Equivariant Image Modeling

• URL: http://arxiv.org/abs/2503.18948v1
• Date: Mon, 24 Mar 2025 17:59:57 GMT
• Title: Equivariant Image Modeling
• Authors: Ruixiao Dong, Mengde Xu, Zigang Geng, Li Li, Han Hu, Shuyang Gu,
• Abstract summary: Current generative models, such as autoregressive and diffusion approaches, decompose high-dimensional data distribution learning into simpler subtasks.<n>We propose a novel equivariant image modeling framework that inherently aligns optimization targets across subtasks.<n>Our approach achieves performance comparable to state-of-the-art AR models while using fewer computational resources.
• Score: 17.87342493584631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current generative models, such as autoregressive and diffusion approaches, decompose high-dimensional data distribution learning into a series of simpler subtasks. However, inherent conflicts arise during the joint optimization of these subtasks, and existing solutions fail to resolve such conflicts without sacrificing efficiency or scalability. We propose a novel equivariant image modeling framework that inherently aligns optimization targets across subtasks by leveraging the translation invariance of natural visual signals. Our method introduces (1) column-wise tokenization which enhances translational symmetry along the horizontal axis, and (2) windowed causal attention which enforces consistent contextual relationships across positions. Evaluated on class-conditioned ImageNet generation at 256x256 resolution, our approach achieves performance comparable to state-of-the-art AR models while using fewer computational resources. Systematic analysis demonstrates that enhanced equivariance reduces inter-task conflicts, significantly improving zero-shot generalization and enabling ultra-long image synthesis. This work establishes the first framework for task-aligned decomposition in generative modeling, offering insights into efficient parameter sharing and conflict-free optimization. The code and models are publicly available at https://github.com/drx-code/EquivariantModeling.

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