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ComfyUI-KJNodes/nodes/image_nodes.py

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import numpy as np
import time
import torch
import torch.nn.functional as F
import torchvision.transforms as T
import io
import base64
import random
import math
import os
import re
import json
import importlib
from PIL.PngImagePlugin import PngInfo
try:
import cv2
except:
print("OpenCV not installed")
pass
from PIL import ImageGrab, ImageDraw, ImageFont, Image, ImageOps
from nodes import MAX_RESOLUTION, SaveImage
from comfy_extras.nodes_mask import composite
import node_helpers
from comfy.cli_args import args
from comfy.utils import ProgressBar, common_upscale
import folder_paths
from comfy import model_management
try:
from server import PromptServer
except:
PromptServer = None
from concurrent.futures import ThreadPoolExecutor
script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
class ImagePass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
},
"optional": {
"image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "passthrough"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Passes the image through without modifying it.
"""
def passthrough(self, image=None):
return image,
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'
}),
},
"optional": {
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"multithread": ("BOOLEAN", {"default": True}),
}
}
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, strength=1.0, multithread=True):
# Skip unnecessary processing
if strength == 0:
return (image_target,)
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")
image_ref = image_ref.cpu()
image_target = image_target.cpu()
batch_size = image_target.size(0)
images_target = image_target.squeeze()
images_ref = image_ref.squeeze()
image_ref_np = images_ref.numpy()
images_target_np = images_target.numpy()
def process(i):
cm = ColorMatcher()
image_target_np_i = 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_i, ref=image_ref_np_i, method=method) # Avoid potential blur when only the fully color-matched image is used
if strength != 1:
image_result = image_target_np_i + strength * (image_result - image_target_np_i)
return torch.from_numpy(image_result)
except Exception as e:
print(f"Thread {i} error: {e}")
return torch.from_numpy(image_target_np_i) # fallback
if multithread and batch_size > 1:
max_threads = min(os.cpu_count() or 1, batch_size)
with ThreadPoolExecutor(max_workers=max_threads) as executor:
out = list(executor.map(process, range(batch_size)))
else:
out = [process(i) for i in range(batch_size)]
out = torch.stack(out, dim=0).to(torch.float32)
out.clamp_(0, 1)
return (out,)
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 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 sorted(os.listdir(full_output_folder)):
# Check if the file matches the expected format
match = re.fullmatch(fr"{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": True}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "concatenate"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the image2 to image1 in the specified direction.
"""
def concatenate(self, image1, image2, direction, match_image_size, first_image_shape=None):
# Check if the batch sizes are different
batch_size1 = image1.shape[0]
batch_size2 = image2.shape[0]
if batch_size1 != batch_size2:
# Calculate the number of repetitions needed
max_batch_size = max(batch_size1, batch_size2)
repeats1 = max_batch_size - batch_size1
repeats2 = max_batch_size - batch_size2
# Repeat the last image to match the largest batch size
if repeats1 > 0:
last_image1 = image1[-1].unsqueeze(0).repeat(repeats1, 1, 1, 1)
image1 = torch.cat([image1.clone(), last_image1], dim=0)
if repeats2 > 0:
last_image2 = image2[-1].unsqueeze(0).repeat(repeats2, 1, 1, 1)
image2 = torch.cat([image2.clone(), last_image2], dim=0)
if match_image_size:
# Use first_image_shape if provided; otherwise, default to image1's shape
target_shape = first_image_shape if first_image_shape is not None else image1.shape
original_height = image2.shape[1]
original_width = image2.shape[2]
original_aspect_ratio = original_width / original_height
if direction in ['left', 'right']:
# Match the height and adjust the width to preserve aspect ratio
target_height = target_shape[1] # B, H, W, C format
target_width = int(target_height * original_aspect_ratio)
elif direction in ['up', 'down']:
# Match the width and adjust the height to preserve aspect ratio
target_width = target_shape[2] # B, H, W, C format
target_height = int(target_width / original_aspect_ratio)
# Adjust image2 to the expected format for common_upscale
image2_for_upscale = image2.movedim(-1, 1) # Move C to the second position (B, C, H, W)
# Resize image2 to match the target size while preserving aspect ratio
image2_resized = common_upscale(image2_for_upscale, target_width, target_height, "lanczos", "disabled")
# Adjust image2 back to the original format (B, H, W, C) after resizing
image2_resized = image2_resized.movedim(1, -1)
else:
image2_resized = image2
# Ensure both images have the same number of channels
channels_image1 = image1.shape[-1]
channels_image2 = image2_resized.shape[-1]
if channels_image1 != channels_image2:
if channels_image1 < channels_image2:
# Add alpha channel to image1 if image2 has it
alpha_channel = torch.ones((*image1.shape[:-1], channels_image2 - channels_image1), device=image1.device)
image1 = torch.cat((image1, alpha_channel), dim=-1)
else:
# Add alpha channel to image2 if image1 has it
alpha_channel = torch.ones((*image2_resized.shape[:-1], channels_image1 - channels_image2), device=image2_resized.device)
image2_resized = torch.cat((image2_resized, alpha_channel), dim=-1)
# Concatenate based on the specified direction
if direction == 'right':
concatenated_image = torch.cat((image1, image2_resized), dim=2) # Concatenate along width
elif direction == 'down':
concatenated_image = torch.cat((image1, image2_resized), dim=1) # Concatenate along height
elif direction == 'left':
concatenated_image = torch.cat((image2_resized, image1), dim=2) # Concatenate along width
elif direction == 'up':
concatenated_image = torch.cat((image2_resized, image1), dim=1) # Concatenate along height
return concatenated_image,
import torch # Make sure you have PyTorch installed
class ImageConcatFromBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
"num_columns": ("INT", {"default": 3, "min": 1, "max": 255, "step": 1}),
"match_image_size": ("BOOLEAN", {"default": False}),
"max_resolution": ("INT", {"default": 4096}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "concat"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates images from a batch into a grid with a specified number of columns.
"""
def concat(self, images, num_columns, match_image_size, max_resolution):
# Assuming images is a batch of images (B, H, W, C)
batch_size, height, width, channels = images.shape
num_rows = (batch_size + num_columns - 1) // num_columns # Calculate number of rows
print(f"Initial dimensions: batch_size={batch_size}, height={height}, width={width}, channels={channels}")
print(f"num_rows={num_rows}, num_columns={num_columns}")
if match_image_size:
target_shape = images[0].shape
resized_images = []
for image in images:
original_height = image.shape[0]
original_width = image.shape[1]
original_aspect_ratio = original_width / original_height
if original_aspect_ratio > 1:
target_height = target_shape[0]
target_width = int(target_height * original_aspect_ratio)
else:
target_width = target_shape[1]
target_height = int(target_width / original_aspect_ratio)
print(f"Resizing image from ({original_height}, {original_width}) to ({target_height}, {target_width})")
# Resize the image to match the target size while preserving aspect ratio
resized_image = common_upscale(image.movedim(-1, 0), target_width, target_height, "lanczos", "disabled")
resized_image = resized_image.movedim(0, -1) # Move channels back to the last dimension
resized_images.append(resized_image)
# Convert the list of resized images back to a tensor
images = torch.stack(resized_images)
height, width = target_shape[:2] # Update height and width
# Initialize an empty grid
grid_height = num_rows * height
grid_width = num_columns * width
print(f"Grid dimensions before scaling: grid_height={grid_height}, grid_width={grid_width}")
# Original scale factor calculation remains unchanged
scale_factor = min(max_resolution / grid_height, max_resolution / grid_width, 1.0)
# Apply scale factor to height and width
scaled_height = height * scale_factor
scaled_width = width * scale_factor
# Round scaled dimensions to the nearest number divisible by 8
height = max(1, int(round(scaled_height / 8) * 8))
width = max(1, int(round(scaled_width / 8) * 8))
if abs(scaled_height - height) > 4:
height = max(1, int(round((scaled_height + 4) / 8) * 8))
if abs(scaled_width - width) > 4:
width = max(1, int(round((scaled_width + 4) / 8) * 8))
# Recalculate grid dimensions with adjusted height and width
grid_height = num_rows * height
grid_width = num_columns * width
print(f"Grid dimensions after scaling: grid_height={grid_height}, grid_width={grid_width}")
print(f"Final image dimensions: height={height}, width={width}")
grid = torch.zeros((grid_height, grid_width, channels), dtype=images.dtype)
for idx, image in enumerate(images):
resized_image = torch.nn.functional.interpolate(image.unsqueeze(0).permute(0, 3, 1, 2), size=(height, width), mode="bilinear").squeeze().permute(1, 2, 0)
row = idx // num_columns
col = idx % num_columns
grid[row*height:(row+1)*height, col*width:(col+1)*width, :] = resized_image
return grid.unsqueeze(0),
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": 0,"min": 0, "max": 255, "step": 1}),
"text_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
"text_y": ("INT", {"default": 256,"min": 0, "max": 4096, "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, text_x, text_y):
out = []
# Generate the sequential numbers for each image
numbers = np.arange(start_from, start_from + batch_size)
font_path = folder_paths.get_full_path("kjnodes_fonts", font)
for number in numbers:
# Create a black image with the number as a random color text
image = Image.new("RGB", (width, height), color='black')
draw = ImageDraw.Draw(image)
# Generate a random color for the text
font_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
font = ImageFont.truetype(font_path, font_size)
# Get the size of the text and position it in the center
text = str(number)
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,)
# Convert the image to a numpy array and normalize the pixel values
image_np = np.array(image).astype(np.float32) / 255.0
image_tensor = torch.from_numpy(image_np).unsqueeze(0)
out.append(image_tensor)
out_tensor = torch.cat(out, dim=0)
return (out_tensor,)
class ImageGrabPIL:
@classmethod
def IS_CHANGED(cls):
return
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "screencap"
CATEGORY = "KJNodes/image"
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):
start_time = time.time()
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.from_numpy(np.array(screen_capture, dtype=np.float32) / 255.0).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)
elapsed_time = time.time() - start_time
print(f"screengrab took {elapsed_time} seconds.")
return (torch.cat(captures, dim=0),)
class Screencap_mss:
@classmethod
def IS_CHANGED(s, **kwargs):
return float("NaN")
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "screencap"
CATEGORY = "KJNodes/image"
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": 10000, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 10000, "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):
from mss import mss
captures = []
with mss() as sct:
bbox = {'top': y, 'left': x, 'width': width, 'height': height}
for _ in range(num_frames):
sct_img = sct.grab(bbox)
img_np = np.array(sct_img)
img_torch = torch.from_numpy(img_np[..., [2, 1, 0]]).float() / 255.0
captures.append(img_torch)
if num_frames > 1:
time.sleep(delay)
return (torch.stack(captures, 0),)
class WebcamCaptureCV2:
@classmethod
def IS_CHANGED(cls):
return
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "capture"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
Captures a frame from a webcam using CV2.
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}),
"cam_index": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
"release": ("BOOLEAN", {"default": False}),
},
}
def capture(self, x, y, cam_index, width, height, release):
# Check if the camera index has changed or the capture object doesn't exist
if not hasattr(self, "cap") or self.cap is None or self.current_cam_index != cam_index:
if hasattr(self, "cap") and self.cap is not None:
self.cap.release()
self.current_cam_index = cam_index
self.cap = cv2.VideoCapture(cam_index)
try:
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
except:
pass
if not self.cap.isOpened():
raise Exception("Could not open webcam")
ret, frame = self.cap.read()
if not ret:
raise Exception("Failed to capture image from webcam")
# Crop the frame to the specified bbox
frame = frame[y:y+height, x:x+width]
img_torch = torch.from_numpy(frame[..., [2, 1, 0]]).float() / 255.0
if release:
self.cap.release()
self.cap = None
return (img_torch.unsqueeze(0),)
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": -1, "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',
'left',
'right',
'overlay'
],
{
"default": 'up'
}),
},
"optional":{
"caption": ("STRING", {"default": "", "forceInput": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "addlabel"
CATEGORY = "KJNodes/text"
DESCRIPTION = """
Creates a new with the given text, and concatenates it to
either above or below the input image.
Note that this changes the input image's height!
