語義瀏覽：影像生成中的可控多樣性

摘要

現代的文本到圖像模型在視覺保真度與提示遵循方面表現卓越。然而，這種嚴格遵循的代價是生成樣本的多樣性不足：產出的結果往往收斂於單一的視覺詮釋。現有的提升多樣性方法所產出的內容多由隨機變異驅動，而非有意義的設計選擇。這促使我們重新定義多樣性任務，要求對生成樣本施加結構化約束。我們提出一種可控多樣性的方法，實現「語義瀏覽」：使用者能瀏覽結構化的圖像庫，透過系統性地探索有意義、可解讀的變異軸線，體驗創意探索。要達到這種語義層級的控制，需要對場景有深入理解。我們利用近期文本到圖像模型訓練於詳盡標題的特性，有效將語義決策與像素生成脫鉤。這帶來了典範轉移：不再依賴文本到圖像模型內的隨機變異，而是直接在文本層級引發多樣性。透過利用豐富的文本表徵，我們讓視覺語言模型能夠操作完整的場景脈絡。為克服標準視覺語言模型常見的通用輸出，我們採用一種代理工作流程，明確強加與原始提示相呼應的結構化變異。我們證明，此方法能產出多樣化且可導航的設計空間，其中每個變異都對應到使用者可理解的具體語義決策。

English

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.