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创意生图 Creative Scene

从零创意生图，也可定向改模特、姿势、搭配。一句描述（可选参考图）→ 图。当用户说「创意生图」「生成一张」「改个姿势」「换模板」「随便来张图」时使用。 Skill: 创意生图 Creative Scene Owner: dlazyai Summary: 从零创意生图，也可定向改模特、姿势、搭配。一句描述（可选参考图）→ 图。当用户说「创意生图」「生成一张」「改个姿势」「换模板」「随便来张图」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:48:30.396Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:41:08.055Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:41:29.342Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:14:45.101Z | user 例行版本更新 2026-10-02 v1.0.15 | 2026-09

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

1.0.19

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.3K downloadsadoption · observed Oct 10, 2026
Latest release
1.0.19release · observed Oct 10, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:creative-scene
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-creative-scene/snapshot"

Documentation

CLAWHUB

146,803 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: creative-scene
version: 1.0.19
description: 从零创意生图，也可定向改模特、姿势、搭配。一句描述（可选参考图）→ 图。当用户说「创意生图」「生成一张」「改个姿势」「换模板」「随便来张图」时使用。
---

# creative-scene — 输入想象中的画面自由创作图片

**一句话造一张图**。没有素材也能开工——描述人物、穿着、场景、视角，直接出图。

本技能同时收录了一套**可直接复制的指令模板**：改模特、改姿势、改搭配——这些是所有其他技能的通用零件。

---

## 生成效果示例

本技能不需要输入素材——只有一句描述。

实际执行的命令：

```bash
dlazy banana-pro \
  --prompt 'A long-haired young Asian woman wearing a white puff-sleeve lace midi dress, sitting at a marble table inside a French-style cafe, fresh flowers and pastries arranged on the table, warm white colour grading, soft window light, front three-quarter view, waist-up framing, atmospheric editorial portrait, photorealistic, shot on 85mm, shallow depth of field, no text, no watermark.' \
  --aspectRatio 3:4 --imageSize 2K \
  --save docs/creative-scene/example-output.jpg
```

对应的五槽位拆解：

| 槽位 | 内容 |
| --- | --- |
| 人物 | `a long-haired young Asian woman` |
| 穿着 | `white puff-sleeve lace midi dress` |
| 场景 | `marble table inside a French-style cafe, fresh flowers and pastries` |
| 视角/景别 | `front three-quarter view, waist-up framing` |
| 氛围/影调 | `warm white colour grading, soft window light, atmospheric editorial` |

**输出**

<img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/creative-scene/example-output.jpg" width="320">

`example-output.jpg` — 3:4 / 2K，18 credits。人物、穿着、法式咖啡馆场景、三分之三正面半身景别与暖白影调全部按描述落位。

这张图可以直接作为后续定向修改的输入——例如接 `把模特的上衣变成黑色高领修身打底衫，保持其他不变` 换搭配。

---

## 1、能力边界

| 模式 | 说明 |
| --- | --- |
| 纯文生图 | 只给描述，凭空造图 |
| 参考图 + 描述 | 带一张图做定向修改（改模特 / 改姿势 / 改搭配） |

| 词库分类 | 说明 |
| --- | --- |
| 室内 / 室外 / 居家 / 街道 | 场景类 |
| 半身 / 全身 / 特写 | 景别类 |
| 场景图 | 完整场景氛围 |
| 改模特 / 改姿势 / 改搭配 | 定向修改类（见第三节） |

**不做**：不生成特定真人的肖像；不生成未成年人的不当内容；不用于伪造商品实拍与使用体验。

---

## 2、描述公式

官方给的参考形式是：**人物 + 穿着，场景氛围，图片视角**。

```text
一个长发小女孩穿灰色打底裤和长款羽绒服白色雪地靴，咖啡店圣诞，全身正面
 └── 人物 ──┘└──────── 穿着 ────────┘  └── 场景 ──┘ └─ 视角 ─┘
```

