# PyPotteryInk: One-Step Diffusion Model for Sketch to Publication-ready Archaeological Drawings

arXiv: https://arxiv.org/abs/2502.06897 · HTML: https://mlsanity.com/paper/2502.06897/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 2 of 2)

- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [Unsupervised Clustering of Roman Potsherds via Variational Autoencoders](https://mlsanity.com/paper/2203.07437/) (2022)

## Built on by (most cited first, 3 of 3)

- [Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models](https://mlsanity.com/paper/2503.11519/) (2025)
- [OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset](https://mlsanity.com/paper/2608.30616/) (2026)
- [PyPottery: an AI-powered end-to-end suite for pottery processing and publication](https://mlsanity.com/paper/2610.02072/) (2026)

## Abstract (arXiv)

Archaeological pottery documentation traditionally requires a time-consuming manual process of converting pencil sketches into publication-ready inked drawings. I present PyPotteryInk, an open-source automated pipeline that transforms archaeological pottery sketches into standardised publication-ready drawings using a one-step diffusion model. Built on a modified img2img-turbo architecture, the system processes drawings in a single forward pass while preserving crucial morphological details and maintaining archaeologic documentation standards and analytical value. The model employs an efficient patch-based approach with dynamic overlap, enabling high-resolution output regardless of input drawing size. I demonstrate the effectiveness of the approach on a dataset of Italian protohistoric pottery drawings, where it successfully captures both fine details like decorative patterns and structural elements like vessel profiles or handling elements. Expert evaluation confirms that the generated drawings meet publication standards while significantly reducing processing time from hours to seconds per drawing. The model can be fine-tuned to adapt to different archaeological contexts with minimal training data, making it versatile across various pottery documentation styles. The pre-trained models, the Python library and comprehensive documentation are provided to facilitate adoption within the archaeological research community.