In advertising, consistency is not a detail: it is a trademark. Think of classic campaigns from Nike, Coca-Cola, or Nubank. You can recognize an ad just by the color palette, the way the typography breathes, or even by the lighting.

With the arrival of artificial intelligence, the challenge has become even more intense. If before art directors had to balance style, typography, lighting, and narrative, today we face the randomness factor of generative models. AI is abundant, but it is also unpredictable. It can open endless possibilities for campaign variations, but without creative direction, each piece runs the risk of looking like part of a different universe.

In this article, we will show how it is possible to use AI to expand campaigns without losing identity, exploring prompt techniques, use of reference images, consistency of characters, and multi-piece workflows.

What visual consistency means in AI campaigns

Consistency is not about copying and pasting the same design everywhere. It is about creating a visual narrative thread that runs through all campaign variations, even when they change in format, proportion, or distribution channel.

Consistency is in the color palette that remains coherent, in the character that appears recognizable in each scene, in the style of photography or illustration that does not dissolve. The audience, upon seeing these variations, should feel that all pieces were born from the same creative direction.

The balance between repetition and variation

The risk of consistency is falling into monotony. A campaign that repeats the same elements too much can become tiresome, while one that varies without control loses its identity. 

The key is to think of consistency as a body: it can change clothes, accessories, or settings, but it is still the same person. That is how strong brands work with their campaigns, not repeating formulas but creating coherent variations.

Why consistency is a challenge in AI

The tools were designed to generate diversity. Small changes in prompts can result in different characters, disconnected visual atmospheres, or contradictory styles. What should be a series of consistent variations can turn into a mosaic of unrelated images.

Brands do not need a single piece but dozens. A post for social networks, an artwork for programmatic media, a video for YouTube, and material for point of sale. Each variation must speak the same language. This is the real challenge, using the abundance of AI without losing the narrative thread that keeps the audience connected.

The risk of “AI Slop”

In recent years, the market began to use the expression “AI Slop” to define that artificial content without identity, plastic, generic. This is the trap of campaigns made in haste or without creative direction. It is easy to be enchanted by the abundance of AI and forget that a brand needs coherence to be recognized.

How to create consistency in AI campaigns

The role of the style guide

Before opening any AI tool, it is essential to have a clear style guide. This means establishing the color palette that will govern the campaign, the types of typography, the lighting mood, the photographic or illustrative language that will be replicated in each piece. 

The strategic use of reference images

One of the most effective ways to ensure consistency is to use a set of reference images as a base.  They function as anchors, reducing the randomness of the models. If the campaign has a KV, it still serves as a base. However, for AI workflows, the safest path is to start from multiple references aligned to the same style and apply them in the tools.

Midjourney, this package turns into a mood board and feeds the Style/Omni Reference. In Visual Electric and Recraft, the set transforms into reusable styles that you apply in series.

set of references, you generate variations that maintain character and aesthetics, exploring angles, scenarios, or small changes in action, always pulling back to the same visual universe. This material becomes the “north” that speeds up approval and ensures coherence.

Prompting techniques to maintain coherence

Prompt engineering is the art of speaking to AI clearly. To maintain consistency, it is essential to fix parameters such as lens type, lighting, color palette, and visual style. 

Once defined, these parameters must be repeated in all variations. It’s like building a visual foundation. From this foundation, it is possible to experiment with controlled variations without losing visual identity.

Character consistency as the center of the narrative

Nothing breaks the connection with the audience more than inconsistent characters. If the face of a model changes in each piece, the sense of identity disappears. The solution is to create a base image and, above all, use the consistency resources that are already part of your flow. 

Midjourney, the Omni Reference helps maintain the same face and features in different scenes without losing style. In Freepik, using Flux or Nano-Banana (Gemini 2.5 Flash Image), you can preserve the appearance and aesthetics while changing context, lighting, or costume. In the prompt, keep it explicit: “same character, same features, identical face.”

Workflows for consistent campaigns

The set of references as a starting point

In any visual project, the KV (master piece) acts as the guide. For AI workflows, this KV can and usually is born from a well-selected set of references. 

It is not only the first approved image but the synthesis of everything the campaign wants to communicate: the atmosphere, the color palette, the identity of the characters, the photographic or illustrative style. 