Fonts are loaded from this folder:
ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
"""
def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction, caption=""):
batch_size = image.shape[0]
width = image.shape[2]
font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf") if font == "TTNorms-Black.otf" else folder_paths.get_full_path("kjnodes_fonts", font)
def process_image(input_image, caption_text):
font = ImageFont.truetype(font_path, font_size)
words = caption_text.split()
lines = []
current_line = []
current_line_width = 0
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
if current_line:
lines.append(" ".join(current_line))
if direction == 'overlay':
pil_image = Image.fromarray((input_image.cpu().numpy() * 255).astype(np.uint8))
else:
if height == -1:
# Adjust the image height automatically
margin = 8
required_height = (text_y + len(lines) * font_size) + margin # Calculate required height
pil_image = Image.new("RGB", (width, required_height), label_color)
else:
# Initialize with a minimal height
label_image = Image.new("RGB", (width, height), label_color)
pil_image = label_image
draw = ImageDraw.Draw(pil_image)
y_offset = text_y
for line in lines:
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 += font_size
processed_image = torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0)
return processed_image
if caption == "":
processed_images = [process_image(img, text) for img in image]
else:
assert len(caption) == batch_size, f"Number of captions {(len(caption))} does not match number of images"
processed_images = [process_image(img, cap) for img, cap in zip(image, caption)]
processed_batch = torch.cat(processed_images, dim=0)
# Combine images based on direction
if direction == 'down':
combined_images = torch.cat((image, processed_batch), dim=1)
elif direction == 'up':
combined_images = torch.cat((processed_batch, image), dim=1)
elif direction == 'left':
processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2)
combined_images = torch.cat((processed_batch, image), dim=2)
elif direction == 'right':
processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2)
combined_images = torch.cat((image, processed_batch), dim=2)
else:
combined_images = processed_batch
return (combined_images,)
class GetImageSizeAndCount:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE","INT", "INT", "INT",)
RETURN_NAMES = ("image", "width", "height", "count",)
FUNCTION = "getsize"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Returns width, height and batch size of the image,
and passes it through unchanged.
"""
def getsize(self, image):
width = image.shape[2]
height = image.shape[1]
count = image.shape[0]
return {"ui": {
"text": [f"{count}x{width}x{height}"]},
"result": (image, width, height, count)
}
class GetLatentSizeAndCount:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
}}
RETURN_TYPES = ("LATENT","INT", "INT", "INT", "INT", "INT")
RETURN_NAMES = ("latent", "batch_size", "channels", "frames", "width", "height")
FUNCTION = "getsize"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Returns latent tensor dimensions,
and passes the latent through unchanged.
"""
def getsize(self, latent):
if len(latent["samples"].shape) == 5:
B, C, T, H, W = latent["samples"].shape
elif len(latent["samples"].shape) == 4:
B, C, H, W = latent["samples"].shape
T = 0
else:
raise ValueError("Invalid latent shape")
return {"ui": {
"text": [f"{B}x{C}x{T}x{H}x{W}"]},
"result": (latent, B, C, T, H, W)
}
class ImageBatchRepeatInterleaving:
RETURN_TYPES = ("IMAGE", "MASK",)
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}),
},
"optional": {
"mask": ("MASK",),
}
}
def repeat(self, images, repeats, mask=None):
original_count = images.shape[0]
total_count = original_count * repeats
repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0)
if mask is not None:
mask = torch.repeat_interleave(mask, repeats=repeats, dim=0)
else:
mask = torch.zeros((total_count, images.shape[1], images.shape[2]),
device=images.device, dtype=images.dtype)
for i in range(original_count):
mask[i * repeats] = 1.0
print("mask shape", mask.shape)
return (repeated_images, mask)
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)
steps = in_img.shape[0]
pbar = 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].to(device))
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/image"
DESCRIPTION = """
Normalize the images to be in the range [-1, 1]
"""
def normalize(self,images):
images = images * 2.0 - 1.0
return (images,)
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 SplitImageChannels:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK")
RETURN_NAMES = ("red", "green", "blue", "mask")
FUNCTION = "split"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Splits image channels into images where the selected channel
is repeated for all channels, and the alpha as a mask.
"""
def split(self, image):
red = image[:, :, :, 0:1] # Red channel
green = image[:, :, :, 1:2] # Green channel
blue = image[:, :, :, 2:3] # Blue channel
alpha = image[:, :, :, 3:4] # Alpha channel
alpha = alpha.squeeze(-1)
# Repeat the selected channel for all channels
red = torch.cat([red, red, red], dim=3)
green = torch.cat([green, green, green], dim=3)
blue = torch.cat([blue, blue, blue], dim=3)
return (red, green, blue, alpha)
class MergeImageChannels:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"red": ("IMAGE",),
"green": ("IMAGE",),
"blue": ("IMAGE",),
},
"optional": {
"alpha": ("MASK", {"default": None}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "merge"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Merges channel data into an image.
"""
def merge(self, red, green, blue, alpha=None):
image = torch.stack([
red[..., 0, None], # Red channel
green[..., 1, None], # Green channel
blue[..., 2, None] # Blue channel
], dim=-1)
image = image.squeeze(-2)
if alpha is not None:
image = torch.cat([image, alpha.unsqueeze(-1)], dim=-1)
return (image,)
class ImagePadForOutpaintMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "expand_image"
CATEGORY = "image"
def expand_image(self, image, left, top, right, bottom, feathering, mask=None):
if mask is not None:
if torch.allclose(mask, torch.zeros_like(mask)):
print("Warning: The incoming mask is fully black. Handling it as None.")
mask = None
B, H, W, C = image.size()
new_image = torch.ones(
(B, H + top + bottom, W + left + right, C),
dtype=torch.float32,
) * 0.5
new_image[:, top:top + H, left:left + W, :] = image
if mask is None:
new_mask = torch.ones(
(B, H + top + bottom, W + left + right),
dtype=torch.float32,
)
t = torch.zeros(
(B, H, W),
dtype=torch.float32
)
else:
# If a mask is provided, pad it to fit the new image size
mask = F.pad(mask, (left, right, top, bottom), mode='constant', value=0)
mask = 1 - mask
t = torch.zeros_like(mask)
if feathering > 0 and feathering * 2 < H and feathering * 2 < W:
for i in range(H):
for j in range(W):
dt = i if top != 0 else H
db = H - i if bottom != 0 else H
dl = j if left != 0 else W
dr = W - j if right != 0 else W
d = min(dt, db, dl, dr)
if d >= feathering:
continue
v = (feathering - d) / feathering
if mask is None:
t[:, i, j] = v * v
else:
t[:, top + i, left + j] = v * v
if mask is None:
new_mask[:, top:top + H, left:left + W] = t
return (new_image, new_mask,)
else:
return (new_image, mask,)
class ImagePadForOutpaintTargetSize:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"target_width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"target_height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"upscale_method": (s.upscale_methods,),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "expand_image"
CATEGORY = "image"
def expand_image(self, image, target_width, target_height, feathering, upscale_method, mask=None):
B, H, W, C = image.size()
new_height = H
new_width = W
# Calculate the scaling factor while maintaining aspect ratio
scaling_factor = min(target_width / W, target_height / H)
# Check if the image needs to be downscaled
if scaling_factor < 1:
image = image.movedim(-1,1)
# Calculate the new width and height after downscaling
new_width = int(W * scaling_factor)
new_height = int(H * scaling_factor)
# Downscale the image
image_scaled = common_upscale(image, new_width, new_height, upscale_method, "disabled").movedim(1,-1)
else:
# If downscaling is not needed, use the original image dimensions
image_scaled = image
# Ensure mask dimensions match image dimensions
if mask is not None:
mask_scaled = mask.unsqueeze(0) # Add an extra dimension for batch size
mask_scaled = F.interpolate(mask_scaled, size=(new_height, new_width), mode="nearest")
mask_scaled = mask_scaled.squeeze(0) # Remove the extra dimension after interpolation
else:
mask_scaled = None
# Calculate how much padding is needed to reach the target dimensions
pad_top = max(0, (target_height - new_height) // 2)
pad_bottom = max(0, target_height - new_height - pad_top)
pad_left = max(0, (target_width - new_width) // 2)
pad_right = max(0, target_width - new_width - pad_left)
# Now call the original expand_image with the calculated padding
return ImagePadForOutpaintMasked.expand_image(self, image_scaled, pad_left, pad_top, pad_right, pad_bottom, feathering, mask_scaled)
class ImagePrepForICLora:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"reference_image": ("IMAGE",),
"output_width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"output_height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
"border_width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
},
"optional": {
"latent_image": ("IMAGE",),
"latent_mask": ("MASK",),
"reference_mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "expand_image"
CATEGORY = "image"
def expand_image(self, reference_image, output_width, output_height, border_width, latent_image=None, reference_mask=None, latent_mask=None):
if reference_mask is not None:
if torch.allclose(reference_mask, torch.zeros_like(reference_mask)):
print("Warning: The incoming mask is fully black. Handling it as None.")
reference_mask = None
image = reference_image
if latent_image is not None:
if image.shape[0] != latent_image.shape[0]:
image = image.repeat(latent_image.shape[0], 1, 1, 1)
B, H, W, C = image.size()
# Handle mask
if reference_mask is not None:
resized_mask = torch.nn.functional.interpolate(
reference_mask.unsqueeze(1),
size=(H, W),
mode='nearest'
).squeeze(1)
print(resized_mask.shape)
image = image * resized_mask.unsqueeze(-1)
# Calculate new width maintaining aspect ratio
new_width = int((W / H) * output_height)
# Resize image to new height while maintaining aspect ratio
resized_image = common_upscale(image.movedim(-1,1), new_width, output_height, "lanczos", "disabled").movedim(1,-1)
# Create padded image
if latent_image is None:
pad_image = torch.zeros((B, output_height, output_width, C), device=image.device)
else:
resized_latent_image = common_upscale(latent_image.movedim(-1,1), output_width, output_height, "lanczos", "disabled").movedim(1,-1)
pad_image = resized_latent_image
if latent_mask is not None:
resized_latent_mask = torch.nn.functional.interpolate(
latent_mask.unsqueeze(1),
size=(pad_image.shape[1], pad_image.shape[2]),
mode='nearest'
).squeeze(1)
if border_width > 0:
border = torch.zeros((B, output_height, border_width, C), device=image.device)
padded_image = torch.cat((resized_image, border, pad_image), dim=2)
if latent_mask is not None:
padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
padded_mask[:, :, (new_width + border_width):] = resized_latent_mask
else:
padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
padded_mask[:, :, :new_width + border_width] = 0
else:
padded_image = torch.cat((resized_image, pad_image), dim=2)
if latent_mask is not None:
padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
padded_mask[:, :, new_width:] = resized_latent_mask
else:
padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device)
padded_mask[:, :, :new_width] = 0
return (padded_image, padded_mask)
class ImageAndMaskPreview(SaveImage):
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
self.compress_level = 4
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"mask_color": ("STRING", {"default": "255, 255, 255"}),
"pass_through": ("BOOLEAN", {"default": False}),
},
"optional": {
"image": ("IMAGE",),
"mask": ("MASK",),
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("composite",)
FUNCTION = "execute"
CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Preview an image or a mask, when both inputs are used
composites the mask on top of the image.
with pass_through on the preview is disabled and the
composite is returned from the composite slot instead,
this allows for the preview to be passed for video combine
nodes for example.
