--- title: "PAN 2" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b0dad2b4-3f72-4c44-83bd-34dbe74c0172" updated: "2026-11-12" --- # PAN 2 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 29B world model from IFM at MBZUAI that turns a scene and described actions into a video of up to 20 seconds, so an agent can preview outcomes. For researchers and agent builders with a large GPU. PAN 2 is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by IFM ## About ### What it is PAN 2 is a world model from the Institute of Foundation Models at MBZUAI. It predicts how a scene changes after actions that you describe in text. You give the current scene as text, with an optional image, plus the actions, and it returns a video of the result that runs up to 20 seconds. This repository holds the 29B diffusion backbone in BF16. ### Who it suits It suits researchers and agent builders who want to see the likely outcome of an action before choosing it, and video makers who want short clips from a described scene. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Researchers | Study world models and simulative reasoning | Compare candidate actions by their simulated outcomes | | Agents | Agent builders | Let an agent preview outcomes before it acts | Simulate each plan and pick the best-looking result | | Media | Video creators | Make a short clip from a first frame and a description | A clip of up to 20 seconds from one image | | Simulation | Developers | Prototype how a scene may change after an action | A scene after a character carries out a step | If you want a light video tool for a small GPU, you can skip it, since the weights alone are about 58.4 GB. ### Where to use it - Python with conda, using the generate.py script from the card - Diffusers and vLLM, which the card says also support the model - Machines with NVIDIA GPUs, since the setup uses CUDA builds of PyTorch | Use case | Who | What they get back | | --- | --- | --- | | Text to video | Creators | A video made from a structured text description | | Image to video | Creators | A video that starts from your first frame | | Action simulation | Agent builders | A video of the outcome of an action you describe | ### How to set it up You need these first: - Python and conda, with Python 3.14 as in the card - A CUDA GPU and disk space for about 58.4 GB of BF16 weights - PyTorch 2.12.1 and torchvision 0.27.1 built for CUDA - The text encoder Qwen3.5-9B and the Wan 2.2 VAE, which download from their own repositories 1. Install the Hugging Face hub tool and download the transformer folder. 2. Convert the weights once to the layout that generate.py reads. 3. Create the conda environment and install the libraries. 4. Download the frozen text encoder and VAE. 5. Point CKPT\_PATH at the converted transformer folder and run generate.py. ```sh pip install -U huggingface_hub hf download IFM/PAN2 --include "transformer/*" --local-dir /path/to/PAN2 python scripts/convert_checkpoint.py to-native \ --input /path/to/PAN2/transformer --output /transformer ``` ```bash conda create -n pan python=3.14 -y && conda activate pan pip install --pre "flash-attn-4[cu13]==4.0.0b29" --no-build-isolation pip install "quack-kernels[cu13]==0.6.4" pip install -r requirements.txt ``` ```bash export HF_HOME=YOUR_HF_PATH hf download Qwen/Qwen3.5-9B hf download Wan-AI/Wan2.2-TI2V-5B-Diffusers ``` ### How to use it Describe the scene and the actions in the structured text form the card expects, add a first frame if you have one, and run generate.py. The part of the card given here ends before the generate command, so read the full card for the exact call. | Tool | What it does | | --- | --- | | generate.py | The script the card uses to make videos | | convert\_checkpoint.py | Converts the released weights to the layout generate.py reads | | Diffusers | Also supports PAN 2, per the card | | vLLM | Also supports PAN 2, per the card | ### Example You give a picture of a cup on a table and write that a hand pushes it off the edge. PAN 2 returns a video of up to 20 seconds that shows what follows. The card does not show this exact run, so treat it as an expectation. ### For newcomers and experts A world model predicts what happens next in a scene. A good first step is to read the card's overview and check that your GPU can hold the weights before you download anything. The card describes two parts. A large language model first reads the scene and the actions, then drafts a structured text description of what the world looks like next. A latent diffusion model then predicts the video that matches it, and the Wan 2.2 decoder turns that into frames. The released checkpoint is the 29B diffusion part, which the card says learned from scratch using videos numbering in the hundreds of millions. Describe the scene and the actions → The language model writes the next world state → The diffusion model predicts the video latents → The decoder renders the video ### Good to know The weights are large, and the 4-bit figure is only Hugging Face's own estimate. Weight size in GB | Label | Value | | --- | --- | | BF16 weights | 58.4 GB | | 4-bit estimate | 14.6 GB | - Output: video at 704x1248 and 24 fps, up to 20 seconds - Tasks: one checkpoint covers text to video and image to video - Conversion: the weights ship in the diffusers layout, so convert them once for generate.py - Separate downloads: the text encoder and VAE come from other repositories - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the text and images you give it, and the extra models you download ## README ````markdown # PAN 2 A 29B world model from IFM at MBZUAI that turns a scene and described actions into a video of up to 20 seconds, so an agent can preview outcomes. For researchers and agent builders with a large GPU. Made by IFM 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: PAN 2 is a world model from the Institute of Foundation Models at MBZUAI. It predicts how a scene changes after actions that you describe in text. You give the current scene as text, with an optional image, plus the actions, and it returns a video of the result that runs up to 20 seconds. This repository holds the 29B diffusion backbone in BF16. Who it suits: It suits researchers and agent builders who want to see the likely outcome of an action before choosing it, and video makers who want short clips from a described scene. ```table Industry | Who uses it | How they use it | Example AI research | Researchers | Study world models and simulative reasoning | Compare candidate actions by their simulated