---
title: LingBot_World_V2_ComfyUI
canonical_url: "https://www.modelscope.cn/models/realrebelai/LingBot_World_V2_ComfyUI"
md_url: "https://www.modelscope.cn/models/realrebelai/LingBot_World_V2_ComfyUI.md"
repository: realrebelai/LingBot_World_V2_ComfyUI
last_updated: 2026-07-11
license: cc-by-nc-sa-4.0
pipeline_tag: image-to-video
tasks:
  - image-to-video
base_model:
  - robbyant/lingbot-world-v2-14b-causal-fast
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
downloads: 60
stars: 1
tags:
  - world-model
  - video
  - gguf
  - comfyui
  - wan
  - camera-control
  - low-vram
---

# LingBot_World_V2_ComfyUI

> LingBot_World_V2_ComfyUI - realrebelai 在 ModelScope 开源的模型。LingBot-World-v2 14B causal-fast — GGUF (8GB-VRAM ready)

realrebelai/LingBot_World_V2_ComfyUI 是 ModelScope 魔搭社区上的image-to-video模型，采用 cc-by-nc-sa-4.0 许可，基于 robbyant/lingbot-world-v2-14b-causal-fast 构建。

- **Repository**: realrebelai/LingBot_World_V2_ComfyUI
- **License**: cc-by-nc-sa-4.0
- **Tasks**: image-to-video
- **Base model**: robbyant/lingbot-world-v2-14b-causal-fast
- **Tags**: world-model, video, gguf, comfyui, wan, camera-control, low-vram
- **Downloads**: 60
- **Stars**: 1
- **Last updated**: 2026-07-11

Source: https://www.modelscope.cn/models/realrebelai/LingBot_World_V2_ComfyUI

---

# LingBot-World-v2 14B causal-fast — GGUF (8GB-VRAM ready)

GGUF quantizations of [robbyant/lingbot-world-v2-14b-causal-fast](https://huggingface.co/robbyant/lingbot-world-v2-14b-causal-fast)
for **[ComfyUI_Rebels_LingBotWorld](https://github.com/RealRebelAI/ComfyUI_Rebels_LingBotWorld)** —
an action-controlled world model running on consumer GPUs (tested: RTX 3070 8GB / 16GB RAM).

You author a camera/movement track (or type one into the Action Builder node); the model
renders the video that track produces from your start image, with genuine action following.
Offline generation, chunked causal sampling, 4 distilled steps per chunk.

## Files
| File | Notes |
|---|---|
| `LingBot-World-14B-Q4_K_S.gguf` (~11.7GB) | tested tier; Q5-bumped v-projections keep effective bpw ~6.7 |
| other tiers (Q4_K_M…Q8_0) | quality ladder; RAM-streamed, VRAM use is unchanged |

Also required: `Wan2.1_VAE.pth`, a **UMT5-XXL GGUF** encoder (loads via ComfyUI-GGUF
CLIPLoader, type `wan`) 

## Settings that matter (8GB)
- Resolution preset **256×448** (default) or 320×544; world-memory window **6+2**
- The KV cache is the world memory: it scales with window × resolution
  (upstream 18+6 @ 480×832 ≈ 21GB — does not fit consumer cards; the sampler
  pre-checks and refuses instead of hanging)
- frame_num 4n+1; start at 21; export 16 fps

## License
**CC BY-NC-SA 4.0** (inherited from upstream): non-commercial, attribution, share-alike.
Quantization is a format conversion only. Quants + nodes by [RealRebelAI](https://huggingface.co/realrebelai).


https://cdn-uploads.huggingface.co/production/uploads/68761990332d15464ccc8dee/OBwm3rX-WJ5s8a-EePAbK.mp4