feat(core): 脚本 agent 真流式思考动画 + 场景图16:9 + 资产卡/审核标修复 + 模型收敛

- 进度区改竖线时间轴:推理模型 reasoning_content 逐字转发(volcano),状态字=模型实时最新一句+秒数+折叠展开思考全文(替代旧 ProgressStream/ThinkingStream)
- 场景基础资产出图改 16:9(run_base_asset_task size + 提取提示词横屏)
- 已出图立绘主卡不再被在途轮询任务误盖转圈
- 审核态随项目详情持久下发(BaseAssetGroup.adopted_asset_review),刷新不丢徽章
- 设定卡加「← 返回」回三选项;模型选择器固定按 created_at 排序(默认豆包2.0Pro)
- 新增 reasoning 转发回归测试

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

core/backend/apps/ai/providers/volcano.py

@@ -97,6 +97,11 @@ class VolcanoArkProvider:
                 if not choices:
                     continue
                 delta = choices[0].get("delta") or {}
+                # 推理模型(豆包 seed-pro / 部分中转 o系/gemini)思考阶段只发 reasoning_content,
+                # 不发 content。必须单独转发,否则整个思考期(可达几十秒~分钟)前端零输出 = 假死。
+                reason = delta.get("reasoning_content")
+                if reason:
+                    yield {"type": "reasoning", "text": reason}
                 piece = delta.get("content")
                 if piece:
                     yield {"type": "delta", "text": piece}

core/backend/apps/ai/script_agent.py

@@ -8,6 +8,7 @@
 
 SSE 事件(每帧 `data: {json}\n\n`,json 带 type):
   tool   {id,label?,status:running|done|error}   —— 工具卡(加载skill/分析商品/生成分镜/提取实体/自检)
+  reasoning {text}                                —— 推理模型思考流(逐字,纯展示,不进答案)
   delta  {text}                                   —— 模型自然语言前言(JSON 部分不外露)
   draft  {draft}                                  —— 规范化后的 ScriptDraft(前端结构化渲染)
   saved  {script_version_id, version}             —— 已落库的 ScriptVersion(含 segments/metadata)
@@ -621,7 +622,15 @@ def stream_script_agent(
                 messages=messages,
                 temperature=0.85,
             ):
-                if ev.get("type") == "delta":
+                et = ev.get("type")
+                if et == "reasoning":
+                    # 思考流:推理模型在出 JSON 前会先想很久,把思考逐字下发给前端(像对话一样可见),
+                    # 不进 full(不是答案正文,纯展示)。这是「卡在生成分镜」假死的根因修复。
+                    rpiece = ev.get("text") or ""
+                    if rpiece:
+                        yield _sse({"type": "reasoning", "text": rpiece})
+                    continue
+                if et == "delta":
                     full.append(ev["text"])
                     if forwarding:
                         text = "".join(full)
@@ -634,7 +643,7 @@ def stream_script_agent(
                             shown = len(visible)
                             if piece.strip():
                                 yield _sse({"type": "delta", "text": piece})
-                elif ev.get("type") == "done":
+                elif et == "done":
                     break
             raw = "".join(full)
             draft = normalize_draft(raw, aspect_ratio=aspect_ratio, total_duration=effective_duration)

core/backend/apps/ai/services.py

@@ -125,11 +125,12 @@ def build_cast_scene_extract_prompt(content: str) -> list[dict[str, str]]:
     """轻量抽取提示词:从镜头脚本里提炼人物 / 场景标签,并给每个标签一句可直接生图的画面提示词。"""
     system = (
         "你是短视频脚本分析助手。请从给定的镜头脚本中提取出现的『人物』和『场景』,"
-        "并为每个人物 / 场景写一句可直接用于文生图的画面提示词(中文,30 字内,描述外形 / 着装 / 环境 / 光线,9:16 竖屏)。"
+        "并为每个人物 / 场景写一句可直接用于文生图的画面提示词(中文,30 字内,描述外形 / 着装 / 环境 / 光线)。"
+        "人物提示词用 9:16 竖屏(出镜角色全身);场景提示词用 16:9 横屏(环境 / 背景空镜)。"
         "人物指出镜的角色(例:女主、同事、闺蜜);场景指画面发生的地点或环境(例:卫生间、地铁、办公室)。"
         "去重,人物与场景各最多 6 个。只输出一个 JSON 对象,不要 markdown 代码块,不要任何额外文字,格式如下：\n"
         '{"cast":[{"name":"女主","prompt":"26岁都市女性,自然妆容,米色针织衫,柔和室内光,9:16竖屏"}],'
-        '"scenes":[{"name":"卫生间","prompt":"现代简约浴室,暖色灯光,干净台面,9:16竖屏"}]}'
+        '"scenes":[{"name":"卫生间","prompt":"现代简约浴室,暖色灯光,干净台面,16:9横屏"}]}'
     )
     return [{"role": "system", "content": system}, {"role": "user", "content": f"镜头脚本如下：\n{content}".strip()}]
 
