A painter with 50 years of figurative work just open-sourced his entire archive. Fine-tune on it.

I am a figurative artist based in New York with work in the collections of the Metropolitan Museum of Art, MoMA, SFMOMA, and the British Museum. I have been painting the human figure since the 1970s. I recently published my catalog raisonne as an open dataset on Hugging Face. Roughly 3,000 to 4,000 documented works spanning five decades, with full metadata, CC-BY-NC-4.0 licensed. My total output is approximately double that and I will keep adding to it. Why this might interest you: This is a single-artist dataset with a consistent primary subject — the human figure — across fifty years and multiple media including oil on canvas, works on paper, drawings, etchings, lithographs, and digital works. The stylistic range within a single sustained practice is significant. It is also one of the few fine art datasets of this size that is properly licensed, artist-controlled, and published with full provenance. Fine-tuning on a dataset this coherent and this large should produce interesting results. I would genuinely love to see what Stable Diffusion generates when trained on fifty years of figurative painting by a single hand. The dataset has had over 2,500 downloads in its first week. I am not a developer. I am the artist. If you experiment with it I want to see what you make. Dataset: huggingface.co/datasets/Hafftka/michael-hafftka-catalog-raisonne