--- title: "Wan2.1-T2V-1.3B-Diffusers" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/366e0512-9024-4a7a-b9bd-35645c681154" updated: "2026-11-13" --- # Wan2.1-T2V-1.3B-Diffusers > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates short 480P videos from a text prompt with the small 1.3B Wan2.1 model, packaged for the diffusers library. Runs on a consumer graphics card with about 8.19 GB of VRAM. Wan2.1-T2V-1.3B-Diffusers 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 small text-to-video model of the Wan2.1 family, with about 1.4B parameters, in a layout the diffusers library can load. You write a prompt and it generates a video. The card says it was built to fit almost any consumer graphics card. ### Who it suits It suits creators and academic teams who want to make video from text without a large GPU cluster. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Video production | Independent creators | Draft short clips from a written description | A quick concept clip for a storyboard | | Marketing | Small content teams | Make moving visuals without a render farm | A short background loop for a social post | | Academia | Teams with limited computing | Use an open video model as a research base | Testing prompt wording on one consumer GPU | | Education | Students | Learn how text-to-video models run locally | Running the model on a gaming graphics card | You can skip it if you need higher resolution than 480P or an image-to-video model, since the larger family members cover those. ### Where to use it - Python code that uses the diffusers library - Windows, macOS or Linux, with Python for the download - A consumer graphics card, which the card says needs about 8.19 GB of VRAM for this model - The Wan2.1 project code, which has multi-GPU inference and a Gradio demo | Use case | Who | What they get back | | --- | --- | --- | | Generate a clip from a prompt | Creators | A short 480P video | | Run on one consumer GPU | Hobbyists | A video without quantization tricks, per the card | | Research on a budget | Academic teams | An open base model for video work | ### How to set it up - Python on your computer - PyTorch 2.4.0 or newer, which the card says to ensure - A graphics card with about 8.19 GB of VRAM - Disk space for about 5.7 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-1.3B-Diffusers ``` 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 it generates a video. The card points to the project's inference code for running it. The part of the card shown here has no diffusers snippet. ### Example A person writes a short prompt describing a scene and runs the model on a graphics card like an RTX 4090. The card says a 5-second 480P video takes about 4 minutes there without optimization. What comes back is that video clip. ### For newcomers and experts If you are new to this, start with a short prompt and expect to wait a few minutes for a few seconds of video. Check that your card has about 8.19 GB of VRAM first. If you know the field, the card says the family has a Wan-VAE that encodes and decodes 1080P video of any length while keeping time information. This 1.3B member is listed as supporting 480P. The card claims strong results against other open and closed models but shows no scores in the part given here. Download the model files → Install the project requirements → Write a text prompt → The model generates the video → Save the 480P clip ### Good to know - Limits: the card lists 480P for this model and shows no diffusers code in the part given here. - Speed: the 4-minute figure is for a 5-second clip on an RTX 4090 without quantization. - Claims: the quality statements come from the authors. - Text: the family can render Chinese and English text in video, according to the card. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the model files to your computer and reads only the prompts you give it. ## README ````markdown # Wan2.1-T2V-1.3B-Diffusers Generates short 480P videos from a text prompt with the small 1.3B Wan2.1 model, packaged for the diffusers library. Runs on a consumer graphics card with about 8.19 GB of VRAM. 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 small text-to-video model of the Wan2.1 family, with about 1.4B parameters, in a layout the diffusers library can load. You write a prompt and it generates a video. The card says it was built to fit almost any consumer graphics card. Who it suits: It suits creators and academic teams who want to make video from text without a large GPU cluster. ```table Industry | Who uses it | How they use it | Example Video production | Independent creators | Draft short clips from a written description | A quick concept clip for a storyboard Marketing | Small content teams | Make moving visuals without a render farm | A short background loop for a social post Academia | Teams with limited computing | Use an open video model as a research base | Testing prompt wording on one consumer GPU Education | Students | Learn how text-to-video models run locally | Running the model on a gaming graphics card ``` You can skip it if you need higher resolution than 480P or an image-to-video model, since the larger family members cover those. Where to use it: - Python code that uses the diffusers library - Windows, macOS or Linux, with Python for the download - A consumer graphics card, which the card says needs about 8.19 GB of VRAM for this model - The Wan2.1 project code, which has multi-GPU inference and a Gradio demo ```table Use case | Who | What they get back Generate a clip from a prompt | Creators | A short 480P video Run on one consumer GPU | Hobbyists | A video without quantization tricks, per the card Research on a budget | Academic teams | An open base model for video work ``` How to set it up: - Python on your computer - PyTorch 2.4.0 or newer, which the card says to ensure - A graphics card with about 8.19 GB of VRAM - Disk space for about 5.7 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-1.3B-Diffusers ``` 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 it generates a video. The card points to the project's inference code for running it. The part of the card shown here has no diffusers snippet. Example: A person writes a short prompt describing a scene and runs the model on a graphics card like an RTX 4090. The card says a 5-second 480P video takes about 4 minutes there without optimization. What comes back is that video clip. For newcomers and experts: If you are new to this, start with a short prompt and expect to wait a few minutes for a few seconds of video. Check that your card has about 8.19 GB of VRAM first. If you know the field, the card says the family has a Wan-VAE that encodes and decodes 1080P video of any length while keeping time information. This 1.3B member is listed as supporting 480P. The card claims strong results against other open and closed models but shows no scores in the part given here. ```flow Download the model files Install the project requirements Write a text prompt The model generates the video Save the 480P clip ``` Good to know: - Limits: the card lists 480P for this model and shows no diffusers code in the part given here. - Speed: the 4-minute figure is for a 5-second clip on an RTX 4090 without quantization. - Claims: the quality statements come from the authors. - Text: the family can render Chinese and English text in video, according to the card. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies the 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: 1.4B - Weights: about 5.7 GB in F32; about 709 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 384,970 - Likes: 168 - License: apache-2.0 - Languages: en, zh - Fine-tunes: 49 - Tags: diffusers, safetensors, video, video-generation, text-to-video ## Links - Model: https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers - Files: https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers/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-1.3B-Diffusers ``` 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-1.3B-Diffusers on Hugging Face](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) ## 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-1.3B-Diffusers (this page) | 384,970 | 168 | 1.4B | apache-2.0 | 2025-04-04 | | [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-14B](https://mindket.com/p/00aa7ba3-e616-4066-817f-ef36df7edbb7) | 111,314 | 1,619 | 14.3B | apache-2.0 | 2025-03-12 | | [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. ## 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: 2025-04-04 - First published: 2025-03-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Wan-AI/Wan2.1-T2V-1.3B-Diffusers on Hugging Face](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/366e0512-9024-4a7a-b9bd-35645c681154) - [Its page on Mindket](https://mindket.com/p/366e0512-9024-4a7a-b9bd-35645c681154)