SplitFlux: Learning to Decouple Content and Style from a Single Image

The study presents a data-driven machine learning approach aimed at optimizing the performance of Large Language Model (LLM) adapters in GPU serving environments. It addresses the challenge of maximizing throughput while preventing request starvation by determining the optimal configuration of concurrent and parallel adapters. The introduction of a Digital Twin for LLM-adapter systems facilitates efficient training data generation, with experiments showing a throughput accuracy within 5.1% of real results.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

Recent advancements in diffusion models have led to significant improvements in audio-driven human video generation, outperforming traditional techniques in quality and controllability. However, challenges remain in achieving lip-sync accuracy, maintaining temporal coherence in long videos, and creating multi-character animations. The proposed framework utilizes a diffusion transformer (DiT) to generate realistic talking videos of any length without the need for training. It incorporates a LoRA-based strategy and a position shift inference method, enhancing lip synchronization and natural body…

The article discusses the evaluation of backdoor attacks against federated model adaptation, particularly focusing on the impact of Parameter-Efficient Fine-Tuning techniques like Low-Rank Adaptation (LoRA). It highlights the security threats posed by backdoor attacks during local training phases and presents findings on backdoor lifespan, indicating that lower LoRA ranks can lead to longer persistence of backdoors. This research emphasizes the need for improved evaluation methods to address these vulnerabilities in Federated Learning.

FAPE-IR introduces a Frequency-Aware Planning and Execution framework for All-in-One Image Restoration (AIO-IR), designed to address multiple image degradations in complex conditions. Unlike existing methods that depend on task-specific designs, FAPE-IR utilizes a frozen Multimodal Large Language Model (MLLM) to analyze degraded images and create frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module, which dynamically selects experts based on the frequency features of the input image, enhancing restoration quality through adversarial training an…