mac
579 lines
26 KiB
JavaScript
579 lines
26 KiB
JavaScript
// canvas.js —— 创意画布主进程中转执行层：复用 router.llmGroup() 的当前中转分组，
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// 统一在主进程发起 image/video/tts 请求并把成品下载转 base64 data URL 返回渲染层。
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// key 只在主进程流转；渲染层被 CSP 拦截的远程 URL 一律由这里下载转 base64。
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const router = require("./router");
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// —— 从 baseUrl 解析出主机名（失败则原样返回）——
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function hostOf(baseUrl) {
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try {
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return new URL(baseUrl).host;
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} catch {
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return baseUrl || "";
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}
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}
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// —— 去尾斜杠 ——
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function trimSlash(u) {
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return (u || "").replace(/\/$/, "");
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}
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// —— 可复用的异步 sleep（不留定时器句柄泄漏隐患）——
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function sleep(ms) {
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return new Promise((resolve) => setTimeout(resolve, ms));
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}
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// —— 统一请求头（与 router.fetchModels 的 User-Agent 一致）——
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function headers(key) {
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return {
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Authorization: "Bearer " + (key || ""),
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"User-Agent": "codex-market",
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"Content-Type": "application/json",
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};
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}
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// —— 下载任意远程资源 → base64 字符串 ——
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async function downloadAsBase64(url) {
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const res = await fetch(url);
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if (!res.ok) throw new Error("下载失败 HTTP " + res.status);
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const buf = Buffer.from(await res.arrayBuffer());
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return buf.toString("base64");
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}
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// —— 组装 data URL ——
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function toDataUrl(mime, b64) {
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return `data:${mime};base64,${b64}`;
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}
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// ============================================================================
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// 素材外链托管：兰空图床（Lsky Pro）——图生图参考图 / 图生视频素材统一走这里换公网直链
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// 桌面端没有对外 Web 服务器，Seedance/火山上游只接受「可访问的图片 URL」，base64 会被拒收；
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// 部分中转的图生图模型也用图片 URL 传参考图。所以 base64 素材先 POST 到兰空 /api/v1/upload
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// 换成 http(s) 直链，再交给上游。
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// 兰空契约：POST multipart/form-data（字段 file）+ 头 Authorization: Bearer <token>，
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// 返回体公网直链在 data.links.url。
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// 默认写死用户自建图床；可用环境变量 CANVAS_UPLOAD_ENDPOINT / CANVAS_UPLOAD_AUTH 覆盖（开发期切换）。
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// ============================================================================
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const MEDIA_HOST = {
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// 兰空图床基址（不带尾斜杠）。上传端点固定为 {base}/api/v1/upload。
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base: trimSlash(process.env.CANVAS_UPLOAD_ENDPOINT || "https://tc.tokendun.com"),
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// 兰空上传 token（个人令牌，长期有效）。留空则上传时报错。
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token: process.env.CANVAS_UPLOAD_AUTH || "9|u2ZJoV8p2KJU6zBdy1MtTbiqMV9TgbHBLvURVWwt",
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};
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// 从图床响应里挑出公网 URL（兰空 data.links.url，兼容 {url}/{data.url}/{data:[{url}]} 等形态）
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function pickUploadedUrl(j) {
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if (!j || typeof j !== "object") return "";
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if (typeof j.url === "string") return j.url;
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const d = j.data;
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if (d && typeof d === "object") {
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// 兰空 Pro：data.links.url
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if (d.links && typeof d.links.url === "string") return d.links.url;
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if (typeof d.url === "string") return d.url;
