Efficient Generative Model Training via Embedded Representation Warmup

• URL: http://arxiv.org/abs/2504.10188v1
• Date: Mon, 14 Apr 2025 12:43:17 GMT
• Title: Efficient Generative Model Training via Embedded Representation Warmup
• Authors: Deyuan Liu, Peng Sun, Xufeng Li, Tao Lin,
• Abstract summary: Diffusion models excel at generating high-dimensional data but fall short in training efficiency and representation quality compared to self-supervised methods.<n>We identify a key bottleneck: the underutilization of high-quality, semantically rich representations during training.<n>We propose Embedded Representation Warmup (ERW), a plug-and-play framework where in the first stage we get the ERW module.
• Score: 6.783363935446626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models excel at generating high-dimensional data but fall short in training efficiency and representation quality compared to self-supervised methods. We identify a key bottleneck: the underutilization of high-quality, semantically rich representations during training notably slows down convergence. Our systematic analysis reveals a critical representation processing region -- primarily in the early layers -- where semantic and structural pattern learning takes place before generation can occur. To address this, we propose Embedded Representation Warmup (ERW), a plug-and-play framework where in the first stage we get the ERW module serves as a warmup that initializes the early layers of the diffusion model with high-quality, pretrained representations. This warmup minimizes the burden of learning representations from scratch, thereby accelerating convergence and boosting performance. Our theoretical analysis demonstrates that ERW's efficacy depends on its precise integration into specific neural network layers -- termed the representation processing region -- where the model primarily processes and transforms feature representations for later generation. We further establish that ERW not only accelerates training convergence but also enhances representation quality: empirically, our method achieves a 40$\times$ acceleration in training speed compared to REPA, the current state-of-the-art methods. Code is available at https://github.com/LINs-lab/ERW.

Related papers

• Parallelly Tempered Generative Adversarial Networks [7.94957965474334]
  A generative adversarial network (GAN) has been a representative backbone model in generative artificial intelligence (AI) This work analyzes the training instability and inefficiency in the presence of mode collapse by linking it to multimodality in the target distribution. With our newly developed GAN objective function, the generator can learn all the tempered distributions simultaneously.
  arXiv  Detail & Related papers  (2024-11-18T18:01:13Z)
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think [72.48325960659822]
  One main bottleneck in training large-scale diffusion models for generation lies in effectively learning these representations.<n>We study this by introducing a straightforward regularization called REPresentation Alignment (REPA), which aligns the projections of noisy input hidden states in denoising networks with clean image representations obtained from external, pretrained visual encoders.<n>The results are striking: our simple strategy yields significant improvements in both training efficiency and generation quality when applied to popular diffusion and flow-based transformers, such as DiTs and SiTs.
  arXiv  Detail & Related papers  (2024-10-09T14:34:53Z)
• Advancing Neural Network Performance through Emergence-Promoting Initialization Scheme [0.0]
  Emergence in machine learning refers to the spontaneous appearance of capabilities that arise from the scale and structure of training data.<n>We introduce a novel yet straightforward neural network initialization scheme that aims at achieving greater potential for emergence.<n>We demonstrate substantial improvements in both model accuracy and training speed, with and without batch normalization.
  arXiv  Detail & Related papers  (2024-07-26T18:56:47Z)
• Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations [11.874972134063638]
  This paper proposes a novel SSL event-to-video reconstruction approach, dubbed EvINR, which eliminates the need for labeled data or optical flow estimation. We use an implicit neural representation (INR), which takes in coordinate $(x, y, t)$ and predicts intensity values, to represent the event generation equation. To make EvINR feasible for online requisites, we propose several acceleration techniques that substantially expedite the training process.
  arXiv  Detail & Related papers  (2024-07-26T04:18:10Z)
• Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation [105.23631749213729]
  We propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-Vision Prompts. We show that our method can converge faster and perform better than CNN-based models in low-data regimes.
  arXiv  Detail & Related papers  (2024-05-22T06:48:43Z)
• 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)
• Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt [103.58323875748427]
  This work offers a novel unsupervised pre-training solution for low-data regimes. Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models. Experimental results show that our method significantly boosts several QEIS models on three datasets.
  arXiv  Detail & Related papers  (2023-02-02T15:49:03Z)
• Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles [60.97922557957857]
  We provide a framework to perform self-supervised model ensembling via a novel method of learning representations directly through gradient descent at inference time. This technique improves representation quality, as measured by k-nearest neighbors, both on the in-domain dataset and in the transfer setting.
  arXiv  Detail & Related papers  (2021-10-19T22:24:57Z)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

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.