@@ -536,7 +537,9 @@ def run_base_asset_task(*, task_id: str) -> None:
         if use_edit and ref_url:
             response = provider.image_edit(model=model_config.name, prompt=prompt, images=[ref_url], size="1536x1024")
         else:
-            response = provider.image_generation(model=model_config.name, endpoint=model_config.endpoint, prompt=prompt)
+            # 场景 = 16:9 横构图(环境/背景空镜);人物立绘 = 9:16 竖全身。默认竖。
+            gen_size = "1536x1024" if kind == BaseAssetGroup.Kind.SCENE else "1024x1536"
+            response = provider.image_generation(model=model_config.name, endpoint=model_config.endpoint, prompt=prompt, size=gen_size)
         media = provider.extract_first_media_url(response)
         with transaction.atomic():
             task.status = AITask.Status.SUCCEEDED

core/backend/apps/ai/tests.py

@@ -142,6 +142,7 @@ from unittest.mock import patch
 from django.test import TestCase
 
 from apps.accounts.models import Team, User
+from apps.ai.providers.volcano import VolcanoArkProvider
 from apps.ai.services import enqueue_standalone_images
 from apps.assets.models import Asset, AssetFile
 from apps.billing.models import CreditAccount
@@ -209,3 +210,49 @@ class StandaloneImageReferenceTests(TestCase):
         enqueue_standalone_images(team=self.team, user=self.user, prompt="平台套图", mode="cover", count=1, product_id=str(self.product.id), ratio="4:5")
         prov.image_generation.assert_called_once()
         prov.image_edit.assert_not_called()
+
+
+class _FakeStreamResp:
+    """模拟 requests 流式响应:支持 with、raise_for_status、可写 encoding、iter_lines。"""
+    status_code = 200
+    encoding = None
+
+    def __init__(self, lines):
+        self._lines = lines
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *exc):
+        return False
+
+    def raise_for_status(self):
+        return None
+
+    def iter_lines(self, decode_unicode=True):  # noqa: ARG002
+        yield from self._lines
+
+
+class ChatStreamReasoningTests(SimpleTestCase):
+    """推理模型(豆包 seed-pro 等)思考期只发 reasoning_content、不发 content。
+    provider 必须把它作为独立 `reasoning` 事件转发——否则脚本 agent 思考期零输出 =
+    前端「卡在生成分镜」假死。本测试锁住该转发,防回归。"""
+
+    def test_reasoning_content_forwarded_as_reasoning_event(self):
+        def _chunk(delta):
+            return "data: " + json.dumps({"choices": [{"delta": delta}]}, ensure_ascii=False)
+
+        lines = [
+            _chunk({"reasoning_content": "先想想"}),
+            _chunk({"reasoning_content": "用户要4镜"}),
+            _chunk({"content": "正在生成"}),
+            _chunk({"content": "脚本…"}),
+            "data: [DONE]",
+        ]
+        prov = VolcanoArkProvider(api_key="k", base_url="http://x")
+        with patch("apps.ai.providers.volcano.requests.post", return_value=_FakeStreamResp(lines)):
+            events = list(prov.chat_completion_stream(model="m", messages=[{"role": "user", "content": "hi"}]))
+
+        self.assertEqual([e["type"] for e in events], ["reasoning", "reasoning", "delta", "delta", "done"])
+        self.assertEqual([e["text"] for e in events if e["type"] == "reasoning"], ["先想想", "用户要4镜"])
+        self.assertEqual("".join(e["text"] for e in events if e["type"] == "delta"), "正在生成脚本…")

core/backend/apps/ai/views.py

@@ -75,7 +75,9 @@ class AITaskViewSet(TeamScopedViewSetMixin, ReadOnlyModelViewSet):
 
