1226 lines
40 KiB
Python
1226 lines
40 KiB
Python
import warnings
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warnings.filterwarnings('ignore', module="torchvision")
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import ast
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import math
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import random
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import os
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import operator as op
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torchvision.transforms.v2 as T
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from nodes import MAX_RESOLUTION, SaveImage
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import folder_paths
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import comfy.utils
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def p(image):
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return image.permute([0,3,1,2])
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def pb(image):
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return image.permute([0,2,3,1])
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# from https://github.com/pythongosssss/ComfyUI-Custom-Scripts
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class AnyType(str):
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def __ne__(self, __value: object) -> bool:
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return False
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any = AnyType("*")
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EPSILON = 1e-5
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class GetImageSize:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("INT", "INT")
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RETURN_NAMES = ("width", "height")
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image):
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return (image.shape[2], image.shape[1],)
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class ImageResize:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
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"height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
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"interpolation": (["nearest", "bilinear", "bicubic", "area", "nearest-exact", "lanczos"],),
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"keep_proportion": ("BOOLEAN", { "default": False }),
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"condition": (["always", "only if bigger", "only if smaller"],),
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}
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT",)
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RETURN_NAMES = ("IMAGE", "width", "height",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, width, height, keep_proportion, interpolation="nearest", condition="always"):
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if keep_proportion is True:
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_, oh, ow, _ = image.shape
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if width == 0 and oh < height:
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width = MAX_RESOLUTION
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elif width == 0 and oh >= height:
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width = ow
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if height == 0 and ow < width:
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height = MAX_RESOLUTION
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elif height == 0 and ow >= width:
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height = ow
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#width = ow if width == 0 else width
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#height = oh if height == 0 else height
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ratio = min(width / ow, height / oh)
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width = round(ow*ratio)
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height = round(oh*ratio)
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outputs = p(image)
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if "always" in condition or ("bigger" in condition and (oh > height or ow > width)) or ("smaller" in condition and (oh < height or ow < width)):
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if interpolation == "lanczos":
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outputs = comfy.utils.lanczos(outputs, width, height)
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else:
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outputs = F.interpolate(outputs, size=(height, width), mode=interpolation)
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outputs = pb(outputs)
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return(outputs, outputs.shape[2], outputs.shape[1],)
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class ImageFlip:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"axis": (["x", "y", "xy"],),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, axis):
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dim = ()
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if "y" in axis:
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dim += (1,)
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if "x" in axis:
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dim += (2,)
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image = torch.flip(image, dim)
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return(image,)
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class ImageCrop:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"width": ("INT", { "default": 256, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
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"height": ("INT", { "default": 256, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
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"position": (["top-left", "top-center", "top-right", "right-center", "bottom-right", "bottom-center", "bottom-left", "left-center", "center"],),
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"x_offset": ("INT", { "default": 0, "min": -99999, "step": 1, }),
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"y_offset": ("INT", { "default": 0, "min": -99999, "step": 1, }),
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}
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}
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RETURN_TYPES = ("IMAGE","INT","INT",)
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RETURN_NAMES = ("IMAGE","x","y",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, width, height, position, x_offset, y_offset):
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_, oh, ow, _ = image.shape
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width = min(ow, width)
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height = min(oh, height)
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if "center" in position:
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x = round((ow-width) / 2)
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y = round((oh-height) / 2)
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if "top" in position:
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y = 0
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if "bottom" in position:
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y = oh-height
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if "left" in position:
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x = 0
