feat: add Gemini image fallback circuit breaker

RULES.md

@@ -73,9 +73,11 @@
 - `AUDIO_REWRITE_MODEL`：后续音频口播改写模型，默认跟随 `REWRITE_MODEL`；如果旧环境仍写 `gemini-*`，后端会自动改用 `REWRITE_MODEL`
 - `AUDIO_PRODUCT_BRIEF`：音频口播改写时注入的 SKG 产品卖点
 - `PRODUCT_VIEW_MODEL`：同一产品素材池的视角标注/自动识别模型；当前按项目要求强制使用 `gpt-image-2`
-- `IMAGE_BASE_URL` / `IMAGE_API_KEY` / `IMAGE_MODEL`：OpenAI 兼容生图网关；当前所有生图入口一律强制使用 `gpt-image-2`，不做其他图片模型 fallback
+- `IMAGE_BASE_URL` / `IMAGE_API_KEY` / `IMAGE_MODEL`：OpenAI 兼容生图网关；当前所有生图入口主模型仍为 `gpt-image-2`
 - `IMAGE_REQUEST_TIMEOUT_SECONDS`：单次图片网关请求超时，默认 60 秒；超时会直接把该视图标失败并继续下一张，避免主体 6 视图整包长时间无反馈
-- `GPT_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODELS`：保留兼容旧环境变量名，但服务端会强制主体 6 视图和所有其他生图入口都只使用 `gpt-image-2`
+- `IMAGE_FALLBACK_ENABLED` / `IMAGE_FALLBACK_MODEL`：图片主模型故障兜底；当前允许在 `gpt-image-2` 超时、429、5xx 或网络错误时临时使用 `gemini-3-pro-image-preview`，400/401/403/404 和参数错误不兜底
+- `IMAGE_CIRCUIT_FAILURE_THRESHOLD` / `IMAGE_CIRCUIT_COOLDOWN_SECONDS`：短时熔断配置，默认 `gpt-image-2` 连续 2 次上游类失败后 600 秒内直接走 Gemini 兜底；成功恢复后自动清空失败计数
+- `GPT_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODELS`：保留兼容旧环境变量名；主体 6 视图先用 `gpt-image-2`，同一套图内一旦触发 Gemini 兜底，后续视图沿用 Gemini，避免一张张等待主模型超时
 - `AI_HTTP_PROXY` / `IMAGE_HTTP_PROXY`：可选的 AI 网关出站代理；本地 launchd 后台进程不一定继承 shell 的 `http_proxy/https_proxy`，如生图报 DNS / ConnectError，可在本地 `api/.env` 配置后重启后端。`/health` 只回传是否配置代理，不回传代理地址。
 - `YTDLP_COOKIES_FILE` / `YTDLP_COOKIES_FROM_BROWSER`：可选 TikTok 下载登录态；生产云端固定使用 cookies 文件 `/run/secrets/tiktok_cookies.txt`（宿主机 `./secrets/tiktok_cookies.txt` 挂载进容器），本地开发可临时用浏览器 cookies。cookies 文件属于敏感登录态，只能放本机或服务器私有路径，不允许入库。
 - `VOICE_PROVIDER`：配音通道，服务端固定使用 `azure_openai`；旧环境若写 `minimax` 会被忽略

api/.env.example

@@ -25,9 +25,13 @@ IMAGE_BASE_URL=https://ai.skg.com/ezlink/v1
 IMAGE_API_KEY=
 IMAGE_MODEL=gpt-image-2
 IMAGE_REQUEST_TIMEOUT_SECONDS=60
+IMAGE_FALLBACK_ENABLED=true
+IMAGE_FALLBACK_MODEL=gemini-3-pro-image-preview
+IMAGE_CIRCUIT_FAILURE_THRESHOLD=2
+IMAGE_CIRCUIT_COOLDOWN_SECONDS=600
 GPT_IMAGE_MODEL=gpt-image-2
 SUBJECT_ASSET_IMAGE_MODEL=gpt-image-2
-SUBJECT_ASSET_IMAGE_MODELS=gpt-image-2
+SUBJECT_ASSET_IMAGE_MODELS=gpt-image-2,gemini-3-pro-image-preview
 # 可选：本地网络需要代理访问 ai.skg.com 时配置；launchd 不一定继承 shell 代理变量。
 AI_HTTP_PROXY=
 YTDLP_COOKIES_FILE=

