We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding. > **💡 Rewriter deployment**: the bundled rewriter uses the single-process > `transformers` backend. For higher throughput, deploy the VLM yourself and call > it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1 > must use the base VLM without the rewriter LoRA, while step 2 must use the same > base VLM with the rewriter LoRA enabled. This can be implemented with two > endpoints, or with one server that can select the adapter per request. See > vLLM / SGLang official docs. Install the optional SGLang dependencies only when using SGLang Diffusion or the fused / FP8 MoE runtime:
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View details ↗We are excited to introduce **LingBot-World**, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. - **High-Fidelity & Diverse Environments**: It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. - **Long-Term Memory & Consistency**: It enables a minute-level horizon while preserving contextual consistency over time, which is also known as long-term memory. - **Real-Time Interactivity & Open Access**: It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning. - Jan 29, 2026: 🎉 We release the technical report, code, and models for LingBot-World. This codebase is built upon Wan2.2. Please refer to their documentation for installation instructions. Clone the repo:
Input Image→ Output Video
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View details ↗We are excited to introduce **LingBot-World**, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. - **High-Fidelity & Diverse Environments**: It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. - **Long-Term Memory & Consistency**: It enables a minute-level horizon while preserving contextual consistency over time, which is also known as long-term memory. - **Real-Time Interactivity & Open Access**: It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning. This codebase is built upon Wan2.2. Please refer to their documentation for installation instructions. Clone the repo: Our model supports video generation at both 480P and 720P resolutions. You can find data samples for inference in the `examples/` directory, which includes the corresponding input images, prompts, and control signals. To enable long video generation, we utilize multi-GPU inference powered by FSDP and DeepSpeed Ulysses. - 480P:
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