Imagen Product Recontext on GCP Vertex AI

Name: Imagen Product Recontext on GCP Vertex AI
Brand: Google DeepMind

Imagen · Google DeepMind

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

Why use Imagen Product Recontext on GCP Vertex AI?

GCP Vertex AI offers Imagen Product Recontext 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

imagen-product-recontext

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("imagen-product-recontext")
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.