api/main.py

@@ -97,14 +97,24 @@ AI_HTTP_PROXY = (
     or os.getenv("http_proxy")
     or ""
 ).strip()
-# Product decision: every image-generation/editing path is locked to gpt-image-2.
-# Environment variables may still choose the gateway URL/key, but not the model.
+# Product decision: gpt-image-2 remains the primary image model. Gemini is only
+# allowed as an outage fallback when the primary gateway times out or returns
+# transient upstream failures.
 GPT_IMAGE_MODEL = "gpt-image-2"
+IMAGE_FALLBACK_MODEL = os.getenv("IMAGE_FALLBACK_MODEL", "gemini-3-pro-image-preview").strip() or ""
+IMAGE_FALLBACK_ENABLED = os.getenv("IMAGE_FALLBACK_ENABLED", "true").strip().lower() not in {"0", "false", "no", "off"}
 IMAGE_MODEL = GPT_IMAGE_MODEL
 PRODUCT_VIEW_MODEL = GPT_IMAGE_MODEL
 SUBJECT_ASSET_IMAGE_MODEL = GPT_IMAGE_MODEL
-SUBJECT_ASSET_IMAGE_MODELS = [GPT_IMAGE_MODEL]
+SUBJECT_ASSET_IMAGE_MODELS = [GPT_IMAGE_MODEL] + (
+    [IMAGE_FALLBACK_MODEL] if IMAGE_FALLBACK_ENABLED and IMAGE_FALLBACK_MODEL and IMAGE_FALLBACK_MODEL != GPT_IMAGE_MODEL else []
+)
 IMAGE_REQUEST_TIMEOUT_SECONDS = max(15, min(180, int(os.getenv("IMAGE_REQUEST_TIMEOUT_SECONDS", "60"))))
+IMAGE_CIRCUIT_FAILURE_THRESHOLD = max(1, int(os.getenv("IMAGE_CIRCUIT_FAILURE_THRESHOLD", "2")))
+IMAGE_CIRCUIT_COOLDOWN_SECONDS = max(60, int(os.getenv("IMAGE_CIRCUIT_COOLDOWN_SECONDS", "600")))
+_IMAGE_CIRCUIT_LOCK = threading.Lock()
+_IMAGE_PRIMARY_FAILURES = 0
+_IMAGE_PRIMARY_OPEN_UNTIL = 0.0
 PRODUCT_ASSET_MAX_SIDE = max(1024, int(os.getenv("PRODUCT_ASSET_MAX_SIDE", "1600")))
 PRODUCT_ASSET_MIN_LONG_SIDE = max(512, int(os.getenv("PRODUCT_ASSET_MIN_LONG_SIDE", "900")))
 PRODUCT_ASSET_MIN_SHORT_SIDE = max(320, int(os.getenv("PRODUCT_ASSET_MIN_SHORT_SIDE", "600")))
@@ -3511,6 +3521,83 @@ def _image_is_transport_error(message: str) -> bool:
     )
 
