Since the start of my Little Chris project I’ve used ChatGTP to help me with all kinds of aspects of machine learning. From which platforms to use, to analysing images, and setting up a dataset, etc. I’ve named the chatbot Rufus for easy conversation. For the next step in the Little Chris project, I want to build a driftwood-based dataset to train a LoRA focused on an Alexander-inspired property. The endless variate of shapes in driftwood keep amazing me.
Day 1
I’ve discussed with Rufus which properties are present in pieces of driftwood. We decided on the property gradients. Gradients refers to controlled transitions and gradual change [1]. Rufus gave advice on the variety and size of my driftwood collection in order to build a robust dataset such as: clear thick to thin gradient, subtle torsion (slight twisting), direction or sense of movement. These type of fragments will express the gradients property.
Day 2

I went to beach to collect a variety of pieces of driftwood. I found about 50 interesting and beautiful pieces (Fig 1.).
Day 3

I’ve asked Rufus how to photograph the wood for the optimal dataset. “The key is to photograph individual pieces against a neutral background, using soft diffuse light, and capturing each object from different angles […] Aim for around 30–40 distinct pieces, resulting in 80–120 images.” (View Fig. 2.) From this big set Rufus helped me to create a strong subset of 42 images with clear gradients properties. The focus was mainly on thick to thin pieces and subtle shape shifts.
Day 4

Training was harder this time then with the pebble dataset. That set was more extensive and uniform. I actually did two runs. One with the 42 images and captions, the second with a smaller set. Training a model is incremental. It yields different version of the model called checkpoints. You can test for the most useful checkpoint by generating an image from each checkpoint using the same prompt e.g.: woodGradient ceramic container, hand formed clay. WoodGradient being the class name of the model used during training (Fig. 3).

It was hard to determine the over or under training of the LoRA’s because this also depends very much on the prompt used. You can also play with the strength of the LoRA checkpoint. This determines the influence of the LoRA compared to the base model, in this case Stable diffusion XL Base 1.0. I tested three checkpoints systematically with the same seed and prompt but with different strengths (Fig. 4.). It seemed I had to increase the strength to 1.4 (1.0 being default). I decided to use a checkpoint from the first training.
Day 5
I decided to adapt the pebble prompt I used earlier. This useful prompt meant I could lower the LoRA strength to 1.0 or 1.1 for inference of the cups, bowls and dishes. Example prompt: woodGradient hand-formed ceramic bowl, open vessel with subtle gradient shape, wide mouth, shallow interior, driftwood-like material character, clay tones with natural colour transition, tactile hand-built surface, quiet asymmetry, grounded mass, soft daylight, neutral background.

I’m especially pleased with the bowls. The results are very diverse but all are beautiful and functional and they can be created in clay.
Reference
1. Salingaros, Nikos A.. “ch. 11 (19). Christopher Alexander’s 15 Fundamental Properties.” In Unified Architectural Theory: Form, Language, Complexity—a Companion to Christopher Alexander’s “The Phenomenon of Life : the Nature of Order, Book 1”, 125-130. Portland, Oregon and Kathmandu, Nepal: Sustasis Foundation and Vajra Books, 2013.




















