mirror of
https://github.com/JimLiu/baoyu-skills.git
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chore: release v1.19.0
This commit is contained in:
Jim Liu 宝玉
@@ -0,0 +1,149 @@
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import path from "node:path";
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import { readFile } from "node:fs/promises";
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import type { CliArgs } from "../types";
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const GOOGLE_MULTIMODAL_MODELS = ["gemini-3-pro-image-preview"];
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const GOOGLE_IMAGEN_MODELS = ["imagen-3.0-generate-002", "imagen-3.0-generate-001"];
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export function getDefaultModel(): string {
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return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image-preview";
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}
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function isGoogleMultimodal(model: string): boolean {
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return GOOGLE_MULTIMODAL_MODELS.some((m) => model.includes(m));
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}
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function isGoogleImagen(model: string): boolean {
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return GOOGLE_IMAGEN_MODELS.some((m) => model.includes(m));
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}
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function getGoogleApiKey(): string | null {
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return process.env.GOOGLE_API_KEY || process.env.GEMINI_API_KEY || null;
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}
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function getGoogleImageSize(args: CliArgs): "1K" | "2K" | "4K" {
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if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";
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return args.quality === "2k" ? "2K" : "1K";
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}
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function buildPromptWithAspect(prompt: string, ar: string | null, quality: CliArgs["quality"]): string {
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let result = prompt;
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if (ar) {
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result += ` Aspect ratio: ${ar}.`;
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}
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if (quality === "2k") {
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result += " High resolution 2048px.";
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}
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return result;
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}
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async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: string }> {
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const buf = await readFile(p);
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const ext = path.extname(p).toLowerCase();
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let mimeType = "image/png";
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if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
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else if (ext === ".gif") mimeType = "image/gif";
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else if (ext === ".webp") mimeType = "image/webp";
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return { data: buf.toString("base64"), mimeType };
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}
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async function generateWithGemini(
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prompt: string,
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model: string,
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args: CliArgs
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): Promise<Uint8Array> {
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const { GoogleGenAI } = await import("@google/genai");
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const apiKey = getGoogleApiKey();
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if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
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const ai = new GoogleGenAI({
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apiKey,
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httpOptions: {
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baseUrl: process.env.GOOGLE_BASE_URL || undefined,
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},
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});
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const input: Array<{ type: "text" | "image"; text?: string; data?: string; mime_type?: string }> = [];
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for (const refPath of args.referenceImages) {
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const { data, mimeType } = await readImageAsBase64(refPath);
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input.push({ type: "image", data, mime_type: mimeType });
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}
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input.push({ type: "text", text: prompt });
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const imageConfig: { image_size: "1K" | "2K" | "4K"; aspect_ratio?: string } = {
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image_size: getGoogleImageSize(args),
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};
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if (args.aspectRatio) {
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imageConfig.aspect_ratio = args.aspectRatio;
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}
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console.log("Generating image with Gemini...", imageConfig);
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const interaction = await ai.interactions.create({
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model,
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input,
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response_modalities: ["image"],
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generation_config: {
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image_config: imageConfig,
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},
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});
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console.log("Generation completed.");
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for (const output of interaction.outputs || []) {
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if (output.type === "image" && output.data) {
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return Uint8Array.from(Buffer.from(output.data, "base64"));
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}
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}
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throw new Error("No image in response");
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}
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async function generateWithImagen(
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prompt: string,
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model: string,
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args: CliArgs
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): Promise<Uint8Array> {
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const { experimental_generateImage: generateImage } = await import("ai");
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const { createGoogleGenerativeAI } = await import("@ai-sdk/google");
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const google = createGoogleGenerativeAI({
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apiKey: getGoogleApiKey() || undefined,
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baseURL: process.env.GOOGLE_BASE_URL,
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});
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const fullPrompt = buildPromptWithAspect(prompt, args.aspectRatio, args.quality);
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const result = await generateImage({
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model: google.image(model),
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prompt: fullPrompt,
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n: args.n,
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aspectRatio: args.aspectRatio || undefined,
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});
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const img = result.images[0];
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if (!img) throw new Error("No image in response");
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if (img.uint8Array) return img.uint8Array;
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if (img.base64) return Uint8Array.from(Buffer.from(img.base64, "base64"));
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throw new Error("Cannot extract image data");
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}
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export async function generateImage(
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prompt: string,
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model: string,
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args: CliArgs
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): Promise<Uint8Array> {
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if (isGoogleImagen(model)) {
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if (args.referenceImages.length > 0) {
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console.error("Warning: Reference images not supported with Imagen models, ignoring.");
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}
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return generateWithImagen(prompt, model, args);
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}
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if (!isGoogleMultimodal(model) && args.referenceImages.length > 0) {
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console.error("Warning: Reference images are only supported with Gemini multimodal models.");
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}
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return generateWithGemini(prompt, model, args);
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}
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@@ -0,0 +1,114 @@
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import type { CliArgs } from "../types";
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export function getDefaultModel(): string {
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return process.env.OPENAI_IMAGE_MODEL || "gpt-image-1.5";
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}
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function parseAspectRatio(ar: string): { width: number; height: number } | null {
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const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
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if (!match) return null;
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const w = parseFloat(match[1]!);
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const h = parseFloat(match[2]!);
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if (w <= 0 || h <= 0) return null;
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return { width: w, height: h };
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}
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type SizeMapping = {
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square: string;
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landscape: string;
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portrait: string;
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};
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function getOpenAISize(
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model: string,
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ar: string | null,
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quality: CliArgs["quality"]
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): string {
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const isDalle3 = model.includes("dall-e-3");
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const isDalle2 = model.includes("dall-e-2");
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if (isDalle2) {
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return "1024x1024";
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}
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const sizes: SizeMapping = isDalle3
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? {
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square: "1024x1024",
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landscape: "1792x1024",
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portrait: "1024x1792",
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}
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: {
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square: "1024x1024",
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landscape: "1536x1024",
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portrait: "1024x1536",
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};
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if (!ar) return sizes.square;
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const parsed = parseAspectRatio(ar);
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if (!parsed) return sizes.square;
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const ratio = parsed.width / parsed.height;
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if (Math.abs(ratio - 1) < 0.1) return sizes.square;
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if (ratio > 1.5) return sizes.landscape;
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if (ratio < 0.67) return sizes.portrait;
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return sizes.square;
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}
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export async function generateImage(
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prompt: string,
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model: string,
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args: CliArgs
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): Promise<Uint8Array> {
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const baseURL = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) throw new Error("OPENAI_API_KEY is required");
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if (args.referenceImages.length > 0) {
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console.error("Warning: Reference images not supported with OpenAI, ignoring.");
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}
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const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
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const body: Record<string, any> = {
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model,
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prompt,
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size,
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};
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if (model.includes("dall-e-3")) {
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body.quality = args.quality === "2k" ? "hd" : "standard";
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}
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const res = await fetch(`${baseURL}/images/generations`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${apiKey}`,
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},
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body: JSON.stringify(body),
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});
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if (!res.ok) {
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const err = await res.text();
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throw new Error(`OpenAI API error: ${err}`);
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}
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const result = (await res.json()) as { data: Array<{ url?: string; b64_json?: string }> };
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const img = result.data[0];
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if (img?.b64_json) {
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return Uint8Array.from(Buffer.from(img.b64_json, "base64"));
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}
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if (img?.url) {
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const imgRes = await fetch(img.url);
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if (!imgRes.ok) throw new Error("Failed to download image");
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const buf = await imgRes.arrayBuffer();
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return new Uint8Array(buf);
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}
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throw new Error("No image in response");
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}
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