自我评估解锁任意步骤文生图生成

摘要

我们提出自评估模型（Self-E），这是一种新颖的文本到图像生成从头训练方法，支持任意步长推理。该模型在采用流匹配式数据学习机制的同时，创新性地引入了自评估机制：通过当前分数估计值对自身生成样本进行动态评估，实现自我教学功能。与传统扩散模型或流模型不同，该方法不依赖通常需要多步推理的局部监督机制；与基于蒸馏的方法相比，它无需预训练教师模型。这种即时局部学习与自驱动全局匹配的结合，成功弥合了两种范式间的鸿沟，实现了支持极低步长的高质量文本到图像生成模型。在大规模文本到图像基准测试中，Self-E不仅在少步生成方面表现卓越，在50步推理时亦可与最先进的流匹配模型媲美。研究进一步发现，其性能随推理步长增加呈单调提升趋势，使得单一模型既能实现超快速少步生成，又能完成高质量长轨迹采样。据我们所知，Self-E是首个支持任意步长的从头训练文本到图像模型，为高效可扩展生成提供了统一框架。

English

We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectively serving as a dynamic self-teacher. Unlike traditional diffusion or flow models, it does not rely solely on local supervision, which typically necessitates many inference steps. Unlike distillation-based approaches, it does not require a pretrained teacher. This combination of instantaneous local learning and self-driven global matching bridges the gap between the two paradigms, enabling the training of a high-quality text-to-image model from scratch that excels even at very low step counts. Extensive experiments on large-scale text-to-image benchmarks show that Self-E not only excels in few-step generation, but is also competitive with state-of-the-art Flow Matching models at 50 steps. We further find that its performance improves monotonically as inference steps increase, enabling both ultra-fast few-step generation and high-quality long-trajectory sampling within a single unified model. To our knowledge, Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.

自我评估解锁任意步骤文生图生成

Self-Evaluation Unlocks Any-Step Text-to-Image Generation

摘要

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