chore: release v1.11.0

skills/baoyu-image-gen/scripts/main.ts

@@ -0,0 +1,576 @@
+import fs from "node:fs";
+import path from "node:path";
+import process from "node:process";
+import { homedir } from "node:os";
+import { mkdir, readFile, writeFile } from "node:fs/promises";
+
+type Provider = "google" | "openai";
+type Quality = "normal" | "2k";
+
+type CliArgs = {
+  prompt: string | null;
+  promptFiles: string[];
+  imagePath: string | null;
+  provider: Provider | null;
+  model: string | null;
+  aspectRatio: string | null;
+  size: string | null;
+  quality: Quality;
+  referenceImages: string[];
+  n: number;
+  json: boolean;
+  help: boolean;
+};
+
+const GOOGLE_MULTIMODAL_MODELS = [
+  "gemini-3-pro-image-preview",
+  "gemini-2.0-flash-exp-image-generation",
+  "gemini-2.5-flash-preview-native-audio-dialog",
+];
+
+const GOOGLE_IMAGEN_MODELS = ["imagen-3.0-generate-002", "imagen-3.0-generate-001"];
+
+const OPENAI_IMAGE_MODELS = ["gpt-image-1.5", "gpt-image-1", "dall-e-3", "dall-e-2"];
+
+function printUsage(): void {
+  console.log(`Usage:
+  npx -y bun scripts/main.ts --prompt "A cat" --image cat.png
+  npx -y bun scripts/main.ts --prompt "A landscape" --image landscape.png --ar 16:9
+  npx -y bun scripts/main.ts --promptfiles system.md content.md --image out.png
+
+Options:
+  -p, --prompt <text>       Prompt text
+  --promptfiles <files...>  Read prompt from files (concatenated)
+  --image <path>            Output image path (required)
+  --provider google|openai  Force provider (auto-detect by default)
+  -m, --model <id>          Model ID
+  --ar <ratio>              Aspect ratio (e.g., 16:9, 1:1, 4:3)
+  --size <WxH>              Size (e.g., 1024x1024)
+  --quality normal|2k       Quality preset (default: normal)
+  --ref <files...>          Reference images (Google multimodal only)
+  --n <count>               Number of images (default: 1)
+  --json                    JSON output
+  -h, --help                Show help
+
+Environment variables:
+  OPENAI_API_KEY            OpenAI API key
+  GOOGLE_API_KEY            Google API key
+  OPENAI_IMAGE_MODEL        Default OpenAI model (gpt-image-1.5)
+  GOOGLE_IMAGE_MODEL        Default Google model (gemini-3-pro-image-preview)
+  OPENAI_BASE_URL           Custom OpenAI endpoint
+  GOOGLE_BASE_URL           Custom Google endpoint
+
+Env file load order: CLI args > process.env > <cwd>/.baoyu-skills/.env > ~/.baoyu-skills/.env`);
+}
+
+function parseArgs(argv: string[]): CliArgs {
+  const out: CliArgs = {
+    prompt: null,
+    promptFiles: [],
+    imagePath: null,
+    provider: null,
+    model: null,
+    aspectRatio: null,
+    size: null,
+    quality: "normal",
+    referenceImages: [],
+    n: 1,
+    json: false,
+    help: false,
+  };
+
+  const positional: string[] = [];
+
+  const takeMany = (i: number): { items: string[]; next: number } => {
+    const items: string[] = [];
+    let j = i + 1;
+    while (j < argv.length) {
+      const v = argv[j]!;
+      if (v.startsWith("-")) break;
+      items.push(v);
+      j++;
+    }
+    return { items, next: j - 1 };
+  };
+
+  for (let i = 0; i < argv.length; i++) {
+    const a = argv[i]!;
+
+    if (a === "--help" || a === "-h") {
+      out.help = true;
+      continue;
+    }
+
+    if (a === "--json") {
+      out.json = true;
+      continue;
+    }
+
+    if (a === "--prompt" || a === "-p") {
+      const v = argv[++i];
+      if (!v) throw new Error(`Missing value for ${a}`);
+      out.prompt = v;
+      continue;
+    }
+
+    if (a === "--promptfiles") {
+      const { items, next } = takeMany(i);
+      if (items.length === 0) throw new Error("Missing files for --promptfiles");
+      out.promptFiles.push(...items);
+      i = next;
+      continue;
+    }
+
+    if (a === "--image") {
+      const v = argv[++i];
+      if (!v) throw new Error("Missing value for --image");
+      out.imagePath = v;
+      continue;
+    }
+
+    if (a === "--provider") {
