您可以將單一的創意簡報轉化為一周的品牌社群視覺效果，並且只需幾分鐘即可完成——但前提是您的 AI 圖片生成器、授權和自動化真正對齊。作為社群經理或代理商，您正在應對高需求的平臺準備圖像，同時擔心成本、生成速度、商業用途權利以及如何將圖像生成嵌入評論回覆、DM 和計畫發文中。
本指南提供了領先 AI 圖片生成工具的實用、決策準備比較，並根據圖像質量、授權清晰度、API 和 Zapier 支援、批量吞吐量和成本進行逐項評分。您將獲得選擇合適平臺的排名矩陣、適合社群渠道的即用提示模板，以及展示如何在回覆、DM 和計畫發文中自動化視覺效果以加快團隊速度並保持合規性的逐步集成範例。
為什麼 AI 圖片生成器對社群媒體團隊很重要
AI 圖片生成器將視覺製作從預先計畫的拍攝轉移到即時創意，讓團隊在帖子、故事、廣告和社群回覆上快速迭代。社群管理者不必等待數天的攝影師或創意簡報，即可生成多個變種——產品顏色搭配、背景處理、作為圖片的風格化字幕——並在幾小時內選擇贏家。這種速度對於像故事、反應性活動掛勾和需要視覺火花的評論回覆等時間敏感格式很重要。
僅按圖像質量評估生成器忽略了社群工作流程的操作現實。對於大批量發佈團隊，您還必須評估：
API & 自動化: 工具是否提供 API 或與 Zapier 兼容的連接器，使其能夠以程式化方式生成 DM、評論回覆或後端系統的圖像？
授權及商業用途: 輸出是否已清除可供廣告和轉售使用，並且是否有使用限制或需提供版權標註？
批量生成、速度及圖像每個成本: 您能否足夠快速地創建數百個變異且在預算範圍內進行 A/B 實驗？
實用提示：通過將生成器打成小批次工作流——通過 API 生成 50 類低解析度變體，檢查質量，然後對贏家進行升級——來測量每個贏家圖像的真實成本。
誰受益最多？社群管理者和社群團隊需要快速反應的資產以應對回覆和危機處理；成長行銷者需要大量變異以進行轉換實驗；代理商需要授權清晰和批量工具以服務於更多客戶；創建者受益於快速樣圖和個性化 DM。像 Blabla 這樣的工具通過自動化 AI 驅動回覆和 DM來補充生成器，將生成的圖像編織進對話中，同時審核內容和將社群互動轉化為銷售——而不會為您安排或發佈帖子。
範例工作流程：根據頂級粉絲評論生成個性化產品樣圖，透過 Blabla 的 AI 回覆自動化將該樣圖發送至 DM 漏斗，並記錄轉換為銷售；或通過 API 批量製作區域廣告創意，運行微測試，然後升級贏家。
評估標準及測試方法
為了以反映真實社群運作的方式比較生成器，我們選擇了以生產優先事項為重點的評估標準和測試方法——一致性、自動化、成本和監管——而不僅僅是視覺保真度。
我們的比較框架專注於產品工作流程中重要的度量標準：
圖像質量: 典型社群格式中的現實主義、構圖、瑕疵和色彩保真度。
風格一致性: 能夠在多個提示和批次中重現品牌外觀。
客製化: 控制提示、負面提示及參數調整。
速度和圖像每個成本: API 延遲、吞吐量及每生成資產的結算成本。
批量支援: 代理規模運行的批量生成、並行請求和速率限制。
API 和 Zapier 支援: 程式化存取、webhook流程及Zapier 動作以進行自動化。
授權及商業用途: 權利的清晰度、版權標註要求及轉售規則。
監管和倫理: 自動化回覆時的內容過濾器、安全工具和假陽性處理。
測試設置和協議
所有工具均提供相同的提示和配置，確保公平的結果。我們目標三種分辨率，反映真實社群用途：
Feed 發文: 1080×1080 px (正方形)
故事/Reel: 1080×1920 px (縱向)
廣告/英雄: 2048×1152 px (寬屏)
批次運行包括 10、50 和 200 張圖像的大小，以測量可擴展行為。對於定時，我們測量了中位數和 95 百分位的 API 延遲和端到端吞吐量；對於成本，我們記錄了每張圖像的計費單位，並推算每 1000張圖像的成本。
提示、基準和評分
