A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models

Recent years have witnessed the rapid progress and broad application of diffusion probabilistic models (DPMs). Sampling from DPMs can be viewed as solving an ordinary differential equation (ODE). Despite the promising performance, the generation of DPMs usually consumes much time due to the large number of function evaluations (NFE). Though recent works have accelerated the sampling to around 20 steps with high-order solvers, the sample quality with less than 10 NFE can still be improved. In this paper, we propose a unified sampling framework (USF) to study the optional strategies for solver. Under this framework, we further reveal that taking different solving strategies at different timesteps may help further decrease the truncation error, and a carefully designed \emph{solver schedule} has the potential to improve the sample quality by a large margin. Therefore, we propose a new sampling framework based on the exponential integral formulation that allows free choices of solver strategy at each step and design specific decisions for the framework. Moreover, we propose $S^3$, a predictor-based search method that automatically optimizes the solver schedule to get a better time-quality trade-off of sampling. We demonstrate that $S^3$ can find outstanding solver schedules which outperform the state-of-the-art sampling methods on CIFAR-10, CelebA, ImageNet, and LSUN-Bedroom datasets. Specifically, we achieve 2.69 FID with 10 NFE and 6.86 FID with 5 NFE on CIFAR-10 dataset, outperforming the SOTA method significantly. We further apply $S^3$ to Stable-Diffusion model and get an acceleration ratio of 2$\times$, showing the feasibility of sampling in very few steps without retraining the neural network

THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively. While previous approaches primarily concentrated on concealing a relatively small amount of information, we propose THInImg, which manages to hide lengthy audio data (and subsequently decode talking head video) inside an identity image by leveraging the properties of human face, which can be effectively utilized for covert communication, transmission and copyright protection. THInImg consists of two parts: the encoder and decoder. Inside the encoder-decoder pipeline, we introduce a novel architecture that substantially increase the capacity of hiding audio in images. Moreover, our framework can be extended to iteratively hide multiple audio clips into an identity image, offering multiple levels of control over permissions. We conduct extensive experiments to prove the effectiveness of our method, demonstrating that THInImg can present up to 80 seconds of high quality talking-head video (including audio) in an identity image with 160x160 resolution.Comment: Accepted at WACV 202

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs), but it can also potentially improve the answer quality on several question categories in terms of diversity and relevance. SoT is an initial attempt at data-centric optimization for efficiency, and reveal the potential of pushing LLMs to think more like a human for answer quality.Comment: Technical report, work in progres

Characterization and Tissue-specific Expression of bHLH Genes in Dimocarpus longan

In plants, the basic helix-loop-helix (bHLH) transcription factors (TFs) play pivotal roles in many biological processes including growth, stress response, and secondary metabolite synthesis. To date, many bHLH genes have been identified and characterized in diverse plant species. However, little is known regarding the bHLH genes in Dimocarpus longan Lour. (D. longan). Based on RNA-seq data, we identified 42 putative bHLH genes from D. longan and determined their putative functions using the NCBI Conserved Domain Search Tool and Pfam databases. The physicochemical properties, phylogenetic relationships, conserved motifs, gene ontology (GO) annotations, protein-protein interactions, and tissue-specific expression patterns of these bHLH genes were systematically explored. In total, ten motifs were found in DlbHLH proteins using MEME, among which two were highly conserved. Phylogenetic tree analysis found that DlbHLH proteins can be divided into nine groups, with group 2 being the largest. GO annotation results showed that the DlHLH genes were involved in various molecular functions. RNA-seq and qRT-PCR results revealed important differences in the expression patterns of 17 of the DlbHLH genes. In particular, DlbHLH-9, DlbHLH-19, DlbHLH-25, DlbHLH-26, and DlbHLH-35 were found to show significantly different expression patterns in root and leaf tissues. The results of this study will further enrich our knowledge regarding bHLH transcription factor genes and lay a foundation for enhancing the production of active secondary metabolites by genetic engineering in D. longan

Transcriptome-wide identification and characterization of WD40 genes, as well as their tissue-specific expression profiles and responses to heat stress in Dimocarpus longan Lour.

The WD40 transcription factor (TF) family is widespread in plants and plays important roles in plant growth and development, transcriptional regulation, and tolerance to abiotic stresses. WD40 TFs have been identified and characterized in a diverse series of plant species. However, little information is available on WD40 genes from D. longan. In this study, a total of 45 DlWD40 genes were identified from D. longan RNA-Seq data, and further analysed by bioinformatics tools. Also, the expression patterns of DlWD40 genes in roots and leaves, as well as responses to heat stress, were evaluated using quantitative real-time PCR (qRT-PCR). We found that the 45 DlWD40 proteins, together with 80 WD40 proteins from Arabidopsis and Zea mays, could be categorized into six groups. Of these, the DlWD40-4 protein was highly homologous to Arabidopsis WDR5a, a protein participating in tolerance to abiotic stresses. Moreover, a total of 25 cis-acting elements, such as abiotic stress and flavonoid biosynthesis elements, were found in the promoters of DlWD40 genes. The DlWD40-33 gene is targeted by miR3627, which has been proposed to be involved in flavonoid biosynthesis. Using qRT-PCR, ten of the 45 DlWD40 genes were demonstrated to have diverse expression patterns between roots and leaves, and these ten DlWD40 genes could also respond to varying durations of a 38 °C heat stress in roots and leaves. The results reported here will provide a basis for the further functional verification of DlWD40 genes in D. longan