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if "right" in position:
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x = ow-width
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x += x_offset
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y += y_offset
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x2 = x+width
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y2 = y+height
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if x2 > ow:
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x2 = ow
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if x < 0:
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x = 0
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if y2 > oh:
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y2 = oh
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if y < 0:
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y = 0
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image = image[:, y:y2, x:x2, :]
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return(image, x, y, )
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class ImageDesaturate:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"factor": ("FLOAT", { "default": 1.00, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, factor):
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grayscale = 0.299 * image[..., 0] + 0.587 * image[..., 1] + 0.114 * image[..., 2]
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grayscale = (1.0 - factor) * image + factor * grayscale.unsqueeze(-1).repeat(1, 1, 1, 3)
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return(grayscale,)
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class ImagePosterize:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"threshold": ("FLOAT", { "default": 0.50, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, threshold):
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image = 0.299 * image[..., 0] + 0.587 * image[..., 1] + 0.114 * image[..., 2]
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#image = image.mean(dim=3, keepdim=True)
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image = (image > threshold).float()
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image = image.unsqueeze(-1).repeat(1, 1, 1, 3)
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return(image,)
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class ImageEnhanceDifference:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image1": ("IMAGE",),
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"image2": ("IMAGE",),
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"exponent": ("FLOAT", { "default": 0.75, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image1, image2, exponent):
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if image1.shape != image2.shape:
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image2 = p(image2)
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image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
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image2 = pb(image2)
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diff_image = image1 - image2
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diff_image = torch.pow(diff_image, exponent)
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diff_image = torch.clamp(diff_image, 0, 1)
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return(diff_image,)
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class ImageExpandBatch:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"size": ("INT", { "default": 16, "min": 1, "step": 1, }),
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"method": (["expand", "repeat all", "repeat first", "repeat last"],)
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, size, method):
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orig_size = image.shape[0]
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if orig_size == size:
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return (image,)
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if size <= 1:
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return (image[:size],)
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if 'expand' in method:
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out = torch.empty([size] + list(image.shape)[1:], dtype=image.dtype, device=image.device)
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if size < orig_size:
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scale = (orig_size - 1) / (size - 1)
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for i in range(size):
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out[i] = image[min(round(i * scale), orig_size - 1)]
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else:
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scale = orig_size / size
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for i in range(size):
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out[i] = image[min(math.floor((i + 0.5) * scale), orig_size - 1)]
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elif 'all' in method:
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out = image.repeat([math.ceil(size / image.shape[0])] + [1] * (len(image.shape) - 1))[:size]
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elif 'first' in method:
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if size < image.shape[0]:
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out = image[:size]
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else:
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out = torch.cat([image[:1].repeat(size-image.shape[0], 1, 1, 1), image], dim=0)
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elif 'last' in method:
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if size < image.shape[0]:
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out = image[:size]
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else:
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out = torch.cat((image, image[-1:].repeat((size-image.shape[0], 1, 1, 1))), dim=0)
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return (out,)
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class ExtractKeyframes:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"threshold": ("FLOAT", { "default": 0.85, "min": 0.00, "max": 1.00, "step": 0.01, }),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("KEYFRAMES", "indexes")
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, threshold):
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window_size = 2
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variations = torch.sum(torch.abs(image[1:] - image[:-1]), dim=[1, 2, 3])
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#variations = torch.sum((image[1:] - image[:-1]) ** 2, dim=[1, 2, 3])
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threshold = torch.quantile(variations.float(), threshold).item()
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keyframes = []
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for i in range(image.shape[0] - window_size + 1):
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window = image[i:i + window_size]
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variation = torch.sum(torch.abs(window[-1] - window[0])).item()
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if variation > threshold:
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keyframes.append(i + window_size - 1)
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return (image[keyframes], ','.join(map(str, keyframes)),)
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"""
