpening, IPAdapterApply,prep_image
|
|
|
|
from .weight_nodes import ScaledSoftControlNetWeightsImport, SoftControlNetWeightsImport, CustomControlNetWeightsImport, \
|
|
SoftT2IAdapterWeightsImport, CustomT2IAdapterWeightsImport
|
|
from .latent_keyframe_nodes import LatentKeyframeGroupNodeImport, LatentKeyframeInterpolationNodeImport, LatentKeyframeBatchedGroupNodeImport, LatentKeyframeNodeImport,calculate_weights
|
|
from .deprecated_nodes import LoadImagesFromDirectory
|
|
from .logger import logger
|
|
import torchvision.transforms as TT
|
|
import torch.nn.functional as F
|
|
|
|
import comfy.utils
|
|
import comfy.model_management
|
|
from comfy.clip_vision import clip_preprocess
|
|
from comfy.ldm.modules.attention import optimized_attention
|
|
# import BytesIO
|
|
from io import BytesIO
|
|
|
|
|
|
|
|
class TimestepKeyframeNodeImport:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
|
|
},
|
|
"optional": {
|
|
"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
|
|
"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
|
|
"latent_keyframe": ("LATENT_KEYFRAME", ),
|
|
"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
|
|
FUNCTION = "load_keyframe"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
|
|
|
|
def load_keyframe(self,
|
|
start_percent: float,
|
|
control_net_weights: ControlNetWeightsTypeImport=None,
|
|
t2i_adapter_weights: T2IAdapterWeightsTypeImport=None,
|
|
latent_keyframe: LatentKeyframeGroupImport=None,
|
|
prev_timestep_keyframe: TimestepKeyframeGroupImport=None):
|
|
if not prev_timestep_keyframe:
|
|
prev_timestep_keyframe = TimestepKeyframeGroupImport()
|
|
keyframe = TimestepKeyframeImport(start_percent, control_net_weights, t2i_adapter_weights, latent_keyframe)
|
|
prev_timestep_keyframe.add(keyframe)
|
|
return (prev_timestep_keyframe,)
|
|
|
|
|
|
class ControlNetLoaderAdvancedImport:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
|
|
},
|
|
"optional": {
|
|
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET", )
|
|
FUNCTION = "load_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
|
|
|
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroupImport=None):
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, timestep_keyframe)
|
|
return (controlnet,)
|
|
|
|
|
|
class DiffControlNetLoaderAdvancedImport:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), )
|
|
},
|
|
"optional": {
|
|
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONTROL_NET", )
|
|
FUNCTION = "load_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
|
|
|
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroupImport, model):
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
|
|
return (controlnet,)
|
|
|
|
|
|
class AdvancedControlNetApplyImport:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"control_net": ("CONTROL_NET", ),
|
|
"image": ("IMAGE", ),
|
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
|
|
},
|
|
"optional": {
|
|
"mask_optional": ("MASK", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONDITIONING","CONDITIONING")
|
|
RETURN_NAMES = ("positive", "negative")
|
|
FUNCTION = "apply_controlnet"
|
|
|
|
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/conditioning"
|
|
|
|
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_optional=None):
|
|
if strength == 0:
|
|
return (positive, negative)
|
|
|
|
control_hint = image.movedim(-1,1)
|
|
cnets = {}
|
|
|
|
out = []
|
|
for conditioning in [positive, negative]:
|
|
c = []
|
|
|
|
for t in conditioning:
|
|
d = t[1].copy()
|
|
|
|
prev_cnet = d.get('control', None)
|
|
if prev_cnet in cnets:
|
|
c_net = cnets[prev_cnet]
|
|
|
|
else:
|
|
c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
|
|
# set cond hint mask
|
|
if mask_optional is not None:
|
|
if is_advanced_controlnet(c_net):
|
|