 
 class ModelConfigViewSet(ReadOnlyModelViewSet):
-    queryset = ModelConfig.objects.select_related("provider").filter(status=ModelConfig.Status.ACTIVE)
+    # 按创建序固定排序:最早创建的 active 模型排第一 = 前端选择器默认项,与 get_default_model 口径一致
+    # (否则 DB 默认序不稳定,可能默认选到 Gemini 等;用户要默认 = 豆包 2.0 Pro,它最早创建)
+    queryset = ModelConfig.objects.select_related("provider").filter(status=ModelConfig.Status.ACTIVE).order_by("created_at")
     serializer_class = ModelConfigSerializer
     search_fields = ["name", "display_name", "capability"]
     ordering_fields = ["created_at", "display_name"]

core/backend/apps/projects/serializers.py

@@ -78,15 +78,24 @@ class BaseAssetGroupSerializer(serializers.ModelSerializer):
     candidate_assets = serializers.PrimaryKeyRelatedField(many=True, read_only=True)
     adopted_asset_url = serializers.SerializerMethodField()
     candidate_asset_urls = serializers.SerializerMethodField()
+    # 采用资产的火山审核态随项目详情下发,刷新后仍能回显绿/红/审核中徽章(前端不再依赖那次性的轮询)
+    adopted_asset_review = serializers.SerializerMethodField()
+    adopted_asset_review_error = serializers.SerializerMethodField()
 
     class Meta:
         model = BaseAssetGroup
-        fields = ["id", "kind", "prompt", "adopted_asset", "adopted_asset_url", "candidate_assets", "candidate_asset_urls", "version", "metadata", "created_at"]
+        fields = ["id", "kind", "prompt", "adopted_asset", "adopted_asset_url", "candidate_assets", "candidate_asset_urls", "adopted_asset_review", "adopted_asset_review_error", "version", "metadata", "created_at"]
         read_only_fields = fields
 
     def get_adopted_asset_url(self, obj) -> str:
         return _asset_preview_url(obj.adopted_asset)
 
+    def get_adopted_asset_review(self, obj) -> str:
+        return getattr(obj.adopted_asset, "review_status", "") or ""
+
+    def get_adopted_asset_review_error(self, obj) -> str:
+        return getattr(obj.adopted_asset, "review_error", "") or ""
+
     def get_candidate_asset_urls(self, obj) -> dict:
         return {str(asset.id): _asset_preview_url(asset) for asset in obj.candidate_assets.all()}
 

core/frontend/src/pipeline-page.css

@@ -530,17 +530,32 @@
   .chat-tagedit-row .tagedit-chip.add { border-color: var(--heat); color: var(--heat); }
   .chat-tagedit-row .tagedit-chip.remove { color: var(--black-alpha-48); text-decoration: line-through; }
 