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class NoiseFromImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"adjust_levels": ("FLOAT", { "default": 1.00, "min": 0.00, "max": 20.00, "step": 0.05, }),
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#"noise_intensity": ("FLOAT", { "default": 1.00, "min": 0.00, "max": 1.00, "step": 0.05, }),
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"noise_density": ("FLOAT", { "default": 0.05, "min": 0.00, "max": 1.00, "step": 0.05, }),
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"noise_scale": ("FLOAT", { "default": 0.2, "min": 0.00, "max": 1.00, "step": 0.05, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image, noise_seed, adjust_levels, noise_density, noise_scale):
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generator = torch.manual_seed(noise_seed)
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image = image.mean(dim=3).unsqueeze(-1).repeat(1, 1, 1, 3)
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# Adjust image levels
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image = (1 - adjust_levels) * torch.mean(image) + adjust_levels * image
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image = torch.clamp(image, 0, 1)
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# Create noise
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fine_noise = torch.rand([image.shape[0], image.shape[1], image.shape[2], image.shape[3]], dtype=image.dtype, layout=image.layout, generator=generator, device="cpu")
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fine_noise = fine_noise * (fine_noise > 1-noise_density).float() # Lower density
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fine_noise = (fine_noise * 16).round() / 16
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coarse_noise = F.interpolate(p(fine_noise), scale_factor=noise_scale, mode='bilinear', align_corners=False)
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coarse_noise = F.interpolate(coarse_noise, size=(image.shape[1], image.shape[2]), mode='bilinear', align_corners=False)
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coarse_noise = pb(coarse_noise)
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# Merge noises
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noise = ((1 - image) * coarse_noise + image * fine_noise)
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noise = torch.clamp(noise, 0, 1)
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noise = image * noise
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# Change noise intensity
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#noise = noise * noise_intensity
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#print(noise.min(), noise.max())
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#noise = torch.clamp(noise, 0, 1)
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# Apply noise to image
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#noise = torch.clamp((1-noise_intensity) * image + noise, 0, 1)
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#out = image + fine_noise * mask * noise_intensity
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return (noise,)
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"""
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class MaskFlip:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"mask": ("MASK",),
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"axis": (["x", "y", "xy"],),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, mask, axis):
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dim = ()
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if "y" in axis:
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dim += (1,)
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if "x" in axis:
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dim += (2,)
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mask = torch.flip(mask, dims=dim)
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return(mask,)
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class MaskBlur:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"mask": ("MASK",),
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"amount": ("FLOAT", { "default": 6.0, "min": 0, "step": 0.5, }),
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}
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, mask, amount):
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size = int(6 * amount +1)
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if size % 2 == 0:
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size+= 1
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blurred = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 1)
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blurred = p(blurred)
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blurred = T.GaussianBlur(size, amount)(blurred)
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blurred = pb(blurred)
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blurred = blurred[:, :, :, 0]
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return(blurred,)
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|
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class MaskPreview(SaveImage):
|
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def __init__(self):
|
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
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self.compress_level = 4
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@classmethod
|
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def INPUT_TYPES(s):
|
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return {
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"required": {"mask": ("MASK",), },
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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|
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FUNCTION = "execute"
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CATEGORY = "essentials"
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|
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def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
|
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preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
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return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
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|
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class MaskBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
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return {
|
|
"required": {
|
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"mask1": ("MASK",),
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"mask2": ("MASK",),
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}
|
|
}
|
|
|
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RETURN_TYPES = ("MASK",)
|
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FUNCTION = "execute"
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CATEGORY = "essentials"
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|
|
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def execute(self, mask1, mask2):
|
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if mask1.shape[1:] != mask2.shape[1:]:
|
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mask2 = F.interpolate(mask2.unsqueeze(1), size=(mask1.shape[1], mask1.shape[2]), mode="bicubic").squeeze(1)
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|
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out = torch.cat((mask1, mask2), dim=0)
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return (out,)
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|
|
class MaskExpandBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
