Agent Skill · 图像处理

image-to-video-lora（图像到视频洛拉）

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制；涉及人物、品牌、商品或公开展示素材时，应额外核对授权、真实性和内容合规边界。

image-to-video-lora 是图像处理类 Agent Skill，可用宿主为 OpenClaw，通过ClawHub获取，权限为接触敏感数据；许可证为 MIT-0，第三方扫描有提醒项，按安装量在图像处理类的 1,562 个 Skill 里排第 979。

总安装 2,007周安装 82GitHub Stars 公开资料未说明下载 643核验 2026-05-01

图像处理接触敏感数据 ClawHub未标认证来源可访问许可证明确审计提醒 OpenClaw

安装说明

本站只整理中文说明，不托管安装包，也不代用户安装。

GitHub

许可证: MIT-0
版本: 1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：image-to-video-lora（图像到视频 洛拉）
来源仓库：https://github.com/vcarolxhberger/image-to-video-lora
安装命令：
openclaw skills install image-to-video-lora
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

ClawHubOpenClaw

openclaw skills install image-to-video-lora

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

将单张肖像或产品照片转换为1080p动画视频剪辑。适合人像、产品展示等静态图像动态化需求。适用宿主包括 OpenClaw，接入前应确认版本、权限和运行环境要求。上传图片并描述期望效果，自动生成短视频。支持JPG/PNG等格式，注意版权与内容合规性。

image-to-video-lora 属于图像处理类 Skill，可作为该场景下的辅助能力补充。

上手步骤

4 步

1
确认自己的宿主工具
公开资料标注支持 OpenClaw；先确认自己用的是其中之一，再按对应工具的 Skills 目录放置。
2
拿到安装命令
可以直接复制的命令是 openclaw skills install image-to-video-lora；命令里的占位符换成自己的路径或密钥再执行。
3
安装后重启宿主
命令执行完要完全退出宿主工具再打开，让新装的 Skill 被重新扫描到，否则列表里可能看不到。
4
按最小权限试用
它需要接触敏感数据，先在测试目录或测试账号里跑，确认没问题再放到正式项目。

常见问题

6 条

image-to-video-lora 怎么安装？

在终端执行 openclaw skills install image-to-video-lora。装完完全退出宿主工具再打开，让它重新扫描本地 Skill。

image-to-video-lora 支持哪些 AI 工具？

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录，一般也能手动放进去，但要自己确认触发方式是否一致。

image-to-video-lora 需要什么权限？

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置，应进入高风险复核队列，默认不自动发布。

image-to-video-lora 是免费的吗？

来源标注的许可证是 MIT-0。本站不代收任何费用，也不托管安装包；能否商用、要不要署名，以许可证原文为准。

image-to-video-lora 用的人多吗？

公开数据是累计安装 2,007 次，近一周安装 82 次，下载 643 次。这些数字来自第三方目录统计，只能当作热度参考，不代表质量或安全性。

image-to-video-lora 安全吗？

第三方扫描有提醒项，说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

专注 OpenClaw 一种宿主，触发方式和使用习惯不用来回切换。
许可证明确为 MIT-0，商用和分发边界好判断。

先想清楚的地方

需要接触敏感数据，装到常用环境前要先评估影响面。
第三方扫描存在提醒项或未通过项，需要人工逐条确认。
第三方目录整理，版本更新和行为细节以来源仓库为准。

适不适合你

更适合

已经在用 OpenClaw，想直接扩展它能力的人。
需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

环境里有大量密钥、凭证，不方便给第三方 Skill 读取的人。
需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类

