How to Maintain Consistent Branding Across AI-Generated Content

When you generate an image or video with AI for your brand, the results can often feel a bit generic. This creates a challenge when you’re trying to generate visual assets for your brand. You often need a specific color palette, a particular typography, or a certain art style. And even if you try to pack all that brand information into your text prompt, it usually isn't enough to get the exact look you need.

Fortunately, there are specialized workflows designed to lock in the specific visual variables your brand requires. In this guide, we will walk you through these practical techniques to help you create highly consistent, on-brand AI images and videos.

How to Keep AI-Generated Images on Brand

Being on-brand is a broad concept that covers everything from your tone of voice and core messaging to your font and color palette. For this guide, we are focusing strictly on the visual side of staying on-brand with AI-generated images and videos. Let's start with images and look at the most important visual variables we need to control.

Color Palette

Your brand likely has a specific color palette that needs to remain consistent across all your marketing assets. To show how you can integrate your exact colors into your AI generations, let's look at a fictional example: Doodle. Doodle is an app that lets users draw a character, then uses AI to instantly color it, animate it, and turn it into a short video story.

The Doodle team wants to explore different UI mockups for a new community gallery page featuring user creations. The team already has a predefined brand color palette they need to follow:

To ensure the AI-generated page mockup accurately reflects these specific colors, we can use a color swatch sheet as an image reference, which is a two-step process. First, we create a color swatch sheet, like the one below, using a free tool like Canva's color palette generator.

Next, we upload this color swatch sheet as an image reference in Leonardo (using the button indicated by the arrow below). To give the model the strongest possible direction, make sure your text prompt also explicitly includes the exact HEX codes from your sheet.

We used GPT Image 1.5 for this task because it’s great at generating UI mockups. Check our image models guide to see the creative applications for all the models on Leonardo.

Typography

The fictional Doodle brand kit also includes a specific font to reflect the playful nature of the app: Sniglet. One of the problems with our previous UI mockup is that it doesn’t use this typography.

To fix this, we will use an image reference again. This time, we need to create a simple visual showing the font:

We take this extra step because font replacement remains one of the hardest challenges in AI image generation. While modern models have become very good at rendering legible text, they still struggle to perfectly match specific, named fonts from a text prompt alone. For this task, we switch to the Nano Banana Pro model, as it is the best option for handling high-fidelity text and complex text transformations.

Inside the Leonardo app, we can edit our current image by adding our font visual as an image reference:

Here is the result we got and the editing prompt we used to make the change:

Sometimes the differences between fonts are hard to spot, so it is best to inspect them letter by letter if the AI-generated image is your final asset. While this level of scrutiny isn't strictly necessary for a UI mockup like ours, you will want to double-check the exact lettering if you are publishing the assets (for example, when creating a social media post with AI).

Brand Logo

You will often need to include your brand's logo in the assets you create. While our previous mockup successfully captured the right typography and color palette, it still lacks the official Doodle logo.

To add it to our design, we can edit the previous image by bringing in the logo file directly as an image reference. Here is the result we got and the editing prompt we used to place it:

To learn more about prompting editing models, check our Nano Banana Prompt Guide.

Extra Brand Elements

Every brand is unique, so you might have additional visual assets that need to be included to keep your content on-brand. A common example is a brand mascot (think of the famous Duolingo owl). Let's try to integrate Doodle’s own mascot into our UI mockup.

We will take the same approach and upload the mascot as an image reference. To avoid making the UI mockup feel too crowded, we will instruct the AI to integrate the mascot into the featured video on the page, rather than placing it loosely in the layout. Here is the editing prompt we used and the final result:

How to Keep AI-Generated Videos on Brand

Keeping a video on-brand requires more than just achieving character consistency. We need to hit the right brand notes for the content, color palette, logo, and other specific brand elements. Just like with images, prompts alone are not enough, so we need to rely on specialized workflows. Let’s walk through a practical example.

The Doodle team wants to create a short video to showcase how their app works: turning a sketch into an animation that comes alive. First, let’s take a look at the final video we want to create, and then we will break down the exact steps to build it.

Create an AI Storyboard

Before generating any motion, it’s important to know exactly where we’re heading with the visual narrative. The best way to organize our shots and maintain a clear direction is by building an AI storyboard.

Start/End (S/E) Workflow

For the first shot, the goal is to show the raw sketch transforming into a fully colored scene. To do this reliably, we use a start frame (the sketch) and an end frame (the final colored result) to anchor the video generation.

Once these two frames are created, we upscale them to avoid distortions. Providing the video model with high-definition images prevents the AI from turning low-resolution noise into visual glitches during the animation.

