691 lines
24 KiB
Python
691 lines
24 KiB
Python
CURRENT_CATEGORY = "🐦🔥 ArchiGraph/🛠️ Utils" # organized by py file
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import os
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import random, uuid, hashlib, time
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import torch
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import numpy as np
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from PIL import Image
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from .anyType import ANY
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from .nodes_opencv import Nparray2Image
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from ..node_registry import comfy_node
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# import comfyui core node
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from server import PromptServer # import comfyui server module to manupulate prompt queue
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from nodes import SaveImage
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import folder_paths
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NPARRAY_DIR = os.path.join(folder_paths.get_output_directory(), "nparrays")
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JSON_DIR = os.path.join(folder_paths.get_output_directory(), "jsons")
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#---------------------------------------------------------------------------------------------------------------------#
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# Helpers
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#---------------------------------------------------------------------------------------------------------------------#
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def resolve_path(path: str) -> str:
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# 1. 展开环境变量（如 %USERPROFILE%）
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path = os.path.expandvars(path)
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# 2. 展开用户目录（如 ~ 表示用户主目录）
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path = os.path.expanduser(path)
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# 3. 转换为绝对路径并规范化
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path = os.path.abspath(path)
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return path
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class JsonSave:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input": (ANY, {
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"tooltip": "Input dictionary/list/tuple to save as JSON."
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}),
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"filename_noExt": ("STRING", {
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"default": "unknown",
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"tooltip": "Filename without extension (extension .json will be added automatically).",
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}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("file_path",)
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OUTPUT_NODE = True
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FUNCTION = "execute"
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NAME = "JSON Save"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Saves a dictionary/list/tuple as a .json file. Nparrays and Tensors will be converted to lists."
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def convert(self, obj):
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if isinstance(obj, np.ndarray):
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return obj.tolist()
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elif isinstance(obj, torch.Tensor):
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return obj.cpu().numpy().tolist()
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raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
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def execute(self, input, filename_noExt):
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import json
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if not os.path.exists(JSON_DIR):
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os.makedirs(JSON_DIR)
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file_path = os.path.join(JSON_DIR, filename_noExt + ".json")
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with open(file_path, 'w') as json_file:
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json.dump(input, json_file, indent=4, default=self.convert)
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return (file_path,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class JsonLoad:
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@classmethod
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def INPUT_TYPES(cls):
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if not os.path.exists(JSON_DIR):
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os.makedirs(JSON_DIR)
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files = []
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else:
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files = sorted([f for f in os.listdir(JSON_DIR) if f.endswith('.json')])
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if len(files) == 0:
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files = ["None"] # Placeholder to avoid empty list issues
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return {
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"required": {
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"jsonfile": (files, ),
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"filename_noExt": ("STRING", {
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"default": "",
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"tooltip": "Filename without extension (extension .json will be added automatically). If specified, npyfile will be ignored.",
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}),
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},
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}
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RETURN_TYPES = (ANY,)
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RETURN_NAMES = ("data",)
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FUNCTION = "execute"
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NAME = "JSON Load"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Loads a dictionary/list/tuple from a .json file."
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def execute(self, jsonfile, filename_noExt=""):
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import json
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if filename_noExt != "":
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jsonfile = filename_noExt + ".json"
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else:
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if jsonfile == "None":
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raise FileNotFoundError("JSON files not found in the jsons directory.")
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file_path = os.path.join(JSON_DIR, jsonfile)
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"JSON file not found: {file_path}")
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with open(file_path, 'r') as json_file:
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data = json.load(json_file)
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return (data,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class NparraySave:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"nparray": ("NPARRAY", {
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"lazy": False,
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}),
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"filename_noExt": ("STRING", {
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"default": "unknown",
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"tooltip": "Filename without extension (extension .npy will be added automatically).",
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}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("file_path",)
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OUTPUT_NODE = True
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FUNCTION = "execute"
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NAME = "Nparray Save"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Saves a numpy array as a .npy file."
