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多单品搭配融图 Image Fusion

多单品融合成一整套 Look。最多 8 张单品图 → 同一模特身上的完整搭配商拍图，每件单品保真。当用户说「多件搭配」「融图」「组一套 look」「搭配图」「几件衣服合成一张」时使用。 Skill: 多单品搭配融图 Image Fusion Owner: dlazyai Summary: 多单品融合成一整套 Look。最多 8 张单品图 → 同一模特身上的完整搭配商拍图，每件单品保真。当用户说「多件搭配」「融图」「组一套 look」「搭配图」「几件衣服合成一张」时使用。 Tags: latest:1.0.19 Version history: v1.0.19 | 2026-10-10T01:50:57.431Z | user 例行版本更新 2026-10-10 v1.0.18 | 2026-10-08T01:43:37.797Z | user 例行版本更新 2026-10-08 v1.0.17 | 2026-10-04T01:43:32.389Z | user 例行版本更新 2026-10-04 v1.0.16 | 2026-10-02T05:16:17.027Z | user 例行版本更新 2026-10-0

OpenClaw
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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:image-fusion
  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-image-fusion/snapshot"

Documentation

CLAWHUB

146,934 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: image-fusion
version: 1.0.19
description: 多单品融合成一整套 Look。最多 8 张单品图 → 同一模特身上的完整搭配商拍图，每件单品保真。当用户说「多件搭配」「融图」「组一套 look」「搭配图」「几件衣服合成一张」时使用。
---

# image-fusion — 多单品自由搭配融图

一次给**最多 8 张单品图**，融合成同一个模特身上的**一整套 Look**。

和 [flat-lay](https://github.com/dlazy-ai/ecommerce-skills/blob/main/skills/flat-lay/skill.md) 的「多件上身」（只支持上装+下装两张）不同，本技能是**任意品类的自由组合**：毛衣 + 阔腿裤 + 帽子 + 项链 + 包 + 鞋，一次出一张完整搭配图。

---

## 生成效果示例

| 输入单品 1 | 输入单品 2 | 输入单品 3 |
| --- | --- | --- |
| <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/image-fusion/item-sweater.jpg" width="200"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/image-fusion/item-hat.jpg" width="200"> | <img src="https://raw.githubusercontent.com/dlazy-ai/ecommerce-skills/main/docs/image-fusion/item-necklace.jpg" width="200"> |
| `item-sweater.jpg` — 军绿麻花针织毛衣 | `item-hat.jpg` — 彩色编织渔夫帽 | `item-necklace.jpg` — 珍珠项链 |

实际执行的命令：

```bash
dlazy seedream-5.0 \
  --prompt '电商搭配商拍图。将参考图中的多件单品组合到同一个模特身上：图1 的军绿色麻花针织圆领毛衣作为上装，图2 的彩色编织渔夫帽戴在头上，图3 的珍珠项链戴在颈部。每件单品必须与参考图完全一致——颜色、织法纹理、图案、材质与细节都不能改。下装自动补一条米白色阔腿长裤，脚穿白色运动鞋。青年亚洲女模特，正面站姿，全身入画，纯浅灰色摄影棚背景，柔和顶光，真实照片质感，无文字无水印。' \
  --images docs/image-fusion/item-sweater.jpg docs/image-fusion/item-hat.jpg docs/image-fusion/item-necklace.jpg \
  --size 3:4 --resolution 2k \
  --save docs/image-fusion/example-output.jpg
```

**输出**

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

`example-output.jpg` — 3:4 / 2K，5 credits。三件单品同时到位，毛衣的麻花织法、渔夫帽的编织配色与项链的珠径都被保留；未提供的下装与鞋按 prompt 指定补齐。

---

## 1、能力边界

| 能力 | 说明 |
| --- | --- |
| 单品数量 | 最多 **8 张** |
| 品类组合 | 上装 / 下装 / 外套 / 连衣裙 / 鞋 / 包 / 帽子 / 围巾 / 首饰 任意混搭 |
| 参考图（可选） | 决定模特姿势、拍摄角度、景别、场景与光线 |
| 模特图（可选） | 锁定人脸与身材，多套 Look 保持同一个模特 |
| 缺件自动补齐 | 只给上装时，下装与鞋由模型按风格自动搭配 |

