--- title: "Wan2.1-T2V-14B" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/00aa7ba3-e616-4066-817f-ef36df7edbb7" updated: "2026-11-13" --- # Wan2.1-T2V-14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates video from a text prompt at 480P or 720P with the 14B Wan2.1 model, and can put Chinese and English text into the picture. For creators and researchers with strong GPU hardware. Wan2.1-T2V-14B is a kit in the Productivity 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 This is the 14B text-to-video model of the Wan2.1 family of open video models, with about 14.3B parameters. You give it a prompt and it generates a video at 480P or 720P. The card says it can also render Chinese and English text inside the video. ### Who it suits It suits creators and researchers who want an open text-to-video model and have hardware for a model of this size. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Video production | Filmmakers and video creators | Generate scenes from written descriptions | A short establishing shot for a project pitch | | Marketing | Content teams | Make motion visuals, including ones with on-screen text | A short clip with a Chinese or English caption in the picture | | Research | Video model researchers | Use an open 14B model as a baseline | Comparing its outputs with another open model | | Games and design | Concept artists | Explore motion ideas from text | Testing how an idea looks in motion | You can skip it if you have a small graphics card, since the 1.3B sibling in the same family is built for consumer GPUs. ### Where to use it - Python on Windows, macOS or Linux, which the download steps need - The Wan2.1 project code, which the card lists with multi-GPU inference and a Gradio demo - Multi-GPU machines, which the card names for the 14B models - Diffusers and ComfyUI, which the card's to-do list shows as not yet ticked | Use case | Who | What they get back | | --- | --- | --- | | Generate a 480P or 720P clip | Creators | A video made from a text prompt | | Put text in a video | Marketers | Chinese or English text rendered in the picture | | Run across several GPUs | Teams with big hardware | Inference spread over multiple GPUs | ### How to set it up - Python on your computer - PyTorch 2.4.0 or newer, which the card says to ensure - A strong GPU setup, because the card gives no VRAM figure for this 14B model - Disk space for about 57.2 GB of full-precision weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Get the Wan2.1 project code and install its requirements. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.1-T2V-14B ``` The card's own dependency step reads like this. ```sh # Ensure torch >= 2.4.0 pip install -r requirements.txt ``` ### How to use it You give the model a text prompt and choose 480P or 720P. The card points to the project's inference code for running it, with a Gradio demo as another route. ### Example A person writes a prompt describing a scene and asks for a 720P result. What comes back is a video clip made from that text. The card gives no sample prompts, run times or clip lengths for this model. ### For newcomers and experts If you are new to this, read the download section on the card first, since the files are large. If your card is modest, try the 1.3B sibling before this one. If you know the field, the card says the family uses a Wan-VAE that encodes and decodes 1080P video of any length with time information kept. It claims state of the art results against open and closed models, with no score table in the part given here. Download the model files → Install the project requirements → Write a text prompt → Pick 480P or 720P → The model generates the video ### Good to know - Limits: the card gives no VRAM figure, run time or clip length for this model. - Integrations: the card's to-do list does not tick the diffusers or ComfyUI items. - Claims: the quality statements come from the authors. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies large model files to your computer and reads only the prompts you give it. ## README ````markdown # Wan2.1-T2V-14B Generates video from a text prompt at 480P or 720P with the 14B Wan2.1 model, and can put Chinese and English text into the picture. For creators and researchers with strong GPU hardware. 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: This is the 14B text-to-video model of the Wan2.1 family of open video models, with about 14.3B parameters. You give it a prompt and it generates a video at 480P or 720P. The card says it can also render Chinese and English text inside the video. Who it suits: It suits creators and researchers who want an open text-to-video model and have hardware for a model of this size. ```table Industry | Who uses it | How they use it | Example Video production | Filmmakers and video creators | Generate scenes from written descriptions | A short establishing shot for a project pitch Marketing | Content teams | Make motion visuals, including ones with on-screen text | A short clip with a Chinese or English caption in the picture Research | Video model researchers | Use an open 14B model as a baseline | Comparing its outputs with another open model Games and design | Concept artists | Explore motion ideas from text | Testing how an idea looks in motion ``` You can skip it if you have a small graphics card, since the 1.3B sibling in the same family is built for consumer GPUs. Where to use it: - Python on Windows, macOS or Linux, which the download steps need - The Wan2.1 project code, which the card lists with multi-GPU inference and a Gradio demo - Multi-GPU machines, which the card names for the 14B models - Diffusers and ComfyUI, which the card's to-do list shows as not yet ticked ```table Use case | Who | What they get back Generate a 480P or 720P clip | Creators | A video made from a text prompt Put text in a video | Marketers | Chinese or English text rendered in the picture Run across several GPUs | Teams with big hardware | Inference spread over multiple GPUs ``` How to set it up: - Python on your computer - PyTorch 2.4.0 or newer, which the card says to ensure - A strong GPU setup, because the card gives no VRAM figure for this 14B model - Disk space for about 57.2 GB of full-precision weights 1. Install the Hugging Face hub package with pip. 2. Download the model files to your computer. 3. Get the Wan2.1 project code and install its requirements. ```sh pip install -U huggingface_hub hf download Wan-AI/Wan2.1-T2V-14B ``` The card's own dependency step reads like this. ```sh # Ensure torch >= 2.4.0 pip install -r requirements.txt ``` How to use it: You give the model a text prompt and choose 480P or 720P. The card points to the project's inference code for running it, with a Gradio demo as another route. Example: A person writes a prompt describing a scene and asks for a 720P result. What comes back is a video clip made from that text. The card gives no sample prompts, run times or clip lengths for this model. For newcomers and experts: If you are new to this, read the download section on the card first, since the files are large. If your card is modest, try the 1.3B sibling before this one. If you know the field, the card says the family uses a Wan-VAE that encodes and decodes 1080P video of any length with time information kept. It claims state of the art results against open and closed models, with no score table in the part given here. ```flow Download the model files Install the project requirements Write a text prompt Pick 480P or 720P The model generates the video ``` Good to know: - Limits: the card gives no VRAM figure, run time or clip length for this model. - Integrations: the card's to-do list does not tick the diffusers or ComfyUI items. - Claims: the quality statements come from the authors. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies large model files to your computer and reads only the prompts you give it. ## On Hugging Face, 13 November 2026 - Task: text to video - Library: diffusers - Parameters: 14.3B - Weights: about 57.2 GB in F32; about 7.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 111,314 - Likes: 1,619 - License: apache-2.0 - Languages: en, zh - Quantized versions: 24 - Fine-tunes: 86 - Tags: diffusers, safetensors, t2v, text-to-video ## Links - Model: https://huggingface.co/Wan-AI/Wan2.1-T2V-14B - Files: https://huggingface.co/Wan-AI/Wan2.1-T2V-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/Wan2.1-T2V-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/Wan2.1-T2V-14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.1-T2V-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 | | --- | --- | --- | --- | --- | --- | | Wan2.1-T2V-14B (this page) | 111,314 | 1,619 | 14.3B | apache-2.0 | 2025-03-12 | | [Wan2.1-T2V-1.3B](https://mindket.com/p/a68fa7f4-135f-45e4-9d81-34c23f1efb55) | 91,394 | 676 | 1.4B | apache-2.0 | 2025-03-01 | | [Wan2.1-T2V-1.3B-Diffusers](https://mindket.com/p/366e0512-9024-4a7a-b9bd-35645c681154) | 384,970 | 168 | 1.4B | apache-2.0 | 2025-04-04 | | [wan-gguf](https://mindket.com/p/d99fdbec-847a-4c58-8647-73862c3d4d99) | 21,309 | 210 | 127M | apache-2.0 | 2025-07-08 | | [controlnet-openpose-sdxl-1.0](https://mindket.com/p/e9415382-28bd-4bbf-bf79-4af2e8a6e250) | 294,030 | 334 | 1.3B | apache-2.0 | 2024-07-09 | | [GLM-Image](https://mindket.com/p/743612c3-c1da-4c92-a1a7-01a5e6ba37c5) | 14,352 | 1,129 | 6.9B | mit | 2026-01-15 | 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 · xnx · 2025-02-25](https://news.ycombinator.com/item?id=43172506): Wan2.1-T2V-14B - [Hacker News · helloericsf · 2025-02-25](https://news.ycombinator.com/item?id=43172776): Alibaba Open Source Large-Scale Video Generative Models: Wan2.1: HF:https://huggingface.co/Wan-AI/Wan2.1-T2V-14B Github:https://github.com/Wan-Video/Wan2.1 Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features: - SOTA Performance: Wan2.1 consistently outperforms… ## 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: 2025-03-12 - First published: 2025-02-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Wan-AI/Wan2.1-T2V-14B on Hugging Face](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/00aa7ba3-e616-4066-817f-ef36df7edbb7) - [Its page on Mindket](https://mindket.com/p/00aa7ba3-e616-4066-817f-ef36df7edbb7)