337 lines
14 KiB
Python
337 lines
14 KiB
Python
import asyncio
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import random
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import time
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import threading
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import hashlib
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import base64
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from io import BytesIO
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import os
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import json
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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 comfy.model_management import InterruptProcessingException
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from ..services.vlm import VisionService
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from ..utils.common import format_api_error, format_model_with_thinking, generate_request_id, log_prepare, log_error, TASK_IMAGE_CAPTION, SOURCE_NODE
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from ..services.thinking_control import build_thinking_suppression
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from .base import VLMNodeBase
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class ImageCaptionNode(VLMNodeBase):
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"""
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图像反推提示词节点
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分析输入图像并生成描述性提示词
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"""
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@classmethod
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def INPUT_TYPES(cls):
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# 从config_manager获取系统提示词配置
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from ..config_manager import config_manager
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system_prompts = config_manager.get_system_prompts()
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# 获取所有vision_prompts作为选项
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vision_prompts = {}
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if system_prompts and 'vision_prompts' in system_prompts:
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vision_prompts = system_prompts['vision_prompts']
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# 构建提示词模板选项（支持分类格式：类别/规则名称）
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prompt_template_options = []
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for key, value in vision_prompts.items():
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# 过滤掉不在后端显示的规则
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show_in = value.get('showIn', ["frontend", "node"])
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if 'node' not in show_in:
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continue
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name = value.get('name', key)
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category = value.get('category', '')
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# 如果有分类，显示为 "类别/规则名称"，否则直接显示规则名称
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display_name = f"{category}/{name}" if category else name
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prompt_template_options.append(display_name)
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# 如果没有选项，添加一个默认选项
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if not prompt_template_options:
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prompt_template_options = ["默认中文反推提示词"]
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# ---动态获取VLM服务/模型列表---
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service_options = cls.get_vlm_service_options()
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default_service = service_options[0] if service_options else "智谱"
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return {
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"required": {
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"image": ("IMAGE",),
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"rule": (prompt_template_options, {"default": prompt_template_options[0] if prompt_template_options else "默认中文反推提示词", "tooltip": "Choose a preset rule for image captioning"}),
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"custom_rule": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable", "tooltip": "Enable to use custom rule content below"}),
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"custom_rule_content": ("STRING", {"multiline": True, "default": "", "placeholder": "在此输入临时规则，仅在启用'临时规则'时生效", "tooltip": "在此输入您的自定义规则内容; 💡输入触发词[R],可以让节点每次都被执行"}),
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"user_prompt": ("STRING", {"multiline": True, "default": "", "placeholder": "输入额外的具体要求，将与规则一起发送给模型", "tooltip": "输入额外的具体要求，将与规则一起发送给模型; 💡输入触发词[R],可以让节点每次都被执行"}),
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"vlm_service": (service_options, {"default": default_service, "tooltip": "Select VLM service and model"}),
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# Ollama Automatic VRAM Unload
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"ollama_auto_unload": ("BOOLEAN", {"default": True, "label_on": "Enable", "label_off": "Disable", "tooltip": "Auto unload Ollama model after generation"}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ("STRING", "STRING")
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RETURN_NAMES = ("prompt_output", "prompt_list")
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OUTPUT_IS_LIST = (False, True)
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FUNCTION = "analyze_image"
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CATEGORY = "✨Prompt Assistant"
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OUTPUT_NODE = False
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@classmethod
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def IS_CHANGED(cls, image=None, rule=None, custom_rule=None, custom_rule_content=None, user_prompt=None, vlm_service=None, ollama_auto_unload=None, unique_id=None):
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"""
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只在输入内容真正变化时才触发重新执行
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使用输入参数的哈希值作为判断依据
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"""
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# 检查是否包含强制刷新符号 [R]
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if cls._check_is_changed_bypass(rule, custom_rule_content, user_prompt):
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return float("nan")
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# 导入图像哈希工具函数
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from ..utils.image import compute_image_hash
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# 计算图像的哈希值
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img_hash = compute_image_hash(image)
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# 组合所有输入的哈希值
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input_hash = hash((
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img_hash,
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rule,
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bool(custom_rule),
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custom_rule_content,
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user_prompt,
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vlm_service,
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bool(ollama_auto_unload)
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))
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return input_hash
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def _analyze_single_image(self, image_data, prompt_template, rule_name, service_id, service, provider_config, unique_id, frame_index=None):
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"""
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分析单张图像（内部方法）
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Args:
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image_data: base64编码的图像数据
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prompt_template: 提示词模板
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rule_name: 规则名称
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service_id: 服务ID
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service: 服务配置
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provider_config: Provider配置
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unique_id: 节点唯一ID
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frame_index: 批次中的帧索引（用于日志标识）
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Returns:
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str: 图像描述结果
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"""
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# 创建请求ID（包含帧索引信息）
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frame_suffix = f"_f{frame_index}" if frame_index is not None else ""
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request_id = generate_request_id("icap", None, unique_id) + frame_suffix
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# 检查是否关闭思维链
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model_full_name = provider_config.get('model')
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disable_thinking_enabled = service.get('disable_thinking', True)
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thinking_extra = build_thinking_suppression(service_id, model_full_name) if disable_thinking_enabled else None
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model_display = format_model_with_thinking(model_full_name, bool(thinking_extra))
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# 获取服务显示名称
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service_display_name = service.get('name', service_id)
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# 准备阶段日志（包含帧信息）
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frame_info = f" [帧 {frame_index + 1}]" if frame_index is not None else ""
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log_prepare(TASK_IMAGE_CAPTION, request_id, SOURCE_NODE, service_display_name, model_display, rule_name + frame_info)
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# 执行图像分析
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result = self._run_vision_task(
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VisionService.analyze_image,
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service_id,
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image_data=image_data,
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request_id=request_id,
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stream_callback=None,
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prompt_content=prompt_template,
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custom_provider=service_id,
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custom_provider_config=provider_config,
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task_type=TASK_IMAGE_CAPTION,
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source=SOURCE_NODE
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)
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if result and result.get('success'):
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description = result.get('data', {}).get('description', '').strip()
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if not description:
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error_msg = 'API返回结果为空，请检查API密钥、模型配置或网络连接'
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log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"分析失败: {error_msg}")
