rui40000andClaude Fable 5
9dd4fbd890
用户实测 anthropic/claude-sonnet-5 报 400：`temperature` is deprecated。 根因：节点无条件把 temperature/top_p/seed/max_tokens 塞进请求体，而 ZenMux 聚合的部分模型弃用/不支持这些采样参数，规则各家不一且随模型迭代变化。 - 新增 _diagnose_param：从 400 错误消息识别是哪个采样参数导致失败 - 请求改为重试循环：命中即剔除该参数（或把 max_tokens 改名为 max_completion_tokens，适配 gpt-5 reasoning 系）后重试 - 只动可选采样参数，绝不触碰 model/messages；非参数类 400 不重试、不死循环 - 正常请求零额外开销；剔除动作打印到控制台，最终仍失败时提示已剔除项 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
429 lines
18 KiB
Python
429 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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ZenMux API 连接节点
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===================
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通过 ZenMux 聚合平台（https://zenmux.ai）调用其收录的所有文本类模型。
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特性：
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- 模型下拉覆盖 ZenMux 全部文本模型，按「厂商/模型名」排序聚类；
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ComfyUI 下拉自带搜索，输入厂商前缀（如 "anthropic/"）即可快速过滤。
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- 每个模型选项后面直接标注输入/输出价格（USD / 百万 token）。
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- usage_stats 输出单次运行的 token 消耗与费用（按快照单价折算，
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汇率可用 usd_to_cny 参数调整）。
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- 具备常规 API 节点的完整参数：api_key、system/user prompt、seed、
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temperature、top_p、max_tokens、以及可选的多模态图像输入与代理。
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- 默认模型 openai/gpt-5.4-nano。
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- 随节点分发 models_snapshot.json，无网络也能列出模型；价格与列表可用
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build_snapshot.py 重新拉取更新。
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注：ZenMux 采用 OpenAI 兼容协议，chat 端点为
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https://zenmux.ai/api/v1/chat/completions
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"""
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import base64
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import io
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import json
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import os
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import re
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import numpy as np
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import requests
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from PIL import Image
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from .model_registry import (
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DEFAULT_MODEL_ID,
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default_model_label,
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all_model_labels,
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label_to_model_id,
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model_label_by_id,
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model_prices,
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)
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# ZenMux 平台固定地址（OpenAI 兼容）
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DEFAULT_BASE_URL = "https://zenmux.ai/api/v1"
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# 输入框默认值不带 "://"——本仓库实测 ComfyUI 前端会吞掉文本框里的
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# 协议片段（见 openai_node.py 的同款处理），后端 _build_chat_url 会自动补 https。
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DEFAULT_BASE_URL_INPUT = "zenmux.ai/api/v1"
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def _clear_proxy_env():
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"""清除可能干扰 requests 的代理环境变量（仅本模块加载时执行一次）。"""
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for key in ('HTTP_PROXY', 'HTTPS_PROXY', 'http_proxy', 'https_proxy'):
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os.environ.pop(key, None)
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_clear_proxy_env()
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def _build_chat_url(base_url: str) -> str:
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"""由 base_url 拼出 chat/completions 端点，容忍用户填了/没填结尾斜杠。"""
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s = (base_url or "").strip()
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if not s:
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s = DEFAULT_BASE_URL
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# 补协议
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if not re.match(r'^https?://', s, flags=re.IGNORECASE):
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s = 'https://' + s.lstrip('/')
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s = s.rstrip('/')
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# 用户可能已经把 /chat/completions 填进去了
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if s.lower().endswith('/chat/completions'):
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return s
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return s + '/chat/completions'
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def _build_proxy_url(raw_proxy: str) -> str:
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"""把 '127.0.0.1:7890' 或 'http://127.0.0.1:7890' 统一成带协议的地址。"""
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s = (raw_proxy or "").strip()
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if not s:
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return ''
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if re.match(r'^https?://', s, flags=re.IGNORECASE):
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return s
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s = re.sub(r'^https?\s*:\s*/*/?\s*', '', s, flags=re.IGNORECASE).strip('/')
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return ('http://' + s) if s else ''
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# 遇到「参数不被支持 / 已弃用」的 400 时，可安全剔除的采样参数
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# （绝不触碰 model / messages 等核心字段）
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_ADJUSTABLE_PARAMS = ("temperature", "top_p", "seed", "max_tokens",
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"max_completion_tokens", "frequency_penalty",
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"presence_penalty", "top_k")
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# 判定「这条 400 是参数问题」的关键词（命中才尝试剔除重试）
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_PARAM_ERR_HINTS = ("deprecat", "unsupport", "not support", "not allowed",
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"invalid", "unexpected", "unknown", "must be", "cannot",
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"not permitted", "removed")
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def _diagnose_param(resp, payload):
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"""
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从 400 响应里判断是哪个采样参数导致失败，返回处理指令：
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("drop", 参数名) —— 从 payload 剔除该参数后重试
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("rename_mct", ...) —— 把 max_tokens 改名为 max_completion_tokens 后重试
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None —— 非参数类错误，不重试
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ZenMux 各模型参数规则不一（claude-sonnet-5 弃用 temperature、gpt-5 reasoning