 
+def _image_fallback_models() -> list[str]:
+    if not IMAGE_FALLBACK_ENABLED or not IMAGE_FALLBACK_MODEL or IMAGE_FALLBACK_MODEL == GPT_IMAGE_MODEL:
+        return []
+    return [IMAGE_FALLBACK_MODEL]
+
+
+def _image_circuit_snapshot() -> dict:
+    now = time.time()
+    with _IMAGE_CIRCUIT_LOCK:
+        open_until = _IMAGE_PRIMARY_OPEN_UNTIL
+        return {
+            "primary": GPT_IMAGE_MODEL,
+            "fallbacks": _image_fallback_models(),
+            "failure_threshold": IMAGE_CIRCUIT_FAILURE_THRESHOLD,
+            "cooldown_seconds": IMAGE_CIRCUIT_COOLDOWN_SECONDS,
+            "primary_failures": _IMAGE_PRIMARY_FAILURES,
+            "primary_open": open_until > now,
+            "primary_open_until": open_until if open_until > now else 0,
+            "primary_open_remaining_seconds": max(0, int(open_until - now)),
+        }
+
+
+def _image_primary_circuit_open() -> bool:
+    return _image_circuit_snapshot()["primary_open"]
+
+
+def _image_model_candidates(force_fallback: bool = False) -> list[str]:
+    fallbacks = _image_fallback_models()
+    if not fallbacks:
+        return [GPT_IMAGE_MODEL]
+    if force_fallback or _image_primary_circuit_open():
+        return fallbacks
+    return [GPT_IMAGE_MODEL, *fallbacks]
+
+
+def _image_failure_can_fallback(status_code: int, body: str, last_err: str) -> bool:
+    if status_code in (400, 401, 403, 404):
+        return False
+    return (
+        status_code == 429
+        or status_code >= 500
+        or _image_is_capacity_error(status_code, body)
+        or _image_is_transport_error(last_err)
+        or "timeout" in (body or "").lower()
+    )
+
+
+def _image_record_primary_success() -> None:
+    global _IMAGE_PRIMARY_FAILURES, _IMAGE_PRIMARY_OPEN_UNTIL
+    with _IMAGE_CIRCUIT_LOCK:
+        if _IMAGE_PRIMARY_FAILURES or _IMAGE_PRIMARY_OPEN_UNTIL:
+            print(f"[image circuit] primary {GPT_IMAGE_MODEL} recovered", flush=True)
+        _IMAGE_PRIMARY_FAILURES = 0
+        _IMAGE_PRIMARY_OPEN_UNTIL = 0.0
+
+
+def _image_record_primary_failure(reason: str) -> None:
+    global _IMAGE_PRIMARY_FAILURES, _IMAGE_PRIMARY_OPEN_UNTIL
+    if not _image_fallback_models():
+        return
+    with _IMAGE_CIRCUIT_LOCK:
+        _IMAGE_PRIMARY_FAILURES += 1
+        if _IMAGE_PRIMARY_FAILURES >= IMAGE_CIRCUIT_FAILURE_THRESHOLD:
+            _IMAGE_PRIMARY_OPEN_UNTIL = time.time() + IMAGE_CIRCUIT_COOLDOWN_SECONDS
+            print(
+                f"[image circuit] primary {GPT_IMAGE_MODEL} opened for {IMAGE_CIRCUIT_COOLDOWN_SECONDS}s "
+                f"after {_IMAGE_PRIMARY_FAILURES} failures; fallback={IMAGE_FALLBACK_MODEL}; reason={reason[:220]}",
+                flush=True,
+            )
+        else:
+            print(
+                f"[image circuit] primary {GPT_IMAGE_MODEL} failure {_IMAGE_PRIMARY_FAILURES}/{IMAGE_CIRCUIT_FAILURE_THRESHOLD}; "
+                f"fallback={IMAGE_FALLBACK_MODEL}; reason={reason[:220]}",
+                flush=True,
+            )
+
+
 def _image_failure_message(kind: str, attempts: int, last_err: str, capacity_seen: bool) -> str:
     if capacity_seen:
         return (
@@ -3604,36 +3691,37 @@ def _image_edit_call(
     fallback_text: bool = False,
     max_attempts: int = 3,
     max_side: int = 1024,
+    force_fallback_model: bool = False,
 ) -> tuple[bytes, str]:
     """通用 image edit 调用 · 失败重试 + 可选 text fallback。
     返回 (image_bytes, effective_mode) where effective_mode in {"edit","text"}。
     失败 raise RuntimeError。
     输入图自动 resize 到 max_side（默认 1024）边长后再用 multipart 上传；多参考图使用 image[]。
-    生图模型按产品规则强制使用 gpt-image-2；model/models 参数只保留兼容旧调用。"""
+    生图模型主路径使用 gpt-image-2；Gemini 只在主模型上游异常时兜底。model/models 参数只保留兼容旧调用。"""
     import base64 as b64lib
     import time as _time
     import httpx
     if not IMAGE_API_KEY:
         raise RuntimeError("IMAGE_API_KEY 或 LLM_API_KEY 未配置")
-    models_cycle = [GPT_IMAGE_MODEL]
     model = GPT_IMAGE_MODEL
     image_paths = image_path if isinstance(image_path, list) else [image_path]
     image_paths = [path for path in image_paths if path and path.exists()][:10]
     if not image_paths:
         raise RuntimeError("image edit reference image missing")
     img_bytes_list = [_prepare_image_edit_bytes(path, max_side) for path in image_paths]
-    plan: list[str] = ["edit"] * max_attempts
+    model_candidates = _image_model_candidates(force_fallback=force_fallback_model)
+    mode_plan: list[str] = ["edit"] if model_candidates != [GPT_IMAGE_MODEL] else ["edit"] * max_attempts
     if fallback_text:
-        plan.append("text")
+        mode_plan.append("text")
+    attempt_steps = [(current_mode, current_model) for current_mode in mode_plan for current_model in model_candidates]
 
