zhouxiaoka/autoclipPublic

AutoClip|一个链接,一键出片。开源 AI 视频剪辑桌面工具,将播客、访谈、课程等长视频自动剪成短视频,生成字幕、封面和发布文案,适配抖音、小红书、TikTok、Reels 与 YouTube Shorts。Open-source AI video clipping & content repurposing.

AI summary: An AI-powered video processing tool that automatically downloads videos and extracts highlights for content creation.

Stars
9.1K
+76 today
Forks
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Watchers
38
Open issues
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Open PRs
3
Contributors
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Commits
246
Branches
53

PythonMITCreated Jul 8, 2025Last push 1d agoLatest release v1.4.0+245 stars this week+952 this month

Quick answers

What is autoclip?
An AI-powered video processing tool that automatically downloads videos and extracts highlights for content creation.
What does autoclip do?
AutoClip is an automated video processing system designed to download videos from platforms like YouTube and Bilibili and intelligently extract highlight clips. It utilizes large language models, specifically Qwen, to analyze the video content and identify the most engaging segments. The system automatically cuts the video into smaller clips and can organize them into curated collections. It features a modern React-based frontend and a robust FastAPI backend supported by a Celery task queue for handling asynchronous processing. This pipeline dramatically reduces the manual effort required for secondary video creation and social media clipping.
Who is autoclip for?
This tool is built for content creators, video editors, and social media managers looking to scale their short-form content production. Users should be familiar with basic Docker or Python setups to deploy the system.
How popular is autoclip on GitHub?
zhouxiaoka/autoclip has 9,134 stars and 1,669 forks on GitHub, and gained 245 stars in the last 7 days.
What license does autoclip use?
zhouxiaoka/autoclip is released under the MIT license.

Star history

since Sep 21, 2026
02.5K5K7.5KSep 2026Sep 2026Sep 2026Oct 2026
9.1K stars as of Oct 3, 2026. Measured daily since Sep 21, 2026; GitHub no longer exposes earlier star timestamps.

Contribution activity

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Signals and awards

derived from tracked data
  • Actively maintained

    Pushed within 48 hours

  • Well documented

    High community health score

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

What autoclip does

AutoClip is an automated video processing system designed to download videos from platforms like YouTube and Bilibili and intelligently extract highlight clips. It utilizes large language models, specifically Qwen, to analyze the video content and identify the most engaging segments. The system automatically cuts the video into smaller clips and can organize them into curated collections. It features a modern React-based frontend and a robust FastAPI backend supported by a Celery task queue for handling asynchronous processing. This pipeline dramatically reduces the manual effort required for secondary video creation and social media clipping.

This tool is built for content creators, video editors, and social media managers looking to scale their short-form content production. Users should be familiar with basic Docker or Python setups to deploy the system.

  • Multi-platform downloads: Easily fetch source videos from platforms like YouTube and Bilibili or upload local files.
  • AI-driven highlights: Uses large language models to analyze content and identify the most compelling moments for clipping.
  • Automated slicing: Intelligently cuts full-length videos into shorter highlight clips without manual intervention.
  • Smart collections: Groups generated clips into curated collections with AI recommendations and drag-and-drop sorting.
  • Asynchronous processing: Relies on a Celery task queue to handle heavy video processing tasks without blocking the UI.

Where teams use it

Social media content creation

Content creators generating short-form clips from long videos for platforms like TikTok or YouTube Shorts.

Stream highlight extraction

Streamers automatically pulling the best moments from hours of live broadcast footage.

Video curation automation

Media managers organizing and categorizing large libraries of video content into digestible highlight reels.

Secondary content production

Video editors accelerating their workflow by letting AI handle the initial rough cut of interesting segments.