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"required": {
|
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"mask": ("MASK",),
|
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"size": ("INT", { "default": 16, "min": 1, "step": 1, }),
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"method": (["expand", "repeat all", "repeat first", "repeat last"],)
|
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}
|
|
}
|
|
|
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RETURN_TYPES = ("MASK",)
|
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FUNCTION = "execute"
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CATEGORY = "essentials"
|
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|
|
def execute(self, mask, size, method):
|
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orig_size = mask.shape[0]
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|
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if orig_size == size:
|
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return (mask,)
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|
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if size <= 1:
|
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return (mask[:size],)
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|
|
if 'expand' in method:
|
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out = torch.empty([size] + list(mask.shape)[1:], dtype=mask.dtype, device=mask.device)
|
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if size < orig_size:
|
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scale = (orig_size - 1) / (size - 1)
|
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for i in range(size):
|
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out[i] = mask[min(round(i * scale), orig_size - 1)]
|
|
else:
|
|
scale = orig_size / size
|
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for i in range(size):
|
|
out[i] = mask[min(math.floor((i + 0.5) * scale), orig_size - 1)]
|
|
elif 'all' in method:
|
|
out = mask.repeat([math.ceil(size / mask.shape[0])] + [1] * (len(mask.shape) - 1))[:size]
|
|
elif 'first' in method:
|
|
if size < mask.shape[0]:
|
|
out = mask[:size]
|
|
else:
|
|
out = torch.cat([mask[:1].repeat(size-mask.shape[0], 1, 1), mask], dim=0)
|
|
elif 'last' in method:
|
|
if size < mask.shape[0]:
|
|
out = mask[:size]
|
|
else:
|
|
out = torch.cat((mask, mask[-1:].repeat((size-mask.shape[0], 1, 1))), dim=0)
|
|
|
|
return (out,)
|
|
|
|
def cubic_bezier(t, p):
|
|
p0, p1, p2, p3 = p
|
|
return (1 - t)**3 * p0 + 3 * (1 - t)**2 * t * p1 + 3 * (1 - t) * t**2 * p2 + t**3 * p3
|
|
|
|
class MaskFromColor:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE", ),
|
|
"red": ("INT", { "default": 255, "min": 0, "max": 255, "step": 1, }),
|
|
"green": ("INT", { "default": 255, "min": 0, "max": 255, "step": 1, }),
|
|
"blue": ("INT", { "default": 255, "min": 0, "max": 255, "step": 1, }),
|
|
"threshold": ("INT", { "default": 0, "min": 0, "max": 127, "step": 1, }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, image, red, green, blue, threshold):
|
|
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
|
|
color = torch.tensor([red, green, blue])
|
|
lower_bound = (color - threshold).clamp(min=0)
|
|
upper_bound = (color + threshold).clamp(max=255)
|
|
lower_bound = lower_bound.view(1, 1, 1, 3)
|
|
upper_bound = upper_bound.view(1, 1, 1, 3)
|
|
mask = (temp >= lower_bound) & (temp <= upper_bound)
|
|
mask = mask.all(dim=-1)
|
|
mask = mask.float()
|
|
|
|
return (mask, )
|
|
|
|
class MaskFromBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"mask": ("MASK", ),
|
|
"start": ("INT", { "default": 0, "min": 0, "step": 1, }),
|
|
"length": ("INT", { "default": -1, "min": -1, "step": 1, }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, mask, start, length):
|
|
if length<0:
|
|
length = mask.shape[0]
|
|
start = min(start, mask.shape[0]-1)
|
|
length = min(mask.shape[0]-start, length)
|
|
return (mask[start:start + length], )
|
|
|
|
class ImageFromBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE", ),
|
|
"start": ("INT", { "default": 0, "min": 0, "step": 1, }),
|
|
"length": ("INT", { "default": -1, "min": -1, "step": 1, }),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, image, start, length):
|
|
if length<0:
|
|
length = image.shape[0]
|
|
start = min(start, image.shape[0]-1)
|
|
length = min(image.shape[0]-start, length)
|
|
return (image[start:start + length], )
|
|
|
|
class ImageCompositeFromMaskBatch:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image_from": ("IMAGE", ),
|
|
"image_to": ("IMAGE", ),
|
|
"mask": ("MASK", )
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, image_from, image_to, mask):
|
|
frames = mask.shape[0]
|
|
|
|
if image_from.shape[1] != image_to.shape[1] or image_from.shape[2] != image_to.shape[2]:
|
|
image_to = p(image_to)
|
|
image_to = comfy.utils.common_upscale(image_to, image_from.shape[2], image_from.shape[1], upscale_method='bicubic', crop='center')
|
|
image_to = pb(image_to)
|
|
|
|
if frames < image_from.shape[0]:
|
|
image_from = image_from[:frames]
|
|
elif frames > image_from.shape[0]:
|
|
image_from = torch.cat((image_from, image_from[-1].unsqueeze(0).repeat(frames-image_from.shape[0], 1, 1, 1)), dim=0)
|
|
|
|
mask = mask.unsqueeze(3).repeat(1, 1, 1, 3)
|
|
|
|
if image_from.shape[1] != mask.shape[1] or image_from.shape[2] != mask.shape[2]:
|
|
mask = p(mask)
|
|
mask = comfy.utils.common_upscale(mask, image_from.shape[2], image_from.shape[1], upscale_method='bicubic', crop='center')
|
|
mask = pb(mask)
|
|
|
|
out = mask * image_to + (1 - mask) * image_from
|
|
|
|
return (out, )
|
|
|
|
class TransitionMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"width": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
|
|
"height": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
|
|
"frames": ("INT", { "default": 16, "min": 1, "max": 9999, "step": 1, }),
|
|
"start_frame": ("INT", { "default": 0, "min": 0, "step": 1, }),
|
|
"end_frame": ("INT", { "default": 9999, "min": 0, "step": 1, }),
|
|
"transition_type": (["horizontal slide", "vertical slide", "horizontal bar", "vertical bar", "center box", "horizontal door", "vertical door", "circle", "fade"],),
|
|
"timing_function": (["linear", "in", "out", "in-out"],)
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, width, height, frames, start_frame, end_frame, transition_type, timing_function):
|
|
if timing_function == 'in':
|
|
tf = [0.0, 0.0, 0.5, 1.0]
|
|
elif timing_function == 'out':
|
|
tf = [0.0, 0.5, 1.0, 1.0]
|
|
elif timing_function == 'in-out':
|
|
tf = [0, 1, 0, 1]
|
|
#elif timing_function == 'back':
|
|
# tf = [0, 1.334, 1.334, 0]
|
|
else:
|
|
tf = [0, 0, 1, 1]
|
|
|
|
out = []
|
|
|
|
end_frame = min(frames, end_frame)
|
|
transition = end_frame - start_frame
|
|
|
|
if start_frame > 0:
|
|
out = out + [torch.full((height, width), 0.0, dtype=torch.float32, device="cpu")] * start_frame
|
|
|
|
for i in range(transition):
|
|
frame = torch.full((height, width), 0.0, dtype=torch.float32, device="cpu")
|
|
progress = i/(transition-1)
|
|
|
|
if timing_function != 'linear':
|
|
progress = cubic_bezier(progress, tf)
|
|
|
|
if "horizontal slide" in transition_type:
|
|
pos = round(width*progress)
|
|
frame[:, :pos] = 1.0
|
|
elif "vertical slide" in transition_type:
|
|
pos = round(height*progress)
|
|
frame[:pos, :] = 1.0
|
|
elif "box" in transition_type:
|
|
box_w = round(width*progress)
|
|
box_h = round(height*progress)
|
|
x1 = (width - box_w) // 2
|
|
y1 = (height - box_h) // 2
|
|
x2 = x1 + box_w
|
|
y2 = y1 + box_h
|
|
frame[y1:y2, x1:x2] = 1.0
|
|
elif "circle" in transition_type:
|
|
radius = math.ceil(math.sqrt(pow(width,2)+pow(height,2))*progress/2)
