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Agent Skill · 图像处理

local-image-gen-aipc(本地图像生成 aipc)

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

local-image-gen-aipc 是图像处理类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在图像处理类的 1,562 个 Skill 里排第 320。

总安装 10,234周安装 414GitHub Stars 公开资料未说明下载 3,213核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.3

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:local-image-gen-aipc(本地图像生成 aipc)
来源仓库:https://github.com/juan-oy/local-image-gen-aipc
安装命令:
openclaw skills install local-image-gen-aipc
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 local-image-gen-aipc

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

基于文本提示生成高质量图像,支持多种风格与分辨率选项。适用于创意素材制作、演示配图和原型设计辅助场景。输出 PNG/JPG 格式文件,可嵌入工作流或直接交付用户。需注意版权声明与人物肖像授权,避免生成侵权或不当内容。local-image-gen-aipc 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install local-image-gen-aipc;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

local-image-gen-aipc 怎么安装?

在终端执行 openclaw skills install local-image-gen-aipc。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

local-image-gen-aipc 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

local-image-gen-aipc 需要什么权限?

权限级别是接触敏感数据。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。

local-image-gen-aipc 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

local-image-gen-aipc 用的人多吗?

公开数据是累计安装 10,234 次,近一周安装 414 次,下载 3,213 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

local-image-gen-aipc 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
local-image-gen-aipc

本地图像生成 aipc · 本页

10,234—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
local-image-gen-aipc
description
>
os
windows
requires
network
setup
required # github.com (pip deps), modelscope.cn (\~10 GB model)
inference
offline
user-invocable
true
allowed-tools
Bash(python *), Bash(powershell *), Bash(git *), Read, Glob, Write, message

Model: snake7gun/Z-Image-Turbo-int4-ov (ModelScope INT4) SKILL_VERSION: v1.0.3

Network usage: Setup downloads pip dependencies (some pinned to git+https commits) from github.com, and the model (~10 GB, resume supported) from modelscope.cn. Inference is fully offline — no network calls once setup is complete.

First time? Before using this skill, run these two scripts once in a terminal:

python setup.py          # creates venv, installs dependencies (~5 min)
python download_model.py # downloads the model (~10 GB, resumable)

Both scripts are in the skill directory alongside this SKILL.md.

Directory layout (all auto-created)

{USERNAME}_openvino\
├── venv\                          ← shared venv (created by setup.py)
└── imagegen\
    ├── state.json                 ← written by setup.py
    ├── generate_image.py          ← deployed by setup.py (versioned)
    ├── Z-Image-Turbo-int4-ov\    ← downloaded by download_model.py (~10 GB)
    └── outputs\YYYYMMDD_HHMMSS_topic.png

Agent instructions

  1. Windows / PowerShell only. Never use Linux commands (ls, rm, cat). Never use && or call.
  2. Every step reads state.json itself — do not pass paths between steps manually.
  3. Use VENV_PY from state.json for all inference and package-import calls — never use system Python for inference or Step 2 DEP_CHECK. Exception: setup.py, check_env.py, download_model.py, and the Step 2 script-write command intentionally run with system Python (they only use stdlib or create/validate the venv, so they must not depend on it).
  4. CRITICAL — Never modify generate_image.py manually. It is deployed and versioned by setup.py. If it fails or is missing, re-run setup.py.
  5. Goal: generate an image and send the preview to the conversation.

Auto-recovery policy — try before asking user:

  • If STATE=MISSING, VENV_PY=BROKEN, PACKAGES_MISSING, or SCRIPTS_STALE: automatically run setup.py (up to 3 attempts). Only ask user if all 3 fail.
  • If MODEL_STATUS=MISSING: automatically run download_model.py (up to 3 attempts). Stop if a single attempt exceeds 20 minutes — download supports resume, partial progress is not lost.
  • Always announce before each attempt: [INFO] Auto-installing environment (attempt N/3)…

[WARNING] Network / proxy handling — when running setup.py or download_model.py:

  1. Check for proxy: read $env:HTTPS_PROXY / $env:HTTP_PROXY; if unset, run netsh winhttp show proxy.
  2. If proxy detected: apply automatically and continue.
  3. If no proxy and download/setup fails: inform the user, suggest configuring one, then retry.

Many networks (corporate / China mainland / campus) require a proxy. Download supports resume — safe to retry after fixing network.

Pipeline — follow exactly in order, no skipping:

Step 0: expand prompt       → EXPANDED_PROMPT, TOPIC
Step 1: verify environment  → VENV_PY, IMAGE_GEN_DIR confirmed ready
         ↳ if STATE=MISSING or VENV_BROKEN or PACKAGES_MISSING: auto-run setup.py (3 attempts)
         ↳ if MODEL_STATUS=MISSING: auto-run download_model.py (3 attempts)
Step 2: verify deps         → DEP_CHECK=PASS
Step 3: generate + send     → [SUCCESS] + image preview

Pre-flight: Verify Runtime Requirements (Required on First Use)

Pre-flight: Checking Python and git...

