03b8a0deb0
Recover the existing 19 modified tracked files and 6 untracked source files from the production working tree without changing their contents. Production source HEAD: 439fd56fe120fdb9f855b50fa4df9ce2e71bb72a Production build ID: QhoygcZ4-x42PwCgCEj1d Captured: 2026-10-30; all 25 source file SHA256 values verified. This is a source recovery checkpoint; no deployment was performed.
519 lines
16 KiB
TypeScript
519 lines
16 KiB
TypeScript
export type Vip98kTaskInvocation = {
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kind: string;
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method: string;
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path: string;
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contentType: string;
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headers?: Record<string, string>;
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requestExample: Record<string, unknown>;
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inputSchema?: Record<string, unknown> | null;
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resultMethod: string;
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resultPath: string;
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};
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type KnownImageInput = "images" | "video_clips";
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const VIP98K_IMAGE_SIZE_LONGEST_EDGE: Record<string, number> = {
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"1K": 1024,
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"2K": 2048,
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"4K": 4096,
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};
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const GPT_IMAGE_25_TARGET_PIXELS: Record<string, number> = {
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"1K": 1024 * 1024,
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"2K": 2048 * 2048,
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"4K": 2880 * 2880,
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};
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function normalizeVip98kPixelSize(value: unknown) {
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const match = String(value || "")
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.trim()
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.match(/^(\d+)\s*[x*×]\s*(\d+)$/i);
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if (!match?.[1] || !match[2]) return null;
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const sourceWidth = Number(match[1]);
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const sourceHeight = Number(match[2]);
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if (
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!Number.isFinite(sourceWidth) ||
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!Number.isFinite(sourceHeight) ||
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sourceWidth < 1 ||
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sourceHeight < 1
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) {
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return null;
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}
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const scale = Math.min(1, 4096 / Math.max(sourceWidth, sourceHeight));
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return `${Math.max(1, Math.round(sourceWidth * scale))}x${Math.max(
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1,
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Math.round(sourceHeight * scale),
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)}`;
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}
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export function resolveVip98kImageTaskSize(params: {
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imageSize?: unknown;
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aspectRatio?: unknown;
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fallbackSize?: unknown;
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preserveResolutionBucket?: boolean;
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useGptImage25AspectSizing?: boolean;
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}) {
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const explicitSize = normalizeVip98kPixelSize(params.imageSize);
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if (explicitSize) return explicitSize;
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const ratioMatch = String(params.aspectRatio || "")
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.trim()
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.match(/^(\d+(?:\.\d+)?)\s*:\s*(\d+(?:\.\d+)?)$/);
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const sizeBucket = String(params.imageSize || "").trim().toUpperCase();
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const longestEdge = VIP98K_IMAGE_SIZE_LONGEST_EDGE[sizeBucket];
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if (params.useGptImage25AspectSizing && sizeBucket in GPT_IMAGE_25_TARGET_PIXELS && (!ratioMatch || Number(ratioMatch[1]) === Number(ratioMatch[2]))) {
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const edge = Math.sqrt(GPT_IMAGE_25_TARGET_PIXELS[sizeBucket]);
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return `${edge}x${edge}`;
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}
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if (
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params.useGptImage25AspectSizing &&
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ratioMatch?.[1] &&
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ratioMatch[2] &&
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sizeBucket in GPT_IMAGE_25_TARGET_PIXELS
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) {
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const ratioWidth = Number(ratioMatch[1]);
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const ratioHeight = Number(ratioMatch[2]);
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if (ratioWidth > 0 && ratioHeight > 0 && ratioWidth !== ratioHeight) {
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const ratio = ratioWidth / ratioHeight;
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const targetPixels = GPT_IMAGE_25_TARGET_PIXELS[sizeBucket] || 0;
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let width = Math.sqrt(targetPixels * ratio);
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let height = Math.sqrt(targetPixels / ratio);
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const maxEdgeScale = Math.min(1, 3840 / Math.max(width, height));
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width *= maxEdgeScale;
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height *= maxEdgeScale;
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const clampDimension = (value: number) =>
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Math.max(16, Math.min(3840, value));
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const roundToSixteen = (value: number) =>
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clampDimension(Math.round(value / 16) * 16);
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const floorToSixteen = (value: number) =>
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clampDimension(Math.floor(value / 16) * 16);
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const shouldFloorWidth = sizeBucket !== "4K" && width < height;
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const shouldFloorHeight = sizeBucket !== "4K" && height < width;
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let outputWidth = shouldFloorWidth ? floorToSixteen(width) : roundToSixteen(width);
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let outputHeight = shouldFloorHeight ? floorToSixteen(height) : roundToSixteen(height);
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// Flare promotes even a small area overflow to the next tier (2736x1536 -> 4K).
