惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

I
InfoQ
Spread Privacy
Spread Privacy
GbyAI
GbyAI
F
Fortinet All Blogs
小众软件
小众软件
B
Blog RSS Feed
博客园_首页
量子位
Y
Y Combinator Blog
美团技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Google DeepMind News
Google DeepMind News
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Vercel News
Vercel News
Last Week in AI
Last Week in AI
F
Full Disclosure
Stack Overflow Blog
Stack Overflow Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
宝玉的分享
宝玉的分享
Microsoft Azure Blog
Microsoft Azure Blog
有赞技术团队
有赞技术团队
A
About on SuperTechFans
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
The Cloudflare Blog
Hugging Face - Blog
Hugging Face - Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
T
Tenable Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Project Zero
Project Zero
C
CXSECURITY Database RSS Feed - CXSecurity.com
Engineering at Meta
Engineering at Meta
博客园 - 叶小钗
S
SegmentFault 最新的问题
T
Threat Research - Cisco Blogs
博客园 - 司徒正美
MyScale Blog
MyScale Blog
云风的 BLOG
云风的 BLOG
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
The GitHub Blog
The GitHub Blog
V
Vulnerabilities – Threatpost
S
Schneier on Security
Latest news
Latest news
I
Intezer
A
Arctic Wolf
T
Threatpost

Hugging Face - Blog

ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents Scaleway on Hugging Face Inference Providers 🔥 Democratizing AI Safety with RiskRubric.ai Public AI on Hugging Face Inference Providers 🔥 `LeRobotDataset:v3.0`: Bringing large-scale datasets to `lerobot` Visible Watermarking with Gradio Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason! Tricks from OpenAI gpt-oss YOU 🫵 can use with transformers Fine-tune Any LLM from the Hugging Face Hub with Together AI Jupyter Agents: training LLMs to reason with notebooks mmBERT: ModernBERT goes Multilingual Welcome EmbeddingGemma, Google's new efficient embedding model SAIR: Accelerating Pharma R&D with AI-Powered Structural Intelligence Make your ZeroGPU Spaces go brrr with ahead-of-time compilation NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset Generate Images with Claude and Hugging Face From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels MCP for Research: How to Connect AI to Research Tools Kimina-Prover-RL Arm & ExecuTorch 0.7: Bringing Generative AI to the masses Neural Super Sampling is here! TextQuests: How Good are LLMs at Text-Based Video Games? 🇵🇭 FilBench - Can LLMs Understand and Generate Filipino? Introducing AI Sheets: a tool to work with datasets using open AI models! Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training Vision Language Model Alignment in TRL ⚡️ Welcome GPT OSS, the new open-source model family from OpenAI! Measuring Open-Source Llama Nemotron Models on DeepResearch Bench 📚 3LM: A Benchmark for Arabic LLMs in STEM and Code Implementing MCP Servers in Python: An AI Shopping Assistant with Gradio Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face Say hello to `hf`: a faster, friendlier Hugging Face CLI ✨ Parquet Content-Defined Chunking TimeScope: How Long Can Your Video Large Multimodal Model Go? Fast LoRA inference for Flux with Diffusers and PEFT Accelerate a World of LLMs on Hugging Face with NVIDIA NIM Arc Virtual Cell Challenge: A Primer Consilium: When Multiple LLMs Collaborate Back to The Future: Evaluating AI Agents on Predicting Future Events Five Big Improvements to Gradio MCP Servers Ettin Suite: SoTA Paired Encoders and Decoders Migrating the Hub from Git LFS to Xet Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models Asynchronous Robot Inference: Decoupling Action Prediction and Execution ScreenEnv: Deploy your full stack Desktop Agent Building the Hugging Face MCP Server Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders Creating custom kernels for the AMD MI300 Upskill your LLMs With Gradio MCP Servers SmolLM3: smol, multilingual, long-context reasoner Three Mighty Alerts Supporting Hugging Face’s Production Infrastructure Efficient MultiModal Data Pipeline Announcing NeurIPS 2025 E2LM Competition: Early Training Evaluation of Language Models Training and Finetuning Sparse Embedding Models with Sentence Transformers Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub Gemma 3n fully available in the open-source ecosystem! Transformers backend integration in SGLang (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware Groq on Hugging Face Inference Providers 🔥 How Long Prompts Block Other Requests - Optimizing LLM Performance Learn the Hugging Face Kernel Hub in 5 Minutes Featherless AI on Hugging Face Inference Providers 🔥 Convert Transformers to ONNX with Hugging Face Optimum Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration Director of Machine Learning Insights [Part 3: Finance Edition] The Annotated Diffusion Model Deep Q-Learning with Space Invaders Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers Introducing Pull Requests and Discussions 🥳 Efficient Table Pre-training without Real Data: An Introduction to TAPEX An Introduction to Q-Learning Part 2/2 How Sempre Health is leveraging the Expert Acceleration Program to accelerate their ML roadmap
Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs
Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, Da · 2026-04-09 · via Hugging Face - Blog

