import numpy as np
import time
import torch
import torch.nn.functional as F
import random
import math
import os
import re
import json
import hashlib
try:
import cv2
except:
print("OpenCV not installed")
pass
from PIL import ImageGrab, ImageDraw, ImageFont, Image, ImageSequence, ImageOps
from nodes import MAX_RESOLUTION, SaveImage
from comfy_extras.nodes_mask import ImageCompositeMasked
from comfy.cli_args import args
from comfy.utils import ProgressBar, common_upscale
import folder_paths
import model_management
script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
class ImagePass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "passthrough"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Passes the image through without modifying it.
"""
def passthrough(self, image):
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}),
}
}
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):
try:
from color_matcher import ColorMatcher
except:
raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
cm = ColorMatcher()
image_ref = image_ref.cpu()
image_target = image_target.cpu()
batch_size = image_target.size(0)
out = []
images_target = image_target.squeeze()
images_ref = image_ref.squeeze()
image_ref_np = images_ref.numpy()
images_target_np = images_target.numpy()
if image_ref.size(0) > 1 and image_ref.size(0) != batch_size:
raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.")
for i in range(batch_size):
image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy()
image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
try:
image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method)
except BaseException as e:
print(f"Error occurred during transfer: {e}")
break
# Apply the strength multiplier
image_result = image_target_np + strength * (image_result - image_target_np)
out.append(torch.from_numpy(image_result))
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 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": False}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "concanate"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the image2 to image1 in the specified direction.
"""
def concanate(self, image1, image2, direction, match_image_size, 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 images to match the largest batch size
image1 = image1.repeat(repeats1, 1, 1, 1)
image2 = image2.repeat(repeats2, 1, 1, 1)
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
# 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,
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/experimental"
DESCRIPTION = """
Captures an area specified by screen coordinates.
Can be used for realtime diffusion with autoqueue.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
},
}
def screencap(self, x, y, width, height, num_frames, delay):
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(cls):
return
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "screencap"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
Captures an area specified by screen coordinates.
Can be used for realtime diffusion with autoqueue.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
"width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
"num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
"delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
},
}
def screencap(self, x, y, width, height, num_frames, delay):
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": 0, "max": 4096, "step": 1}),
"font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}),
"font_color": ("STRING", {"default": "white"}),
"label_color": ("STRING", {"default": "black"}),
"font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
"text": ("STRING", {"default": "Text"}),
"direction": (
[ 'up',
'down',
'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):
if direction == 'overlay':
pil_image = Image.fromarray((input_image.cpu().numpy() * 255).astype(np.uint8))
else:
label_image = Image.new("RGB", (width, height), label_color)
pil_image = label_image
draw = ImageDraw.Draw(pil_image)
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))
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 # Move to the next line
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 ImageBatchRepeatInterleaving:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "repeat"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Repeats each image in a batch by the specified number of times.
Example batch of 5 images: 0, 1 ,2, 3, 4
with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"repeats": ("INT", {"default": 1, "min": 1, "max": 4096}),
},
}
def repeat(self, images, repeats):
repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0)
return (repeated_images, )
class 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 = 0
new_width = 0
# 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)
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 = mask
else:
# If downscaling is not needed, use the original image dimensions
image_scaled = image
mask_scaled = mask
# 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 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"
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
preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted)
if pass_through:
return (preview, )
return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
class CrossFadeImages:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "crossfadeimages"
CATEGORY = "KJNodes/image"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images_1": ("IMAGE",),
"images_2": ("IMAGE",),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
"transition_start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
"start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
"end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
},
}
def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level):
def crossfade(images_1, images_2, alpha):
crossfade = (1 - alpha) * images_1 + alpha * images_2
return crossfade
def ease_in(t):
return t * t
def ease_out(t):
return 1 - (1 - t) * (1 - t)
def ease_in_out(t):
return 3 * t * t - 2 * t * t * t
def bounce(t):
if t < 0.5:
return self.ease_out(t * 2) * 0.5
else:
return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5
def elastic(t):
return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
def glitchy(t):
return t + 0.1 * math.sin(40 * t)
def exponential_ease_out(t):
return 1 - (1 - t) ** 4
easing_functions = {
"linear": lambda t: t,
"ease_in": ease_in,
"ease_out": ease_out,
"ease_in_out": ease_in_out,
"bounce": bounce,
"elastic": elastic,
"glitchy": glitchy,
"exponential_ease_out": exponential_ease_out,
}
crossfade_images = []
alphas = torch.linspace(start_level, end_level, transitioning_frames)
for i in range(transitioning_frames):
alpha = alphas[i]
image1 = images_1[i + transition_start_index]
image2 = images_2[i + transition_start_index]
easing_function = easing_functions.get(interpolation)
alpha = easing_function(alpha) # Apply the easing function to the alpha value
crossfade_image = crossfade(image1, image2, alpha)
crossfade_images.append(crossfade_image)
# Convert crossfade_images to tensor
crossfade_images = torch.stack(crossfade_images, dim=0)
# Get the last frame result of the interpolation
last_frame = crossfade_images[-1]
# Calculate the number of remaining frames from images_2
remaining_frames = len(images_2) - (transition_start_index + transitioning_frames)
# Crossfade the remaining frames with the last used alpha value
for i in range(remaining_frames):
alpha = alphas[-1]
image1 = images_1[i + transition_start_index + transitioning_frames]
image2 = images_2[i + transition_start_index + transitioning_frames]
easing_function = easing_functions.get(interpolation)
alpha = easing_function(alpha) # Apply the easing function to the alpha value
crossfade_image = crossfade(image1, image2, alpha)
crossfade_images = torch.cat([crossfade_images, crossfade_image.unsqueeze(0)], dim=0)
# Append the beginning of images_1
beginning_images_1 = images_1[:transition_start_index]
crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
return (crossfade_images, )
class GetImageRangeFromBatch:
RETURN_TYPES = ("IMAGE", "MASK", )
FUNCTION = "imagesfrombatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Creates a new batch using images from the input,
batch, starting from start_index.