提示類型模擬生產需求：產品英雄照、生活方式 UGC、品牌化平坦化、可疊加的廣告和縮圖。範例提示：“明亮的產品英雄照、最小的陰影、白色背景、45度角、細節豐富。”再現性使用固定的種子支持時。
主觀質量被五名審核員（社群經理、設計師和成長行銷者）按現實、品牌契合度和可編輯性（0–5分制）打分。客觀指標與主觀得分相結合，使用加權公式優先考慮一致性和社群工作流程的 API 可靠性。實用提示：在發布前通過 Blabla 路由生成的草稿，以進行自動化監管並提供 AI 支持的評論或 DM 回覆，使視覺和文本作品配對。
我們在移動端 Feed 模型中進行盲測，以測量點擊意圖和編輯時間；實用的自動化注意事項：標準化提示模板和負面提示，以減少批次的編輯負擔。
並排比較：Midjourney、DALL·E、Stable Diffusion 及主要替代品
既然我們了解了評估標準和測試方法，讓我們探索一下在團隊最關注的社群工作流程中，領先的生成器如何並排表現——Feed、故事和廣告資產，以及將圖像轉化為對話的自動化層。
快速框架： 此比較涵蓋了Midjourney、DALL·E（OpenAI）、Stable Diffusion的變化、Runway、Adobe Firefly和Blabla。注意：Blabla 不是圖像生成器；它是一個AI 社群互動平臺，能將生成的圖像集成到評論回覆和 DM 中，自動化對話流程並監控互動行為。在列出 Blabla 的地方，我們評估每個生成器在通過 Blabla 的自動化和監管功能時的表現。
圖像質量、美學範圍和客製化
在相同的 Feed、故事和廣告提示中，各工具顯示了顯著的強項：
Midjourney – 在創意、風格化美學和寫實/奇幻融合中最強。對於 Feed/輪播提示，它一致產生令人眼前一亮的構圖，帶有陰鬱的照明和豐富的紋理。故障模塊：當提示過載時，偶爾會出現面部不對稱和過度裝飾細節。
DALL·E – 在乾淨、文字表述和產品展示方面可靠。它在影像真實性和插畫輸出方面平衡得很好，使其在廣告中主題清晰。故障模塊：圖像中的簡單文字可能難以辨認；與 Midjourney 相比，構圖可能較為保守。
Stable Diffusion（及調整檢查點）– 在使用調校過的模型和風格預設時，最靈活於品牌一致的風格。當控制了圖示和負面提示時，它在批次之間製作一致的輸出方面非常出色。故障模塊：開箱即用的模型可能會對面部和小文本生成瑕疵，除非進行後處理。
Runway – 在動作和序列連續性方面很強；對於靜止圖像，它在穩定性擴散上的競爭力不遜色，但在團隊需要快速視頻或動畫故事變體時表現出色。故障模塊：除非鎖定色彩配置，否則批次間會出現色彩變化。
Adobe Firefly – 為設計工作流程優化，具備可靠的授權條款以供商業使用，預測色彩再現和緊密的 Adobe 工具集成。故障模塊：與 Midjourney 相比，構圖較不冒險。
Blabla – 再次強調，它不是影像引擎。Blabla 的重要之處在於它自動化了由以上引擎生成的圖像的分配和對話使用：將最高質量的生成器輸出路由到個性化評論回覆、DM 或管控回應中以提高互動。
速度和吞吐量：延遲、并行性和圖像每個成本
在典型社群工作流程（批次由 10–100 張圖像、Feed 和故事解析度組成）測量如下：
Midjourney – 單個影像延遲依隊列和模型而異（快速模式對較高品質模式）。預期最快模式下每張影像需 5–20 秒；成本隨質量設置擴展。并行性受益於付費級別，但批量生成比本地 SD 批處理慢。
DALL·E – 單次提示通過 API 通常每張影像需 3–10 秒，圖像每個成本居中；適當併發性，但大型批次運行會變得昂貴。
Stable Diffusion – 自置主機或批量優化雲端執行下以最快速度運行：GPU 群集下每張影像少於 5 秒。若基礎設施能在卷中攤銷並可達最低圖像每個成本。管控的 SD 提供者也能提供競爭力的批量端點。
Abridor – 梁一雪；在創作故事序列特別競爭力。在仍影像方面其取决於模組和 GPU 配置，通常每影像需4～15秒.