With AI, this role becomes even more crucial. Without a clear master piece, each variation runs the risk of turning into its own aesthetic. Therefore, details need to be polished during post-production to ensure that the final result is strong enough to sustain the multiplication that will come.

The reference bank as a repository

Once the master piece is defined, it is necessary to organize a reference bank. It stores fixed characters, color palettes, graphics, and compositions that will serve as a guide for each new variation. This bank functions as a living manual of the campaign, always consulted before generating new images.

This bank is dynamic. It grows as the campaign evolves, receiving new additions with each production round. More than a static file, it should be constantly consulted and updated. 

The big mistake of those who work with AI is to believe that it is enough to save some images on the computer. A professional workflow requires organization, separating folders by character, marking approved versions, documenting prompts used, and even recording the seeds for reproduction.

This care creates an ecosystem of references that not only ensures consistency but also speeds up the process. Instead of starting from scratch with each variation, the team has at hand a visual arsenal that guides creative decisions.

Batch production and curation

The next step is to produce variations in batches, changing secondary elements without losing the visual core. This stage usually generates an excess of options, and that’s where curation comes in. 

Selecting the most coherent variations manually is what ensures that the campaign does not dilute. This combination of abundance generated by AI and the human eye of curation is what differentiates consistent campaigns from disposable campaigns.

AI tools to maintain consistency

The choice of tool is a decisive part of the process of ensuring visual consistency in campaigns. Each software carries a different “aesthetic brain,” with its own strengths and limitations. The idea is not to choose just one but to understand the role of each within the workflow: some are perfect for generating static images with style control, while others shine in movement, creating video variations with narrative fluency.

Consistency in images generated with AI

Creating consistent images requires tools capable of communicating well with prompts, working with reference images, and maintaining stable characters and styles. In the market, some have already become standards for creative professionals.

Midjourney

Midjourney continues to be the fastest shortcut to a campaign visual with high aesthetic appeal. Use Style Reference when you want to replicate palette, texture, lighting, or treatment, without copying the subject, and use Character/Omni Reference when the priority is to keep the same person/object in various scenes. 

In parallel, create a project mood board and use it in all prompts to ensure consistency among pieces. To reduce randomness between variations, fix a seed and change only one element at a time (setting, costume, or action).

• Define the focus: style or character.

• Style: apply Style Reference (--sref) with 1–3 images and paste the style block from the mood board into the prompt.

• Character: use Character/Omni Reference (--cref/Omni) with the base face/object image.

• Control: fix --seed [n] to reduce randomness. Maintain the same proportion and change just one element at a time. If you notice deviation, reuse the same reference and the same block and generate again.

Visual Electric

Visual Electric starts with your mood board and transforms it into a reusable project style (the “Custom Style”). In practice, you gather color, texture, light, and composition references within the canvas, save this set as a style, and start to use it in every new generation. When creating the pieces, you select this style, send the references (character or product) as visual references, and maintain the same palette. The result is simple: the mood remains identical from one image to another while you change only the context, framing, or action.

• Assemble the mood board: gather 6–12 references.

• Create the Custom Style: select the mood board set and save it as project style (name it for reuse).

• Activate the style when generating: choose the Custom Style whenever creating new images.

• Use references: send the reference (character or product).

• Fix the palette in Colors: define the campaign colors to maintain the same chromatic treatment.

• Generate in batches with control: change only one element at a time (setting, pose, or action) and maintain the same proportion. If any output is off, reapply the same Custom Style and reference and generate again.

Stable Diffusion and LoRA

Stable Diffusion is the most open and malleable tool. Its learning curve is higher, but the level of control is unmatched. The use of LoRAs (Low-Rank Adaptations) allows you to train specific nuances, be it a character, a lighting pattern, or even a brand aesthetic. When well configured, it is the resource that provides the most predictability and consistency on a large scale.

• Choose a stable checkpoint: use a base model (e.g., SDXL) compatible with the desired campaign aesthetic.

• Train or load a LoRA: upload 15 to 30 images, varying angles and contexts. The LoRA will learn the unique characteristics that need to be maintained.

