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add docs
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eycjur
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Azure AI Image Editing
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Azure AI provides powerful image editing capabilities using FLUX models from Black Forest Labs to modify existing images based on text descriptions.
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## Overview
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| Property | Details |
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|----------|---------|
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| Description | Azure AI Image Editing uses FLUX models to modify existing images based on text prompts. |
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| Provider Route on LiteLLM | `azure_ai/` |
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| Provider Doc | [Azure AI FLUX Models ↗](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659) |
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| Supported Operations | [`/images/edits`](#image-editing) |
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## Setup
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### API Key & Base URL & API Version
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```python showLineNumbers
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# Set your Azure AI API credentials
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import os
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os.environ["AZURE_AI_API_KEY"] = "your-api-key-here"
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os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint" # e.g., https://your-endpoint.eastus2.inference.ai.azure.com/
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os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview" # Example API version
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```
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Get your API key and endpoint from [Azure AI Studio](https://ai.azure.com/).
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## Supported Models
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| Model Name | Description | Cost per Image |
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|------------|-------------|----------------|
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| `azure_ai/FLUX.1-Kontext-pro` | FLUX 1 Kontext Pro model with enhanced context understanding for editing | $0.04 |
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## Image Editing
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### Usage - LiteLLM Python SDK
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<Tabs>
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<TabItem value="basic-edit" label="Basic Usage">
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```python showLineNumbers title="Basic Image Editing"
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import os
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import base64
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from pathlib import Path
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import litellm
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# Set your API credentials
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os.environ["AZURE_AI_API_KEY"] = "your-api-key-here"
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os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint"
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os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview"
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# Edit an image with a prompt
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response = litellm.image_edit(
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model="azure_ai/FLUX.1-Kontext-pro",
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image=open("path/to/your/image.png", "rb"),
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prompt="Add a winter theme with snow and cold colors",
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api_base=os.environ["AZURE_AI_API_BASE"],
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api_key=os.environ["AZURE_AI_API_KEY"],
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api_version=os.environ["AZURE_AI_API_VERSION"]
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)
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img_base64 = response.data[0].get("b64_json")
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img_bytes = base64.b64decode(img_base64)
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path = Path("edited_image.png")
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path.write_bytes(img_bytes)
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```
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</TabItem>
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<TabItem value="async-edit" label="Async Usage">
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```python showLineNumbers title="Async Image Editing"
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import os
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import base64
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from pathlib import Path
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import litellm
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import asyncio
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# Set your API credentials
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os.environ["AZURE_AI_API_KEY"] = "your-api-key-here"
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os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint"
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os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview"
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async def edit_image():
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# Edit image asynchronously
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response = await litellm.aimage_edit(
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model="azure_ai/FLUX.1-Kontext-pro",
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image=open("path/to/your/image.png", "rb"),
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prompt="Make this image look like a watercolor painting",
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api_base=os.environ["AZURE_AI_API_BASE"],
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api_key=os.environ["AZURE_AI_API_KEY"],
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api_version=os.environ["AZURE_AI_API_VERSION"]
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)
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img_base64 = response.data[0].get("b64_json")
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img_bytes = base64.b64decode(img_base64)
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path = Path("async_edited_image.png")
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path.write_bytes(img_bytes)
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# Run the async function
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asyncio.run(edit_image())
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```
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</TabItem>
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<TabItem value="advanced-edit" label="Advanced Parameters">
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```python showLineNumbers title="Advanced Image Editing with Parameters"
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import os
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import base64
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from pathlib import Path
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import litellm
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# Set your API credentials
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os.environ["AZURE_AI_API_KEY"] = "your-api-key-here"
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os.environ["AZURE_AI_API_BASE"] = "your-azure-ai-endpoint"
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os.environ["AZURE_AI_API_VERSION"] = "2025-04-01-preview"
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# Edit image with additional parameters
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response = litellm.image_edit(
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model="azure_ai/FLUX.1-Kontext-pro",
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image=open("path/to/your/image.png", "rb"),
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prompt="Add magical elements like floating crystals and mystical lighting",
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api_base=os.environ["AZURE_AI_API_BASE"],
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api_key=os.environ["AZURE_AI_API_KEY"],
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api_version=os.environ["AZURE_AI_API_VERSION"],
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n=1
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)
