La lógica que seguí cuando pensé el diseño de este website/portfolio fue que el contenido y los casos sean los protagonistas. Que si algo iba a generar impacto eso iba a ser el case study o el artículo en sí, y no el propio website que le sale a competir con animaciones locas o templates raros.
Por eso me mantuve en una estética simple, neutra, blanco y negro. Todo ordenadito prolijo, cuidando las jerarquías, fácil de navegar y encontrar las cosas. Esa misma lógica conceptual quería seguir para el diseño de mis propias business cards. Blanco y negro, Helvetica, un QR chiquito y listo.
Todo bien de un lado. Y el otro lado vacío? Mmm🤔. Ademas hacerlas de papel así normalito? Había que encontrar una solución.
1. Diseñando el personaje
2. Guion
3. Caracterizando mi personaje como Rocky Balboa
4. Building Locations
4. Creating Frames with pj
5. Animando escenas: Hablar de los modelos Kling + Element. detallado.
6. Gonna Fly AI theme music
7. Resultado final: el video
2. Character Configurator
Moving on to the Character Configurator, which allows us to fully customize our model by selecting Gender, Age, Ethnicity, Body, Hair, Shirt, and Pants.
I decided to define these specific characteristics, but you can make the configurator as detailed as you want. You could define eye color, whether the character wears a watch or necklace, accessories, and much more. The possibilities are virtually endless.
Credits to the Hinge Health Image Generator, from which I adapted several descriptions, including Gender, Ethnicity, Body, Hair, and more.
To build this configurator, we essentially create a Text Array Node for each characteristic. For example, Gender is an Array containing the items Man and Woman. This Array node is then connected to a List Selector, allowing us to quickly switch between the available options.
Each List Selector — Gender, Age, Ethnicity, Body, Pants, Shirt, and Hair — is then grouped under our Variable 2: Character Configurator, which is connected to our Variable Master Prompt.
4. Location Picker
For this step, I decided to keep things simple by describing the location, and it actually gave me great results. For example, I used “Concrete skatepark with bowls and ledges”, and the location was represented pretty much exactly as I had envisioned it.
Of course, at this stage, if we wanted full control over the location, we could also use the image description process we used to define the visual aesthetic or to describe the shoes. We could even define an entire set of variables dedicated only for the location. The key is that everyone can expand the workflow and add as much detail as they consider relevant to their own campaign.
Ok, returning to our flow, the Location Picker outputs an Array containing all the items we define. We then connect it to a List node, which defines our Variable 4: Location Selector. And we leave it floating around. No, kidding!, this one is also connected to our Variable Master Prompt.
5. Pose Definer
Now we reach the Pose Definer, which for me is one of the key components of this flow. This is the point where we start directing our model and translating the concept of our photographic shot. This is when our subject tells the story.
As we’ve been doing throughout the workflow, we upload a reference image, a selfie or a sketch on a napkin and connect it to an LLM node together with a prompt such as:
Analyze the provided image and extract ONLY the model’s pose. Do NOT: describe visual style, describe model clothes, describe model shoes, model skin tone, model gender.
Produce a clean, standalone description that can be used to generate new images in the same model position but with an entirely new aesthetic.
The goal here is to separate the pose from everything else happening in the reference image. We don’t want the LLM to carry over any visual characteristics from the reference — only the position, body orientation, gesture, and overall composition of the model.
This gives us a clean pose description that can later be combined with our previously defined aesthetic, model, shoes, and location, giving us much more control over the final shot.
If you’ve made it this far, you already know that Variable 5: Pose is, of course, connected to our Variable Prompt Master.
Final: Variable Master Prompt
Finally! This guy has been talking about variables throughout the entire case. We’ve made it to the node where all the other nodes converge. The final node. The chosen one. We’ve reached the Variable Prompt Master.
So, to recap, here are all the characteristics we generated for each of our variables throughout the workflow:
- Variable 1: Aesthetics Builder
- Variable 2: Character Description
- Variable 3: Shoe Description
- Variable 4: Location Generator
- Variable 5: Pose Definer
This is where everything we’ve built throughout the workflow finally comes together. Each variable brings a specific piece of information to the final prompt, giving us control over every key aspect of the shot while keeping the whole system modular and easy to iterate.
The final step is to put our Variable Master Prompt into action and see what happens. We connect it to an image model, in my case I used Gemini 3 (Nano Banana Pro) which gave me very good results at a cost of 11 credits per “shoot.”
In addition to our variables, the prompt is completed with general instructions. For example, we tell the model to act as a professional photographer with 20 years of experience, along with other guidelines that help define the overall output.
By combining all five variables, we end up with a highly configurable and scalable flow that can be adapted to different AI-generated photography productions depending on what we need.
Here are some of the shots I generated for my sneaker campaign:
Conclusions
Workflow Cost in Credits
The reality is that the credit cost throughout the workflow is extremely low. The LLMs responsible for describing the characteristics of our variables cost 1 credit each. Basically nothing. So the only significant cost comes from the image generation itself: 11 credits per image when using Nano Banana Pro — which is still very affordable.
Of course, there’s another factor that depends entirely on how well you ‘feed’ and define each variable. If, for example, you don’t do a good job defining the visual aesthetic of your campaign and simply throw in a few random reference images, the final results probably won’t be the best.
And that’s when you’ll start wasting credits on unnecessary iterations. But if you take the time to properly define each variable, this workflow becomes practically free — especially considering the level of control, flexibility, and scalability it gives you.
Final Takeaways:
I asked AI whether AI itself can replace a professional photography production. The answer was:
Yes, artificial intelligence can replace a photography production for many commercial and low-cost tasks, such as e-commerce catalogs, advertising backgrounds, and product mockups. However, it cannot completely replace it when real product fidelity, genuine human experiences, or the capture of spontaneous moments are required.
I can imagine some photographers looking at the breakdown of my workflow and horrifying themselves. And honestly, they might have a point. I’m probably leaving 500,000 variables up to chance compared to what happens in a professional photography shoot.
But that’s also the point: I’m an art director, and a photographer is a photographer. My job is to design and unify the overall visual concept — the scenarios, objects, aesthetic, and visual language of the project.
The photographer, on the other hand, is responsible for bringing that shot to life, applying their knowledge of camera operation, composition, framing, lighting, shadows, lenses, and all the technical and artistic decisions that come with producing a real photograph. In that sense, a professional photographer will undoubtedly be able to define and control many more aspects of the actual shot than I can through this workflow, leaving far fewer variables to chance.
So, can an AI photography production speed up my work and save my client money? Yes. Are the results on the same level as a real professional photography production? No.
And I think that’s ultimately the most important takeaway. AI-generated photography is another tool in the creative toolbox. The value lies in knowing when it makes sense to use it, when it doesn’t, and what compromises you’re willing to make depending on the project, the budget, and the final objective.
Each of us will have to decide when — and when not — to use a workflow like this to generate AI photography for advertising campaigns.