• Apply the LoRA in the prompts: when generating new images, combine the main model with the LoRA using weights (e.g., <lora:name:0.8>). Adjust the intensity to balance fidelity and creative freedom.

Luma AI

Although it is better known for its strength in video, Luma has also been exploring consistency in three-dimensional images. Its 3D reconstruction technology opens up space to maintain cohesion among different angles of the same object or character, which is essential in product campaigns.

• Capture the master piece in 3D: use the NeRF capture feature (with video or image sequence) to record the object or character from multiple angles.

• Generate the 3D model: the platform automatically reconstructs the geometry and textures, ensuring visual fidelity.

• Produce controlled variations: from the same 3D model, generate renders in different lighting, backgrounds, or framing without losing the identity of the product or character.

Freepik

Freepik has bet on the integration between image bank and AI generation. Its big advantage is allowing consistent variations from commercially validated styles, which facilitates campaigns that need scale without losing coherence. 

Despite gathering various models, the highlight goes to Flux and Nano-Banana (Gemini 2.5 Flash Image), which we use to maintain consistency while varying context. In practice, Flux is great for preserving aesthetics and realism among variations, while Nano-Banana shines in contextual editing while maintaining the identity of the character/product when changing settings, objects, or small details. 

1. Upload your reference: use the master piece or set of references as a base.

2. Choose the model: Flux or Nano-Banana.

3. Generate variations: ask for versions maintaining the style and proportions of the submitted image.

Higgsfield: SOUL ID

The differential of Higgsfield lies in the SOUL ID, a technology aimed at character consistency. This function ensures that faces and expressions are maintained from one piece to another, allowing the construction of narratives in which the same character appears in multiple contexts without losing identity.

1. Creation of the SOUL ID: upload a set of images of the character or main face. The system generates a unique ID that serves as “visual DNA.”

2. Association with the character: confirm and adjust the SOUL ID, ensuring that the captured features are faithful to what needs to be replicated.

3. Usage in prompts: when creating new pieces, add the SOUL ID to the prompt. This ensures that the character maintains face, expression, and proportions consistency.

Flora AI

Flora positions itself as an image creation AI with greater aesthetic sensitivity. Its proposal is to work consistency in an “organic” way, preserving textures, palette, and atmosphere between variations.

1. Upload the reference: upload the central image, such as a character, product, or reference scene.

2. Fix the main element: define in the prompt that the face, object, or item must be preserved in all variations.

Krea AI

Krea AI is one of the most practical tools for consistency, especially in campaigns with volume. Its ability to fix characters and generate controlled variations is a valuable shortcut for art directors.

1. Upload the reference: upload the image of the character or product.

2. Activate the consistency feature: select the Character Consistency function for faces or Product Consistency for objects.

3. Set fixed parameters: define color, visual style, or proportions that must not change from one variation to another.

Consistency in AI-generated videos

If maintaining consistency in images is already a challenge, in videos, the degree of complexity increases. Here, it is not enough to replicate a character or a color palette: it is necessary to ensure fluidity between frames, narrative coherence, and aesthetic that does not break in movement. Some tools are emerging as a reference for this type of work.

Kling AI

Kling AI stands out for creating long takes with good continuity, but the way to anchor references changes depending on the mode. In Elements, you guide the generation with 1–4 images (character, product, setting) to compose a cohesive video from scratch. In Multi-Elements, you upload a reference video and replace, add, or remove items within it. 

Elements (image-to-video with multiple images): ideal for generating a new scene while keeping the character/product coherent throughout the take. You combine 1–4 reference images (e.g., face, car, environment) and describe the action or camera.

Multi-Elements (editing over reference video): ideal for preserving identity in an existing video (replacing actor/object, adjusting costume, inserting product) while maintaining original light, aesthetics, and movement. It is in this mode that you upload video as a base.

1. Choose the right mode: want to generate from scratch with visual references? Use Elements (1–4 images). Want to edit a video preserving the look/movement? Use Multi-Elements (upload the reference video).

2. Define what does not change: character/face, product/form, palette, and light. Repeat the same terms across attempts.

3. Describe the action/camera without altering the style: focus on movement (approach, shift, spin) while maintaining the same “mood” between versions.