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def execute(self, nparray, filename_noExt):
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if not os.path.exists(NPARRAY_DIR):
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os.makedirs(NPARRAY_DIR)
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file_path = os.path.join(NPARRAY_DIR, filename_noExt + ".npy")
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np.save(file_path, nparray)
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return (file_path,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class NparrayLoad:
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@classmethod
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def INPUT_TYPES(cls):
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if not os.path.exists(NPARRAY_DIR):
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os.makedirs(NPARRAY_DIR)
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files = []
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else:
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files = sorted([f for f in os.listdir(NPARRAY_DIR) if f.endswith('.npy')])
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if len(files) == 0:
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files = ["None"] # Placeholder to avoid empty list issues
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return {
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"required": {
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"npyfile": (files, ),
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"filename_noExt": ("STRING", {
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"default": "",
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"tooltip": "Filename without extension (extension .npy will be added automatically). If specified, npyfile will be ignored.",
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}),
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},
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}
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RETURN_TYPES = ("NPARRAY",)
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RETURN_NAMES = ("nparray",)
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FUNCTION = "execute"
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NAME = "Nparray Load"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Loads a numpy array from a .npy file."
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def execute(self, npyfile, filename_noExt=""):
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if filename_noExt != "":
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npyfile = filename_noExt + ".npy"
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else:
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if npyfile == "None":
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raise FileNotFoundError("Npy files not found in the nparrays directory.")
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file_path = os.path.join(NPARRAY_DIR, npyfile)
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"Npy file not found: {file_path}")
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nparray = np.load(file_path)
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return (nparray,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class SaveNparrayImage(SaveImage):
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"nparrays": ("NPARRAY", {"tooltip": "The nparray images to save."}),
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"filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."})
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},
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"hidden": {
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"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
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},
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}
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OUTPUT_NODE = True
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RETURN_TYPES = ()
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FUNCTION = "execute"
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NAME = "Save Nparray Image"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Saves the input nparray images to your ComfyUI output directory."
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def execute(self, nparrays, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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images = Nparray2Image().execute(nparrays)[0]
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return super().save_images(images, filename_prefix, prompt, extra_pnginfo)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class PreviewNparrayImage(SaveNparrayImage):
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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 = 1
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":
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{"nparrays": ("NPARRAY", ), },
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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NAME = "Preview Nparray Image"
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DESCRIPTION = "Previews the input nparray images by saving them to a temporary directory."
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class Printf:
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'''
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TODO: Can be used to unravel list and show specified elements only.
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'''
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"any": (ANY, {
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"forceInput": True,
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"tooltip": "Select objects to print."
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}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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OUTPUT_NODE = True
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INPUT_IS_LIST = False # if the input is a list, workflow will pass each element separately
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OUTPUT_IS_LIST = (True,)
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("string",)
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FUNCTION = "execute"
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NAME = "Printf"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Print object info to console for debugging. Supports any input type."
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def execute(self, any, unique_id=None, extra_pnginfo=None):
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# prepare text, caution: text is parse at frontend as list of strings
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text = []
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if isinstance(any, np.ndarray):
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text.append(f"Type: Nparray, Shape: {any.shape}, Dtype: {any.dtype}")
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elif isinstance(any, torch.Tensor):
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text.append(f"Type: Tensor, Shape: {any.shape}, Dtype: {any.dtype}, Device: {any.device}")
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elif isinstance(any, list):
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text.append(f"Type: List, Length: {len(any)}")
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for i, item in enumerate(any):
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text.append(f"[{i}]: Type: {type(item)}, Value:\n{item}")
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elif isinstance(any, dict):
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text.append(f"Type: Dict, Length: {len(any)}")
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for key, value in any.items():
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text.append(f"Key: {key}, Type: {type(value)}, Value:\n{value}")
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elif isinstance(any, tuple):
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text.append(f"Type: Tuple, Length: {len(any)}")
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for i, item in enumerate(any):
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text.append(f"[{i}]: Type: {type(item)}, Value:\n{item}")
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else:
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text.append(f"Type: {type(any)}, Value:\n{any}")
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return {"ui": {"text": text}, "result": (text,)}
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class SliceList:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input_list": (ANY, {
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"tooltip": "Input list to slice."
