import torch import torch.nn.functional as F from torchvision.transforms import functional as TF import scipy.ndimage import matplotlib.pyplot as plt import numpy as np from PIL import ImageFilter, Image, ImageDraw, ImageFont from contextlib import nullcontext import json import re import os import io import model_management from nodes import MAX_RESOLUTION import folder_paths from ..utility.utility import tensor2pil, pil2tensor from comfy.utils import ProgressBar script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts")) class AnyType(str): """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" def __ne__(self, __value: object) -> bool: return False any = AnyType("*") class INTConstant: @classmethod def INPUT_TYPES(s): return {"required": { "value": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, } RETURN_TYPES = ("INT",) RETURN_NAMES = ("value",) FUNCTION = "get_value" CATEGORY = "KJNodes/constants" def get_value(self, value): return (value,) class FloatConstant: @classmethod def INPUT_TYPES(s): return {"required": { "value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.001}), }, } RETURN_TYPES = ("FLOAT",) RETURN_NAMES = ("value",) FUNCTION = "get_value" CATEGORY = "KJNodes/constants" def get_value(self, value): return (value,) class StringConstant: @classmethod def INPUT_TYPES(cls): return { "required": { "string": ("STRING", {"default": '', "multiline": False}), } } RETURN_TYPES = ("STRING",) FUNCTION = "passtring" CATEGORY = "KJNodes/constants" def passtring(self, string): return (string, ) class StringConstantMultiline: @classmethod def INPUT_TYPES(cls): return { "required": { "string": ("STRING", {"default": "", "multiline": True}), "strip_newlines": ("BOOLEAN", {"default": True}), } } RETURN_TYPES = ("STRING",) FUNCTION = "stringify" CATEGORY = "KJNodes/constants" def stringify(self, string, strip_newlines): new_string = [] for line in io.StringIO(string): if not line.strip().startswith("\n") and strip_newlines: line = line.replace("\n", '') new_string.append(line) new_string = "\n".join(new_string) return (new_string, ) class CreateFluidMask: RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "createfluidmask" CATEGORY = "KJNodes/masking/generate" @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}), "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}), "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}), "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}), "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}), }, } #using code from https://github.com/GregTJ/stable-fluids def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration): from ..utility.fluid import Fluid from scipy.spatial import erf out = [] masks = [] RESOLUTION = width, height DURATION = frames INFLOW_PADDING = inflow_padding INFLOW_DURATION = inflow_duration INFLOW_RADIUS = inflow_radius INFLOW_VELOCITY = inflow_velocity INFLOW_COUNT = inflow_count print('Generating fluid solver, this may take some time.') fluid = Fluid(RESOLUTION, 'dye') center = np.floor_divide(RESOLUTION, 2) r = np.min(center) - INFLOW_PADDING points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False) points = tuple(np.array((np.cos(p), np.sin(p))) for p in points) normals = tuple(-p for p in points) points = tuple(r * p + center for p in points) inflow_velocity = np.zeros_like(fluid.velocity) inflow_dye = np.zeros(fluid.shape) for p, n in zip(points, normals): mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY inflow_dye[mask] = 1 for f in range(DURATION): print(f'Computing frame {f + 1} of {DURATION}.') if f <= INFLOW_DURATION: fluid.velocity += inflow_velocity fluid.dye += inflow_dye curl = fluid.step()[1] # Using the error function to make the contrast a bit higher. # Any other sigmoid function e.g. smoothstep would work. curl = (erf(curl * 2) + 1) / 4 color = np.dstack((curl, np.ones(fluid.shape), fluid.dye)) color = (np.clip(color, 0, 1) * 255).astype('uint8') image = np.array(color).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] mask = image[:, :, :, 0] masks.append(mask) out.append(image) if invert: return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),) return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) class CreateAudioMask: 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": 255, "step": 1}), "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), }, } def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame): def ease_in(t): return t * t def ease_out(t): return 1 - (1 - t) * (1 - t) def ease_in_out(t): return 3 * t * t - 2 * t * t * t batch_size = frames out = [] image_batch = np.zeros((batch_size, height, width), dtype=np.float32) if midpoint_frame == 0: midpoint_frame = batch_size // 2 for i in range(batch_size): if i <= midpoint_frame: t = i / midpoint_frame if interpolation == "ease_in": t = ease_in(t) elif interpolation == "ease_out": t = ease_out(t) elif interpolation == "ease_in_out": t = ease_in_out(t) color = start_level - t * (start_level - midpoint_level) else: t = (i - midpoint_frame) / (batch_size - midpoint_frame) if interpolation == "ease_in": t = ease_in(t) elif interpolation == "ease_out": t = ease_out(t) elif interpolation == "ease_in_out": t = ease_in_out(t) color = midpoint_level - t * (midpoint_level - end_level) color = np.clip(color, 0, 255) image = np.full((height, width), color, dtype=np.float32) image_batch[i] = image output = torch.from_numpy(image_batch) mask = output out.append(mask) if invert: return (1.0 - torch.cat(out, dim=0),) return (torch.cat(out, dim=0),) class CreateFadeMaskAdvanced: RETURN_TYPES = ("MASK",) FUNCTION = "createfademask" CATEGORY = "KJNodes/masking/generate" DESCRIPTION = """ Create a batch of masks interpolated between given frames and values. Uses same syntax as Fizz' BatchValueSchedule. First value is the frame index (not that this starts from 0, not 1) and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0 For example the default values: 0:(0.0) 7:(1.0) 15:(0.0) Would create a mask batch fo 16 frames, starting from black, interpolating with the chosen curve to fully white at the 8th frame, and interpolating from that to fully black at the 16th frame. """ @classmethod def INPUT_TYPES(s): return { "required": { "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 16,"min": 2, "max": 255, "step": 1}), "width": ("INT", {"default": 512,"min": 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 ScaleBatchPromptSchedule: RETURN_TYPES = ("STRING",) FUNCTION = "scaleschedule" CATEGORY = "KJNodes" DESCRIPTION = """ Scales a batch schedule from Fizz' nodes BatchPromptSchedule to a different frame count. """ @classmethod def INPUT_TYPES(s): return { "required": { "input_str": ("STRING", {"forceInput": True,"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n"}), "old_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), "new_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), }, } def scaleschedule(self, old_frame_count, input_str, new_frame_count): print("input_str:", input_str) pattern = r'"(\d+)"\s*:\s*"(.*?)"