README

main branch
AutoClip

AutoClip

开源 AI 高光剪辑工具

导入长视频,AI 分析字幕、找出精彩片段,自动剪成短视频与合集。

GitHub release GitHub stars License: MIT

AutoClip — GitHub Trending AutoClip — Trendshift Python daily ranking AutoClip — Trendshift daily ranking, all languages

下载桌面版 · 快速开始 · 项目网站 · 使用文档 · 问题反馈

简体中文 · English · 日本語 · 한국어 · Español · Português · Русский · Français

适合访谈、播客、课程、口播与直播回放。提供桌面应用、Docker Web 界面和 CLI / MCP,可交互使用,也可批量处理。

界面预览

导入视频与项目管理

AI 切片结果 Studio 预览与修改
AI 切片结果 Studio 预览与修改

v1.4.0 界面实拍(含后续 Studio 修复):导入视频、查看真实切片结果,再进入 Studio 修改成片。截图为中文界面,案例字幕与生成标题保留英文。

截图版本与案例来源

特别感谢 ❤️

88API
88API
感谢 88API Token聚合平台 赞助 AutoClip!聚合 GPT、Claude、Gemini、Grok、DeepSeek、Kimi、GLM 等语言与编程模型,适合字幕分析、高光筛选与标题生成。
🎨 多媒体能力:平台提供 GPT-Image、Gemini、Grok 等图片模型,Seedance、Veo、MiniMax Hailuo H3、Kling、Grok 等视频模型,以及 Whisper、TTS 等语音能力;AutoClip 可接入兼容的分析、封面生图与字幕转写接口。
🏷️ 服务与结算:据合作方介绍,由海外企业运营,提供人工客服、正规发票,充值比例 1:1;具体模型、服务与结算条件以平台页面为准。
🎁 新用户福利:通过 专属推广链接注册可获体验额度,用于测试模型能力,领取条件以活动页面为准。 接入说明
Infistar
Infistar.cc
无限星河
感谢 Infistar.cc 无限星河 赞助 AutoClip!提供多模型 API 服务,适合长视频字幕分析、高光筛选与标题生成等工作流。
⚙️ 兼容接入:在 AutoClip 选择 OpenAI 兼容接口,填写 Base URL、API Key 和可用模型,即可配置使用。
🧩 多模型选择:合作方提供 Claude、GPT、Gemini、DeepSeek 等系列模型,可选择支持兼容接口的型号,对比字幕分析与高光筛选效果。
🏷️ 价格与服务:据合作方介绍,部分模型低至官方定价的 0.1 折,支持人民币结算、开票及模型验真;具体型号、价格与服务条件以平台页面为准。
🎁 AutoClip 专属福利:通过 专属推广链接注册可领取 $5 体验额度,领取条件以活动页面为准。 接入说明

功能特性

点击任意缩略图查看大图。

多种导入方式

本地视频、YouTube 或 B 站链接,可附带 SRT 字幕;无字幕时可使用本地 Whisper 转写。

多种导入方式
AI 内容分析

基于字幕生成大纲与话题时间线,为片段评分、提取标题。

AI 内容分析
片段与合集

自动生成切片,预览结果,并按需要调整合集顺序。

片段与合集
多平台导出

提供抖音、小红书、YouTube Shorts 和 B 站预设,支持烧录字幕与标题卡。

多平台导出
封面与发布

自 v1.3.2 起支持自动封面、立即发布和定时发布;海外平台通过 Upload-Post 连接,B 站单独配置。

封面与发布 封面与发布

演示环境未连接发布账号;这里展示发布入口和封面设置,并非已发布结果。

发布管理

查看发布记录与月历,管理待发布任务;也可只下载成片。

发布管理
模型可选

支持通义千问、OpenAI 兼容接口、Gemini 等云端服务,以及 Ollama / LM Studio 本地模型。

模型可选
批量与 Agent 工作流

用 CLI 编排批量任务,或通过 MCP 调用视频处理能力。

批量与 Agent 工作流

CLI / MCP 无图形界面:此图是实际命令帮助输出的展示页截图。

多语言界面

支持中、英、日、韩、西、葡、俄、法,可在顶栏切换或跟随系统;素材与生成内容保留原文。

多语言界面

英文界面与语言选择菜单;素材和生成内容保持原语言。

查看发布平台、账号要求与导出细节

海外发布使用你自己的 Upload-Post 账号,支持平台以账号实际连接情况为准,包括 TikTok、Instagram、YouTube、Facebook、LinkedIn、X、Threads、Pinterest、Bluesky、Discord 和 Google Business。B 站在设置中单独填写 Cookie(包含 SESSDATA、bili_jct、DedeUserID),支持一个账号。

