Merge pull request #5523 from MicrosoftDocs/main

6/13/2025 AM Publish

Author: Taojunshen

articles/ai-services/openai/includes/dall-e-rest.md

@@ -18,7 +18,7 @@ Use this guide to get started calling the Azure OpenAI in Azure AI Foundry Model
 - <a href="https://www.python.org/" target="_blank">Python 3.8 or later version</a>.
 - The following Python libraries installed: `os`, `requests`, `json`.
 - An Azure OpenAI resource created in a supported region. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
-- Then, you need to deploy a `dalle3` model with your Azure resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+- Then, you need to deploy a `gpt-image-1` or `dalle3` model with your Azure resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
 ## Setup 
 
@@ -41,6 +41,98 @@ Go to your resource in the Azure portal. On the navigation pane, select **Keys a
 
 Create a new Python file named _quickstart.py_. Open the new file in your preferred editor or IDE.
 
+#### [GPT-image-1](#tab/gpt-image-1)
+
+1. Replace the contents of _quickstart.py_ with the following code. Change the value of `prompt` to your preferred text. Also set `deployment` to the deployment name you chose when you deployed the GPT-image-1 model.
+    
+    ```python
+    import os
+    import requests
+    import base64
+    from PIL import Image
+    from io import BytesIO
+    
+    # set environment variables
+    endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+    subscription_key = os.getenv("AZURE_OPENAI_API_KEY")
+    
+    deployment = "gpt-image-1" # the name of your GPT-image-1 deployment
+    api_version = "2025-04-01-preview" # or later version
+    
+    def decode_and_save_image(b64_data, output_filename):
+      image = Image.open(BytesIO(base64.b64decode(b64_data)))
+      image.show()
+      image.save(output_filename)
+    
+    def save_all_images_from_response(response_data, filename_prefix):
+      for idx, item in enumerate(response_data['data']):
+        b64_img = item['b64_json']
+        filename = f"{filename_prefix}_{idx+1}.png"
+        decode_and_save_image(b64_img, filename)
+        print(f"Image saved to: '{filename}'")
+    
+    base_path = f'openai/deployments/{deployment}/images'
+    params = f'?api-version={api_version}'
+    
+    generation_url = f"{endpoint}{base_path}/generations{params}"
+    generation_body = {
+      "prompt": "girl falling asleep",
+      "n": 1,
+      "size": "1024x1024",
+      "quality": "medium",
+      "output_format": "png"
+    }
+    generation_response = requests.post(
+      generation_url,
+      headers={
+        'Api-Key': subscription_key,
+        'Content-Type': 'application/json',
+      },
+      json=generation_body
+    ).json()
+    save_all_images_from_response(generation_response, "generated_image")
+    
+    # In addition to generating images, you can edit them.
+    edit_url = f"{endpoint}{base_path}/edits{params}"
+    edit_body = {
+      "prompt": "girl falling asleep",
+      "n": 1,
+      "size": "1024x1024",
+      "quality": "medium"
+    }
+    files = {
+      "image": ("generated_image_1.png", open("generated_image_1.png", "rb"), "image/png"),
+      # You can use a mask to specify which parts of the image you want to edit.
+      # The mask must be the same size as the input image.
+      # "mask": ("mask.png", open("mask.png", "rb"), "image/png"),
+    }
+    edit_response = requests.post(
+      edit_url,
+      headers={'Api-Key': subscription_key},
+      data=edit_body,
+      files=files
+    ).json()
+    save_all_images_from_response(edit_response, "edited_image")
+    ```
+
+    The script makes a synchronous image generation API call.
+
+    > [!IMPORTANT]
+    > Remember to remove the key from your code when you're done, and never post your key publicly. For production, use a secure way of storing and accessing your credentials. For more information, see [Azure Key Vault](/azure/key-vault/general/overview).
+
+1. Run the application with the `python` command:
+
+    ```console
+    python quickstart.py
+    ```
+
+    Wait a few moments to get the response.
+
+
+
+#### [DALL-E](#tab/dall-e-3)
+
+
 1. Replace the contents of _quickstart.py_ with the following code. Change the value of `prompt` to your preferred text.
 
