mirror of
https://github.com/QuantumNous/new-api.git
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395 lines
17 KiB
JavaScript
395 lines
17 KiB
JavaScript
export const meta = {
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apiVersion: 1,
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key: "vertex-ai",
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name: "Google Veo (Vertex AI)",
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icon: "VertexAI.Color",
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description: {
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en: "Google Veo video generation on Vertex AI (text-to-video and image-to-video)",
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zh: "Google Veo 视频生成（文生视频、图生视频），Vertex AI 版本",
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},
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version: "1.0.0",
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channelTypes: [41],
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author: { name: "QuantumNous" },
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models: ["veo-3.0-generate-001", "veo-3.0-fast-generate-001", "veo-3.1-generate-preview", "veo-3.1-fast-generate-preview"],
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fetchMode: "per_task",
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auth: { type: "oauth2_jwt" },
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usageSchema: {
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seconds: {
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type: "number",
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unit: "second",
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description: {
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en: "Requested video duration in seconds. Allowed values: 4, 6, 8.",
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zh: "请求的视频时长，单位为秒。允许值为 4、6、8。",
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},
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},
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resolution: {
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enum: ["720p", "1080p", "4k"],
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description: { en: "Requested video output resolution.", zh: "请求的输出视频分辨率。" },
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},
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generate_audio: {
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type: "boolean",
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description: {
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en: "Whether audio is generated. Default true. Audio and muted tiers have different prices.",
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zh: "是否生成音频。默认为 true。有声与静音档位计费不同。",
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},
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},
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},
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usageExamples: [
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{ label: "8s 720p audio", facts: { seconds: 8, resolution: "720p", generate_audio: true } },
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{ label: "8s 720p muted", facts: { seconds: 8, resolution: "720p", generate_audio: false } },
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{ label: "8s 1080p audio", facts: { seconds: 8, resolution: "1080p", generate_audio: true } },
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{ label: "8s 4k audio", facts: { seconds: 8, resolution: "4k", generate_audio: true } },
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],
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protocols: [{ name: "openai_responses", supports: ["stream", "sync", "background"] }, "openai_video"],
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};
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function trimmed(value) {
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return String(value || "").trim();
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}
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function responsesInput(req) {
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const texts = [],
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images = [];
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const input = req.input;
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if (typeof input === "string") texts.push(input);
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else if (Array.isArray(input)) {
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for (const item of input) {
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if (typeof item === "string") {
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texts.push(item);
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continue;
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}
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if (!item || typeof item !== "object" || Array.isArray(item)) continue;
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const content = item.content === undefined ? [item] : Array.isArray(item.content) ? item.content : [item.content];
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for (const part of content) {
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if (typeof part === "string") {
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texts.push(part);
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continue;
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}
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if (!part || typeof part !== "object" || Array.isArray(part)) continue;
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if (["input_text", "text"].includes(part.type) && typeof part.text === "string") texts.push(part.text);
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if (["input_image", "image_url"].includes(part.type)) {
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let image = part.image_url;
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if (image && typeof image === "object") image = image.url;
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if (trimmed(image)) images.push(trimmed(image));
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}
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}
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}
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}
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return {
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prompt: texts
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.filter(function (text) {
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return trimmed(text);
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})
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.join("\n"),
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images: images,
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};
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}
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function validImageInput(value) {
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value = trimmed(value);
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if (!value) return false;
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if (value.startsWith("data:")) {
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const comma = value.indexOf(",");
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return comma >= 0 && Boolean(value.slice(comma + 1));
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}
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return /^[A-Za-z0-9+/]+={0,2}$/.test(value);
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}
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function sizeParts(size) {
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const p = String(size || "")
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.toLowerCase()
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.split("x");
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return p.length === 2 ? [Number(p[0]), Number(p[1])] : null;
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}
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function resolution(size) {
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const p = sizeParts(size);
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if (!p) return "720p";
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const m = Math.max(p[0], p[1]);
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return m >= 3840 ? "4k" : m >= 1920 ? "1080p" : "720p";
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}
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function aspect(size) {
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const p = sizeParts(size);
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return !p || p[0] <= 0 || p[1] <= 0 ? "16:9" : p[1] > p[0] ? "9:16" : "16:9";
