← 返回 Skills 市场
juan-oy

Z-image Local image generation with OpenVINO (no API key)

作者 juan-OY · GitHub ↗ · v1.0.3 · MIT-0
cross-platform ⚠ suspicious
333
总下载
0
收藏
0
当前安装
4
版本数
在 OpenClaw 中安装
/install local-image-gen-aipc
功能描述
generate an image, create a picture, draw something, make an image of, text to image, paint a picture, illustrate, visualize, local image generation, AI art,...
使用说明 (SKILL.md)

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

Network usage: Setup downloads pip dependencies (some pinned to git+https commits)\r from github.com, and the model (~10 GB, resume supported) from modelscope.cn.\r Inference is fully offline — no network calls once setup is complete.\r \r First time? Before using this skill, run these two scripts once in a terminal:\r \r

python setup.py          # creates venv, installs dependencies (~5 min)\r
python download_model.py # downloads the model (~10 GB, resumable)\r
```\r
\r
Both scripts are in the skill directory alongside this SKILL.md.\r
\r
## Directory layout (all auto-created)\r
\r
```\r
{USERNAME}_openvino\\r
├── venv\                          ← shared venv (created by setup.py)\r
└── imagegen\\r
    ├── state.json                 ← written by setup.py\r
    ├── generate_image.py          ← deployed by setup.py (versioned)\r
    ├── Z-Image-Turbo-int4-ov\    ← downloaded by download_model.py (~10 GB)\r
    └── outputs\YYYYMMDD_HHMMSS_topic.png\r
```\r
\r
---\r
\r
##  Agent instructions\r
\r
1. **Windows / PowerShell only.** Never use Linux commands (`ls`, `rm`, `cat`). Never use `&&` or `call`.\r
2. **Every step reads `state.json` itself** — do not pass paths between steps manually.\r
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).\r
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`.\r
5. **Goal**: generate an image and send the preview to the conversation.\r
\r
**Auto-recovery policy — try before asking user:**\r
\r
* 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.\r
* 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.\r
* Always announce before each attempt: `[INFO] Auto-installing environment (attempt N/3)…`\r
\r
**[WARNING] Network / proxy handling** — when running `setup.py` or `download_model.py`:\r
1. Check for proxy: read `$env:HTTPS_PROXY` / `$env:HTTP_PROXY`; if unset, run `netsh winhttp show proxy`.\r
2. If proxy detected: apply automatically and continue.\r
3. If no proxy and download/setup fails: inform the user, suggest configuring one, then retry.\r
\r
Many networks (corporate / China mainland / campus) require a proxy. Download supports resume — safe to retry after fixing network.\r
\r
**Pipeline — follow exactly in order, no skipping:**\r
\r
```\r
Step 0: expand prompt       → EXPANDED_PROMPT, TOPIC\r
Step 1: verify environment  → VENV_PY, IMAGE_GEN_DIR confirmed ready\r
         ↳ if STATE=MISSING or VENV_BROKEN or PACKAGES_MISSING: auto-run setup.py (3 attempts)\r
         ↳ if MODEL_STATUS=MISSING: auto-run download_model.py (3 attempts)\r
Step 2: verify deps         → DEP_CHECK=PASS\r
Step 3: generate + send     → [SUCCESS] + image preview\r
```\r
## Pre-flight: Verify Runtime Requirements (Required on First Use)\r
\r
> Pre-flight: Checking Python and git...\r
\r
### Check Python Version\r
\r
```powershell\r
python --version\r
```\r
\r
**Interpretation:**\r
\r
| Output | Action |\r
|------|------|\r
| `Python 3.10.x` or higher | [OK] `PYTHON_OK`, continue to git check |\r
| `Python 3.8 / 3.9` | Version too low; upgrade is required (see below) |\r
| `'python' is not recognized as an internal or external command` | Python is not installed; install is required (see below) |\r
| `Python was not found; run without arguments...` | **Windows Store alias** — see below |\r
\r
**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:\r
\r
```powershell\r
where.exe python 2>$null | Where-Object { $_ -notlike "*WindowsApps*" } | Select-Object -First 1\r
```\r
\r
