yangxiangyuan
202 lines
9.3 KiB
JavaScript
202 lines
9.3 KiB
JavaScript
/* ============================================================
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* tool_gen_travel_citytour_photos.js —— 用百炼生成「游山玩水」样例的 AI 示意图
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*
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* 背景：Trae 自带的 text_to_image 接口（coresg）对任意 prompt 只返回同一张固定图，
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* 无法逐节点出图；改用百炼（DashScope）文生图。
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* 实测可用两条通道：
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* A) multimodal-generation + qwen-image 系列 —— 直接返回图片 URL，无需轮询
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* B) text2image/image-synthesis + wanx/wan2.x —— 异步任务，需轮询
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* 脚本按优先级依次尝试，谁通用谁，避免单点故障。
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*
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* 产物：public/tools/thought_lab/labs/travel_citytour/assets/photos/*.jpg
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* 运行：node dev_test_scripts/tools/tool_gen_travel_citytour_photos.js
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* 说明：密钥来自本机私有凭据文件，不写入仓库；本脚本只读密钥、只写图片。
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* ============================================================ */
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const fs = require('fs')
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const path = require('path')
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const crypto = require('crypto')
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const envPath = path.join(process.env.USERPROFILE || 'C:\\Users\\Administrator', 'Toolbox_local_creds.env.local')
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if (fs.existsSync(envPath)) {
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for (const line of fs.readFileSync(envPath, 'utf8').split(/\r?\n/)) {
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if (!line || /^\s*#/.test(line)) continue
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const i = line.indexOf('=')
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if (i < 0) continue
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const k = line.slice(0, i).trim()
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const v = line.slice(i + 1).trim().replace(/^["']|["']$/g, '')
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if (k && !(k in process.env)) process.env[k] = v
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}
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}
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const KEY = process.env.DASHSCOPE_API_KEY || ''
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const BASE = 'https://dashscope.aliyuncs.com'
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const OUT_DIR = path.join(
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process.cwd(), 'public', 'tools', 'thought_lab', 'labs', 'travel_citytour', 'assets', 'photos'
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)
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fs.mkdirSync(OUT_DIR, { recursive: true })
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// 优先级：先试直接返回的 qwen-image 系列，再退到异步的 wanx 系列
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const MODEL_CHAIN = [
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{ id: 'qwen-image-3.0-pro', kind: 'multimodal' },
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{ id: 'qwen-image-3.0', kind: 'multimodal' },
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{ id: 'qwen-image-max', kind: 'multimodal' },
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{ id: 'qwen-image-plus', kind: 'multimodal' },
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{ id: 'wan2.2-t2i-plus', kind: 'synthesis', size: '1280*720' },
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{ id: 'wanx2.1-t2i-plus', kind: 'synthesis', size: '1280*720' }
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]
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// 尺寸候选：页面卡片里只显示 150px 高，用不到 1664×928，
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// 按「够用且省体积」的顺序试；qwen-image 对尺寸有白名单，故逐个降级尝试。
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const MULTIMODAL_SIZES = [
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process.env.TC_IMG_SIZE || '',
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'1024*576',
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'1280*720',
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'1328*1328'
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].filter(Boolean)
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const STYLE = 'realistic travel photograph, natural daylight, no text, no watermark, no logo'
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const JOBS = [
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{ file: 'rudong_cover.jpg', prompt: 'coastal highway along the Yellow Sea tidal flat in Jiangsu China, wind turbines in the distance, autumn afternoon, wide landscape' },
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{ file: 'rudong_hike1_embankment.jpg', prompt: 'long concrete sea embankment levee path above a mudflat, fishing nets and small boats below, overcast coastal sky, empty path' },
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{ file: 'rudong_bengcha.jpg', prompt: 'old Chinese town street with grey brick houses, stone slab lane, red lanterns, quiet morning light, low-rise traditional buildings' },
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{ file: 'rudong_lunch_local.jpg', prompt: 'small local seafood restaurant table in a Jiangsu coastal town, clam soup, steamed fish, plain ceramic bowls, simple interior' },
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{ file: 'rudong_bathhouse.jpg', prompt: 'outdoor hot spring pool in a resort garden at dusk, steam rising from the water, wooden deck, greenery around' },
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{ file: 'rudong_carsleep_park.jpg', prompt: 'quiet parking lot beside a city park in a small Chinese town at night, warm street lamps, one car parked alone, trees' },
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{ file: 'rudong_hike2_forest.jpg', prompt: 'coastal shelterbelt forest dirt trail, tall trees on both sides, dappled sunlight through leaves, green tunnel path' }
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]
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const sleep = (ms) => new Promise((r) => setTimeout(r, ms))
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async function genMultimodal(model, prompt, size) {
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const r = await fetch(BASE + '/api/v1/services/aigc/multimodal-generation/generation', {
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method: 'POST',
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headers: { Authorization: 'Bearer ' + KEY, 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model,
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input: { messages: [{ role: 'user', content: [{ text: prompt + ', ' + STYLE }] }] },
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parameters: { size, n: 1, prompt_extend: true }
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}),
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signal: AbortSignal.timeout(120000)
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})
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const t = await r.text()
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let j = null
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try { j = JSON.parse(t) } catch {}
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if (r.status !== 200) throw new Error('HTTP ' + r.status + ' ' + (j && j.message ? j.message : t.slice(0, 140)))
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const content = j && j.output && j.output.choices && j.output.choices[0] &&
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j.output.choices[0].message && j.output.choices[0].message.content
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const url = Array.isArray(content) ? (content.find((c) => c.image) || {}).image : null
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if (!url) throw new Error('未返回图片：' + t.slice(0, 200))
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return url
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}
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async function genSynthesis(model, prompt, size) {
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const r = await fetch(BASE + '/api/v1/services/aigc/text2image/image-synthesis', {
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method: 'POST',
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headers: {