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系要求 max_completion_tokens 等），靠错误消息动态识别，免维护静态清单。
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"""
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try:
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msg = (resp.json().get("error", {}) or {}).get("message", "") or resp.text or ""
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except Exception:
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msg = getattr(resp, "text", "") or ""
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low = msg.lower()
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# 特例：OpenAI reasoning 系要求用 max_completion_tokens 替代 max_tokens
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if "max_completion_tokens" in low and "max_tokens" in payload:
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return ("rename_mct", "max_tokens")
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# 反引号/引号里的参数名优先（错误消息通常形如 `temperature` is deprecated）
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for name in re.findall(r"""[`'"]([a-zA-Z_]+)[`'"]""", msg):
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if name in payload and name in _ADJUSTABLE_PARAMS:
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return ("drop", name)
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# 回退：错误像参数问题时，扫描 payload 里哪个参数名出现在消息中
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if any(h in low for h in _PARAM_ERR_HINTS):
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for name in _ADJUSTABLE_PARAMS:
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if name in payload and name in low:
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return ("drop", name)
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return None
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class ZenMuxNode:
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"""ZenMux API 连接节点。"""
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# ────────── 输入定义 ──────────
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@classmethod
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def INPUT_TYPES(cls):
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model_labels = all_model_labels()
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default_label = default_model_label()
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return {
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"required": {
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"api_key": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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# 全量模型标签（含价格），已按厂商排序聚类；
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# 下拉搜索框输入厂商前缀（如 "qwen/"）即可过滤。
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"model": (model_labels, {
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"default": default_label,
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}),
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"system_prompt": ("STRING", {
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"default": "You are a helpful assistant.",
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"multiline": True,
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}),
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"user_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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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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},
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"optional": {
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"temperature": ("FLOAT", {
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"default": 0.7,
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"min": 0.0,
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"max": 2.0,
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"step": 0.1,
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}),
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"top_p": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.05,
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}),
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"max_tokens": ("INT", {
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"default": 1024,
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"min": 1,
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"max": 200000,
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}),
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# 多模态图像输入（模型需支持 image 输入才有意义）
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"image_1": ("IMAGE",),
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"image_2": ("IMAGE",),
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"image_3": ("IMAGE",),
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"image_4": ("IMAGE",),
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"image_5": ("IMAGE",),
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"image_6": ("IMAGE",),
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"detail": (["auto", "low", "high"], {
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"default": "auto",
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}),
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"image_max_size": ("INT", {
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"default": 1024,
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"min": 256,
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"max": 4096,
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"step": 64,
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}),
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# 高级：一般无需改动，留空即用官方地址（无需写 https://，会自动补全）
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"base_url": ("STRING", {
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"default": DEFAULT_BASE_URL_INPUT,
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"multiline": False,
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}),
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"proxy_url": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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# usage_stats 里人民币换算用的汇率，可按当日牌价自行调整
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"usd_to_cny": ("FLOAT", {
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"default": 7.2,
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"min": 0.1,
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"max": 100.0,
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"step": 0.01,
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}),
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},
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}
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RETURN_TYPES = ("STRING", "STRING", "STRING")
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RETURN_NAMES = ("text", "model_id", "usage_stats")
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FUNCTION = "generate"
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CATEGORY = "Rui-Node🐶/AI模型🤖"
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# ────────── 宽松校验 ──────────
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@classmethod
