# 语义浏览：图像生成的可控多样性

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-22 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.virxact.com/items/cmqs5kkh6002wslfuxjv7tdu6
- 原文链接：https://arxiv.org/abs/2606.23679

## AI 摘要

现有文本到图像模型虽保真度高，但生成样本单一。现有多样性方法往往产生偶然变化而非有意义的设计选择。本文提出可控多样性方法“语义浏览”，让用户沿可解释变化轴系统遍历结构化图像画廊。核心思路是将语义决策与像素生成分离，直接在文本层面诱导多样性：利用视觉语言模型（VLM）操作完整场景上下文，并通过智能体工作流强制执行与原始提示一致的结构化变化。该方法生成多样且可导航的设计空间，每种变化对应一个可理解的语义决策。

## 正文

Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a single visual interpretation. Existing methods to improve diversity produce outputs driven by incidental variations rather than meaningful design choices. This motivates a new variant of the diversity task where structure is enforced on the generated samples. We introduce a method for controlled diversity that enables Semantic Browsing, where users can navigate structured image galleries and experience creative exploration through a systematic traversal of meaningful, interpretable axes of variation. Achieving this level of semantic control requires a deep understanding of the scene. We exploit the fact that recent text-to-image models are trained on elaborated captions, effectively decoupling semantic decision-making from pixel generation. This enables a paradigm shift: instead of relying on stochastic variation within the text-to-image model, we induce diversity directly at the text level. By leveraging rich textual representations, we allow a Vision Language Model (VLM) to operate on the full scene context. To overcome the generic outputs typical of standard VLMs, we employ an agentic workflow that explicitly enforces structured variation attuned to the original prompt. We demonstrate that our method produces diverse and navigable design spaces where every variation corresponds to a specific, user-understandable semantic decision.