|
|
c_x = width // 2
|
|
c_y = height // 2
|
|
# is this real life? Am I hallucinating?
|
|
x = torch.arange(0, width, dtype=torch.float32, device="cpu")
|
|
y = torch.arange(0, height, dtype=torch.float32, device="cpu")
|
|
y, x = torch.meshgrid((y, x), indexing="ij")
|
|
circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2)
|
|
frame[circle] = 1.0
|
|
elif "horizontal bar" in transition_type:
|
|
bar = round(height*progress)
|
|
y1 = (height - bar) // 2
|
|
y2 = y1 + bar
|
|
frame[y1:y2, :] = 1.0
|
|
elif "vertical bar" in transition_type:
|
|
bar = round(width*progress)
|
|
x1 = (width - bar) // 2
|
|
x2 = x1 + bar
|
|
frame[:, x1:x2] = 1.0
|
|
elif "horizontal door" in transition_type:
|
|
bar = math.ceil(height*progress/2)
|
|
if bar > 0:
|
|
frame[:bar, :] = 1.0
|
|
frame[-bar:, :] = 1.0
|
|
elif "vertical door" in transition_type:
|
|
bar = math.ceil(width*progress/2)
|
|
if bar > 0:
|
|
frame[:, :bar] = 1.0
|
|
frame[:, -bar:] = 1.0
|
|
elif "fade" in transition_type:
|
|
frame[:,:] = progress
|
|
|
|
out.append(frame)
|
|
|
|
if end_frame < frames:
|
|
out = out + [torch.full((height, width), 1.0, dtype=torch.float32, device="cpu")] * (frames - end_frame)
|
|
|
|
out = torch.stack(out, dim=0)
|
|
|
|
return (out, )
|
|
|
|
def min_(tensor_list):
|
|
# return the element-wise min of the tensor list.
|
|
x = torch.stack(tensor_list)
|
|
mn = x.min(axis=0)[0]
|
|
return torch.clamp(mn, min=0)
|
|
|
|
def max_(tensor_list):
|
|
# return the element-wise max of the tensor list.
|
|
x = torch.stack(tensor_list)
|
|
mx = x.max(axis=0)[0]
|
|
return torch.clamp(mx, max=1)
|
|
|
|
# From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
|
|
class ImageCAS:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"amount": ("FLOAT", {"default": 0.8, "min": 0, "max": 1, "step": 0.05}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "essentials"
|
|
FUNCTION = "execute"
|
|
|
|
def execute(self, image, amount):
|
|
img = F.pad(p(image), pad=(1, 1, 1, 1)).cpu()
|
|
|
|
a = img[..., :-2, :-2]
|
|
b = img[..., :-2, 1:-1]
|
|
c = img[..., :-2, 2:]
|
|
d = img[..., 1:-1, :-2]
|
|
e = img[..., 1:-1, 1:-1]
|
|
f = img[..., 1:-1, 2:]
|
|
g = img[..., 2:, :-2]
|
|
h = img[..., 2:, 1:-1]
|
|
i = img[..., 2:, 2:]
|
|
|
|
# Computing contrast
|
|
cross = (b, d, e, f, h)
|
|
mn = min_(cross)
|
|
mx = max_(cross)
|
|
|
|
diag = (a, c, g, i)
|
|
mn2 = min_(diag)
|
|
mx2 = max_(diag)
|
|
mx = mx + mx2
|
|
mn = mn + mn2
|
|
|
|
# Computing local weight
|
|
inv_mx = torch.reciprocal(mx + EPSILON)
|
|
amp = inv_mx * torch.minimum(mn, (2 - mx))
|
|
|
|
# scaling
|
|
amp = torch.sqrt(amp)
|
|
w = - amp * (amount * (1/5 - 1/8) + 1/8)
|
|
div = torch.reciprocal(1 + 4*w)
|
|
|
|
output = ((b + d + f + h)*w + e) * div
|
|
output = output.clamp(0, 1)
|
|
#output = torch.nan_to_num(output) # this seems the only way to ensure there are no NaNs
|
|
|
|
output = pb(output)
|
|
|
|
return (output,)
|
|
|
|
operators = {
|
|
ast.Add: op.add,
|
|
ast.Sub: op.sub,
|
|
ast.Mult: op.mul,
|
|
ast.Div: op.truediv,
|
|
ast.FloorDiv: op.floordiv,
|
|
ast.Pow: op.pow,
|
|
ast.BitXor: op.xor,
|
|
ast.USub: op.neg,
|
|
ast.Mod: op.mod,
|
|
}
|
|
|
|
op_functions = {
|
|
'min': min,
|
|
'max': max
|
|
}
|
|
|
|
class SimpleMath:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"optional": {
|
|
"a": ("INT,FLOAT", { "default": 0.0, "step": 0.1 }),
|
|
"b": ("INT,FLOAT", { "default": 0.0, "step": 0.1 }),
|
|
},
|
|
"required": {
|
|
"value": ("STRING", { "multiline": False, "default": "" }),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("INT", "FLOAT", )
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, value, a = 0.0, b = 0.0):
|
|
def eval_(node):
|
|
if isinstance(node, ast.Num): # number
|
|
return node.n
|
|
elif isinstance(node, ast.Name): # variable
|
|
if node.id == "a":
|
|
return a
|
|
if node.id == "b":
|
|
return b
|
|