拆成五个槽位，缺哪个补哪个：

| 槽位 | 例子 |
| --- | --- |
| 人物 | `长发女生` `卷毛非洲小女孩` `35 岁商务男性` `混血模特` |
| 穿着 | `白色泡泡袖蕾丝连衣裙` `军绿麻花毛衣 + 米白阔腿裤 + 白色运动鞋` |
| 场景 | `法式装修风的咖啡店内，鲜花布置` `新疆草原，身后有草原动物` `T台走秀` |
| 视角/景别 | `全身正面` `半身侧面` `下半身图` `颈部特写` `半身背面转头` |
| 氛围/影调 | `明亮的暖白色调，氛围感` `复古色调，写真` `冷色光，明亮色调` |

**电商向的经验**：`写真`、`氛围感`、`明亮的暖白色调` 这类词能显著提升出图的商业可用度；`全身正面` / `半身侧面` 这类明确的景别词能大幅降低构图返工。

---

## 3、三类定向修改模板（可直接复制）

带参考图时，用下面的句式做定向修改。**共同点：都以「保持其余不变」结尾**——这是不跑偏的关键。

**改模特**

```text
将主体改为一个[年龄]岁的[种族][性别][职业类型]模特，她/他有着[五官特征]、[发型]、[肤色]。
保持相同的服装、姿势和背景。
```

细分维度也可以单独改：

| 维度 | 模板 |
| --- | --- |
| 肤色 | `将肤色改为[浅色/浅桃色/温暖的香槟色/蜜色/可可色]。保持原有的面部结构、种族特征、所有面部特征的大小和位置、身体姿态、体型以及光照条件完全不变。` |
| 身材 | `将身材改为[苗条/丰满/稍微丰满/肌肉型]。保持主体完全不变，只改变身材。` |
| 发型 | `将发型改为[双麻花辫/凌乱的丸子头/长发大波浪/低马尾/精灵短发/寸头/背头]。保持角色与背景的一致性，同时保留相同的面部特征、姿势和表情。` |
| 五官 | `将[瞳孔颜色/唇色/眉毛/睫毛/腮红]改为[具体描述]。保持角色与背景的一致性，同时保留相同的姿势和表情。` |

**改姿势**

```text
将姿势改为[姿势描述]。保持完全相同的拍摄角度、面部结构、肤色和体型。
```

常用姿势：`正面站立，双臂自然下垂` / `以自然的步伐朝相机方向走来` / `侧身站立，上半身朝向相机扭转，双手放在背后以展示腰线和侧面轮廓` / `背对镜头站立，回头看向肩膀` / `像模特一样走在T台中间的步伐，一脚向前并扭动臀部` / `坐在地板上，向后倾，用双手在身后支撑身体`

**改搭配**

```text
把模特的[上衣/下装/鞋子/配饰]变成[具体描述]，保持其他不变
```

例：

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "creative-scene",
  "version": "1.0.19",
  "publishedAt": 1791596910396
}

references/model-flags.md

# `banana-pro` 参数清单

本技能默认用的模型的完整参数。日常只需要「参数约定」里那几个，
这份清单在需要用到非常规参数时再看。

**CRITICAL INSTRUCTION FOR AGENT**:
Run the `dlazy banana-pro` command to get results.

```bash
dlazy banana-pro -h

Options:
  --prompt <prompt>            Prompt
  --images [images...]         Images [image: url or local path] (max 14)
  --aspectRatio <aspectRatio>  Aspect Ratio [default: auto] (choices: "auto",
                               "1:1", "4:3", "3:4", "16:9", "9:16", "21:9")
  --imageSize <imageSize>      Image Size [default: 1K] (choices: "1K", "2K",
                               "4K")
  --dry-run                    Print payload without executing the tool
  --no-wait                    Return generateId immediately for async tasks
  --timeout <seconds>          Max seconds to wait for async completion
                               (default: "1800")
  --input <jsonOrFile>         Inline JSON or @path/to/file.json — merged under
                               flag values (flags win)
  --save <path>                Download the result asset to this local path
                               (mkdir + retry handled for you). A destination
                               path — NOT a response format; for stdout shape
                               use --format
  --batch <n>                  Fan-out N parallel runs (cloud tools only)
                               (default: "1")
  -h, --help                   display help for command
```

> Any flag also accepts pipe references — `-` (auto-pick from upstream stdin), `@N` (n-th output), `@N.path` (jsonpath into output), `@*` (all primary values), `@stdin` / `@stdin:path` (whole envelope). See `dlazy --help` for details.