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if (Array.isArray(d) && d[0] && typeof d[0].url === "string") return d[0].url;
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}
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return "";
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}
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// —— base64 dataURL → 兰空公网直链 ——
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// 端点/令牌未配置 / 上传失败 / 未拿到合法 URL → 抛错（绝不把 base64 兜底发给上游）。
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async function uploadMedia(dataUrl) {
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if (!MEDIA_HOST.base || !MEDIA_HOST.token) {
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throw new Error(
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"素材需要图床外链：图床地址或上传令牌未配置（桌面端无法把 base64 图直接交给上游）。"
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);
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}
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// dataURL → mime + Buffer → Blob，组 multipart（Node 18+ 原生 FormData/Blob）
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const m = /^data:([^;,]+)?(;base64)?,(.*)$/is.exec(String(dataUrl || ""));
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if (!m) throw new Error("参考图不是合法的 data URL");
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const mime = m[1] || "image/png";
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const buf = Buffer.from(m[3], m[2] ? "base64" : "utf-8");
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const ext = (mime.split("/")[1] || "png").replace(/[^a-z0-9]/gi, "") || "png";
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const fd = new FormData();
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fd.append("file", new Blob([buf], { type: mime }), "ref." + ext);
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const endpoint = MEDIA_HOST.base + "/api/v1/upload";
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let res;
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try {
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// 注意：不手设 Content-Type，让 fetch 自动带 multipart boundary
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res = await fetch(endpoint, {
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method: "POST",
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headers: { Authorization: "Bearer " + MEDIA_HOST.token, Accept: "application/json", "User-Agent": "codex-market" },
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body: fd,
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});
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} catch (e) {
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throw new Error("素材上传请求失败：" + String((e && e.message) || e));
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}
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if (!res.ok) {
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const detail = await res.text().catch(() => "");
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throw new Error(`素材上传失败(${res.status})：${detail.slice(0, 200)}`);
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}
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let j;
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try {
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j = await res.json();
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} catch {
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throw new Error("图床返回非 JSON，无法解析上传结果");
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}
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// 兰空失败时也返回 200 + {status:false,message}，据此给出明确错误
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if (j && j.status === false) throw new Error("图床上传被拒：" + (j.message || "未知原因"));
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const url = pickUploadedUrl(j);
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if (!url || !/^https?:\/\//i.test(url)) throw new Error("图床未返回合法的公网 URL");
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return url;
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}
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// —— 单个素材 → 公网直链：data URL 上传兰空换外链；http(s) 原样；空串返回 "" ——
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async function resolveMat(v) {
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const s = String(v || "").trim();
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if (!s) return "";
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if (/^data:/i.test(s)) return await uploadMedia(s);
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if (/^https?:\/\//i.test(s)) return s;
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throw new Error("素材需为 data URL 或公网 http(s) 地址");
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}
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// 当前中转是否可用：ready = 有可用 llmGroup。host 从 baseUrl 解析域名。
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function status() {
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const llm = router.llmGroup();
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if (!llm) return { ready: false, provider: null, reason: "未配置可用中转分组" };
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return {
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ready: true,
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provider: { name: llm.name || "", model: llm.model || "", host: hostOf(llm.baseUrl) },
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};
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}
