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ReVersion: Diffusion-Based Relation Inversion from Images

Ziqi Huang, Tianxing Wu, Yuming Jiang, Kelvin C. K. Chan, Ziwei Liu

arXiv:2303.13495v2cs.CV

TL;DR

Existing diffusion inversion methods focus mainly on object appearance, while learning relations between objects from exemplars remains underexplored. ReVersion learns a relation prompt with a frozen text-to-image diffusion model, using linguistic steering and interaction-focused sampling. The paper reports that its framework can generate prompted entities following relations found in exemplars, while noting dependence on Stable Diffusion's generative capabilities.

  • Problem

    Existing diffusion inversion methods mainly capture object appearance, while how to invert relations between objects remains comparatively unexplored.

    Source: §1 · p. 2

  • Method

    ReVersion learns a relation prompt with a frozen pre-trained text-to-image diffusion model, steering it toward relation-dense regions and emphasizing high-level interactions over appearance details.

    Source: §1 · p. 2

  • Results

    Qualitative results report that ReVersion synthesizes the entities in an inference template and preserves the relation shared by the exemplar images.

    Source: §6.2 · p. 6

  • Takeaways & Limitations

    Relation Inversion offers a generative approach for extracting relations from exemplars and applying them to new image content.

    Source: §1 · p. 2, §7 · p. 10

    Performance depends on Stable Diffusion's generative capabilities, which may yield suboptimal synthesis for entities such as human bodies and faces.

    Source: §6.7 · p. 9

Abstract

from arXiv · show

Diffusion models gain increasing popularity for their generative capabilities. Recently, there have been surging needs to generate customized images by inverting diffusion models from exemplar images, and existing inversion methods mainly focus on capturing object appearances (i.e., the "look"). However, how to invert object relations, another important pillar in the visual world, remains unexplored. In this work, we propose the Relation Inversion task, which aims to learn a specific relation (represented as "relation prompt") from exemplar images. Specifically, we learn a relation prompt with a frozen pre-trained text-to-image diffusion model. The learned relation prompt can then be applied to generate relation-specific images with new objects, backgrounds, and styles. To tackle the Relation Inversion task, we propose the ReVersion Framework. Specifically, we propose a novel "relation-steering contrastive learning" scheme to steer the relation prompt towards relation-dense regions, and disentangle it away from object appearances. We further devise "relation-focal importance sampling" to emphasize high-level interactions over low-level appearances (e.g., texture, color). To comprehensively evaluate this new task, we contribute the ReVersion Benchmark, which provides various exemplar images with diverse relations. Extensive experiments validate the superiority of our approach over existing methods across a wide range of visual relations. Our proposed task and method could be good inspirations for future research in various domains like generative inversion, few-shot learning, and visual relation detection.

1 INTRODUCTION

Existing diffusion inversion methods primarily capture object appearance, leaving relations between objects comparatively unexplored. ReVersion learns a reusable relation prompt using linguistic steering and interaction-focused sampling, and introduces a benchmark spanning diverse relations.

1 INTRODUCTION

  • ReVersion introduces Relation Inversion: learning a prompt for a relation shared across exemplar images, then composing it with prompts to generate new objects, styles, and backgrounds.The framework uses linguistic priors and relation-focal importance sampling; the paper also contributes a benchmark for the task.Source: §1 · p. 2
  • Appearance inversion captures an object's look, but extracting abstract object relations is harder and pixel-level reconstruction alone is insufficient.The paper motivates linguistic and visual priors for representing high-level relations.Source: §1 · p. 2
  • Relation-steering contrastive learning pulls the relation prompt toward basis prepositions and away from nouns and adjectives associated with object appearance.The method is motivated by the observed clustering of words by part of speech in the text embedding space.Source: §1 · p. 2, Figure 2 · p. 2
  • Relation-focal importance sampling emphasizes high-level interactions over lower-level details such as object color and texture.Source: §1 · p. 2
  • The ReVersion Benchmark provides exemplar images spanning relations from simple spatial arrangements to complex interactive behaviors.The benchmark is presented as an evaluation tool for future Relation Inversion research.Source: §1 · p. 2

2 RELATED WORK

The related work situates ReVersion among diffusion-based image synthesis and inversion methods. It distinguishes generative extraction of open-world relations from appearance inversion and discriminative relation classification.