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}
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function duration(req) {
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let n = Number(req.duration);
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if (!Number.isFinite(n) || n <= 0) n = 8;
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return n;
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}
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function region(ctx, model) {
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const setting = ctx.userSetting || {};
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const configured = setting.vertexRegion || setting.apiVersion || "global";
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if (configured && typeof configured === "object") return configured[model] || configured.default || "global";
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return String(configured || "global");
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}
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function apiBase(baseUrl, project, location) {
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const base = String(baseUrl || "").replace(/\/$/, "");
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if (base) return base + (base.endsWith("/v1") ? "" : "/v1") + "/projects/" + project + "/locations/" + location;
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return "https://" + (location === "global" ? "" : location + "-") + "aiplatform.googleapis.com/v1/projects/" + project + "/locations/" + location;
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}
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function modelURL(ctx, project, location, model, action) {
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return apiBase(ctx.baseUrl, project, location) + "/publishers/google/models/" + model + ":" + action;
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}
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function decodeTaskId(value) {
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return utils.base64URLDecode(value);
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}
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function operationPart(name, marker) {
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const start = name.indexOf(marker);
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if (start < 0) return "";
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return name.slice(start + marker.length).split("/")[0];
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}
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function dataVideo(response) {
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const videos = response.videos || [];
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const first = videos[0] || {};
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const data = first.bytesBase64Encoded || response.bytesBase64Encoded || response.video || "";
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if (!data || String(data).startsWith("data:") || String(data).startsWith("http")) return data;
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const enc = first.mimeType || first.encoding || response.encoding || "mp4";
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return "data:" + (String(enc).includes("/") ? enc : "video/" + enc) + ";base64," + data;
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}
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export function buildSubmitRequest(ctx) {
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if (ctx.authError) throw new Error(ctx.authError);
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const req = ctx.requestBody || {},
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metadata = Object.assign({}, req.metadata || {}),
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model = ctx.upstreamModel || "veo-3.0-generate-001";
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if (Number(req.duration) > 0) metadata.durationSeconds = Number(req.duration);
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if (!metadata.resolution && req.size) metadata.resolution = resolution(req.size);
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if (!metadata.aspectRatio && req.size) metadata.aspectRatio = aspect(req.size);
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if (metadata.resolution) metadata.resolution = String(metadata.resolution).toLowerCase();
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metadata.sampleCount = 1;
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const instance = { prompt: req.prompt };
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const image = (req.images || [])[0];
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if (image && typeof image === "object" && !Array.isArray(image) && image.__fileRef) {
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let mime = String(image.mimeType || "").toLowerCase();
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if (mime !== "image/jpeg" && mime !== "image/png") {
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const files = ctx.files || [];
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for (const file of files) {
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if (file.ref === image.__fileRef || file.field === "input_reference") {
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mime = String(file.mimeType || "").toLowerCase();
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break;
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}
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}
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}
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if (mime !== "image/jpeg" && mime !== "image/png") throw new Error("input image must be image/jpeg or image/png");
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instance.image = { bytesBase64Encoded: image, mimeType: mime };
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} else if (image) {
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const text = String(image);
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const comma = text.indexOf(",");
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let mime = "";
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let data = text;
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if (text.startsWith("data:")) {
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mime = text.slice(5, comma).split(";")[0].toLowerCase();
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data = text.slice(comma + 1);
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} else if (text.startsWith("iVBORw0KGgo")) mime = "image/png";
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else if (text.startsWith("/9j/")) mime = "image/jpeg";
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if (mime !== "image/jpeg" && mime !== "image/png") throw new Error("input image must be image/jpeg or image/png");
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instance.image = { bytesBase64Encoded: data, mimeType: mime };
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}
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return {
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url: modelURL(ctx, ctx.auth.projectId, region(ctx, model), model, "predictLongRunning"),
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method: "POST",
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headers: { "Content-Type": "application/json", Accept: "application/json", Authorization: ctx.authHeader, "x-goog-user-project": ctx.auth.projectId },
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body: { instances: [instance], parameters: metadata },
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action: image ? "image_to_video" : "text_to_video",
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};
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}
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export function parseSubmitResponse(ctx, resp) {
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const body = resp.body || {};
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if (!String(body.name || "").trim()) throw new Error("missing operation name");
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return { taskId: utils.base64URL(body.name), taskData: body };
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}
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export function extractUsage(ctx) {
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const req = ctx.requestBody || {};
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const metadata = req.metadata || {};
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return {
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seconds: duration(req),
