import { Buffer } from 'node:buffer'; import { existsSync, mkdirSync, readFileSync, readdirSync, writeFileSync } from 'node:fs'; import path from 'node:path'; const repoRoot = process.cwd(); const outputDir = path.join( repoRoot, 'public', 'branding', 'taonier-logo-hands-concepts', ); const defaultTimeoutMs = 420000; const concepts = [ { id: 'taonier-hands-v2-cradle', title: '掌心星核', prompt: '无文字扁平矢量 Logo,产品名“陶泥儿”。图形是上下两片抽象软掌,轻轻护住中央小星核,像把脑洞捏成作品。主流 App icon,简单、亲和、醒目、小尺寸清晰。珊瑚红、青绿、奶油白,最多 3 色。不要真实手指、播放三角、聊天气泡、笑脸、眼睛、花朵、褐色、文字、字母、3D。', }, { id: 'taonier-hands-v2-clap', title: '合掌成型', prompt: '无文字扁平矢量 Logo,产品名“陶泥儿”。用上下两片圆润软形表现合掌捏合,中间一个小圆点正在成型,表达 AI 把灵感变成小游戏作品。图形完整、现代、亲和、可记忆。珊瑚红、薄荷青、奶白,最多 3 色。不要真实手掌、聊天气泡、播放键、笑脸、眼睛、花朵、褐色、碎元素、3D、文字。', }, { id: 'taonier-hands-v2-bowl', title: '软掌托碗', prompt: '无文字扁平矢量 Logo,产品名“陶泥儿”。下方一片软掌像托碗,上方一片小软形轻压,中央浮出小星点,表达轻托脑洞、一捏成型。品牌感、主流、温暖、干净。青绿主形、珊瑚红辅助、奶白中心,最多 3 色。不要眼睛、嘴巴、聊天气泡、播放键、真实手、花朵、褐色、3D、文字。', }, { id: 'taonier-hands-v2-seal', title: '双掌印记', prompt: '无文字扁平矢量 Logo,产品名“陶泥儿”。两片抽象软掌上下扣合,形成一个圆润印记,中间留出小星形负空间,像双手捏出的创意标记。简洁、亲和、高识别、适合 App icon。珊瑚红、奶油白、青绿,最多 3 色。不要真实手指、宗教手势、医疗标识、聊天气泡、播放三角、眼睛、花朵、褐色、3D、文字。', }, { id: 'taonier-hands-v2-pop', title: '掌心开捏', prompt: '无文字扁平矢量 Logo,产品名“陶泥儿”。上下两片软掌像打开的胶囊,中央小星点从掌心弹出,表达脑洞被捏出来。年轻、亲和、醒目、主流娱乐创作 App 风格。亮珊瑚红、薄荷青、奶白,最多 3 色。不要聊天气泡、播放键、笑脸、眼睛、花朵、真实手指、褐色、碎元素、3D、文字。', }, ]; const args = new Map(); for (let index = 2; index < process.argv.length; index += 1) { const raw = process.argv[index]; if (!raw.startsWith('--')) { continue; } const next = process.argv[index + 1]; if (next && !next.startsWith('--')) { args.set(raw, next); index += 1; } else { args.set(raw, true); } } function readDotenv(fileName) { const filePath = path.join(repoRoot, fileName); if (!existsSync(filePath)) { return {}; } const values = {}; for (const line of readFileSync(filePath, 'utf8').split(/\r?\n/u)) { const trimmed = line.trim(); if (!trimmed || trimmed.startsWith('#')) { continue; } const match = /^([A-Za-z_][A-Za-z0-9_]*)=(.*)$/u.exec(trimmed); if (!match) { continue; } let value = match[2].trim(); if ( (value.startsWith('"') && value.endsWith('"')) || (value.startsWith("'") && value.endsWith("'")) ) { value = value.slice(1, -1); } values[match[1]] = value; } return values; } function resolveEnv() { const loaded = { ...readDotenv('.env.example'), ...readDotenv('.env.local'), ...readDotenv('.env.secrets.local'), ...process.env, }; return { baseUrl: String(loaded.VECTOR_ENGINE_BASE_URL || '') .trim() .replace(/\/+$/u, ''), apiKey: String(loaded.VECTOR_ENGINE_API_KEY || '').trim(), timeoutMs: Number.parseInt( String(loaded.VECTOR_ENGINE_IMAGE_REQUEST_TIMEOUT_MS || defaultTimeoutMs), 10, ), }; } function buildUrl(baseUrl) { return baseUrl.endsWith('/v1') ? `${baseUrl}/images/generations` : `${baseUrl}/v1/images/generations`; } function collectStringsByKey(value, targetKey, output) { if (Array.isArray(value)) { value.forEach((entry) => collectStringsByKey(entry, targetKey, output)); return; } if (!value || typeof value !== 'object') { return; } for (const [key, nested] of Object.entries(value)) { if (key === targetKey) { if (typeof nested === 'string' && nested.trim()) { output.push(nested.trim()); } if (Array.isArray(nested)) { nested.forEach((entry) => { if (typeof entry === 'string' && entry.trim()) { output.push(entry.trim()); } }); } } collectStringsByKey(nested, targetKey, output); } } function extractImageUrls(payload) { const urls = []; collectStringsByKey(payload, 'url', urls); collectStringsByKey(payload, 'image', urls); collectStringsByKey(payload, 'image_url', urls); return [...new Set(urls)].filter((url) => /^https?:\/\//u.test(url)); } function extractBase64Images(payload) { const