Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
Show-1 combines pixel and latent diffusion models for efficient text-to-video generation, using only 15G GPU memory.
Key Findings
Methodology
Show-1 integrates pixel and latent diffusion models. It initially uses pixel-based VDMs to generate low-resolution videos with strong text-video correlation. Subsequently, latent-based VDMs upsample these to high resolution, removing artifacts. This approach improves text-video alignment and reduces computational costs significantly.
Key Results
- Compared to latent VDMs, Show-1 excels in text-video alignment, reducing GPU memory usage from 72G to 15G.
- Show-1 achieves state-of-the-art performance on UCF101, MSR-VTT, and VBench benchmarks.
- Show-1 can be adapted for motion customization and video stylization through simple temporal attention layer finetuning.
Significance
Show-1 is significant in the text-to-video domain, addressing high computational costs of pixel VDMs and imprecise text-video alignment of latent VDMs. Its efficient computation and precise alignment offer new possibilities for academia and industry, especially in resource-constrained environments.
Technical Contribution
Show-1's technical contribution lies in its novel integration of pixel and latent diffusion models, forming an efficient text-to-video generation framework. Through innovative expert translation, Show-1 maintains high-quality video generation while significantly reducing computational resource demands.
Novelty
Show-1 is the first hybrid model combining pixel and latent diffusion models, solving their respective drawbacks and achieving efficient text-to-video generation. Compared to prior methods, Show-1 significantly improves text-video alignment and computational efficiency.
Limitations
- In extremely high-resolution video generation, computational resource bottlenecks may still exist.
- Limited capability in handling complex motion scenarios.
Future Work
Future research directions include further optimizing the model's computational efficiency, exploring more diverse application scenarios, and enhancing the handling of complex motion and high-resolution videos.
AI Executive Summary
In the text-to-video generation field, existing methods mainly rely on either pixel or latent diffusion models, each with drawbacks. Pixel models excel in text-video alignment but are computationally expensive; latent models are efficient but lack alignment precision.
The Show-1 model innovatively combines these two approaches. It first uses pixel diffusion models to generate low-resolution videos, ensuring strong text-video correlation. Then, latent diffusion models upsample these to high resolution, removing artifacts and corruptions. This method not only enhances alignment precision but also significantly reduces computational costs.
Experimental results show that Show-1 achieves state-of-the-art performance on multiple standard video generation benchmarks, reducing GPU memory usage from 72G to 15G. Additionally, Show-1 can be adapted for motion customization and video stylization through simple temporal attention layer finetuning. This breakthrough offers new possibilities in the text-to-video generation field.
Deep Analysis
Background
Recent advancements in text-to-video generation have been driven by large-scale pre-trained diffusion models. Existing methods are mainly categorized into pixel and latent diffusion models. Pixel models directly denoise pixel values, achieving better text alignment but at high computational costs; latent models operate in latent space, offering computational efficiency but lacking alignment precision.
Core Problem
The core problem is how to maintain text-video alignment precision while reducing computational costs. Existing methods either compromise on precision or consume excessive computational resources, limiting practical application.
Innovation
Show-1's core innovation lies in combining pixel and latent diffusion models into an efficient text-to-video generation framework. By using pixel models for low-resolution stages to ensure alignment precision and latent models for high-resolution upsampling, it addresses each method's drawbacks.
Methodology
- �� Use pixel diffusion models to generate low-resolution videos, ensuring text-video alignment.
- �� Upsample using latent diffusion models, removing artifacts and corruptions.
- �� Employ expert translation to optimize latent model upsampling.
- �� Fine-tune temporal attention layers for motion customization and video stylization.
Experiments
Experiments were conducted on UCF101, MSR-VTT, and VBench benchmarks, using the same T5 text encoder and LAION pre-trained weights. Baselines included pure pixel and latent diffusion models, with evaluation metrics focusing on text-video alignment precision and computational cost.
Results
Show-1 excels in text-video alignment, reducing GPU memory usage from 72G to 15G. It achieves state-of-the-art performance on multiple benchmarks, especially in handling complex motion scenarios, outperforming existing methods.
Applications
Show-1 can be used for efficient video generation in resource-constrained environments. With simple finetuning, it can also achieve video motion customization and stylization, offering broad application prospects.
Limitations & Outlook
While Show-1 achieves breakthroughs in computational efficiency and alignment precision, computational resource bottlenecks may still exist in extremely high-resolution video generation. Additionally, its capability in handling complex motion scenarios is limited, requiring further optimization.
Plain Language Accessible to non-experts
Imagine a factory where the pixel diffusion model acts as the initial production line, producing low-resolution products that closely match the design blueprint. The latent diffusion model is the advanced production line, refining these initial products to enhance quality and resolution. This way, the factory ensures high-quality products while reducing production costs.
ELI14 Explained like you're 14
Imagine you're making a video game. The pixel diffusion model is like building a basic scene with LEGO, simple but matching your design. Then, the latent diffusion model is like painting your LEGO scene, making it more detailed and realistic. This way, you can create a cool game scene with fewer bricks and paint!
Glossary
Diffusion Model
A generative model that synthesizes data by progressively denoising. Used for generating high-quality text-to-video outputs.
Used to generate high-quality videos from text.
Pixel-based VDM
A diffusion model that directly denoises pixel values.
Used to generate low-resolution videos ensuring text alignment.
Latent-based VDM
A diffusion model operating in latent space.
Used for efficient high-resolution upsampling of low-resolution videos.
Expert Translation Method
A method to optimize latent model upsampling effects.
Used to remove artifacts and corruptions from low-resolution videos.
Temporal Attention Layer
An attention mechanism for capturing temporal dynamics in videos.
Used for motion customization and video stylization.
Open Questions Unanswered questions from this research
- 1 How to maintain computational efficiency at extremely high resolutions?
- 2 How to further enhance handling of complex motion scenarios?
Applications
Immediate Applications
Efficient Video Generation
Show-1 can be used to generate high-quality videos in resource-constrained environments, such as mobile devices and cloud computing.
Long-term Vision
Video Stylization and Customization
With simple finetuning, Show-1 can be used for video stylization and motion customization, offering broad application prospects.
Abstract
Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution, which can also remove potential artifacts and corruptions from low-resolution videos. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). Furthermore, our Show-1 model can be readily adapted for motion customization and video stylization applications through simple temporal attention layer finetuning. Our model achieves state-of-the-art performance on standard video generation benchmarks. Our code and model weights are publicly available at https://github.com/showlab/Show-1.