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ComfyUI-KJNodes/nodes.py
kijai bdc5a71e8a Update nodes.py
2024-04-07 16:03:48 +03:00

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import torch
import torch.nn.functional as F
from torchvision.transforms import Resize, CenterCrop, InterpolationMode
from torchvision.transforms import functional as TF
import scipy.ndimage
import matplotlib.pyplot as plt
import numpy as np
from PIL import ImageFilter, Image, ImageDraw, ImageFont
import json
import re
import os
import random
import math
import model_management
from nodes import MAX_RESOLUTION
import folder_paths
script_directory = os.path.dirname(os.path.abspath(__file__))
folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts"))
class INTConstant:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
}
RETURN_TYPES = ("INT",)
RETURN_NAMES = ("value",)
FUNCTION = "get_value"
CATEGORY = "KJNodes/constants"
def get_value(self, value):
return (value,)
class FloatConstant:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.001}),
},
}
RETURN_TYPES = ("FLOAT",)
RETURN_NAMES = ("value",)
FUNCTION = "get_value"
CATEGORY = "KJNodes/constants"
def get_value(self, value):
return (value,)
class StringConstant:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"string": ("STRING", {"default": '', "multiline": False}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "passtring"
CATEGORY = "KJNodes/constants"
def passtring(self, string):
return (string, )
class CreateFluidMask:
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "createfluidmask"
CATEGORY = "KJNodes/masking/generate"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}),
"inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}),
"inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}),
"inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}),
"inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}),
},
}
#using code from https://github.com/GregTJ/stable-fluids
def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration):
from .fluid import Fluid
from scipy.spatial import erf
out = []
masks = []
RESOLUTION = width, height
DURATION = frames
INFLOW_PADDING = inflow_padding
INFLOW_DURATION = inflow_duration
INFLOW_RADIUS = inflow_radius
INFLOW_VELOCITY = inflow_velocity
INFLOW_COUNT = inflow_count
print('Generating fluid solver, this may take some time.')
fluid = Fluid(RESOLUTION, 'dye')
center = np.floor_divide(RESOLUTION, 2)
r = np.min(center) - INFLOW_PADDING
points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False)
points = tuple(np.array((np.cos(p), np.sin(p))) for p in points)
normals = tuple(-p for p in points)
points = tuple(r * p + center for p in points)
inflow_velocity = np.zeros_like(fluid.velocity)
inflow_dye = np.zeros(fluid.shape)
for p, n in zip(points, normals):
mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS
inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY
inflow_dye[mask] = 1
for f in range(DURATION):
print(f'Computing frame {f + 1} of {DURATION}.')
if f <= INFLOW_DURATION:
fluid.velocity += inflow_velocity
fluid.dye += inflow_dye
curl = fluid.step()[1]
# Using the error function to make the contrast a bit higher.
# Any other sigmoid function e.g. smoothstep would work.
curl = (erf(curl * 2) + 1) / 4
color = np.dstack((curl, np.ones(fluid.shape), fluid.dye))
color = (np.clip(color, 0, 1) * 255).astype('uint8')
image = np.array(color).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
mask = image[:, :, :, 0]
masks.append(mask)
out.append(image)
if invert:
return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),)
return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
class CreateAudioMask:
def __init__(self):
try:
import librosa
self.librosa = librosa
except ImportError:
print("Can not import librosa. Install it with 'pip install librosa'")
RETURN_TYPES = ("IMAGE",)
FUNCTION = "createaudiomask"
CATEGORY = "KJNodes/deprecated"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}),
"scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}),
"audio_path": ("STRING", {"default": "audio.wav"}),
"width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
},
}
def createaudiomask(self, frames, width, height, invert, audio_path, scale):
# Define the number of images in the batch
batch_size = frames
out = []
masks = []
if audio_path == "audio.wav": #I don't know why relative path won't work otherwise...
audio_path = os.path.join(script_directory, audio_path)
audio, sr = self.librosa.load(audio_path)
spectrogram = np.abs(self.librosa.stft(audio))
for i in range(batch_size):
image = Image.new("RGB", (width, height), "black")
draw = ImageDraw.Draw(image)
frame = spectrogram[:, i]
circle_radius = int(height * np.mean(frame))
circle_radius *= scale
circle_center = (width // 2, height // 2) # Calculate the center of the image
draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius),
(circle_center[0] + circle_radius, circle_center[1] + circle_radius)],
fill='white')
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
mask = image[:, :, :, 0]
masks.append(mask)
out.append(image)
if invert:
return (1.0 - torch.cat(out, dim=0),)
return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
class CreateGradientMask:
RETURN_TYPES = ("MASK",)
FUNCTION = "createmask"
CATEGORY = "KJNodes/masking/generate"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
},
}
def createmask(self, frames, width, height, invert):
# Define the number of images in the batch
batch_size = frames
out = []
# Create an empty array to store the image batch
image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
# Generate the black to white gradient for each image
for i in range(batch_size):
gradient = np.linspace(1.0, 0.0, width, dtype=np.float32)
time = i / frames # Calculate the time variable
offset_gradient = gradient - time # Offset the gradient values based on time
image_batch[i] = offset_gradient.reshape(1, -1)
output = torch.from_numpy(image_batch)
mask = output
out.append(mask)
if invert:
return (1.0 - torch.cat(out, dim=0),)
return (torch.cat(out, dim=0),)
class CreateFadeMask:
RETURN_TYPES = ("MASK",)
FUNCTION = "createfademask"
CATEGORY = "KJNodes/deprecated"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 2,"min": 2, "max": 255, "step": 1}),
"width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
"start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
"midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
"end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
"midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
},
}
def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame):
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
batch_size = frames
out = []
image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
if midpoint_frame == 0:
midpoint_frame = batch_size // 2
for i in range(batch_size):
if i <= midpoint_frame:
t = i / midpoint_frame
if interpolation == "ease_in":
t = ease_in(t)
elif interpolation == "ease_out":
t = ease_out(t)
elif interpolation == "ease_in_out":
t = ease_in_out(t)
color = start_level - t * (start_level - midpoint_level)
else:
t = (i - midpoint_frame) / (batch_size - midpoint_frame)
if interpolation == "ease_in":
t = ease_in(t)
elif interpolation == "ease_out":
t = ease_out(t)
elif interpolation == "ease_in_out":
t = ease_in_out(t)
color = midpoint_level - t * (midpoint_level - end_level)
color = np.clip(color, 0, 255)
image = np.full((height, width), color, dtype=np.float32)
image_batch[i] = image
output = torch.from_numpy(image_batch)
mask = output
out.append(mask)
if invert:
return (1.0 - torch.cat(out, dim=0),)
return (torch.cat(out, dim=0),)
class CreateFadeMaskAdvanced:
RETURN_TYPES = ("MASK",)
FUNCTION = "createfademask"
CATEGORY = "KJNodes/masking/generate"
DESCRIPTION = """
Create a batch of masks interpolated between given frames and values.
Uses same syntax as Fizz' BatchValueSchedule.
First value is the frame index (not that this starts from 0, not 1)
and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0
For example the default values:
0:(0.0)
7:(1.0)
15:(0.0)
Would create a mask batch fo 16 frames, starting from black,
interpolating with the chosen curve to fully white at the 8th frame,
and interpolating from that to fully black at the 16th frame.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 16,"min": 2, "max": 255, "step": 1}),
"width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
},
}
def createfademask(self, frames, width, height, invert, points_string, interpolation):
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
# Parse the input string into a list of tuples
points = []
points_string = points_string.rstrip(',\n')
for point_str in points_string.split(','):
frame_str, color_str = point_str.split(':')
frame = int(frame_str.strip())
color = float(color_str.strip()[1:-1]) # Remove parentheses around color
points.append((frame, color))
# Check if the last frame is already in the points
if len(points) == 0 or points[-1][0] != frames - 1:
# If not, add it with the color of the last specified frame
points.append((frames - 1, points[-1][1] if points else 0))
# Sort the points by frame number
points.sort(key=lambda x: x[0])
batch_size = frames
out = []
image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
# Index of the next point to interpolate towards
next_point = 1
for i in range(batch_size):
while next_point < len(points) and i > points[next_point][0]:
next_point += 1
# Interpolate between the previous point and the next point
prev_point = next_point - 1
t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0])
if interpolation == "ease_in":
t = ease_in(t)
elif interpolation == "ease_out":
t = ease_out(t)
elif interpolation == "ease_in_out":
t = ease_in_out(t)
elif interpolation == "linear":
pass # No need to modify `t` for linear interpolation
color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1])
color = np.clip(color, 0, 255)
image = np.full((height, width), color, dtype=np.float32)
image_batch[i] = image
output = torch.from_numpy(image_batch)
mask = output
out.append(mask)
if invert:
return (1.0 - torch.cat(out, dim=0),)
return (torch.cat(out, dim=0),)
class ScaleBatchPromptSchedule:
RETURN_TYPES = ("STRING",)
FUNCTION = "scaleschedule"
CATEGORY = "KJNodes"
DESCRIPTION = """
Scales a batch schedule from Fizz' nodes BatchPromptSchedule
to a different frame count.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input_str": ("STRING", {"forceInput": True,"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n"}),
"old_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}),
"new_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}),
},
}
def scaleschedule(self, old_frame_count, input_str, new_frame_count):
print("input_str:", input_str)
pattern = r'"(\d+)"\s*:\s*"(.*?)"(?:,|\Z)'
frame_strings = dict(re.findall(pattern, input_str))
# Calculate the scaling factor
scaling_factor = (new_frame_count - 1) / (old_frame_count - 1)
# Initialize a dictionary to store the new frame numbers and strings
new_frame_strings = {}
# Iterate over the frame numbers and strings
for old_frame, string in frame_strings.items():
# Calculate the new frame number
new_frame = int(round(int(old_frame) * scaling_factor))
# Store the new frame number and corresponding string
new_frame_strings[new_frame] = string
# Format the output string
output_str = ', '.join([f'"{k}":"{v}"' for k, v in sorted(new_frame_strings.items())])
print(output_str)
return (output_str,)
class CrossFadeImages:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "crossfadeimages"
CATEGORY = "KJNodes/image"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images_1": ("IMAGE",),
"images_2": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
"end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
},
}
def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level):
def crossfade(images_1, images_2, alpha):
crossfade = (1 - alpha) * images_1 + alpha * images_2
return crossfade
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
def bounce(t):
if t < 0.5:
return self.ease_out(t * 2) * 0.5
else:
return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5
def elastic(t):
return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
def glitchy(t):
return t + 0.1 * math.sin(40 * t)
def exponential_ease_out(t):
return 1 - (1 - t) ** 4
easing_functions = {
"linear": lambda t: t,
"ease_in": ease_in,
"ease_out": ease_out,
"ease_in_out": ease_in_out,
"bounce": bounce,
"elastic": elastic,
"glitchy": glitchy,
"exponential_ease_out": exponential_ease_out,
}
crossfade_images = []
alphas = torch.linspace(start_level, end_level, transitioning_frames)
for i in range(transitioning_frames):
alpha = alphas[i]
image1 = images_1[i + transition_start_index]
image2 = images_2[i + transition_start_index]
easing_function = easing_functions.get(interpolation)
alpha = easing_function(alpha) # Apply the easing function to the alpha value
crossfade_image = crossfade(image1, image2, alpha)
crossfade_images.append(crossfade_image)
# Convert crossfade_images to tensor
crossfade_images = torch.stack(crossfade_images, dim=0)
# Get the last frame result of the interpolation
last_frame = crossfade_images[-1]
# Calculate the number of remaining frames from images_2
remaining_frames = len(images_2) - (transition_start_index + transitioning_frames)
# Crossfade the remaining frames with the last used alpha value
for i in range(remaining_frames):
alpha = alphas[-1]
image1 = images_1[i + transition_start_index + transitioning_frames]
image2 = images_2[i + transition_start_index + transitioning_frames]
easing_function = easing_functions.get(interpolation)
alpha = easing_function(alpha) # Apply the easing function to the alpha value
crossfade_image = crossfade(image1, image2, alpha)
crossfade_images = torch.cat([crossfade_images, crossfade_image.unsqueeze(0)], dim=0)
# Append the beginning of images_1
beginning_images_1 = images_1[:transition_start_index]
crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
return (crossfade_images, )
class GetImageRangeFromBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "imagesfrombatch"
CATEGORY = "KJNodes/image"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
},
}
def imagesfrombatch(self, images, start_index, num_frames):
if start_index == -1:
start_index = len(images) - num_frames
if start_index < 0 or start_index >= len(images):
raise ValueError("GetImageRangeFromBatch: Start index is out of range")
end_index = start_index + num_frames
if end_index > len(images):
raise ValueError("GetImageRangeFromBatch: End index is out of range")
chosen_images = images[start_index:end_index]
return (chosen_images, )
class GetImagesFromBatchIndexed:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "indexedimagesfrombatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Selects and returns the images at the specified indices as an image batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
},
}
def indexedimagesfrombatch(self, images, indexes):
# Parse the indexes string into a list of integers
index_list = [int(index.strip()) for index in indexes.split(',')]
# Convert list of indices to a PyTorch tensor
indices_tensor = torch.tensor(index_list, dtype=torch.long)
# Select the images at the specified indices
chosen_images = images[indices_tensor]
return (chosen_images,)
class GetLatentsFromBatchIndexed:
RETURN_TYPES = ("LATENT",)
FUNCTION = "indexedlatentsfrombatch"
CATEGORY = "KJNodes"
DESCRIPTION = """
Selects and returns the latents at the specified indices as an latent batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latents": ("LATENT",),
"indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
},
}
def indexedlatentsfrombatch(self, latents, indexes):
samples = latents.copy()
latent_samples = samples["samples"]
# Parse the indexes string into a list of integers
index_list = [int(index.strip()) for index in indexes.split(',')]
# Convert list of indices to a PyTorch tensor
indices_tensor = torch.tensor(index_list, dtype=torch.long)
# Select the latents at the specified indices
chosen_latents = latent_samples[indices_tensor]
samples["samples"] = chosen_latents
return (samples,)
class ReplaceImagesInBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "replace"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Replaces the images in a batch, starting from the specified start, with the replacement images.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"original_images": ("IMAGE",),
"replacement_images": ("IMAGE",),
"start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
},
}
def replace(self, original_images, replacement_images, start_index):
images = None
if start_index >= len(original_images):
raise ValueError("GetImageRangeFromBatch: Start index is out of range")
end_index = start_index + len(replacement_images)
if end_index > len(original_images):
raise ValueError("GetImageRangeFromBatch: End index is out of range")
# Create a copy of the original_images tensor
original_images_copy = original_images.clone()
original_images_copy[start_index:end_index] = replacement_images
images = original_images_copy
return (images, )
class ReverseImageBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "reverseimagebatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Reverses the order of the images in a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
},
}
def reverseimagebatch(self, images):
reversed_images = torch.flip(images, [0])
return (reversed_images, )
class CreateTextMask:
RETURN_TYPES = ("IMAGE", "MASK",)
FUNCTION = "createtextmask"
CATEGORY = "KJNodes/text"
DESCRIPTION = """
Creates a text image and mask.