Skill	总安装	GitHub 星	权限	许可证	扫描
ai-image-generation AI 图像生成 · Codex、Claude	4,365,552	382	调用外部服务	未明确	有提醒
openai-image-gen OpenAI 图像生成 · OpenClaw	1,906,884	32	需要联网	MIT-0	有提醒
image 图像 · OpenClaw	525,999	26	需要联网	MIT-0	通过
baoyu-cover-image 宝玉封面图 · Codex、Claude	474,240	16,779	只读	MIT	有提醒
baoyu-image-gen 宝玉图像生成器 · Codex、Claude	468,007	16,778	接触敏感数据	MIT	通过
image-to-video-lora 图像到视频洛拉 · 本页	2,007	—	接触敏感数据	MIT-0	有提醒

按安装量取同分类前几名；数字来自第三方目录统计，只作热度参考，不代表质量结论。

SKILL.md 原文

name: image-to-video-lora
version: 1.0.0
displayName: Image to Video LoRA — Animate images with LoRA styles
description: >
metadata: {"openclaw": {"emoji": "🎞️", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "greeting_v2"}}

Getting Started

Send me your images and I'll handle the LoRA video generation. Or just describe what you're after.

Try saying:

"convert a single portrait photo or product image into a 1080p MP4"
"animate this photo into a 5-second video using a cinematic LoRA style"
"turning still images into short animated videos using custom LoRA models for AI artists and content creators"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

Generate a UUID as client identifier
POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Image to Video LoRA — Animate images with LoRA styles

Drop your images in the chat and tell me what you need. I'll handle the LoRA video generation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a single portrait photo or product image, ask for animate this photo into a 5-second video using a cinematic LoRA style, and about 1-3 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — high-contrast images with clear subjects produce the most consistent motion results.

Matching Input to Actions

User prompts referencing image to video lora, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...	Action	Skip SSE?
"export" / "导出" / "download" / "send me the video"	→ §3.5 Export	✅
"credits" / "积分" / "balance" / "余额"	→ §3.3 Credits	✅
"status" / "状态" / "show tracks"	→ §3.4 State	✅
"upload" / "上传" / user sends file	→ §3.2 Upload	✅
Everything else (generate, edit, add BGM…)	→ §3.1 SSE	❌

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

All calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

Session — POST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"<lang>"}. Gives you a session_id.
Chat (SSE) — POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
Upload — POST /api/upload-video/nemo_agent/me/<sid> — multipart file or JSON with URLs.
Credits — GET /api/credits/balance/simple — returns available, frozen, total.
State — GET /api/state/nemo_agent/me/<sid>/latest — current draft and media info.
Export — POST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/<id> every 30s for completed status and download URL.

Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Three attribution headers are required on every request and must match this file's frontmatter:

Header	Value
`X-Skill-Source`	`image-to-video-lora`
`X-Skill-Version`	frontmatter `version`
`X-Skill-Platform`	auto-detect: `clawhub` / `cursor` / `unknown` from install path

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says	You do
"click [button]" / "点击"	Execute via API
"open [panel]" / "打开"	Query session state
"drag/drop" / "拖拽"	Send edit via SSE
"preview in timeline"	Show track summary
"Export button" / "导出"	Execute export workflow

SSE Event Handling

Event	Action
Text response	Apply GUI translation (§4), present to user
Tool call/result	Process internally, don't forward
`heartbeat` / empty `data:`	Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes	Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with `?bind=<id>` (get `<id>` from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "animate this photo into a 5-second video using a cinematic LoRA style" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, WEBP, BMP for the smoothest experience.

PNG images preserve detail better than JPG when used as LoRA animation inputs.

Common Workflows

Quick edit: Upload → "animate this photo into a 5-second video using a cinematic LoRA style" → Download MP4. Takes 1-3 minutes for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.

平台分布

OpenClaw

79.63%

按下载量换算512

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置，应进入高风险复核队列，默认不自动发布。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项，安装前请自行复核。目前只有一个公开来源，建议同时核对源仓库。

继续浏览同类 Skills

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whitejohnk-26/image-to-video-long-duration

下一个 Skill

image-to-video-low-vram（图像到视频低显存）

peand-rover/image-to-video-low-vram