To add a start and end frame on Leonardo, click the icon to the left of the prompt bar:

We generate the motion using the Veo 3.1 model and this prompt:

Next, we use this exact same Start and End Frame workflow to generate our final shot featuring the company logo.

Here is the prompt we used and the result we got using the Kling 3.0 model:

Make sure to check our video models guide to understand the creative applications of each model available on Leonardo.

Image-to-Video (I2V) Workflow

For the close-up shots, we only need to provide the AI with a starting image. This Image-to-Video (I2V) workflow is the most reliable approach because it stops AI from guessing what the subject should look like and forces it to calculate the physics of the movement.

To add a start frame on Leonardo, click the icon to the left of the prompt bar:

Here is the result we got and the prompt we used to guide the motion:

Next, we repeat this same process for the remaining two close-ups.

At the end, we generate shot 5 using the same I2V workflow (we reuse the end frame of shot 1). Once we have all the shots ready, we stitch them together in a free editor, such as Canva Video Editor, and add some copyright-free music to make the entire sequence feel continuous. The result is the on-brand video we saw at the beginning of this section.

Why Brand Consistency Can Be Tricky With AI

Now that we have covered the practical workflows, we have better context to discuss why brand consistency is a challenge in the first place. Maintaining your brand's rules is difficult because of how the underlying technology is built. Let's look at the core challenges that cause AI generations to miss the mark.

The Generic Output Trap

Foundational AI models are typically trained on massive, unfiltered datasets. When you ask AI to generate a commercial asset, the system typically outputs the average of similar images from its training data. The result is a safe, middle-ground image. While it might look perfectly balanced, it lacks the distinct character, specific colors, and unique layout that make your brand stand out.

Identity Drift and Temporal Instability

When transitioning from images to video generation, AI models often struggle to keep things looking exactly the same from one second to the next. Your product (or any other on-brand element) can warp, mutate, or completely change mid-video. If we had not used the Start and End frames for our fictional Doodle animation, the AI might have morphed the magenta mascot into something completely unrecognizable while it was moving.

Missing the Human Touch

With the practical workflows we've learned, you can solve the technical side of problems, but that won’t be enough. To generate truly on-brand assets with AI, perhaps the most important aspect is knowing your brand inside and out.

AI models are excellent at following structural instructions and matching colors, but they lack empathy. The human is the one who has the emotional intelligence to understand that very complex connection between the brand and its customers. It is up to the human to guide the generation process so the final image or video captures the authentic emotional tone of your messaging.

Best Practices for On-Brand AI Content

Mastering specific workflows like image reference, I2V, and Start/End frames gives you a great technical foundation. To make this process repeatable across all your campaigns, you need to establish a few core habits. Here are the best practices for keeping your AI generation process controlled and on-brand.

Use Image References

Text prompts are a great starting point, but they sometimes fall short when you need exact color matches or specific typography. To keep your assets on-brand, rely on image references. By uploading a color palette, a specific font visual, or your official logo, you guide the AI to use your exact visual assets instead of guessing based on a text description.

Use I2V and S/E Workflows in Videos

When you move into video generation, avoid relying entirely on prompts. Asking AI to simultaneously invent your brand's look and calculate the physics of motion often leads to glitches and unstable characters. Instead, use an Image-to-Video (I2V) workflow by starting with a static, on-brand image. For even more control, use a Start/End (S/E) workflow to give the AI exact beginning and concluding frames, eliminating the need for the model to guess where the motion should go.

Upscale Your Images

Before you turn a static image into a video, always upscale it. If you feed a low-resolution image into a video model, the AI might misinterpret random pixel noise as actual physical shapes, leading to strange visual artifacts during the animation. Providing an upscaled starting frame helps a lot with reducing these errors.

Build a Centralized Prompt Library for Your Team

Your brand consistency quickly breaks down if everyone on your team is using different prompts and workflows to generate content. Once you find the exact combination of prompts and workflows that capture your brand identity, document it. Create a shared, centralized prompt library so every team member works from the exact same foundation.

Master Prompt Writing

While we’ve said in this blog that prompting alone is not enough for on-brand content, it is still a very important part of the process. Knowing how to write good prompts goes hand-in-hand with the workflows we’ve covered. If you are looking for a place to start learning, check out our detailed prompt guides:

• How To Write AI Image Prompts
• Veo 3 Prompt Guide
• Nano Banana Prompt Guide
• Kling AI Prompt Guide
• Sora AI Prompt Guide

The Most Important Part of the Process: The Human

Maintaining brand consistency with AI requires a deliberate approach that goes beyond basic text prompts and uses specific, controlled workflows such as image references and Start/End video frames.

But the most critical part of this entire process isn't the technology itself. It's the human behind it. The best results happen when you combine the speed and visual execution of AI with human empathy and strategic direction.