Looks for fonts from this folder:
ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
If start_rotation and/or end_rotation are different values,
creates animation between them.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"invert": ("BOOLEAN", {"default": False}),
"frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
"text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}),
"font_color": ("STRING", {"default": "white"}),
"text": ("STRING", {"default": "HELLO!", "multiline": True}),
"font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
"width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}),
"end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}),
},
}
def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation):
# Define the number of images in the batch
batch_size = frames
out = []
masks = []
rotation = start_rotation
if start_rotation != end_rotation:
rotation_increment = (end_rotation - start_rotation) / (batch_size - 1)
font_path = folder_paths.get_full_path("kjnodes_fonts", font)
# Generate the text
for i in range(batch_size):
image = Image.new("RGB", (width, height), "black")
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(font_path, font_size)
# Split the text into words
words = text.split()
# Initialize variables for line creation
lines = []
current_line = []
current_line_width = 0
try: #new pillow
# Iterate through words to create lines
for word in words:
word_width = font.getbbox(word)[2]
if current_line_width + word_width <= width - 2 * text_x:
current_line.append(word)
current_line_width += word_width + font.getbbox(" ")[2] # Add space width
else:
lines.append(" ".join(current_line))
current_line = [word]
current_line_width = word_width
except: #old pillow
for word in words:
word_width = font.getsize(word)[0]
if current_line_width + word_width <= width - 2 * text_x:
current_line.append(word)
current_line_width += word_width + font.getsize(" ")[0] # Add space width
else:
lines.append(" ".join(current_line))
current_line = [word]
current_line_width = word_width
# Add the last line if it's not empty
if current_line:
lines.append(" ".join(current_line))
# Draw each line of text separately
y_offset = text_y
for line in lines:
text_width = font.getlength(line)
text_height = font_size
text_center_x = text_x + text_width / 2
text_center_y = y_offset + text_height / 2
try:
draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga'])
except:
draw.text((text_x, y_offset), line, font=font, fill=font_color)
y_offset += text_height # Move to the next line
if start_rotation != end_rotation:
image = image.rotate(rotation, center=(text_center_x, text_center_y))
rotation += rotation_increment
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
mask = image[:, :, :, 0]
masks.append(mask)
out.append(image)
if invert:
return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),)
return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
class GrowMaskWithBlur:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
"incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"tapered_corners": ("BOOLEAN", {"default": True}),
"flip_input": ("BOOLEAN", {"default": False}),
"blur_radius": ("FLOAT", {
"default": 0.0,
"min": 0.0,
"max": 100,
"step": 0.1
}),
"lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"fill_holes": ("BOOLEAN", {"default": False}),
},
}
CATEGORY = "KJNodes/masking"
RETURN_TYPES = ("MASK", "MASK",)
RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "expand_mask"
DESCRIPTION = """
# GrowMaskWithBlur
- mask: Input mask or mask batch
- expand: Expand or contract mask or mask batch by a given amount
- incremental_expandrate: increase expand rate by a given amount per frame
- tapered_corners: use tapered corners
- flip_input: flip input mask
- blur_radius: value higher than 0 will blur the mask
- lerp_alpha: alpha value for interpolation between frames
- decay_factor: decay value for interpolation between frames
- fill_holes: fill holes in the mask (slow)"""
def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
alpha = lerp_alpha
decay = decay_factor
if flip_input:
mask = 1.0 - mask
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
[c, 1, c]])
growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu()
out = []
previous_output = None
current_expand = expand
for m in growmask:
output = m.numpy()
for _ in range(abs(round(current_expand))):
if current_expand < 0:
output = scipy.ndimage.grey_erosion(output, footprint=kernel)
else:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
if current_expand < 0:
current_expand -= abs(incremental_expandrate)
else:
current_expand += abs(incremental_expandrate)
if fill_holes:
binary_mask = output > 0
output = scipy.ndimage.binary_fill_holes(binary_mask)
output = output.astype(np.float32) * 255
output = torch.from_numpy(output)
if alpha < 1.0 and previous_output is not None:
# Interpolate between the previous and current frame
output = alpha * output + (1 - alpha) * previous_output
if decay < 1.0 and previous_output is not None:
# Add the decayed previous output to the current frame
output += decay * previous_output
output = output / output.max()
previous_output = output
out.append(output)
if blur_radius != 0:
# Convert the tensor list to PIL images, apply blur, and convert back
for idx, tensor in enumerate(out):
# Convert tensor to PIL image
pil_image = tensor2pil(tensor.cpu().detach())[0]
# Apply Gaussian blur
pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius))
# Convert back to tensor
out[idx] = pil2tensor(pil_image)
blurred = torch.cat(out, dim=0)
return (blurred, 1.0 - blurred)
else:
return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
class ColorToMask:
RETURN_TYPES = ("MASK",)
FUNCTION = "clip"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Converts chosen RGB value to a mask
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"invert": ("BOOLEAN", {"default": False}),
"red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
},
}
def clip(self, images, red, green, blue, threshold, invert):
color = np.array([red, green, blue])
images = 255. * images.cpu().numpy()
images = np.clip(images, 0, 255).astype(np.uint8)
images = [Image.fromarray(image) for image in images]
images = [np.array(image) for image in images]
black = [0, 0, 0]
white = [255, 255, 255]
if invert:
black, white = white, black
new_images = []
for image in images:
new_image = np.full_like(image, black)
color_distances = np.linalg.norm(image - color, axis=-1)
complement_indexes = color_distances <= threshold
new_image[complement_indexes] = white
new_images.append(new_image)
new_images = np.array(new_images).astype(np.float32) / 255.0
new_images = torch.from_numpy(new_images).permute(3, 0, 1, 2)
return new_images
class ConditioningMultiCombine:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 20, "step": 1}),
"conditioning_1": ("CONDITIONING", ),
"conditioning_2": ("CONDITIONING", ),
},
}
RETURN_TYPES = ("CONDITIONING", "INT")
RETURN_NAMES = ("combined", "inputcount")
FUNCTION = "combine"
CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Combines multiple conditioning nodes into one
"""
def combine(self, inputcount, **kwargs):
from nodes import ConditioningCombine
cond_combine_node = ConditioningCombine()
cond = kwargs["conditioning_1"]
for c in range(1, inputcount):
new_cond = kwargs[f"conditioning_{c + 1}"]
cond = cond_combine_node.combine(new_cond, cond)[0]
return (cond, inputcount,)
class CondPassThrough:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
},
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("positive", "negative")
FUNCTION = "passthrough"
CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Simply passes through the positive and negative conditioning,
workaround for Set node not allowing bypassed inputs.
"""
def passthrough(self, positive, negative):
return (positive, negative,)
def append_helper(t, mask, c, set_area_to_bounds, strength):
n = [t[0], t[1].copy()]
_, h, w = mask.shape
n[1]['mask'] = mask
n[1]['set_area_to_bounds'] = set_area_to_bounds
n[1]['mask_strength'] = strength
c.append(n)
class ConditioningSetMaskAndCombine:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive_1": ("CONDITIONING", ),
"negative_1": ("CONDITIONING", ),
"positive_2": ("CONDITIONING", ),
"negative_2": ("CONDITIONING", ),
"mask_1": ("MASK", ),
"mask_2": ("MASK", ),
"mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength):
c = []
c2 = []
set_area_to_bounds = False
if set_cond_area != "default":
set_area_to_bounds = True
if len(mask_1.shape) < 3:
mask_1 = mask_1.unsqueeze(0)
if len(mask_2.shape) < 3:
mask_2 = mask_2.unsqueeze(0)
for t in positive_1:
append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
for t in positive_2:
append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
for t in negative_1:
append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
for t in negative_2:
append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
return (c, c2)
class ConditioningSetMaskAndCombine3:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive_1": ("CONDITIONING", ),
"negative_1": ("CONDITIONING", ),
"positive_2": ("CONDITIONING", ),
"negative_2": ("CONDITIONING", ),
"positive_3": ("CONDITIONING", ),
"negative_3": ("CONDITIONING", ),
"mask_1": ("MASK", ),
"mask_2": ("MASK", ),
"mask_3": ("MASK", ),
"mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength):
c = []
c2 = []
set_area_to_bounds = False
if set_cond_area != "default":
set_area_to_bounds = True
if len(mask_1.shape) < 3:
mask_1 = mask_1.unsqueeze(0)
if len(mask_2.shape) < 3:
mask_2 = mask_2.unsqueeze(0)
if len(mask_3.shape) < 3:
mask_3 = mask_3.unsqueeze(0)
for t in positive_1:
append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
for t in positive_2:
append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
for t in positive_3:
append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
for t in negative_1:
append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
for t in negative_2:
append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
for t in negative_3:
append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
return (c, c2)
class ConditioningSetMaskAndCombine4:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive_1": ("CONDITIONING", ),
"negative_1": ("CONDITIONING", ),
"positive_2": ("CONDITIONING", ),
"negative_2": ("CONDITIONING", ),
"positive_3": ("CONDITIONING", ),
"negative_3": ("CONDITIONING", ),
"positive_4": ("CONDITIONING", ),
"negative_4": ("CONDITIONING", ),
"mask_1": ("MASK", ),
"mask_2": ("MASK", ),
"mask_3": ("MASK", ),
"mask_4": ("MASK", ),
"mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength):
c = []
c2 = []
set_area_to_bounds = False
if set_cond_area != "default":
set_area_to_bounds = True
if len(mask_1.shape) < 3:
mask_1 = mask_1.unsqueeze(0)
if len(mask_2.shape) < 3:
mask_2 = mask_2.unsqueeze(0)
if len(mask_3.shape) < 3:
mask_3 = mask_3.unsqueeze(0)
if len(mask_4.shape) < 3:
mask_4 = mask_4.unsqueeze(0)
for t in positive_1:
append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
for t in positive_2:
append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
for t in positive_3:
append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
for t in positive_4:
append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength)
for t in negative_1:
append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
for t in negative_2:
append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
for t in negative_3:
append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
for t in negative_4:
append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength)
return (c, c2)
class ConditioningSetMaskAndCombine5:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive_1": ("CONDITIONING", ),
"negative_1": ("CONDITIONING", ),
"positive_2": ("CONDITIONING", ),
"negative_2": ("CONDITIONING", ),
"positive_3": ("CONDITIONING", ),
"negative_3": ("CONDITIONING", ),
"positive_4": ("CONDITIONING", ),
"negative_4": ("CONDITIONING", ),
"positive_5": ("CONDITIONING", ),
"negative_5": ("CONDITIONING", ),
"mask_1": ("MASK", ),
"mask_2": ("MASK", ),
"mask_3": ("MASK", ),
"mask_4": ("MASK", ),
"mask_5": ("MASK", ),
"mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"set_cond_area": (["default", "mask bounds"],),
}
}
RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength):
c = []
c2 = []
set_area_to_bounds = False
if set_cond_area != "default":
set_area_to_bounds = True
if len(mask_1.shape) < 3:
mask_1 = mask_1.unsqueeze(0)
if len(mask_2.shape) < 3:
mask_2 = mask_2.unsqueeze(0)
if len(mask_3.shape) < 3:
mask_3 = mask_3.unsqueeze(0)
if len(mask_4.shape) < 3:
mask_4 = mask_4.unsqueeze(0)
if len(mask_5.shape) < 3:
mask_5 = mask_5.unsqueeze(0)
for t in positive_1:
append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
for t in positive_2:
append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
for t in positive_3:
append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
for t in positive_4:
append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength)
for t in positive_5:
append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength)
for t in negative_1:
append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
for t in negative_2:
append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
for t in negative_3:
append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
for t in negative_4:
append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength)
for t in negative_5:
append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength)
return (c, c2)
class VRAM_Debug:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"empty_cache": ("BOOLEAN", {"default": True}),
"gc_collect": ("BOOLEAN", {"default": True}),
"unload_all_models": ("BOOLEAN", {"default": False}),
},
"optional":{
"image_passthrough": ("IMAGE",),
"model_passthrough": ("MODEL",),
}
}
RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",)
RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after")
FUNCTION = "VRAMdebug"
CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Placed between model or image chain, performs comfy model management functions and reports free VRAM before and after the functions
"""
def VRAMdebug(self, gc_collect,empty_cache, unload_all_models,image_passthrough=None, model_passthrough=None):
freemem_before = model_management.get_free_memory()
print("VRAMdebug: free memory before: ", freemem_before)
if empty_cache:
model_management.soft_empty_cache()
if unload_all_models:
model_management.unload_all_models()
if gc_collect:
import gc
gc.collect()
freemem_after = model_management.get_free_memory()
print("VRAMdebug: free memory after: ", freemem_after)
print("VRAMdebug: freed memory: ", freemem_after - freemem_before)
return (image_passthrough, model_passthrough, freemem_before, freemem_after)
class AnyType(str):
"""A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
def __ne__(self, __value: object) -> bool:
return False
any = AnyType("*")
class SomethingToString:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"input": (any, {}),
},
"optional": {
"prefix": ("STRING", {"default": ""}),
"suffix": ("STRING", {"default": ""}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "stringify"
CATEGORY = "KJNodes/text"
DESCRIPTION = """
Converts any type to a string.
