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feat(image-to-image): 新增图生图全局技能 v1.0.0 (#27)
## 概要 / Summary 新增图生图（image-to-image）全局默认技能，支持用户基于已有图片进行 AI 编辑和变换。 Add a new global builtin skill for image-to-image editing, allowing users to edit and transform existing images with AI. ## 变更内容 / Changes - 新增 `skills/image-to-image/SKILL.md`（英文） - 新增 `skills/image-to-image/SKILL.zh-CN.md`（中文） - `builtin-skills.json` 注册为全局默认技能 **技术实现**： - 模型：`gpt-image-2`（通过 DesireCore Cloud 提供） - 端点：`/images/generations` + `image` 字段（与文生图共用端点，image 字段触发图生图模式） - 零代码改动：纯技能文件实现，media-proxy 已支持透传 image 字段 **与文生图（dashscope-image-gen）的区分**： - 用户有参考图 → 图生图（本技能） - 用户无参考图 → 文生图（dashscope-image-gen） ## 测试 / Test Plan - [x] dev 模式端到端测试：用户发送图片 + 编辑指令 → 成功生成编辑后的图片 - [x] API 验证：`gpt-image-2` + `/images/generations` + `image` base64 字段 → 返回 b64_json 成功
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GitHub
@@ -7,6 +7,7 @@
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"discover-agent",
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"docx",
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"frontend-design",
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"image-to-image",
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"mail-operations",
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"manage-skills",
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"manage-teams",
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263
skills/image-to-image/SKILL.md
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263
skills/image-to-image/SKILL.md
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---
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name: image-to-image
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description: >-
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Use this skill when the user wants to edit, transform, or modify an existing
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image using AI. Supports image-to-image generation via the OpenAI-compatible
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images/generations API with gpt-image-2 model.
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Use when 用户提到 图生图、修改图片、编辑图片、图片变换、改图、
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换背景、换风格、图片编辑、以图生图、参考图、基于这张图、
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把这张图改成、在这张图上、image edit、img2img。
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license: Complete terms in LICENSE.txt
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version: 1.0.0
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type: procedural
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risk_level: low
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status: enabled
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disable-model-invocation: false
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provider: auto
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tags:
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- media
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- image
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- editing
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- img2img
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requires:
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tools:
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- Bash
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metadata:
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author: desirecore
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updated_at: '2026-06-12'
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i18n:
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default_locale: en-US
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source_locale: zh-CN
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locales:
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- zh-CN
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- en-US
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zh-CN:
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name: 图生图
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short_desc: 基于参考图片的 AI 图像编辑与变换技能
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description: >-
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当用户希望基于已有图片进行编辑、变换或修改时使用此技能。支持通过 gpt-image-2 模型进行图生图，使用 OpenAI 兼容的 images/generations API。用户提到 图生图、修改图片、编辑图片、图片变换、改图、换背景、换风格、图片编辑、以图生图、参考图、基于这张图、把这张图改成、在这张图上。
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body: ./SKILL.zh-CN.md
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source_hash: sha256:b3d5e7f01a2c4e8f
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translated_by: human
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en-US:
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name: Image-to-Image
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short_desc: AI image editing and transformation based on reference images
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description: "Use this skill when the user wants to edit, transform, or modify an existing image using AI. Supports image-to-image via gpt-image-2 model through the OpenAI-compatible images/generations API. Trigger keywords: image edit, img2img, modify image, transform image, change background, change style, based on this image, edit this image."