* A path is printed → **record this literal string as `SYSTEM_PYTHON`** (system-level Python, used only for `setup.py`, `check_env.py`, `download_model.py`). For every command marked `[SYSTEM PYTHON]`, substitute the full literal path for `python`. Do NOT use this path for inference — inference must always use `VENV_PY`. Do NOT use a `$variable` across tool calls — each call is a new shell; always embed the literal path directly.\r
* Nothing printed → Python is not installed — install it (see below).\r
\r
**If Python is missing or outdated**, run this one-command silent installer in PowerShell (recommended, no admin required):\r
\r
```powershell\r
$f = "$env:TEMP\python-installer.exe"\r
Invoke-WebRequest "https://www.python.org/ftp/python/3.12.10/python-3.12.10-amd64.exe" -OutFile $f\r
Start-Process $f -ArgumentList "/quiet InstallAllUsers=0 PrependPath=1 Include_pip=1" -Wait\r
Remove-Item $f\r
```\r
\r
> `PrependPath=1` adds Python to PATH automatically; `Include_pip=1` installs pip; `InstallAllUsers=0` avoids requiring administrator privileges.\r
\r
After installation, **restart the terminal**, then run `python --version` and confirm it reports `Python 3.12.x`.\r
\r
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.\r
\r
### Check git\r
\r
```bat\r
git --version\r
```\r
\r
**Interpretation:**\r
\r
| Output | Action |\r
|------|------|\r
| `git version 2.x.x` | [OK] `GIT_OK`, Pre-flight passed |\r
| `'git' is not recognized as an internal or external command` |  git is not installed; install is required (see below) |\r
\r
**If git is missing**, run this one-command silent installer in PowerShell:\r
\r
```powershell\r
$f = "$env:TEMP\git-installer.exe"\r
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\r
Start-Process $f -ArgumentList "/VERYSILENT /NORESTART /NOCANCEL /SP- /CLOSEAPPLICATIONS /RESTARTAPPLICATIONS /COMPONENTS=icons,ext\\reg\\shellhere,assoc,assoc_sh" -Wait\r
Remove-Item $f\r
```\r
\r
After installation, **restart the terminal**, then run `git --version` to confirm.\r
\r
If you prefer manual installation: open **https://git-scm.com/download/win**, download the installer, and proceed with default options.\r
\r
> git is required for the `git+https://` dependency in `requirements_imagegen.txt`; without git, `pip install` will fail with `git: command not found`.\r
\r
**Pre-flight pass criteria**: `python --version` is >= 3.10 and `git --version` returns a valid version string.\r
\r
Status message: `[OK] Python and git are ready. Starting main workflow.`\r
\r
---\r
\r
## Skill Contract (Input / Output)\r
\r
### Accepted Inputs\r
\r
1. **Text description** (required) — English or Chinese prompt describing the image to generate.\r
2. **Optional parameters:**\r
   * `steps`: inference steps, default 9 (higher = more detail)\r
   * `size`: `512×512` | `768×768` | `1024×1024`, default `512×512`\r
   * `seed`: integer (default 42) or `-1` for random\r
   * `output`: custom absolute output path for the PNG file\r
\r
### Output on Success\r
\r
Returns the PNG file path in stdout as `[SUCCESS] \x3Cpath>`, and sends a preview via the `message` tool.\r
\r
```json\r
{\r
    "image_path": "C:\\...\\outputs\\20260412_153000_panda_bamboo.png",\r
    "topic": "panda_bamboo",\r
    "prompt": "A giant panda sitting in a lush bamboo forest...",\r
    "steps": 9,\r
    "size": "512x512",\r
    "seed": 42,\r
    "device": "GPU.0"\r
}\r
```\r
\r
### Output on Failure\r
\r
```json\r
{\r
    "error": "human-readable description",\r
    "stage": "environment | model | inference",\r
    "recoverable": true\r
}\r
```\r
\r
---\r
\r
## Step 0: expand prompt (LLM only — no tools)\r
\r
Do two things simultaneously: **① expand the prompt** and **② extract a topic slug** (English snake_case, used for the filename).\r
\r
Expansion structure: `[subject] [action/pose] [environment] [lighting/mood] [style] [quality tags]`\r
\r
Prompts can be English or Chinese — no translation needed. Topic slug must always be English to avoid path encoding issues.\r
