LifeLike Sketcher leverages a pretrained base model trained on billions of images from all over the internet. It maps concepts into a latent space—a shared multidimensional space where visual ideas and their textual descriptions are represented as unified values. This extensive training enables it to create polished art and realistic photographs of virtually anything.
Fine-tuning on real photographs of my own hand-drawn images ensures the model accurately captures the physical properties of the material and its medium—such as how ink pools on a whiteboard surface, how Sharpie ink is absorbed by paper, and how stroke speed or pressure can affect thickness and density of chalk strokes. This ongoing training process over two years has been dedicated not just to making the model skilled at replicating a particular style, but ironically, to teach it how to be a less refined artist—rushed, imperfect, talentless, exactly as needed for mentalism or magic performances. Conventional diffusion models are predisposed to produce polished, professional-looking art—mimicking a style reminiscent of famous artists or real photographs. In contrast, LifeLike Sketcher's custom training ensures it can also produce casual, even 'bad' drawings that are convincing enough to pass as genuine scribbles in a live performance. It's this painstaking approach that truly sets LifeLike Sketcher apart from standard text-to-image solutions.
LifeLike sketcher is a diffusion model. This means it starts with random noise and iteratively "denoises" it. Each iteration assesses whether the model is moving closer or further from the desired result, like a "warmer" or "colder" game, resulting in the final sketch. This process runs on state-of-the-art NVIDIA H100 GPUs in the cloud, delivering results in roughly 3-5 seconds.