# if not in the form of a batch, make it so
|
|
if len(mask_optional.shape) < 3:
|
|
mask_optional = mask_optional.unsqueeze(0)
|
|
c_net.set_cond_hint_mask(mask_optional)
|
|
c_net.set_previous_controlnet(prev_cnet)
|
|
cnets[prev_cnet] = c_net
|
|
|
|
d['control'] = c_net
|
|
d['control_apply_to_uncond'] = False
|
|
n = [t[0], d]
|
|
c.append(n)
|
|
out.append(c)
|
|
return (out[0], out[1])
|
|
|
|
|
|
class MaskGeneratorNode:
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "generate_masks"
|
|
CATEGORY = "Steerable-Motion/Interpolation"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"number_of_masks": ("INT", {"default": 16, "min": 1, "max": 100, "step": 1}),
|
|
"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"width": ("INT", {"default": 512, "min": 16, "max": 4096, "step": 1}),
|
|
"height": ("INT", {"default": 512, "min": 16, "max": 4096, "step": 1}),
|
|
},
|
|
}
|
|
|
|
def generate_masks(self, number_of_masks, strength, width, height):
|
|
|
|
masks = []
|
|
for _ in range(number_of_masks):
|
|
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
|
|
|
|
|
|
|
|
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": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
|
|
"dynamic_key_frame_influence_values": ("STRING", {"multiline": True, "default": "1.0,1.0,1.0,0.5"}),
|
|
"type_of_cn_strength_distribution": (["linear", "dynamic"],),
|
|
"linear_cn_strength_value": ("STRING", {"multiline": False, "default": "(0.0,0.4)"}),
|
|
"dynamic_cn_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}),
|
|
# "interpolation": (["ease-in-out", "ease-in", "ease-out"],),
|
|
"buffer": ("INT", {"default": 4, "min": 0, "max": 16, "step": 1}),
|
|
"relative_ipadapter_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1}),
|
|
"relative_ipadapter_influence": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1}),
|
|
"ipadapter_noise": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE","CONDITIONING","CONDITIONING","MODEL",)
|
|
RETURN_NAMES = ("GRAPH","POSITIVE", "NEGATIVE","MODEL")
|
|
FUNCTION = "combined_function"
|
|
|
|
CATEGORY = "Steerable-Motion/Interpolation"
|
|
|
|
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_cn_strength_distribution,linear_cn_strength_value,dynamic_cn_strength_values,
|
|
soft_scaled_cn_weights_multiplier,buffer,relative_ipadapter_strength,
|
|
relative_ipadapter_influence,ipadapter_noise):
|
|
|
|
def calculate_dynamic_influence_ranges(keyframe_positions, key_frame_influence_values, allow_extension=True):
|
|
if len(keyframe_positions) < 2 or len(keyframe_positions) != len(key_frame_influence_values):
|
|
return []
|
|
|
|
influence_ranges = []
|
|
for i, position in enumerate(keyframe_positions):
|
|
influence_factor = key_frame_influence_values[i]
|
|
|
|
# Calculate the base range size
|
|
range_size = influence_factor * (keyframe_positions[-1] - keyframe_positions[0]) / (len(keyframe_positions) - 1) / 2
|
|
|
|
# Calculate symmetric start and end influence
|
|
start_influence = position - range_size
|
|
end_influence = position + range_size
|
|
|
|
# Adjust start and end influence to not exceed previous and next keyframes
|
|
if not allow_extension:
|
|
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:
|
|
shifted_ranges.append((start + buffer, end + buffer))
|
|
return shifted_ranges
|
|
|
|
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 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":
|
|
# 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
|
|
dynamic_values = [float(influence.strip()) for influence in dynamic_key_frame_influence_values.split(',')]
|
|
elif isinstance(dynamic_key_frame_influence_values, list):
|
|
dynamic_values = dynamic_key_frame_influence_values
|
|
else:
|
|
raise ValueError("Invalid type for dynamic_key_frame_influence_values. Must be string or list.")
