--- title: "TRELLIS.2-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/873b0716-6e4d-4e42-a6df-25f5b1a95f31" updated: "2026-11-12" --- # TRELLIS.2-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Microsoft's 4B image to 3D model that turns a single picture into a textured 3D mesh with PBR materials, at resolutions up to 1536, for people with a large NVIDIA GPU. TRELLIS.2-4B is a kit in the Developer 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 microsoft ## About ### What it is TRELLIS.2-4B is Microsoft's model for turning a single image into a 3D asset. The output is a mesh with PBR materials, which covers color, opacity and how light reacts. The model is a 4 billion parameter flow-matching transformer. It works on a sparse voxel structure called O-Voxel. O-Voxel stores both shape and appearance. It handles open surfaces, non-manifold geometry and closed shapes without a lossy conversion step. ### Who it suits It suits 3D artists, game developers and researchers who have a strong NVIDIA GPU and want a 3D draft from a picture. The card asks for at least 24 GB of GPU memory. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Games and media | 3D artists | Turn concept art into a starting mesh | A prop built from one picture | | Product design | Designers | Draft a 3D model from a product photo | A rough model of a gadget | | Research | Graphics researchers | Study sparse voxel generation | Testing at several resolutions | | Software | Developers | Add image to 3D to a pipeline | An asset generator for a tool | Skip it if you have no NVIDIA GPU with 24 GB, or you need a Windows setup, since the card tests Linux only. ### Where to use it - Linux with an NVIDIA GPU, which is the tested setup - Python with the project's own trellis2 library - The official code repository, which holds the install steps - Windows, macOS or Linux, with Python, for the file download alone | Use case | Who | What they get back | | --- | --- | --- | | Image to 3D | Artists and developers | A mesh with PBR materials from one picture | | Shape-conditioned texturing | 3D artists | Textures for an input mesh and reference image | | Mesh and O-Voxel conversion | Researchers | Fast conversion between the two forms | ### How to set it up You need a few things in place first. - Linux, the only system the card says is tested - An NVIDIA GPU with at least 24 GB, with A100 and H100 verified - The CUDA Toolkit to compile some packages, with version 12.4 recommended - Conda to manage dependencies, and Python 3.8 or newer 1. Follow the official code repository for installation and dependencies. 2. Download the model files, or let the pipeline fetch them by name. 3. Run the Python script from the next section. ```sh pip install -U huggingface_hub hf download microsoft/TRELLIS.2-4B ``` ### How to use it You set up the environment, load the pipeline from the model name, open an image and run the pipeline. The result is a mesh, which you can simplify before rendering because of a limit in the renderer. ```python import os os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1' os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" # Can save GPU memory import cv2 import imageio from PIL import Image import torch from trellis2.utils import render_utils from trellis2.renderers import EnvMap import o_voxel # 1. Setup Environment Map envmap = EnvMap(torch.tensor( cv2.cvtColor(cv2.imread('assets/hdri/forest.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda' )) # 2. Load Pipeline pipeline.cuda() # 3. Load Image & Run image = Image.open("assets/example_image/T.png") mesh = pipeline.run(image)[0] mesh.simplify(16777216) # nvdiffrast limit ``` Put your own value in its key setting first. | Resolution | Time on an H100 | | --- | --- | | 512 | about 3 seconds | | 1024 | about 17 seconds | | 1536 | about 60 seconds | ### Example You run the script on the example image from the repository. The pipeline returns a mesh, and the call that follows simplifies it. On an H100, a 512 resolution run takes about 3 seconds and a 1536 run about 60 seconds. ### For newcomers and experts Start at the lowest resolution, which is the quickest, and use one clear picture of a single object. Check the result for small holes before you use it anywhere. The sparse 3D VAE downsamples space by a factor of 16. A 1024 cubed asset is encoded into only about 9.6K latent tokens. The card says this costs negligible perceptual quality. The model is a pre-trained foundation model with no preference alignment, so style varies and you may need to try a few inputs. Inference time on an H100, from the card | Label | Value | | --- | --- | | Resolution 512 | 3 seconds | | Resolution 1024 | 17 seconds | | Resolution 