MODEL PROFILE

Hy-Nomas

腾讯 · Hy-Nomas

**Hy-Nomas** is a video world generation model that supports free-form, multi-turn interaction through text instructions and camera control. It generates a world one video segment at a time, allowing users to introduce objects, change styles and scenes, describe actions, or control the camera as generation continues. The model combines a segment-wise autoregressive diffusion transformer (DiT), a Qwen text encoder, camera-conditioning modules, and a PixelUMM video decoder. A persistent key/value (KV) cache carries context between segments. The distilled inference configuration uses **8 denoising steps**, with **720 × 1280** output at **24 FPS**. A separate configuration supports video generation on a **single NVIDIA GeForce RTX 4090** using INT8 DiT weights, an INT8 KV cache, an FP8 decoder, and CPU offload. - **Free-form interaction:** Guide successive segments with instructions about subjects, actions, appearance, style, and environment. - **Camera control:** Supply camera trajectories alongside text to control viewpoint and movement. - **Continuous generation:** Reuse earlier context through a KV cache when extending the same world. - **Distilled inference:** Generate each segment with 8 DiT denoising steps and decode its latents with a fine-tuned PixelUMM decoder. - **Flexible deployment:** Use the standard sequence-parallel configuration or the single-GPU configuration, and interact through the web demo.

license:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/31 03:08:28
OVERVIEW

Model overview

**Hy-Nomas** is a video world generation model that supports free-form, multi-turn interaction through text instructions and camera control. It generates a world one video segment at a time, allowing users to introduce objects, change styles and scenes, describe actions, or control the camera as generation continues. The model combines a segment-wise autoregressive diffusion transformer (DiT), a Qwen text encoder, camera-conditioning modules, and a PixelUMM video decoder. A persistent key/value (KV) cache carries context between segments. The distilled inference configuration uses **8 denoising steps**, with **720 × 1280** output at **24 FPS**. A separate configuration supports video generation on a **single NVIDIA GeForce RTX 4090** using INT8 DiT weights, an INT8 KV cache, an FP8 decoder, and CPU offload. - **Free-form interaction:** Guide successive segments with instructions about subjects, actions, appearance, style, and environment. - **Camera control:** Supply camera trajectories alongside text to control viewpoint and movement. - **Continuous generation:** Reuse earlier context through a KV cache when extending the same world. - **Distilled inference:** Generate each segment with 8 DiT denoising steps and decode its latents with a fine-tuned PixelUMM decoder. - **Flexible deployment:** Use the standard sequence-parallel configuration or the single-GPU configuration, and interact through the web demo.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。