EvoStory: a model-agnostic "blueprint-to-render" framework for long-form narrative consistency
Abstract
In recent years, diffusion models have achieved significant progress in single-image generation. However, they still face a 'semantic gap' between linear text and two-dimensional spatial consistency in long-form narrative image generation, leading characters to easily fall into failure modes of 'identity erasure' and 'semantic overriding'. Existing technologies mostly rely on implicit attention or temporary memory, lacking explicit structural control and cross-cycle asset reuse capabilities, and are highly coupled with underlying models. To address these challenges, this paper proposes EvoStory, a Model-Agnostic structured visual narrative generation framework. We innovatively introduce a Narrative-to-Blueprint protocol, transforming ambiguous natural language into an intermediate representation layer containing spatial layouts and high-fidelity character matrices, acting as a Constraint Injector Middleware. The core of this framework adopts a Dual Decoupling strategy: in the temporal dimension, separating characters' inherent identity from stage-specific features to support long-cycle character dynamic evolution; in the logical dimension, using region masks and geometric skeletons to completely separate narrative structure from visual rendering, eradicating feature collapse and fusion issues in complex multi-character interactions. Experiments show that EvoStory can not only precisely drive local SDXL workflows to achieve absolute spatial hard control, but also serve as a…
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