From c8f3f91d38dd128d47da60874190c98d24e4d21e Mon Sep 17 00:00:00 2001 From: seaislee1209 Date: Sun, 21 Jun 2026 16:36:29 +0800 Subject: [PATCH] =?UTF-8?q?feat(images):=20=E6=9C=9F2=20=E5=9B=BE=E7=89=87?= =?UTF-8?q?=E8=B6=B4=E4=B8=89=E7=B1=BB=20=E2=80=94=20=E6=A8=A1=E7=89=B9?= =?UTF-8?q?=E4=B8=8A=E8=BA=AB=E5=9B=BE/=E5=B9=B3=E5=8F=B0=E5=A5=97?= =?UTF-8?q?=E5=9B=BE/=E8=87=AA=E7=94=B1=E5=88=9B=E4=BD=9C=20=E5=BD=92?= =?UTF-8?q?=E7=B1=BB+=E6=88=90=E7=BB=84+=E6=8E=A5=E6=A8=A1=E7=89=B9?= =?UTF-8?q?=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 后端: - 独立生图三类归类:model+product→model_tryon(模特上身图,引用模特库,不送审) / cover→platform_kit(平台套图) / image→free_create(自由创作);生成演员(model 无 product)仍→person(视频角色,留期3) - 成组:每次提交一个 batch_id 串起整批,落 asset.metadata;另记 mode + model_entity_id(上身图溯源模特库) - generate-image 端点透传 model_entity_id;资产库 _tab_q+summary 加 tryon/kits/creations 三类桶 - 单测 StandaloneCategoryTests 直跑 worker 验四态归类全绿(绕开异步 .delay) 前端: - 资产库 library.tsx 加三类 tab(模特上身图/平台套图/自由创作)+ 计数 + 筛选维度 - ai-tools 模特选择器数据源 person→模特库(listModels 映射,选中传形象图当参考 = 引用模特库),ActorLibrary 同源 - api.ts submitGenerateImage 加 model_entity_id 验收:tsc+build 全绿;无头 0 console error(资产库三类 tab 各归类正确、模特选择器 5 张取自模特库) 基线既有 3 失败(StandaloneImageReferenceTests 异步漂移)零新增 Co-Authored-By: Claude Opus 4.8 --- core/backend/apps/ai/services.py | 30 +++++++++---- core/backend/apps/ai/tests.py | 63 +++++++++++++++++++++++++++ core/backend/apps/ai/views.py | 3 +- core/backend/apps/assets/views.py | 14 +++++- core/frontend/src/api.ts | 2 +- core/frontend/src/routes/ai-tools.tsx | 42 +++++++++++++----- core/frontend/src/routes/library.tsx | 10 +++-- core/qa/visual-parity/_models-p2.mjs | 52 ++++++++++++++++++++++ 8 files changed, 187 insertions(+), 29 deletions(-) create mode 100644 core/qa/visual-parity/_models-p2.mjs diff --git a/core/backend/apps/ai/services.py b/core/backend/apps/ai/services.py index d532fed..bc11597 100644 --- a/core/backend/apps/ai/services.py +++ b/core/backend/apps/ai/services.py @@ -1494,10 +1494,13 @@ def create_export_job(*, timeline, user) -> ExportJob: return ExportJob.objects.create(timeline=timeline, status=ExportJob.Status.QUEUED) +# 图片趴三类(模特库+资产模型重构 期2): +# · model + product → model_tryon(模特上身图);model 无 product → person(视频角色「生成演员」,留期3) +# · cover → platform_kit(平台套图);image → free_create(自由创作) _STANDALONE_CATEGORY = { "model": Asset.Category.PERSON, - "cover": Asset.Category.PRODUCT_IMAGE, - "image": Asset.Category.PRODUCT_IMAGE, + "cover": Asset.Category.PLATFORM_KIT, + "image": Asset.Category.FREE_CREATE, } _STANDALONE_TASK_TYPE = { "model": AITask.Type.PERSON_IMAGE, @@ -1535,7 +1538,7 @@ def _reap_stale_standalone_image_tasks(*, team) -> None: continue -def enqueue_standalone_images(*, team, user, prompt: str, mode: str = "image", count: int = 1, product_id: str | None = None, reference_product: bool = False, model_id: str | None = None, ratio: str | None = None) -> list[AITask]: +def enqueue_standalone_images(*, team, user, prompt: str, mode: str = "image", count: int = 1, product_id: str | None = None, reference_product: bool = False, model_id: str | None = None, model_entity_id: str | None = None, ratio: str | None = None) -> list[AITask]: """独立生图(图片创作 / 模特上身图 / 平台套图)改为**异步**:本函数在 Web 请求里只做「建任务 + 预留额度」这种秒级的活,真正 ~30s 的 ARK 出图交给 Celery worker(generate_standalone_image_task)。 @@ -1550,6 +1553,8 @@ def enqueue_standalone_images(*, team, user, prompt: str, mode: str = "image", c raise ValueError("no active image model configured") task_type = _STANDALONE_TASK_TYPE.get(mode, AITask.Type.PRODUCT_IMAGE) count = max(1, min(int(count or 1), 12)) + # 本次提交 = 一组(模特上身图组 / 平台套图组):同一 batch_id 串起这批图,前端可成组展示。 + batch_id = str(uuid.uuid4()) tasks: list[AITask] = [] for index in range(count): cost = estimate_cost(model_config) @@ -1561,7 +1566,7 @@ def enqueue_standalone_images(*, team, user, prompt: str, mode: str = "image", c status=AITask.Status.CREATED, model_config=model_config, idempotency_key=f"standalone-image:{team.id}:{uuid.uuid4()}", - request_payload={"model": model_config.name, "endpoint": model_config.endpoint, "prompt": prompt, "mode": mode, "index": index, "product_id": str(product_id) if product_id else None, "reference_product": bool(reference_product), "model_id": str(model_id) if model_id else None, "ratio": str(ratio) if ratio else None}, + request_payload={"model": model_config.name, "endpoint": model_config.endpoint, "prompt": prompt, "mode": mode, "index": index, "product_id": str(product_id) if product_id else None, "reference_product": bool(reference_product), "model_id": str(model_id) if model_id else None, "model_entity_id": str(model_entity_id) if model_entity_id else None, "batch_id": batch_id, "ratio": str(ratio) if ratio else None}, estimated_cost=cost, ) # 预留额度若余额不足会抛 ValueError,在同步的 Web 请求里立刻反馈给前端(不会先建半套任务) @@ -1588,10 +1593,10 @@ def run_standalone_image_task(*, task_id: str) -> None: mode = str(payload.get("mode") or "image") index = int(payload.get("index") or 0) product_id = payload.get("product_id") or None - # 模特上身图(mode=model 且绑了商品)= 该商品的商品图,归到对应商品的 AI 资产,不进人物库; - # 「生成演员」同样走 mode=model 但无 product_id,仍归人物库(PERSON)。 + # 模特上身图(mode=model 且绑了商品)= 图片趴「模特上身图」(引用模特库),归 model_tryon、不送审; + # 「生成演员」同样走 mode=model 但无 product_id = 视频角色,仍归 person(送审,留期3 收编为「角色」)。 if mode == "model" and product_id: - category = Asset.Category.PRODUCT_IMAGE + category = Asset.Category.MODEL_TRYON else: category = _STANDALONE_CATEGORY.get(mode, Asset.Category.UNCATEGORIZED) model_config = task.model_config @@ -1652,11 +1657,18 @@ def run_standalone_image_task(*, task_id: str) -> None: object_key = f"teams/{team.id}/standalone/{asset_id}{suffix}" stored = TosStorage().upload_fileobj(fileobj=fileobj, object_key=object_key, content_type=content_type) asset_label = {"model": "模特上身图", "cover": "平台套图", "image": "图片创作"}.get(mode, mode) + # 资产元数据:product_id(商品详情页据此只展示该商品素材)+ batch_id(成组)+ mode + model_entity_id(上身图溯源模特库) + asset_meta: dict = {"mode": mode} + if product_id: + asset_meta["product_id"] = str(product_id) + if payload.get("batch_id"): + asset_meta["batch_id"] = str(payload["batch_id"]) + if payload.get("model_entity_id"): + asset_meta["model_entity_id"] = str(payload["model_entity_id"]) asset = Asset.objects.create( id=asset_id, team=team, created_by=user, name=f"AI 生成 · {asset_label} · {index + 