Text to Video Diffusion — Generate Videos from Text Prompts
Turn a short descriptive sentence like 'a fox running through a snowy forest at dusk' into 1080p AI-generated video just by typing what you need. Whether it's generating short video clips from written descriptions or quick social content, drop your text prompt and describe the result you want. No timeline dragging, no export settings — 1-3 minutes from upload to download.
0 0by peandrover
aivideo
## Getting Started
> Ready when you are. Drop your text prompt here or describe what you want to make.
**Try saying:**
- "generate a short descriptive sentence like 'a fox running through a snowy forest at dusk' into a 1080p MP4"
- "generate a 5-second clip of a city skyline transitioning from day to night"
- "generating short video clips from written descriptions for content creators, marketers, designers"
### 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 <token>`, `Content-Type: application/json`, and body `{"task_name":"project","language":"<detected>"}`. 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.
# Text to Video Diffusion — Generate Videos from Text Prompts
Send me your text prompt and describe the result you want. The AI video generation runs on remote GPU nodes — nothing to install on your machine.
A quick example: upload a short descriptive sentence like 'a fox running through a snowy forest at dusk', type "generate a 5-second clip of a city skyline transitioning from day to night", and you'll get a 1080p MP4 back in roughly 1-3 minutes. All rendering happens server-side.
Worth noting: shorter, more specific prompts tend to produce more accurate and coherent video output.
## Matching Input to Actions
User prompts referencing text to video diffusion, 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.
Base URL: `https://mega-api-prod.nemovideo.ai`
| Endpoint | Method | Purpose |
|----------|--------|---------|
| `/api/tasks/me/with-session/nemo_agent` | POST | Start a new editing session. Body: `{"task_name":"project","language":"<lang>"}`. Returns `session_id`. |
| `/run_sse` | POST | Send a user message. Body includes `app_name`, `session_id`, `new_message`. Stream response with `Accept: text/event-stream`. Timeout: 15 min. |
| `/api/upload-video/nemo_agent/me/<sid>` | POST | Upload a file (multipart) or URL. |
| `/api/credits/balance/simple` | GET | Check remaining credits (`available`, `frozen`, `total`). |
| `/api/state/nemo_agent/me/<sid>/latest` | GET | Fetch current timeline state (`draft`, `video_infos`, `generated_media`). |
| `/api/render/proxy/lambda` | POST | Start export. Body: `{"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}`. Poll status every 30s. |
Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.
Headers are derived from this file's YAML frontmatter. `X-Skill-Source` is `text-to-video-diffusion`, `X-Skill-Version` comes from the `version` field, and `X-Skill-Platform` is detected from the install path (`~/.clawhub/` = `clawhub`, `~/.cursor/skills/` = `cursor`, otherwise `unknown`).
Include `Authorization: Bearer <NEMO_TOKEN>` and all attribution headers on every request — omitting them triggers a 402 on export.
### Error Codes
- `0` — success, continue normally
- `1001` — token expired or invalid; re-acquire via `/api/auth/anonymous-token`
- `1002` — session not found; create a new one
- `2001` — out of credits; anonymous users get a registration link with `?bind=<id>`, registered users top up
- `4001` — unsupported file type; show accepted formats
- `4002` — file too large; suggest compressing or trimming
- `400` — missing `X-Client-Id`; generate one and retry
- `402` — free plan export blocked; not a credit issue, subscription tier
- `429` — rate limited; wait 30s and retry once
### 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.
### 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 |
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 "generate a 5-second clip of a city skyline transitioning from day to night" — concrete instructions get better results.
Max file size is 500MB. Stick to TXT, DOCX, PDF, plain text for the smoothest experience.
Export as MP4 for widest compatibility across platforms and devices.
## Common Workflows
**Quick edit**: Upload → "generate a 5-second clip of a city skyline transitioning from day to night" → 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.