---

换其他后端时参数由 `scripts/gen.mjs` 统一翻译，见 [`provider-cli.md`](provider-cli.md)。

references/provider-cli.md

<!-- 由 scripts/build-skills.mjs 从 shared/references/provider-cli.md 同步生成，不要直接改这里。 -->
# 后端调用参考

技能正文只写「要生成什么」。认证、计费、错误码、输出结构这些每个技能都一样的东西放在这里，
**用到时再读**，不占技能的常驻上下文。

---

## 一、认证

### 默认后端 dLazy

```bash
dlazy login            # 设备码流程，远程 shell 也能用，自动写入本地配置
dlazy auth set <KEY>   # 已有 key 时直接写入
```

key 存在用户配置目录（macOS/Linux `~/.dlazy/config.json`，Windows `%USERPROFILE%\.dlazy\config.json`），
权限限本机用户。也可以每次调用用环境变量 `DLAZY_API_KEY` 传入。

手动获取：登录 [dlazy.com](https://dlazy.com) → [API Key 页面](https://dlazy.com/dashboard/organization/api-key)。
key 按组织隔离，可随时轮换或吊销。

### 其他后端

本技能库不锁定单一厂商。配好任意一家的 key 即可跑：

| 后端 | 环境变量 | 说明 |
| --- | --- | --- |
| `dlazy` | `dlazy login` 或 `DLAZY_API_KEY` | 默认，最省事 |
| `openai` | `OPENAI_API_KEY` | 走 `/v1/images/edits` 与 `/v1/images/generations` |
| `gemini` | `GEMINI_API_KEY` | Nano Banana 系列 |
| `fal` | `FAL_KEY` | |
| `replicate` | `REPLICATE_API_TOKEN` | |
| `ark` | `ARK_API_KEY` + `ARK_MODEL` | 火山方舟，模型 ID 需按开通情况填 |

选路优先级：`--provider` 参数 > `PROVIDER` 环境变量 > 第一个配了 key 的 > `dlazy`。

```bash
node scripts/gen.mjs --doctor     # 看当前哪个后端可用
```

各后端的模型 ID 可用 `GEN_MODEL_OPENAI` / `GEN_MODEL_GEMINI` / `GEN_MODEL_FAL` /
`GEN_MODEL_REPLICATE` / `GEN_MODEL_ARK` 覆盖。**厂商目录会变，以各家最新文档为准。**

---

## 二、两种调用方式

### 方式 A：统一入口（推荐）

```bash
node scripts/gen.mjs --task <技能名> --prompt '...' --images a.jpg b.jpg --save out.jpg
```

它负责：后端选路、默认尺寸档位、失败重试（429/5xx 指数退避）、落盘建目录、成本估算。

```bash
node scripts/gen.mjs --task flat-lay --prompt '...' --dry-run   # 不调用不计费，只看要发什么
node scripts/gen.mjs --help
```

### 方式 B：直接用 dLazy CLI

不想引入 Node 依赖时，技能正文里的 `dlazy ...` 命令可以原样执行，效果等价。

```bash
npx @dlazy/[email protected] <command>     # 不装全局二进制
```

- CLI 源码：[github.com/dlazy-ai/cli](https://github.com/dlazy-ai/cli) · npm 包 `@dlazy/cli`

---

## 三、数据流向

调用 dLazy 时：提示词与参数发往 `api.dlazy.com`；传入的本地图片会上传到 `files.dlazy.com`
供模型读取；产出 URL 同样托管在 `files.dlazy.com`。这是云端生成 API 的通用形态。
换成其他后端时，数据流向对应厂商，不经过 dLazy。

---

## 四、输出结构

`gen.mjs`（加 `--json`）：

```json
{
  "ok": true,
  "task": "flat-lay",
  "provider": "dlazy",
  "model": "gpt-image-2",
  "files": ["docs/flat-lay/output-sku001.jpg"],
  "texts": [],
  "estimatedCredits": 60,
  "elapsedMs": 58213
}
```

dLazy CLI 原生：

```json
{
  "ok": true,
  "result": {
    "tool": "gpt-image-2",
    "data": { "urls": ["https://files.dlazy.com/data/ai/....jpg"] },
    "savedPath": "docs/flat-lay/example-output.jpg"
  }
}
```