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body: ./SKILL.md
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source_hash: sha256:b3d5e7f01a2c4e8f
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translated_by: human
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market:
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icon: >-
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0
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24 24" fill="none"><rect x="3" y="3" width="18" height="18" rx="3"
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stroke="#007AFF" stroke-width="1.5" fill="#007AFF"
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fill-opacity="0.1"/><path d="M6 15l3-4 2.5 3 3.5-5 4 6H6z" fill="#007AFF"
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fill-opacity="0.3" stroke="#007AFF" stroke-width="1.2"
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stroke-linejoin="round"/><path d="M14 4l3 3-3 3" stroke="#007AFF"
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stroke-width="1.5" stroke-linecap="round"
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stroke-linejoin="round"/><path d="M17 7H11" stroke="#007AFF"
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stroke-width="1.5" stroke-linecap="round"/></svg>
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short_desc: 基于参考图片的 AI 图像编辑与变换技能
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category: media
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maintainer:
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name: DesireCore Official
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verified: true
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channel: latest
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---
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# image-to-image Skill
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## Mandatory Rules (violations cause failure)
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1. **STRICTLY follow the execution steps below** — do NOT improvise, explore alternative endpoints, or try models not listed in this document
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2. **Must access agent-service over HTTPS** — the API address is already provided in the system prompt under "本机 API" section (e.g. `https://127.0.0.1:PORT`); use it directly with `-k` to skip certificate verification
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3. **Must upload to media-store via `/api/media/upload`** — `/tmp` is only a transient download/decode location, never use a local path as the final output
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4. **Must use the `dc-media://` protocol to display images** — the only form the frontend can render correctly
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5. **Use Bash curl throughout** — do not use the HttpRequest tool or Python (except for the b64 extraction script)
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6. **Use `/images/generations` endpoint with `image` field** — same endpoint as text-to-image, the `image` field triggers image-to-image mode
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7. **Only use `gpt-image-2` model** — do NOT try dall-e-3, wan2.7-image, or any other model for image-to-image
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## Provider & Default Compute
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This skill uses the `gpt-image-2` model through the DesireCore Cloud provider. You do NOT need to specify a provider — just pass `"serviceType": "image_gen"` and the system will automatically route to the correct provider.
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- **DesireCore Cloud** (default, always available): The built-in compute provider supports `gpt-image-2` for image-to-image. Users can use it immediately without any configuration.
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**Never** try to query provider lists, read compute.json, or explore available models through API calls. The model listed above is guaranteed to work.
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## When to Use This Skill
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This skill should be triggered when the user:
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- Sends an image and asks to modify/edit/transform it
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- Asks to change the style, background, or content of an existing image
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- Wants to use a reference image to generate a new image
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- Uses keywords like "图生图", "修改图片", "编辑图片", "基于这张图", "img2img"
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**Do NOT use this skill** when the user simply asks to "generate an image" or "draw something" without providing a reference image — use the `dashscope-image-gen` skill instead.
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## How to Identify the User's Input Image
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The user's input image can come from several sources:
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1. **Image in the current conversation**: The user uploads an image in chat. It appears in the conversation as `dc-media://<mediaId>` or as an image attachment. Extract the `mediaId` (format: `xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.png`).
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2. **Image from a previous generation**: The user refers to a previously generated image (e.g. "modify the image above", "change the pig picture"). Look back in the conversation for the most recent `dc-media://<mediaId>` reference.
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3. **Image URL**: The user provides an external URL. Download it first with `curl -sL "<url>" -o /tmp/input-image.png`.
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## Full Execution Flow (strictly follow these steps)
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### How to get the API address
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The system prompt already contains the agent-service API address under "本机 API" (e.g. `Agent Service: https://127.0.0.1:61000`). Extract the URL from there and use it directly.
