[Paper Review] Compositional Visual Generation with Composable Diffusion Models

Nan Liu, Shuang Li+3 more|arXiv (Cornell University)|Jun 3, 2022

Computational and Text Analysis Methods4 citations

TL;DR

This paper proposes composable diffusion models that interpret individual diffusion models as energy-based models, enabling explicit composition of multiple models at inference time to generate complex, compositional images. By introducing conjunction and negation operators, the method achieves zero-shot generalization to novel combinations of objects, relations, and attributes—outperforming baselines by 24.02% on CLEVR for three-object composition with improved image quality and fidelity.

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/

Motivation & Objective

• To address the limitations of existing text-guided diffusion models in understanding complex, compositional descriptions such as object relations and attribute bindings.
• To enable zero-shot compositional generation of images with novel combinations of concepts not seen during training.
• To develop a method that composes pre-trained diffusion models at inference time without additional training, using explicit compositional operators.
• To improve generalization and image quality in generating complex scenes involving multiple objects, relations, and attributes.
• To demonstrate the effectiveness of interpreting diffusion models as energy-based models for structured, scalable composition in visual generation.

Proposed method

• Interprets trained diffusion models as energy-based models (EBMs), enabling explicit composition through energy function combination.
• Introduces two compositional operators: conjunction (logical AND) and negation (logical NOT) for combining diffusion models at inference time.
• Uses the energy-based formulation to compose multiple diffusion models, each responsible for a specific component (e.g., object, attribute, relation), and combines their energy functions additively.
• Applies the composed energy function to perform denoising diffusion sampling, generating images that reflect all specified components.
• Employs a differentiable sampling process that allows gradient-free, end-to-end image generation from composed model energies.
• Leverages pre-trained models like GLIDE and Stable Diffusion as base components, enabling zero-shot composition without fine-tuning.

Experimental results

Research questions

• RQ1 Can diffusion models be composed at inference time using a structured, differentiable framework to generate complex visual compositions?
• RQ2 Can the proposed method generalize to novel combinations of objects, relations, and attributes not seen during training?
• RQ3 How does the performance of the composed diffusion model compare to existing baselines in terms of accuracy and image quality on compositional visual generation tasks?
• RQ4 What are the failure modes of the compositional approach, and how do they relate to limitations in the pre-trained base models?
• RQ5 Can the method effectively bind attributes to objects and preserve relational structure in generated images?

Key findings

• The proposed method achieved a 24.02% higher accuracy than the best baseline on the CLEVR dataset for generating images with three objects in specified positions.
• On object relation composition, the method outperformed StyleGAN2, LACE, GLIDE, and EBM in binary classification accuracy while maintaining better FID scores than EBM.
• The method generated higher-quality images than EBM, with sharper object boundaries and improved photorealism, despite EBM's superior accuracy in relation understanding.
• DALL-E 2 and GLIDE failed to understand object relations despite generating high-quality images, highlighting a key limitation in compositional reasoning.
• Failure cases were primarily due to pre-trained models not understanding certain concepts (e.g., 'person'), confusing attributes (e.g., 'red bear' vs. 'bear in a red forest'), or compositional collapse in central image regions.
• The method demonstrated effective zero-shot generalization to unseen combinations of concepts across language, objects, relations, and facial attributes.