import torch import torch.nn.functional as F from torchvision.transforms import functional as TF from PIL import Image, ImageDraw, ImageFilter, ImageFont import scipy.ndimage import numpy as np from contextlib import nullcontext import os import model_management from comfy.utils import ProgressBar from comfy.utils import common_upscale from nodes import MAX_RESOLUTION import folder_paths from ..utility.utility import tensor2pil, pil2tensor script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) class BatchCLIPSeg: def __init__(self): pass @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "text": ("STRING", {"multiline": False}), "threshold": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 10.0, "step": 0.001}), "binary_mask": ("BOOLEAN", {"default": True}), "combine_mask": ("BOOLEAN", {"default": False}), "use_cuda": ("BOOLEAN", {"default": True}), }, "optional": { "blur_sigma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), "opt_model": ("CLIPSEGMODEL", ), "prev_mask": ("MASK", {"default": None}), "image_bg_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "invert": ("BOOLEAN", {"default": False}), } } CATEGORY = "KJNodes/masking" RETURN_TYPES = ("MASK", "IMAGE", ) RETURN_NAMES = ("Mask", "Image", ) FUNCTION = "segment_image" DESCRIPTION = """ Segments an image or batch of images using CLIPSeg. """ def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda, blur_sigma=0.0, opt_model=None, prev_mask=None, invert= False, image_bg_level=0.5): from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation import torchvision.transforms as transforms offload_device = model_management.unet_offload_device() device = model_management.get_torch_device() if not use_cuda: device = torch.device("cpu") dtype = model_management.unet_dtype() if opt_model is None: checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', 'clipseg-rd64-refined-fp16') if not hasattr(self, "model"): try: if not os.path.exists(checkpoint_path): from huggingface_hub import snapshot_download snapshot_download(repo_id="Kijai/clipseg-rd64-refined-fp16", local_dir=checkpoint_path, local_dir_use_symlinks=False) self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) except: checkpoint_path = "CIDAS/clipseg-rd64-refined" self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) processor = CLIPSegProcessor.from_pretrained(checkpoint_path) else: self.model = opt_model['model'] processor = opt_model['processor'] self.model.to(dtype).to(device) B, H, W, C = images.shape images = images.to(device) autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device) with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): PIL_images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ] prompt = [text] * len(images) input_prc = processor(text=prompt, images=PIL_images, return_tensors="pt") for key in input_prc: input_prc[key] = input_prc[key].to(device) outputs = self.model(**input_prc) mask_tensor = torch.sigmoid(outputs.logits) mask_tensor = (mask_tensor - mask_tensor.min()) / (mask_tensor.max() - mask_tensor.min()) mask_tensor = torch.where(mask_tensor > (threshold), mask_tensor, torch.tensor(0, dtype=torch.float)) print(mask_tensor.shape) if len(mask_tensor.shape) == 2: mask_tensor = mask_tensor.unsqueeze(0) mask_tensor = F.interpolate(mask_tensor.unsqueeze(1), size=(H, W), mode='nearest') mask_tensor = mask_tensor.squeeze(1) self.model.to(offload_device) if binary_mask: mask_tensor = (mask_tensor > 0).float() if blur_sigma > 0: kernel_size = int(6 * int(blur_sigma) + 1) blur = transforms.GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma)) mask_tensor = blur(mask_tensor) if combine_mask: mask_tensor = torch.max(mask_tensor, dim=0)[0] mask_tensor = mask_tensor.unsqueeze(0).repeat(len(images),1,1) del outputs model_management.soft_empty_cache() if prev_mask is not None: if prev_mask.shape != mask_tensor.shape: prev_mask = F.interpolate(prev_mask.unsqueeze(1), size=(H, W), mode='nearest') mask_tensor = mask_tensor + prev_mask.to(device) torch.clamp(mask_tensor, min=0.0, max=1.0) if invert: mask_tensor = 1 - mask_tensor image_tensor = images * mask_tensor.unsqueeze(-1) + (1 - mask_tensor.unsqueeze(-1)) * image_bg_level image_tensor = torch.clamp(image_tensor, min=0.0, max=1.0).cpu().float() mask_tensor = mask_tensor.cpu().float() return mask_tensor, image_tensor, class DownloadAndLoadCLIPSeg: def __init__(self): pass @classmethod def INPUT_TYPES(s): return {"required": { "model": ( [ 'Kijai/clipseg-rd64-refined-fp16', 'CIDAS/clipseg-rd64-refined', ], ), }, } CATEGORY = "KJNodes/masking" RETURN_TYPES = ("CLIPSEGMODEL",) RETURN_NAMES = ("clipseg_model",) FUNCTION = "segment_image" DESCRIPTION = """ Downloads and loads CLIPSeg model with huggingface_hub, to ComfyUI/models/clip_seg """ def segment_image(self, model): from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', os.path.basename(model)) if not hasattr(self, "model"): if not os.path.exists(checkpoint_path): from huggingface_hub import snapshot_download snapshot_download(repo_id=model, local_dir=checkpoint_path, local_dir_use_symlinks=False) self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) processor = CLIPSegProcessor.from_pretrained(checkpoint_path) clipseg_model = {} clipseg_model['model'] = self.model clipseg_model['processor'] = processor return clipseg_model, class CreateTextMask: RETURN_TYPES = ("IMAGE", "MASK",) FUNCTION = "createtextmask" CATEGORY = "KJNodes/text" DESCRIPTION = """ Creates a text image and mask. Looks for fonts from this folder: ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts If start_rotation and/or end_rotation are different values, creates animation between them. """ @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}), "font_color": ("STRING", {"default": "white"}), "text": ("STRING", {"default": "HELLO!", "multiline": True}), "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}), "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}), }, } def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation): # Define the number of images in the batch batch_size = frames out = [] masks = [] rotation = start_rotation if start_rotation != end_rotation: rotation_increment = (end_rotation - start_rotation) / (batch_size - 1) font_path = folder_paths.get_full_path("kjnodes_fonts", font) # Generate the text for i in range(batch_size): image = Image.new("RGB", (width, height), "black") draw = ImageDraw.Draw(image) font = ImageFont.truetype(font_path, font_size) # Split the text into words words = text.split() # Initialize variables for line creation lines = [] current_line = [] current_line_width = 0 try: #new pillow # Iterate through words to create lines for word in words: word_width = font.getbbox(word)[2] if current_line_width + word_width <= width - 2 * text_x: current_line.append(word) current_line_width += word_width + font.getbbox(" ")[2] # Add space width else: lines.append(" ".join(current_line)) current_line = [word] current_line_width = word_width except: #old pillow for word in words: word_width = font.getsize(word)[0] if current_line_width + word_width <= width - 2 * text_x: current_line.append(word) current_line_width += word_width + font.getsize(" ")[0] # Add space width else: lines.append(" ".join(current_line)) current_line = [word] current_line_width = word_width # Add the last line if it's not empty if current_line: lines.append(" ".join(current_line)) # Draw each line of text separately y_offset = text_y for line in lines: text_width = font.getlength(line) text_height = font_size text_center_x = text_x + text_width / 2 text_center_y = y_offset + text_height / 2 try: draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) except: draw.text((text_x, y_offset), line, font=font, fill=font_color) y_offset += text_height # Move to the next line if start_rotation != end_rotation: image = image.rotate(rotation, center=(text_center_x, text_center_y)) rotation += rotation_increment image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] mask = image[:, :, :, 0] masks.append(mask) out.append(image) if invert: return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),) return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) class ColorToMask: RETURN_TYPES = ("MASK",) FUNCTION = "clip" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Converts chosen RGB value to a mask. With batch inputs, the **per_batch** controls the number of images processed at once. """ @classmethod def INPUT_TYPES(s): return { "required": { "images": ("IMAGE",), "invert": ("BOOLEAN", {"default": False}), "red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), }, } def clip(self, images, red, green, blue, threshold, invert, per_batch): color = torch.tensor([red, green, blue], dtype=torch.uint8) black = torch.tensor([0, 0, 0], dtype=torch.uint8) white = torch.tensor([255, 255, 255], dtype=torch.uint8) if invert: black, white = white, black steps = images.shape[0] pbar = ProgressBar(steps) tensors_out = [] for start_idx in range(0, images.shape[0], per_batch): # Calculate color distances color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1) # Create a mask based on the threshold mask = color_distances <= threshold # Apply the mask to create new images