https://github.com/lazauk/aifoundry-gpt-image-1-editing
Practical demo of image editing with Azure OpenAI's GPT-image-1 via direct API calls and Azure Entra ID auths.
https://github.com/lazauk/aifoundry-gpt-image-1-editing
ai api azure entra-id foundry gpt-image-1 image-editing openai
Last synced: 3 months ago
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Practical demo of image editing with Azure OpenAI's GPT-image-1 via direct API calls and Azure Entra ID auths.
- Host: GitHub
- URL: https://github.com/lazauk/aifoundry-gpt-image-1-editing
- Owner: LazaUK
- License: mit
- Created: 2025-06-06T23:47:07.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-08T14:22:49.000Z (4 months ago)
- Last Synced: 2025-07-03T05:36:51.446Z (3 months ago)
- Topics: ai, api, azure, entra-id, foundry, gpt-image-1, image-editing, openai
- Language: Jupyter Notebook
- Homepage:
- Size: 4.3 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Editing with Azure OpenAI GPT-image-1
This repo demonstrates how to use Azure OpenAI **GPT-image-1**, a powerful image generation model in Azure AI Foundry. The provided Jupyter notebook showcases image editing capabilities, allowing you to modify existing images based on textual prompts.
This demo leverages the *requests* library for direct API interaction and *azure-identity* library for secure Azure Entra ID authentication.
___
🎧 You may listen to the audio podcast, ["Image Makeovers with GPT-image-1: Behind the Code"](https://youtu.be/heyLTbfDo6k), where we explore the content of this notebook in a beginner-friendly way.
___
## 📑 Table of Contents:
* [Part 1: Configuring Solution Environment](#part-1-configuring-solution-environment)
* [Part 2: Performing Image Edits and Visualisation](#part-2-performing-image-edits-and-visualisation)
---
## Part 1: Configuring Solution Environment
To use the notebook, set up your Azure OpenAI environment and install Python packages.
### 1.1 Azure OpenAI Service Setup
Ensure you have an **Azure OpenAI Service** resource with the **GPT-image-1 model** deployed.
### 1.2 Authentication
This demo uses **Azure Entra ID** authentication via `DefaultAzureCredential` from `azure.identity`. This credential type automatically handles various authentication methods.
To enable this, ensure your environment is set up for Azure authentication, e.g., by logging in via `az login` (Azure CLI) or setting relevant environment variables for service principals.
### 1.3 Environment Variables
Set the following environment variables, pointing to your Azure OpenAI GPT-image-1 deployment:
| Environment Variable | Description |
| :----------------------- | :-------------------------------------------------------------------------------------- |
| AOAI_API_BASE | Your Azure OpenAI endpoint URL (e.g., https://.openai.azure.com). |
| AOAI_DEPLOYMENT_NAME | The name of your GPT-image-1 deployment (e.g., gpt-image-1). |
| AOAI_API_VERSION | The API version for image edits (e.g., 2025-04-01-preview). |
### 1.4 Install Required Python packages
``` Bash
pip install requests Pillow ipython azure-identity
```
## Part 2: Performing Image Edits and Visualisation
The `GPT-image-1.ipynb` notebook outlines the core image editing logic:
* **API URL Construction:** Builds the endpoint URL using environment variables.
* **Input Image:** Loads the image from `INPUT_IMAGE_PATH` (e.g., `./input_image.jpeg`).
* **Secure Authentication:** Obtains an access token using `DefaultAzureCredential`.
* **API Request:** Sends a `multipart/form-data` POST request to the GPT-image-1 API, including:
* The input image file.
* A `prompt` string (e.g., `"Add a giant, friendly robot behind the bird."`).
* Parameters like `n` (number of images, typically 1) and `size` (e.g., `1024x1024`), along with other options.
* **Response Processing:** On success, it decodes the base64-encoded edited image, saves it to `OUTPUT_IMAGE_PATH` (e.g., `./output_image_edited.png`), and displays it.
* **Error Handling:** Includes robust error reporting for API failures.
Example API call structure from the notebook:
``` Python
# Input image
files = {
"image": (
os.path.basename(INPUT_IMAGE_PATH),
image_file,
"image/jpeg")
}
# Input prompt and parameters
data = {
"prompt": PROMPT,
"n": 1,
"size": "1024x1024",
"quality": "medium",
"output_compression": 100,
"output_format": "jpeg"
}
# Entra ID Auth
credential = DefaultAzureCredential()
token = credential.get_token("https://cognitiveservices.azure.com/.default")
headers = {
"Authorization": f"Bearer {token.token}"
}
# Image API request
print("Making API call to edit the image...")
response = requests.post(
API_URL,
headers = headers,
files = files,
data = data
)
```
> [!NOTE]
> Feel free to modify the PROMPT string to experiment with different image editing requests.
### Example Input:
This is an example of an input image that could be used for editing in this notebook.
### Example Output:
Below is an example of an edited image, based on an input image and a prompt like "`Add a giant, friendly robot behind the bird.`"
> [!NOTE]
> Your actual output may vary.