(?:,|\Z)' frame_strings = dict(re.findall(pattern, input_str)) # Calculate the scaling factor scaling_factor = (new_frame_count - 1) / (old_frame_count - 1) # Initialize a dictionary to store the new frame numbers and strings new_frame_strings = {} # Iterate over the frame numbers and strings for old_frame, string in frame_strings.items(): # Calculate the new frame number new_frame = int(round(int(old_frame) * scaling_factor)) # Store the new frame number and corresponding string new_frame_strings[new_frame] = string # Format the output string output_str = ', '.join([f'"{k}":"{v}"' for k, v in sorted(new_frame_strings.items())]) print(output_str) return (output_str,) class GetLatentsFromBatchIndexed: RETURN_TYPES = ("LATENT",) FUNCTION = "indexedlatentsfrombatch" CATEGORY = "KJNodes" DESCRIPTION = """ Selects and returns the latents at the specified indices as an latent batch. """ @classmethod def INPUT_TYPES(s): return { "required": { "latents": ("LATENT",), "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), }, } def indexedlatentsfrombatch(self, latents, indexes): samples = latents.copy() latent_samples = samples["samples"] # Parse the indexes string into a list of integers index_list = [int(index.strip()) for index in indexes.split(',')] # Convert list of indices to a PyTorch tensor indices_tensor = torch.tensor(index_list, dtype=torch.long) # Select the latents at the specified indices chosen_latents = latent_samples[indices_tensor] samples["samples"] = chosen_latents return (samples,) class CreateTextMask: RETURN_TYPES = ("IMAGE", "MASK",) FUNCTION = "createtextmask" CATEGORY = "KJNodes/text" DESCRIPTION = """ Creates a text image and mask. Looks for fonts from this folder: ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts If start_rotation and/or end_rotation are different values, creates animation between them. """ @classmethod def INPUT_TYPES(s): return { "required": { "invert": ("BOOLEAN", {"default": False}), "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}), "font_color": ("STRING", {"default": "white"}), "text": ("STRING", {"default": "HELLO!", "multiline": True}), "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}), "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}), }, } def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation): # Define the number of images in the batch batch_size = frames out = [] masks = [] rotation = start_rotation if start_rotation != end_rotation: rotation_increment = (end_rotation - start_rotation) / (batch_size - 1) font_path = folder_paths.get_full_path("kjnodes_fonts", font) # Generate the text for i in range(batch_size): image = Image.new("RGB", (width, height), "black") draw = ImageDraw.Draw(image) font = ImageFont.truetype(font_path, font_size) # Split the text into words words = text.split() # Initialize variables for line creation lines = [] current_line = [] current_line_width = 0 try: #new pillow # Iterate through words to create lines for word in words: word_width = font.getbbox(word)[2] if current_line_width + word_width <= width - 2 * text_x: current_line.append(word) current_line_width += word_width + font.getbbox(" ")[2] # Add space width else: lines.append(" ".join(current_line)) current_line = [word] current_line_width = word_width except: #old pillow for word in words: word_width = font.getsize(word)[0] if current_line_width + word_width <= width - 2 * text_x: current_line.append(word) current_line_width += word_width + font.getsize(" ")[0] # Add space width else: lines.append(" ".join(current_line)) current_line = [word] current_line_width = word_width # Add the last line if it's not empty if current_line: lines.append(" ".join(current_line)) # Draw each line of text separately y_offset = text_y for line in lines: text_width = font.getlength(line) text_height = font_size text_center_x = text_x + text_width / 2 text_center_y = y_offset + text_height / 2 try: draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) except: draw.text((text_x, y_offset), line, font=font, fill=font_color) y_offset += text_height # Move to the next line if start_rotation != end_rotation: image = image.rotate(rotation, center=(text_center_x, text_center_y)) rotation += rotation_increment image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] mask = image[:, :, :, 0] masks.append(mask) out.append(image) if invert: return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),) return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) class GrowMaskWithBlur: @classmethod def INPUT_TYPES(cls): return { "required": { "mask": ("MASK",), "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), "tapered_corners": ("BOOLEAN", {"default": True}), "flip_input": ("BOOLEAN", {"default": False}), "blur_radius": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 100, "step": 0.1 }), "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }, "optional": { "fill_holes": ("BOOLEAN", {"default": False}), }, } CATEGORY = "KJNodes/masking" RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "expand_mask" DESCRIPTION = """ # GrowMaskWithBlur - mask: Input mask or mask batch - expand: Expand or contract mask or mask batch by a given amount - incremental_expandrate: increase expand rate by a given amount per frame - tapered_corners: use tapered corners - flip_input: flip input mask - blur_radius: value higher than 0 will blur the mask - lerp_alpha: alpha value for interpolation between frames - decay_factor: decay value for interpolation between frames - fill_holes: fill holes in the mask (slow)""" def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False): alpha = lerp_alpha decay = decay_factor if flip_input: mask = 1.0 - mask c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu() out = [] previous_output = None current_expand = expand for m in growmask: output = m.numpy() for _ in range(abs(round(current_expand))): if current_expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) if current_expand < 0: current_expand -= abs(incremental_expandrate) else: current_expand += abs(incremental_expandrate) if fill_holes: binary_mask = output > 0 output = scipy.ndimage.binary_fill_holes(binary_mask) output = output.astype(np.float32) * 255 output = torch.from_numpy(output) if alpha < 1.0 and previous_output is not None: # Interpolate between the previous and current frame output = alpha * output + (1 - alpha) * previous_output if decay < 1.0 and previous_output is not None: # Add the decayed previous output to the current frame output += decay * previous_output output = output / output.max() previous_output = output out.append(output) if blur_radius != 0: # Convert the tensor list to PIL images, apply blur, and convert back for idx, tensor in enumerate(out): # Convert tensor to PIL image pil_image = tensor2pil(tensor.cpu().detach())[0] # Apply Gaussian blur pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius)) # Convert back to tensor out[idx] = pil2tensor(pil_image) blurred = torch.cat(out, dim=0) return (blurred, 1.0 - blurred) else: return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) class ColorToMask: RETURN_TYPES = ("MASK",) FUNCTION = "clip" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Converts chosen RGB value to a mask. 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 ConditioningMultiCombine: @classmethod def INPUT_TYPES(s): return { "required": { "inputcount": ("INT", {"default": 2, "min": 2, "max": 20, "step": 1}), "conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", ), }, } RETURN_TYPES = ("CONDITIONING", "INT") RETURN_NAMES = ("combined", "inputcount") FUNCTION = "combine" CATEGORY = "KJNodes/masking/conditioning" DESCRIPTION = """ Combines multiple conditioning nodes into one """ def combine(self, inputcount, **kwargs): from nodes import ConditioningCombine cond_combine_node = ConditioningCombine() cond = kwargs["conditioning_1"] for c in range(1, inputcount): new_cond = kwargs[f"conditioning_{c + 1}"] cond = cond_combine_node.combine(new_cond, cond)[0] return (cond, inputcount,) class 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 JoinStrings: @classmethod def INPUT_TYPES(cls): return { "required": { "string1": ("STRING", {"default": '', "forceInput": True}), "string2": ("STRING", {"default": '', "forceInput": True}), "delimiter": ("STRING", {"default": ' ', "multiline": False}), } } RETURN_TYPES = ("STRING",) FUNCTION = "joinstring" CATEGORY = "KJNodes/constants" def joinstring(self, string1, string2, delimiter): joined_string = string1 + delimiter + string2 return (joined_string, ) class JoinStringMulti: @classmethod def INPUT_TYPES(s): return { "required": { "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), "string_1": ("STRING", {"default": '', "forceInput": True}), "string_2": ("STRING", {"default": '', "forceInput": True}), "delimiter": ("STRING", {"default": ' ', "multiline": False}), "return_list": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("STRING",) RETURN_NAMES = ("string",) FUNCTION = "combine" CATEGORY = "KJNodes" DESCRIPTION = """ Creates single string, or a list of strings, from multiple input strings. You can set how many inputs the node has, with the **inputcount** and clicking update. """ def combine(self, inputcount, delimiter, **kwargs): string = kwargs["string_1"] return_list = kwargs["return_list"] strings = [string] # Initialize a list with the first string for c in range(1, inputcount): new_string = kwargs[f"string_{c + 1}"] if return_list: strings.append(new_string) # Add new string to the list else: string = string + delimiter + new_string if return_list: return (strings,) # Return the list of strings else: return (string,) # Return the combined string class CondPassThrough: @classmethod def INPUT_TYPES(s): return { "required": { "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), }, } RETURN_TYPES = ("CONDITIONING", "CONDITIONING",) RETURN_NAMES = ("positive", "negative") FUNCTION = "passthrough" CATEGORY = "KJNodes/misc" DESCRIPTION = """ Simply passes through the positive and negative conditioning, workaround for Set node not allowing bypassed inputs. """ def passthrough(self, positive, negative): return (positive, negative,) def append_helper(t, mask, c, set_area_to_bounds, strength): n = [t[0], t[1].copy()] _, h, w = mask.shape n[1]['mask'] = mask n[1]['set_area_to_bounds'] = set_area_to_bounds n[1]['mask_strength'] = strength c.append(n) class ConditioningSetMaskAndCombine: @classmethod def INPUT_TYPES(cls): return { "required": { "positive_1": ("CONDITIONING", ), "negative_1": ("CONDITIONING", ), "positive_2": ("CONDITIONING", ), "negative_2": ("CONDITIONING", ), "mask_1": ("MASK", ), "mask_2": ("MASK", ), "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), } } RETURN_TYPES = ("CONDITIONING","CONDITIONING",) RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" CATEGORY = "KJNodes/masking/conditioning" DESCRIPTION = """ Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes """ def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength): c = [] c2 = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask_1.shape) < 3: mask_1 = mask_1.unsqueeze(0) if len(mask_2.shape) < 3: mask_2 = mask_2.unsqueeze(0) for t in positive_1: append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) for t in positive_2: append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) for t in negative_1: append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) for t in negative_2: append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) return (c, c2) class ConditioningSetMaskAndCombine3: @classmethod def INPUT_TYPES(cls): return { "required": { "positive_1": ("CONDITIONING", ), "negative_1": ("CONDITIONING", ), "positive_2": ("CONDITIONING", ), "negative_2": ("CONDITIONING", ), "positive_3": ("CONDITIONING", ), "negative_3": ("CONDITIONING", ), "mask_1": ("MASK", ), "mask_2": ("MASK", ), "mask_3": ("MASK", ), "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), } } RETURN_TYPES = ("CONDITIONING","CONDITIONING",) RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" CATEGORY = "KJNodes/masking/conditioning" DESCRIPTION = """ Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes """ def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength): c = [] c2 = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask_1.shape) < 3: mask_1 = mask_1.unsqueeze(0) if len(mask_2.shape) < 3: mask_2 = mask_2.unsqueeze(0) if len(mask_3.shape) < 3: mask_3 = mask_3.unsqueeze(0) for t in positive_1: append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) for t in positive_2: append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) for t in positive_3: append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) for t in negative_1: append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) for t in negative_2: append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) for t in negative_3: append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) return (c, c2) class ConditioningSetMaskAndCombine4: @classmethod def INPUT_TYPES(cls): return { "required": { "positive_1": ("CONDITIONING", ), "negative_1": ("CONDITIONING", ), "positive_2": ("CONDITIONING", ), "negative_2": ("CONDITIONING", ), "positive_3": ("CONDITIONING", ), "negative_3": ("CONDITIONING", ), "positive_4": ("CONDITIONING", ), "negative_4": ("CONDITIONING", ), "mask_1": ("MASK", ), "mask_2": ("MASK", ), "mask_3": ("MASK", ), "mask_4": ("MASK", ), "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), } } RETURN_TYPES = ("CONDITIONING","CONDITIONING",) RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" CATEGORY = "KJNodes/masking/conditioning" DESCRIPTION = """ Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes """ def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength): c = [] c2 = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask_1.shape) < 3: mask_1 = mask_1.unsqueeze(0) if len(mask_2.shape) < 3: mask_2 = mask_2.unsqueeze(0) if len(mask_3.shape) < 3: mask_3 = mask_3.unsqueeze(0) if len(mask_4.shape) < 3: mask_4 = mask_4.unsqueeze(0) for t in positive_1: append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) for t in positive_2: append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) for t in positive_3: append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) for t in positive_4: append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) for t in negative_1: append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) for t in negative_2: append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) for t in negative_3: append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) for t in negative_4: append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) return (c, c2) class ConditioningSetMaskAndCombine5: @classmethod def INPUT_TYPES(cls): return { "required": { "positive_1": ("CONDITIONING", ), "negative_1": ("CONDITIONING", ), "positive_2": ("CONDITIONING", ), "negative_2": ("CONDITIONING", ), "positive_3": ("CONDITIONING", ), "negative_3": ("CONDITIONING", ), "positive_4": ("CONDITIONING", ), "negative_4": ("CONDITIONING", ), "positive_5": ("CONDITIONING", ), "negative_5": ("CONDITIONING", ), "mask_1": ("MASK", ), "mask_2": ("MASK", ), "mask_3": ("MASK", ), "mask_4": ("MASK", ), "mask_5": ("MASK", ), "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "set_cond_area": (["default", "mask bounds"],), } } RETURN_TYPES = ("CONDITIONING","CONDITIONING",) RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" CATEGORY = "KJNodes/masking/conditioning" DESCRIPTION = """ Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes """ def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength): c = [] c2 = [] set_area_to_bounds = False if set_cond_area != "default": set_area_to_bounds = True if len(mask_1.shape) < 3: mask_1 = mask_1.unsqueeze(0) if len(mask_2.shape) < 3: mask_2 = mask_2.unsqueeze(0) if len(mask_3.shape) < 3: mask_3 = mask_3.unsqueeze(0) if len(mask_4.shape) < 3: mask_4 = mask_4.unsqueeze(0) if len(mask_5.shape) < 3: mask_5 = mask_5.unsqueeze(0) for t in positive_1: append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) for t in positive_2: append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) for t in positive_3: append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) for t in positive_4: append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) for t in positive_5: append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength) for t in negative_1: append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) for t in negative_2: append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) for t in negative_3: append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) for t in negative_4: append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) for t in negative_5: append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength) return (c, c2) class VRAM_Debug: @classmethod def INPUT_TYPES(s): return { "required": { "empty_cache": ("BOOLEAN", {"default": True}), "gc_collect": ("BOOLEAN", {"default": True}), "unload_all_models": ("BOOLEAN", {"default": False}), }, "optional": { "any_input": (any, {}), "image_pass": ("IMAGE",), "model_pass": ("MODEL",), } } RETURN_TYPES = (any, "IMAGE","MODEL","INT", "INT",) RETURN_NAMES = ("any_output", "image_pass", "model_pass", "freemem_before", "freemem_after") FUNCTION = "VRAMdebug" CATEGORY = "KJNodes/misc" DESCRIPTION = """ Returns the inputs unchanged, they are only used as triggers, and performs comfy model management functions and garbage collection, reports free VRAM before and after the operations. """ def VRAMdebug(self, gc_collect,empty_cache, unload_all_models, image_pass=None, model_pass=None, any_input=None): freemem_before = model_management.get_free_memory() print("VRAMdebug: free memory before: ", freemem_before) if empty_cache: model_management.soft_empty_cache() if unload_all_models: model_management.unload_all_models() if gc_collect: import gc gc.collect() freemem_after = model_management.get_free_memory() print("VRAMdebug: free memory after: ", freemem_after) print("VRAMdebug: freed memory: ", freemem_after - freemem_before) return (any_input, image_pass, model_pass, freemem_before, freemem_after) class SomethingToString: @classmethod def INPUT_TYPES(s): return { "required": { "input": (any, {}), }, "optional": { "prefix": ("STRING", {"default": ""}), "suffix": ("STRING", {"default": ""}), } } RETURN_TYPES = ("STRING",) FUNCTION = "stringify" CATEGORY = "KJNodes/text" DESCRIPTION = """ Converts any type to a string. """ def stringify(self, input, prefix="", suffix=""): if isinstance(input, (int, float, bool)): stringified = str(input) elif isinstance(input, list): print("input is a list") stringified = ', '.join(str(item) for item in input) else: return if prefix: # Check if prefix is not empty stringified = prefix + stringified # Add the prefix if suffix: # Check if suffix is not empty stringified = stringified + suffix # Add the suffix return (stringified,) class Sleep: @classmethod def INPUT_TYPES(s): return { "required": { "input": (any, {}), "minutes": ("INT", {"default": 0, "min": 0, "max": 1439}), "seconds": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 59.99, "step": 0.01}), }, } RETURN_TYPES = (any,) FUNCTION = "sleepdelay" CATEGORY = "KJNodes/misc" DESCRIPTION = """ Delays the execution for the input amount of time. """ def sleepdelay(self, input, minutes, seconds): total_seconds = minutes * 60 + seconds time.sleep(total_seconds) return input, class EmptyLatentImagePresets: @classmethod def INPUT_TYPES(cls): return { "required": { "dimensions": ( [ '512 x 512', '768 x 512', '960 x 512', '1024 x 512', '1536 x 640', '1344 x 768', '1216 x 832', '1152 x 896', '1024 x 1024', ], { "default": '512 x 512' }), "invert": ("BOOLEAN", {"default": False}), "batch_size": ("INT", { "default": 1, "min": 1, "max": 4096 }), }, } RETURN_TYPES = ("LATENT", "INT", "INT") RETURN_NAMES = ("Latent", "Width", "Height") FUNCTION = "generate" CATEGORY = "KJNodes" def generate(self, dimensions, invert, batch_size): from nodes import EmptyLatentImage result = [x.strip() for x in dimensions.split('x')] if invert: width = int(result[1].split(' ')[0]) height = int(result[0]) else: width = int(result[0]) height = int(result[1].split(' ')[0]) latent = EmptyLatentImage().generate(width, height, batch_size)[0] return (latent, int(width), int(height),) class BatchCLIPSeg: def __init__(self): pass @classmethod def INPUT_TYPES(s): return {"required": { "images": ("IMAGE",), "text": ("STRING", {"multiline": False}), "threshold": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.001}), "binary_mask": ("BOOLEAN", {"default": True}), "combine_mask": ("BOOLEAN", {"default": False}), "use_cuda": ("BOOLEAN", {"default": True}), }, } CATEGORY = "KJNodes/masking" RETURN_TYPES = ("MASK",) RETURN_NAMES = ("Mask",) FUNCTION = "segment_image" DESCRIPTION = """ Segments an image or batch of images using CLIPSeg. """ def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda): from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation out = [] height, width, _ = images[0].shape if use_cuda and torch.cuda.is_available(): device = torch.device("cuda") else: device = torch.device("cpu") dtype = model_management.unet_dtype() model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") model.to(dtype) model.to(device) images = images.to(device) processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") pbar = ProgressBar(images.shape[0]) 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(): for image in images: image = (image* 255).type(torch.uint8) prompt = text input_prc = processor(text=prompt, images=image, return_tensors="pt") # Move the processed input to the device for key in input_prc: input_prc[key] = input_prc[key].to(device) outputs = model(**input_prc) tensor = torch.sigmoid(outputs[0]) tensor_thresholded = torch.where(tensor > threshold, tensor, torch.tensor(0, dtype=torch.float)) tensor_normalized = (tensor_thresholded - tensor_thresholded.min()) / (tensor_thresholded.max() - tensor_thresholded.min()) tensor = tensor_normalized # Resize the mask if len(tensor.shape) == 3: tensor = tensor.unsqueeze(0) resized_tensor = F.interpolate(tensor, size=(height, width), mode='nearest') # Remove the extra dimensions resized_tensor = resized_tensor[0, 0, :, :] pbar.update(1) out.append(resized_tensor) results = torch.stack(out).cpu().float() if combine_mask: combined_results = torch.max(results, dim=0)[0] results = combined_results.unsqueeze(0).repeat(len(images),1,1) if binary_mask: results = results.round() return results, class GetMaskSize: @classmethod def INPUT_TYPES(s): return {"required": { "mask": ("MASK",), }} RETURN_TYPES = ("MASK","INT", "INT", ) RETURN_NAMES = ("mask", "width", "height",) FUNCTION = "getsize" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Returns the width and height of the mask, and passes through the mask unchanged. """ def getsize(self, mask): width = mask.shape[2] height = mask.shape[1] return (mask, width, height,) 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: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }), "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, "display": "number" }), "keep_proportions": ("BOOLEAN", { "default": False }), } } RETURN_TYPES = ("MASK", "INT", "INT",) RETURN_NAMES = ("mask", "width", "height",) FUNCTION = "resize" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Resizes the mask or batch of masks to the specified width and height. """ def resize(self, mask, width, height, keep_proportions): if keep_proportions: _, oh, ow, _ = mask.shape width = ow if width == 0 else width height = oh if height == 0 else height ratio = min(width / ow, height / oh) width = round(ow*ratio) height = round(oh*ratio) outputs = mask.unsqueeze(0) # Add an extra dimension for batch size outputs = F.interpolate(outputs, size=(height, width), mode="nearest") outputs = outputs.squeeze(0) # Remove the extra dimension after interpolation return(outputs, outputs.shape[2], outputs.shape[1],) class OffsetMask: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }), "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }), "roll": ("BOOLEAN", { "default": False }), "incremental": ("BOOLEAN", { "default": False }), "padding_mode": ( [ 'empty', 'border', 'reflection', ], { "default": 'empty' }), } } RETURN_TYPES = ("MASK",) RETURN_NAMES = ("mask",) FUNCTION = "offset" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Offsets the mask by the specified amount. - mask: Input mask or mask batch - x: Horizontal offset - y: Vertical offset - angle: Angle in degrees - roll: roll edge wrapping - duplication_factor: Number of times to duplicate the mask to form a batch - border padding_mode: Padding mode for the mask """ def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"): # Create duplicates of the mask batch mask = mask.repeat(duplication_factor, 1, 1).clone() batch_size, height, width = mask.shape if angle != 0 and incremental: for i in range(batch_size): rotation_angle = angle * (i+1) mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0) elif