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Update nodes.py
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nodes.py
84
nodes.py
@ -4,6 +4,9 @@ import torch.nn.functional as F
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import scipy.ndimage
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import numpy as np
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from PIL import ImageColor, Image, ImageDraw, ImageFont
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from PIL.PngImagePlugin import PngInfo
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import json
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import re
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import os
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import librosa
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from scipy.special import erf
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@ -11,6 +14,8 @@ from .fluid import Fluid
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import comfy.model_management
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import math
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from nodes import MAX_RESOLUTION
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import folder_paths
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from comfy.cli_args import args
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script_dir = os.path.dirname(os.path.abspath(__file__))
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@ -998,7 +1003,80 @@ class ColorMatch:
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break
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out.append(torch.from_numpy(image_result))
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return (torch.stack(out, dim=0).to(torch.float32), )
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class SaveImageWithAlpha:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"images": ("IMAGE", ),
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"mask": ("MASK", ),
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"filename_prefix": ("STRING", {"default": "ComfyUI"})},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images_alpha"
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OUTPUT_NODE = True
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CATEGORY = "image"
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def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
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filename_prefix += self.prefix_append
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
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results = list()
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def file_counter():
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max_counter = 0
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# Loop through the existing files
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for existing_file in os.listdir(full_output_folder):
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# Check if the file matches the expected format
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match = re.fullmatch(f"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file)
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if match:
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# Extract the numeric portion of the filename
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file_counter = int(match.group(1))
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# Update the maximum counter value if necessary
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if file_counter > max_counter:
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max_counter = file_counter
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return max_counter
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for image, alpha in zip(images, mask):
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i = 255. * image.cpu().numpy()
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a = 255. * alpha.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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if a.shape == img.size[::-1]: # Check if the mask has the same size as the image
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print("Applying mask")
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a = np.clip(a, 0, 255).astype(np.uint8)
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img.putalpha(Image.fromarray(a, mode='L'))
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else:
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raise ValueError("SaveImageWithAlpha: Mask size does not match")
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metadata = None
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if not args.disable_metadata:
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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# Increment the counter by 1 to get the next available value
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counter = file_counter() + 1
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file = f"{filename}_{counter:05}.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type
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})
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return { "ui": { "images": results } }
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NODE_CLASS_MAPPINGS = {
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"INTConstant": INTConstant,
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"FloatConstant": FloatConstant,
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@ -1019,7 +1097,8 @@ NODE_CLASS_MAPPINGS = {
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"CrossFadeImages": CrossFadeImages,
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"EmptyLatentImagePresets": EmptyLatentImagePresets,
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"ColorMatch": ColorMatch,
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"GetImageRangeFromBatch": GetImageRangeFromBatch
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"GetImageRangeFromBatch": GetImageRangeFromBatch,
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"SaveImageWithAlpha": SaveImageWithAlpha
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"INTConstant": "INT Constant",
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@ -1040,5 +1119,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"SomethingToString": "SomethingToString",
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"EmptyLatentImagePresets": "EmptyLatentImagePresets",
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"ColorMatch": "ColorMatch",
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"GetImageRangeFromBatch": "GetImageRangeFromBatch"
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"GetImageRangeFromBatch": "GetImageRangeFromBatch",
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"SaveImageWithAlpha": "SaveImageWithAlpha"
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}
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