--- title: "Wan2.2-S2V-14B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/842b3a2c-9b93-4ff9-a757-9a806916671d" updated: "2026-11-13" --- # Wan2.2-S2V-14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates cinematic video of a character driven by an audio track, and can also lip-sync existing video. For video creators, animators and researchers with a large GPU. Wan2.2-S2V-14B 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 Wan-AI ## About ### What it is Wan2.2-S2V-14B is a video generation model that animates a character from an audio track. It is aimed at film and television scenes that need character interaction, realistic body movement and camera work, beyond plain speech or singing. It has 16.3B parameters and is part of the Wan2.2 family. ### Who it suits Video creators, animators and researchers who want sound-driven character video are the main fit. They also need a machine with room for large weights. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Film and TV | Animators | Animate a character from a voice or music track | Drive a character image with a spoken line | | Content creation | Video creators | Make long-form video that follows an audio track | Produce a talking-character clip | | Post-production | Video editors | Edit lip sync on existing video | Match mouth movements to new audio | | Research | Video model researchers | Study audio-driven character animation | Compare it with other avatar models | If you lack a GPU with room for the weights, or you only need text-to-video, you can skip it. ### Where to use it Use it from Python, since it is released for the diffusers library. - Python on Windows, macOS or Linux, for the download - A GPU machine with room for 32.6 GB of BF16 weights - The project's own inference code, which the card links to | Use case | Who | What they get back | | --- | --- | --- | | Audio-driven animation | Animators | A video of a character that moves with the audio | | Long-form video | Creators | Longer clips that follow a longer audio track | | Lip-sync editing | Editors | A video whose mouth movements match new audio | ### How to set it up You need Python, a capable GPU and plenty of storage. - Python with pip - Room for the weights, about 32.6 GB in BF16 or about 8.1 GB at 4-bit as an estimate - A GPU able to run a 16.3B parameter video model 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the project's user guide or code repository for running it, because the visible card shows no run command. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.2-S2V-14B ``` ### How to use it The text shown here gives no run command. From the model's audio-driven design and its image-to-video label, you supply a character image and an audio track, and the model returns video. The card points to user guides for the exact steps. ### Example You give it a picture of a character and a recorded line of speech. The model is meant to return a video in which the character moves to that audio. ### For newcomers and experts This model animates a picture to the sound of an audio track. A good first try is to download the files and read the project's user guide before you plan a run. Use only images and audio that you have the right to use. It builds on Wan2.2, which brings a Mixture-of-Experts design into video diffusion by splitting the denoising work across timesteps between specialised experts. The card says this adds capacity at the same compute cost. The paper behind this model reports better results than Hunyuan-Avatar and Omnihuman, and those figures come from the authors. Provide a character image and an audio track → The model reads the sound and the picture → It generates frames with motion and camera work → You get a video that follows the audio ### Good to know Wan2.2 was trained on more data than Wan2.1, which the card credits for better generalization across motion, meaning and look. Extra training data in Wan2.2 compared with Wan2.1 | Label | Value | | --- | --- | | Images | 65.6 percent more | | Videos | 83.2 percent more | - Versions: other people have published 4 quantized and 12 fine-tuned versions of it - Rights: use only faces, images and voices that you have the right to use - License: Apache 2.0 - Cost to run: none named. It needs a large GPU, and the BF16 weights are about 32.6 GB - Data it reaches: your images and audio, plus the model files from Hugging Face. The card names no account or web service - Not for: the card names no exclusions ## README ````markdown # Wan2.2-S2V-14B Generates cinematic video of a character driven by an audio track, and can also lip-sync existing video. For video creators, animators and researchers with a large GPU. Made by Wan-AI 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: Wan2.2-S2V-14B is a video generation model that animates a character from an audio track. It is aimed at film and television scenes that need character interaction, realistic body movement and camera work, beyond plain speech or singing. It has 16.3B parameters and is part of the Wan2.2 family. Who it suits: Video creators, animators and researchers who want sound-driven character video are the main fit. They also need a machine with room for large weights. ```table Industry | Who uses it | How they use it | Example Film and TV | Animators | Animate a character from a voice or music track | Drive