--- title: "Wan2.2-I2V-A14B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/05752d53-43be-488a-9d2b-5171ad35fa63" updated: "2026-11-13" --- # Wan2.2-I2V-A14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns a still image into a video at 480P or 720P, using a Mixture-of-Experts design from Wan-AI. For creators and researchers who generate video from pictures. Wan2.2-I2V-A14B 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-I2V-A14B is the image-to-video model in the Wan2.2 family from Wan-AI. It supports 480P and 720P output. Its Mixture-of-Experts design aims for steadier video, fewer unrealistic camera moves and better handling of stylized scenes. ### Who it suits Video creators, researchers and developers who want to animate a still picture with an open model they run themselves. Prompts in English and Chinese are listed as supported. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Video production | Video creators | Animate a still frame into a short clip | Turn a concept image into a moving shot | | Research | Video generation researchers | Study a Mixture-of-Experts video diffusion model | Run the model and compare it with Wan2.1 | | Tooling | Developers | Build image-to-video into their own apps or workflows | Call the model from the project's inference code | | Stylized content | Illustrators | Bring stylized scenes to life | Animate an illustrated scene at 720P | You can skip it if you only have a text prompt and no starting image. The same family has separate text-to-video models. ### Where to use it - Windows, macOS and Linux with Python, through the project's own code - ComfyUI, which the card says integrates Wan2.2 - Diffusers, through the separate Diffusers version of this model - Multi-GPU setups, since the card ships multi-GPU inference code | Use case | Who | What they get back | | --- | --- | --- | | Animating a picture | Creators | A video clip that starts from your image | | Higher-resolution output | Producers | Video at 480P or 720P | | Research comparison | Researchers | Output to compare against Wan2.1 | ### How to set it up You need Python, the project's code and its dependencies. - Python, with torch version 2.4.0 or newer - The Wan2.2 code repository from GitHub - The model weights, downloaded from Hugging Face 1. Clone the Wan2.2 repository from GitHub and move into its folder. 2. Install the dependencies. If flash\_attn fails to install, install the other packages first. 3. Download the model weights. ```sh cd Wan2.2 ``` ```sh # Ensure torch >= 2.4.0 # If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last pip install -r requirements.txt ``` ```bash pip install -U huggingface_hub hf download Wan-AI/Wan2.2-I2V-A14B ``` ### How to use it The card's run steps go on to a model download and a generation command, which are cut off in the part shown here. Take the exact command from the full card. You provide a starting image and a text prompt, and choose 480P or 720P. ### Example You give it a picture of a mountain village and a prompt about clouds drifting and smoke rising. It returns a short video that starts from your picture and adds that motion at the resolution you chose. ### For newcomers and experts Image-to-video means you start with one picture and the model works out the frames that follow. A first small step is to get the repository running with its dependencies, then try one image at 480P. The design splits the denoising process across timesteps between specialized expert models. The card says this adds capacity without raising the computational cost. Wan2.2 also trained on more data than Wan2.1, and added aesthetic labels for lighting, composition, contrast and color tone. The 5B hybrid model with the 16x16x4 compression VAE is a separate checkpoint, not this one. Start from one still image → Add a text prompt for the motion → Expert models denoise in turn → Get a 480P or 720P video Extra training data over Wan2.1 | Label | Value | | --- | --- | | Images | 65.6% | | Videos | 83.2% | ### Good to know - This repository holds the 14B-class image-to-video checkpoint. The 5B model is a separate release. - The card lists ComfyUI and Diffusers integrations as done. - Installing flash\_attn can fail, so the card suggests installing it last. - License: apache-2.0. - Cost to run: none named, but multi-GPU inference code hints at heavy hardware. - Data it reaches: files from Hugging Face and GitHub, plus the images and prompts you supply. - Not for: the card names no limits. ## README ````markdown # Wan2.2-I2V-A14B Turns a still image into a video at 480P or 720P, using a Mixture-of-Experts design from Wan-AI. For creators and researchers who generate video from pictures. 