elif isinstance(node, ast.BinOp): # <left> <operator> <right>
|
|
return operators[type(node.op)](eval_(node.left), eval_(node.right))
|
|
elif isinstance(node, ast.UnaryOp): # <operator> <operand> e.g., -1
|
|
return operators[type(node.op)](eval_(node.operand))
|
|
elif isinstance(node, ast.Call): # custom function
|
|
if node.func.id in op_functions:
|
|
args =[eval_(arg) for arg in node.args]
|
|
return op_functions[node.func.id](*args)
|
|
else:
|
|
return 0
|
|
|
|
result = eval_(ast.parse(value, mode='eval').body)
|
|
|
|
if math.isnan(result):
|
|
result = 0.0
|
|
|
|
return (round(result), result, )
|
|
|
|
class ModelCompile():
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"fullgraph": ("BOOLEAN", { "default": False }),
|
|
"dynamic": ("BOOLEAN", { "default": False }),
|
|
"mode": (["default", "reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"],),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL", )
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, model, fullgraph, dynamic, mode):
|
|
work_model = model.clone()
|
|
torch._dynamo.config.suppress_errors = True
|
|
work_model.model.diffusion_model = torch.compile(work_model.model.diffusion_model, dynamic=dynamic, fullgraph=fullgraph, mode=mode)
|
|
return( work_model, )
|
|
|
|
class ConsoleDebug:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"value": (any, {}),
|
|
},
|
|
"optional": {
|
|
"prefix": ("STRING", { "multiline": False, "default": "Value:" })
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
OUTPUT_NODE = True
|
|
|
|
def execute(self, value, prefix):
|
|
print(f"\033[96m{prefix} {value}\033[0m")
|
|
|
|
return (None,)
|
|
|
|
class DebugTensorShape:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"tensor": (any, {}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
OUTPUT_NODE = True
|
|
|
|
def execute(self, tensor):
|
|
shapes = []
|
|
def tensorShape(tensor):
|
|
if isinstance(tensor, dict):
|
|
for k in tensor:
|
|
tensorShape(tensor[k])
|
|
elif isinstance(tensor, list):
|
|
for i in range(len(tensor)):
|
|
tensorShape(tensor[i])
|
|
elif hasattr(tensor, 'shape'):
|
|
shapes.append(list(tensor.shape))
|
|
|
|
tensorShape(tensor)
|
|
|
|
print(f"\033[96mShapes found: {shapes}\033[0m")
|
|
|
|
return (None,)
|
|
|
|
class BatchCount:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"batch": (any, {}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("INT",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, batch):
|
|
count = 0
|
|
if hasattr(batch, 'shape'):
|
|
count = batch.shape[0]
|
|
elif isinstance(batch, dict) and 'samples' in batch:
|
|
count = batch['samples'].shape[0]
|
|
elif isinstance(batch, list) or isinstance(batch, dict):
|
|
count = len(batch)
|
|
|
|
return (count, )
|
|
|
|
class ImageSeamCarving:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"width": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
|
|
"height": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
|
|
"energy": (["backward", "forward"],),
|
|
"order": (["width-first", "height-first"],),
|
|
},
|
|
"optional": {
|
|
"keep_mask": ("MASK",),
|
|
"drop_mask": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
CATEGORY = "essentials"
|
|
FUNCTION = "execute"
|
|
|
|
def execute(self, image, width, height, energy, order, keep_mask=None, drop_mask=None):
|
|
try:
|
|
from .carve import seam_carving
|
|
except ImportError as e:
|
|
raise Exception(e)
|
|
|
|
img = p(image)
|
|
|
|
if keep_mask is not None:
|
|
#keep_mask = keep_mask.reshape((-1, 1, keep_mask.shape[-2], keep_mask.shape[-1])).movedim(1, -1)
|
|
keep_mask = p(keep_mask.unsqueeze(-1))
|
|
|
|
if keep_mask.shape[2] != img.shape[2] or keep_mask.shape[3] != img.shape[3]:
|
|
keep_mask = F.interpolate(keep_mask, size=(img.shape[2], img.shape[3]), mode="bilinear")
|
|
if drop_mask is not None:
|
|
drop_mask = p(drop_mask.unsqueeze(-1))
|
|
|
|
