This thesis addresses the problem of generating large, seamless bark textures of spruce logs. The problem arises from two facts. First, a log is cylindrical along its circumference, and only a narrow strip can be photographed at high resolution. Second, diffusion models, the current state of the art for image synthesis, are typically trained on patches of 512 x 512 pixels, while the full circumference spans several thousand. The goal is to fine-tune a generative model that composes the full circumferential texture from limited captures, closes it seamlessly into a torus, and preserves a natural distribution of classes: bark, knots, and mechanical damage.
We propose a two-stage pipeline. In the first stage, a Markov Random Field generates a coarse semantic map that controls the global class proportions. In the second stage, an existing latent diffusion model based on the DiffInfinite approach is adapted and fine-tuned on our data to produce a high-resolution image conditioned on that map. We extend the approach with modular patch indexing and periodic blending during decoding, so that the generated texture tiles seamlessly under the chosen geometry, either toroidal or cylindrical. The U-Net architecture itself is unchanged.
Together with the Biotechnical Faculty we captured 140 photographs of 15 spruce logs in the field and built a dataset of 270 bark image and mask pairs. We fine-tuned the diffusion model for 200.000 steps; broad augmentation of the training patches lowered FID and KID
substantially, and lighter augmentation in the final phase lowered them further. We also observed that after a few thousand steps the loss function no longer reflects visual quality progress, while FID and KID computed on patches continue to track it well. The generated texture was wrapped seamlessly around a demonstration cylindrical log in Blender.
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