--- title: "Wan2.2 TI2V 5B GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5ed488a5-d03d-47a5-a0a8-d0d76c94f553" updated: "2026-11-13" --- # Wan2.2 TI2V 5B GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A 5B-parameter text-to-video model converted to GGUF for ComfyUI, about 2.5 GB at 4-bit by estimate, for English and Chinese prompts. It suits ComfyUI users who want video generation on their own computer. Wan2.2 TI2V 5B GGUF 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 QuantStack ## About ### What it is Wan2.2 TI2V 5B GGUF is a direct conversion of the Wan-AI Wan2.2-TI2V-5B video model into the GGUF format, made by QuantStack. It has 5B parameters, makes video from text, and takes about 2.5 GB at 4-bit by estimate. Because it is a quantized copy, the original model's license terms and usage limits still apply. It has been downloaded about 495,000 times in the last month. ### Who it suits It suits ComfyUI users who want to generate video clips on their own computer and are happy to add a custom node. The card is short, so some reading of the original model's notes helps. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Creative hobby | ComfyUI users | Generate video clips from written prompts | A short clip of waves at dusk | | Media | Visual designers | Sketch moving scenes before a full production | Testing the motion of a product idea | | Research | Video model researchers | Compare a quantized copy with the original model | Checking how output differs at 4-bit | People who use other tools than ComfyUI should check first, since the card only names the ComfyUI-GGUF custom node. ### Where to use it - ComfyUI with the ComfyUI-GGUF custom node by city96 - Windows, macOS or Linux, wherever ComfyUI runs - Prompts in English or Chinese | Use case | Who | What they get back | | --- | --- | --- | | Text to video | Hobbyists and designers | A generated clip from a written prompt | | Smaller downloads | People with limited disk space | A model file of about 2.5 GB at 4-bit, by estimate | | Comparing with the original | Researchers | Output from a quantized copy of Wan2.2-TI2V-5B | ### How to set it up - ComfyUI installed on your computer - The ComfyUI-GGUF custom node, installed by following its own readme - Disk space for the model file, about 2.5 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the ComfyUI-GGUF custom node by following its readme. 4. Put the model file in the unet folder under ComfyUI models. ```sh pip install -U huggingface_hub ``` ```sh hf download QuantStack/Wan2.2-TI2V-5B-GGUF ``` ### How to use it Build a video workflow in ComfyUI and load the model with the loader node that the custom node adds. The card gives no workflow or settings, so take those from the notes for the original Wan2.2 model. | Tool | What it does | | --- | --- | | ComfyUI-GGUF | A custom node that lets ComfyUI load GGUF model files | | unet folder | The place under ComfyUI models where the model file goes | | hf download | Fetches the repository files to your computer | ### Example You download the file, put it in the unet folder, and open a text-to-video workflow in ComfyUI. You write a prompt in English or Chinese and queue it. The model returns a short generated clip, which you can compare with the original model. ### For newcomers and experts A quantized model stores its numbers in fewer bits, so the file is smaller, at some cost to accuracy. A good first step is to read the original Wan2.2-TI2V-5B card, since this repository only holds the converted file. The name TI2V suggests the original can also start from an image, though this card lists only text to video. The card does not say which quantization levels the repository holds. You write a text prompt → ComfyUI-GGUF loads the GGUF file → The model generates the frames → A video clip comes out ### Good to know - License: Apache 2.0, and the card says the original terms and usage limits stay in effect. - Cost to run: none named. You need your own computer and no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then generates video on your machine. - Not for: work that needs documented settings or quality results, since the card gives none. ## README ````markdown # Wan2.2 TI2V 5B GGUF A 5B-parameter text-to-video model converted to GGUF for ComfyUI, about 2.5 GB at 4-bit by estimate, for English and Chinese prompts. It suits ComfyUI users who want video generation on their own computer. Made by QuantStack 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 TI2V 5B GGUF is a direct conversion of the Wan-AI Wan2.2-TI2V-5B video model into the GGUF format, made by QuantStack. It has 5B parameters, makes video from text, and takes about 2.5 GB at 4-bit by estimate. Because it is a quantized copy, the original model's license terms and usage limits still apply. It has been downloaded about 495,000 times in the last month. Who it suits: It