1}", asset_type=Asset.Type.IMAGE, source=Asset.Source.AI_GENERATED, category=category, origin_task=task, - # 记下生图时选中的商品,商品详情页据此只展示「该商品」的 AI 素材(而非全团队) - metadata={"product_id": str(product_id)} if product_id else {}, + metadata=asset_meta, ) AssetFile.objects.create(asset=asset, object_key=stored.object_key, bucket=stored.bucket, content_type=stored.content_type, size_bytes=stored.size_bytes, is_primary=True) except Exception as exc: # noqa: BLE001 — 失败要退费并把错误记进 AITask 供前端轮询读取;不向上抛(避免 celery 重试二次扣费) diff --git a/core/backend/apps/ai/tests.py b/core/backend/apps/ai/tests.py index 82ff267..3e54d81 100644 --- a/core/backend/apps/ai/tests.py +++ b/core/backend/apps/ai/tests.py @@ -212,6 +212,69 @@ class StandaloneImageReferenceTests(TestCase): prov.image_edit.assert_not_called() +class StandaloneCategoryTests(TestCase): + """图片趴三类归类(期2):模特上身图→model_tryon / 平台套图→platform_kit / 自由创作→free_create; + 生成演员(model 无 product)仍→person(视频角色)。直接跑 worker 函数避开异步 .delay。""" + + def setUp(self): + self.user = User.objects.create_user(username="catowner", password="pass") + self.team = Team.objects.create(name="CT", owner=self.user) + CreditAccount.objects.create(team=self.team, balance="100.0000") + self.product = Product.objects.create(team=self.team, created_by=self.user, title="测试商品") + cover = Asset.objects.create( + team=self.team, created_by=self.user, name="主图", asset_type=Asset.Type.IMAGE, + source=Asset.Source.UPLOAD, category=Asset.Category.PRODUCT_IMAGE, + ) + AssetFile.objects.create(asset=cover, object_key="c.png", bucket="b", content_type="image/png", preview_url="http://x/cover.png", is_primary=True) + self.product.cover_asset = cover + self.product.save(update_fields=["cover_asset"]) + + def _patch_provider(self): + provider = patch("apps.ai.services.get_image_provider").start() + prov = provider.return_value + prov.image_edit.return_value = {"data": [{"url": "http://x/out.png"}]} + prov.image_generation.return_value = {"data": [{"url": "http://x/out.png"}]} + prov.extract_first_media_url.return_value = "http://x/out.png" + media = patch("apps.ai.services.VolcanoArkProvider.media_to_bytes").start() + media.return_value = (BytesIO(b"img"), "image/png") + store = patch("apps.ai.services.TosStorage").start() + stored = store.return_value.upload_fileobj.return_value + stored.object_key, stored.bucket, stored.content_type, stored.size_bytes = "o.png", "b", "image/png", 3 + self.addCleanup(patch.stopall) + return prov + + def _run(self, mode, *, product_id=None, model_id=None): + from apps.ai.services import run_standalone_image_task + + self._patch_provider() + tasks = enqueue_standalone_images( + team=self.team, user=self.user, prompt="x", mode=mode, count=1, + product_id=product_id, model_id=model_id, model_entity_id="ent-1", ratio="4:5", + ) + run_standalone_image_task(task_id=str(tasks[0].id)) + return Asset.objects.filter(origin_task=tasks[0]).first() + + def test_model_tryon_category(self): + a = self._run("model", product_id=str(self.product.id)) + self.assertEqual(a.category, Asset.Category.MODEL_TRYON) + self.assertEqual(a.metadata.get("mode"), "model") + self.assertTrue(a.metadata.get("batch_id")) # 成组 + self.assertEqual(a.metadata.get("model_entity_id"), "ent-1") # 