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resolution: String(metadata.resolution || resolution(req.size) || "720p").toLowerCase(),
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generate_audio: metadata.generateAudio !== false,
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};
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}
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export function extractUsageOnComplete() {
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return null;
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}
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export function buildQueryRequest(ctx) {
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const name = decodeTaskId(ctx.taskId),
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project = operationPart(name, "projects/"),
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location = operationPart(name, "locations/"),
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model = operationPart(name, "models/");
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if (!project || !model) throw new Error("cannot extract project or model from operation name");
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return {
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url: modelURL(ctx, project, location || "us-central1", model, "fetchPredictOperation"),
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method: "POST",
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headers: { "Content-Type": "application/json", Accept: "application/json", Authorization: ctx.authHeader, "x-goog-user-project": ctx.auth.projectId },
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body: { operationName: name },
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};
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}
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export function parseTaskResult(ctx, body) {
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if (body.error && body.error.message) return { status: "FAILURE", progress: "100%", reason: body.error.message };
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if (!body.done) return { status: "IN_PROGRESS", progress: "50%" };
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const url = dataVideo(body.response || {});
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return { status: "SUCCESS", progress: "100%", url: url, remoteUrl: url };
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}
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export function listArtifacts() {
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return [];
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}
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export function buildContentRequest() {
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throw new Error("artifact_not_found");
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}
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function completionMessage(ctx, task) {
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const request = (ctx && ctx.requestBody) || {};
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const model = trimmed(request.model) || trimmed((task.properties || {}).origin_model_name) || "veo-3.0-generate-001";
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const status = String(task.status || "SUCCESS").toUpperCase();
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return (
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"Video generation for " +
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model +
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" completed (" +
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duration(request) +
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" seconds, status " +
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status +
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"). Retrieve the video through the native /v1/videos task flow."
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);
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}
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export const protocols = {
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openai_responses: {
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decodeRequest: function (ctx) {
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if (!ctx.body || ctx.body.kind !== "json") throw new Error("JSON body required");
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const req = ctx.body.value;
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if (!req || typeof req !== "object" || Array.isArray(req)) throw new Error("request body must be an object");
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const model = trimmed(req.model);
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if (!model) throw new Error("model is required");
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if (req.input !== undefined && typeof req.input !== "string" && !Array.isArray(req.input)) throw new Error("input must be a string or array");
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if (req.images !== undefined && !Array.isArray(req.images)) throw new Error("images must be an array");
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if (req.metadata !== undefined && (!req.metadata || typeof req.metadata !== "object" || Array.isArray(req.metadata)))
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throw new Error("metadata must be an object");
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const input = responsesInput(req);
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const prompt = input.prompt || trimmed(req.prompt);
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const images = [];
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for (const image of [req.image, req.input_reference].concat(req.images || [], input.images)) {
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if (trimmed(image) && !images.includes(trimmed(image))) images.push(trimmed(image));
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}
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if (!prompt && images.length === 0) throw new Error("input is required");
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if (images.length && !validImageInput(images[0])) throw new Error("input image must be a data URL or base64 value");
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const metadata = Object.assign({}, req.metadata || {});
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if (Object.prototype.hasOwnProperty.call(req, "resolution")) metadata.resolution = req.resolution;
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const requestBody = { model: model, prompt: prompt, metadata: metadata };
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if (images.length) requestBody.images = images;
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if (Object.prototype.hasOwnProperty.call(req, "seconds")) requestBody.duration = req.seconds;
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else if (Object.prototype.hasOwnProperty.call(req, "duration")) requestBody.duration = req.duration;
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if (Object.prototype.hasOwnProperty.call(req, "size")) requestBody.size = req.size;
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return { kind: "submit", model: model, action: images.length ? "image_to_video" : "text_to_video", requestBody: requestBody };
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},
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renderEvents: function (ctx, task, previousState) {
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const status = String(task.status || "UNKNOWN").toUpperCase();
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const value = Number(String(task.progress || "").replace("%", ""));
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const progress = Number.isFinite(value) && value >= 0 && value <= 100 ? value : null;
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const state = { status: status, progress: progress };
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if (status === "SUCCESS") {
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const events = previousState && previousState.status === status ? [] : [{ type: "output", data: completionMessage(ctx, task) }];
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return { events: events, state: state, done: true };
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}
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if (status === "FAILURE")
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return { events: [{ type: "error", code: "task_failed", message: task.fail_reason || "task failed" }], state: state, done: true };
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if (previousState && previousState.status === status && previousState.progress === progress) return { events: [], state: state, done: false };
