Multi-Architecture Multi-Expert Diffusion Models

• URL: http://arxiv.org/abs/2306.04990v2
• Date: Wed, 27 Dec 2023 07:51:56 GMT
• Title: Multi-Architecture Multi-Expert Diffusion Models
• Authors: Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, Seungtaek Choi
• Abstract summary: We introduce Multi-architecturE Multi-Expert diffusion models (MEME) MEME operates 3.3 times faster than baselines while improving image generation quality (FID scores) by 0.62 (FFHQ) and 0.37 (CelebA) We argue that MEME opens a new design choice for diffusion models that can be easily applied in other scenarios, such as large multi-expert models.
• Score: 18.463425624382115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME). We identify the need for tailored operations at different time-steps in diffusion processes and leverage this insight to create compact yet high-performing models. MEME assigns distinct architectures to different time-step intervals, balancing convolution and self-attention operations based on observed frequency characteristics. We also introduce a soft interval assignment strategy for comprehensive training. Empirically, MEME operates 3.3 times faster than baselines while improving image generation quality (FID scores) by 0.62 (FFHQ) and 0.37 (CelebA). Though we validate the effectiveness of assigning more optimal architecture per time-step, where efficient models outperform the larger models, we argue that MEME opens a new design choice for diffusion models that can be easily applied in other scenarios, such as large multi-expert models.

Related papers

• Aggregation of Multi Diffusion Models for Enhancing Learned Representations [4.126721111013567]
  This paper introduces a novel algorithm, Aggregation of Multi Diffusion Models (AMDM) AMDM synthesizes features from multiple diffusion models into a specified model, enhancing its learned representations to activate specific features for fine-grained control. Experimental results demonstrate that AMDM significantly improves fine-grained control without additional training or inference time.
  arXiv  Detail & Related papers  (2024-10-02T06:16:06Z)
• Efficient Multi-Task Large Model Training via Data Heterogeneity-aware Model Management [35.06717005729781]
  Recent foundation models are capable of handling multiple machine learning (ML) tasks and multiple data modalities with the unified base model structure and several specialized model components. Development of such multi-task (MT) multi-modal (MM) models poses significant model management challenges to existing training systems. We build a prototype system and evaluate it on various large MT MM models. Experiments demonstrate the superior performance and efficiency of our system, with speedup ratio up to 71% compared to state-of-the-art training systems.
  arXiv  Detail & Related papers  (2024-09-05T09:10:40Z)
• Diffusion Models For Multi-Modal Generative Modeling [32.61765315067488]
  We propose a principled way to define a diffusion model by constructing a unified multi-modal diffusion model in a common diffusion space. We propose several multimodal generation settings to verify our framework, including image transition, masked-image training, joint image-label and joint image-representation generative modeling.
  arXiv  Detail & Related papers  (2024-07-24T18:04:17Z)
• DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception [66.88792390480343]
  We propose DEEM, a simple but effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder. DEEM exhibits enhanced robustness and a superior capacity to alleviate model hallucinations while utilizing fewer trainable parameters, less pre-training data, and a smaller base model size.
  arXiv  Detail & Related papers  (2024-05-24T05:46:04Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Memory-Efficient Fine-Tuning for Quantized Diffusion Model [12.875837358532422]
  We introduce TuneQDM, a memory-efficient fine-tuning method for quantized diffusion models. Our method consistently outperforms the baseline in both single-/multi-subject generations.
  arXiv  Detail & Related papers  (2024-01-09T03:42:08Z)
• Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures [12.703947839247693]
  Diffusion models, emerging as powerful deep generative tools, excel in various applications. However, their remarkable generative performance is hindered by slow training and sampling. This is due to the necessity of tracking extensive forward and reverse diffusion trajectories. We present a multi-stage framework inspired by our empirical findings to tackle these challenges.
  arXiv  Detail & Related papers  (2023-12-14T17:48:09Z)
• Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models [51.5543321122664]
  This paper investigates how to better leverage large-scale pre-trained uni-modal models to enhance discriminative multi-modal learning. We introduce Multi-Modal Low-Rank Adaptation learning (MMLoRA)
  arXiv  Detail & Related papers  (2023-10-08T15:01:54Z)
• AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration [57.846038404893626]
  We propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 $times$ 64 with only four steps, compared to 138.66 with DDIM.
  arXiv  Detail & Related papers  (2023-09-19T08:57:24Z)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• When Ensembling Smaller Models is More Efficient than Single Large Models [52.38997176317532]
  We show that ensembles can outperform single models with both higher accuracy and requiring fewer total FLOPs to compute. This presents an interesting observation that output diversity in ensembling can often be more efficient than training larger models.
  arXiv  Detail & Related papers  (2020-05-01T18:56:18Z)

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.