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 from PIL.PngImagePlugin import PngInfo 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": { }, "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}), } } 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": True}), }} 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 # 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/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(s, **kwargs): return float("NaN") 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": 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 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 = 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) 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 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",), "image_2": ("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}), }, } def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs): 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, } image_1 = kwargs["image_1"] 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[f"image_{c + 1}"] 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 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 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",), "image_2": ("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"}), }, } 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] easing_function = easing_functions[interpolation] for c in range(1, inputcount): frames = [] new_image = kwargs[f"image_{c + 1}"] 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 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 = """ Randomizes image order within 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 = 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' }), "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", ), "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",), "crop": (["disabled","center"],), } } 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, 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) 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],) 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 class LoadImagesFromFolderKJ: @classmethod def INPUT_TYPES(s): return { "required": { "folder": ("STRING", {"default": ""}), }, "optional": { "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), } } RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",) RETURN_NAMES = ("image", "mask", "count", "image_path",) FUNCTION = "load_images" CATEGORY = "image" def load_images(self, folder, image_load_cap, start_index): if not os.path.isdir(folder): raise FileNotFoundError(f"Folder '{folder} cannot be found.'") dir_files = os.listdir(folder) if len(dir_files) == 0: raise FileNotFoundError(f"No files in directory '{folder}'.") # Filter files by extension valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] dir_files = sorted(dir_files) dir_files = [os.path.join(folder, x) for x in dir_files] # 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 has_non_empty_mask = False for image_path in dir_files: if os.path.isdir(image_path) and os.path.ex: continue if limit_images and image_count >= image_load_cap: break i = Image.open(image_path) i = ImageOps.exif_transpose(i) 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) has_non_empty_mask = True else: mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") images.append(image) masks.append(mask) image_path_list.append(image_path) image_count += 1 if len(images) == 1: return (images[0], masks[0], 1, image_path_list) elif len(images) > 1: image1 = images[0] mask1 = None for image2 in images[1:]: if image1.shape[1:] != image2.shape[1:]: image2 = common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1) image1 = torch.cat((image1, image2), dim=0) for mask2 in masks[1:]: if has_non_empty_mask: if image1.shape[1:3] != mask2.shape: mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False) mask2 = mask2.squeeze(0) else: mask2 = mask2.unsqueeze(0) else: mask2 = mask2.unsqueeze(0) if mask1 is None: mask1 = mask2 else: mask1 = torch.cat((mask1, mask2), dim=0) return (image1, mask1, len(images), image_path_list) 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.output_dir = folder_paths.get_output_directory() 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 = "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 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]) if output_folder != "output": if not os.path.exists(output_folder): os.makedirs(output_folder, exist_ok=True) full_output_folder = output_folder 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 { "ui": { "images": results }, "result": (file,) } 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 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 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),)