910 lines
29 KiB
TypeScript
910 lines
29 KiB
TypeScript
import "server-only";
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import sharp from "sharp";
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import { getEnabledAiModelsWithProvider } from "@/lib/db/queries";
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import { fetchImageBufferFromUrl } from "@/lib/ai/rmbg/service";
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import { createGeminiImage } from "@/lib/ai/providers/gemini-image";
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import { createAlibabaImage } from "@/lib/ai/providers/alibaba-image";
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import { createBflImage } from "@/lib/ai/providers/bfl-image";
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import { createRuxaImage } from "@/lib/ai/providers/ruxa-image";
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import { createFalImage } from "@/lib/ai/providers/fal-image";
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import { createOpenAIImage } from "@/lib/ai/providers/openai-image";
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import { createStepfunImage } from "@/lib/ai/providers/stepfun-image";
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import { createMidjourney } from "@/lib/ai/providers/midjourney";
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import { createRunway } from "@/lib/ai/providers/runway";
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import { createKrea } from "@/lib/ai/providers/krea";
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import { createVolcanoVision } from "@/lib/ai/providers/volcano-vision";
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import { filterReachableRemoteUrls } from "@/lib/net/url-access";
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import {
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acquireCanvasImageModelSlot,
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getCanvasImageModelQueueStatus,
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type CanvasImageModelQueueStatus,
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normalizeCanvasImageModelKey,
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recordCanvasImageModelExecutionDuration,
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} from "./image-concurrency";
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import {
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recordAiModelHealthFailure,
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recordAiModelHealthSuccess,
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shouldFailoverAiModelError,
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} from "@/lib/ai/runtime/model-health";
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import { uploadFile } from "@/lib/storage/service";
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export interface CanvasLocalEditCompositeRegion {
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x: number;
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y: number;
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width: number;
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height: number;
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}
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export interface CanvasLocalEditCompositeConfig {
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sourceImageUrl: string;
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regions: CanvasLocalEditCompositeRegion[];
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}
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export interface CanvasGenerateImageRequest {
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prompt: string;
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negativePrompt?: string;
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model?: string;
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aspectRatio?: string;
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imageSize?: string;
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imageCount?: number;
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promptExtend?: boolean;
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referenceImage?: string;
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referenceImages?: string[];
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billingFeature?: "multi-view" | "repaint";
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ownerKey?: string;
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debugLabel?: string;
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debugMeta?: Record<string, unknown>;
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localEditComposite?: CanvasLocalEditCompositeConfig;
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allowFailover?: boolean;
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internalFailoverTriedModelRefs?: string[];
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onQueueStatusChange?: (
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status: CanvasImageModelQueueStatus & {
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modelName?: string | null;
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modelRef?: string | null;
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}
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) => void | Promise<void>;
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}
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export interface ParsedImage {
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thumbnailUrl: string;
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originalUrl: string;
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}
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export interface CanvasGenerateImageResult {
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images: ParsedImage[];
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model: string;
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modelRef: string;
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prompt: string;
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}
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const SUPPORTED_CANVAS_PROVIDER_TYPES = new Set([
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"gemini",
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"grsai-gpt-image",
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"alibaba-image",
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"bfl-image",
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"ruxa-image",
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"fal-image",
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"openai-image",
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"stepfun-image",
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"midjourney",
