World model that transforms a rough 3D environment into realistic video one segment at a time, producing a segment in 5.1 seconds on one H200. More efficient than Nvidia's earlier method; code and training scripts released.

Streaming Code World Model
Research code that streams video generation chunk by chunk from a rough 3D proxy, built on Code World Model.
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Streaming Code World Model at a glance
- What is Streaming Code World Model?
- Streaming Code World Model is a model. Research code that streams video generation chunk by chunk from a rough 3D proxy, built on Code World Model.
- Does Streaming Code World Model work in the EU?
- Available in the EU. Code and checkpoints are publicly downloadable from GitHub and Hugging Face with no regional restriction mentioned; the license was not stated, so downstream terms (CWM, MiniMax-H3) should be checked. Last verified 14 Nov 2026.
- How much does Streaming Code World Model cost?
- Open source. Code and LoRA checkpoints are free to download; requires H200-class GPU hardware.
- Where was Streaming Code World Model shown?
- In 1 creator video by AI Search. Most recently by AI Search on 18 Oct 2026, at 31:07 in “Quantum breakthrough, Gemini 4 Argon, MOSS Image, KLING 4, OpenAI spy, Muse Wonder: AI NEWS”.
Sources: the vendor's own pages (linked above) and creator videos at the linked timestamps. EU status is information, not legal advice; see how we check.
Streaming Code World Model is a research implementation that turns a streaming version of Code World Model (CWM) into a causal video generator. Each 0.71 s chunk is generated from the start image, the proxy (a coarse 3D scene rendering) so far, and earlier chunks, with no access to future frames. It uses a sliding 6-chunk attention span with KV cache reuse via RoPE rotation.
Training uses diffusion forcing with a noisy history to slow long-horizon drift, followed by 4-step DMD self-forcing distillation. The README reports a 4-step model rendering a chunk in 5.1 s on one NVIDIA H200 at 1344×768, about 9× faster than 35-step sampling, with lower pixel MSE on four held-out CARLA clips. It is not yet real time. The training data are 342 CARLA clips (3.7 h).
Checkpoints are LoRAs for the released MiniMax-H3 model, hosted on Hugging Face, with sampling and training scripts in the repository. Inference needs an H200-class GPU (123 GB peak in bf16, 108 GB with FP8); 80 GB GPUs are not supported yet. The Slurm scripts assume 8-GPU nodes. The repository license was not stated in the material provided.
- Causal chunk-by-chunk streaming video generation from a coarse 3D proxy
- 4-step DMD self-forcing distillation, 5.1 s per 0.71 s chunk on one H200
- Diffusion forcing with noisy history to reduce long-horizon drift
- Sliding 6-chunk attention span with RoPE-based KV cache reuse
- Optional FP8 and FlashAttention-3; training and sampling scripts released
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