VINO：一个具有交错多模态上下文的统一视觉生成器
VINO: A Unified Visual Generator with Interleaved OmniModal Context
AI总结：
VINO通过统一的视觉生成框架实现图像和视频的生成与编辑，利用交错多模态上下文条件处理，提升多任务视觉创作能力。
摘要：
我们提出了VINO，一个统一的视觉生成器，能够在单一框架内进行图像和视频生成与编辑。与依赖于任务特定模型或为每种模态独立模块不同，VINO使用一个共享的扩散骨干网络，该网络根据文本、图像和视频进行条件处理，从而在单一模型下支持广泛视觉创作和编辑任务。具体而言，VINO将视觉语言模型（VLM）与多模态扩散变换器（MMDiT）相结合，其中多模态输入被编码为交错的条件标记，然后用于引导扩散过程。这种设计支持多参考定位、长形式指令跟随以及在静态和动态内容之间保持一致的身份，同时避免了模态特定的架构组件。为了训练这样的统一系统，我们引入了一个多阶段训练流水线，逐步将视频生成基础模型扩展为一个能够处理图像和视频输入输出的统一、多任务生成器。在多样化的生成和编辑基准上，VINO展示了强大的视觉质量、忠实的指令跟随、改进的参考和属性保持以及更可控的多身份编辑。我们的结果强调了可扩展统一视觉生成的实用路径，并展示了交错、上下文计算作为通用视觉创作基础的前景。
英文摘要：
We present VINO, a unified visual generator that performs image and video generation and editing within a single framework. Instead of relying on task-specific models or independent modules for each modality, VINO uses a shared diffusion backbone that conditions on text, images and videos, enabling a broad range of visual creation and editing tasks under one model. Specifically, VINO couples a vision-language model (VLM) with a Multimodal Diffusion Transformer (MMDiT), where multimodal inputs are encoded as interleaved conditioning tokens, and then used to guide the diffusion process. This design supports multi-reference grounding, long-form instruction following, and coherent identity preservation across static and dynamic content, while avoiding modality-specific architectural components. To train such a unified system, we introduce a multi-stage training pipeline that progressively expands a video generation base model into a unified, multi-task generator capable of both image and video input and output. Across diverse generation and editing benchmarks, VINO demonstrates strong visual quality, faithful instruction following, improved reference and attribute preservation, and more controllable multi-identity edits. Our results highlight a practical path toward scalable unified visual generation, and the promise of interleaved, in-context computation as a foundation for general-purpose visual creation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
