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modelscope-img-generatormodelscope 图像生成器

Agent Skill

modelscope-img-generator 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,540

周安装

149

GitHub Stars

1

下载量

1,240
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

2

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:modelscope-img-generator(modelscope 图像生成器)
来源仓库:https://github.com/windandliberty/modelscope-img-generator
安装命令:
openclaw skills install modelscope-img-generator
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 modelscope-img-generator

简介

使用 ModelScope API 生成图像。用于图像生成请求。支持文字转图像+图像转图像;可配置的模型;使用--输入图像。

SKILL.md

name
ModelScope-Img
description
Generate images with ModelScope API. Use for image generation requests. Supports text-to-image + image-to-image; configurable models; use --input-image.

ModelScope Image Generation

Generate new images or edit existing ones using ModelScope's community models.

⚠️ Security Notice

This tool ONLY connects to the official ModelScope API endpoint.

  • Fixed Endpoint: https://api-inference.modelscope.cn/
  • Endpoint is NOT configurable: There is NO ability to customize or redirect the API endpoint
  • This prevents any possibility of API key or data being redirected to untrusted servers

Prerequisites

Required before use:

  1. ModelScope API Key - Get from https://modelscope.cn

- Set as environment variable: export MODELSCOPE_API_KEY="your-key-here" - Or pass directly: --api-key "your-key-here"

  1. uv - Python package manager (install via pip install uv)
  2. Python 3.10+ - Required runtime

Python dependencies (auto-installed by uv):

  • requests>=2.31.0
  • pillow>=10.0.0

Usage

Run the script using absolute path (do NOT cd to skill directory first):

Generate new image:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "your image description" --filename "output-name.jpg" [--model MODEL_ID] [--api-key KEY]

Edit existing image:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "editing instructions" --filename "output-name.jpg" --input-image "path/to/input.jpg" [--model MODEL_ID] [--api-key KEY]

Important: Always run from the user's current working directory so images are saved where the user is working, not in the skill directory.

Models

The script supports various ModelScope community models. Use --model to specify:

  • Default: Tongyi-MAI/Z-Image-Turbo (fast, good quality)
  • Other options: Browse ModelScope Model Hub for more image generation models

Popular alternatives:

  • MusePublic/wukong-1.8B - Chinese style
  • Other community models available on ModelScope

LoRA Support

Enhance generation with LoRA adapters using --lora:

# Single LoRA
uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "anime girl" --filename output.jpg --lora "my-lora-repo"

# Multiple LoRAs with weights (must sum to 1.0)
uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "anime girl" --filename output.jpg --lora "lora1:0.6,lora2:0.4"

API Key

The script checks for API key in this order:

  1. --api-key argument (use if user provided key in chat)
  2. MODELSCOPE_API_KEY environment variable

If neither is available, the script exits with an error message.

Preflight + Common Failures (fast fixes)

  • Preflight:

- command -v uv (must exist) - test -n "$MODELSCOPE_API_KEY" (or pass --api-key) - If editing: test -f "path/to/input.jpg"

  • Common failures:

- Error: No API key provided. → set MODELSCOPE_API_KEY or pass --api-key - Error: Input image not found: → wrong path / unreadable file; verify --input-image points to a real image - API errors → wrong key, no access, or model not available; try a different key or model - Task timeout → generation took too long; try a simpler prompt or different model

Filename Generation

Generate filenames with the pattern: yyyy-mm-dd-hh-mm-ss-name.jpg

Format: {timestamp}-{descriptive-name}.jpg

  • Timestamp: Current date/time in format yyyy-mm-dd-hh-mm-ss (24-hour format)
  • Name: Descriptive lowercase text with hyphens
  • Keep the descriptive part concise (1-5 words typically)
  • Use context from user's prompt or conversation
  • If unclear, use random identifier (e.g., x9k2, a7b3)

Examples:

  • Prompt "A serene Japanese garden" → 2025-11-23-14-23-05-japanese-garden.jpg
  • Prompt "sunset over mountains" → 2025-11-23-15-30-12-sunset-mountains.jpg
  • Prompt "create an image of a robot" → 2025-11-23-16-45-33-robot.jpg
  • Unclear context → 2025-11-23-17-12-48-x9k2.jpg

Image Editing

When the user wants to modify an existing image:

  1. Check if they provide an image path or reference an image in the current directory
  2. Use --input-image parameter with the path to the image
  3. The prompt should contain editing instructions (e.g., "make the sky more dramatic", "remove the person", "change to cartoon style")
  4. Common editing tasks: add/remove elements, change style, adjust colors, blur background, etc.

Prompt Handling

For generation: Pass user's image description as-is to --prompt. Only rework if clearly insufficient.

For editing: Pass editing instructions in --prompt (e.g., "add a rainbow in the sky", "make it look like a watercolor painting")

Preserve user's creative intent in both cases.

Prompt Templates (high hit-rate)

Use templates when the user is vague or when edits must be precise.

  • Generation template:

- "Create an image of: <subject>. Style: <style>. Composition: <camera/shot>. Lighting: <lighting>. Background: <background>. Color palette: <palette>. Avoid: <list>."

  • Editing template (preserve everything else):

- "Change ONLY: <single change>. Keep identical: subject, composition/crop, pose, lighting, color palette, background, text, and overall style. Do not add new objects. If text exists, keep it unchanged."

Output

  • Saves image to current directory (or specified path if filename includes directory)
  • Script outputs the full path to the generated image
  • Do not read the image back - just inform the user of the saved path

Examples

Generate new image:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "A serene Japanese garden with cherry blossoms" --filename "2025-11-23-14-23-05-japanese-garden.jpg"

Generate with specific model:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "Chinese ink painting landscape" --filename "ink-landscape.jpg" --model "MusePublic/wukong-1.8B"

Edit existing image:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "make the sky more dramatic with storm clouds" --filename "2025-11-23-14-25-30-dramatic-sky.jpg" --input-image "original-photo.jpg"

Generate with LoRA:

uv run ~/.codex/skills/ModelScope_img_generator/scripts/generate_img.py --prompt "anime style portrait" --filename "anime-portrait.jpg" --lora "anime-lora:0.8"

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

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。

平台分布

OpenClaw

88.05%
按下载量换算1,092

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。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

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