Adobe Firefly – 有通常的延遲和企業吞吐，尊照 Adobe 商業授權和合規特徵的成本。
Blabla – 不生成影像，因此延遲不適用；相反，測量好Blabla如何影響端到端交付時間：Blabla 的自動化能將人的處理時間從數小時減少至數分鐘，通過自動選擇生成的影像，附加到AI回覆並發送DM 或評論回覆（在平臺政策允許的地方）。這減少了人工策展的開銷並降低有效的交互成本。
客製化與控制：微調，图像到图像及种子控制
为了品牌一致性需要在批次之间实现预测结果。以下是哪些工具对比:
Stable Diffusion—在客製化方面最佳：您可以微调检查点、锁定种子、使用图像到图像的去噪控制，并自主管理模型以便每次批次符合品牌指南。实用提示：将 50 至 200 个品牌图像创建成小型微调检查点，以在数千个输出中稳固颜色和色调。
Midjourney—在提示参量中提供风格预设和种子控制；对于创意多样性来说非常优秀，但在制度化严格的品牌外观上跨越庞大批次并不和 SD 一样简单。
DALL·E—提供提示工程杠杆和编辑工具的一贯性(使用用于填补的遮罩)，但对比 SD 在检查点微调上控制不多。
Runway & Adobe Firefly—都提供风格控制和强大的图像到图像工作流程；Firefly 的设计模板帮助维持品牌资产的可预测结果。
Blabla—擅长于操作控制而非像素控制：它让您对特定的生成输出进行映射到回覆模板，执行监督规则，A/B 测试哪个生成样式驱动更高的 DM 转化，并应用模板使得所有自动回覆遵循语气和合规要求。示例: 如果网红评论触发自动对话流，Blabla 可以在第一条 DM 中选择一个 Stable Diffusion 产品图像（品牌调和）和一个 Midjourney 生活方式变体进行后续处理，基于互动规则。
实际实例及常见故障模式
测试同样的提示(产品照，生活方式肖像，故事垂直广告)产出的实际建议如下:
博文产品拍摄提示 — "干净的白色背景，3/4视图，品牌标记在下方": DALL·E 提供最具文字解释性的、商业准备的视觉；SD 需要品牌检查点才能可靠匹配标记位置；Midjourney 产生了艺术照明但不一致的标记清晰度。
生活方式肖像提示 — "年轻职业人士，城市天台，黄金时段": Midjourney 在氛围和戏剧性照明领先；SD 在种子固定时产生可重复的变异；DALL·E 很直接但不那么具电影魅力。
故事垂直广告 — "30秒故事框，醒目标题区域，CTA空闲空间": Runway 和 Firefly 产生可编辑资产具有一致的色彩，SD 在批次下快速生产多个可行框架，Midjourney 需要手动裁剪和修饰以提升文字清晰度。
各个生成器中的常见故障模式: 面部不对称、奇怪的手指渲染、嵌入的文字不清晰以及标记的透视错误。实际修正: 使用图像到图像进行增量编辑，为批次一致性锁定种子，并在自动化之前对生成的图像进行快速文本清晰度和品牌色彩检查。
Midjourney、DALL·E和Stable Diffusion的具体比较
简单说: Midjourney = 最高的创意装饰和风格差异；DALL·E = 可靠、文字解释和适合产品；Stable Diffusion = 最可控和按规模成本效益最高。针对社群团队需要既出众创意又需预期批次，混合方式最佳：以 Midjourney 为英雄影像原型化，以 Stable Diffusion 标准化海量资产，并使用 DALL·E 为产品详细镜头。
最后，Blabla 将这些选择整合进操作中：通过自动回复和 DM、应用监督规则、和将正确的生成输出引导至对话中，Blabla 节省了数小时的手动工作，提高了回复率，并帮助品牌从垃圾邮件和滥用中保护自己——无需为您额外安排或发布帖文将生成图像转化为可量化的互动。
价格、计划和社群活动的真实图像每个成本
现在既已比较视觉质量和 API 支持，让我们来分析一下价格如何真正影响大型社群活动和对话用户案例。
计划大致分为四类模式，每种模式对于社群团队有利弊：
免费层级 — 每日图像数有限或低解析度积分；有助于实验，但不是规模化。
