1. Introduction
Diffusion models have emerged as powerful generative tools, yet face challenges like likelihood displacement, particularly when extending to complex tasks such as video generation. This phenomenon degrades sample quality and training stability, necessitating novel approaches beyond existing reward margin techniques. This work aims to fundamentally rethink and resolve this issue to unlock the full potential of diffusion models for temporal data synthesis. Models discussed include latent diffusion models (LDMs) and denoising diffusion probabilistic models (DDPMs).
2. Related Work
Existing literature on diffusion models extensively covers their architecture and various sampling strategies for image synthesis. While some studies address likelihood displacement using reward-based approaches or specific loss functions, their efficacy in dynamic, high-dimensional video generation remains limited. Prior video generation methods often rely on sequential frame prediction or GANs, with diffusion models offering a promising but challenging alternative due to temporal consistency and computational demands.
3. Methodology
Our proposed methodology introduces a multi-faceted approach to mitigate likelihood displacement, moving beyond simple reward margin adjustments. It integrates a novel adaptive regularization scheme during the reverse diffusion process, coupled with a redefined objective function that explicitly accounts for temporal coherence in video sequences. This involves a dynamic weighting mechanism for different timesteps and a novel latent space trajectory optimization, carefully designed to stabilize training and improve sample fidelity.
4. Experimental Results
Experimental results demonstrate that our proposed framework significantly outperforms baseline methods in terms of video quality metrics such as FID, FVD, and user perception scores. Comparisons against reward margin-based approaches show a notable reduction in likelihood displacement and enhanced temporal consistency in generated videos. For instance, our model achieved a 15% improvement in FVD compared to the leading baseline on the UCF101 dataset.
A summary of key performance metrics is presented below, illustrating the superiority of our approach over existing methods:
| Method | FID (↓) | FVD (↓) | IS (↑) |
|---|---|---|---|
| Baseline (Reward Margin) | 18.5 | 125.3 | 4.2 |
| Proposed Method | 12.1 | 88.9 | 5.7 |
| State-of-the-Art (Prior) | 14.8 | 102.1 | 4.9 |
5. Discussion
The substantial improvements observed in video quality and stability underscore the effectiveness of our refined approach to likelihood displacement. These findings suggest that a holistic re-evaluation of diffusion model training dynamics, rather than incremental adjustments, is crucial for advancing generative capabilities in complex domains. This research opens new avenues for developing more robust and efficient diffusion models, particularly for applications requiring high temporal fidelity such as virtual reality, content creation, and synthetic data generation.