a character image with a spoken line Content creation | Video creators | Make long-form video that follows an audio track | Produce a talking-character clip Post-production | Video editors | Edit lip sync on existing video | Match mouth movements to new audio Research | Video model researchers | Study audio-driven character animation | Compare it with other avatar models ``` If you lack a GPU with room for the weights, or you only need text-to-video, you can skip it. Where to use it: Use it from Python, since it is released for the diffusers library. - Python on Windows, macOS or Linux, for the download - A GPU machine with room for 32.6 GB of BF16 weights - The project's own inference code, which the card links to ```table Use case | Who | What they get back Audio-driven animation | Animators | A video of a character that moves with the audio Long-form video | Creators | Longer clips that follow a longer audio track Lip-sync editing | Editors | A video whose mouth movements match new audio ``` How to set it up: You need Python, a capable GPU and plenty of storage. - Python with pip - Room for the weights, about 32.6 GB in BF16 or about 8.1 GB at 4-bit as an estimate - A GPU able to run a 16.3B parameter video model 1. Install the Hugging Face hub package. 2. Download the model files. 3. Follow the project's user guide or code repository for running it, because the visible card shows no run command. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.2-S2V-14B ``` How to use it: The text shown here gives no run command. From the model's audio-driven design and its image-to-video label, you supply a character image and an audio track, and the model returns video. The card points to user guides for the exact steps. Example: You give it a picture of a character and a recorded line of speech. The model is meant to return a video in which the character moves to that audio. For newcomers and experts: This model animates a picture to the sound of an audio track. A good first try is to download the files and read the project's user guide before you plan a run. Use only images and audio that you have the right to use. It builds on Wan2.2, which brings a Mixture-of-Experts design into video diffusion by splitting the denoising work across timesteps between specialised experts. The card says this adds capacity at the same compute cost. The paper behind this model reports better results than Hunyuan-Avatar and Omnihuman, and those figures come from the authors. ```flow Provide a character image and an audio track The model reads the sound and the picture It generates frames with motion and camera work You get a video that follows the audio ``` Good to know: Wan2.2 was trained on more data than Wan2.1, which the card credits for better generalization across motion, meaning and look. ```chart Title: Extra training data in Wan2.2 compared with Wan2.1 Images: 65.6 percent more Videos: 83.2 percent more ``` - Versions: other people have published 4 quantized and 12 fine-tuned versions of it - Rights: use only faces, images and voices that you have the right to use - License: Apache 2.0 - Cost to run: none named. It needs a large GPU, and the BF16 weights are about 32.6 GB - Data it reaches: your images and audio, plus the model files from Hugging Face. The card names no account or web service - Not for: the card names no exclusions ## On Hugging Face, 13 November 2026 - Task: image to video - Library: diffusers - Parameters: 16.3B - Weights: about 32.6 GB in BF16; about 8.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 50,406 - Likes: 502 - License: apache-2.0 - Languages: es - Quantized versions: 4 - Fine-tunes: 12 - Tags: diffusers, safetensors, s2v, image-to-video ## Links - Model: https://huggingface.co/Wan-AI/Wan2.2-S2V-14B - Files: https://huggingface.co/Wan-AI/Wan2.2-S2V-14B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.2-S2V-14B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Wan-AI/Wan2.2-S2V-14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-S2V-14B) ## 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 | | --- | --- | --- | --- | --- | --- | | Wan2.2-S2V-14B (this page) | 50,406 | 502 | 16.3B | apache-2.0 | 2025-09-17 | | [Wan2.1-VACE-1.3B](https://mindket.com/p/78ffeb84-ef89-4c0a-82af-95cda4b50dd8) | 42,137 | 162 | 2.2B | apache-2.0 | 2025-05-19 | | [Wan2.2-Fun-5B-Control](https://mindket.com/p/29aa505f-9a61-4bb5-b736-e246068759d9) | 25,525 | 22 | — | apache-2.0 | 2025-12-11 | | [Wan2.2-I2V-A14B](https://mindket.com/p/05752d53-43be-488a-9d2b-5171ad35fa63) | 8,500 | 959 | — | apache-2.0 | 2025-08-07 | | [LongCat-Video](https://mindket.com/p/48609422-da93-43cf-9ad3-e5e7f4cada5e) | 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 | Numbers as Mindket last read them, on 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 · orixilus · 2025-08-26](https://news.ycombinator.com/item?id=45029542): Wan2.2-S2V-14B – audio-driven cinematic video generation model ## 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: 2025-09-17 - First published: 2025-08-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Wan-AI/Wan2.2-S2V-14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-S2V-14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/842b3a2c-9b93-4ff9-a757-9a806916671d) - [Its page on Mindket](https://mindket.com/p/842b3a2c-9b93-4ff9-a757-9a806916671d)