4. Generate short takes (3–6 s) and review frame by frame if the face/form have remained identical.

5. Iterate with control: if “it escapes,” resend the same references (Elements) or the same video (Multi-Elements) and reduce the complexity of the action.

Google Veo 3

The Veo 3 is Google’s answer to the consistency challenge. It interprets prompts accurately and allows anchoring generation in image or short clip. In practice, it maintains camera style, lighting, and atmosphere between takes by reusing the same reference and repeating a “style block” (lens, camera movement, light, and palette). 

The official API supports text → video, image → video, and the use of reference images (up to three as assets or one as style), in addition to parameters such as seed and resolution 720p/1080p.

1. Send the reference in image or short clip and define the aspect ratio.

2. Standardize the style block (lens, camera grammar, light, palette) and repeat the same text in all prompts.

3. Generate short takes (3–6 s) and maintain the description of movement without changing the style block.

4. Replicate between takes: reuse the same reference (or referenceImages) and, if it makes sense, fix the seed to reduce distortions in variations. 

Runway Gen-4

The Gen-4 raised the consistency between frames and, with Gen-4 References, you can use up to 3 reference images to preserve characters and locations between scenes. It is possible to save references and call them by @name in the prompts, which facilitates replicating the same visual across multiple pieces without distorting the style.

1. Upload up to 3 references (character, location, style) in Gen-4 References.

2. Save and label the references. Reuse with @name in the prompt.
   Repeat the style block (lens, camera movement, light, palette) in all takes and vary only the context/action.

3. Review: if you notice distortion in face/form/color, adjust the references (combinations) and regenerate while maintaining the same set.

Luma Dream Machine

Luma has built its name by offering consistency in 3D and video, allowing characters and environments to be explored immersively. The Dream Machine is capable of maintaining aesthetic integrity even in complex camera movements.

1. Send the reference: image or short clip of the character/product.

2. Standardize the style (lens, camera movement, lighting, palette, proportion) and repeat the same text in all scenes.

3. Generate short scenes (3–6 s) and review frame by frame; if there are distortions, increase the strength of the reference or simplify the movement.

Seedance

Seedance is one of the most experimental tools, but it already catches attention for the way it maintains fluidity among variations of dance and body movement. Its initial focus is niche, but the learning of body and expression consistency can be applied in advertising narratives.

1. Send the reference: image or short clip of the character/product.

2. Standardize the style in the prompt and repeat in all scenes: image proportion, lighting, palette, and description of costumes/objects that cannot change.

3. Define the movement with progression: start with simple actions (short steps, slow spins) and increase complexity only after validating that features and proportions remain.

4. Review frame by frame: look for signs of deformation. If it occurs, regenerate by reusing the same reference and simplify the movement.

HeyGen

HeyGen became popular for its realistic avatars and, for consistency, shines by allowing the same avatar to speak different scripts in varied scenarios, with lip sync and language control, maintaining narrative continuity across pieces. You can create a custom avatar (with base video and consent video) or choose one of the gallery avatars and replicate it in multiple scenes. 

1. Consent video: frontal light, face fully visible, no filters. It must be the same person who will become the avatar.

2. Create the avatar: upload a base video of the face (neutral, well-lit) and complete the validation.

3. Standardize voice and language: choose the same voice across all pieces. For versions in other languages, enable Lip Sync.

4. Lock framing and style: define frame, light, and background. Save as a preset and reuse it in all videos.

5. Generate variations: change only the text/script (and, if necessary, the language), keeping avatar, voice, and framing.

6. Review quality: check lip sync, face stability, and color/light consistency. If there is deformation, regenerate using the same preset.

Conclusion

Consistency is giving method for the brand language to survive variations. In practice, campaigns gain momentum when the flow is always the same: creative direction first, technique later. 

The style guide sets limits and possibilities, the prompts repeat what needs to be repeated, the tools enter as extensions of the gaze, not as a shortcut to “more of the same.” This is how recognizable characters, faithful products, and an atmosphere that runs through formats without losing its way are maintained.

Take this article to your next job as a mental checklist: refined set of references, standardized parameters, reused reference, controlled variations, and a good round of curation. Having done that, the lingering question is not whether AI will “decharacterize” your campaign, but how far you can take the same identity, with rhythm, coherence, and presence.