feat: 错误提示中文化、GPT Image 余额查询、Nano Banana 自动重试
- gemini_client: 更新 429/504 中文错误文案，与产品文案对齐 - gpt_image_client: 新增 query_balance_sync / format_balance_info 余额查询方法 - gpt_image: 每次执行后打印余额日志（finally 块保证触发） - nano_banana_pro: 单张生成支持自动重试，遇 429/503/504 最多重试 4 次，指数退避 2-32s，终端显示显眼重试状态 - nano_banana_pro: 关闭调试日志（DEBUG_LOG_ENABLED = False） Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
co-authored by
Claude Sonnet 4.5
Claude Sonnet 4.5
parent
949f7bb180
commit
fe3cc65b71
+60
-48
@@ -141,20 +141,44 @@ class O1keyGPTImage:
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raise ValueError("未授权！") from None
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raise
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# ── 4. 解析批量提示词 ─────────────────────────────────────────────────
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batch_prompts = parse_batch_prompts(prompt)
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try:
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# ── 4. 解析批量提示词 ─────────────────────────────────────────────
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batch_prompts = parse_batch_prompts(prompt)
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# ── 5. 调用 API ───────────────────────────────────────────────────────
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all_pil_images = []
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# ── 5. 调用 API ───────────────────────────────────────────────────
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all_pil_images = []
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if batch_prompts:
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# 批量模式：逐条提示词调用
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total = len(batch_prompts)
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print(f"[o1key GPT Image] 批量模式 | {total} 条提示词 | 每条生成 {生图数量} 张")
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for idx, p in enumerate(batch_prompts, 1):
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if batch_prompts:
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# 批量模式：逐条提示词调用
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total = len(batch_prompts)
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print(f"[o1key GPT Image] 批量模式 | {total} 条提示词 | 每条生成 {生图数量} 张")
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for idx, p in enumerate(batch_prompts, 1):
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try:
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pil_images = client.run_sync(
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prompt=p,
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model=模型,
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quality=quality,
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background="auto",
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size=size,
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n=生图数量,
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seed=seed,
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image_tensor=图片,
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mask_tensor=遮罩,
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)
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all_pil_images.extend(pil_images)
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ✓ {snippet}")
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except Exception as e:
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error_msg = str(e).split('\n')[0]
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ❌ {snippet} → {error_msg}")
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else:
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# 单提示词模式
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if not prompt or not prompt.strip():
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raise ValueError("提示词不能为空")
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try:
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pil_images = client.run_sync(
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prompt=p,
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prompt=prompt,
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model=模型,
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quality=quality,
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background="auto",
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@@ -165,47 +189,35 @@ class O1keyGPTImage:
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mask_tensor=遮罩,
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)
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all_pil_images.extend(pil_images)
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ✓ {snippet}")
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except Exception as e:
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error_msg = str(e).split('\n')[0]
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ❌ {snippet} → {error_msg}")
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else:
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# 单提示词模式
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if not prompt or not prompt.strip():
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raise ValueError("提示词不能为空")
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try:
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pil_images = client.run_sync(
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prompt=prompt,
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model=模型,
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quality=quality,
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background="auto",
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size=size,
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n=生图数量,
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seed=seed,
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image_tensor=图片,
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mask_tensor=遮罩,
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)
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all_pil_images.extend(pil_images)
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except Exception as e:
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error_msg = str(e).split('\n')[0]
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print(f"[o1key GPT Image] ❌ {error_msg}")
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raise RuntimeError(error_msg) from None
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print(f"[o1key GPT Image] ❌ {error_msg}")
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raise RuntimeError(error_msg) from None
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# ── 6. 检查是否有可用图像 ─────────────────────────────────────────────
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if not all_pil_images:
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raise RuntimeError("所有提示词均生成失败，无可用图像输出")
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# ── 6. 检查是否有可用图像 ─────────────────────────────────────────
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if not all_pil_images:
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raise RuntimeError("所有提示词均生成失败，无可用图像输出")
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# ── 7. PIL → tensor ───────────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(all_pil_images)
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# ── 7. PIL → tensor ───────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(all_pil_images)
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# ── 8. 完成日志 ───────────────────────────────────────────────────────
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elapsed = time.time() - start_time
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print(
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f"[o1key GPT Image] 完成！耗时 {elapsed:.1f}s，"
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f"输出 {output_tensor.shape[0]} 张 "
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f"{output_tensor.shape[2]}×{output_tensor.shape[1]}"
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)
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# ── 8. 完成日志 ───────────────────────────────────────────────────
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elapsed = time.time() - start_time
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print(
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f"[o1key GPT Image] 完成！耗时 {elapsed:.1f}s，"
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f"输出 {output_tensor.shape[0]} 张 "
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f"{output_tensor.shape[2]}×{output_tensor.shape[1]}"
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)
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return (output_tensor,)
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return (output_tensor,)
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finally:
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self._print_balance(client)
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def _print_balance(self, client):
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try:
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balance_data = client.query_balance_sync()
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balance_info = client.format_balance_info(balance_data)
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print(f"[o1key GPT Image] {balance_info}")
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except Exception:
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pass
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+32
-15
@@ -52,7 +52,7 @@ except ImportError:
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# ============================================================================
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# 是否启用调试日志（打印完整的 API 响应内容）
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# 设置为 True 以启用调试日志，False 以禁用
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DEBUG_LOG_ENABLED = True
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DEBUG_LOG_ENABLED = False
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# 是否启用请求体日志（打印发送给 API 的请求体，base64 图片数据将自动截断）
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# 设置为 True 以启用请求体日志，False 以禁用
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REQUEST_LOG_ENABLED = False
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@@ -665,20 +665,37 @@ class NanoBananaPro:
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else:
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# 单提示词模式
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if 生图数量 == 1:
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# 单张：同步生成，输出 tensor
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generated_images = self.client.generate_sync(
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prompt=prompt,
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model=模型,
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resolution=分辨率,
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aspect_ratio=宽高比,
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batch_size=1,
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images=input_images,
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progress_callback=progress_callback,
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debug=DEBUG_LOG_ENABLED,
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debug_request=REQUEST_LOG_ENABLED,
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enable_grounding=enable_grounding,
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enable_image_search=enable_image_search,
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)
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# 单张：同步生成，自动重试（429/503/504）
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_RETRY_CODES = ("429", "503", "504")
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_MAX_RETRIES = 5
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for _attempt in range(1, _MAX_RETRIES + 1):
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try:
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generated_images = self.client.generate_sync(
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prompt=prompt,
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model=模型,
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resolution=分辨率,
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aspect_ratio=宽高比,
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batch_size=1,
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images=input_images,
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progress_callback=progress_callback,
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debug=DEBUG_LOG_ENABLED,
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debug_request=REQUEST_LOG_ENABLED,
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enable_grounding=enable_grounding,
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enable_image_search=enable_image_search,
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)
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break
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except RuntimeError as e:
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error_msg = str(e)
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if any(code in error_msg for code in _RETRY_CODES) and _attempt < _MAX_RETRIES:
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_wait = 2 ** _attempt
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print(f"{'=' * 60}")
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print(f"⚠️ Nano Banana Pro 自动重试 [{_attempt}/{_MAX_RETRIES - 1}]")
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print(f" 原因：{error_msg}")
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print(f" 等待 {_wait}s 后重试...")
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print(f"{'=' * 60}")
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time.sleep(_wait)
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else:
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raise
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else:
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# 多张：异步并发，内存输出
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