VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models

• URL: http://arxiv.org/abs/2412.00156v2
• Date: Tue, 03 Dec 2024 07:18:25 GMT
• Title: VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models
• Authors: Taesung Kwon, Jong Chul Ye,
• Abstract summary: In this paper, we propose a framework for solving high-definition video inverse problems using latent image diffusion models. Our approach leverages latent-space diffusion models to achieve enhanced video quality and resolution. Unlike previous methods, our approach supports multiple aspect ratios and delivers HD-resolution reconstructions in under 2.5 minutes on a single GPU.
• Score: 58.464465016269614
• License:
• Abstract: In this paper, we propose a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements in spatio-temporal optimization for video inverse problems using image diffusion models, our approach leverages latent-space diffusion models to achieve enhanced video quality and resolution. To address the high computational demands of processing high-resolution frames, we introduce a pseudo-batch consistent sampling strategy, allowing efficient operation on a single GPU. Additionally, to improve temporal consistency, we present batch-consistent inversion, an initialization technique that incorporates informative latents from the measurement frame. By integrating with SDXL, our framework achieves state-of-the-art video reconstruction across a wide range of spatio-temporal inverse problems, including complex combinations of frame averaging and various spatial degradations, such as deblurring, super-resolution, and inpainting. Unlike previous methods, our approach supports multiple aspect ratios (landscape, vertical, and square) and delivers HD-resolution reconstructions (exceeding 1280x720) in under 2.5 minutes on a single NVIDIA 4090 GPU.

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