Scaling Concept With Text-Guided Diffusion Models
Chao Huang, Susan Liang, Yunlong Tang, Yapeng Tian, Anurag Kumar,, Chenliang Xu
TL;DR
This paper introduces ScalingConcept, a method to enhance or suppress concepts in text-guided diffusion models, enabling new zero-shot applications like pose generation and sound editing by decomposing and scaling concepts without adding new elements.
Contribution
The paper presents a novel concept scaling technique for diffusion models and a new dataset for evaluating concept enhancement in generative tasks.
Findings
Concepts can be decomposed in diffusion models.
Scaling concepts improves control over generated content.
Enables zero-shot applications in image and audio domains.
Abstract
Text-guided diffusion models have revolutionized generative tasks by producing high-fidelity content from text descriptions. They have also enabled an editing paradigm where concepts can be replaced through text conditioning (e.g., a dog to a tiger). In this work, we explore a novel approach: instead of replacing a concept, can we enhance or suppress the concept itself? Through an empirical study, we identify a trend where concepts can be decomposed in text-guided diffusion models. Leveraging this insight, we introduce ScalingConcept, a simple yet effective method to scale decomposed concepts up or down in real input without introducing new elements. To systematically evaluate our approach, we present the WeakConcept-10 dataset, where concepts are imperfect and need to be enhanced. More importantly, ScalingConcept enables a variety of novel zero-shot applications across image and audio…
Peer Reviews
Decision·Submitted to ICLR 2025
- The paper conducts a thorough empirical analysis of the concept removal phenomenon in text-guided diffusion models, establishing a solid foundation for the proposed method. - The ScalingConcept method is versatile, showcasing applications across multiple domains (image and audio) without additional fine-tuning. - The introduction of the WeakConcept-10 dataset is a valuable contribution that provides a benchmark for evaluating concept scaling methods. - The experiments are well-designed, wit
- While the method performs well on the WeakConcept-10 dataset, its generalizability to other datasets or more complex concepts remains uncertain. For example, in what scenario we will need this technique? It is applicable to combine removal and addition by existing methods to achieve the same performance. Further validation on diverse and challenging datasets would strengthen the paper. In addition, the paper does not thoroughly address the scalability of the method to larger datasets or higher
1. The paper is logically structured, and all figures and tables are well-presented. 2. The paper presents a novel motivation, introducing a simple and understandable paradigm for text-to-image generation based on the scaling concept. The authors also conduct empirical investigations on existing image and audio generation tasks to support their claims. 3. The proposed ScalingConcept method seems promising in various downstream tasks, including Object Stitching, Pose Generation, and Creative Enha
1. The scientific question in this paper is not straightforward. The author seems inspired by their observations without clearly pointing out the flaws of existing methods. 2. The empirical study is inconsistent with the proposed method's idea. For example, Figure 2 shows the removal concepts using different prompts during the reconstruction. However, the proposed method removes image concepts by using a blank prompt. 3. The analysis of Figure 3 lacks illustrative examples. As noted in point 2,
1. The paper explores a novel aspect of scaling rather than replacing concepts, which adds a fresh perspective to the application of diffusion models. 2. ScalingConcept is simple and effective, which indicates practical applicability in enhancing or suppressing concepts. 3. ScalingConcept's ability to support novel zero-shot applications is across both image and audio domains.
1. There is no mention of how ScalingConcept compares to existing methods or techniques, which might raise questions on its relative performance improvements. 2. The paper does not discuss the complexity of applying ScalingConcept in diffusion models. 3. It is necessary to demonstrate the method's scalability and effectiveness across diverse datasets beyond WeakConcept-10. 4. In the application zoo, only a few cases are provided. It is unclear whether the method is effective universally.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications · Music Technology and Sound Studies
MethodsDiffusion