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img_base64 = response.data[0].get("b64_json")
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img_bytes = base64.b64decode(img_base64)
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path = Path("advanced_edited_image.png")
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path.write_bytes(img_bytes)
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```
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</TabItem>
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</Tabs>
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### Usage - LiteLLM Proxy Server
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#### 1. Configure your config.yaml
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```yaml showLineNumbers title="Azure AI Image Editing Configuration"
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model_list:
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- model_name: azure-flux-kontext-edit
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litellm_params:
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model: azure_ai/FLUX.1-Kontext-pro
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api_key: os.environ/AZURE_AI_API_KEY
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api_base: os.environ/AZURE_AI_API_BASE
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api_version: os.environ/AZURE_AI_API_VERSION
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model_info:
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mode: image_edit
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general_settings:
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master_key: sk-1234
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```
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#### 2. Start LiteLLM Proxy Server
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```bash showLineNumbers title="Start LiteLLM Proxy Server"
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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#### 3. Make image editing requests with OpenAI Python SDK
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<Tabs>
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<TabItem value="openai-edit-sdk" label="OpenAI SDK">
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```python showLineNumbers title="Azure AI Image Editing via Proxy - OpenAI SDK"
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="sk-1234" # Your proxy API key
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)
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# Edit image with FLUX Kontext Pro
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response = client.images.edit(
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model="azure-flux-kontext-edit",
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image=open("path/to/your/image.png", "rb"),
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prompt="Transform this image into a beautiful oil painting style",
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)
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img_base64 = response.data[0].b64_json
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img_bytes = base64.b64decode(img_base64)
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path = Path("proxy_edited_image.png")
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path.write_bytes(img_bytes)
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```
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</TabItem>
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<TabItem value="litellm-edit-sdk" label="LiteLLM SDK">
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```python showLineNumbers title="Azure AI Image Editing via Proxy - LiteLLM SDK"
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import litellm
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# Edit image through proxy
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response = litellm.image_edit(
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model="litellm_proxy/azure-flux-kontext-edit",
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image=open("path/to/your/image.png", "rb"),
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prompt="Add a mystical forest background with magical creatures",
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api_base="http://localhost:4000",
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api_key="sk-1234"
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)
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img_base64 = response.data[0].b64_json
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img_bytes = base64.b64decode(img_base64)
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path = Path("proxy_edited_image.png")
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path.write_bytes(img_bytes)
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```
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</TabItem>
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<TabItem value="curl-edit" label="cURL">
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```bash showLineNumbers title="Azure AI Image Editing via Proxy - cURL"
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curl --location 'http://localhost:4000/v1/images/edits' \
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--header 'Authorization: Bearer sk-1234' \
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--form 'model="azure-flux-kontext-edit"' \
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--form 'prompt="Convert this image to a vintage sepia tone with old-fashioned effects"' \
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--form 'image=@"path/to/your/image.png"'
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```
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</TabItem>
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</Tabs>
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## Supported Parameters
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Azure AI Image Editing supports the following OpenAI-compatible parameters:
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| Parameter | Type | Description | Default | Example |
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|-----------|------|-------------|---------|---------|
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| `image` | file | The image file to edit | Required | File object or binary data |
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| `prompt` | string | Text description of the desired changes | Required | `"Add snow and winter elements"` |
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| `model` | string | The FLUX model to use for editing | Required | `"azure_ai/FLUX.1-Kontext-pro"` |
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| `n` | integer | Number of edited images to generate (You can specify only 1) | `1` | `1` |
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| `api_base` | string | Your Azure AI endpoint URL | Required | `"https://your-endpoint.eastus2.inference.ai.azure.com/"` |
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| `api_key` | string | Your Azure AI API key | Required | Environment variable or direct value |
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| `api_version` | string | API version for Azure AI | Required | `"2025-04-01-preview"` |
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## Getting Started
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1. Create an account at [Azure AI Studio](https://ai.azure.com/)
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2. Deploy a FLUX model in your Azure AI Studio workspace
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3. Get your API key and endpoint from the deployment details
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4. Set your `AZURE_AI_API_KEY`, `AZURE_AI_API_BASE` and `AZURE_AI_API_VERSION` environment variables
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5. Prepare your source image
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6. Use `litellm.image_edit()` to modify your images with text instructions
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## Additional Resources
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- [Azure AI Studio Documentation](https://docs.microsoft.com/en-us/azure/ai-services/)
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- [FLUX Models Announcement](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/black-forest-labs-flux-1-kontext-pro-and-flux1-1-pro-now-available-in-azure-ai-f/4434659)
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@@ -383,6 +383,7 @@ const sidebars = {
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items: [
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"providers/azure_ai",
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"providers/azure_ai_img",
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"providers/azure_ai_img_edit",
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]
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},
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{
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