-  /* ── 行33 · 进度提示流 ── */
-  .progress-stream { display: flex; flex-direction: column; gap: 6px; }
-  .progress-stream .ps-row { display: flex; align-items: flex-start; gap: 8px; font-size: 13px; line-height: 1.5; animation: psRowIn .28s ease; }
-  .progress-stream .ps-row .ps-dot { width: 6px; height: 6px; margin-top: 6px; border-radius: 50%; background: var(--heat); flex: 0 0 6px; }
-  .progress-stream .ps-row.past { color: var(--black-alpha-48); }
-  .progress-stream .ps-row.past .ps-dot { background: var(--black-alpha-24); }
-  .progress-stream .ps-row.active { color: var(--accent-black); }
-  .progress-stream .ps-row.active .ps-dot { animation: prog-pulse 1.2s ease-in-out infinite; }
-  .progress-stream.done { display: inline-flex; flex-direction: row; align-items: center; gap: 7px; font-size: 13px; color: var(--accent-forest); }
-  .progress-stream.done .ps-check { width: 16px; height: 16px; display: grid; place-items: center; background: var(--forest-bg); color: var(--accent-forest); border-radius: 50%; flex: 0 0 16px; }
-  .progress-stream.done .ps-check svg { width: 10px; height: 10px; }
+  /* ── 行33 · 进度时间轴(竖线串步骤 · 当前步光扫+秒数 · 思考折叠展开)── */
+  .progress-timeline { display: flex; flex-direction: column; }
+  .progress-timeline .pt-row { display: flex; gap: 10px; animation: ptRowIn .28s ease; }
+  .progress-timeline .pt-rail { position: relative; flex: 0 0 12px; display: flex; justify-content: center; }
+  .progress-timeline .pt-rail::after { content: ""; position: absolute; top: 11px; bottom: -5px; left: 50%; width: 1px; transform: translateX(-50%); background: var(--border-faint); }
+  .progress-timeline .pt-row:last-child .pt-rail::after { display: none; }
+  .progress-timeline .pt-dot { width: 7px; height: 7px; margin-top: 4px; border-radius: 50%; background: var(--heat); flex: 0 0 7px; z-index: 1; }
+  .progress-timeline .pt-row:not(.done):not(.active) .pt-dot { background: var(--ink-4); }
+  .progress-timeline .pt-row.active .pt-dot { animation: pt-pulse 1.4s ease-in-out infinite; }
+  .progress-timeline .pt-main { flex: 1; min-width: 0; padding-bottom: 9px; }
+  .progress-timeline .pt-line { display: flex; align-items: center; gap: 8px; font-size: 13px; line-height: 1.45; }
+  .progress-timeline .pt-text { flex: 1; min-width: 0; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; color: var(--ink-2); }
+  .progress-timeline .pt-row.done .pt-text { color: var(--ink-3); }
+  .progress-timeline .pt-text.shimmer {
+    background: linear-gradient(90deg, var(--ink-2) 0%, var(--ink-2) 42%, var(--ink-4) 50%, var(--ink-2) 58%, var(--ink-2) 100%);
+    background-size: 220% 100%; -webkit-background-clip: text; background-clip: text;
+    -webkit-text-fill-color: transparent; color: transparent; animation: pt-sweep 1.6s linear infinite;
+  }
+  .progress-timeline .pt-sec { font-family: var(--font-mono); font-size: 11px; color: var(--ink-4); letter-spacing: .02em; flex: 0 0 auto; }
+  .progress-timeline .pt-caret { flex: 0 0 auto; padding: 0 2px; background: none; border: 0; cursor: pointer; color: var(--ink-4); font-size: 15px; line-height: 1; transition: transform .18s ease; }
+  .progress-timeline .pt-caret:hover { color: var(--ink-2); }
+  .progress-timeline .pt-caret.open { transform: rotate(90deg); }
+  .progress-timeline .pt-think { margin-top: 6px; font-size: 12px; line-height: 1.6; color: var(--ink-3); white-space: pre-wrap; word-break: break-word; max-height: 168px; overflow-y: auto; border-left: 1px solid var(--border-faint); padding-left: 8px; }
+  @keyframes ptRowIn { from { opacity: 0; transform: translateY(-3px); } to { opacity: 1; transform: none; } }
+  @keyframes pt-pulse { 0%, 100% { box-shadow: 0 0 0 2px var(--heat-12); } 50% { box-shadow: 0 0 0 5px transparent; } }
+  @keyframes pt-sweep { from { background-position: 140% 0; } to { background-position: -40% 0; } }
 
   /* ── 行32 · 长文本折叠 ── */
   .clamp-lines { display: -webkit-box; -webkit-line-clamp: var(--clamp-lines, 10); -webkit-box-orient: vertical; overflow: hidden; white-space: pre-wrap; word-break: break-word; }

core/frontend/src/routes/pipeline.tsx

@@ -364,27 +364,58 @@ function DraftShotCard({ draft, onCommit, onCancel }: {
   );
 }
 