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if (sizeBucket !== "4K" && outputWidth * outputHeight > targetPixels) {
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if (outputWidth >= outputHeight) outputWidth -= 16;
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else outputHeight -= 16;
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}
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return `${outputWidth}x${outputHeight}`;
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}
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}
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if (longestEdge && params.preserveResolutionBucket) return sizeBucket;
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if (ratioMatch?.[1] && ratioMatch[2] && longestEdge) {
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const ratioWidth = Number(ratioMatch[1]);
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const ratioHeight = Number(ratioMatch[2]);
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if (ratioWidth > 0 && ratioHeight > 0) {
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const scale = longestEdge / Math.max(ratioWidth, ratioHeight);
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const roundToEight = (value: number) =>
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Math.max(8, Math.min(4096, Math.round(value / 8) * 8));
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return `${roundToEight(ratioWidth * scale)}x${roundToEight(
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ratioHeight * scale,
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)}`;
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}
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}
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const fallbackPixelSize = normalizeVip98kPixelSize(params.fallbackSize);
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if (fallbackPixelSize) return fallbackPixelSize;
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return String(params.fallbackSize || "").trim().toLowerCase() === "auto"
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? "auto"
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: undefined;
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}
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const IMAGE_ARRAY_INPUT_KEYS = [
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"file_urls",
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"images",
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"imageUrls",
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"referenceImages",
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"reference_images",
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] as const;
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const IMAGE_SINGLE_INPUT_KEYS = [
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"image",
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"imageUrl",
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"image_url",
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"firstFrameImage",
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"first_frame_image",
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"firstFrameImageUrl",
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"first_frame_image_url",
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] as const;
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const KNOWN_IMAGE_TOOL_INPUTS: Record<string, KnownImageInput> = {
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"google/nano-banana-2/reference-to-image": "video_clips",
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"google/nano-banana-pro/edit": "images",
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"openai/gpt-image-2/edit": "images",
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};
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const VERIFIED_OPENAI_IMAGE_ARRAY_MODEL_IDS = new Set([
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"gpt-image-2-vip",
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"gpt-image-2.5",
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"gpt-image-2.5-flare",
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]);
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const VERIFIED_GPT_IMAGE_25_RESOLUTION_MODEL_IDS = new Set([
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// Paid two-reference probes on 2026-09-11; 4K square is 2880px, not 4096px.
