Imagen Video: High Definition Video Generation with Diffusion Models (2210.02303v1)

Abstract: We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we scale up the system as a high definition text-to-video model including design decisions such as the choice of fully-convolutional temporal and spatial super-resolution models at certain resolutions, and the choice of the v-parameterization of diffusion models. In addition, we confirm and transfer findings from previous work on diffusion-based image generation to the video generation setting. Finally, we apply progressive distillation to our video models with classifier-free guidance for fast, high quality sampling. We find Imagen Video not only capable of generating videos of high fidelity, but also having a high degree of controllability and world knowledge, including the ability to generate diverse videos and text animations in various artistic styles and with 3D object understanding. See https://imagen.research.google/video/ for samples.

Imagen Video: High Definition Video Generation with Diffusion Models

The paper "Imagen Video: High Definition Video Generation with Diffusion Models" presents a novel approach to generating high-definition (HD) videos from text inputs using a cascade of video diffusion models. This methodology leverages advancements in text-to-image generation and extends them to the temporal domain of video generation, providing a comprehensive pipeline that maintains high fidelity across both spatial and temporal domains.

Key Contributions and Architectural Highlights

Cascaded Diffusion Models

The core innovation of Imagen Video lies in its cascaded diffusion model architecture, consisting of a base video generation model followed by successive spatial and temporal super-resolution models. Specifically, the architecture begins with the generation of low-resolution video frames that are progressively enhanced to HD quality through a series of super-resolution steps. This cascading method scales effectively to handle the increased dimensions inherent in video data, enabling the production of 1280×768 resolution videos at 24 frames per second.

Diffusion Model Techniques

1. Base Video Generation Model: The base video model employs a Video U-Net architecture that integrates spatial and temporal convolutions with attention mechanisms, allowing for the generation of temporally coherent and spatially detailed video segments. This model alone supports up to 128 frames.
2. Super-Resolution Models: The spatial super-resolution models enhance frame resolution, while temporal super-resolution models interpolate frames to maintain consistency and smoothness in motion. These models ensure that the generated videos maintain high fidelity and continuity at every resolution scale.

Important Findings and Techniques

Text Conditioning and $v$-Prediction

Text conditioning is achieved using embeddings from a frozen T5-XXL text encoder, which has proven crucial for generating high-quality videos consistent with text prompts. Additionally, the use of the $v$-prediction parameterization (where $v \equiv \alpha_t - \sigma_t x$) is emphasized for its numerical stability and ability to avoid common artifacts like color shifting, especially in higher-resolution models.

Classifier-Free Guidance

To ensure the generated videos closely align with text prompts, classifier-free guidance is employed. This method involves adjusting the denoising model’s predictions by emphasizing the text-conditioned signal, substantially enhancing perceptual quality and alignment. Dynamic clipping and oscillating guidance weights are used to mitigate saturation issues, a common problem in large guidance settings.

Evaluation and Performance

The efficacy of the proposed architecture is validated through extensive experiments that showcase the Imagen Video system's ability to generate diverse and detailed videos. The paper provides comprehensive evaluation metrics such as FID, FVD, and CLIP scores, with the results suggesting that the proposed $v$-parameterization converges more rapidly than $$-prediction in terms of sample quality metrics.</p> <h3 class='paper-heading'>Implications and Future Work</h3> <p>The introduction of Imagen Video signifies significant progress toward generating complex visual content purely from textual descriptions, expanding the potential applications of generative models. In practice, this technology could revolutionize creative industries such as animation, filmmaking, and game design by automating the generation of consistent and high-fidelity video content.</p> <p>However, ethical concerns must be addressed, particularly regarding the misuse of generative models for producing deceptive or harmful content. The paper acknowledges these risks and underscores the importance of implementing robust filter mechanisms and further developing ethical guidelines for deploying such technologies.</p> <h3 class='paper-heading'>Conclusion</h3> <p>Imagen Video represents a significant step forward in the field of generative modeling by successfully scaling text-to-image diffusion models to video generation. The cascade of video diffusion models, text conditioning using frozen T5-XXL embeddings, and advanced techniques like classifier-free guidance and$$v$-prediction contribute to its ability to generate high-definition, temporally coherent videos from text inputs. Future advancements are expected to further enhance the performance and applicability of such models, ensuring they remain aligned with ethical standards in AI development.