angle > 0: for i in range(batch_size): mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0) if roll: if incremental: for i in range(batch_size): shift_x = min(x*(i+1), width-1) shift_y = min(y*(i+1), height-1) if shift_x != 0: mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1) if shift_y != 0: mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0) else: shift_x = min(x, width-1) shift_y = min(y, height-1) if shift_x != 0: mask = torch.roll(mask, shifts=shift_x, dims=2) if shift_y != 0: mask = torch.roll(mask, shifts=shift_y, dims=1) else: for i in range(batch_size): if incremental: temp_x = min(x * (i+1), width-1) temp_y = min(y * (i+1), height-1) else: temp_x = min(x, width-1) temp_y = min(y, height-1) if temp_x > 0: if padding_mode == 'empty': mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode) elif temp_x < 0: if padding_mode == 'empty': mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode) if temp_y > 0: if padding_mode == 'empty': mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode) elif temp_y < 0: if padding_mode == 'empty': mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0) elif padding_mode in ['replicate', 'reflect']: mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode) return mask, class WidgetToString: @classmethod def IS_CHANGED(cls, **kwargs): return float("NaN") @classmethod def INPUT_TYPES(cls): return { "required": { "id": ("INT", {"default": 0}), "widget_name": ("STRING", {"multiline": False}), "return_all": ("BOOLEAN", {"default": False}), }, "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "prompt": "PROMPT"}, } RETURN_TYPES = ("STRING", ) FUNCTION = "get_widget_value" CATEGORY = "KJNodes/text" DESCRIPTION = """ Selects a node and it's specified widget and outputs the value as a string. To see node id's, enable node id display from Manager badge menu. """ def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, return_all=False): workflow = extra_pnginfo["workflow"] print(workflow) results = [] for node in workflow["nodes"]: print(node) node_id = node["id"] if node_id != id: continue values = prompt[str(node_id)] if "inputs" in values: if return_all: results.append(', '.join(f'{k}: {str(v)}' for k, v in values["inputs"].items())) elif widget_name in values["inputs"]: v = str(values["inputs"][widget_name]) # Convert to string here return (v, ) else: raise NameError(f"Widget not found: {id}.{widget_name}") if not results: raise NameError(f"Node not found: {id}") return (', '.join(results).strip(', '), ) class CreateShapeMask: RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) FUNCTION = "createshapemask" CATEGORY = "KJNodes/masking/generate" 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 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 rng = np.random.default_rng(seed) out = [] coords = coordinate_grid((frame_width, frame_height)) # Calculate the number of frames for each transition frames_per_transition = frames // transitions # Generate a base set of parameters base_params = { "coords": random_transform(coords, rng), "depth": depth, "distortion": distortion, } for t in range(transitions): # Generate a second set of parameters that is at most max_diff away from the base parameters params1 = base_params.copy() params2 = base_params.copy() params1['coords'] = random_transform(coords, rng) params2['coords'] = random_transform(coords, rng) for i in range(frames_per_transition): # Compute the interpolation factor alpha = i / frames_per_transition # Interpolate between the two sets of parameters params = params1.copy() params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords'] tex = magic(**params) dpi = frame_width / 10 fig = plt.figure(figsize=(10, 10), dpi=dpi) ax = fig.add_subplot(111) plt.subplots_adjust(left=0, right=1, bottom=0, top=1) ax.get_yaxis().set_ticks([]) ax.get_xaxis().set_ticks([]) ax.imshow(tex, aspect='auto') fig.canvas.draw() img = np.array(fig.canvas.renderer._renderer) plt.close(fig) pil_img = Image.fromarray(img).convert("L") mask = torch.tensor(np.array(pil_img)) / 255.0 out.append(mask) return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) class BboxToInt: @classmethod def INPUT_TYPES(cls): return { "required": { "bboxes": ("BBOX",), "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}), }, } RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",) RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",) FUNCTION = "bboxtoint" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Returns selected index from bounding box list as integers. """ def bboxtoint(self, bboxes, index): x_min, y_min, width, height = bboxes[index] center_x = int(x_min + width / 2) center_y = int(y_min + height / 2) return (x_min, y_min, width, height, center_x, center_y,) class BboxVisualize: @classmethod def INPUT_TYPES(cls): return { "required": { "images": ("IMAGE",), "bboxes": ("BBOX",), "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}), }, } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("images",) FUNCTION = "visualizebbox" DESCRIPTION = """ Visualizes the specified bbox on the image. """ CATEGORY = "KJNodes/masking" def visualizebbox(self, bboxes, images, line_width): image_list = [] for image, bbox in zip(images, bboxes): x_min, y_min, width, height = bbox image = image.permute(2, 0, 1) img_with_bbox = image.clone() # Define the color for the bbox, e.g., red color = torch.tensor([1, 0, 0], dtype=torch.float32) # Draw lines for each side of the bbox with the specified line width for lw in range(line_width): # Top horizontal line img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None] # Bottom horizontal line img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None] # Left vertical line img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None] # Right vertical line img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None] img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0) image_list.append(img_with_bbox) return (torch.cat(image_list, dim=0),) class DummyLatentOut: @classmethod def INPUT_TYPES(cls): return { "required": { "latent": ("LATENT",), } } RETURN_TYPES = ("LATENT",) FUNCTION = "dummy" CATEGORY = "KJNodes/misc" OUTPUT_NODE = True DESCRIPTION = """ Does nothing, used to trigger generic workflow output. A way to get previews in the UI without saving anything to disk. """ def dummy(self, latent): return (latent,) class FlipSigmasAdjusted: @classmethod def INPUT_TYPES(s): return {"required": {"sigmas": ("SIGMAS", ), "divide_by_last_sigma": ("BOOLEAN", {"default": False}), "divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}), "offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}), } } RETURN_TYPES = ("SIGMAS", "STRING",) RETURN_NAMES = ("SIGMAS", "sigmas_string",) CATEGORY = "KJNodes/noise" FUNCTION = "get_sigmas_adjusted" def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by): sigmas = sigmas.flip(0) if sigmas[0] == 0: sigmas[0] = 0.0001 adjusted_sigmas = sigmas.clone() #offset sigma for i in range(1, len(sigmas)): offset_index = i - offset_by if 0 <= offset_index < len(sigmas): adjusted_sigmas[i] = sigmas[offset_index] else: adjusted_sigmas[i] = 0.0001 if adjusted_sigmas[0] == 0: adjusted_sigmas[0] = 0.0001 if divide_by_last_sigma: adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1] sigma_np_array = adjusted_sigmas.numpy() array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf) adjusted_sigmas = adjusted_sigmas / divide_by return (adjusted_sigmas, array_string,) class CustomSigmas: @classmethod def INPUT_TYPES(s): return {"required": { "sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}), "interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), } } RETURN_TYPES = ("SIGMAS",) RETURN_NAMES = ("SIGMAS",) CATEGORY = "KJNodes/noise" FUNCTION = "customsigmas" DESCRIPTION = """ Creates a sigmas tensor from a string of comma separated values. Examples: Nvidia's optimized AYS 10 step schedule for SD 1.5: 14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029 SDXL: 14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029 