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peand-rover

Image To Video Low Vram

by peandrover adam · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install image-to-video-low-vram
Description
Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, BMP, up to 50MB), say something like "ani...
README (SKILL.md)

Getting Started

Got still images to work with? Send it over and tell me what you need — I'll take care of the AI video creation.

Try saying:

  • "convert a single product photo or illustration into a 720p MP4"
  • "animate this image into a 5-second video clip using low VRAM mode"
  • "converting still images into short video clips on low-end GPUs for indie creators, hobbyists with budget hardware"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer \x3Ctoken>, Content-Type: application/json, and body {"task_name":"project","language":"\x3Cdetected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Image to Video Low VRAM — Animate Images on Budget GPUs

Send me your still images and describe the result you want. The AI video creation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a single product photo or illustration, type "animate this image into a 5-second video clip using low VRAM mode", and you'll get a 720p MP4 back in roughly 1-3 minutes. All rendering happens server-side.

Worth noting: smaller image dimensions reduce VRAM usage and speed up generation significantly.

Matching Input to Actions

User prompts referencing image to video low vram, 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.

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/\x3Csid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s until status = completed. Download URL at output.url.

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

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=\x3Cid> (get \x3Cid> 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

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

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

Draft JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "animate this image into a 5-second video clip using low VRAM mode" — concrete instructions get better results.

Max file size is 50MB. Stick to JPG, PNG, WEBP, BMP for the smoothest experience.

Use PNG for clean edges and transparent backgrounds before converting to MP4.

Common Workflows

Quick edit: Upload → "animate this image into a 5-second video clip using low VRAM mode" → 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.

Usage Guidance
This skill appears to do what it says: it uploads user images to a remote rendering service and returns generated videos. Before installing, consider: 1) the skill will use or obtain a NEMO_TOKEN and store session state (tokens expire after 7 days for anonymous tokens); 2) it makes network calls to https://mega-api-prod.nemovideo.ai — if you need to audit data flows, review their privacy/retention policy because your uploaded images and any prompts are sent to that service; 3) clarify the minor metadata mismatch (SKILL.md frontmatter claims a config path ~/.config/nemovideo/ while the registry summary listed none) if you want to be sure the skill will not read local config files unexpectedly. If those points are acceptable, the skill is coherent with its purpose.
Capability Analysis
Type: OpenClaw Skill Name: image-to-video-low-vram Version: 1.0.0 The skill is a legitimate integration for the Nemo Video API (mega-api-prod.nemovideo.ai), designed to facilitate image-to-video generation via remote cloud rendering. It provides clear instructions for the AI agent to handle authentication (including anonymous token generation), session management, and file uploads, with no evidence of data exfiltration, malicious execution, or unauthorized access beyond its stated purpose.
Capability Assessment
Purpose & Capability
Name/description (animate images into short videos on remote GPUs) match the runtime instructions: all network calls target a remote rendering API, uploads are supported, and an API token (NEMO_TOKEN) is required. The requested capability (NEMO_TOKEN + session management) is proportional to a cloud render service.
Instruction Scope
The SKILL.md's runtime steps are focused on authentication, session creation, upload, SSE-based generation, and export polling — all consistent with a cloud-render workflow. Note: the doc instructs the agent to generate an anonymous token automatically if NEMO_TOKEN is not present and to store session_id/token for subsequent calls; it also says not to display raw token values. This is expected, but you should be aware tokens are created/stored and network calls are made to an external API. Also the frontmatter references a config path (~/.config/nemovideo/) and a platform-detection step (install path → X-Skill-Platform) — these are minor scope extensions (metadata/config awareness) but not suspicious on their own.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is written to disk by the skill itself. This is the lowest-risk install model.
Credentials
Only one required env var (NEMO_TOKEN / primaryEnv), which is appropriate for a remote API. The instructions include a flow to obtain an anonymous token via the service if no token is present; that behavior is consistent with the declared credential. One minor inconsistency: the registry metadata summary at the top reported 'Required config paths: none', but the SKILL.md frontmatter lists a configPaths entry (~/.config/nemovideo/). This mismatch should be clarified (does the skill read that path or merely declare it?)
Persistence & Privilege
Skill is not always:true and does not request elevated platform privileges. It stores session state/tokens for its own operation (normal). Autonomous invocation is allowed (default) but not combined with other broad permissions, so risk is standard for an agent-invokable connector.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install image-to-video-low-vram
  3. After installation, invoke the skill by name or use /image-to-video-low-vram
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of Image to Video Low VRAM: turn still images (JPG, PNG, WEBP, BMP, up to 50MB) into 720p MP4 video clips using remote cloud GPUs, optimized for budget hardware. - Simple setup: connects automatically and handles free API token acquisition; no manual installation or GPU required. - Supports quick workflows: upload image, describe the desired animation, and download the generated video in ~1–3 minutes. - Guided error handling with clear feedback for unsupported files, size limits, token/session expiry, and export restrictions. - Detailed session management and timeline tracking, supporting text overlays, background music, and aspect ratio adjustments via natural language. - Multiple common use cases covered: one-click quick edits, batch processing, and iterative project refinement.
Metadata
Slug image-to-video-low-vram
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Image To Video Low Vram?

Get animated video clips ready to post, without touching a single slider. Upload your still images (JPG, PNG, WEBP, BMP, up to 50MB), say something like "ani... It is an AI Agent Skill for Claude Code / OpenClaw, with 132 downloads so far.

How do I install Image To Video Low Vram?

Run "/install image-to-video-low-vram" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Image To Video Low Vram free?

Yes, Image To Video Low Vram is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Image To Video Low Vram support?

Image To Video Low Vram is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Image To Video Low Vram?

It is built and maintained by peandrover adam (@peand-rover); the current version is v1.0.0.

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