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If for any reason you cannot find it in the system prompt, use this fallback:
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```bash
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PORT=$(cat "${DESIRECORE_HOME:-$HOME/.desirecore}/agent-service.port")
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# Then use https://127.0.0.1:${PORT}
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```
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### Step 1: Download the input image and convert to base64
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Download the user's image from media-store and convert it to a base64 data URL:
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```bash
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# Download from media-store (replace <mediaId> with the actual media ID)
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curl -sk "https://127.0.0.1:${PORT}/api/media/<mediaId>" -o /tmp/input-image.png
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# Convert to base64 data URL (NEVER print base64 to terminal)
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python3 -c "
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import base64, sys
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with open('/tmp/input-image.png', 'rb') as f:
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data = f.read()
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b64 = base64.b64encode(data).decode()
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# Detect MIME type from magic bytes
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mime = 'image/png'
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if data[:2] == b'\xff\xd8':
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mime = 'image/jpeg'
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elif data[:4] == b'RIFF' and data[8:12] == b'WEBP':
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mime = 'image/webp'
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data_url = f'data:{mime};base64,{b64}'
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with open('/tmp/input-image-dataurl.txt', 'w') as f:
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f.write(data_url)
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print(f'OK: data URL saved, image size: {len(data)} bytes, mime: {mime}')
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"
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```
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**CRITICAL**: The base64 data URL can be very large (1-5MB). NEVER print it to the terminal. Always save to file.
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### Step 2: Generate the edited image (single curl command)
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Call `/images/generations` with the `image` field through media-proxy. **You MUST use this exact request structure**:
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```bash
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# Read data URL from file and build request
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IMAGE_DATA_URL=$(cat /tmp/input-image-dataurl.txt)
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# Save response to temp file to avoid base64 flooding the terminal
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curl -sk -X POST "https://127.0.0.1:${PORT}/api/media-proxy" \
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-H "Content-Type: application/json" \
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-d "{
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\"serviceType\": \"image_gen\",
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\"endpoint\": \"/images/generations\",
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\"body\": {
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\"model\": \"gpt-image-2\",
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\"prompt\": \"Replace this with the edit description in English\",
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\"image\": \"${IMAGE_DATA_URL}\",
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\"size\": \"1024x1024\",
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\"n\": 1
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},
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\"responseType\": \"json\"
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}" -o /tmp/img2img-response.json
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# Check success and extract b64_json to image file (NEVER cat the response to stdout)
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python3 -c "
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import json, base64, sys
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with open('/tmp/img2img-response.json') as f:
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resp = json.load(f)
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if not resp.get('success'):
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print('ERROR:', json.dumps(resp, ensure_ascii=False)[:500])
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sys.exit(1)
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b64 = resp['data']['data'][0]['b64_json']
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with open('/tmp/img2img-output.png', 'wb') as f:
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f.write(base64.b64decode(b64))
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print('OK: saved to /tmp/img2img-output.png')
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"
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```
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**CRITICAL**: The response contains a large base64 image (~2MB). NEVER print the raw response or b64_json to the terminal. Always save to file with `-o` and extract with the python3 script above.
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**IMPORTANT**: Combine Step 1 and Step 2 into a single Bash tool call to minimize round trips. The full script should: download image → convert to base64 → build curl request → extract result.
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**Response format** (saved in `/tmp/img2img-response.json`):
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```json
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{
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"success": true,
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"data": {
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"created": 1781060911,
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"data": [{"b64_json": "<very large base64 string>"}],
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"size": "1024x1024"
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}
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}
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```
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### Step 3: Upload to media-store
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```bash
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curl -sk -X POST "https://127.0.0.1:${PORT}/api/media/upload" \
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-F "file=@/tmp/img2img-output.png;type=image/png"
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```
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Pick the `mediaId` field from the JSON response (format `xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.png`).
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### Step 4: Render the image via the dc-media protocol
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In your reply text, write Markdown image syntax directly:
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```
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```
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The frontend will translate `dc-media://` into a reachable image URL and render it.
