FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation (2502.05179v3)

Abstract: DiT diffusion models have achieved great success in text-to-video generation, leveraging their scalability in model capacity and data scale. High content and motion fidelity aligned with text prompts, however, often require large model parameters and a substantial number of function evaluations (NFEs). Realistic and visually appealing details are typically reflected in high resolution outputs, further amplifying computational demands especially for single stage DiT models. To address these challenges, we propose a novel two stage framework, FlashVideo, which strategically allocates model capacity and NFEs across stages to balance generation fidelity and quality. In the first stage, prompt fidelity is prioritized through a low resolution generation process utilizing large parameters and sufficient NFEs to enhance computational efficiency. The second stage establishes flow matching between low and high resolutions, effectively generating fine details with minimal NFEs. Quantitative and visual results demonstrate that FlashVideo achieves state-of-the-art high resolution video generation with superior computational efficiency. Additionally, the two-stage design enables users to preview the initial output and accordingly adjust the prompt before committing to full-resolution generation, thereby significantly reducing computational costs and wait times as well as enhancing commercial viability.

• The paper demonstrates FlashVideo's two-stage framework that balances low-res prompt fidelity with high-res detail enhancement for efficient video generation.
• It employs a large 5B-parameter model for low-resolution generation and a lighter 2B-parameter model with a flow-matching algorithm for rapid high-resolution output.
• Experiments reveal that FlashVideo reduces generation time from 2150 seconds to 102 seconds while achieving a VBench-Long score of 82.49, underscoring its effectiveness.

Efficient High-Resolution Video Generation with FlashVideo

This essay elaborates on the research work "Flowing Fidelity to Detail for Efficient High-Resolution Video Generation," which introduces FlashVideo, a two-stage framework aimed at enhancing computational efficiency in text-to-video generation while maintaining high fidelity in content and motion. The framework focuses on overcoming the excessive computational demands typical of high-resolution video generation via DiT diffusion models, which often necessitate large model parameters and numerous function evaluations.

The paper discusses the prevalent challenges in text-to-video (T2V) generation, highlighting the computational complexities associated with achieving high content and motion fidelity aligned with text prompts. High-resolution outputs are desirable for realism and visual appeal, but single-stage DiT models tend to amplify computational demands. FlashVideo introduces a novel architecture addressing these challenges through a strategic allocation of model capacity and function evaluations across two distinct stages.

Two-Stage Framework

Stage 1: Low-Resolution Generation

The first stage emphasizes prompt fidelity through low-resolution video generation. This phase leverages large model parameters with sufficient NFEs to ensure high semantic fidelity and motion alignment with the input prompt while maintaining computational efficiency. The use of a large model with 5 billion parameters in combination with 50 evaluation steps at a 270p resolution allows this stage to produce a preview result in 30 seconds, facilitating prompt refinement without incurring full-resolution computational costs.

Stage 2: High-Resolution Enhancement

In the second stage, FlashVideo enhances the low-resolution output to high-resolution (1080p) video, focusing on refining fine details with minimal computational overhead. A lighter model, featuring 2 billion parameters, is employed alongside an innovative flow-matching algorithm that connects the low and high-resolution stages. This operation substantially reduces the number of function evaluations, requiring only four steps, and consequently decreases the generation time for 1080p videos to 102 seconds. This efficiency marks a significant improvement over existing state-of-the-art models, which may require up to 2150 seconds for similar outputs.

Experimental Results and Implications

FlashVideo demonstrates superior quantitative and qualitative results across various benchmark standards. The framework achieves an impressive total score of 82.49 on VBench-Long, outperforming existing models in both semantic alignment and aesthetic quality. Notably, the approach excels in preserving motion fidelity even in complex scenes, thanks to its robust handling of 3D full attention mechanisms. The two-stage design not only reduces computational costs but also enhances commercial viability by allowing users to preview the initial output rapidly.

The adaptive allocation of model resources across stages opens up new possibilities in video generation, hinting at future directions in model efficiency and scalability. Such strategic deployment in a multitasking system could inspire further research into optimization techniques within AI frameworks, particularly for applications demanding high-resolution outputs.

Future Research Directions

This research contributes significantly to the T2V domain, presenting an approach that balances fidelity and efficiency. Future studies could explore further refinements to the flow-matching processes to decrease NFEs even more, potentially leveraging hybrid models that integrate different architectures for better efficiency. Another area worth exploring is the application of the framework in other generative domains, such as text-to-image or audio synthesis, where similar computational constraints apply.

The findings from FlashVideo suggest broader implications for scaling AI applications while managing computational resources. As AI systems continue to grow in complexity and application scope, efficient design strategies like those employed in FlashVideo could be instrumental in deploying solutions at scale, ensuring both performance and accessibility remain within reach in ever-demanding digital environments.