Towards Controllable Image Generation through Representation-Conditioned Diffusion Models
Representation-conditioned diffusion models using DINO embeddings achieve high-quality, controllable image generation on LSUN and CelebA datasets.
Key Findings
Methodology
This paper proposes a framework for conditioning diffusion models on image representations extracted from a pretrained self-supervised model, DINO. Specifically, input images are encoded into a 768-dimensional representation space via the DINO encoder. Then, a latent diffusion model (LDM) employs a conditional denoising U-Net that takes these representations as conditioning inputs to guide image generation in the latent space. The approach builds upon and extends Representation Conditioned Generation (RCG) by integrating MoCo v3 encoder and MAGE generator concepts within the LDM framework. Training is conducted on LSUN Churches and CelebA datasets, utilizing a pretrained VAE for latent compression. The representation space is explored via perturbations and linear interpolations to identify semantic directions, enabling smooth and partially disentangled control over generated images.
Key Results
- On the LSUN Churches dataset, the Representation Conditioned Diffusion Model (RCDM) maintains image quality and content consistency at perturbation strengths λ > 0.4, whereas Diffusion Inversion exhibits significant degradation under the same conditions, demonstrating RCDM's superior robustness and stability.
- In the CelebA dataset, linear interpolation in the representation space yields semantically smooth transitions in generated images with RCDM, outperforming Stable Diffusion and Diffusion Inversion methods, which show blurring or abrupt changes at interpolation midpoints.
- Using both supervised attribute mean vectors and unsupervised PCA, RCDM enables controllable modifications of image attributes such as blonde hair and baldness. Although disentanglement is less pronounced than in GANs, variations in forehead size, hair length, and background color indicate promising interpretability.
Significance
This work is the first to systematically evaluate self-supervised representation-conditioned diffusion models for controllable image generation and quality enhancement. Unlike conventional conditioning approaches relying on text prompts or semantic maps requiring extensive annotations, RCDM leverages unsupervised learned representations, reducing data dependency. The demonstrated smooth semantic transitions and partial disentanglement in the representation space provide new insights into latent space design for diffusion models, advancing the field toward more flexible and precise image generation. This has significant implications for both academic research and industrial applications where annotation costs and control granularity are critical challenges.
Technical Contribution
The technical contributions include: 1) innovatively employing pretrained self-supervised DINO representations as conditioning inputs to improve unconditional diffusion model generation quality; 2) constructing an operable latent space enabling semantic direction control characterized by smoothness and partial disentanglement; 3) integrating conditional denoising U-Net within the latent diffusion model framework for efficient training across multiple datasets; 4) systematically exploring semantic directions via supervised attribute averaging and unsupervised PCA, validating the representation space's controllability and interpretability, thereby extending diffusion model capabilities beyond prior methods.
Novelty
This study is the first to deeply investigate the use of self-supervised visual representations (DINO) as conditioning variables for diffusion models to achieve controllable image generation. Unlike prior approaches dependent on text or semantic annotations, the fundamental innovation lies in leveraging unsupervised learned representations to enable high-quality, controllable generation without additional labels, filling a research gap in latent space design for diffusion models.
Limitations
- The semantic disentanglement and interpretability of the representation space remain limited compared to GAN-based models, with attribute correlations impacting precise control.
- Experiments are confined to two datasets (LSUN Churches and CelebA), leaving generalization and cross-domain adaptability unverified.
- The approach relies on pretrained representations and does not realize end-to-end unconditional generation training, limiting model autonomy and flexibility.
Future Work
Future directions include enhancing disentanglement in the representation space to improve semantic direction independence and interpretability; exploring fusion of multiple self-supervised encoders and multimodal representations to boost control capabilities; developing end-to-end unconditional generation training for greater autonomy; and applying the method to practical tasks such as image editing and data augmentation to broaden diffusion model applications.