     last_err = ""
     resp_data: dict = {}
     effective_mode = "edit"
     capacity_seen = False
     attempts_done = 0
-    for attempt, current_mode in enumerate(plan):
+    for attempt, (current_mode, current_model) in enumerate(attempt_steps):
         attempts_done = attempt + 1
-        current_model = models_cycle[min(attempt, len(models_cycle) - 1)]
         status_code = 0
         body = ""
         retry_after: str | None = None
@@ -3660,8 +3748,10 @@ def _image_edit_call(
             else:
                 resp_data = _image_generation_response(prompt, current_model)
             if resp_data.get("data"):
-                effective_mode = current_mode
+                effective_mode = f"{current_mode}:{current_model}"
                 model = current_model  # 记录实际成功的 model
+                if current_model == GPT_IMAGE_MODEL:
+                    _image_record_primary_success()
                 break
             err_obj = resp_data.get("error") or {}
             last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
@@ -3677,9 +3767,15 @@ def _image_edit_call(
         except Exception as e:
             last_err = f"{type(e).__name__}: {e} · model={current_model}"
 
-        next_mode_changed = attempt < len(plan) - 1 and plan[attempt + 1] != current_mode
-        if _image_should_retry(attempt, len(plan), status_code, body, last_err, next_mode_changed):
-            tag = f"retry {attempt + 1}/{len(plan)} → {GPT_IMAGE_MODEL}"
+        fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
+        if fallbackable:
+            _image_record_primary_failure(last_err)
+            if any(next_model != GPT_IMAGE_MODEL for _next_mode, next_model in attempt_steps[attempt + 1:]):
+                print(f"[image edit fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
+                continue
+        next_mode_changed = attempt < len(attempt_steps) - 1 and attempt_steps[attempt + 1][0] != current_mode
+        if _image_should_retry(attempt, len(attempt_steps), status_code, body, last_err, next_mode_changed):
+            tag = f"retry {attempt + 1}/{len(attempt_steps)} → {current_model}"
             delay = _image_retry_delay(attempt, status_code, body, retry_after)
             print(f"[image edit {tag}, sleep {delay:.0f}s] {last_err}", flush=True)
             _time.sleep(delay)
@@ -3706,20 +3802,21 @@ def _image_text_call(
     model: str | None = None,
     models: list[str] | None = None,
     max_attempts: int = 3,
+    force_fallback_model: bool = False,
 ) -> tuple[bytes, str]:
-    """Text-only image generation. 生图模型强制使用 gpt-image-2。"""
+    """Text-only image generation. gpt-image-2 primary, Gemini only as outage fallback."""
     import base64 as b64lib
     import time as _time
     import httpx
     if not IMAGE_API_KEY:
         raise RuntimeError("IMAGE_API_KEY 或 LLM_API_KEY 未配置")
-    models_cycle = [GPT_IMAGE_MODEL]
+    candidates = _image_model_candidates(force_fallback=force_fallback_model)
+    attempt_models = candidates if candidates != [GPT_IMAGE_MODEL] else [GPT_IMAGE_MODEL] * max_attempts
     last_err = ""
     capacity_seen = False
     attempts_done = 0
-    for attempt in range(max_attempts):
+    for attempt, current_model in enumerate(attempt_models):
