Virtual Try-On on GCP Vertex AI

Name: Virtual Try-On on GCP Vertex AI
Brand: Google DeepMind

Imagen · Google DeepMind

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Last refreshed 2026-05-11. Next refresh: weekly.

Why use Virtual Try-On on GCP Vertex AI?

GCP Vertex AI offers Virtual Try-On with competitive pricing. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.

Input / 1M

Output / 1M

Cache

Not sourced

Batch

Not sourced

Setup recipe

Python + curl

Install

pip install google-cloud-aiplatform

Auth

export GOOGLE_CLOUD_PROJECT=...

Call

import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")

Model ID

virtual-try-on

Request example

import os
import vertexai
from vertexai.generative_models import GenerativeModel

# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("virtual-try-on")
response = model.generate_content("Hello")
print(response.text)

Gotchas

For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.