在切片页面打开发布,可立即发布或定时发布。标题和描述可不填,默认使用片段标题;字幕烧录和约 4 秒的片头标题卡默认开启。可见范围在平台支持时默认「仅自己」,其中 TikTok、YouTube、B 站支持 private / 仅自己。封面与标题卡行为以所用版本的 Release 说明为准。

竖屏账号导出为 9:16,不按 60 秒截断;只发 B 站用横屏,只有 LinkedIn、X 这类横屏账号时保留原画。竖屏平台与 B 站同次发布时分别渲染。

项目页提供发布记录和月历,可取消待发布排期。「排这一周」安排在周一、周三、周五 09:00,仅用于海外平台,不包含 B 站。

导入视频 → 字幕 / 语音转写 → AI 分析 → 片段与合集 → 导出 / 发布

快速开始

你的使用场景 建议方式 准备事项
在电脑上剪辑视频 桌面版 macOS Apple Silicon 或 Windows x64
自建 Web 服务 / Linux Docker Docker 与 Compose v2
批量处理 / 接入 Agent CLI / MCP Python 3.10+(建议 3.11)与 FFmpeg

桌面版:第一次出片

  1. 下载安装。 前往 Releases:macOS Apple Silicon 选 .dmg,Windows 10 / 11 x64 选 -setup.exe。桌面版内置 Python 和 FFmpeg;Intel Mac / Linux 可使用 Docker 或 CLI。实际系统要求以对应 Release 为准。
  2. 配置模型。 在设置中选择服务商,填写 API Key 与模型名,测试连接后保存;使用本地模型时,先启动 Ollama 或 LM Studio。
  3. 导入视频。 建议先用 3–5 分钟短样片,可同时导入 SRT。无字幕时先在设置中准备本地 Whisper 组件与语音模型。
  4. 预览并导出。 检查片段起止时间、标题和内容完整性,选择导出预设,或连接账号后发布。

完整安装与首次使用指南 · 遇到问题?

Docker / Web 部署

需要 Docker 和 Docker Compose v2。以下命令在仓库根目录执行:

git clone https://github.com/zhouxiaoka/autoclip.git
cd autoclip
cp env.example .env

启动前编辑 .env:选择 LLM_PROVIDER,填写对应服务的 API Key 和模型名;也可以启动后在设置页配置。

mkdir -p data logs uploads
docker compose up -d --build

打开 Web 界面;API 文档 在后端启动后可用。部署细节见 Docker 指南(中文)。

Linux 上若绑定目录出现权限错误,先执行以下命令修正项目数据目录的归属,再重新启动服务:

docker compose run --rm --no-deps --user root --entrypoint sh autoclip -c 'chown -R autoclip:autoclip /app/data /app/logs /app/uploads'
docker compose up -d
CLI / MCP 安装与使用

需要 Python 3.10+(建议 3.11)和 PATH 中可用的 FFmpeg。以下安装示例使用 macOS / Linux shell;Windows PowerShell 用 venv\Scripts\Activate.ps1 激活虚拟环境。CLI 本地处理不需要 Redis。

git clone https://github.com/zhouxiaoka/autoclip.git
cd autoclip
python3 -m venv venv
source venv/bin/activate
python -m pip install -r requirements.txt
python -m pip install -e .

