UniSlider：让图像编辑滑块拖动更符合感知均匀
UniSlider: Perceptually Uniform Sliders for Continuous Image Editing
做 AI 图像编辑的可以看看，它把滑块拖动和实际感知变化对齐了，300 个样本的基准上加用户研究，比之前方法都稳。
现有生成式编辑方法的滑块只是对强度参数（adapter 系数、prompt 权重或插值因子）的缩放，导致图像随滑块移动出现回退、长时间无变化或突变。UniSlider 将滑块与强度解耦，用 LoRA 在少步编辑骨干上训练，使感知距离随滑块值线性增长。推理时再通过自适应采样对强度做重映射，弥补低秩适配器无法完全均匀的局限，无需额外训练或参数。在包含 300 个连续编辑样本的新基准上，UniSlider 在均匀性、单调性、编辑保真度和身份保持四项指标上超过所有已有方法，并在用户研究中获得偏好。
UniSlider: Perceptually Uniform Sliders for Continuous Image Editing
Sliders provide an intuitive interface for continuous image editing. In current generative approaches, however, the slider is simply a rescaling of the method's strength parameter, such as an adapter coefficient, a prompt weight, or an interpolation factor. This strength relates poorly to perceptual change. The image can partially revert as the slider moves, long stretches of the range produce no visible difference, and short intervals transform the image abruptly. Remapping the strength could fix this uneven pace, but only if the trajectory is monotone, which current methods do not enforce. We therefore distinguish the slider from the strength, and require perceptual distance from the input to grow linearly with the slider value. We introduce UniSlider, a lightweight LoRA trained on a few-step editing backbone so that its strength approximates this ideal slider. Few-step sampling lets us impose this objective in pixel space without intermediate ground truth, and the backbone's output is preserved at full strength. However, a low-rank adapter cannot make the strength fully uniform. Our slider is thus an inference-time remapping of the strength, obtained by adaptive sampling. Since training optmizes to make the trajectory monotone, this remapping closes the remaining gap without extra training or parameters. On a new benchmark of 300 continuous edits evaluating uniformity, monotonicity, edit fidelity, and identity preservation, UniSlider outperforms all prior methods and is preferred in a user study.
