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| license: other | |
| license_name: minimax-h3-community | |
| license_link: LICENSE | |
| tags: | |
| - video-generation | |
| - streaming | |
| - interactive | |
| - world-model | |
| - minimax-h3 | |
| # StreamRender-H3: Interactive Coding Worlds with Real-time Streaming Diffusion Render | |
| **Project page:** https://nvlabs.github.io/Sana/Sol-Engine/StreamRender-H3/ | |
| StreamRender-H3 splits an interactive world into a **Code Game Engine** and a **Streaming Diffusion Render**. The engine keeps the world state in code (maps, physics, rules) and streams a semantic map for every frame. A two-step streaming video model built on MiniMax-H3 Ref2VA renders those maps into photorealistic video as the player drives, with the look set by a reference image and a prompt. | |
| - Two denoising steps per chunk in the distilled student. | |
| - Diffusion render of 217.96 ms per streaming step on 8 GB200 GPUs, a 3.67× speedup over the PyTorch baseline. | |
| - Fast Causal H3 VAE decodes 2 latent frames at 720p in 24 ms on one GB200, built by a self-improving agent loop. | |
| ## Released runtime assets | |
| This repository contains the complete model assets for the streaming runtime. | |
| | Directory | Component | Weight provenance | | |
| | --- | --- | --- | | |
| | `backbone/` | Full 50-layer streaming H3 renderer, 1008b step224 EMA, LoRA merged | Trained renderer | | |
| | `causal_decoder/` | Fast Causal H3 VAE, distilled ViT24 decoder step6000 | Trained decoder | | |
| | `qwen_model/`, `qwen_dcp/` | H3-compatible Qwen conditioning model in safetensors and DCP formats | Unchanged upstream model; DCP is a storage conversion | | |
| | `tae/` | Official H3 TAE weights for streaming reference encoding | Unchanged upstream weights | | |
| | `audio_vae/` | H3 audio codec required by the model contract | Unchanged upstream weights | | |
| | `tokenizer/`, `qwen_processor/` | Conditioning tokenizer and processor | Upstream runtime assets | | |
| | `configs/`, `assets.json` | Runtime configuration, environment versions, relative asset manifest | Deployment configuration | | |
| | `reference_image/` | Default B10_e-track-3_bluehour reference picture | Example initialization asset | | |
| There are five distinct model components. Qwen's two storage formats are the | |
| same model. The causal decoder is constructed directly from its architecture | |
| configuration and student checkpoint; no native H3 video VAE checkpoint is | |
| needed. Streaming reference encoding uses TAE, and video decoding uses the | |
| trained causal decoder. | |
| ## Download and use | |
| Code and full deployment instructions: | |
| [NVlabs/Sana — StreamRender-H3](https://github.com/NVlabs/Sana/tree/sol-engine/models/streamrender_h3). | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download("Efficient-Large-Model/StreamRender-H3", | |
| local_dir="./StreamRender-H3") | |
| ``` | |
| The complete assets are approximately 206.87 GB. Reconstruct the exact | |
| merged backbone from its nine safetensors shards (another 66.28 GB of disk): | |
| ```bash | |
| python StreamRender-H3/download_assets.py | |
| cd Sana/models/streamrender_h3 | |
| python scripts/preflight.py --assets /absolute/path/to/StreamRender-H3/assets.json | |
| GPUS=8 PYTHON_BIN=python bash scripts/launch_local.sh \ | |
| --config /absolute/path/to/StreamRender-H3/configs/runtime.json \ | |
| --assets /absolute/path/to/StreamRender-H3/assets.json | |
| ``` | |
| Use the documented Python/CUDA environment and install frontend dependencies | |
| first. The bundle includes compatible Qwen DCP weights, so no separate DCP | |
| conversion or personal model repository is required. The default prompt and | |
| reference image are B10_e-track-3_bluehour. Alternative semantic-video examples | |
| and their media download instructions are in the runtime's `examples/` folder. | |
| The runtime uses eight GPUs in one NVLink domain, two full denoising evaluations, | |
| CFG=1 and S=2/W=2/C=2. Qwen conditions on the prompt and first picture once per | |
| session. `SHA256SUMS` records file integrity. Each component retains its | |
| applicable upstream license; see `LICENSE`, `LICENSE-Qwen.txt`, | |
| `LICENSE-TAE.txt`, and `NOTICE`. | |
| ## License | |
| The H3 renderer, distilled causal decoder and H3 audio codec follow the | |
| MiniMax H3 Community License, including its territory, use and redistribution | |
| terms; see `LICENSE` and `NOTICE`. Qwen components retain their Apache-2.0 | |
| license (`LICENSE-Qwen.txt`). TAE retains its upstream MIT license and | |
| attribution (`LICENSE-TAE.txt`). This bundle is not covered by a single | |
| blanket license. The runtime code's license does not replace model licenses. | |
| ## Citation | |
| ```bibtex | |
| @misc{streamrenderh3_2026, | |
| title = {StreamRender-H3: Interactive Coding Worlds with Real-time Streaming Diffusion Render}, | |
| author = {Yanzuo Lu and Tian Ye and Yitong Li and Shuchen Xue and Haozhe Liu and Song Han and Enze Xie}, | |
| year = {2026}, | |
| howpublished = {\url{https://nvlabs.github.io/Sana/Sol-Engine/StreamRender-H3/}}, | |
| note = {Project page} | |
| } | |
| ``` | |
| ## Acknowledgements | |
| We thank the [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3) team | |
| for the base video generation model and native video codec. Our streaming | |
| renderer and distilled causal decoder build on these components. Their | |
| upstream license and attribution notices remain applicable. | |