Adding in Vextra
The Vextra Nodes have been added to AegisFlow Utility Nodes due to an installer error on the original repo. that has been ignored by that maintainer.
This commit is contained in:
Major Studio
+434
-4
@@ -5,8 +5,10 @@
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import sys
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import torch
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import numpy as np
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from PIL import Image, ImageFilter
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from PIL import Image, ImageFilter, ImageDraw, ImageFont
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from PIL import Image
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import subprocess
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import math
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p310_plus = (sys.version_info >= (3, 10))
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@@ -682,6 +684,421 @@ class af_pipe_out_xl:
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return (image, mask, sdxl_tuple, latent, model, vae, clip, positive, negative, refiner_model, refiner_vae, refiner_clip, refiner_positive, refiner_negative, image_width, image_height, refiner_negative, latent_width, latent_height, discord, )
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# Vextra Nodes; These are having issues being imported due to some errors occurring on the original nodes; maintainer has not been available to fix the issue and as such we are including them here
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# with full credit to the original developer diontimmer. Not all of their nodes are present, but just the ones we use:
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class Flatten_Colors():
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"""
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This node provides a simple interface to apply PixelSort blur to the output image.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"number_of_colors": ("INT", {"default": 5, "min": 1, "max": 4000, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "flatten"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def flatten(self, images, number_of_colors):
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#create empty tensor with the same shape as images
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total_images = []
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for image in images:
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image = self.tensor_to_pil(image)
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image = image.convert('P', palette=Image.ADAPTIVE, colors=number_of_colors)
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# convert to tensor
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out_image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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def or_convert(im, mode):
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return im if im.mode == mode else im.convert(mode)
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def hue_rotate(im, deg=0):
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cos_hue = math.cos(math.radians(deg))
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sin_hue = math.sin(math.radians(deg))
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matrix = [
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.213 + cos_hue * .787 - sin_hue * .213,
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.715 - cos_hue * .715 - sin_hue * .715,
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.072 - cos_hue * .072 + sin_hue * .928,
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0,
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.213 - cos_hue * .213 + sin_hue * .143,
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.715 + cos_hue * .285 + sin_hue * .140,
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.072 - cos_hue * .072 - sin_hue * .283,
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0,
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.213 - cos_hue * .213 - sin_hue * .787,
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.715 - cos_hue * .715 + sin_hue * .715,
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.072 + cos_hue * .928 + sin_hue * .072,
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0,
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]
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rotated = or_convert(im, 'RGB').convert('RGB', matrix)
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return or_convert(rotated, im.mode)
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class HueRotation():
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"hue_rotation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 360.0, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_hr"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def apply_hr(self, images, hue_rotation):
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#create empty tensor with the same shape as images
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total_images = []
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for image in images:
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image = self.tensor_to_pil(image)
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image = hue_rotate(image, hue_rotation)
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# convert to tensor
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out_image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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COLOR_MODES = {
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'RGB': 'RGB',
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'RGBA': 'RGBA',
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'luminance': 'L',
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'luminance_alpha': 'LA',
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'cmyk': 'CMYK',
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'ycbcr': 'YCbCr',
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'lab': 'LAB',
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'hsv': 'HSV',
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'single_channel': '1',
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}
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class Swap_Color_Mode():
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"""
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This node provides a simple interface to apply PixelSort blur to the output image.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"color_mode": (['default', 'luminance', 'single_channel', 'RGB', 'RGBA', 'lab', 'hsv', 'cmyk', 'ycbcr'],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "do_swap"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def do_swap(self, images, color_mode='default'):
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total_images = []
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for image in images:
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image = self.tensor_to_pil(image)
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if color_mode != 'default':
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correct_color_mode = COLOR_MODES[color_mode]
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image = image.convert(correct_color_mode)
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# convert to tensor
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out_image = np.array(image).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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try:
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import pilgram
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except ModuleNotFoundError:
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# install pixelsort in current venv
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subprocess.check_call([sys.executable, "-m", "pip", "install", "pilgram"])
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import pilgram
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class ApplyFilter():
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"instagram_filter": ([
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"_1977",
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"aden",
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"brannan",
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"brooklyn",
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"clarendon",
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"earlybird",
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"gingham",
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"hudson",
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"inkwell",
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"kelvin",
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"lark",
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"lofi",
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"maven",
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"mayfair",
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"moon",
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"nashville",
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"perpetua",
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"reyes",
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"rise",
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"slumber",
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"stinson",
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"toaster",
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"valencia",
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"walden",
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"willow",
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"xpro2",
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],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_filter"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def apply_filter(self, images, instagram_filter):
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#create empty tensor with the same shape as images
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total_images = []
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filter_fn = getattr(pilgram, instagram_filter)
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for image in images:
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image = self.tensor_to_pil(image)
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image = filter_fn(image)
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# convert to tensor
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out_image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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try:
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from glitch_this import ImageGlitcher
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except ModuleNotFoundError:
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# install pixelsort in current venv
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subprocess.check_call([sys.executable, "-m", "pip", "install", "glitch-this"])
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from glitch_this import ImageGlitcher
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class GlitchThis():
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"glitch_amount": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.01}),
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"color_offset": (['Disable', 'Enable'],),
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"scan_lines": (['Disable', 'Enable'],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_glitch"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def string2bool(self, v):
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return v == 'Enable'
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def apply_glitch(self, images, glitch_amount=1, color_offset='Disable', scan_lines='Disable', seed=0):
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color_offset = self.string2bool(color_offset)
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scan_lines = self.string2bool(scan_lines)
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glitcher = ImageGlitcher()
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#create empty tensor with the same shape as images
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total_images = []
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for image in images:
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image = self.tensor_to_pil(image)
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image = glitcher.glitch_image(image, glitch_amount, color_offset=color_offset, scan_lines=scan_lines, seed=seed)
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# convert to tensor
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out_image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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class FontText():
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"""
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This node provides a simple interface to apply PixelSort blur to the output image.