mask_out = torch.where(mask.unsqueeze(-1), white, black).float() mask_out = mask_out.mean(dim=-1) tensors_out.append(mask_out.cpu()) batch_count = mask_out.shape[0] pbar.update(batch_count) tensors_out = torch.cat(tensors_out, dim=0) tensors_out = torch.clamp(tensors_out, min=0.0, max=1.0) return tensors_out, class CreateFluidMask: RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "createfluidmask" CATEGORY = "KJNodes/masking/generate" @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}), "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}), "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}), "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}), "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}), }, } #using code from https://github.com/GregTJ/stable-fluids def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration): from ..utility.fluid import Fluid try: from scipy.special import erf except: from scipy.spatial import erf out = [] masks = [] RESOLUTION = width, height DURATION = frames INFLOW_PADDING = inflow_padding INFLOW_DURATION = inflow_duration INFLOW_RADIUS = inflow_radius INFLOW_VELOCITY = inflow_velocity INFLOW_COUNT = inflow_count print('Generating fluid solver, this may take some time.') fluid = Fluid(RESOLUTION, 'dye') center = np.floor_divide(RESOLUTION, 2) r = np.min(center) - INFLOW_PADDING points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False) points = tuple(np.array((np.cos(p), np.sin(p))) for p in points) normals = tuple(-p for p in points) points = tuple(r * p + center for p in points) inflow_velocity = np.zeros_like(fluid.velocity) inflow_dye = np.zeros(fluid.shape) for p, n in zip(points, normals): mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY inflow_dye[mask] = 1 for f in range(DURATION): print(f'Computing frame {f + 1} of {DURATION}.') if f <= INFLOW_DURATION: fluid.velocity += inflow_velocity fluid.dye += inflow_dye curl = fluid.step()[1] # Using the error function to make the contrast a bit higher. # Any other sigmoid function e.g. smoothstep would work. curl = (erf(curl * 2) + 1) / 4 color = np.dstack((curl, np.ones(fluid.shape), fluid.dye)) color = (np.clip(color, 0, 1) * 255).astype('uint8') image = np.array(color).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] mask = image[:, :, :, 0] masks.append(mask) out.append(image) if invert: return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),) return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) class CreateAudioMask: RETURN_TYPES = ("IMAGE",) FUNCTION = "createaudiomask" CATEGORY = "KJNodes/deprecated" @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}), "scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}), "audio_path": ("STRING", {"default": "audio.wav"}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), }, } def createaudiomask(self, frames, width, height, invert, audio_path, scale): try: import librosa except ImportError: raise Exception("Can not import librosa. Install it with 'pip install librosa'") batch_size = frames out = [] masks = [] if audio_path == "audio.wav": #I don't know why relative path won't work otherwise... audio_path = os.path.join(script_directory, audio_path) audio, sr = librosa.load(audio_path) spectrogram = np.abs(librosa.stft(audio)) for i in range(batch_size): image = Image.new("RGB", (width, height), "black") draw = ImageDraw.Draw(image) frame = spectrogram[:, i] circle_radius = int(height * np.mean(frame)) circle_radius *= scale circle_center = (width // 2, height // 2) # Calculate the center of the image draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius), (circle_center[0] + circle_radius, circle_center[1] + circle_radius)], fill='white') image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] mask = image[:, :, :, 0] masks.append(mask) out.append(image) if invert: return (1.0 - torch.cat(out, dim=0),) return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) class CreateGradientMask: RETURN_TYPES = ("MASK",) FUNCTION = "createmask" CATEGORY = "KJNodes/masking/generate" @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), }, } def createmask(self, frames, width, height, invert): # Define the number of images in the batch batch_size = frames out = [] # Create an empty array to store the image batch image_batch = np.zeros((batch_size, height, width), dtype=np.float32) # Generate the black to white gradient for each image for i in range(batch_size): gradient = np.linspace(1.0, 0.0, width, dtype=np.float32) time = i / frames # Calculate the time variable offset_gradient = gradient - time # Offset the gradient