按需付费 / 积分包 — 按图像或像素购买积分；对小型突发性但线性规模化成本具有预见性。
订阅 — 以月费定额或平价无限尝试的方式提供监控；对稳定创作者来说不错。
企业/协商合同 — 自定义SLA、大量定价、专用吞吐量和商业活动的授权。
要计算图像每个成本，考虑不仅是标题价格。包括：
基础生成成本 — 请求的解析度每图像或每积分价格。
升级和编辑 — 每项升级、变异或图像到图像处理可能会加倍或三倍使用积分。
解析度和输出格式 — 高解析度广告创意成本超过移动故事尺寸的导出。
速率限制及并行性 — 慢吞吐量会增加工程或编排成本。
超额费用和未使用积分 — 预付包可能携带浪费；按需付费可能出乎意料地飙升。
实际示例计算（近似）：
10,000图像的月度活动 — Feed广告，混合分辨率： 如果供应商对标准分辨率收费 $0.08/图像，高解析度/升级收费 $0.20，70/30分比例发出的费用为（7,000×$0.08）+(3,000×$0.20) = $560 + $600 = $1,160 → 每图像 $0.116。
广告创意与短视频故事批次:广告创意(较大，常升级）可能平均每图像 $0.18–$0.30；故事尺寸批次(优化低解析度)可以是每个图像$0.04–$0.10。对于 1000资产:广告 ≈ $180–$300，故事 ≈ $40–$100。
哪些供应商提供可预测的大量定价？
主要提供商的企业供应（协商合同）提供月度承诺量、SLA 和封顶超额费用——最适合每月 10k+ 图像。
部分平台出售批量积分包带梯级折扣；其他仅提供订阅因而节流吞吐而不是单价降低。
操作提示：基于用户案例（回复/DM相对于广告制作)建模管道，并添加10%的其它工具以用于修饰和升级。对于对话自动化，使用Blabla协调何时以及如何请求和引入生成图像到回复——这将消耗集中到消费中，以便监控积分使用情况，限制支出，同时将支出与转化指标捆绑于一起，而不用额外安排自己帖子发布。
商业社群使用的授权、监控、版權及倫理考慮
既然我們理解了價格、计划與圖像每個真實成本，接下來讓我們檢查使用AI影像商業時所需的授权、监控、版權及倫理保障。
大多数主要生成器都包含商業用途條款但限制不同：
OpenAI（DALL·E）：用户生成影像通常允许商業使用；检查归属和模型更新。
Midjourney：付费层包括商業许可权；免费/alpha输出可能有限制。
Stable Diffusion：许可取决于检查点模型和训练数据；某些检查点明确用于商業使用而社区模型可能不行。
Adobe Firefly：设计用于商業创作工作，生成资产具有透許許可。
Runway和其他企业供应商：提供商業许可和为企业提供的保障选项。
版权和来源风险需要积极控制。AI 模型可能会复制受版权保护的元素或生成与真实作品或人物相似的图像。减少法律风险的实际步骤：
使用具有明确商業许可的模型和已记录培训源策略的模型。
保留提示和种子日志、时间戳记以及模块版本元数据以便于查证。
针对高价值资产运行逆向图像检查以检测现有作品的近似复制品。
获得对可识别的人物或品牌商标的授权；避免生成知名版权角色的精确副本。
监控功能不同，直接影响自动化工作流程。内置过滤器在生成时阻止裸露、仇恨符号或暴力；用户安全政策决定允许的内容。对于社群自动化：
在自动回应或发送图像至 DM 前实施监控层。
配置升级规则使高风险信息转移到人工处理。
使用诸如Blabla这样的平台在评论和 DM 中强制执行监控，应用AI安全过滤器，并在政策阈值触发时暂停自动回覆。
广告和对话使用的伦理准则：
未经同意，切勿使用生成的肖像表示认可。
视频可能造成误导时必须披露生成媒体（例如模拟推荐词）。
对 DM/评论，标记和人工审查任何可能是深度伪装、政治或高度具有诱导性的内容。
在活动创意中保持透明审核路径和可见披露政策。
实际提示：对于活动资产保留一个合规文件夹，其中包含模型许可 PDF、发布表单、提示和可导出的监控日志；配置 Blabla to to 标记并档案索引过的对话，以便法律和创意团队快速审核图像来源和监控决定。