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"""
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Input Types
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"""
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return {
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"required": {
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"images": ("IMAGE",),},
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"optional": {
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"font_ttf": ("STRING", {"default": 'C:/Windows/Fonts/arial.ttf'}),
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"size": ("INT", {"default": 50, "min": 2, "max": 1000, "step": 1}),
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"x": ("INT", {"default": 50, "min": 2, "max": 10000, "step": 1}),
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"y": ("INT", {"default": 50, "min": 2, "max": 10000, "step": 1}),
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"text": ("STRING", {"default": "Hello World", "multiline": True}),
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"color": ("STRING", {"default": 'rgba(255, 255, 255, 255)'}),
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"anchor": (["Bottom Left Corner", "Center"],),
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"rotate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 360.0, "step": 0.1}),
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"color_mode": (["RGB", "RGBA"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "do_font"
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CATEGORY = "AegisFlow/fx"
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def tensor_to_pil(self, img):
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if img is not None:
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i = 255. * img.cpu().numpy().squeeze()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return img
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def do_font(self, images, font_ttf, size, x, y, color, anchor, rotate, color_mode, text):
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#create empty tensor with the same shape as images
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total_images = []
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center_anchor = True if anchor == 'Center' else False
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if color.startswith('#'):
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color_rgba = tuple(int(color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
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else:
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color_rgba = tuple(map(int, color.strip('rgba()').split(',')))
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for image in images:
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image = self.tensor_to_pil(image)
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add_text_to_image(image, font_ttf, size, x, y, text, color_rgba, center_anchor, rotate)
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# convert to tensor
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out_image = np.array(image.convert(color_mode)).astype(np.float32) / 255.0
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out_image = torch.from_numpy(out_image).unsqueeze(0)
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total_images.append(out_image)
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total_images = torch.cat(total_images, 0)
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return (total_images,)
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def add_text_to_image(img, font_ttf, size, x, y, text, color_rgb, center=False, rotate=0):
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draw = ImageDraw.Draw(img)
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myFont = ImageFont.truetype(font_ttf, size)
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text_width, text_height = draw.textsize(text, font=myFont)
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if center:
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x -= text_width // 2
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y -= text_height // 2
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if rotate != 0:
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text_img = Image.new('RGBA', img.size, (255, 255, 255, 0))
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text_draw = ImageDraw.Draw(text_img)
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text_draw.text((x, y), text, font=myFont, fill=color_rgb)
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text_img = text_img.rotate(rotate, resample=Image.BICUBIC, expand=True)
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img.paste(text_img, (0, 0), text_img)
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else:
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draw.text((x, y), text, font=myFont, fill=color_rgb)
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return img
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@@ -707,7 +1124,13 @@ NODE_CLASS_MAPPINGS = {
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"af_pipe_in_15": af_pipe_in_15,
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"af_pipe_out_15": af_pipe_out_15,
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"af_pipe_in_xl": af_pipe_in_xl,
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"af_pipe_out_xl": af_pipe_out_xl
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"af_pipe_out_xl": af_pipe_out_xl,
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"Flatten Colors": Flatten_Colors,
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"Hue Rotation": HueRotation,
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"Swap Color Mode": Swap_Color_Mode,
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"Apply Instagram Filter": ApplyFilter,
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"GlitchThis Effect": GlitchThis,
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"Add Text To Image": FontText
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -730,8 +1153,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"af_pipe_in_15": "MultiPipe 1.5 In",
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"af_pipe_out_15": "MultiPipe 1.5 Out",
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"af_pipe_in_xl": "MultiPipe XL In",
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"af_pipe_out_xl": "MultiPipe XL Out"
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"af_pipe_out_xl": "MultiPipe XL Out",
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"Flatten Colors": "Flatten Colors-Vextra",
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"Hue Rotation": "Hue Rotation-Vextra",
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"Swap Color Mode": "Swap Color Mode-Vextra",
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"Apply Instagram Filter": "Instagram Filters-Vextra",
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"GlitchThis Effect": "Glitch-Vextra",
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"Add Text To Image": "Add Font Text-Vextra"
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}
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WEB_DIRECTORY = "./js"
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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Reference in New Issue
Block a user