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// —— 读图片流式 SSE：逐行取 data:{...}，partial 帧回调 onPartial，completed/末帧作最终图 ——
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// 返回 { ok:true, mime, dataUrl } 或 null（非 SSE / 没拿到任何帧 → 交由调用方回退非流式）。
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async function readImageSse(res, onPartial) {
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const decoder = new TextDecoder();
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let buf = "";
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let lastB64 = ""; // 最近一帧（流自然结束时用作最终图）
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let finalB64 = ""; // completed 事件的最终图
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let sawData = false;
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for await (const chunk of res.body) {
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buf += decoder.decode(chunk, { stream: true });
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let i;
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while ((i = buf.indexOf("\n")) >= 0) {
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const line = buf.slice(0, i).trim();
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buf = buf.slice(i + 1);
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if (!line.startsWith("data:")) continue;
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sawData = true;
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const data = line.slice(5).trim();
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if (data === "[DONE]") continue;
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let ev;
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try {
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ev = JSON.parse(data);
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} catch {
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continue;
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}
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const b64 = ev && (ev.b64_json || (ev.data && ev.data[0] && ev.data[0].b64_json));
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if (!b64) continue;
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const type = String((ev && ev.type) || "");
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lastB64 = b64;
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if (/partial/i.test(type)) {
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if (onPartial) {
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try {
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onPartial(toDataUrl("image/png", b64));
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} catch {}
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}
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} else if (/completed/i.test(type)) {
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finalB64 = b64;
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}
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}
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}
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if (!sawData) return null; // 首包/全程都不是 data: → 不是 SSE，回退
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const b64 = finalB64 || lastB64;
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if (!b64) return null;
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return { ok: true, mime: "image/png", dataUrl: toDataUrl("image/png", b64) };
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}
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// —— 回退：非流式单次 POST —— gpt-image 不加 response_format，其它模型加 b64_json 容错 ——
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async function runImageNonStream(base, llm, params, url, model) {
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const isGptImage = /gpt-image/i.test(model);
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const body = {
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model,
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prompt: params.prompt,
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size: params.size || "1024x1024",
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...(params.extra || {}),
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};
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if (!isGptImage) body.response_format = "b64_json";
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// 参考图外链（图生图）——runImage 已解析好 _refUrl，非流式回退同样带上
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if (params._refUrl) body[params.refField || "image"] = params._refUrl;
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try {
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const res = await fetch(url, { method: "POST", headers: headers(llm.key), body: JSON.stringify(body) });
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if (!res.ok) {
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const detail = await res.text().catch(() => "");
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return { ok: false, error: `图像接口 ${res.status}：POST ${url} model=${model}${detail ? " → " + detail.slice(0, 240) : ""}` };
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}
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const j = await res.json();
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const item = (j && j.data && j.data[0]) || {};
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let b64 = item.b64_json;
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const sourceUrl = item.url || "";
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if (!b64 && item.url) b64 = await downloadAsBase64(item.url); // url 形式：主进程再下载转 base64