Check Python Version

python --version

Interpretation:

OutputAction
Python 3.10.x or higher[OK] PYTHON_OK, continue to git check
Python 3.8 / 3.9Version too low; upgrade is required (see below)
'python' is not recognized as an internal or external commandPython is not installed; install is required (see below)
Python was not found; run without arguments...Windows Store alias — see below

If output contains "run without arguments to install from the Microsoft Store", the Windows Store App Execution Alias is shadowing the real Python. Do NOT ask the user to change settings, and do NOT write helper scripts. Run this command to find the real Python:

where.exe python 2>$null | Where-Object { $_ -notlike "*WindowsApps*" } | Select-Object -First 1
  • A path is printed → record this literal string as SYSTEM_PYTHON (system-level Python, used only for setup.py, check_env.py, download_model.py). For every command marked [SYSTEM PYTHON], substitute the full literal path for python. Do NOT use this path for inference — inference must always use VENV_PY. Do NOT use a $variable across tool calls — each call is a new shell; always embed the literal path directly.
  • Nothing printed → Python is not installed — install it (see below).

If Python is missing or outdated, run this one-command silent installer in PowerShell (recommended, no admin required):

$f = "$env:TEMP\python-installer.exe"
Invoke-WebRequest "https://www.python.org/ftp/python/3.12.10/python-3.12.10-amd64.exe" -OutFile $f
Start-Process $f -ArgumentList "/quiet InstallAllUsers=0 PrependPath=1 Include_pip=1" -Wait
Remove-Item $f
PrependPath=1 adds Python to PATH automatically; Include_pip=1 installs pip; InstallAllUsers=0 avoids requiring administrator privileges.

After installation, restart the terminal, then run python --version and confirm it reports Python 3.12.x.

If you prefer manual installation: download https://www.python.org/ftp/python/3.12.10/python-3.12.10-amd64.exe and make sure to check "Add python.exe to PATH" during setup.

Check git

git --version

Interpretation:

OutputAction
git version 2.x.x[OK] GIT_OK, Pre-flight passed
'git' is not recognized as an internal or external commandgit is not installed; install is required (see below)

If git is missing, run this one-command silent installer in PowerShell:

$f = "$env:TEMP\git-installer.exe"
Invoke-WebRequest "https://github.com/git-for-windows/git/releases/download/v2.49.0.windows.1/Git-2.49.0-64-bit.exe" -OutFile $f
Start-Process $f -ArgumentList "/VERYSILENT /NORESTART /NOCANCEL /SP- /CLOSEAPPLICATIONS /RESTARTAPPLICATIONS /COMPONENTS=icons,ext\\
eg\\shellhere,assoc,assoc_sh" -Wait
Remove-Item $f

After installation, restart the terminal, then run git --version to confirm.

If you prefer manual installation: open https://git-scm.com/download/win, download the installer, and proceed with default options.

git is required for the git+https:// dependency in requirements_imagegen.txt; without git, pip install will fail with git: command not found.

Pre-flight pass criteria: python --version is >= 3.10 and git --version returns a valid version string.

Status message: [OK] Python and git are ready. Starting main workflow.


Skill Contract (Input / Output)

Accepted Inputs

  1. Text description (required) — English or Chinese prompt describing the image to generate.
  2. Optional parameters:

* steps: inference steps, default 9 (higher = more detail) * size: 512×512 | 768×768 | 1024×1024, default 512×512 * seed: integer (default 42) or -1 for random * output: custom absolute output path for the PNG file

Output on Success

Returns the PNG file path in stdout as [SUCCESS] <path>, and sends a preview via the message tool.

{
    "image_path": "C:\\...\\outputs\\20260412_153000_panda_bamboo.png",
    "topic": "panda_bamboo",
    "prompt": "A giant panda sitting in a lush bamboo forest...",
    "steps": 9,
    "size": "512x512",
    "seed": 42,
    "device": "GPU.0"
}

Output on Failure

{
    "error": "human-readable description",
    "stage": "environment | model | inference",
    "recoverable": true
}

Step 0: expand prompt (LLM only — no tools)

Do two things simultaneously: ① expand the prompt and ② extract a topic slug (English snake_case, used for the filename).

Expansion structure: [subject] [action/pose] [environment] [lighting/mood] [style] [quality tags]

Prompts can be English or Chinese — no translation needed. Topic slug must always be English to avoid path encoding issues.

