HyperAI

In the field of AI image generation, there is often a conflict between style and subject matter that is difficult to achieve simultaneously.Style-driven, prioritizes the generation of artistic expressions with similar styles.For example, if you are asked to generate a "Cubist-style portrait of Picasso," the AI will prioritize ensuring that the color and brushstrokes are recognizable as Picasso's style at a glance, while the details of the portrait will be greatly reduced.Theme-driven approaches focus on the pursuit of theme consistency, and their core task is to "accurately generate specified content."When given the prompt "a cat wearing a red bow tie", the AI will ensure that the generated result is consistent with the subject you described. If the scene setting is required to be "in the office", the generated background may be blurred.

Based on this,ByteDance's UXO team launched USO, a unified framework for decoupling and restructuring content and style.By constructing a large-scale triplet dataset, employing a disentangled learning scheme to simultaneously align style features and separate content and style, and introducing style reward learning (SRL) to further improve model performance, this framework enables the free combination of themes and styles, generating ideal images with high subject consistency, strong style fidelity, and a natural, non-plastic feel.

USO improves model performance through cross-task collaborative decoupling, reaching the SOTA level of open source models in both subject consistency and style similarity.It breaks the isolation between style and theme in traditional image generation and achieves the goal of having both.

Currently, HyperAI's official website has launched "USO: Unified Style and Subject-Driven Image Generation Model". Come and try it out!

Online use：https://go.hyper.ai/VWz1i

From September 1st to September 5th, here’s a quick overview of the hyper.ai official website updates:

* High-quality public datasets: 10

* High-quality tutorial selection: 5

* This week's recommended papers: 5

* Community article interpretation: 6 articles

* Popular encyclopedia entries: 5

* Top conferences with deadline in September: 5

Visit the official website:hyper.ai

Selected public datasets

1. MV3DPT Multi-view 3D point tracking dataset

MV3DPT is a benchmark dataset specifically built for the task of multi-view arbitrary 3D point tracking. It aims to provide a foundation for research on stable online tracking of arbitrary 3D points in dynamic scenes from multiple camera perspectives. This dataset covers both synthetic and real scenes, fusions data from multiple perspectives, and enables robust prediction under occlusion. It is suitable for training and evaluating 3D point tracking models and has widespread application in computer vision and robotics.

Direct use: https://go.hyper.ai/xs6Kt

2. StepEval Audio Paralinguistic Paralinguistic Understanding Evaluation Dataset

StepEval Audio Paralinguistic is an audio paralinguistic understanding evaluation dataset released by the StepFun AI team. It aims to evaluate the ability of AI models to understand paralinguistic information (such as gender, age, intonation, emotion, etc.) in speech.

Direct use: https://go.hyper.ai/d65ah

3. Landslide4Sense landslide remote sensing benchmark dataset

Landslide4Sense is a multi-source satellite remote sensing benchmark dataset for landslide detection. The dataset covers landslide scenes in multiple regions from 2015 to 2021. It is unified into 128×128 image blocks with a resolution of approximately 10 m/pixel. Each sample contains 14 bands (Sentinel-2 multispectral B1–B12 + ALOS PALSAR-derived slope and DEM).

Direct use: https://go.hyper.ai/nDDwN

4. AlphaEarth Core Embedding Dataset

AlphaEarth is a global geospatial embedding dataset released by the Google DeepMind and Google Earth Engine teams. It aims to compress multi-source remote sensing and geographic data into unified, reusable spatiotemporal embeddings, enabling more efficient mapping and monitoring under conditions of scarce annotations.

Direct use：https://go.hyper.ai/EYcNz

5. AetherCode top programming competition benchmark dataset

AetherCode is a programming competition evaluation dataset released by ByteDance and the MAP team. It aims to more realistically evaluate the algorithmic reasoning and coding capabilities of large models through difficult questions from top competitions such as IOI, ICPC, and USACO, as well as high-quality test cases verified by experts.

Direct use: https://go.hyper.ai/oBpK1

6. MedChatZH Chinese Medical Conversation Command Dataset

MedChatZH is a Chinese medical conversation dataset released by East China University of Science and Technology. It aims to improve the understanding and generation capabilities of Chinese medical consultation dialogues (especially in TCM scenarios) through continuous pre-training on TCM classics and fine-tuning on medical instruction data.

