AI Persona Content Pipeline (Stable Diffusion)

The problem

AI-generated content had become a market of its own, and the client wanted a virtual persona in the style of Aitana Lopez (@fit_aitana): a photoreal character whose images could be published every day to build an audience. A stock model can produce a convincing face once; the hard part is producing the same face, in the same aesthetic, on demand. The job was to make that consistency repeatable.

What I built

• Stable Diffusion running through the Automatic1111 web UI, installed and configured on a local machine with an NVIDIA 2080 Ti GPU so training and generation stayed in-house with quick turnaround.
• A collected and preprocessed dataset that defined the persona's look and the target aesthetic.
• A fine-tuned Stable Diffusion model trained on that dataset, using the 2080 Ti for the training runs.
• A daily content-generation routine that produced realistic, engaging images on a fixed cadence to keep the account active and growing.

How it was delivered

I was the sole builder across all four stages: environment setup, data preparation, model training and ongoing generation. Keeping the whole stack on one local GPU kept processing efficient and turnaround short, so changes to the aesthetic could be tested and regenerated the same day.

Outcome

A fine-tuned model that produces consistent, realistic imagery to a defined aesthetic, and a daily content cadence built to grow the persona's follower base. The result is a generative-AI content pipeline rather than a one-off image: the same character, on brand, every day.