**不做**：不改单品的颜色、图案、材质与版型；不做尺码推荐；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**

本技能的素材约束比其他技能宽：

- 数量：**最多 8 张**
- 大小：**20KB ~ 15MB**
- 分辨率：**不超过 8192×8192**
- 格式：**jpg / jpeg / png / heic / webp**

**输入建议**

| 做法 | 说明 |
| --- | --- |
| ✅ 每张只放一件单品 | 一张图里混着上衣和裤子，模型会分不清该穿哪件 |
| ✅ 纯色/透明背景平铺图 | 干扰最少，颜色最准 |
| ✅ 按穿着顺序传图 | 上装 → 下装 → 外套 → 鞋 → 包 → 配饰，prompt 里的 image N 与之对应 |
| ❌ 同类目重复 | 传两件上衣，模型只会选一件或把它们混在一起 |
| ❌ 已经有人穿着的图 | 会把原模特的身体一起带进来 |

---

## 3、单品清单 → prompt 映射

融图的关键是**逐件点名**：每张图对应一个明确的穿着位置，不点名的单品会被忽略。

```text
图1 → 上装：军绿色麻花针织圆领毛衣（作为上装穿在身上）
图2 → 帽子：彩色编织渔夫帽（戴在头上）
图3 → 项链：珍珠项链（戴在颈部）
未提供 → 下装：自动补米白色阔腿长裤
未提供 → 鞋：自动补白色运动鞋
```

写成 prompt：

```text
将参考图中的多件单品组合到同一个模特身上：
图1 的<单品描述>作为上装，图2 的<单品描述>戴在头上，图3 的<单品描述>戴在颈部。
每件单品必须与参考图完全一致——颜色、织法纹理、图案、材质与细节都不能改。
下装自动补一条<描述>，脚穿<描述>。
```

**层次冲突要显式排序**：同时给外套和上装时写 `图2 的外套敞开穿在图1 的上装外面，露出内搭`，否则模型会二选一。

---

## 4、统一整组视觉

一个店铺往往要出十几套 Look，视觉必须统一，否则详情页看起来像拼凑的。固定这四项：

| 要固定的 | 写法 |
| --- | --- |
| 模特 | 传同一张模特图，或 prompt 里固定 `青年亚洲女模特，鹅蛋脸，中长黑直发` |
| 姿势与景别 | 传同一张参考图，或固定 `正面站姿，全身入画` |
| 背景与光线 | 固定 `纯浅灰色摄影棚背景，柔和顶光` |
| 构图留白 | 固定 `人物居中，头顶留白约画面高度 8%` |

只换单品清单，

_meta.json

{
  "ownerId": "kn7c5wgeajfcfvdfb5ceemvdb984cjpd",
  "slug": "image-fusion",
  "version": "1.0.19",
  "publishedAt": 1791597057431
}

references/model-flags.md

# `seedream-5.0` 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 seedream-5.0` command to get results.

```bash
dlazy seedream-5.0 -h

Options:
  --prompt <prompt>          Prompt
  --images [images...]       Images [image: url or local path] (max 10)
  --resolution <resolution>  Resolution [default: 2k] (choices: "2k", "3k",
                             "4k")
  --size <size>              Size [default: 16:9] (choices: "1:1", "4:3",
                             "3:4", "16:9", "9:16", "3:2", "2:3", "21:9")
  --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/image-fusion",
      "sourceUrl": "https://clawhub.ai/dlazyai/skills/image-fusion",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:32:17.355Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
      "value": "OpenClaw",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-image-fusion/contract",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-image-fusion/contract",
      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:32:17.355Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "1.3K downloads",
      "href": "https://clawhub.ai/dlazyai/image-fusion",
      "sourceUrl": "https://clawhub.ai/dlazyai/image-fusion",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-10T19:32:17.355Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "1.0.19",
      "href": "https://clawhub.ai/dlazyai/image-fusion",
      "sourceUrl": "https://clawhub.ai/dlazyai/image-fusion",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:50:57.431Z",
      "isPublic": true
    },
    {
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      "category": "security",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-image-fusion/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-dlazyai-image-fusion/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/image-fusion",
      "sourceUrl": "https://clawhub.ai/dlazyai/image-fusion",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-10-10T01:50:57.431Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 10, 2026.

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