285 lines
12 KiB
Python
285 lines
12 KiB
Python
import asyncio
|
|
import random
|
|
import time
|
|
import threading
|
|
import hashlib
|
|
import base64
|
|
from io import BytesIO
|
|
import os
|
|
import json
|
|
|
|
import torch
|
|
import numpy as np
|
|
from PIL import Image
|
|
from comfy.model_management import InterruptProcessingException
|
|
|
|
from ..services.vlm import VisionService
|
|
from ..utils.common import format_api_error, format_model_with_thinking, generate_request_id, log_prepare, log_error, TASK_IMAGE_CAPTION, SOURCE_NODE
|
|
from ..services.thinking_control import build_thinking_suppression
|
|
from .base import VLMNodeBase
|
|
|
|
|
|
class ImageCaptionNode(VLMNodeBase):
|
|
"""
|
|
图像反推提示词节点
|
|
分析输入图像并生成描述性提示词
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
# 从config_manager获取系统提示词配置
|
|
from ..config_manager import config_manager
|
|
system_prompts = config_manager.get_system_prompts()
|
|
|
|
# 获取所有vision_prompts作为选项
|
|
vision_prompts = {}
|
|
if system_prompts and 'vision_prompts' in system_prompts:
|
|
vision_prompts = system_prompts['vision_prompts']
|
|
|
|
# 构建提示词模板选项（支持分类格式：类别/规则名称）
|
|
prompt_template_options = []
|
|
for key, value in vision_prompts.items():
|
|
# 过滤掉不在后端显示的规则
|
|
show_in = value.get('showIn', ["frontend", "node"])
|
|
if 'node' not in show_in:
|
|
continue
|
|
|
|
name = value.get('name', key)
|
|
category = value.get('category', '')
|
|
# 如果有分类，显示为 "类别/规则名称"，否则直接显示规则名称
|
|
display_name = f"{category}/{name}" if category else name
|
|
prompt_template_options.append(display_name)
|
|
|
|
# 如果没有选项，添加一个默认选项
|
|
if not prompt_template_options:
|
|
prompt_template_options = ["默认中文反推提示词"]
|
|
|
|
# ---动态获取VLM服务/模型列表---
|
|
service_options = cls.get_vlm_service_options()
|
|
default_service = service_options[0] if service_options else "智谱"
|
|
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"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"}),
|
|
"custom_rule": ("BOOLEAN", {"default": False, "label_on": "Enable", "label_off": "Disable", "tooltip": "Enable to use custom rule content below"}),
|
|
"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"}),
|
|
"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"}),
|
|
"vlm_service": (service_options, {"default": default_service, "tooltip": "Select VLM service and model"}),
|
|
# Ollama Automatic VRAM Unload
|
|
"ollama_auto_unload": ("BOOLEAN", {"default": True, "label_on": "Enable", "label_off": "Disable", "tooltip": "Auto unload Ollama model after generation"}),
|
|
},
|
|
"hidden": {
|
|
"unique_id": "UNIQUE_ID",
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("prompt_output",)
|
|
FUNCTION = "analyze_image"
|
|
CATEGORY = "✨Prompt Assistant"
|
|
OUTPUT_NODE = False
|
|
|
|
@classmethod
|
|
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):
|
|
"""
|
|
只在输入内容真正变化时才触发重新执行
|
|
使用输入参数的哈希值作为判断依据
|
|
"""
|
|
# 导入图像哈希工具函数
|
|
from ..utils.image import compute_image_hash
|
|
|
|
# 计算图像的哈希值
|
|
img_hash = compute_image_hash(image)
|
|
|
|
# 组合所有输入的哈希值
|
|
input_hash = hash((
|
|
img_hash,
|
|
rule,
|
|
bool(custom_rule),
|
|
custom_rule_content,
|
|
user_prompt,
|
|
vlm_service,
|
|
bool(ollama_auto_unload)
|
|
))
|
|
|
|
return input_hash
|
|
|
|
def analyze_image(self, image, rule, custom_rule, custom_rule_content, user_prompt, vlm_service, ollama_auto_unload, unique_id=None):
|
|
"""
|
|
分析图像并生成提示词
|
|
|
|
Args:
|
|
图像: 输入的图像数据
|
|
规则模板: 选择的提示词模板
|
|
临时规则: 是否启用临时规则
|
|
临时规则内容: 临时规则的内容
|
|
用户提示词: 用户补充的提示词
|
|
视觉服务: 选择的视觉服务
|
|
|
|
Returns:
|
|
tuple: 分析结果
|
|
"""
|
|
try:
|
|
# 检查输入
|
|
if image is None:
|
|
raise ValueError("输入图像不能为空")
|
|
|
|
# 将图像转换为base64编码
|
|
image_data = self._image_to_base64(image)
|
|
|
|
# 获取提示词模板内容
|
|
prompt_template = None
|
|
|
|
# 确定使用的规则
|
|
rule_name = "Custom Rule" if (custom_rule and custom_rule_content) else rule
|
|
|
|
if custom_rule and custom_rule_content:
|
|
prompt_template = custom_rule_content
|
|
else:
|
|
# 从config_manager获取系统提示词配置
|
|
from ..config_manager import config_manager
|
|
system_prompts = config_manager.get_system_prompts()
|
|
|
|
# 获取vision_prompts
|
|
vision_prompts = {}
|
|
if system_prompts and 'vision_prompts' in system_prompts:
|
|
vision_prompts = system_prompts['vision_prompts']
|
|
|
|
# 查找选定的提示词模板（按显示名称匹配）
|
|
# 显示名称格式：有分类时为 "类别/规则名称"，无分类时为 "规则名称"
|
|
template_found = False