|
|
|
|
# Trim the dynamic_values to match the length of keyframe_positions
|
|
return dynamic_values[:len(keyframe_positions)]
|
|
|
|
else:
|
|
# Create a list with the linear_key_frame_influence_value for each keyframe
|
|
return [linear_key_frame_influence_value for _ in keyframe_positions]
|
|
|
|
def extract_start_and_endpoint_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":
|
|
# If dynamic_key_frame_influence_values is a list of characters representing tuples, process it
|
|
if isinstance(dynamic_key_frame_influence_values[0], str) and dynamic_key_frame_influence_values[0] == "(":
|
|
# Join the characters to form a single string and evaluate to convert into a list of tuples
|
|
string_representation = ''.join(dynamic_key_frame_influence_values)
|
|
dynamic_values = eval(f'[{string_representation}]')
|
|
else:
|
|
# If it's already a list of tuples or a single tuple, use it directly
|
|
dynamic_values = dynamic_key_frame_influence_values if isinstance(dynamic_key_frame_influence_values, list) else [dynamic_key_frame_influence_values]
|
|
return dynamic_values
|
|
else:
|
|
# Return a list of tuples with the linear_key_frame_influence_value as a tuple repeated for each position
|
|
return [linear_key_frame_influence_value for _ in keyframe_positions]
|
|
|
|
def create_mask_batch(last_key_frame_position, weights, frames):
|
|
# Hardcoded dimensions
|
|
width, height = 512, 512
|
|
|
|
# Calculate the reversed weights in a generalizable way (e.g., 0.6 becomes 0.4, 0.1 becomes 0.9)
|
|
reversed_weights = [1.0 - weight for weight in weights]
|
|
|
|
# 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 adjust_influence_range(batch_index_from, batch_index_to_excl, last_key_frame_position, scale_factor, buffer):
|
|
# Calculate the midpoint of the current range
|
|
midpoint = (batch_index_from + batch_index_to_excl) // 2
|
|
|
|
# Calculate the new range length
|
|
new_range_length = int((batch_index_to_excl - batch_index_from) * scale_factor)
|
|
|
|
# Adjusting both sides of the range
|
|
if batch_index_from == 0:
|
|
# Start is anchored at 0
|
|
new_batch_index_from = 0
|
|
new_batch_index_to_excl = batch_index_from + new_range_length
|
|
elif batch_index_to_excl == last_key_frame_position:
|
|
# End is anchored at last_key_frame_position
|
|
new_batch_index_from = batch_index_to_excl - new_range_length
|
|
new_batch_index_to_excl = last_key_frame_position
|
|
else:
|
|
# No anchoring, adjust both sides around the midpoint
|
|
new_batch_index_from = midpoint - new_range_length // 2
|
|
new_batch_index_to_excl = midpoint + new_range_length // 2
|
|
|
|
# Remove minimum and maximum constraints
|
|
|
|
return new_batch_index_from, new_batch_index_to_excl
|
|
|
|
def adjust_strength_values(strength_from, strength_to, multiplier):
|
|
mid_point = (strength_from + strength_to) / 2
|
|
range_half = abs(strength_to - strength_from) / 2
|
|
|
|
# Adjust the range with the multiplier
|
|
new_range_half = min(range_half * multiplier, 0.5)
|
|
|
|
# Calculate new strength values, ensuring they stay within [0.0, 1.0]
|
|
new_strength_from = max(mid_point - new_range_half, 0.0)
|
|
new_strength_to = min(mid_point + new_range_half, 1.0)
|
|
|
|
# Preserve the order of the original strength values
|
|
if strength_from > strength_to:
|
|
new_strength_from, new_strength_to = new_strength_to, new_strength_from
|
|
|
|
return (new_strength_from, new_strength_to)
|
|
|
|
def plot_weight_comparison(cn_frame_numbers, cn_weights, ipadapter_frame_numbers, ipadapter_weights, buffer):
|
|
plt.figure(figsize=(12, 8))
|
|
|
|
# Defining colors for each set of data
|
|
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
|
|
|
|
# Alternating the data sets with labels and colors
|
|
max_length = max(len(cn_frame_numbers), len(ipadapter_frame_numbers))
|
|
label_counter = 1 if buffer < 0 else 0 # Start from 1 if buffer < 0, else start from 0
|
|
for i in range(max_length):
|
|
