41 research outputs found
LoRA-Enhanced Distillation on Guided Diffusion Models
Diffusion models, such as Stable Diffusion (SD), offer the ability to
generate high-resolution images with diverse features, but they come at a
significant computational and memory cost. In classifier-free guided diffusion
models, prolonged inference times are attributed to the necessity of computing
two separate diffusion models at each denoising step. Recent work has shown
promise in improving inference time through distillation techniques, teaching
the model to perform similar denoising steps with reduced computations.
However, the application of distillation introduces additional memory overhead
to these already resource-intensive diffusion models, making it less practical.
To address these challenges, our research explores a novel approach that
combines Low-Rank Adaptation (LoRA) with model distillation to efficiently
compress diffusion models. This approach not only reduces inference time but
also mitigates memory overhead, and notably decreases memory consumption even
before applying distillation. The results are remarkable, featuring a
significant reduction in inference time due to the distillation process and a
substantial 50% reduction in memory consumption. Our examination of the
generated images underscores that the incorporation of LoRA-enhanced
distillation maintains image quality and alignment with the provided prompts.
In summary, while conventional distillation tends to increase memory
consumption, LoRA-enhanced distillation offers optimization without any
trade-offs or compromises in quality
Markov Tensor Theory and Cascade, Reachability, and Routing in Complex Networks
University of Minnesota Ph.D. dissertation. 2017. Major: Electrical Engineering. Advisor: Zhi-Li Zhang. 1 computer file (PDF); 180 pages.In this dissertation, we study and characterize the networks as the medium and substrate for communications, interactions, and flows by addressing various crucial problems under the general topics of cascade, reachability, and routing. These are general problem domains common in several applications and from a variety of networks. We address these problems in a unified way by a theoretical platform that we have developed in this research, which we call Markov Tensor Theory. How does a phenomena, influence, or a failure cascade in a network and what are the key factors in this cascade? We study the influence cascade in social networks and introduce the Heat Conduction (HC) Model which captures both social influence and non-social influence, and extends many of the existing non-progressive models. We then prove that selecting the optimal seed set of influential nodes for maximizing the influence spread is NP-hard for HC, however, by establishing the submodularity of influence spread, we tackle the influence maximization problem with a scalable and provably near-optimal greedy algorithm. We also study failure cascade in inter-dependent networks where we considered the effects of cascading failures both within and across different layers. In this study, we investigate how different couplings (i.e., inter-dependencies) between network elements across layers affect the cascading failure dynamics. How failures or disruptions affect the network in terms of reachability of entities from each other, how to identify the reachabilities efficiently after failures, and who are the pivotal players in the reachabilities? We develop an oracle to answer dynamic reachabilities efficiently for failure-prone networks with frequent reachability query requirement. Founded on the concept of reachability, we also introduce and provide a formulation for finding articulation points, measuring network load balancing, and computing pivotality ranking of nodes. Once the reachabilities are determined, how to quickly and robustly route a flow from a part of the network to the other part of a network under the failures? To avoid solely relying on the shortest path and generate alternative paths on one hand, and to correct the degeneracy of hitting time distance, on the other hand, we develop a novel routing continuum method from shortest-path routing to all-path routing which provides both a closed form formulation for computing the continuum distances and an efficient routing strategy. We also devise an oracle for efficiently answering to single-source shortest path queries as well as finding the replacement paths in the case of multiple failures. For these studies, we develop Markov Tensor Theory as a platform of powerful theories and tools founded on Markov chain theory and random walk methods which supports the general weighted and directed networks
The effect of myocardial fibrosis on left ventricular diastolic function assessed by non-invasive cardiac magnetic resonance and echocardiography
A social work study on measuring the effects of culturing factors influencing consumerism
This paper presents a social work study to measure the impact of various cultural factors on consumerism in city of Esfahan, Iran. The proposed study uses clustering technique to choose a sample of 300 families out of 70,000 residences of this city. The study designs a questionnaire in Likert scale and distributes it among participants. Cronbach alpha have been calculated as 0.85 and 0.92 for cultural factors and consumerism, respectively. The results of Pearson correlation ratios indicate that there are statistically meaningful relationships between consumerism and four components of culture including religious believes, tendency to luxuries, using different multi medias and adherence to traditions