StableDrag Technology Release: Dragging Anchor Points for High-Quality Image Editing

Krea Integrates Gemini's Text-to-Image and Image Editing: Chat Interface Receives a Practical Leap

Recently, the AI creative platform Krea announced the successful integration of Google Gemini's text-to-image and image editing capabilities, further enhancing the platform's generative capabilities and user experience. This update significantly improves the practicality of the Krea Chat interface, transforming it from a simple dialogue tool into a comprehensive creative platform integrating image generation and editing. This advancement is considered a significant step for Krea in the AI-driven creative design field.

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.

OpenAI Unveils New Image Generation Model, Challenging Google's One-Sentence Image Editing

Adobe Stock Launches AI Customization Tool for Easy Stock Image Editing

Adobe recently announced an upgrade to its Adobe Stock service, introducing an AI-powered feature called "Customize." This new functionality allows users to quickly adjust and optimize stock images without leaving the Adobe Stock platform. Users can perform simple image edits and generate AI variations, eliminating the need to spend significant time on complex edits in applications like Photoshop. Adobe Stock

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.

AI Image Editing Reaches Mid-Range Market: Samsung's Galaxy A Series Offers 6 Years of System Updates

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.

Adobe Launches AI-Powered Bulk Create: Significantly Simplifying Image Editing and Update Workflows