values based on time image_batch[i] = offset_gradient.reshape(1, -1) output = torch.from_numpy(image_batch) mask = output out.append(mask) if invert: return (1.0 - torch.cat(out, dim=0),) return (torch.cat(out, dim=0),) class CreateFadeMask: RETURN_TYPES = ("MASK",) FUNCTION = "createfademask" CATEGORY = "KJNodes/deprecated" @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 2,"min": 2, "max": 10000, "step": 1}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), }, } def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame): def ease_in(t): return t * t def ease_out(t): return 1 - (1 - t) * (1 - t) def ease_in_out(t): return 3 * t * t - 2 * t * t * t batch_size = frames out = [] image_batch = np.zeros((batch_size, height, width), dtype=np.float32) if midpoint_frame == 0: midpoint_frame = batch_size // 2 for i in range(batch_size): if i <= midpoint_frame: t = i / midpoint_frame if interpolation == "ease_in": t = ease_in(t) elif interpolation == "ease_out": t = ease_out(t) elif interpolation == "ease_in_out": t = ease_in_out(t) color = start_level - t * (start_level - midpoint_level) else: t = (i - midpoint_frame) / (batch_size - midpoint_frame) if interpolation == "ease_in": t = ease_in(t) elif interpolation == "ease_out": t = ease_out(t) elif interpolation == "ease_in_out": t = ease_in_out(t) color = midpoint_level - t * (midpoint_level - end_level) color = np.clip(color, 0, 255) image = np.full((height, width), color, dtype=np.float32) image_batch[i] = image output = torch.from_numpy(image_batch) mask = output out.append(mask) if invert: return (1.0 - torch.cat(out, dim=0),) return (torch.cat(out, dim=0),) class CreateFadeMaskAdvanced: RETURN_TYPES = ("MASK",) FUNCTION = "createfademask" CATEGORY = "KJNodes/masking/generate" DESCRIPTION = """ Create a batch of masks interpolated between given frames and values. Uses same syntax as Fizz' BatchValueSchedule. First value is the frame index (not that this starts from 0, not 1) and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0 For example the default values: 0:(0.0) 7:(1.0) 15:(0.0) Would create a mask batch fo 16 frames, starting from black, interpolating with the chosen curve to fully white at the 8th frame, and interpolating from that to fully black at the 16th frame. """ @classmethod def INPUT_TYPES(s): return { "required": { "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 16,"min": 2, "max": 10000, "step": 1}), "width": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), "height": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), }, } def createfademask(self, frames, width, height, invert, points_string, interpolation): def ease_in(t): return t * t def ease_out(t): return 1 - (1 - t) * (1 - t) def ease_in_out(t): return 3 * t * t - 2 * t * t * t # Parse the input string into a list of tuples points = [] points_string = points_string.rstrip(',\n') for point_str in points_string.split(','): frame_str, color_str = point_str.split(':') frame = int(frame_str.strip()) color = float(color_str.strip()[1:-1]) # Remove parentheses around color points.append((frame, color)) # Check if the last frame is already in the points if len(points) == 0 or points[-1][0] != frames - 1: # If not, add it with the color of the last specified frame points.append((frames - 1, points[-1][1] if points else 0)) # Sort the points by frame number points.sort(key=lambda x: x[0]) batch_size = frames out = [] image_batch = np.zeros((batch_size, height, width), dtype=np.float32) # Index of the next point to interpolate towards next_point = 1 for i in range(batch_size): while next_point < len(points) and i > points[next_point][0]: next_point += 1 # Interpolate between the previous point and the next point prev_point = next_point - 1 t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0]) if interpolation == "ease_in": t = ease_in(t) elif interpolation == "ease_out": t = ease_out(t) elif interpolation == "ease_in_out": t = ease_in_out(t) elif interpolation == "linear": pass # No need to modify `t` for linear interpolation color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1]) color = np.clip(color, 0, 255) image = np.full((height, width), color, dtype=np.float32) image_batch[i] = image output = torch.from_numpy(image_batch) mask = output out.append(mask) if invert: return (1.0 - torch.cat(out, dim=0),) return (torch.cat(out, dim=0),) class CreateMagicMask: RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "createmagicmask" CATEGORY = "KJNodes/masking/generate" @classmethod def INPUT_TYPES(s): return { "required": { "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), "depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}), "distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}), "seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}), "transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}), "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), }, } def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height): from ..utility.magictex import coordinate_grid, random_transform, magic import matplotlib.pyplot as plt rng = np.random.default_rng(seed) out = [] coords = coordinate_grid((frame_width, frame_height)) # Calculate the number of frames for each transition frames_per_transition = frames // transitions # Generate a base set of parameters base_params = { "coords": random_transform(coords, rng), "depth": depth, "distortion": distortion, } for t in range(transitions): # Generate a second set of parameters that is at most max_diff away from the base parameters params1 = base_params.copy() params2 = base_params.copy() params1['coords'] = random_transform(coords, rng) params2['coords'] = random_transform(coords, rng) for i in range(frames_per_transition): # Compute the interpolation factor alpha = i / frames_per_transition # Interpolate between the two sets of parameters params = params1.copy() params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords'] tex = magic(**params) dpi = frame_width / 10 fig = plt.figure(figsize=(10, 10), dpi=dpi) ax = fig.add_subplot(111) plt.subplots_adjust(left=0, right=1, bottom=0, top=1) ax.get_yaxis().set_ticks([]) ax.get_xaxis().set_ticks([]) ax.imshow(tex, aspect='auto') fig.canvas.draw() img = np.array(fig.canvas.renderer._renderer) plt.close(fig) pil_img = Image.fromarray(img).convert("L") mask = torch.tensor(np.array(pil_img)) / 255.0 out.append(mask) return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) class CreateShapeMask: RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "createshapemask" CATEGORY = "KJNodes/masking/generate" DESCRIPTION = """ Creates a mask or batch of masks with the specified shape. Locations are center locations. Grow value is the amount to grow the shape on each frame, creating animated masks. """ @classmethod def INPUT_TYPES(s): return { "required": { "shape": ( [ 'circle', 'square', 'triangle', ], { "default": 'circle' }), "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), "grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), }, } def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape): # Define the number of images in the batch batch_size = frames out = [] color = "white" for i in range(batch_size): image = Image.new("RGB", (frame_width, frame_height), "black") draw = ImageDraw.Draw(image) # Calculate the size for this frame and ensure it's not less than 0 current_width = max(0, shape_width + i*grow) current_height = max(0, shape_height + i*grow) if shape == 'circle' or shape == 'square': # Define the bounding box for the shape left_up_point = (location_x - current_width // 2, location_y - current_height // 2) right_down_point = (location_x + current_width // 2, location_y + current_height // 2) two_points = [left_up_point, right_down_point] if shape == 'circle': draw.ellipse(two_points, fill=color) elif shape == 'square': draw.rectangle(two_points, fill=color) elif shape == 'triangle': # Define the points for the triangle left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right top_point = (location_x, location_y - current_height // 2) # top point draw.polygon([top_point, left_up_point, right_down_point], fill=color) image = pil2tensor(image) mask = image[:, :, :, 0] out.append(mask) outstack = torch.cat(out, dim=0) return (outstack, 1.0 - outstack,) class CreateVoronoiMask: RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "createvoronoi" CATEGORY = "KJNodes/masking/generate" @classmethod def INPUT_TYPES(s): return { "required": { "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}), "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}), "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), }, } def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height): from scipy.spatial import Voronoi # Define the number of images in the batch batch_size = frames out = [] # Calculate aspect ratio aspect_ratio = frame_width / frame_height # Create start and end points for each point, considering the aspect ratio start_points = np.random.rand(num_points, 2) start_points[:, 0] *= aspect_ratio end_points = np.random.rand(num_points, 2) end_points[:, 0] *= aspect_ratio for i in range(batch_size): # Interpolate the points' positions based on the current