\r
Quality tags: `photorealistic`, `8K resolution`, `cinematic lighting`, `masterpiece`\r
\r
|Input|Topic slug|Expanded prompt|\r
|-|-|-|\r
|a panda|`panda_bamboo`|A giant panda sitting in a lush bamboo forest, sunlight filtering through leaves, photorealistic, 8K, wildlife photography|\r
|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|\r
\r
Show the result before proceeding:\r
\r
```\r
Input:    {user description}\r
   Expanded: {full prompt}\r
   Topic:    {topic_slug}\r
```\r
\r
---\r
\r
## Step 1: verify environment and model\r
\r
> Step 1/3: checking environment and model…\r
\r
```powershell\r
# [SYSTEM PYTHON] check_env.py validates the venv — must NOT use venv python here\r
python "\x3Cskill_dir>\check_env.py"\r
```\r
\r
**Output interpretation:**\r
\r
| Output | Meaning | Action |\r
|---|---|---|\r
| `MODEL_STATUS=READY` + `VENV_PY=...` | OK | Proceed to Step 2 |\r
| `SCRIPTS_STALE=old->new` | `generate_image.py` outdated | Auto-run setup.py (see below) |\r
| `STATE=MISSING` | setup.py never run | Auto-run setup.py (see below) |\r
| `VENV_PY=BROKEN` | venv corrupted | Auto-run setup.py (see below) |\r
| `PACKAGES_MISSING: ...` | packages missing from venv | Auto-run setup.py (see below) |\r
| `MODEL_STATUS=MISSING  missing=[...]` | download incomplete | Auto-run download_model.py (see below) |\r
\r
**On success**: record `VENV_PY` and `IMAGE_GEN_DIR` from stdout, proceed to Step 2.\r
\r
> If `SCRIPTS_STALE` also appears alongside `MODEL_STATUS=READY`, auto-run setup.py before Step 2 (see below).\r
\r
---\r
\r
### If SCRIPTS_STALE or STATE=MISSING or VENV_PY=BROKEN or PACKAGES_MISSING → auto-run setup.py\r
\r
`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.\r
\r
`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.\r
\r
Announce and run (up to 3 attempts):\r
\r
```\r
[INFO] Environment not initialized — auto-installing (attempt 1/3)…\r
```\r
\r
```powershell\r
# [SYSTEM PYTHON] setup.py creates the venv — must NOT use venv python here\r
python "\x3Cskill_dir>\setup.py"\r
```\r
\r
Re-run `python "\x3Cskill_dir>\check_env.py"` after each attempt. If all 3 fail, show manual fallback below.\r
\r
---\r
\r
### If MODEL_STATUS=MISSING → auto-run download_model.py\r
\r
Announce to user and ask how to proceed:\r
\r
```\r
Model not found — download required (~10 GB)\r
   Estimated time:\r
   • 100 Mbps → ~15 min\r
   •  50 Mbps → ~30 min\r
   •  10 Mbps → ~2 hr\r
   Download supports resume — safe to interrupt and retry.\r
\r
   [Y] Start auto-download\r
   [N] I'll download manually — show me the link\r
```\r
\r
**Auto-download** (up to 3 attempts, stop if a single attempt exceeds 20 minutes):\r
\r
```powershell\r
# [SYSTEM PYTHON] download_model.py uses stdlib only — must NOT use venv python here\r
python "\x3Cskill_dir>\download_model.py"\r
```\r
\r
Re-run `python "\x3Cskill_dir>\check_env.py"` after each attempt.\r
\r
**Manual download fallback:**\r
\r
ModelScope page: **https://modelscope.cn/models/snake7gun/Z-Image-Turbo-int4-ov/files**\r
\r
Place all files under `\x3CIMAGE_GEN_DIR>\Z-Image-Turbo-int4-ov\`. Required subdirs:\r
\r
```\r
Z-Image-Turbo-int4-ov\\r
├── transformer\\r
├── vae_decoder\\r
└── text_encoder\\r
```\r
\r
Then re-run `python "\x3Cskill_dir>\check_env.py"` to verify.\r
\r
---\r
\r
### Manual fallback (only if all 3 setup auto-attempts fail)\r
\r
Show user:\r
\r
```\r
[WARN] Auto-install failed. Please run manually in a terminal:\r
\r
1) Install environment:\r
   python "\x3Cskill_dir>\setup.py"\r
   Takes ~5 min, fully automated.\r
\r
2) Download model (~10 GB):\r
   python "\x3Cskill_dir>\download_model.py"\r
   Resumable — safe to interrupt and retry.\r
\r
Come back here when done.\r
```\r
\r
---\r
\r
## Step 2: verify dependencies\r
\r
> Step 2/3: verifying dependencies…\r
\r
**Verify dependencies** (run via VENV_PY):\r
\r
```\r
$env:PYTHONUTF8 = "1"\r
& "\x3CVENV_PY>" -c "\r