-// 行33 · 进度提示流气泡:steps 逐条滚动;done 后折叠成一行「已完成」结果
-function ProgressStream({ steps, done }: { steps?: string[]; done?: boolean }) {
+// 行33 · 进度时间轴节点:每步一个点,竖线串起;模型调用那步(id=generate)会进入「思考」态。
+type StepNode = { id: string; label: string; done?: boolean; think?: boolean };
+
+// 行33 · 进度时间轴(参考主流 AI 应用的流式+推理体感):
+// 竖线串起每步;当前步状态字「光扫」+ 秒数;generate 步思考态用模型实时思考最新一句做状态字,
+// 右侧 › 折叠/展开思考全文(默认收起);完成后收成「已完成思考 · 用时 Xs」。非推理模型无思考节点,照常跑步骤。
+function ProgressTimeline({ steps, reasoning, stream, done, startedAt }: { steps?: StepNode[]; reasoning?: string; stream?: string; done?: boolean; startedAt?: number }) {
+  const [openThink, setOpenThink] = useState(false);
+  const [now, setNow] = useState(() => Date.now());
+  const bodyRef = useRef<HTMLDivElement>(null);
+  useEffect(() => {
+    if (done) return;
+    const t = window.setInterval(() => setNow(Date.now()), 1000);
+    return () => window.clearInterval(t);
+  }, [done]);
+  useEffect(() => { if (openThink && bodyRef.current) bodyRef.current.scrollTop = bodyRef.current.scrollHeight; }, [reasoning, openThink]);
+
   const list = steps ?? [];
-  if (done) {
-    return (
-      <div className="progress-stream done">
-        <span className="ps-check" aria-hidden="true">
-          <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2.2" strokeLinecap="round" strokeLinejoin="round"><path d="M20 6 9 17l-5-5" /></svg>
-        </span>
-        已完成 {list.length} 步思考 · 镜头脚本已生成
-      </div>
-    );
-  }
+  const elapsed = startedAt ? Math.max(0, Math.round((now - startedAt) / 1000)) : 0;
+  const activeId = !done ? ([...list].reverse().find((s) => !s.done)?.id ?? null) : null;
+  // 思考流最新一句(按句末标点/换行切)= 动态状态字,不写死
+  const latestThought = (() => {
+    const parts = (reasoning ?? "").split(/[\n。.!?！？;；]/).map((s) => s.trim()).filter(Boolean);
+    return parts.length ? parts[parts.length - 1] : "";
+  })();
+  const preamble = (stream ?? "").trim();
+
   return (
-    <div className="progress-stream">
-      {list.map((step, i) => {
-        const isLast = i === list.length - 1;
+    <div className={`progress-timeline${done ? " done" : ""}`}>
+      {list.map((s) => {
+        const rowDone = !!s.done || !!done;
+        const active = s.id === activeId;
+        const hasThink = s.id === "generate" && (reasoning ?? "").length > 0;
+        let text = s.label;
+        if (s.id === "generate") {
+          if (rowDone) text = hasThink ? `已完成思考 · 用时 ${elapsed}s` : (preamble || s.label);
+          else if (s.think) text = latestThought || "思考";
+          else text = preamble || s.label;
+        }
         return (
-          <div className={`ps-row${isLast ? " active" : " past"}`} key={i}>
-            <span className="ps-dot" aria-hidden="true"></span>
-            <span className="ps-text">{step}</span>
+          <div className={`pt-row${rowDone ? " done" : ""}${active ? " active" : ""}`} key={s.id}>
+            <span className="pt-rail" aria-hidden="true"><span className="pt-dot"></span></span>
+            <div className="pt-main">
+              <div className="pt-line">
+                <span className={`pt-text${active ? " shimmer" : ""}`}>{text}</span>
+                {active && startedAt ? <span className="pt-sec">{elapsed}s</span> : null}
+                {hasThink ? (
+                  <button type="button" className={`pt-caret${openThink ? " open" : ""}`} aria-label={openThink ? "收起思考" : "展开思考"} onClick={() => setOpenThink((v) => !v)}>›</button>
+                ) : null}
+              </div>
+              {hasThink && openThink ? <div className="pt-think" ref={bodyRef}>{reasoning}</div> : null}
+            </div>
           </div>
         );
       })}
@@ -811,7 +842,7 @@ export function PipelinePage(props: {
   const chatBodyRef = useRef<HTMLDivElement | null>(null);
   // 对话记录(本地会话态):生成动作可追溯,不再是「点了按钮、对话区永远空着」
   // kind=progress:进度提示流(行33),steps 逐条滚动出现,done 后折叠成一行结果
-  type ChatMsg = { id: number; role: "ai" | "user"; text: string; time: string; kind?: "progress"; steps?: string[]; stream?: string; done?: boolean; auto?: boolean };
+  type ChatMsg = { id: number; role: "ai" | "user"; text: string; time: string; kind?: "progress"; steps?: StepNode[]; stream?: string; reasoning?: string; done?: boolean; startedAt?: number; auto?: boolean };
