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----
-license: apache-2.0
-library_name: lightx2v
-base_model:
- - robbyant/lingbot-video-moe-30b-a3b
-pipeline_tag: text-to-video
-tags:
- - video-generation
- - text-to-video
- - image-to-video
- - text-to-image
- - distillation
- - lora
----
-
-# 🎬 LightLingBot-Video
-
-> Few-step distilled LoRA checkpoints for LingBot-Video, accelerated by
-> [LightX2V](https://github.com/ModelTC/LightX2V).
-
-LightLingBot-Video provides distilled LoRA checkpoints for fast LingBot-Video
-inference. The same LoRA configuration supports text-to-video (T2V),
-text-to-image (T2I), and image-to-video (I2V) generation.
-
-## 📋 Table of Contents
-
-- [Features](#-features)
-- [Installation](#-installation)
-- [Model Preparation](#-model-preparation)
-- [Inference](#-inference)
-- [Generation Results](#-generation-results)
-- [Notes](#-notes)
-- [Acknowledgements](#-acknowledgements)
-
-## ✨ Features
-
-- ⚡ **4-step inference** with distilled LoRA checkpoints
-- 🎬 **T2V, T2I, and I2V** generation
-- 🔧 **One unified LoRA configuration** shared by all tasks
-- 🚀 **LightX2V integration** for efficient inference and deployment
-- 🎯 **Two distilled LoRA versions** for comparison
-
-## 📦 Installation
-
-```bash
-git clone https://github.com/ModelTC/LightX2V.git
-cd LightX2V
-python3 -m pip install -e .
-```
-
-Follow the
-[LightX2V installation guide](https://github.com/ModelTC/LightX2V)
-to install the optional acceleration dependencies required by your hardware.
-
-## 📥 Model Preparation
-
-Download the LingBot-Video base model and the distilled LoRA repository:
-
-```bash
-huggingface-cli download robbyant/lingbot-video-moe-30b-a3b \
- --local-dir /path/to/LingBot-Video
-
-huggingface-cli download lightx2v/LightLingBot-Video \
- --local-dir /path/to/LightLingBot-Video
-```
-
-Before inference:
-
-1. Set `model_path` in the corresponding script under
- `scripts/lingbot_video/`.
-2. Set the LoRA checkpoint path in
- `configs/lingbot_video/lingbot_video_lora.json`.
-3. Adjust `CUDA_VISIBLE_DEVICES` in the script if needed.
-4. For I2V, also set `image_path` in the I2V script.
-
-## 🚀 Inference
-
-Run the following commands from the LightX2V repository root. These commands
-use the shell scripts under `scripts/lingbot_video`; no `examples` entry point
-is required.
-
-### Text-to-Video
-
-```bash
-bash scripts/lingbot_video/run_lingbot_video_t2v.sh
-```
-
-### Text-to-Image
-
-```bash
-bash scripts/lingbot_video/run_lingbot_video_t2i.sh
-```
-
-### Image-to-Video
-
-```bash
-bash scripts/lingbot_video/run_lingbot_video_i2v.sh
-```
-
-### Recommended Settings
-
-| Setting | Base model | Distilled LoRA |
-|:--|:--:|:--:|
-| Inference steps | 40 | 4 |
-| LoRA strength | — | 1.0 |
-| Resolution | 832 × 480 | 832 × 480 |
-| Guidance scale | 3.0 | 1.0 |
-| Sample shift | 3.0 | 3.0 |
-
-## 🎬 Generation Results
-
-### Text-to-Video (T2V)
-
-| Method | Sample 1 | Sample 2 | Sample 3 |
-|:--|:--:|:--:|:--:|
-| 40 steps | | | |
-| 4-step LoRA v1 | | | |
-| 4-step LoRA v2 | | | |
-
-### Text-to-Image (T2I)
-
-| Method | Sample 1 | Sample 2 | Sample 3 | Sample 4 |
-|:--|:--:|:--:|:--:|:--:|
-| 40 steps | 
| 
| 
| 
|
-| 4-step LoRA v1 | 
| 
| 
| 
|
-| 4-step LoRA v2 | 
| 
| 
| 
|
-
-### Image-to-Video (I2V)
-
-| Method | Sample 1 | Sample 2 | Sample 3 | Sample 4 |
-|:--|:--:|:--:|:--:|:--:|
-| 40 steps | | | | |
-| 4-step LoRA v1 | | | | |
-| 4-step LoRA v2 | | | | |
-
-## ⚠️ Notes
-
-- The distilled LoRA configuration uses 4 inference steps and disables CFG.
-- Merged LoRA inference requires original, non-quantized DiT weights.
-- Prompts and negative prompts should follow the structured JSON format used
- in `scripts/lingbot_video/`.
-- T2V, T2I, and I2V share
- `configs/lingbot_video/lingbot_video_lora.json`.
-- Speed and memory usage depend on the GPU, attention backend, resolution, and
- video length.
-
-## 🤝 Acknowledgements
-
-- [LingBot-Video](https://huggingface.co/inclusionAI/LingBot-Video)
-- [LightX2V](https://github.com/ModelTC/LightX2V)
-
-If you find this project helpful, please give
-[LightX2V](https://github.com/ModelTC/LightX2V) a ⭐.