生成虚拟试穿图片

借助虚拟试穿功能，您可以生成人物图片，以便虚拟试穿服装产品。您提供一张人物图片和一张服装商品图片，然后虚拟试穿功能会生成一张人物穿着该商品的图片。

在 Colab 中试用虚拟试戴

准备工作

  1. 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.

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

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI API.

    Enable the API

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

    Go to project selector

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the Vertex AI API.

    Enable the API

    8.

  8. 为您的环境设置身份验证。

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

    Python

    如需在本地开发环境中使用本页面上的 Python 示例，请安装并初始化 gcloud CLI，然后使用您的用户凭证设置应用默认凭证。

    1. Install the Google Cloud CLI.

    2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

    3. 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.

    Google Cloud

    REST

    如需在本地开发环境中使用本页面上的 REST API 示例，请使用您提供给 gcloud CLI 的凭证。

      Install the Google Cloud CLI.

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

    如需了解详情，请参阅 Google Cloud 身份验证文档中的使用 REST 时进行身份验证。

    9. 生成图片

    Python

    安装

    pip install --upgrade google-genai

    如需了解详情，请参阅 SDK 参考文档。

    设置环境变量以将 Gen AI SDK 与 Vertex AI 搭配使用： 

    # 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

    在使用任何请求数据之前，请先进行以下替换：

    • REGION：项目所在的区域。如需详细了解支持的区域，请参阅 Vertex AI 上的生成式 AI 位置。
    • PROJECT_ID：您的 Google Cloud 项目 ID。
    • BASE64_PERSON_IMAGE：人物图片的 Base64 编码。
    • BASE64_PRODUCT_IMAGE：商品的 Base64 编码图片。
    • IMAGE_COUNT：要生成的图片数量。接受的值范围为 1 到 4。
    • GCS_OUTPUT_PATH：用于存储虚拟试穿输出的 Cloud Storage 路径。

    HTTP 方法和网址：

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

    请求 JSON 正文： 

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

    如需发送请求，请选择以下方式之一：

    curl

    将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

    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

    将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

    $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
    请求返回图片对象。在此示例中，系统会返回两个图片对象，其中包含两个预测对象（以 base64 编码的图片形式）。 
    {
  "predictions": [
    {
      "mimeType": "image/png",
      "bytesBase64Encoded": "BASE64_IMG_BYTES"
    },
    {
      "bytesBase64Encoded": "BASE64_IMG_BYTES",
      "mimeType": "image/png"
    }
  ]
}