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"runway",
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"krea",
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"volcano-vision",
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]);
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function summarizeReferenceImageHosts(urls: string[]) {
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return Array.from(
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new Set(
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urls
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.map((value) => {
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try {
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return new URL(value).host;
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} catch {
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return "";
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}
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})
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.filter(Boolean)
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)
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);
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}
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function parseImagesFromMarkdown(markdown: string): ParsedImage[] {
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const imageRegex = /!\[([^\]]*)\]\(([^)]+)\)/g;
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const matches = [...markdown.matchAll(imageRegex)];
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const parsed: ParsedImage[] = [];
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for (const match of matches) {
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const altText = match[1] || "";
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const thumbnailUrl = match[2];
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let originalUrl = thumbnailUrl;
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if (altText.includes("thumb|||")) {
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const content = altText.substring(altText.indexOf("thumb|||") + 8);
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const parts = content.split("|||");
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originalUrl = parts[0] || thumbnailUrl;
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} else if (altText.includes("thumb$$")) {
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const content = altText.substring(altText.indexOf("thumb$$") + 7);
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const parts = content.split("$$");
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originalUrl = parts[0] || thumbnailUrl;
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} else if (altText.includes("thumb|")) {
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const content = altText.substring(altText.indexOf("thumb|") + 6);
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const parts = content.split("|");
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originalUrl = parts[0] || thumbnailUrl;
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}
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parsed.push({ thumbnailUrl, originalUrl });
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}
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return parsed;
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}
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function bufferToArrayBuffer(buffer: Buffer): ArrayBuffer {
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return buffer.buffer.slice(
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buffer.byteOffset,
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buffer.byteOffset + buffer.byteLength
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) as ArrayBuffer;
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}
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function clampRatio(value: number) {
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if (!Number.isFinite(value)) return 0;
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return Math.min(1, Math.max(0, value));
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}
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function normalizeLocalEditCompositeConfig(
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value: CanvasGenerateImageRequest["localEditComposite"]
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): CanvasLocalEditCompositeConfig | null {
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if (!value || typeof value !== "object") return null;
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const sourceImageUrl = String(value.sourceImageUrl || "").trim();
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if (!/^https?:\/\//i.test(sourceImageUrl)) return null;
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if (!Array.isArray(value.regions) || value.regions.length === 0) return null;
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const regions = value.regions
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.map((region) => {
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const x = clampRatio(Number(region?.x));
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const y = clampRatio(Number(region?.y));
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const width = clampRatio(Number(region?.width));
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const height = clampRatio(Number(region?.height));
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return {
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x,
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y,
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width: Math.min(width, Math.max(0, 1 - x)),
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height: Math.min(height, Math.max(0, 1 - y)),
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};
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})
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.filter((region) => region.width > 0 && region.height > 0);
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return regions.length > 0 ? { sourceImageUrl, regions } : null;
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}
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function buildLocalEditCompositeMaskSvg(params: {
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width: number;
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height: number;
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regions: CanvasLocalEditCompositeRegion[];
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}) {
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const { width, height, regions } = params;
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const rects = regions
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.map((region) => {