SVD: 700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002 """ def customsigmas(self, sigmas_string, interpolate_to_steps): sigmas_list = sigmas_string.split(', ') sigmas_float_list = [float(sigma) for sigma in sigmas_list] sigmas_tensor = torch.tensor(sigmas_float_list) if len(sigmas_tensor) != interpolate_to_steps: sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps) return (sigmas_tensor,) def loglinear_interp(self, t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ t_steps_np = t_steps.numpy() xs = np.linspace(0, 1, len(t_steps_np)) ys = np.log(t_steps_np[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() interped_ys_tensor = torch.tensor(interped_ys) return interped_ys_tensor class InjectNoiseToLatent: @classmethod def INPUT_TYPES(s): return {"required": { "latents":("LATENT",), "strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}), "noise": ("LATENT",), "normalize": ("BOOLEAN", {"default": False}), "average": ("BOOLEAN", {"default": False}), }, "optional":{ "mask": ("MASK", ), "mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}), "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), } } RETURN_TYPES = ("LATENT",) FUNCTION = "injectnoise" CATEGORY = "KJNodes/noise" def injectnoise(self, latents, strength, noise, normalize, average, mix_randn_amount=0, seed=None, mask=None): samples = latents.copy() if latents["samples"].shape != noise["samples"].shape: raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape") if average: noised = (samples["samples"].clone() + noise["samples"].clone()) / 2 else: noised = samples["samples"].clone() + noise["samples"].clone() * strength if normalize: noised = noised / noised.std() if mask is not None: mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noised.shape[2], noised.shape[3]), mode="bilinear") mask = mask.expand((-1,noised.shape[1],-1,-1)) if mask.shape[0] < noised.shape[0]: mask = mask.repeat((noised.shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]] noised = mask * noised + (1-mask) * latents["samples"] if mix_randn_amount > 0: if seed is not None: torch.manual_seed(seed) rand_noise = torch.randn_like(noised) noised = ((1 - mix_randn_amount) * noised + mix_randn_amount * rand_noise) / ((mix_randn_amount**2 + (1-mix_randn_amount)**2) ** 0.5) samples["samples"] = noised return (samples,) class SoundReactive: @classmethod def INPUT_TYPES(s): return {"required": { "sound_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 99999, "step": 0.01}), "start_range_hz": ("INT", {"default": 150, "min": 0, "max": 9999, "step": 1}), "end_range_hz": ("INT", {"default": 2000, "min": 0, "max": 9999, "step": 1}), "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 99999, "step": 0.01}), "smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "normalize": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("FLOAT","INT",) RETURN_NAMES =("sound_level", "sound_level_int",) FUNCTION = "react" CATEGORY = "KJNodes/audio" DESCRIPTION = """ Reacts to the sound level of the input. Uses your browsers sound input options and requires. Meant to be used with realtime diffusion with autoqueue. """ def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize): sound_level *= multiplier if normalize: sound_level /= 255 sound_level_int = int(sound_level) return (sound_level, sound_level_int, ) class GenerateNoise: @classmethod def INPUT_TYPES(s): return {"required": { "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), "multiplier": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 4096, "step": 0.01}), "constant_batch_noise": ("BOOLEAN", {"default": False}), "normalize": ("BOOLEAN", {"default": False}), }, "optional": { "model": ("MODEL", ), "sigmas": ("SIGMAS", ), } } RETURN_TYPES = ("LATENT",) FUNCTION = "generatenoise" CATEGORY = "KJNodes/noise" DESCRIPTION = """ Generates noise for injection or to be used as empty latents on samplers with add_noise off. """ def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None): generator = torch.manual_seed(seed) noise = torch.randn([batch_size, 4, height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") if sigmas is not None: sigma = sigmas[0] - sigmas[-1] sigma /= model.model.latent_format.scale_factor noise *= sigma noise *=multiplier if normalize: noise = noise / noise.std() if constant_batch_noise: noise = noise[0].repeat(batch_size, 1, 1, 1) return ({"samples":noise}, ) def camera_embeddings(elevation, azimuth): elevation = torch.as_tensor([elevation]) azimuth = torch.as_tensor([azimuth]) embeddings = torch.stack( [ torch.deg2rad( (90 - elevation) - (90) ), # Zero123 polar is 90-elevation torch.sin(torch.deg2rad(azimuth)), torch.cos(torch.deg2rad(azimuth)), torch.deg2rad( 90 - torch.full_like(elevation, 0) ), ], dim=-1).unsqueeze(1) return embeddings def interpolate_angle(start, end, fraction): # Calculate the difference in angles and adjust for wraparound if necessary diff = (end - start + 540) % 360 - 180 # Apply fraction to the difference interpolated = start + fraction * diff # Normalize the result to be within the range of -180 to 180 return (interpolated + 180) % 360 - 180 class StableZero123_BatchSchedule: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), "elevation_points_string": ("STRING", {"default": "0:(0.0),\n7:(0.0),\n15:(0.0)\n", "multiline": True}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "KJNodes/experimental" def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) def ease_in(t): return t * t def ease_out(t): return 1 - (1 - t) * (1 - t) def ease_in_out(t): return 3 * t * t - 2 * t * t * t # Parse the azimuth input string into a list of tuples azimuth_points = [] azimuth_points_string = azimuth_points_string.rstrip(',\n') for point_str in azimuth_points_string.split(','): frame_str, azimuth_str = point_str.split(':') frame = int(frame_str.strip()) azimuth = float(azimuth_str.strip()[1:-1]) azimuth_points.append((frame, azimuth)) # Sort the points by frame number azimuth_points.sort(key=lambda x: x[0]) # Parse the elevation input string into a list of tuples elevation_points = [] elevation_points_string = elevation_points_string.rstrip(',\n') for point_str in elevation_points_string.split(','): frame_str, elevation_str = point_str.split(':') frame = int(frame_str.strip()) elevation_val = float(elevation_str.strip()[1:-1]) elevation_points.append((frame, elevation_val)) # Sort the points by frame number elevation_points.sort(key=lambda x: x[0]) # Index of the next point to interpolate towards next_point = 1 next_elevation_point = 1 positive_cond_out = [] positive_pooled_out = [] negative_cond_out = [] negative_pooled_out = [] #azimuth interpolation for i in range(batch_size): # Find the interpolated azimuth for the current frame while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: next_point += 1 # If next_point is equal to the length of points, we've gone past the last point if next_point == len(azimuth_points): next_point -= 1 # Set next_point to the last index of points prev_point = max(next_point - 1, 0) # Ensure prev_point is not less than 0 # Calculate fraction if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: # Prevent division by zero fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) if interpolation == "ease_in": fraction = ease_in(fraction) elif interpolation == "ease_out": fraction = ease_out(fraction) elif interpolation == "ease_in_out": fraction = ease_in_out(fraction) # Use the new interpolate_angle function interpolated_azimuth = interpolate_angle(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) else: interpolated_azimuth = azimuth_points[prev_point][1] # Interpolate the elevation next_elevation_point = 1 while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: next_elevation_point += 1 if next_elevation_point == len(elevation_points): next_elevation_point -= 1 prev_elevation_point = max(next_elevation_point - 1, 0) if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) if interpolation == "ease_in": fraction = ease_in(fraction) elif interpolation == "ease_out": fraction = ease_out(fraction) elif interpolation == "ease_in_out": fraction = ease_in_out(fraction) interpolated_elevation = interpolate_angle(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) else: interpolated_elevation = elevation_points[prev_elevation_point][1] cam_embeds = camera_embeddings(interpolated_elevation, interpolated_azimuth) cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1) positive_pooled_out.append(t) positive_cond_out.append(cond) negative_pooled_out.append(torch.zeros_like(t)) negative_cond_out.append(torch.zeros_like(pooled)) # Concatenate the conditions and pooled outputs final_positive_cond = torch.cat(positive_cond_out, dim=0) final_positive_pooled = torch.cat(positive_pooled_out, dim=0) final_negative_cond = torch.cat(negative_cond_out, dim=0) final_negative_pooled = torch.cat(negative_pooled_out, dim=0) # Structure the final output final_positive = [[final_positive_cond, {"concat_latent_image": final_positive_pooled}]] final_negative = [[final_negative_cond, {"concat_latent_image": final_negative_pooled}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) return (final_positive, final_negative, {"samples": latent}) def linear_interpolate(start, end, fraction): return start + (end - start) * fraction class SV3D_BatchSchedule: @classmethod def INPUT_TYPES(s): return {"required": { "clip_vision": ("CLIP_VISION",), "init_image": ("IMAGE",), "vae": ("VAE",), "width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}), "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}), "elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}), }} RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" CATEGORY = "KJNodes/experimental" DESCRIPTION = """ Allow scheduling of the azimuth and elevation conditions for SV3D. Note that SV3D is still a video model and the schedule needs to always go forward https://huggingface.co/stabilityai/sv3d """ def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) encode_pixels = pixels[:,:,:,:3] t = vae.encode(encode_pixels) def ease_in(t): return t * t def ease_out(t): return 1 - (1 - t) * (1 - t) def ease_in_out(t): return 3 * t * t - 2 * t * t * t # Parse the azimuth input string into a list of tuples azimuth_points = [] azimuth_points_string = azimuth_points_string.rstrip(',\n') for point_str in azimuth_points_string.split(','): frame_str, azimuth_str = point_str.split(':') frame = int(frame_str.strip()) azimuth = float(azimuth_str.strip()[1:-1]) azimuth_points.append((frame, azimuth)) # Sort the points by frame number azimuth_points.sort(key=lambda x: x[0]) # Parse the elevation input string into a list of tuples elevation_points = [] elevation_points_string = elevation_points_string.rstrip(',\n') for point_str in elevation_points_string.split(','): frame_str, elevation_str = point_str.split(':') frame = int(frame_str.strip()) elevation_val = float(elevation_str.strip()[1:-1]) elevation_points.append((frame, elevation_val)) # Sort the points by frame number elevation_points.sort(key=lambda x: x[0]) # Index of the next point to interpolate towards next_point = 1 next_elevation_point = 1 elevations = [] azimuths = [] # For azimuth interpolation for i in range(batch_size): # Find the interpolated azimuth for the current frame while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: next_point += 1 if next_point == len(azimuth_points): next_point -= 1 prev_point = max(next_point - 1, 0) if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) # Apply the ease function to the fraction if interpolation == "ease_in": fraction = ease_in(fraction) elif interpolation == "ease_out": fraction = ease_out(fraction) elif interpolation == "ease_in_out": fraction = ease_in_out(fraction) interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) else: interpolated_azimuth = azimuth_points[prev_point][1] # Interpolate the elevation next_elevation_point = 1 while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: next_elevation_point += 1 if next_elevation_point == len(elevation_points): next_elevation_point -= 1 prev_elevation_point = max(next_elevation_point - 1, 0) if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) # Apply the ease function to the fraction if interpolation == "ease_in": fraction = ease_in(fraction) elif interpolation == "ease_out": fraction = ease_out(fraction) elif interpolation == "ease_in_out": fraction = ease_in_out(fraction) interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) else: interpolated_elevation = elevation_points[prev_elevation_point][1] azimuths.append(interpolated_azimuth) elevations.append(interpolated_elevation) print("azimuths", azimuths) print("elevations", elevations) # Structure the final output final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) return (final_positive, final_negative, {"samples": latent}) class RemapMaskRange: @classmethod def INPUT_TYPES(s): return { "required": { "mask": ("MASK",), "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), } } RETURN_TYPES = ("MASK",) RETURN_NAMES = ("mask",) FUNCTION = "remap" CATEGORY = "KJNodes/masking" DESCRIPTION = """ Sets new min and max values for the mask. """ def remap(self, mask, min, max): # Find the maximum value in the mask mask_max = torch.max(mask) # If the maximum mask value is zero, avoid division by zero by setting it to 1 mask_max = mask_max if mask_max > 0 else 1 # Scale the mask values to the new range defined by min and max # The highest pixel value in the mask will be scaled to max scaled_mask = (mask / mask_max) * (max - min) + min # Clamp the values to ensure they are within [0.0, 1.0] scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0) return (scaled_mask, ) class LoadResAdapterNormalization: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "resadapter_path": (folder_paths.get_filename_list("checkpoints"), ) } } RETURN_TYPES = ("MODEL",) FUNCTION = "load_res_adapter" CATEGORY = "KJNodes/experimental" def load_res_adapter(self, model, resadapter_path): print("ResAdapter: Checking ResAdapter path") resadapter_full_path = folder_paths.get_full_path("checkpoints", resadapter_path) if not os.path.exists(resadapter_full_path): raise Exception("Invalid model path") else: print("ResAdapter: Loading ResAdapter normalization weights") prefix_to_remove = 'diffusion_model.' model_clone = model.clone() norm_state_dict = comfy.utils.load_torch_file(resadapter_full_path) new_values = {key[len(prefix_to_remove):]: value for key, value in norm_state_dict.items() if key.startswith(prefix_to_remove)} print("ResAdapter: Attempting to add patches with ResAdapter weights") try: for key in model.model.diffusion_model.state_dict().keys(): if key in new_values: original_tensor = model.model.diffusion_model.state_dict()[key] new_tensor = new_values[key].to(model.model.diffusion_model.dtype) if original_tensor.shape == new_tensor.shape: model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor) else: print("ResAdapter: No match for key: ",key) except: raise Exception("Could not patch model, this way of patching was added to ComfyUI on March 3rd 2024, is your ComfyUI up to date?") print("ResAdapter: Added resnet normalization patches") return (model_clone, ) class Superprompt: @classmethod def INPUT_TYPES(s): return { "required": { "instruction_prompt": ("STRING", {"default": 'Expand the following prompt to add more detail', "multiline": True}), "prompt": ("STRING", {"default": '', "multiline": True, "forceInput": True}), "max_new_tokens": ("INT", {"default": 128, "min": 1, "max": 4096, "step": 1}), } } RETURN_TYPES = ("STRING",) FUNCTION = "process" CATEGORY = "KJNodes/text" DESCRIPTION = """ # SuperPrompt A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions. Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts. https://huggingface.co/roborovski/superprompt-v1 """ def process(self, instruction_prompt, prompt, max_new_tokens): device = model_management.get_torch_device() from transformers import T5Tokenizer, T5ForConditionalGeneration checkpoint_path = os.path.join(script_directory, "models","superprompt-v1") tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False) model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device) model.to(device) input_text = instruction_prompt + ": " + prompt print(input_text) input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) outputs = model.generate(input_ids, max_new_tokens=max_new_tokens) out = (tokenizer.decode(outputs[0])) out = out.replace('', '') out = out.replace('', '') print(out) return (out, ) class CameraPoseVisualizer: @classmethod def INPUT_TYPES(s): return {"required": { "pose_file_path": ("STRING", {"default": '', "multiline": False}), "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), "use_exact_fx": ("BOOLEAN", {"default": False}), "relative_c2w": ("BOOLEAN", {"default": True}), "use_viewer": ("BOOLEAN", {"default": False}), }, "optional": { "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "plot" CATEGORY = "KJNodes/misc" DESCRIPTION = """ Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. """ def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None): import matplotlib as mpl import matplotlib.pyplot as plt from torchvision.transforms import ToTensor x_min = -2.0 * scale x_max = 2.0 * scale y_min = -2.0 * scale y_max = 2.0 * scale z_min = -2.0 * scale z_max = 2.0 * scale plt.rcParams['text.color'] = '#999999' self.fig = plt.figure(figsize=(18, 7)) self.fig.patch.set_facecolor('#353535') self.ax = self.fig.add_subplot(projection='3d') self.ax.set_facecolor('#353535') # Set the background color here self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) self.plotly_data = None # plotly data traces self.ax.set_aspect("auto") self.ax.set_xlim(x_min, x_max) self.ax.set_ylim(y_min, y_max) self.ax.set_zlim(z_min, z_max) self.ax.set_xlabel('x', color='#999999') self.ax.set_ylabel('y', color='#999999') self.ax.set_zlabel('z', color='#999999') for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): text.set_color('#999999') print('initialize camera pose visualizer') if pose_file_path != "": with open(pose_file_path, 'r') as f: poses = f.readlines() w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]] fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]] print(poses) elif cameractrl_poses is not None: poses = cameractrl_poses w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses] fxs = [pose[1] for pose in cameractrl_poses] else: raise ValueError("Please provide either pose_file_path or cameractrl_poses") total_frames = len(w2cs) transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) last_row = np.zeros((1, 4)) last_row[0, -1] = 1.0 w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs] c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w) for frame_idx, c2w in enumerate(c2ws): self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval, zval=(fxs[frame_idx] if use_exact_fx else zval)) # Create the colorbar cmap = mpl.cm.rainbow norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') # Change the colorbar label colorbar.set_label('Frame', color='#999999') # Change the label and its color # Change the tick colors colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color # Change the tick frequency # Assuming you want to set the ticks at every 10th frame ticks = np.arange(0, total_frames, 10) colorbar.ax.yaxis.set_ticks(ticks) plt.title('') plt.draw() buf = io.BytesIO() plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) buf.seek(0) img = Image.open(buf) tensor_img = ToTensor()(img) buf.close() tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) if use_viewer: time.sleep(1) plt.show() return (tensor_img,) def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3): from mpl_toolkits.mplot3d.art3d import Poly3DCollection vertex_std = np.array([[0, 0, 0, 1], [base_xval, -base_xval * hw_ratio, zval, 1], [base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, -base_xval * hw_ratio, zval, 1]]) vertex_transformed = vertex_std @ extrinsic.T meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) self.ax.add_collection3d( Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) def customize_legend(self, list_label): from matplotlib.patches import Patch list_handle = [] for idx, label in enumerate(list_label): color = plt.cm.rainbow(idx / len(list_label)) patch = Patch(color=color, label=label) list_handle.append(patch) plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) def get_c2w(self, w2cs, transform_matrix, relative_c2w): if relative_c2w: target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] else: ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] ret_poses = [transform_matrix @ x for x in ret_poses] return np.array(ret_poses, dtype=np.float32) class StabilityAPI_SD3: @classmethod def INPUT_TYPES(cls): return { "required": { "prompt": ("STRING", {"multiline": True}), "n_prompt": ("STRING", {"multiline": True}), "seed": ("INT", {"default": 123,"min": 0, "max": 4294967294, "step": 1}), "model": ( [ 'sd3', 'sd3-turbo', ], { "default": 'sd3' }), "aspect_ratio": ( [ '1:1', '16:9', '21:9', '2:3', '3:2', '4:5', '5:4', '9:16', '9:21', ], { "default": '1:1' }), "output_format": ( [ 'png', 'jpeg', ], { "default": 'jpeg' }), }, "optional": { "api_key": ("STRING", {"multiline": True}), "image": ("IMAGE",), "img2img_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "disable_metadata": ("BOOLEAN", {"default": True}), }, } RETURN_TYPES = ("IMAGE",) FUNCTION = "apicall" CATEGORY = "KJNodes/experimental" DESCRIPTION = """ ## Calls StabilityAI API Although you may have multiple keys in your account, you should use the same key for all requests to this API. Get your API key here: https://platform.stability.ai/account/keys Recommended to set the key in the config.json -file under this node packs folder. # WARNING: Otherwise the API key may get saved in the image metadata even with "disable_metadata" on if the workflow includes save nodes separate from this node. sd3 requires 6.5 credits per generation sd3-turbo requires 4 credits per generation If no image is provided, mode is set to text-to-image """ def apicall(self, prompt, n_prompt, model, seed, aspect_ratio, output_format, img2img_strength=0.5, image=None, disable_metadata=True, api_key=""): from comfy.cli_args import args if disable_metadata: args.disable_metadata = True else: args.disable_metadata = False import requests from torchvision import transforms data = { "mode": "text-to-image", "prompt": prompt, "model": model, "seed": seed, "output_format": output_format } if image is not None: image = image.permute(0, 3, 1, 2).squeeze(0) to_pil = transforms.ToPILImage() pil_image = to_pil(image) # Save the PIL Image to a BytesIO object buffer = io.BytesIO() pil_image.save(buffer, format='PNG') buffer.seek(0) files = {"image": ("image.png", buffer, "image/png")} data["mode"] = "image-to-image" data["image"] = pil_image data["strength"] = img2img_strength else: data["aspect_ratio"] = aspect_ratio, files = {"none": ''} if model != "sd3-turbo": data["negative_prompt"] = n_prompt headers={ "accept": "image/*" } if api_key != "": headers["authorization"] = api_key else: config_file_path = os.path.join(script_directory,"config.json") with open(config_file_path, 'r') as file: config = json.load(file) api_key_from_config = config.get("sai_api_key") headers["authorization"] = api_key_from_config response = requests.post( f"https://api.stability.ai/v2beta/stable-image/generate/sd3", headers=headers, files = files, data = data, ) if response.status_code == 200: # Convert the response content to a PIL Image image = Image.open(io.BytesIO(response.content)) # Convert the PIL Image to a PyTorch tensor transform = transforms.ToTensor() tensor_image = transform(image) tensor_image = tensor_image.unsqueeze(0) tensor_image = tensor_image.permute(0, 2, 3, 1).cpu().float() return (tensor_image,) else: try: # Attempt to parse the response as JSON error_data = response.json() raise Exception(f"Server error: {error_data}") except json.JSONDecodeError: # If the response is not valid JSON, raise a different exception raise Exception(f"Server error: {response.text}")