Direct use: https://go.hyper.ai/gNRfB

7. HBFMID human fracture image dataset

HBFMID is a medical imaging dataset designed to support fracture detection and classification tasks. The dataset incorporates multimodal images, covers multiple body parts, and displays a variety of formats. It is fully enhanced and clearly segmented, making it suitable for training and evaluating fracture detection and classification models. It is particularly valuable in medical image analysis and deep learning research.

Direct access: https://go.hyper.ai/IPIOE

8. HH-RLHF Human Preference Dataset

HH-RLHF is a human preference dataset released by Anthropic, which mainly consists of two parts: beneficial/harmless human preference data (PM Data) and red team dialogue data (non-PM Data).

Direct use: https://go.hyper.ai/u98TI

9. UQ Unsolved Questions Dataset

The UQ dataset is an evaluation benchmark released by Stanford University in collaboration with the University of Washington, the University of North Carolina, and other institutions. It aims to evaluate the reasoning, factuality, and browsing capabilities of cutting-edge large models by using real and difficult "unanswered questions" by human society.

Direct use: https://go.hyper.ai/BW5qz

10. Llama Nemotron VLM v1 Multimodal Image and Text Dataset

Llama Nemotron VLM v1 is a high-quality image and text dataset released by NVIDIA for VLM post-training. It is used to support the Llama-3.1-Nemotron-Nano-VL-8B-V1 document understanding model released by NVIDIA (supporting document question answering, graph question answering, AI2D and other scenarios).

Direct use: https://go.hyper.ai/KVW6Z

Selected Public Tutorials

1. Hunyuan-GameCraft-1.0: Interactive Game Video Generation Framework

Hunyuan-GameCraft-1.0 is a framework for generating highly dynamic interactive game videos, jointly developed by the Tencent Hunyuan team and Huazhong University of Science and Technology. By unifying keyboard and mouse input into a shared camera representation space, it enables precise motion control and supports complex interactive input.

Run online: https://go.hyper.ai/c48zV

2. Hunyuan-MT-7B: Translation Model Demo

Hunyuan-MT-7B is a lightweight translation model released by the Tencent Hunyuan team. It has only 7 billion parameters and supports translation between 33 languages and 5 ethnic Chinese languages/dialects. It can accurately understand online slang, ancient poetry, social conversations, etc., and perform free translation based on the context. It proposes a training paradigm covering the entire chain from pre-training to integrated reinforcement.

Run online: https://go.hyper.ai/nv9gJ

3. USO: A Unified Style and Subject-Driven Image Generation Model

USO is a unified framework for decoupling and reorganizing content and style launched by ByteDance's UXO team. It can freely combine any subject with any style in any scene, generating images with high subject consistency, strong style fidelity, and a natural, non-plastic feel. Experiments have shown that it has reached the top level of open source models in both subject consistency and style similarity.

Run online: https://go.hyper.ai/VWz1i

4. MiniCPM-V 4.5: The Strongest End-to-End Multimodal Model

MiniCPM-V 4.5 is an extremely efficient, large-scale, on-device model developed open-source by the Natural Language Processing Laboratory of Tsinghua University and Mianbi Intelligence. It excels in multiple areas, including images, videos, and optical character recognition (OCR). It achieves a particular breakthrough in understanding high-refresh-rate videos, enabling it to accurately recognize content. The model supports hybrid inference modes, balancing performance and responsiveness.

Run online: https://go.hyper.ai/o3Ns5

5. BioEmu: Generative Deep Learning System

BioEmu, a generative deep learning system developed by the AI for Science team at Microsoft Research, efficiently simulates the dynamic structures and equilibrium conformations of proteins. The system can generate thousands of protein structure samples per hour on a single GPU, significantly outperforming traditional molecular dynamics (MD) simulations.

Run online: https://go.hyper.ai/YV75B

💡We have also established a Stable Diffusion tutorial exchange group. Welcome friends to scan the QR code and remark [SD tutorial] to join the group to discuss various technical issues and share application results~

This week's paper recommendation

1. R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning

This paper proposes a multimodal large language model, R-4B, capable of automated reasoning and decision-making. It can adaptively decide whether to activate the thinking process based on the complexity of the problem. Its core concept is to use a dual-mode annealing mechanism to endow the model with both "thinking" and "non-thinking" capabilities. It also employs a dual-mode strategy optimization method to improve the model's ability to accurately determine whether to activate the reasoning process.

Paper link: https://go.hyper.ai/3Nq23

2. EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control

This paper proposes EO-Robotics, which consists of the EO-1 model and the EO-Data1.5M dataset. EO-1 is a unified embodied foundational model that achieves superior performance in multimodal embodied reasoning and robotic control tasks through interleaved vision-text-action pre-training.

