Self-Evaluation Unlocks Any-Step Text-to-Image Generation
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.
69541fd4869a8627b452c8ca
Xin Yu
69541fd4869a8627b452c8cb
Xiaojuan Qi
69541fd4869a8627b452c8cc
Zhengqi Li
69541fd4869a8627b452c8cd
Kai Zhang
69541fd4869a8627b452c8ce
Richard Zhang
69541fd4869a8627b452c8cf
Zhe Lin
69541fd4869a8627b452c8d0
Eli Shechtman
69541fd4869a8627b452c8d1
Tianyu Wang
69541fd4869a8627b452c8d2
Yotam Nitzan
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
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.
Self-E is a novel self-evaluating text-to-image model trained from scratch that supports any-step generation and combines local learning with self-driven global matching to achieve high quality even at low step counts.
Self-Evaluating Model (Self-E) Flow Matching model self-evaluation mechanism score estimates dynamic self-teacher diffusion or flow models local supervision distillation-based approaches pretrained teacher instantaneous local learning self-driven global matching few-step generation any-step text-to-image model unified framework