"""
@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 = len(images) - num_frames
if start_index < 0 or start_index >= len(images):
raise ValueError("Start index is out of range")
end_index = start_index + num_frames
if end_index > len(images):
raise ValueError("End index is out of range")
chosen_images = images[start_index:end_index]
# Process masks if provided
if masks is not None:
if start_index == -1:
start_index = 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 = start_index + num_frames
if end_index > len(masks):
raise ValueError("End index is out of range for masks")
chosen_masks = masks[start_index:end_index]
return (chosen_images, chosen_masks,)
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}),
},
}
def insertimagesfrombatch(self, original_images, images_to_insert, 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)
# Ensure the images_to_insert is a tensor
if not isinstance(images_to_insert, torch.Tensor):
images_to_insert = torch.tensor(images_to_insert)
# Insert the images at the specified indices
for index, image in zip(indices_tensor, images_to_insert):
original_images[index] = image
return (original_images,)
class ReplaceImagesInBatch:
RETURN_TYPES = ("IMAGE",)
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": {
"original_images": ("IMAGE",),
"replacement_images": ("IMAGE",),
"start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
},
}
def replace(self, original_images, replacement_images, start_index):
images = None
if start_index >= len(original_images):
raise ValueError("GetImageRangeFromBatch: Start index is out of range")
end_index = start_index + len(replacement_images)
if end_index > len(original_images):
raise ValueError("GetImageRangeFromBatch: End index is out of range")
# Create a copy of the original_images tensor
original_images_copy = original_images.clone()
original_images_copy[start_index:end_index] = replacement_images
images = original_images_copy
return (images, )
class ReverseImageBatch:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "reverseimagebatch"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Reverses the order of the images in a batch.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
},
}
def reverseimagebatch(self, images):
reversed_images = torch.flip(images, [0])
return (reversed_images, )
class ImageBatchMulti:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
"image_1": ("IMAGE", ),
"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"]
for c in range(1, inputcount):
new_image = kwargs[f"image_{c + 1}"]
image, = image_batch_node.batch(image, new_image)
return (image,)
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'
}),
},
}
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, **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 * 0.5, new_image * 0.5)
elif blending == "subtract":
image = torch.sub(image * 0.5, new_image * 0.5)
elif blending == "multiply":
image = torch.mul(image * 0.5, new_image * 0.5)
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", ),
"image_2": ("IMAGE", ),
"direction": (
[ 'right',
'down',
'left',
'up',
],
{
"default": 'right'
}),
"match_image_size": ("BOOLEAN", {"default": False}),
},
}
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[f"image_{c + 1}"]
image, = ImageConcanate.concanate(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=80, method=4)
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": 8, }),
"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
"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",),
}
}
RETURN_TYPES = ("IMAGE", "INT", "INT",)
RETURN_NAMES = ("IMAGE", "width", "height",)
FUNCTION = "resize"
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Resizes the image to the specified width and height.
Size can be retrieved from the inputs, and the final scale
is determined in this order of importance:
- get_image_size
- width_input and height_input
- width and height widgets
Keep proportions keeps the aspect ratio of the image, by
highest dimension.
"""
def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, width_input=None, height_input=None, get_image_size=None):
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)
image = image.movedim(-1,1)
scaled = common_upscale(image, width, height, upscale_method, 'disabled')
scaled = scaled.movedim(1,-1)
return(scaled, scaled.shape[2], scaled.shape[1],)
class LoadAndResizeImage:
_color_channels = ["alpha", "red", "green", "blue"]
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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, ),
},
}
CATEGORY = "KJNodes/image"
RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT",)
RETURN_NAMES = ("image", "mask", "width", "height",)
FUNCTION = "load_image"
def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel):
image_path = folder_paths.get_annotated_filepath(image)
import node_helpers
img = node_helpers.pillow(Image.open, image_path)
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 i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.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,]
mask = None
c = mask_channel[0].upper()
if c in i.getbands():
if resize:
i = i.resize((width, height), Image.Resampling.BILINEAR)
mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)
if 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)
@classmethod
def IS_CHANGED(s, image):
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