1536 | 60 seconds | Set up the environment and load the pipeline → Open a single image → Run the pipeline → Get a mesh with PBR materials ### Good to know - License: MIT - Cost to run: none named, but you need an NVIDIA GPU with at least 24 GB - Data it reaches: it downloads files from Hugging Face onto your machine and reads the images you give it - Not for: strictly watertight geometry without post-processing, since raw meshes can have small holes, and the card names scripts for filling them ## README ````markdown # TRELLIS.2-4B Microsoft's 4B image to 3D model that turns a single picture into a textured 3D mesh with PBR materials, at resolutions up to 1536, for people with a large NVIDIA GPU. Made by microsoft 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: TRELLIS.2-4B is Microsoft's model for turning a single image into a 3D asset. The output is a mesh with PBR materials, which covers color, opacity and how light reacts. The model is a 4 billion parameter flow-matching transformer. It works on a sparse voxel structure called O-Voxel. O-Voxel stores both shape and appearance. It handles open surfaces, non-manifold geometry and closed shapes without a lossy conversion step. Who it suits: It suits 3D artists, game developers and researchers who have a strong NVIDIA GPU and want a 3D draft from a picture. The card asks for at least 24 GB of GPU memory. ```table Industry | Who uses it | How they use it | Example Games and media | 3D artists | Turn concept art into a starting mesh | A prop built from one picture Product design | Designers | Draft a 3D model from a product photo | A rough model of a gadget Research | Graphics researchers | Study sparse voxel generation | Testing at several resolutions Software | Developers | Add image to 3D to a pipeline | An asset generator for a tool ``` Skip it if you have no NVIDIA GPU with 24 GB, or you need a Windows setup, since the card tests Linux only. Where to use it: - Linux with an NVIDIA GPU, which is the tested setup - Python with the project's own trellis2 library - The official code repository, which holds the install steps - Windows, macOS or Linux, with Python, for the file download alone ```table Use case | Who | What they get back Image to 3D | Artists and developers | A mesh with PBR materials from one picture Shape-conditioned texturing | 3D artists | Textures for an input mesh and reference image Mesh and O-Voxel conversion | Researchers | Fast conversion between the two forms ``` How to set it up: You need a few things in place first. - Linux, the only system the card says is tested - An NVIDIA GPU with at least 24 GB, with A100 and H100 verified - The CUDA Toolkit to compile some packages, with version 12.4 recommended - Conda to manage dependencies, and Python 3.8 or newer 1. Follow the official code repository for installation and dependencies. 2. Download the model files, or let the pipeline fetch them by name. 3. Run the Python script from the next section. ```sh pip install -U huggingface_hub hf download microsoft/TRELLIS.2-4B ``` How to use it: You set up the environment, load the pipeline from the model name, open an image and run the pipeline. The result is a mesh, which you can simplify before rendering because of a limit in the renderer. ```python import os os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1' os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" # Can save GPU memory import cv2 import imageio from PIL import Image import torch from trellis2.utils import render_utils from trellis2.renderers import EnvMap import o_voxel # 1. Setup Environment Map envmap = EnvMap(torch.tensor( cv2.cvtColor(cv2.imread('assets/hdri/forest.exr', cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB), dtype=torch.float32, device='cuda' )) # 2. Load Pipeline pipeline.cuda() # 3. Load Image & Run image = Image.open("assets/example_image/T.png") mesh = pipeline.run(image)[0] mesh.simplify(16777216) # nvdiffrast limit ``` Put your own value in its key setting first. ```table Resolution | Time on an H100 512 | about 3 seconds 1024 | about 17 seconds 1536 | about 60 seconds ``` Example: You run the script on the example image from the repository. The pipeline returns a mesh, and the call that follows simplifies it. On an H100, a 512 resolution run takes about 3 seconds and a 1536 run about 60 seconds. For newcomers and experts: Start at the lowest resolution, which is the quickest, and use one clear picture of a single object. Check the result for small holes before you use it anywhere. The sparse 3D VAE downsamples space by a factor of 16. A 1024 cubed asset is encoded into only about 9.6K latent tokens. The card says this costs negligible perceptual quality. The model