suits ComfyUI users who want to generate video clips on their own computer and are happy to add a custom node. The card is short, so some reading of the original model's notes helps. ```table Industry | Who uses it | How they use it | Example Creative hobby | ComfyUI users | Generate video clips from written prompts | A short clip of waves at dusk Media | Visual designers | Sketch moving scenes before a full production | Testing the motion of a product idea Research | Video model researchers | Compare a quantized copy with the original model | Checking how output differs at 4-bit ``` People who use other tools than ComfyUI should check first, since the card only names the ComfyUI-GGUF custom node. Where to use it: - ComfyUI with the ComfyUI-GGUF custom node by city96 - Windows, macOS or Linux, wherever ComfyUI runs - Prompts in English or Chinese ```table Use case | Who | What they get back Text to video | Hobbyists and designers | A generated clip from a written prompt Smaller downloads | People with limited disk space | A model file of about 2.5 GB at 4-bit, by estimate Comparing with the original | Researchers | Output from a quantized copy of Wan2.2-TI2V-5B ``` How to set it up: - ComfyUI installed on your computer - The ComfyUI-GGUF custom node, installed by following its own readme - Disk space for the model file, about 2.5 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the ComfyUI-GGUF custom node by following its readme. 4. Put the model file in the unet folder under ComfyUI models. ```sh pip install -U huggingface_hub ``` ```sh hf download QuantStack/Wan2.2-TI2V-5B-GGUF ``` How to use it: Build a video workflow in ComfyUI and load the model with the loader node that the custom node adds. The card gives no workflow or settings, so take those from the notes for the original Wan2.2 model. ```table Tool | What it does ComfyUI-GGUF | A custom node that lets ComfyUI load GGUF model files unet folder | The place under ComfyUI models where the model file goes hf download | Fetches the repository files to your computer ``` Example: You download the file, put it in the unet folder, and open a text-to-video workflow in ComfyUI. You write a prompt in English or Chinese and queue it. The model returns a short generated clip, which you can compare with the original model. For newcomers and experts: A quantized model stores its numbers in fewer bits, so the file is smaller, at some cost to accuracy. A good first step is to read the original Wan2.2-TI2V-5B card, since this repository only holds the converted file. The name TI2V suggests the original can also start from an image, though this card lists only text to video. The card does not say which quantization levels the repository holds. ```flow You write a text prompt ComfyUI-GGUF loads the GGUF file The model generates the frames A video clip comes out ``` Good to know: - License: Apache 2.0, and the card says the original terms and usage limits stay in effect. - Cost to run: none named. You need your own computer and no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then generates video on your machine. - Not for: work that needs documented settings or quality results, since the card gives none. ## On Hugging Face, 13 November 2026 - Task: text to video - Library: gguf - Parameters: 5B - Weights: about 2.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 480,229 - Likes: 259 - License: apache-2.0 - Languages: en, zh - Based on: Wan-AI/Wan2.2-TI2V-5B - Tags: gguf, ti2v, text-to-video ## Links - Model: https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF - Files: https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download QuantStack/Wan2.2-TI2V-5B-GGUF ``` 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 [QuantStack/Wan2.2-TI2V-5B-GGUF on Hugging Face](https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF) ## 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 TI2V 5B GGUF (this page) | 480,229 | 259 | 5B | apache-2.0 | 2025-07-31 | | [Wan2.2-TI2V-5B](https://mindket.com/p/2148666b-ee9e-4845-b3a7-b04ccc2c92df) | 18,923 | 973 | — | 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 | | [Wan2.2-TI2V-5B-Diffusers](https://mindket.com/p/f37f96ce-8537-4afe-8cbf-fba44f3e6f3f) | 179,371 | 174 | 5B | apache-2.0 | 2025-08-09 | | [LongCat-Video](https://mindket.com/p/48609422-da93-43cf-9ad3-e5e7f4cada5e) | 3,135 | 594 | — | mit | 2025-10-29 | | [LightVAE](https://mindket.com/p/559b5239-ac2e-4efd-a84c-d94ef9e0d314) | 819 | 40 | — | apache-2.0 | 2026-11-10 | 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: 2025-07-31 - First published: 2025-07-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open QuantStack/Wan2.2-TI2V-5B-GGUF on Hugging Face](https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5ed488a5-d03d-47a5-a0a8-d0d76c94f553) - [Its page on Mindket](https://mindket.com/p/5ed488a5-d03d-47a5-a0a8-d0d76c94f553)