Existing open-source datasets for arbitrary-instruction image editing remain suboptimal, while a plug-and-play editing module compatible with community-prevalent generative models is notably absent. In this paper, we first introduce the X2Edit Dataset, a comprehensive dataset covering 14 diverse editing tasks, including subject-driven generation. We utilize the industry-leading unified image generation models and expert models to construct the data. Meanwhile, we design reasonable editing instructions with the VLM and implement various scoring mechanisms to filter the data. As a result, we construct 3.7 million high-quality data with balanced categories. Second, to better integrate seamlessly with community image generation models, we design task-aware MoE-LoRA training based on FLUX.1, with only 8% of the parameters of the full model. To further improve the final performance, we utilize the internal representations of the diffusion model and define positive/negative samples based on image editing types to introduce contrastive learning. Extensive experiments demonstrate that the model's editing performance is competitive among many excellent models. Additionally, the constructed dataset exhibits substantial advantages over existing open-source datasets.

Large language models have achieved notable success across various domains, yet efficient inference is still limited by the quadratic computation complexity of the attention mechanism. The inference consists of prefilling and decoding phases. Although several attempts have been made to accelerate decoding, the inefficiency of the prefilling phase, especially for long-context tasks, remains a challenge. In this paper, we observe a locality in query criticality during the prefilling phase of long-context processing: adjacent query tokens tend to focus on similar subsets of the past Key-Value (KV) cache. Based on this observation, we propose CritiPrefill, a criticality-based segment-wise prefilling method. This method partitions the input sequence’s queries and KV cache into segments and blocks, utilizing a segment-wise algorithm to estimate the query criticality. By pruning noncritical computations between query segments and cache blocks in the self-attention mechanism, the prefilling process can be significantly accelerated. Extensive evaluations on multiple long-context datasets show up to 2.7x speedup on Llama3-8B and 3.0x speedup on Yi-9B for 128K context length on a single A100 GPU, with minimal quality degradation.

Training GNNs over large graphs faces a severe data processing bottleneck, involving both sampling and feature loading. To tackle this issue, we introduce F 2 CGT, a fast GNN training system incorporating feature compression. To avoid potential accuracy degradation, we propose a two-level, hybrid feature compression approach that applies different compression methods to various graph nodes. This differentiated choice strikes a balance between rounding errors, compression ratios, model accuracy loss, and preprocessing costs. Our theoretical analysis proves that this approach offers convergence and comparable model accuracy as the conventional training without feature compression. Additionally, we also co-design the on-GPU cache sub-system with compression-enabled training within F 2 CGT. The new cache sub-system, driven by a cost model, runs new cache policies to carefully choose graph nodes with high access frequencies, and well partitions the spare GPU memory for various types of graph data, for improving cache hit rates. Finally, extensive evaluation of F 2 CGT on two popular GNN models and four datasets, including three large public datasets, demonstrates that F 2 CGT achieves a compression ratio of up to 128 and provides GNN training speedups of 1.23-2.56× and 3.58--71.46× for single-machine and distributed training, respectively, with up to 32 GPUs and marginal accuracy loss.