AI Executive Summary
Diffusion models have recently revolutionized generative modeling by enabling high-quality image synthesis and editing. However, controlling these models to produce specific outputs remains challenging, especially without relying on large annotated datasets. Traditional conditioning methods often depend on text prompts or semantic maps, which require extensive labeling and complex prompt engineering, limiting flexibility and precision. Addressing this challenge, the authors propose a novel framework that conditions diffusion models on image representations extracted from a pretrained self-supervised model, DINO. This approach leverages unsupervised learned features to guide image generation, reducing dependency on annotations while enhancing controllability and quality.
The method involves encoding images into a 768-dimensional representation space via the DINO encoder. These representations condition a latent diffusion model (LDM) employing a conditional denoising U-Net to generate images in the latent space. Training is performed on two diverse datasets—LSUN Churches and CelebA—using a pretrained variational autoencoder (VAE) for latent compression. This design not only improves unconditional generation quality but also provides a structured, operable latent space for semantic control.
Technically, the authors explore the representation space through perturbations and linear interpolations, revealing smooth semantic transitions and partial disentanglement. Experimental results demonstrate that RCDM maintains image quality under higher perturbation levels compared to Diffusion Inversion. Moreover, linear interpolation between representation vectors yields semantically coherent image morphing superior to Stable Diffusion and Diffusion Inversion. Both supervised attribute mean vector addition and unsupervised principal component analysis (PCA) identify meaningful semantic directions, enabling controllable attribute modifications such as hair color and style.
The significance of this work lies in its departure from annotation-heavy conditioning approaches, introducing a self-supervised representation-based conditioning paradigm that enhances both quality and controllability. This contributes to fundamental understanding and practical capabilities in diffusion model latent space design, with potential impact across academic research and industry applications where flexible, annotation-free control is desirable.
Looking forward, the authors suggest improving disentanglement and interpretability of the representation space, integrating multimodal and stronger encoders, achieving end-to-end unconditional training, and applying the framework to image editing and data augmentation. While current limitations include limited disentanglement, dataset scope, and reliance on pretrained representations, this study lays foundational groundwork for controllable, high-quality diffusion-based image generation using self-supervised representations.
Deep Analysis
Background
The field of generative modeling has evolved rapidly from Generative Adversarial Networks (GANs) to diffusion models. GANs, such as StyleGAN, are known for their structured and disentangled latent spaces enabling fine-grained image editing, but suffer from training instability and mode collapse. Diffusion models have emerged as a robust alternative, offering stable training and high-fidelity image synthesis. Despite their success, diffusion models lack the well-structured latent spaces characteristic of GANs, limiting precise control over generated content. Conventional conditional diffusion approaches rely heavily on text prompts or semantic maps, requiring extensive labeled data and sophisticated prompt engineering. Meanwhile, self-supervised learning (SSL) models like DINO and MoCo v3 have demonstrated the ability to learn rich visual representations without labels. This paper leverages such SSL representations as conditioning inputs to diffusion models, aiming to enhance generation quality and controllability without annotation dependencies.
Core Problem
While diffusion models excel in generating high-quality images, their latent spaces are not inherently structured or disentangled, posing challenges for precise control over image attributes. Existing conditional diffusion methods depend on text or semantic labels, which are costly to obtain and often insufficient for fine-grained control. Moreover, unconditional diffusion models typically produce lower quality outputs. The core problem addressed is how to construct a well-structured, operable latent space for diffusion models based on unsupervised learned representations, enabling both improved unconditional generation quality and controllable image synthesis without reliance on annotated data.
Innovation
The paper introduces several key innovations:
- �� Utilizing pretrained self-supervised DINO visual representations as conditioning variables for diffusion models, eliminating the need for text or semantic annotations.
- �� Integrating a conditional denoising U-Net within the latent diffusion model framework to guide generation based on these representations, enhancing unconditional generation quality.
- �� Exploring the representation space via perturbations and linear interpolations to identify semantic directions exhibiting smooth transitions and partial disentanglement.
- �� Employing both supervised attribute averaging and unsupervised PCA to discover meaningful semantic directions, enabling controllable attribute manipulation.