import json, site\r
from pathlib import Path\r
\r
EXPECTED_COMMITS = {\r
    'optimum_intel': '2f62e5ae',\r
    'diffusers':     'a1f36ee3',\r
}\r
\r
def get_git_commit(pkg_name):\r
    dirs = site.getsitepackages()\r
    try: dirs += [site.getusersitepackages()]\r
    except Exception: pass\r
    for d in dirs:\r
        for dist in Path(d).glob(f'{pkg_name}*.dist-info'):\r
            url_file = dist / 'direct_url.json'\r
            if url_file.exists():\r
                data = json.loads(url_file.read_text(encoding='utf-8'))\r
                return data.get('vcs_info', {}).get('commit_id', 'no_vcs_info')\r
    return 'not_found'\r
\r
results = {}\r
for pkg, imp in [('openvino','openvino'),('torch','torch'),('Pillow','PIL'),('modelscope','modelscope')]:\r
    try:\r
        ver = getattr(__import__(imp), '__version__', 'OK')\r
        results[pkg] = ('OK', ver)\r
    except ImportError as e:\r
        results[pkg] = ('MISSING', str(e))\r
\r
try:\r
    from optimum.intel import OVZImagePipeline\r
    results['OVZImagePipeline'] = ('OK', 'importable')\r
except ImportError as e:\r
    results['OVZImagePipeline'] = ('MISSING', str(e))\r
\r
for pkg_name, exp in EXPECTED_COMMITS.items():\r
    actual = get_git_commit(pkg_name)\r
    if actual == 'not_found':\r
        results[f'{pkg_name}@commit'] = ('MISSING', 'not installed via git+https')\r
    elif actual.startswith(exp):\r
        results[f'{pkg_name}@commit'] = ('OK', actual[:16])\r
    else:\r
        results[f'{pkg_name}@commit'] = ('WRONG', f'got {actual[:16]} want {exp}...')\r
\r
all_ok = all(v[0] == 'OK' for v in results.values())\r
for k, (status, detail) in results.items():\r
    icon = '[OK]' if status == 'OK' else ('[WARN]' if status == 'WRONG' else '[MISSING]')\r
    print(f'  {icon} {k}: {detail}')\r
print('DEP_CHECK=PASS' if all_ok else 'DEP_CHECK=FAIL')\r
"\r
```\r
\r
|Output|Action|\r
|-|-|\r
|`DEP_CHECK=PASS`|[PASS] Proceed to Step 3|\r
|`DEP_CHECK=FAIL` (MISSING)|[FAIL] Re-run `setup.py` and retry|\r
|`DEP_CHECK=FAIL` (`@commit` WRONG)|[FAIL] Force reinstall: `& "\x3CVENV_PY>" -m pip uninstall optimum-intel diffusers -y` then `& "\x3CVENV_PY>" -m pip install -r "\x3Cskill_dir>\requirements_imagegen.txt" --no-cache-dir`|\r
\r
---\r
\r
## Step 3: generate image and send preview\r
\r
> Step 3/3: running inference…\r
\r
Run these two commands separately:\r
\r
```\r
$env:PYTHONUTF8 = "1"\r
```\r
\r
```\r
& "\x3CVENV_PY>" "\x3CIMAGE_GEN_DIR>\generate_image.py" --prompt "EXPANDED_PROMPT" --topic "TOPIC" --steps 9 --seed 42\r
```\r
\r
**Pass**: stdout contains `[SUCCESS]`. Record `OUTPUT_PATH` from the `[SUCCESS]` line.\r
\r
Send preview via `message` tool:\r
\r
```\r
action: "send"  filePath: "OUTPUT_PATH"  message: "[OK] TOPIC"\r
```\r
\r
**Final announcement:**\r
\r
```\r
[OK] Done! Path: \x3COUTPUT_PATH>\r
Prompt: {expanded prompt}\r
steps=9, 512×512, seed=42 | device: {CPU/GPU}\r
```\r
---\r
\r
## Parameters\r
\r
|Param|Default|Notes|\r
|-|-|-|\r
|`--prompt`|required|English or Chinese|\r
|`--topic`|empty|English snake_case slug for filename|\r
|`--steps`|9|Higher = more detail; no hard limit|\r
|`--width--height`|512|512 | 768|768  1024|1024 recommended|\r
|`--seed`|42|-1 = random|\r
|`--output`|auto|Custom absolute output path|\r
\r
> `guidance_scale` is fixed at `0.0` and not exposed as a parameter.\r
\r
---\r
\r
## Troubleshooting\r
\r
|Error|Cause|Fix|\r
|-|-|-|\r
|`STATE=MISSING`|setup.py never run|Run `python "\x3Cskill_dir>\setup.py"`|\r
|`VENV_PY=BROKEN`|venv corrupted|Re-run `python "\x3Cskill_dir>\setup.py"` — rebuilds venv automatically|\r
|`PACKAGES_MISSING: ...`|venv ok but packages missing|Re-run `python "\x3Cskill_dir>\setup.py"` — reinstalls missing packages; skips steps already done|\r
|`MODEL_STATUS=MISSING`|download never run or interrupted|Run `python "\x3Cskill_dir>\download_model.py"` — resumes automatically|\r
|`DEP_CHECK=FAIL` (MISSING)|packages not installed in venv|Re-run `setup.py`|\r