Diffusion Models

  • Text-to-image diffusion models convert user-provided descriptions into images, and ReVersion builds on Stable Diffusion.Source: §2 · p. 3

Relation Modeling

  • Prior relation-modeling work detects and classifies image relations within predefined closed sets, whereas Relation Inversion targets arbitrary open-world relations for content creation.Source: §2 · p. 3

Diffusion-Based Inversion

  • Diffusion-based inversion learns text embeddings for exemplar-image concepts; ReVersion instead extracts high-level object relations rather than appearance details such as color or texture.The paper notes that relation extraction requires understanding image composition and object relationships.Source: §2 · p. 3

3 THE RELATION INVERSION TASK

Relation Inversion extracts a common relation from exemplar images by optimizing a relation prompt. The acquired prompt can then be used to generate images of novel objects interacting through that relation.

3 THE RELATION INVERSION TASK

  • The task assumes that two dominant entities in each exemplar interact through a common relation, represented in a coarse caption as entity A, relation prompt, and entity B.Source: §3 · p. 3
  • Optimizing the relation prompt aims to represent the relation shared by the exemplar images, enabling synthesis with novel interacting objects.The paper also identifies relation inference from exemplars as a broader direction for research.Source: §3 · p. 3

4 THE REVERSION FRAMEWORK

ReVersion learns a relation prompt from exemplar images for use in generating new entities interacting through that relation. Its framework combines contrastive steering away from appearance semantics with timestep sampling that emphasizes high-level relations.

4.1 Preliminaries

  • Given exemplar images sharing a relation, the framework learns a prompt that can be used with new entities to generate that relation.The prompt is optimized in a pre-trained text-to-image diffusion model whose weights remain frozen.Source: §4.1 · p. 3, §4.2 · p. 4, Figure 3 · p. 4

4.2 Relation-Steering Contrastive Learning

  • Pixel-level reconstruction alone can associate the prompt with object appearance rather than relations, leaking exemplar appearances and producing incorrect relations.The framework therefore adds relation-focused objectives to the reconstruction objective.Source: §4.2 · p. 4
  • Relation-steering contrastive learning uses prepositions as positives and other part-of-speech words, such as nouns and adjectives, as negatives.This steers the prompt toward relation-dense text embeddings and away from appearance-related semantics.Source: §4.2 · p. 4

4.3 Relation-Focal Importance Sampling

  • Relation-focal importance sampling assigns higher sampling probability to larger diffusion timesteps to emphasize high-level relations over details such as color and texture.The sampling function's skewness increases with α, set to 0.5 in the experiments.Source: §4.3 · p. 4

5 THE REVERSION BENCHMARK

The ReVersion Benchmark provides varied relations, entities, exemplar images, and text descriptions for evaluating Relation Inversion. It includes inference templates with different object entities and objective-metric comparisons against existing methods and ablations.

5 THE REVERSION BENCHMARK

  • The benchmark defines ten relations spanning basic spatial arrangements, entity interactions, and abstract concepts, with varied entities.It is intended to support qualitative and quantitative evaluation of Relation Inversion methods.Source: §5 · p. 5
  • For each relation, the benchmark provides four to ten exemplar images with different entities and annotated text templates at multiple detail levels.It also supplies 100 inference templates per relation, totaling 1,000 templates for evaluation.Source: §5 · p. 5
  • Table 1 compares objective metrics for the proposed method, existing methods, and ablation variants.The supplied table caption identifies the comparison scope but does not provide metric values.Source: Table 1 · p. 6, §5 · p. 6

6 EXPERIMENTS

Experiments compare ReVersion with existing methods and test its components across human preference, objective metrics, and qualitative cases. The reported results favor ReVersion, while the authors note its dependence on Stable Diffusion’s generation capabilities.