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const left = Math.max(0, Math.round(region.x * width));
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const top = Math.max(0, Math.round(region.y * height));
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const rectWidth = Math.min(
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width - left,
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Math.max(1, Math.round(region.width * width))
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);
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const rectHeight = Math.min(
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height - top,
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Math.max(1, Math.round(region.height * height))
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);
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return `<rect x="${left}" y="${top}" width="${rectWidth}" height="${rectHeight}" fill="#ffffff" />`;
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})
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.join("");
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return Buffer.from(`
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<svg width="${width}" height="${height}" viewBox="0 0 ${width} ${height}" xmlns="http://www.w3.org/2000/svg">
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<rect x="0" y="0" width="${width}" height="${height}" fill="rgba(0,0,0,0)" />
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${rects}
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</svg>
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`);
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}
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async function applyLocalEditCompositeToImages(params: {
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images: ParsedImage[];
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composite: CanvasLocalEditCompositeConfig | null;
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debugLabel: string;
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}) {
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const { images, composite, debugLabel } = params;
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if (!composite || images.length === 0) return images;
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const sourceBuffer = await sharp(
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await fetchImageBufferFromUrl(composite.sourceImageUrl),
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{ failOn: "none" }
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)
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.rotate()
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.png({ compressionLevel: 6 })
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.toBuffer();
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const sourceMetadata = await sharp(sourceBuffer, { failOn: "none" }).metadata();
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const sourceWidth = Number(sourceMetadata.width || 0);
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const sourceHeight = Number(sourceMetadata.height || 0);
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if (sourceWidth <= 0 || sourceHeight <= 0) {
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throw new Error("局部编辑结果合成失败：无法识别原图尺寸");
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}
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const maskSvg = buildLocalEditCompositeMaskSvg({
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height: sourceHeight,
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regions: composite.regions,
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width: sourceWidth,
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});
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const mergedImages: ParsedImage[] = [];
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for (const [index, image] of images.entries()) {
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const generatedUrl = image.originalUrl || image.thumbnailUrl;
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const generatedBuffer = await fetchImageBufferFromUrl(generatedUrl);
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const overlayBuffer = await sharp(generatedBuffer, { failOn: "none" })
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.rotate()
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.resize(sourceWidth, sourceHeight, { fit: "cover", position: "center" })
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.ensureAlpha()
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.composite([{ input: maskSvg, blend: "dest-in" }])
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.png({ compressionLevel: 6 })
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.toBuffer();
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const mergedBuffer = await sharp(sourceBuffer, { failOn: "none" })
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.ensureAlpha()
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.composite([{ input: overlayBuffer, left: 0, top: 0 }])
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.png({ compressionLevel: 6 })
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.toBuffer();
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const thumbnailBuffer = await sharp(mergedBuffer, { failOn: "none" })
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.resize(512, 512, { fit: "inside", withoutEnlargement: true })
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.png({ compressionLevel: 6 })
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.toBuffer();
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const suffix = `${Date.now()}-${index + 1}-${Math.random().toString(36).slice(2, 8)}`;
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const [originalUpload, thumbnailUpload] = await Promise.all([
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uploadFile(
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`canvas-local-edit-composited-${suffix}.png`,
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bufferToArrayBuffer(mergedBuffer)
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),
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uploadFile(
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`canvas-local-edit-composited-thumb-${suffix}.png`,
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bufferToArrayBuffer(thumbnailBuffer)
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),
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]);
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mergedImages.push({
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originalUrl: originalUpload.url,
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thumbnailUrl: thumbnailUpload.url,
