Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient

• URL: http://arxiv.org/abs/2411.17787v1
• Date: Tue, 26 Nov 2024 15:13:15 GMT
• Title: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient
• Authors: Zigeng Chen, Xinyin Ma, Gongfan Fang, Xinchao Wang,
• Abstract summary: Collaborative Decoding (CoDe) is a novel efficient decoding strategy tailored for the Visual Auto-Regressive ( VAR) framework. CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales. CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98.
• Score: 52.96232442322824
• License:
• Abstract: In the rapidly advancing field of image generation, Visual Auto-Regressive (VAR) modeling has garnered considerable attention for its innovative next-scale prediction approach. This paradigm offers substantial improvements in efficiency, scalability, and zero-shot generalization. Yet, the inherently coarse-to-fine nature of VAR introduces a prolonged token sequence, leading to prohibitive memory consumption and computational redundancies. To address these bottlenecks, we propose Collaborative Decoding (CoDe), a novel efficient decoding strategy tailored for the VAR framework. CoDe capitalizes on two critical observations: the substantially reduced parameter demands at larger scales and the exclusive generation patterns across different scales. Based on these insights, we partition the multi-scale inference process into a seamless collaboration between a large model and a small model. The large model serves as the 'drafter', specializing in generating low-frequency content at smaller scales, while the smaller model serves as the 'refiner', solely focusing on predicting high-frequency details at larger scales. This collaboration yields remarkable efficiency with minimal impact on quality: CoDe achieves a 1.7x speedup, slashes memory usage by around 50%, and preserves image quality with only a negligible FID increase from 1.95 to 1.98. When drafting steps are further decreased, CoDe can achieve an impressive 2.9x acceleration ratio, reaching 41 images/s at 256x256 resolution on a single NVIDIA 4090 GPU, while preserving a commendable FID of 2.27. The code is available at https://github.com/czg1225/CoDe

Related papers

• Improving the Diffusability of Autoencoders [54.920783089085035]
  Latent diffusion models have emerged as the leading approach for generating high-quality images and videos. We perform a spectral analysis of modern autoencoders and identify inordinate high-frequency components in their latent spaces. We hypothesize that this high-frequency component interferes with the coarse-to-fine nature of the diffusion synthesis process and hinders the generation quality.
  arXiv  Detail & Related papers  (2025-02-20T18:45:44Z)
• Masked Generative Nested Transformers with Decode Time Scaling [21.34984197218021]
  In this work, we aim to address the bottleneck of inference computational efficiency in visual generation algorithms. We design a decode time model scaling schedule to utilize compute effectively, and we can cache and reuse some of the computation. Our experiments show that with almost $3times$ less compute than baseline, our model obtains competitive performance.
  arXiv  Detail & Related papers  (2025-02-01T09:41:01Z)
• SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer [45.720721058671856]
  SoftVQ-VAE is a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token. Our approach compresses 256x256 and 512x512 images using as few as 32 or 64 1-dimensional tokens. Remarkably, SoftVQ-VAE improves inference throughput by up to 18x for generating 256x256 images and 55x for 512x512 images.
  arXiv  Detail & Related papers  (2024-12-14T20:29:29Z)
• M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation [39.97174784206976]
  We show that this scale-wise autoregressive framework can be effectively decoupled into textitintra-scale modeling We apply linear-complexity mechanisms like Mamba to substantially reduce computational overhead. Experiments demonstrate that our method outperforms existing models in both image quality and generation speed.
  arXiv  Detail & Related papers  (2024-11-15T18:54:42Z)
• LinFusion: 1 GPU, 1 Minute, 16K Image [71.44735417472043]
  We introduce a low-rank approximation of a wide spectrum of popular linear token mixers. We find that the distilled model, termed LinFusion, achieves performance on par with or superior to the original SD. Experiments on SD-v1.5, SD-v2.1, and SD-XL demonstrate that LinFusion enables satisfactory and efficient zero-shot cross-resolution generation.
  arXiv  Detail & Related papers  (2024-09-03T17:54:39Z)
• Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization [26.926712014346432]
  This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, setting new state-of-the-art FID scores of 1.70 on ImageNet 256 x 256 and 2.89 on ImageNet 512 x 512.
  arXiv  Detail & Related papers  (2024-06-13T17:59:58Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting [48.59338619051709]
  HiFi4G is an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame.
  arXiv  Detail & Related papers  (2023-12-06T12:36:53Z)
• ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression [18.05997169440533]
  We propose ConvNeXt-ChARM, an efficient ConvNeXt-based transform coding framework, paired with a compute-efficient channel-wise auto-regressive auto-regressive. We show that ConvNeXt-ChARM brings consistent and significant BD-rate (PSNR) reductions estimated on average to 5.24% and 1.22% over the versatile video coding (VVC) reference encoder (VTM-18.0) and the state-of-the-art learned image compression method SwinT-ChARM.
  arXiv  Detail & Related papers  (2023-07-12T11:45:54Z)
• Highly Efficient Salient Object Detection with 100K Parameters [137.74898755102387]
  We propose a flexible convolutional module, namely generalized OctConv (gOctConv), to efficiently utilize both in-stage and cross-stages multi-scale features. We build an extremely light-weighted model, namely CSNet, which achieves comparable performance with about 0.2% (100k) of large models on popular object detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-12T07:00:46Z)

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.