Title: T2I-ConBench: Text-to-Image Benchmark for Continual Post-training
Primary area: datasets and benchmarks
Keywords: Continual Learning, Continual Post-training, Text-to-image Generation, Diffusion Model

Abstract: Continual post‑training adapts a single text‑to‑image diffusion model to learn new tasks without incurring the cost of separate models, but naïve post-training causes forgetting of pretrained knowledge and undermines zero‑shot compositionality. We observe that the absence of a standardized evaluation protocol hampers related research for continual post‑training. To address this, we introduce **T2I‑ConBench**, a unified benchmark for continual post-training of text-to-image models. T2I-ConBench focuses on two practical scenarios, *item customization* and *domain enhancement*, and analyzes four dimensions: (1) retention of generality, (2) target-task performance, (3) catastrophic forgetting, and (4) cross-task generalization. It combines automated metrics, human‑preference modeling, and vision‑language QA for comprehensive assessment. We benchmark ten representative methods across three realistic task sequences and find that no approach excels on all fronts. Even joint “oracle” training does not succeed for every task, and cross-task generalization remains unsolved.