# Label for cn_strength
|
|
if i < len(cn_frame_numbers):
|
|
if i == 0 and buffer > 0:
|
|
label = 'cn_strength_buffer'
|
|
else:
|
|
label = f'cn_strength_{label_counter}'
|
|
plt.plot(cn_frame_numbers[i], cn_weights[i], marker='o', color=colors[i % len(colors)], label=label)
|
|
|
|
# Label for ipa_strength
|
|
if i < len(ipadapter_frame_numbers):
|
|
if i == 0 and buffer > 0:
|
|
label = 'ipa_strength_buffer'
|
|
else:
|
|
label = 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()
|
|
max_weight = max([weight.max() for weight in cn_weights + ipadapter_weights]) * 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 = TT.ToTensor()(img)
|
|
|
|
img_tensor = img_tensor.unsqueeze(0)
|
|
|
|
img_tensor = img_tensor.permute([0, 2, 3, 1])
|
|
|
|
return (img_tensor,)
|
|
|
|
keyframe_positions = get_keyframe_positions(type_of_frame_distribution, dynamic_frame_distribution_values, images, linear_frame_distribution_value)
|
|
cn_strength_values = extract_start_and_endpoint_values(type_of_cn_strength_distribution, dynamic_cn_strength_values, keyframe_positions, linear_cn_strength_value)
|
|
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)
|
|
cn_strength_values = [literal_eval(val) if isinstance(val, str) else val for val in cn_strength_values]
|
|
|
|
cn_frame_numbers = []
|
|
cn_weights = []
|
|
ipadapter_frame_numbers = []
|
|
ipadapter_weights = []
|
|
|
|
last_key_frame_position = (keyframe_positions[-1]) + buffer
|
|
control_net = []
|
|
for i, (start, end) in enumerate(influence_ranges):
|
|
batch_index_from, batch_index_to_excl = influence_ranges[i]
|
|
ipadapter_strength_multiplier = relative_ipadapter_strength
|
|
ipadapter_influence_multiplier = relative_ipadapter_influence
|
|
|
|
if i == 0:
|
|
if buffer > 0: # First image with buffer
|
|
image = images[0]
|
|
strength_from = strength_to = cn_strength_values[0][1] if len(cn_strength_values) > 0 else (1.0, 1.0)
|
|
revert_direction_at_midpoint = False
|
|
ipadapter_strength_multiplier = 1.0
|
|
ipadapter_influence_multiplier = 1.0
|
|
interpolation = "ease-in-out"
|
|
else:
|
|
continue # Skip first image without buffer
|
|
elif i == 1: # First image
|
|
image = images[0]
|
|
strength_to, strength_from = cn_strength_values[0] if len(cn_strength_values) > 0 else (0.0, 1.0)
|
|
revert_direction_at_midpoint = False
|
|
interpolation = "ease-in"
|
|
|
|
|
|
|
|
elif i == len(images): # Last image
|
|
image = images[i-1]
|
|
strength_from, strength_to = cn_strength_values[i-1] if i-1 < len(cn_strength_values) else (0.0, 1.0)
|
|
revert_direction_at_midpoint = False
|
|
interpolation = "ease-out"
|
|
|
|
else: # Middle images
|
|
image = images[i-1]
|
|
strength_from, strength_to = cn_strength_values[i-1] if i-1 < len(cn_strength_values) else (0.0, 1.0)
|
|
revert_direction_at_midpoint = True
|
|
interpolation = "ease-in-out"
|
|
|
|
|
|
latent_keyframe_interpolation_node = LatentKeyframeInterpolationNodeImport()
|
|
weights, frame_numbers, latent_keyframe, = latent_keyframe_interpolation_node.load_keyframe(batch_index_from,strength_from,batch_index_to_excl,strength_to,interpolation,revert_direction_at_midpoint,last_key_frame_position,i,len(influence_ranges),buffer)
|
|
|
|
cn_frame_numbers.append(frame_numbers)
|
|
cn_weights.append(weights)
|
|
|
|
scaled_soft_control_net_weights = ScaledSoftControlNetWeightsImport()
|
|
control_net_weights, _ = scaled_soft_control_net_weights.load_weights(soft_scaled_cn_weights_multiplier,False)
|
|
|
|
timestep_keyframe_node = TimestepKeyframeNodeImport()
|
|
timestep_keyframe, = timestep_keyframe_node.load_keyframe(start_percent=0.0,control_net_weights=control_net_weights,t2i_adapter_weights=None,latent_keyframe=latent_keyframe,prev_timestep_keyframe=None)
|
|
|
|
control_net_loader = ControlNetLoaderAdvancedImport()
|
|
control_net, = control_net_loader.load_controlnet(control_net_name, timestep_keyframe)
|
|
|
|
apply_advanced_control_net = AdvancedControlNetApplyImport()
|
|