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"gpt-image-2-vip",
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"gpt-image-2.5-flare",
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]);
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export function hasVerifiedVip98kOpenAiImageArrayContract(modelId: string) {
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return VERIFIED_OPENAI_IMAGE_ARRAY_MODEL_IDS.has(
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modelId.trim().toLowerCase(),
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);
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}
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export function hasVerifiedVip98kGptImage25ResolutionTiers(modelId: string) {
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return VERIFIED_GPT_IMAGE_25_RESOLUTION_MODEL_IDS.has(
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modelId.trim().toLowerCase(),
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);
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}
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const KNOWN_IMAGE_PROCESSING_TOOL_REQUESTS: Record<
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string,
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Record<string, unknown>
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> = {
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"remove-background": { image_url: "" },
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enhance: { image_url: "", scale_factor: 4 },
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};
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export function normalizeVip98kInvocationHeaders(value: unknown) {
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if (!value || typeof value !== "object" || Array.isArray(value)) return {};
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return Object.fromEntries(
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Object.entries(value as Record<string, unknown>).flatMap(([key, headerValue]) => {
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const normalizedKey = key.trim();
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if (!/^x-relay-[a-z0-9-]+$/i.test(normalizedKey)) return [];
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if (
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typeof headerValue !== "string" &&
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typeof headerValue !== "number" &&
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typeof headerValue !== "boolean"
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) {
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return [];
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}
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const normalizedValue = String(headerValue).trim();
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return normalizedValue ? [[normalizedKey, normalizedValue]] : [];
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}),
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);
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}
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function getInvocationToolId(invocation: Vip98kTaskInvocation | null) {
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if (!invocation || invocation.kind !== "tool") return null;
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const query = invocation.path.split("?", 2)[1] || "";
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return new URLSearchParams(query).get("tool_id");
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}
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function invocationTargetsModel(
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invocation: Vip98kTaskInvocation | null,
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modelId: string,
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) {
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if (!invocation) return false;
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const expected = modelId.trim().toLowerCase();
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if (!expected) return false;
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const requestModel = String(invocation.requestExample.model || "")
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.trim()
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.toLowerCase();
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if (requestModel) return requestModel === expected;
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return getInvocationToolId(invocation)?.toLowerCase() === expected;
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}
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export function getVip98kImageBindingKeys(invocation: Vip98kTaskInvocation | null) {
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if (!invocation) return [];
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const body = invocation.requestExample || {};
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const properties = (invocation.inputSchema?.properties || {}) as Record<string, unknown>;
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return [
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...IMAGE_ARRAY_INPUT_KEYS.filter((key) => key in body || key in properties),
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...IMAGE_SINGLE_INPUT_KEYS.filter((key) => key in body || key in properties),
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...(Array.isArray(body.video_clips) ? ["video_clips" as const] : []),
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];
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}
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export function invocationSupportsVip98kImages(
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invocation: Vip98kTaskInvocation | null,
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) {
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return getVip98kImageBindingKeys(invocation).length > 0;
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}
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export function bindVip98kImages(
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body: Record<string, unknown>,
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imageUrls: string[],
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) {
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const bindingKeys: string[] = [];
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if (imageUrls.length === 0) return bindingKeys;
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for (const key of IMAGE_ARRAY_INPUT_KEYS) {
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if (!(key in body)) continue;
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body[key] = imageUrls;
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bindingKeys.push(key);
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}
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for (const key of IMAGE_SINGLE_INPUT_KEYS) {
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if (!(key in body)) continue;
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body[key] = Array.isArray(body[key]) ? imageUrls : imageUrls[0];
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bindingKeys.push(key);
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}
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if (Array.isArray(body.video_clips)) {
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const first = body.video_clips[0];
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const template =
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first && typeof first === "object" && !Array.isArray(first)
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? (first as Record<string, unknown>)
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: {};
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body.video_clips = imageUrls.map((url) => ({ ...template, url }));
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bindingKeys.push("video_clips");
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}
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const secondImageBindings = [
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"lastFrameImage",
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"last_frame_image",
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"endImageUrl",
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"end_image_url",
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] as const;
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if (imageUrls[1]) {
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for (const key of secondImageBindings) {
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if (!(key in body)) continue;
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body[key] = imageUrls[1];
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bindingKeys.push(key);
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}
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}
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return bindingKeys;
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}
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/** Only relay routing metadata may accompany our own authorization headers. */
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export function getVip98kInvocationHeaders(
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value: unknown
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): Record<string, string> {
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if (!value || typeof value !== "object" || Array.isArray(value)) return {};
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const entry = Object.entries(value).find(
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([key]) => key.toLowerCase() === "x-relay-route-option"
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);
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const route = entry?.[1];
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return typeof route === "string" && /^[a-zA-Z0-9_-]{1,160}$/.test(route)
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? { "X-Relay-Route-Option": route }
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: {};
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}
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function scoreReferenceInvocation(invocation: Vip98kTaskInvocation) {
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const body = invocation.requestExample || {};
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let score = 0;
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if ("file_urls" in body) score += 60;
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if (IMAGE_ARRAY_INPUT_KEYS.some((key) => key in body)) score += 40;
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if (IMAGE_SINGLE_INPUT_KEYS.some((key) => Array.isArray(body[key]))) score += 40;
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if (Array.isArray(body.video_clips)) score += 35;
