稳定艺术家：在扩散潜空间中引导语义
The Stable Artist: Steering Semantics in Diffusion Latent Space
AI总结：
本文提出 Stable Artist 图像编辑方法，通过语义引导 SEGA 沿可变语义方向控制扩散过程，实现细粒度图像编辑、构图风格调整及潜空间概念探测。
摘要：
大型文本条件生成扩散模型近来因其仅从文本生成高保真图像的出色性能而备受关注。然而，以一次性方式实现高质量结果几乎不可行。相反，文本引导的图像生成需要用户对输入进行许多细微调整，以便迭代地雕琢出所设想的图像。但是，对输入提示的细微改动往往会导致生成完全不同的图像，因此艺术家的控制力在粒度上受到限制。为了提供灵活性，我们提出了 Stable Artist，这是一种能够对图像生成过程进行细粒度控制的图像编辑方法。其核心组件是语义引导（SEGA），它沿着可变数量的语义方向引导扩散过程。这使得可以对图像进行细微编辑、改变构图和风格，以及优化整体艺术构思。此外，SEGA 还能对潜空间进行探测，以深入了解模型所学概念的表示，甚至是诸如“碳排放”之类的复杂概念。我们在多个任务上展示了 Stable Artist，展现了高质量的图像编辑与合成能力。
英文摘要：
Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity. To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as 'carbon emission'. We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition.
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。
