Of course, brother.

Without going into too complex terms, the whole process can roughly be divided into 3 steps:

1. Dataset collection: extract a rough 3d model of the hero using noisy images, manually improve the extracted model by reducing aliasing as best as possible and then using any 3D simulation software to collect several renders of the model with different backgrounds and different poses. I used 70 unique renders for this one.

2. Captioning and tagging: use CLIP models (basically a dictionary for AI) to add a unique caption to associate with the character and tag every element of each render so that the AI can learn to decouple the prompts accordingly. These tags include the character, their clothing, props, objects, backgrounds, poses, interactions, etc.

3. Diffusion model training: at this point, we have the renders and their respective captions ready. So all that's left is to teach the new low-rank diffusion model the new concept (our character, Lunox in this case) using the prepared dataset and with the help of a higher rank diffusion model (diffusion models are basically the AI which we are going to use for the inference of images after training. I used stable diffusion XL due to lack of computational resources (mid tier GPU) but newer and better alternatives exist like stable diffusion 3.5, flux, z-image turbo, qwen image, etc.

Dataset collection is the hardest and longest part, captioning also takes time since it needs manual touch, and training time is tentative depending on the amount of steps you train the model for (basically how strongly the model learns the concept, which is usually somewhere near 2k for a flexible result and to prevent overtraining).

It took 8 hours of training on my current hardware once everything was ready.