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}),
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"start": ("INT", {
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"default": 0,
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"min": 0,
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"tooltip": "Start index for slicing."
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}),
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"length": ("INT", {
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"default": 1,
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"min": 1,
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"tooltip": "Length for slicing."
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}),
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},
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}
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RETURN_TYPES = (ANY,)
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RETURN_NAMES = ("sliced",)
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FUNCTION = "execute"
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NAME = "Slicing List"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Extract a list from the input list from start index with specified length. Supports any input type."
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def execute(self, input_list, start, length):
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if not (isinstance(input_list, list) or isinstance(input_list, tuple)):
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raise ValueError("Input is not a list or tuple.")
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sliced = input_list[start : start + length]
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return (sliced,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class SliceSublist:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"input_list": (ANY, {
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"tooltip": "Input list to slice."
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}),
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"start": ("INT", {
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"default": 0,
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"min": 0,
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"tooltip": "Start index for slicing."
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}),
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"length": ("INT", {
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"default": 1,
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"min": 1,
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"tooltip": "Length for slicing."
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}),
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},
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}
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RETURN_TYPES = (ANY,)
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RETURN_NAMES = ("sliced",)
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FUNCTION = "execute"
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NAME = "Slicing Sublist"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Extract a sublist from the input list from start index with specified length. Supports any input type."
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def execute(self, input_list, start, length):
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if not (isinstance(input_list, list) or isinstance(input_list, tuple)):
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raise ValueError("Input is not a list or tuple.")
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sliced = []
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for sublist in input_list:
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if not (isinstance(sublist, list) or isinstance(sublist, tuple)):
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sliced.append([]) # append empty list if sublist is not a list
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else:
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sliced.append(sublist[start : start + length])
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return (sliced,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class SaveImageToPath:
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def __init__(self):
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE", {"tooltip": "The images to save."}),
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"target_folder": ("STRING", {
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"default": "%USERPROFILE%",
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"tooltip": "Target size for resizing."
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}),
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"filename": ("STRING", {
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"default": "output",
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"tooltip": "Filename without extension (extension .png will be added automatically).",
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}),
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},
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}
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OUTPUT_NODE = True
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RETURN_TYPES = ()
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FUNCTION = "execute"
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NAME = "Save Image To Path"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "TODO."
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def execute(self, images, target_folder, filename):
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target_folder = resolve_path(target_folder)
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if not os.path.exists(target_folder):
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try:
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os.makedirs(target_folder, exist_ok=True)
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except Exception as e:
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print(f"AG {self.NAME}: Failed to create target folder: {target_folder}")
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return ()
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file_path = os.path.join(target_folder, f"{filename}.png")
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i = 255. * images[0].cpu().numpy()
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image = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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image.save(file_path, compress_level=4)
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print(f"AG {self.NAME}: {file_path} created.")
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return () # need to return something to avoid error in comfyui
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class FilenameList:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"num_files": ("INT", {
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"default": 10,
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"min": 1,
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"tooltip": "Number of filenames to generate."
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}),
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"filename_prefix": ("STRING", {
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"default": "AG",
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"tooltip": "Prefix for the filenames."
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}),
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"num_digit": ("INT", {
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"default": 4,
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"tooltip": "Number of digits in the filename."
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}),
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"num_start": ("INT", {
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"default": 0,
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"min": 0,
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"tooltip": "Starting number for the filename."
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}),
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},
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"optional": {
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"optional_idx": ("INT", {
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"default": -1,
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"min": -1,
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"tooltip": "If specified (not -1), generate only the filename for this index."
