Xpersona
agentCLAWHUBUnverified

服装图一键上身 Flat Lay

服装平铺图一键上身试穿。服装平铺图 + 姿势参考图 → 模特上身商拍图，款式、颜色、织法、版型保持不变。当用户说「平铺图转模特图」「衣服上身」「虚拟试穿」「AI 试衣」「让模特穿上」时使用。 Skill: 服装图一键上身 Flat Lay Owner: dlazyai Summary: 服装平铺图一键上身试穿。服装平铺图 + 姿势参考图 → 模特上身商拍图，款式、颜色、织法、版型保持不变。当用户说「平铺图转模特图」「衣服上身」「虚拟试穿」「AI 试衣」「让模特穿上」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:50:30.495Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:43:17.593Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:43:12.267Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:16:03.044Z | user 例行版本更新 2026-10-02

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:flat-lay
  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-flat-lay/snapshot"

Documentation

CLAWHUB

146,821 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: flat-lay
version: 1.0.19
description: 服装平铺图一键上身试穿。服装平铺图 + 姿势参考图 → 模特上身商拍图，款式、颜色、织法、版型保持不变。当用户说「平铺图转模特图」「衣服上身」「虚拟试穿」「AI 试衣」「让模特穿上」时使用。
---

# flat-lay — 服装图一键上身试穿

把一张**服装平铺图**变成**模特上身商拍图**，不用约模特、不用租场地、不用摄影棚。

本技能用 dLazy 的 `gpt-image-2` 实现：以「服装图 + 参考图」双图参考做图像编辑合成，服装保真、姿势场景照抄参考图。

---

## 生成效果示例

| 输入：服装平铺图 | 输入：参考图 |
| --- | --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/flat-lay/garment-flatlay.jpg" width="300"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/flat-lay/pose-reference.jpg" width="300"> |
| `garment-flatlay.jpg` — 军绿色麻花针织圆领毛衣，800×800 | `pose-reference.jpg` — 男青年正面站姿、浅灰墙棚拍，768×1024 |

实际执行的命令：

```bash
dlazy gpt-image-2 \
  --prompt 'E-commerce on-model product photography. Image 1 is the garment flat-lay: an olive-green cable-knit crewneck sweater. Image 2 is the pose/scene reference. Dress the model from image 2 in the garment from image 1, replacing the grey T-shirt. Keep the garment 100% faithful: identical olive-green color, cable-knit and diamond texture, oversized drop-shoulder fit, ribbed collar and cuffs, and the small woven label on the right cuff. Reproduce the reference exactly for pose, camera angle, crop, body proportions, lighting and the plain light-grey studio wall background. Photorealistic full-frame catalog shot, sharp fabric detail, natural soft light, no text or watermark.' \
  --images docs/flat-lay/garment-flatlay.jpg docs/flat-lay/pose-reference.jpg \
  --size 1024x1536 --quality high --imageFormat jpeg \
  --save docs/flat-lay/example-output.jpg
```

**输出**

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

`example-output.jpg` — 1024×1536，60 credits，约 60s。

麻花织法、菱形提花、落肩版型、袖口罗纹与右袖织标均被保留；姿势、景别、光线与浅灰背景照抄参考图。

---

## 一、能力边界

| 能力 | 说明 |
| --- | --- |
| 单件上身 | 上传 1 张单件衣服（上装 / 连衣裙 / 连体衣）平铺图或真人上身图 |
| 多件上身 | 分别上传「上装图」+「下装图」，合成为同一个模特身上的一整套 Look |
| 参考图 | 决定模特姿势、拍摄角度、景别、场景与光线；可用素材库，也可用自有商拍图 |
| 指定模特 | 可选。锁定同一张脸，保证同店铺多 SKU 视觉统一；不指定则由参考图中的模特形象决定 |
| 生成策略 | 通用 / 颜色饱和度优化 / 材质增强 / 崩坏问题优化 / 精准选区 |

**不做**：不改款式、不改颜色、不改印花、不修改吊牌文字；不用于伪造他人肖像的商业代言。

---

## 二、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**

**推荐的输入类型（✅）**

| 类型 | 说明 |
| --- | --- |
| 上装平铺图 | 纯色背景、平铺展开、完整可见 |
| 连体衣 / 连衣裙平铺图 | 单件整体 |
| 真人上身图 | 已有的真人商拍图，用于换姿势换场景 |

**会明显拉低效果的输入（❌）**

| 问题 | 说明 |
| --- | --- |
| 商品被遮挡 | 模特手臂、包袋、道具压住衣服主体 |
| 套装商品 | 一张图里上装+下装+鞋子，单件上身识别不了 → 请改用「多件上身」并拆成两张 |
| 商品不清晰 | 模糊、过曝、低分辨率、强色偏 |

---

## 三、参考图与模特的选择维度

**参考图**（决定姿势与场景，是出图风格的主导变量）

- 维度：`单图 / 套图`
- 类目：`女装 / 男装 / 童装`（多选）
- 地区：`国内 / 海外`（多选）
- 类型：`电商 / 种草`（多选）
- 筛选：性别 `男 / 女`；年龄 `婴儿 / 小童 / 大童 / 青少年 / 青年人 / 中年人 / 老年人`；肤色 `欧美人 / 非洲人 / 亚洲人 / 其他肤色`
- 也可直接用自有参考图（支持批量），或按图搜同类姿势

**模特**（可选，锁定人脸）

- 维度：性别 / 年龄 / 肤色 / 身材，或随机指定
- 指定模特会增加约 1 分钟生成时间

选择建议：

- 想要**款式还原优先** → 参考图选正面站姿、纯色背景、景别与商品一致（上装选半身，连衣裙选全身）。
- 想要**氛围种草优先** → 参考图选带场景的街拍 / 室内生活场景，接受轻微版型偏差。
- **多 SKU 批量** → 固定同一张参考图 + 同一个模

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "flat-lay",
  "version": "1.0.19",
  "publishedAt": 1791597030495
}

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.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/dlazyai/skills/flat-lay",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/flat-lay",
      "sourceType": "profile",
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      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-flat-lay/contract",
      "sourceType": "contract",
      "confidence": "medium",
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      "confidence": "medium",
      "observedAt": "2026-10-10T21:00:05.294Z",
      "isPublic": true
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      "isPublic": true
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      "confidence": "medium",
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      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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