Compositional Visual Generation with Composable Diffusion Models (2206.01714v6)

Abstract: Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects. In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined. The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALLE-2. These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation. Project page: https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/

• The paper introduces a compositional approach that integrates multiple diffusion models as energy-based models to capture complex visual concepts.
• The methodology employs conjunction and negation operators to combine distinct visual components, achieving a 24% accuracy improvement on the CLEVR dataset.
• The results demonstrate enhanced flexibility and fidelity in visual generation, paving the way for robust AI systems capable of synthesizing semantically rich images.

Compositional Visual Generation with Composable Diffusion Models: An Overview

The paper, "Compositional Visual Generation with Composable Diffusion Models," addresses the significant challenge of effectively capturing the compositional nature of visual concepts. The focus is on enhancing the flexibility of diffusion models in generating complex and semantically rich images. Diffusion models have shown promise in generating photorealistic images from natural language descriptions; however, they often falter when faced with intricate compositions of concepts. This paper introduces a novel method for generating complex visuals by composing sets of diffusion models catering to individual components of an image.

Methodology

The paper interprets diffusion models as Energy-Based Models (EBMs), allowing explicit compositionality. This approach involves treating the image generation task as a process of combining multiple diffusion models, with each model dedicated to capturing a different aspect or component of the image. By leveraging the interpretive capability of EBMs, the authors have proposed two operators, Conjunction (AND) and Negation (NOT), which enable the composition of different concepts in visual generation. This composition facilitates the generation of images that are not only complex but also more faithful to their textual descriptions than what current models like DALL-E 2 can achieve.

Numerical Results

The paper presents substantial empirical evidence demonstrating the method's effectiveness. The proposed models were tested on various datasets, including CLEVR, Relational CLEVR, and FFHQ, across multiple design settings – single-component and multi-component compositions. In the CLEVR dataset, the accuracy in composing three objects using the proposed model showed an improvement of 24.02% over the best-performing baseline. This significant enhancement underscores the model's efficacy in zero-shot generalization, crucial for real-world applications involving unseen concept combinations.

Implications and Future Directions

The implications of this research are manifold. Practically, it could pave the way for more robust and flexible AI systems capable of understanding and generating complex visual content from textual input. Theoretically, this work contributes to the growing evidence that hybrid model architectures, incorporating principles from different neural network paradigms, can surpass the capabilities of traditional models in addressing specific limitations.

Looking ahead, there are intriguing avenues for further exploration. One potential direction is the composition of diffusion models trained on heterogeneous datasets, which remains challenging. Addressing this could enhance the collaborative capability of multiple models trained independently, potentially leading to a more unified semantic understanding across varied domains. Additionally, integrating mechanisms to handle even more abstract concept combinations could push the boundaries of what these models can achieve.

In conclusion, this paper makes a compelling case for the power of compositionality in visual generative modeling, presenting a methodological advancement that improves on the expressiveness and generalization of diffusion models. With further development, this approach holds promise for revolutionizing AI-based visual content generation.