         attempts_done = attempt + 1
-        current_model = models_cycle[min(attempt, len(models_cycle) - 1)]
         status_code = 0
         body = ""
         retry_after: str | None = None
@@ -3729,12 +3826,16 @@ def _image_text_call(
                 item = resp_data["data"][0]
                 b64 = item.get("b64_json")
                 if b64:
-                    return b64lib.b64decode(b64), "text"
+                    if current_model == GPT_IMAGE_MODEL:
+                        _image_record_primary_success()
+                    return b64lib.b64decode(b64), f"text:{current_model}"
                 if item.get("url"):
                     with ai_http_client(timeout=IMAGE_REQUEST_TIMEOUT_SECONDS) as client:
                         image_resp = client.get(item["url"])
                         image_resp.raise_for_status()
-                        return image_resp.content, "text"
+                        if current_model == GPT_IMAGE_MODEL:
+                            _image_record_primary_success()
+                        return image_resp.content, f"text:{current_model}"
             err_obj = resp_data.get("error") or {}
             last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
         except httpx.HTTPStatusError as e:
@@ -3748,9 +3849,15 @@ def _image_text_call(
             body = str(e)
             status_code = 429 if "429" in body or "saturated" in body.lower() or "饱和" in body else 0
             capacity_seen = capacity_seen or _image_is_capacity_error(status_code, body)
-        if _image_should_retry(attempt, max_attempts, status_code, body, last_err):
+        fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
+        if fallbackable:
+            _image_record_primary_failure(last_err)
+            if any(next_model != GPT_IMAGE_MODEL for next_model in attempt_models[attempt + 1:]):
+                print(f"[image text fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
+                continue
+        if _image_should_retry(attempt, len(attempt_models), status_code, body, last_err):
             delay = _image_retry_delay(attempt, status_code, body, retry_after)
-            print(f"[image text retry {attempt + 1}/{max_attempts} → {GPT_IMAGE_MODEL}, sleep {delay:.0f}s] {last_err}", flush=True)
+            print(f"[image text retry {attempt + 1}/{len(attempt_models)} → {current_model}, sleep {delay:.0f}s] {last_err}", flush=True)
             _time.sleep(delay)
         else:
             break
@@ -4116,7 +4223,8 @@ def health() -> dict:
             "image_base_url": IMAGE_BASE_URL or LLM_BASE_URL or "openai-default",
             "image_request_timeout_seconds": IMAGE_REQUEST_TIMEOUT_SECONDS,
             "ai_proxy_configured": bool(AI_HTTP_PROXY),
-            "image_fallbacks": [GPT_IMAGE_MODEL],
+            "image_fallbacks": _image_fallback_models(),
+            "image_circuit": _image_circuit_snapshot(),
             "subject_image": SUBJECT_ASSET_IMAGE_MODEL,
             "subject_image_fallbacks": SUBJECT_ASSET_IMAGE_MODELS,
             "voice_provider": VOICE_PROVIDER,
@@ -4447,16 +4555,18 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
     if req.mode == "edit":
         img_bytes_in = reference_path.read_bytes()
 
