EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models

• URL: http://arxiv.org/abs/2406.17462v2
• Date: Wed, 11 Dec 2024 09:23:17 GMT
• Title: EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models
• Authors: Vidya Prasad, Hans van Gorp, Christina Humer, Ruud J. G. van Sloun, Anna Vilanova, Nicola Pezzotti,
• Abstract summary: Diffusion models rely on iterative refinement to generate images from noise.<n>EvolvED presents a holistic view of the iterative generative process in diffusion models.<n>Central to EvolvED is a novel evolutionary embedding algorithm that encodes iterative steps while maintaining semantic relations.
• Score: 14.582985391135232
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion models, widely used in image generation, rely on iterative refinement to generate images from noise. Understanding this data evolution is important for model development and interpretability, yet challenging due to its high-dimensional, iterative nature. Prior works often focus on static or instance-level analyses, missing the iterative and holistic aspects of the generative path. While dimensionality reduction can visualize image evolution for few instances, it does preserve the iterative structure. To address these gaps, we introduce EvolvED, a method that presents a holistic view of the iterative generative process in diffusion models. EvolvED goes beyond instance exploration by leveraging predefined research questions to streamline generative space exploration. Tailored prompts aligned with these questions are used to extract intermediate images, preserving iterative context. Targeted feature extractors trace the evolution of key image attribute evolution, addressing the complexity of high-dimensional outputs. Central to EvolvED is a novel evolutionary embedding algorithm that encodes iterative steps while maintaining semantic relations. It enhances the visualization of data evolution by clustering semantically similar elements within each iteration with t-SNE, grouping elements by iteration, and aligning an instance's elements across iterations. We present rectilinear and radial layouts to represent iterations and support exploration. We apply EvolvED to diffusion models like GLIDE and Stable Diffusion, demonstrating its ability to provide valuable insights into the generative process.

Related papers

• Scaling Image and Video Generation via Test-Time Evolutionary Search [41.715197824076746]
  Test-time scaling (TTS) has emerged as a promising direction for improving generative model performance by allocating additional computation at inference time.<n>EvoSearch is a novel, generalist, and efficient TTS method that effectively enhances the scalability of both image and video generation across diffusion and flow models.
  arXiv  Detail & Related papers  (2025-05-23T08:25:46Z)
• Heuristically Adaptive Diffusion-Model Evolutionary Strategy [1.8299322342860518]
  Diffusion Models represent a significant advancement in generative modeling. Our research reveals a fundamental connection between diffusion models and evolutionary algorithms. Our framework marks a major algorithmic transition, offering increased flexibility, precision, and control in evolutionary optimization processes.
  arXiv  Detail & Related papers  (2024-11-20T16:06:28Z)
• Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution [29.744990195972587]
  Diffusion models have revolutionized image synthesis, garnering significant research interest in recent years. We study discriminative algorithms operating on diffusion trajectories. Our approach demonstrates the presence of patterns across steps that can be leveraged for classification.
  arXiv  Detail & Related papers  (2024-11-12T00:20:11Z)
• Diffusion Models are Evolutionary Algorithms [1.8299322342860518]
  We show that diffusion models inherently perform evolutionary algorithms, naturally encompassing selection, mutation, and reproductive isolation. We propose the Diffusion Evolution method: an evolutionary algorithm utilizing iterative denoising. We also introduce Latent Space Diffusion Evolution, which finds solutions for evolutionary tasks in high-dimensional complex parameter space.
  arXiv  Detail & Related papers  (2024-10-03T14:47:46Z)
• Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding [84.3224556294803]
  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.
  arXiv  Detail & Related papers  (2024-08-15T16:47:59Z)
• Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency [25.632973225129728]
  We study the geometric properties of the diffusion model, whose forward diffusion process and reverse generation process construct a series of distributions on a manifold. We show that the generation rate is highly correlated with intuitive visual properties, such as visual saliency, of the image component. We propose an efficient and differentiable scheme to estimate the generation rate for a given image component over time, giving rise to a generation curve.
  arXiv  Detail & Related papers  (2024-06-07T07:32:41Z)
• Heat Death of Generative Models in Closed-Loop Learning [63.83608300361159]
  We study the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. We show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to degenerate.
  arXiv  Detail & Related papers  (2024-04-02T21:51:39Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data [55.748186000425996]
  Recent advancements show that diffusion models can generate high-quality images. We study this phenomenon in a hierarchical generative model of data. Our analysis characterises the relationship between time and scale in diffusion models.
  arXiv  Detail & Related papers  (2024-02-26T19:52:33Z)
• Lecture Notes in Probabilistic Diffusion Models [0.5361320134021585]
  Diffusion models are loosely modelled based on non-equilibrium thermodynamics. The diffusion model learns the data manifold to which the original and thus the reconstructed data samples belong. Diffusion models have -- unlike variational autoencoder and flow models -- latent variables with the same dimensionality as the original data.
  arXiv  Detail & Related papers  (2023-12-16T09:36:54Z)
• Iterative Token Evaluation and Refinement for Real-World Super-Resolution [77.74289677520508]
  Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. We propose an Iterative Token Evaluation and Refinement framework for RWSR. We show that ITER is easier to train than Generative Adversarial Networks (GANs) and more efficient than continuous diffusion models.
  arXiv  Detail & Related papers  (2023-12-09T17:07:32Z)
• InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models [35.566528358691336]
  InfoDiffusion is an algorithm that augments diffusion models with low-dimensional latent variables. InfoDiffusion relies on a learning objective regularized with the mutual information between observed and hidden variables. We find that InfoDiffusion learns disentangled and human-interpretable latent representations that are competitive with state-of-the-art generative and contrastive methods.
  arXiv  Detail & Related papers  (2023-06-14T21:48:38Z)
• 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)
• Effective Data Augmentation With Diffusion Models [65.09758931804478]
  We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.
  arXiv  Detail & Related papers  (2023-02-07T20:42:28Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• Dimensionality-Varying Diffusion Process [52.52681373641533]
  Diffusion models learn to reverse a signal destruction process to generate new data. We make a theoretical generalization of the forward diffusion process via signal decomposition. We show that our strategy facilitates high-resolution image synthesis and improves FID of diffusion model trained on FFHQ at $1024times1024$ resolution from 52.40 to 10.46.
  arXiv  Detail & Related papers  (2022-11-29T09:05:55Z)
• From Points to Functions: Infinite-dimensional Representations in Diffusion Models [23.916417852496608]
  Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution. We show that a combination of information content from different time steps gives a strictly better representation for the downstream task.
  arXiv  Detail & Related papers  (2022-10-25T05:30:53Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)
• Unsupervised Discovery of Disentangled Manifolds in GANs [74.24771216154105]
  Interpretable generation process is beneficial to various image editing applications. We propose a framework to discover interpretable directions in the latent space given arbitrary pre-trained generative adversarial networks.
  arXiv  Detail & Related papers  (2020-11-24T02:18:08Z)

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.