outcomes Agents | Agent builders | Let an agent preview outcomes before it acts | Simulate each plan and pick the best-looking result Media | Video creators | Make a short clip from a first frame and a description | A clip of up to 20 seconds from one image Simulation | Developers | Prototype how a scene may change after an action | A scene after a character carries out a step ``` If you want a light video tool for a small GPU, you can skip it, since the weights alone are about 58.4 GB. Where to use it: - Python with conda, using the generate.py script from the card - Diffusers and vLLM, which the card says also support the model - Machines with NVIDIA GPUs, since the setup uses CUDA builds of PyTorch ```table Use case | Who | What they get back Text to video | Creators | A video made from a structured text description Image to video | Creators | A video that starts from your first frame Action simulation | Agent builders | A video of the outcome of an action you describe ``` How to set it up: You need these first: - Python and conda, with Python 3.14 as in the card - A CUDA GPU and disk space for about 58.4 GB of BF16 weights - PyTorch 2.12.1 and torchvision 0.27.1 built for CUDA - The text encoder Qwen3.5-9B and the Wan 2.2 VAE, which download from their own repositories 1. Install the Hugging Face hub tool and download the transformer folder. 2. Convert the weights once to the layout that generate.py reads. 3. Create the conda environment and install the libraries. 4. Download the frozen text encoder and VAE. 5. Point CKPT_PATH at the converted transformer folder and run generate.py. ```sh pip install -U huggingface_hub hf download IFM/PAN2 --include "transformer/*" --local-dir /path/to/PAN2 python scripts/convert_checkpoint.py to-native \ --input /path/to/PAN2/transformer --output /transformer ``` ```bash conda create -n pan python=3.14 -y && conda activate pan pip install --pre "flash-attn-4[cu13]==4.0.0b29" --no-build-isolation pip install "quack-kernels[cu13]==0.6.4" pip install -r requirements.txt ``` ```bash export HF_HOME=YOUR_HF_PATH hf download Qwen/Qwen3.5-9B hf download Wan-AI/Wan2.2-TI2V-5B-Diffusers ``` How to use it: Describe the scene and the actions in the structured text form the card expects, add a first frame if you have one, and run generate.py. The part of the card given here ends before the generate command, so read the full card for the exact call. ```table Tool | What it does generate.py | The script the card uses to make videos convert_checkpoint.py | Converts the released weights to the layout generate.py reads Diffusers | Also supports PAN 2, per the card vLLM | Also supports PAN 2, per the card ``` Example: You give a picture of a cup on a table and write that a hand pushes it off the edge. PAN 2 returns a video of up to 20 seconds that shows what follows. The card does not show this exact run, so treat it as an expectation. For newcomers and experts: A world model predicts what happens next in a scene. A good first step is to read the card's overview and check that your GPU can hold the weights before you download anything. The card describes two parts. A large language model first reads the scene and the actions, then drafts a structured text description of what the world looks like next. A latent diffusion model then predicts the video that matches it, and the Wan 2.2 decoder turns that into frames. The released checkpoint is the 29B diffusion part, which the card says learned from scratch using videos numbering in the hundreds of millions. ```flow Describe the scene and the actions The language model writes the next world state The diffusion model predicts the video latents The decoder renders the video ``` Good to know: The weights are large, and the 4-bit figure is only Hugging Face's own estimate. ```chart Title: Weight size in GB BF16 weights: 58.4 GB 4-bit estimate: 14.6 GB ``` - Output: video at 704x1248 and 24 fps, up to 20 seconds - Tasks: one checkpoint covers text to video and image to video - Conversion: the weights ship in the diffusers layout, so convert them once for generate.py - Separate downloads: the text encoder and VAE come from other repositories - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the text and images you give it, and the extra models you download ## On Hugging Face, 13 November 2026 - Task: image text to video - Library: diffusers - Parameters: 29.2B - Weights: about 58.4 GB in BF16; about 14.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 10 - Likes: 83 - License: apache-2.0 - Languages: en - Fine-tunes: 1 - Tags: diffusers, safetensors, world-model, world-simulation, text-to-video, image-to-video, image-text-to-video, diffusion-transformer ## Links - Model: https://huggingface.co/IFM/PAN2 - Files: https://huggingface.co/IFM/PAN2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download IFM/PAN2 ``` 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 [IFM/PAN2 on Hugging Face](https://huggingface.co/IFM/PAN2) ## 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 | | --- | --- | --- | --- | --- | --- | | PAN 2 (this page) | 10 | 83 | 29.2B | apache-2.0 | 2026-11-12 | | [Drishya Tiny](https://mindket.com/p/8cff9a9e-f919-49f0-9510-298c1142400e) | 163 | 6 | 212M | cc-by-nc-4.0 | 2026-10-28 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | | [GTA world model](https://mindket.com/p/3a29ddef-e378-421f-8918-88b4da9ac428) | 0 | 11 | — | apache-2.0 | 2026-11-11 | | [RFD4-Proteina-Diffusers](https://mindket.com/p/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05) | 403 | 5 | 178M | bsd-3-clause | 2026-10-20 | | [GenFirst](https://mindket.com/p/dc71e7f3-3967-4818-9bb6-b0c990168fb9) | 0 | 7 | — | cc-by-nc-4.0 | 2026-11-03 | 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 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · AI Daily Post · 2026-11-11](https://bsky.app/profile/aidailypost.com/post/3mzziu24o752h): MBZUAI IFM just dropped PAN 2, a 29B diffusion‑transformer world model that lets robots imagine and generate video‑level physics. Open‑source under Apache 2.0 – the next leap for physical AI. Dive in\! \#PAN2 \#PhysicalAI \#WorldModel 🔗 aidailypost.com/news/mbzuai-... ## 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: 2026-11-12 - First published: 2026-10-07 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IFM/PAN2 on Hugging Face](https://huggingface.co/IFM/PAN2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b0dad2b4-3f72-4c44-83bd-34dbe74c0172) - [Its page on Mindket](https://mindket.com/p/b0dad2b4-3f72-4c44-83bd-34dbe74c0172)