"""
def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None):
if mask is not None and image is None:
preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
elif mask is None and image is not None:
preview = image
elif mask is not None and image is not None:
mask_adjusted = mask * mask_opacity
mask_image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3).clone()
if ',' in mask_color:
color_list = np.clip([int(channel) for channel in mask_color.split(',')], 0, 255) # RGB format
else:
mask_color = mask_color.lstrip('#')
color_list = [int(mask_color[i:i+2], 16) for i in (0, 2, 4)] # Hex format
mask_image[:, :, :, 0] = color_list[0] / 255 # Red channel
mask_image[:, :, :, 1] = color_list[1] / 255 # Green channel
mask_image[:, :, :, 2] = color_list[2] / 255 # Blue channel
destination, source = node_helpers.image_alpha_fix(image, mask_image)
destination = destination.clone().movedim(-1, 1)
preview = composite(destination, source.movedim(-1, 1), 0, 0, mask_adjusted, 1, True).movedim(1, -1)
if pass_through:
return (preview, )
return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
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 ease_out(t * 2) * 0.5
else:
return 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,
}
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": -4096, "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):
crossfade_images = []
if transition_start_index < 0:
transition_start_index = len(images_1) + transition_start_index
if transition_start_index < 0:
raise ValueError("Transition start index is out of range for images_1.")
transitioning_frames = min(transitioning_frames, len(images_1) - transition_start_index, len(images_2))
alphas = torch.linspace(start_level, end_level, transitioning_frames)
for i in range(transitioning_frames):
alpha = alphas[i]
image1 = images_1[transition_start_index + i]
image2 = images_2[i]
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)
# Append the beginning of images_1 (before the transition)
beginning_images_1 = images_1[:transition_start_index]
crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
# Append the remaining frames of images_2 (after the transition)
remaining_images_2 = images_2[transitioning_frames:]
if len(remaining_images_2) > 0:
crossfade_images = torch.cat([crossfade_images, remaining_images_2], dim=0)
return (crossfade_images, )
class CrossFadeImagesMulti:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "crossfadeimages"
CATEGORY = "KJNodes/image"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
},
"optional": {
"image_2": ("IMAGE",),
}
}
def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs):
image_1 = kwargs["image_1"]
first_image_shape = image_1.shape
first_image_device = image_1.device
height = image_1.shape[1]
width = image_1.shape[2]
easing_function = easing_functions[interpolation]
for c in range(1, inputcount):
frames = []
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
new_image_height = new_image.shape[1]
new_image_width = new_image.shape[2]
if new_image_height != height or new_image_width != width:
new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled")
new_image = new_image.movedim(1, -1) # Move channels back to the last dimension
last_frame_image_1 = image_1[-1]
first_frame_image_2 = new_image[0]
for frame in range(transitioning_frames):
t = frame / (transitioning_frames - 1)
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
frame_image = crossfade(last_frame_image_1, first_frame_image_2, alpha_tensor)
frames.append(frame_image)
frames = torch.stack(frames)
image_1 = torch.cat((image_1, frames, new_image), dim=0)
return image_1,
def transition_images(images_1, images_2, alpha, transition_type, blur_radius, reverse):
width = images_1.shape[1]
height = images_1.shape[0]
mask = torch.zeros_like(images_1, device=images_1.device)
alpha = alpha.item()
if reverse:
alpha = 1 - alpha
#transitions from matteo's essential nodes
if "horizontal slide" in transition_type:
pos = round(width * alpha)
mask[:, :pos, :] = 1.0
elif "vertical slide" in transition_type:
pos = round(height * alpha)
mask[:pos, :, :] = 1.0
elif "box" in transition_type:
box_w = round(width * alpha)
box_h = round(height * alpha)
x1 = (width - box_w) // 2
y1 = (height - box_h) // 2
x2 = x1 + box_w
y2 = y1 + box_h
mask[y1:y2, x1:x2, :] = 1.0
elif "circle" in transition_type:
radius = math.ceil(math.sqrt(pow(width, 2) + pow(height, 2)) * alpha / 2)
c_x = width // 2
c_y = height // 2
x = torch.arange(0, width, dtype=torch.float32, device="cpu")
y = torch.arange(0, height, dtype=torch.float32, device="cpu")
y, x = torch.meshgrid((y, x), indexing="ij")
circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2)
mask[circle] = 1.0
elif "horizontal door" in transition_type:
bar = math.ceil(height * alpha / 2)
if bar > 0:
mask[:bar, :, :] = 1.0
mask[-bar:,:, :] = 1.0
elif "vertical door" in transition_type:
bar = math.ceil(width * alpha / 2)
if bar > 0:
mask[:, :bar,:] = 1.0
mask[:, -bar:,:] = 1.0
elif "fade" in transition_type:
mask[:, :, :] = alpha
mask = gaussian_blur(mask, blur_radius)
return images_1 * (1 - mask) + images_2 * mask
def gaussian_blur(mask, blur_radius):
if blur_radius > 0:
kernel_size = int(blur_radius * 2) + 1
if kernel_size % 2 == 0:
kernel_size += 1 # Ensure kernel size is odd
sigma = blur_radius / 3
x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32)
x = torch.exp(-0.5 * (x / sigma) ** 2)
kernel1d = x / x.sum()
kernel2d = kernel1d[:, None] * kernel1d[None, :]
kernel2d = kernel2d.to(mask.device)
kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1])
mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension
mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1])
mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C]
return mask
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,
}
class TransitionImagesMulti:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "transition"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates transitions between images.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
"transitioning_frames": ("INT", {"default": 2,"min": 2, "max": 4096, "step": 1}),
"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
"reverse": ("BOOLEAN", {"default": False}),
"device": (["CPU", "GPU"], {"default": "CPU"}),
},
"optional": {
"image_2": ("IMAGE",),
}
}
def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs):
gpu = model_management.get_torch_device()
image_1 = kwargs["image_1"]
height = image_1.shape[1]
width = image_1.shape[2]
first_image_shape = image_1.shape
first_image_device = image_1.device
easing_function = easing_functions[interpolation]
for c in range(1, inputcount):
frames = []
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device)
new_image_height = new_image.shape[1]
new_image_width = new_image.shape[2]
if new_image_height != height or new_image_width != width:
new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled")
new_image = new_image.movedim(1, -1) # Move channels back to the last dimension
last_frame_image_1 = image_1[-1]
first_frame_image_2 = new_image[0]
if device == "GPU":
last_frame_image_1 = last_frame_image_1.to(gpu)
first_frame_image_2 = first_frame_image_2.to(gpu)
if reverse:
last_frame_image_1, first_frame_image_2 = first_frame_image_2, last_frame_image_1
for frame in range(transitioning_frames):
t = frame / (transitioning_frames - 1)
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
frame_image = transition_images(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse)
frames.append(frame_image)
frames = torch.stack(frames).cpu()
image_1 = torch.cat((image_1, frames, new_image), dim=0)
return image_1.cpu(),
class TransitionImagesInBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "transition"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates transitions between images in a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}),
"reverse": ("BOOLEAN", {"default": False}),
"device": (["CPU", "GPU"], {"default": "CPU"}),
},
}
#transitions from matteo's essential nodes
def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse):
if images.shape[0] == 1:
return images,
gpu = model_management.get_torch_device()
easing_function = easing_functions[interpolation]
images_list = []
pbar = ProgressBar(images.shape[0] - 1)
for i in range(images.shape[0] - 1):
frames = []
image_1 = images[i]
image_2 = images[i + 1]
if device == "GPU":
image_1 = image_1.to(gpu)
image_2 = image_2.to(gpu)
if reverse:
image_1, image_2 = image_2, image_1
for frame in range(transitioning_frames):
t = frame / (transitioning_frames - 1)
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device)
frame_image = transition_images(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse)
frames.append(frame_image)
pbar.update(1)
frames = torch.stack(frames).cpu()
images_list.append(frames)
images = torch.cat(images_list, dim=0)
return images.cpu(),
class ImageBatchJoinWithTransition:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "transition_batches"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Transitions between two batches of images, starting at a specified index in the first batch.
During the transition, frames from both batches are blended frame-by-frame, so the video keeps playing.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images_1": ("IMAGE",),
"images_2": ("IMAGE",),
"start_index": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],),
"transitioning_frames": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
"reverse": ("BOOLEAN", {"default": False}),
"device": (["CPU", "GPU"], {"default": "CPU"}),
},
}
def transition_batches(self, images_1, images_2, start_index, interpolation, transition_type, transitioning_frames, blur_radius, reverse, device):
if images_1.shape[0] == 0 or images_2.shape[0] == 0:
raise ValueError("Both input batches must have at least one image.")
if start_index < 0:
start_index = images_1.shape[0] + start_index
if start_index < 0 or start_index > images_1.shape[0]:
raise ValueError("start_index is out of range.")
gpu = model_management.get_torch_device()
easing_function = easing_functions[interpolation]
out_frames = []
# Add images from images_1 up to start_index
if start_index > 0:
out_frames.append(images_1[:start_index])
# Determine how many frames we can blend
max_transition = min(transitioning_frames, images_1.shape[0] - start_index, images_2.shape[0])
# Blend corresponding frames from both batches
for i in range(max_transition):
img1 = images_1[start_index + i]
img2 = images_2[i]
if device == "GPU":
img1 = img1.to(gpu)
img2 = img2.to(gpu)
if reverse:
img1, img2 = img2, img1
t = i / (max_transition - 1) if max_transition > 1 else 1.0
alpha = easing_function(t)
alpha_tensor = torch.tensor(alpha, dtype=img1.dtype, device=img1.device)
frame_image = transition_images(img1, img2, alpha_tensor, transition_type, blur_radius, reverse)
out_frames.append(frame_image.cpu().unsqueeze(0))
# Add remaining images from images_2 after transition
if images_2.shape[0] > max_transition:
out_frames.append(images_2[max_transition:])
# Concatenate all frames
out = torch.cat(out_frames, dim=0)
return (out.cpu(),)
class ShuffleImageBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "shuffle"
CATEGORY = "KJNodes/image"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
},
}
def shuffle(self, images, seed):
torch.manual_seed(seed)
B, H, W, C = images.shape
indices = torch.randperm(B)
shuffled_images = images[indices]
return shuffled_images,
class GetImageRangeFromBatch:
RETURN_TYPES = ("IMAGE", "MASK", )
FUNCTION = "imagesfrombatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Returns a range of images from a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
},
"optional": {
"images": ("IMAGE",),
"masks": ("MASK",),
}
}
def imagesfrombatch(self, start_index, num_frames, images=None, masks=None):
chosen_images = None
chosen_masks = None
# Process images if provided
if images is not None:
if start_index == -1:
start_index = max(0, len(images) - num_frames)
if start_index < 0 or start_index >= len(images):
raise ValueError("Start index is out of range")
end_index = min(start_index + num_frames, len(images))
chosen_images = images[start_index:end_index]
# Process masks if provided
if masks is not None:
if start_index == -1:
start_index = max(0, len(masks) - num_frames)
if start_index < 0 or start_index >= len(masks):
raise ValueError("Start index is out of range for masks")
end_index = min(start_index + num_frames, len(masks))
chosen_masks = masks[start_index:end_index]
return (chosen_images, chosen_masks,)
class ImageBatchExtendWithOverlap:
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", )
RETURN_NAMES = ("source_images", "start_images", "extended_images")
OUTPUT_TOOLTIPS = (
"The original source images (passthrough)",
"The input images used as the starting point for extension",
"The extended images with overlap, if no new images are provided this will be empty",
)
FUNCTION = "imagesfrombatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Helper node for video generation extension
First input source and overlap amount to get the starting frames for the extension.