加 `--no-wait` 的异步任务不返回 `data`，返回 `task: { generateId, status }`，
用 `dlazy status <generateId> --wait` 轮询。

文本类模型（如质检）产出在 `result.data.texts[0]`：

```bash
dlazy claude-sonnet-5 --prompt '...' --images x.jpg \
  | python3 -c 'import sys,json;print(json.load(sys.stdin)["result"]["data"]["texts"][0])'
```

---

## 五、错误处理

| Code | 类型 | 示例 |
| --- | --- | --- |
| 401 | 未授权 / 无 key | `ok: false, code: "unauthorized"` |
| 501 | 缺必填参数 | `error: required option '--prompt <prompt>' not specified` |
| 502 | 本地文件读不到 | `Error: Image file not found: ...` |
| 503 | 余额不足 | `ok: false, code: "insufficient_balance"` |
| 503 | 服务端错误 | `HTT

scripts/lib/tasks.json

{
  "_note": "技能 → 默认模型与参数。dlazy 列为默认后端的模型名；其他后端走 providers.mjs 的通用映射，可用 GEN_MODEL_<PROVIDER> 覆盖。",
  "_credits": { "gpt-image-2": 60, "seedream-5.0": 30, "seedream-5.0-pro": 45, "banana-pro": 25, "claude-sonnet-5": 3 },
  "tasks": {
    "flat-lay":                { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "wear-everything":         { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "image-fusion":            { "model": "seedream-5.0",     "size": "3:4",       "resolution": "2k" },
    "one-shot":                { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "fission-pattern":         { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "item-detail":             { "model": "seedream-5.0-pro", "size": "3:4",       "resolution": "2k" },
    "creative-scene":          { "model": "banana-pro",       "size": "1024x1536", "format": "jpeg" },
    "batch-image":             { "model": "seedream-5.0",     "size": "3:4",       "resolution": "2k" },
    "to-3d":                   { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "clothing-extraction":     { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "fabric-on-body":          { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "clothing-detail":         { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "clothing-grass-planting": { "model": "gpt-image-2",      "size": "1024x1536", "quality": "medium", "format": "jpeg" },
    "item-selling-point":      { "model": "seedream-5.0-pro", "size": "1:1",       "resolution": "2k" },
    "item-change-background":  { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "remove-watermark":        { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "material-enhancement":    { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "item-repair":             { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "detect-task":             { "model": "claude-sonnet-5",  "text": true },
    "listing-optimizer":       { "model": "gpt-image-2",      "size": "1024x1024", "quality": "high",   "format": "jpeg" },
    "cross-border-localize":   { "model": "seedream-5.0-pro", "size": "1:1",       "resolution": "2k" },
    "brand-kit":               { "model": "gpt-image-2",      "size": "1024x1536", "quality": "high",   "format": "jpeg" },
    "platform-compliance":     { "model": "claude-sonnet-5",  "text": true },
    "main-image-video":        { "model": "$DLAZY_VIDEO_MODEL", "video": true },
    "product-

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Machine-readable data

The same record, as JSON, for agents and crawlers.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/dlazyai/skills/creative-scene",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/creative-scene",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:41:33.285Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
      "value": "OpenClaw",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-creative-scene/contract",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-creative-scene/contract",
      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:41:33.285Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "1.3K downloads",
      "href": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceUrl": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T20:41:33.285Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "1.0.19",
      "href": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceUrl": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:48:30.396Z",
      "isPublic": true
    },
    {
      "factKey": "handshake_status",
      "category": "security",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-creative-scene/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-creative-scene/trust",
      "sourceType": "trust",
      "confidence": "medium",
      "observedAt": null,
      "isPublic": true
    }
  ],
  "events": [
    {
      "eventType": "release",
      "title": "Release 1.0.19",
      "description": "例行版本更新 2026-10-10",
      "href": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceUrl": "https://clawhub.ai/dlazyai/creative-scene",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:48:30.396Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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