One Attention, One Scale: Phase-Aligned Rotary Positional Embeddings for Mixed-Resolution Diffusion Transformer
This solves a structural problem for diffusion transformer users by enabling high-fidelity mixed-resolution generation without retraining.
The paper tackled the failure of rotary positional embeddings in mixed-resolution diffusion transformers by identifying phase aliasing from linear interpolation, and introduced Cross-Resolution Phase-Aligned Attention (CRPA) as a training-free fix that stabilizes generation and outperforms previous state-of-the-art methods.
We identify a core failure mode that occurs when using the usual linear interpolation on rotary positional embeddings (RoPE) for mixed-resolution denoising with Diffusion Transformers. When tokens from different spatial grids are mixed, the attention mechanism collapses. The issue is structural. Linear coordinate remapping forces a single attention head to compare RoPE phases sampled at incompatible rates, creating phase aliasing that destabilizes the score landscape. Pretrained DiTs are especially brittle-many heads exhibit extremely sharp, periodic phase selectivity-so even tiny cross-rate inconsistencies reliably cause blur, artifacts, or full collapse. To this end, our main contribution is Cross-Resolution Phase-Aligned Attention (CRPA), a training-free drop-in fix that eliminates this failure at its source. CRPA modifies only the RoPE index map for each attention call: all Q/K positions are expressed on the query's stride so that equal physical distances always induce identical phase increments. This restores the precise phase patterns that DiTs rely on. CRPA is fully compatible with pretrained DiTs, stabilizes all heads and layers uniformly. We demonstrate that CRPA enables high-fidelity and efficient mixed-resolution generation, outperforming previous state-of-the-art methods on image and video generation.