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## Parameter Mapping
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### Size selection
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Pass `size` in the `body` object:
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| User intent | size value |
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|---------|-----------|
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| Square / default | "1024x1024" |
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| Landscape / wide | "1536x1024" |
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| Portrait / tall | "1024x1536" |
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| Auto (let model decide) | "auto" |
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### Prompt guidelines for image-to-image
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- **Be specific about what to change**: "Change the background to a beach scene" is better than "edit this image"
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- **Describe the desired result**: "A pig wearing an astronaut suit in space" rather than "make it space-themed"
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- **English prompts usually produce better results**; Chinese is also supported
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- **Mention what to preserve**: "Keep the main subject but change the background" helps maintain fidelity
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## Error Handling
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| Error | Meaning | Action |
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|-------|---------|--------|
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| `"No matching provider"` | No enabled provider supports `image_gen` | Tell user to enable a provider with image_gen support in settings |
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| `"API Key not configured"` | API Key missing | Tell user to configure API key |
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| `statusCode: 401` | API Key invalid or expired | Tell user to check API key |
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| `statusCode: 429` | Rate limited | Wait and retry once |
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| `statusCode: 400` | Bad parameters | Check model name and size; ensure image is valid base64 |
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| Image download fails | mediaId not found or expired | Ask user to re-upload the image |
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**On any error**: Do NOT try alternative models, alternative endpoints, or read config files. Report the error to the user clearly.
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## Notes
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- Image-to-image calls are synchronous and typically return in 15-60 seconds
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- Input image is sent as base64 in the request body, which increases request size significantly
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- The `gpt-image-2` model can handle various edit types: style transfer, background change, object modification, artistic transformation
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- When the user does not specify a size, default to `1024x1024`
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- If the user's input image is very large, consider mentioning that processing may take longer
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198
skills/image-to-image/SKILL.zh-CN.md
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198
skills/image-to-image/SKILL.zh-CN.md
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<!-- locale: zh-CN -->
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# image-to-image 技能
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## 强制规则（违反将导致功能失败）
