"FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video Generation"
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https://arxiv.org/abs/2502.05179
The challenge in text-to-video generation lies in achieving high visual quality at high resolutions due to immense computational costs. Single-stage diffusion models require substantial parameters and function evaluations, making high-resolution video generation inefficient.
This paper introduces FlashVideo, a two-stage framework. It efficiently generates high-resolution videos by decoupling prompt fidelity and visual quality optimization.
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📌 FlashVideo smartly addresses high-resolution video generation costs. It uses a two-stage diffusion process. Stage one for content, stage two for detail. This division significantly boosts efficiency without sacrificing quality.
📌 Flow matching in stage two is a key innovation. It warps low-resolution video into high-resolution. This avoids expensive, full denoising. FlashVideo leverages pre-existing structure for efficient upscaling.
📌 The preview capability offers practical value. Users can assess low-resolution output quickly. This reduces computational waste. FlashVideo enhances user experience and commercial viability through efficient, staged generation.
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Methods Explored in this Paper 🔧:
→ FlashVideo uses a two-stage approach for efficient high-resolution video generation.
→ Stage I prioritizes prompt fidelity at a low 270p resolution. It employs a 5 billion parameter Diffusion Transformer (DiT) model with 50 function evaluations. This stage focuses on content and motion accuracy.
→ Stage II enhances visual quality at a high 1080p resolution. It uses a lighter 2 billion parameter DiT model with only 4 function evaluations. This stage emphasizes fine-grained details.
→ FlashVideo uses flow matching in Stage II to directly transform the low-resolution video from Stage I into a high-resolution video. This avoids starting from Gaussian noise, unlike traditional cascade models. Flow matching reduces the number of function evaluations needed in Stage II.
→ 3D Rotary Position Embedding (RoPE) is used in both stages for efficient spatiotemporal modeling, improving scalability to higher resolutions. Full 3D attention in Stage II maintains temporal consistency of details in videos with motion.
→ Latent degradation and pixel degradation techniques are used during Stage II training to simulate low-quality input and enhance detail generation and structural accuracy of small objects.
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Key Insights 💡:
→ Decoupling prompt fidelity and visual quality into two stages significantly improves efficiency and quality in high-resolution video generation.
→ Flow matching enables efficient detail enhancement in the second stage with minimal function evaluations by transforming the low-resolution video instead of generating from noise.
→ Using a larger model and more function evaluations at a lower resolution (Stage I) for content creation is computationally cheaper than doing so at high resolution.
→ A smaller model and fewer function evaluations (Stage II) are sufficient for detail enhancement when guided by the output of Stage I via flow matching.
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Results 📊:
→ FlashVideo achieves a VBench-Long total score of 82.99, outperforming other open-source models.
→ Function evaluation time for 1080p video generation is reduced to 102 seconds, which is about 1/20th of a single-stage DiT model and 5 times faster than vanilla cascade frameworks.
→ On Texture100 dataset, FlashVideo achieves MUSIQ score of 58.69, MANIQA score of 0.296, CLIPIQA score of 0.439, and NIQE score of 4.501, demonstrating superior frame and video quality compared to video enhancement methods like VEnhancer and Upscale-A-Video.