values = []; collectStringsByKey(payload, 'b64_json', values); return values; } function inferExtensionFromBytes(bytes) { if (bytes.subarray(0, 8).equals(Buffer.from('\x89PNG\r\n\x1A\n', 'binary'))) { return 'png'; } if (bytes.subarray(0, 3).equals(Buffer.from([0xff, 0xd8, 0xff]))) { return 'jpg'; } if ( bytes.subarray(0, 4).toString('ascii') === 'RIFF' && bytes.subarray(8, 12).toString('ascii') === 'WEBP' ) { return 'webp'; } return 'png'; } async function fetchJson(url, options, timeoutMs) { const abortController = new AbortController(); const timer = setTimeout(() => abortController.abort(), timeoutMs); try { const response = await fetch(url, { ...options, signal: abortController.signal, }); const text = await response.text(); if (!response.ok) { throw new Error(`VectorEngine ${response.status}: ${text.slice(0, 600)}`); } return JSON.parse(text); } catch (error) { if (error?.name === 'AbortError') { throw new Error(`VectorEngine request timed out after ${timeoutMs}ms`); } throw error; } finally { clearTimeout(timer); } } async function downloadUrl(url, timeoutMs) { const abortController = new AbortController(); const timer = setTimeout(() => abortController.abort(), timeoutMs); try { const response = await fetch(url, { signal: abortController.signal }); if (!response.ok) { throw new Error(`download ${response.status}`); } return Buffer.from(await response.arrayBuffer()); } catch (error) { if (error?.name === 'AbortError') { throw new Error(`Generated image download timed out after ${timeoutMs}ms`); } throw error; } finally { clearTimeout(timer); } } async function generateConcept(env, concept) { const requestBody = { model: 'gpt-image-2-all', prompt: concept.prompt, n: 1, size: '1024x1024', }; const payload = await fetchJson( buildUrl(env.baseUrl), { method: 'POST', headers: { Authorization: `Bearer ${env.apiKey}`, Accept: 'application/json', 'Content-Type': 'application/json', }, body: JSON.stringify(requestBody), }, env.timeoutMs, ); const urls = extractImageUrls(payload); const b64Images = extractBase64Images(payload); let bytes; if (urls[0]) { bytes = await downloadUrl(urls[0], env.timeoutMs); } else if (b64Images[0]) { bytes = Buffer.from(b64Images[0], 'base64'); } else { throw new Error(`VectorEngine returned no image for ${concept.id}`); } mkdirSync(outputDir, { recursive: true }); const extension = inferExtensionFromBytes(bytes); const outputPath = path.join(outputDir, `${concept.id}.${extension}`); writeFileSync(outputPath, bytes); return outputPath; } const dryRun = args.has('--dry-run') || !args.has('--live'); const onlyIds = String(args.get('--only') || '') .split(',') .map((value) => value.trim()) .filter(Boolean); const selected = concepts.filter( (concept) => !onlyIds.length || onlyIds.includes(concept.id), ); if (dryRun) { console.log( JSON.stringify( { mode: 'dry-run', outputDir, count: selected.length, requests: selected.map((concept) => ({ id: concept.id, title: concept.title, body: { model: 'gpt-image-2-all', prompt: concept.prompt, n: 1, size: '1024x1024', }, })), }, null, 2, ), ); process.exit(0); } const env = resolveEnv(); if (!env.baseUrl || !env.apiKey) { console.error( JSON.stringify({ ok: false, error: 'Missing VECTOR_ENGINE_BASE_URL or VECTOR_ENGINE_API_KEY', hasBaseUrl: Boolean(env.baseUrl), hasApiKey: Boolean(env.apiKey), }), ); process.exit(1); } const generated = []; for (const concept of selected) { console.log(`Generating ${concept.id}...`); generated.push(await generateConcept(env, concept)); } console.log( JSON.stringify( { ok: true, count: generated.length, files: generated, verifiedFiles: readdirSync(outputDir).sort(), }, null, 2, ), );