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const event = { type: "progress", message: status.toLowerCase() };
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if (progress !== null) event.progress = progress;
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return { events: [event], state: state, done: false };
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},
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renderFinal: function (ctx, task) {
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return {
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output: [
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{
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type: "message",
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status: "completed",
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role: "assistant",
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content: [{ type: "output_text", text: completionMessage(ctx, task), annotations: [], logprobs: [] }],
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},
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],
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metadata: { vendor: "vertex", artifact_mode: "native_videos" },
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};
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},
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},
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};
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const legacyRenderers = {
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openai_video: function (task) {
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const model = (task.properties || {}).origin_model_name || "veo-3.0-generate-001";
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const statuses = { NOT_START: "queued", SUBMITTED: "queued", QUEUED: "queued", IN_PROGRESS: "in_progress", SUCCESS: "completed", FAILURE: "failed" };
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const out = {
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id: task.task_id,
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object: "video",
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model,
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status: statuses[task.status] || "unknown",
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progress: Number(String(task.progress || "0").replace("%", "")),
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created_at: task.created_at,
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};
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if (Number(task.updated_at) > 0) out.completed_at = Number(task.updated_at);
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return out;
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},
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};
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protocols.openai_video = {
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decodeRequest: function (ctx) {
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if (!ctx.body || (ctx.body.kind !== "json" && ctx.body.kind !== "multipart")) throw new Error("JSON or multipart body required");
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let req;
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let hasInputReferenceFile = false;
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if (ctx.body.kind === "json") {
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if (!ctx.body.value || Array.isArray(ctx.body.value)) throw new Error("JSON object required");
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req = Object.assign({}, ctx.body.value);
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} else {
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const first = function (name) {
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const values = (ctx.body.fields || {})[name] || [];
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if (values.length > 1) throw new Error(name + " must be provided once");
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return values[0];
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};
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req = {};
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const fields = ctx.body.fields || {};
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for (const name of Object.keys(fields)) {
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req[name] = first(name);
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}
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for (const file of ctx.body.files || []) {
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if (file.field !== "input_reference") throw new Error("unexpected file field: " + file.field);
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if (hasInputReferenceFile) throw new Error("input_reference must be provided once");
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const mime = String(file.mimeType || "").toLowerCase();
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if (mime !== "image/jpeg" && mime !== "image/png") throw new Error("input_reference must be image/jpeg or image/png");
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hasInputReferenceFile = true;
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}
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if (req.metadata !== undefined) {
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let parsed;
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try {
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parsed = JSON.parse(req.metadata);
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} catch {
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throw new Error("metadata must be a JSON object string");
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}
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if (!parsed || typeof parsed !== "object" || Array.isArray(parsed)) throw new Error("metadata must be a JSON object string");
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req.metadata = parsed;
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}
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if (req.seconds !== undefined) req.seconds = Number(req.seconds);
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else if (req.duration !== undefined) req.seconds = Number(req.duration);
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}
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const seconds = req.seconds === undefined ? req.duration : req.seconds;
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if (seconds !== undefined) {
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const n = Number(seconds);
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if (n !== 4 && n !== 6 && n !== 8) throw new Error("seconds must be one of 4, 6, or 8");
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req.duration = n;
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}
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const providedResolution = req.resolution !== undefined ? req.resolution : req.metadata && req.metadata.resolution;
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if (providedResolution !== undefined && providedResolution !== "") {
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const value = String(providedResolution).toLowerCase();
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if (value !== "720p" && value !== "1080p" && value !== "4k") throw new Error("resolution must be one of 720p, 1080p, or 4k");
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}
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if (hasInputReferenceFile) {
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req.images = [{ __fileRef: "request_file:input_reference", encoding: "base64", maxBytes: 20971520 }];
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}
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return {
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kind: "submit",
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model: ctx.model,
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action: hasInputReferenceFile || req.input_reference || req.image ? "image_to_video" : "text_to_video",
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requestBody: Object.assign({}, req, { model: ctx.model }),
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};
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},
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render: function (ctx, task) {
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return legacyRenderers.openai_video(task);
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},
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};
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