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return description
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else:
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error_msg = result.get('error', '分析失败，未知错误') if result else '分析服务未返回结果'
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if error_msg == "任务被中断":
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raise InterruptProcessingException()
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log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"分析失败: {error_msg}")
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def analyze_image(self, image, rule, custom_rule, custom_rule_content, user_prompt, vlm_service, ollama_auto_unload, unique_id=None):
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"""
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分析图像并生成提示词（支持 batch 遍历）
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Args:
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image: 输入的图像数据，支持单张或 batch
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rule: 选择的提示词模板
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custom_rule: 是否启用临时规则
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custom_rule_content: 临时规则的内容
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user_prompt: 用户补充的提示词
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vlm_service: 选择的视觉服务
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ollama_auto_unload: Ollama 自动卸载开关
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unique_id: 节点唯一ID
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Returns:
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tuple: 分析结果，batch 输入时结果用换行符分隔
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"""
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request_id = None # 初始化，用于异常处理
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try:
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# 检查输入
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if image is None:
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raise ValueError("输入图像不能为空")
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# ---准备提示词模板---
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prompt_template = None
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rule_name = "Custom Rule" if (custom_rule and custom_rule_content) else rule
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if custom_rule and custom_rule_content:
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prompt_template = custom_rule_content
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else:
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from ..config_manager import config_manager
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system_prompts = config_manager.get_system_prompts()
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vision_prompts = {}
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if system_prompts and 'vision_prompts' in system_prompts:
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vision_prompts = system_prompts['vision_prompts']
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# 按显示名称匹配
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template_found = False
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for key, value in vision_prompts.items():
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name = value.get('name', key)
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category = value.get('category', '')
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display_name = f"{category}/{name}" if category else name
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if display_name == rule:
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prompt_template = value.get('content')
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template_found = True
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break
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if not template_found:
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# 兼容旧格式
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for key, value in vision_prompts.items():
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if value.get('name') == rule or key == rule:
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prompt_template = value.get('content')
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template_found = True
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break
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if not template_found or not prompt_template:
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prompt_template = "请详细描述这张图片的内容，包括主体、场景、风格、色彩等要素。"
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rule_name = "Default Rule"
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# 拼接用户提示词
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if user_prompt and user_prompt.strip():
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prompt_template = f"{prompt_template}\n\n用户补充要求：\n{user_prompt}"
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# ---解析服务配置---
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service_id, model_name = self.parse_service_model(vlm_service)
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if not service_id:
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raise ValueError(f"Invalid service selection: {vlm_service}")
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from ..config_manager import config_manager
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service = config_manager.get_service(service_id)
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if not service:
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raise ValueError(f"Service config not found: {vlm_service}")
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# 构建 provider_config
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vlm_models = service.get('vlm_models', [])
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target_model = None
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if model_name:
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target_model = next((m for m in vlm_models if m.get('name') == model_name), None)
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if not target_model:
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target_model = next((m for m in vlm_models if m.get('is_default')),
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vlm_models[0] if vlm_models else None)
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if not target_model:
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raise ValueError(f"Service {vlm_service} has no available models")
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provider_config = {
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'provider': service_id,
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'model': target_model.get('name', ''),
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'base_url': service.get('base_url', ''),
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'api_key': service.get('api_key', ''),
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'temperature': target_model.get('temperature', 0.7),
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'max_tokens': target_model.get('max_tokens', 500),
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'top_p': target_model.get('top_p', 0.9),
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}
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if service.get('type') == 'ollama':
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provider_config['auto_unload'] = ollama_auto_unload
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# ---处理 batch 输入---
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# 检查是否为 4D tensor（batch 格式）
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if len(image.shape) == 4 and image.shape[0] > 1:
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# Batch 模式：逐帧处理
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batch_size = image.shape[0]
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results = []
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for i in range(batch_size):
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# 提取单帧并转换为 base64
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single_frame = image[i:i+1] # 保持 4D 形状 [1, H, W, C]
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image_data = self._image_to_base64(single_frame)
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# 分析单张图像
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description = self._analyze_single_image(
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image_data=image_data,
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prompt_template=prompt_template,
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rule_name=rule_name,
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service_id=service_id,
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service=service,
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provider_config=provider_config,
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unique_id=unique_id,
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frame_index=i
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)
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results.append(description)
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# 输出：合并结果 + 列表
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combined_result = "\n---\n".join(results)
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return (combined_result, results)
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else:
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# 单张模式
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image_data = self._image_to_base64(image)
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description = self._analyze_single_image(
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image_data=image_data,
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prompt_template=prompt_template,
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rule_name=rule_name,
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service_id=service_id,
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service=service,
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provider_config=provider_config,
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unique_id=unique_id,
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frame_index=None
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)
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return (description, [description])
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except InterruptProcessingException:
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raise
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except Exception as e:
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error_msg = format_api_error(e, vlm_service)
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if request_id:
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log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
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raise RuntimeError(f"分析异常: {error_msg}")
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# 节点映射，用于向ComfyUI注册节点
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NODE_CLASS_MAPPINGS = {
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"ImageCaptionNode": ImageCaptionNode,
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}
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# 节点显示名称映射
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ImageCaptionNode": "✨图像反推提示词",
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} |