+      const v = argv[++i];
+      if (v !== "google" && v !== "openai") throw new Error(`Invalid provider: ${v}`);
+      out.provider = v;
+      continue;
+    }
+
+    if (a === "--model" || a === "-m") {
+      const v = argv[++i];
+      if (!v) throw new Error(`Missing value for ${a}`);
+      out.model = v;
+      continue;
+    }
+
+    if (a === "--ar") {
+      const v = argv[++i];
+      if (!v) throw new Error("Missing value for --ar");
+      out.aspectRatio = v;
+      continue;
+    }
+
+    if (a === "--size") {
+      const v = argv[++i];
+      if (!v) throw new Error("Missing value for --size");
+      out.size = v;
+      continue;
+    }
+
+    if (a === "--quality") {
+      const v = argv[++i];
+      if (v !== "normal" && v !== "2k") throw new Error(`Invalid quality: ${v}`);
+      out.quality = v;
+      continue;
+    }
+
+    if (a === "--ref" || a === "--reference") {
+      const { items, next } = takeMany(i);
+      if (items.length === 0) throw new Error(`Missing files for ${a}`);
+      out.referenceImages.push(...items);
+      i = next;
+      continue;
+    }
+
+    if (a === "--n") {
+      const v = argv[++i];
+      if (!v) throw new Error("Missing value for --n");
+      out.n = parseInt(v, 10);
+      if (isNaN(out.n) || out.n < 1) throw new Error(`Invalid count: ${v}`);
+      continue;
+    }
+
+    if (a.startsWith("-")) {
+      throw new Error(`Unknown option: ${a}`);
+    }
+
+    positional.push(a);
+  }
+
+  if (!out.prompt && out.promptFiles.length === 0 && positional.length > 0) {
+    out.prompt = positional.join(" ");
+  }
+
+  return out;
+}
+
+async function loadEnvFile(p: string): Promise<Record<string, string>> {
+  try {
+    const content = await readFile(p, "utf8");
+    const env: Record<string, string> = {};
+    for (const line of content.split("\n")) {
+      const trimmed = line.trim();
+      if (!trimmed || trimmed.startsWith("#")) continue;
+      const idx = trimmed.indexOf("=");
+      if (idx === -1) continue;
+      const key = trimmed.slice(0, idx).trim();
+      let val = trimmed.slice(idx + 1).trim();
+      if ((val.startsWith('"') && val.endsWith('"')) || (val.startsWith("'") && val.endsWith("'"))) {
+        val = val.slice(1, -1);
+      }
+      env[key] = val;
+    }
+    return env;
+  } catch {
+    return {};
+  }
+}
+
+async function loadEnv(): Promise<void> {
+  const home = homedir();
+  const cwd = process.cwd();
+
+  const homeEnv = await loadEnvFile(path.join(home, ".baoyu-skills", ".env"));
+  const cwdEnv = await loadEnvFile(path.join(cwd, ".baoyu-skills", ".env"));
+
+  for (const [k, v] of Object.entries(homeEnv)) {
+    if (!process.env[k]) process.env[k] = v;
+  }
+  for (const [k, v] of Object.entries(cwdEnv)) {
+    if (!process.env[k]) process.env[k] = v;
+  }
+}
+
+async function readPromptFromFiles(files: string[]): Promise<string> {
+  const parts: string[] = [];
+  for (const f of files) {
+    parts.push(await readFile(f, "utf8"));
+  }
+  return parts.join("\n\n");
+}
+
+async function readPromptFromStdin(): Promise<string | null> {
+  if (process.stdin.isTTY) return null;
+  try {
+    const t = await Bun.stdin.text();
+    const v = t.trim();
+    return v.length > 0 ? v : null;
+  } catch {
+    return null;
+  }
+}
+
+function normalizeOutputImagePath(p: string): string {
+  const full = path.resolve(p);
+  const ext = path.extname(full);
+  if (ext) return full;
+  return `${full}.png`;
+}
+
+function detectProvider(args: CliArgs): Provider {
+  if (args.provider) return args.provider;
+
+  const hasGoogle = !!process.env.GOOGLE_API_KEY;
+  const hasOpenai = !!process.env.OPENAI_API_KEY;
+
+  if (hasGoogle && !hasOpenai) return "google";
+  if (hasOpenai && !hasGoogle) return "openai";
+  if (hasGoogle && hasOpenai) return "google";
+
+  throw new Error(
+    "No API key found. Set GOOGLE_API_KEY or OPENAI_API_KEY.\n" +
+      "Create ~/.baoyu-skills/.env or <cwd>/.baoyu-skills/.env with your keys."
