OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation

We introduce the Orthogonal Panel-Relative Operator (OPRO), a novel parameter-efficient adaptation method for tiled-panel In-Context Generation (ICG) that utilizes the pre-trained Diffusion Transformers (DiTs). OPRO works by composing learnable, panel-specific orthogonal operators onto the backbone's frozen positional encodings. This design provides two properties: 1) Isometry, which maintains feature geometry to promote stable fine-tuning, and 2) Same-Panel Invariance, which perfectly preserves the model's powerful pre-trained intra-panel synthesis capabilities. We conduct a controlled analysis demonstrating that OPRO's effectiveness is not limited to RoPE but consistently enhances performance across various positional encodings that satisfy orthogonality. By enabling effective panel-relative learning while simultaneously protecting the backbone's core synthesis power, OPRO consistently improves ICG-based instructional image editing methods, including state-of-the-art methods ICEdit.