将AI影像生成器整合至社群自动化中(API、Zapier、批量工作流)
既然我们已经处理了授权和監管，接下来检查如何将影像生成器插入社群自动化堆栈。
API 要点: 鉴权、端点、速率限制和响应格式决定了生成器是否准备好生产使用。用API密钥或OAuth安全地做，确认可用端点(同步生成，异步作业，资产检索，webhook)。提前测试速率限制和并发连接: 模拟峰值评论量和测量失败呼叫和429行为。检查响应格式 — 直接影像URL，base64负载或JSON包装 —并确认所需的元数据字段（模型ID，提示，种子，安全标志）。实用的测试在扩大之前: 在负载下测量中位数和p95延迟，验证webhook发送，确认幂等性或提供唯一请求ID，并验证错误代码和建议重试窗口。
Zapier 和无代码整合: 使其对无开发人员可用。实用的流程包括:
新的社群评论 -> Zap -> 用提示模板调用图像生成器（提到产品变种）-> 上传图像至云存储 -> Blabla接收URL并用图像回复评论。
新的博文 -> Zap -> 批量生成6个英雄影像变体 -> 将影像放入供调度员使用的共享文件夹中。
具有一流的Zapier或无代码支持的供应商通常包括 OpenAI (DALL·E 通过 OpenAI 的整合)、Stability 提供商、Runway、Adobe Firefly 和 Blabla；某些工具需要中间件或社区构建连接器。提示: 首选支持异步作业完成 webhooks 的工具在 Zapier 中操作时。
批量和大规模生成策略 降低延时和成本。并行化工作池但尊重速率限制；将相似的提示分组成批以可以使用缓存调节；使用提供的异步批量端点提交多个作业并接收回调。后备图像路由至关键: 将失败作业路由至缓存的默认图像，轻量级模板渲染器或由策展过的股份提套集避免用户悬而未决。依据供应商对比批量特征—有些按图收费，其他按 GPU时间或优先队列收费 —并测量您预期并发时的每个图像成本。
为生产而考虑:
缓存生成艺术在 CDN 和去重是相同提示以节省呼叫。
使用提示模板变量（用户名，产品颜色）在规模上个性化UGC并与轻量的覆盖而非完全再生成结合。
对于实时注解回覆，预先生成常用变体或使用快速缩略图而完整文件渲染；定义延迟 SLA 并测量 p95。
实施通过指数退避的重试，幂等性密钥，电路断路器及警报。
Blabla 补充这些模式通过自动化注解和 DM 工作流程，应用品牌安全的 AI 回覆，节省手动工作的小时数，提高回复率并在整合生成图像至对话自动化时保护您的品牌避免垃圾邮件或仇恨。一起测量成本、延迟和交互提升：在完整部署前追踪每次回复的成本、图像回复转换提升和错误率，并月度迭代之后。
提示工程、品牌一致性和最终建议
既然我们已经覆盖了整合和批量工作流，让我们聚焦于提示工程和社群工作流的最终选择。
实用提示配方:
博文模板: "{产品}的写实产品拍摄在极简背景上，自然温暖的光线，品牌色彩：{hex}，构图：居中，浅景深，标题准备框架。" 用负面提示如 "无水印，无文字，无人" 并附上1–2参考影像以保证一致颜色分级。
故事/广告模板: "垂直生活方式影像，充能氛围，模型使用{产品}，动作模糊，高对比度，上方20%安全区域用于文本叠加。" 增加风格标记如 "电影化，高色彩饱和"。
使用风格标记（例如"复古，平面插图，豪华"）并在提示库中保留共享标记列表。
扩展技术:
使用变量和模板：编程替换 {产品}、{颜色}、{CTA}。
种子控制实现批次重复变异；使用递增种子进行批次以保持多样性。
生成后过滤：根据主要颜色、构图自动标记输出，并在发布之前运行自动监控检查。
依据使用案例选择哪些生成器:
单一高质量广告创意：供应商 A（最高保真度）。
高产故事批次：供应商 B（快速，低图像每个成本）。
API驱动个性化：供应商 C（坚实API，低延迟）。
预算考量：供应商D（积分制，可预见）。
发起清单:
法律签字，监控配置，成本上限，10×测试矩阵(尺寸，提示种子)，监控仪表板。
最终赢家:平衡保真度、API 可靠性和成本。Blabla 通过自动化此类生成的创意评注和 DM 回复来补充这些生成器，以节省时间，增加交互率，并在规模化活动中保护品牌声誉。通过匹配保真度、吞吐量和监控需求与您的活动目标来选择赢家。谨慎开始。
並排比較：Midjourney、DALL·E、Stable Diffusion 及主要替代品
以下是简洁的、以能力为重点的比较，帮助您快速缩小选择。涵盖供应商级详细信息例如深入质量基准、监控状况和许可条款分别在其他地方总结——参见部分 3 和部分 4 以便获得这些供应商级备忘。
模型 | 核心优势 | 最适宜哪种用途 | 灵活性与部署 |
|---|---|---|---|
Midjourney | 高度风格化，创意美学；擅长艺术和概念渲染 | 概念艺术，风格化插图，创意探索 | 云托管，提示驱动工作流程（Discord 界面）；订阅制访问 |
DALL·E | 擅长照片写实和混合风格；优秀的图像融合和布局 | 产品视觉效果，照片写实场景，混合创意/写实输出 | 云API和WEB应用访问；整合到更广泛的平台工具 |
Stable Diffusion | 开源和高度定制化；广泛的模型和工具社区 | 研究，定制，本地/嵌入式部署和生产管道 | 本地部署或托管服务；支持微调，检查点和控制模块 |
主要替代品（实例） | 多样化 — 例如，Adobe Firefly 专注于设计工作流程；Google Imagen 以高保真写实为目标 | 设计集成的工作流程，研究级保真或平台特定集成 | 供应商可变的可用性与进入选择包括云API、创意应用程序插件和研究预览 |
这个一目了然的比较突出了功能上的差异和典型的使用案例，不重复详细的供应商级评估。关于性能测量、影响因素/监控行为和许可具体，请参考部分 3 和部分 4。
价格、计划和社群活动的真实图像每个成本
图像生成工具的价格依据模型、使用模式和输出需求而变化。以下是简洁的、关注活动的指南，帮助您估算真实图像每个成本、选择合适的计划并控制支出。
影响图像每个成本的因素
模型层级：更高质量或溢价模型每请求成本更高。
解析度和输出: 生成多个变异、高解析度图像或更新升级会增加成本。
迭代和提示：更多的调整和重新运行提高了使用的总积分。
后处理：编辑、屏蔽或批量调整大小可增加计算成本或需要单独的API调用。
储存和交付: 托管资产和 CDN 带宽增加的不仅是生成积分活动成本。
常见计划和结算风格
供应商常提供:
免费或试用层级 — 限制积分用于测试和小规模内容。
按需付费 — 按图或按标记收费无长期承诺；最适合变动量。
订阅层级 — 月度积分块在折扣率上用于可预测的工作量。
企业协议 — 自定义定价，更高的吞吐量，优先支持和使用报告。
快速成本示例（说明性）
通过添加模型成本 + 变异 + 后处理来估算每个图像支出。以下是用于指导的示例范围，会随供应商有所变化：
单一低分辨率概念影像：$0.02–$0.10
用于社群发文的多重变异及升级：$0.10–$0.60
具有编辑的高分辨率，多次迭代创意：$0.60–$2.00+
示例计算：如果活动需要100个社群影像，每个生成3个变异和1个最终升级，乘以基础图像率4（3个变异 + 1个升级）以获得近似总量。
如何降低图像每个真实成本
批量生成: 在单次会话中创建多种变异以减少开销。
优化提示: 当提示准确时需要更少的迭代。
使用较低层级草稿: 仅保留溢价模怎样供最终创意。
重复使用资产: 模板和一致布局减少生成需求。
监控并限制支出: 设置每日或项目限额以防止惊奇。
监控和编排
要管理众多生成资产并关注积分，使用编排层（例如 Blabla)集中请求，跟踪每个活动的积分使用并生成使用报告。这使您可以执行预算，按社群渠道审计支出，并在不开设个别供应商控制面板的情况下自动批量处理。
底线:计算您需要的总输出量（包括草稿、变异和最终编辑），选择符合您批量的计划，使用编排和监控以控制并预测您的图像每个真实成本。
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