+  );
+}
+
+function getDefaultModel(provider: Provider): string {
+  if (provider === "google") {
+    return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image-preview";
+  }
+  return process.env.OPENAI_IMAGE_MODEL || "gpt-image-1.5";
+}
+
+function isGoogleMultimodal(model: string): boolean {
+  return GOOGLE_MULTIMODAL_MODELS.some((m) => model.includes(m));
+}
+
+function isGoogleImagen(model: string): boolean {
+  return GOOGLE_IMAGEN_MODELS.some((m) => model.includes(m));
+}
+
+function buildPromptWithAspect(prompt: string, ar: string | null, quality: Quality): string {
+  let result = prompt;
+  if (ar) {
+    result += ` Aspect ratio: ${ar}.`;
+  }
+  if (quality === "2k") {
+    result += " High resolution 2048px.";
+  }
+  return result;
+}
+
+function parseAspectRatio(ar: string): { width: number; height: number } | null {
+  const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
+  if (!match) return null;
+  const w = parseFloat(match[1]!);
+  const h = parseFloat(match[2]!);
+  if (w <= 0 || h <= 0) return null;
+  return { width: w, height: h };
+}
+
+function getOpenAISize(ar: string | null, quality: Quality): string {
+  const base = quality === "2k" ? 2048 : 1024;
+
+  if (!ar) return `${base}x${base}`;
+
+  const parsed = parseAspectRatio(ar);
+  if (!parsed) return `${base}x${base}`;
+
+  const ratio = parsed.width / parsed.height;
+
+  if (Math.abs(ratio - 1) < 0.1) return `${base}x${base}`;
+  if (ratio > 1.5) return quality === "2k" ? "2048x1024" : "1792x1024";
+  if (ratio < 0.67) return quality === "2k" ? "1024x2048" : "1024x1792";
+  return `${base}x${base}`;
+}
+
+async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: string }> {
+  const buf = await readFile(p);
+  const ext = path.extname(p).toLowerCase();
+  let mimeType = "image/png";
+  if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
+  else if (ext === ".gif") mimeType = "image/gif";
+  else if (ext === ".webp") mimeType = "image/webp";
+  return { data: buf.toString("base64"), mimeType };
+}
+
+async function generateWithGoogleMultimodal(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const { generateText } = await import("ai");
+  const { createGoogleGenerativeAI } = await import("@ai-sdk/google");
+
+  const google = createGoogleGenerativeAI({
+    apiKey: process.env.GOOGLE_API_KEY,
+    baseURL: process.env.GOOGLE_BASE_URL,
+  });
+
+  const fullPrompt = buildPromptWithAspect(prompt, args.aspectRatio, args.quality);
+
+  const messages: any[] = [];
+  const content: any[] = [];
+
+  for (const refPath of args.referenceImages) {
+    const { data, mimeType } = await readImageAsBase64(refPath);
+    content.push({ type: "image", image: data, mimeType });
+  }
+  content.push({ type: "text", text: fullPrompt });
+
+  messages.push({ role: "user", content });
+
+  const result = await generateText({
+    model: google(model, { useSearchGrounding: false }),
+    messages,
+    providerOptions: {
+      google: {
+        responseModalities: ["TEXT", "IMAGE"],
+      },
+    },
+  });
+
+  const files = (result as any).files;
+  if (!files || files.length === 0) {
+    const expRes = (result as any).response?.body?.candidates?.[0]?.content?.parts;
+    if (expRes) {
+      for (const part of expRes) {
+        if (part.inlineData?.data) {
+          return Uint8Array.from(Buffer.from(part.inlineData.data, "base64"));
+        }
+      }
+    }
+    throw new Error("No image in response");
+  }
+
+  const img = files[0];
+  if (img.uint8Array) return img.uint8Array;
+  if (img.base64) return Uint8Array.from(Buffer.from(img.base64, "base64"));
+
+  throw new Error("Cannot extract image data");
+}
+
+async function generateWithGoogleImagen(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const { experimental_generateImage: generateImage } = await import("ai");
+  const { createGoogleGenerativeAI } = await import("@ai-sdk/google");
+
+  const google = createGoogleGenerativeAI({
+    apiKey: process.env.GOOGLE_API_KEY,
+    baseURL: process.env.GOOGLE_BASE_URL,
+  });
+
+  const fullPrompt = buildPromptWithAspect(prompt, args.aspectRatio, args.quality);
+
+  const result = await generateImage({
+    model: google.image(model),