Back to Articles

Waypoint-1.5 Weights on the Hub

Try it

waypoint 1.5

What is Waypoint-1.5?

Waypoint-1.5 is Overworld’s next real-time video world model, built to bring interactive generative worlds to the hardware people actually own.

The first release of Waypoint showed that real-time generative worlds were possible. It proved that interactive world models could be more than passive video demos, and that locally runnable systems could begin to close the gap between generating a world and actually stepping into one.

Waypoint-1.5 builds directly on that foundation. This release improves visual fidelity, expands the range of hardware that can run the model locally, and takes another step toward interactive world simulation without datacenter-scale compute.

On desktop hardware including RTX 3090 through 5090, Waypoint-1.5 can generate real-time environments at up to 720p and 60 FPS. This release also introduces a 360p tier designed to run smoothly across a much broader range of consumer hardware, including gaming laptops, and (soon) Apple Silicon Macs.

What’s new in Waypoint-1.5?

The biggest change in Waypoint-1.5 is accessibility.

With Waypoint-1, we proved the core experience. With Waypoint-1.5, we wanted to make that experience available on more machines without giving up real-time interactivity. That meant building two model tiers: a 720p model for higher-performance hardware, and a 360p model optimized for broader deployment.

We also scaled training dramatically. Waypoint-1.5 was trained on nearly 100x more data than Waypoint-1, which significantly improves the model’s ability to generate more coherent environments and more consistent motion over time.

Under the hood, Waypoint-1.5 also incorporates more efficient video modeling techniques to reduce redundant computation across frames. That matters because real-time world models are not judged only by how a single frame looks. They are judged by whether the world responds instantly, stays coherent as you move through it, and remains usable on local hardware.

Why this matters for world models

A lot of recent progress in generative video and world models has focused on visual fidelity. Those results matter, but fidelity alone is not what makes an interactive world feel real.

What people remember is responsiveness. They remember whether the environment reacts to them, whether motion stays coherent, whether the world holds together as they explore it, and whether the whole experience feels immediate instead of delayed.

That is the gap we care about most: the difference between watching a generated scene and actually being inside one.

If world models only run on large GPU clusters, they remain impressive demos. If they run locally on consumer hardware, they become something much more useful: a foundation for interactive entertainment, creative tooling, simulation, and AI-native environments people can actually explore.

Waypoint-1.5 is designed around that idea: not just better videos, but more responsive and explorable worlds that remain accessible on consumer hardware.

How to experience Waypoint-1.5

There are two ways to play Waypoint-1.5.

The first is local execution through Overworld Biome. This release is designed to run across a wide range of hardware configurations, and the updated Biome runtime makes local setup much simpler. With the new installer flow, users can go from download to running the model locally in minutes.

The second is Overworld Stream, which lets you try Waypoint-1.5 instantly in the browser with no local setup required.

Whether you want immediate access or full local control, Waypoint-1.5 is built to support both.

Additionally, we provide World Engine, our flexible, easy to use, core inference library powering our official clients, along with nearly a dozen third party clients and libraries.

The path forward

Waypoint started with a simple question: what would it take for generative worlds to become truly interactive?

Early generative systems showed that models could produce convincing images and videos. But building environments that people can explore, control, and interact with in real time is a different challenge entirely.

Waypoint-1.5 is another step in that direction, improving fidelity and expanding hardware accessibility while continuing to push real-time interactive generation onto local machines.

We think the future of world models will not be defined only by what they can render, but by whether people can actually inhabit and interact with them in real time.

Download Waypoint-1.5, run it locally with Biome, or jump in instantly on Overworld.stream.

And if you build something fun, strange, or unexpectedly immersive with it, we’d love to see it.

Stay in touch