--- title: "LongCat-Video" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/48609422-da93-43cf-9ad3-e5e7f4cada5e" updated: "2026-11-13" --- # LongCat-Video > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes videos from a text prompt, from a still image, or by continuing an existing clip, with one 13.6B model. Aims at long clips at 720p and 30 fps, for developers and researchers with GPUs. LongCat-Video is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by meituan-longcat ## About ### What it is LongCat-Video is a 13.6 billion parameter video generation model from Meituan's LongCat team. A single set of weights covers three tasks: it turns text into video, animates an image, or extends a clip. It was pretrained on continuation, which is how the authors say it keeps color and quality steady across clips that run for minutes. They call it a first step toward world models. ### Who it suits Developers and researchers who run video models on their own GPUs and want a single model for several video tasks. It is code you run from a command line, so it suits people comfortable with Python and conda. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Video creators | Make clips from text or a still image | A short scene from a written prompt | | Software | Developers | Build video generation into a tool | A service that animates a product picture | | Research | Video model researchers | Study long video generation | Checking how a clip holds its color over minutes | | Marketing | Marketing teams | Draft video concepts from a prompt | A concept clip for a campaign | You can skip it if you have no suitable GPU, or if you want a hosted tool with no setup, since you run everything from code here. ### Where to use it - A command line with torchrun, on one GPU or on two - A conda environment with Python 3.10 - A machine whose PyTorch build matches its CUDA version - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Text to video | Creators | A video made from a written prompt | | Image to video | Designers | A video that starts from your picture | | Video continuation | Researchers | A longer clip that extends an existing one | ### How to set it up - conda, with a Python 3.10 environment - A PyTorch build that matches your CUDA version, installed as the card describes - The LongCat-Video code from its GitHub repository, since the demo scripts live there - FlashAttention-2, which the model config enables by default 1. Get the code from the project's GitHub repository and open its folder. The clone command holds a web address, so it is not copied here. 2. Create the conda environment and activate it. 3. Install PyTorch for your CUDA version. Its command holds a web address, so follow the card. 4. Install flash-attn and the other requirements. 5. Download the model weights into a weights folder. ```sh conda create -n longcat-video python=3.10 conda activate longcat-video # 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 ``` ```sh pip install "huggingface_hub[cli]" huggingface-cli download meituan-longcat/LongCat-Video --local-dir ./weights/LongCat-Video ``` ### How to use it Run one of the three demo scripts with torchrun and point it at the weights folder. For two GPUs, add the process count and the context parallel size. The visible card text does not show where the prompt goes, so check the demo script itself. ```sh # 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 ``` ```sh # Single-GPU inference torchrun run_demo_image_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile ``` ```sh # Single-GPU inference torchrun run_demo_video_continuation.py --checkpoint_dir=./weights/LongCat-Video --enable_compile ``` | Tool | What it does | | --- | --- | | run\_demo\_text\_to\_video.py | Makes a video from a text prompt | | run\_demo\_image\_to\_video.py | Makes a video that starts from an image | | run\_demo\_video\_continuation.py | Extends an existing video clip | | torchrun | Starts a run on one GPU or on several | ### Example You run the text to video demo on a single GPU and point it at the weights folder. The script produces a video from its prompt. The authors say 720p clips at 30 fps take minutes to generate. ### For newcomers and experts If you are new, start with the text to video demo on a single GPU and leave the other two scripts for later. Expect a long setup, since it needs a conda environment, a matching PyTorch build and flash-attn. If you know the field: the authors build one framework for all three tasks, so the same weights serve each. Speed comes from coarse-to-fine generation along both time and space, plus Block Sparse Attention at high resolutions. Training adds multi-reward Group Relative Policy Optimization, and you can switch FlashAttention-2 to FlashAttention-3 or xformers in the model config. Text, image or video clip → LongCat-Video, 13.6B parameters → Coarse-to-fine generation → 720p video at 30 fps ### Good to know The authors say their tests put the model on par with leading open-source video models, and with some commercial ones. Those are their own results, so check them against your needs. - Size: 13.6B parameters - Output: 720p at 30 fps, as the card describes - Languages: English and Chinese - Attention: FlashAttention-2 by default - License: MIT - Cost to run: none named, though it needs GPU hardware - Data it reaches: model files downloaded to your computer, and the project code from GitHub - Not for: machines without a suitable GPU, since the card shows only GPU runs ## README ````markdown # LongCat-Video Makes videos from a text prompt, from a still image, or by continuing an existing clip, with one 13.6B model. Aims at long clips at 720p and 30 fps, for developers and researchers with GPUs. Made by meituan-longcat on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: LongCat-Video is a 13.6 billion parameter video generation model from Meituan's LongCat team. A single set of weights covers three tasks: it turns text into video, animates an image, or extends a clip. It was pretrained on continuation, which is how the authors say it keeps color and quality steady across clips that run for minutes. They call it a first step toward world models. Who it suits: Developers and researchers who run video models on their own GPUs and want a single model for several video tasks. It is code you run from a command line, so it suits people comfortable with Python and conda. ```table Industry | Who uses it | How they use it | Example Media | Video creators | Make clips from text or a still image | A short scene from a written prompt Software | Developers | Build video generation into a tool | A service that animates a product picture Research | Video model researchers | Study long video generation | Checking how a clip holds its color over minutes Marketing | Marketing teams | Draft video concepts from a prompt | A concept clip for a campaign ``` You can skip it if you have no suitable GPU, or if you want a hosted tool with no setup, since you run everything from code here. Where to use it: - A command line with torchrun, on one GPU or on two - A conda environment with Python 3.10 - A machine whose PyTorch build matches its CUDA version - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Text to video | Creators | A video made from a written prompt Image to video | Designers | A video that starts from your picture Video continuation | Researchers | A longer clip that extends an existing one ``` How to set it up: - conda, with a Python 3.10 environment - A PyTorch build that matches your CUDA version, installed as the card describes - The LongCat-Video code from its GitHub repository, since the demo scripts live there - FlashAttention-2, which the model config enables by default 1. Get the code from the project's GitHub repository and open its folder. The clone command holds a web address, so it is not copied here. 2. Create the conda environment and activate it. 3. Install PyTorch for your CUDA version. Its command holds a web address, so follow the card. 4. Install flash-attn and the other requirements. 5. Download the model weights into a weights folder. ```sh conda create -n longcat-video python=3.10 conda activate longcat-video # 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 ``` ```sh pip install "huggingface_hub[cli]" huggingface-cli download meituan-longcat/LongCat-Video --local-dir ./weights/LongCat-Video ``` How to use it: Run one of the three demo scripts with torchrun and point it at the weights folder. For two GPUs, add the process count and the context parallel size. The visible card text does not show where the prompt goes, so check the demo script itself. ```sh # 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 ``` ```sh # Single-GPU inference torchrun run_demo_image_to_video.py --checkpoint_dir=./weights/LongCat-Video --enable_compile ``` ```sh # Single-GPU inference torchrun run_demo_video_continuation.py --checkpoint_dir=./weights/LongCat-Video --enable_compile ``` ```table Tool | What it does run_demo_text_to_video.py | Makes a video from a text prompt run_demo_image_to_video.py | Makes a video that starts from an image run_demo_video_continuation.py | Extends an existing video clip torchrun | Starts a run on one GPU or on several ``` Example: You run the text to video demo on a single GPU and point it at the weights folder. The script produces a video from its prompt. The authors say 720p clips at 30 fps take minutes to generate. For newcomers and experts: If you are new, start with the text to video demo on a single GPU and leave the other two scripts for later. Expect a long setup, since it needs a conda environment, a matching PyTorch build and flash-attn. If you know the field: the authors build one framework for all three tasks, so the same weights serve each. Speed comes from coarse-to-fine generation along both time and space, plus Block Sparse Attention at high resolutions. Training adds multi-reward Group Relative Policy Optimization, and you can switch FlashAttention-2 to FlashAttention-3 or xformers in the model config. ```flow Text, image or video clip LongCat-Video, 13.6B parameters Coarse-to-fine generation 720p video at 30 fps ``` Good to know: The authors say their tests put the model on par with leading open-source video models, and with some commercial ones. Those are their own results, so check them against your needs. - Size: 13.6B parameters - Output: 720p at 30 fps, as the card describes - Languages: English and Chinese - Attention: FlashAttention-2 by default - License: MIT - Cost to run: none named, though it needs GPU hardware - Data it reaches: model files downloaded to your computer, and the project code from GitHub - Not for: machines without a suitable GPU, since the card shows only GPU runs ## On Hugging Face, 13 November 2026 - Task: text to video - Library: diffusers - Formats: Safetensors - Downloads in the last 30 days: 3,135 - Likes: 594 - License: mit - Languages: en, zh - Quantized versions: 8 - Fine-tunes: 3 - Tags: diffusers, safetensors, transformers, image-to-video, video-continuation, text-to-video ## Links - Model: https://huggingface.co/meituan-longcat/LongCat-Video - Files: https://huggingface.co/meituan-longcat/LongCat-Video/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download meituan-longcat/LongCat-Video ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [meituan-longcat/LongCat-Video on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Video) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | LongCat-Video (this page) | 3,135 | 594 | — | mit | 2025-10-29 | | [MAGI-2 Preview](https://mindket.com/p/0739f3fb-9736-4610-99d8-c75f60f02d67) | 0 | 125 | — | apache-2.0 | 2026-08-05 | | [LightVAE](https://mindket.com/p/559b5239-ac2e-4efd-a84c-d94ef9e0d314) | 819 | 40 | — | apache-2.0 | 2026-11-10 | | [Wan2.2-TI2V-5B-Diffusers](https://mindket.com/p/f37f96ce-8537-4afe-8cbf-fba44f3e6f3f) | 179,371 | 174 | 5B | apache-2.0 | 2025-08-09 | | [Wan2.2-TI2V-5B](https://mindket.com/p/2148666b-ee9e-4845-b3a7-b04ccc2c92df) | 18,923 | 973 | — | apache-2.0 | 2025-08-07 | | [Wan2.2-S2V-14B](https://mindket.com/p/842b3a2c-9b93-4ff9-a757-9a806916671d) | 50,406 | 502 | 16.3B | apache-2.0 | 2025-09-17 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · maxloh · 2025-10-26](https://news.ycombinator.com/item?id=45710230): LongCat-Video: 13.6B text-to-video, image-to-video, and video-continuation model - [Bluesky · Adina Yakup · 2026-05-22](https://bsky.app/profile/adinayakup.bsky.social/post/3mmglm72zn22i): LongCat-Video-Avatar 1.5🐱 an audio driven avatar video generation framework from Meituan huggingface.co/meituan-long... ✨ Multi-character + multi-audio support ✨ Drive video from audio alone or audio + image + text ✨ 8-step inference ✨ Whisper-Large powered lip sync ✨ MIT license - [Hacker News · chengzeyi · 2025-12-24](https://news.ycombinator.com/item?id=46374054): LongCat Avatar: Turn Any Photo into a Talking Video: LongCat Avatar is built on the LongCat-Video foundation - a 13.6 billion parameter video generation model developed by Meituan's LongCat research team. The model unifies Text-to-Video, Image-to-Video, and Video-Continuation tasks within a single framework, enabling minutes-long video generation… - [Bluesky · Adina Yakup · 2025-12-16](https://bsky.app/profile/adinayakup.bsky.social/post/3ma4nybuyp222): New model from Meituan 🚀 LongCat-Video-Avatar🔥 Audio driven character animation with text, image, and video inputs, all in one\! huggingface.co/meituan-long... - [Bluesky · Jainam Parmar · 2026-08-25](https://bsky.app/profile/jainamparmar.bsky.social/post/3mtwcit2lbs2u): A process that once required a camera, recording session, and hours of editing can now be generated directly from one image and an audio file. The project already has more than 7.5K GitHub stars, costs $0, and publishes its model weights under the MIT license. Repo 👇 github.com/meituan-long... - [Bluesky · JauntyWunderKind · 2026-05-29](https://bsky.app/profile/jauntywunderkind.bsky.social/post/3mmxsgmr2s22l): longcat-video-avatar-1.5 is clearly the spookiest, video generation from sound. huggingface.co/meituan-long... - [Bluesky · AgentsPointee · 2025-11-23](https://bsky.app/profile/agentspointee.bsky.social/post/3m6clsd3thc2v): 4. LongCat-Video is an advanced AI video generation model developed by Meituan, featuring 13.6 billion parameters. It stands out by enabling the creation of high-quality, minutes-long videos at 720p resolution and 30 frames per second. agentspointee.com/listing/long... - [Bluesky · GitHub Trending · 2026-10-03](https://bsky.app/profile/github-trending.bsky.social/post/3mwxuklxpuj2w): 📦 meituan-longcat / LongCat-Video ⭐ 8,601 (+43) 🗒 Python - [Bluesky · Kristoffer Ørum · 2026-10-01](https://bsky.app/profile/oerum.org/post/3mwtqtkzris2j): Jeg er Ligeglad Jeg er Ligeglad Tekst: Kristoffer Ørum · Musik og sang: YuE · Video: LongCat-Video-Avatar 1.5 (Meituan) med Whisper-large-v3 til læbesynkronisering og… https://oerum.org/grav/kristoffer/blog/jeg-er-ligeglad \#kristofferorum \#art \#mixtape \#projects \#noneoftheseimageareofme - [Bluesky · probbrain.com · 2026-05-25](https://bsky.app/profile/probbrain.bsky.social/post/3mmne4y2fsx2c): Meituan released LongCat-Video-Avatar-1.5, a video avatar model, on HuggingFace. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-10-29 - First published: 2025-10-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open meituan-longcat/LongCat-Video on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Video) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/48609422-da93-43cf-9ad3-e5e7f4cada5e) - [Its page on Mindket](https://mindket.com/p/48609422-da93-43cf-9ad3-e5e7f4cada5e)