if drop_mask.shape[2] != img.shape[2] or drop_mask.shape[3] != img.shape[3]:
|
|
drop_mask = F.interpolate(drop_mask, size=(img.shape[2], img.shape[3]), mode="bilinear")
|
|
|
|
out = []
|
|
for i in range(img.shape[0]):
|
|
resized = seam_carving(
|
|
T.ToPILImage()(img[i]),
|
|
size=(width, height),
|
|
energy_mode=energy,
|
|
order=order,
|
|
keep_mask=T.ToPILImage()(keep_mask[i]) if keep_mask is not None else None,
|
|
drop_mask=T.ToPILImage()(drop_mask[i]) if drop_mask is not None else None,
|
|
)
|
|
out.append(T.ToTensor()(resized))
|
|
|
|
out = torch.stack(out)
|
|
out = pb(out)
|
|
|
|
return(out, )
|
|
|
|
class CLIPTextEncodeSDXLSimplified:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
|
|
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
|
|
"text": ("STRING", {"multiline": True, "default": ""}),
|
|
"clip": ("CLIP", ),
|
|
}}
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, clip, width, height, text):
|
|
crop_w = 0
|
|
crop_h = 0
|
|
width = width*4
|
|
height = height*4
|
|
target_width = width
|
|
target_height = height
|
|
text_g = text_l = text
|
|
|
|
tokens = clip.tokenize(text_g)
|
|
tokens["l"] = clip.tokenize(text_l)["l"]
|
|
if len(tokens["l"]) != len(tokens["g"]):
|
|
empty = clip.tokenize("")
|
|
while len(tokens["l"]) < len(tokens["g"]):
|
|
tokens["l"] += empty["l"]
|
|
while len(tokens["l"]) > len(tokens["g"]):
|
|
tokens["g"] += empty["g"]
|
|
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
|
|
return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], )
|
|
|
|
class SDXLResolutionPicker:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"resolution": (["704x1408 (0.5)","704x1344 (0.52)","768x1344 (0.57)","768x1280 (0.6)","832x1216 (0.68)","832x1152 (0.72)","896x1152 (0.78)","896x1088 (0.82)","960x1088 (0.88)","960x1024 (0.94)","1024x1024 (1.0)","1024x960 (1.07)","1088x960 (1.13)","1088x896 (1.21)","1152x896 (1.29)","1152x832 (1.38)","1216x832 (1.46)","1280x768 (1.67)","1344x768 (1.75)","1344x704 (1.91)","1408x704 (2.0)","1472x704 (2.09)","1536x640 (2.4)","1600x640 (2.5)","1664x576 (2.89)","1728x576 (3.0)",], {"default": "1024x1024 (1.0)"}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("INT","INT",)
|
|
RETURN_NAMES = ("width", "height",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
def execute(self, resolution):
|
|
width, height = resolution.split(" ")[0].split("x")
|
|
|
|
return (width, height,)
|
|
|
|
LUTS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "luts")
|
|
# From https://github.com/yoonsikp/pycubelut/blob/master/pycubelut.py (MIT license)
|
|
class ImageApplyLUT:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"lut_file": ([f for f in os.listdir(LUTS_DIR) if f.endswith('.cube')], ),
|
|
"log_colorspace": ("BOOLEAN", { "default": False }),
|
|
"clip_values": ("BOOLEAN", { "default": False }),
|
|
}}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "execute"
|
|
CATEGORY = "essentials"
|
|
|
|
# TODO: check if we can do without numpy
|
|
def execute(self, image, lut_file, log_colorspace, clip_values):
|
|
from colour.io.luts.iridas_cube import read_LUT_IridasCube
|
|
|
|
lut = read_LUT_IridasCube(os.path.join(LUTS_DIR, lut_file))
|
|
lut.name = lut_file
|
|
|
|
if clip_values:
|
|
if lut.domain[0].max() == lut.domain[0].min() and lut.domain[1].max() == lut.domain[1].min():
|
|
lut.table = np.clip(lut.table, lut.domain[0, 0], lut.domain[1, 0])
|
|
else:
|
|
if len(lut.table.shape) == 2: # 3x1D
|
|
for dim in range(3):
|
|
lut.table[:, dim] = np.clip(lut.table[:, dim], lut.domain[0, dim], lut.domain[1, dim])
|
|
else: # 3D
|
|
for dim in range(3):
|
|
lut.table[:, :, :, dim] = np.clip(lut.table[:, :, :, dim], lut.domain[0, dim], lut.domain[1, dim])
|
|
|
|
out = []
|
|
for img in image: # TODO: is this more resrouce efficient?
|
|
img = img.numpy().copy()
|
|
|
|
is_non_default_domain = not np.array_equal(lut.domain, np.array([[0., 0., 0.], [1., 1., 1.]]))