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1. **严格按下方步骤执行** — 禁止自行探索其他端点、尝试本文档未列出的模型、或读取配置文件
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2. **必须用 HTTPS 访问 agent-service** — API 地址已在系统提示词的"本机 API"部分提供（如 `https://127.0.0.1:PORT`），直接使用，加 `-k` 跳过证书验证
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3. **必须通过 `/api/media/upload` 上传到 media-store** — 禁止保存到本地路径
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4. **必须使用 `dc-media://` 协议展示图片** — 唯一能让前端正确渲染的方式
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5. **全程使用 Bash curl** — 不要使用 HttpRequest 工具或 Python（b64 提取脚本除外）
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6. **使用 `/images/generations` 端点并附带 `image` 字段** — 与文生图相同的端点，`image` 字段触发图生图模式
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7. **只使用 `gpt-image-2` 模型** — 禁止尝试 dall-e-3、wan2.7-image 或任何其他模型进行图生图
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## 供应商与默认算力
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本技能通过 DesireCore Cloud 供应商使用 `gpt-image-2` 模型。**无需指定供应商**，只需传 `"serviceType": "image_gen"`，系统会自动路由到正确的供应商。
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- **DesireCore Cloud**（默认，始终可用）：内置算力供应商已支持 `gpt-image-2` 图生图。用户无需任何配置即可直接使用。
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**禁止**尝试查询供应商列表、读取 compute.json、或通过 API 探索可用模型。上方列出的模型保证可用。
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## 何时使用此技能
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当用户出现以下情况时触发此技能：
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- 发送了一张图片并要求修改/编辑/变换
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- 要求更换图片的风格、背景或内容
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- 想基于参考图片生成新图片
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- 使用了关键词：图生图、修改图片、编辑图片、基于这张图、img2img
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**不要在以下情况使用此技能**：用户仅要求"生成图片"或"画一张"而没有提供参考图 — 此时应使用 `dashscope-image-gen` 技能。
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## 如何识别用户的输入图片
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用户的输入图片可能来自以下来源：
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1. **当前对话中的图片**：用户在聊天中上传了图片，它在对话中以 `dc-media://<mediaId>` 或图片附件形式出现。提取 `mediaId`（格式：`xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.png`）。
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2. **之前生成的图片**：用户引用了之前生成的图片（如"修改上面那张图"、"把那张小猪图改一下"）。回溯对话找到最近的 `dc-media://<mediaId>` 引用。
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3. **图片 URL**：用户提供了外部 URL。先用 `curl -sL "<url>" -o /tmp/input-image.png` 下载。
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## 完整执行流程（严格按此步骤执行）
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### 获取 API 地址
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系统提示词中"本机 API"部分已包含 agent-service 的地址（如 `Agent Service: https://127.0.0.1:61000`）。直接从中提取 URL 使用。
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如果无法从系统提示词中找到，使用以下兜底方式：
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```bash
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PORT=$(cat "${DESIRECORE_HOME:-$HOME/.desirecore}/agent-service.port")
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||||
# 然后使用 https://127.0.0.1:${PORT}
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||||
```
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### 第一步：下载输入图片并转为 base64
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从 media-store 下载用户图片并转为 base64 data URL：
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```bash
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# 从 media-store 下载（将 <mediaId> 替换为实际的 media ID）
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curl -sk "https://127.0.0.1:${PORT}/api/media/<mediaId>" -o /tmp/input-image.png
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|
||||
# 转为 base64 data URL（禁止将 base64 打印到终端）
|
||||
python3 -c "
|
||||
import base64, sys
|
||||
with open('/tmp/input-image.png', 'rb') as f:
|
||||
data = f.read()
|
||||
b64 = base64.b64encode(data).decode()
|
||||
# 通过魔数检测 MIME 类型
|
