microsoft/mai-image-2.5/edit

image-to-image

MAI-Image 2.5 Edit API by MICROSOFT

microsoft/mai-image-2.5/edit

Edit

Microsoft's flagship image-to-image editing model, enabling precise, controllable edits to existing images through natural language instructions.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.058 per run. For $10 you can run this model approximately 172 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "microsoft/mai-image-2.5/edit",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "microsoft/mai-image-2.5/edit",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Response

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

{
  "model": "microsoft/mai-image-2.5/edit"
}

Output Schema

The API returns a prediction response with the generated output URLs.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 300+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

No examples available

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MAI-Image-2.5 Edit (Image-to-Image)

MAI-Image-2.5 Edit is Microsoft's flagship image-to-image editing model, enabling precise, controllable edits to existing images through natural language instructions. Built on the same diffusion-based generative architecture as MAI-Image-2.5, it excels at surgical, targeted modifications — removing or replacing objects, updating in-image text, correcting artifacts, and adapting layouts — while preserving the original composition and visual identity. Released on June 2, 2026.

Key Capabilities

Surgical object editing — Make targeted object edits: remove, replace, recolor, or reposition specific elements without affecting the rest of the image.
Inpainting & outpainting — Fill in missing regions, remove unwanted content, and extend image boundaries seamlessly.
Text updates in images — Accurately update signage, labels, packaging text, and other embedded text within images.
Artifact cleanup — Remove motion blur, noise, compression artifacts, and other visual defects.
Layout adaptation — Adjust composition, crop, or reframe scenes while maintaining subject consistency.
Face and identity consistency — Preserve facial features and identity across edits and modifications.
Visual reasoning — Understands spatial relationships, lighting, and scene structure to apply edits that look natural and coherent.
High-fidelity portraits — Generates or edits expressive, natural-looking portraits with accurate facial structure and lighting.
Accurate text rendering — Maintains or improves text legibility in labels, posters, and signage.
Product, branding & commercial design — Well suited for product imagery refinement, marketing visuals, and commercial creative workflows.

Pricing

Pricing is based on three components: the input text tokens in the prompt, a fixed input image fee, and a fixed per-output-image fee.

SKU	Description	Unit Price
`sku_input_1m_token`	Price per 1M input (prompt) tokens	$5.00
`sku_input_image`	Fixed fee per input image	$0.008
`sku_output_image`	Fixed fee per generated/edited output image	$0.05
`sku_base`	Combined base cost (input image + output image)	$0.058

Pricing Formula

cost = countTokens(prompt) / 1,000,000 × $5.00 + $0.05 + $0.008

Which simplifies to:

cost = countTokens(prompt) / 1,000,000 × $5.00 + $0.058

For most prompts (a few hundred tokens), the token cost is negligible and the effective cost is approximately $0.058 per edit.

Examples

Prompt Length	Token Count	Token Cost	Base Fee	Total
Short (e.g., 50 tokens)	50	~$0.000250	$0.058	~$0.0583
Medium (e.g., 500 tokens)	500	~$0.002500	$0.058	~$0.0605
Long (e.g., 2,000 tokens)	2,000	~$0.010000	$0.058	~$0.0680

For detailed pricing configuration, see models/microsoft/mai/price/microsoft-mai-image-2.5-edit.json.

Best Use Cases

Product retouching — Remove backgrounds, fix lighting, swap colors, or clean up blemishes in product images.
Marketing asset updates — Update text, logos, or seasonal elements in existing creatives without a full reshoot.
Photo restoration — Remove artifacts, clean up old photos, or fix quality issues.
Content localization — Update in-image text across languages for different markets.
Design iteration — Rapidly test layout, color, or composition variations on an existing design.
E-commerce — Update product variants (colors, styles) from a single base image.

Pro Tips

Be explicit about what to change and what to preserve (e.g., "change the red car to blue, keep the background unchanged").
For text edits, quote the exact text to replace and the replacement string.
Describe the desired result, not just what to remove — "make the background a white studio" is better than "remove the background".
For consistent style across edits, keep the same prompt structure across iterations.

Technical Specifications

Spec	Value
Model Developer	Microsoft AI
Release Date	June 2, 2026
Input Format	Text prompt + image (JPEG or PNG, multipart/form-data)
Output Format	PNG image (base64-encoded)
Max Prompt Length	32,000 tokens
Max Output Resolution	1,048,576 total pixels (e.g., 1024×1024)
Supported Languages	English (primary)

MAI-Image-2.5 Text-to-Image — Same model family for generating images from text only.
MAI-Image-2.5-Flash Edit — Faster, lower-cost image editing variant.
MAI-Image-2.5-Flash Text-to-Image — Faster, lower-cost text-to-image variant.

MAI-Image 2.5 Edit API by MICROSOFT

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

Please log in to view request history

MAI-Image-2.5 Edit (Image-to-Image)

Key Capabilities

Pricing

Pricing Formula

Examples

Best Use Cases

Pro Tips

Technical Specifications

Related Models

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One API for All Media AI.

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INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example