"""
def stringify(self, input, prefix="", suffix=""):
if isinstance(input, (int, float, bool)):
stringified = str(input)
if prefix: # Check if prefix is not empty
stringified = prefix + stringified # Add the prefix
if suffix: # Check if suffix is not empty
stringified = stringified + suffix # Add the suffix
else:
return
return (stringified,)
class EmptyLatentImagePresets:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"dimensions": (
[ '512 x 512',
'768 x 512',
'960 x 512',
'1024 x 512',
'1536 x 640',
'1344 x 768',
'1216 x 832',
'1152 x 896',
'1024 x 1024',
],
{
"default": '512 x 512'
}),
"invert": ("BOOLEAN", {"default": False}),
"batch_size": ("INT", {
"default": 1,
"min": 1,
"max": 4096
}),
},
}
RETURN_TYPES = ("LATENT", "INT", "INT")
RETURN_NAMES = ("Latent", "Width", "Height")
FUNCTION = "generate"
CATEGORY = "KJNodes"
def generate(self, dimensions, invert, batch_size):
from nodes import EmptyLatentImage
result = [x.strip() for x in dimensions.split('x')]
if invert:
width = int(result[1].split(' ')[0])
height = int(result[0])
else:
width = int(result[0])
height = int(result[1].split(' ')[0])
latent = EmptyLatentImage().generate(width, height, batch_size)[0]
return (latent, int(width), int(height),)
class ColorMatch:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image_ref": ("IMAGE",),
"image_target": ("IMAGE",),
"method": (
[
'mkl',
'hm',
'reinhard',
'mvgd',
'hm-mvgd-hm',
'hm-mkl-hm',
], {
"default": 'mkl'
}),
},
}
CATEGORY = "KJNodes/image"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "colormatch"
DESCRIPTION = """
color-matcher enables color transfer across images which comes in handy for automatic
color-grading of photographs, paintings and film sequences as well as light-field
and stopmotion corrections.
The methods behind the mappings are based on the approach from Reinhard et al.,
the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution
to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram
matching. As shown below our HM-MVGD-HM compound outperforms existing methods.
https://github.com/hahnec/color-matcher/
"""
def colormatch(self, image_ref, image_target, method):
try:
from color_matcher import ColorMatcher
except:
raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
cm = ColorMatcher()
image_ref = image_ref.cpu()
image_target = image_target.cpu()
batch_size = image_target.size(0)
out = []
images_target = image_target.squeeze()
images_ref = image_ref.squeeze()
image_ref_np = images_ref.numpy()
images_target_np = images_target.numpy()
if image_ref.size(0) > 1 and image_ref.size(0) != batch_size:
raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.")
for i in range(batch_size):
image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy()
image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
try:
image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method)
except BaseException as e:
print(f"Error occurred during transfer: {e}")
break
out.append(torch.from_numpy(image_result))
return (torch.stack(out, dim=0).to(torch.float32), )
class SaveImageWithAlpha:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"mask": ("MASK", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_images_alpha"
OUTPUT_NODE = True
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Saves an image and mask as .PNG with the mask as the alpha channel.
"""
def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
from comfy.cli_args import args
from PIL.PngImagePlugin import PngInfo
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
if mask.dtype == torch.float16:
mask = mask.to(torch.float32)
def file_counter():
max_counter = 0
# Loop through the existing files
for existing_file in os.listdir(full_output_folder):
# Check if the file matches the expected format
match = re.fullmatch(f"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file)
if match:
# Extract the numeric portion of the filename
file_counter = int(match.group(1))
# Update the maximum counter value if necessary
if file_counter > max_counter:
max_counter = file_counter
return max_counter
for image, alpha in zip(images, mask):
i = 255. * image.cpu().numpy()
a = 255. * alpha.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
# Resize the mask to match the image size
a_resized = Image.fromarray(a).resize(img.size, Image.LANCZOS)
a_resized = np.clip(a_resized, 0, 255).astype(np.uint8)
img.putalpha(Image.fromarray(a_resized, mode='L'))
metadata = None
if not args.disable_metadata:
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
# Increment the counter by 1 to get the next available value
counter = file_counter() + 1
file = f"{filename}_{counter:05}.png"
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
return { "ui": { "images": results } }
class ImageConcanate:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"direction": (
[ 'right',
'down',
'left',
'up',
],
{
"default": 'right'
}),
"match_image_size": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "concanate"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the image2 to image1 in the specified direction.
"""
def concanate(self, image1, image2, direction, match_image_size):
if match_image_size:
image2 = torch.nn.functional.interpolate(image2, size=(image1.shape[2], image1.shape[3]), mode="bilinear")
if direction == 'right':
row = torch.cat((image1, image2), dim=2)
elif direction == 'down':
row = torch.cat((image1, image2), dim=1)
elif direction == 'left':
row = torch.cat((image2, image1), dim=2)
elif direction == 'up':
row = torch.cat((image2, image1), dim=1)
return (row,)
class ImageGridComposite2x2:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"image3": ("IMAGE",),
"image4": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "compositegrid"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the 4 input images into a 2x2 grid.
"""
def compositegrid(self, image1, image2, image3, image4):
top_row = torch.cat((image1, image2), dim=2)
bottom_row = torch.cat((image3, image4), dim=2)
grid = torch.cat((top_row, bottom_row), dim=1)
return (grid,)
class ImageGridComposite3x3:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"image3": ("IMAGE",),
"image4": ("IMAGE",),
"image5": ("IMAGE",),
"image6": ("IMAGE",),
"image7": ("IMAGE",),
"image8": ("IMAGE",),
"image9": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "compositegrid"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the 9 input images into a 3x3 grid.
"""
def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9):
top_row = torch.cat((image1, image2, image3), dim=2)
mid_row = torch.cat((image4, image5, image6), dim=2)
bottom_row = torch.cat((image7, image8, image9), dim=2)
grid = torch.cat((top_row, mid_row, bottom_row), dim=1)
return (grid,)
class ImageBatchTestPattern:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"batch_size": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"start_from": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
"font_size": ("INT", {"default": 255,"min": 8, "max": 4096, "step": 1}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "generatetestpattern"
CATEGORY = "KJNodes/text"
def generatetestpattern(self, batch_size, font, font_size, start_from, width, height):
out = []
# Generate the sequential numbers for each image
numbers = np.arange(batch_size)
font_path = folder_paths.get_full_path("kjnodes_fonts", font)
# Create an image for each number
for i, number in enumerate(numbers):
# Create a black image with the number as a random color text
image = Image.new("RGB", (width, height), color=0)
draw = ImageDraw.Draw(image)
# Draw a border around the image
border_width = 10
border_color = (255, 255, 255) # white color
border_box = [(border_width, border_width), (width - border_width, height - border_width)]
draw.rectangle(border_box, fill=None, outline=border_color)
# Generate a random color for the text
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
font = ImageFont.truetype(font_path, font_size)
text_width = font_size
text_height = font_size
text_x = (width - text_width / 2) // 2
text_y = (height - text_height) // 2
try:
draw.text((text_x, text_y), str(number), font=font, fill=color, features=['-liga'])
except:
draw.text((text_x, text_y), str(number), font=font, fill=color)
# Convert the image to a numpy array and normalize the pixel values
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
out.append(image)
return (torch.cat(out, dim=0),)
#based on nodes from mtb https://github.com/melMass/comfy_mtb
from .utility import tensor2pil, pil2tensor
class BatchCropFromMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"original_images": ("IMAGE",),
"masks": ("MASK",),
"crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
"bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = (
"IMAGE",
"IMAGE",
"BBOX",
"INT",
"INT",
)
RETURN_NAMES = (
"original_images",
"cropped_images",
"bboxes",
"width",
"height",
)
FUNCTION = "crop"
CATEGORY = "KJNodes/masking"
def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
if alpha == 0:
return prev_bbox_size
return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
def smooth_center(self, prev_center, curr_center, alpha=0.5):
if alpha == 0:
return prev_center
return (
round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])
)
def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
bounding_boxes = []
cropped_images = []
self.max_bbox_width = 0
self.max_bbox_height = 0
# First, calculate the maximum bounding box size across all masks
curr_max_bbox_width = 0
curr_max_bbox_height = 0
for mask in masks:
_mask = tensor2pil(mask)[0]
non_zero_indices = np.nonzero(np.array(_mask))
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
width = max_x - min_x
height = max_y - min_y
curr_max_bbox_width = max(curr_max_bbox_width, width)
curr_max_bbox_height = max(curr_max_bbox_height, height)
# Smooth the changes in the bounding box size
self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha)
self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)
# Apply the crop size multiplier
self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
# Then, for each mask and corresponding image...
for i, (mask, img) in enumerate(zip(masks, original_images)):
_mask = tensor2pil(mask)[0]
non_zero_indices = np.nonzero(np.array(_mask))
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
# Calculate center of bounding box
center_x = np.mean(non_zero_indices[1])
center_y = np.mean(non_zero_indices[0])
curr_center = (round(center_x), round(center_y))
# If this is the first frame, initialize prev_center with curr_center
if not hasattr(self, 'prev_center'):
self.prev_center = curr_center
# Smooth the changes in the center coordinates from the second frame onwards
if i > 0:
center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
else:
center = curr_center
# Update prev_center for the next frame
self.prev_center = center
# Create bounding box using max_bbox_width and max_bbox_height
half_box_width = round(self.max_bbox_width / 2)
half_box_height = round(self.max_bbox_height / 2)
min_x = max(0, center[0] - half_box_width)
max_x = min(img.shape[1], center[0] + half_box_width)
min_y = max(0, center[1] - half_box_height)
max_y = min(img.shape[0], center[1] + half_box_height)
# Append bounding box coordinates
bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
# Crop the image from the bounding box
cropped_img = img[min_y:max_y, min_x:max_x, :]
# Calculate the new dimensions while maintaining the aspect ratio
new_height = min(cropped_img.shape[0], self.max_bbox_height)
new_width = round(new_height * bbox_aspect_ratio)
# Resize the image
resize_transform = Resize((new_height, new_width))
resized_img = resize_transform(cropped_img.permute(2, 0, 1))
# Perform the center crop to the desired size
crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary
cropped_resized_img = crop_transform(resized_img)
cropped_images.append(cropped_resized_img.permute(1, 2, 0))
cropped_out = torch.stack(cropped_images, dim=0)
return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )
def bbox_to_region(bbox, target_size=None):
bbox = bbox_check(bbox, target_size)
return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
def bbox_check(bbox, target_size=None):
if not target_size:
return bbox
new_bbox = (
bbox[0],
bbox[1],
min(target_size[0] - bbox[0], bbox[2]),
min(target_size[1] - bbox[1], bbox[3]),
)
return new_bbox
class BatchUncrop:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"original_images": ("IMAGE",),
"cropped_images": ("IMAGE",),
"bboxes": ("BBOX",),
"border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"border_top": ("BOOLEAN", {"default": True}),
"border_bottom": ("BOOLEAN", {"default": True}),
"border_left": ("BOOLEAN", {"default": True}),
"border_right": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "uncrop"
CATEGORY = "KJNodes/masking"
def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right):
def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right):
draw = ImageDraw.Draw(image)
width, height = image.size
if border_top:
draw.rectangle((0, 0, width, border_width), fill=border_color)
if border_bottom:
draw.rectangle((0, height - border_width, width, height), fill=border_color)
if border_left:
draw.rectangle((0, 0, border_width, height), fill=border_color)
if border_right:
draw.rectangle((width - border_width, 0, width, height), fill=border_color)
return image
if len(original_images) != len(cropped_images):
raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
# Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
if len(bboxes) > len(original_images):
print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
bboxes = bboxes[:len(original_images)]
elif len(bboxes) < len(original_images):
raise ValueError("There should be at least as many bboxes as there are original and cropped images")
input_images = tensor2pil(original_images)
crop_imgs = tensor2pil(cropped_images)
out_images = []
for i in range(len(input_images)):
img = input_images[i]
crop = crop_imgs[i]
bbox = bboxes[i]
# uncrop the image based on the bounding box
bb_x, bb_y, bb_width, bb_height = bbox
paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
# scale factors
scale_x = crop_rescale
scale_y = crop_rescale
# scaled paste_region
paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
# rescale the crop image to fit the paste_region
crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
crop_img = crop.convert("RGB")
if border_blending > 1.0:
border_blending = 1.0
elif border_blending < 0.0:
border_blending = 0.0
blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
blend = img.convert("RGBA")
mask = Image.new("L", img.size, 0)
mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right)
mask.paste(mask_block, paste_region)
blend.paste(crop_img, paste_region)
mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
blend.putalpha(mask)
img = Image.alpha_composite(img.convert("RGBA"), blend)
out_images.append(img.convert("RGB"))
return (pil2tensor(out_images),)
class BatchCropFromMaskAdvanced:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"original_images": ("IMAGE",),
"masks": ("MASK",),
"crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = (
"IMAGE",
"IMAGE",
"MASK",
"IMAGE",
"MASK",
"BBOX",
"BBOX",
"INT",
"INT",
)
RETURN_NAMES = (
"original_images",
"cropped_images",
"cropped_masks",
"combined_crop_image",
"combined_crop_masks",
"bboxes",
"combined_bounding_box",
"bbox_width",
"bbox_height",
)
FUNCTION = "crop"
CATEGORY = "KJNodes/masking"
def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
def smooth_center(self, prev_center, curr_center, alpha=0.5):
return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))
def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
bounding_boxes = []
combined_bounding_box = []
cropped_images = []
cropped_masks = []
cropped_masks_out = []
combined_crop_out = []
combined_cropped_images = []
combined_cropped_masks = []
def calculate_bbox(mask):
non_zero_indices = np.nonzero(np.array(mask))
# handle empty masks
min_x, max_x, min_y, max_y = 0, 0, 0, 0
if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0:
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
width = max_x - min_x
height = max_y - min_y
bbox_size = max(width, height)
return min_x, max_x, min_y, max_y, bbox_size
combined_mask = torch.max(masks, dim=0)[0]
_mask = tensor2pil(combined_mask)[0]
new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask)
center_x = (new_min_x + new_max_x) / 2
center_y = (new_min_y + new_max_y) / 2
half_box_size = round(combined_bbox_size // 2)
new_min_x = max(0, round(center_x - half_box_size))
new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size))
new_min_y = max(0, round(center_y - half_box_size))
new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size))
combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y))
self.max_bbox_size = 0
# First, calculate the maximum bounding box size across all masks
curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks)
# Smooth the changes in the bounding box size
self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha)
# Apply the crop size multiplier
self.max_bbox_size = round(self.max_bbox_size * crop_size_mult)
# Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is
self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16
if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]:
# max_bbox_size can only be as big as our input's width or height, and it has to be even
self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2
# Then, for each mask and corresponding image...
for i, (mask, img) in enumerate(zip(masks, original_images)):
_mask = tensor2pil(mask)[0]
non_zero_indices = np.nonzero(np.array(_mask))
# check for empty masks
if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0:
min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
# Calculate center of bounding box
center_x = np.mean(non_zero_indices[1])
center_y = np.mean(non_zero_indices[0])
curr_center = (round(center_x), round(center_y))
# If this is the first frame, initialize prev_center with curr_center
if not hasattr(self, 'prev_center'):
self.prev_center = curr_center
# Smooth the changes in the center coordinates from the second frame onwards
if i > 0:
center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
else:
center = curr_center
# Update prev_center for the next frame
self.prev_center = center
# Create bounding box using max_bbox_size
half_box_size = self.max_bbox_size // 2
min_x = max(0, center[0] - half_box_size)
max_x = min(img.shape[1], center[0] + half_box_size)
min_y = max(0, center[1] - half_box_size)
max_y = min(img.shape[0], center[1] + half_box_size)
# Append bounding box coordinates
bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
# Crop the image from the bounding box
cropped_img = img[min_y:max_y, min_x:max_x, :]
cropped_mask = mask[min_y:max_y, min_x:max_x]
# Resize the cropped image to a fixed size
new_size = max(cropped_img.shape[0], cropped_img.shape[1])
resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1]))
resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)
resized_img = resize_transform(cropped_img.permute(2, 0, 1))
# Perform the center crop to the desired size
# Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions.
crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2])))
cropped_resized_img = crop_transform(resized_img)
cropped_images.append(cropped_resized_img.permute(1, 2, 0))
cropped_resized_mask = crop_transform(resized_mask)
cropped_masks.append(cropped_resized_mask)
combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :]
combined_cropped_images.append(combined_cropped_img)
combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x]
combined_cropped_masks.append(combined_cropped_mask)
else:
bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
cropped_images.append(img)
cropped_masks.append(mask)
combined_cropped_images.append(img)
combined_cropped_masks.append(mask)
cropped_out = torch.stack(cropped_images, dim=0)
combined_crop_out = torch.stack(combined_cropped_images, dim=0)
cropped_masks_out = torch.stack(cropped_masks, dim=0)
combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0)
return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size)
class FilterZeroMasksAndCorrespondingImages:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"masks": ("MASK",),
},
"optional": {
"original_images": ("IMAGE",),
},
}
RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
FUNCTION = "filter"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Filter out all the empty (i.e. all zero) mask in masks
Also filter out all the corresponding images in original_images by indexes if provide
original_images (optional): If provided, need have same length as masks.
"""
def filter(self, masks, original_images=None):
non_zero_masks = []
non_zero_mask_images = []
zero_mask_images = []
zero_mask_images_indexes = []
masks_num = len(masks)
also_process_images = False
if original_images is not None:
imgs_num = len(original_images)
if len(original_images) == masks_num:
also_process_images = True
else:
print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})")
for i in range(masks_num):
non_zero_num = np.count_nonzero(np.array(masks[i]))
if non_zero_num > 0:
non_zero_masks.append(masks[i])
if also_process_images:
non_zero_mask_images.append(original_images[i])
else:
zero_mask_images.append(original_images[i])
zero_mask_images_indexes.append(i)
non_zero_masks_out = torch.stack(non_zero_masks, dim=0)
non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None
if also_process_images:
non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0)
if len(zero_mask_images) > 0:
zero_mask_images_out = torch.stack(zero_mask_images, dim=0)
zero_mask_images_out_indexes = zero_mask_images_indexes
return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes)
class InsertImageBatchByIndexes:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"images_to_insert": ("IMAGE",),
"insert_indexes": ("INDEXES",),
},
}
RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("images_after_insert", )
FUNCTION = "insert"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
It inserts the images_to_insert into images according to insert_indexes
Returns:
images_after_insert: updated original images with origonal sequence order
"""
def insert(self, images, images_to_insert, insert_indexes):
images_after_insert = images
if images_to_insert is not None and insert_indexes is not None:
images_to_insert_num = len(images_to_insert)
insert_indexes_num = len(insert_indexes)
if images_to_insert_num == insert_indexes_num:
images_after_insert = []
i_images = 0
for i in range(len(images) + images_to_insert_num):
if i in insert_indexes:
images_after_insert.append(images_to_insert[insert_indexes.index(i)])
else:
images_after_insert.append(images[i_images])
i_images += 1
images_after_insert = torch.stack(images_after_insert, dim=0)
else:
print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})")
return (images_after_insert, )
def bbox_to_region(bbox, target_size=None):
bbox = bbox_check(bbox, target_size)
return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
def bbox_check(bbox, target_size=None):
if not target_size:
return bbox
new_bbox = (
bbox[0],
bbox[1],
min(target_size[0] - bbox[0], bbox[2]),
min(target_size[1] - bbox[1], bbox[3]),
)
return new_bbox
class BatchUncropAdvanced:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"original_images": ("IMAGE",),
"cropped_images": ("IMAGE",),
"cropped_masks": ("MASK",),
"combined_crop_mask": ("MASK",),
"bboxes": ("BBOX",),
"border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
"crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"use_combined_mask": ("BOOLEAN", {"default": False}),
"use_square_mask": ("BOOLEAN", {"default": True}),
},
"optional": {
"combined_bounding_box": ("BBOX", {"default": None}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "uncrop"
CATEGORY = "KJNodes/masking"
def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None):
def inset_border(image, border_width=20, border_color=(0)):
width, height = image.size
bordered_image = Image.new(image.mode, (width, height), border_color)
bordered_image.paste(image, (0, 0))
draw = ImageDraw.Draw(bordered_image)
draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
return bordered_image
if len(original_images) != len(cropped_images):
raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
# Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
if len(bboxes) > len(original_images):
print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
bboxes = bboxes[:len(original_images)]
elif len(bboxes) < len(original_images):
raise ValueError("There should be at least as many bboxes as there are original and cropped images")
crop_imgs = tensor2pil(cropped_images)
input_images = tensor2pil(original_images)
out_images = []
for i in range(len(input_images)):
img = input_images[i]
crop = crop_imgs[i]
bbox = bboxes[i]
if use_combined_mask:
bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0]
paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
mask = combined_crop_mask[i]
else:
bb_x, bb_y, bb_width, bb_height = bbox
paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
mask = cropped_masks[i]
# scale paste_region
scale_x = scale_y = crop_rescale
paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
# rescale the crop image to fit the paste_region
crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
crop_img = crop.convert("RGB")
#border blending
if border_blending > 1.0:
border_blending = 1.0
elif border_blending < 0.0:
border_blending = 0.0
blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
blend = img.convert("RGBA")
if use_square_mask:
mask = Image.new("L", img.size, 0)
mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
mask_block = inset_border(mask_block, round(blend_ratio / 2), (0))
mask.paste(mask_block, paste_region)
else:
original_mask = tensor2pil(mask)[0]
original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]))
mask = Image.new("L", img.size, 0)
mask.paste(original_mask, paste_region)
mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
blend.paste(crop_img, paste_region)
blend.putalpha(mask)
img = Image.alpha_composite(img.convert("RGBA"), blend)
out_images.append(img.convert("RGB"))
return (pil2tensor(out_images),)
class BatchCLIPSeg:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"images": ("IMAGE",),
"text": ("STRING", {"multiline": False}),
"threshold": ("FLOAT", {"default": 0.15,"min": 0.0, "max": 10.0, "step": 0.01}),
"binary_mask": ("BOOLEAN", {"default": True}),
"combine_mask": ("BOOLEAN", {"default": False}),
"use_cuda": ("BOOLEAN", {"default": True}),
},
}
CATEGORY = "KJNodes/masking"
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("Mask",)
FUNCTION = "segment_image"
DESCRIPTION = """
Segments an image or batch of images using CLIPSeg.
"""
def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
out = []
height, width, _ = images[0].shape
if use_cuda and torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
model.to(device) # Ensure the model is on the correct device
images = images.to(device)
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
pbar = comfy.utils.ProgressBar(images.shape[0])
for image in images:
image = (image* 255).type(torch.uint8)
prompt = text
input_prc = processor(text=prompt, images=image, padding="max_length", return_tensors="pt")
# Move the processed input to the device
for key in input_prc:
input_prc[key] = input_prc[key].to(device)
outputs = model(**input_prc)
tensor = torch.sigmoid(outputs[0])
tensor_thresholded = torch.where(tensor > threshold, tensor, torch.tensor(0, dtype=torch.float))
tensor_normalized = (tensor_thresholded - tensor_thresholded.min()) / (tensor_thresholded.max() - tensor_thresholded.min())
tensor = tensor_normalized
# Resize the mask
resized_tensor = F.interpolate(tensor.unsqueeze(0), size=(height, width), mode='bilinear', align_corners=False)
# Remove the extra dimensions
resized_tensor = resized_tensor[0, 0, :, :]
pbar.update(1)
out.append(resized_tensor)
results = torch.stack(out).cpu()
if combine_mask:
combined_results = torch.max(results, dim=0)[0]
results = combined_results.unsqueeze(0).repeat(len(images),1,1)
if binary_mask:
results = results.round()
return results,
class RoundMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"mask": ("MASK",),
}}
RETURN_TYPES = ("MASK",)
FUNCTION = "round"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Rounds the mask or batch of masks to a binary mask.
<img src="https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40" width="300" height="250" alt="RoundMask example">
"""
def round(self, mask):
mask = mask.round()
return (mask,)
class ResizeMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }),
"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }),
"keep_proportions": ("BOOLEAN", { "default": False }),
}
}
RETURN_TYPES = ("MASK", "INT", "INT",)
RETURN_NAMES = ("mask", "width", "height",)
FUNCTION = "resize"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Resizes the mask or batch of masks to the specified width and height.
"""
def resize(self, mask, width, height, keep_proportions):
if keep_proportions:
_, oh, ow, _ = mask.shape
width = ow if width == 0 else width
height = oh if height == 0 else height
ratio = min(width / ow, height / oh)
width = round(ow*ratio)
height = round(oh*ratio)
outputs = mask.unsqueeze(0) # Add an extra dimension for batch size
outputs = F.interpolate(outputs, size=(height, width), mode="nearest")
outputs = outputs.squeeze(0) # Remove the extra dimension after interpolation
return(outputs, outputs.shape[2], outputs.shape[1],)
class OffsetMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }),
"duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }),
"roll": ("BOOLEAN", { "default": False }),
"incremental": ("BOOLEAN", { "default": False }),
"padding_mode": (
[
'empty',
'border',
'reflection',
], {
"default": 'empty'
}),
}
}
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "offset"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Offsets the mask by the specified amount.