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if (!b64) return { ok: false, error: `图像接口未返回 b64_json 或 url：POST ${url} model=${model}` };
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return { ok: true, mime: "image/png", dataUrl: toDataUrl("image/png", b64), url: sourceUrl, _model: model };
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} catch (e) {
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return { ok: false, error: `图像接口请求失败：POST ${url} model=${model} → ${String((e && e.message) || e)}` };
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}
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}
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// —— 文生图：stream-first —— POST {base}{endpointPath||"/v1/images/generations"} ——
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// model 必须由用户填写（图像模型），为空不回退聊天模型，直接报错。
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// 优先流式（partial 渐进帧经 onPartial 回传）；非 2xx / 非 SSE / 未取到帧 → 回退非流式。
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async function runImage(base, llm, params, onPartial) {
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const model = params.model;
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if (!model) return { ok: false, error: "请先填写图像模型" };
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// 图生图：参考图(base64) → 兰空图床外链，只换一次，供流式/非流式两条路复用
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if (params.image && !params._refUrl) {
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const img = String(params.image).trim();
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if (/^data:/i.test(img)) {
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try {
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params._refUrl = await uploadMedia(img);
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} catch (e) {
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return { ok: false, error: "参考图上传图床失败：" + String((e && e.message) || e) };
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}
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} else if (/^https?:\/\//i.test(img)) {
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params._refUrl = img; // 已是公网URL，直接用
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}
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}
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const path = params.endpointPath || "/v1/images/generations";
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const url = base + path;
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try {
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const body = {
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model,
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prompt: params.prompt,
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size: params.size || "1024x1024",
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n: 1,
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stream: true,
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partial_images: 2,
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...(params.extra || {}),
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};
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// 参考图外链塞进请求体（字段名可配，默认 image）
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if (params._refUrl) body[params.refField || "image"] = params._refUrl;
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const res = await fetch(url, {
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method: "POST",
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headers: headers(llm.key),
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body: JSON.stringify(body),
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});
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const ct = res.headers.get("content-type") || "";
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if (res.ok && res.body && /event-stream/i.test(ct)) {
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const streamed = await readImageSse(res, onPartial);
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if (streamed && streamed.ok) return { ...streamed, _model: model };
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}
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// 非 2xx / 非 SSE / 流里没拿到帧 → 回退非流式
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} catch {
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// 流式请求异常 → 回退非流式
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}
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return runImageNonStream(base, llm, params, url, model);
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}
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// —— TTS 配音：POST {base}/v1/audio/speech（响应为二进制音频）——
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async function runTts(base, llm, params) {
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const model = params.model || "tts-1";
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const body = {
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model,
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input: params.text,
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voice: params.voice || "alloy",
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response_format: params.format || "mp3",
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};
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if (params.speed !== undefined && params.speed !== null && params.speed !== "") body.speed = params.speed;
