---
license: other
license_name: minimax-h3-community
license_link: LICENSE
library_name: pytorch
tags:
- video-generation
- streaming
- game-engine
- streamrender
language:
- en
---

# StreamRender-H3 runtime assets

The complete inference asset bundle for the code-driven StreamRender-H3
runtime: controllable game engine → semantic reference frames → streaming
TAE encode → two full H3 render evaluations → causal ViT24 decode → browser feedback.

Runtime source and deployment instructions:
[NVlabs/Sana, sol-engine/models/streamrender_h3](https://github.com/NVlabs/Sana/tree/sol-engine/models/streamrender_h3),
validated integration commit `fcef8d4287d5f45e1a5e839c940b7e14c91c71fc`.

## Included components

| Directory | Component |
| --- | --- |
| `backbone/` | Latest 1008b step224 merged EMA H3 backbone, all 50 layers, standard safetensors shards |
| `tae/` | Official TAE weights used by the online reference encoder |
| `causal_decoder/` | Fast causal ViT24 decoder step6000, release 20261005-v1 |
| `qwen_model/` | Qwen3-VL text/visual conditioning model, original safetensors shards and configuration |
| `qwen_dcp/` | Corresponding distributed checkpoint used by the runtime loader, including `.metadata` |
| `tokenizer/`, `qwen_processor/` | Exact tokenizer/processor assets |
| `native_vae/` | Native H3 video VAE checkpoint used to construct the decoder architecture |
| `audio_vae/` | H3 audio codec checkpoint required by the model contract |
| `reference_image/` | Reference image for the validated opening racing scene |
| `configs/` | Runtime inference profile and tested environment versions |

This is the current deployment bundle, not a collection of historical training
checkpoints. Optimizer states are excluded. Both Qwen formats are included to
preserve the tested loader setup. Total payload is approximately **217 GB**.
The original backbone contains 535 tensors, 66,280,488,840 bytes, SHA256:
`56c3ee3270db40354ef70fcbea16248809914cfa8f51acb57dae7d30fa4bb597`.
Sharding preserves every tensor and permits byte-identical reconstruction.

## Download and run

Keep model caches and downloads on a sufficiently large storage volume.

```python
from huggingface_hub import snapshot_download
snapshot_download("yitongl/StreamRender-H3", local_dir="/your/code/models/StreamRender-H3")
```

The current runtime expects one merged backbone file. Reconstruct it first
(requires an additional 66.28 GB; the script checks the original SHA256):

```bash
python /your/code/models/StreamRender-H3/download_assets.py
cd Sana/models/streamrender_h3
python scripts/preflight.py --assets /your/code/models/StreamRender-H3/assets.json
GPUS=8 PYTHON_BIN=python bash scripts/launch_local.sh \
  --config /your/code/models/StreamRender-H3/configs/runtime.json \
  --assets /your/code/models/StreamRender-H3/assets.json
```

Use the runtime's documented Python/CUDA/FA4 environment and install its
frontend dependencies. The tested deployment uses eight GB200 GPUs within
one NVLink domain, two denoising evaluations, CFG=1, S=2/W=2/C=2, 832×480
reference encoding and 1344×768 output. Qwen consumes prompt and first image
once per session. The included `assets.json` uses relative paths.

The actual browser-to-H3-to-browser integration was verified in a same-rack
eight-GPU run that exited successfully. The longer validation measured a
295.58 ms median GPU-resident RGB-to-RGB-ready interval and approximately
1.03 s browser publication intervals. These are different timing boundaries;
the browser test is not a claim of 24-fps end-to-end feedback.

## License and attribution

This bundle is **not blanket Apache/MIT licensed**. H3 weights and derivatives
are governed by the included MiniMax H3 Community License, including its
territorial and redistribution terms. Qwen components retain Apache-2.0
terms. TAE source attribution is preserved separately. See `LICENSE`,
`LICENSE-Qwen.txt`, `LICENSE-TAE.txt`, `NOTICE` and `SOURCE_SNAPSHOT.json`.
Consult your applicable authorization when using or redistributing the bundle.