Then on another copy of the node provide the newly generated frames and choose how to overlap them.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"source_images": ("IMAGE", {"tooltip": "The source images to extend"}),
"overlap": ("INT", {"default": 13,"min": 1, "max": 4096, "step": 1, "tooltip": "Number of overlapping frames between source and new images"}),
"overlap_side": (["source", "new_images"], {"default": "source", "tooltip": "Which side to overlap on"}),
"overlap_mode": (["cut", "linear_blend", "ease_in_out"], {"default": "linear_blend", "tooltip": "Method to use for overlapping frames"}),
},
"optional": {
"new_images": ("IMAGE", {"tooltip": "The new images to extend with"}),
}
}
def imagesfrombatch(self, source_images, overlap, overlap_side, overlap_mode, new_images=None):
if overlap >= len(source_images):
return source_images, source_images, source_images
if new_images is not None:
if source_images.shape[1:3] != new_images.shape[1:3]:
raise ValueError(f"Source and new images must have the same shape: {source_images.shape[1:3]} vs {new_images.shape[1:3]}")
# Determine where to place the overlap
prefix = source_images[:-overlap]
if overlap_side == "source":
blend_src = source_images[-overlap:]
blend_dst = new_images[:overlap]
elif overlap_side == "new_images":
blend_src = new_images[:overlap]
blend_dst = source_images[-overlap:]
suffix = new_images[overlap:]
if overlap_mode == "linear_blend":
blended_images = [
crossfade(blend_src[i], blend_dst[i], (i + 1) / (overlap + 1))
for i in range(overlap)
]
blended_images = torch.stack(blended_images, dim=0)
extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
elif overlap_mode == "ease_in_out":
blended_images = []
for i in range(overlap):
t = (i + 1) / (overlap + 1)
eased_t = ease_in_out(t)
blended_image = crossfade(blend_src[i], blend_dst[i], eased_t)
blended_images.append(blended_image)
blended_images = torch.stack(blended_images, dim=0)
extended_images = torch.cat((prefix, blended_images, suffix), dim=0)
elif overlap_mode == "cut":
extended_images = torch.cat((prefix, suffix), dim=0)
if overlap_side == "new_images":
extended_images = torch.cat((source_images, new_images[overlap:]), dim=0)
elif overlap_side == "source":
extended_images = torch.cat((source_images[:-overlap], new_images), dim=0)
else:
extended_images = torch.zeros((1, 64, 64, 3), device="cpu")
start_images = source_images[-overlap:]
return (source_images, start_images, extended_images)
class GetLatentRangeFromBatch:
RETURN_TYPES = ("LATENT", )
FUNCTION = "latentsfrombatch"
CATEGORY = "KJNodes/latents"
DESCRIPTION = """
Returns a range of latents from a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latents": ("LATENT",),
"start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": -1, "max": 4096, "step": 1}),
},
}
def latentsfrombatch(self, latents, start_index, num_frames):
chosen_latents = None
samples = latents["samples"]
if len(samples.shape) == 4:
B, C, H, W = samples.shape
num_latents = B
elif len(samples.shape) == 5:
B, C, T, H, W = samples.shape
num_latents = T
if start_index == -1:
start_index = max(0, num_latents - num_frames)
if start_index < 0 or start_index >= num_latents:
raise ValueError("Start index is out of range")
end_index = num_latents if num_frames == -1 else min(start_index + num_frames, num_latents)
if len(samples.shape) == 4:
chosen_latents = samples[start_index:end_index]
elif len(samples.shape) == 5:
chosen_latents = samples[:, :, start_index:end_index]
return ({"samples": chosen_latents.contiguous(),},)
class InsertLatentToIndex:
RETURN_TYPES = ("LATENT", )
FUNCTION = "insert"
CATEGORY = "KJNodes/latents"
DESCRIPTION = """
Inserts a latent at the specified index into the original latent batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"source": ("LATENT",),
"destination": ("LATENT",),
"index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}),
},
}
def insert(self, source, destination, index):
samples_destination = destination["samples"]
samples_source = source["samples"].to(samples_destination)
if len(samples_source.shape) == 4:
B, C, H, W = samples_source.shape
num_latents = B
elif len(samples_source.shape) == 5:
B, C, T, H, W = samples_source.shape
num_latents = T
if index >= num_latents or index < 0:
raise ValueError(f"Index {index} out of bounds for tensor with {num_latents} latents")
if len(samples_source.shape) == 4:
joined_latents = torch.cat([
samples_destination[:index],
samples_source,
samples_destination[index+1:]
], dim=0)
else:
joined_latents = torch.cat([
samples_destination[:, :, :index],
samples_source,
samples_destination[:, :, index+1:]
], dim=2)
return ({"samples": joined_latents,},)
class ImageBatchFilter:
RETURN_TYPES = ("IMAGE", "STRING",)
RETURN_NAMES = ("images", "removed_indices",)
FUNCTION = "filter"
CATEGORY = "KJNodes/image"
DESCRIPTION = "Removes empty images from a batch"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"empty_color": ("STRING", {"default": "0, 0, 0"}),
"empty_threshold": ("FLOAT", {"default": 0.01,"min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional": {
"replacement_image": ("IMAGE",),
}
}
def filter(self, images, empty_color, empty_threshold, replacement_image=None):
B, H, W, C = images.shape
input_images = images.clone()
empty_color_list = [int(color.strip()) for color in empty_color.split(',')]
empty_color_tensor = torch.tensor(empty_color_list, dtype=torch.float32).to(input_images.device)
color_diff = torch.abs(input_images - empty_color_tensor)
mean_diff = color_diff.mean(dim=(1, 2, 3))
empty_indices = mean_diff <= empty_threshold
empty_indices_string = ', '.join([str(i) for i in range(B) if empty_indices[i]])
if replacement_image is not None:
B_rep, H_rep, W_rep, C_rep = replacement_image.shape
replacement = replacement_image.clone()
if (H_rep != images.shape[1]) or (W_rep != images.shape[2]) or (C_rep != images.shape[3]):
replacement = common_upscale(replacement.movedim(-1, 1), W, H, "lanczos", "center").movedim(1, -1)
input_images[empty_indices] = replacement[0]
return (input_images, empty_indices_string,)
else:
non_empty_images = input_images[~empty_indices]
return (non_empty_images, empty_indices_string,)
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 InsertImagesToBatchIndexed:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "insertimagesfrombatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Inserts images at the specified indices into the original image batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"original_images": ("IMAGE",),
"images_to_insert": ("IMAGE",),
"indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
},
"optional": {
"mode": (["replace", "insert"],),
}
}
def insertimagesfrombatch(self, original_images, images_to_insert, indexes, mode="replace"):
if indexes == "":
return (original_images,)
input_images = original_images.clone()
# 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)
# Ensure the images_to_insert is a tensor
if not isinstance(images_to_insert, torch.Tensor):
images_to_insert = torch.tensor(images_to_insert)
if mode == "replace":
# Replace the images at the specified indices
for index, image in zip(indices_tensor, images_to_insert):
input_images[index] = image
else:
# Create a list to hold the new image sequence
new_images = []
insert_offset = 0
for i in range(len(input_images) + len(indices_tensor)):
if insert_offset < len(indices_tensor) and i == indices_tensor[insert_offset]:
# Use modulo to cycle through images_to_insert
new_images.append(images_to_insert[insert_offset % len(images_to_insert)])
insert_offset += 1
else:
new_images.append(input_images[i - insert_offset])
# Convert the list back to a tensor
input_images = torch.stack(new_images, dim=0)
return (input_images,)
class PadImageBatchInterleaved:
RETURN_TYPES = ("IMAGE", "MASK",)
RETURN_NAMES = ("images", "masks",)
FUNCTION = "pad"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Inserts empty frames between the images in a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"empty_frames_per_image": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"pad_frame_value": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
"add_after_last": ("BOOLEAN", {"default": False}),
},
}
def pad(self, images, empty_frames_per_image, pad_frame_value, add_after_last):
B, H, W, C = images.shape
# Handle single frame case specifically
if B == 1:
total_frames = 1 + empty_frames_per_image if add_after_last else 1
else:
# Original B images + (B-1) sets of empty frames between them
total_frames = B + (B-1) * empty_frames_per_image
# Add additional empty frames after the last image if requested
if add_after_last:
total_frames += empty_frames_per_image
# Create new tensor with zeros (empty frames)
padded_batch = torch.ones((total_frames, H, W, C),
dtype=images.dtype,
device=images.device) * pad_frame_value
# Create mask tensor (1 for original frames, 0 for empty frames)
mask = torch.zeros((total_frames, H, W),
dtype=images.dtype,
device=images.device)
# Fill in original images at their new positions
for i in range(B):
if B == 1:
# For single frame, just place it at the beginning
new_pos = 0
else:
# Each image is separated by empty_frames_per_image blank frames
new_pos = i * (empty_frames_per_image + 1)
padded_batch[new_pos] = images[i]
mask[new_pos] = 1.0 # Mark this as an original frame
return (padded_batch, mask)
class ReplaceImagesInBatch:
RETURN_TYPES = ("IMAGE", "MASK",)
FUNCTION = "replace"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Replaces the images in a batch, starting from the specified start index,
with the replacement images.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
},
"optional": {
"original_images": ("IMAGE",),
"replacement_images": ("IMAGE",),
"original_masks": ("MASK",),
"replacement_masks": ("MASK",),
}
}
def replace(self, original_images=None, replacement_images=None, start_index=1, original_masks=None, replacement_masks=None):
images = None
masks = None
if original_images is not None and replacement_images is not None:
if start_index >= len(original_images):
raise ValueError("ReplaceImagesInBatch: Start index is out of range")
end_index = start_index + len(replacement_images)
if end_index > len(original_images):
raise ValueError("ReplaceImagesInBatch: End index is out of range")
original_images_copy = original_images.clone()
if original_images_copy.shape[2] != replacement_images.shape[2] or original_images_copy.shape[3] != replacement_images.shape[3]:
replacement_images = common_upscale(replacement_images.movedim(-1, 1), original_images_copy.shape[1], original_images_copy.shape[2], "lanczos", "center").movedim(1, -1)
original_images_copy[start_index:end_index] = replacement_images
images = original_images_copy
else:
images = torch.zeros((1, 64, 64, 3))
if original_masks is not None and replacement_masks is not None:
if start_index >= len(original_masks):
raise ValueError("ReplaceImagesInBatch: Start index is out of range")
end_index = start_index + len(replacement_masks)
if end_index > len(original_masks):
raise ValueError("ReplaceImagesInBatch: End index is out of range")
original_masks_copy = original_masks.clone()
if original_masks_copy.shape[1] != replacement_masks.shape[1] or original_masks_copy.shape[2] != replacement_masks.shape[2]:
replacement_masks = common_upscale(replacement_masks.unsqueeze(1), original_masks_copy.shape[1], original_masks_copy.shape[2], "nearest-exact", "center").squeeze(0)
original_masks_copy[start_index:end_index] = replacement_masks
masks = original_masks_copy
else:
masks = torch.zeros((1, 64, 64))
return (images, masks)
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 ImageBatchMulti:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
},
"optional": {
"image_2": ("IMAGE", ),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "combine"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates an image batch from multiple images.
You can set how many inputs the node has,
with the **inputcount** and clicking update.
"""
def combine(self, inputcount, **kwargs):
from nodes import ImageBatch
image_batch_node = ImageBatch()
image = kwargs["image_1"].cpu()
first_image_shape = image.shape
for c in range(1, inputcount):
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).cpu()
image, = image_batch_node.batch(image, new_image)
return (image,)
class ImageTensorList:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "append"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates an image list from the input images.
"""
def append(self, image1, image2):
image_list = []
if isinstance(image1, torch.Tensor) and isinstance(image2, torch.Tensor):
image_list = [image1, image2]
elif isinstance(image1, list) and isinstance(image2, torch.Tensor):
image_list = image1 + [image2]
elif isinstance(image1, torch.Tensor) and isinstance(image2, list):
image_list = [image1] + image2
elif isinstance(image1, list) and isinstance(image2, list):
image_list = image1 + image2
return image_list,
class ImageAddMulti:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
"image_2": ("IMAGE", ),
"blending": (
[ 'add',
'subtract',
'multiply',
'difference',
],
{
"default": 'add'
}),
"blend_amount": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "add"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Add blends multiple images together.
You can set how many inputs the node has,
with the **inputcount** and clicking update.
"""
def add(self, inputcount, blending, blend_amount, **kwargs):
image = kwargs["image_1"]
for c in range(1, inputcount):
new_image = kwargs[f"image_{c + 1}"]
if blending == "add":
image = torch.add(image * blend_amount, new_image * blend_amount)
elif blending == "subtract":
image = torch.sub(image * blend_amount, new_image * blend_amount)
elif blending == "multiply":
image = torch.mul(image * blend_amount, new_image * blend_amount)
elif blending == "difference":
image = torch.sub(image, new_image)
return (image,)
class ImageConcatMulti:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
"direction": (
[ 'right',
'down',
'left',
'up',
],
{
"default": 'right'
}),
"match_image_size": ("BOOLEAN", {"default": False}),
},
"optional": {
"image_2": ("IMAGE", ),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "combine"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates an image from multiple images.
You can set how many inputs the node has,
with the **inputcount** and clicking update.