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}),
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},
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}
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RETURN_TYPES = (ANY,)
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RETURN_NAMES = ("filenames",)
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FUNCTION = "execute"
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NAME = "Filename List"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Generates a list of filenames based on the given prefix and number format."
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def execute(self, num_files: int, filename_prefix: str, num_digit: int, num_start: int, optional_idx: int = -1):
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if optional_idx == -1:
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filenames = [f"{filename_prefix}_{i:0{num_digit}}.png" for i in range(num_start, num_start + num_files)]
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else:
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filenames = f"{filename_prefix}_{optional_idx:0{num_digit}}.png"
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return (filenames,)
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#---------------------------------------------------------------------------------------------------------------------#
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@comfy_node
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class FilepathGeneratorMaker:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"target_folder": ("STRING", {
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"default": "%USERPROFILE%",
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"tooltip": "Target folder to enumerate files from. Subfolders are not included."
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}),
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"file_extension": ("STRING", {
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"default": "",
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"tooltip": "File extension to filter by. If empty, all 'png/jpg/jpeg' are included."
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}),
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},
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}
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RETURN_TYPES = (ANY,)
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RETURN_NAMES = ("enumerator",)
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FUNCTION = "execute"
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NAME = "Filepath Generator Maker"
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CATEGORY = CURRENT_CATEGORY
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DESCRIPTION = "Returns a generator for filepaths from the specified target folder with the given file extension."
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def execute(self, target_folder, file_extension):
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target_folder = resolve_path(target_folder)
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if not os.path.exists(target_folder):
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print(f"AG {self.NAME}: Target folder does not exist: {target_folder}")
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return (None,)
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from pathlib import Path
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def maker():
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nonlocal target_folder
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nonlocal file_extension
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def file_generator(folder, extension):
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path = Path(folder)
|
||
if extension != "":
|
||
yield from path.rglob(f"*.{extension.lstrip('.')}") # ensure extension starts with '.'
|
||
else:
|
||
for ext in ['*.png', '*.jpg', '*.jpeg']:
|
||
yield from path.rglob(ext)
|
||
|
||
return file_generator(target_folder, file_extension)
|
||
|
||
return (maker,)
|
||
|
||
#---------------------------------------------------------------------------------------------------------------------#
|
||
@comfy_node
|
||
class IterateItems:
|
||
def __init__(self):
|
||
self.index = 0
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"items": (ANY, {
|
||
"tooltip": "Input a list to iterate over its items."
|
||
}),
|
||
"iteration_on": ("BOOLEAN", {
|
||
"default": True,
|
||
"tooltip": "Always re-run this node, even if the input list is unchanged."
|
||
}),
|
||
},
|
||
"hidden": {
|
||
"unique_id": "UNIQUE_ID",
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = (ANY, "INT", )
|
||
RETURN_NAMES = ("item", "index", )
|
||
FUNCTION = "execute"
|
||
NAME = "Iterate Items"
|
||
CATEGORY = CURRENT_CATEGORY
|
||
DESCRIPTION = "Iterate over each item in the input list. Outputs one item at a time."
|
||
|
||
INPUT_IS_LIST = False
|
||
OUTPUT_IS_LIST = (False, False, )
|
||
|
||
def execute(self, items, iteration_on, unique_id=None):
|
||
# sanity check
|
||
if not (isinstance(items, list) or isinstance(items, tuple)):
|
||
print("Input is not a list or tuple, return as is.")
|
||
return (items, 0, )
|
||
|
||
# get list item
|
||
item = items[self.index]
|
||
print(f"AG {self.NAME}: iterated {self.index + 1} times.")
