HSE and Yandex Propose Method to Speed Up Neural Networks for Image Generation
A team of scientists at HSE FCS and Yandex Research has proposed a method that reduces computational costs and accelerates text-to-image generation in diffusion models without compromising quality. These models currently set the standard for text-to-image generation, but their use is limited by high computational loads, the company said in a statement.
It is further specified that the developed method—Scale-wise Distillation of Diffusion Models (SwD)—avoids redundant computations during image generation, allowing results to be produced in just 0.3–0.4 seconds.
According to one of the authors, the generation process in diffusion models typically requires dozens of steps involving high-resolution computations. However, in the early stages, only the general structure of the image is formed, and fine details are not yet distinguishable; as a result, some of these computations are redundant. The proposed SwD framework addresses this problem in two ways. First, generation begins at a low resolution and is progressively refined as noise is reduced, eliminating redundant computations in the early stages. Second, the method uses distillation of pre-trained models such as FLUX and Stable Diffusion 3.5, whereby a simpler student model learns to replicate the output of a more complex one, reducing the number of generation steps from dozens to just 4–6.
The authors propose a new loss function for training—Maximum Mean Discrepancy (MMD), which compares how the teacher model 'sees' an image at its internal processing levels with how the student model represents the same image. Unlike traditional approaches, this method does not require auxiliary models, simplifying and accelerating training, the company emphasises. Moreover, MMD can be used as a standalone distillation (knowledge compression) technique: in experiments, the time per training iteration was reduced sevenfold compared to more complex combined approaches.
The new approach reduces generation time from several seconds to just 0.3–0.4 seconds while maintaining visual quality. As a result, SwD makes modern diffusion models faster and more cost-efficient to use, improving their accessibility for practical applications, the company statement says.
The solution is described in a paper to be presented at ICLR 2026, one of the leading conferences on artificial intelligence.
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