feat: sync latest local version as authoritative codebase
Complete rewrite/sync of comfyui_o1key custom nodes. Treat this commit as the current canonical version. Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
@@ -0,0 +1,642 @@
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"""
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全能生图（批量）节点
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ComfyUI 自定义节点，用于批量处理图像生成任务
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支持多文件夹加载、1:1/笛卡尔积配对、智能命名保存
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"""
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import time
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import math
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import random
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import asyncio
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import aiohttp
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from concurrent.futures import ThreadPoolExecutor
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from typing import Optional, Tuple, List
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from PIL import Image
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import torch
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import numpy as np
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from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
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from ..utils.file_utils import (
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ImageInfo,
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load_images_from_folder,
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pair_images_by_name,
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pair_images_cartesian,
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generate_timestamp_filename,
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save_image,
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)
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from ..clients.openai_client import OpenAIAPIClient
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from ..models_config import (
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get_enabled_models,
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get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
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get_model_supported_resolutions, get_all_supported_resolutions
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)
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# 导入 ComfyUI 原生进度条
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try:
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from comfy.utils import ProgressBar
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PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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PROGRESS_BAR_AVAILABLE = False
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print("⚠️ 全能生图（批量）: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
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# 导入 ComfyUI 的文件夹路径管理
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try:
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import folder_paths
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FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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FOLDER_PATHS_AVAILABLE = False
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print("⚠️ 全能生图（批量）: folder_paths 不可用，将无法使用默认保存路径")
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# 内存监控（可选）
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try:
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import psutil
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MEMORY_MONITOR_AVAILABLE = True
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except ImportError:
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MEMORY_MONITOR_AVAILABLE = False
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print("⚠️ 全能生图（批量）: psutil 不可用，内存监控功能禁用")
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# ============================================================================
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# 调试日志配置
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# ============================================================================
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DEBUG_LOG_ENABLED = False
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REQUEST_LOG_ENABLED = False
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# ============================================================================
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class BatchQuanNengShengTu:
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"""
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全能生图（批量）节点
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功能：
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- 从多个文件夹加载图片
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- 支持三种配对模式：
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* 按相同图片命名 - 索引配对（文件夹之间按位置配对）
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* 1*N - 笛卡尔积配对（所有可能组合）
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* 不配对 - 固定参考图模式（文件夹图片依次与所有参考图组合）
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- 批量调用 API 生成图像
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- 智能命名保存（保留原始文件名）
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- 并发控制（默认最大 10）
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注意：
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- 「不配对」模式只支持单个文件夹
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- 支持的模型列表从 models_config.py 动态加载
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"""
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MODELS = None
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ASPECT_RATIOS = [
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"1:1", "4:3", "3:4", "16:9", "9:16",
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"2:3", "3:2", "4:5", "5:4", "21:9",
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"1:4", "4:1", "1:8", "8:1"
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]
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RESOLUTIONS = ["512", "1K", "2K", "4K"]
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PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"]
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def __init__(self):
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"""初始化节点"""
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self.client = None
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def resize_to_megapixels(
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self,
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image: Image.Image,
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target_megapixels: float
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) -> Image.Image:
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"""将图像缩放到指定的总像素数，保持纵横比"""
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current_pixels = image.width * image.height
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target_pixels = int(target_megapixels * 1_000_000)
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if abs(current_pixels - target_pixels) / target_pixels < 0.05:
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return image
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scale = (target_pixels / current_pixels) ** 0.5
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new_width = max(1, int(image.width * scale))
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new_height = max(1, int(image.height * scale))
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return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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@classmethod
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def INPUT_TYPES(cls):
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"""定义输入参数"""
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enabled_models = get_enabled_models()
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enabled_models = [m for m in enabled_models if "限时特价" not in m]
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if not enabled_models:
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enabled_models = ["请在 models_config.py 中启用至少一个模型"]
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all_aspect_ratios = get_all_supported_aspect_ratios()
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if not all_aspect_ratios:
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all_aspect_ratios = cls.ASPECT_RATIOS
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all_resolutions = get_all_supported_resolutions()
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if not all_resolutions:
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all_resolutions = cls.RESOLUTIONS
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optional_inputs = {}
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for i in range(1, 10):
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optional_inputs[f"参考图{i}"] = ("IMAGE",)
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optional_inputs["图片配对模式"] = (cls.PAIRING_MODES, {
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"default": "不配对"
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})
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return {
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"required": {
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"提示词": ("STRING", {
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"default": "一个中国女子的OOTD",
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"multiline": True
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}),
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"模型": (enabled_models, {
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"default": enabled_models[0]
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}),
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"宽高比": (all_aspect_ratios, {
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"default": "1:1"
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}),
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"分辨率": (all_resolutions, {
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"default": "2K"
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}),
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"像素缩放": ("BOOLEAN", {
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"default": False,
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"label_on": "打开",
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"label_off": "关闭"
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}),
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"分辨率像素": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 100.0,
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"step": 0.1,
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"display": "number"
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff
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}),
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"文件夹1": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹2": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹3": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹4": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹5": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹6": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹7": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹8": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"文件夹9": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"保存路径": ("STRING", {
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"default": "",
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"multiline": False
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})
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},
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"optional": optional_inputs
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("输出图像",)
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FUNCTION = "process_batch"
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CATEGORY = "image/batch"
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def _load_folders(
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self,
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folder1: str,
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folder2: Optional[str],