|
||
|
||
# import json
|
||
# print(json.dumps(current_item)) # for debugging
|
||
|
||
# -----------------------------------------------------
|
||
# hack the prompt queue to add a new item to the queue
|
||
# -----------------------------------------------------
|
||
if iteration_on:
|
||
queue = PromptServer.instance.prompt_queue
|
||
current_item = queue.currently_running[queue.task_counter - 1]
|
||
|
||
# item structure: (number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive)
|
||
new_item = list(current_item) # copy current item
|
||
new_item[0] = queue.task_counter # update index
|
||
new_item[1] = str(uuid.uuid4()) # generate new prompt_id
|
||
new_item[3]["create_time"] = int(time.time() * 1000) # timestamp in milliseconds
|
||
|
||
# add new item to queue
|
||
if self.index < len(items) - 1:
|
||
queue.put(new_item)
|
||
|
||
current_idx = self.index
|
||
self.index = (self.index + 1) % len(items) # will return to 0 when finished
|
||
|
||
return (item, current_idx, )
|
||
|
||
return (item, 0, )
|
||
|
||
@classmethod
|
||
def IS_CHANGED(cls, items, iteration_on, unique_id=None):
|
||
if iteration_on:
|
||
return float("nan") # for faster update
|
||
|
||
input_str = f"{items}-{iteration_on}"
|
||
return hashlib.sha256(input_str.encode("utf-8")).hexdigest()
|
||
|
||
#---------------------------------------------------------------------------------------------------------------------#
|
||
@comfy_node
|
||
class IterateGenerator:
|
||
def __init__(self):
|
||
self.index = 0
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"generatorMaker": (ANY, {
|
||
"tooltip": "Input a generator to iterate"
|
||
}),
|
||
"enabled": ("BOOLEAN", {
|
||
"default": True,
|
||
"tooltip": "Only run when enabled."
|
||
}),
|
||
},
|
||
"hidden": {
|
||
"unique_id": "UNIQUE_ID",
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = (ANY, "INT")
|
||
RETURN_NAMES = ("item", "index")
|
||
FUNCTION = "execute"
|
||
NAME = "Iterate Generator"
|
||
CATEGORY = CURRENT_CATEGORY
|
||
DESCRIPTION = "Iterate over each item from the generator. Outputs one item at a time."
|
||
|
||
INPUT_IS_LIST = False
|
||
OUTPUT_IS_LIST = (False,False)
|
||
|
||
def execute(self, generatorMaker, enabled, unique_id=None):
|
||
# when not enabled, exit
|
||
if not enabled:
|
||
print(f"AG {self.NAME}: Iterator not enabled.")
|
||
return (None, 0, )
|
||
|
||
# store generator at start
|
||
if self.index == 0:
|
||
self.generator = generatorMaker()
|
||
|
||
# iterate
|
||
try:
|
||
item = next(self.generator)
|
||
except StopIteration:
|
||
self.index = 0
|
||
print(f"AG {self.NAME}: StopIteration encountered.")
|
||
return (None, 0, )
|
||
|
||
# get list item
|
||
print(f"AG {self.NAME}: iterated {self.index + 1} times.")
|
||
|
||
# -----------------------------------------------------
|
||
# hack the prompt queue to add a new item to the queue
|
||
# -----------------------------------------------------
|
||
queue = PromptServer.instance.prompt_queue
|
||
current_item = queue.currently_running[queue.task_counter - 1]
|
||
|
||
# item structure: (number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive)
|
||
new_item = list(current_item) # copy current item
|
||
new_item[0] = queue.task_counter # update index
|
||
new_item[1] = str(uuid.uuid4()) # generate new prompt_id
|
||
new_item[3]["create_time"] = int(time.time() * 1000) # timestamp in milliseconds
|
||
|
||
# add new item to queue
|
||
queue.put(new_item)
|
||
|
||
# return index
|
||
current_idx = self.index
|
||
self.index += 1
|
||
|
||
return (item, current_idx, )
|
||
|
||
@classmethod
|
||
def IS_CHANGED(cls, generatorMaker, enabled, unique_id=None):
|
||
if enabled:
|
||
return float("nan") # enforce update
|
||
return "unchanged" # don't update |