- mask: Input mask or mask batch
- x: Horizontal offset
- y: Vertical offset
- angle: Angle in degrees
- roll: roll edge wrapping
- duplication_factor: Number of times to duplicate the mask to form a batch
- border padding_mode: Padding mode for the mask
"""
def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
# Create duplicates of the mask batch
mask = mask.repeat(duplication_factor, 1, 1).clone()
batch_size, height, width = mask.shape
if angle != 0 and incremental:
for i in range(batch_size):
rotation_angle = angle * (i+1)
mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0)
elif angle > 0:
for i in range(batch_size):
mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0)
if roll:
if incremental:
for i in range(batch_size):
shift_x = min(x*(i+1), width-1)
shift_y = min(y*(i+1), height-1)
if shift_x != 0:
mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1)
if shift_y != 0:
mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0)
else:
shift_x = min(x, width-1)
shift_y = min(y, height-1)
if shift_x != 0:
mask = torch.roll(mask, shifts=shift_x, dims=2)
if shift_y != 0:
mask = torch.roll(mask, shifts=shift_y, dims=1)
else:
for i in range(batch_size):
if incremental:
temp_x = min(x * (i+1), width-1)
temp_y = min(y * (i+1), height-1)
else:
temp_x = min(x, width-1)
temp_y = min(y, height-1)
if temp_x > 0:
if padding_mode == 'empty':
mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1)
elif padding_mode in ['replicate', 'reflect']:
mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
elif temp_x < 0:
if padding_mode == 'empty':
mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1)
elif padding_mode in ['replicate', 'reflect']:
mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
if temp_y > 0:
if padding_mode == 'empty':
mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0)
elif padding_mode in ['replicate', 'reflect']:
mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
elif temp_y < 0:
if padding_mode == 'empty':
mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0)
elif padding_mode in ['replicate', 'reflect']:
mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
return mask,
class WidgetToString:
@classmethod
def IS_CHANGED(cls, **kwargs):
return float("NaN")
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"id": ("INT", {"default": 0}),
"widget_name": ("STRING", {"multiline": False}),
"return_all": ("BOOLEAN", {"default": False}),
},
"hidden": {"extra_pnginfo": "EXTRA_PNGINFO",
"prompt": "PROMPT"},
}
RETURN_TYPES = ("STRING", )
FUNCTION = "get_widget_value"
CATEGORY = "KJNodes/text"
DESCRIPTION = """
Selects a node and it's specified widget and outputs the value as a string.
To see node id's, enable node id display from Manager badge menu.
"""
def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, return_all=False):
workflow = extra_pnginfo["workflow"]
results = []
for node in workflow["nodes"]:
node_id = node["id"]
if node_id != id:
continue
values = prompt[str(node_id)]
if "inputs" in values:
if return_all:
results.append(', '.join(f'{k}: {str(v)}' for k, v in values["inputs"].items()))
elif widget_name in values["inputs"]:
v = str(values["inputs"][widget_name]) # Convert to string here
return (v, )
else:
raise NameError(f"Widget not found: {id}.{widget_name}")
if not results:
raise NameError(f"Node not found: {id}")
return (', '.join(results).strip(', '), )
class CreateShapeMask:
RETURN_TYPES = ("MASK", "MASK",)
RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "createshapemask"
CATEGORY = "KJNodes/masking/generate"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"shape": (
[ 'circle',
'square',
'triangle',
],
{
"default": 'circle'
}),
"frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
"location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
"location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
"grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
"frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
"shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
},
}
def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape):
# Define the number of images in the batch
batch_size = frames
out = []
color = "white"
for i in range(batch_size):
image = Image.new("RGB", (frame_width, frame_height), "black")
draw = ImageDraw.Draw(image)
# Calculate the size for this frame and ensure it's not less than 0
current_width = max(0, shape_width + i*grow)
current_height = max(0, shape_height + i*grow)
if shape == 'circle' or shape == 'square':
# Define the bounding box for the shape
left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
two_points = [left_up_point, right_down_point]
if shape == 'circle':
draw.ellipse(two_points, fill=color)
elif shape == 'square':
draw.rectangle(two_points, fill=color)
elif shape == 'triangle':
# Define the points for the triangle
left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
top_point = (location_x, location_y - current_height // 2) # top point
draw.polygon([top_point, left_up_point, right_down_point], fill=color)
image = pil2tensor(image)
mask = image[:, :, :, 0]
out.append(mask)
outstack = torch.cat(out, dim=0)
return (outstack, 1.0 - outstack,)
class CreateVoronoiMask:
RETURN_TYPES = ("MASK", "MASK",)
RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "createvoronoi"
CATEGORY = "KJNodes/masking/generate"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
"num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}),
"line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}),
"speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
"frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
},
}
def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height):
from scipy.spatial import Voronoi
# Define the number of images in the batch
batch_size = frames
out = []
# Calculate aspect ratio
aspect_ratio = frame_width / frame_height
# Create start and end points for each point, considering the aspect ratio
start_points = np.random.rand(num_points, 2)
start_points[:, 0] *= aspect_ratio
end_points = np.random.rand(num_points, 2)
end_points[:, 0] *= aspect_ratio
for i in range(batch_size):
# Interpolate the points' positions based on the current frame
t = (i * speed) / (batch_size - 1) # normalize to [0, 1] over the frames
t = np.clip(t, 0, 1) # ensure t is in [0, 1]
points = (1 - t) * start_points + t * end_points # lerp
# Adjust points for aspect ratio
points[:, 0] *= aspect_ratio
vor = Voronoi(points)
# Create a blank image with a white background
fig, ax = plt.subplots()
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1]) # adjust x limits
ax.axis('off')
ax.margins(0, 0)
fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100) # adjust figure size
ax.fill_between([0, 1], [0, 1], color='white')
# Plot each Voronoi ridge
for simplex in vor.ridge_vertices:
simplex = np.asarray(simplex)
if np.all(simplex >= 0):
plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width)
fig.canvas.draw()
img = np.array(fig.canvas.renderer._renderer)
plt.close(fig)
pil_img = Image.fromarray(img).convert("L")
mask = torch.tensor(np.array(pil_img)) / 255.0
out.append(mask)
return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
class CreateMagicMask:
RETURN_TYPES = ("MASK", "MASK",)
RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "createmagicmask"
CATEGORY = "KJNodes/masking/generate"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
"depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}),
"distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}),
"seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}),
"transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}),
"frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
},
}
def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height):
from .magictex import coordinate_grid, random_transform, magic
rng = np.random.default_rng(seed)
out = []
coords = coordinate_grid((frame_width, frame_height))
# Calculate the number of frames for each transition
frames_per_transition = frames // transitions
# Generate a base set of parameters
base_params = {
"coords": random_transform(coords, rng),
"depth": depth,
"distortion": distortion,
}
for t in range(transitions):
# Generate a second set of parameters that is at most max_diff away from the base parameters
params1 = base_params.copy()
params2 = base_params.copy()
params1['coords'] = random_transform(coords, rng)
params2['coords'] = random_transform(coords, rng)
for i in range(frames_per_transition):
# Compute the interpolation factor
alpha = i / frames_per_transition
# Interpolate between the two sets of parameters
params = params1.copy()
params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords']
tex = magic(**params)
dpi = frame_width / 10
fig = plt.figure(figsize=(10, 10), dpi=dpi)
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
ax.get_yaxis().set_ticks([])
ax.get_xaxis().set_ticks([])
ax.imshow(tex, aspect='auto')
fig.canvas.draw()
img = np.array(fig.canvas.renderer._renderer)
plt.close(fig)
pil_img = Image.fromarray(img).convert("L")
mask = torch.tensor(np.array(pil_img)) / 255.0
out.append(mask)
return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
class BboxToInt:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bboxes": ("BBOX",),
"index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
},
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
FUNCTION = "bboxtoint"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Returns selected index from bounding box list as integers.
"""
def bboxtoint(self, bboxes, index):
x_min, y_min, width, height = bboxes[index]
center_x = int(x_min + width / 2)
center_y = int(y_min + height / 2)
return (x_min, y_min, width, height, center_x, center_y,)
class BboxVisualize:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"bboxes": ("BBOX",),
"line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "visualizebbox"
DESCRIPTION = """
Visualizes the specified bbox on the image.
"""
CATEGORY = "KJNodes/masking"
def visualizebbox(self, bboxes, images, line_width):
image_list = []
for image, bbox in zip(images, bboxes):
x_min, y_min, width, height = bbox
image = image.permute(2, 0, 1)
img_with_bbox = image.clone()
# Define the color for the bbox, e.g., red
color = torch.tensor([1, 0, 0], dtype=torch.float32)
# Draw lines for each side of the bbox with the specified line width
for lw in range(line_width):
# Top horizontal line
img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
# Bottom horizontal line
img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
# Left vertical line
img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
# Right vertical line
img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
image_list.append(img_with_bbox)
return (torch.cat(image_list, dim=0),)
class SplitBboxes:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bboxes": ("BBOX",),
"index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
},
}
RETURN_TYPES = ("BBOX","BBOX",)
RETURN_NAMES = ("bboxes_a","bboxes_b",)
FUNCTION = "splitbbox"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Splits the specified bbox list at the given index into two lists.
"""
def splitbbox(self, bboxes, index):
bboxes_a = bboxes[:index] # Sub-list from the start of bboxes up to (but not including) the index
bboxes_b = bboxes[index:] # Sub-list from the index to the end of bboxes
return (bboxes_a, bboxes_b,)
from PIL import ImageGrab
import time
class ImageGrabPIL:
@classmethod
def IS_CHANGED(cls):
return
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "screencap"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
Captures an area specified by screen coordinates.
Can be used for realtime diffusion with autoqueue.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
},
}
def screencap(self, x, y, width, height, num_frames, delay):
captures = []
bbox = (x, y, x + width, y + height)
for _ in range(num_frames):
# Capture screen
screen_capture = ImageGrab.grab(bbox=bbox)
screen_capture_torch = torch.tensor(np.array(screen_capture), dtype=torch.float32) / 255.0
screen_capture_torch = screen_capture_torch.unsqueeze(0)
captures.append(screen_capture_torch)
# Wait for a short delay if more than one frame is to be captured
if num_frames > 1:
time.sleep(delay)
return (torch.cat(captures, dim=0),)
class DummyLatentOut:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"latent": ("LATENT",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "dummy"
CATEGORY = "KJNodes/misc"
OUTPUT_NODE = True
DESCRIPTION = """
Does nothing, used to trigger generic workflow output.
A way to get previews in the UI without saving anything to disk.
"""
def dummy(self, latent):
return (latent,)
class FlipSigmasAdjusted:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"sigmas": ("SIGMAS", ),
"divide_by_last_sigma": ("BOOLEAN", {"default": False}),
"divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}),
"offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}),
}
}
RETURN_TYPES = ("SIGMAS", "STRING",)
RETURN_NAMES = ("SIGMAS", "sigmas_string",)
CATEGORY = "KJNodes/noise"
FUNCTION = "get_sigmas_adjusted"
def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by):
sigmas = sigmas.flip(0)
if sigmas[0] == 0:
sigmas[0] = 0.0001
adjusted_sigmas = sigmas.clone()
#offset sigma
for i in range(1, len(sigmas)):
offset_index = i - offset_by
if 0 <= offset_index < len(sigmas):
adjusted_sigmas[i] = sigmas[offset_index]
else:
adjusted_sigmas[i] = 0.0001
if adjusted_sigmas[0] == 0:
adjusted_sigmas[0] = 0.0001
if divide_by_last_sigma:
adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1]
sigma_np_array = adjusted_sigmas.numpy()
array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf)
adjusted_sigmas = adjusted_sigmas / divide_by
return (adjusted_sigmas, array_string,)
class InjectNoiseToLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latents":("LATENT",),
"strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}),
"noise": ("LATENT",),
"normalize": ("BOOLEAN", {"default": False}),
"average": ("BOOLEAN", {"default": False}),
},
"optional":{
"mask": ("MASK", ),
"mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}),
"seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "injectnoise"
CATEGORY = "KJNodes/noise"
def injectnoise(self, latents, strength, noise, normalize, average, mix_randn_amount=0, seed=None, mask=None):
samples = latents.copy()
if latents["samples"].shape != noise["samples"].shape:
raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape")
if average:
noised = (samples["samples"].clone() + noise["samples"].clone()) / 2
else:
noised = samples["samples"].clone() + noise["samples"].clone() * strength
if normalize:
noised = noised / noised.std()
if mask is not None:
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noised.shape[2], noised.shape[3]), mode="bilinear")
mask = mask.expand((-1,noised.shape[1],-1,-1))
if mask.shape[0] < noised.shape[0]:
mask = mask.repeat((noised.shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]]
noised = mask * noised + (1-mask) * latents["samples"]
if mix_randn_amount > 0:
if seed is not None:
torch.manual_seed(seed)
rand_noise = torch.randn_like(noised)
noised = ((1 - mix_randn_amount) * noised + mix_randn_amount *
rand_noise) / ((mix_randn_amount**2 + (1-mix_randn_amount)**2) ** 0.5)
samples["samples"] = noised
return (samples,)
class AddLabel:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image":("IMAGE",),
"text_x": ("INT", {"default": 10, "min": 0, "max": 4096, "step": 1}),
"text_y": ("INT", {"default": 2, "min": 0, "max": 4096, "step": 1}),
"height": ("INT", {"default": 48, "min": 0, "max": 4096, "step": 1}),
"font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}),
"font_color": ("STRING", {"default": "white"}),
"label_color": ("STRING", {"default": "black"}),
"font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
"text": ("STRING", {"default": "Text"}),
"direction": (
[ 'up',
'down',
],
{
"default": 'up'
}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "addlabel"
CATEGORY = "KJNodes/text"
def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction):
batch_size = image.shape[0]
width = image.shape[2]
if font == "TTNorms-Black.otf":
font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf")
else:
font_path = folder_paths.get_full_path("kjnodes_fonts", font)
label_image = Image.new("RGB", (width, height), label_color)
draw = ImageDraw.Draw(label_image)
font = ImageFont.truetype(font_path, font_size)
try:
draw.text((text_x, text_y), text, font=font, fill=font_color, features=['-liga'])
except:
draw.text((text_x, text_y), text, font=font, fill=font_color)
label_image = np.array(label_image).astype(np.float32) / 255.0
label_image = torch.from_numpy(label_image)[None, :, :, :]
# Duplicate the label image for the entire batch
label_batch = label_image.repeat(batch_size, 1, 1, 1)
if direction == 'down':
combined_images = torch.cat((image, label_batch), dim=1)
elif direction == 'up':
combined_images = torch.cat((label_batch, image), dim=1)
return (combined_images,)
class SoundReactive:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"sound_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 99999, "step": 0.01}),
"start_range_hz": ("INT", {"default": 150, "min": 0, "max": 9999, "step": 1}),
"end_range_hz": ("INT", {"default": 2000, "min": 0, "max": 9999, "step": 1}),
"multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 99999, "step": 0.01}),
"smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"normalize": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("FLOAT","INT",)
RETURN_NAMES =("sound_level", "sound_level_int",)
FUNCTION = "react"
CATEGORY = "KJNodes/audio"
def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize):
sound_level *= multiplier
if normalize:
sound_level /= 255
sound_level_int = int(sound_level)
return (sound_level, sound_level_int, )
class GenerateNoise:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
"multiplier": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 4096, "step": 0.01}),
"constant_batch_noise": ("BOOLEAN", {"default": False}),
"normalize": ("BOOLEAN", {"default": False}),
},
"optional": {
"model": ("MODEL", ),
"sigmas": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generatenoise"
CATEGORY = "KJNodes/noise"
DESCRIPTION = """
Generates noise for injection or to be used as empty latents on samplers with add_noise off.