|`DEP_CHECK=FAIL` (`@commit` WRONG)|PyPI release installed instead of pinned commit|Uninstall optimum-intel + diffusers, reinstall with `--no-cache-dir`|\r
|`@commit` shows `not installed via git+https`|git was missing when pip ran|Confirm git is installed, re-run `setup.py`|\r
|`[ERROR] Model incomplete`|Download interrupted mid-file|Re-run `download_model.py` — resumes automatically|\r
|`[ERROR] state.json not found`|state.json missing|Re-run Step 1|\r
|`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)|\r
|`RuntimeError` on GPU|Insufficient VRAM|Lower resolution or hardcode `return "CPU"` in `get_device()`|\r
|Black  noisy output|Too few steps|Use `--steps` ≥ 4; 9 recommended|\r
|Download timeout|Network issue or proxy needed|Configure proxy and retry — download supports resume|\r
|`RuntimeError: stack expects each tensor to be equal size`|openvino 2026.1.0 breaks `OVZImagePipeline.forward` — `pooled_projections` tensors have mismatched sequence lengths|Downgrade: `& "\x3CVENV_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.|\r
\r
\r
\r
安全使用建议
What to consider before installing this skill: - Functionality and platform: This skill is designed for Windows (PowerShell, drive-letter paths, explorer), but the registry metadata does not restrict OS. Only install/use on a Windows machine. - Disk, bandwidth, and time: Setup + model download will fetch packages and a ~10 GB model and create a venv. Ensure you have free disk space and are comfortable with the network usage. - Automatic actions: The SKILL.md instructs the agent to auto-run setup and download scripts (up to 3 attempts) when state is missing or packages are broken. If you allow the skill to run autonomously, it may install packages and download large files without asking each time — consider running setup.py and download_model.py manually yourself the first time. - Sources and code review: The setup installs pinned git+https dependencies from GitHub and uses modelscope.snapshot_download to fetch the model from modelscope.cn. If you require stronger assurance, review the referenced GitHub commits and the model provider before proceeding. - Permissions and scope: The scripts will write under {USERNAME}_openvino on whichever drive has most free space, scanning drives A:–Z: to locate prior installs. This is intended to find or create an installation folder but is broader filesystem access than a pure in-memory tool — be comfortable with the skill creating/modifying files in your drives. - Recommended safe steps before enabling autonomously: 1) Inspect the code (setup.py, download_model.py, check_env.py) yourself or in a sandbox. 2) Run setup.py and download_model.py manually from a terminal to observe behavior and confirm network sources. 3) Ensure you have necessary Python/git and that installations occur in an environment you control (or a dedicated VM). 4) If you prefer, disable autonomous invocation for this skill or only allow user-invoked runs so you can approve installs/downloads. Given these operational and minor metadata inconsistencies, the package is coherent with its stated purpose but has behaviors (automatic installs, drive scanning, Windows-only code) that warrant caution — especially if you enable autonomous execution.
功能分析
Type: OpenClaw Skill Name: local-image-gen-aipc Version: 1.0.3 The skill bundle is a legitimate tool for setting up a local image generation environment on Windows using Intel OpenVINO. It includes comprehensive scripts (setup.py, download_model.py, check_env.py) to manage a Python virtual environment, install dependencies from official sources (GitHub, PyPI), and download a large model (~10 GB) from ModelScope. While it requests the agent to perform high-risk actions like running silent installers for Python and Git if they are missing, these actions are explicitly documented, use official URLs, and are necessary for the stated purpose of offline AI inference.