6.1 Comparison Methods

  • The experiments use Stable Diffusion and compare against Text-to-Image Generation, Textual Inversion, and DreamBooth.The reported setup generates images at 512 × 512 resolution.Source: §6 · p. 6, §6.1 · p. 6

6.3 Quantitative Comparisons via Human Evaluation

  • Human evaluators favor ReVersion over the comparison methods on Relation Accuracy, Entity Accuracy, and Overall Quality.The study sampled 20 image groups and involved 68 evaluators.Source: Table 2 · p. 6, §6.3 · pp. 6, 8

6.2 Qualitative Comparisons

  • ReVersion generates entities in the inference prompt while capturing their exemplar relation, unlike the comparison methods’ relation failures or appearance leakage.Text-to-Image Generation and DreamBooth can generate the named entities but fail to compose them in the desired relation; Textual Inversion can leak exemplar appearance.Source: §6.2 · p. 6, Figure 5 · p. 7

6.4 Quantitative Comparisons via Objective Metrics

  • Objective metrics show ReVersion outperforms all compared methods in relation accuracy and Textual Inversion in entity accuracy.Its entity score is comparable to Text-to-Image Generation and DreamBooth, while its relation score significantly surpasses them.Source: §6.4 · p. 8

6.5 Ablation Study

  • Removing relation-steering or importance sampling worsens both relation and entity accuracy.Without steering, exemplar appearances can leak into the prompt; without importance sampling, it can focus on low-level details instead of relations.Source: §6.5 · pp. 8–9

6.6 Further Analysis

  • ReVersion transfers a complex “holding hands, leaning backwards” relation to new entities more effectively than describe-and-text-to-image.It also applies inverted relations in scenes with diverse backgrounds and styles.Source: §6.6 · p. 9

6.7 Limitations and Potential Societal Impacts

  • ReVersion’s performance depends on Stable Diffusion’s generative capabilities, which can produce suboptimal human bodies and faces.The authors also identify potential malicious use of its entity-relational composition on real human figures.Source: §6.7 · p. 9

7 CONCLUSION

The conclusion presents Relation Inversion as learning a prompt for relations shared across exemplar images. ReVersion combines relation-steering and importance sampling, and the paper identifies several areas for future work.

7 CONCLUSION

  • Relation Inversion learns a prompt to capture a relation shared across multiple exemplar images.The conclusion presents this as the paper’s first step toward the task.Source: §7 · p. 10
  • ReVersion combines relation-steering contrastive learning with relation-focal importance sampling to focus on relations rather than visual details.The framework also contributes the ReVersion Benchmark for performance evaluation.Source: §7 · p. 10
  • The authors identify generative model inversion, representation learning, few-shot learning, visual relation detection, and scene graph generation as potential future directions.They frame Relation Inversion as a possible inspiration for work in these areas.Source: §7 · p. 10

SUPPLEMENTARY

The supplementary file provides further experimental details and elaborates on the ReVersion Benchmark.

SUPPLEMENTARY

  • It elaborates on the ReVersion Benchmark and explains basis prepositions.Source: SUPPLEMENTARY · p. 13
  • It also discusses limitations and potential societal impacts and presents additional qualitative results.Source: SUPPLEMENTARY · p. 13

A MORE EXPERIMENTAL DETAILS

A.1 Implementation Details of ReVersion

The appendix specifies ReVersion’s Stable Diffusion setup, human preference-voting procedure, objective metrics, and where it documents the Describe-and-T2I comparison process.

A.1 Implementation Details of ReVersion

  • ReVersion uses Stable Diffusion 1.5 at 512×512 resolution, initializing ⟨R⟩ with “and” and optimizing it for 3,000 steps with AdamW.The denoising and steering loss weights are 1.0 and 0.01, respectively; inference uses classifier-free guidance weight 7.5.Source: §A.1 · p. 13

A.2 Human Evaluation

  • The user-study form used preference voting to compare methods, with result order randomized for unbiased responses; Table 1 offered four choices, while Table 2’s ablation offered three.Source: §A.2 · p. 13

A.3 Objective Evaluation Metrics

  • Relation Score is the percentage of generated images classified as following the exemplar relation, using PSGFormer features and linear SVMs.Entity Score is the CLIP cosine similarity between the generated image and a revised prompt containing only the specified entities.Source: §A.3 · p. 13

A.4 Implementation of “Describe and Text-to-Image”

  • The appendix points to Figure A12 for the detailed process of the Describe-and-Text-to-Image baseline used in the main-paper comparison.The comparison is described as appearing in Section 6.6 and Figure 6.Source: §A.4 · p. 13

B REVERSION BENCHMARK DETAILS

The ReVersion Benchmark organizes diverse relation exemplars, descriptions, and inference templates for evaluating relation inversion, and uses basis prepositions to steer prompt learning.

