Towards Controllable Image Generation through Representation-Conditioned Diffusion Models

• URL: http://arxiv.org/abs/2605.27343v1
• Date: Tue, 26 May 2026 17:46:25 GMT
• Title: Towards Controllable Image Generation through Representation-Conditioned Diffusion Models
• Abstract summary: Diffusion models have emerged as powerful tools for high-quality image generation and editing.<n> Conventional approaches rely on conditioning mechanisms, such as text prompts or semantic maps.<n>We explore diffusion models conditioned on representations from a pre-trained self-supervised model.
• Score: 1.9517610560768623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs remains a challenge. Conventional approaches rely on conditioning mechanisms, such as text prompts or semantic maps, which require extensively annotated datasets. In this preliminary work, we explore diffusion models conditioned on representations from a pre-trained self-supervised model. The self-conditioning mechanism not only improves the quality of unconditional image generation, but also provides a representation space that can be used to control the generation. We explore this conditioning space by identifying directions of variations, and demonstrate promising properties in terms of smoothness and disentanglement.

Related papers

• GloTok: Global Perspective Tokenizer for Image Reconstruction and Generation [51.95701097588426]
  We introduce a Global Perspective Tokenizer (GloTok) to model a more uniform semantic distribution of tokenized features.<n>A residual learning module is proposed to recover the fine-grained details to minimize the reconstruction error caused by quantization.<n>Experiments on the standard ImageNet-1k benchmark clearly show that our proposed method achieves state-of-the-art reconstruction performance and generation quality.
  arXiv  Detail & Related papers  (2025-11-18T06:40:26Z)
• Nested Diffusion Models Using Hierarchical Latent Priors [23.605302440082994]
  We introduce nested diffusion models, an efficient and powerful hierarchical generative framework.<n>Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels.<n>To construct these latent variables, we leverage a pre-trained visual encoder, which learns strong semantic visual representations.
  arXiv  Detail & Related papers  (2024-12-08T16:13:39Z)
• LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation [17.169772329737913]
  We present LocRef-Diffusion, a tuning-free model capable of personalized customization of multiple instances' appearance and position within an image. To enhance the precision of instance placement, we introduce a layout-net, which controls instance generation locations. To improve the appearance fidelity to reference images, we employ an appearance-net that extracts instance appearance features.
  arXiv  Detail & Related papers  (2024-11-22T08:44:39Z)
• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks.<n>Our approach enables versatile capabilities via different inference-time sampling schemes.<n>Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• Image Inpainting via Tractable Steering of Diffusion Models [48.16994134964729]
  This paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior.<n>Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs)
  arXiv  Detail & Related papers  (2023-11-28T21:14:02Z)
• Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis [62.07413805483241]
  Steered Diffusion is a framework for zero-shot conditional image generation using a diffusion model trained for unconditional generation. We present experiments using steered diffusion on several tasks including inpainting, colorization, text-guided semantic editing, and image super-resolution.
  arXiv  Detail & Related papers  (2023-09-30T02:03:22Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Conditional Generation from Unconditional Diffusion Models using Denoiser Representations [94.04631421741986]
  We propose adapting pre-trained unconditional diffusion models to new conditions using the learned internal representations of the denoiser network. We show that augmenting the Tiny ImageNet training set with synthetic images generated by our approach improves the classification accuracy of ResNet baselines by up to 8%.
  arXiv  Detail & Related papers  (2023-06-02T20:09:57Z)
• Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC [102.64648158034568]
  diffusion models have quickly become the prevailing approach to generative modeling in many domains. We propose an energy-based parameterization of diffusion models which enables the use of new compositional operators. We find these samplers lead to notable improvements in compositional generation across a wide set of problems.
  arXiv  Detail & Related papers  (2023-02-22T18:48:46Z)
• On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models [27.472482893004862]
  Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. In this work, we explore techniques to condition diffusion models with carefully crafted input noise artifacts.
  arXiv  Detail & Related papers  (2022-05-08T13:18:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.