   const nowHm = () => new Date().toTimeString().slice(0, 5);
   const msgIdRef = useRef(1);
   const nextMsgId = () => msgIdRef.current++;
@@ -898,7 +929,7 @@ export function PipelinePage(props: {
   async function runScriptGeneration(prompt: string, userLabel?: string, source?: string, mode?: "auto" | "theme" | "revise", targetIndex?: number) {
     pushMsg("user", userLabel || prompt);
     const progressId = nextMsgId();
-    setChatMsgs((list) => [...list, { id: progressId, role: "ai", text: "", kind: "progress", steps: [], stream: "", done: false, time: nowHm() }]);
+    setChatMsgs((list) => [...list, { id: progressId, role: "ai", text: "", kind: "progress", steps: [], stream: "", reasoning: "", done: false, startedAt: Date.now(), time: nowHm() }]);
     // 指定镜号 = 精准改一镜,强制走 revise(后端读全脚本上下文、只动那一镜)
     const agentMode = targetIndex != null ? "revise" : (mode ?? mapSourceToMode(source ?? chatMode));
     const baseVersionId = agentMode === "revise" ? currentScript?.id : undefined;
@@ -919,14 +950,35 @@ export function PipelinePage(props: {
         (evt) => {
           receivedEvent = true;
           if (evt.type === "tool") {
-            // 工具卡:running 时把 label 滚进进度流(done/error 暂只用于结束态)
-            if (evt.status === "running" && typeof evt.label === "string") {
-              const label = evt.label;
-              setChatMsgs((list) => list.map((m) => (m.id === progressId ? { ...m, steps: [...(m.steps ?? []), label] } : m)));
-            }
-          } else if (evt.type === "delta" && typeof evt.text === "string") {
+            // 工具卡 → 时间轴节点:running 入列/置活动,done 标完成(extract/check 只发 done 也入列)
+            const sid = String((evt as { id?: unknown }).id || "");
+            if (!sid) return;
+            const label = typeof evt.label === "string" ? evt.label : undefined;
+            const running = evt.status === "running";
+            setChatMsgs((list) => list.map((m) => {
+              if (m.id !== progressId) return m;
+              const steps = [...(m.steps ?? [])];
+              const idx = steps.findIndex((s) => s.id === sid);
+              if (idx === -1) steps.push({ id: sid, label: label ?? sid, done: !running });
+              else steps[idx] = { ...steps[idx], ...(label ? { label } : {}), ...(running ? {} : { done: true }) };
+              return { ...m, steps };
+            }));
+          } else if (evt.type === "reasoning" && typeof evt.text === "string") {
+            // 推理模型思考流:逐字累积,并把 generate 步置「思考态」(状态字 = 思考最新一句)
             const piece = evt.text;
-            setChatMsgs((list) => list.map((m) => (m.id === progressId ? { ...m, stream: (m.stream ?? "") + piece } : m)));
+            setChatMsgs((list) => list.map((m) => {
+              if (m.id !== progressId) return m;
+              const steps = (m.steps ?? []).map((s) => (s.id === "generate" ? { ...s, think: true } : s));
+              return { ...m, reasoning: (m.reasoning ?? "") + piece, steps };
+            }));
+          } else if (evt.type === "delta" && typeof evt.text === "string") {
+            // 出正文 = 思考结束:generate 步退出思考态,状态字转成模型那句真前言
+            const piece = evt.text;
+            setChatMsgs((list) => list.map((m) => {
+              if (m.id !== progressId) return m;
+              const steps = (m.steps ?? []).map((s) => (s.id === "generate" ? { ...s, think: false } : s));
+              return { ...m, stream: (m.stream ?? "") + piece, steps };
+            }));
           } else if (evt.type === "saved") {
             ok = true;
           } else if (evt.type === "error") {
@@ -2194,8 +2246,7 @@ export function PipelinePage(props: {
                           <div className="bubble">
                             {msg.kind === "progress"
                               ? <>
-                                  <ProgressStream steps={msg.steps} done={msg.done} />
-                                  {msg.stream ? <div style={{ marginTop: 6, opacity: 0.85, whiteSpace: "pre-wrap" }}>{msg.stream}</div> : null}
+                                  <ProgressTimeline steps={msg.steps} reasoning={msg.reasoning} stream={msg.stream} done={msg.done} startedAt={msg.startedAt} />
