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.

利用基于文本的生成式模型生成高保真度图像的表现十分引人注目。然而，通过微调输入来实现对图像的高度控制仍然有很大的挑战。为了解决这一问题，研究者提出了一种称为稳定艺术家的图像编辑方法，它利用语义引导来沿着语义方向控制扩散过程中的不同变量数量，从而实现对图像的微调。通过这种方法，可以灵活地调整图像的构图、样式和整体艺术概念，并深入了解模型所学概念的表现，包括复杂的概念，如 “碳排放”。研究结果表明，在多项任务中，该方法能实现高质量的图像编辑和构图。