SF-V: Single Forward Video Generation Model (2406.04324v2)

Abstract: Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computational costs. In this work, we propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained video diffusion models. We show that, through the adversarial training, the multi-steps video diffusion model, i.e., Stable Video Diffusion (SVD), can be trained to perform single forward pass to synthesize high-quality videos, capturing both temporal and spatial dependencies in the video data. Extensive experiments demonstrate that our method achieves competitive generation quality of synthesized videos with significantly reduced computational overhead for the denoising process (i.e., around $23\times$ speedup compared with SVD and $6\times$ speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing. More visualization results are made publicly available at https://snap-research.github.io/SF-V.

SF-V: Single Forward Video Generation Model

The paper "SF-V: Single Forward Video Generation Model" introduces a novel approach to video generation using diffusion-based models enhanced with adversarial training. The model, SF-V, aims to address the computational inefficiency of traditional diffusion-based video generation methods by reducing the number of denoising steps required for high-quality video synthesis.

Introduction and Motivation

Diffusion-based models have shown great promise in generating high-fidelity videos by iteratively denoising video frames. However, the iterative nature of the sampling process in these models incurs high computational costs, making them less practical for real-time applications. To tackle this, the paper leverages adversarial training to fine-tune pre-trained video diffusion models, enabling the generation of videos in a single forward pass. This approach substantially reduces the computational overhead while preserving the quality of the generated videos.

Methodology

The proposed SF-V model is a refinement over the Stable Video Diffusion (SVD) framework. The key innovation lies in the integration of adversarial training in the latent space to distill a multi-step video diffusion model into a single-step generator.

Training Framework

The training leverages adversarial models comprising a generator and a discriminator. The generator is initialized with the pre-trained SVD model weights, which allows it to retain the high-fidelity synthesis capability. The discriminator is designed with spatial and temporal heads to ensure that the generated videos maintain both high image quality and motion consistency.

1. Latent Adversarial Training: The authors incorporate latent adversarial training, where noise is added to the real and generated samples to help the discriminator distinguish between them. This enables the generator to learn effective noise reduction strategies in a single forward pass.
2. Spatial-Temporal Discriminator Heads: To better capture the spatiotemporal dynamics, the discriminator is equipped with separate spatial and temporal heads. The spatial heads process each frame individually, while the temporal heads address motion consistency across frames.

Experiments and Results

Extensive experiments were conducted to validate the efficacy of SF-V. The evaluation metrics focused on Fréchet Video Distance (FVD) and visual quality comparisons against existing models like SVD and AnimateLCM.

1. Quantitative Comparisons: SF-V achieved an impressive speedup, reducing the denoising process time by around 23 times compared to SVD and by 6 times compared to AnimateLCM, while maintaining or surpassing the visual quality. The FVD scores indicated that SF-V could achieve comparable performance to SVD with 16 denoising steps.
2. Qualitative Comparisons: Visual inspections of the generated videos highlighted the high quality and temporal coherence of SF-V outputs. The model's ability to produce diverse and natural motions in various video sequences was particularly notable.

Implications and Future Directions

The practical implications of this research are substantial:

1. Efficiency: SF-V brings real-time video synthesis and editing closer to reality by significantly reducing computational overheads.
2. Scalability: The potential to scale diffusion models to generate longer and more complex video sequences is enhanced with the reduced resource requirements.

Speculation on Future Developments

Looking forward, several avenues for future research can be considered:

1. Further Optimization: While the focus was primarily on reducing the denoising steps, additional optimizations could be made to the temporal VAE decoder and the image conditioning encoder to further speed up the overall process.
2. Enhanced Applications: The SF-V framework could be extended to more complex video generation tasks, such as interactive content creation or real-time video editing in augmented reality (AR) and virtual reality (VR) environments.

Conclusion

The paper presents a novel and highly efficient approach to video generation by leveraging adversarial training to streamline the diffusion process. The SF-V model stands out by achieving a balance between computational efficiency and generation quality, thereby paving the way for more practical applications of video synthesis technologies.