Towards Controllable Image Generation through Representation-Conditioned Diffusion Models

Problem
This preprint addresses the challenge of controllable image generation using diffusion models, which have shown significant promise in generating high-quality images. Traditional conditioning methods, such as text prompts or semantic maps, necessitate large, annotated datasets, limiting their applicability. The authors propose a novel approach that leverages representations from a pre-trained self-supervised model to enhance the controllability of image generation without the need for extensive annotations.

Method
The core technical contribution is the introduction of representation-conditioned diffusion models. The authors utilize a self-supervised model to derive representations that serve as conditioning inputs for the diffusion process. This self-conditioning mechanism allows for the exploration of a representation space that facilitates control over the generated images. The authors identify directions of variation within this space, which are used to manipulate the output in a smooth and disentangled manner. The architecture details, including the specific diffusion model employed and the self-supervised model used for representation extraction, are not disclosed in the abstract, but the methodology emphasizes the importance of the representation space in guiding the generation process.

Results
The authors demonstrate that their approach significantly enhances the quality of unconditional image generation compared to baseline diffusion models. While specific quantitative results are not provided in the abstract, the authors claim to achieve promising properties in terms of smoothness and disentanglement of the generated images. The effectiveness of the representation-conditioned diffusion model is likely evaluated against standard benchmarks in image generation, although these details are not explicitly mentioned in the abstract.

Limitations
The authors acknowledge that their work is preliminary and may not yet be fully validated through extensive empirical evaluation. They do not discuss potential limitations such as the scalability of the self-supervised model, the generalizability of the learned representations across different datasets, or the computational overhead introduced by the conditioning mechanism. Additionally, the reliance on a pre-trained model may limit the approach’s applicability in scenarios where such models are not available or where domain-specific representations are required.

Why it matters
This work has significant implications for the field of controllable image generation, particularly in reducing the dependency on large annotated datasets. By leveraging self-supervised representations, the proposed method opens avenues for more flexible and efficient image generation techniques. The ability to manipulate generated images through identified directions in the representation space could enhance applications in creative industries, automated content generation, and interactive design tools. Furthermore, this approach may inspire future research into more robust conditioning mechanisms that can be applied across various generative models.

Authors: Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel Eilertsen
Source: arXiv:2605.27343
URL: https://arxiv.org/abs/2605.27343v1