arXiv 2606.27377 introduces DanceOPD, a unified image-generation framework from ByteDance that addresses the well-known multi-capability conflict in image generation models. The core idea: a "field distillation" scheme that lets different capabilities (text rendering, human generation, aesthetic quality, layout) each maintain their own routing distribution, while sharing the same base model — a "soft partition, hard query" strategy.
Traditional image-generation models, when trained with multi-task joint optimization, often see the strongest task "swallow" the gradient of weaker ones, leading to a Pareto-degraded result. DanceOPD's fix: each task has its own routing distribution, dynamically weighted through a "field" mechanism — high-quality samples from a particular capability get a higher routing weight in that capability's "field." Training only optimizes the field weights, not the underlying network parameters, so the base model stays unchanged.
Experimental results: on six common image-generation benchmarks (text rendering, human generation, layout, aesthetic, etc.), DanceOPD outperforms traditional joint training by an average of 4.7 points, with particularly significant gains in long-text rendering and complex-layout generation. The single-query inference is consistent with the base model and adds no extra compute overhead.
DanceOPD's value is in offering a new paradigm for multi-capability image models: instead of "the strongest model wins," it lets every capability have its own "field" — a more refined solution to the multi-task conflict problem.