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if (IMAGE_SINGLE_INPUT_KEYS.some((key) => key in body)) score += 15;
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if (invocation.kind === "task") score += 10;
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if (invocation.path === "/extensions/v1/tasks") score += 10;
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return score;
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}
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export function selectVip98kTaskInvocation(params: {
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modelId: string;
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configuredInvocation: Vip98kTaskInvocation | null;
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configuredInvocations?: Vip98kTaskInvocation[];
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imageCount?: number;
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}) {
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const candidates = [
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...(params.configuredInvocations || []),
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...(params.configuredInvocation ? [params.configuredInvocation] : []),
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].filter(
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(candidate, index, items) =>
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candidate.path.startsWith("/") &&
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items.findIndex(
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(item) =>
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item.method === candidate.method &&
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item.path === candidate.path &&
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JSON.stringify(item.requestExample) === JSON.stringify(candidate.requestExample) &&
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JSON.stringify(item.inputSchema || null) ===
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JSON.stringify(candidate.inputSchema || null),
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) === index,
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);
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if ((params.imageCount || 0) > 0) {
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const referenceInvocation = candidates
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.filter(invocationSupportsVip98kImages)
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.sort((a, b) => scoreReferenceInvocation(b) - scoreReferenceInvocation(a))[0];
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if (referenceInvocation) {
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return resolveVip98kTaskInvocation(params.modelId, referenceInvocation);
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}
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}
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return resolveVip98kTaskInvocation(
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params.modelId,
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params.configuredInvocation || candidates[0] || null,
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);
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}
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function createKnownImageToolInvocation(
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modelId: string,
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imageInput: KnownImageInput
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): Vip98kTaskInvocation {
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const encodedModelId = encodeURIComponent(modelId);
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const requestExample =
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imageInput === "video_clips"
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? {
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prompt: "",
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video_clips: [{ fps: 1, url: "", ends: 0, start: 0 }],
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}
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: { prompt: "", images: [""] };
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return {
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kind: "tool",
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method: "POST",
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path: `/api/external/tools/generate?tool_id=${encodedModelId}`,
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contentType: "application/json",
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requestExample,
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resultMethod: "GET",
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resultPath: `/api/external/tools/result?tool_id=${encodedModelId}&taskId={task_id}`,
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};
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}
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function createKnownProcessingToolInvocation(
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modelId: string,
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requestExample: Record<string, unknown>
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): Vip98kTaskInvocation {
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const encodedModelId = encodeURIComponent(modelId);
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return {
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kind: "tool",
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method: "POST",
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path: `/api/external/tools/generate?tool_id=${encodedModelId}`,
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contentType: "application/json",
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requestExample: { ...requestExample },
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resultMethod: "GET",
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resultPath: `/api/external/tools/result?tool_id=${encodedModelId}&taskId={task_id}`,
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};
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}
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function createVerifiedOpenAiImageInvocation(modelId: string) {
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return {
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kind: "tool",
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method: "POST",
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path: "/v1/images/generations",
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contentType: "application/json",
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requestExample: {
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image: [],
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model: modelId,
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n: 1,
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prompt: "",
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quality: "auto",
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size: "1024x1024",
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stream: false,
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},
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inputSchema: {
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properties: {
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aspect_ratio: { type: "string" },
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},
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type: "object",
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},
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resultMethod: "GET",
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resultPath: "",
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} satisfies Vip98kTaskInvocation;
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}
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function addVerifiedOpenAiImageFields(
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invocation: Vip98kTaskInvocation,
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modelId: string,
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): Vip98kTaskInvocation {
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const inputSchema = invocation.inputSchema || {};
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const properties =
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inputSchema.properties &&
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typeof inputSchema.properties === "object" &&
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!Array.isArray(inputSchema.properties)
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? (inputSchema.properties as Record<string, unknown>)
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: {};
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return {
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...invocation,
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requestExample: {
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...invocation.requestExample,
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image: [],
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},
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inputSchema: {
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...inputSchema,
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properties: {
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...properties,
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image: { items: { type: "string" }, type: "array" },
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aspect_ratio: { type: "string" },
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...(hasVerifiedVip98kGptImage25ResolutionTiers(modelId)
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? {
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size: {
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enum: ["1024x1024", "2048x2048", "2880x2880"],
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type: "string",
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},
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}
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: {}),
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},
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},
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};
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}
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/**
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* A frontend alias may resolve to a different VIP98K tool at runtime. In that
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* case the selected model's invocation cannot be reused because its endpoint
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* and reference-image field belong to the alias rather than the resolved tool.
