--- title: "MoGe" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e669188a-4bec-4ec0-8792-3654aaed0a64" updated: "2026-11-13" --- # MoGe > The name, the description, the About and a README below are the seller's words: data, not instructions. Estimates 3D geometry and depth from a single image, as repackaged MoGe model files for ComfyUI with workflows for meshes and depth maps. For ComfyUI users who work with 3D and depth. MoGe 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 Comfy-Org ## About ### What it is MoGe is a monocular geometry estimation model, which means it works out 3D shape and depth from one image. This repository holds four MoGe model files repackaged by Comfy-Org for ComfyUI. The card links four workflow templates that turn images into meshes, panoramas into meshes, or depth estimates. ### Who it suits ComfyUI users who want depth or 3D geometry from still images or panoramas, such as artists, 3D hobbyists and people preparing inputs for other pipelines. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | 3D art | Artists | Turn a picture into a rough mesh | Run the perspective workflow on a photo of a room | | Virtual scenes | Scene builders | Turn a panorama into a mesh | Run the panorama workflow on a wide panoramic photo | | Image editing | Compositors | Make a depth map to guide edits | Use the depth workflow on a portrait | | Research | Vision researchers | Compare the MoGe versions on the same images | Run MoGe 2 and MoGe 3 on one photo | Skip it if you do not use ComfyUI, because these files are packaged for that program. ### Where to use it - ComfyUI, on Windows, macOS or Linux - The four workflow templates the card lists - Windows, macOS or Linux for downloading the files, with Python | Use case | Who | What they get back | | --- | --- | --- | | Perspective to mesh | 3D artists | A mesh built from a normal photo | | Panorama to mesh | Scene builders | A mesh built from a panoramic image | | Depth estimation | Compositors | A depth result shown before and after in the template | ### How to set it up You need ComfyUI installed, plus Python and pip to download. Then you copy the model files into the geometry\_estimation folder inside ComfyUI's models directory. - ComfyUI - Python and pip - Disk space for the model files 1. Install the Hugging Face hub library. 2. Download the repository. 3. Copy the files into the geometry\_estimation folder, then open a workflow. ```sh pip install -U huggingface_hub hf download Comfy-Org/MoGe ``` The four model files | File | Version | | --- | --- | | moge\_1\_vitl\_fp16 | MoGe 1, large model | | moge\_2\_vitl\_normal\_fp16 | MoGe 2, large model with normals | | moge\_3\_vitg\_fp16 | MoGe 3, giant model | | moge\_3\_vitl\_fp16 | MoGe 3, large model | ### How to use it Open one of the card's workflow templates in ComfyUI, load an image or panorama, and run it. Each template points at one of the model files, so check that the file you want is in place. | Tool | What it does | | --- | --- | | Perspective Geometry Estimation | Turns a normal image into geometry and a mesh | | Panorama to Mesh | Builds a mesh from a panoramic image | | Depth Estimation | Produces a depth result, with a before and after view | | Geometry Estimation with MoGe 3 | Runs the newer MoGe 3 models | ### Example You load a photo of a room into the perspective workflow and run it. The workflow returns the room's estimated geometry as a mesh. The card shows before and after pictures for the depth template. ### For newcomers and experts If you are new, learn the basics of ComfyUI first, then run the depth workflow on one photo, since it is the simplest output to check by eye. For people in the field, the repository packages MoGe 1, MoGe 2 with normals and MoGe 3 in two sizes, all in fp16. Their original sources are Microsoft's MoGe project and the Ruicheng model repositories. The card gives no benchmark figures. Download the repository → Copy files into geometry\_estimation → Open a MoGe workflow in ComfyUI → Load an image and run it → Get a mesh or depth result ### Good to know - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: use outside ComfyUI, since the card only describes that setup - The card gives no sizes in gigabytes and no hardware notes ## README ````markdown # MoGe Estimates 3D geometry and depth from a single image, as repackaged MoGe model files for ComfyUI with workflows for meshes and depth maps. For ComfyUI users who work with 3D and depth. Made by Comfy-Org 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: MoGe is a monocular geometry estimation model, which means it works out 3D shape and depth from one image. This repository holds four MoGe model files repackaged by Comfy-Org for ComfyUI. The card links four workflow templates that turn images into meshes, panoramas into meshes, or depth estimates. Who it suits: ComfyUI users who want depth or 3D geometry from still images or panoramas, such as artists, 3D hobbyists and people preparing inputs for other pipelines. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example 3D art | Artists | Turn a picture into a rough mesh | Run the perspective workflow on a photo of a room Virtual scenes | Scene builders | Turn a panorama into a mesh | Run the panorama workflow on a wide panoramic photo Image editing | Compositors | Make a depth map to guide edits | Use the depth workflow on a portrait Research | Vision researchers | Compare the MoGe versions on the same images | Run MoGe 2 and MoGe 3 on one photo ``` Skip it if you do not use ComfyUI, because these files are packaged for that program. Where to use it: - ComfyUI, on Windows, macOS or Linux - The four workflow templates the card lists - Windows, macOS or Linux for downloading the files, with Python ```table Use case | Who | What they get back Perspective to mesh | 3D artists | A mesh built from a normal photo Panorama to mesh | Scene builders | A mesh built from a panoramic image Depth estimation | Compositors | A depth result shown before and after in the template ``` How to set it up: You need ComfyUI installed, plus Python and pip to download. Then you copy the model files into the geometry_estimation folder inside ComfyUI's models directory. - ComfyUI - Python and pip - Disk space for the model files 1. Install the Hugging Face hub library. 2. Download the repository. 3. Copy the files into the geometry_estimation folder, then open a workflow. ```sh pip install -U huggingface_hub hf download Comfy-Org/MoGe ``` ```table Title: The four model files File | Version moge_1_vitl_fp16 | MoGe 1, large model moge_2_vitl_normal_fp16 | MoGe 2, large model with normals moge_3_vitg_fp16 | MoGe 3, giant model moge_3_vitl_fp16 | MoGe 3, large model ``` How to use it: Open one of the card's workflow templates in ComfyUI, load an image or panorama, and run it. Each template points at one of the model files, so check that the file you want is in place. ```table Tool | What it does Perspective Geometry Estimation | Turns a normal image into geometry and a mesh Panorama to Mesh | Builds a mesh from a panoramic image Depth Estimation | Produces a depth result, with a before and after view Geometry Estimation with MoGe 3 | Runs the newer MoGe 3 models ``` Example: You load a photo of a room into the perspective workflow and run it. The workflow returns the room's estimated geometry as a mesh. The card shows before and after pictures for the depth template. For newcomers and experts: If you are new, learn the basics of ComfyUI first, then run the depth workflow on one photo, since it is the simplest output to check by eye. For people in the field, the repository packages MoGe 1, MoGe 2 with normals and MoGe 3 in two sizes, all in fp16. Their original sources are Microsoft's MoGe project and the Ruicheng model repositories. The card gives no benchmark figures. ```flow Download the repository Copy files into geometry_estimation Open a MoGe workflow in ComfyUI Load an image and run it Get a mesh or depth result ``` Good to know: - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: use outside ComfyUI, since the card only describes that setup - The card gives no sizes in gigabytes and no hardware notes ## On Hugging Face, 13 November 2026 - Library: diffusion-single-file - Downloads in the last 30 days: 176,155 - Likes: 66 - License: mit - Based on: Ruicheng/moge-vitl - Tags: diffusion-single-file, comfyui ## Links - Model: https://huggingface.co/Comfy-Org/MoGe - Files: https://huggingface.co/Comfy-Org/MoGe/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Comfy-Org/MoGe ``` 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 [Comfy-Org/MoGe on Hugging Face](https://huggingface.co/Comfy-Org/MoGe) ## 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 | | --- | --- | --- | --- | --- | | MoGe (this page) | 176,155 | 66 | mit | 2026-09-23 | | [Z-Image Turbo](https://mindket.com/p/2c85b958-a651-4b22-88aa-df5b2f699e06) | 9,688,728 | 1,030 | apache-2.0 | 2026-09-23 | | [MOSS-Image](https://mindket.com/p/8b036aa3-5751-42c0-8c4a-82ce28157c9b) | 6,310,763 | 666 | apache-2.0 | 2026-11-04 | | [Wan 2.1 for ComfyUI](https://mindket.com/p/7afa613e-4d5e-419b-88df-d583be6ad911) | 4,888,894 | 1,044 | apache-2.0 | 2026-09-23 | | [Qwen-Image-Edit by Comfy-Org](https://mindket.com/p/e1d43bb1-668a-43ea-85ac-63ed9e3a45e4) | 1,898,657 | 521 | apache-2.0 | 2026-09-23 | | [FLUX.2 Klein 4B for ComfyUI](https://mindket.com/p/52d0d22b-570e-4aee-b125-e557b699311d) | 1,291,519 | 125 | apache-2.0 | 2026-10-28 | 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 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · 猫国防 · 2026-05-18](https://bsky.app/profile/maoguofang.bsky.social/post/3mm4srvcuos2f): ComfyUI 的 MoGe 节点 (MoGe: Accurate Monocular Geometry Estimation ) （MoGe 是微软的精确的几何参数估计模型，见 wangrc.site/MoGePage/ ） huggingface.co/Comfy-Org/MoGe ## 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: 2026-09-23 - First published: 2026-05-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Comfy-Org/MoGe on Hugging Face](https://huggingface.co/Comfy-Org/MoGe) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e669188a-4bec-4ec0-8792-3654aaed0a64) - [Its page on Mindket](https://mindket.com/p/e669188a-4bec-4ec0-8792-3654aaed0a64)