Amateur Photographer Trains First Klein 9B LoRA on AMD Strix Halo, Sparks AI Art Community Interest

Amateur Photographer Trains First Klein 9B LoRA on AMD Strix Halo, Sparks AI Art Community Interest

In a groundbreaking experiment that bridges personal artistry and cutting-edge AI development, amateur photographer Mikkoph has successfully trained his first Klein 9B LoRA model using an AMD Strix Halo system running Linux. The project, documented in a detailed Reddit post on r/StableDiffusion, represents one of the earliest public demonstrations of consumer-grade AI fine-tuning on AMD’s ROCm platform, challenging the industry’s dominant NVIDIA-centric paradigm.

Mikkoph’s goal was simple yet profound: to create a personalized generative model that replicates his unique photographic style. Using a curated dataset of 55 original photographs—images for which he holds full copyright—he trained a Low-Rank Adaptation (LoRA) model on the Klein 9B base architecture. The result, now publicly available on Hugging Face as mikkoph-style, is designed to be activated with the trigger phrase "by mikkoph" during image generation.

Technically, the project leveraged SimpleTuner—a lightweight, open-source training framework—and ROCm nightly 7.12, AMD’s open-source compute platform for GPU acceleration. Mikkoph configured the model with a learning rate of 4e-4 (a typo he later admitted to), a rank of 16, and 1,000 training steps across 55 images. He enabled EMA (Exponential Moving Average) and used Flow 2, a setting in SimpleTuner optimized for fine detail capture. The entire training process took approximately six hours, a remarkably short duration given the complexity of the task.

According to Mikkoph’s post-mortem analysis, the resulting LoRA performs well in text-to-image generation, consistently producing visuals that reflect his signature lighting, composition, and tonal palette. However, it showed limited effectiveness in image-to-image editing unless the source image was a controlled studio shot. He noted that the final checkpoint after 1,000 steps yielded a more subtle effect than the one at 600 steps, requiring higher strength values (above 1.0) to achieve noticeable results—a common phenomenon in LoRA fine-tuning where overfitting can dilute impact.

The broader implications of this project extend beyond aesthetics. Mikkoph emphasized that his motivation was ethical: by using only his own copyrighted images, he avoided the legal and moral ambiguities that plague many AI training datasets. This approach aligns with growing calls in the AI community for transparency and consent in model training, as highlighted by recent policy debates at the European Parliament and the U.S. Copyright Office.

Technical observers have praised the project for its accessibility. "This is a textbook example of democratized AI," said Dr. Lena Torres, an AI ethics researcher at MIT. "Using off-the-shelf hardware and open-source tools, a non-engineer created a professional-grade model. It proves that you don’t need a GPU cluster to personalize AI—you just need intention and the right documentation."

While the LoRA’s performance in img2img remains limited, its compatibility with other style models suggests potential for hybrid creative workflows. Users on Reddit have already begun combining "by mikkoph" with other LoRAs to produce novel visual hybrids, indicating that even imperfect models can serve as catalysts for innovation.

As AI-generated art continues to blur lines between creator and tool, Mikkoph’s experiment stands as a quiet manifesto: personal style, when ethically preserved and technically refined, can become a powerful force in machine learning. His journey—from shutterbug to model trainer—may well inspire a new generation of artists to train not just what AI sees, but what it feels.