frame t = (i * speed) / (batch_size - 1) # normalize to [0, 1] over the frames t = np.clip(t, 0, 1) # ensure t is in [0, 1] points = (1 - t) * start_points + t * end_points # lerp # Adjust points for aspect ratio points[:, 0] *= aspect_ratio vor = Voronoi(points) # Create a blank image with a white background fig, ax = plt.subplots() plt.subplots_adjust(left=0, right=1, bottom=0, top=1) ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1]) # adjust x limits ax.axis('off') ax.margins(0, 0) fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100) # adjust figure size ax.fill_between([0, 1], [0, 1], color='white') # Plot each Voronoi ridge for simplex in vor.ridge_vertices: simplex = np.asarray(simplex) if np.all(simplex >= 0): plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width) fig.canvas.draw() img = np.array(fig.canvas.renderer._renderer) plt.close(fig) pil_img = Image.fromarray(img).convert("L") mask = torch.tensor(np.array(pil_img)) / 255.0 out.append(mask) return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) class GetMaskSizeAndCount: @classmethod def INPUT_TYPES(s): return {"required": { "mask": ("MASK",), }} RETURN_TYPES = ("MASK","INT", "INT", "INT",) RETURN_NAMES = ("mask", "width", "height", "count",) FUNCTION = "getsize" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Returns the width, height and batch size of the mask, and passes it through unchanged. """ def getsize(self, mask): width = mask.shape[2] height = mask.shape[1] count = mask.shape[0] return {"ui": { "text": [f"{count}x{width}x{height}"]}, "result": (mask, width, height, count) } class GrowMaskWithBlur: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), "tapered_corners": ("BOOLEAN", {"default": True}), "flip_input": ("BOOLEAN", {"default": False}), "blur_radius": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 100, "step": 0.1 }), "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }, "optional": { "fill_holes": ("BOOLEAN", {"default": False}), }, } CATEGORY = "KJNodes/masking" RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "expand_mask" DESCRIPTION = """ # GrowMaskWithBlur - mask: Input mask or mask batch - expand: Expand or contract mask or mask batch by a given amount - incremental_expandrate: increase expand rate by a given amount per frame - tapered_corners: use tapered corners - flip_input: flip input mask - blur_radius: value higher than 0 will blur the mask - lerp_alpha: alpha value for interpolation between frames - decay_factor: decay value for interpolation between frames - fill_holes: fill holes in the mask (slow)""" def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False): alpha = lerp_alpha decay = decay_factor if flip_input: mask = 1.0 - mask c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu() out = [] previous_output = None current_expand = expand for m in growmask: output = m.numpy().astype(np.float32) for _ in range(abs(round(current_expand))): if current_expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) if current_expand < 0: current_expand -= abs(incremental_expandrate) else: current_expand += abs(incremental_expandrate) if fill_holes: binary_mask = output > 0 output = scipy.ndimage.binary_fill_holes(binary_mask) output = output.astype(np.float32) * 255 output = torch.from_numpy(output) if alpha < 1.0 and previous_output is not None: # Interpolate between the previous and current frame output = alpha * output + (1 - alpha) * previous_output if decay < 1.0 and previous_output is not None: # Add the decayed previous output to the current frame output += decay * previous_output output = output / output.max() previous_output = output out.append(output) if blur_radius != 0: # Convert the tensor list to PIL images, apply blur, and convert back for idx, tensor in enumerate(out): # Convert tensor to PIL image pil_image = tensor2pil(tensor.cpu().detach())[0] # Apply Gaussian blur pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius)) # Convert back to tensor out[idx] = pil2tensor(pil_image) blurred = torch.cat(out, dim=0) return (blurred, 1.0 - blurred) else: return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) class MaskBatchMulti: @classmethod def INPUT_TYPES(s): return { "required": { "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), "mask_1": ("MASK", ), "mask_2": ("MASK", ), }, } RETURN_TYPES = ("MASK",) RETURN_NAMES = ("masks",) FUNCTION = "combine" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Creates an image batch from multiple masks. You can set how many inputs the node has, with the **inputcount** and clicking update. """ def