Quality tags: photorealistic, 8K resolution, cinematic lighting, masterpiece

|Input|Topic slug|Expanded prompt| |-|-|-| |a panda|panda_bamboo|A giant panda sitting in a lush bamboo forest, sunlight filtering through leaves, photorealistic, 8K, wildlife photography| |赛博朋克城市|cyberpunk_city|未来感都市夜景,霓虹灯倒映在湿漉漉的街道,赛博朋克风,电影级,8K|

Show the result before proceeding:

Input:    {user description}
   Expanded: {full prompt}
   Topic:    {topic_slug}

Step 1: verify environment and model

Step 1/3: checking environment and model…
# [SYSTEM PYTHON] check_env.py validates the venv — must NOT use venv python here
python "<skill_dir>\check_env.py"

Output interpretation:

OutputMeaningAction
MODEL_STATUS=READY + VENV_PY=...OKProceed to Step 2
SCRIPTS_STALE=old->newgenerate_image.py outdatedAuto-run setup.py (see below)
STATE=MISSINGsetup.py never runAuto-run setup.py (see below)
VENV_PY=BROKENvenv corruptedAuto-run setup.py (see below)
PACKAGES_MISSING: ...packages missing from venvAuto-run setup.py (see below)
MODEL_STATUS=MISSING missing=[...]download incompleteAuto-run download_model.py (see below)

On success: record VENV_PY and IMAGE_GEN_DIR from stdout, proceed to Step 2.

If SCRIPTS_STALE also appears alongside MODEL_STATUS=READY, auto-run setup.py before Step 2 (see below).

If SCRIPTS_STALE or STATE=MISSING or VENV_PY=BROKEN or PACKAGES_MISSING → auto-run setup.py

SCRIPTS_STALE means the venv and model are both OK but generate_image.py in IMAGE_GEN_DIR is an older version. setup.py is idempotent — it skips venv/packages if already healthy and only redeploys the script.

PACKAGES_MISSING means the venv exists but required packages (openvino, torch, optimum.intel) are not installed. Re-running setup.py re-installs only what is missing.

Announce and run (up to 3 attempts):

[INFO] Environment not initialized — auto-installing (attempt 1/3)…
# [SYSTEM PYTHON] setup.py creates the venv — must NOT use venv python here
python "<skill_dir>\setup.py"

Re-run python "<skill_dir>\check_env.py" after each attempt. If all 3 fail, show manual fallback below.


If MODEL_STATUS=MISSING → auto-run download_model.py

Announce to user and ask how to proceed:

Model not found — download required (~10 GB)
   Estimated time:
   • 100 Mbps → ~15 min
   •  50 Mbps → ~30 min
   •  10 Mbps → ~2 hr
   Download supports resume — safe to interrupt and retry.

   [Y] Start auto-download
   [N] I'll download manually — show me the link

Auto-download (up to 3 attempts, stop if a single attempt exceeds 20 minutes):

# [SYSTEM PYTHON] download_model.py uses stdlib only — must NOT use venv python here
python "<skill_dir>\download_model.py"

Re-run python "<skill_dir>\check_env.py" after each attempt.

Manual download fallback:

ModelScope page: https://modelscope.cn/models/snake7gun/Z-Image-Turbo-int4-ov/files

Place all files under <IMAGE_GEN_DIR>\Z-Image-Turbo-int4-ov\. Required subdirs:

Z-Image-Turbo-int4-ov\
├── transformer\
├── vae_decoder\
└── text_encoder\

Then re-run python "<skill_dir>\check_env.py" to verify.


Manual fallback (only if all 3 setup auto-attempts fail)

Show user:

[WARN] Auto-install failed. Please run manually in a terminal:

1) Install environment:
   python "<skill_dir>\setup.py"
   Takes ~5 min, fully automated.

2) Download model (~10 GB):
   python "<skill_dir>\download_model.py"
   Resumable — safe to interrupt and retry.

Come back here when done.

Step 2: verify dependencies

Step 2/3: verifying dependencies…

Verify dependencies (run via VENV_PY):

$env:PYTHONUTF8 = "1"
& "<VENV_PY>" -c "
import json, site
from pathlib import Path

EXPECTED_COMMITS = {
    'optimum_intel': '2f62e5ae',
    'diffusers':     'a1f36ee3',
}

def get_git_commit(pkg_name):
    dirs = site.getsitepackages()
    try: dirs += [site.getusersitepackages()]
    except Exception: pass
    for d in dirs:
        for dist in Path(d).glob(f'{pkg_name}*.dist-info'):
            url_file = dist / 'direct_url.json'
            if url_file.exists():
                data = json.loads(url_file.read_text(encoding='utf-8'))
                return data.get('vcs_info', {}).get('commit_id', 'no_vcs_info')
    return 'not_found'

results = {}
for pkg, imp in [('openvino','openvino'),('torch','torch'),('Pillow','PIL'),('modelscope','modelscope')]:
    try:
        ver = getattr(__import__(imp), '__version__', 'OK')
        results[pkg] = ('OK', ver)
    except ImportError as e:
        results[pkg] = ('MISSING', str(e))

