You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs

Abstract: Recently, some works have tried to combine diffusion and Generative Adversarial Networks (GANs) to alleviate the computational cost of the iterative denoising inference in Diffusion Models (DMs). However, existing works in this line suffer from either training instability and mode collapse or subpar one-step generation learning efficiency. To address these issues, we introduce YOSO, a novel generative model designed for rapid, scalable, and high-fidelity one-step image synthesis with high training stability and mode coverage. Specifically, we smooth the adversarial divergence by the denoising generator itself, performing self-cooperative learning. We show that our method can serve as a one-step generation model training from scratch with competitive performance. Moreover, we extend our YOSO to one-step text-to-image generation based on pre-trained models by several effective training techniques (i.e., latent perceptual loss and latent discriminator for efficient training along with the latent DMs; the informative prior initialization (IPI), and the quick adaption stage for fixing the flawed noise scheduler). Experimental results show that YOSO achieves the state-of-the-art one-step generation performance even with Low-Rank Adaptation (LoRA) fine-tuning. In particular, we show that the YOSO-PixArt-$\alpha$ can generate images in one step trained on 512 resolution, with the capability of adapting to 1024 resolution without extra explicit training, requiring only ~10 A800 days for fine-tuning. Our code is provided at https://github.com/Luo-Yihong/YOSO.

• The paper presents YOSO, a model that combines diffusion processes with GANs using self-cooperative learning to enable one-step text-to-image synthesis.
• The method enhances training stability and scalability, allowing seamless adaptation from 512 to 1024 resolution while maintaining competitive image quality.
• Experimental results demonstrate reduced computational requirements and high-fidelity image generation, highlighting its potential for real-time content creation.

Insightful Overview of "You Only Sample Once: Taming One-Step Text-To-Image Synthesis by Self-Cooperative Diffusion GANs"

The paper "You Only Sample Once: Taming One-Step Text-To-Image Synthesis by Self-Cooperative Diffusion GANs" introduces YOSO, a novel generative adversarial network architecture designed to enhance the efficiency and quality of text-to-image synthesis. YOSO integrates the diffusion process with GANs, thereby achieving instantaneous, high-fidelity image generation from text descriptions using a single inference step.

Key Contributions

The authors of this study make several significant advancements in the domain of generative models:

1. Introduction of YOSO: The primary contribution lies in the development of YOSO, which exploits a self-cooperative learning strategy. This design smooths the data distribution through a denoising generator without requiring iterative noise adjustment steps typically seen in traditional diffusion models.
2. Self-Cooperative Diffusion GANs: The study elaborates on the hybridization of Diffusion Models (DMs) with GANs, enhancing training stability and sample quality by leveraging self-cooperative learning. This approach allows for the efficient training of one-step generation models directly from scratch.
3. Scalability and Flexibility: YOSO demonstrates its capability not only as a stand-alone generative model but also as a fine-tuning technique for pre-trained diffusion models. It is shown to extend seamlessly to text-to-image tasks, making it adaptable from resolutions of 512 to 1024 without explicit additional training at the higher resolution.
4. Diffusion Transformer and LoRA Fine-Tuning: Furthermore, the research showcases the implementation of the first diffusion transformer capable of one-step image generation, and the adaptation of Low Rank Adaptation (LoRA) for these tasks, reflecting YOSO's robust flexibility.

Numerical and Experimental Insights

• Efficiency: YOSO achieves computational efficiency by requiring approximately 10 A800 days for training, a noteworthy reduction compared to many conventional models.
• Generative Performance: The model's image generation from scratch and through text-to-image synthesis fine-tuning proved competitive in qualitative and quantitative benchmarks, effectively maintaining image quality comparable to state-of-the-art models even in resource-constrained settings.

Theoretical and Practical Implications

The synthesis of diffusion processes with GANs opens up avenues for deploying rapid, high-quality image generation in practical applications, potentially transforming tasks such as real-time video synthesis, virtual reality environment creation, and user-driven content generation across multimedia platforms. The hybrid model emboldens theoretical perspectives on convergence, training stability, and scalability, offering a blueprint for future advanced generative models.

Speculations on Future Developments

Future developments following this research could entail:

• Adaptation to Larger Models and Datasets: As computational resources grow, scaling YOSO to larger models could further narrow the quality gap between one-step and multi-step synthesis models.
• Integration with Automated Machine Learning Techniques: Leveraging AutoML could calibrate YOSO's parameters and configurations automatically for diverse datasets, optimizing model performance without extensive manual intervention.
• Enhanced Conditional Controls and Customization: Refinement in controllable attributes could yield more precise outcomes, aligning closely with specified text prompts or contextual requirements, making the model usable in highly-customized content generation tasks.

In conclusion, this paper's exploration of integrating diffusion processes with GAN architectures shines a promising light on the future of efficient and scalable generative models, offering a substantial enhancement over current methods in speed, quality, and practicality.