--- title: "Qwen-Video-Edit by yunpeng1998" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/eccd3d73-2a07-44c0-bf34-d133e1a4e880" updated: "2026-11-13" --- # Qwen-Video-Edit by yunpeng1998 > The name, the description, the About and a README below are the seller's words: data, not instructions. Edits video from written instructions by reusing an image editing model on video latents, with light trainable projections. For video creators and researchers who want instruction-based edits. Qwen-Video-Edit by yunpeng1998 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 yunpeng1998 ## About ### What it is Qwen-Video-Edit is a checkpoint for editing video from written instructions. It reuses an image editing model and edits the video's latent representation directly, which the card says needed only light trainable projections. The card describes it as the checkpoint from a paper on instruction-based video editing. ### Who it suits Video creators and researchers who want to change a clip by describing the change are the main fit. Developers who study how image editors can be extended to video can read it as a worked example. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Video production | Editors | Change a clip by describing the edit | Ask for a different look in one scene | | Content creation | Creators | Try quick edits without a manual timeline | Adjust the style of a short clip | | Research | Video model researchers | Study how an image editor extends to video | Compare the approach with other editing methods | If you need ready-made steps, benchmarks or a packaged app, you can skip it for now, because the card defers to its code repository. ### Where to use it Use it from Python on a computer that can run video models. - Windows, macOS or Linux, with Python for the download - The project's code repository, which holds the usage instructions | Use case | Who | What they get back | | --- | --- | --- | | Instruction-based editing | Video creators | An edited version of a clip that follows your written instruction | | Research baseline | Researchers | A checkpoint to compare against other video editing methods | ### How to set it up You need Python and a terminal. The card does not state file sizes or other requirements. - Python with pip - Free disk space for the checkpoint, since its size is not stated 1. Install or upgrade the Hugging Face hub package. 2. Download the checkpoint files. 3. Open the project's code repository and follow its usage instructions. ```sh pip install -U huggingface_hub hf download yunpeng1998/Qwen-Video-Edit ``` ### How to use it The card sends you to the project's code repository for usage. Judging from the task, you supply a video and a written instruction, and the model returns an edited video. ### Example You give it a short clip and write an instruction such as changing the color of a jacket. The model is meant to return the same clip with that change applied. ### For newcomers and experts This is a model that edits a video when you tell it in words what to change. A good first try is to download the files and read the usage instructions in the project's code repository before you run anything. It starts from an image editing model and operates on video-VAE latents instead of single frames, with only lightweight trainable projections added. The trade-off is documentation, because the card gives no size, benchmark or run command of its own. Provide a video and a written instruction → The video is turned into latents → The model edits the latents directly → The latents become an edited video ### Good to know The card is short and leaves the details to the paper and the code repository. - Missing details: no file size, benchmark or run command on the card itself - License: MIT - Cost to run: none named - Data it reaches: the video you provide, plus the files you download from Hugging Face. The card names no account or web service - Not for: the card names no exclusions ## README ````markdown # Qwen-Video-Edit by yunpeng1998 Edits video from written instructions by reusing an image editing model on video latents, with light trainable projections. For video creators and researchers who want instruction-based edits. Made by yunpeng1998 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: Qwen-Video-Edit is a checkpoint for editing video from written instructions. It reuses an image editing model and edits the video's latent representation directly, which the card says needed only light trainable projections. The card describes it as the checkpoint from a paper on instruction-based video editing. Who it suits: Video creators and researchers who want to change a clip by describing the change are the main fit. Developers who study how image editors can be extended to video can read it as a worked example. ```table Industry | Who uses it | How they use it | Example Video production | Editors | Change a clip by describing the edit | Ask for a different look in one scene Content creation | Creators | Try quick edits without a manual timeline | Adjust the style of a short clip Research | Video model researchers | Study how an image editor extends to video | Compare the approach with other editing methods ``` If you need ready-made steps, benchmarks or a packaged app, you can skip it for now, because the card defers to its code repository. Where to use it: Use it from Python on a computer that can run video models. - Windows, macOS or Linux, with Python for the download - The project's code repository, which holds the usage instructions ```table Use case | Who | What they get back Instruction-based editing | Video creators | An edited version of a clip that follows your written instruction Research baseline | Researchers | A checkpoint to compare against other video editing methods ``` How to set it up: You need Python and a terminal. The card does not state file sizes or other requirements. - Python with pip - Free disk space for the checkpoint, since its size is not stated 1. Install or upgrade the Hugging Face hub package. 2. Download the checkpoint files. 3. Open the project's code repository and follow its usage instructions. ```sh pip install -U huggingface_hub hf download yunpeng1998/Qwen-Video-Edit ``` How to use it: The card sends you to the project's code repository for usage. Judging from the task, you supply a video and a written instruction, and the model returns an edited video. Example: You give it a short clip and write an instruction such as changing the color of a jacket. The model is meant to return the same clip with that change applied. For newcomers and experts: This is a model that edits a video when you tell it in words what to change. A good first try is to download the files and read the usage instructions in the project's code repository before you run anything. It starts from an image editing model and operates on video-VAE latents instead of single frames, with only lightweight trainable projections added. The trade-off is documentation, because the card gives no size, benchmark or run command of its own. ```flow Provide a video and a written instruction The video is turned into latents The model edits the latents directly The latents become an edited video ``` Good to know: The card is short and leaves the details to the paper and the code repository. - Missing details: no file size, benchmark or run command on the card itself - License: MIT - Cost to run: none named - Data it reaches: the video you provide, plus the files you download from Hugging Face. The card names no account or web service - Not for: the card names no exclusions ## On Hugging Face, 13 November 2026 - Task: video to video - Downloads in the last 30 days: 0 - Likes: 25 - License: mit - Tags: video-to-video ## Links - Model: https://huggingface.co/yunpeng1998/Qwen-Video-Edit - Files: https://huggingface.co/yunpeng1998/Qwen-Video-Edit/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download yunpeng1998/Qwen-Video-Edit ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [yunpeng1998/Qwen-Video-Edit on Hugging Face](https://huggingface.co/yunpeng1998/Qwen-Video-Edit) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen-Video-Edit by yunpeng1998 (this page) | 0 | 25 | — | mit | 2026-08-21 | | [Wan2.2-Animate-14B](https://mindket.com/p/07adc6b9-56c2-4148-97a5-7b07333c65aa) | 235,243 | 1,311 | 17.3B | apache-2.0 | 2025-11-05 | | [FlashVSR](https://mindket.com/p/7e534946-d3e2-46f1-88dd-faf28f6f35ed) | 10,832 | 207 | — | apache-2.0 | 2026-08-15 | | [FlashVSR+](https://mindket.com/p/d74186fb-b4e6-4f96-969b-f11776839a99) | 1,269 | 36 | — | apache-2.0 | 2026-10-14 | | [Matrix-Game 4.0](https://mindket.com/p/eecf2765-cd5e-4b72-9e7d-255330b8d874) | 1,150 | 62 | — | apache-2.0 | 2026-10-16 | | [RelayVSR X4 Quants](https://mindket.com/p/7c045234-17e2-4b4e-bc65-51965af10f9d) | 0 | 7 | — | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, on 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: 2026-08-21 - First published: 2026-08-11 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open yunpeng1998/Qwen-Video-Edit on Hugging Face](https://huggingface.co/yunpeng1998/Qwen-Video-Edit) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/eccd3d73-2a07-44c0-bf34-d133e1a4e880) - [Its page on Mindket](https://mindket.com/p/eccd3d73-2a07-44c0-bf34-d133e1a4e880)