能力评估
Purpose & Capability
The skill claims offline local image generation using OpenVINO and the included scripts (setup.py, download_model.py, check_env.py) implement that. The requested network access (GitHub for pip deps and modelscope.cn for a ~10 GB model) is proportional to the stated purpose. Minor incoherence: SKILL.md and the scripts are Windows-specific (drive letters, PowerShell, explorer) but the registry metadata has no OS restriction; that mismatch could cause confusion or failed runs on non-Windows hosts.
Instruction Scope
Runtime instructions direct the agent to run setup.py and download_model.py (which create a venv and install pinned dependencies from GitHub and modelscope). The agent is instructed to auto-run those scripts under certain error states (auto-recovery policy: up to 3 attempts) and to read state.json and environment proxy variables. The scripts also scan all drive letters to find or create installation directories. These behaviors go beyond simple 'read-only' checks and entail installing packages, writing files, and downloading large model artifacts — which are coherent with the skill's goal but are operationally significant and potentially surprising if performed autonomously.
Install Mechanism
There is no platform install spec in the registry (instruction-only), but included setup.py will create a venv and pip-install dependencies, including git+https installs from specific GitHub commits and packages from the PyTorch extra index and modelscope. Sources (github.com, modelscope.cn) are expected for this task. The downloads and installs are legitimate for local inference but are substantial (network usage and disk I/O, ~10 GB model plus packages).
Credentials
The skill declares no required environment variables or credentials. At runtime it reads USERNAME (to construct paths) and proxy variables (HTTPS_PROXY/HTTP_PROXY) to handle network configuration; this is reasonable for its setup and download steps. It does not request unrelated secrets or credentials.
Persistence & Privilege
always:false (good). However the SKILL.md explicitly tells the agent to auto-run setup.py and download_model.py in auto-recovery, including performing up to 3 unattended attempts and using system Python for certain steps. If the agent is allowed autonomous invocation, this enables the skill to perform network downloads, install packages into a venv, write state.json and files to disk, and open Explorer — all without an additional explicit user prompt. That autonomous-install behavior is operationally powerful and should be considered before enabling the skill.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install local-image-gen-aipc
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /local-image-gen-aipc 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.3
**local-image-gen-aipc v1.0.3** - Added `check_env.py` for improved environment verification and troubleshooting. - Updated agent instructions, auto-recovery, and pipeline flows for strict environment/package/script checks. - Enhanced pre-flight validation: now includes robust runtime Python and git version/availability checks, with clear setup/remediation instructions. - Clarified handling of Python Store alias situations and proxy support for setup/model download. - Now enforces that only system Python is used for setup scripts; venv Python is reserved for inference and package imports.
v1.0.2
v1.0.2 introduces standalone setup scripts for easier installation and auto-recovery. - Added `setup.py` and `download_model.py` for simplified environment and model setup. - Environment verification now uses `state.json` for robust tracking of venv and model status. - Automated recovery: missing or broken setups trigger up to 3 auto-install attempts before prompting the user. - All python calls during inference now use the venv python (`VENV_PY`) recorded in `state.json`. - SKILL.md rewritten for simpler, more reliable installation and a streamlined agent pipeline.
v1.0.1
change description
v1.0.0
- Initial release: local, offline text-to-image generation on Windows using Z-Image-Turbo via Intel OpenVINO (Xe/Arc iGPU prioritized, CPU fallback). - Full support for bilingual prompts (English & Chinese); auto-creates outputs with English topic slug filenames. - One-time setup downloads Python, Git, pip dependencies (some via pinned Git commits), and ~10 GB model from ModelScope; all inference is fully offline after setup. - Step-by-step agent instructions for installation, environment checks, venv creation, dependency pinning, and model management. - Designed for transparent workflow: clear output at each stage, error handling guidance, and user-friendly directory structure.
元数据
Slug local-image-gen-aipc
版本 1.0.3
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 4
常见问题

Z-image Local image generation with OpenVINO (no API key) 是什么?

generate an image, create a picture, draw something, make an image of, text to image, paint a picture, illustrate, visualize, local image generation, AI art,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 333 次。

如何安装 Z-image Local image generation with OpenVINO (no API key)?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install local-image-gen-aipc」即可一键安装,无需额外配置。

Z-image Local image generation with OpenVINO (no API key) 是免费的吗?

是的,Z-image Local image generation with OpenVINO (no API key) 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Z-image Local image generation with OpenVINO (no API key) 支持哪些平台?

Z-image Local image generation with OpenVINO (no API key) 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Z-image Local image generation with OpenVINO (no API key)?

由 juan-OY(@juan-oy)开发并维护,当前版本 v1.0.3。

💬 留言讨论