US20250111866
2025-04-03
Physics
G11B27/031
The patent application introduces a method for editing videos using image diffusion technology. It involves receiving an input video and a prompt specifying the desired edit. A keyframe from the video is identified and edited using a generative neural network based on the prompt. This edited keyframe serves as a reference for editing subsequent frames, ensuring consistency throughout the video.
Image diffusion models have become popular for their ability to generate diverse and high-quality images. They are particularly effective in editing real images and generating new ones based on textual prompts. These models, such as Denoising Diffusion Probabilistic Model (DDPM) and its variant DDIM, have been widely used for text-to-image generation, achieving impressive results.
The proposed technique leverages pre-trained image diffusion models to edit videos without requiring additional training or fine-tuning. By editing a keyframe and propagating those edits across the video, the method reduces visual artifacts like flickering. Attention layer manipulation and latent updates at each diffusion step ensure seamless integration of edits throughout the video.
This approach offers a training-free solution for video editing, utilizing existing image generation models. It eliminates the need for extensive pre-processing or computational overhead during inference, making it practical for real-world applications. The system can be implemented in various computing environments, such as standalone applications or integrated into cloud-based services.
The described technology has broad applications in video editing, allowing for controlled modifications based on user prompts. It paves the way for new advancements in video content creation and modification, providing tools that are both efficient and effective in maintaining visual quality across edited videos.