+    prompt: fullPrompt,
+    n: args.n,
+    aspectRatio: args.aspectRatio || undefined,
+  });
+
+  const img = result.images[0];
+  if (!img) throw new Error("No image in response");
+
+  if (img.uint8Array) return img.uint8Array;
+  if (img.base64) return Uint8Array.from(Buffer.from(img.base64, "base64"));
+
+  throw new Error("Cannot extract image data");
+}
+
+async function generateWithOpenAI(
+  prompt: string,
+  model: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  const baseURL = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
+  const apiKey = process.env.OPENAI_API_KEY;
+
+  if (!apiKey) throw new Error("OPENAI_API_KEY is required");
+
+  const size = args.size || getOpenAISize(args.aspectRatio, args.quality);
+
+  const body: Record<string, any> = {
+    model,
+    prompt,
+    size,
+  };
+
+  if (model.includes("dall-e-3")) {
+    body.quality = args.quality === "2k" ? "hd" : "standard";
+  }
+
+  const res = await fetch(`${baseURL}/images/generations`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      Authorization: `Bearer ${apiKey}`,
+    },
+    body: JSON.stringify(body),
+  });
+
+  if (!res.ok) {
+    const err = await res.text();
+    throw new Error(`OpenAI API error: ${err}`);
+  }
+
+  const result = (await res.json()) as { data: Array<{ url?: string; b64_json?: string }> };
+  const img = result.data[0];
+
+  if (img?.b64_json) {
+    return Uint8Array.from(Buffer.from(img.b64_json, "base64"));
+  }
+
+  if (img?.url) {
+    const imgRes = await fetch(img.url);
+    if (!imgRes.ok) throw new Error("Failed to download image");
+    const buf = await imgRes.arrayBuffer();
+    return new Uint8Array(buf);
+  }
+
+  throw new Error("No image in response");
+}
+
+async function generate(
+  provider: Provider,
+  model: string,
+  prompt: string,
+  args: CliArgs
+): Promise<Uint8Array> {
+  if (provider === "google") {
+    if (isGoogleMultimodal(model)) {
+      return generateWithGoogleMultimodal(prompt, model, args);
+    }
+    if (isGoogleImagen(model)) {
+      if (args.referenceImages.length > 0) {
+        console.error("Warning: Reference images not supported with Imagen models, ignoring.");
+      }
+      return generateWithGoogleImagen(prompt, model, args);
+    }
+    return generateWithGoogleMultimodal(prompt, model, args);
+  }
+
+  if (args.referenceImages.length > 0) {
+    console.error("Warning: Reference images not supported with OpenAI, ignoring.");
+  }
+  return generateWithOpenAI(prompt, model, args);
+}
+
+async function main(): Promise<void> {
+  const args = parseArgs(process.argv.slice(2));
+
+  if (args.help) {
+    printUsage();
+    return;
+  }
+
+  await loadEnv();
+
+  let prompt: string | null = args.prompt;
+  if (!prompt && args.promptFiles.length > 0) prompt = await readPromptFromFiles(args.promptFiles);
+  if (!prompt) prompt = await readPromptFromStdin();
+
+  if (!prompt) {
+    console.error("Error: Prompt is required");
+    printUsage();
+    process.exitCode = 1;
+    return;
+  }
+
+  if (!args.imagePath) {
+    console.error("Error: --image is required");
+    printUsage();
+    process.exitCode = 1;
+    return;
+  }
+
+  const provider = detectProvider(args);
+  const model = args.model || getDefaultModel(provider);
+  const outputPath = normalizeOutputImagePath(args.imagePath);
+
+  let imageData: Uint8Array;
+  let retried = false;
+
+  while (true) {
+    try {
+      imageData = await generate(provider, model, prompt, args);
+      break;
+    } catch (e) {
+      if (!retried) {
+        retried = true;
+        console.error("Generation failed, retrying...");
+        continue;
+      }
+      throw e;
+    }
+  }
+
+  const dir = path.dirname(outputPath);
+  await mkdir(dir, { recursive: true });
+  await writeFile(outputPath, imageData);
+
+  if (args.json) {
+    console.log(
+      JSON.stringify(
+        {
+          savedImage: outputPath,
+          provider,
+          model,
+          prompt: prompt.slice(0, 200),
+        },
+        null,
+        2
+      )
+    );
+  } else {
+    console.log(outputPath);
+  }
+}
+
+main().catch((e) => {
+  const msg = e instanceof Error ? e.message : String(e);
+  console.error(msg);
+  process.exit(1);
+});