positive, negative = apply_advanced_control_net.apply_controlnet(positive,negative,control_net,image.unsqueeze(0),1.0,0.0,1.0)
|
|
|
|
prepped_image, = prep_image(image=image.unsqueeze(0), interpolation="LANCZOS", crop_position="pad", sharpening=0.0)
|
|
|
|
ipadapter_application = IPAdapterApply()
|
|
|
|
ipa_strength_from, ipa_strength_to = adjust_strength_values(strength_from, strength_to, ipadapter_strength_multiplier)
|
|
|
|
ipa_batch_index_from, ipa_batch_index_to_excl = adjust_influence_range(batch_index_from, batch_index_to_excl, last_key_frame_position, ipadapter_influence_multiplier, buffer)
|
|
|
|
ipa_weights, ipa_frame_numbers = calculate_weights(ipa_batch_index_from,ipa_batch_index_to_excl,ipa_strength_from, ipa_strength_to,interpolation, revert_direction_at_midpoint, last_key_frame_position,i, len(influence_ranges),buffer)
|
|
|
|
|
|
ipadapter_frame_numbers.append(ipa_frame_numbers)
|
|
ipadapter_weights.append(ipa_weights)
|
|
|
|
|
|
masks = create_mask_batch(last_key_frame_position, weights, frame_numbers)
|
|
|
|
model, = ipadapter_application.apply_ipadapter(ipadapter=ipadapter, model=model, weight=1.0, clip_vision=clip_vision, image=prepped_image, weight_type="original", noise=ipadapter_noise, embeds=None, attn_mask=masks, start_at=0.0, end_at=1.0, unfold_batch=True)
|
|
|
|
comparison_diagram, = plot_weight_comparison(cn_frame_numbers, cn_weights, ipadapter_frame_numbers, ipadapter_weights, buffer)
|
|
|
|
return comparison_diagram, positive, negative, model
|
|
|
|
# NODE MAPPING
|
|
NODE_CLASS_MAPPINGS = {
|
|
# Combined
|
|
"BatchCreativeInterpolation": BatchCreativeInterpolationNode
|
|
# "MaskGenerator": MaskGeneratorNode
|
|
# "FILMVFIImport": FILMVFINode
|
|
# Keyframes
|
|
# "TimestepKeyframe": TimestepKeyframeNodeImport,
|
|
# "LatentKeyframeImport": LatentKeyframeNodeImport,
|
|
# "LatentKeyframeGroupImport": LatentKeyframeGroupImportNode,
|
|
# "LatentKeyframeBatchedGroupImport": LatentKeyframeBatchedGroupNodeImport,
|
|
# "LatentKeyframeTiming": LatentKeyframeInterpolationNodeImport,
|
|
# Loaders
|
|
# "ControlNetLoaderAdvancedImport": ControlNetLoaderAdvancedImport,
|
|
# "DiffControlNetLoaderAdvancedImport": DiffControlNetLoaderAdvancedImport,
|
|
# Conditioning
|
|
# "ACN_AdvancedControlNetApplyImport": AdvancedControlNetApplyImport,
|
|
# Weights
|
|
# "ScaledSoftControlNetWeightsImport": ScaledSoftControlNetWeightsImport,
|
|
# "SoftControlNetWeights": SoftControlNetWeights,
|
|
# "CustomControlNetWeights": CustomControlNetWeights,
|
|
# "SoftT2IAdapterWeights": SoftT2IAdapterWeights,
|
|
# "CustomT2IAdapterWeights": CustomT2IAdapterWeights,
|
|
# Image
|
|
# "LoadImagesFromDirectory": LoadImagesFromDirectory
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
# Combined
|
|
"BatchCreativeInterpolation": "Batch Creative Interpolation 🎞️🅟🅞🅜"
|
|
# "MaskGenerator": "Mask Generator 🎞️🅟🅞🅜"
|
|
# Keyframes
|
|
# "TimestepKeyframe": "Timestep Keyframe 🎞️🅟🅞🅜",
|
|
# "LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
|
|
# "LatentKeyframeGroupImport": "Latent Keyframe Group 🛂🅐🅒🅝",
|
|
# "LatentKeyframeBatchedGroup": "Latent Keyframe Batched Group 🛂🅐🅒🅝",
|
|
# "LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
|
|
# Loaders
|
|
# "ControlNetLoaderAdvancedImport": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
|
|
# "DiffControlNetLoaderAdvancedImport": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
|
|
# Conditioning
|
|
# "ACN_AdvancedControlNetApplyImport": "Apply Advanced ControlNet 🛂🅐🅒🅝",
|
|
# Weights
|
|
# "ScaledSoftControlNetWeightsImport": "Scaled Soft ControlNet Weights 🛂🅐🅒🅝",
|
|
# "SoftControlNetWeights": "Soft ControlNet Weights 🛂🅐🅒🅝",
|
|
# "CustomControlNetWeights": "Custom ControlNet Weights 🛂🅐🅒🅝",
|
|
# "SoftT2IAdapterWeights": "Soft T2IAdapter Weights 🛂🅐🅒🅝",
|
|
# "CustomT2IAdapterWeights": "Custom T2IAdapter Weights 🛂🅐🅒🅝",
|
|
# Image
|
|
# "LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
|
|
}
|