文本嵌入插值在连续图像操控中的不合理有效性
The Unreasonable Effectiveness of Text Embedding Interpolation for Continuous Image Steering
AI总结：
本文提出一种无需训练的连续可控图像编辑框架，通过文本嵌入空间的简单操控实现平滑编辑控制，引入弹性范围搜索确保连续性，并在文本条件模态间实现泛化。
摘要：
我们提出一种无需训练的连续可控图像编辑框架，用于文本条件生成模型的测试时间编辑。与依赖额外训练或手动干预的先前方法不同，我们发现仅在文本嵌入空间中进行简单的操控即可实现平滑的编辑控制。给定目标概念（如增强逼真度或改变面部表情），我们使用大型语言模型自动构造一组去偏对比提示对，从中计算生成器文本编码器空间中的操控向量。然后将此向量直接添加到输入提示表示中以沿所需语义轴控制生成。为获得连续控制，我们提出一种弹性范围搜索过程，自动确定有效的操控幅度区间，避免欠操控（无编辑）和过操控（改变其他属性）。在该区间内添加相同向量的缩放版本可产生平滑且连续的编辑。由于我们的方法仅修改文本表示，因此可以自然地跨文本条件模态泛化，包括图像和视频生成。为量化操控连续性，我们引入一个新的评估度量标准，测量编辑强度下的语义变化均匀性。我们比较了不同方法的连续编辑行为，发现尽管其简单和轻量设计，我们的方法在性能上与训练基方法相当，优于其他无需训练的方法。
英文摘要：
We present a training-free framework for continuous and controllable image editing at test time for text-conditioned generative models. In contrast to prior approaches that rely on additional training or manual user intervention, we find that a simple steering in the text-embedding space is sufficient to produce smooth edit control. Given a target concept (e.g., enhancing photorealism or changing facial expression), we use a large language model to automatically construct a small set of debiased contrastive prompt pairs, from which we compute a steering vector in the generator's text-encoder space. We then add this vector directly to the input prompt representation to control generation along the desired semantic axis. To obtain a continuous control, we propose an elastic range search procedure that automatically identifies an effective interval of steering magnitudes, avoiding both under-steering (no-edit) and over-steering (changing other attributes). Adding the scaled versions of the same vector within this interval yields smooth and continuous edits. Since our method modifies only textual representations, it naturally generalizes across text-conditioned modalities, including image and video generation. To quantify the steering continuity, we introduce a new evaluation metric that measures the uniformity of semantic change across edit strengths. We compare the continuous editing behavior across methods and find that, despite its simplicity and lightweight design, our approach is comparable to training-based alternatives, outperforming other training-free methods.
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。