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});
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}
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console.info("[Canvas Generate Image] Local edit composite applied:", {
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debugLabel,
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imageCount: mergedImages.length,
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regionCount: composite.regions.length,
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sourceHeight,
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sourceWidth,
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});
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return mergedImages;
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}
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function getProviderBatchLimit(providerType: string) {
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switch (providerType) {
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case "ruxa-image":
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case "bfl-image":
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case "openai-image":
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case "stepfun-image":
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case "midjourney":
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case "runway":
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return 1;
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case "krea":
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return 4;
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case "gemini":
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case "grsai-gpt-image":
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return 2;
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case "alibaba-image":
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case "fal-image":
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return 6;
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default:
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return 2;
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}
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}
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type CanvasModelWithProvider = Awaited<
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ReturnType<typeof getEnabledAiModelsWithProvider>
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>[number];
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function getCanvasModelRefKeys(item: CanvasModelWithProvider) {
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return [
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String(item.model.id || "").trim(),
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String(item.model.modelId || "").trim(),
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].filter(Boolean);
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}
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function findCanvasFailoverModel(params: {
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selectedModel: CanvasModelWithProvider;
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imageModels: CanvasModelWithProvider[];
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failedModelRefs: string[];
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}) {
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const failedRefs = new Set(
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params.failedModelRefs.map((item) => String(item || "").trim()).filter(Boolean)
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);
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const selectedFallbackRefs = [
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params.selectedModel.model.fallbackModelId1,
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params.selectedModel.model.fallbackModelId2,
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]
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.map((item) => String(item || "").trim())
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.filter(Boolean);
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const isUsableFallback = (item: CanvasModelWithProvider) => {
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if (!item.model.isEnabled || item.model.isSubModel || item.model.excludeFromFailover) {
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return false;
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}
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if (!item.model.supportsImageGen) return false;
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if (!SUPPORTED_CANVAS_PROVIDER_TYPES.has(item.provider.type)) return false;
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return getCanvasModelRefKeys(item).every((key) => !failedRefs.has(key));
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};
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for (const fallbackRef of selectedFallbackRefs) {
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const configured = params.imageModels.find(
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(item) =>
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isUsableFallback(item) &&
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(item.model.id === fallbackRef || item.model.modelId === fallbackRef)
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);
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if (configured) return configured;
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}
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const sameProviderType = params.imageModels.find(
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(item) =>
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isUsableFallback(item) &&
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item.provider.type === params.selectedModel.provider.type
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);
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if (sameProviderType) return sameProviderType;
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return params.imageModels.find(isUsableFallback) || null;
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}
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export async function generateCanvasImage(
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request: CanvasGenerateImageRequest
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): Promise<CanvasGenerateImageResult> {
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const startedAt = Date.now();
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const prompt = String(request.prompt || "").trim();
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if (!prompt) {
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throw new Error("请输入提示词");
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}
