Diffusion Models without Classifier-free Guidance

• URL: http://arxiv.org/abs/2502.12154v1
• Date: Mon, 17 Feb 2025 18:59:50 GMT
• Title: Diffusion Models without Classifier-free Guidance
• Authors: Zhicong Tang, Jianmin Bao, Dong Chen, Baining Guo,
• Abstract summary: Model-guidance (MG) is a novel objective for training diffusion model addresses and removes commonly used guidance (CFG) Our innovative approach transcends the standard modeling and incorporates the posterior probability of conditions. Our method significantly accelerates the training process, doubles inference speed, and achieve exceptional quality that parallel surpass even concurrent diffusion models with CFG.
• Score: 41.59396565229466
• License:
• Abstract: This paper presents Model-guidance (MG), a novel objective for training diffusion model that addresses and removes of the commonly used Classifier-free guidance (CFG). Our innovative approach transcends the standard modeling of solely data distribution to incorporating the posterior probability of conditions. The proposed technique originates from the idea of CFG and is easy yet effective, making it a plug-and-play module for existing models. Our method significantly accelerates the training process, doubles the inference speed, and achieve exceptional quality that parallel and even surpass concurrent diffusion models with CFG. Extensive experiments demonstrate the effectiveness, efficiency, scalability on different models and datasets. Finally, we establish state-of-the-art performance on ImageNet 256 benchmarks with an FID of 1.34. Our code is available at https://github.com/tzco/Diffusion-wo-CFG.

Related papers

• An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques [12.470257882838126]
  We propose the Diffusion-Based Discriminative Model Enhancement Framework (DBMEF) This framework seamlessly integrates discriminative and generative models in a training-free manner. DBMEF can effectively enhance the classification accuracy and capability of discriminative models in a plug-and-play manner.
  arXiv  Detail & Related papers  (2024-12-12T08:46:22Z)
• Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models [33.09663675904689]
  We investigate efficient diffusion training from the perspective of dataset pruning. Inspired by the principles of data-efficient training for generative models such as generative adversarial networks (GANs), we first extend the data selection scheme used in GANs to DM training. To further improve the generation performance, we employ a class-wise reweighting approach.
  arXiv  Detail & Related papers  (2024-09-27T20:21:19Z)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• Fixed Point Diffusion Models [13.035518953879539]
  Fixed Point Diffusion Model (FPDM) is a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model, transforming the diffusion process into a sequence of closely-related fixed point problems. We conduct experiments with state-of-the-art models on ImageNet, FFHQ, CelebA-HQ, and LSUN-Church, demonstrating substantial improvements in performance and efficiency.
  arXiv  Detail & Related papers  (2024-01-16T18:55:54Z)
• 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)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• On Distillation of Guided Diffusion Models [94.95228078141626]
  We propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from. For standard diffusion models trained on the pixelspace, our approach is able to generate images visually comparable to that of the original model. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps.
  arXiv  Detail & Related papers  (2022-10-06T18:03:56Z)

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.