"""
def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None):
generator = torch.manual_seed(seed)
noise = torch.randn([batch_size, 4, height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu")
if sigmas is not None:
sigma = sigmas[0] - sigmas[-1]
sigma /= model.model.latent_format.scale_factor
noise *= sigma
noise *=multiplier
if normalize:
noise = noise / noise.std()
if constant_batch_noise:
noise = noise[0].repeat(batch_size, 1, 1, 1)
return ({"samples":noise}, )
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
azimuth = torch.as_tensor([azimuth])
embeddings = torch.stack(
[
torch.deg2rad(
(90 - elevation) - (90)
), # Zero123 polar is 90-elevation
torch.sin(torch.deg2rad(azimuth)),
torch.cos(torch.deg2rad(azimuth)),
torch.deg2rad(
90 - torch.full_like(elevation, 0)
),
], dim=-1).unsqueeze(1)
return embeddings
def interpolate_angle(start, end, fraction):
# Calculate the difference in angles and adjust for wraparound if necessary
diff = (end - start + 540) % 360 - 180
# Apply fraction to the difference
interpolated = start + fraction * diff
# Normalize the result to be within the range of -180 to 180
return (interpolated + 180) % 360 - 180
class StableZero123_BatchSchedule:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
"azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
"elevation_points_string": ("STRING", {"default": "0:(0.0),\n7:(0.0),\n15:(0.0)\n", "multiline": True}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "KJNodes/experimental"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
# Parse the azimuth input string into a list of tuples
azimuth_points = []
azimuth_points_string = azimuth_points_string.rstrip(',\n')
for point_str in azimuth_points_string.split(','):
frame_str, azimuth_str = point_str.split(':')
frame = int(frame_str.strip())
azimuth = float(azimuth_str.strip()[1:-1])
azimuth_points.append((frame, azimuth))
# Sort the points by frame number
azimuth_points.sort(key=lambda x: x[0])
# Parse the elevation input string into a list of tuples
elevation_points = []
elevation_points_string = elevation_points_string.rstrip(',\n')
for point_str in elevation_points_string.split(','):
frame_str, elevation_str = point_str.split(':')
frame = int(frame_str.strip())
elevation_val = float(elevation_str.strip()[1:-1])
elevation_points.append((frame, elevation_val))
# Sort the points by frame number
elevation_points.sort(key=lambda x: x[0])
# Index of the next point to interpolate towards
next_point = 1
next_elevation_point = 1
positive_cond_out = []
positive_pooled_out = []
negative_cond_out = []
negative_pooled_out = []
#azimuth interpolation
for i in range(batch_size):
# Find the interpolated azimuth for the current frame
while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
next_point += 1
# If next_point is equal to the length of points, we've gone past the last point
if next_point == len(azimuth_points):
next_point -= 1 # Set next_point to the last index of points
prev_point = max(next_point - 1, 0) # Ensure prev_point is not less than 0
# Calculate fraction
if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: # Prevent division by zero
fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0])
if interpolation == "ease_in":
fraction = ease_in(fraction)
elif interpolation == "ease_out":
fraction = ease_out(fraction)
elif interpolation == "ease_in_out":
fraction = ease_in_out(fraction)
# Use the new interpolate_angle function
interpolated_azimuth = interpolate_angle(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction)
else:
interpolated_azimuth = azimuth_points[prev_point][1]
# Interpolate the elevation
next_elevation_point = 1
while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
next_elevation_point += 1
if next_elevation_point == len(elevation_points):
next_elevation_point -= 1
prev_elevation_point = max(next_elevation_point - 1, 0)
if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
if interpolation == "ease_in":
fraction = ease_in(fraction)
elif interpolation == "ease_out":
fraction = ease_out(fraction)
elif interpolation == "ease_in_out":
fraction = ease_in_out(fraction)
interpolated_elevation = interpolate_angle(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction)
else:
interpolated_elevation = elevation_points[prev_elevation_point][1]
cam_embeds = camera_embeddings(interpolated_elevation, interpolated_azimuth)
cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1)
positive_pooled_out.append(t)
positive_cond_out.append(cond)
negative_pooled_out.append(torch.zeros_like(t))
negative_cond_out.append(torch.zeros_like(pooled))
# Concatenate the conditions and pooled outputs
final_positive_cond = torch.cat(positive_cond_out, dim=0)
final_positive_pooled = torch.cat(positive_pooled_out, dim=0)
final_negative_cond = torch.cat(negative_cond_out, dim=0)
final_negative_pooled = torch.cat(negative_pooled_out, dim=0)
# Structure the final output
final_positive = [[final_positive_cond, {"concat_latent_image": final_positive_pooled}]]
final_negative = [[final_negative_cond, {"concat_latent_image": final_negative_pooled}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (final_positive, final_negative, {"samples": latent})
def linear_interpolate(start, end, fraction):
return start + (end - start) * fraction
class SV3D_BatchSchedule:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
"azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}),
"elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
Allow scheduling of the azimuth and elevation conditions for SV3D.
Note that SV3D is still a video model and the schedule needs to always go forward
"""
def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
encode_pixels = pixels[:,:,:,:3]
t = vae.encode(encode_pixels)
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
# Parse the azimuth input string into a list of tuples
azimuth_points = []
azimuth_points_string = azimuth_points_string.rstrip(',\n')
for point_str in azimuth_points_string.split(','):
frame_str, azimuth_str = point_str.split(':')
frame = int(frame_str.strip())
azimuth = float(azimuth_str.strip()[1:-1])
azimuth_points.append((frame, azimuth))
# Sort the points by frame number
azimuth_points.sort(key=lambda x: x[0])
# Parse the elevation input string into a list of tuples
elevation_points = []
elevation_points_string = elevation_points_string.rstrip(',\n')
for point_str in elevation_points_string.split(','):
frame_str, elevation_str = point_str.split(':')
frame = int(frame_str.strip())
elevation_val = float(elevation_str.strip()[1:-1])
elevation_points.append((frame, elevation_val))
# Sort the points by frame number
elevation_points.sort(key=lambda x: x[0])
# Index of the next point to interpolate towards
next_point = 1
next_elevation_point = 1
elevations = []
azimuths = []
# For azimuth interpolation
for i in range(batch_size):
# Find the interpolated azimuth for the current frame
while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
next_point += 1
if next_point == len(azimuth_points):
next_point -= 1
prev_point = max(next_point - 1, 0)
if azimuth_points[next_point][0] != azimuth_points[prev_point][0]:
fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0])
# Apply the ease function to the fraction
if interpolation == "ease_in":
fraction = ease_in(fraction)
elif interpolation == "ease_out":
fraction = ease_out(fraction)
elif interpolation == "ease_in_out":
fraction = ease_in_out(fraction)
interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction)
else:
interpolated_azimuth = azimuth_points[prev_point][1]
# Interpolate the elevation
next_elevation_point = 1
while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
next_elevation_point += 1
if next_elevation_point == len(elevation_points):
next_elevation_point -= 1
prev_elevation_point = max(next_elevation_point - 1, 0)
if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
# Apply the ease function to the fraction
if interpolation == "ease_in":
fraction = ease_in(fraction)
elif interpolation == "ease_out":
fraction = ease_out(fraction)
elif interpolation == "ease_in_out":
fraction = ease_in_out(fraction)
interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction)
else:
interpolated_elevation = elevation_points[prev_elevation_point][1]
azimuths.append(interpolated_azimuth)
elevations.append(interpolated_elevation)
print("azimuths", azimuths)
print("elevations", elevations)
# Structure the final output
final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (final_positive, final_negative, {"samples": latent})
class ImageBatchRepeatInterleaving:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "repeat"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Repeats each image in a batch by the specified number of times.
Example batch of 5 images: 0, 1 ,2, 3, 4
with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"repeats": ("INT", {"default": 1, "min": 1, "max": 4096}),
},
}
def repeat(self, images, repeats):
repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0)
return (repeated_images, )
class NormalizedAmplitudeToMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"normalized_amp": ("NORMALIZED_AMPLITUDE",),
"width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
"frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}),
"location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
"location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
"size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
"shape": (
[
'none',
'circle',
'square',
'triangle',
],
{
"default": 'none'
}),
"color": (
[
'white',
'amplitude',
],
{
"default": 'amplitude'
}),
},}
CATEGORY = "KJNodes/audio"
RETURN_TYPES = ("MASK",)
FUNCTION = "convert"
DESCRIPTION = """
Works as a bridge to the AudioScheduler -nodes:
https://github.com/a1lazydog/ComfyUI-AudioScheduler
Creates masks based on the normalized amplitude.
"""
def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color):
# Ensure normalized_amp is an array and within the range [0, 1]
normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
# Offset the amplitude values by rolling the array
normalized_amp = np.roll(normalized_amp, frame_offset)
# Initialize an empty list to hold the image tensors
out = []
# Iterate over each amplitude value to create an image
for amp in normalized_amp:
# Scale the amplitude value to cover the full range of grayscale values
if color == 'amplitude':
grayscale_value = int(amp * 255)
elif color == 'white':
grayscale_value = 255
# Convert the grayscale value to an RGB format
gray_color = (grayscale_value, grayscale_value, grayscale_value)
finalsize = size * amp
if shape == 'none':
shapeimage = Image.new("RGB", (width, height), gray_color)
else:
shapeimage = Image.new("RGB", (width, height), "black")
draw = ImageDraw.Draw(shapeimage)
if shape == 'circle' or shape == 'square':
# Define the bounding box for the shape
left_up_point = (location_x - finalsize, location_y - finalsize)
right_down_point = (location_x + finalsize,location_y + finalsize)
two_points = [left_up_point, right_down_point]
if shape == 'circle':
draw.ellipse(two_points, fill=gray_color)
elif shape == 'square':
draw.rectangle(two_points, fill=gray_color)
elif shape == 'triangle':
# Define the points for the triangle
left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left
right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right
top_point = (location_x, location_y) # top point
draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color)
shapeimage = pil2tensor(shapeimage)
mask = shapeimage[:, :, :, 0]
out.append(mask)
return (torch.cat(out, dim=0),)
class OffsetMaskByNormalizedAmplitude:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"normalized_amp": ("NORMALIZED_AMPLITUDE",),
"mask": ("MASK",),
"x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"rotate": ("BOOLEAN", { "default": False }),
"angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
}
}
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "offset"
CATEGORY = "KJNodes/audio"
DESCRIPTION = """
Works as a bridge to the AudioScheduler -nodes:
https://github.com/a1lazydog/ComfyUI-AudioScheduler
Offsets masks based on the normalized amplitude.
"""
def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
# Ensure normalized_amp is an array and within the range [0, 1]
offsetmask = mask.clone()
normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
batch_size, height, width = mask.shape
if rotate:
for i in range(batch_size):
rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier))
rotation_angle = rotation_amp
offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0)
if x != 0 or y != 0:
for i in range(batch_size):
offset_amp = normalized_amp[i] * 10
shift_x = min(x*offset_amp, width-1)
shift_y = min(y*offset_amp, height-1)
if shift_x != 0:
offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1)
if shift_y != 0:
offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0)
return offsetmask,
class ImageTransformByNormalizedAmplitude:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"normalized_amp": ("NORMALIZED_AMPLITUDE",),
"zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
"x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
"cumulative": ("BOOLEAN", { "default": False }),
"image": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "amptransform"
CATEGORY = "KJNodes/audio"
DESCRIPTION = """
Works as a bridge to the AudioScheduler -nodes:
https://github.com/a1lazydog/ComfyUI-AudioScheduler
Transforms image based on the normalized amplitude.
