ComfyUI Creations

I'm a trained artist. Before I touched AI, I spent years drawing, painting, and building my own visual language. When I started working with Stable Diffusion, I didn't want to just type prompts and see what happened. I wanted to bring my own aesthetic into the model itself.

So I trained LoRA files on my own artwork using AI Toolkit. Now when I generate, the model isn't just pulling from its training data. It's pulling from my style, my compositions, the way I see form and color.

The workflows I build use multi-step diffusion. Before the VAE decodes the final image, I run it through different stages with different prompts, different LoRAs, different ControlNets. Sometimes those prompts conflict with each other. That tension is where the interesting stuff happens.

Step one might establish composition. Step two introduces color and texture from a different LoRA. Step three applies edge conditioning or pose guidance. By the time the image finishes, it's been shaped by multiple passes, each one adding intention and chance at the same time.

I'm not looking for perfection. I'm looking for the sweet spot between control and surprise. If I control everything, the work feels dead. If I let the model do whatever it wants, it's generic AI slop.

The multi-step approach lets me guide the generation without locking it down. I set the stage, introduce conflicting ideas, and let the diffusion process negotiate between them. The model makes choices I didn't explicitly make, but within a space I defined.

That's why these pieces don't look like typical AI output. They've been through a more complex flow, shaped by my own trained models and intentional workflow design. The randomness is there, but it's constrained randomness. Chaos with guardrails.