本地模型示例:先安装并启动 Ollama,再下载模型。无字幕视频需要 faster-whisper,首次转写会下载语音模型;已有字幕可用 --srt talk.srt。

ollama pull qwen2.5:7b
python -m pip install faster-whisper
autoclip doctor --provider ollama
autoclip run talk.mp4 --provider ollama --json

把 PROJECT_ID 替换为处理结果中的项目 ID,即可导出 Shorts 格式;用 autoclip mcp 启动 stdio MCP 服务:

autoclip export PROJECT_ID --preset shorts
autoclip mcp

在 MCP 客户端中将 command 设为虚拟环境里 autoclip 的绝对路径,args 设为 ["mcp"]。opencode 用户一条命令即可接入:autoclip mcp install opencode(见 OpenCode 接入)。详见 CLI / MCP 指南(中文)和 Agent skill(中文)。

模型配置

方式 配置
云端 API 在设置中选择服务商,填写自己的 API Key 和模型名。OpenAI 兼容服务还可配置 Base URL。
Ollama 默认地址 http://localhost:11434/v1,默认模型 qwen2.5:7b,无需 API Key。
LM Studio 加载模型并启动 Local Server,默认地址 http://localhost:1234/v1,选择服务实际提供的模型。

Docker 访问宿主机模型服务时,localhost 指向容器自身,需要改为容器可访问的宿主机地址。视频剪辑在本地进行;云端字幕分析会向所选服务商发送字幕文本,下载视频与模型仍需要网络。

模型配置指南 · 本地模型与容器配置 · Infistar 接入

常见问题

需要付费或 API Key 吗?

AutoClip 免费、开源(MIT)。云端模型由所选服务商计费,需要自己的 API Key;Ollama / LM Studio 本地预设无需云端 Key,但需要模型和相应硬件。自 v1.3.2 起,海外发布需要你自己的 Upload-Post 账号。免费档、付费档,以及 TikTok、YouTube、Instagram 等的每日额度,以 Upload-Post 自己的页面为准,不是 AutoClip 的承诺。

我的视频会上传吗?

剪辑留在你的设备上。使用云端模型时,字幕文本会发送给该服务商。成片只有在你点「发布」之后才会离开这台机器,发到你已连接的平台;也可以只下载、不发布。统计与错误报告取决于版本和设置,详见隐私说明。

没有字幕也能使用吗?

可以,需要先准备本地 Whisper 组件和语音模型。已有字幕时可同时导入 SRT;准确字幕通常能减少转写等待和识别错误。

为什么没有生成片段?

先检查失败阶段:字幕是否为空、模型连接是否成功、评分阈值是否过高,以及 FFmpeg 和磁盘是否正常。可以尝试把评分阈值从 0.7 降到 0.5,但不保证一定有片段。

什么视频更适合?处理要多久?

当前分析主要基于字幕,适合访谈、播客、课程和口播。纯视觉动作或音乐类视频效果可能有限。耗时取决于时长、硬件、模型与导出设置,建议先用自备的 3–5 分钟短样片验证。

素材准备和公开链接示例见 第一次出片指南。

完整排错指南 · 已知问题

文档

你想了解 文档
安装与首次出片 安装指南
自建服务与自动化 Docker 部署 · CLI / MCP · Agent skill · OpenCode 接入
配置模型与排错 模型配置 · 常见问题
版本与隐私 更新日志 · 隐私说明
参与开发与翻译 贡献指南 · 翻译维护

README 提供八种语言,深入文档以中文为主;安装与排错指南另有英文版本。

参与贡献与联系

欢迎提交修复、使用反馈和翻译改进。如果 AutoClip 帮到了你,欢迎给项目一个 Star。

个人业余维护,回复时间不固定,不提供即时客服或一对一部署服务。提问前请先查看 FAQ 与 已知问题;社区分类见 欢迎说明 和 社区看板。

感谢 FastAPI、React、Tauri、FFmpeg、yt-dlp、Whisper,以及所有贡献者。项目采用 MIT License。

社区成就与 Star History

以下徽章由 Trendshift 提供,点击可查看 AutoClip 的上榜记录。GitHub Trending 与 Trendshift 是不同榜单;徽章展示平台记录的成就,不代表当前实时排名。

