  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.
