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Large, text-conditioned generative diffusion models have recently gained alot of attention for their impressive performance in generating high-fidelityimages from text alone. However, achieving high-quality results is almostunfeasible in a one-shot fashion. On the contrary, text-guided image generationinvolves the user making many slight changes to inputs in order to iterativelycarve out the envisioned image. However, slight changes to the input promptoften lead to entirely different images being generated, and thus the controlof the artist is limited in its granularity. To provide flexibility, we presentthe Stable Artist, an image editing approach enabling fine-grained control ofthe image generation process. The main component is semantic guidance (SEGA)which steers the diffusion process along variable numbers of semanticdirections. This allows for subtle edits to images, changes in composition andstyle, as well as optimization of the overall artistic conception. Furthermore,SEGA enables probing of latent spaces to gain insights into the representationof concepts learned by the model, even complex ones such as 'carbon emission'.We demonstrate the Stable Artist on several tasks, showcasing high-qualityimage editing and composition.

The Stable Artist: Steering Semantics in Diffusion Latent Space