LACON: Training Text-to-Image Model from Uncurated Data
Summary: arXiv:2603.26866v1 Announce Type: cross
Introduction
The landscape of text-to-image generation has transformed dramatically in recent years, primarily due to the advent of expansive, high-quality datasets. Traditionally, these datasets have been curated using a filter-first approach, which often results in the removal of a significant amount of raw data deemed low-quality. This raises a critical question: Is the discarded data truly devoid of value, or does it harbor potential that has yet to be unlocked? This article explores the findings of a new research framework known as LACON (Labeling-and-Conditioning), which offers a fresh perspective on leveraging uncurated data for training text-to-image models.
Understanding LACON
LACON proposes a paradigm shift in the way text-to-image models are trained. Instead of strictly filtering out low-quality data, LACON takes advantage of the full spectrum of data quality by employing quality signals such as aesthetic scores and watermark probabilities. These signals serve as explicit, quantitative condition labels that guide the training process.
Key Features of LACON
- Repurposing Quality Signals: LACON utilizes existing quality measures to classify and incorporate uncurated data effectively.
- Full Spectrum Learning: By embracing both low and high-quality content, LACON allows the generative model to learn from the entire data distribution.
- Improved Generation Quality: Initial results indicate that models trained using the LACON framework outperform those trained solely on filtered datasets, even when operating within the same computational budget.
Benefits of Utilizing Uncurated Data
The findings from the LACON framework suggest that uncurated data holds significant value in the development of text-to-image generation models. Here are some of the benefits:
- Enhanced Model Robustness: Training on a wider variety of data types can help models generalize better to unseen inputs.
- Minimized Data Waste: By not discarding potentially useful data, researchers can maximize the utility of available resources.
- Cost-Effective Training: Leveraging uncurated data can reduce the costs associated with dataset curation and preparation.
Conclusion
The LACON framework introduces a compelling argument for the value of uncurated data in the training of text-to-image models. By re-evaluating the traditional filter-first paradigm and harnessing the potential of low-quality data, LACON opens new avenues for research and innovation in generative modeling. As the field progresses, embracing this approach may lead to more robust, versatile, and high-quality generative models that can shape the future of visual content creation.