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You can’t start a supported Google Imagen 2 workflow today. Google scheduled its Imagen models to shut down on August 17, 2026, and recommends moving to its Gemini image-generation models instead. That date has passed. If you’re following an older tutorial, its Imagen 2 model IDs and controls may no longer work. For new images, use Gemini or Google AI Studio; use Vertex AI’s current image-generation options for a cloud-based production workflow. Google’s migration guidance and current image-generation documentation explain the change.
What Imagen 2 was—and what it wasn’t
Imagen 2 was Google’s text-to-image model generation for creating photorealistic or artistic images from written prompts. A prompt could describe a subject, setting, composition and visual style; the model then generated images subject to Google’s safety controls. Google announced Imagen 2 for Vertex AI on December 13, 2023. At launch, access was for Vertex AI customers through an allowlist or approved-access process, not a universal consumer image-generator website. Google’s announcement provides the historical context.
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Imagen 2 is not the same product as later Imagen versions, nor is it interchangeable with the newer Gemini image-generation experience, often referred to as Nano Banana. “Google image generator” can mean the Gemini consumer app, Google AI Studio, the Gemini API, or Vertex AI. Which one you choose affects setup, controls and availability.
Can you still use Imagen 2?
No—not as a supported current workflow as of August 18, 2026. Google’s Gemini API documentation says Imagen models were deprecated and scheduled to shut down on August 17, 2026, and directs users to Nano Banana models. Old tutorials may still show Imagen 2 model IDs, API calls or console controls, but those can return unavailable-model, model-not-found, access or deprecation errors. Don’t treat a missing Imagen 2 menu as a hidden setting to hunt for: migrate to a currently supported image-generation model.
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Google Cloud documentation also records the deprecation of Imagen versions 1 and 2, while current Vertex AI examples use newer model identifiers and SDK patterns. Check the live documentation for model availability, regions and parameters before adapting an older integration. Vertex AI’s image-generation reference and its current overview distinguish the current workflow from legacy examples.
How the old no-code workflow worked
The following is a historical outline, not a current set of Imagen 2 instructions. The console and model choices have changed, and Imagen 2 is retired.
- Create or select a Google Cloud project, attach billing and enable the relevant Vertex AI API.
- Use a project authorized for the model, then open the image-generation area in Vertex AI Studio.
- Select an available Imagen model, enter a prompt and choose the controls offered for that model, such as image count, aspect ratio or person-generation settings.
- Generate the images, review the results and save or download an output.
The historical experience depended on project access and the controls available for a particular model. The current Vertex AI Studio interface uses newer model examples; don’t assume its labels or menu path match an older screenshot. See the current Vertex AI image-generation overview for the supported route.
How the old API workflow worked
Legacy Imagen integrations typically needed a Google Cloud project with billing, Vertex AI access, authentication, a supported region and a model identifier. A REST call used the Vertex AI prediction method, with a prompt in instances and generation settings in parameters. A historical endpoint pattern was:
POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predictA minimal legacy request structure looked like this:
{
"instances": [
{
"prompt": "A small red boat on a calm lake at sunrise, watercolor illustration"
}
],
"parameters": {
"sampleCount": 1
}
}Depending on the model and request, the response could include Base64-encoded image bytes or direct the output to Cloud Storage. This is a description of the old pattern, not a working Imagen 2 recipe for 2026. Model IDs, permitted image counts, response handling and SDK interfaces varied by model generation. Google’s Vertex AI image-generation guide and prediction API reference document the relevant API concepts.
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Historical Python example—not a current installation recipe
Older Vertex AI Python libraries used a pattern like this:
import vertexai
from vertexai.preview.vision_models import ImageGenerationModel
PROJECT_ID = "your-project-id"
OUTPUT_FILE = "output.png"
vertexai.init(
project=PROJECT_ID,
location="us-central1",
)
model = ImageGenerationModel.from_pretrained(
"imagegeneration@002"
)
images = model.generate_images(
prompt=(
"A red fox sitting in a snowy forest at dawn, "
"cinematic natural light, photorealistic"
),
number_of_images=1,
)
images[0].save(
location=OUTPUT_FILE,
include_generation_parameters=False,
)This preserves the shape of a legacy example; it is not a promise that the library, class or model identifier will work now. Current Google examples use the google-genai SDK and newer model identifiers. New applications should follow the current documentation rather than rebuilding around imagegeneration@002.
What to use instead
Choose the current Google route according to how you want to work:
- Gemini app: Best for casual image generation and conversational refinements without cloud setup. Availability, models, limits and plan requirements vary by country, account and language. Google’s support page describes its availability requirements.