溯源模特库 + + def test_platform_kit_category(self): + a = self._run("cover", product_id=str(self.product.id)) + self.assertEqual(a.category, Asset.Category.PLATFORM_KIT) + + def test_free_create_category(self): + a = self._run("image") + self.assertEqual(a.category, Asset.Category.FREE_CREATE) + + def test_generate_actor_stays_person(self): + # 生成演员:mode=model 但无 product → 视频角色,仍 person(送审范围内) + a = self._run("model") + self.assertEqual(a.category, Asset.Category.PERSON) + + class _FakeStreamResp: """模拟 requests 流式响应:支持 with、raise_for_status、可写 encoding、iter_lines。""" status_code = 200 diff --git a/core/backend/apps/ai/views.py b/core/backend/apps/ai/views.py index e256ca7..b78c3d7 100644 --- a/core/backend/apps/ai/views.py +++ b/core/backend/apps/ai/views.py @@ -32,10 +32,11 @@ class GenerateImageView(APIView): product_id = str(request.data.get("product_id") or "").strip() or None reference_product = bool(request.data.get("reference_product")) model_id = str(request.data.get("model_id") or "").strip() or None + model_entity_id = str(request.data.get("model_entity_id") or "").strip() or None ratio = str(request.data.get("ratio") or "").strip() or None team = get_current_team(request.user) try: - tasks = enqueue_standalone_images(team=team, user=request.user, prompt=prompt, mode=mode, count=count, product_id=product_id, reference_product=reference_product, model_id=model_id, ratio=ratio) + tasks = enqueue_standalone_images(team=team, user=request.user, prompt=prompt, mode=mode, count=count, product_id=product_id, reference_product=reference_product, model_id=model_id, model_entity_id=model_entity_id, ratio=ratio) except ValueError as exc: # 无可用模型 / 余额不足等,立即反馈 return Response({"detail": str(exc)}, status=status.HTTP_400_BAD_REQUEST) return Response( diff --git a/core/backend/apps/assets/views.py b/core/backend/apps/assets/views.py index 83fc19c..5a1e7dd 100644 --- a/core/backend/apps/assets/views.py +++ b/core/backend/apps/assets/views.py @@ -30,7 +30,11 @@ class AssetPagination(PageNumberPagination): # 资产库 tab → 查询条件。与前端 assetTab(library.tsx)完全一致,供服务端按页过滤/计数。 -_KNOWN_CATS = ["person", "scene", "product_image", "final_video", "upload"] +# 图片趴三类(期2):tryon=模特上身图 / kits=平台套图 / creations=自由创作。 +_KNOWN_CATS = [ + "person", "scene", "product_image", "final_video", "upload", + "model_tryon", "platform_kit", "free_create", +] def _tab_q(tab: str) -> Q: @@ -40,6 +44,12 @@ def _tab_q(tab: str) -> Q: return Q(category="scene") if tab == "products": return Q(category="product_image") + if tab == "tryon": # 模特上身图(图片趴) + return Q(category="model_tryon") + if tab == "kits": # 平台套图(图片趴) + return Q(category="platform_kit") + if tab == "creations": # 自由创作(图片趴) + return Q(category="free_create") if tab == "uploads": return Q(category="upload") if tab == "finals": # final_video,或「未归类但是视频」 @@ -100,7 +110,7 @@ class AssetViewSet(TeamScopedViewSetMixin, ModelViewSet): def summary(self, request): """资产库 tab 计数(人物/场景/商品图/成片/我的上传/未分类),供 tab 徽标——不必取全量。""" base = Asset.objects.filter(team=self.get_team()) - tabs = ["people", "scenes", "products", "finals", "uploads", "unclassified"] + tabs = ["people", "scenes", "products", "tryon", "kits", "creations", "finals", "uploads", "unclassified"] return Response({t: base.filter(_tab_q(t)).count() for t in tabs}) @action(detail=False, methods=["get"]) diff --git a/core/frontend/src/api.ts b/core/frontend/src/api.ts index 8f6cbf0..0998d03 100644 --- a/core/frontend/src/api.ts +++ b/core/frontend/src/api.ts @@ -511,7 +511,7 @@ export const api = { return request>("/api/ai/tasks/"); }, // 异步生图:提交后秒级返回任务列表(慢出图在 worker 跑),再用 generateImageStatus 轮询取结果 - submitGenerateImage(payload: { prompt: string; mode?: "image" | "model" | "cover"; count?: number; product_id?: string; reference_product?: boolean; model_id?: string; ratio?: string }) { + submitGenerateImage(payload: { prompt: string; mode?: "image" | "model" | "cover"; count?: number; product_id?: string; reference_product?: boolean; model_id?: string; model_entity_id?: string; ratio?: string }) { return request<{ tasks: { id: string; status: string }[] }>("/api/ai/generate-image/", { method: "POST", body: JSON.stringify(payload) }); }, generateImageStatus(ids: string[]) { diff --git a/core/frontend/src/routes/ai-tools.tsx b/core/frontend/src/routes/ai-tools.tsx index 0ea2941..d3d5ac1 100644 --- a/core/frontend/src/routes/ai-tools.tsx +++ b/core/frontend/src/routes/ai-tools.tsx @@ -1,4 +1,4 @@ -import { Fragment, useEffect, useMemo, useRef, useState } from "react"; +import { Fragment, useCallback, useEffect, useMemo, useRef, useState } from "react"; import { createPortal } from "react-dom"; import type { ChangeEvent } from "react"; import { @@ -23,7 +23,7 @@ import { WandSparkles, X } from "lucide-react"; -import type { AITask, Asset, ModelConfig, Product } from "../types"; +import type { AITask, Asset, ModelConfig, ModelEntity, Product } from "../types"; import { api } from "../api"; import { ActorLibrary } from "../components/actor-library"; import { SkeletonRows } from "../components/loading"; @@ -580,16 +580,34 @@ export function ImageWorkbenchPage({ const imageModels = modelConfigs.filter((model) => model.capability.includes("image")); - /* 模特卡数据来源:服务端按 category=person 懒加载(不再吃全局 assets 全量数组); - 无则回退到基线占位模特卡 Ava/Luna/Mia/Zoe */ + /* 模特卡数据来源:期2 改为「模特库」(顶级实体,引用其形象图当上身图参考)。 + 映射 ModelEntity→Asset 形:id=形象图资产 id(选中即作 model_id 参考图),metadata 记 model_entity_id 溯源。 + 无模特则回退到基线占位卡 Ava/Luna/Mia/Zoe。深埋 ActorLibrary 弹窗同源,逻辑不动。 */ const [personAssets, setPersonAssets] = useState([]); - useEffect(() => { - let alive = true; - api.assetsPage({ category: "person", pageSize: 200 }) - .then((res) => { if (alive) setPersonAssets(res.results); }) - .catch(() => { if (alive) setPersonAssets([]); }); - return () => { alive = false; }; + const loadModels = useCallback(() => { + api.listModels({ pageSize: 200 }) + .then((res) => { + const mapped: Asset[] = res.results + .filter((m: ModelEntity) => m.portrait_asset) + .map((m: ModelEntity) => ({ + id: m.portrait_asset as string, + name: m.name, + asset_type: "image", + source: m.source === "upload" ? "upload" : "ai_generated", + category: "model_portrait", + description: m.description || "", + metadata: { model_entity_id: m.id }, + files: m.portrait + ? [{ id: m.portrait_asset as string, object_key: "", bucket: "", content_type: "image/png", size_bytes: 0, preview_url: m.portrait, is_primary: true }] + : [], + created_at: m.created_at, + updated_at: m.updated_at, + })); + setPersonAssets(mapped); + }) + .catch(() => setPersonAssets([])); }, []); + useEffect(() => { loadModels(); }, [loadModels]); useEffect(() => { if (product) setPrompt(meta.promptTemplate(product.title)); @@ -1524,7 +1542,7 @@ export function ImageWorkbenchPage({ onPick={(assetId, assetName) => { setPickedIds([assetId]); setPickedModelName(assetName || ""); - void api.assetsPage({ category: "person", pageSize: 200 }).then((res) => setPersonAssets(res.results)).catch(() => {}); + loadModels(); setActorLibOpen(false); }} onGenerate={(p) => onGenerate({ prompt: p, mode: "model", count: 1 })} @@ -1537,7 +1555,7 @@ export function ImageWorkbenchPage({ return api.uploadAsset(fd).catch(() => null); }} onRename={(assetId, name) => api.updateAsset(assetId, { name }).catch(() => null)} - onRefresh={() => { void api.assetsPage({ category: "person", pageSize: 200 }).then((res) => setPersonAssets(res.results)).catch(() => {}); }} + onRefresh={() => { loadModels(); }} /> {/* P0④:商品库全屏选择器(pl-modal · 多选 + 右上勾 + 已选汇总 · restraint token) */} diff --git a/core/frontend/src/routes/library.tsx b/core/frontend/src/routes/library.tsx index 9a6a6cc..fcf6532 100644 --- a/core/frontend/src/routes/library.tsx +++ b/core/frontend/src/routes/library.tsx @@ -34,10 +34,12 @@ const UPLOAD_KINDS: Array<{ value: string; label: string; accept: string; hint: { value: "subtitle", label: "字幕", accept: ".srt,.vtt,.ass", hint: "SRT / VTT / ASS" } ]; -type LibTab = "people" | "scenes" | "products" | "finals" | "uploads" | "unclassified"; +// 图片趴三类(期2):tryon=模特上身图 / kits=平台套图 / creations=自由创作 +type LibTab = "people" | "scenes" | "products" | "tryon" | "kits" | "creations" | "finals" | "uploads" | "unclassified"; const LIB_TABS: Array<{ key: LibTab; label: string }> = [ { key: "people", label: "人物" }, { key: "scenes", label: "场景" }, { key: "products", label: "商品图" }, + { key: "tryon", label: "模特上身图" }, { key: "kits", label: "平台套图" }, { key: "creations", label: "自由创作" }, { key: "finals", label: "成片" }, { key: "uploads", label: "我的上传" }, { key: "unclassified", label: "未分类" } ]; @@ -47,11 +49,11 @@ const LIB_CHIPS: Array<{ key: string; label: string; tabs: LibTab[] }> = [ { key: "age", label: "年龄段", tabs: ["people"] }, { key: "role", label: "角色标签", tabs: ["people"] }, { key: "sceneType", label: "场景类型", tabs: ["scenes"] }, - { key: "product", label: "关联商品", tabs: ["products"] }, + { key: "product", label: "关联商品", tabs: ["products", "tryon", "kits"] }, { key: "project", label: "关联项目", tabs: ["finals"] }, { key: "duration", label: "时长", tabs: ["finals"] }, { key: "kind", label: "资产类型", tabs: ["uploads"] }, - { key: "source", label: "来源", tabs: ["people", "scenes", "products", "uploads"] } + { key: "source", label: "来源", tabs: ["people", "scenes", "products", "tryon", "kits", "creations", "uploads"] } ]; // metadata 里可能用的中文属性键(真实存在才渲染,缺则不显;属性区不造假) @@ -425,7 +427,7 @@ export function LibraryPage({ onUpload, onDelete }: { onUpload: (formData: FormD // ── 服务端懒加载:列表按页拉、tab 计数走 