LTX-Video: Realtime Video Latent Diffusion

• URL: http://arxiv.org/abs/2501.00103v1
• Date: Mon, 30 Dec 2024 19:00:25 GMT
• Title: LTX-Video: Realtime Video Latent Diffusion
• Authors: Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, Ofir Bibi,
• Abstract summary: LTX-Video is a transformer-based latent diffusion model. It seamlessly integrates the Video-VAE and the denoising transformer. It achieves faster-than-real-time generation, producing 5 seconds of 24 fps video 768 atx512 resolution in just 2 seconds on an Nvidia H100 GPU.
• Score: 4.7789714048042775
• License:
• Abstract: We introduce LTX-Video, a transformer-based latent diffusion model that adopts a holistic approach to video generation by seamlessly integrating the responsibilities of the Video-VAE and the denoising transformer. Unlike existing methods, which treat these components as independent, LTX-Video aims to optimize their interaction for improved efficiency and quality. At its core is a carefully designed Video-VAE that achieves a high compression ratio of 1:192, with spatiotemporal downscaling of 32 x 32 x 8 pixels per token, enabled by relocating the patchifying operation from the transformer's input to the VAE's input. Operating in this highly compressed latent space enables the transformer to efficiently perform full spatiotemporal self-attention, which is essential for generating high-resolution videos with temporal consistency. However, the high compression inherently limits the representation of fine details. To address this, our VAE decoder is tasked with both latent-to-pixel conversion and the final denoising step, producing the clean result directly in pixel space. This approach preserves the ability to generate fine details without incurring the runtime cost of a separate upsampling module. Our model supports diverse use cases, including text-to-video and image-to-video generation, with both capabilities trained simultaneously. It achieves faster-than-real-time generation, producing 5 seconds of 24 fps video at 768x512 resolution in just 2 seconds on an Nvidia H100 GPU, outperforming all existing models of similar scale. The source code and pre-trained models are publicly available, setting a new benchmark for accessible and scalable video generation.

Related papers

• Pyramidal Flow Matching for Efficient Video Generative Modeling [67.03504440964564]
  This work introduces a unified pyramidal flow matching algorithm. It sacrifices the original denoising trajectory as a series of pyramid stages, where only the final stage operates at the full resolution. The entire framework can be optimized in an end-to-end manner and with a single unified Diffusion Transformer (DiT)
  arXiv  Detail & Related papers  (2024-10-08T12:10:37Z)
• ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler [53.98558445900626]
  Current image-to-video diffusion models, while powerful in generating videos from a single frame, need adaptation for two-frame conditioned generation. We introduce a novel, bidirectional sampling strategy to address these off-manifold issues without requiring extensive re-noising or fine-tuning. Our method employs sequential sampling along both forward and backward paths, conditioned on the start and end frames, respectively, ensuring more coherent and on-manifold generation of intermediate frames.
  arXiv  Detail & Related papers  (2024-10-08T03:01:54Z)
• xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations [120.52120919834988]
  xGen-SynVideo-1 is a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens. DiT model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios.
  arXiv  Detail & Related papers  (2024-08-22T17:55:22Z)
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer [55.515836117658985]
  We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer. It can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
  arXiv  Detail & Related papers  (2024-08-12T11:47:11Z)
• Hierarchical Patch Diffusion Models for High-Resolution Video Generation [50.42746357450949]
  We develop deep context fusion, which propagates context information from low-scale to high-scale patches in a hierarchical manner. We also propose adaptive computation, which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation.
  arXiv  Detail & Related papers  (2024-06-12T01:12:53Z)
• Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation [115.09597127418452]
  Latent-Shift is an efficient text-to-video generation method based on a pretrained text-to-image generation model. We show that Latent-Shift achieves comparable or better results while being significantly more efficient.
  arXiv  Detail & Related papers  (2023-04-17T17:57:06Z)
• VDTR: Video Deblurring with Transformer [24.20183395758706]
  Videoblurring is still an unsolved problem due to the challenging-temporal modeling process. This paper presents VDTR, an effective Transformer-based model that makes the first attempt to adapt for Transformer video dering.
  arXiv  Detail & Related papers  (2022-04-17T14:22:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.