Xpersona
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换模特换背景 One Shot

已有模特图换模特、换背景。一张模特图 → 多人群多场景版本，商品本身不变。当用户说「换模特」「换背景」「换人种」「一图多版」「同一件衣服换个人」时使用。 Skill: 换模特换背景 One Shot Owner: dlazyai Summary: 已有模特图换模特、换背景。一张模特图 → 多人群多场景版本，商品本身不变。当用户说「换模特」「换背景」「换人种」「一图多版」「同一件衣服换个人」时使用。 Tags: latest:1.0.18 Version history: v1.0.18 | 2026-10-10T01:54:37.370Z | user 例行版本更新 2026-10-10 v1.0.17 | 2026-10-08T01:46:28.489Z | user 例行版本更新 2026-10-08 v1.0.16 | 2026-10-04T01:46:10.655Z | user 例行版本更新 2026-10-04 v1.0.15 | 2026-10-02T05:18:18.503Z | user 例行版本更新 2026-10-02 v1.0.14 | 2026-09-3

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

1.0.18

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
1.0.18release · observed Oct 10, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170j1ymymrxasgd00dsk7tckx84cf45:one-shot
  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-one-shot/snapshot"

Documentation

CLAWHUB

146,677 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: one-shot
version: 1.0.18
description: 已有模特图换模特、换背景。一张模特图 → 多人群多场景版本，商品本身不变。当用户说「换模特」「换背景」「换人种」「一图多版」「同一件衣服换个人」时使用。
---

# one-shot — 同一商品替换模特和背景

拿一张**已经拍好的模特图或人台图**，在**商品完全不动**的前提下换掉人、换掉背景，或两者都换。

和 [flat-lay](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/flat-lay/skill.md) 的区别：flat-lay 从平铺图**造**一张模特图；本技能是把**已有**的模特图**裂变**成多个人群 / 场景版本——一次拍摄，覆盖国内海外、不同年龄段、不同肤色的投放需求。

---

## 生成效果示例

| 输入：原模特图 |
| --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/one-shot/source-model.jpg" width="280"> |
| `source-model.jpg` — 男青年身穿灰色落肩短袖，浅灰棚拍背景，768×1024 |

实际执行的命令（换模特换背景）：

```bash
dlazy gpt-image-2 \
  --prompt 'Replace the model and the background of this e-commerce photo while keeping the garment untouched. Keep the grey oversized short-sleeve T-shirt exactly as it is: same slate-grey colour, same drop-shoulder cut, same white chest logo, same folds and hem. Replace the person with a different male model of similar build and age, and replace the plain studio wall with a sunlit outdoor city street with soft bokeh. Keep the same pose, camera angle, crop and framing. Photorealistic catalog shot, natural light, no text, no watermark.' \
  --images docs/one-shot/source-model.jpg \
  --size 1024x1536 --quality medium --imageFormat jpeg \
  --save docs/one-shot/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1536。灰色落肩短袖的颜色、版型、胸前 logo 与下摆弧线保持不变；模特换成另一位男青年，棚拍灰墙换成树影斑驳的街景，姿势与景别沿用原图。

---

## 1、能力边界

| 模式 | 说明 |
| --- | --- |
| 换模特换背景 | 人和场景全换，只留商品 |
| 只换背景 | 保留原模特（脸、身材、姿势），换掉环境 |
| 只换模特 | 保留原场景与构图，换掉人 |

| 附加能力 | 说明 |
| --- | --- |
| 指定模特 | 性别 / 年龄 / 肤色 / 身材，锁定同一张脸做多 SKU |
| 参考图 | 决定新的姿势与场景，支持套图 |
| 参考图相似度 | `50% 相似`（借风格，保留原构图）/ `100% 相似`（严格照抄参考图） |
| 智能匹配模特位置 | 自动对齐新模特与原商品的身体位置，避免衣服错位 |
| 自动修手 | 生成后自动修正手部结构 |
| 人台图转真人 | 输入人形模特（假人）图，输出真人上身图 |

**不做**：不改商品的款式、颜色、图案与版型；不做换脸到特定真人；不用于伪造他人肖像代言。

---

## 2、输入素材规则

生成前先自检这几条硬性约束：

- 大小：**20KB ~ 15MB**
- 分辨率：**大于 400×400**
- 格式：**jpg / jpeg / png / webp**

**输入类型**：模特图 **或** 人台图（衣服已经穿在真人 / 假人身上的图）。

**输入建议**

| 做法 | 说明 |
| --- | --- |
| ✅ 商品完整无遮挡 | 手臂、包袋压住衣服的地方换人后容易崩 |
| ✅ 商品在画面中占比够大 | 太小的商品换人时细节会被重绘丢失 |
| ✅ 光线均匀 | 强逆光、大面积阴影会让新模特的光影对不上 |
| ❌ 多人同框 | 模型分不清该换哪个人 |
| ❌ 商品被裁切 | 出画的部分只能靠猜，容易长出错误结构 |

---

## 3、三种模式怎么选

| 你的目标 | 选模式 | prompt 要写死的不变量 |
| --- | --- | --- |
| 同款衣服卖给欧美市场 | 换模特换背景 | 商品（款式/颜色/图案/版型/褶皱） |
| 同一套图换季节氛围 | 只换背景 | 商品 + 模特（脸/发型/身材/姿势） |
| 人台图转真人图 | 只换模特 | 商品 + 场景 + 构图 + 光线 |
| 一张图裂变成多年龄段版本 | 只换模特 | 商品 + 场景 + 姿势 + 景别 |

**「参考图相似度」的等价写法**

| 档位 | 写进 prompt |
| --- | --- |
| 50% 相似 | `Borrow the mood, colour grading and lighting style from image 2, but keep the original pose, camera angle and crop from image 1.` |
| 100% 相似 | `Reproduce image 2 exactly for pose, camera angle, crop, background and lighting.` |

**「自动修手」的等价写法**：`Hands must be anatomically correct — five distinct fingers per hand, natural knuckles, no fused or

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "one-shot",
  "version": "1.0.18",
  "publishedAt": 1791597277370
}

references/model-flags.md

# `gpt-image-2` 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 gpt-image-2` command to get results.

```bash
dlazy gpt-image-2 -h

Options:
  --prompt <prompt>            Prompt
  --images [images...]         Images [image: url or local path] (max 5)
  --size <size>                Size [default: auto] (choices: "1024x1024",
                               "1536x1024", "1024x1536", "2048x2048",
                               "2048x1152", "3840x2160", "2160x3840", "auto")
  --imageFormat <imageFormat>  Image Format [default: jpeg] (choices: "jpeg",
                               "png", "webp")
  --quality <quality>          Quality [default: medium] (choices: "low",
                               "medium", "high")
  --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.

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Record generated Oct 10, 2026.

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