Diffusion Transformer models can generate images with remarkable fidelity and detail, yet training them at ultra-high resolutions remains extremely costly due to the self-attention mechanism's quadratic scaling with the number of image tokens. In this paper, we introduce Dynamic Position Extrapolation (DyPE), a novel, training-free method that enables pre-trained diffusion transformers to synthesize images at resolutions far beyond their training data, with no additional sampling cost. DyPE takes advantage of the spectral progression inherent to the diffusion process, where low-frequency structures converge early, while high-frequencies take more steps to resolve. Specifically, DyPE dynamically adjusts the model's positional encoding at each diffusion step, matching their frequency spectrum with the current stage of the generative process. This approach allows us to generate images at resolutions that exceed the training resolution dramatically, e.g., 16 million pixels using FLUX. On multiple benchmarks, DyPE consistently improves performance and achieves state-of-the-art fidelity in ultra-high-resolution image generation, with gains becoming even more pronounced at higher resolutions. Project page is available at https://noamissachar.github.io/DyPE/.

翻译：扩散Transformer模型能够生成具有卓越保真度和细节的图像，然而，由于自注意力机制的计算复杂度随图像令牌数量呈二次方增长，在超高分辨率下训练这些模型仍然极其昂贵。本文提出动态位置外推（DyPE），这是一种新颖的免训练方法，它使预训练的扩散Transformer能够以远超其训练数据的分辨率合成图像，且无需额外的采样成本。DyPE利用了扩散过程固有的频谱演进特性：低频结构早期收敛，而高频细节需要更多步骤才能解析。具体而言，DyPE在扩散过程的每一步动态调整模型的位置编码，使其频谱与当前生成阶段相匹配。这种方法使我们能够以显著超出训练分辨率的分辨率生成图像，例如使用FLUX模型生成1600万像素的图像。在多个基准测试中，DyPE持续提升性能，并在超高分辨率图像生成中实现了最先进的保真度，且分辨率越高，性能增益越显著。项目页面请访问 https://noamissachar.github.io/DyPE/。