Star History

View on GitHub

Recent activity

commits and pull requests

Recent open issues

view all

Discussions

all 9

Releases and announcements

11 total
  1. AutoClip Desktop v1.4.0v1.4.0Sep 27, 20262.5K downloads

    ## AutoClip Desktop v1.4.0 ### 新增 - 游戏视觉分析:自行配置多模态模型,在确认后识别录屏中的独立事件,生成可编辑高光与推广草稿。 - 双分析路线:保留低成本字幕分析;旧配置默认字幕模式。模型配置与调用授权分离,付费视觉初筛需显式启用。 - 统一导入、确认、编辑与导出:快速推荐制作类型,支持手动校正;未确认导入可恢复,确认后才正式理解和剪辑。 - 字幕高光与推广草稿接入共用编辑器;六款文字模板、语言配置、候选边界调整及导出历史。 ### 改进与修复 - 有界视觉扫描与有效候选保留,按独立事件组织高光,最多六段,不按时间相邻强行合成全片。 - 推广草稿的自动画幅匹配为竖屏,用户手动选择优先;保持原音轨并支持本机渲染与原生保存。 - 旧项目重分析显式确认,文字任务不隐式回退到视觉接口;导出调度失败可恢复。 - 保留便携 Python 运行时资源签名,统一八语界面与匿名业务统计。 ### 验收范围与已知限制 - macOS Apple Silicon 已有候选包安装、非空数据升级/回退、原生编辑/导出/保存和真实跑酷样本验证。单一样本不代表所有游戏质量;输出仍需人工检查边界、裁切和文案,不承诺广告投放效果。 - Windows 提供构建产物,实际安装、导入与保存尚待实机验收。 - 本次不含 CTA 动效、生成式品牌尾卡、卡拉 OK / 逐词字幕;后续独立迭代。 - 使用云端视觉模型会发送抽样画面及必要文字;字幕路线发送相关字幕/文案,费用取决于所选服务商。剪辑与渲染在本机完成。 - macOS 为 ad-hoc 签名、未公证;Windows 安装包未做系统代码签名。更新包签名与系统代码签名是不同机制。 ### 下载 / Download | 平台 | 文件 | 首次打开 | |---|---|---| | macOS(Apple Silicon)| `AutoClip.Desktop_1.4.0_aarch64.dmg` | 未公证:右键应用 → **打开** | | Windows 10/11 x64 | `AutoClip.Desktop_1.4.0_x64-setup.exe` | 未签名:SmartScreen → **更多信息 → 仍要运行** | 两个包都内置便携 Python 与静态 ffmpeg,不需要预装任何东西;Windows 安装包按用户安装,不需要管理员权限,缺 WebView2 会自动下载。 Intel Mac / Linux 暂无安装包,请用 Docker(`DOCKER.md`)或 `pip install -e .` 跑 `autoclip` CLI。 Built-in portable Python + static ffmpeg, nothing to install. macOS

