iMontage：统一、通用、高动态的多对多图像生成
iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation
把预训练视频模型的时间连贯先验迁移到多图生成，用最小侵入的适配策略保留原始运动先验，为图像集生成与编辑提供统一框架，可复用于需要跨图一致性的多图/故事板生成任务。
原文依据 repurpose a powerful video model into an all-in-one image generator
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论预训练视频模型习得了生成高质量、时序连贯内容的强大先验。尽管这些模型在时序连贯性上表现出色，其动态性往往受限于训练数据的连续性本质。我们假设，通过将图像数据中丰富且不受约束的内容多样性注入这一连贯的时序框架，可以生成既具有自然过渡、又具备远为广阔动态范围的图像集。为此，我们提出 iMontage，一个统一框架，旨在将强大的视频模型改造为一体化图像生成器。该框架接收并生成可变长度的图像集，统一了广泛的图像生成与编辑任务。为实现这一目标，我们提出一种简洁且最小侵入的适配策略，并辅以量身定制的数据整理流程与训练范式。该方法使模型能够获得广泛的图像操控能力，同时不破坏其宝贵的原始运动先验。iMontage 在多项主流多进多出任务上表现优异，不仅保持强大的跨图像上下文一致性，还能生成动态性非凡、超越常规范围的场景。我们的代码与模型权重将公开发布。
英文原文摘要 1309 字符
Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Our code and model weights will be made publicly available.