|
|
for key, value in vision_prompts.items():
|
|
name = value.get('name', key)
|
|
category = value.get('category', '')
|
|
# 构建与下拉列表一致的显示名称
|
|
display_name = f"{category}/{name}" if category else name
|
|
if display_name == rule:
|
|
prompt_template = value.get('content')
|
|
template_found = True
|
|
break
|
|
|
|
if not template_found:
|
|
# 允许用规则名称或键名直接匹配（兼容旧格式）
|
|
for key, value in vision_prompts.items():
|
|
if value.get('name') == rule or key == rule:
|
|
prompt_template = value.get('content')
|
|
template_found = True
|
|
break
|
|
|
|
# 如果没有找到提示词模板，使用默认值
|
|
if not template_found or not prompt_template:
|
|
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。"
|
|
rule_name = "Default Rule"
|
|
|
|
# 拼接用户提示词
|
|
if user_prompt and user_prompt.strip():
|
|
prompt_template = f"{prompt_template}\n\n用户补充要求：\n{user_prompt}"
|
|
|
|
# ---解析服务/模型字符串---
|
|
service_id, model_name = self.parse_service_model(vlm_service)
|
|
if not service_id:
|
|
raise ValueError(f"Invalid service selection: {vlm_service}")
|
|
|
|
# ---获取服务配置---
|
|
from ..config_manager import config_manager
|
|
service = config_manager.get_service(service_id)
|
|
if not service:
|
|
raise ValueError(f"Service config not found: {vlm_service}")
|
|
|
|
# ---构建provider_config---
|
|
# 查找指定的模型或默认模型
|
|
vlm_models = service.get('vlm_models', [])
|
|
target_model = None
|
|
|
|
if model_name:
|
|
# 查找指定的模型
|
|
target_model = next((m for m in vlm_models if m.get('name') == model_name), None)
|
|
|
|
if not target_model:
|
|
# 使用默认模型或第一个模型
|
|
target_model = next((m for m in vlm_models if m.get('is_default')),
|
|
vlm_models[0] if vlm_models else None)
|
|
|
|
if not target_model:
|
|
raise ValueError(f"Service {vlm_service} has no available models")
|
|
|
|
# 构建配置对象
|
|
provider_config = {
|
|
'provider': service_id,
|
|
'model': target_model.get('name', ''),
|
|
'base_url': service.get('base_url', ''),
|
|
'api_key': service.get('api_key', ''),
|
|
'temperature': target_model.get('temperature', 0.7),
|
|
'max_tokens': target_model.get('max_tokens', 500),
|
|
'top_p': target_model.get('top_p', 0.9),
|
|
}
|
|
|
|
# Ollama特殊处理:添加auto_unload配置
|
|
if service.get('type') == 'ollama':
|
|
provider_config['auto_unload'] = ollama_auto_unload
|
|
|
|
# 创建请求ID
|
|
request_id = generate_request_id("icap", None, unique_id)
|
|
|
|
# 检查是否关闭思维链
|
|
model_full_name = provider_config.get('model')
|
|
disable_thinking_enabled = service.get('disable_thinking', True)
|
|
thinking_extra = build_thinking_suppression(service_id, model_full_name) if disable_thinking_enabled else None
|
|
model_display = format_model_with_thinking(model_full_name, bool(thinking_extra))
|
|
|
|
# 获取服务显示名称
|
|
service_display_name = service.get('name', service_id)
|
|
|
|
# 准备阶段日志
|
|
log_prepare(TASK_IMAGE_CAPTION, request_id, SOURCE_NODE, service_display_name, model_display, rule_name)
|
|
|
|
# 执行图像分析
|
|
result = self._run_vision_task(
|
|
VisionService.analyze_image,
|
|
service_id,
|
|
image_data=image_data,
|
|
request_id=request_id,
|
|
stream_callback=None,
|
|
prompt_content=prompt_template,
|
|
custom_provider=service_id,
|
|
custom_provider_config=provider_config,
|
|
task_type=TASK_IMAGE_CAPTION,
|
|
source=SOURCE_NODE
|
|
)
|
|
|
|
if result and result.get('success'):
|
|
description = result.get('data', {}).get('description', '').strip()
|
|
if not description:
|
|
error_msg = 'API返回结果为空，请检查API密钥、模型配置或网络连接'
|
|
log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"分析失败: {error_msg}")
|
|
|
|
# 服务层已经打印了完成日志，这里不再重复
|
|
return (description,)
|
|
else:
|
|
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'
|
|
# 如果是中断错误,直接抛出InterruptProcessingException,不打印日志(由基类打印)
|
|
if error_msg == "任务被中断":
|
|
raise InterruptProcessingException()
|
|
log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"分析失败: {error_msg}")
|
|
|
|
except InterruptProcessingException:
|
|
# 不打印日志,由基类统一打印
|
|
raise
|
|
except Exception as e:
|
|
error_msg = format_api_error(e, vlm_service)
|
|
log_error(TASK_IMAGE_CAPTION, request_id, error_msg, source=SOURCE_NODE)
|
|
raise RuntimeError(f"分析异常: {error_msg}")
|
|
|
|
|
|
# 节点映射，用于向ComfyUI注册节点
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ImageCaptionNode": ImageCaptionNode,
|
|
}
|
|
|
|
# 节点显示名称映射
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ImageCaptionNode": "✨图像反推提示词",
|
|
} |