Paper link: https://go.hyper.ai/cTtge

3. ASE: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code

This paper proposes ASE (AI Code Generation Security Evaluation), a repository-level benchmark for evaluating secure code generation. ASE builds tasks from real open-source repositories containing known vulnerabilities (CVEs), fully preserving repository-level context, including build systems and cross-file dependencies.

Paper link: https://go.hyper.ai/irGB2

4. Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

This paper explores how to apply the video modality to 3D asset generation, covering the entire process from dataset construction to model design. It proposes the first large-scale video dataset Droplet3D-4M with multi-view hierarchical annotation and trains the Droplet3D model, a generative model that supports image input and dense text input.

Paper link: https://go.hyper.ai/BWwsV

5. VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use

This paper proposes a unified and modular framework, VerlTool, that formalizes ARLT as multi-round trajectories with multimodal observation labels (text/images/video), breaking through the paradigm limitations of traditional single-round RLVR. Researchers trained and evaluated models on tasks such as mathematical reasoning, knowledge question answering, SQL generation, visual reasoning, web search, and software engineering, achieving performance comparable to specialized systems while providing a unified training infrastructure.

Paper link: https://go.hyper.ai/NeCSC

More AI frontier papers:https://go.hyper.ai/iSYSZ

Community article interpretation

1. Global Water Health Diagnosis: A team from the Hong Kong University of Science and Technology proposed a spatiotemporal interpolation and prediction model to accurately predict the spatiotemporal distribution of chlorophyll a in coastal areas.

To address the issue of coastal ecosystem health diagnosis, a team from the Hong Kong University of Science and Technology proposed the Spatiotemporal Interpolation and Prediction (STIMP) model. By integrating specially designed modules, it achieved accurate prediction of the spatiotemporal distribution of chlorophyll a, providing a new path for predicting marine chlorophyll a under spatiotemporal constraints.

View the full report: https://go.hyper.ai/trOfg

2. From GPT-3 Director to Anthropic CTO, Tom Brown discusses his entrepreneurial experience, scaling laws, and chip supply chain dependence.

In an interview with Y Combinator, Anthropic CTO Tom Brown recounted his journey from startup to AI research. He discussed "demand fit" and the impact of "scaling laws," explained his reasons for leaving OpenAI to found Anthropic, discussed the challenges and breakthroughs encountered during the iteration of the Claude series of models, and revealed Anthropic's considerations regarding its multi-chip strategy and security vision.

View the full report: https://go.hyper.ai/d3CFR

3. The CoTCN model developed by the Institute of Atmospheric Physics has significantly improved the accuracy of global sea surface temperature forecasts, with a 1-day SST forecast error of only 0.2°C.

At the 2025 CCF Global High Performance Computing Conference, a team led by Researcher Lin Pengfei from the Institute of Atmospheric Physics, Chinese Academy of Sciences, presented a significant research achievement. The team successfully developed the CoTCN deep learning model, a coupled Transformer and CNN framework. This model achieved a breakthrough in short-term global sea surface temperature forecasting, providing key technical support for marine environmental forecasting.

View the full report: https://go.hyper.ai/Wb1yK

4. Meta AI et al. proposed a new protein dynamic fusion characterization framework, FusionProt, which enables iterative information exchange and achieves state-of-the-art performance in multiple tasks.

A research team from the Technion-Israel Institute of Technology and Meta AI has proposed a novel protein representation learning framework called FusionProt. This framework uses innovative learnable fusion tokens to iteratively exchange information between protein model structures (PLMs) and protein 3D structures, achieving state-of-the-art performance on a variety of biological tasks.

View the full report: https://go.hyper.ai/ZZq4Q

5. From high-paying poachers from OpenAI/Google to a sudden hiring halt: Meta MSL's key personnel review: Half are Chinese and 751 TP3T PhDs are the main force

In mid-August 2025, the Wall Street Journal broke the news: Meta, having just completed a massive AI talent hunt, suddenly suspended hiring for its artificial intelligence department. Subsequently, a large number of employees were reported to have resigned.

View the full report: https://go.hyper.ai/KMCvz

High-quality Fusion of Style and Theme! The USO Framework Achieves Both Through Decoupling and reward-based Learning; 1,000 TCM Classics! East China University of Science and Technology Releases MedChatZH to Help AI Better Understand TCM.

Selected public datasets

Selected Public Tutorials

This week's paper recommendation

Community article interpretation

Popular Encyclopedia Articles