---
title: "LingBot-Video"
description: "LingBot-Video from Robbyant (Ant Group): text-to-image and text-to-video; Runner parity pending."
modelId: "lingbot-video"
pageSource: generated
---

LingBot-Video is Robbyant's video generation family, recorded for text-to-image, text-to-video, text+image-to-video, and video refinement. Two checkpoint lines are recorded: a dense 1.3B model and a MoE 30B-A3B model that ships with a refiner. WorldFoundry flattens the inference package in-tree under synthesis/visual_generation/lingbot_video and deliberately excludes the prompt rewriter, training, and benchmark sources.

<ModelPaperFigures modelId="lingbot-video" locale="en" />

The recorded paper is [arXiv 2607.07675](https://arxiv.org/abs/2607.07675).

Project page: [technology.robbyant.com/lingbot-video](https://technology.robbyant.com/lingbot-video). Code: [Robbyant/lingbot-video](https://github.com/Robbyant/lingbot-video).

<ModelCommandBuilder modelId="lingbot-video" locale="en" />

## What it is for

Use `lingbot-video` for the family's joint T2V/I2V inference path. The catalog records both task profiles on this page.

The dense 1.3B and MoE 30B-A3B (3B active) checkpoints share the inference surface; the MoE line adds a refiner stage. The preserved code paths include direct Diffusers, optional SGLang, FSDP, context-parallel, and CFG-parallel execution. The upstream-recommended stack pins a dedicated Transformers 5 / Diffusers 0.39 environment. It is published by Robbyant (Ant Group).

Catalog tasks include text-to-image, text-to-video, text-image-to-video, and video-refinement. Catalog aliases include `lingbot-video-dense` and `lingbot-video-moe`. Documented modalities: text and image in, image and video out.

Recorded evaluation entry points include [VBench](/docs/evaluation/benchmark-hub/vbench) (General T2V quality suite for both checkpoint lines).

## Runtime contract

Optional `prompt`, `prompt_json`, `negative_prompt`, `backend`, and `mode`; artifacts `generated_image`, `generated_video`, and `lingbot_video.mp4`.

CLI task profiles: `t2v`, `ti2v`, and `t2i`. WorldFoundry binds `LingBotVideoGenerationPipeline`.

Family status: **Runner parity pending**.

Recorded `t2v` defaults: height=480, width=832, seed=42, fps=24 Recorded `ti2v` defaults: height=480, width=832, seed=42, fps=24.

Full checkpoint-backed artifact and visual parity are pending. The released Dense 1.3B checkpoint already has a recorded 121-frame, 832x480 quality-oriented Workspace artifact. Note one quirk from the profile: T2I produces an image even though the default artifact type is the T2V video route. Dense and MoE weights are external (apache-2.0) and never tracked in git.

## Running in WorldFoundry

The catalog launch is `worldfoundry-eval run lingbot-video --pipeline.task-profile t2v`. The command builder above switches `--pipeline.task-profile` among recorded tasks and still emits `worldfoundry-eval run`.

It runs in the unified environment `worldfoundry-unified-cu128` (Python 3.11, CUDA 12.8, torch==2.11.0+cu128).

Recorded weight repositories include `robbyant/lingbot-video-dense-1.3b` and `robbyant/lingbot-video-moe-30b-a3b`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh lingbot-video`. Add `--download` to fetch missing public checkpoints.

Recorded licenses include apache-2.0; status: **Runner parity pending**.

<ModelRelatedRecipes modelId="lingbot-video" locale="en" />