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[doc] standardize diffusion cookbook model pages (#34247)

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Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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Mick
2026-08-21 10:25:40 +08:00
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co-authored by Claude Opus 5
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docs/cookbook/diffusion/LingBot-World/LingBot-World-2.0.mdx
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@@ -7,13 +7,13 @@ tag: REALTIME
import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
<DiffusionModelTags tags={["realtime", "world model", "causal DiT", "camera control"]} />
<DiffusionModelTags tags={["realtime", "world model", "14B causal DiT", "camera control", "interactive KV window"]} />
## 1. Model Introduction
lingbot-world-v2-14b-causal-fast-diffusers is a realtime camera-controlled video world model. In SGLang-diffusion, it belongs to the realtime causal path: the server keeps a live session, samples control signals per chunk, reuses causal DiT state, and decodes video frames incrementally.
[LingBot World 2.0](https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast-diffusers) is the 14B realtime camera-controlled world model in the LingBot family. It generates continuously from a live causal session, accepts control updates between chunks, and reuses DiT and VAE state for incremental frame delivery.
This is different from offline diffusion video models such as Wan or LTX. Offline models denoise a bounded latent sequence for one request. Realtime world models generate a continuing stream, so the runtime must manage session state, control events, causal attention cache, and VAE decode cache.
Choose 2.0 when the larger model and interactive KV-window path justify an 8-GPU deployment. Like the earlier LingBot release, it prioritizes responsive control and bounded causal state over the full-clip global context of offline video diffusion.
## 2. Deployment