combine(self, inputcount, **kwargs): mask = kwargs["mask_1"] for c in range(1, inputcount): new_mask = kwargs[f"mask_{c + 1}"] if mask.shape[1:] != new_mask.shape[1:]: new_mask = F.interpolate(new_mask.unsqueeze(1), size=(mask.shape[1], mask.shape[2]), mode="bicubic").squeeze(1) mask = torch.cat((mask, new_mask), dim=0) return (mask,) class OffsetMask: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }), "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }), "roll": ("BOOLEAN", { "default": False }), "incremental": ("BOOLEAN", { "default": False }), "padding_mode": ( [ 'empty', 'border', 'reflection', ], { "default": 'empty' }), } } RETURN_TYPES = ("MASK",) RETURN_NAMES = ("mask",) FUNCTION = "offset" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Offsets the mask by the specified amount. - mask: Input mask or mask batch - x: Horizontal offset - y: Vertical offset - angle: Angle in degrees - roll: roll edge wrapping - duplication_factor: Number of times to duplicate the mask to form a batch - border padding_mode: Padding mode for the mask """ def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"): # Create duplicates of the mask batch mask = mask.repeat(duplication_factor, 1, 1).clone() batch_size, height, width = mask.shape if angle != 0 and incremental: for i in range(batch_size): rotation_angle = angle * (i+1) mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0) elif angle > 0: for i in range(batch_size): mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0) if roll: if incremental: for i in range(batch_size): shift_x = min(x*(i+1), width-1) shift_y = min(y*(i+1), height-1) if shift_x != 0: mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1) if shift_y != 0: mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0) else: shift_x = min(x, width-1) shift_y = min(y, height-1) if shift_x != 0: mask = torch.roll(mask, shifts=shift_x, dims=2) if shift_y != 0: mask = torch.roll(mask, shifts=shift_y, dims=1) else: for i in range(batch_size): if incremental: temp_x = min(x * (i+1), width-1) temp_y = min(y * (i+1), height-1) else: temp_x = min(x, width-1) temp_y = min(y, height-1) if temp_x > 0: if padding_mode == 'empty': mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode) elif temp_x < 0: if padding_mode == 'empty': mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode) if temp_y > 0: if padding_mode == 'empty': mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode) elif temp_y < 0: if padding_mode == 'empty': mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode) return mask, class RoundMask: @classmethod def INPUT_TYPES(s): return {"required": { "mask": ("MASK",), }} RETURN_TYPES = ("MASK",) FUNCTION = "round" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Rounds the mask or batch of masks to a binary mask. RoundMask example """ def round(self, mask): mask = mask.round() return (mask,) class ResizeMask: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }), "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }), "keep_proportions": ("BOOLEAN", { "default": False }), "upscale_method": (s.upscale_methods,), "crop": (["disabled","center"],), } } RETURN_TYPES = ("MASK", "INT", "INT",) RETURN_NAMES = ("mask", "width", "height",) FUNCTION = "resize" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Resizes the mask or batch of masks to the specified width and height. """ def resize(self, mask, width, height, keep_proportions, upscale_method,crop): if keep_proportions: _, oh, ow = mask.shape width = ow if width == 0 else width height = oh if height == 0 else height ratio = min(width / ow, height / oh) width = round(ow*ratio) height = round(oh*ratio) outputs = mask.unsqueeze(1) outputs = common_upscale(outputs, width, height, upscale_method, crop) outputs = outputs.squeeze(1) return(outputs, outputs.shape[2], outputs.shape[1],) class RemapMaskRange: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), } } RETURN_TYPES = ("MASK",) RETURN_NAMES = ("mask",) FUNCTION = "remap" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Sets new min and max values for the mask. """ def remap(self, mask, min, max): # Find the maximum value in the mask mask_max = torch.max(mask) # If the maximum mask value is zero, avoid division by zero by setting it to 1 mask_max = mask_max if mask_max > 0 else 1 # Scale the mask values to the new range defined by min and max # The highest pixel value in the mask will be scaled to max scaled_mask = (mask / mask_max) * (max - min) + min # Clamp the values to ensure they are within [0.0, 1.0] scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0) return (scaled_mask, )