MagistrTheOne
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ARACHNE-X-ULTRA-VIDEO

@@ -12,222 +12,101 @@ tags:
 - video-continuation
 ---
-# LongCat-Video
 <div align="center">
-  <img src="assets/longcat_logo.svg" width="45%" alt="LongCat-Video" />
 </div>
 <hr>
-<div align="center" style="line-height: 1;">
-  <a href='https://meituan-longcat.github.io/LongCat-Video/'><img src='https://img.shields.io/badge/Project-Page-green'></a>
   <a href='https://huggingface.co/papers/2510.22200'><img src='https://img.shields.io/badge/Paper-HuggingFace-red'></a>
-  <a href='https://huggingface.co/meituan-longcat/LongCat-Video'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href='https://github.com/meituan-longcat/LongCat-Flash-Chat/blob/main/figures/wechat_official_accounts.png'><img src='https://img.shields.io/badge/WeChat-LongCat-brightgreen?logo=wechat&logoColor=white'></a>
-  <a href='https://x.com/Meituan_LongCat'><img src='https://img.shields.io/badge/Twitter-LongCat-white?logo=x&logoColor=white'></a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href='https://huggingface.co/meituan-longcat/LongCat-Video/blob/main/LICENSE'><img src='https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53'></a>
 </div>
-## Model Introduction
-We introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across *Text-to-Video*, *Image-to-Video*, and *Video-Continuation* generation tasks. It particularly excels in efficient and high-quality long video generation, representing our first step toward world models.
-### Key Features
-- 🌟 **Unified architecture for multiple tasks**: LongCat-Video unifies *Text-to-Video*, *Image-to-Video*, and *Video-Continuation* tasks within a single video generation framework. It natively supports all these tasks with a single model and consistently delivers strong performance across each individual task.
-- 🌟 **Long video generation**: LongCat-Video is natively pretrained on *Video-Continuation* tasks, enabling it to produce minutes-long videos without color drifting or quality degradation.
-- 🌟 **Efficient inference**: LongCat-Video generates $720p$, $30fps$ videos within minutes by employing a coarse-to-fine generation strategy along both the temporal and spatial axes. Block Sparse Attention further enhances efficiency, particularly at high resolutions
-- 🌟 **Strong performance with multi-reward RLHF**: Powered by multi-reward Group Relative Policy Optimization (GRPO), comprehensive evaluations on both internal and public benchmarks demonstrate that LongCat-Video achieves performance comparable to leading open-source video generation models as well as the latest commercial solutions.
-For more detail, please refer to the comprehensive [***LongCat-Video Technical Report***](https://huggingface.co/papers/2510.22200).
-## 🎥 Teaser Video
 <div align="center">
-  <video src="https://github.com/user-attachments/assets/00fa63f0-9c4e-461a-a79e-c662ad596d7d" width="2264" height="384"> </video>
 </div>
-## Quick Start
-### Installation
-Clone the repo:
-```shell
-git clone https://github.com/meituan-longcat/LongCat-Video
-cd LongCat-Video
-```
-Install dependencies:
-```shell
-# create conda environment
-conda create -n longcat-video python=3.10
-conda activate longcat-video
-# install torch (configure according to your CUDA version)
 pip install torch==2.6.0+cu124 torchvision==0.21.0+cu124 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
-# install flash-attn-2
-pip install ninja
-pip install psutil
-pip install packaging
-pip install flash_attn==2.7.4.post1
-# install other requirements
 pip install -r requirements.txt
-```
-FlashAttention-2 is enabled in the model config by default; you can also change the model config to use FlashAttention-3 or xformers.
-### Model Download
-| Models | Download Link |
-| --- | --- |
-| LongCat-Video | 🤗 [Huggingface](https://huggingface.co/meituan-longcat/LongCat-Video) |
-Download models using huggingface-cli:
-```shell
-pip install "huggingface_hub[cli]"
-huggingface-cli download meituan-longcat/LongCat-Video --local-dir ./weights/LongCat-Video
-```
-### Run Text-to-Video
-```shell
-# Single-GPU inference
-torchrun run_demo_text_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile
-# Multi-GPU inference
-torchrun --nproc_per_node=2 run_demo_text_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile
-```
-### Run Image-to-Video
-```shell
-# Single-GPU inference
-torchrun run_demo_image_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile
-# Multi-GPU inference
-torchrun --nproc_per_node=2 run_demo_image_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile
-```
-### Run Video-Continuation
-```shell
-# Single-GPU inference
-torchrun run_demo_video_continuation.py --checkpoint_dir=./weights/LongCat-Video --enable_compile
-# Multi-GPU inference
-torchrun --nproc_per_node=2 run_demo_video_continuation.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile
-```
-### Run Long-Video Generation
-```shell
-# Single-GPU inference
-torchrun run_demo_long_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile
-# Multi-GPU inference
-torchrun --nproc_per_node=2 run_demo_long_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile
-```
-### Run Interactive Video Generation
-```shell
-# Single-GPU inference
-torchrun run_demo_interactive_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile
-# Multi-GPU inference
-torchrun --nproc_per_node=2 run_demo_interactive_video.py --context_parallel_size=2 --checkpoint_dir=./weights/LongCat-Video --enable_compile
-```
-### Run Streamlit
-```shell
-# Single-GPU inference
 streamlit run ./run_streamlit.py --server.fileWatcherType none --server.headless=false
-```
-## Evaluation Results
-### Text-to-Video
-The *Text-to-Video* MOS evaluation results on our internal benchmark.
-| **MOS score** | **Veo3** | **PixVerse-V5** | **Wan 2.2-T2V-A14B** | **LongCat-Video** |
-|---------------|-------------------|--------------------|-------------|-------------|
-| **Accessibility** | Proprietary | Proprietary | Open Source | Open Source |
-| **Architecture** | - | - | MoE | Dense |
-| **# Total Params** | - | - | 28B | 13.6B |
-| **# Activated Params** | - | - | 14B | 13.6B |
-| Text-Alignment↑ | 3.99 | 3.81 | 3.70 | 3.76 |
-| Visual Quality↑ | 3.23 | 3.13 | 3.26 | 3.25 |
-| Motion Quality↑ | 3.86 | 3.81 | 3.78 | 3.74 |
-| Overall Quality↑ | 3.48 | 3.36 | 3.35 | 3.38 |
-### Image-to-Video
-The *Image-to-Video* MOS evaluation results on our internal benchmark.
-| **MOS score** | **Seedance 1.0** | **Hailuo-02** | **Wan 2.2-I2V-A14B** | **LongCat-Video** |
-|---------------|-------------------|--------------------|-------------|-------------|
-| **Accessibility** | Proprietary | Proprietary | Open Source | Open Source |
-| **Architecture** | - | - | MoE | Dense |
-| **# Total Params** | - | - | 28B | 13.6B |
-| **# Activated Params** | - | - | 14B | 13.6B |
-| Image-Alignment↑ | 4.12 | 4.18 | 4.18 | 4.04 |
-| Text-Alignment↑ | 3.70 | 3.85 | 3.33 | 3.49 |
-| Visual Quality↑ | 3.22 | 3.18 | 3.23 | 3.27 |
-| Motion Quality↑ | 3.77 | 3.80 | 3.79 | 3.59 |
-| Overall Quality↑ | 3.35 | 3.27 | 3.26 | 3.17 |
-## Community Works
-Community works are welcome! Please PR or inform us in Issue to add your work.
-- [CacheDiT](https://github.com/vipshop/cache-dit) offers Fully Cache Acceleration support for LongCat-Video with DBCache and TaylorSeer, achieved nearly 1.7x speedup without obvious loss of precision. Visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_longcat_video.py) for more details.
-## License Agreement
-The **model weights** are released under the **MIT License**.
-Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.
-See the [LICENSE](https://huggingface.co/meituan-longcat/LongCat-Video/blob/main/LICENSE) file for the full license text.
-## Usage Considerations
-This model has not been specifically designed or comprehensively evaluated for every possible downstream application.
-Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios.
-It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.
-Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
-## Citation
-We kindly encourage citation of our work if you find it useful.
-```
-@misc{meituanlongcatteam2025longcatvideotechnicalreport,
-      title={LongCat-Video Technical Report},
-      author={Meituan LongCat Team and Xunliang Cai and Qilong Huang and Zhuoliang Kang and Hongyu Li and Shijun Liang and Liya Ma and Siyu Ren and Xiaoming Wei and Rixu Xie and Tong Zhang},
-      year={2025},
       eprint={2510.22200},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2510.22200},
 }
-```
-## Acknowledgements
-We would like to thank the contributors to the [Wan](https://huggingface.co/Wan-AI), [UMT5-XXL](https://huggingface.co/google/umt5-xxl), [Diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
-## Contact
-Please contact us at <a href="mailto:longcat-team@meituan.com">longcat-team@meituan.com</a> or join our WeChat Group if you have any questions.

 - video-continuation
 ---
+ARACHNE-X-ULTRA
 <div align="center">
+  <img src="assets/arachne_logo.svg" width="45%" alt="ARACHNE-X-ULTRA" />
 </div>
 <hr>
+<div align="center">
+  <a href='https://nullxes.com/arachne-x-ultra'><img src='https://img.shields.io/badge/Project-Page-green'></a>
   <a href='https://huggingface.co/papers/2510.22200'><img src='https://img.shields.io/badge/Paper-HuggingFace-red'></a>
+  <a href='https://huggingface.co/nullxes/ARACHNE-X-ULTRA'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
+  <a href='mailto:ceo@nullxes.com'><img src='https://img.shields.io/badge/Contact-ceo@nullxes.com-lightgrey'></a>
 </div>
 <div align="center">
+  <a href='https://huggingface.co/nullxes/ARACHNE-X-ULTRA/blob/main/LICENSE'><img src='https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53'></a>
 </div>
+Model Introduction
+We introduce ARACHNE-X-ULTRA, a foundational video generation model with 13.6B parameters, delivering strong performance across Text-to-Video, Image-to-Video, and Video-Continuation generation tasks. It particularly excels in efficient, high-quality long video generation — representing NULLXES's first step toward world models.
