Direct Answer: Scaling Assets for Vertical Ad Placement
To resize and outpaint a 1:1 product image into a 9:16 format for Meta or TikTok ads, you need a workflow that intelligently fills the negative space. In the RelayMe platform at app.relayme.ai, you can achieve this by using an outpainting node that keeps your central product intact while extending the background to fit vertical mobile screens.
This approach is intended for creative teams who need high-volume, platform-specific adaptations without relying on manual Photoshop work. A critical limitation is that outpainting models require a stable, high-quality base image; adding complex or branded elements via outpainting may require multiple iterations to ensure visual consistency.
Place the outpaint node after the source image; the canvas layout is the node-based workflow.
Constructing Your No-Code Outpainting Workflow
The RelayMe visual editor allows you to drag and drop nodes to create your ad production line. Start by uploading your source product image, then connect it to an image generation node using models like Nano Banana Pro (Gemini 3 Pro Image) or GPT Image 2.5 Flare. By adjusting the output dimensions to 9:16 in the model parameters, the AI understands the need to synthesize new, harmonious background pixels.
You can manage your production costs by tracking credits per task. As of September 20, 2026, pricing varies by channel, with some GPT-Image2 channels listing rates at $0.10 per image. Because these costs are transparent in your task history, you can audit your spend and optimize your workflow for efficiency before scaling your campaign.
For product-specific inputs and review steps, continue with the product image workflow.
Hosted product-photo tools are a different package. See the Pebblely alternative and the Photoroom alternative for dated public prices.
Model Selection and Performance Optimization
Not all models handle aspect ratio expansion equally. Within RelayMe, you can 'fan out' a single source image prompt to multiple nodes—such as Nano Banana 2 and GPT Image 2.5 Sunburst—simultaneously. This allows you to compare how different architectures interpret the peripheral data of your product photo.
If your product requires precise detail retention, opt for models known for higher fidelity in specific textures. Always review the output against your brand guidelines. Because RelayMe is a workflow platform, you can save your preferred settings as a template, ensuring that every subsequent product image follows the exact same sizing and stylistic constraints.
Check the current pricing and credit rules before selecting a model; a dated example is not a current quote.
Workflow Strategy: When to Stop Outpainting
My professional recommendation: Do not use outpainting for images containing complex, text-heavy packaging or logos that must remain legible and unwarped. AI generation is best suited for extending atmospheric backgrounds, textures, or simple surfaces. If your product packaging is the primary hook, use a simple background extension rather than a generative model that might attempt to 'hallucinate' or interpret text on your packaging.
This limitation ensures you maintain professional control over your creative assets. By using a 'mask' or 'canvas' approach to place your high-quality, pre-cut product image onto an AI-generated vertical background, you maximize both the speed of the RelayMe workflow and the integrity of your brand's visual identity.
Save the aspect-ratio settings as a template using the AI image workflow guide, and check outpaint credit use on pricing.
Managing Costs and Scaling Your Ad Library
Scale your operations by utilizing the RelayMe credit system, which offers tiered plans starting from the Starter plan at $19/month for 20,000 credits. For teams managing large ad libraries, the Pro plan provides 110,000 credits for $90/month. Because credits in your pay-as-you-go top-up pool never expire, you can purchase capacity during low-volume periods and deploy it during peak campaign windows.
Always inspect the task history record to see how specific model-channel combinations impact your credit balance. If you find one model delivers better results at a lower cost, update your primary template node. This creates a data-driven feedback loop where your ad production becomes both faster and more cost-efficient over time.