  2. AutoClip Desktop v1.3.5v1.3.5Sep 25, 20261.2K downloads

    ## AutoClip Desktop v1.3.5 ### 改进 - 导入失败的监控分类:缺字幕、缺少 API Key,以及未预期的导入故障,会以不同异常类型分开上报。Sentry 里对应 fingerprint 为 `import-processing` / `missing-subtitle`、`import-processing` / `missing-key`;未预期故障仍是 `ImportProcessingError`,沿用默认栈归组。日志里带 `kind=missing-subtitle`、`kind=missing-key` 或 `kind=unexpected`。这只改善监控分类,不改变导入是否成功,也不改变字幕质量。 ### 修复 - YouTube / 链接导入时,项目卡不再停在约 5%:下载进度会写入数据库,进度条会跟上列表里的真实百分比。这不表示所有 YouTube 下载或 Whisper 转写都会成功(Related #163 / #183) - 切片预览改为原生 video。播放地址没有 .mp4 后缀时,ReactPlayer 不会挂上播放器,弹层只剩一块黑底。新切片转成 H.264 / AAC 并前移索引,应用内能解出画面。已经生成的旧切片不会重编码;解不开时预览会说明去下载,用系统播放器打开(Related #175) - 走 DeepSeek 官方接口做切片分析时关闭思考模式,并限制单次输出长度。思考模式默认开着,推理内容按输出 token 计费,长字幕会比可见回答贵很多。长视频仍按约 30 分钟一块、分几步调用;刷新页面不会重新计费,重新开始处理才会(Related #175) - 时间线为空时不再误导去改模型设置(Related #182) - 本地导入调用 Whisper 时,不再因为多传了时间戳、标点或超时参数,在转写开始前就失败。未安装时仍指到「设置 → 转写」 - Whisper 只返回空白片段,或上次留下的字幕文件没有正文时,不再把空文件当成转写成功 - 本地导入没有可用字幕时,失败说明会指到「设置 → 转写」,也可以在重新导入时附上 .srt。已经失败、只记下「字幕文件不存在」的项目,打开后同样能看到这条去向(#186) - 桌面端连续查看项目、下载或生成合集时,不再因为数据库连接占满而打不开列表和详情(#175) - 切片标题含中文等非英文字符时,预览和下载不再因为响应头无法编码而失败 ### 下载 / Download | 平台 | 文件 | 首次打开 | |---|---|---| | macOS(Apple Silicon)| `AutoClip.Desktop_1.3.5_aarch64.dmg` | 未公证:右键应用 → **打开** | | Windows 10/11 x64 |

  3. AutoClip Desktop v1.3.4v1.3.4Sep 24, 20261.5K downloads

    ## AutoClip Desktop v1.3.4 ### 修复 - macOS 桌面端切片生成后可以预览,下载会保存到「下载」文件夹(#166 / #168) - 片源处理进度显示更准:不再因读错字段停在约 5%;真下载失败仍需看日志(Related #163 / #169) - 导入任务失败时,Celery 结果可正确记为 FAILURE(#20 / #170) - 无字幕或未安装 Whisper 时,失败提示指到「设置 → 转写」(#165 族 / #171) - 没有可用模型或连接测试失败时,提示需自备 API Key,并指到「设置 → 模型」(Related #161/#162 / #174) ### 下载 / Download | 平台 | 文件 | 首次打开 | |---|---|---| | macOS(Apple Silicon)| `AutoClip.Desktop_1.3.4_aarch64.dmg` | 未公证:右键应用 → **打开** | | Windows 10/11 x64 | `AutoClip.Desktop_1.3.4_x64-setup.exe` | 未签名:SmartScreen → **更多信息 → 仍要运行** | 两个包都内置便携 Python 与静态 ffmpeg,不需要预装任何东西;Windows 安装包按用户安装,不需要管理员权限,缺 WebView2 会自动下载。 Intel Mac / Linux 暂无安装包,请用 Docker(`DOCKER.md`)或 `pip install -e .` 跑 `autoclip` CLI。 Built-in portable Python + static ffmpeg, nothing to install. macOS build is ad-hoc signed (right-click → Open on first launch); Windows build is unsigned (SmartScreen → More info → Run anyway). If a file is missing, that platform's build failed for this tag — see the Desktop Build workflow run. ### 反馈 / Feedback - 已知问题与当前状态:#96 · 能复现的故障走 [Issue 模板](https://github.com/zhouxiaoka/autoclip/issues/new/choose) · 想法与用法走 [Discussions](https://github.com/zhouxiaoka/autoclip/discussions)