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const res = await fetch(base + "/v1/audio/speech", {
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method: "POST",
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headers: headers(llm.key),
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body: JSON.stringify(body),
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});
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if (!res.ok) {
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const detail = await res.text().catch(() => "");
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return { ok: false, error: `TTS 接口返回 HTTP ${res.status}：${detail.slice(0, 300)}` };
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}
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const b64 = Buffer.from(await res.arrayBuffer()).toString("base64");
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return { ok: true, mime: "audio/mpeg", dataUrl: toDataUrl("audio/mpeg", b64), _model: model };
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}
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// —— 递归深挖视频地址（覆盖 result_url、data.content.video_url、data.data.content.video_url 等嵌套）——
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// port 自 提示词提取/server.js
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function findVideoUrl(o) {
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if (!o || typeof o === "string") return "";
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if (Array.isArray(o)) {
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for (const v of o) {
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const u = findVideoUrl(v);
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if (u) return u;
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}
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return "";
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}
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if (typeof o === "object") {
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for (const k of ["video_url", "result_url", "url"]) {
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const v = o[k];
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if (typeof v === "string" && /^https?:\/\//.test(v)) return v;
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}
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for (const v of Object.values(o)) {
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const u = findVideoUrl(v);
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if (u) return u;
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}
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}
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return "";
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}
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// —— 归一任务状态：各中转大小写/用词不一 —— port 自 提示词提取/server.js
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function normalizeVideoStatus(raw) {
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const s = String(raw || "").toLowerCase();
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if (["succeeded", "success", "succeed", "done", "completed", "complete", "finished"].includes(s)) return "succeeded";
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if (["failed", "fail", "failure", "error", "cancelled", "canceled", "timeout", "timed_out", "expired"].includes(s)) return "failed";
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if (["in_progress", "running", "processing", "generating"].includes(s)) return "running";
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return s || "queued";
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}
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function joinUrl(base, p) {
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return trimSlash(base) + (String(p || "").startsWith("/") ? p : "/" + p);
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}
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// —— Seedance/TokenDun 视频：异步任务制（建任务 → 轮询 → 下载成品转 base64）——
|
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// mode: "text"(文生视频，只用 prompt) | "image"(图生视频)。model 须用户填写。
|
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// 图生视频素材两条来路：params.imageUrl(手填公网URL，优先) 或 params.image(上游 base64)。
|
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// base64 会先经 uploadMedia() 换成公网直链再发上游（Seedance 绝不接受 base64）。
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async function runVideo(base, llm, params) {
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const model = params.model;
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if (!model) return { ok: false, error: "请先填写视频模型" };
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const createUrl = base + (params.endpointPath || "/v1/video/generations");
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// 输入模式：text/first/firstlast/reference/video；兼容旧值 image→first
|
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let mode = String(params.mode || "text");
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if (mode === "image") mode = "first";
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// —— body 组装：严格照中转文档（pulseaify Seedance）——扁平字段，非火山 metadata 格式 ——
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// prompt 必填；duration 整数(5/10/15)；ratio 顶层；quality 可选；async:true 走异步轮询
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const body = { model, prompt: (params.prompt || "").trim(), async: true };
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const dur = parseInt(params.seconds, 10);