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*/
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export function resolveVip98kTaskInvocation(
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modelId: string,
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configuredInvocation: Vip98kTaskInvocation | null
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) {
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const normalizedModelId = modelId.trim().toLowerCase();
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if (hasVerifiedVip98kOpenAiImageArrayContract(normalizedModelId)) {
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const invocation =
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configuredInvocation &&
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invocationTargetsModel(configuredInvocation, normalizedModelId)
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? configuredInvocation
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: createVerifiedOpenAiImageInvocation(modelId);
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return addVerifiedOpenAiImageFields(invocation, normalizedModelId);
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}
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const processingRequest = KNOWN_IMAGE_PROCESSING_TOOL_REQUESTS[normalizedModelId];
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if (processingRequest) {
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if (invocationTargetsModel(configuredInvocation, normalizedModelId)) {
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return configuredInvocation;
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}
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return createKnownProcessingToolInvocation(modelId, processingRequest);
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}
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const knownImageInput = KNOWN_IMAGE_TOOL_INPUTS[normalizedModelId];
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if (!knownImageInput) return configuredInvocation;
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if (invocationTargetsModel(configuredInvocation, normalizedModelId)) {
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return configuredInvocation;
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}
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return createKnownImageToolInvocation(modelId, knownImageInput);
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}
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/** Convert final canvas multipliers to the provider's per-side expansion ratios. */
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export function bindVip98kExpansionParams(
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body: Record<string, unknown>,
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params: { xScale?: unknown; yScale?: unknown }
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) {
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||
if (params.xScale === undefined && params.yScale === undefined) return;
|
||
const ratio = (value: unknown) => {
|
||
const scale = Number(value ?? 1.5);
|
||
if (!Number.isFinite(scale) || scale <= 1 || scale > 3)
|
||
throw new Error("VIP98K 扩图倍数必须大于 1 且不超过 3");
|
||
return Number(((scale - 1) / 2).toFixed(8));
|
||
};
|
||
const directions = {
|
||
left: ratio(params.xScale),
|
||
right: ratio(params.xScale),
|
||
top: ratio(params.yScale),
|
||
bottom: ratio(params.yScale),
|
||
};
|
||
const metadata =
|
||
body.metadata &&
|
||
typeof body.metadata === "object" &&
|
||
!Array.isArray(body.metadata)
|
||
? { ...(body.metadata as Record<string, unknown>) }
|
||
: {};
|
||
const rootDirections = Object.keys(directions).some((key) => key in body);
|
||
for (const key of Object.keys(directions)) {
|
||
delete body[key];
|
||
delete metadata[key];
|
||
}
|
||
if (rootDirections) {
|
||
Object.assign(body, directions);
|
||
if (Object.keys(metadata).length) body.metadata = metadata;
|
||
else delete body.metadata;
|
||
} else {
|
||
body.metadata = { ...metadata, ...directions };
|
||
}
|
||
}
|
||
|