"""
def combine(self, inputcount, direction, match_image_size, **kwargs):
image = kwargs["image_1"]
first_image_shape = None
if first_image_shape is None:
first_image_shape = image.shape
for c in range(1, inputcount):
new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape))
image, = ImageConcanate.concatenate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape)
first_image_shape = None
return (image,)
class PreviewAnimation:
def __init__(self):
self.output_dir = folder_paths.get_temp_directory()
self.type = "temp"
self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
self.compress_level = 1
methods = {"default": 4, "fastest": 0, "slowest": 6}
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"fps": ("FLOAT", {"default": 8.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
},
"optional": {
"images": ("IMAGE", ),
"masks": ("MASK", ),
},
}
RETURN_TYPES = ()
FUNCTION = "preview"
OUTPUT_NODE = True
CATEGORY = "KJNodes/image"
def preview(self, fps, images=None, masks=None):
filename_prefix = "AnimPreview"
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
results = list()
pil_images = []
if images is not None and masks is not None:
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)
for mask in masks:
if pil_images:
mask_np = mask.cpu().numpy()
mask_np = np.clip(mask_np * 255, 0, 255).astype(np.uint8) # Convert to values between 0 and 255
mask_img = Image.fromarray(mask_np, mode='L')
img = pil_images.pop(0) # Remove and get the first image
img = img.convert("RGBA") # Convert base image to RGBA
# Create a new RGBA image based on the grayscale mask
rgba_mask_img = Image.new("RGBA", img.size, (255, 255, 255, 255))
rgba_mask_img.putalpha(mask_img) # Use the mask image as the alpha channel
# Composite the RGBA mask onto the base image
composited_img = Image.alpha_composite(img, rgba_mask_img)
pil_images.append(composited_img) # Add the composited image back
elif images is not None and masks is None:
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)
elif masks is not None and images is None:
for mask in masks:
mask_np = 255. * mask.cpu().numpy()
mask_img = Image.fromarray(np.clip(mask_np, 0, 255).astype(np.uint8))
pil_images.append(mask_img)
else:
print("PreviewAnimation: No images or masks provided")
return { "ui": { "images": results, "animated": (None,), "text": "empty" }}
num_frames = len(pil_images)
c = len(pil_images)
for i in range(0, c, num_frames):
file = f"{filename}_{counter:05}_.webp"
pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], lossless=False, quality=50, method=0)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
counter += 1
animated = num_frames != 1
return { "ui": { "images": results, "animated": (animated,), "text": [f"{num_frames}x{pil_images[0].size[0]}x{pil_images[0].size[1]}"] } }
class ImageResizeKJ:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"upscale_method": (s.upscale_methods,),
"keep_proportion": ("BOOLEAN", { "default": False }),
"divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
},
"optional" : {
#"width_input": ("INT", { "forceInput": True}),
#"height_input": ("INT", { "forceInput": True}),
"get_image_size": ("IMAGE",),
"crop": (["disabled","center", 0], { "tooltip": "0 will do the default center crop, this is a workaround for the widget order changing with the new frontend, as in old workflows the value of this widget becomes 0 automatically" }),
}
}
RETURN_TYPES = ("IMAGE", "INT", "INT",)
RETURN_NAMES = ("IMAGE", "width", "height",)
FUNCTION = "resize"
CATEGORY = "KJNodes/image"
DEPRECATED = True
DESCRIPTION = """
DEPRECATED!
Due to ComfyUI frontend changes, this node should no longer be used, please check the
v2 of the node. This node is only kept to not completely break older workflows.
"""
def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by,
width_input=None, height_input=None, get_image_size=None, crop="disabled"):
B, H, W, C = image.shape
if width_input:
width = width_input
if height_input:
height = height_input
if get_image_size is not None:
_, height, width, _ = get_image_size.shape
if keep_proportion and get_image_size is None:
# If one of the dimensions is zero, calculate it to maintain the aspect ratio
if width == 0 and height != 0:
ratio = height / H
width = round(W * ratio)
elif height == 0 and width != 0:
ratio = width / W
height = round(H * ratio)
elif width != 0 and height != 0:
# Scale based on which dimension is smaller in proportion to the desired dimensions
ratio = min(width / W, height / H)
width = round(W * ratio)
height = round(H * ratio)
else:
if width == 0:
width = W
if height == 0:
height = H
if divisible_by > 1 and get_image_size is None:
width = width - (width % divisible_by)
height = height - (height % divisible_by)
if crop == 0: #workaround for old workflows
crop = "center"
image = image.movedim(-1,1)
image = common_upscale(image, width, height, upscale_method, crop)
image = image.movedim(1,-1)
return(image, image.shape[2], image.shape[1],)
class ImageResizeKJv2:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"upscale_method": (s.upscale_methods,),
"keep_proportion": (["stretch", "resize", "pad", "pad_edge", "pad_edge_pixel", "crop", "pillarbox_blur", "total_pixels"], { "default": False }),
"pad_color": ("STRING", { "default": "0, 0, 0", "tooltip": "Color to use for padding."}),
"crop_position": (["center", "top", "bottom", "left", "right"], { "default": "center" }),
"divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
},
"optional" : {
"mask": ("MASK",),
"device": (["cpu", "gpu"],),
#"per_batch": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, "tooltip": "Process images in sub-batches to reduce memory usage. 0 disables sub-batching."}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK",)
RETURN_NAMES = ("IMAGE", "width", "height", "mask",)
FUNCTION = "resize"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Resizes the image to the specified width and height.
Size can be retrieved from the input.
Keep proportions keeps the aspect ratio of the image, by
highest dimension.
"""
def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, unique_id, device="cpu", mask=None, per_batch=64):
B, H, W, C = image.shape
if device == "gpu":
if upscale_method == "lanczos":
raise Exception("Lanczos is not supported on the GPU")
device = model_management.get_torch_device()
else:
device = torch.device("cpu")
pillarbox_blur = keep_proportion == "pillarbox_blur"
# Initialize padding variables
pad_left = pad_right = pad_top = pad_bottom = 0
if keep_proportion in ["resize", "total_pixels"] or keep_proportion.startswith("pad") or pillarbox_blur:
if keep_proportion == "total_pixels":
total_pixels = width * height
aspect_ratio = W / H
new_height = int(math.sqrt(total_pixels / aspect_ratio))
new_width = int(math.sqrt(total_pixels * aspect_ratio))
# If one of the dimensions is zero, calculate it to maintain the aspect ratio
elif width == 0 and height == 0:
new_width = W
new_height = H
elif width == 0 and height != 0:
ratio = height / H
new_width = round(W * ratio)
new_height = height
elif height == 0 and width != 0:
ratio = width / W
new_width = width
new_height = round(H * ratio)
elif width != 0 and height != 0:
ratio = min(width / W, height / H)
new_width = round(W * ratio)
new_height = round(H * ratio)
else:
new_width = width
new_height = height
if keep_proportion.startswith("pad") or pillarbox_blur:
# Calculate padding based on position
if crop_position == "center":
pad_left = (width - new_width) // 2
pad_right = width - new_width - pad_left
pad_top = (height - new_height) // 2
pad_bottom = height - new_height - pad_top
elif crop_position == "top":
pad_left = (width - new_width) // 2
pad_right = width - new_width - pad_left
pad_top = 0
pad_bottom = height - new_height
elif crop_position == "bottom":
pad_left = (width - new_width) // 2
pad_right = width - new_width - pad_left
pad_top = height - new_height
pad_bottom = 0
elif crop_position == "left":
pad_left = 0
pad_right = width - new_width
pad_top = (height - new_height) // 2
pad_bottom = height - new_height - pad_top
elif crop_position == "right":
pad_left = width - new_width
pad_right = 0
pad_top = (height - new_height) // 2
pad_bottom = height - new_height - pad_top
width = new_width
height = new_height
else:
if width == 0:
width = W
if height == 0:
height = H
if divisible_by > 1:
width = width - (width % divisible_by)
height = height - (height % divisible_by)
# Preflight estimate (log-only when batching is active)
if per_batch != 0 and B > per_batch:
try:
bytes_per_elem = image.element_size() # typically 4 for float32
est_total_bytes = B * height * width * C * bytes_per_elem
est_mb = est_total_bytes / (1024 * 1024)
msg = f"<tr><td>Resize v2</td><td>estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}</td></tr>"
if unique_id and PromptServer is not None:
try:
PromptServer.instance.send_progress_text(msg, unique_id)
except:
pass
else:
print(f"[ImageResizeKJv2] estimated output ~{est_mb:.2f} MB; batching {per_batch}/{B}")
except:
pass
def _process_subbatch(in_image, in_mask, pad_left, pad_right, pad_top, pad_bottom):
# Avoid unnecessary clones; only move if needed
out_image = in_image if in_image.device == device else in_image.to(device)
out_mask = None if in_mask is None else (in_mask if in_mask.device == device else in_mask.to(device))
# Crop logic
if keep_proportion == "crop":
old_height = out_image.shape[-3]
old_width = out_image.shape[-2]
old_aspect = old_width / old_height
new_aspect = width / height
if old_aspect > new_aspect:
crop_w = round(old_height * new_aspect)
crop_h = old_height
else:
crop_w = old_width
crop_h = round(old_width / new_aspect)
if crop_position == "center":
x = (old_width - crop_w) // 2
y = (old_height - crop_h) // 2
elif crop_position == "top":
x = (old_width - crop_w) // 2
y = 0
elif crop_position == "bottom":
x = (old_width - crop_w) // 2
y = old_height - crop_h
elif crop_position == "left":
x = 0
y = (old_height - crop_h) // 2
elif crop_position == "right":
x = old_width - crop_w
y = (old_height - crop_h) // 2
out_image = out_image.narrow(-2, x, crop_w).narrow(-3, y, crop_h)
if out_mask is not None:
out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h)
out_image = common_upscale(out_image.movedim(-1,1), width, height, upscale_method, crop="disabled").movedim(1,-1)
if out_mask is not None:
if upscale_method == "lanczos":
out_mask = common_upscale(out_mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1,-1)[:, :, :, 0]
else:
out_mask = common_upscale(out_mask.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1)
# Pad logic
if (keep_proportion.startswith("pad") or pillarbox_blur) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):
padded_width = width + pad_left + pad_right
padded_height = height + pad_top + pad_bottom
if divisible_by > 1:
width_remainder = padded_width % divisible_by
height_remainder = padded_height % divisible_by
if width_remainder > 0:
extra_width = divisible_by - width_remainder
pad_right += extra_width
if height_remainder > 0:
extra_height = divisible_by - height_remainder
pad_bottom += extra_height
pad_mode = (
"pillarbox_blur" if pillarbox_blur else
"edge" if keep_proportion == "pad_edge" else
"edge_pixel" if keep_proportion == "pad_edge_pixel" else
"color"
)
out_image, out_mask = ImagePadKJ.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, pad_mode, mask=out_mask)
return out_image, out_mask
# If batching disabled (per_batch==0) or batch fits, process whole batch
if per_batch == 0 or B <= per_batch:
out_image, out_mask = _process_subbatch(image, mask, pad_left, pad_right, pad_top, pad_bottom)
else:
chunks = []
mask_chunks = [] if mask is not None else None
total_batches = (B + per_batch - 1) // per_batch
current_batch = 0
for start_idx in range(0, B, per_batch):
current_batch += 1
end_idx = min(start_idx + per_batch, B)
sub_img = image[start_idx:end_idx]
sub_mask = mask[start_idx:end_idx] if mask is not None else None
sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask, pad_left, pad_right, pad_top, pad_bottom)
chunks.append(sub_out_img.cpu())
if mask is not None:
mask_chunks.append(sub_out_mask.cpu() if sub_out_mask is not None else None)
# Per-batch progress update
if unique_id and PromptServer is not None:
try:
PromptServer.instance.send_progress_text(
f"<tr><td>Resize v2</td><td>batch {current_batch}/{total_batches} · images {end_idx}/{B}</td></tr>",
unique_id
)
except:
pass
else:
try:
print(f"[ImageResizeKJv2] batch {current_batch}/{total_batches} · images {end_idx}/{B}")
except:
pass
out_image = torch.cat(chunks, dim=0)
if mask is not None and any(m is not None for m in mask_chunks):
out_mask = torch.cat([m for m in mask_chunks if m is not None], dim=0)
else:
out_mask = None
# Progress UI
if unique_id and PromptServer is not None:
try:
num_elements = out_image.numel()
element_size = out_image.element_size()
memory_size_mb = (num_elements * element_size) / (1024 * 1024)
PromptServer.instance.send_progress_text(
f"<tr><td>Output: </td><td><b>{out_image.shape[0]}</b> x <b>{out_image.shape[2]}</b> x <b>{out_image.shape[1]} | {memory_size_mb:.2f}MB</b></td></tr>",
unique_id
)
except:
pass
return (out_image.cpu(), out_image.shape[2], out_image.shape[1], out_mask.cpu() if out_mask is not None else torch.zeros(64,64, device=torch.device("cpu"), dtype=torch.float32))
import pathlib
class LoadAndResizeImage:
_color_channels = ["alpha", "red", "green", "blue"]
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f.name for f in pathlib.Path(input_dir).iterdir() if f.is_file()]
return {"required":
{
"image": (sorted(files), {"image_upload": True}),
"resize": ("BOOLEAN", { "default": False }),
"width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"repeat": ("INT", { "default": 1, "min": 1, "max": 4096, "step": 1, }),
