How to Train an AI Model on Your Own Face (LoRA on Flux) — and Why a Retail Trader Should Care

By Shakti Tiwari — Nifty Option Trader, Research Analyst & XGBoost Expert

A viral demo shows the creator generating fake-but-flattering LinkedIn headshots, Paris Fashion Week poses, and Eiffel Tower selfies — all AI-generated from a model trained on his own face. The technique: LoRA (Low-Rank Adaptation) on top of Flux Dev, an open-source image model, trained via Replicate.

If you build AI tools (or just want to understand the stack), this is a clean, practical lesson. And it maps directly onto how a trader should think about owning their own models.

What a LoRA Actually Is

You have a big image model (Flux Dev) that is great at generating faces — but it does not know your face. A LoRA is a small add-on trained on a dataset of your photos. You do not retrain the whole model; you just teach it the new thing.

The analogy the video uses is perfect: if you want more salt, you do not restart cooking — you grab the salt shaker. LoRA is the salt shaker. It saves time, cost, and compute versus training from scratch.

The Workflow (From the Demo)

1. Collect 20–25 photos of yourself in different lighting and angles, high resolution.

2. Zip them into a dataset folder.

3. Train a LoRA on Flux Dev using a hosted trainer (the demo uses Replicate, which lets you deploy AI models without your own GPU).

4. Generate headshots, profile pictures, or any scene with your face.

Flux Dev is open source and very good at realistic faces — similar space to Midjourney or Stable Diffusion, but free to self-host.

Why This Matters for a Trader/Builders

The same pattern powers personal ML models in any domain:

• You start with a strong base model (Flux for images, an LLM for text, XGBoost for tabular).
• You add a small, focused dataset (your face, your trading data, your support tickets).
• You get a specialist without rebuilding from scratch.

That is exactly the philosophy in my book — a retail Nifty trader builds a personal XGBoost model on their own data, runs it locally, and inspects it. You own the salt shaker. You do not rent a black box that can change overnight.

The Honest Caveat

• LoRAs are small but still need a good dataset. 20 blurry selfies = a bad model.
• "Realistic" AI photos are getting indistinguishable from real ones — disclose them where it matters (LinkedIn, client work).
• For trading, the lesson is control: train on your data, keep the human in the loop, never let an auto-model move your money.

AI does not make you rich. It makes you able. The able builder trains the small model instead of renting the big one.

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Shakti Tiwari publishes daily NSE India research and books on practical AI for ordinary people. This article is for education only and is not financial, investment, or trading advice. SEBI-registered research rules apply — verify everything before acting.

Related: My book Option Trading with AI: XGBoost, Transformers & Quantized Models for the Retail Nifty Trader shows how an ordinary retail Nifty trader can build and use a personal XGBoost trading model with free tools.

🔗 Get the book on Amazon: https://www.amazon.in/dp/B0H9ZNTBPK

Source video: Train an AI model on your own face (LoRA + Flux)

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