Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code

Text-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process commences by acquiring a noisy latent vector corresponding to the source image via the diffusion model. This vector is subsequently fed into separate source and target diffusion branches for editing. The accuracy of this inversion process significantly impacts the final editing outcome, influencing both essential content preservation of the source image and edit fidelity according to the target prompt. Prior inversion techniques aimed at finding a unified solution in both the source and target diffusion branches. However, our theoretical and empirical analyses reveal that disentangling these branches leads to a distinct separation of responsibilities for preserving essential content and ensuring edit fidelity. Building on this insight, we introduce "Direct Inversion," a novel technique achieving optimal performance of both branches with just three lines of code. To assess image editing performance, we present PIE-Bench, an editing benchmark with 700 images showcasing diverse scenes and editing types, accompanied by versatile annotations and comprehensive evaluation metrics. Compared to state-of-the-art optimization-based inversion techniques, our solution not only yields superior performance across 8 editing methods but also achieves nearly an order of speed-up

Generative Model Watermarking Based on Human Visual System

Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing. However, the existing watermarking methods designed for generative models do not take into account the effects of different channels of sample images on watermarking. As a result, the watermarking performance is still limited. To tackle this problem, in this paper, we first analyze the effects of embedding watermark information on different channels. Then, based on the characteristics of human visual system (HVS), we introduce two HVS-based generative model watermarking methods, which are realized in RGB color space and YUV color space respectively. In RGB color space, the watermark is embedded into the R and B channels based on the fact that HVS is more sensitive to G channel. In YUV color space, the watermark is embedded into the DCT domain of U and V channels based on the fact that HVS is more sensitive to brightness changes. Experimental results demonstrate the effectiveness of the proposed work, which improves the fidelity of the model to be protected and has good universality compared with previous methods.Comment: https://scholar.google.com/citations?user=IdiF7M0AAAAJ&hl=e

New circuit switching techniques in on-chip networks

Network on Chip (NoC) is proposed as a promising technology to address the communication challenges in deep sub-micron era. NoC brings network-based communication into the on-chip environment and tackles the problems like long wire complexities, bandwidth scaling and so on. After more than a decade's evolution and development, there are many NoC architectures and solutions available. Nevertheless, NoCs can be classi_ed into two categories: packet switched NoC and circuit switched NoC. In this thesis, targeting circuit switched NoC, we present our innovations and considerations on circuit switched NoCs in three areas, namely, connection setup method, time division multiplexing (TDM) technology and spatial division multiplexing (SDM) technology. Connection setup technique deeply inuences the architecture and performance of a circuit switched NoC, since circuit switched NoC requires to set up connections before launching data transfer. We propose a novel parallel probe based method for dynamic distributed connection setup. This setup method on one hand searches all the possible minimal paths in parallel. On the other hand, it also has a mechanism to reduce resource occupation during the path search process by reclaiming redundant paths. With this setup method, connections are more likely to be established because of the exploration on the path diversity. TDM based NoC constitutes a sub-category of circuit switched NoC. We propose a double time-wheel technique to facilitate a probe based connection setup in TDM NoCs. With this technique, path search algorithms used in connection setup are no longer limited to deterministic routing algorithms. Moreover, the hardware cost can be reduced, since setup requests and data flows can co-exist in one network. Apart from the double time-wheel technique for connection setup, we also propose a highway technique that can enhance the slot utilization during data transfer. This technique can accelerate the transfer of a data flow while maintaining the throughput guarantee and the packet order. SDM based NoC constitutes another sub-category of circuit switched NoC. SDM NoC can benefit from high clock frequency and simple synchronization efforts. To better support the dynamic connection setup in SDM NoCs, we design a single cycle allocator for channel allocation inside each router. This allocator can guarantee both strong fairness and maximal matching quality. We also build up a circuit switched NoC, which can support multiple channels and multiple networks, to study different ways of organizing channels and setting up connections. Finally, we make a comparison between circuit switched NoC and packet switched NoC. We show the strengths and weaknesses on each of them by analysis and evaluation.QC 20151109</p

Analysis and Evaluation of Circuit Switched NoC and Packet Switched NoC

Abstract-Circuit switched NoC has, compared to packet switching, a longer setup time, guaranteed throughput and latency, higher clock frequency, lower HW complexity, and higher energy efficiency. Depending on packet size and throughput requirements they exhibit better or worse performance. In this paper we designed a circuit switched NoC and compared that with packet switched NoC. By speculation and analysis, we propose that, as packet size increases, performance decreases for packet switched NoC, while it increases for circuit switched NoC. By close examination on the router architecture, we suggest that circuit switched NoC can operate at a higher clock frequency than packet switched NoC, and thus at zero load above a certain packet size circuit switched NoC could be bette

Flexible distributed control plane deployment

For large-scale programmable networks, flexible deployment of distributed control planes is essential for service availability and performance. However, existing approaches only focus on placing controllers whereas the consequent control traffic is often ignored. In this paper, we propose a black-box optimization framework offering the additional steps for quantifying the effect of the consequent control traffic when deploying a distributed control plane. Evaluating different implementations of the framework over real-world topologies shows that close to optimal solutions can be achieved. Moreover, experiments indicate that running a method for controller placement without considering the control traffic, cause excessive bandwidth usage (worst cases varying between 20.1%-50.1% more) and congestion, compared to our approach

Control under Intermittent Network Partitions

We propose a novel distributed leader election algorithm to deal with the controller and control service availability issues in programmable networks, such as Software Defined Networks (SDN) or programmable Radio Access Network (RAN).  Our approach can deal with a wide range of network failures, especially intermittent network partitions, where splitting and merging of a network repeatedly occur.  In contrast to traditional leader election algorithms that mainly focus on the (eventual) consensus on one leader, the proposed algorithm aims at optimizing control service availability, stability and reducing the controller state synchronization effort during intermittent network partitioning situations. To this end, we design a new framework that enables dynamic leader election based on real-time estimates acquired from statistical monitoring. With this framework, the proposed leader election algorithm has the capability of being flexibly configured to achieve different optimization objectives, while adapting to various failure patterns. Compared with two existing algorithms,  our approach can significantly reduce the synchronization overhead (up to 12x) due to controller state updates, and maintain up to twice more nodes under a controller

Parallel Probing: Dynamic and Constant Time Setup Procedure in Circuit Switching NoC

Abstract- We propose a circuit switching Network-on-chip with a parallel probe searching setup method, which can search the entire network in constant time, only dependent on the network size but independent of the network load. Under a specific search policy, the setup procedure is guaranteed to terminate in time 3D+6 cycles, where D is the geometric distance between source and destination. If a path can be found, the method succeeds in 3D+6 cycles; if a path cannot be found, it fails in maximum 3D+6 cycles. Compared to previous work, our method can reduce the setup time and enhance the success rate of setups. Our experiments show that compared with a sequential probe searching method, this method can reduce the search time by up to 20%. Compared with a centralized channel allocator method, this method can enhance the success rate by up to 20%. 1. INTRODUCTION AND RELATE

Scene Classification With Recurrent Attention of VHR Remote Sensing Images

International audienceScene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches