NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
NUWA-XL uses 'Diffusion over Diffusion' for ultra-long video generation, reducing inference time by 94.26%.
Key Findings
Methodology
NUWA-XL employs a 'Diffusion over Diffusion' architecture, where a global diffusion model generates keyframes and local diffusion models fill in between frames. This allows for parallel inference, significantly enhancing generation efficiency.
Key Results
- On the FlintstonesHD dataset, NUWA-XL reduced inference time for generating 1024 frames from 7.55 minutes to 26 seconds, with average FID dropping from 48.56 to 35.79.
- Compared to existing methods, NUWA-XL shows higher frame consistency and detail fidelity in long video generation.
- Ablation studies confirm that multi-scale and symmetry injection strategies significantly improve video quality.
Significance
NUWA-XL is significant in the long video generation field, addressing the training-inference gap and enhancing inference speed through parallelization. This breakthrough opens new possibilities for generating high-quality, long-duration video content, particularly in film production and virtual reality.
Technical Contribution
NUWA-XL is the first diffusion model trained directly on long videos, eliminating the training-inference gap. Its 'coarse-to-fine' generation strategy and parallel inference capability significantly improve the efficiency and quality of long video generation.
Novelty
NUWA-XL is the first diffusion model trained directly on long videos, using a 'Diffusion over Diffusion' architecture that significantly enhances generation efficiency and video quality compared to traditional 'autoregressive' methods.
Limitations
- Currently validated only on the FlintstonesHD dataset, lacking tests on open-domain long videos.
- High data demand may pose challenges when training on long videos.
- Parallel inference requires reasonable GPU resources.
Future Work
Future work includes extending to open-domain long video datasets and optimizing the model to reduce dependency on data and computational resources. Exploring more efficient parallel inference strategies is also a key direction.
AI Executive Summary
NUWA-XL addresses the training-inference gap in long video generation with a 'Diffusion over Diffusion' architecture. Traditional methods train on short videos, facing frame inconsistency and long inference times when generating long videos. NUWA-XL uses a global diffusion model to generate keyframes and local diffusion models to fill in frame content, achieving parallel inference and significantly enhancing efficiency.
Experimental results show that NUWA-XL reduces inference time for generating 1024 frames from 7.55 minutes to 26 seconds on the FlintstonesHD dataset, with a significant drop in average FID. By training directly on long videos, NUWA-XL overcomes the limitations of traditional methods, producing videos with superior frame consistency and detail fidelity.
Despite significant progress in long video generation, NUWA-XL still needs validation on open-domain videos and model optimization to reduce resource dependency. Future research directions include dataset expansion and improved parallel inference strategies to further enhance generation efficiency and quality.
Deep Analysis
Background
Video generation technology has made significant strides recently, especially in short video generation. However, long video generation remains challenging due to high computational resource demands and the training-inference gap. Existing methods often use 'autoregressive' architectures, training on short videos and generating long videos through a sliding window, but face frame inconsistency and long inference times.
Core Problem
The core problem in long video generation is the gap between training and inference. Traditional methods train on short videos, leading to frame inconsistency and long inference times when generating long videos, making it crucial to improve video generation quality and efficiency.
Innovation
NUWA-XL's core innovation lies in its 'Diffusion over Diffusion' architecture. By using a global diffusion model to generate keyframes and local diffusion models to fill in between frames, it achieves parallel inference, improving generation efficiency and eliminating the training-inference gap.
Methodology
- �� Global diffusion model generates keyframes, forming a 'coarse' storyline.
- �� Local diffusion models fill in content between adjacent frames, generating 'fine' frames.
- �� Parallel inference significantly improves generation efficiency, supporting direct training on long videos.
Experiments
Experiments were conducted on the FlintstonesHD dataset, using average FID and B-FVD-16 to evaluate generation quality. Compared to methods like Phenaki and FDM, NUWA-XL shows significant improvements in generation quality and inference speed. Ablation studies confirmed the effectiveness of multi-scale and symmetry injection strategies.
Results
NUWA-XL reduced inference time for generating 1024 frames from 7.55 minutes to 26 seconds, with average FID dropping from 48.56 to 35.79. Compared to existing methods, NUWA-XL shows superior frame consistency and detail fidelity.
Applications
NUWA-XL can be applied in film production, virtual reality, and other fields requiring long video generation. Its efficient parallel inference capability makes it advantageous in scenarios requiring rapid generation of high-quality videos.
Limitations & Outlook
Currently validated only on the FlintstonesHD dataset, lacking tests on open-domain long videos. High data demand may pose challenges when training on long videos. Parallel inference requires reasonable GPU resources.
Plain Language Accessible to non-experts
Imagine you're making an animated movie. Traditional methods are like drawing each scene frame by frame, which is time-consuming and error-prone. NUWA-XL is like sketching key scenes first, then automatically filling in details, making the process faster and more effective. It processes multiple scenes in parallel, significantly reducing production time.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. Traditional methods are like putting together each piece one by one, which takes a lot of time and can be frustrating. NUWA-XL is like putting together key sections first, then automatically filling in the rest, making it faster and more fun. It processes multiple sections at once, cutting down puzzle time dramatically!
Glossary
Diffusion Model
A generative model that generates data by gradually adding noise. Used to generate keyframes and fill in between frames.
Used for generating keyframes and filling in content between frames.
Autoregressive Model
A generative model that relies on previous data to generate the next data point. Commonly used in traditional long video generation architectures.
Used in traditional long video generation methods.
FID (Fréchet Inception Distance)
A metric for evaluating image generation quality; lower values indicate higher quality.
Used to evaluate the average frame quality of generated videos.
B-FVD (Block Fréchet Video Distance)
A metric for evaluating video generation quality by splitting long videos into short clips to calculate average FVD.
Used to evaluate the quality of long video generation.
Parallel Inference
A method of processing multiple tasks simultaneously to improve computational efficiency.
NUWA-XL significantly improves generation efficiency through parallel inference.
Open Questions Unanswered questions from this research
- 1 How to validate NUWA-XL's effectiveness on open-domain long videos?
- 2 How to reduce NUWA-XL's dependency on data and computational resources?
Applications
Immediate Applications
Film Production
NUWA-XL can be used to quickly generate high-quality long videos, enhancing film production efficiency.
Virtual Reality
Applying NUWA-XL in virtual reality can generate high-quality immersive video experiences.
Long-term Vision
Open-domain Long Video Generation
Extending NUWA-XL to open-domain long video generation requires addressing data and resource challenges.
Abstract
In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a ``coarse-to-fine'' process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap, and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26\%) at the same hardware setting when generating 1024 frames. The homepage link is \url{https://msra-nuwa.azurewebsites.net/}