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const negativePrompt =
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typeof request.negativePrompt === "string" ? request.negativePrompt : undefined;
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const model = typeof request.model === "string" ? request.model : undefined;
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const aspectRatio =
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typeof request.aspectRatio === "string" && request.aspectRatio.trim()
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? request.aspectRatio.trim()
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: "auto";
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const imageSize =
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typeof request.imageSize === "string" && request.imageSize.trim()
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? request.imageSize.trim()
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: "2K";
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const imageCount =
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typeof request.imageCount === "number" && Number.isFinite(request.imageCount)
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? Math.max(1, Math.min(12, Math.floor(request.imageCount)))
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: 1;
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const promptExtend =
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typeof request.promptExtend === "boolean" ? request.promptExtend : undefined;
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const ownerKey =
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typeof request.ownerKey === "string" && request.ownerKey.trim()
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? request.ownerKey.trim()
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: undefined;
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const referenceImage =
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typeof request.referenceImage === "string" ? request.referenceImage : undefined;
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const referenceImages = Array.isArray(request.referenceImages)
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? request.referenceImages
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: [];
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const debugLabel = String(request.debugLabel || "").trim() || "canvas-image";
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const debugMeta =
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request.debugMeta && typeof request.debugMeta === "object"
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? request.debugMeta
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: undefined;
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const localEditComposite = normalizeLocalEditCompositeConfig(
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request.localEditComposite
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);
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const allModels = await getEnabledAiModelsWithProvider();
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const imageModels = allModels.filter((m) => m.model.supportsImageGen);
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if (!imageModels || imageModels.length === 0) {
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throw new Error("暂无可用的图片生成模型");
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}
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let selectedModel = model
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? imageModels.find((m) => m.model.modelId === model || m.model.id === model)
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: imageModels.find((m) => m.model.isDefault) || imageModels[0];
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if (!selectedModel) {
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selectedModel = imageModels[0];
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}
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let provider = selectedModel.provider;
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if (!SUPPORTED_CANVAS_PROVIDER_TYPES.has(provider.type)) {
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if (request.allowFailover === false) {
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throw new Error(
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"当前图片模型类型暂不支持画布生成，请在后台选择 Gemini/Grsai GPT Image/火山 Seedream/阿里/BFL/RUXA/FAL/OpenAI/StepFun/Runway/Krea 图片模型。"
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);
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}
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let fallbackModel = selectedModel;
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let fallbackProvider = provider;
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for (const candidate of imageModels) {
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const candidateProvider = candidate.provider;
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if (!SUPPORTED_CANVAS_PROVIDER_TYPES.has(candidateProvider.type)) continue;
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fallbackModel = candidate;
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fallbackProvider = candidateProvider;
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break;
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}
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if (!SUPPORTED_CANVAS_PROVIDER_TYPES.has(fallbackProvider.type)) {
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throw new Error(
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"当前图片模型类型暂不支持画布生成，请在后台选择 Gemini/Grsai GPT Image/火山 Seedream/阿里/BFL/RUXA/FAL/OpenAI/StepFun/Runway/Krea 图片模型。"
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);
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}
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if (fallbackModel.model.id !== selectedModel.model.id) {
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console.warn("[Canvas Generate Image] Fallback to supported image model:", {
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requestedModel: selectedModel.model.modelId || selectedModel.model.id,
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fallbackModel: fallbackModel.model.modelId || fallbackModel.model.id,
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requestedProviderType: provider.type,
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fallbackProviderType: fallbackProvider.type,
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});
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}
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selectedModel = fallbackModel;
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provider = fallbackProvider;
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}
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const normalizedReferenceImages = Array.from(
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new Set(
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[
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...referenceImages,
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...(referenceImage ? [referenceImage] : []),
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]
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.map((item) => String(item || "").trim())
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.filter((url) => /^https?:\/\//i.test(url))
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)
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).slice(0, 12);
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const { kept: reachableReferenceImages, dropped: unreachableReferenceImages } =
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await filterReachableRemoteUrls(normalizedReferenceImages, { timeoutMs: 3000 });
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const effectiveReferenceImages = normalizedReferenceImages;
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if (unreachableReferenceImages.length > 0) {
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console.warn("[Canvas Generate Image] Local reachability check failed for some reference images:", {
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count: unreachableReferenceImages.length,
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urls: unreachableReferenceImages.slice(0, 6),
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hosts: summarizeReferenceImageHosts(unreachableReferenceImages),
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});
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}
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if (normalizedReferenceImages.length > 0 && reachableReferenceImages.length === 0) {
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console.warn(
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"[Canvas Generate Image] All reference images failed local reachability check; proceeding with original URLs.",
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{
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count: normalizedReferenceImages.length,
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hosts: summarizeReferenceImageHosts(normalizedReferenceImages),
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debugLabel,
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}
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);
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}
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console.info("[Canvas Generate Image] Started:", {
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debugLabel,
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promptLength: prompt.length,
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aspectRatio,
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imageSize,
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imageCount,
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requestedReferenceImageCount: normalizedReferenceImages.length,
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reachableReferenceImageCount: reachableReferenceImages.length,
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effectiveReferenceImageCount: effectiveReferenceImages.length,
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referenceImageHosts: summarizeReferenceImageHosts(effectiveReferenceImages),
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selectedModelRef: selectedModel.model.id,
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selectedModelId: selectedModel.model.modelId,
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providerType: provider.type,
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...debugMeta,
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hasLocalEditComposite: Boolean(localEditComposite),
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|
});
|
|
|
|
const messages = [
|
|
{
|
|
role: "user" as const,
|
|
content:
|
|
effectiveReferenceImages.length > 0
|
|
? [
|
|
{ type: "text" as const, text: prompt },
|
|
...effectiveReferenceImages.map((url) => ({
|
|
type: "image_url" as const,
|
|
image_url: { url },
|
|
})),
|
|
]
|
|
: prompt,
|
|
},
|
|
];
|
|
|
|
const imageParams = {
|
|
aspectRatio,
|
|
imageSize,
|
|
imageCount: 1,
|
|
negativePrompt,
|
|
promptExtend,
|
|
};
|
|
|
|
let executeGenerateBatch:
|
|
| ((batchImageCount: number) => Promise<string>)
|
|
| null = null;
|
|
|
|
if (provider.type === "gemini" || provider.type === "grsai-gpt-image") {
|
|
const geminiProvider = createGeminiImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = geminiProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "alibaba-image") {
|
|
const alibabaProvider = createAlibabaImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = alibabaProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "bfl-image") {
|
|
const bflProvider = createBflImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = bflProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (_batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams,
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "ruxa-image") {
|
|
const ruxaProvider = createRuxaImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = ruxaProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
imageGenConfig: selectedModel.model.imageGenConfig || null,
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "fal-image") {
|
|
const falProvider = createFalImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = falProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "openai-image") {
|
|
const openAIImageProvider = createOpenAIImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = openAIImageProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "stepfun-image") {
|
|
const stepfunProvider = createStepfunImage({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = stepfunProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (_batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams,
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "midjourney") {
|
|
const midjourneyProvider = createMidjourney({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = midjourneyProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "runway") {
|
|
const runwayProvider = createRunway({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = runwayProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (_batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams,
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "krea") {
|
|
const kreaProvider = createKrea({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = kreaProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else if (provider.type === "volcano-vision") {
|
|
const volcanoVisionProvider = createVolcanoVision({