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def VALIDATE_INPUTS(cls, model):
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"""
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接管 model 下拉的校验，替代 ComfyUI 内置的「值必须在候选列表里」
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检查：价格快照更新后，旧工作流里保存的标签（带旧价格）不再逐字
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匹配新列表，但只要能解析出 model id 就应放行，避免整个工作流
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被判为无效。
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"""
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if label_to_model_id(model) is None:
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return f"无法从 '{model}' 解析出 ZenMux 模型 id"
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return True
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# ────────── 图像编码 ──────────
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@staticmethod
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def _encode_image(img_tensor, max_size):
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"""把单张图像张量 [H,W,C]（0~1）编码为 base64 JPEG。"""
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img_np = np.clip(img_tensor.cpu().numpy(), 0, 1)
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pil = Image.fromarray((img_np * 255).astype(np.uint8), 'RGB')
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w, h = pil.size
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if max(w, h) > max_size:
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r = max_size / max(w, h)
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pil = pil.resize((max(1, int(w * r)), max(1, int(h * r))), Image.LANCZOS)
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buf = io.BytesIO()
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pil.save(buf, format='JPEG', quality=85)
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return base64.b64encode(buf.getvalue()).decode('utf-8')
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# ────────── 消耗统计 ──────────
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@staticmethod
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def _fmt_cost(v):
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"""费用格式化：最多 6 位小数并去尾零；未知为 '?'。"""
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if v is None:
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return "?"
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s = f"{v:.6f}".rstrip("0").rstrip(".")
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return s if s else "0"
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@staticmethod
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def _build_usage_stats(model_id, usage, usd_to_cny, out_text=None):
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"""
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由 API 响应的 usage、输出文本与快照单价生成四行消耗统计：
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token消耗，输入：XXX，输出：XXX
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输出文字数量：XXX
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模型类型：openai/gpt-5.4-nano [入$0.2/M 出$1.25/M]
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价格换算，美元：XXX，人民币：XXX
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usage 缺失/单价未知的项以 '?' 呈现；请求未发生时传 usage=None 记为 0 消耗。
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out_text 为模型返回的文本，字数按字符数计（含标点）；无输出记 0。
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"""
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if not isinstance(usage, dict):
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usage = {"prompt_tokens": 0, "completion_tokens": 0}
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in_tok = usage.get("prompt_tokens")
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out_tok = usage.get("completion_tokens")
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in_price, out_price = model_prices(model_id)
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usd = None
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if (isinstance(in_tok, (int, float)) and isinstance(out_tok, (int, float))
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and in_price is not None and out_price is not None):
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usd = in_tok / 1e6 * in_price + out_tok / 1e6 * out_price
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cny = usd * usd_to_cny if usd is not None else None
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n_chars = len(out_text) if isinstance(out_text, str) else 0
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tok = lambda t: str(int(t)) if isinstance(t, (int, float)) else "?" # noqa: E731
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return (f"token消耗，输入：{tok(in_tok)}，输出：{tok(out_tok)}\n"
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f"输出文字数量：{n_chars}\n"
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f"模型类型：{model_label_by_id(model_id)}\n"
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f"价格换算，美元：{ZenMuxNode._fmt_cost(usd)}，人民币：{ZenMuxNode._fmt_cost(cny)}")
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# ────────── 主函数 ──────────
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def generate(
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self,
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api_key,
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model,
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system_prompt,
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user_prompt,
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seed,
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temperature=0.7,
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top_p=1.0,
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max_tokens=1024,
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image_1=None,
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image_2=None,
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image_3=None,
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image_4=None,
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image_5=None,
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image_6=None,
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detail="auto",
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image_max_size=1024,
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base_url=DEFAULT_BASE_URL,
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proxy_url="",
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usd_to_cny=7.2,
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):
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# ---- 1. 从下拉标签解析真实 model id ----
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model_id = label_to_model_id(model) or DEFAULT_MODEL_ID
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chat_url = _build_chat_url(base_url)
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print(f"[Rui-Node] ZenMux -> {chat_url} model={model_id}")
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# 请求未成功前的兜底统计（0 消耗）
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zero_stats = self._build_usage_stats(model_id, None, usd_to_cny)
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if not (api_key or "").strip():
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return ("（错误：未填写 api_key，请在节点里填入 ZenMux 的 API Key）",
