Sun Yat-sen University et al. propose the ScaleLong diffusion model: a few lines of code stabilize UNet training

Les lunettes intelligentes Xiaomi avec véritable IA pourraient sortir le mois prochain : architecture à double puce et fonctionnalités IA

Ces lunettes intelligentes à véritable IA adopteront une architecture à double puce et intégreront un appareil photo dont les performances devraient rivaliser avec celles des Meta Ray-Ban. Elles pourront non seulement percevoir le monde réel grâce à leur objectif, mais aussi proposer une expérience interactive riche et variée grâce à l’IA. Par exemple, les utilisateurs pourront identifier des objets, reconnaître automatiquement les restaurants et obtenir instantanément des informations pratiques telles que les prix et les notes, une fonctionnalité similaire au système de vision intelligent d’Apple.

AccVideo Achieves High-Quality Video Generation with Synthetic Data, Boosting Speed by 8.5x

Diffusion models have garnered significant attention in the field of AI video generation due to their superior performance. However, their inherent iterative denoising nature leads to a time-consuming and computationally expensive generation process, significantly hindering their widespread adoption. Recently, a research team from Beihang University, the University of Hong Kong, and the Shanghai AI Laboratory jointly released AccVideo, an innovative technology. This method, employing a novel and efficient distillation approach combined with a synthetic dataset, successfully boosts the generation speed of video diffusion models by an impressive 8.5 times.

LanPaint: Zero-Shot Image Inpainting with Diffusion Models

Recently, developer scraed released LanPaint on GitHub, a zero-shot image inpainting tool. This tool aims to help users achieve high-quality image inpainting results on any Stable Diffusion (SD) model, including custom-trained models. LanPaint achieves this by iteratively prompting the model to 'think' before denoising, resulting in more seamless and accurate inpainting. A key feature of LanPaint is its zero-shot capability; users can start using it immediately without any training.

Google AI Introduces a Fundamental Framework for Scaling Inference Time in Diffusion Models

A research team from New York University, MIT, and Google has recently proposed an innovative framework aimed at addressing the bottleneck of inference time scaling in diffusion models. This groundbreaking research transcends the traditional method of simply increasing the denoising steps, opening up new avenues for improving generative model performance. The framework primarily unfolds from two dimensions: one is leveraging feedback from validators, and the second is implementing algorithms to discover better noise candidates. The research team builds on a pre-trained SiT-XL model with a resolution of 256×256, maintaining 250 fixed denoising steps.

Microsoft Launches Innovative AI Model MatterGen to Create New Materials Based on Specific Needs

Microsoft Research has launched a powerful AI system called MatterGen that can generate new materials with specific properties, potentially accelerating the development of key technologies such as batteries and solar panels. The introduction of MatterGen signifies a fundamental shift in how scientists discover new materials. Unlike traditional methods that screen millions of existing compounds, MatterGen directly generates new materials based on desired characteristics, akin to how AI image generators create visuals from textual descriptions.

New Technology for Coloring Line Art MangaNinja: Input Line Art and Reference Images for Accurate Coloring

Recently, a line art coloring method called MangaNinja has garnered widespread attention, allowing users to color target line art based on reference images with just an input of the line art and the reference image. This technology is based on a diffusion model and focuses on reference-image-guided line art coloring, significantly enhancing the accuracy of coloring and interactive control capabilities. The research team employed two innovative designs to ensure precise transfer of character details. First, they introduced a patch rearrangement module to facilitate correspondence learning between the reference color images and the target line art. Secondly, they adopted a point-driven approach.

Single Image Transforms into 3D Wonderland: Efficiently Building High-Quality 3D Scenes with Wonderland

For a long time, efficiently generating high-quality, wide-angle 3D scenes from a single image has been a challenge faced by researchers. Traditional methods often rely on multi-view data or require time-consuming per-scene optimization, and they struggle with background quality and reconstruction of unseen areas. Existing technologies, when dealing with single-view 3D scene generation, often produce errors or distortions in occluded areas due to insufficient information, along with blurry backgrounds and difficulties in inferring the geometric structure of unseen regions. While regression-based models can synthesize new views in a feedforward manner, they struggle with complex...

ByteDance Releases Infinity: A New Breakthrough in Auto-Regressive Text-to-Image Generation, Outperforming Diffusion Models

In the field of artificial intelligence, ByteDance's commercial technology team presents the latest achievement, the Infinity model, which has become a new leader in the auto-regressive text-to-image generation domain with its outstanding performance and innovative technology. This newly open-sourced model not only surpasses Stable Diffusion 3 in image generation quality but also demonstrates significant advantages in inference speed. The core innovation of the Infinity model lies in its use of a Bitwise Token auto-regressive framework, which predicts the next level of resolution by +1 or -1.

VMix: A Plug-and-Play Extension to Enhance the Aesthetics of Diffusion Models by Byte and University of Science and Technology of China

New Breakthrough in Image Super-Resolution! InvSR Tool Transforms Blurry Photos to HD with One Click

Are you still troubled by unclear photos? A brand new image super-resolution tool called InvSR has emerged, allowing images to be instantly transformed into clear and sharp ones with minimal processing steps. The strength of this tool lies in its utilization of the rich prior knowledge embedded in large pre-trained diffusion models, achieving efficient and high-quality image resolution enhancement. The core technology of InvSR is its innovative partial noise prediction strategy. It cleverly constructs the intermediate state of the diffusion model as the starting point for the sampling process and employs a deep noise.