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arxiv: 2605.27343 · v1 · pith:BRKBX4AJnew · submitted 2026-05-26 · 💻 cs.CV · cs.LG

Towards Controllable Image Generation through Representation-Conditioned Diffusion Models

Nithesh Chandher Karthikeyan , Jonas Unger , Gabriel Eilertsen This is my paper

Pith reviewed 2026-06-29 18:01 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords diffusion modelsself-supervised representationscontrollable image generationself-conditioninglatent space controlimage synthesisrepresentation conditioningunconditional generation
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The pith

Conditioning diffusion models on representations from pre-trained self-supervised models improves unconditional generation quality and enables control via variation directions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that diffusion models can be conditioned on representations extracted from an off-the-shelf pre-trained self-supervised model. This self-conditioning boosts the quality of images produced without any external guidance and creates a space where directions can be identified to steer the output. The authors demonstrate that changes along these directions are smooth and that different directions affect separate attributes. A reader would care because this sidesteps the usual requirement for large annotated datasets such as text prompts or semantic maps. If the claim holds, existing self-supervised models become a practical source for both higher-quality generation and controllable editing.

Core claim

The central claim is that 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. The authors explore this conditioning space by identifying directions of variations and demonstrate promising properties in terms of smoothness and disentanglement.

What carries the argument

The self-conditioning mechanism that conditions the diffusion model on representations from a pre-trained self-supervised model, serving as both a quality booster and a controllable latent space.

If this is right

  • Unconditional image generation quality increases without external conditions.
  • Directions identified in the representation space allow control over generated images.
  • Changes along those directions remain smooth.
  • Different directions control separate attributes due to disentanglement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This approach could reduce dependence on text or semantic annotations for guiding diffusion models.
  • The same conditioning space might support image editing by moving along the discovered directions.
  • Different choices of pre-trained self-supervised model could be tested to find which representations yield the strongest control.
  • The method may transfer to other generative architectures beyond diffusion models.

Load-bearing premise

Representations extracted from an off-the-shelf pre-trained self-supervised model form a suitable latent space for both quality improvement and controllable generation without further adaptation or task-specific training.

What would settle it

If quantitative metrics show no improvement in unconditional generation quality, or if varying along the identified directions produces abrupt or entangled changes instead of smooth disentangled ones.

Figures

Figures reproduced from arXiv: 2605.27343 by Gabriel Eilertsen, Jonas Unger, Nithesh Chandher Karthikeyan.

Figure 1
Figure 1. Figure 1: The Representation Conditioned Diffusion Model consists of [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Image variations are created by linearly interpolating between two embedding vectors, derived from the corresponding reference image or [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Images generated by perturbing the embedding vector [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Images generated by identifying semantic directions within the representation space of diffusion models. (a) supervised approach using the [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper explores conditioning diffusion models on representations extracted from pre-trained self-supervised models. It claims that this self-conditioning mechanism improves the quality of unconditional image generation while also yielding a representation space that supports controllable generation via identification of variation directions, with promising properties in smoothness and disentanglement, all without requiring annotated datasets such as text prompts or semantic maps.

Significance. If the empirical claims hold, the approach could offer a data-efficient alternative for controllable diffusion-based generation by repurposing existing SSL models, reducing reliance on labeled conditioning data and potentially enabling new editing capabilities through the learned representation space.

major comments (2)
  1. [Abstract] Abstract: The manuscript asserts that self-conditioning 'improves the quality of unconditional image generation' and yields 'promising properties in terms of smoothness and disentanglement,' yet provides no quantitative metrics, baselines, ablation studies, or error analysis to support these claims. This absence is load-bearing, as the central contribution is an empirical exploration whose validity cannot be assessed from the given text.
  2. [Abstract] Abstract: The approach assumes that representations from an off-the-shelf pre-trained SSL model (e.g., SimCLR/DINO-style) simultaneously enhance generation quality and form a latent space with traversable, disentangled directions. However, SSL objectives explicitly encourage invariance to augmentations and other factors, which may collapse the very axes needed for control; no discussion, justification, or test of this compatibility is provided.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments. As this is presented as preliminary work, we address the concerns regarding empirical support and theoretical compatibility below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The manuscript asserts that self-conditioning 'improves the quality of unconditional image generation' and yields 'promising properties in terms of smoothness and disentanglement,' yet provides no quantitative metrics, baselines, ablation studies, or error analysis to support these claims. This absence is load-bearing, as the central contribution is an empirical exploration whose validity cannot be assessed from the given text.

    Authors: We agree that the current version lacks quantitative metrics, baselines, and ablations to support the claims on generation quality and representation properties. The manuscript is explicitly preliminary and relies on qualitative demonstrations. In revision we will add FID scores for unconditional generation quality, quantitative measures of smoothness and disentanglement, and comparisons against standard unconditional diffusion baselines. revision: yes

  2. Referee: [Abstract] Abstract: The approach assumes that representations from an off-the-shelf pre-trained SSL model (e.g., SimCLR/DINO-style) simultaneously enhance generation quality and form a latent space with traversable, disentangled directions. However, SSL objectives explicitly encourage invariance to augmentations and other factors, which may collapse the very axes needed for control; no discussion, justification, or test of this compatibility is provided.

    Authors: The potential tension between SSL invariance objectives and the need for traversable variation axes is a substantive point. While our empirical observations indicate that usable semantic variation remains, the manuscript provides no explicit discussion or targeted test of this compatibility. We will add a dedicated paragraph in the revised version analyzing preserved versus collapsed factors and, where feasible, supporting analysis. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical exploration only

full rationale

The paper presents an empirical study of conditioning diffusion models on frozen off-the-shelf self-supervised representations. No equations, fitted parameters, or derivation steps are described that would reduce any claim to a self-referential definition or a renamed input. The abstract and provided text contain no self-citations invoked as load-bearing uniqueness theorems, no ansatzes smuggled via prior work, and no predictions that are statistically forced by construction. The central claims rest on reported experimental outcomes (quality improvement and observed smoothness/disentanglement) rather than any algebraic or definitional reduction to the inputs themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract contains no derivations, fitted parameters, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5631 in / 910 out tokens · 30703 ms · 2026-06-29T18:01:12.548754+00:00 · methodology

discussion (0)

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