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if (!isNaN(dur) && dur > 0) body.duration = dur; // -1/空/auto → 省略，用中转默认(10)
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if (params.ratio && params.ratio !== "auto") body.ratio = params.ratio; // 顶层 ratio
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// 注：文档的 quality 字段示例值是 "hd"，与 UI 的 720p/1080p 不同类，未确认合法值前不下发，避免 400
|
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try {
|
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// 参考图/素材：本中转支持 image(单图) / images(多图) / ref_assets；base64 先传兰空换公网URL
|
||
if (mode === "first") {
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const img = await resolveMat(params.imageUrl || params.image);
|
||
if (!img) return { ok: false, error: "首帧图生视频需要首帧图：连上游文生图 / 上传 / 手填公网URL" };
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body.image = img;
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||
} else if (mode === "firstlast") {
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// 本中转文档无「首尾帧」字段，退化为多图参考 images:[首,尾]
|
||
const first = await resolveMat(params.image);
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||
const last = await resolveMat(params.lastFrameImage);
|
||
const arr = [first, last].filter(Boolean);
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||
if (arr.length < 2) return { ok: false, error: "首尾帧模式需首帧和尾帧两张图（本中转会作多图参考处理）" };
|
||
body.images = arr;
|
||
} else if (mode === "reference") {
|
||
const refs = Array.isArray(params.referenceImages) ? params.referenceImages : [];
|
||
const resolved = (await Promise.all(refs.map((r) => resolveMat(r)))).filter(Boolean);
|
||
if (!resolved.length) return { ok: false, error: "多参考图模式需至少 1 张参考图" };
|
||
body.images = resolved;
|
||
} else if (mode === "video") {
|
||
return { ok: false, error: "该中转不支持「参考视频」模式，请改用 文生 / 首帧 / 多参考图" };
|
||
}
|
||
// 文/图生视频 prompt 都必填（文档要求）
|
||
if (!body.prompt) return { ok: false, error: "请填写提示词（本接口文生/图生视频都需要 prompt）" };
|
||
|
||
// —— 建任务 ——
|
||
const cres = await fetch(createUrl, {
|
||
method: "POST",
|
||
headers: headers(llm.key),
|
||
body: JSON.stringify(body),
|
||
});
|
||
if (!cres.ok) {
|
||
const detail = await cres.text().catch(() => "");
|
||
return { ok: false, error: `视频建任务失败(${cres.status})：${detail.slice(0, 300)}` };
|
||
}
|
||
let data;
|
||
try {
|
||
data = await cres.json();
|
||
} catch {
|
||
const detail = await cres.text().catch(() => "");
|
||
return { ok: false, error: `视频建任务返回非 JSON：${detail.slice(0, 300)}` };
|
||
}
|
||
const taskId =
|
||
(data.data && (data.data.task_id || data.data.id)) ||
|
||
data.task_id ||
|
||
data.id ||
|
||
(data.result && (data.result.task_id || data.result.id));
|
||
if (!taskId) return { ok: false, error: `视频建任务未返回任务ID：${JSON.stringify(data).slice(0, 300)}` };
|
||
|
||
// —— 轮询：每 5s，最多 120 次(~10min)，对齐 提示词提取 ——
|
||
// poll 是 GET：只带 Authorization（不要带 Content-Type，个别网关对「GET 携带 body 类头」会返回非 2xx 而被误跳过）
|
||
const pollUrl = createUrl + "/" + encodeURIComponent(taskId);
|
||
const pollHeaders = { Authorization: "Bearer " + (llm.key || ""), "User-Agent": "codex-market" };
|
||
let lastPoll = ""; // 记录最近一次轮询的异常/状态，超时时回传便于排查
|
||
for (let i = 0; i < 120; i++) {
|
||
await sleep(5000);
|
||
let pd;
|
||
try {
|
||
const pr = await fetch(pollUrl, { headers: pollHeaders });
|
||
if (!pr.ok) {
|
||
lastPoll = `HTTP ${pr.status}: ` + (await pr.text().catch(() => "")).slice(0, 200);
|
||
continue;
|
||
}
|
||
pd = await pr.json();
|
||
} catch (e) {
|
||
lastPoll = "请求异常：" + String((e && e.message) || e);
|
||
continue;
|
||
}
|
||
const d = pd && pd.data && typeof pd.data === "object" && !Array.isArray(pd.data) ? pd.data : pd;
|
||
const st = normalizeVideoStatus((d && (d.status || d.state)) || (pd && pd.status));
|
||
lastPoll = "status=" + st;
|
||
if (st === "succeeded") {
|
||
const url = findVideoUrl(pd);
|
||
if (!url) return { ok: false, error: `视频任务完成但未找到视频地址：${JSON.stringify(pd).slice(0, 300)}` };
|
||
const b64 = await downloadAsBase64(url);
|
||
return { ok: true, mime: "video/mp4", dataUrl: toDataUrl("video/mp4", b64), url, _model: model };
|
||
}
|
||
if (st === "failed") {
|
||
const err = (d && d.error && (d.error.message || d.error)) || (d && (d.fail_reason || d.message || d.reason)) || (pd && pd.message) || st;
|
||
const raw = JSON.stringify(pd).slice(0, 500);
|
||
return { ok: false, error: "视频任务失败：" + (typeof err === "string" ? err : JSON.stringify(err)) + "\n上游原始返回：" + raw };
|
||
}
|
||
// queued/running → 继续等
|
||
}
|
||
return { ok: false, error: "视频轮询 10 分钟未完成（taskId=" + taskId + "，最近一次轮询：" + (lastPoll || "无") + "）" };
|
||
} catch (e) {
|
||
return { ok: false, error: "视频生成异常：" + String((e && e.message) || e) };
|
||
}
|
||
}
|
||
|
||
// —— Grok 视频：独立新增接口，按 /v1/videos/generations + /v1/videos/{request_id} 调用 ——
|
||
async function runGrokVideo(base, llm, params) {
|
||
let model = params.model || "grok-imagine-video";
|
||
const prompt = (params.prompt || "").trim();
|
||
if (!prompt) return { ok: false, error: "请填写 Grok 视频提示词" };
|
||
|
||
const body = { model, prompt };
|
||
if (params.resolution && params.resolution !== "auto") body.resolution = params.resolution;
|
||
const dur = parseInt(params.seconds, 10);
|
||
if (!isNaN(dur) && dur > 0) body.duration = dur;
|
||
|
||
let mode = String(params.mode || "text");
|
||
if (mode === "image") mode = "first";
|
||
try {
|
||
if (
|
||
mode === "text" &&
|
||
/grok-imagine-video-1\.5/i.test(model) &&
|
||
body.resolution === "1080p" &&
|
||
Number(body.duration) === 15
|
||
) {
|
||
return { ok: false, error: "1.5 的 1080p/15s 需要首帧图；请改用首帧图生视频，或降低文生视频规格" };
|
||
}
|
||
