tensorslab-image

Original：🇺🇸 English

Translated

1 scriptsChecked / no sensitive code detected

Generate and edit images using TensorsLab's AI models. Supports text-to-image, image-to-image generation, plus advanced editing: avatar generation, watermark removal, object erasure, face replacement, and general image editing. Features automatic prompt enhancement, progress tracking, and local file saving. Requires TENSORSLAB_API_KEY environment variable.

3installs

Sourcetensorslab/skills

Added on2026-02-28

NPX Install

npx skill4agent add tensorslab/skills tensorslab-image

SKILL.md Content

View Translation Comparison →

TensorsLab Image Generation

Overview

This skill enables AI-powered image generation through TensorsLab's API, supporting both text-to-image and image-to-image workflows. The agent enhances user prompts with detailed visual descriptions before calling the API, ensuring high-quality outputs.

Authentication Check

Before any image generation, verify the API key is configured:

bash

# Check if API key is set
echo $TENSORSLAB_API_KEY

If not set, display this friendly message:

您好！要生成高质量的内容，您需要先进行简单的配置：
1. 访问 https://test.tensorai.tensorslab.com/ 登录并订阅。
2. 在控制台中获取您的专属 API Key。
3. 将其保存为环境变量：
   - Windows (PowerShell): $env:TENSORSLAB_API_KEY="您的Key"
   - Mac/Linux: export TENSORSLAB_API_KEY="您的Key"

Models

Model	Description	Best For
seedreamv45	Latest enhanced model	General purpose, highest quality
seedreamv4	Standard model	Fast generation, good quality
zimage	Alternative model	Specific artistic styles

Default:

seedreamv4

Workflow

For additional scenarios beyond basic generation (avatar generation, watermark removal, object erasure, face replacement), see references/scenarios.md.

1. Text-to-Image Generation

User request: "画一个在月球上吃热狗的宇航员"

Agent processing:

Extract the core subject and action
Enhance prompt with details (lighting, composition, style, atmosphere)
Call API with enriched prompt
Monitor progress with heartbeat updates
Download to
```
./tensorslab_output/
```

Example enhanced prompt:

An astronaut sitting on the lunar surface, eating a hot dog with mustard,
cinematic lighting, Earth visible in the background, highly detailed,
photorealistic, 8k quality, dramatic shadows from the low sun angle

2. Image-to-Image Generation

User request: "把 cat.png 的背景换成太空" or "参考 sketch.png 渲染成 3D 模型"

Agent processing:

Extract image file paths (absolute or relative to current directory)
Enhance prompt with transformation instructions
Upload source images with prompt
Monitor and download results

Parameters for image-to-image:

```
sourceImage
```
: Array of image files (for local upload)
```
imageUrl
```
: URL of source image
```
prompt
```
: Description of desired transformation

3. Image Editing (General Purpose)

General-purpose editing for any local image modifications.

User request examples:

"把这张图的天空改成日落色"
"给人物加上墨镜"
"把头发颜色染成粉色"

Agent processing:

Extract image file path
Parse the specific editing instruction (what to change, where)
Build enhanced prompt with precise editing guidance
Call API with source image and editing prompt
Save result to
```
./tensorslab_output/
```

Example enhanced prompt:

Change the sky to sunset colors with warm orange and pink gradients,
matching the existing lighting conditions and atmospheric perspective,
seamless blend at the horizon line

For avatar generation, watermark removal, object erasure, and face replacement scenarios, see references/scenarios.md.

4. Resolution Options

Supported formats:

Aspect ratios:
```
9:16
```
,
```
16:9
```
,
```
3:4
```
,
```
4:3
```
,
```
1:1
```
,
```
2:3
```
,
```
3:2
```
Resolution levels:
```
2K
```
,
```
4K
```
Specific dimensions:
```
WxH
```
format (e.g.,
```
2048x2048
```
,
```
1920x1080
```
)
- Constraint: Total pixels must be between 3,686,400 and 16,777,216

Using the Script

Execute the Python script directly:

bash

# Text-to-image
python scripts/tensorslab_image.py "a cat on the moon"

# With specific resolution
python scripts/tensorslab_image.py "sunset over mountains" --resolution 16:9

# Image-to-image
python scripts/tensorslab_image.py "watercolor style" --source cat.png

# Specify model
python scripts/tensorslab_image.py "cyberpunk city" --model seedreamv45

# Custom output directory
python scripts/tensorslab_image.py "a beautiful landscape" --output-dir ./my_images

Task Status Flow

Status	Code	Meaning
Queued	1	Task waiting in queue
Processing	2	Currently generating
Completed	3	Done, images ready
Failed	4	Error occurred

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://test.tensorai.tensorslab.com"/ 充值"
9999	General error	Show the specific error message

Output

All images 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}.{ext}

- e.g.,

abcd_1234567890_0.png

After completion, inform user:

🎉 您的图片处理完毕！已存放于 ./tensorslab_output/{filename}

Resources

scripts/tensorslab_image.py: Main API client with full CLI support
references/api_reference.md: Detailed API documentation
references/scenarios.md: Advanced usage scenarios (avatar generation, watermark removal, object erasure, face replacement)

tensorslab-image

NPX Install

Tags

SKILL.md Content

TensorsLab Image Generation

Overview

Authentication Check

Models

Workflow

1. Text-to-Image Generation

2. Image-to-Image Generation

3. Image Editing (General Purpose)

4. Resolution Options

Using the Script

Task Status Flow

Error Handling

Output

Resources