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folder3: Optional[str],
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folder4: Optional[str],
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enable_scaling: bool,
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target_megapixels: float,
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folder5: Optional[str] = None,
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folder6: Optional[str] = None,
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folder7: Optional[str] = None,
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folder8: Optional[str] = None,
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folder9: Optional[str] = None,
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) -> List[List[ImageInfo]]:
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"""加载所有文件夹中的图片"""
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folders = [folder1, folder2, folder3, folder4, folder5, folder6, folder7, folder8, folder9]
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all_images = []
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for i, folder in enumerate(folders, 1):
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if folder and folder.strip():
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try:
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images = load_images_from_folder(folder)
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if images:
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if enable_scaling:
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scaled_images = []
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for img_info in images:
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scaled_img = self.resize_to_megapixels(
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img_info.image,
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target_megapixels
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)
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scaled_info = ImageInfo(
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image=scaled_img,
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filename=img_info.filename,
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extension=img_info.extension,
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source_path=img_info.source_path
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)
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scaled_images.append(scaled_info)
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images = scaled_images
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all_images.append(images)
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except ValueError as e:
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print(f"全能生图（批量）: 文件夹{i} 加载失败 - {e}")
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return all_images
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def _create_pairs(
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self,
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image_lists: List[List[ImageInfo]],
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pairing_mode: str,
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manual_images: Optional[List[ImageInfo]] = None
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) -> List[Tuple[ImageInfo, ...]]:
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"""根据配对模式创建图片组合"""
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if pairing_mode == "不配对":
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if len(image_lists) > 1:
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raise ValueError("「不配对」模式只支持单个文件夹，请清空其他文件夹路径")
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if image_lists and manual_images:
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folder_images = image_lists[0]
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pairs = []
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for img in folder_images:
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pair = (img,) + tuple(manual_images)
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pairs.append(pair)
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return pairs
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elif image_lists:
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return [(img,) for img in image_lists[0]]
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else:
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return []
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if not image_lists:
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return []
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if len(image_lists) == 1:
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base_pairs = [(img,) for img in image_lists[0]]
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elif pairing_mode == "按相同图片命名":
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base_pairs = list(pair_images_by_name(*image_lists))
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else:
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base_pairs = list(pair_images_cartesian(*image_lists))
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if manual_images:
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manual_tuple = tuple(manual_images)
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base_pairs = [pair + manual_tuple for pair in base_pairs]
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return base_pairs
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async def _generate_single_task(
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self,
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session: aiohttp.ClientSession,
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prompt: str,
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model: str,
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resolution: str,
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aspect_ratio: str,
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images: List[ImageInfo],
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output_folder: str,
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task_index: int,
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base_filename: str = None,
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) -> dict:
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"""执行单个生成任务"""
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result = {
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"task_index": task_index,
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"prompt": prompt,
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"success": False,
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"generated_count": 0,
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"saved_files": [],
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"error": None
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}
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try:
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input_pil_images = [info.image for info in images]
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gen_result = await self.client.generate_single_async(
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prompt=prompt,
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model=model,
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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images=input_pil_images,
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session=session,
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debug=DEBUG_LOG_ENABLED,
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debug_request=REQUEST_LOG_ENABLED,
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enable_grounding=False,
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enable_image_search=False
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)
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if gen_result:
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images_list, _ = gen_result
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import os
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for gen_img in images_list:
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if base_filename:
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base_name = base_filename
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counter = 0
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while True:
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filename = f"{base_name}.png" if counter == 0 else f"{base_name}+{counter}.png"
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output_path = os.path.join(output_folder, filename)
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if not os.path.exists(output_path):
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break
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counter += 1
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else:
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output_path = generate_timestamp_filename(
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output_folder=output_folder,
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extension=".png"
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)
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save_image(gen_img, output_path)
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result["saved_files"].append(output_path)
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gen_img = None
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result["success"] = True
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result["generated_count"] = len(images_list)
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except Exception as e:
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result["error"] = str(e)
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return result
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async def _process_batch_async(
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self,
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pairs: List[Tuple[ImageInfo, ...]],
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prompt: str,
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model: str,
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resolution: str,
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aspect_ratio: str,
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output_folder: str,
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pbar=None,
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prompts_per_task: Optional[List[str]] = None,
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) -> List[dict]:
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"""异步批量处理所有任务"""
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if self.client is None:
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self.client = OpenAIAPIClient()
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total_tasks = len(pairs)
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max_concurrent = 10
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print(f"全能生图（批量）: 检测到 {total_tasks} 个任务")
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all_results = []
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completed = 0
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success_count = 0
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fail_count = 0
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num_batches = math.ceil(total_tasks / max_concurrent)
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if num_batches > 1:
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print(f"全能生图（批量）: 任务数 {total_tasks} 超过并发上限 {max_concurrent}，将分 {num_batches} 批执行")
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connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
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async with aiohttp.ClientSession(connector=connector) as session:
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for batch_idx in range(num_batches):
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start_idx = batch_idx * max_concurrent
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end_idx = min(start_idx + max_concurrent, total_tasks)
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batch_pairs = pairs[start_idx:end_idx]
|
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||||
if num_batches > 1:
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print(f"全能生图（批量）: 执行第 {batch_idx + 1}/{num_batches} 批 ({start_idx + 1}-{end_idx})...")