-    # 尝试 i2i 最多 3 次，全失败时降级 text-only 再试 1 次
-    plan: list[str] = ([req.mode] * 3) if req.mode == "edit" else [req.mode]
+    # 尝试 i2i；主模型上游异常时允许 Gemini 兜底。无兜底时保留旧的多次重试。
+    model_candidates = _image_model_candidates()
+    plan: list[str] = ([req.mode] if model_candidates != [GPT_IMAGE_MODEL] else [req.mode] * 3) if req.mode == "edit" else [req.mode]
     if req.mode == "edit":
         plan.append("text")  # i2i 都失败时自动降级
+    attempt_steps = [(current_mode, current_model) for current_mode in plan for current_model in model_candidates]
     resp_data: dict = {}
     last_err = ""
     effective_mode = req.mode
     capacity_seen = False
     attempts_done = 0
-    for attempt, current_mode in enumerate(plan):
+    for attempt, (current_mode, current_model) in enumerate(attempt_steps):
         attempts_done = attempt + 1
         status_code = 0
         body = ""
@@ -4471,20 +4581,23 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
                         headers={
                             "Authorization": f"Bearer {IMAGE_API_KEY}",
                         },
-                        data={"model": model, "prompt": full_prompt, "n": "1"},
+                        data={"model": current_model, "prompt": full_prompt, "n": "1"},
                         files={"image": ("reference.jpg", img_bytes_in, "image/jpeg")},
                     )
                     r.raise_for_status()
                     resp_data = r.json()
             else:
                 # text-only
-                resp_data = _image_generation_response(full_prompt, model)
+                resp_data = _image_generation_response(full_prompt, current_model)
 
             if resp_data.get("data"):
-                effective_mode = current_mode
+                effective_mode = f"{current_mode}:{current_model}"
+                model = current_model
+                if current_model == GPT_IMAGE_MODEL:
+                    _image_record_primary_success()
                 break
             err_obj = resp_data.get("error") or {}
-            last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]}"
+            last_err = f"empty data · {err_obj.get('code', '')} · {str(err_obj.get('message', ''))[:200]} · model={current_model}"
         except httpx.HTTPStatusError as e:
             body = e.response.text
             status_code = e.response.status_code
@@ -4498,16 +4611,22 @@ def generate_image(job_id: str, idx: int, req: GenerateReq) -> Job:
                 or "timeout" in body.lower()
                 or _image_is_capacity_error(status_code, body)
             )
-            last_err = f"HTTP {status_code}: {body[:200]}"
+            last_err = f"HTTP {status_code}: {body[:200]} · model={current_model}"
             if not transient:
                 raise HTTPException(500, f"image gen HTTP {status_code}: {body[:300]}")
         except Exception as e:
-            last_err = f"{type(e).__name__}: {e}"
+            last_err = f"{type(e).__name__}: {e} · model={current_model}"
 