These innovations collectively establish a new paradigm for controllable diffusion-based image generation grounded in unsupervised representation learning.
Methodology
- �� Pretrained Encoder: Use the DINO self-supervised vision transformer to encode input images into 768-dimensional representation vectors capturing semantic content.
- �� Latent Compression: Employ a pretrained variational autoencoder (VAE) with KL-divergence loss to compress images into a latent space, reducing computational complexity.
- �� Conditional Diffusion Model Training: Train a latent diffusion model (LDM) with a conditional denoising U-Net that takes noisy latent variables and corresponding DINO representations as conditioning inputs.
- �� Datasets: Train and evaluate on LSUN Churches and CelebA datasets to cover diverse semantic domains.
- �� Representation Space Exploration:
- Perturbation: Add Gaussian noise to representation vectors and observe effects on generated image quality and content.
- Interpolation: Linearly interpolate between two representation vectors to generate smooth semantic transitions in images.
- �� Semantic Direction Discovery:
- Supervised: Compute mean representation vectors for images with specific attributes (e.g., blonde hair) and add these to reference representations to modify attributes.
- Unsupervised: Apply Principal Component Analysis (PCA) to representation vectors to identify principal semantic directions.
- �� Evaluation: Qualitative assessment of image quality, smoothness of transitions, and controllability of attribute modifications.
Experiments
Experiments utilize two publicly available datasets: LSUN Churches, containing large-scale church building images suitable for structural semantic generation, and CelebA, comprising over 200,000 face images with rich attribute annotations enabling semantic direction exploration. The pretrained DINO encoder extracts 768-dimensional representations from these datasets. A pretrained VAE compresses images into latent space for the latent diffusion model. The conditional denoising U-Net is trained with noisy latent variables and corresponding DINO representations. Baselines include Diffusion Inversion and Stable Diffusion. Experimental protocols involve varying perturbation strengths, performing linear interpolation in representation space, and discovering semantic directions via supervised attribute means and unsupervised PCA. Evaluation focuses on visual quality, robustness under noise, smoothness of semantic transitions, and effectiveness of attribute control.
Results
Key findings include:
- �� On LSUN Churches, RCDM maintains high image quality and content consistency at perturbation levels λ > 0.4, whereas Diffusion Inversion suffers significant degradation, indicating superior robustness.
- �� On CelebA, linear interpolation between representation vectors in RCDM yields semantically smooth and coherent image morphing, outperforming Stable Diffusion and Diffusion Inversion, which exhibit blurring or abrupt changes.
- �� Supervised semantic direction addition enables controllable attribute modification (e.g., blonde hair, baldness), though attribute correlations (e.g., baldness and gender) affect disentanglement.
- �� Unsupervised PCA reveals principal semantic directions corresponding to forehead size, hair length, background color, and more, demonstrating promising interpretability despite less disentanglement than GANs.
Applications
Potential applications include:
- �� Unsupervised Image Editing: Enables flexible modification of image attributes without requiring labeled data, facilitating personalized image adjustments and artistic creation.
- �� Data Augmentation: Generates diverse, semantically controlled images to enrich training datasets, improving generalization and robustness of downstream vision tasks.
- �� Computer Vision Research: Provides a platform to study diffusion model latent space structure and semantic disentanglement, advancing theoretical understanding and method development.
- �� Creative Industries: Offers tools for artists and designers to control image generation via semantic representations, enhancing digital content creation workflows.
Limitations & Outlook
Despite promising results, limitations include:
- �� Limited semantic disentanglement and interpretability compared to GAN-based models, with attribute correlations impacting control precision.
- �� Experiments restricted to two datasets, leaving generalization and cross-domain adaptability untested.
- �� Dependence on pretrained representations without end-to-end unconditional generation training, limiting model autonomy.
- �� Limited baseline comparisons, lacking broader method benchmarking.
- �� High computational resource requirements for training and inference, constraining practical deployment.
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.
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