177 lines
6.4 KiB
Python
177 lines
6.4 KiB
Python
import requests
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import json
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import 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 io import BytesIO
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import os
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import base64
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import re
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from .modelscope_image_node import load_config, load_api_tokens, save_api_tokens, tensor_to_base64_url
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# 检查openai库是否可用
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try:
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from openai import OpenAI
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OPENAI_AVAILABLE = True
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except ImportError:
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OPENAI_AVAILABLE = False
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class ModelScopeImageCaptionNode:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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if not OPENAI_AVAILABLE:
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return {
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"required": {
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"error_message": ("STRING", {
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"default": "请先安装openai库: pip install openai",
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"multiline": True
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}),
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}
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}
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saved_tokens = load_api_tokens()
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# 定义支持的模型列表
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supported_models = [
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"Qwen/Qwen3-VL-8B-Instruct",
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"Qwen/Qwen3-VL-235B-A22B-Instruct"
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]
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return {
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"required": {
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"image": ("IMAGE",),
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"api_tokens": ("STRING", {
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"default": f"***已保存{len(saved_tokens)}个Token***" if saved_tokens else "",
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"placeholder": "请输入API Token（支持多个，用逗号/换行分隔）",
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"multiline": True
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}),
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},
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"optional": {
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"prompt": ("STRING", {
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"multiline": True,
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"default": "详细描述这张图片的内容，包括主体、背景、颜色、风格等信息"
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}),
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# 添加模型下拉选择
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"model": (supported_models, {
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"default": "Qwen/Qwen3-VL-8B-Instruct" # 默认选中原模型
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}),
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"max_tokens": ("INT", {
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"default": 1000,
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"min": 100,
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"max": 4000
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}),
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"temperature": ("FLOAT", {
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"default": 0.7,
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"min": 0.1,
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"max": 2.0,
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"step": 0.1
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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 = ("description",)
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FUNCTION = "generate_caption"
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CATEGORY = "ModelScopeAPI"
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def parse_api_tokens(self, token_input):
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"""解析输入的多个API Token（支持逗号、分号、换行分隔）"""
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if not token_input or token_input.strip() in ["", f"***已保存{len(load_api_tokens())}个Token***"]:
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return load_api_tokens()
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# 支持多种分隔符拆分Token
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tokens = re.split(r'[,;\n]+', token_input)
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return [token.strip() for token in tokens if token.strip()]
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# 调整参数顺序，加入model参数（与INPUT_TYPES顺序匹配）
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def generate_caption(self, image=None, api_tokens="", prompt="详细描述这张图片的内容", model="Qwen/Qwen3-VL-8B-Instruct", max_tokens=1000, temperature=0.7):
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if not OPENAI_AVAILABLE:
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return ("请先安装openai库: pip install openai",)
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# 解析Token列表（支持多个）
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tokens = self.parse_api_tokens(api_tokens)
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if not tokens:
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raise Exception("请提供至少一个有效的API Token")
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# 保存新Token（如果有变化）
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saved_tokens = load_api_tokens()
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if api_tokens.strip() not in ["", f"***已保存{len(saved_tokens)}个Token***"]:
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if save_api_tokens(tokens):
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print(f"✅ 已保存 {len(tokens)} 个API Token")
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else:
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print("⚠️ API Token保存失败，但不影响当前使用")
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try:
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print(f"🔍 开始生成图像描述...")
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print(f"📝 提示词: {prompt}")
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print(f"🤖 模型: {model}") # 显示选中的模型
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print(f"🔑 可用Token数量: {len(tokens)}")
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# 转换图像为base64格式
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image_url = tensor_to_base64_url(image)
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print(f"🖼️ 图像已转换为base64格式")
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# 构建消息体
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messages = [{
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'role': 'user',
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'content': [{
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'type': 'text',
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'text': prompt,
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}, {
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'type': 'image_url',
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'image_url': {
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'url': image_url,
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},
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}],
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}]
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# 轮询尝试每个Token
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last_exception = None
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for i, token in enumerate(tokens):
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try:
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print(f"🔄 尝试使用第 {i+1}/{len(tokens)} 个Token...")
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# 初始化OpenAI客户端
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client = OpenAI(
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base_url='https://api-inference.modelscope.cn/v1',
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api_key=token
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)
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# 调用API（使用选中的模型）
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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stream=False
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)
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# 成功获取结果
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description = response.choices[0].message.content
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print(f"✅ 第 {i+1} 个Token调用成功!")
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print(f"📄 结果预览: {description[:100]}...")
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return (description,)
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except Exception as e:
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last_exception = e
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print(f"❌ 第 {i+1} 个Token调用失败: {str(e)}")
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if i < len(tokens) - 1:
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print(f"⏳ 准备尝试下一个Token...")
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# 所有Token都失败
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raise Exception(f"所有Token均调用失败: {str(last_exception)}")
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except Exception as e:
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error_msg = f"图像描述生成失败: {str(e)}"
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print(f"❌ {error_msg}")
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return (error_msg,)
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# 节点映射
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NODE_CLASS_MAPPINGS = {
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"ModelScopeImageCaptionNode": ModelScopeImageCaptionNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ModelScopeImageCaptionNode": "ModelScope 图像描述生成"
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} |