图像处理敏感数据clawhub未标认证来源可访问clear审计提醒

image-to-video-lora（图像到视频洛拉）

Agent Skill

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。

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OpenClaw

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

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安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

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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 warning/failed 结果，不能写成本站确认安全。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/vcarolxhberger/image-to-video-lora

SKILL.md 来源

clawhub.ai