TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows

Zhenglin Cheng, Peng Sun, Jianguo Li, Tao Lin

Keywords: few-step generation, text-to-image generation, multi-modal generative models

TL;DR: We propose a simple yet effective framework for training one-step generative models without the demand of pretrained models for distillation or standard adversarial training, which is helpful when training large few-step generative models.

Abstract:

Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation.
These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)).
While various few-step methods aim to accelerate the inference, existing solutions have clear limitations.
Prominent distillation-based methods, such as progressive and consistency distillation, either require an iterative distillation procedure or show significant degradation at very few steps (< 4-NFE).
Meanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models.
To this end, we propose TwinFlow, a simple yet effective framework for training 1-step generative models that bypasses the need for distillation from pre-trained models and avoids standard adversarial training, making it ideal for building large-scale, efficient models.
On text-to-image tasks, our method achieves a GenEval score of 0.83 in 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework).
**Notably, we demonstrate the scalability of TwinFlow by transforming Qwen-Image-20B---the current largest open-source multi-modal generative model---into an efficient few-step generator**. With just 1-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and DPG-Bench benchmarks, reducing computational cost by $100\times$ with minor quality degradation.
Our code and models will be made publicly available.

Primary Area: generative models

Submission Number: 24044