生成虛擬試穿圖片

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這項產品或功能是生成式 AI 預先發布版產品，使用時須遵守《Google Cloud 服務專屬條款》的「正式發布前產品條款」，以及《 生成式 AI 預先發布版產品附加條款》。就這項 Generative AI 預先發布版服務而言，客戶可選擇將其用於生產或商業用途，或向第三方揭露生成內容，並可按照《Cloud 資料處理附加條款》所述方式處理個人資料，但須遵守 Google Cloud 使用協議中的義務和限制。正式發布前的產品是按照「原樣」提供，支援範圍可能有限。詳情請參閱推出階段說明。

虛擬試穿功能可生成人物圖像，虛擬試穿服飾產品。你提供人物和服裝產品的圖片，虛擬試裝功能就會生成人物穿著該產品的圖片。

在 Colab 中試用虛擬試裝功能

事前準備

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

1. 為環境設定驗證方法。

   Select the tab for how you plan to use the samples on this page:

   Python

   如要在本機開發環境中使用本頁的 Python 範例，請安裝並初始化 gcloud CLI，然後使用使用者憑證設定應用程式預設憑證。

   安裝 Google Cloud CLI。

   如果您使用外部識別資訊提供者 (IdP)，請先 使用聯合身分登入 gcloud CLI。

   If you're using a local shell, then create local authentication credentials for your user account:

   gcloud auth application-default login

   You don't need to do this if you're using Cloud Shell.

   If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

   詳情請參閱 Google Cloud 驗證說明文件中的「 為本機開發環境設定 ADC」。

   REST

   如要在本機開發環境中使用本頁的 REST API 範例，請使用您提供給 gcloud CLI 的憑證。

   安裝 Google Cloud CLI。

   如果您使用外部識別資訊提供者 (IdP)，請先 使用聯合身分登入 gcloud CLI。

   詳情請參閱 Google Cloud 驗證說明文件中的「Authenticate for using REST」。

   生成圖像

   Python

   安裝

   pip install --upgrade google-genai

   詳情請參閱 SDK 參考說明文件。

   設定環境變數，透過 Vertex AI 使用 Gen AI SDK：

   # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
   # with appropriate values for your project.
   export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
   export GOOGLE_CLOUD_LOCATION=global
   export GOOGLE_GENAI_USE_VERTEXAI=True

   from google import genai
   from google.genai.types import RecontextImageSource, ProductImage
   
   client = genai.Client()
   
   # TODO(developer): Update and un-comment below line
   # output_file = "output-image.png"
   
   image = client.models.recontext_image(
       model="virtual-try-on-preview-08-04",
       source=RecontextImageSource(
           person_image=Image.from_file(location="test_resources/man.png"),
           product_images=[
               ProductImage(product_image=Image.from_file(location="test_resources/sweater.jpg"))
           ],
       ),
   )
   
   image.generated_images[0].image.save(output_file)
   
   print(f"Created output image using {len(image.generated_images[0].image.image_bytes)} bytes")
   # Example response:
   # Created output image using 1234567 bytes
   

   REST

   使用任何要求資料之前，請先替換以下項目：

   • REGION：專案所在的區域。如要進一步瞭解支援的區域，請參閱「Vertex AI 的生成式 AI 服務地區」。
   • PROJECT_ID：您的 Google Cloud 專案 ID。
   • BASE64_PERSON_IMAGE：採用 Base64 編碼的人像圖片。
   • BASE64_PRODUCT_IMAGE：產品圖片的 Base64 編碼圖片。
   • IMAGE_COUNT：要生成的圖片數量。可接受的值範圍為 1 到 4。
   • GCS_OUTPUT_PATH：儲存虛擬試穿輸出內容的 Cloud Storage 路徑。

   HTTP 方法和網址：

   POST https://REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/publishers/google/models/virtual-try-on-preview-08-04:predict

   JSON 要求主體：

   {
     "instances": [
       {
         "personImage": {
           "image": {
             "bytesBase64Encoded": "BASE64_PERSON_IMAGE"
           }
         },
         "productImages": [
           {
             "image": {
               "bytesBase64Encoded": "BASE64_PRODUCT_IMAGE"
             }
           }
         ]
       }
     ],
     "parameters": {
       "sampleCount": IMAGE_COUNT,
       "storageUri": "GCS_OUTPUT_PATH"
     }
   }
   

   如要傳送要求，請選擇以下其中一個選項：

   curl

   注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI，或使用 Cloud Shell，自動登入 gcloud CLI。 您可以執行 gcloud auth list 查看目前有效的帳戶。

   將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

   curl -X POST \
        -H "Authorization: Bearer $(gcloud auth print-access-token)" \
        -H "Content-Type: application/json; charset=utf-8" \
        -d @request.json \
        "https://REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/publishers/google/models/virtual-try-on-preview-08-04:predict"

   PowerShell

   注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。 您可以執行 gcloud auth list 查看目前有效的帳戶。

   將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

   $cred = gcloud auth print-access-token
   $headers = @{ "Authorization" = "Bearer $cred" }
   
   Invoke-WebRequest `
       -Method POST `
       -Headers $headers `
       -ContentType: "application/json; charset=utf-8" `
       -InFile request.json `
       -Uri "https://REGION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/publishers/google/models/virtual-try-on-preview-08-04:predict" | Select-Object -Expand Content

   要求會傳回圖片物件。在本例中，系統會傳回兩個圖片物件，並以 Base64 編碼圖片的形式提供兩個預測物件。

   {
     "predictions": [
       {
         "mimeType": "image/png",
         "bytesBase64Encoded": "BASE64_IMG_BYTES"
       },
       {
         "bytesBase64Encoded": "BASE64_IMG_BYTES",
         "mimeType": "image/png"
       }
     ]
   }
   

   提供意見