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-I2V-A14B is the image-to-video model in the Wan2.2 family from Wan-AI. It supports 480P and 720P output. Its Mixture-of-Experts design aims for steadier video, fewer unrealistic camera moves and better handling of stylized scenes. Who it suits: Video creators, researchers and developers who want to animate a still picture with an open model they run themselves. Prompts in English and Chinese are listed as supported. ```table Industry | Who uses it | How they use it | Example Video production | Video creators | Animate a still frame into a short clip | Turn a concept image into a moving shot Research | Video generation researchers | Study a Mixture-of-Experts video diffusion model | Run the model and compare it with Wan2.1 Tooling | Developers | Build image-to-video into their own apps or workflows | Call the model from the project's inference code Stylized content | Illustrators | Bring stylized scenes to life | Animate an illustrated scene at 720P ``` You can skip it if you only have a text prompt and no starting image. The same family has separate text-to-video models. Where to use it: - Windows, macOS and Linux with Python, through the project's own code - ComfyUI, which the card says integrates Wan2.2 - Diffusers, through the separate Diffusers version of this model - Multi-GPU setups, since the card ships multi-GPU inference code ```table Use case | Who | What they get back Animating a picture | Creators | A video clip that starts from your image Higher-resolution output | Producers | Video at 480P or 720P Research comparison | Researchers | Output to compare against Wan2.1 ``` How to set it up: You need Python, the project's code and its dependencies. - Python, with torch version 2.4.0 or newer - The Wan2.2 code repository from GitHub - The model weights, downloaded from Hugging Face 1. Clone the Wan2.2 repository from GitHub and move into its folder. 2. Install the dependencies. If flash_attn fails to install, install the other packages first. 3. Download the model weights. ```sh cd Wan2.2 ``` ```sh # Ensure torch >= 2.4.0 # If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last pip install -r requirements.txt ``` ```bash pip install -U huggingface_hub hf download Wan-AI/Wan2.2-I2V-A14B ``` How to use it: The card's run steps go on to a model download and a generation command, which are cut off in the part shown here. Take the exact command from the full card. You provide a starting image and a text prompt, and choose 480P or 720P. Example: You give it a picture of a mountain village and a prompt about clouds drifting and smoke rising. It returns a short video that starts from your picture and adds that motion at the resolution you chose. For newcomers and experts: Image-to-video means you start with one picture and the model works out the frames that follow. A first small step is to get the repository running with its dependencies, then try one image at 480P. The design splits the denoising process across timesteps between specialized expert models. The card says this adds capacity without raising the computational cost. Wan2.2 also trained on more data than Wan2.1, and added aesthetic labels for lighting, composition, contrast and color tone. The 5B hybrid model with the 16x16x4 compression VAE is a separate checkpoint, not this one. ```flow Start from one still image Add a text prompt for the motion Expert models denoise in turn Get a 480P or 720P video ``` ```chart Title: Extra training data over Wan2.1 Images: 65.6% Videos: 83.2% ``` Good to know: - This repository holds the 14B-class image-to-video checkpoint. The 5B model is a separate release. - The card lists ComfyUI and Diffusers integrations as done. - Installing flash_attn can fail, so the card suggests installing it last. - License: apache-2.0. - Cost to run: none named, but multi-GPU inference code hints at heavy hardware. - Data it reaches: files from Hugging Face and GitHub, plus the images and prompts you supply. - Not for: the card names no limits. ## On Hugging Face, 13 November 2026 - Task: image to video - Library: wan2.2 - Formats: Safetensors - Downloads in the last 30 days: 8,500 - Likes: 959 - License: apache-2.0 - Languages: en, zh - Quantized versions: 59 - Fine-tunes: 143 - Tags: wan2.2, diffusers, safetensors, image-to-video ## Links - Model: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B - Files: https://huggingface.co/Wan-AI/Wan2.2-I2V-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-I2V-A14B ``` 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-I2V-A14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-I2V-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-I2V-A14B (this page) | 8,500 | 959 | — | apache-2.0 | 2025-08-07 | | [Wan2.2-Fun-5B-Control](https://mindket.com/p/29aa505f-9a61-4bb5-b736-e246068759d9) | 25,525 | 22 | — | apache-2.0 | 2025-12-11 | | [LightVAE](https://mindket.com/p/559b5239-ac2e-4efd-a84c-d94ef9e0d314) | 819 | 40 | — | apache-2.0 | 2026-11-10 | | [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 | | [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 | 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: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Wan-AI/Wan2.2-I2V-A14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/05752d53-43be-488a-9d2b-5171ad35fa63) - [Its page on Mindket](https://mindket.com/p/05752d53-43be-488a-9d2b-5171ad35fa63)