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model_id, zero_stats)
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# ---- 2. 收集图像 ----
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images = [
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img for img in (image_1, image_2, image_3, image_4, image_5, image_6)
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if img is not None
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]
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# ---- 3. 构造 messages ----
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messages = []
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if system_prompt and system_prompt.strip():
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messages.append({"role": "system", "content": system_prompt})
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if images:
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parts = []
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if user_prompt and user_prompt.strip():
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parts.append({"type": "text", "text": user_prompt})
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for img in images:
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b64 = self._encode_image(img[0], image_max_size)
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parts.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{b64}",
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"detail": detail,
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},
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})
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if not parts:
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parts.append({"type": "text", "text": " "})
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messages.append({"role": "user", "content": parts})
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else:
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text = (user_prompt or "").strip()
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if not text:
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return ("（错误：未提供图片也未提供提示词，请至少填写 user_prompt）",
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model_id, zero_stats)
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messages.append({"role": "user", "content": text})
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# ---- 4. 请求 ----
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key.strip()}",
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}
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payload = {
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"model": model_id,
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"messages": messages,
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"seed": seed,
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"temperature": temperature,
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"top_p": top_p,
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"max_tokens": max_tokens,
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}
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proxies = None
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p = _build_proxy_url(proxy_url)
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if p:
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proxies = {"http": p, "https": p}
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resp = None
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dropped = [] # 记录被剔除/改名的参数，供最终报错时提示
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try:
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# 自适应参数重试：ZenMux 聚合的部分模型弃用/不支持某些采样参数
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# （claude-sonnet-5 弃用 temperature、gpt-5 reasoning 系要 max_completion_tokens
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# 等），命中即剔除/改名后重试，正常请求不受影响、零额外开销。
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for _ in range(len(_ADJUSTABLE_PARAMS) + 2):
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resp = requests.post(chat_url, headers=headers, json=payload,
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proxies=proxies, timeout=180)
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if resp.status_code == 400:
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fix = _diagnose_param(resp, payload)
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if fix:
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action, param = fix
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if action == "rename_mct":
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payload["max_completion_tokens"] = payload.pop("max_tokens")
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dropped.append("max_tokens→max_completion_tokens")
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print("[Rui-Node] ZenMux: 该模型要求 max_completion_tokens，"
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"已改名重试")
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else:
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payload.pop(param, None)
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dropped.append(param)
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print(f"[Rui-Node] ZenMux: 该模型不支持参数 '{param}'，"
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"已剔除后重试")
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continue
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break
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resp.raise_for_status()
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data = resp.json()
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if data.get("choices"):
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msg = data["choices"][0].get("message", {})
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content = msg.get("content", "")
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if isinstance(content, list): # 少数模型返回分段内容
|
||
content = "".join(
|
||
seg.get("text", "") for seg in content if isinstance(seg, dict)
|
||
)
|
||
stats = self._build_usage_stats(model_id, data.get("usage"),
|
||
usd_to_cny, content or "")
|
||
return (content or "", model_id, stats)
|
||
# 格式异常：无正文可计字数，但 usage 仍尽量取真实值
|
||
stats = self._build_usage_stats(model_id, data.get("usage"), usd_to_cny)
|
||
return (f"API 返回格式异常: {json.dumps(data, ensure_ascii=False)[:800]}",
|
||
model_id, stats)
|
||
except requests.exceptions.ConnectionError as e:
|
||
return (f"连接失败（请检查网络/代理）: {e}", model_id, zero_stats)
|
||
except requests.exceptions.Timeout:
|
||
return ("请求超时（180s），请检查网络或 ZenMux 服务状态。", model_id, zero_stats)
|
||
except requests.exceptions.HTTPError:
|
||
code = resp.status_code if resp is not None else "?"
|
||
body = resp.text[:600] if resp is not None else ""
|
||
hint = f"（已尝试剔除参数 {', '.join(dropped)} 仍失败）" if (code == 400 and dropped) else ""
|
||
return (f"HTTP 错误 {code}: {body}{hint}", model_id, zero_stats)
|
||
except Exception as e:
|
||
return (f"请求异常: {type(e).__name__}: {e}", model_id, zero_stats)
|
||
|
||
|
||
# ────────── ComfyUI 注册 ──────────
|
||
NODE_CLASS_MAPPINGS = {
|
||
"ZenMuxAPINode": ZenMuxNode,
|
||
}
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"ZenMuxAPINode": "ZenMux API 连接 / ZenMux API Connector",
|
||
}
|