LACON: Training Text-to-Image Model from Uncurated Data

The success of modern text-to-image generation is largely at-tributed to massive, high-quality datasets. Currently, these datasets arecurated through a filter-first paradigm that aggressively discards low-quality raw data based on the assumption that it is detrimental to modelperformance. Is the discarded bad data truly useless, or does it hold un-tapped potential? In this work, we critically re-examine this question. Wepropose LACON (Labeling-and-Conditioning), a novel training frame-work that exploits the underlying uncurated data distribution. Insteadof filtering, LACON re-purposes quality signals, such as aesthetic scoresand watermark probabilities as explicit, quantitative condition labels.The generative model is then trained to learn the full spectrum of dataquality, from bad to good. By learning the explicit boundary betweenhigh- and low-quality content, LACON achieves superior generation qual-ity compared to baselines trained only on filtered data using the samecompute budget, proving the significant value of uncurated data. Ourproject page is available at https://zhiyangliang.github.io/LACON.