                                 </>
                               : <CollapsibleText text={msg.text} maxLines={10} />}
                           </div>
@@ -2222,6 +2273,8 @@ export function PipelinePage(props: {
                             </label>
                             <div className="setup-rec">根据商品目标人群推荐</div>
                             <div className="setup-foot">
+                              {/* ← 返回:收起设定卡,回到「AI全生/一句话主题/自带脚本」三选项页 */}
+                              <button type="button" className="btn btn-ghost btn-sm" onClick={() => setSetupOpen(false)}>← 返回</button>
                               <button type="button" className="btn btn-ghost btn-sm" onClick={() => {
                                 // 重新推荐:随机换一组,模拟「再推荐」(纯前端,后端会从 prompt 推断)
                                 setSetupStyle(SETUP_STYLE_KEYS[Math.floor(Math.random() * SETUP_STYLE_KEYS.length)] || setupStyle);
@@ -2529,8 +2582,13 @@ export function PipelinePage(props: {
                               const mainUrl = groupMainUrl(grp);
                               const promptValue = assetPromptDraft[entity.key] ?? grp.prompt ?? "";
                               const entBK = `ent:${kind}:${entity.key}`;
-                              const busy = isBusy(entBK) || isBusy(`${entBK}:tri`) || pendingHas(kind, entity.name);
-                              const rs = kind === "person" && grp.adopted_asset ? assetReview(grp.adopted_asset) : "";
+                              // 已生成卡(有 mainUrl)只在本次主动重跑(本地 isBusy)时转圈;
+                              // 不再被轮询到的在途任务(可能是残留/重复/三视图同名任务)误盖成「生成中」——
+                              // 否则刷新后已出图的卡会无故转圈。仅当还没出图(mainUrl 空)才用 pendingHas 兜底。
+                              const busy = isBusy(entBK) || isBusy(`${entBK}:tri`) || (!mainUrl && pendingHas(kind, entity.name));
+                              // 审核态:优先本地轮询的实时态(processing→终态),回退项目详情持久下发的 adopted_asset_review
+                              // (刷新后轮询 map 为空也能回显;不再依赖 byId,因为页面没喂全量 assets)
+                              const rs = kind === "person" && grp.adopted_asset ? (reviews[grp.adopted_asset] || grp.adopted_asset_review || "") : "";
                               return (
                                 <div className="asset-card-2" data-asset-kind={kind} data-asset-id={entity.key} key={entity.key}>
                                   <div className={`placeholder thumb-2${mainUrl && !busy ? " has-mock-media" : ""}`} style={mainUrl && !busy ? { ...mediaStyle(mainUrl), cursor: "pointer" } : { cursor: "pointer" }}
@@ -2545,7 +2603,7 @@ export function PipelinePage(props: {
                                       <strong style={{ fontSize: "13.5px", cursor: "pointer" }} onClick={() => openAssetDetail(kind, entity)}>{entity.name}</strong>
                                       <span className="spacer"></span>
                                       {rs === "active" && <span className="pill ok" title="火山真人审核已通过"><span className="dot"></span>审核✓</span>}
-                                      {rs === "failed" && <span className="pill err" title={`审核未过:${byId.get(grp.adopted_asset!)?.review_error || "建议改提示词重新生成"}`}><span className="dot"></span>审核✗</span>}
+                                      {rs === "failed" && <span className="pill err" title={`审核未过:${grp.adopted_asset_review_error || byId.get(grp.adopted_asset!)?.review_error || "建议改提示词重新生成"}`}><span className="dot"></span>审核✗</span>}
                                       {rs === "processing" && <span className="pill neutral"><span className="dot"></span>审核中</span>}
                                     </div>
                                     <PromptBox key={entity.key} value={promptValue} onChange={(v) => setAssetPromptDraft((m) => ({ ...m, [entity.key]: v }))} />

core/frontend/src/types.ts

@@ -208,6 +208,9 @@ export type Project = {
     adopted_asset_url?: string;
     candidate_assets: string[];
     candidate_asset_urls?: Record<string, string>;
+    // 采用资产的火山审核态(随项目详情持久下发,刷新后仍可回显徽章)
+    adopted_asset_review?: string;
+    adopted_asset_review_error?: string;
     // 流程步骤4 · label = 该资产对应的脚本提取标签(人物/场景名),把生成结果归回标签卡
     metadata?: { label?: string } & Record<string, unknown>;
     created_at?: string;