  4. AutoClip Desktop v1.3.3v1.3.3Sep 23, 2026943 downloads

    ## AutoClip Desktop v1.3.3 ### 修复 - 非 Mac 安装 Whisper 时,不再误报「仅支持 Apple Silicon」并拦住安装(#145) - 处理完成后进度弹窗不再空转;项目目录找不到时,状态查询不再整页失败(#152) - 分类列表还没加载完时,导入入口不再崩溃(#153) - 本地 Whisper 转写失败时给出可读提示,应用继续运行(#154) - 升级后若项目里还有旧的状态或类型值,启动时自动改写,项目列表能打开(#155) - 桌面端下载 Whisper 模型时,不再因进度条写控制台而失败(#157) ### 下载 / Download | 平台 | 文件 | 首次打开 | |---|---|---| | macOS(Apple Silicon)| `AutoClip.Desktop_1.3.3_aarch64.dmg` | 未公证:右键应用 → **打开** | | Windows 10/11 x64 | `AutoClip.Desktop_1.3.3_x64-setup.exe` | 未签名:SmartScreen → **更多信息 → 仍要运行** | 两个包都内置便携 Python 与静态 ffmpeg,不需要预装任何东西;Windows 安装包按用户安装,不需要管理员权限,缺 WebView2 会自动下载。 Intel Mac / Linux 暂无安装包,请用 Docker(`DOCKER.md`)或 `pip install -e .` 跑 `autoclip` CLI。 Built-in portable Python + static ffmpeg, nothing to install. macOS build is ad-hoc signed (right-click → Open on first launch); Windows build is unsigned (SmartScreen → More info → Run anyway). If a file is missing, that platform's build failed for this tag — see the Desktop Build workflow run. ### 反馈 / Feedback - 已知问题与当前状态:#96 · 能复现的故障走 [Issue 模板](https://github.com/zhouxiaoka/autoclip/issues/new/choose) · 想法与用法走 [Discussions](https://github.com/zhouxiaoka/autoclip/discussions)

  5. AutoClip Desktop v1.3.2v1.3.2Sep 22, 2026588 downloads

    ## AutoClip Desktop v1.3.2 ### 新增 - **发布到海外平台(Upload-Post)**:切片可经 [Upload-Post](https://www.upload-post.com) 一次发到 TikTok / Instagram / YouTube Shorts / Facebook / LinkedIn / X / Threads / Pinterest / Bluesky 等, 与 B 站投稿并列;先按预设渲成片(竖屏平台默认 `shorts`),再异步提交并轮询各平台结果。视频处理仍全部在本地。 入口:`autoclip publish`、MCP `publish_clip` / `get_publish_status` / `list_publish_profiles`、API `/api/v1/publish/upload-post/*`; 配置用 `UPLOAD_POST_API_KEY` / `UPLOAD_POST_USER`、`autoclip publish --api-key … --user … --save`,或设置页「发布」。 切片的「发布导出」里可以「发到海外平台」:勾选已连接的平台,默认先私密试发(`docs/PUBLISH_UPLOAD_POST.md`)。 发布客户端按当前 Upload-Post 文档和 OpenAPI 重写:过期账号不拿来发,Reddit 暂不发送,超长 YouTube 标题单独截短,提交后每 10 秒查一次结果。无效 key 打线上接口会得到 401。进程中断后未提交的任务会标成失败,而不是一直停在排队。 应用里的发布收成一条:切片上打开发布,现在发或定时,成片画幅跟着账号走。项目里看记录并取消还没发出的排期。记录可以换成月历;「排这一周」把还没发的切片按分数填进周一、三、五的 09:00,确认后才提交。 B 站和海外账号在同一处配置、同一页勾选。设置里粘贴 Cookie 并校验,发布页出现 B 站;只发 B 站时渲成横屏,和竖屏账号一起发时各自渲。定时要晚于现在两小时,还没到点的可以取消。抖音、小红书、快手仍然没有投稿接口。 - **自动封面**:设置页「封面」可配 OpenAI 兼容、Seedream(火山方舟)或通义万相。发布页可预览、改文案、重新生成。B 站投稿优先用设计封面;生图失败或未生成时截帧兜底,封面失败不挡投稿。Seedream 参考帧走 generations 的 image 字段,校对用豆包视觉。 ### 改进 - **设置「发布」把 B 站和海外拆开**:B 站 Cookie 输入和保存放在最上面,不再跟在海外密钥后面,避免一进来找不到贴 Cookie 的地方。 - **设置页模型名单跟得上服务商**:下拉换成

