TensorsLab Video Generation

Overview

This skill enables AI-powered video generation through TensorsLab's API, supporting both text-to-video and image-to-video workflows. Video generation is a time-intensive process - tasks typically take several minutes to complete.

Script Path

The Python scripts for this skill are located in the scripts/ subdirectory relative to this SKILL.md file. Always use the absolute path when executing scripts. Determine the absolute path based on where this skill is installed.

For example, if this SKILL.md is at /path/to/skills/tl-video/SKILL.md, then:

Auth script: python "/path/to/skills/tl-video/scripts/tensorslab_auth.py"
Video script: python "/path/to/skills/tl-video/scripts/tensorslab_video.py"

When executing, construct the command using the resolved absolute path:

python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" <args>

Authorization

BEFORE any video generation, you must ensure you are authorized with TensorsLab.

1. Automatic Authorization

The authorization script will automatically check if an API key already exists in the TENSORSLAB_API_KEY environment variable or in ~/.tensorslab/.env before proceeding. (Note: When you need to verify the environment variable, ONLY check if it exists. NEVER display or print the actual API key value.)

Run:

python "<absolute_path_to_skill_dir>/scripts/tensorslab_auth.py"

On Windows/macOS: This will open a browser for authorization. Wait for "Authorization Successful!" before proceeding.

On Linux: Browser auth is automatically skipped. The script will print instructions to get your API key manually from TensorsLab Console. Set it via:

export TENSORSLAB_API_KEY=your_api_key_here

After authorization, the API key is stored in ~/.tensorslab/.env and you don't need to re-authorize unless the key expires.

2. Key Persistence

When the API key is provided via export TENSORSLAB_API_KEY=xxx or --api-key argument and a generation task succeeds, the key is automatically saved to ~/.tensorslab/.env. Future sessions will pick it up without needing to export again.

Models

Model	Description	Best For	Max Duration
seedancev2	Highest quality, much slower generation	Cinematic content where quality is priority	15s
seedancev15pro	Pro quality	High-end productions	10s
seedancev1profast	Fast generation with solid quality	Default model, quick previews	10s

Default model: seedancev1profast (recommended for most requests)

Notes:

seedancev2 has the best visual quality but is significantly slower than 1.x models.

Workflow

1. Text-to-Video Generation

User request: "做一段 10 秒钟横屏的宇宙飞船穿梭星际的视频"

Constraints:

Do NOT pass sourceImage or imageUrl for text-to-video generation.

Agent processing:

Extract parameters: duration=10, ratio="16:9"
Enhance prompt with cinematic details, camera movements, scene descriptions
Call API with enriched prompt
Monitor progress with heartbeat updates (every 60 seconds)
Download to ./tensorslab_output/

Example enhanced prompt:

Cinematic wide shot of a spaceship rapidly flying through space, passing glowing
nebulae and distant stars, lens flares, dramatic camera movement, epic scale,
movie-quality visual effects, smooth 24fps motion

2. Image-to-Video Generation

User request: "让这张人物合影 family.jpg 动起来" or "让风景照动起来"

Agent processing:

Extract image file paths (1-2 images supported)
Enhance prompt with motion instructions
Monitor progress with heartbeat updates
Download results

Parameters for image-to-video:

sourceImage: Array of image files (1-2 images max)
imageUrl: Comma-separated URLs of source images (Must be standard HTTP/HTTPS URLs. Do NOT use local paths like /tmp/xxx.png here)
prompt: Description of desired motion/animation

Model limitation:

seedancev2 does not support human-face source images for image-to-video.
If the source image contains a human face, use seedancev1profast (default) or seedancev15pro instead.

3. Resolution and Aspect Ratio

Aspect ratios:

9:16 - Vertical (TikTok, Reels, Shorts) - default
16:9 - Horizontal (YouTube, standard video)
Other ratios available depending on model

Resolutions:

480p - SD quality, faster generation
720p - HD quality - default
1080p - Full HD
1440p - 2K quality (seedancev2 only)

4. Duration Options

seedancev2: 5-15 seconds
Other models: 5-10 seconds

Longer videos take proportionally more time to generate. seedancev2 is notably slower even at the same duration/resolution.

5. Special Features (seedancev2 only)

Feature	Parameter	Description
Audio Generation	`generate_audio=1`	Generate soundtrack with video
Last Frame	`return_last_frame=1`	Also return final frame as image

Progress Tracking

Video generation takes several minutes. Keep users informed:

⏳ Waiting for video generation to complete...
   (This may take several minutes - please be patient)
🔄 Status: Processing (elapsed: 45s)
🚀 正在渲染电影级大片，已耗时 60 秒，请稍安勿躁...
🚀 正在渲染电影级大片，已耗时 120 秒，请稍安勿躁...
✅ Task completed!

Heartbeat interval: Print encouraging message every 60 seconds.

Using the Script

依赖：脚本需要 requests 和 pyyaml 库，首次使用前执行：
pip install requests pyyaml

Execute the Python script directly:

# Text-to-video (default 5s, vertical 9:16)
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "a spaceship flying through space"

# 10 second horizontal video
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "sunset over ocean waves" --duration 10 --ratio 16:9

# Image-to-video with local file
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "make this photo come alive" --source portrait.jpg

# Image-to-video with URL
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "make this photo come alive" --image-url https://example.com/portrait.jpg

# Fast preview
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "abstract flowing colors" --model seedancev1profast

# High quality with audio
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "epic mountain timelapse" --resolution 1440p --duration 10 --audio

# Custom output directory
python "<absolute_path_to_skill_dir>/scripts/tensorslab_video.py" "a sunset timelapse" --output-dir ./my_videos

Task Status Flow

Status	Code	Meaning
Pending	1	Task waiting in queue
Processing	2	Currently generating
Completed	3	Done, video ready
Failed	4	Error occurred
Uploading	5	Uploading generated video

Error Handling

Translate API errors to user-friendly messages:

Error Code	Meaning	User Message
9000	Insufficient credits	"亲，积分用完啦，请前往 https://tensorai.tensorslab.com/ 充值"
9999	General error	Show the specific error message

Output

All videos are saved to output directory with naming pattern:

Default: ./tensorslab_output/ (current working directory)
Custom: Use --output-dir or -o to specify a different path
Naming: {task_id}_{index}.mp4 - e.g., abcd_1234567890_0.mp4

URL mapping: The script also saves file-to-URL mappings in ./tensorslab_output/urls.yaml. This file tracks the original URLs for each downloaded file and accumulates entries across multiple runs. When you need the original URL of a generated video, read this file.

# Example urls.yaml content
abcd_1234567890_0.mp4: https://tensorai.tensorslab.com/videos/abcd_1234567890_0.mp4

After completion, the script outputs both the local file path and the remote URL. Inform user with both:

🎉 您的视频处理完毕！
   - File: ./tensorslab_output/{filename}
   - URL: {remote_url}

Tips for Better Results

Text-to-Video

Include cinematic terms: "wide shot", "close-up", "pan", "dolly"
Describe motion: "flying rapidly", "slowly drifting", "zooming in"
Specify style: "cinematic", "documentary style", "dreamy"

Image-to-Video

Describe the desired motion: "gentle sway", "subtle movement"
For landscapes: "clouds moving", "water flowing", "leaves rustling"

Resources

scripts/tensorslab_video.py: Main API client with full CLI support
references/api_reference.md: Detailed API documentation

tl-video