Explore AnimateDiff’s performance with user rankings and CLIP metrics, comparing text alignment, domain similarity, and motion smoothness in video generation.

AnimateDiff enables efficient animation of personalized T2Is, outperforming other models with MotionLoRA for improved shot control and composition.

Explore the training and inference process of AnimateDiff, detailing how the domain adapter, motion module, and MotionLoRA work in tandem.

The AnimateDiff motion module uses temporal Transformers to model motion dynamics, enabling smooth, high-quality animations from 2D diffusion models.

Addressing domain gaps between high-quality image datasets and lower-quality video datasets, the proposed domain adapter uses LoRA to mitigate quality issues.

MotionLoRA aids efficient fine-tuning of motion modules in AnimateDiff, adapting to unique motion patterns like zooming or panning with minimal reference videos

This paper lays the groundwork for AnimateDiff by explaining Stable Diffusion's latent space operations and LoRA's efficient model fine-tuning.

A comprehensive overview of advancements in text-to-image diffusion models, including personalization techniques and strides in animating these models.

Discover AnimateDiff, a groundbreaking framework that animates text-to-image diffusion models without fine-tuning.

Ablative study of AnimateDiff reveals how domain adapter, motion module design, and MotionLoRA improve visual quality, motion learning, and efficiency.

This statement outlines efforts to ensure the reproducibility of AI research

AnimateDiff acknowledges the potential misuse of generative AI for harmful content and is committed to upholding ethical standards.

AnimateDiff transforms personalized T2I models into high-quality animations, using MotionLoRA for motion personalization and seamless animation generation.

AnimateDiff's ability to separate visual content and motion priors allows for precise control over video generation.