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|
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tasks = []
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for i, pair in enumerate(batch_pairs):
|
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task_prompt = prompts_per_task[start_idx + i] if prompts_per_task else prompt
|
||||
|
||||
base_filename = None
|
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if pair and len(pair) > 0:
|
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first_image = pair[0]
|
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if hasattr(first_image, 'filename'):
|
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base_filename = first_image.filename
|
||||
|
||||
task = asyncio.create_task(
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self._generate_single_task(
|
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session=session,
|
||||
prompt=task_prompt,
|
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model=model,
|
||||
resolution=resolution,
|
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aspect_ratio=aspect_ratio,
|
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images=list(pair),
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output_folder=output_folder,
|
||||
task_index=start_idx + i,
|
||||
base_filename=base_filename,
|
||||
)
|
||||
)
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tasks.append(task)
|
||||
|
||||
batch_results = []
|
||||
for coro in asyncio.as_completed(tasks):
|
||||
result_data = None
|
||||
try:
|
||||
result = await coro
|
||||
if isinstance(result, Exception):
|
||||
result_data = {"success": False, "error": str(result), "generated_count": 0, "saved_files": []}
|
||||
else:
|
||||
result_data = result
|
||||
batch_results.append(result_data)
|
||||
except Exception as e:
|
||||
result_data = {"success": False, "error": str(e), "generated_count": 0, "saved_files": []}
|
||||
batch_results.append(result_data)
|
||||
|
||||
completed += 1
|
||||
|
||||
if result_data and result_data.get("success", False):
|
||||
success_count += 1
|
||||
print(f"全能生图（批量）: 任务 {completed}/{total_tasks} 成功 ✓")
|
||||
else:
|
||||
fail_count += 1
|
||||
error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
|
||||
print(f"全能生图（批量）: 任务 {completed}/{total_tasks} 失败 ✗ - {error_msg}")
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
all_results.extend(batch_results)
|
||||
|
||||
import gc
|
||||
gc.collect()
|
||||
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
return all_results
|
||||
|
||||
def process_batch(
|
||||
self,
|
||||
提示词: str,
|
||||
模型: str,
|
||||
宽高比: str,
|
||||
分辨率: str,
|
||||
像素缩放: bool,
|
||||
分辨率像素: float,
|
||||
seed: int,
|
||||
文件夹1: str,
|
||||
文件夹2: str,
|
||||
文件夹3: str,
|
||||
文件夹4: str,
|
||||
文件夹5: str,
|
||||
文件夹6: str,
|
||||
文件夹7: str,
|
||||
文件夹8: str,
|
||||
文件夹9: str,
|
||||
保存路径: str,
|
||||
**kwargs
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""批量处理图像生成"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
random.seed(seed)
|
||||
np.random.seed(seed % (2**32))
|
||||
|
||||
if self.client is None:
|
||||
self.client = OpenAIAPIClient()
|
||||
|
||||
supported_resolutions = get_model_supported_resolutions(模型)
|
||||
if supported_resolutions and 分辨率 not in supported_resolutions:
|
||||
raise ValueError(
|
||||
f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
|
||||
f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
|
||||
)
|
||||
|
||||
supported_ratios = get_model_supported_aspect_ratios(模型)
|
||||
if supported_ratios and 宽高比 not in supported_ratios:
|
||||
raise ValueError(
|
||||
f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
|
||||
f"该模型支持的宽高比：{', '.join(supported_ratios)}"
|
||||
)
|
||||
