# Wan 2.7 Image-to-Image — Atlas Cloud API > Edits and recomposes images with Wan 2.7 image using text instructions, multi-image references, and optional interaction boxes. This is the machine-readable API reference for **Wan 2.7 Image-to-Image** on Atlas Cloud, a unified API platform for 400+ AI models across text, image, video, audio and 3D. - **Model ID**: `alibaba/wan-2.7/image-edit` - **Built by**: Alibaba - **Modality**: Image - **Model page**: https://www.atlascloud.ai/models/alibaba/wan-2.7/image-edit - **API key**: https://www.atlascloud.ai/console/api-keys - **Docs**: https://www.atlascloud.ai/docs ## Pricing on Atlas Cloud - $0.03 per image - Pay-as-you-go. No minimum spend, no subscription required. > **These are the authoritative Atlas Cloud rates for this model.** Any price that > appears in the vendor description further down refers to a different platform or > a different model variant and does not apply here. ## Use this model from an AI agent Atlas Cloud ships three first-party integration surfaces. All three authenticate with the same API key via the `ATLASCLOUD_API_KEY` environment variable. ### MCP server The official MCP server (`atlascloud-mcp`) exposes this model to any MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose, Claude Desktop. One-line install: ```bash # Claude Code claude mcp add atlascloud -- npx -y atlascloud-mcp # OpenAI Codex CLI codex mcp add atlascloud -- npx -y atlascloud-mcp # Gemini CLI gemini mcp add atlascloud -- npx -y atlascloud-mcp export ATLASCLOUD_API_KEY="your-api-key" ``` Then ask in plain English; the agent calls `atlas_generate_image` with `model: "alibaba/wan-2.7/image-edit"`. The server fetches each model's schema and validates parameters before submitting, so invalid requests fail fast without spending credits. MCP docs: https://www.atlascloud.ai/docs/mcp-server ### Agent Skills `atlas-cloud-skills` is a portable skill package (API reference, code templates in Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and 12+ other agents: ```bash npx skills add AtlasCloudAI/atlas-cloud-skills export ATLASCLOUD_API_KEY="your-api-key" ``` Skills docs: https://www.atlascloud.ai/docs/skills ### CLI The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs are polled and downloaded automatically (use `--no-download` when a script only needs the output URLs): ```bash # Install (Homebrew, npm, or shell installer) brew install AtlasCloudAI/tap/atlascloud # npm install -g atlascloud-cli # curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh atlas auth login atlas generate image alibaba/wan-2.7/image-edit -p "Your prompt here" ``` CLI docs: https://www.atlascloud.ai/docs/cli ## HTTP API reference - **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id` - **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes - **Model ID**: `alibaba/wan-2.7/image-edit` ## API Information This model can be used via our HTTP API or more conveniently via our client libraries. See the input and output schema below, as well as the usage examples. ### Input Schema The API accepts the following input parameters: - **`model`** (`string`, _required_): Model name. - Default: `"alibaba/wan-2.7/image-edit"` - Options: "alibaba/wan-2.7/image-edit" - **`prompt`** (`string`, _required_): Text instruction used to edit the image. Maximum length is 5000 characters. - **`images`** (`array[string]`, _required_): Input images, each provided as a URL or Base64 string. The first image is treated as the primary input; subsequent images serve as references. - Min items: 1 - Max items: 9 - **`size`** (`string`, _optional_): Output image resolution. - Default: `"2K"` - Options: "1K", "2K" - **`n`** (`integer`, _optional_): Number of images to generate. - Default: `1` - Min: 1 - Max: 4 - **`thinking_mode`** (`boolean`, _optional_): Whether to enable thinking mode for higher-quality image generation. - Default: `true` - **`seed`** (`integer`, _optional_): Random seed for image generation. Range: 0 to 2147483647. Use -1 for a random seed. - Default: `-1` - Min: -1 - Max: 2147483647 - **`enable_sync_mode`** (`boolean`, _optional_): Whether to wait for generation to finish and return the final result directly in the response. - Default: `false` - **`enable_base64_output`** (`boolean`, _optional_): Whether to return the generated image as a Base64 string instead of a URL. - Default: `false` **Required Parameters Example**: ```json { "model": "alibaba/wan-2.7/image-edit", "prompt": "", "images": [ "" ] } ``` **Full Example**: ```json { "model": "alibaba/wan-2.7/image-edit", "prompt": "", "images": [ "" ], "size": "2K", "n": 1, "thinking_mode": true, "seed": -1, "enable_sync_mode": false, "enable_base64_output": false } ``` ### Output Schema The API returns the following output format: - **`code`** (`integer`, _optional_): - **`message`** (`string`, _optional_): - **`data`** (`string`, _optional_): **Example Response**: ```json { "code": 0, "message": "", "data": null } ``` ## Usage Examples ### cURL ```bash # Step 1: Start generation (async) curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \ -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "alibaba/wan-2.7/image-edit", "prompt": "", "images": [ "" ], "size": "2K", "n": 1, "thinking_mode": true, "seed": -1, "enable_sync_mode": false, "enable_base64_output": false }' # Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}} # Step 2: Poll for result (replace {prediction_id} with the id returned above) curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \ -H "Authorization: Bearer $ATLASCLOUD_API_KEY" # Keep polling until status is "completed", "succeeded" or "failed" # When completed, outputs will contain the generated content URL(s) ``` ## Additional Resources ### Documentation - [Model Playground](https://www.atlascloud.ai/models/alibaba/wan-2.7/image-edit) ## About this model _Vendor-supplied description. Any pricing or endpoint mentioned below refers to_ _other platforms — use the Atlas Cloud values above._ ### Alibaba WAN 2.7 Image Edit **Alibaba WAN 2.7 Image Edit** enables instruction-driven image editing and multi-image composition. It is designed for workflows where you want to preserve the source scene, combine references, and steer the result with natural language. #### Why creators love it * **Instruction-based editing:** Rewrite clothing, materials, color, scene details, or object relationships with a plain-language prompt. * **Single-image and multi-image input:** Works well for focused edits as well as reference-driven composition. * **Reference composition:** Combine multiple images and a text instruction in one edit request. * **Practical controls:** Supports `size`, `n`, `watermark`, `thinking_mode`, and `seed`. #### Perfect for * Marketing teams adapting hero visuals for multiple campaigns. * E-commerce teams refreshing product shots and styled composites. * Users who want Wan 2.7 editing quality in the standard tier. * Creative teams that need reliable visual revisions without rebuilding an image from scratch. #### How to Use 1. Provide a main source image, and add extra references when style, objects, or composition details need to be guided. 2. Write a prompt that explains the desired edit clearly and specifically. 3. Choose the target size and decide whether you want one result or multiple variations. 4. Review the edited outputs and keep the one that best balances fidelity and creative change. #### Pro tips * Be explicit about what should change and what should remain stable. * Use a single output when you want tighter control, and multiple outputs when exploring different directions. --- Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/alibaba/wan-2.7/image-edit · Docs: https://www.atlascloud.ai/docs