feat(images): 期2 图片趴三类 — 模特上身图/平台套图/自由创作归类+成组+接模特库

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 <noreply@anthropic.com>
2026-06-21 16:36:29 +08:00
parent 44e90233ff
commit c8f3f91d38
8 changed files with 187 additions and 29 deletions
@@ -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)
@@ -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
@@ -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(
@@ -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"])
@@ -511,7 +511,7 @@ export const api = {
    return request<Paginated<AITask>>("/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[]) {
@@ -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<Asset[]>([]);
-  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) */}
@@ -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<Asset[]>([]);
  const [total, setTotal] = useState(0);
-  const [counts, setCounts] = useState<Record<LibTab, number>>({ people: 0, scenes: 0, products: 0, finals: 0, uploads: 0, unclassified: 0 });
+  const [counts, setCounts] = useState<Record<LibTab, number>>({ 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<string, string[]> }>({ sources: [], kinds: [], metadata: {} });
  const [loading, setLoading] = useState(false);
  const [page, setPage] = useState(1);
@@ -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));