Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding

• URL: http://arxiv.org/abs/2408.08252v5
• Date: Fri, 25 Oct 2024 02:50:44 GMT
• Title: Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
• Authors: Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Shuiwang Ji, Aviv Regev, Sergey Levine, Masatoshi Uehara,
• Abstract summary: Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. We aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Our algorithm integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future.
• Score: 84.3224556294803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preserving the naturalness of these design spaces. Existing methods for achieving this goal often require ``differentiable'' proxy models (\textit{e.g.}, classifier guidance or DPS) or involve computationally expensive fine-tuning of diffusion models (\textit{e.g.}, classifier-free guidance, RL-based fine-tuning). In our work, we propose a new method to address these challenges. Our algorithm is an iterative sampling method that integrates soft value functions, which looks ahead to how intermediate noisy states lead to high rewards in the future, into the standard inference procedure of pre-trained diffusion models. Notably, our approach avoids fine-tuning generative models and eliminates the need to construct differentiable models. This enables us to (1) directly utilize non-differentiable features/reward feedback, commonly used in many scientific domains, and (2) apply our method to recent discrete diffusion models in a principled way. Finally, we demonstrate the effectiveness of our algorithm across several domains, including image generation, molecule generation, and DNA/RNA sequence generation. The code is available at \href{https://github.com/masa-ue/SVDD}{https://github.com/masa-ue/SVDD}.

Related papers

• DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization [22.546989373687655]
  We propose a novel pruning method that derives an efficient diffusion model via a more intelligent and differentiable pruner. Our approach achieves 4.4 x speedup for SD-1.5 without any loss of accuracy, significantly outperforming the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-22T12:18:24Z)
• Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design [56.957070405026194]
  We propose an algorithm that enables direct backpropagation of rewards through entire trajectories generated by diffusion models. DRAKES can generate sequences that are both natural-like and yield high rewards.
  arXiv  Detail & Related papers  (2024-10-17T15:10:13Z)
• Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review [63.31328039424469]
  This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to optimize downstream reward functions. We explain the application of various RL algorithms, including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning.
  arXiv  Detail & Related papers  (2024-07-18T17:35:32Z)
• Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation [10.793646707711442]
  We present a framework for training generative models for density estimation. We use the score-based diffusion model to generate labeled data. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner.
  arXiv  Detail & Related papers  (2023-10-22T23:56:19Z)
• Mirror Diffusion Models for Constrained and Watermarked Generation [41.27274841596343]
  Mirror Diffusion Models (MDM) is a new class of diffusion models that generate data on convex constrained sets without losing tractability. For safety and privacy purposes, we also explore constrained sets as a new mechanism to embed invisible but quantitative information in generated data. Our work brings new algorithmic opportunities for learning tractable diffusion on complex domains.
  arXiv  Detail & Related papers  (2023-10-02T14:26:31Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Infinite-Dimensional Diffusion Models [4.342241136871849]
  We formulate diffusion-based generative models in infinite dimensions and apply them to the generative modeling of functions. We show that our formulations are well posed in the infinite-dimensional setting and provide dimension-independent distance bounds from the sample to the target measure. We also develop guidelines for the design of infinite-dimensional diffusion models.
  arXiv  Detail & Related papers  (2023-02-20T18:00:38Z)
• Fast Sampling of Diffusion Models via Operator Learning [74.37531458470086]
  We use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
  arXiv  Detail & Related papers  (2022-11-24T07:30:27Z)
• A Survey on Generative Diffusion Model [75.93774014861978]
  Diffusion models are an emerging class of deep generative models. They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space. This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
  arXiv  Detail & Related papers  (2022-09-06T16:56:21Z)
• A Sparsity-promoting Dictionary Model for Variational Autoencoders [16.61511959679188]
  Structuring the latent space in deep generative models is important to yield more expressive models and interpretable representations. We propose a simple yet effective methodology to structure the latent space via a sparsity-promoting dictionary model.
  arXiv  Detail & Related papers  (2022-03-29T17:13:11Z)

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.