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Authorization: 'Bearer ' + KEY,
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'Content-Type': 'application/json',
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'X-DashScope-Async': 'enable'
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},
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body: JSON.stringify({
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model,
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input: { prompt: prompt + ', ' + STYLE },
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parameters: { size, n: 1 }
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}),
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signal: AbortSignal.timeout(60000)
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})
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const t = await r.text()
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let j = null
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try { j = JSON.parse(t) } catch {}
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if (r.status !== 200 || !j || !j.output || !j.output.task_id) {
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throw new Error('HTTP ' + r.status + ' ' + (j && j.message ? j.message : t.slice(0, 140)))
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}
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const taskId = j.output.task_id
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for (let i = 0; i < 40; i++) {
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await sleep(3000)
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const pr = await fetch(BASE + '/api/v1/tasks/' + taskId, { headers: { Authorization: 'Bearer ' + KEY } })
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const pj = JSON.parse(await pr.text())
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const st = pj && pj.output && pj.output.task_status
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if (st === 'SUCCEEDED') {
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const url = ((pj.output.results || [])[0] || {}).url
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if (!url) throw new Error('任务成功但无图片 URL')
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return url
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}
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if (st === 'FAILED') throw new Error('任务失败：' + (pj.output.message || pj.message || ''))
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}
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throw new Error('任务超时')
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}
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async function download(url, file) {
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const r = await fetch(url, { signal: AbortSignal.timeout(120000) })
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if (!r.ok) throw new Error('下载失败 HTTP ' + r.status)
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const buf = Buffer.from(await r.arrayBuffer())
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if (buf.length < 10000) throw new Error('文件过小，疑似异常：' + buf.length)
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const full = path.join(OUT_DIR, file)
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fs.writeFileSync(full, buf)
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return { size: buf.length, md5: crypto.createHash('md5').update(buf).digest('hex') }
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}
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async function main() {
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if (!KEY) { console.log('缺少 DASHSCOPE_API_KEY'); process.exit(1) }
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// 先做一次连通性预检，确定这次用哪条通道、哪个尺寸
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let chosen = null
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for (const m of MODEL_CHAIN) {
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if (chosen) break
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if (m.kind === 'multimodal') {
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for (const size of MULTIMODAL_SIZES) {
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process.stdout.write('[preflight] ' + m.id + ' size=' + size + ' … ')
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try {
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await genMultimodal(m.id, 'a simple red apple on a wooden table', size)
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console.log('可用（直接返回）')
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chosen = { id: m.id, kind: 'multimodal', size }
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break
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} catch (e) {
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console.log('不可用：' + e.message)
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}
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}
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} else {
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process.stdout.write('[preflight] ' + m.id + ' size=' + m.size + ' … ')
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try {
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// 异步模型预检只验证能否建任务，避免多花钱
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const r = await fetch(BASE + '/api/v1/services/aigc/text2image/image-synthesis', {
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method: 'POST',
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headers: { Authorization: 'Bearer ' + KEY, 'Content-Type': 'application/json', 'X-DashScope-Async': 'enable' },
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body: JSON.stringify({ model: m.id, input: { prompt: 'test' }, parameters: { size: m.size, n: 1 } })
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})
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const j = JSON.parse(await r.text())
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if (!(j && j.output && j.output.task_id)) throw new Error(j && j.message ? j.message : 'HTTP ' + r.status)
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console.log('可用（异步任务）')
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chosen = m
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} catch (e) {
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console.log('不可用：' + e.message)
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}
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}
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}
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if (!chosen) { console.log('没有可用模型/尺寸组合'); process.exit(1) }
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console.log('[chosen] ' + chosen.id + ' size=' + chosen.size + ' → 输出目录 ' + OUT_DIR)
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const results = []
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for (const job of JOBS) {
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process.stdout.write('[gen] ' + job.file + ' … ')
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try {
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const url = chosen.kind === 'multimodal'
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? await genMultimodal(chosen.id, job.prompt, chosen.size)
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: await genSynthesis(chosen.id, job.prompt, chosen.size)
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const d = await download(url, job.file)
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results.push({ file: job.file, ...d })
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console.log('OK ' + d.size + ' bytes ' + d.md5.slice(0, 12))
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} catch (e) {
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console.log('FAIL ' + e.message)
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}
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}
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console.log('\n===== 汇总 =====')
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results.forEach((r) => console.log(r.file + ' ' + r.size + ' ' + r.md5))
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const uniq = new Set(results.map((r) => r.md5))
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console.log('生成 ' + results.length + '/' + JOBS.length + ' 张，去重后 ' + uniq.size + ' 张')
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console.log(uniq.size === results.length ? '✓ 每张都不同，可逐节点使用' : '❌ 存在重复图，不可用')
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}
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main().catch((e) => { console.error('FATAL', e && e.message); process.exit(1) }) |