"""
def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
# Ensure normalized_amp is an array and within the range [0, 1]
normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
transformed_images = []
# Initialize the cumulative zoom factor
prev_amp = 0.0
for i in range(image.shape[0]):
img = image[i] # Get the i-th image in the batch
amp = normalized_amp[i] # Get the corresponding amplitude value
# Incrementally increase the cumulative zoom factor
if cumulative:
prev_amp += amp
amp += prev_amp
# Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision
img = img.permute(2, 0, 1)
# Convert PyTorch tensor to PIL Image for processing
pil_img = TF.to_pil_image(img)
# Calculate the crop size based on the amplitude
width, height = pil_img.size
crop_size = int(min(width, height) * (1 - amp * zoom_scale))
crop_size = max(crop_size, 1)
# Calculate the crop box coordinates (centered crop)
left = (width - crop_size) // 2
top = (height - crop_size) // 2
right = (width + crop_size) // 2
bottom = (height + crop_size) // 2
# Crop and resize back to original size
cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size)
resized_img = TF.resize(cropped_img, (height, width))
# Convert back to tensor in CxHxW format
tensor_img = TF.to_tensor(resized_img)
# Convert the tensor back to BxHxWxC format
tensor_img = tensor_img.permute(1, 2, 0)
# Offset the image based on the amplitude
offset_amp = amp * 10 # Calculate the offset magnitude based on the amplitude
shift_x = min(x_offset * offset_amp, img.shape[1] - 1) # Calculate the shift in x direction
shift_y = min(y_offset * offset_amp, img.shape[0] - 1) # Calculate the shift in y direction
# Apply the offset to the image tensor
if shift_x != 0:
tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1)
if shift_y != 0:
tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0)
# Add to the list
transformed_images.append(tensor_img)
# Stack all transformed images into a batch
transformed_batch = torch.stack(transformed_images)
return (transformed_batch,)
def parse_coordinates(coordinates_str):
coordinates = {}
pattern = r'(\d+):\((\d+),(\d+)\)'
matches = re.findall(pattern, coordinates_str)
for match in matches:
index, x, y = map(int, match)
coordinates[index] = (x, y)
return coordinates
def interpolate_coordinates(coordinates_dict, batch_size):
sorted_coords = sorted(coordinates_dict.items())
interpolated = {}
for i, ((index1, (x1, y1)), (index2, (x2, y2))) in enumerate(zip(sorted_coords, sorted_coords[1:])):
distance = index2 - index1
x_step = (x2 - x1) / distance
y_step = (y2 - y1) / distance
for j in range(distance):
interpolated_x = round(x1 + j * x_step)
interpolated_y = round(y1 + j * y_step)
interpolated[index1 + j] = (interpolated_x, interpolated_y)
interpolated[sorted_coords[-1][0]] = sorted_coords[-1][1]
# Ensure we have coordinates for all indices in the batch
last_index, last_coords = sorted_coords[-1]
for i in range(last_index + 1, batch_size):
interpolated[i] = last_coords
return interpolated
def interpolate_coordinates_with_curves(coordinates_dict, batch_size):
from scipy.interpolate import CubicSpline
sorted_coords = sorted(coordinates_dict.items())
x_coords, y_coords = zip(*[coord for index, coord in sorted_coords])
# Create the spline curve functions
indices = np.array([index for index, coord in sorted_coords])
cs_x = CubicSpline(indices, x_coords)
cs_y = CubicSpline(indices, y_coords)
# Generate interpolated coordinates using the spline functions
interpolated_indices = np.arange(0, batch_size)
interpolated_x = cs_x(interpolated_indices)
interpolated_y = cs_y(interpolated_indices)
# Round the interpolated coordinates and create the dictionary
interpolated = {i: (round(x), round(y)) for i, (x, y) in enumerate(zip(interpolated_x, interpolated_y))}
return interpolated
def plot_to_tensor(coordinates_dict, interpolated_dict, height, width, box_size):
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import matplotlib.patches as patches
original_x, original_y = zip(*coordinates_dict.values())
interpolated_x, interpolated_y = zip(*interpolated_dict.values())
fig, ax = plt.subplots(figsize=(width/100, height/100), dpi=100)
ax.scatter(original_x, original_y, color='blue', label='Original Points')
ax.scatter(interpolated_x, interpolated_y, color='red', alpha=0.5, label='Interpolated Points')
ax.plot(interpolated_x, interpolated_y, color='grey', linestyle='--', linewidth=0.5)
# Draw a box at each interpolated coordinate
for x, y in interpolated_dict.values():
rect = patches.Rectangle((x - box_size/2, y - box_size/2), box_size, box_size,
linewidth=1, edgecolor='green', facecolor='none')
ax.add_patch(rect)
ax.set_title('Interpolated Coordinates')
ax.set_xlabel('X Coordinate')
ax.set_ylabel('Y Coordinate')
ax.legend()
ax.set_xlim(0, width) # Set the x-axis to match the input latent width
ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
canvas = FigureCanvas(fig)
canvas.draw()
width, height = fig.get_size_inches() * fig.get_dpi()
image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
image_tensor = torch.from_numpy(image_np).float() / 255.0
image_tensor = image_tensor.unsqueeze(0)
plt.close(fig)
return image_tensor
class GLIGENTextBoxApplyBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_to": ("CONDITIONING", ),
"latents": ("LATENT", ),
"clip": ("CLIP", ),
"gligen_textbox_model": ("GLIGEN", ),
"text": ("STRING", {"multiline": True}),
"width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
"coordinates": ("STRING", {"multiline": True}),
"interpolation": (
[
'straight',
'CubicSpline',
],
{
"default": 'CubicSpline'
}),
}}
RETURN_TYPES = ("CONDITIONING", "IMAGE",)
FUNCTION = "append"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
Experimental, does not function yet as ComfyUI base changes are needed
"""
def append(self, latents, conditioning_to, clip, gligen_textbox_model, text, width, height, coordinates, interpolation):
coordinates_dict = parse_coordinates(coordinates)
batch_size = sum(tensor.size(0) for tensor in latents.values())
c = []
cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
# Interpolate coordinates for the entire batch
if interpolation == 'CubicSpline':
interpolated_coords = interpolate_coordinates_with_curves(coordinates_dict, batch_size)
if interpolation == 'straight':
interpolated_coords = interpolate_coordinates(coordinates_dict, batch_size)
plot_image_tensor = plot_to_tensor(coordinates_dict, interpolated_coords, 512, 512, height)
for t in conditioning_to:
n = [t[0], t[1].copy()]
position_params_batch = [[] for _ in range(batch_size)] # Initialize a list of empty lists for each batch item
for i in range(batch_size):
x_position, y_position = interpolated_coords[i]
position_param = (cond_pooled, height // 8, width // 8, y_position // 8, x_position // 8)
position_params_batch[i].append(position_param) # Append position_param to the correct sublist
print("x ",x_position, "y ", y_position)
prev = []
if "gligen" in n[1]:
prev = n[1]['gligen'][2]
else:
prev = [[] for _ in range(batch_size)]
# Concatenate prev and position_params_batch, ensuring both are lists of lists
# and each sublist corresponds to a batch item
combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
n[1]['gligen'] = ("position", gligen_textbox_model, combined_position_params)
c.append(n)
return (c, plot_image_tensor,)
class ImageUpscaleWithModelBatched:
@classmethod
def INPUT_TYPES(s):
return {"required": { "upscale_model": ("UPSCALE_MODEL",),
"images": ("IMAGE",),
"per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Same as ComfyUI native model upscaling node, but allows setting sub-batches for reduced VRAM usage.
"""
def upscale(self, upscale_model, images, per_batch):
device = model_management.get_torch_device()
upscale_model.to(device)
in_img = images.movedim(-1,-3).to(device)
steps = in_img.shape[0]
pbar = comfy.utils.ProgressBar(steps)
t = []
for start_idx in range(0, in_img.shape[0], per_batch):
sub_images = upscale_model(in_img[start_idx:start_idx+per_batch])
t.append(sub_images.cpu())
# Calculate the number of images processed in this batch
batch_count = sub_images.shape[0]
# Update the progress bar by the number of images processed in this batch
pbar.update(batch_count)
upscale_model.cpu()
t = torch.cat(t, dim=0).permute(0, 2, 3, 1).cpu()
return (t,)
class ImageNormalize_Neg1_To_1:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "normalize"
CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Normalize the images to be in the range [-1, 1]
"""
def normalize(self,images):
images = images * 2.0 - 1.0
return (images,)
import comfy.sample
from nodes import CLIPTextEncode
folder_paths.add_model_folder_path("intristic_loras", os.path.join(script_directory, "intristic_loras"))
class Intrinsic_lora_sampling:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"lora_name": (folder_paths.get_filename_list("intristic_loras"), ),
"task": (
[
'depth map',
'surface normals',
'albedo',
'shading',
],
{
"default": 'depth map'
}),
"text": ("STRING", {"multiline": True, "default": ""}),
"clip": ("CLIP", ),
"vae": ("VAE", ),
"per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
},
"optional": {
"image": ("IMAGE",),
"optional_latent": ("LATENT",),
},
}
RETURN_TYPES = ("IMAGE", "LATENT",)
FUNCTION = "onestepsample"
CATEGORY = "KJNodes"
DESCRIPTION = """
https://github.com/duxiaodan/intrinsic-lora
"""
def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None):
pbar = comfy.utils.ProgressBar(3)
if optional_latent is None:
image_list = []
for start_idx in range(0, image.shape[0], per_batch):
sub_pixels = vae.vae_encode_crop_pixels(image[start_idx:start_idx+per_batch])
image_list.append(vae.encode(sub_pixels[:,:,:,:3]))
sample = torch.cat(image_list, dim=0)
else:
sample = optional_latent["samples"]
noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu")
prompt = task + "," + text
positive, = CLIPTextEncode.encode(self, clip, prompt)
negative = positive #negative shouldn't do anything in this scenario
pbar.update(1)
#custom model sampling to pass latent through as it is
class X0_PassThrough(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
def calculate_input(self, sigma, noise):
return noise
sampling_base = comfy.model_sampling.ModelSamplingDiscrete
sampling_type = X0_PassThrough
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
#load lora
model_clone = model.clone()
lora_path = folder_paths.get_full_path("intristic_loras", lora_name)
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_clone_with_lora = comfy.sd.load_lora_for_models(model_clone, None, lora, 1.0, 0)[0]
model_clone_with_lora.add_object_patch("model_sampling", model_sampling)
samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample,
denoise=1.0, disable_noise=True, start_step=0, last_step=1,
force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)}
pbar.update(1)
decoded = []
for start_idx in range(0, samples["samples"].shape[0], per_batch):
decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch]))
image_out = torch.cat(decoded, dim=0)
pbar.update(1)
if task == 'depth map':
imax = image_out.max()
imin = image_out.min()
image_out = (image_out-imin)/(imax-imin)
image_out = torch.max(image_out, dim=3, keepdim=True)[0].repeat(1, 1, 1, 3)
elif task == 'surface normals':
image_out = F.normalize(image_out * 2 - 1, dim=3) / 2 + 0.5
image_out = 1.0 - image_out
else:
image_out = image_out.clamp(-1.,1.)
return (image_out, samples,)
class RemapMaskRange:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
"max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
}
}
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "remap"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Sets new min and max values for the mask.
"""
def remap(self, mask, min, max):
# Find the maximum value in the mask
mask_max = torch.max(mask)
# If the maximum mask value is zero, avoid division by zero by setting it to 1
mask_max = mask_max if mask_max > 0 else 1
# Scale the mask values to the new range defined by min and max
# The highest pixel value in the mask will be scaled to max
scaled_mask = (mask / mask_max) * (max - min) + min
# Clamp the values to ensure they are within [0.0, 1.0]
scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0)
return (scaled_mask, )
class LoadResAdapterNormalization:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"resadapter_path": (folder_paths.get_filename_list("checkpoints"), )
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_res_adapter"
CATEGORY = "KJNodes/experimental"
def load_res_adapter(self, model, resadapter_path):
print("ResAdapter: Checking ResAdapter path")
resadapter_full_path = folder_paths.get_full_path("checkpoints", resadapter_path)
if not os.path.exists(resadapter_full_path):
raise Exception("Invalid model path")
else:
print("ResAdapter: Loading ResAdapter normalization weights")
prefix_to_remove = 'diffusion_model.'
model_clone = model.clone()
norm_state_dict = comfy.utils.load_torch_file(resadapter_full_path)
new_values = {key[len(prefix_to_remove):]: value for key, value in norm_state_dict.items() if key.startswith(prefix_to_remove)}
print("ResAdapter: Attempting to add patches with ResAdapter weights")
try:
for key in model.model.diffusion_model.state_dict().keys():
if key in new_values:
original_tensor = model.model.diffusion_model.state_dict()[key]
new_tensor = new_values[key].to(model.model.diffusion_model.dtype)
if original_tensor.shape == new_tensor.shape:
model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor)
else:
print("ResAdapter: No match for key: ",key)
except:
raise Exception("Could not patch model, this way of patching was added to ComfyUI on March 3rd 2024, is your ComfyUI up to date?")
print("ResAdapter: Added resnet normalization patches")
return (model_clone, )
class Superprompt:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"instruction_prompt": ("STRING", {"default": 'Expand the following prompt to add more detail', "multiline": True}),
"prompt": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"max_new_tokens": ("INT", {"default": 128, "min": 1, "max": 4096, "step": 1}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "process"
CATEGORY = "KJNodes/text"
DESCRIPTION = """
SuperPrompt
A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions.
Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts.
https://huggingface.co/roborovski/superprompt-v1
"""
def process(self, instruction_prompt, prompt, max_new_tokens):
device = model_management.get_torch_device()
from transformers import T5Tokenizer, T5ForConditionalGeneration
checkpoint_path = os.path.join(script_directory, "models","superprompt-v1")
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False)
model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device)
model.to(device)
input_text = instruction_prompt + ": " + prompt
print(input_text)
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
outputs = model.generate(input_ids, max_new_tokens=max_new_tokens)
out = (tokenizer.decode(outputs[0]))
out = out.replace('<pad>', '')
out = out.replace('</s>', '')
print(out)
return (out, )
class RemapImageRange:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
"max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
"clamp": ("BOOLEAN", {"default": True}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "remap"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Remaps the image values to the specified range.
"""
def remap(self, image, min, max, clamp):
if image.dtype == torch.float16:
image = image.to(torch.float32)
image = min + image * (max - min)
if clamp:
image = torch.clamp(image, min=0.0, max=1.0)
return (image, )
class CameraPoseVisualizer:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"pose_file_path": ("STRING", {"default": 'pose file path here', "multiline": False}),
"sample_stride": ("INT", {"default": 1,"min": 0, "max": 100, "step": 1}),
"frames": ("INT", {"default": 16,"min": 0, "max": 100, "step": 1}),
"base_xval": ("FLOAT", {"default": 0.5,"min": 0, "max": 100, "step": 0.01}),
"zval": ("FLOAT", {"default": 2.0,"min": 0, "max": 100, "step": 0.01}),
"use_exact_fx": ("BOOLEAN", {"default": True}),
"relative_c2w": ("BOOLEAN", {"default": True}),
"x_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}),
"x_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}),
"y_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}),
"y_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}),
"z_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}),
"z_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}),
"use_viewer": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "plot"
CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Visualizes the camera poses from a .txt file with RealEstate camera intrinsics and coordinates in a 3D plot.
"""
def plot(self, pose_file_path, sample_stride, frames, base_xval, zval, use_exact_fx, relative_c2w, x_min, x_max, y_min, y_max, z_min, z_max, use_viewer):
import matplotlib as mpl
import matplotlib.pyplot as plt
import io
from torchvision.transforms import ToTensor
self.fig = plt.figure(figsize=(18, 7))
self.ax = self.fig.add_subplot(projection='3d')
self.plotly_data = None # plotly data traces
self.ax.set_aspect("auto")
self.ax.set_xlim(x_min, x_max)
self.ax.set_ylim(y_min, y_max)
self.ax.set_zlim(z_min, z_max)
self.ax.set_xlabel('x')
self.ax.set_ylabel('y')
self.ax.set_zlabel('z')
print('initialize camera pose visualizer')
with open(pose_file_path, 'r') as f:
poses = f.readlines()
w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]]
fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]]
cropped_length = frames * sample_stride
total_frames = len(w2cs)
start_frame_ind = random.randint(0, max(0, total_frames - cropped_length - 1))
end_frame_ind = min(start_frame_ind + cropped_length, total_frames)
frame_ind = np.linspace(start_frame_ind, end_frame_ind - 1, frames, dtype=int)
w2cs = [w2cs[x] for x in frame_ind]
transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4)
last_row = np.zeros((1, 4))
last_row[0, -1] = 1.0
w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w)
for frame_idx, c2w in enumerate(c2ws):
self.extrinsic2pyramid(c2w, frame_idx / frames, hw_ratio=1/1, base_xval=base_xval,
zval=(fxs[frame_idx] if use_exact_fx else zval))
cmap = mpl.cm.rainbow
norm = mpl.colors.Normalize(vmin=0, vmax=frames)
self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical', label='Frame Number')
plt.title('Extrinsic Parameters')
plt.draw()
buf = io.BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
buf.seek(0)
img = Image.open(buf)
tensor_img = ToTensor()(img)
buf.close()
tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0)
if use_viewer:
time.sleep(1)
plt.show()
return (tensor_img,)
def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3):
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
vertex_std = np.array([[0, 0, 0, 1],
[base_xval, -base_xval * hw_ratio, zval, 1],
[base_xval, base_xval * hw_ratio, zval, 1],
[-base_xval, base_xval * hw_ratio, zval, 1],
[-base_xval, -base_xval * hw_ratio, zval, 1]])
vertex_transformed = vertex_std @ extrinsic.T
meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]],
[vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]],
[vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]],
[vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]],
[vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]]
color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map)
self.ax.add_collection3d(
Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35))
def customize_legend(self, list_label):
from matplotlib.patches import Patch
list_handle = []
for idx, label in enumerate(list_label):
color = plt.cm.rainbow(idx / len(list_label))
patch = Patch(color=color, label=label)
list_handle.append(patch)
plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle)
def get_c2w(self, w2cs, transform_matrix, relative_c2w):
if relative_c2w:
target_cam_c2w = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]
])
abs2rel = target_cam_c2w @ w2cs[0]
ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
else:
ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
ret_poses = [transform_matrix @ x for x in ret_poses]
return np.array(ret_poses, dtype=np.float32)
NODE_CLASS_MAPPINGS = {
"INTConstant": INTConstant,
"FloatConstant": FloatConstant,
"ConditioningMultiCombine": ConditioningMultiCombine,
"ConditioningSetMaskAndCombine": ConditioningSetMaskAndCombine,
"ConditioningSetMaskAndCombine3": ConditioningSetMaskAndCombine3,
"ConditioningSetMaskAndCombine4": ConditioningSetMaskAndCombine4,
"ConditioningSetMaskAndCombine5": ConditioningSetMaskAndCombine5,
"GrowMaskWithBlur": GrowMaskWithBlur,
"ColorToMask": ColorToMask,
"CreateGradientMask": CreateGradientMask,
"CreateTextMask": CreateTextMask,
"CreateAudioMask": CreateAudioMask,
"CreateFadeMask": CreateFadeMask,
"CreateFadeMaskAdvanced": CreateFadeMaskAdvanced,
"CreateFluidMask" :CreateFluidMask,
"VRAM_Debug" : VRAM_Debug,
"SomethingToString" : SomethingToString,
"CrossFadeImages": CrossFadeImages,
"EmptyLatentImagePresets": EmptyLatentImagePresets,
"ColorMatch": ColorMatch,
"GetImageRangeFromBatch": GetImageRangeFromBatch,
"SaveImageWithAlpha": SaveImageWithAlpha,
"ReverseImageBatch": ReverseImageBatch,
"ImageGridComposite2x2": ImageGridComposite2x2,
"ImageGridComposite3x3": ImageGridComposite3x3,
"ImageConcanate": ImageConcanate,
"ImageBatchTestPattern": ImageBatchTestPattern,
"ReplaceImagesInBatch": ReplaceImagesInBatch,
"BatchCropFromMask": BatchCropFromMask,
"BatchCropFromMaskAdvanced": BatchCropFromMaskAdvanced,
"FilterZeroMasksAndCorrespondingImages": FilterZeroMasksAndCorrespondingImages,
"InsertImageBatchByIndexes": InsertImageBatchByIndexes,
"BatchUncrop": BatchUncrop,
"BatchUncropAdvanced": BatchUncropAdvanced,
"BatchCLIPSeg": BatchCLIPSeg,
"RoundMask": RoundMask,
"ResizeMask": ResizeMask,
"OffsetMask": OffsetMask,
"WidgetToString": WidgetToString,
"CreateShapeMask": CreateShapeMask,
"CreateVoronoiMask": CreateVoronoiMask,
"CreateMagicMask": CreateMagicMask,
"BboxToInt": BboxToInt,
"SplitBboxes": SplitBboxes,
"ImageGrabPIL": ImageGrabPIL,
"DummyLatentOut": DummyLatentOut,
"FlipSigmasAdjusted": FlipSigmasAdjusted,
"InjectNoiseToLatent": InjectNoiseToLatent,
"AddLabel": AddLabel,
"SoundReactive": SoundReactive,
"GenerateNoise": GenerateNoise,
"StableZero123_BatchSchedule": StableZero123_BatchSchedule,
"SV3D_BatchSchedule": SV3D_BatchSchedule,
"GetImagesFromBatchIndexed": GetImagesFromBatchIndexed,
"ImageBatchRepeatInterleaving": ImageBatchRepeatInterleaving,
"NormalizedAmplitudeToMask": NormalizedAmplitudeToMask,
"OffsetMaskByNormalizedAmplitude": OffsetMaskByNormalizedAmplitude,
"ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude,
"GetLatentsFromBatchIndexed": GetLatentsFromBatchIndexed,
"StringConstant": StringConstant,
"GLIGENTextBoxApplyBatch": GLIGENTextBoxApplyBatch,
"CondPassThrough": CondPassThrough,
"ImageUpscaleWithModelBatched": ImageUpscaleWithModelBatched,
"ScaleBatchPromptSchedule": ScaleBatchPromptSchedule,
"ImageNormalize_Neg1_To_1": ImageNormalize_Neg1_To_1,
"Intrinsic_lora_sampling": Intrinsic_lora_sampling,
"RemapMaskRange": RemapMaskRange,
"LoadResAdapterNormalization": LoadResAdapterNormalization,
"Superprompt": Superprompt,
"RemapImageRange": RemapImageRange,
"CameraPoseVisualizer": CameraPoseVisualizer,
"BboxVisualize": BboxVisualize
}
NODE_DISPLAY_NAME_MAPPINGS = {
"INTConstant": "INT Constant",
"FloatConstant": "Float Constant",
"ConditioningMultiCombine": "Conditioning Multi Combine",
"ConditioningSetMaskAndCombine": "ConditioningSetMaskAndCombine",
"ConditioningSetMaskAndCombine3": "ConditioningSetMaskAndCombine3",
"ConditioningSetMaskAndCombine4": "ConditioningSetMaskAndCombine4",
"ConditioningSetMaskAndCombine5": "ConditioningSetMaskAndCombine5",
"GrowMaskWithBlur": "GrowMaskWithBlur",
"ColorToMask": "ColorToMask",
"CreateGradientMask": "CreateGradientMask",
"CreateTextMask" : "CreateTextMask",
"CreateFadeMask" : "CreateFadeMask (Deprecated)",
"CreateFadeMaskAdvanced" : "CreateFadeMaskAdvanced",
"CreateFluidMask" : "CreateFluidMask",
"CreateAudioMask" : "CreateAudioMask (Deprecated)",
"VRAM_Debug" : "VRAM Debug",
"CrossFadeImages": "CrossFadeImages",
"SomethingToString": "SomethingToString",
"EmptyLatentImagePresets": "EmptyLatentImagePresets",
"ColorMatch": "ColorMatch",
"GetImageRangeFromBatch": "GetImageRangeFromBatch",
"SaveImageWithAlpha": "SaveImageWithAlpha",
"ReverseImageBatch": "ReverseImageBatch",
"ImageGridComposite2x2": "ImageGridComposite2x2",
"ImageGridComposite3x3": "ImageGridComposite3x3",
"ImageConcanate": "ImageConcatenate",
"ImageBatchTestPattern": "ImageBatchTestPattern",
"ReplaceImagesInBatch": "ReplaceImagesInBatch",
"BatchCropFromMask": "BatchCropFromMask",
"BatchCropFromMaskAdvanced": "BatchCropFromMaskAdvanced",
"FilterZeroMasksAndCorrespondingImages": "FilterZeroMasksAndCorrespondingImages",
"InsertImageBatchByIndexes": "InsertImageBatchByIndexes",
"BatchUncrop": "BatchUncrop",
"BatchUncropAdvanced": "BatchUncropAdvanced",
"BatchCLIPSeg": "BatchCLIPSeg",
"RoundMask": "RoundMask",
"ResizeMask": "ResizeMask",
"OffsetMask": "OffsetMask",
"WidgetToString": "WidgetToString",
"CreateShapeMask": "CreateShapeMask",
"CreateVoronoiMask": "CreateVoronoiMask",
"CreateMagicMask": "CreateMagicMask",
"BboxToInt": "BboxToInt",
"SplitBboxes": "SplitBboxes",
"ImageGrabPIL": "ImageGrabPIL",
"DummyLatentOut": "DummyLatentOut",
"FlipSigmasAdjusted": "FlipSigmasAdjusted",
"InjectNoiseToLatent": "InjectNoiseToLatent",
"AddLabel": "AddLabel",
"SoundReactive": "SoundReactive",
"GenerateNoise": "GenerateNoise",
"StableZero123_BatchSchedule": "StableZero123_BatchSchedule",
"SV3D_BatchSchedule": "SV3D_BatchSchedule",
"GetImagesFromBatchIndexed": "GetImagesFromBatchIndexed",
"ImageBatchRepeatInterleaving": "ImageBatchRepeatInterleaving",
"NormalizedAmplitudeToMask": "NormalizedAmplitudeToMask",
"OffsetMaskByNormalizedAmplitude": "OffsetMaskByNormalizedAmplitude",
"ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude",
"GetLatentsFromBatchIndexed": "GetLatentsFromBatchIndexed",
"StringConstant": "StringConstant",
"GLIGENTextBoxApplyBatch": "GLIGENTextBoxApplyBatch",
"CondPassThrough": "CondPassThrough",
"ImageUpscaleWithModelBatched": "ImageUpscaleWithModelBatched",
"ScaleBatchPromptSchedule": "ScaleBatchPromptSchedule",
"ImageNormalize_Neg1_To_1": "ImageNormalize_Neg1_To_1",
"Intrinsic_lora_sampling": "Intrinsic_lora_sampling",
"RemapMaskRange": "RemapMaskRange",
"LoadResAdapterNormalization": "LoadResAdapterNormalization",
"Superprompt": "Superprompt",
"RemapImageRange": "RemapImageRange",
"CameraPoseVisualizer": "CameraPoseVisualizer",
"BboxVisualize": "BboxVisualize",
}
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