AnimateDiff-Lightning: Cross-Model Diffusion Distillation for Fast Video Generation

Temel Kavramlar

Progressive adversarial diffusion distillation enhances video generation speed and quality.

Özet

Introduction

Video generative models gaining attention.
AnimateDiff popular for video generation.

Background

Diffusion models crucial for video generation.
Progressive adversarial diffusion distillation explained.

Method

Shared distilled motion module on multiple base models.
Model and data preparation detailed.

Evaluation

Qualitative comparison with AnimateLCM and original model.
Quantitative evaluation against different image base models.

Ablation

Effects of cross-model distillation and unseen base models discussed.

Conclusion

AnimateDiff-Lightning offers fast video generation with state-of-the-art results.

References

Various references cited in the content.

İstatistikler

State-of-the-art generative models are slow due to iterative diffusion process.
AnimateLCM can generate great quality videos with eight inference steps but shows artifacts with four steps.
Our proposed AnimateDiff-Lightning out-competes AnimateLCM in generating better quality videos in fewer inference steps.

Alıntılar

"Our proposed AnimateDiff-Lightning can generate better quality videos in fewer inference steps, out-competing the prior video distillation method AnimateLCM."
"Among all methods, AnimateDiff is one of the most popular video generation models."

Önemli Bilgiler Şuradan Elde Edildi

AnimateDiff-Lightning

by Shanchuan Li... : arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12706.pdf

Daha Derin Sorular

How can cross-model diffusion distillation be applied to other modalities beyond video

Cross-model diffusion distillation can be applied to other modalities beyond video by adapting the same principles and techniques used in video generation to different domains. For instance, in text-to-image generation, one could train a shared distilled module on multiple base image models simultaneously. This approach would involve keeping the weights of the image base model unchanged while updating only the motion module during distillation. By spreading different base models across processing units and modifying frameworks like PyTorch Distributed Data Parallel (DDP) to prevent synchronization issues, one can ensure that the distilled module is trained effectively on various base models.

What are the potential drawbacks or limitations of progressive adversarial diffusion distillation

Progressive adversarial diffusion distillation has several potential drawbacks or limitations that need to be considered:

Complexity: The method involves training a student network to predict future flow locations based on teacher networks through progressive steps, which can increase complexity.
Adversarial Loss Trade-off: Balancing quality and mode coverage with adversarial loss introduces challenges as exact matching with mean squared error may not always be achievable due to reduced model capacity.
Numerical Instability: Issues related to numerical instability may arise when using epsilon formulations for prediction tasks, leading to artifacts like heavy noise or brightness flickers in generated content.
Training Overhead: Training large-scale diffusion models with progressive adversarial distillation requires significant computational resources and time.

How might the use of Motion LoRAs impact the fine-grained control over generated videos

The use of Motion LoRAs (Low-rank Adaptation modules) can significantly impact fine-grained control over generated videos by allowing for precise adjustments in camera motions such as zooming, panning, tilting, rolling, etc., within each frame of the video sequence. These modules enhance motion dynamics and enable more detailed manipulation of movement patterns throughout the generated content. By incorporating Motion LoRAs into generative models like AnimateDiff-Lightning, users gain greater flexibility in shaping specific aspects of video output according to their preferences or creative requirements.