if (mode === "first") {
|
||
const img = await resolveMat(params.imageUrl || params.image);
|
||
if (!img) return { ok: false, error: "Grok 图生视频需要首帧图：上传 / 手填公网URL / 连接上游文生图" };
|
||
body.image = { image_url: img };
|
||
} else if (mode === "reference") {
|
||
const refs = Array.isArray(params.referenceImages) ? params.referenceImages : [];
|
||
const resolved = (await Promise.all(refs.map((r) => resolveMat(r)))).filter(Boolean);
|
||
if (!resolved.length) return { ok: false, error: "Grok 多参考图模式请至少提供 1 张参考图" };
|
||
model = "grok-imagine-video";
|
||
body.model = model;
|
||
body.resolution = "720p";
|
||
if (!body.aspect_ratio) body.aspect_ratio = params.aspectRatio || "16:9";
|
||
body.reference_images = resolved.map((url) => ({ url }));
|
||
} else if (mode !== "text") {
|
||
return { ok: false, error: "Grok 视频新增接口只支持「文生视频」「首帧图生视频」和「多参考图」" };
|
||
}
|
||
|
||
const createUrl = joinUrl(base, params.endpointPath || "/v1/videos/generations");
|
||
const cres = await fetch(createUrl, {
|
||
method: "POST",
|
||
headers: headers(llm.key),
|
||
body: JSON.stringify(body),
|
||
});
|
||
if (!cres.ok) {
|
||
const detail = await cres.text().catch(() => "");
|
||
return { ok: false, error: `Grok 视频建任务失败(${cres.status})：${detail.slice(0, 300)}` };
|
||
}
|
||
const data = await cres.json();
|
||
const requestId = data.request_id || data.id || (data.data && (data.data.request_id || data.data.id));
|
||
if (!requestId) return { ok: false, error: `Grok 视频建任务未返回 request_id：${JSON.stringify(data).slice(0, 300)}` };
|
||
|
||
const pollUrl = joinUrl(base, "/v1/videos/" + encodeURIComponent(requestId));
|
||
const pollHeaders = { Authorization: "Bearer " + (llm.key || ""), Accept: "application/json", "User-Agent": "codex-market" };
|
||
let lastPoll = "";
|
||
for (let i = 0; i < 120; i++) {
|
||
await sleep(5000);
|
||
let pd;
|
||
try {
|
||
const pr = await fetch(pollUrl, { headers: pollHeaders });
|
||
if (!pr.ok) {
|
||
lastPoll = `HTTP ${pr.status}: ` + (await pr.text().catch(() => "")).slice(0, 200);
|
||
continue;
|
||
}
|
||
pd = await pr.json();
|
||
} catch (e) {
|
||
lastPoll = "请求异常：" + String((e && e.message) || e);
|
||
continue;
|
||
}
|
||
const d = pd && pd.data && typeof pd.data === "object" && !Array.isArray(pd.data) ? pd.data : pd;
|
||
const st = normalizeVideoStatus((d && (d.status || d.state)) || (pd && pd.status));
|
||
lastPoll = "status=" + st;
|
||
if (st === "succeeded") {
|
||
const url = findVideoUrl(pd);
|
||
if (!url) return { ok: false, error: `Grok 视频已完成但未找到视频地址：${JSON.stringify(pd).slice(0, 500)}` };
|
||
const b64 = await downloadAsBase64(url);
|
||
return { ok: true, mime: "video/mp4", dataUrl: toDataUrl("video/mp4", b64), url, _model: model };
|
||
}
|
||
if (st === "failed") {
|
||
const err = (d && d.error && (d.error.message || d.error)) || (d && (d.fail_reason || d.message || d.reason)) || (pd && pd.message) || st;
|
||
return { ok: false, error: "Grok 视频任务失败：" + (typeof err === "string" ? err : JSON.stringify(err)) };
|
||
}
|
||
}
|
||
return { ok: false, error: "Grok 视频轮询 10 分钟未完成（request_id=" + requestId + "，最近一次轮询：" + (lastPoll || "无") + "）" };
|
||
} catch (e) {
|
||
return { ok: false, error: "Grok 视频生成异常：" + String((e && e.message) || e) };
|
||
}
|
||
}
|
||
|
||
// 统一执行入口：dispatch 到 image/video/tts，返回 { ok, mime, dataUrl, meta } 或 { ok:false, error }。
|
||
// onPartial：可选，仅 image 流式生成时用来回传渐进帧（undefined 时安全忽略）。
|
||
async function run({ kind, params }, onPartial) {
|
||
const p = params || {};
|
||
// 解析渠道：指定 groupId 则按 id 取该分组，否则回退当前可用 llmGroup
|
||
const g = p.groupId ? router.groupById(p.groupId) : router.llmGroup();
|
||
if (!g) return { ok: false, error: "未配置可用中转分组" };
|
||
if (!g.baseUrl || !g.key) return { ok: false, error: "所选分组缺少地址或密钥" };
|
||
const base = trimSlash(g.baseUrl);
|
||
const host = hostOf(g.baseUrl);
|
||
const t0 = Date.now();
|
||
try {
|
||
let r;
|
||
if (kind === "image") r = await runImage(base, g, p, onPartial);
|
||
else if (kind === "tts") r = await runTts(base, g, p);
|
||
else if (kind === "video") r = await runVideo(base, g, p);
|
||
else if (kind === "grokVideo") r = await runGrokVideo(base, g, p);
|
||
else return { ok: false, error: "未知的画布任务类型：" + kind };
|
||
const model = (r && r._model) || p.model || g.model;
|
||
if (r && r._model !== undefined) delete r._model;
|
||
if (r && r.ok) r.meta = { model, ms: Date.now() - t0, host };
|
||
return r;
|
||
} catch (e) {
|
||
return { ok: false, error: String((e && e.message) || e), meta: { model: p.model || g.model, ms: Date.now() - t0, host } };
|
||
}
|
||
}
|
||
|
||
// 列出全部可用中转分组（供前端每个节点的「分组」下拉）：返回 activeId 与精简分组信息。
|
||
function groups() {
|
||
const list = router.usableGroups();
|
||
if (!list || !list.length) return { ok: false, error: "未配置可用中转分组" };
|
||
const st = router.status();
|
||
const activeId = (st && st.activeId) || (router.llmGroup() || {}).id || null;
|
||
return {
|
||
ok: true,
|
||
activeId,
|
||
groups: list.map((g) => ({ id: g.id, name: g.name || "", model: g.model || "", host: hostOf(g.baseUrl) })),
|
||
};
|
||
}
|
||
|
||
// 拉取指定/当前中转分组可用的模型列表（复用 router.fetchModels），供前端填充模型下拉/datalist。
|
||
async function listModels(groupId) {
|
||
const g = groupId ? router.groupById(groupId) : router.llmGroup();
|
||
if (!g) return { ok: false, error: "未配置可用中转分组" };
|
||
if (!g.baseUrl || !g.key) return { ok: false, error: "所选分组缺少地址或密钥" };
|
||
try {
|
||
const models = await router.fetchModels(g.baseUrl, g.key);
|
||
return { ok: true, models };
|
||
} catch (e) {
|
||
return { ok: false, error: String((e && e.message) || e) };
|
||
}
|
||
}
|
||
|
||
module.exports = { status, groups, run, listModels, downloadAsBase64 };
|