"keep_proportion": ("BOOLEAN", { "default": False }),
"divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
"mask_channel": (s._color_channels, {"tooltip": "Channel to use for the mask output"}),
"background_color": ("STRING", { "default": "", "tooltip": "Fills the alpha channel with the specified color."}),
},
}
CATEGORY = "KJNodes/image"
RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING",)
RETURN_NAMES = ("image", "mask", "width", "height","image_path",)
FUNCTION = "load_image"
def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel, background_color):
from PIL import ImageColor, Image, ImageOps, ImageSequence
import numpy as np
import torch
image_path = folder_paths.get_annotated_filepath(image)
import node_helpers
img = node_helpers.pillow(Image.open, image_path)
# Process the background_color
if background_color:
try:
# Try to parse as RGB tuple
bg_color_rgba = tuple(int(x.strip()) for x in background_color.split(','))
except ValueError:
# If parsing fails, it might be a hex color or named color
if background_color.startswith('#') or background_color.lower() in ImageColor.colormap:
bg_color_rgba = ImageColor.getrgb(background_color)
else:
raise ValueError(f"Invalid background color: {background_color}")
bg_color_rgba += (255,) # Add alpha channel
else:
bg_color_rgba = None # No background color specified
output_images = []
output_masks = []
w, h = None, None
excluded_formats = ['MPO']
W, H = img.size
if resize:
if keep_proportion:
ratio = min(width / W, height / H)
width = round(W * ratio)
height = round(H * ratio)
else:
if width == 0:
width = W
if height == 0:
height = H
if divisible_by > 1:
width = width - (width % divisible_by)
height = height - (height % divisible_by)
else:
width, height = W, H
for frame in ImageSequence.Iterator(img):
frame = node_helpers.pillow(ImageOps.exif_transpose, frame)
if frame.mode == 'I':
frame = frame.point(lambda i: i * (1 / 255))
if frame.mode == 'P':
frame = frame.convert("RGBA")
elif 'A' in frame.getbands():
frame = frame.convert("RGBA")
# Extract alpha channel if it exists
if 'A' in frame.getbands() and bg_color_rgba:
alpha_mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0
alpha_mask = 1. - torch.from_numpy(alpha_mask)
bg_image = Image.new("RGBA", frame.size, bg_color_rgba)
# Composite the frame onto the background
frame = Image.alpha_composite(bg_image, frame)
else:
alpha_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
image = frame.convert("RGB")
if len(output_images) == 0:
w = image.size[0]
h = image.size[1]
if image.size[0] != w or image.size[1] != h:
continue
if resize:
image = image.resize((width, height), Image.Resampling.BILINEAR)
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
c = mask_channel[0].upper()
if c in frame.getbands():
if resize:
frame = frame.resize((width, height), Image.Resampling.BILINEAR)
mask = np.array(frame.getchannel(c)).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
if c == 'A' and bg_color_rgba:
mask = alpha_mask
elif c == 'A':
mask = 1. - mask
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1 and img.format not in excluded_formats:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
if repeat > 1:
output_image = output_image.repeat(repeat, 1, 1, 1)
output_mask = output_mask.repeat(repeat, 1, 1)
return (output_image, output_mask, width, height, image_path)
# @classmethod
# def IS_CHANGED(s, image, **kwargs):
# image_path = folder_paths.get_annotated_filepath(image)
# m = hashlib.sha256()
# with open(image_path, 'rb') as f:
# m.update(f.read())
# return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
import hashlib
class LoadImagesFromFolderKJ:
# Dictionary to store folder hashes
folder_hashes = {}
@classmethod
def IS_CHANGED(cls, folder, **kwargs):
if folder and not os.path.isabs(folder) and args.base_directory:
folder = os.path.join(args.base_directory, folder)
if not folder or not os.path.isdir(folder):
return float("NaN")
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga']
include_subfolders = kwargs.get('include_subfolders', False)
file_data = []
if include_subfolders:
for root, _, files in os.walk(folder):
for file in files:
if any(file.lower().endswith(ext) for ext in valid_extensions):
path = os.path.join(root, file)
try:
mtime = os.path.getmtime(path)
file_data.append((path, mtime))
except OSError:
pass
else:
for file in sorted(os.listdir(folder)):
if any(file.lower().endswith(ext) for ext in valid_extensions):
path = os.path.join(folder, file)
try:
mtime = os.path.getmtime(path)
file_data.append((path, mtime))
except OSError:
pass
file_data.sort()
combined_hash = hashlib.md5()
combined_hash.update(folder.encode('utf-8'))
combined_hash.update(str(len(file_data)).encode('utf-8'))
for path, mtime in file_data:
combined_hash.update(f"{path}:{mtime}".encode('utf-8'))
current_hash = combined_hash.hexdigest()
old_hash = cls.folder_hashes.get(folder)
cls.folder_hashes[folder] = current_hash
if old_hash == current_hash:
return old_hash
return current_hash
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"folder": ("STRING", {"default": ""}),
"width": ("INT", {"default": 1024, "min": -1, "step": 1}),
"height": ("INT", {"default": 1024, "min": -1, "step": 1}),
"keep_aspect_ratio": (["crop", "pad", "stretch",],),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
"include_subfolders": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",)
RETURN_NAMES = ("image", "mask", "count", "image_path",)
FUNCTION = "load_images"
CATEGORY = "KJNodes/image"
DESCRIPTION = """Loads images from a folder into a batch, images are resized and loaded into a batch."""
def load_images(self, folder, width, height, image_load_cap, start_index, keep_aspect_ratio, include_subfolders=False):
if folder and not os.path.isabs(folder) and args.base_directory:
folder = os.path.join(args.base_directory, folder)
if not folder or not os.path.isdir(folder):
raise FileNotFoundError(f"Folder '{folder}' cannot be found.")
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga']
image_paths = []
if include_subfolders:
for root, _, files in os.walk(folder):
for file in files:
if any(file.lower().endswith(ext) for ext in valid_extensions):
image_paths.append(os.path.join(root, file))
else:
for file in sorted(os.listdir(folder)):
if any(file.lower().endswith(ext) for ext in valid_extensions):
image_paths.append(os.path.join(folder, file))
dir_files = sorted(image_paths)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{folder}'.")
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
image_path_list = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
pbar = ProgressBar(len(dir_files))
for image_path in dir_files:
if os.path.isdir(image_path):
continue
if limit_images and image_count >= image_load_cap:
break
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
# Resize image to maximum dimensions
if width == -1 and height == -1:
width = i.size[0]
height = i.size[1]
if i.size != (width, height):
i = self.resize_with_aspect_ratio(i, width, height, keep_aspect_ratio)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
if mask.shape != (height, width):
mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0),
size=(height, width),
mode='bilinear',
align_corners=False).squeeze()
else:
mask = torch.zeros((height, width), dtype=torch.float32, device="cpu")
images.append(image)
masks.append(mask)
image_path_list.append(image_path)
image_count += 1
pbar.update(1)
if len(images) == 1:
return (images[0], masks[0], 1, image_path_list)
elif len(images) > 1:
image1 = images[0]
mask1 = masks[0].unsqueeze(0)
for image2 in images[1:]:
image1 = torch.cat((image1, image2), dim=0)
for mask2 in masks[1:]:
mask1 = torch.cat((mask1, mask2.unsqueeze(0)), dim=0)
return (image1, mask1, len(images), image_path_list)
def resize_with_aspect_ratio(self, img, width, height, mode):
if mode == "stretch":
return img.resize((width, height), Image.Resampling.LANCZOS)
img_width, img_height = img.size
aspect_ratio = img_width / img_height
target_ratio = width / height
if mode == "crop":
# Calculate dimensions for center crop
if aspect_ratio > target_ratio:
# Image is wider - crop width
new_width = int(height * aspect_ratio)
img = img.resize((new_width, height), Image.Resampling.LANCZOS)
left = (new_width - width) // 2
return img.crop((left, 0, left + width, height))
else:
# Image is taller - crop height
new_height = int(width / aspect_ratio)
img = img.resize((width, new_height), Image.Resampling.LANCZOS)
top = (new_height - height) // 2
return img.crop((0, top, width, top + height))
elif mode == "pad":
pad_color = self.get_edge_color(img)
# Calculate dimensions for padding
if aspect_ratio > target_ratio:
# Image is wider - pad height
new_height = int(width / aspect_ratio)
img = img.resize((width, new_height), Image.Resampling.LANCZOS)
padding = (height - new_height) // 2
padded = Image.new('RGBA', (width, height), pad_color)
padded.paste(img, (0, padding))
return padded
else:
# Image is taller - pad width
new_width = int(height * aspect_ratio)
img = img.resize((new_width, height), Image.Resampling.LANCZOS)
padding = (width - new_width) // 2
padded = Image.new('RGBA', (width, height), pad_color)
padded.paste(img, (padding, 0))
return padded
def get_edge_color(self, img):
from PIL import ImageStat
"""Sample edges and return dominant color"""
width, height = img.size
img = img.convert('RGBA')
# Create 1-pixel high/wide images from edges
top = img.crop((0, 0, width, 1))
bottom = img.crop((0, height-1, width, height))
left = img.crop((0, 0, 1, height))
right = img.crop((width-1, 0, width, height))
# Combine edges into single image
edges = Image.new('RGBA', (width*2 + height*2, 1))
edges.paste(top, (0, 0))
edges.paste(bottom, (width, 0))
edges.paste(left.resize((height, 1)), (width*2, 0))
edges.paste(right.resize((height, 1)), (width*2 + height, 0))
# Get median color
stat = ImageStat.Stat(edges)
median = tuple(map(int, stat.median))
return median
class ImageGridtoBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", ),
"columns": ("INT", {"default": 3, "min": 1, "max": 8, "tooltip": "The number of columns in the grid."}),
"rows": ("INT", {"default": 0, "min": 1, "max": 8, "tooltip": "The number of rows in the grid. Set to 0 for automatic calculation."}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "decompose"
CATEGORY = "KJNodes/image"
DESCRIPTION = "Converts a grid of images to a batch of images."
def decompose(self, image, columns, rows):
B, H, W, C = image.shape
print("input size: ", image.shape)
# Calculate cell width, rounding down
cell_width = W // columns
if rows == 0:
# If rows is 0, calculate number of full rows
rows = H // cell_height
else:
# If rows is specified, adjust cell_height
cell_height = H // rows
# Crop the image to fit full cells
image = image[:, :rows*cell_height, :columns*cell_width, :]
# Reshape and permute the image to get the grid
image = image.view(B, rows, cell_height, columns, cell_width, C)
image = image.permute(0, 1, 3, 2, 4, 5).contiguous()
image = image.view(B, rows * columns, cell_height, cell_width, C)
# Reshape to the final batch tensor
img_tensor = image.view(-1, cell_height, cell_width, C)
return (img_tensor,)
class SaveImageKJ:
def __init__(self):
self.type = "output"
self.prefix_append = ""
self.compress_level = 4
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE", {"tooltip": "The images to save."}),
"filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
"output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}),
},
"optional": {
"caption_file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}),
"caption": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}),
},
"hidden": {
"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("filename",)
FUNCTION = "save_images"
OUTPUT_NODE = True
CATEGORY = "KJNodes/image"
DESCRIPTION = "Saves the input images to your ComfyUI output directory."
def save_images(self, images, output_folder, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, caption=None, caption_file_extension=".txt"):
filename_prefix += self.prefix_append
if os.path.isabs(output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder, exist_ok=True)
full_output_folder = output_folder
_, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_folder, images[0].shape[1], images[0].shape[0])
else:
self.output_dir = folder_paths.get_output_directory()
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()
for (batch_number, image) in enumerate(images):
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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]))
filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
base_file_name = f"{filename_with_batch_num}_{counter:05}_"
file = f"{base_file_name}.png"
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
if caption is not None:
txt_file = base_file_name + caption_file_extension
file_path = os.path.join(full_output_folder, txt_file)
with open(file_path, 'w') as f:
f.write(caption)
counter += 1
return file,
class SaveStringKJ:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
self.compress_level = 4
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"string": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}),
"filename_prefix": ("STRING", {"default": "text", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
"output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}),
},
"optional": {
"file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}),
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("filename",)
FUNCTION = "save_string"
OUTPUT_NODE = True
CATEGORY = "KJNodes/misc"
DESCRIPTION = "Saves the input string to your ComfyUI output directory."
