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Diffusion Self-Guidance for Controllable Image Generation (2306.00986v3)

Published 1 Jun 2023 in cs.CV, cs.LG, and stat.ML

Abstract: Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides greater control over generated images by guiding the internal representations of diffusion models. We demonstrate that properties such as the shape, location, and appearance of objects can be extracted from these representations and used to steer sampling. Self-guidance works similarly to classifier guidance, but uses signals present in the pretrained model itself, requiring no additional models or training. We show how a simple set of properties can be composed to perform challenging image manipulations, such as modifying the position or size of objects, merging the appearance of objects in one image with the layout of another, composing objects from many images into one, and more. We also show that self-guidance can be used to edit real images. For results and an interactive demo, see our project page at https://dave.ml/selfguidance/

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Authors (5)
  1. Dave Epstein (9 papers)
  2. Allan Jabri (17 papers)
  3. Ben Poole (46 papers)
  4. Aleksander Holynski (37 papers)
  5. Alexei A. Efros (100 papers)
Citations (183)
View on Semantic Scholar

Summary

An Overview of "Diffusion Self-Guidance for Controllable Image Generation"

The paper presents a novel approach to refining the control over image generation via large-scale generative models, focusing specifically on diffusion models. The proposed method, termed "self-guidance," surpasses the current limitations posed by textual descriptions by leveraging internal signals within pretrained diffusion models to steer the image generation process. This strategy enables the nuanced manipulation of object properties such as shape, location, and appearance, offering an elevated level of user control and flexibility.

Methods and Contributions

The paper’s core contribution lies in the self-guidance mechanism, which harnesses the rich representations encoded in the attention maps and activations of pretrained diffusion models. Unlike conventional methods that require external models or fine-tuning with additional paired data, self-guidance operates without auxiliary models, yielding control by reorienting intermediate features within the diffusion framework.

  1. Object Property Manipulation: The authors meticulously extract properties like object shape, position, and size from the attention maps. These properties are leveraged as guidance terms integrated into the sampling process, enabling deterministic adjustments of targeted image components.
  2. Versatile Image Manipulations: The self-guidance approach supports a wide array of sophisticated image manipulations. Whether merging appearances of objects from distinct images or altering specific object attributes within real images, the technique showcases a degree of control previously unattainable with diffusion models.
  3. Compositionality and Real Image Editing: The method’s compositional nature allows for innovative manipulations, such as blending the layout of one image with the appearance of another. Notably, the strategy extends to handling real images, laying groundwork for edits that preserve intrinsic scene structure while altering specific objects.

Numerical Results and Claims

The paper substantively corroborates its claims through various empirical demonstrations. The authors provide examples of complex image adjustments, quantitatively supported by visual outcomes that illustrate profound control over object properties in diverse contexts. The experiments establish that manipulating only intermediate representations—without further model training—can deliver precise and intentional changes.

Implications and Future Directions

Practically, the implications of self-guidance are substantial, especially for creative industries seeking precise image generation tooling. Theoretically, the approach paves the path for exploring how internal model representations can further facilitate nuanced control over generative outputs. Although the current application focuses on visual domains, the underlying principles might be adapted for multi-modal generative models in future research.

Moving forward, potential research avenues include enhancing disentanglement between interacting objects and refining control over more abstract attributes. Additionally, investigating the effects of intervening on different layers and resolutions could yield further insights into model interpretability and robustness.

In summary, the introduction of diffusion self-guidance marks a meaningful stride in image generation, promising enriched control and offering valuable insights into the operational depth of diffusion models. The methodology extends an important toolset for both theoretical inquiry and practical application, catalyzing further developments in the field of AI-driven generative modeling.

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