|
|
apiKey: provider.apiKey,
|
|
baseURL: provider.baseUrl,
|
|
});
|
|
const modelInstance = volcanoVisionProvider.chat(selectedModel.model.modelId);
|
|
executeGenerateBatch = async (batchImageCount) => {
|
|
const result = await modelInstance.doGenerate({
|
|
messages,
|
|
imageParams: {
|
|
...imageParams,
|
|
imageCount: batchImageCount,
|
|
},
|
|
});
|
|
return result.text || "";
|
|
};
|
|
} else {
|
|
throw new Error(
|
|
`不支持的图片生成模型类型: ${provider.type || "unknown"}，请改用 Gemini/Grsai GPT Image/火山 Seedream/阿里/BFL/RUXA/FAL/OpenAI/StepFun/Midjourney/Runway/Krea 图片模型`
|
|
);
|
|
}
|
|
|
|
const providerBatchLimit = getProviderBatchLimit(provider.type);
|
|
const targetImageCount = Math.max(1, imageCount);
|
|
const collectedImages: ParsedImage[] = [];
|
|
const maxBatches = Math.max(1, Math.ceil(targetImageCount / providerBatchLimit));
|
|
const resolvedModelKey = normalizeCanvasImageModelKey(
|
|
selectedModel.model.modelId || selectedModel.model.id || model
|
|
);
|
|
const slot = await acquireCanvasImageModelSlot({
|
|
modelKey: resolvedModelKey,
|
|
ownerKey,
|
|
debugLabel,
|
|
debugMeta: {
|
|
selectedModelId: selectedModel.model.modelId,
|
|
selectedModelRef: selectedModel.model.id,
|
|
providerType: provider.type,
|
|
...debugMeta,
|
|
},
|
|
onQueued: request.onQueueStatusChange
|
|
? async (queueStatus) => {
|
|
await request.onQueueStatusChange?.({
|
|
...queueStatus,
|
|
modelName: selectedModel.model.displayName || null,
|
|
modelRef: selectedModel.model.id || null,
|
|
});
|
|
}
|
|
: undefined,
|
|
});
|
|
|
|
if (slot.waitMs > 0 && request.onQueueStatusChange) {
|
|
const runningStatus = await getCanvasImageModelQueueStatus({
|
|
modelKey: resolvedModelKey,
|
|
});
|
|
await request.onQueueStatusChange({
|
|
...runningStatus,
|
|
status: "running",
|
|
queuePosition: 0,
|
|
estimatedWaitMs: 0,
|
|
modelName: selectedModel.model.displayName || null,
|
|
modelRef: selectedModel.model.id || null,
|
|
});
|
|
}
|
|
|
|
let caughtError: unknown = null;
|
|
|
|
try {
|
|
for (let batchIndex = 0; batchIndex < maxBatches; batchIndex += 1) {
|
|
const remaining = targetImageCount - collectedImages.length;
|
|
if (remaining <= 0) break;
|
|
const batchImageCount = Math.min(providerBatchLimit, remaining);
|
|
const batchStartedAt = Date.now();
|
|
console.info("[Canvas Generate Image] Batch started:", {
|
|
debugLabel,
|
|
batchIndex: batchIndex + 1,
|
|
totalBatches: maxBatches,
|
|
batchImageCount,
|
|
currentCollectedCount: collectedImages.length,
|
|
providerType: provider.type,
|
|
selectedModelId: selectedModel.model.modelId,
|
|
...debugMeta,
|
|
});
|
|
const imageMarkdown = await executeGenerateBatch(batchImageCount);
|
|
const parsedImages = parseImagesFromMarkdown(imageMarkdown);
|
|
console.info("[Canvas Generate Image] Batch completed:", {
|
|
debugLabel,
|
|
batchIndex: batchIndex + 1,
|
|
totalBatches: maxBatches,
|
|
batchImageCount,
|
|
parsedImageCount: parsedImages.length,
|
|
elapsedMs: Date.now() - batchStartedAt,
|
|
...debugMeta,
|
|
});
|
|
if (parsedImages.length === 0) {
|
|
if (collectedImages.length > 0) {
|
|
break;
|
|
}
|
|
throw new Error("图片生成失败");
|
|
}
|
|
collectedImages.push(...parsedImages.slice(0, batchImageCount));
|
|
}
|
|
|
|
if (collectedImages.length === 0) {
|
|
throw new Error("图片生成失败");
|
|
}
|
|
|
|
const finalImages = await applyLocalEditCompositeToImages({
|
|
composite: localEditComposite,
|
|
debugLabel,
|
|
images: collectedImages.slice(0, targetImageCount),
|
|
});
|
|
console.info("[Canvas Generate Image] Completed:", {
|
|
debugLabel,
|
|
elapsedMs: Date.now() - startedAt,
|
|
waitMs: slot.waitMs,
|
|
singleModelConcurrency: slot.maxConcurrent,
|
|
finalImageCount: finalImages.length,
|
|
providerType: provider.type,
|
|
selectedModelRef: selectedModel.model.id,
|
|
selectedModelId: selectedModel.model.modelId,
|
|
...debugMeta,
|
|
});
|
|
|
|
await recordAiModelHealthSuccess({
|
|
model: selectedModel.model,
|
|
}).catch((healthError) => {
|
|
console.warn("[Canvas Generate Image] Record model health success failed:", {
|
|
selectedModelRef: selectedModel.model.id,
|
|
selectedModelId: selectedModel.model.modelId,
|
|
error: healthError instanceof Error ? healthError.message : String(healthError),
|
|
});
|
|
});
|
|
|
|
recordCanvasImageModelExecutionDuration(
|
|
resolvedModelKey,
|
|
Date.now() - startedAt
|
|
);
|
|
|
|
return {
|
|
images: finalImages,
|
|
model: selectedModel.model.displayName,
|
|
modelRef: selectedModel.model.id,
|
|
prompt,
|
|
};
|
|
} catch (error) {
|
|
console.error("[Canvas Generate Image] Failed:", {
|
|
debugLabel,
|
|
elapsedMs: Date.now() - startedAt,
|
|
waitMs: slot.waitMs,
|
|
singleModelConcurrency: slot.maxConcurrent,
|
|
providerType: provider.type,
|
|
selectedModelRef: selectedModel.model.id,
|
|
selectedModelId: selectedModel.model.modelId,
|
|
error: error instanceof Error ? error.message : String(error),
|
|
...debugMeta,
|
|
});
|
|
caughtError = error;
|
|
} finally {
|
|
await slot.release();
|
|
}
|
|
|
|
await recordAiModelHealthFailure({
|
|
model: selectedModel.model,
|
|
provider,
|
|
error: caughtError,
|
|
context: debugLabel,
|
|
elapsedMs: Date.now() - startedAt,
|
|
metadata: {
|
|
aspectRatio,
|
|
imageSize,
|
|
imageCount,
|
|
requestedReferenceImageCount: normalizedReferenceImages.length,
|
|
...debugMeta,
|
|
},
|
|
}).catch((healthError) => {
|
|
console.warn("[Canvas Generate Image] Record model health failure failed:", {
|
|
selectedModelRef: selectedModel.model.id,
|
|
selectedModelId: selectedModel.model.modelId,
|
|
error: healthError instanceof Error ? healthError.message : String(healthError),
|
|
});
|
|
});
|
|
|
|
const failedModelRefs = Array.from(
|
|
new Set([
|
|
...(Array.isArray(request.internalFailoverTriedModelRefs)
|
|
? request.internalFailoverTriedModelRefs
|
|
: []),
|
|
selectedModel.model.id,
|
|
selectedModel.model.modelId,
|
|
].filter(Boolean))
|
|
);
|
|
const allowFailover = request.allowFailover !== false;
|
|
const fallbackModel = allowFailover && shouldFailoverAiModelError(caughtError)
|
|
? findCanvasFailoverModel({
|
|
selectedModel,
|
|
imageModels,
|
|
failedModelRefs,
|
|
})
|
|
: null;
|
|
|
|
if (fallbackModel) {
|
|
console.warn("[Canvas Generate Image] Failover to alternate image model:", {
|
|
debugLabel,
|
|
failedModelRef: selectedModel.model.id,
|
|
failedModelId: selectedModel.model.modelId,
|
|
fallbackModelRef: fallbackModel.model.id,
|
|
fallbackModelId: fallbackModel.model.modelId,
|
|
fallbackProviderType: fallbackModel.provider.type,
|
|
error: caughtError instanceof Error ? caughtError.message : String(caughtError),
|
|
...debugMeta,
|
|
});
|
|
|
|
return await generateCanvasImage({
|
|
...request,
|
|
model: fallbackModel.model.id,
|
|
internalFailoverTriedModelRefs: failedModelRefs,
|
|
debugMeta: {
|
|
...debugMeta,
|
|
failoverFromModelId: selectedModel.model.modelId,
|
|
failoverFromModelRef: selectedModel.model.id,
|
|
},
|
|
});
|
|
}
|
|
|
|
throw caughtError;
|
|
}
|