def save_string(self, string, output_folder, filename_prefix="text", file_extension=".txt"):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
if output_folder and not os.path.isabs(output_folder) and args.base_directory:
output_folder = os.path.join(args.base_directory, output_folder)
if output_folder != "output":
if not os.path.exists(output_folder):
os.makedirs(output_folder, exist_ok=True)
full_output_folder = output_folder
base_file_name = f"{filename_prefix}_{counter:05}_"
results = list()
txt_file = base_file_name + file_extension
file_path = os.path.join(full_output_folder, txt_file)
with open(file_path, 'w') as f:
f.write(string)
return results,
to_pil_image = T.ToPILImage()
class FastPreview:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE", ),
"format": (["JPEG", "PNG", "WEBP"], {"default": "JPEG"}),
"quality" : ("INT", {"default": 75, "min": 1, "max": 100, "step": 1}),
},
}
RETURN_TYPES = ()
FUNCTION = "preview"
CATEGORY = "KJNodes/experimental"
OUTPUT_NODE = True
DESCRIPTION = "Experimental node for faster image previews by displaying through base64 it without saving to disk."
def preview(self, image, format, quality):
pil_image = to_pil_image(image[0].permute(2, 0, 1))
with io.BytesIO() as buffered:
pil_image.save(buffered, format=format, quality=quality)
img_bytes = buffered.getvalue()
img_base64 = base64.b64encode(img_bytes).decode('utf-8')
return {
"ui": {"bg_image": [img_base64]},
"result": ()
}
class ImageCropByMaskAndResize:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
"mask": ("MASK", ),
"base_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"padding": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"min_crop_resolution": ("INT", { "default": 128, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"max_crop_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
},
}
RETURN_TYPES = ("IMAGE", "MASK", "BBOX", )
RETURN_NAMES = ("images", "masks", "bbox",)
FUNCTION = "crop"
CATEGORY = "KJNodes/image"
def crop_by_mask(self, mask, padding=0, min_crop_resolution=None, max_crop_resolution=None):
iy, ix = (mask == 1).nonzero(as_tuple=True)
h0, w0 = mask.shape
if iy.numel() == 0:
x_c = w0 / 2.0
y_c = h0 / 2.0
width = 0
height = 0
else:
x_min = ix.min().item()
x_max = ix.max().item()
y_min = iy.min().item()
y_max = iy.max().item()
width = x_max - x_min
height = y_max - y_min
if width > w0 or height > h0:
raise Exception("Masked area out of bounds")
x_c = (x_min + x_max) / 2.0
y_c = (y_min + y_max) / 2.0
if min_crop_resolution:
width = max(width, min_crop_resolution)
height = max(height, min_crop_resolution)
if max_crop_resolution:
width = min(width, max_crop_resolution)
height = min(height, max_crop_resolution)
if w0 <= width:
x0 = 0
w = w0
else:
x0 = max(0, x_c - width / 2 - padding)
w = width + 2 * padding
if x0 + w > w0:
x0 = w0 - w
if h0 <= height:
y0 = 0
h = h0
else:
y0 = max(0, y_c - height / 2 - padding)
h = height + 2 * padding
if y0 + h > h0:
y0 = h0 - h
return (int(x0), int(y0), int(w), int(h))
def crop(self, image, mask, base_resolution, padding=0, min_crop_resolution=128, max_crop_resolution=512):
mask = mask.round()
image_list = []
mask_list = []
bbox_list = []
# First, collect all bounding boxes
bbox_params = []
aspect_ratios = []
for i in range(image.shape[0]):
x0, y0, w, h = self.crop_by_mask(mask[i], padding, min_crop_resolution, max_crop_resolution)
bbox_params.append((x0, y0, w, h))
aspect_ratios.append(w / h)
# Find maximum width and height
max_w = max([w for x0, y0, w, h in bbox_params])
max_h = max([h for x0, y0, w, h in bbox_params])
max_aspect_ratio = max(aspect_ratios)
# Ensure dimensions are divisible by 16
max_w = (max_w + 15) // 16 * 16
max_h = (max_h + 15) // 16 * 16
# Calculate common target dimensions
if max_aspect_ratio > 1:
target_width = base_resolution
target_height = int(base_resolution / max_aspect_ratio)
else:
target_height = base_resolution
target_width = int(base_resolution * max_aspect_ratio)
for i in range(image.shape[0]):
x0, y0, w, h = bbox_params[i]
# Adjust cropping to use maximum width and height
x_center = x0 + w / 2
y_center = y0 + h / 2
x0_new = int(max(0, x_center - max_w / 2))
y0_new = int(max(0, y_center - max_h / 2))
x1_new = int(min(x0_new + max_w, image.shape[2]))
y1_new = int(min(y0_new + max_h, image.shape[1]))
x0_new = x1_new - max_w
y0_new = y1_new - max_h
cropped_image = image[i][y0_new:y1_new, x0_new:x1_new, :]
cropped_mask = mask[i][y0_new:y1_new, x0_new:x1_new]
# Ensure dimensions are divisible by 16
target_width = (target_width + 15) // 16 * 16
target_height = (target_height + 15) // 16 * 16
cropped_image = cropped_image.unsqueeze(0).movedim(-1, 1) # Move C to the second position (B, C, H, W)
cropped_image = common_upscale(cropped_image, target_width, target_height, "lanczos", "disabled")
cropped_image = cropped_image.movedim(1, -1).squeeze(0)
cropped_mask = cropped_mask.unsqueeze(0).unsqueeze(0)
cropped_mask = common_upscale(cropped_mask, target_width, target_height, 'bilinear', "disabled")
cropped_mask = cropped_mask.squeeze(0).squeeze(0)
image_list.append(cropped_image)
mask_list.append(cropped_mask)
bbox_list.append((x0_new, y0_new, x1_new, y1_new))
return (torch.stack(image_list), torch.stack(mask_list), bbox_list)
class ImageCropByMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE", ),
"mask": ("MASK", ),
},
}
RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("image", )
FUNCTION = "crop"
CATEGORY = "KJNodes/image"
DESCRIPTION = "Crops the input images based on the provided mask."
def crop(self, image, mask):
B, H, W, C = image.shape
mask = mask.round()
# Find bounding box for each batch
crops = []
for b in range(B):
# Get coordinates of non-zero elements
rows = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=1)
cols = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=0)
# Find boundaries
y_min, y_max = torch.where(rows)[0][[0, -1]]
x_min, x_max = torch.where(cols)[0][[0, -1]]
# Crop image and mask
crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :]
crops.append(crop)
# Stack results back together
cropped_images = torch.cat(crops, dim=0)
return (cropped_images, )
class ImageUncropByMask:
@classmethod
def INPUT_TYPES(s):
return {"required":
{
"destination": ("IMAGE",),
"source": ("IMAGE",),
"mask": ("MASK",),
"bbox": ("BBOX",),
},
}
CATEGORY = "KJNodes/image"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "uncrop"
def uncrop(self, destination, source, mask, bbox=None):
output_list = []
B, H, W, C = destination.shape
for i in range(source.shape[0]):
x0, y0, x1, y1 = bbox[i]
bbox_height = y1 - y0
bbox_width = x1 - x0
# Resize source image to match the bounding box dimensions
#resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False)
resized_source = common_upscale(source[i].unsqueeze(0).movedim(-1, 1), bbox_width, bbox_height, "lanczos", "disabled")
resized_source = resized_source.movedim(1, -1).squeeze(0)
# Resize mask to match the bounding box dimensions
resized_mask = common_upscale(mask[i].unsqueeze(0).unsqueeze(0), bbox_width, bbox_height, "bilinear", "disabled")
resized_mask = resized_mask.squeeze(0).squeeze(0)
# Calculate padding values
pad_left = x0
pad_right = W - x1
pad_top = y0
pad_bottom = H - y1
# Pad the resized source image and mask to fit the destination dimensions
padded_source = F.pad(resized_source, pad=(0, 0, pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
padded_mask = F.pad(resized_mask, pad=(pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0)
# Ensure the padded mask has the correct shape
padded_mask = padded_mask.unsqueeze(2).expand(-1, -1, destination[i].shape[2])
# Ensure the padded source has the correct shape
padded_source = padded_source.unsqueeze(2).expand(-1, -1, -1, destination[i].shape[2]).squeeze(2)
# Combine the destination and padded source images using the mask
result = destination[i] * (1.0 - padded_mask) + padded_source * padded_mask
output_list.append(result)
return (torch.stack(output_list),)
class ImageCropByMaskBatch:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", ),
"masks": ("MASK", ),
"width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1, }),
"preserve_size": ("BOOLEAN", {"default": False}),
"bg_color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", )
RETURN_NAMES = ("images", "masks",)
FUNCTION = "crop"
CATEGORY = "KJNodes/image"
DESCRIPTION = "Crops the input images based on the provided masks."
def crop(self, image, masks, width, height, bg_color, padding, preserve_size):
B, H, W, C = image.shape
BM, HM, WM = masks.shape
mask_count = BM
if HM != H or WM != W:
masks = F.interpolate(masks.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
print(masks.shape)
output_images = []
output_masks = []
bg_color = [int(x.strip())/255.0 for x in bg_color.split(",")]
# For each mask
for i in range(mask_count):
curr_mask = masks[i]
# Find bounds
y_indices, x_indices = torch.nonzero(curr_mask, as_tuple=True)
if len(y_indices) == 0 or len(x_indices) == 0:
continue
# Get exact bounds with padding
min_y = max(0, y_indices.min().item() - padding)
max_y = min(H, y_indices.max().item() + 1 + padding)
min_x = max(0, x_indices.min().item() - padding)
max_x = min(W, x_indices.max().item() + 1 + padding)
# Ensure mask has correct shape for multiplication
curr_mask = curr_mask.unsqueeze(-1).expand(-1, -1, C)
# Crop image and mask together
cropped_img = image[0, min_y:max_y, min_x:max_x, :]
cropped_mask = curr_mask[min_y:max_y, min_x:max_x, :]
crop_h, crop_w = cropped_img.shape[0:2]
new_w = crop_w
new_h = crop_h
if not preserve_size or crop_w > width or crop_h > height:
scale = min(width/crop_w, height/crop_h)
new_w = int(crop_w * scale)
new_h = int(crop_h * scale)
# Resize RGB
resized_img = common_upscale(cropped_img.permute(2,0,1).unsqueeze(0), new_w, new_h, "lanczos", "disabled").squeeze(0).permute(1,2,0)
resized_mask = torch.nn.functional.interpolate(
cropped_mask.permute(2,0,1).unsqueeze(0),
size=(new_h, new_w),
mode='nearest'
).squeeze(0).permute(1,2,0)
else:
resized_img = cropped_img
resized_mask = cropped_mask
# Create empty tensors
new_img = torch.zeros((height, width, 3), dtype=image.dtype)
new_mask = torch.zeros((height, width), dtype=image.dtype)
# Pad both
pad_x = (width - new_w) // 2
pad_y = (height - new_h) // 2
new_img[pad_y:pad_y+new_h, pad_x:pad_x+new_w, :] = resized_img
if len(resized_mask.shape) == 3:
resized_mask = resized_mask[:,:,0] # Take first channel if 3D
new_mask[pad_y:pad_y+new_h, pad_x:pad_x+new_w] = resized_mask
output_images.append(new_img)
output_masks.append(new_mask)
if not output_images:
return (torch.zeros((0, height, width, 3), dtype=image.dtype),)
out_rgb = torch.stack(output_images, dim=0)
out_masks = torch.stack(output_masks, dim=0)
# Apply mask to RGB
mask_expanded = out_masks.unsqueeze(-1).expand(-1, -1, -1, 3)
background_color = torch.tensor(bg_color, dtype=torch.float32, device=image.device)
out_rgb = out_rgb * mask_expanded + background_color * (1 - mask_expanded)
return (out_rgb, out_masks)
class ImagePadKJ:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", ),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"pad_mode": (["edge", "edge_pixel", "color", "pillarbox_blur"],),
"color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}),
},
"optional": {
"mask": ("MASK", ),
"target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}),
"target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", )
RETURN_NAMES = ("images", "masks",)
FUNCTION = "pad"
CATEGORY = "KJNodes/image"
DESCRIPTION = "Pad the input image and optionally mask with the specified padding."
