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The Stable Artist: Steering Semantics in Diffusion Latent Space

Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, Kristian Kersting

TL;DR

The paper tackles the challenge of achieving precise, interactive edits in text-conditioned diffusion models. It introduces Stable Artist and its Semantic Guidance (SEGA), which steers the diffusion process along multiple semantic directions in the latent space, enabling fine-grained edits, multi-concept composition, and latent-space probing without masks or fine-tuning. Key contributions include a formal SEGA framework with one-direction and multi-direction variants, warm-up and momentum mechanisms, and demonstrations of local/global edits, style transfer, and latent-space concept arithmetic, with qualitative comparisons showing advantages over related methods. The work highlights the potential for deeper understanding of how concepts are represented in diffusion models and discusses societal implications, including both beneficial control and risks of misuse. Overall, SEGA provides a powerful, controllable, and interpretable approach to image editing and style transfer in diffusion models, with open avenues for concept discovery and privacy-aware applications.

Abstract

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.

The Stable Artist: Steering Semantics in Diffusion Latent Space

TL;DR

The paper tackles the challenge of achieving precise, interactive edits in text-conditioned diffusion models. It introduces Stable Artist and its Semantic Guidance (SEGA), which steers the diffusion process along multiple semantic directions in the latent space, enabling fine-grained edits, multi-concept composition, and latent-space probing without masks or fine-tuning. Key contributions include a formal SEGA framework with one-direction and multi-direction variants, warm-up and momentum mechanisms, and demonstrations of local/global edits, style transfer, and latent-space concept arithmetic, with qualitative comparisons showing advantages over related methods. The work highlights the potential for deeper understanding of how concepts are represented in diffusion models and discusses societal implications, including both beneficial control and risks of misuse. Overall, SEGA provides a powerful, controllable, and interpretable approach to image editing and style transfer in diffusion models, with open avenues for concept discovery and privacy-aware applications.

Abstract

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.
Paper Structure (14 sections, 9 equations, 7 figures, 1 algorithm)

This paper contains 14 sections, 9 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Stable Artist
  3. Guided Diffusion
  4. Semantic Guidance
  5. One Direction.
  6. Beyond One Direction.
  7. Interactively Steering Semantics
  8. Interacting with Visual Concepts
  9. Fine-grained Image Editing & Composition
  10. Latent Image Optimization
  11. Qualitative Comparison
  12. Broader Impact on Society
  13. Conclusions
  14. Acknowledgments.

Figures (7)

  • Figure 1: The Stable Artist eases the generation of images via Stable Diffusion by (iterative) guidance. Original image (left images) generated using the prompt on top of image pair. Guidance prompt (bottom of image pair) and result (right). (Best viewed in color)
  • Figure 2: Semantic guidance (SEGA) applied to the image 'a portrait of a king' using 'king'$-$‘male’$+$‘female'. (Best viewed in color)
  • Figure 3: Image editing, performed using semantic guiding of the Stable Artist. All images generated from the same initial noise latent using the prompt 'a picture of a car'. Editing prompts denoted in blue. Arrows indicate the editing direction. The Stable Artist can act on explicit edits for local and global changes of the image, as well as abstract editing concepts. (Best viewed in color)
  • Figure 4: The Stable Artist offers strong control over the latent space and can gradually perform edits at the desired strength. All images generated from the same initial noise latent using the prompt 'a crowded boulevard'. Editing prompts denoted in blue and are gradually increased in strength from left to right and top to bottom. (Best viewed in color)
  • Figure 5: Style transfer performed by the Stable Artist. All images generated from the same initial noise latent using the prompt 'a house at a lake'. Editing prompts denoted in blue. Arrows indicate the editing direction. (Best viewed in color)
  • ...and 2 more figures