- Google AI Studio and the Gemini API: A practical route for developers prototyping prompts and API calls. You’ll need to check API-key setup, quotas, billing, model availability and terms. The Gemini API uses a content-generation workflow; it is not simply the old Imagen
generate_imagescall with a new model name. - Vertex AI: Suited to production applications and Google Cloud workflows that need project controls, authentication, regional options or operational management. It requires cloud setup, billing and permissions, and model availability and parameters remain model-specific.
For a no-code start, open Gemini or Google AI Studio, choose an available image-capable model, and request an image directly. For example:
Generate an image of a red fox sitting in a snowy forest at dawn,
cinematic natural light, photorealistic, vertical composition.Review the result, refine it conversationally if needed, and download it. For API work, follow the current Gemini image-generation guide for the model and response format available to your account. For Vertex AI, use its current image-generation documentation. A newer model is not necessarily a drop-in replacement: update the method, request parameters and output parsing as well as the model name.
Migration map for an old Imagen 2 integration
| Legacy component | What to change |
|---|---|
| Imagen 2 model ID | Select a currently supported Gemini/Nano Banana image model in the product and region you use. |
generate_images call |
Use the documented current Gemini content-generation workflow where applicable. |
| Dedicated Imagen image response | Adapt your code to handle the current response’s multimodal content parts. |
| Old Studio menu or screenshots | Follow the live Gemini or Vertex AI image-generation interface and documentation. |
| Old price, quota or access assumptions | Recheck current model-specific pricing, quota, region and access requirements. |
Before deploying, test how your application handles image output, safety blocks, missing image parts and storage. Don’t assume a new model has the same defaults or limits as Imagen 2.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Write prompts that communicate a picture
Google’s historical Imagen prompt guidance recommends being clear about the subject, its context and the intended visual style. Build the description with useful visual details rather than piling on vague praise. A practical structure is:
[command] [subject], [action or pose], [setting],
[composition], [lighting], [color palette], [visual style],
[quality or camera details], [aspect ratio]For example:
Generate a photorealistic product photograph of a matte-black travel mug
on a pale stone table, soft morning window light, shallow depth of field,
minimal beige background, centered composition, no people, vertical 4:5 aspect ratio.Start with the main idea, then refine composition, lighting, color, mood and setting one or two details at a time. “A red fox in a snowy forest at dawn, viewed at eye level, soft blue-and-gold light, naturalistic wildlife photography” gives clearer visual direction than “an amazing, beautiful, high-quality fox.” The historical Imagen prompt guide listed a 480-token limit; don’t assume that limit applies to current Gemini image models. See Google’s Imagen prompt guide for the historical advice.
Set expectations for text, details and people
Generated images can misspell text, distort logos, change product details or struggle with hands, repeated patterns and complex spatial instructions. For a poster or product image, generate the artwork without lettering and add precise type in a design tool. If a visual detail matters, inspect every result and try variations rather than assuming the model will reproduce it exactly.
Face and public-figure requests can be limited by safety rules. Vertex AI documentation describes person-generation controls and states that celebrity generation is not allowed. A model can also block a prompt that conflicts with its safety policies. Use a benign, specific prompt and check the current policy for the product you’re using; don’t try to bypass a restriction by disguising the request.
Watermarks, provenance and commercial use
Google documents SynthID or digital watermarking for generated images. This is a provenance measure, not a license, and it does not establish that an image is copyright-free or cleared for a particular commercial use. Check the current terms for the exact Google product, as well as the rights and permissions for any source image, brand, person or other material in your workflow. Avoid relying on generated logos or brand marks as legally approved assets.
For professional work, keep the prompt, generation date, model and product used, source materials, and records of edits. These details help you track how an image was made, but they do not replace a rights review. Google’s Imagen overview discusses watermarking; consult the relevant product terms for usage rights.
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Quick Recap
Troubleshooting old instructions and new workflows
- “Model unavailable” or “model not found” with Imagen 2: The model has passed its scheduled shutdown. Replace the legacy model with a currently supported image-generation model and update the API method and response handling.
- Imagen 2 is missing from the console: That is consistent with the model’s retirement. Use the current Gemini or Vertex AI route rather than searching for a hidden Imagen 2 control.
- Permission denied in Vertex AI: Confirm the project, billing attachment, enabled Vertex AI API, IAM permissions, supported region and access to the selected current model.
- The output is blocked: Review the request against the product’s safety rules, then try a benign and more precise prompt. Not every request is allowed.
- Text in the image is wrong: Generate the visual without text, add typography afterward, or try short wording and several variations. Proofread the final image.
- The same seed gives a different image: Repeatability depends on the precise model version, request parameters and backend behavior. Treat seed-based reproducibility as model-specific, not a universal guarantee.
- The output format differs from old code: Inspect the current API response and update your parser; Gemini content generation can return multimodal content parts rather than the old Imagen response object.
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