|
|
dom_scale = None
|
|
if is_non_default_domain:
|
|
dom_scale = lut.domain[1] - lut.domain[0]
|
|
img = img * dom_scale + lut.domain[0]
|
|
if log_colorspace:
|
|
img = img ** (1/2.2)
|
|
img = lut.apply(img)
|
|
if log_colorspace:
|
|
img = img ** (2.2)
|
|
if is_non_default_domain:
|
|
img = (img - lut.domain[0]) / dom_scale
|
|
|
|
img = torch.from_numpy(img)
|
|
out.append(img)
|
|
|
|
out = torch.stack(out)
|
|
|
|
return (out, )
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"GetImageSize+": GetImageSize,
|
|
|
|
"ImageResize+": ImageResize,
|
|
"ImageCrop+": ImageCrop,
|
|
"ImageFlip+": ImageFlip,
|
|
|
|
"ImageDesaturate+": ImageDesaturate,
|
|
"ImagePosterize+": ImagePosterize,
|
|
"ImageCASharpening+": ImageCAS,
|
|
"ImageSeamCarving+": ImageSeamCarving,
|
|
"ImageEnhanceDifference+": ImageEnhanceDifference,
|
|
"ImageExpandBatch+": ImageExpandBatch,
|
|
"ImageFromBatch+": ImageFromBatch,
|
|
"ImageCompositeFromMaskBatch+": ImageCompositeFromMaskBatch,
|
|
"ExtractKeyframes+": ExtractKeyframes,
|
|
"ImageApplyLUT+": ImageApplyLUT,
|
|
#"NoiseFromImage+": NoiseFromImage,
|
|
|
|
"MaskBlur+": MaskBlur,
|
|
"MaskFlip+": MaskFlip,
|
|
"MaskPreview+": MaskPreview,
|
|
"MaskBatch+": MaskBatch,
|
|
"MaskExpandBatch+": MaskExpandBatch,
|
|
"TransitionMask+": TransitionMask,
|
|
"MaskFromColor+": MaskFromColor,
|
|
"MaskFromBatch+": MaskFromBatch,
|
|
|
|
"SimpleMath+": SimpleMath,
|
|
"ConsoleDebug+": ConsoleDebug,
|
|
"DebugTensorShape+": DebugTensorShape,
|
|
|
|
"ModelCompile+": ModelCompile,
|
|
"BatchCount+": BatchCount,
|
|
|
|
"CLIPTextEncodeSDXL+": CLIPTextEncodeSDXLSimplified,
|
|
"SDXLResolutionPicker+": SDXLResolutionPicker,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"GetImageSize+": "🔧 Get Image Size",
|
|
"ImageResize+": "🔧 Image Resize",
|
|
"ImageCrop+": "🔧 Image Crop",
|
|
"ImageFlip+": "🔧 Image Flip",
|
|
|
|
"ImageDesaturate+": "🔧 Image Desaturate",
|
|
"ImagePosterize+": "🔧 Image Posterize",
|
|
"ImageCASharpening+": "🔧 Image Contrast Adaptive Sharpening",
|
|
"ImageSeamCarving+": "🔧 Image Seam Carving",
|
|
"ImageEnhanceDifference+": "🔧 Image Enhance Difference",
|
|
"ImageExpandBatch+": "🔧 Image Expand Batch",
|
|
"ImageFromBatch+": "🔧 Image From Batch",
|
|
"ImageCompositeFromMaskBatch+": "🔧 Image Composite From Mask Batch",
|
|
"ExtractKeyframes+": "🔧 Extract Keyframes (experimental)",
|
|
"ImageApplyLUT+": "🔧 Image Apply LUT",
|
|
#"NoiseFromImage+": "🔧 Noise From Image",
|
|
|
|
"MaskBlur+": "🔧 Mask Blur",
|
|
"MaskFlip+": "🔧 Mask Flip",
|
|
"MaskPreview+": "🔧 Mask Preview",
|
|
"MaskBatch+": "🔧 Mask Batch",
|
|
"MaskExpandBatch+": "🔧 Mask Expand Batch",
|
|
"TransitionMask+": "🔧 Transition Mask",
|
|
"MaskFromColor+": "🔧 Mask From Color",
|
|
"MaskFromBatch+": "🔧 MaskFromBatch",
|
|
|
|
"SimpleMath+": "🔧 Simple Math",
|
|
"ConsoleDebug+": "🔧 Console Debug",
|
|
"DebugTensorShape+": "🔧 Tensor Shape Debug",
|
|
|
|
"ModelCompile+": "🔧 Compile Model",
|
|
"BatchCount+": "🔧 Batch Count",
|
|
|
|
"CLIPTextEncodeSDXL+": "🔧 SDXLCLIPTextEncode",
|
|
"SDXLResolutionPicker+": "🔧 SDXL Resolutions",
|
|
}
|