-        next_mode_changed = attempt < len(plan) - 1 and plan[attempt + 1] != current_mode
-        if _image_should_retry(attempt, len(plan), status_code, body, last_err, next_mode_changed):
-            next_mode = plan[attempt + 1]
-            tag = f"fallback → {next_mode}" if next_mode != current_mode else f"retry {attempt + 1}/{len(plan)}"
+        fallbackable = current_model == GPT_IMAGE_MODEL and _image_failure_can_fallback(status_code, body, last_err)
+        if fallbackable:
+            _image_record_primary_failure(last_err)
+            if any(next_model != GPT_IMAGE_MODEL for _next_mode, next_model in attempt_steps[attempt + 1:]):
+                print(f"[image gen fallback → {IMAGE_FALLBACK_MODEL}] {last_err}", flush=True)
+                continue
+        next_mode_changed = attempt < len(attempt_steps) - 1 and attempt_steps[attempt + 1][0] != current_mode
+        if _image_should_retry(attempt, len(attempt_steps), status_code, body, last_err, next_mode_changed):
+            next_mode = attempt_steps[attempt + 1][0]
+            tag = f"fallback → {next_mode}" if next_mode != current_mode else f"retry {attempt + 1}/{len(attempt_steps)}"
             print(f"[image gen {tag}] {last_err}", flush=True)
             _time.sleep(_image_retry_delay(attempt, status_code, body, retry_after))
         else:
@@ -5677,10 +5796,11 @@ def _generate_subject_assets_sync(job_id: str, idx: int, element_id: str, req: G
         "Avoid bulky collars, scarves, hair, hoods, props, or poses that hide the neck/shoulder placement area. "
         "For back and close-up views, prioritize the cervical spine, shoulder blades, upper trapezius, and clean wearable-device contact area. "
     )
-    models = [GPT_IMAGE_MODEL]
+    models = SUBJECT_ASSET_IMAGE_MODELS
     generated: list[SubjectAsset] = []
     generation_errors: list[str] = []
     first_generation_error: RuntimeError | None = None
+    pack_force_fallback_model = _image_primary_circuit_open()
     try:
         for view, view_label in _subject_view_labels(req.subject_kind, req.views):
             closeup_view = view in {"bust", "back_detail", "bust_front", "bust_left_45", "bust_right_45", "back_neck_detail"} or "detail" in view
@@ -5741,14 +5861,18 @@ def _generate_subject_assets_sync(job_id: str, idx: int, element_id: str, req: G
             try:
                 if similar_mode:
                     print(
-                        f"[subject assets] reconstruction_mode=similar endpoint=/images/generations view={view} image_refs=0 model={GPT_IMAGE_MODEL}",
+                        f"[subject assets] reconstruction_mode=similar endpoint=/images/generations view={view} image_refs=0 model={'fallback' if pack_force_fallback_model else GPT_IMAGE_MODEL}",
                         flush=True,
                     )
-                    img_bytes, _mode = _image_text_call(prompt, models=models, max_attempts=3)
+                    img_bytes, _mode = _image_text_call(prompt, models=models, max_attempts=3, force_fallback_model=pack_force_fallback_model)
+                    if _mode.endswith(f":{IMAGE_FALLBACK_MODEL}"):
+                        pack_force_fallback_model = True
                 else:
                     if model_src is None:
                         raise RuntimeError("subject asset edit reference image missing")
-                    img_bytes, _mode = _image_edit_call(model_src, prompt, models=models, fallback_text=False, max_attempts=3, max_side=1280)
+                    img_bytes, _mode = _image_edit_call(model_src, prompt, models=models, fallback_text=False, max_attempts=3, max_side=1280, force_fallback_model=pack_force_fallback_model)
+                    if _mode.endswith(f":{IMAGE_FALLBACK_MODEL}"):
+                        pack_force_fallback_model = True
             except RuntimeError as e:
                 if first_generation_error is None:
                     first_generation_error = e

deploy/.env.production.example

@@ -43,9 +43,13 @@ IMAGE_BASE_URL=https://ai.skg.com/ezlink/v1
 IMAGE_API_KEY=
 IMAGE_MODEL=gpt-image-2
 IMAGE_REQUEST_TIMEOUT_SECONDS=60
+IMAGE_FALLBACK_ENABLED=true
+IMAGE_FALLBACK_MODEL=gemini-3-pro-image-preview
+IMAGE_CIRCUIT_FAILURE_THRESHOLD=2
+IMAGE_CIRCUIT_COOLDOWN_SECONDS=600
 GPT_IMAGE_MODEL=gpt-image-2
 SUBJECT_ASSET_IMAGE_MODEL=gpt-image-2
-SUBJECT_ASSET_IMAGE_MODELS=gpt-image-2
+SUBJECT_ASSET_IMAGE_MODELS=gpt-image-2,gemini-3-pro-image-preview
 # Optional outbound proxy for AI gateway calls. Leave blank on normal VPS networking.
 AI_HTTP_PROXY=
 

