335 lines
14 KiB
Python
335 lines
14 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", "tooltip": "在此输入您的自定义规则内容"}),
|
||
"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"}),
|
||
"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"}),
|
||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
|
||
},
|
||
"hidden": {
|
||
"unique_id": "UNIQUE_ID",
|
||
}
|
||
}
|
||
|
||
RETURN_TYPES = ("STRING", "STRING")
|
||
RETURN_NAMES = ("prompt_output", "prompt_list")
|
||
OUTPUT_IS_LIST = (False, True)
|
||
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, seed=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),
|
||
seed
|
||
))
|
||
|
||
return input_hash
|
||
|
||
def _analyze_single_image(self, image_data, prompt_template, rule_name, service_id, service, provider_config, unique_id, frame_index=None):
|
||
"""
|
||
分析单张图像（内部方法）
|
||
|
||
Args:
|
||
image_data: base64编码的图像数据
|
||
prompt_template: 提示词模板
|
||
rule_name: 规则名称
|
||
service_id: 服务ID
|
||
service: 服务配置
|
||
provider_config: Provider配置
|
||
unique_id: 节点唯一ID
|
||
frame_index: 批次中的帧索引（用于日志标识）
|
||
|
||
Returns:
|
||
str: 图像描述结果
|
||
"""
|
||
# 创建请求ID（包含帧索引信息）
|
||
frame_suffix = f"_f{frame_index}" if frame_index is not None else ""
|
||
request_id = generate_request_id("icap", None, unique_id) + frame_suffix
|
||
|
||
# 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（包含帧信息）
|
||
frame_info = f" [帧 {frame_index + 1}]" if frame_index is not None else ""
|
||
log_prepare(TASK_IMAGE_CAPTION, request_id, SOURCE_NODE, service_display_name, model_display, rule_name + frame_info)
|
||
|
||
# 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}")
|
||
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'
|
||
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}")
|
||
|
||
def analyze_image(self, image, rule, custom_rule, custom_rule_content, user_prompt, vlm_service, ollama_auto_unload, seed=None, unique_id=None):
|
||
"""
|
||
分析图像并生成提示词（支持 batch 遍历）
|
||
|
||
Args:
|
||
image: 输入的图像数据，支持单张或 batch
|
||
rule: 选择的提示词模板
|
||
custom_rule: 是否启用临时规则
|
||
custom_rule_content: 临时规则的内容
|
||
user_prompt: 用户补充的提示词
|
||
vlm_service: 选择的视觉服务
|
||
ollama_auto_unload: Ollama 自动卸载开关
|
||
unique_id: 节点唯一ID
|
||
|
||
Returns:
|
||
tuple: 分析结果，batch 输入时结果用换行符分隔
|
||
"""
|
||
request_id = None # 初始化，用于异常处理
|
||
|
||
try:
|
||
# 检查输入
|
||
if image is None:
|
||
raise ValueError("输入图像不能为空")
|
||
|
||
# ---准备提示词模板---
|
||
prompt_template = None
|
||
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:
|
||
from ..config_manager import config_manager
|
||
system_prompts = config_manager.get_system_prompts()
|
||
|
||
vision_prompts = {}
|
||
if system_prompts and 'vision_prompts' in system_prompts:
|
||
vision_prompts = system_prompts['vision_prompts']
|
||
|
||
# 按显示名称匹配
|
||
template_found = False
|
||
for key, value in vision_prompts.items():
|
||
name = value.get('name', key)
|
||
category = value.get('category', '')
|
||
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:
|
||
# 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
|
||
|
||
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}"
|
||
|
||
# ---解析服务配置---
|
||
service_id, model_name = self.parse_service_model(vlm_service)
|
||
if not service_id:
|
||
raise ValueError(f"Invalid service selection: {vlm_service}")
|
||
|
||
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
|
||
vlm_models = service.get('vlm_models', [])
|
||
target_model = None
|
||
|
||
if model_name:
|
||
target_model = next((m for m in vlm_models if m.get('name') == model_name), None)
|
||
|
||
if not target_model:
|
||
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")
|
||
|
||
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),
|
||
}
|
||
|
||
if service.get('type') == 'ollama':
|
||
provider_config['auto_unload'] = ollama_auto_unload
|
||
|
||
# ---处理 batch 输入---
|
||
# 检查是否为 4D tensor（batch 格式）
|
||
if len(image.shape) == 4 and image.shape[0] > 1:
|
||
# Batch 模式：逐帧处理
|
||
batch_size = image.shape[0]
|
||
results = []
|
||
|
||
for i in range(batch_size):
|
||
# 提取单帧并转换为 base64
|
||
single_frame = image[i:i+1] # 保持 4D 形状 [1, H, W, C]
|
||
image_data = self._image_to_base64(single_frame)
|
||
|
||
# 分析单张图像
|
||
description = self._analyze_single_image(
|
||
image_data=image_data,
|
||
prompt_template=prompt_template,
|
||
rule_name=rule_name,
|
||
service_id=service_id,
|
||
service=service,
|
||
provider_config=provider_config,
|
||
unique_id=unique_id,
|
||
frame_index=i
|
||
)
|
||
results.append(description)
|
||
|
||
# 输出：合并结果 + 列表
|
||
combined_result = "\n---\n".join(results)
|
||
return (combined_result, results)
|
||
else:
|
||
# 单张模式
|
||
image_data = self._image_to_base64(image)
|
||
description = self._analyze_single_image(
|
||
image_data=image_data,
|
||
prompt_template=prompt_template,
|
||
rule_name=rule_name,
|
||
service_id=service_id,
|
||
service=service,
|
||
provider_config=provider_config,
|
||
unique_id=unique_id,
|
||
frame_index=None
|
||
)
|
||
return (description, [description])
|
||
|
||
except InterruptProcessingException:
|
||
raise
|
||
except Exception as e:
|
||
error_msg = format_api_error(e, vlm_service)
|
||
if request_id:
|
||
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",
|
||
} |