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README.md

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 license: apache-2.0
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+library_name: world_engine
+tags:
+  - world-model
+  - interactive-video
+  - generative-worlds
+  - real-time
+  - consumer-gpu
+  - diffusion
+  - transformer
+---
+
+![Overworld](assets/Overworld_Loop.webm)
+
+# Waypoint-1.5-1B
+
+Waypoint-1.5-1B is the smallest dense model in Overworld’s Waypoint-1.5 family of real-time interactive video world models. Waypoint-1.5 is designed around local, real-time generation on consumer hardware ranging from older RTX 30XX to RTX 50XX.
+
+## Model Details
+
+- **Developed by:** Overworld
+- **Model type:** Real-time interactive video world model
+- **Model family:** Waypoint-1.5
+- **Parameter count:** 1.2B
+- **Context length / frame context:** 512 frames
+- **Input modalities:** Starting image or video conditioning, control inputs
+- **Output:** Interactive generated video frames / world rollout
+- **License:** Apache 2
+- **Paper:** Coming soon
+- **Demo:** [overworld.stream](https://www.overworld.stream/)
+- **Runtime / inference library:** [Overworldai/Biome](https://github.com/Overworldai/Biome)
+
+## Model Summary
+
+Waypoint-1.5 is Overworld’s next-generation real-time video world model release. It builds on the original Waypoint-1 release by improving visual fidelity, expanding the range of consumer hardware that can run the model, and pushing further toward responsive, interactive world simulation without datacenter-scale compute.
+
+At the family level, Waypoint-1.5 targets real-time generation at up to **720p and 60 FPS**, and introduces **two model tiers**: a **720p** model for higher-performance systems and a [**360p** model](https://huggingface.co/Overworld/Waypoint-1.5-1B-360P) intended to run smoothly across a broader range of gaming PCs and Apple Silicon Macs. The release was also trained on **substantially more data than Waypoint-1**, improving coherence and motion consistency over longer interactions.
+
+## What makes Waypoint-1.5 different
+
+Waypoint-1.5 is built around a simple product constraint: generative worlds should be usable as **interactive systems**, not just watched as offline demos.
+
+Compared with a conventional video generation workflow, the Waypoint family is designed for:
+
+- **Real-time interaction** rather than offline batch generation
+- **Low-latency responsiveness** to user inputs
+- **Local execution** on consumer hardware
+- **Persistent world rollouts** where coherence across time matters as much as single-frame fidelity
+
+In practice, this means the model is intended to be used inside an interactive runtime that can condition generation on prompt context, previous frames, and live control inputs.
+
+## Intended Use
+
+This model is intended for:
+
+- Research on real-time world models and interactive video generation
+- Prototyping AI-native game and simulation experiences
+- Creative tools for interactive environments, world exploration, and live generative scenes
+- Experimentation with low-latency generative systems on local hardware
+- Education and research into control-conditioned video generation
+
+## Out-of-Scope Use
+
+This model is **not** intended for:
+
+- Generating illegal content or content that exploits, sexualizes, or endangers minors
+- Generating non-consensual sexual content or explicit sexual content where prohibited
+- Impersonation, harassment, or deceptive identity-based content
+- Generating copyrighted characters, branded IP, or celebrity likenesses in ways that infringe rights or violate platform rules
+- Safety-critical decision-making, surveillance, or high-stakes automated systems
+- Any deployment that removes reasonable safeguards while serving end users at scale
+
+## Usage
+
+This checkpoint is intended to be used with Overworld’s interactive runtime stack.
+
+- Use our [world_engine](https://github.com/Wayfarer-Labs/world_engine) inference library directly
+- Play on our official desktop client, [Biome](https://github.com/Overworldai/Biome/)
+
+
+### Recommended setup
+
+- **Recommended GPU / device:** RTX 5090
+- **Expected FPS on reference hardware:** 56 FPS
+
+
+### Architecture
+
+- **Backbone:** Autoregressive Diffusion Transformer
+- **Autoencoder:** [Tiny Hunyuan Autoencoder (taehv1_5)](https://github.com/madebyollin/taehv) — 4x temporal compression, 8x spatial compression, 32 latent channels
+
+### Training Data
+
+Waypoint-1.5 was trained on **nearly 100× more data than Waypoint-1**, with the release emphasizing better coherence, motion consistency, and broader hardware accessibility.
+
+## Performance
+
+### Waypoint 1 vs Waypoint 1.5
+
+| | Waypoint 1 | Waypoint 1.5 |
+|---|---|---|
+| Resolution | 360P | 720P |
+| Context window | 2 seconds | 10 seconds |
+| 4-step unquantized (5090) | 20 FPS | 56 FPS |
+| 4-step w8a8 quantized (5090) | N/A | 72 FPS |
+| 4-step w8a8 (3090) | N/A | 30 FPS |
+
+![Generation Throughput — Waypoint 1 vs Waypoint 1.5](assets/perf_chart.png)
+
+## Limitations
+
+This model has important limitations.
+
+- It is a generative world model, not a simulator with guaranteed physical accuracy.
+- Long interactive rollouts may drift, collapse, or become inconsistent.
+- The model may produce unstable geometry, object persistence failures, or implausible motion.
+- Performance is hardware-dependent and may vary significantly by runtime stack and settings.
+- Safety mitigations available in hosted deployments may not transfer fully to raw checkpoint use.
+- Outputs may reflect biases, omissions, or unsafe patterns present in training data or learned world priors.
+
+## Safety
+
+Please see our blog post, ["Engineering Safety for Interactive World Models"](https://over.world/blog/engineering-safety-for-interactive-world-models) for details.
+
+## Contact
+
+- [Website](http://over.world/)
+- [Discord](https://discord.gg/MEmQa7Wux4)
+- [X/Twitter](https://x.com/overworld_ai)