Core Concepts
BCM efficiently unifies generation and inversion tasks within one framework, enhancing sample quality and reducing reconstruction error.
Abstract
The content introduces Bidirectional Consistency Models (BCM) as an advancement in consistency models, focusing on unifying generation and inversion tasks. It discusses the iterative nature of diffusion models, the challenges in inversion tasks, and the proposed BCM's ability to efficiently handle both tasks. The note covers the introduction, methods, experiments, results, and related works.
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Introduction
- Discusses the iterative nature of diffusion models and challenges in inversion tasks.
- Introduces Bidirectional Consistency Model (BCM) to unify generation and inversion tasks efficiently.
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Methods
- Describes the network parameterization, bidirectional consistency training, sampling schemes, and inversion process of BCM.
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Experiments and Results
- Showcases image generation, inversion, applications like interpolation, inpainting, restoration of compressed images, and defending black-box adversarial attacks.
- Compares BCM's performance with ODE-based diffusion models and consistency models.
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Related Works
- Discusses existing methods to accelerate diffusion models' generation and inversion tasks.
Stats
Recently, Consistency Models (CMs) have emerged to address the challenge of iterative generation and inversion tasks.
BCM achieves comparable or better results with fewer NFEs compared to ODE-based diffusion models.
BCM significantly improves defense against black-box adversarial attacks, enhancing accuracies to over 64% at strong attack budgets.
Quotes
"Our proposed method enables one-step generation and inversion while also allowing the use of additional steps to enhance generation quality or reduce reconstruction error." - Content