T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback

• URL: http://arxiv.org/abs/2405.18750v2
• Date: Fri, 11 Oct 2024 07:50:49 GMT
• Title: T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback
• Authors: Jiachen Li, Weixi Feng, Tsu-Jui Fu, Xinyi Wang, Sugato Basu, Wenhu Chen, William Yang Wang,
• Abstract summary: We introduce T2V-Turbo, which integrates feedback from a mixture of differentiable reward models into the consistency distillation process of a pre-trained T2V model. Remarkably, the 4-step generations from our T2V-Turbo achieve the highest total score on VBench, even surpassing Gen-2 and Pika.
• Score: 111.40967379458752
• License:
• Abstract: Diffusion-based text-to-video (T2V) models have achieved significant success but continue to be hampered by the slow sampling speed of their iterative sampling processes. To address the challenge, consistency models have been proposed to facilitate fast inference, albeit at the cost of sample quality. In this work, we aim to break the quality bottleneck of a video consistency model (VCM) to achieve $\textbf{both fast and high-quality video generation}$. We introduce T2V-Turbo, which integrates feedback from a mixture of differentiable reward models into the consistency distillation (CD) process of a pre-trained T2V model. Notably, we directly optimize rewards associated with single-step generations that arise naturally from computing the CD loss, effectively bypassing the memory constraints imposed by backpropagating gradients through an iterative sampling process. Remarkably, the 4-step generations from our T2V-Turbo achieve the highest total score on VBench, even surpassing Gen-2 and Pika. We further conduct human evaluations to corroborate the results, validating that the 4-step generations from our T2V-Turbo are preferred over the 50-step DDIM samples from their teacher models, representing more than a tenfold acceleration while improving video generation quality.

Related papers

• DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization [50.30051934609654]
  We introduce a distillation method that combines variational score distillation and consistency distillation to achieve few-step video generation. Our method demonstrates state-of-the-art performance in few-step generation for 10-second videos (128 frames at 12 FPS) One-step distillation accelerates the teacher model's diffusion sampling by up to 278.6 times, enabling near real-time generation.
  arXiv  Detail & Related papers  (2024-12-20T09:07:36Z)
• From Slow Bidirectional to Fast Autoregressive Video Diffusion Models [52.32078428442281]
  Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models.
  arXiv  Detail & Related papers  (2024-12-10T18:59:50Z)
• FrameBridge: Improving Image-to-Video Generation with Bridge Models [23.19370431940568]
  Image-to-video (I2V) generation is gaining increasing attention with its wide application in video synthesis. We present FrameBridge, taking the given static image as the prior of video target and establishing a tractable bridge model between them. We propose two techniques, SNR- Fine-tuning (SAF) and neural prior, which improve the fine-tuning efficiency of diffusion-based T2V models to FrameBridge and the synthesis quality of bridge-based I2V models respectively.
  arXiv  Detail & Related papers  (2024-10-20T12:10:24Z)
• T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design [79.7289790249621]
  Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals. We highlight the crucial importance of tailoring datasets to specific learning objectives. We demonstrate the potential of this approach by extracting motion guidance from the training datasets and incorporating it into the ODE solver.
  arXiv  Detail & Related papers  (2024-10-08T04:30:06Z)
• Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching [51.70360630470263]
  Video-to-audio (V2A) generation aims to synthesize content-matching audio from silent video. We propose Frieren, a V2A model based on rectified flow matching. Experiments indicate that Frieren achieves state-of-the-art performance in both generation quality and temporal alignment.
  arXiv  Detail & Related papers  (2024-06-01T06:40:22Z)
• Improved Techniques for Training Consistency Models [13.475711217989975]
  We present improved techniques for consistency training, where consistency models learn directly from data without distillation. We propose a lognormal noise schedule for the consistency training objective, and propose to double total discretization steps every set number of training iterations. These modifications enable consistency models to achieve FID scores of 2.51 and 3.25 on CIFAR-10 and ImageNet $64times 64$ respectively in a single sampling step.
  arXiv  Detail & Related papers  (2023-10-22T05:33:38Z)
• Consistency Models [89.68380014789861]
  We propose a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training.
  arXiv  Detail & Related papers  (2023-03-02T18:30:16Z)

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.