Unraveling the Temporal Dynamics of the Unet in Diffusion Models
- URL: http://arxiv.org/abs/2312.14965v1
- Date: Sun, 17 Dec 2023 04:40:33 GMT
- Title: Unraveling the Temporal Dynamics of the Unet in Diffusion Models
- Authors: Vidya Prasad, Chen Zhu-Tian, Anna Vilanova, Hanspeter Pfister, Nicola
Pezzotti, Hendrik Strobelt
- Abstract summary: Diffusion models introduce Gaussian noise into training data and reconstruct the original data iteratively.
Central to this iterative process is a single Unet, adapting across time steps to facilitate generation.
Recent work revealed the presence of composition and denoising phases in this generation process.
- Score: 33.326244121918634
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Diffusion models have garnered significant attention since they can
effectively learn complex multivariate Gaussian distributions, resulting in
diverse, high-quality outcomes. They introduce Gaussian noise into training
data and reconstruct the original data iteratively. Central to this iterative
process is a single Unet, adapting across time steps to facilitate generation.
Recent work revealed the presence of composition and denoising phases in this
generation process, raising questions about the Unets' varying roles. Our study
dives into the dynamic behavior of Unets within denoising diffusion
probabilistic models (DDPM), focusing on (de)convolutional blocks and skip
connections across time steps. We propose an analytical method to
systematically assess the impact of time steps and core Unet components on the
final output. This method eliminates components to study causal relations and
investigate their influence on output changes. The main purpose is to
understand the temporal dynamics and identify potential shortcuts during
inference. Our findings provide valuable insights into the various generation
phases during inference and shed light on the Unets' usage patterns across
these phases. Leveraging these insights, we identify redundancies in GLIDE (an
improved DDPM) and improve inference time by ~27% with minimal degradation in
output quality. Our ultimate goal is to guide more informed optimization
strategies for inference and influence new model designs.
Related papers
- Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling [2.91204440475204]
Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models.
They rely on sequential denoising steps during sample generation.
We propose a novel method that integrates denoising phases directly into the model's architecture.
arXiv Detail & Related papers (2024-05-31T08:19:44Z) - Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification [3.0398616939692777]
Techniques like adversarial learning, contrastive learning, diffusion denoising learning, and ordinary reconstruction learning have become standard.
The study aims to elucidate the advantages of pre-training techniques and fine-tuning strategies to enhance the learning process of neural networks.
arXiv Detail & Related papers (2024-05-29T15:44:51Z) - DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - Data Attribution for Diffusion Models: Timestep-induced Bias in
Influence Estimation [58.20016784231991]
Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts.
We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep.
We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
arXiv Detail & Related papers (2024-01-17T07:58:18Z) - Not All Steps are Equal: Efficient Generation with Progressive Diffusion
Models [62.155612146799314]
We propose a novel two-stage training strategy termed Step-Adaptive Training.
In the initial stage, a base denoising model is trained to encompass all timesteps.
We partition the timesteps into distinct groups, fine-tuning the model within each group to achieve specialized denoising capabilities.
arXiv Detail & Related papers (2023-12-20T03:32:58Z) - One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion
Schedule Flaws and Enhancing Low-Frequency Controls [77.42510898755037]
One More Step (OMS) is a compact network that incorporates an additional simple yet effective step during inference.
OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters.
Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.
arXiv Detail & Related papers (2023-11-27T12:02:42Z) - Data Augmentation for Seizure Prediction with Generative Diffusion Model [26.967247641926814]
Seizure prediction is of great importance to improve the life of patients.
The severe imbalance problem between preictal and interictal data still poses a great challenge.
Data augmentation is an intuitive way to solve this problem.
We propose a novel data augmentation method with diffusion model called DiffEEG.
arXiv Detail & Related papers (2023-06-14T05:44:53Z) - Knowledge Diffusion for Distillation [53.908314960324915]
The representation gap between teacher and student is an emerging topic in knowledge distillation (KD)
We state that the essence of these methods is to discard the noisy information and distill the valuable information in the feature.
We propose a novel KD method dubbed DiffKD, to explicitly denoise and match features using diffusion models.
arXiv Detail & Related papers (2023-05-25T04:49:34Z) - TIER-A: Denoising Learning Framework for Information Extraction [4.010975396240077]
Deep learning models often overfit on noisy data points, leading to poor performance.
In this work, we examine the role of information entropy in the overfitting process.
We propose a simple yet effective co-regularization joint-training framework.
arXiv Detail & Related papers (2022-11-13T11:28:56Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z)
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.