--- title: "Wan2.2-Fun-5B-Control" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/29aa505f-9a61-4bb5-b736-e246068759d9" updated: "2026-11-13" --- # Wan2.2-Fun-5B-Control > The name, the description, the About and a README below are the seller's words: data, not instructions. Steers generated video with a control condition such as Canny, Depth, Pose or MLSD, or with a trajectory. For video creators and researchers working with the 5B Wan2.2 base in the videox\_fun library. Wan2.2-Fun-5B-Control 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 alibaba-pai ## About ### What it is A video control model that steers the clip it generates with a condition such as Canny edges, Depth, Pose or MLSD, or with a trajectory. It belongs to the Wan2.2-Fun family and is built on Wan-AI/Wan2.2-TI2V-5B. The weights are Safetensors files of 23.0 GB, trained at 121 frames and 24 frames per second. Hugging Face lists it as an image to video model for the videox\_fun library. ### Who it suits Creators and researchers who want to direct how a generated video moves, instead of leaving all the motion to a text prompt. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Video production | Video editors | Guide a generated clip with a pose or depth video | Make a still character follow a recorded motion | | Advertising | Creative teams | Animate a reference image under a chosen control condition | Turn one picture into a short moving clip with set motion | | Animation | Animators | Steer movement along a trajectory | Move an object along a planned path | | Research | AI video researchers | Compare Canny, Depth, Pose and MLSD conditions on a 5B base | Check how each condition changes the same scene | You can skip it if you only want video from a text prompt or from first and last frames, because the sibling Wan2.2-Fun-5B-InP covers that. ### Where to use it It runs through the videox\_fun library, and the card points to Hugging Face and ModelScope for the weights. - The videox\_fun library, which the Hugging Face listing names for this model - Windows, macOS or Linux with Python, to download the files - Hugging Face or ModelScope, where the card links the weights | Use case | Who | What they get back | | --- | --- | --- | | Pose-driven clips | Video creators | A video that follows the motion of a pose control video | | Edge or depth guided clips | Designers | A video that keeps the structure of Canny edges or depth maps | | Trajectory control | Animators | A video whose movement follows a chosen path | ### How to set it up Get the files first, then follow the card's own run steps. The card has Quick start and How to use sections for that part. - Python, for the Hugging Face download commands - About 23.0 GB of free disk space for the weights - The videox\_fun code project by aigc-apps, which the card links 1. Install the Hugging Face tools with pip. 2. Download the model files to your computer. 3. Open the card's Quick start and How to use sections and run the weights in videox\_fun. ```bash pip install -U huggingface_hub hf download alibaba-pai/Wan2.2-Fun-5B-Control ``` ### How to use it You pick a control condition, prepare the matching control input, and let the model produce a video that follows it. The card says it supports multilingual prediction, with English and Chinese listed. The exact run commands sit in the card's How to use section. | Tool | What it does | | --- | --- | | Canny | Control condition based on edge maps | | Depth | Control condition based on depth maps | | Pose | Control condition based on body pose | | MLSD | Control condition based on straight line segments | | Trajectory control | Steers movement along a chosen path | ### Example Pair a reference image with a pose control video, the combination the card shows for its 14B Control sibling. You get back a video in which the pictured subject moves the way the pose clip does. ### For newcomers and experts A control model lets you steer a video with more than words. You give it a picture or a motion clip, and it follows that guidance. A first small try is to run the two download commands, then read the card's Quick start section before you generate anything. For experts, this is the 5B Control variant, built on Wan-AI/Wan2.2-TI2V-5B and trained at 121 frames and 24 frames per second. The 14B Control siblings were trained at 81 frames and 16 frames per second with 512, 768 and 1024 resolutions. The trade is size against capacity, since the 5B files take 23.0 GB and the 14B files take 64.0 GB. Choose a control condition → Prepare a control video or trajectory → Run the weights in videox\_fun → Collect the generated video ### Good to know The card lists six weight sets in all, and their storage sizes compare as follows. Storage size of each Wan2.2-Fun weight set | Label | Value | | --- | --- | | Wan2.2-Fun-A14B-InP | 64.0 GB | | Wan2.2-Fun-A14B-Control | 64.0 GB | | Wan2.2-Fun-A14B-Control-Camera | 64.0 GB | | Wan2.2-Fun-5B-InP | 23.0 GB | | Wan2.2-Fun-5B-Control | 23.0 GB | | Wan2.2-Fun-5B-Control-Camera | 23.0 GB | - Family: InP, Control and Control-Camera each come at 14B and 5B sizes. - Other versions: Hugging Face shows one quantized and five fine-tuned versions made by other people. - License: Apache-2.0. - Cost to run: none named, no API key, account or paid service, only your own computer. - Data it reaches: it downloads the weights from Hugging Face and reads the images and videos you give it. - Not for: the card names no limits in the text given here. ## README ````markdown # Wan2.2-Fun-5B-Control Steers generated video with a control condition such as Canny, Depth, Pose or MLSD, or with a trajectory. For video creators and researchers working with the 5B Wan2.2 base in the videox_fun library. Made by alibaba-pai 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: A video control model that steers the clip it generates with a condition such as Canny edges, Depth, Pose or MLSD, or with a trajectory. It belongs to the Wan2.2-Fun family and is built on Wan-AI/Wan2.2-TI2V-5B. The weights are Safetensors files of 23.0 GB, trained at 121 frames and 24 frames per second. Hugging Face lists it as an image to video model for the videox_fun library. Who it suits: Creators and researchers who want to direct how a generated video moves, instead of leaving all the motion to a text prompt. ```table Industry | Who uses it | How they use it | Example Video production | Video editors | Guide a generated clip with a pose or depth video | Make a still character follow a recorded motion Advertising | Creative teams | Animate a reference image under a chosen control condition | Turn one picture into a short moving clip with set motion Animation | Animators | Steer movement along a trajectory | Move an object along a planned path Research | AI video researchers | Compare Canny, Depth, Pose and MLSD conditions on a 5B base | Check how each condition changes the same scene ``` You can skip it if you only want video from a text prompt or from first and last frames, because the sibling Wan2.2-Fun-5B-InP covers that. Where to use it: It runs through the videox_fun library, and the card points to Hugging Face and ModelScope for the weights. - The videox_fun library, which the Hugging Face listing names for this model - Windows, macOS or Linux with Python, to download the files - Hugging Face or ModelScope, where the card links the weights ```table Use case | Who | What they get back Pose-driven clips | Video creators | A video that follows the motion of a pose control video Edge or depth guided clips | Designers | A video that keeps the structure of Canny edges or depth maps Trajectory control | Animators | A video whose movement follows a chosen path ``` How to set it up: Get the files first, then follow the card's own run steps. The card has Quick start and How to use sections for that part. - Python, for the Hugging Face download commands - About 23.0 GB of free disk space for the weights - The videox_fun code project by aigc-apps, which the card links 1. Install the Hugging Face tools with pip. 2. Download the model files to your computer. 3. Open the card's Quick start and How to use sections and run the weights in videox_fun. ```bash pip install -U huggingface_hub hf download alibaba-pai/Wan2.2-Fun-5B-Control ``` How to use it: You pick a control condition, prepare the matching control input, and let the model produce a video that follows it. The card says it supports multilingual prediction, with English and Chinese listed. The exact run commands sit in the card's How to use section. ```table Tool | What it does Canny | Control condition based on edge maps Depth | Control condition based on depth maps Pose | Control condition based on body pose MLSD | Control condition based on straight line segments Trajectory control | Steers movement along a chosen path ``` Example: Pair a reference image with a pose control video, the combination the card shows for its 14B Control sibling. You get back a video in which the pictured subject moves the way the pose clip does. For newcomers and experts: A control model lets you steer a video with more than words. You give it a picture or a motion clip, and it follows that guidance. A first small try is to run the two download commands, then read the card's Quick start section before you generate anything. For experts, this is the 5B Control variant, built on Wan-AI/Wan2.2-TI2V-5B and trained at 121 frames and 24 frames per second. The 14B Control siblings were trained at 81 frames and 16 frames per second with 512, 768 and 1024 resolutions. The trade is size against capacity, since the 5B files take 23.0 GB and the 14B files take 64.0 GB. ```flow Choose a control condition Prepare a control video or trajectory Run the weights in videox_fun Collect the generated video ``` Good to know: The card lists six weight sets in all, and their storage sizes compare as follows. ```chart Title: Storage size of each Wan2.2-Fun weight set Wan2.2-Fun-A14B-InP: 64.0 GB Wan2.2-Fun-A14B-Control: 64.0 GB Wan2.2-Fun-A14B-Control-Camera: 64.0 GB Wan2.2-Fun-5B-InP: 23.0 GB Wan2.2-Fun-5B-Control: 23.0 GB Wan2.2-Fun-5B-Control-Camera: 23.0 GB ``` - Family: InP, Control and Control-Camera each come at 14B and 5B sizes. - Other versions: Hugging Face shows one quantized and five fine-tuned versions made by other people. - License: Apache-2.0. - Cost to run: none named, no API key, account or paid service, only your own computer. - Data it reaches: it downloads the weights from Hugging Face and reads the images and videos you give it. - Not for: the card names no limits in the text given here. ## On Hugging Face, 13 November 2026 - Task: image to video - Library: videox_fun - Formats: Safetensors - Downloads in the last 30 days: 25,525 - Likes: 22 - License: apache-2.0 - Languages: en, zh - Based on: Wan-AI/Wan2.2-TI2V-5B - Quantized versions: 1 - Fine-tunes: 5 - Tags: videox_fun, diffusers, safetensors, ti2v, video, video-generation, wan2.2, image-to-video ## Links - Model: https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control - Files: https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download alibaba-pai/Wan2.2-Fun-5B-Control ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [alibaba-pai/Wan2.2-Fun-5B-Control on Hugging Face](https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control) ## 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 | License | Last change | | --- | --- | --- | --- | --- | | Wan2.2-Fun-5B-Control (this page) | 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-I2V-A14B](https://mindket.com/p/05752d53-43be-488a-9d2b-5171ad35fa63) | 8,500 | 959 | apache-2.0 | 2025-08-07 | | [Kandinsky S2V 1.0 Lite distilled](https://mindket.com/p/f3c31aa6-b788-41b6-adfc-83375408384a) | 84 | 4 | mit | 2026-11-03 | | [Kandinsky S2V 1.0 Lite](https://mindket.com/p/f1bf63ce-2d08-41f2-be11-442773800783) | 73 | 9 | mit | 2026-11-03 | 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: 2025-12-11 - First published: 2025-08-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open alibaba-pai/Wan2.2-Fun-5B-Control on Hugging Face](https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/29aa505f-9a61-4bb5-b736-e246068759d9) - [Its page on Mindket](https://mindket.com/p/29aa505f-9a61-4bb5-b736-e246068759d9)