def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None):
B, H, W, C = image.shape
# Resize masks to image dimensions if necessary
if mask is not None:
BM, HM, WM = mask.shape
if HM != H or WM != W:
mask = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
# Parse background color
bg_color = [int(x.strip())/255.0 for x in color.split(",")]
if len(bg_color) == 1:
bg_color = bg_color * 3 # Grayscale to RGB
bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device)
# Calculate padding sizes with extra padding
if target_width is not None and target_height is not None:
if extra_padding > 0:
image = common_upscale(image.movedim(-1, 1), W - extra_padding, H - extra_padding, "lanczos", "disabled").movedim(1, -1)
B, H, W, C = image.shape
padded_width = target_width
padded_height = target_height
pad_left = (padded_width - W) // 2
pad_right = padded_width - W - pad_left
pad_top = (padded_height - H) // 2
pad_bottom = padded_height - H - pad_top
else:
pad_left = left + extra_padding
pad_right = right + extra_padding
pad_top = top + extra_padding
pad_bottom = bottom + extra_padding
padded_width = W + pad_left + pad_right
padded_height = H + pad_top + pad_bottom
# Pillarbox blur mode
if pad_mode == "pillarbox_blur":
def _gaussian_blur_nchw(img_nchw, sigma_px):
if sigma_px <= 0:
return img_nchw
radius = max(1, int(3.0 * float(sigma_px)))
k = 2 * radius + 1
x = torch.arange(-radius, radius + 1, device=img_nchw.device, dtype=img_nchw.dtype)
k1 = torch.exp(-(x * x) / (2.0 * float(sigma_px) * float(sigma_px)))
k1 = k1 / k1.sum()
kx = k1.view(1, 1, 1, k)
ky = k1.view(1, 1, k, 1)
c = img_nchw.shape[1]
kx = kx.repeat(c, 1, 1, 1)
ky = ky.repeat(c, 1, 1, 1)
img_nchw = F.conv2d(img_nchw, kx, padding=(0, radius), groups=c)
img_nchw = F.conv2d(img_nchw, ky, padding=(radius, 0), groups=c)
return img_nchw
out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
for b in range(B):
scale_fill = max(padded_width / float(W), padded_height / float(H)) if (W > 0 and H > 0) else 1.0
bg_w = max(1, int(round(W * scale_fill)))
bg_h = max(1, int(round(H * scale_fill)))
src_b = image[b].movedim(-1, 0).unsqueeze(0)
bg = common_upscale(src_b, bg_w, bg_h, "bilinear", crop="disabled")
y0 = max(0, (bg_h - padded_height) // 2)
x0 = max(0, (bg_w - padded_width) // 2)
y1 = min(bg_h, y0 + padded_height)
x1 = min(bg_w, x0 + padded_width)
bg = bg[:, :, y0:y1, x0:x1]
if bg.shape[2] != padded_height or bg.shape[3] != padded_width:
pad_h = padded_height - bg.shape[2]
pad_w = padded_width - bg.shape[3]
pad_top_fix = max(0, pad_h // 2)
pad_bottom_fix = max(0, pad_h - pad_top_fix)
pad_left_fix = max(0, pad_w // 2)
pad_right_fix = max(0, pad_w - pad_left_fix)
bg = F.pad(bg, (pad_left_fix, pad_right_fix, pad_top_fix, pad_bottom_fix), mode="replicate")
sigma = max(1.0, 0.006 * float(min(padded_height, padded_width)))
bg = _gaussian_blur_nchw(bg, sigma_px=sigma)
if C >= 3:
r, g, bch = bg[:, 0:1], bg[:, 1:2], bg[:, 2:3]
luma = 0.2126 * r + 0.7152 * g + 0.0722 * bch
gray = torch.cat([luma, luma, luma], dim=1)
desat = 0.20
rgb = torch.cat([r, g, bch], dim=1)
rgb = rgb * (1.0 - desat) + gray * desat
bg[:, 0:3, :, :] = rgb
dim = 0.35
bg = torch.clamp(bg * dim, 0.0, 1.0)
out_image[b] = bg.squeeze(0).movedim(0, -1)
out_image[:, pad_top:pad_top+H, pad_left:pad_left+W, :] = image
# Mask handling for pillarbox_blur
if mask is not None:
fg_mask = mask
out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
out_masks[:, pad_top:pad_top+H, pad_left:pad_left+W] = fg_mask
else:
out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
out_masks[:, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
return (out_image, out_masks)
# Standard pad logic (edge/color)
out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
for b in range(B):
if pad_mode == "edge":
# Pad with edge color (mean)
top_edge = image[b, 0, :, :]
bottom_edge = image[b, H-1, :, :]
left_edge = image[b, :, 0, :]
right_edge = image[b, :, W-1, :]
out_image[b, :pad_top, :, :] = top_edge.mean(dim=0)
out_image[b, pad_top+H:, :, :] = bottom_edge.mean(dim=0)
out_image[b, :, :pad_left, :] = left_edge.mean(dim=0)
out_image[b, :, pad_left+W:, :] = right_edge.mean(dim=0)
out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
elif pad_mode == "edge_pixel":
# Pad with exact edge pixel values
for y in range(pad_top):
out_image[b, y, pad_left:pad_left+W, :] = image[b, 0, :, :]
for y in range(pad_top+H, padded_height):
out_image[b, y, pad_left:pad_left+W, :] = image[b, H-1, :, :]
for x in range(pad_left):
out_image[b, pad_top:pad_top+H, x, :] = image[b, :, 0, :]
for x in range(pad_left+W, padded_width):
out_image[b, pad_top:pad_top+H, x, :] = image[b, :, W-1, :]
out_image[b, :pad_top, :pad_left, :] = image[b, 0, 0, :]
out_image[b, :pad_top, pad_left+W:, :] = image[b, 0, W-1, :]
out_image[b, pad_top+H:, :pad_left, :] = image[b, H-1, 0, :]
out_image[b, pad_top+H:, pad_left+W:, :] = image[b, H-1, W-1, :]
out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
else:
# Pad with specified background color
out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0)
out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
if mask is not None:
out_masks = torch.nn.functional.pad(
mask,
(pad_left, pad_right, pad_top, pad_bottom),
mode='replicate'
)
else:
out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
for m in range(B):
out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
return (out_image, out_masks)
# extends https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
class LoadVideosFromFolder:
@classmethod
def __init__(cls):
try:
cls.vhs_nodes = importlib.import_module("ComfyUI-VideoHelperSuite.videohelpersuite")
except ImportError:
try:
cls.vhs_nodes = importlib.import_module("comfyui-videohelpersuite.videohelpersuite")
except ImportError:
# Fallback to sys.modules search for Windows compatibility
import sys
vhs_module = None
for module_name in sys.modules:
if 'videohelpersuite' in module_name and 'videohelpersuite' in sys.modules[module_name].__dict__:
vhs_module = sys.modules[module_name]
break
if vhs_module is None:
# Try direct access to the videohelpersuite submodule
for module_name in sys.modules:
if module_name.endswith('videohelpersuite'):
vhs_module = sys.modules[module_name]
break
if vhs_module is not None:
cls.vhs_nodes = vhs_module
else:
raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
except ImportError:
raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"video": ("STRING", {"default": "X://insert/path/"},),
"force_rate": ("FLOAT", {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}),
"custom_width": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}),
"custom_height": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}),
"frame_load_cap": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "disable": 0}),
"skip_first_frames": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
"select_every_nth": ("INT", {"default": 1, "min": 1, "max": 1000, "step": 1}),
"output_type": (["batch", "grid"], {"default": "batch"}),
"grid_max_columns": ("INT", {"default": 4, "min": 1, "max": 16, "step": 1, "disable": 1}),
"add_label": ( "BOOLEAN", {"default": False} ),
},
"hidden": {
"force_size": "STRING",
"unique_id": "UNIQUE_ID"
},
}
CATEGORY = "KJNodes/misc"
RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("IMAGE", )
FUNCTION = "load_video"
def load_video(self, output_type, grid_max_columns, add_label=False, **kwargs):
if kwargs.get('video') and not os.path.isabs(kwargs['video']) and args.base_directory:
kwargs['video'] = os.path.join(args.base_directory, kwargs['video'])
if self.vhs_nodes is None:
raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.")
videos_list = []
filenames = []
for f in sorted(os.listdir(kwargs['video'])):
if os.path.isfile(os.path.join(kwargs['video'], f)):
file_parts = f.split('.')
if len(file_parts) > 1 and (file_parts[-1].lower() in ['webm', 'mp4', 'mkv', 'gif', 'mov']):
videos_list.append(os.path.join(kwargs['video'], f))
filenames.append(f)
print(videos_list)
kwargs.pop('video')
loaded_videos = []
for idx, video in enumerate(videos_list):
video_tensor = self.vhs_nodes.load_video_nodes.load_video(video=video, **kwargs)[0]
if add_label:
# Add filename label above video (without extension)
if video_tensor.dim() == 4:
_, h, w, c = video_tensor.shape
else:
h, w, c = video_tensor.shape
# Remove extension from filename
label_text = filenames[idx].rsplit('.', 1)[0]
font_size = max(16, w // 20)
try:
font = ImageFont.truetype("arial.ttf", font_size)
except:
font = ImageFont.load_default()
dummy_img = Image.new("RGB", (w, 10), (0,0,0))
draw = ImageDraw.Draw(dummy_img)
text_bbox = draw.textbbox((0,0), label_text, font=font)
extra_padding = max(12, font_size // 2) # More padding under the font
label_height = text_bbox[3] - text_bbox[1] + extra_padding
label_img = Image.new("RGB", (w, label_height), (0,0,0))
draw = ImageDraw.Draw(label_img)
draw.text((w//2 - (text_bbox[2]-text_bbox[0])//2, 4), label_text, font=font, fill=(255,255,255))
label_np = np.asarray(label_img).astype(np.float32) / 255.0
label_tensor = torch.from_numpy(label_np)
if c == 1:
label_tensor = label_tensor.mean(dim=2, keepdim=True)
elif c == 4:
alpha = torch.ones((label_height, w, 1), dtype=label_tensor.dtype)
label_tensor = torch.cat([label_tensor, alpha], dim=2)
if video_tensor.dim() == 4:
label_tensor = label_tensor.unsqueeze(0).expand(video_tensor.shape[0], -1, -1, -1)
video_tensor = torch.cat([label_tensor, video_tensor], dim=1)
else:
video_tensor = torch.cat([label_tensor, video_tensor], dim=0)
loaded_videos.append(video_tensor)
if output_type == "batch":
out_tensor = torch.cat(loaded_videos)
elif output_type == "grid":
rows = (len(loaded_videos) + grid_max_columns - 1) // grid_max_columns
# Pad the last row if needed
total_slots = rows * grid_max_columns
while len(loaded_videos) < total_slots:
loaded_videos.append(torch.zeros_like(loaded_videos[0]))
# Create grid by rows
row_tensors = []
for row_idx in range(rows):
start_idx = row_idx * grid_max_columns
end_idx = start_idx + grid_max_columns
row_videos = loaded_videos[start_idx:end_idx]
# Pad all videos in this row to the same height
heights = [v.shape[1] for v in row_videos]
max_height = max(heights)
padded_row_videos = []
for v in row_videos:
pad_height = max_height - v.shape[1]
if pad_height > 0:
# Pad (frames, H, W, C) or (H, W, C)
if v.dim() == 4:
pad = (0,0, 0,0, 0,pad_height, 0,0) # (C,W,H,F)
v = torch.nn.functional.pad(v, (0,0,0,0,0,pad_height,0,0))
else:
v = torch.nn.functional.pad(v, (0,0,0,0,pad_height,0))
padded_row_videos.append(v)
row_tensor = torch.cat(padded_row_videos, dim=2) # Concatenate horizontally
row_tensors.append(row_tensor)
out_tensor = torch.cat(row_tensors, dim=1) # Concatenate rows vertically
print(out_tensor.shape)
return out_tensor,
@classmethod
def IS_CHANGED(s, video, **kwargs):
if s.vhs_nodes is not None:
return s.vhs_nodes.utils.hash_path(video)
return None
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