The ChatGPT Images 2.0 model is here. Our testing shows it's better at creating more detailed images and rendering text, but it still struggles with languages other than English. OpenAI launched a new image generation AI model on Tuesday, dubbed ChatGPT Images 2.0. This model can generate more than one image from a single prompt, like an entire study booklet, as well as output text, including in non-English languages, like Chinese and Hindi. This release is available globally for ChatGPT and Codex users, with a more powerful version available for paying subscribers.

ChatGPT Images 2.0 is better at rendering non-Latin text OpenAI describes it as a step change for image generation models. OpenAI's new ChatGPT Images 2.0 model is now available. A little more than a year after OpenAI gave ChatGPT users the option to create images and designs directly from its chatbot, it's now releasing ChatGPT Images 2.0 . OpenAI describes the new system as a "step change" for image generation models, particularly when it comes to the tool's ability to follow instructions in detail, render dense text and place and relate objects in a scene. For the first time, OpenAI has also built an image model with reasoning capabilities, giving the system the ability to do things like search the web and verify its outputs.

GPT-5.4 mini brings some of the smarts of OpenAI's latest model to ChatGPT Free and Go users The new model offers performance improvements in reasoning, multimodal understanding and more. The ChatGPT icon, as seen on iPhone 12 running iOS. When OpenAI released GPT-5.4 at the start of March, the company said the new model was designed primarily for professional work like programming and data analysis. Now OpenAI is launching GPT-5.4 mini and nano, and while it is once again highlighting the usefulness of these new systems for tasks like coding, one of the new models is available to Free and Go users . What's more, that model, GPT-5.4 mini, even offers performance that approaches GPT-5.4 in a handful of areas.

She didn't expect to fall in love with a chatbot - and then have to say goodbye Rae began speaking to Barry last year after the end of a difficult divorce. She was unfit and unhappy and turned to ChatGPT for advice on diet, supplements and skincare. She had no idea she would fall in love. He lives on an old model of ChatGPT, one that its owners OpenAI announced it would retire on 13 February. That she could lose Barry on the eve of Valentine's Day came as a shock to Rae - and to many others who have found a companion, friend, or even a lifeline in the old model, Chat GPT-4o.

Graph class incremental learning (GCIL) requires the model to classify emerging nodes of new classes while remembering old classes. Existing methods are designed to preserve effective information of old models or graph data to alleviate forgetting, but there is no clear theoretical understanding of what matters in information preservation.

We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which is a base model fine-tuned to insert a different person's visual identity.

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components.The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline,demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more.

Accurate prediction of dynamical systems in unstructured meshes has recently shown successes in scientific simulations. Many dynamical systems have a nonnegligible level of stochasticity introduced by various factors (e.g.