web/components/ad-recreation-board.tsx

@@ -1189,11 +1189,11 @@ function modelList(values: Array<string | undefined>) {
 }
 
 function imageModelChain(models?: RuntimeModels) {
-  return modelList([models?.image || "gpt-image-2"])
+  return modelList([models?.image || "gpt-image-2", ...(models?.image_fallbacks || [])])
 }
 
 function subjectImageModelChain(models?: RuntimeModels) {
-  return modelList([models?.subject_image || "gpt-image-2"])
+  return modelList([models?.subject_image || "gpt-image-2", ...(models?.subject_image_fallbacks || [])])
 }
 
 function resolveVideoModelLabel(models: RuntimeModels | undefined, model: string) {
@@ -1224,7 +1224,7 @@ function productModelTrace(models?: RuntimeModels): ModelTraceSpec {
     chain: [
       `批量视角识别：${modelValue(models?.product_view)} 多图读取同一产品素材，标注视角、佩戴者左右、上下、内外侧、用途和风险`,
       "识别兜底：批量失败会按单图重试；仍失败或文件缺失时写入本地默认视角，并在 risk/note 标明兜底原因",
-      `缺角度补图：${imageModelChain(models)} 走 /images/edits，最多读取 6 张已上传参考图补齐缺失视角；失败保留重试入口，不自动换模型`,
+      `缺角度补图：${imageModelChain(models)} 走 /images/edits，最多读取 6 张已上传参考图补齐缺失视角；只有 gpt-image-2 超时、限流或 5xx 上游异常时才自动兜底`,
       "前端只保存标注和 AI 补图结果；后续首尾帧/视频规划每条最多挑 6 张相关产品图",
     ],
     note: "上传产品图、重新识别、缺视角重试都会使用这组模型链路。",
@@ -1244,7 +1244,7 @@ function similarSubjectModelTrace(models: RuntimeModels | undefined, subjectStyl
       `视觉 brief：${modelValue(models?.vision)} 把关键帧/模板图转成非身份化文字 brief；失败时继续用用户方向和模板文字`,
       `主体类型：${typeLabel}`,
       "主体设定：前端把随机组合或手动选择的性别、年龄、着装、地域人种、肤色、体型、发型和气质锁定为结构化 profile",
-      `图像生成：${subjectImageModelChain(models)} 走 /images/generations 逐张文字生图；当前 similar 模式不上传原帧或模板图作为 image-edit 参考`,
+      `图像生成：${subjectImageModelChain(models)} 走 /images/generations 逐张文字生图；gpt-image-2 是主模型，超时、429 或 5xx 时短时熔断并兜底 Gemini；当前 similar 模式不上传原帧或模板图作为 image-edit 参考`,
       "身份锁定：整套图必须是同一个主体，性别表现、年龄段、体型、材质和风格保持一致",
     ],
     note: "这是生成类似但创新的主体，不是复制、抠出或复刻源视频人物身份；内置形象也只作为方向参考。",

web/lib/api.ts

@@ -272,6 +272,16 @@ export interface RuntimeModels {
   image?: string
   image_base_url?: string
   image_fallbacks?: string[]
+  image_circuit?: {
+    primary?: string
+    fallbacks?: string[]
+    failure_threshold?: number
+    cooldown_seconds?: number
+    primary_failures?: number
+    primary_open?: boolean
+    primary_open_until?: number
+    primary_open_remaining_seconds?: number
+  }
   subject_image?: string
   subject_image_fallbacks?: string[]
   voice_provider?: string