+Key Features
+🕷️ Unified architecture for multiple tasks: ARACHNE-X-ULTRA unifies Text-to-Video, Image-to-Video, and Video-Continuation within a single framework. One model, natively supports all tasks, consistently strong across each.
+🕷️ Long video generation: Natively pretrained on Video-Continuation tasks, enabling minutes-long videos without color drift or quality degradation.
+🕷️ Efficient inference: Generates 720p, 30fps video within minutes via a coarse-to-fine generation strategy along temporal and spatial axes. Block Sparse Attention further boosts efficiency at high resolutions.
+🕷️ Multi-reward RLHF: Powered by multi-reward Group Relative Policy Optimization (GRPO). Comprehensive evaluations on internal and public benchmarks show performance comparable to leading open-source models and the latest commercial solutions.
+For full details, refer to the ARACHNE-X-ULTRA Technical Report.
+Quick Start
+Installation
+shellgit clone https://github.com/nullxes/ARACHNE-X-ULTRA
+cd ARACHNE-X-ULTRA
+shellconda create -n arachne python=3.10
+conda activate arachne
 pip install torch==2.6.0+cu124 torchvision==0.21.0+cu124 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
+pip install ninja psutil packaging flash_attn==2.7.4.post1
 pip install -r requirements.txt
+FlashAttention-2 is enabled by default. You can switch to FlashAttention-3 or xformers via model config.
+Model Download
+ModelLinkARACHNE-X-ULTRA🤗 Huggingface
+shellpip install "huggingface_hub[cli]"
+huggingface-cli download nullxes/ARACHNE-X-ULTRA --local-dir ./weights/ARACHNE-X-ULTRA
+Inference
+shell# Text-to-Video (single GPU)
+torchrun run_demo_text_to_video.py --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Text-to-Video (multi-GPU)
+torchrun --nproc_per_node=2 run_demo_text_to_video.py --context_parallel_size=2 --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Image-to-Video
+torchrun run_demo_image_to_video.py --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Video-Continuation
+torchrun run_demo_video_continuation.py --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Long Video Generation
+torchrun run_demo_long_video.py --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Interactive Video Generation
+torchrun run_demo_interactive_video.py --checkpoint_dir=./weights/ARACHNE-X-ULTRA --enable_compile
+# Streamlit UI
 streamlit run ./run_streamlit.py --server.fileWatcherType none --server.headless=false
+Evaluation Results
+Text-to-Video (Internal MOS Benchmark)
+MOS scoreVeo3PixVerse-V5Wan 2.2-T2V-A14BARACHNE-X-ULTRAAccessibilityProprietaryProprietaryOpen SourceOpen SourceArchitecture——MoEDenseTotal Params——28B13.6BActivated Params——14B13.6BText-Alignment ↑3.993.813.703.76Visual Quality ↑3.233.133.263.25Motion Quality ↑3.863.813.783.74Overall Quality ↑3.483.363.353.38
+Image-to-Video (Internal MOS Benchmark)
+MOS scoreSeedance 1.0Hailuo-02Wan 2.2-I2V-A14BARACHNE-X-ULTRAAccessibilityProprietaryProprietaryOpen SourceOpen SourceArchitecture——MoEDenseTotal Params——28B13.6BActivated Params——14B13.6BImage-Alignment ↑4.124.184.184.04Text-Alignment ↑3.703.853.333.49Visual Quality ↑3.223.183.233.27Motion Quality ↑3.773.803.793.59Overall Quality ↑3.353.273.263.17
+Community Works
+Community contributions are welcome. Open a PR or file an Issue to have your work listed.
+CacheDiT — Fully cached acceleration for ARACHNE-X-ULTRA via DBCache and TaylorSeer, achieving ~1.7× speedup with minimal precision loss. See their example.
+License
+Model weights are released under the MIT License. All contributions to this repository are licensed under MIT unless otherwise stated. This license does not grant rights to use NULLXES trademarks or patents.
+See the LICENSE file for full terms.
+Usage Considerations
+ARACHNE-X-ULTRA has not been specifically designed or comprehensively evaluated for every possible downstream application. Developers should account for known limitations of large generative models, carefully assess accuracy, safety, and fairness before deploying in sensitive or high-risk scenarios, and ensure compliance with all applicable laws and regulations — including data protection, privacy, and content safety requirements.
+Citation
+bibtex@misc{nullxes2025arachnexultra,
+      title={ARACHNE-X-ULTRA Technical Report},
+      author={NULLXES Team},
+      year={2026},
       eprint={2510.22200},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2510.22200},
 }
+Acknowledgements
+We thank the contributors to Wan, UMT5-XXL, Diffusers, and HuggingFace for their open research.
+Contact
+Questions? Reach out at ceo@nullxes.com