try:
    from optimum.intel import OVZImagePipeline
    results['OVZImagePipeline'] = ('OK', 'importable')
except ImportError as e:
    results['OVZImagePipeline'] = ('MISSING', str(e))

for pkg_name, exp in EXPECTED_COMMITS.items():
    actual = get_git_commit(pkg_name)
    if actual == 'not_found':
        results[f'{pkg_name}@commit'] = ('MISSING', 'not installed via git+https')
    elif actual.startswith(exp):
        results[f'{pkg_name}@commit'] = ('OK', actual[:16])
    else:
        results[f'{pkg_name}@commit'] = ('WRONG', f'got {actual[:16]} want {exp}...')

all_ok = all(v[0] == 'OK' for v in results.values())
for k, (status, detail) in results.items():
    icon = '[OK]' if status == 'OK' else ('[WARN]' if status == 'WRONG' else '[MISSING]')
    print(f'  {icon} {k}: {detail}')
print('DEP_CHECK=PASS' if all_ok else 'DEP_CHECK=FAIL')
"

|Output|Action| |-|-| |DEP_CHECK=PASS|[PASS] Proceed to Step 3| |DEP_CHECK=FAIL (MISSING)|[FAIL] Re-run setup.py and retry| |DEP_CHECK=FAIL (@commit WRONG)|[FAIL] Force reinstall: & "<VENV_PY>" -m pip uninstall optimum-intel diffusers -y then & "<VENV_PY>" -m pip install -r "<skill_dir>\ equirements_imagegen.txt" --no-cache-dir|


Step 3: generate image and send preview

Step 3/3: running inference…

Run these two commands separately:

$env:PYTHONUTF8 = "1"
& "<VENV_PY>" "<IMAGE_GEN_DIR>\generate_image.py" --prompt "EXPANDED_PROMPT" --topic "TOPIC" --steps 9 --seed 42

Pass: stdout contains [SUCCESS]. Record OUTPUT_PATH from the [SUCCESS] line.

Send preview via message tool:

action: "send"  filePath: "OUTPUT_PATH"  message: "[OK] TOPIC"

Final announcement:

[OK] Done! Path: <OUTPUT_PATH>
Prompt: {expanded prompt}
steps=9, 512×512, seed=42 | device: {CPU/GPU}

Parameters

|Param|Default|Notes| |-|-|-| |--prompt|required|English or Chinese| |--topic|empty|English snake_case slug for filename| |--steps|9|Higher = more detail; no hard limit| |--width--height|512|512 | 768|768 1024|1024 recommended| |--seed|42|-1 = random| |--output|auto|Custom absolute output path|

guidance_scale is fixed at 0.0 and not exposed as a parameter.

Troubleshooting

|Error|Cause|Fix| |-|-|-| |STATE=MISSING|setup.py never run|Run python "<skill_dir>\setup.py"| |VENV_PY=BROKEN|venv corrupted|Re-run python "<skill_dir>\setup.py" — rebuilds venv automatically| |PACKAGES_MISSING: ...|venv ok but packages missing|Re-run python "<skill_dir>\setup.py" — reinstalls missing packages; skips steps already done| |MODEL_STATUS=MISSING|download never run or interrupted|Run python "<skill_dir>\download_model.py" — resumes automatically| |DEP_CHECK=FAIL (MISSING)|packages not installed in venv|Re-run setup.py| |DEP_CHECK=FAIL (@commit WRONG)|PyPI release installed instead of pinned commit|Uninstall optimum-intel + diffusers, reinstall with --no-cache-dir| |@commit shows not installed via git+https|git was missing when pip ran|Confirm git is installed, re-run setup.py| |[ERROR] Model incomplete|Download interrupted mid-file|Re-run download_model.py — resumes automatically| |[ERROR] state.json not found|state.json missing|Re-run Step 1| |SCRIPTS_STALE=old->new|generate_image.py in IMAGE_GEN_DIR is outdated|Auto-run setup.py — it redeploys only the script, skips venv/packages/model (fast)| |RuntimeError on GPU|Insufficient VRAM|Lower resolution or hardcode return "CPU" in get_device()| |Black noisy output|Too few steps|Use --steps ≥ 4; 9 recommended| |Download timeout|Network issue or proxy needed|Configure proxy and retry — download supports resume| |RuntimeError: stack expects each tensor to be equal size|openvino 2026.1.0 breaks OVZImagePipeline.forward — pooled_projections tensors have mismatched sequence lengths|Downgrade: & "<VENV_PY>" -m pip install openvino==2026.0.0 --force-reinstall. Then re-run Step 2 and Step 3. If re-running setup.py, it will now install the pinned version automatically.|

平台分布

OpenClaw

78.95%
按下载量换算2,537

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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