MagicVideo: Efficient Video Generation With Latent Diffusion Models (2211.11018v2)

Abstract: We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. MagicVideo can generate smooth video clips that are concordant with the given text descriptions. Due to a novel and efficient 3D U-Net design and modeling video distributions in a low-dimensional space, MagicVideo can synthesize video clips with 256x256 spatial resolution on a single GPU card, which takes around 64x fewer computations than the Video Diffusion Models (VDM) in terms of FLOPs. In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model. Besides, we introduce two new designs to adapt the U-Net denoiser trained on image tasks to video data: a frame-wise lightweight adaptor for the image-to-video distribution adjustment and a directed temporal attention module to capture temporal dependencies across frames. Thus, we can exploit the informative weights of convolution operators from a text-to-image model for accelerating video training. To ameliorate the pixel dithering in the generated videos, we also propose a novel VideoVAE auto-encoder for better RGB reconstruction. We conduct extensive experiments and demonstrate that MagicVideo can generate high-quality video clips with either realistic or imaginary content. Refer to \url{https://magicvideo.github.io/#} for more examples.

MagicVideo: Efficient Video Generation with Latent Diffusion Models

The paper "MagicVideo: Efficient Video Generation with Latent Diffusion Models" presents a novel framework for text-to-video generation, leveraging the efficiency of latent diffusion models (LDMs). This framework, dubbed MagicVideo, is designed to generate high-quality video clips that align well with given textual prompts, achieving a significant reduction in computational cost compared to existing models.

The authors propose MagicVideo, a framework that utilizes a 3D U-Net architecture adapted for video generation in a latent space, enabling the synthesis of 256x256 resolution video clips on a single GPU. The computational efficiency claimed is substantial, with the framework reportedly requiring approximately 64 times fewer FLOPs than the Video Diffusion Models (VDM). This efficiency gain is primarily achieved through modeling video distributions in a low-dimensional latent space, facilitated by a pre-trained variational autoencoder (VAE).

A key innovation in the MagicVideo framework is the introduction of a novel 3D U-Net design featuring a frame-wise lightweight adaptor and a directed temporal attention module. These innovations enable the adaptation of a text-to-image model's convolutional operators for video data, thereby leveraging pre-trained image model weights to accelerate video model training. The frame-wise adaptor facilitates image-to-video distribution adjustments, while the directed temporal attention module captures temporal dependencies across video frames, enhancing motion consistency.

Moreover, the framework also proposes VideoVAE, a novel auto-encoder aimed at improving RGB video reconstruction quality by addressing pixel dithering issues in generated videos. Through extensive experimentation, the authors demonstrate the capability of MagicVideo to generate videos with realistic and imaginative content, as exemplified by the outputs faithful to varying text prompts in the paper.

Numerically, MagicVideo is presented as a computationally efficient solution, capable of generating content with high temporal coherence and fidelity, surpassing existing methods such as those described in recent video diffusion models. The practical implications are significant, as this framework lowers the resource barrier to entry for high-quality video generation, making it more accessible for a range of applications including entertainment and art creation.

Theoretically, the paper contributes to the ongoing conversation on the utility of LDMs beyond image generation, presenting robust evidence for their application in video data modeling. The use of LDMs in video generation highlights the potential for further innovations in diffusion models, especially in the area of temporal data.

Future research directions stemming from this work could involve exploring higher resolution video generation while maintaining efficiency, and extending the approach to other modalities such as audio or 3D environments. Additionally, addressing the ethical implications and biases inherent in utilizing large pre-trained datasets for generative models remains a critical area for further examination.

In conclusion, the MagicVideo framework marks an advance in efficient video generation, leveraging latent diffusion techniques to achieve significant computational savings while maintaining high output quality. The paper offers both practical methods and theoretical insights that may inspire further research and development in the field of AI-driven media generation.