--- title: "Wan2.2-T2V-A14B" kind: "Kit" category: "Marketing & SEO" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/82ad8f3a-4fb9-4c7b-b7f0-b2d8d581c5f1" updated: "2026-11-12" --- # Wan2.2-T2V-A14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates 5-second videos from a text prompt at 480P or 720P with a mixture-of-experts video model. Suits creators and developers who want an open text-to-video model. Wan2.2-T2V-A14B is a kit in the Marketing \& SEO category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. 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-T2V-A14B is the text-to-video model of the Wan2.2 family from Wan-AI. It writes 5-second clips at 480P or 720P from a prompt. It uses a mixture-of-experts design in which different expert models handle different stages of the denoising process. ### Who it suits Creators, researchers and developers who want an open text-to-video model that they can run themselves, with ComfyUI or Diffusers support. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Film and media | Video creators | Generate short clips from a written scene | A 5-second 720P clip of a described scene | | Marketing | Content teams | Draft motion ideas before a shoot | A short visual concept from a text brief | | Research | AI researchers | Study mixture-of-experts video diffusion | Compare it with the 5B hybrid model | If you only have a small graphics card, you can skip it, since the card says the 5B model is the one built for consumer cards. ### Where to use it - The Wan2.2 command-line code, with multi-GPU inference - ComfyUI integration - Diffusers integration - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Text to video | Creators | A 5-second clip at 480P or 720P | | Cinematic style control | Filmmakers | Video shaped by lighting, composition and color terms in the prompt | | Research baseline | Researchers | An open model to compare against | ### How to set it up - Python with pip, and torch 2.4.0 or newer - One or more strong GPUs, since the card ships multi-GPU inference code - The Wan2.2 code repository, with its requirements installed 1. Install the Hugging Face hub package. 2. Download the model files. 3. Get the Wan2.2 code, move into its folder and install its requirements. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.2-T2V-A14B ``` ```sh cd Wan2.2 ``` ```sh # Ensure torch >= 2.4.0 pip install -r requirements.txt ``` ### How to use it Run the Wan2.2 inference code with a text prompt and the downloaded model folder, or use the ComfyUI or Diffusers integration. The card's Run section lists the exact generate options. ### Example You describe a scene with lighting and camera detail and ask for a 5-second 720P clip. The model returns a video of that scene. ### For newcomers and experts If you are new, try the ComfyUI or Diffusers route before the command line, and start at 480P to keep runs light. Wan2.2 splits denoising across timesteps between specialized expert models. That enlarges total capacity while the computation per step stays the same. Training data included aesthetic labels for lighting, composition, contrast and color tone, and a larger set than Wan2.1, with 65.6 percent more images and 83.2 percent more videos. You write a text prompt → One expert handles early denoising steps → Another expert handles later steps → A 5-second 480P or 720P video comes out ### Good to know - This repo holds the T2V-A14B model only, and other Wan2.2 models sit in separate repos - The card says it beats leading commercial models on most dimensions of its own Wan-Bench 2.0 - License: Apache 2.0 - Cost to run: none named, you supply the GPUs - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: clips longer than 5 seconds or resolutions above 720P ## README ````markdown # Wan2.2-T2V-A14B Generates 5-second videos from a text prompt at 480P or 720P with a mixture-of-experts video model. Suits creators and developers who want an open text-to-video model. 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-T2V-A14B is the text-to-video model of the Wan2.2 family from Wan-AI. It writes 5-second clips at 480P or 720P from a prompt. It uses a mixture-of-experts design in which different expert models handle different stages of the denoising process. Who it suits: Creators, researchers and developers who want an open text-to-video model that they can run themselves, with ComfyUI or Diffusers support. ```table Industry | Who uses it | How they use it | Example Film and media | Video creators | Generate short clips from a written scene | A 5-second 720P clip of a described scene Marketing | Content teams | Draft motion ideas before a shoot | A short visual concept from a text brief Research | AI researchers | Study mixture-of-experts video diffusion | Compare it with the 5B hybrid model ``` If you only have a small graphics card, you can skip it, since the card says the 5B model is the one built for consumer cards. Where to use it: - The Wan2.2 command-line code, with multi-GPU inference - ComfyUI integration - Diffusers integration - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Text to video | Creators | A 5-second clip at 480P or 720P Cinematic style control | Filmmakers | Video shaped by lighting, composition and color terms in the prompt Research baseline | Researchers | An open model to compare against ``` How to set it up: - Python with pip, and torch 2.4.0 or newer - One or more strong GPUs, since the card ships multi-GPU inference code - The Wan2.2 code repository, with its requirements installed 1. Install the Hugging Face hub package. 2. Download the model files. 3. Get the Wan2.2 code, move into its folder and install its requirements. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.2-T2V-A14B ``` ```sh cd Wan2.2 ``` ```sh # Ensure torch >= 2.4.0 pip install -r requirements.txt ``` How to use it: Run the Wan2.2 inference code with a text prompt and the downloaded model folder, or use the ComfyUI or Diffusers integration. The card's Run section lists the exact generate options. Example: You describe a scene with lighting and camera detail and ask for a 5-second 720P clip. The model returns a video of that scene. For newcomers and experts: If you are new, try the ComfyUI or Diffusers route before the command line, and start at 480P to keep runs light. Wan2.2 splits denoising across timesteps between specialized expert models. That enlarges total capacity while the computation per step stays the same. Training data included aesthetic labels for lighting, composition, contrast and color tone, and a larger set than Wan2.1, with 65.6 percent more images and 83.2 percent more videos. ```flow You write a text prompt One expert handles early denoising steps Another expert handles later steps A 5-second 480P or 720P video comes out ``` Good to know: - This repo holds the T2V-A14B model only, and other Wan2.2 models sit in separate repos - The card says it beats leading commercial models on most dimensions of its own Wan-Bench 2.0 - License: Apache 2.0 - Cost to run: none named, you supply the GPUs - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: clips longer than 5 seconds or resolutions above 720P ## On Hugging Face, 13 November 2026 - Task: text to video - Library: wan2.2 - Formats: Safetensors - Downloads in the last 30 days: 3,339 - Likes: 610 - License: apache-2.0 - Quantized versions: 13 - Fine-tunes: 51 - Tags: wan2.2, diffusers, safetensors, text-to-video ## Links - Model: https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B - Files: https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B/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-T2V-A14B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Wan-AI/Wan2.2-T2V-A14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) ## 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-T2V-A14B (this page) | 3,339 | 610 | — | apache-2.0 | 2025-08-07 | | [Vimoo-1.0](https://mindket.com/p/09f3b856-d029-4da0-96d6-b026dab46b10) | 57 | 18 | 11.7B | apache-2.0 | 2026-11-12 | | [Minimax-h3\_Singularity](https://mindket.com/p/fda25159-4ede-4763-ba58-a8dca57b57f0) | 1,145,318 | 986 | — | apache-2.0 | 2026-10-23 | | [HiDream-I1-Full](https://mindket.com/p/2a0ddaa1-ce5b-41a1-abaa-32b8d72cb97a) | 695 | 1,015 | 17.1B | mit | 2025-07-17 | | [Minimax H3 Latent Upscaler](https://mindket.com/p/9bba4b81-33e6-4e9d-bb7e-6a37970e6e76) | 354,859 | 489 | — | apache-2.0 | 2026-11-10 | | [SenseNova 6.8 Code2Video](https://mindket.com/p/631aef12-4230-43ae-9166-6f21f7973171) | 1,460 | 107 | 27.4B | apache-2.0 | 2026-11-03 | 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 · laiwuchiyuan · 2025-07-30](https://news.ycombinator.com/item?id=44731318): Alibaba open-sources Wan2.2: 27B-param MoE video models, 720 p 24 FPS on a 4090: Alibaba’s Tongyi Wanxiang team just dropped Wan2.2—three new video-generation checkpoints under Apache-2.0: • Wan2.2-T2V-A14B – 27 B total / 14 B active, first open-source text-to-video model built on a mixture-of-experts (MoE) diffusion backbone. • Wan2.2-I2V-A14B – same MoE architecture for… ## 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: 2025-08-07 - First published: 2025-07-24 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Wan-AI/Wan2.2-T2V-A14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/82ad8f3a-4fb9-4c7b-b7f0-b2d8d581c5f1) - [Its page on Mindket](https://mindket.com/p/82ad8f3a-4fb9-4c7b-b7f0-b2d8d581c5f1)