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When work happens

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SunMonTueWedThuFriSat036912151821Sun 0:00 — 4 commitsSun 1:00 — 5 commitsSun 2:00 — 3 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 0 commitsSun 12:00 — 3 commitsSun 13:00 — 1 commitsSun 14:00 — 1 commitsSun 15:00 — 2 commitsSun 16:00 — 0 commitsSun 17:00 — 1 commitsSun 18:00 — 0 commitsSun 19:00 — 1 commitsSun 20:00 — 0 commitsSun 21:00 — 2 commitsSun 22:00 — 4 commitsSun 23:00 — 2 commitsMon 0:00 — 3 commitsMon 1:00 — 1 commitsMon 2:00 — 4 commitsMon 3:00 — 3 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 0 commitsMon 9:00 — 1 commitsMon 10:00 — 1 commitsMon 11:00 — 1 commitsMon 12:00 — 1 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 2 commitsMon 16:00 — 0 commitsMon 17:00 — 1 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 5 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 4 commitsTue 0:00 — 10 commitsTue 1:00 — 3 commitsTue 2:00 — 16 commitsTue 3:00 — 6 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 2 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 1 commitsTue 10:00 — 3 commitsTue 11:00 — 1 commitsTue 12:00 — 4 commitsTue 13:00 — 1 commitsTue 14:00 — 0 commitsTue 15:00 — 6 commitsTue 16:00 — 8 commitsTue 17:00 — 3 commitsTue 18:00 — 2 commitsTue 19:00 — 1 commitsTue 20:00 — 0 commitsTue 21:00 — 2 commitsTue 22:00 — 1 commitsTue 23:00 — 2 commitsWed 0:00 — 0 commitsWed 1:00 — 1 commitsWed 2:00 — 4 commitsWed 3:00 — 3 commitsWed 4:00 — 1 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 1 commitsWed 8:00 — 0 commitsWed 9:00 — 1 commitsWed 10:00 — 3 commitsWed 11:00 — 4 commitsWed 12:00 — 2 commitsWed 13:00 — 0 commitsWed 14:00 — 0 commitsWed 15:00 — 1 commitsWed 16:00 — 3 commitsWed 17:00 — 2 commitsWed 18:00 — 2 commitsWed 19:00 — 6 commitsWed 20:00 — 3 commitsWed 21:00 — 7 commitsWed 22:00 — 1 commitsWed 23:00 — 0 commitsThu 0:00 — 0 commitsThu 1:00 — 2 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 0 commitsThu 11:00 — 3 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 2 commitsThu 15:00 — 1 commitsThu 16:00 — 2 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 0 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 0 commitsFri 9:00 — 0 commitsFri 10:00 — 0 commitsFri 11:00 — 0 commitsFri 12:00 — 0 commitsFri 13:00 — 3 commitsFri 14:00 — 1 commitsFri 15:00 — 0 commitsFri 16:00 — 0 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 3 commitsFri 20:00 — 0 commitsFri 21:00 — 1 commitsFri 22:00 — 0 commitsFri 23:00 — 1 commitsSat 0:00 — 1 commitsSat 1:00 — 3 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 0 commitsSat 16:00 — 0 commitsSat 17:00 — 4 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 7 commitsSat 21:00 — 3 commitsSat 22:00 — 1 commitsSat 23:00 — 6 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits144 (87%)
Community commits22 (13%)

166 commits in total over the last year.

DateListRankStars gained
Sep 22, 2026daily#8+395
Sep 21, 2026daily#8+395
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