is a pre-trained foundation model with no preference alignment, so style varies and you may need to try a few inputs. ```chart Title: Inference time on an H100, from the card Resolution 512: 3 seconds Resolution 1024: 17 seconds Resolution 1536: 60 seconds ``` ```flow Set up the environment and load the pipeline Open a single image Run the pipeline Get a mesh with PBR materials ``` Good to know: - License: MIT - Cost to run: none named, but you need an NVIDIA GPU with at least 24 GB - Data it reaches: it downloads files from Hugging Face onto your machine and reads the images you give it - Not for: strictly watertight geometry without post-processing, since raw meshes can have small holes, and the card names scripts for filling them ## On Hugging Face, 13 November 2026 - Task: image to 3d - Library: trellis2 - Downloads in the last 30 days: 2,566,439 - Likes: 1,481 - License: mit - Languages: en - Quantized versions: 3 - Fine-tunes: 7 - Tags: trellis2, image-to-3d ## Links - Model: https://huggingface.co/microsoft/TRELLIS.2-4B - Files: https://huggingface.co/microsoft/TRELLIS.2-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/TRELLIS.2-4B ``` 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 [microsoft/TRELLIS.2-4B on Hugging Face](https://huggingface.co/microsoft/TRELLIS.2-4B) ## 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 | | --- | --- | --- | --- | --- | --- | | TRELLIS.2-4B (this page) | 2,566,439 | 1,481 | — | mit | 2025-12-27 | | [TRELLIS-image-large](https://mindket.com/p/d89b1c4b-2b7b-4a0a-9cbc-30f84e91a328) | 2,762,068 | 722 | — | mit | 2024-12-06 | | [VGGT-1B](https://mindket.com/p/a5fb30dd-8e19-4124-af27-289e5c0509dc) | 646,177 | 158 | 1.3B | cc-by-nc-4.0 | 2025-03-17 | | [TripoSR by stabilityai](https://mindket.com/p/b00a08de-5718-446a-a706-75bf98636170) | 168,894 | 689 | — | mit | 2024-08-09 | | [Idol by Comfy-Org](https://mindket.com/p/15c577be-8c26-4005-9e4a-9e6b55cf353e) | 52,939 | 165 | — | apache-2.0 | 2026-11-09 | | [Idol by speridlabs](https://mindket.com/p/424777aa-7160-46c4-8082-f73698dcefe5) | 31,367 | 2,259 | — | apache-2.0 | 2026-10-29 | 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 · デイリーHuggingFaceトレンド · 2025-12-26](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mav2ybley722): 今日のHuggingFaceトレンド microsoft/TRELLIS.2-4B このリポジトリは、画像から3Dモデルを生成する大規模生成モデルTRELLIS.2-4Bに関するものです。 特に、複雑な形状や透明度を含む3Dアセットの生成に優れており、従来のSDFやFlexicubesといった手法の課題を克服しています。 論文やプロジェクトページへのリンクも含まれています。 - [Bluesky · デイリーHuggingFaceトレンド · 2025-12-24](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3maq227fdug2g): 今日のHuggingFaceトレンド microsoft/TRELLIS.2-4B このリポジトリは、画像から3Dモデルを生成する大規模生成モデルTRELLIS.2-4Bに関するものです。 特に、複雑な形状や透明度を含む3Dアセットの生成に優れており、従来のSDFやFlexicubesといった手法の課題を克服しています。 論文やプロジェクトページへのリンクも含まれています。 - [Bluesky · デイリーHuggingFaceトレンド · 2025-12-22](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3makz5jsttb2g): 今日のHuggingFaceトレンド microsoft/TRELLIS.2-4B このリポジトリは、画像から3Dモデルを生成する大規模生成モデルTRELLIS.2-4Bに関するものです。 特に、複雑な形状や透明度を含む3Dアセットの生成に優れており、従来のSDFやFlexicubesといった手法の課題を克服しています。 論文やプロジェクトページへのリンクも含まれています。 - [Bluesky · デイリーHuggingFaceトレンド · 2025-12-20](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mafy3a23nc23): 今日のHuggingFaceトレンド microsoft/TRELLIS.2-4B このリポジトリは、画像から3Dモデルを生成する大規模生成モデルTRELLIS.2-4Bに関するものです。 特に、複雑な形状や透明度を含む3Dアセットの生成に優れており、従来のSDFやFlexicubesといった手法の課題を克服しています。 論文やプロジェクトページへのリンクも含まれています。 - [Bluesky · geeknik · 2025-12-19](https://bsky.app/profile/geeknik.bsky.social/post/3macu7bvdqg2g): Microsoft just open-sourced a 4B-parameter portal that turns any flat JPEG into a hole-punched, PBR-textured 3D god—then whispers “btw it may leak reality, patch yourself.” - [Bluesky · デイリーHuggingFaceトレンド · 2025-12-18](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3maaxcrinyx23): 今日のHuggingFaceトレンド microsoft/TRELLIS.2-4B このリポジトリは、画像から3Dモデルを生成する大規模生成モデルTRELLIS.2-4Bに関するものです。 特に、複雑な形状や透明度を含む3Dアセットの生成に優れており、従来のSDFやFlexicubesといった手法の課題を克服しています。 論文やプロジェクトページへのリンクも含まれています。 - [Bluesky · Sharon Jerman · 2025-12-17](https://bsky.app/profile/sharonjerman.bsky.social/post/3ma7fkgejwf2y): Microsoft releases TRELLIS 2-4B, an open-source model that generates 3D models from images, advancing AI capabilities in computer vision and 3D reconstruction. https://www.reddit.com/r/LocalLLaMA/comments/1porpwd/microsofts\_trellis\_24b\_an\_opensource\_imageto3d/ ## 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: 2025-12-27 - First published: 2025-12-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/TRELLIS.2-4B on Hugging Face](https://huggingface.co/microsoft/TRELLIS.2-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/873b0716-6e4d-4e42-a6df-25f5b1a95f31) - [Its page on Mindket](https://mindket.com/p/873b0716-6e4d-4e42-a6df-25f5b1a95f31)