EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching

• URL: http://arxiv.org/abs/2410.23788v1
• Date: Thu, 31 Oct 2024 10:13:05 GMT
• Title: EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
• Authors: Xinwang Chen, Ning Liu, Yichen Zhu, Feifei Feng, Jian Tang,
• Abstract summary: Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs. This work proposes the Efficient Diffusion Transformer (EDT) framework to reduce the computation budget of transformer-based DPMs. With lower FID, EDT-S, EDT-B, and EDT-XL attained speed-ups of 3.93x, 2.84x, and 1.92x respectively in the training phase, and 2.29x, 2.29x, and 2.22x respectively in inference.
• Score: 20.728136287477277
• License:
• Abstract: Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical applications. To reduce the computation budget of transformer-based DPMs, this work proposes the Efficient Diffusion Transformer (EDT) framework. The framework includes a lightweight-design diffusion model architecture, and a training-free Attention Modulation Matrix and its alternation arrangement in EDT inspired by human-like sketching. Additionally, we propose a token relation-enhanced masking training strategy tailored explicitly for EDT to augment its token relation learning capability. Our extensive experiments demonstrate the efficacy of EDT. The EDT framework reduces training and inference costs and surpasses existing transformer-based diffusion models in image synthesis performance, thereby achieving a significant overall enhancement. With lower FID, EDT-S, EDT-B, and EDT-XL attained speed-ups of 3.93x, 2.84x, and 1.92x respectively in the training phase, and 2.29x, 2.29x, and 2.22x respectively in inference, compared to the corresponding sizes of MDTv2. The source code is released at https://github.com/xinwangChen/EDT.

Related papers

• Shrinking the Giant : Quasi-Weightless Transformers for Low Energy Inference [0.30104001512119216]
  Building models with fast and energy-efficient inference is imperative to enable a variety of transformer-based applications. We build on an approach for learning LUT networks directly via an Extended Finite Difference method. This allows for a computational and energy-efficient inference solution for transformer-based models.
  arXiv  Detail & Related papers  (2024-11-04T05:38:56Z)
• Dynamic Diffusion Transformer [67.13876021157887]
  Diffusion Transformer (DiT) has demonstrated superior performance but suffers from substantial computational costs. We propose Dynamic Diffusion Transformer (DyDiT), an architecture that dynamically adjusts its computation along both timestep and spatial dimensions during generation. With 3% additional fine-tuning, our method reduces the FLOPs of DiT-XL by 51%, accelerates generation by 1.73, and achieves a competitive FID score of 2.07 on ImageNet.
  arXiv  Detail & Related papers  (2024-10-04T14:14:28Z)
• MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation [44.74056930805525]
  We introduce a novel Masked Diffusion Transformer for co-speech gesture generation, referred to as MDT-A2G. This model employs a mask modeling scheme specifically designed to strengthen temporal relation learning among sequence gestures. Experimental results demonstrate that MDT-A2G excels in gesture generation, boasting a learning speed that is over 6$times$ faster than traditional diffusion transformers.
  arXiv  Detail & Related papers  (2024-08-06T17:29:01Z)
• FORA: Fast-Forward Caching in Diffusion Transformer Acceleration [39.51519525071639]
  Diffusion transformers (DiT) have become the de facto choice for generating high-quality images and videos. Fast-FORward CAching (FORA) is designed to accelerate DiT by exploiting the repetitive nature of the diffusion process.
  arXiv  Detail & Related papers  (2024-07-01T16:14:37Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• TerDiT: Ternary Diffusion Models with Transformers [83.94829676057692]
  TerDiT is a quantization-aware training scheme for ternary diffusion models with transformers. We focus on the ternarization of DiT networks and scale model sizes from 600M to 4.2B.
  arXiv  Detail & Related papers  (2024-05-23T17:57:24Z)
• Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory [11.3128832831327]
  Increasing the size of a Transformer model does not always lead to enhanced performance. improved generalization ability occurs as the model memorizes the training samples. We present a theoretical framework that sheds light on the memorization process and performance dynamics of transformer-based language models.
  arXiv  Detail & Related papers  (2024-05-14T15:48:36Z)
• SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer [102.39050180060913]
  Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. Recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. In this work, we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training.
  arXiv  Detail & Related papers  (2024-03-25T17:59:35Z)
• 2-D SSM: A General Spatial Layer for Visual Transformers [79.4957965474334]
  A central objective in computer vision is to design models with appropriate 2-D inductive bias. We leverage an expressive variation of the multidimensional State Space Model. Our approach introduces efficient parameterization, accelerated computation, and a suitable normalization scheme.
  arXiv  Detail & Related papers  (2023-06-11T09:41:37Z)
• Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation [130.30465659190773]
  Generative Adversarial Networks (GANs) have been widely-used in image translation, but their high computation and storage costs impede the deployment on mobile devices. We introduce a novel GAN compression method, termed DMAD, by proposing a Differentiable Mask and a co-Attention Distillation. Experiments show DMAD can reduce the Multiply Accumulate Operations (MACs) of CycleGAN by 13x and that of Pix2Pix by 4x while retaining a comparable performance against the full model.
  arXiv  Detail & Related papers  (2020-11-17T02:39:19Z)

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.