|
||||
manual_images = []
|
||||
for i in range(1, 10):
|
||||
key = f"参考图{i}"
|
||||
if key in kwargs and kwargs[key] is not None:
|
||||
pil_imgs = tensor_to_pil(kwargs[key])
|
||||
for pil_img in pil_imgs:
|
||||
manual_images.append(ImageInfo(
|
||||
image=pil_img,
|
||||
filename=f"manual_{i}",
|
||||
extension=".png",
|
||||
source_path=""
|
||||
))
|
||||
|
||||
if 像素缩放 and manual_images:
|
||||
scaled_manual = []
|
||||
for img_info in manual_images:
|
||||
scaled_img = self.resize_to_megapixels(img_info.image, 分辨率像素)
|
||||
scaled_manual.append(ImageInfo(
|
||||
image=scaled_img,
|
||||
filename=img_info.filename,
|
||||
extension=img_info.extension,
|
||||
source_path=img_info.source_path
|
||||
))
|
||||
manual_images = scaled_manual
|
||||
|
||||
folder_images = self._load_folders(
|
||||
文件夹1, 文件夹2, 文件夹3, 文件夹4,
|
||||
像素缩放, 分辨率像素,
|
||||
文件夹5, 文件夹6, 文件夹7, 文件夹8, 文件夹9
|
||||
)
|
||||
|
||||
pairing_mode = kwargs.get("图片配对模式", "不配对")
|
||||
pairs = self._create_pairs(folder_images, pairing_mode, manual_images if manual_images else None)
|
||||
|
||||
if not pairs:
|
||||
raise ValueError("没有可处理的图片组合，请检查文件夹路径和参考图输入")
|
||||
|
||||
batch_prompts = parse_batch_prompts(提示词)
|
||||
prompts_per_task = None
|
||||
|
||||
if batch_prompts:
|
||||
if len(batch_prompts) != len(pairs):
|
||||
raise ValueError(
|
||||
f"批量提示词数量 ({len(batch_prompts)}) 与任务数量 ({len(pairs)}) 不匹配！\n"
|
||||
f"请确保提示词数量与图片组合数量一致"
|
||||
)
|
||||
prompts_per_task = batch_prompts
|
||||
print(f"全能生图（批量）: 批量提示词模式 - {len(batch_prompts)} 个提示词")
|
||||
|
||||
output_folder = 保存路径.strip() if 保存路径 else ""
|
||||
if not output_folder and FOLDER_PATHS_AVAILABLE:
|
||||
output_folder = folder_paths.get_output_directory()
|
||||
|
||||
if not output_folder:
|
||||
raise ValueError("无法确定保存路径，请指定保存路径或确保 folder_paths 可用")
|
||||
|
||||
import os
|
||||
os.makedirs(output_folder, exist_ok=True)
|
||||
print(f"全能生图（批量）: 保存路径 → {output_folder}")
|
||||
|
||||
pbar = None
|
||||
if PROGRESS_BAR_AVAILABLE:
|
||||
pbar = ProgressBar(len(pairs))
|
||||
|
||||
def run_async():
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
return loop.run_until_complete(
|
||||
self._process_batch_async(
|
||||
pairs=pairs,
|
||||
prompt=提示词,
|
||||
model=模型,
|
||||
resolution=分辨率,
|
||||
aspect_ratio=宽高比,
|
||||
output_folder=output_folder,
|
||||
pbar=pbar,
|
||||
prompts_per_task=prompts_per_task,
|
||||
)
|
||||
)
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||||
future = executor.submit(run_async)
|
||||
results = future.result(timeout=3600)
|
||||
|
||||
success_count = sum(1 for r in results if r.get("success", False))
|
||||
fail_count = len(results) - success_count
|
||||
all_saved_files = []
|
||||
for r in results:
|
||||
all_saved_files.extend(r.get("saved_files", []))
|
||||
|
||||
elapsed = time.time() - start_time
|
||||
print(f"全能生图（批量）: 完成！总耗时 {elapsed:.2f}s | 成功: {success_count}/{len(pairs)} | 失败: {fail_count}")
|
||||
|
||||
output_images = []
|
||||
max_output = 10
|
||||
recent_files = all_saved_files[-min(max_output, len(all_saved_files)):]
|
||||
for file_path in recent_files:
|
||||
try:
|
||||
img = Image.open(file_path)
|
||||
output_images.append(img)
|
||||
except Exception as e:
|
||||
print(f"全能生图（批量）: 无法加载 {file_path} - {e}")
|
||||
|
||||
if not output_images:
|
||||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||||
output_images = [placeholder]
|
||||
|
||||
output_tensor = pil_to_tensor(output_images)
|
||||
print(f"全能生图（批量）: 共保存 {len(all_saved_files)} 张图片，节点输出最后 {len(output_images)} 张")
|
||||
|
||||
import gc
|
||||
gc.collect()
|
||||
return (output_tensor,)
|
||||
|
||||
except Exception as e:
|
||||
print(f"全能生图（批量）: ❌ {str(e)}")
|
||||
raise
|
||||
|
||||
Reference in New Issue
Block a user