B REVERSION BENCHMARK DETAILS

  • The benchmark is presented as a resource for qualitative and quantitative evaluation of relation inversion methods.The full benchmark is stated to be publicly available.Source: B REVERSION BENCHMARK DETAILS · p. 13

B.1 Relations

  • Its ten relations range from spatial arrangements such as “on top of” to interactions and abstract relations such as “shakes hands with” and “is carved by.”The relation descriptions are approximations; the exemplar images best describe the relations.Source: §B.1 · p. 13

B.2 Exemplar Images

  • Exemplars span varied entities, including people and animals, with “sits back to back” illustrating the same relation across different scenarios.Text descriptions range from class-name mentions to detailed entity and scene descriptions, with ⟨R⟩ substituted during optimization.Source: §B.2 · pp. 13, 15, §B.3 · p. 15

B.4 Inference Templates

  • Each relation has 100 inference templates with diverse entity combinations, and each template generates 10 images for quantitative evaluation.This yields 1,000 synthesized images for each inverted relation prompt.Source: §B.4 · p. 15

C FURTHER EXPLANATIONS ON BASIS PREPOSITIONS

D LIMITATIONS

The reported limitations stem from Stable Diffusion’s image-generation capabilities, while supplementary examples show relation prompts used across varied styles and entity combinations.

D LIMITATIONS

  • ReVersion’s synthesis quality is capped by Stable Diffusion’s generative capabilities, so difficult rendering cases can remain suboptimal.The authors describe developing better text-to-image diffusion models as an orthogonal direction for improving performance.Source: D LIMITATIONS · p. 15
  • When Stable Diffusion renders multiple entities, it can blend class characteristics, and ReVersion may inherit this issue for different-class entities.The example describes rabbit ears and cat-like fur color and texture appearing together.Source: D LIMITATIONS · p. 15
  • Even when ReVersion captures the relation, Stable Diffusion’s human face and body rendering can leave those subjects suboptimal.The authors identify faces and bodies as the specific quality boundary.Source: D LIMITATIONS · p. 15

E POTENTIAL SOCIETAL IMPACTS

  • Because inverted relations can generate diverse combinations, the authors warn that they could also depict real people interacting in ways they never did.They advise using ReVersion only for proper recreational purposes.Source: E POTENTIAL SOCIETAL IMPACTS · p. 16

F MORE QUALITATIVE RESULTS

  • The supplementary results show inverted relations adapting to diverse styles and backgrounds and linking arbitrary entity combinations with the exemplar relation.Examples include “painted on” and material relations across enumerated entity combinations.Source: §F.1 · p. 16, §F.2 · p. 16

F.3 Additional Qualitative Results

Additional qualitative examples show relation prompts applied to varied entity descriptions and backgrounds, while the accompanying captions describe robustness across styles and entity combinations.

F.3 Additional Qualitative Results

  • Further examples include bear–bear and girl–boy prompts with added setting or clothing details.The supplied examples include wooded-area, field, and white-background descriptions.Source: §F.3 · p. 17
  • The supplementary examples vary entity names, attributes, and scene descriptions while retaining the relation prompt between the entities.Examples include cat–cat, boy–boy, girl–boy, and woman–man prompts, with backgrounds or clothing details in some templates.Source: §F.3 · p. 17
  • The figure captions report that inverted relations adapt across diverse styles and backgrounds and can link arbitrary entity combinations.Examples include “painted on” and “is made of the material of / is carved by” relations.Source: Figure A15 · p. 18, Figure A16 · p. 19, Figure A17 · p. 20

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