summary、筛选项走 facets(不再前端取全量再切片)── const [items, setItems] = useState([]); const [total, setTotal] = useState(0); - const [counts, setCounts] = useState>({ people: 0, scenes: 0, products: 0, finals: 0, uploads: 0, unclassified: 0 }); + const [counts, setCounts] = useState>({ people: 0, scenes: 0, products: 0, tryon: 0, kits: 0, creations: 0, finals: 0, uploads: 0, unclassified: 0 }); const [facets, setFacets] = useState<{ sources: string[]; kinds: string[]; metadata: Record }>({ sources: [], kinds: [], metadata: {} }); const [loading, setLoading] = useState(false); const [page, setPage] = useState(1); diff --git a/core/qa/visual-parity/_models-p2.mjs b/core/qa/visual-parity/_models-p2.mjs new file mode 100644 index 0000000..81620f0 --- /dev/null +++ b/core/qa/visual-parity/_models-p2.mjs @@ -0,0 +1,52 @@ +// 期2 图片趴三类走查:资产库三类 tab(归类) + 图片生成页模特选择器接模特库 + 抓 console/pageerror。 +import { chromium } from "playwright"; +import fs from "node:fs"; +import path from "node:path"; + +const BASE = process.env.BASE || "http://localhost:5188"; +const OUT = path.resolve("../../../_qa_shots/models-p2"); +fs.mkdirSync(OUT, { recursive: true }); + +const browser = await chromium.launch({ headless: true }); +const r = { consoleErrors: [], library: {}, picker: {} }; +const ctx = await browser.newContext({ viewport: { width: 1440, height: 900 } }); +const p = await ctx.newPage(); +p.on("console", (m) => { if (m.type() === "error") r.consoleErrors.push(m.text()); }); +p.on("pageerror", (e) => r.consoleErrors.push("PAGEERR:" + e.message)); + +// 登录 +await p.goto(BASE + "/login", { waitUntil: "load" }); +await p.waitForTimeout(400); +await p.fill("#auth-username", "airshelf"); +await p.fill("#auth-pwd", "Restraint2026"); +await p.click("button.btn-cta"); +await p.waitForFunction(() => !location.pathname.startsWith("/login"), { timeout: 12000 }); + +// ── 资产库:三类 tab 存在 + 计数 + 逐 tab 截图 ── +await p.goto(BASE + "/library", { waitUntil: "load" }); +await p.waitForTimeout(2600); +for (const key of ["tryon", "kits", "creations"]) { + r.library[key + "_tabExists"] = await p.locator(`.tab[data-tab="${key}"]`).count(); +} +async function openTab(key) { + await p.locator(`.tab[data-tab="${key}"]`).click(); + await p.waitForTimeout(2200); + const cards = await p.locator(".asset-card, .lib-card, .ph-frame, .a-card").count(); + await p.screenshot({ path: path.join(OUT, `library-${key}.png`), fullPage: true }); + return cards; +} +r.library.tryonCards = await openTab("tryon"); +r.library.kitsCards = await openTab("kits"); +r.library.creationsCards = await openTab("creations"); + +// ── 图片生成页:模特上身图 → 模特选择器接模特库 ── +await p.goto(BASE + "/model-photo", { waitUntil: "load" }); +await p.waitForTimeout(2600); +r.picker.url = p.url(); +r.picker.modelCards = await p.locator(".model-grid .model-card").count(); +r.picker.firstModelName = await p.locator(".model-grid .model-card .m-name").first().innerText().catch(() => ""); +await p.screenshot({ path: path.join(OUT, "model-photo-picker.png"), fullPage: true }); + +await browser.close(); +console.log(JSON.stringify(r, null, 2)); +fs.writeFileSync(path.join(OUT, "summary.json"), JSON.stringify(r, null, 2));