||||
mime = 'image/png'
|
||||
if data[:2] == b'\xff\xd8':
|
||||
mime = 'image/jpeg'
|
||||
elif data[:4] == b'RIFF' and data[8:12] == b'WEBP':
|
||||
mime = 'image/webp'
|
||||
data_url = f'data:{mime};base64,{b64}'
|
||||
with open('/tmp/input-image-dataurl.txt', 'w') as f:
|
||||
f.write(data_url)
|
||||
print(f'OK: data URL saved, image size: {len(data)} bytes, mime: {mime}')
|
||||
"
|
||||
```
|
||||
|
||||
**关键警告**：base64 data URL 可能非常大（1-5MB）。**禁止**将其打印到终端。始终保存到文件。
|
||||
|
||||
### 第二步：生成编辑后的图片（单次 curl 调用）
|
||||
|
||||
通过 media-proxy 调用 `/images/generations` 并附带 `image` 字段。**必须严格使用以下请求结构**：
|
||||
|
||||
```bash
|
||||
# 从文件读取 data URL 并构建请求
|
||||
IMAGE_DATA_URL=$(cat /tmp/input-image-dataurl.txt)
|
||||
|
||||
# 将响应保存到临时文件，避免 base64 数据灌入终端
|
||||
curl -sk -X POST "https://127.0.0.1:${PORT}/api/media-proxy" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d "{
|
||||
\"serviceType\": \"image_gen\",
|
||||
\"endpoint\": \"/images/generations\",
|
||||
\"body\": {
|
||||
\"model\": \"gpt-image-2\",
|
||||
\"prompt\": \"这里替换为编辑描述（建议英文效果更好）\",
|
||||
\"image\": \"${IMAGE_DATA_URL}\",
|
||||
\"size\": \"1024x1024\",
|
||||
\"n\": 1
|
||||
},
|
||||
\"responseType\": \"json\"
|
||||
}" -o /tmp/img2img-response.json
|
||||
|
||||
# 检查成功并将 b64_json 提取为图片文件（禁止将响应内容输出到终端）
|
||||
python3 -c "
|
||||
import json, base64, sys
|
||||
with open('/tmp/img2img-response.json') as f:
|
||||
resp = json.load(f)
|
||||
if not resp.get('success'):
|
||||
print('ERROR:', json.dumps(resp, ensure_ascii=False)[:500])
|
||||
sys.exit(1)
|
||||
b64 = resp['data']['data'][0]['b64_json']
|
||||
with open('/tmp/img2img-output.png', 'wb') as f:
|
||||
f.write(base64.b64decode(b64))
|
||||
print('OK: saved to /tmp/img2img-output.png')
|
||||
"
|
||||
```
|
||||
|
||||
**关键警告**：响应包含大约 2MB 的 base64 图片数据。**禁止**将原始响应或 b64_json 打印到终端。始终使用 `-o` 保存到文件，再用上面的 python3 脚本提取。
|
||||
|
||||
**重要**：尽量将第一步和第二步合并到单次 Bash 工具调用中，减少往返次数。完整脚本应：下载图片 → 转 base64 → 构建 curl 请求 → 提取结果。
|
||||
|
||||
**响应格式**（保存在 `/tmp/img2img-response.json` 中）：
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"created": 1781060911,
|
||||
"data": [{"b64_json": "<非常大的 base64 字符串>"}],
|
||||
"size": "1024x1024"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 第三步：上传到 media-store
|
||||
|
||||
```bash
|
||||
curl -sk -X POST "https://127.0.0.1:${PORT}/api/media/upload" \
|
||||
-F "file=@/tmp/img2img-output.png;type=image/png"
|
||||
```
|
||||
|
||||
从上传 JSON 响应中提取 `mediaId` 字段（格式如 `xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.png`）。
|
||||
|
||||
### 第四步：用 dc-media 协议展示图片
|
||||
|
||||
在你的回复文本中直接写 Markdown 图片语法：
|
||||
|
||||
```
|
||||
|
||||
```
|
||||
|
||||
前端会自动将 `dc-media://` 转为可访问的图片 URL 并渲染出来。
|
||||
|
||||
## 参数映射
|
||||
|
||||
### 尺寸选择
|
||||
|
||||
`size` 放在 `body` 对象中：
|
||||
|
||||
| 用户意图 | size 参数 |
|
||||
|---------|-----------|
|
||||
| 正方形/默认 | "1024x1024" |
|
||||
| 横版/宽屏 | "1536x1024" |
|
||||
| 竖版/高图 | "1024x1536" |
|
||||
| 自动（让模型决定） | "auto" |
|
||||
|
||||
### 图生图的提示词建议
|
||||
|
||||
- **明确说明要改什么**："将背景换成海滩" 比 "编辑这张图" 更好
|
||||
- **描述期望的结果**："一只穿着宇航服的小猪在太空中" 而不是 "改成太空主题"
|
||||
- **英文提示词通常效果更好**，中文也支持
|
||||
- **说明要保留什么**："保持主体不变，只更换背景" 有助于保持原图特征
|
||||
|
||||
## 错误处理
|
||||
|
||||
| 错误 | 含义 | 处理方式 |
|
||||
|------|------|---------|
|
||||
| `"未找到匹配的供应商"` | 没有启用的供应商支持 `image_gen` | 告知用户在设置中启用支持 image_gen 的供应商 |
|
||||
| `"未配置 API Key"` | 未填写 API Key | 告知用户配置 API Key |
|
||||
| `statusCode: 401` | API Key 无效或过期 | 告知用户检查 API Key |
|
||||
| `statusCode: 429` | 频率限制 | 等待后重试一次 |
|
||||
| `statusCode: 400` | 参数错误 | 检查模型名和尺寸；确保图片是有效的 base64 |
|
||||
| 图片下载失败 | mediaId 不存在或已过期 | 请用户重新上传图片 |
|
||||
|
||||
**遇到任何错误时**：禁止尝试其他模型、其他端点、或读取配置文件。直接向用户清晰报告错误即可。
|
||||
|
||||
## 注意事项
|
||||
|
||||
- 图生图调用是同步的，通常 15-60 秒返回
|
||||
- 输入图片以 base64 形式放在请求体中，会显著增加请求大小
|
||||
- `gpt-image-2` 模型支持多种编辑类型：风格迁移、背景替换、对象修改、艺术变换
|
||||
- 如果用户未明确要求尺寸，默认使用 `1024x1024`
|
||||
- 如果用户的输入图片很大，可以提示处理时间可能较长
|
||||
Reference in New Issue
Block a user