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[ICCV 2025] Video-T1: Test-Time Scaling for Video Generation
| Date | Stars |
|---|---|
| 2026-07-24 | 317 |
| 2026-07-25 | 317 |
| 2026-07-28 | 317 |
| 2026-07-30 | 317 |
| 2026-08-06 | 317 |
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<div align="center">
# ✨Video-T1: Test-Time Scaling for Video Generation✨
<p align="center">
<a href="https://liuff19.github.io/">Fangfu Liu</a><sup>1*</sup>,
<a href="https://hanyang-21.github.io/">Hanyang Wang</a><sup>1*</sup>,
<a href="https://github.com/JamesCai23">Yimo Cai</a><sup>1</sup>,
<a href="https://iseesaw.github.io/">Kaiyan Zhang</a><sup>1</sup>,
<a href="https://xiaohangzhan.github.io/">Xiaohang Zhan</a><sup></sup>,
<a href="https://duanyueqi.github.io/">Yueqi Duan</a><sup>1</sup>,
<br>
<sup>*</sup>Equal Contribution.
<br>
<sup>1</sup>Tsinghua University
</p>
<h3 align="center">ICCV 2025 🔥 </h3>
<a href='https://arxiv.org/abs/2503.18942'><img src='https://img.shields.io/badge/arXiv-2503.18942-b31b1b.svg'></a>
<a href='https://liuff19.github.io/Video-T1/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a><img src='https://img.shields.io/badge/License-MIT-blue'></a>
<a href='https://mp.weixin.qq.com/s/HtJHXGgTAhi-uBWSsgqOKQ'><img src='https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-%E4%B8%AD%E6%96%87%E4%BB%8B%E7%BB%8D-green'></a>
</div>
Video-T1: We present the generative effects and performance improvements of video generation under test-time scaling (TTS)
settings. The videos generated with TTS are of higher quality and more consistent with the prompt than those generated without TTS.
</div>
## 📢 News
- `2025.3.24` 🤗🤗🤗 We release *Video-T1: Test-time Scaling for Video Generation*
## 🎉 Results
</div>
Results of Test-Time Scaling for Video Generation. As the number of samples in the search space increases by scaling test-time
computation (TTS), the models’ performance exhibits consistent improvement.
## 🌟 Pipeline
</div>
Pipeline of Test-Time Scaling for Video Generation. Top: Random Linear Search for TTS video generation is to randomly
sample Gaussian noises, prompt the video generator to generate a sequence of video clips through step-by-step denoising in a linear manner,
and select the highest score from the test verifiers. Bottom: Tree of Frames (ToF) Search for TTS video generation is to divide the video
generation process into three stages: (a) the first stage performs image-level alignment that influences the later frames; (b) the second stage
is to apply dynamic prompt in test verifiers V to focus on motion stability, physical plausibility to provide feedback that guides heuristic
searching process; (c) the last stage assesses the overall quality of the video and select the video with highest alignment with text prompts.
## 🔧 Installation
### Dependencies:
```
git clone https://github.com/liuff19/Video-T1.git
cd VideoT1
conda create -n videot1 python==3.10
conda activate videot1
pip install -r requirements.txt
git clone https://github.com/LLaVA-VL/LLaVA-NeXT && cd LLaVA-NeXT && pip install --no-deps -e ".[train]"
```
### Model Checkpoints:
You need to download the following models:
- **Pyramid-Flow** model checkpoint (for video generation)
- **VisionReward-Video** model checkpoint (for video reward guidance)
- (Optional) **Image-CoT-Generation** model checkpoint (for ImageCoT)
- (Optional) **DeepSeek-R1-Distill-Llama-8B (Or other LLMs)** model checkpoint (for hierarchical prompts)
## 💻 Inference
### 1. Quick start
```bash
cd VideoT1
# Modify videot1.py to assign checkpoints correctly.
python -m videot1.py --prompt "A cat wearing sunglasses and working as a lifeguard at a pool." --video_name cat_lifeguard
```
### 2.Inference Code
For inference, please refer to **videot1.py** for usage.
```python
# Import Pipeline and Base Model
from pyramid_flow.pyramid_dit import PyramidDiTForVideoGeneration
from pipeline.videot1_pipeline import VideoT1Generator
# Initialize Pyramid-Flow Model
pyExcerpt of 7,355 characters
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matched fp:f6556b615fd49582, topic:video-generation, desc:video generation, readme:video generation