     You also need to replace `<dalle3>` in the URL with the deployment name you chose when you deployed the DALL-E 3 model. Entering the model name will result in an error unless you chose a deployment name that is identical to the underlying model name. If you encounter an error, double check to make sure that you don't have a doubling of the `/` at the separation between your endpoint and `/openai/deployments`.
@@ -83,6 +175,8 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
 
     Wait a few moments to get the response.
 
+---
+
 ## Output
 
 The output from a successful image generation API call looks like the following example. The `url` field contains a URL where you can download the generated image. The URL stays active for 24 hours.
@@ -99,7 +193,7 @@ The output from a successful image generation API call looks like the following
 } 
 ```
 
-The Image APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see [Content filtering](../concepts/content-filter.md). For examples of error responses, see the [DALL-E how-to guide](../how-to/dall-e.md).
+The Image APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see [Content filtering](../concepts/content-filter.md). For examples of error responses, see the [Image generation how-to guide](../how-to/dall-e.md).
 
 The system returns an operation status of `Failed` and the `error.code` value in the message is set to `contentFilter`. Here's an example:
 

articles/ai-services/speech-service/embedded-speech.md

@@ -248,7 +248,7 @@ embeddedSpeechConfig.setSpeechSynthesisOutputFormat(SpeechSynthesisOutputFormat.
 
 You can find ready to use embedded speech samples at [GitHub](https://aka.ms/embedded-speech-samples). For remarks on projects from scratch, see samples specific documentation:
 
-- [C# (.NET 6.0)](https://aka.ms/embedded-speech-samples-csharp)
+- [C# (.NET 8.0)](https://aka.ms/embedded-speech-samples-csharp)
 - [C# (.NET MAUI)](https://aka.ms/embedded-speech-samples-csharp-maui)
 - [C# for Unity](https://aka.ms/embedded-speech-samples-csharp-unity)
 ::: zone-end

articles/ai-services/speech-service/includes/quickstarts/voice-live-api/realtime-python.md

@@ -353,7 +353,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 
                     if event.get("type") == "session.created":
                         session = event.get("session")
-                        logger.info(f"Session created: {session.get("id")}")
+                        logger.info(f"Session created: {session.get('id')}")
 
                     elif event.get("type") == "response.audio.delta":
                         if event.get("item_id") != last_audio_item_id:

articles/ai-services/speech-service/includes/release-notes/release-notes-sdk.md

@@ -893,7 +893,7 @@ This table shows the previous and new object names for real-time diarization and
 ### Speech SDK 1.16.0: 2021-March release
 
 > [!NOTE]
-> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019. Download it [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
+> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.
 
 #### New features
 
@@ -938,7 +938,7 @@ This table shows the previous and new object names for real-time diarization and
 ### Speech SDK 1.15.0: 2021-January release
 
 > [!NOTE]
-> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019. Download it [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
+> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.
 
 #### Highlights summary
 - Smaller memory and disk footprint making the SDK more efficient.
@@ -992,7 +992,7 @@ This table shows the previous and new object names for real-time diarization and
 ### Speech SDK 1.14.0: 2020-October release
 
 > [!NOTE]
-> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019. Download it [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
+> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.
 
 #### New features
 - **Linux**: Added support for Debian 10 and Ubuntu 20.04 LTS.
@@ -1040,7 +1040,7 @@ Stay healthy!
 ### Speech SDK 1.13.0: 2020-July release
 
 > [!NOTE]
-> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019. Download and install it from [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
+> The Speech SDK on Windows depends on the shared Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.
 
 #### New features
 - **C#**: Added support for asynchronous conversation transcription. See documentation [here](../../get-started-stt-diarization.md).

articles/ai-services/speech-service/includes/spx-setup.md

@@ -12,7 +12,7 @@ ms.author: eur
 Follow these steps to install the Speech CLI on Windows:
 
 1. Install the [Microsoft Visual C++ Redistributable for Visual Studio](/cpp/windows/latest-supported-vc-redist#latest-microsoft-visual-c-redistributable-version) for your platform. Installing it for the first time might require a restart.
-1. Install [.NET 6](/dotnet/core/install/windows?tabs=net60#runtime-information).
+1. Install [.NET 8](/dotnet/core/install/windows?tabs=net60#runtime-information).
 1. Install the Speech CLI via the .NET CLI by entering this command:
 
    ```dotnetcli
@@ -44,7 +44,7 @@ The following Linux distributions are supported for x64 architectures that use t
 
 Follow these steps to install the Speech CLI on Linux on an x64 CPU:
 
-1. Install the [.NET 6](/dotnet/core/install/linux).
+1. Install the [.NET 8](/dotnet/core/install/linux).
 2. Install the Speech CLI via the .NET CLI by entering this command:
 
    ```dotnetcli
@@ -64,7 +64,7 @@ Enter `spx` to see help for the Speech CLI.
 
 Follow these steps to install the Speech CLI on macOS 10.14 or later:
 
-1. Install [.NET 6](/dotnet/core/install/macos#runtime-information).
+1. Install [.NET 8](/dotnet/core/install/macos#runtime-information).
 1. Install the Speech CLI via the .NET CLI by entering this command:
 
    ```dotnetcli

articles/machine-learning/concept-train-model-git-integration.md

@@ -9,7 +9,7 @@ ms.topic: concept-article
 author: Blackmist
 ms.author: larryfr
 ms.reviewer: osiotugo
-ms.date: 06/12/2024
+ms.date: 06/13/2025
 ms.custom: sdkv2, build-2023
 ---
 # Git integration for Azure Machine Learning
@@ -78,7 +78,7 @@ The command displays the contents of your public key file. Copy the output.
 > To copy and paste in the terminal window, use these keyboard shortcuts, depending on your operating system:
 > 
 > - Windows: Ctrl+C or Ctrl+Insert to copy, Ctrl+V or Ctrl+Shift+V to paste.
-> - MacOS: Cmd+C to copy and Cmd+V to paste.
+> - macOS: Cmd+C to copy and Cmd+V to paste.
 > 
 > Some browsers might not support clipboard permissions properly.
 

articles/machine-learning/how-to-manage-workspace-cli.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.author: larryfr
 author: Blackmist
 ms.reviewer: deeikele
-ms.date: 06/17/2024
+ms.date: 06/13/2025
 ms.topic: how-to
 ms.custom: devx-track-azurecli, cliv2
 ---

articles/machine-learning/how-to-manage-workspace-terraform.md

@@ -9,7 +9,7 @@ ms.custom: devx-track-terraform
 ms.author: larryfr
 author: Blackmist
 ms.reviewer: deeikele
-ms.date: 06/25/2024
+ms.date: 06/13/2025
 ms.topic: how-to
 ms.tool: terraform
 ---

articles/machine-learning/how-to-secure-inferencing-vnet.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.reviewer: None
 ms.author: larryfr
 author: Blackmist
-ms.date: 05/31/2024
+ms.date: 06/13/2025
 ---
 
 # Secure an Azure Machine Learning inferencing environment with virtual networks
@@ -56,7 +56,7 @@ To use Azure Kubernetes Service cluster for secure inference, use the following
 1. Create or configure a [secure Kubernetes inferencing environment](how-to-secure-kubernetes-inferencing-environment.md).
 2. Deploy [Azure Machine Learning extension](how-to-deploy-kubernetes-extension.md).
 3. [Attach the Kubernetes cluster to the workspace](how-to-attach-kubernetes-anywhere.md).
-4. Model deployment with Kubernetes online endpoint can be done using CLI v2, Python SDK v2 and Studio UI.
+4. Model deployment with Kubernetes online endpoint can be done using CLI v2, Python SDK v2, and Studio UI.
 
    * CLI v2 - https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/kubernetes
    * Python SDK V2 - https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/kubernetes