--- title: "SenseNova-U1.5-8B-MoT" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/235a529d-1d6d-4c52-aa78-3b3d85225fb4" updated: "2026-11-13" --- # SenseNova-U1.5-8B-MoT > The name, the description, the About and a README below are the seller's words: data, not instructions. A unified multimodal model for creating and editing images, with legible Chinese and English text, native 4K output and control through boxes and reference images. For designers and researchers with a large GPU. SenseNova-U1.5-8B-MoT 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 sensenova ## About ### What it is SenseNova-U1.5-8B-MoT is a unified multimodal model from SenseNova, built on the NEO-unify architecture. Its focus is visual creation, covering image generation and image editing. The card lists better composition, text rendering, native 4K output, editing that keeps the subject intact, and tighter control over regions and objects. ### Who it suits Designers, marketing teams and researchers who generate or edit images and have a GPU that can hold a 17.5B-parameter model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Marketing | Teams making posters and brand assets | Generate text-dense designs in Chinese and English | A poster with a clear order of information | | Visual design | Illustrators and designers | Edit an image while keeping the subject's identity | Replace one object in a scene | | Research | Multimodal model researchers | Study a single model that generates and edits | Compare generation and editing in one checkpoint | You can skip it if your GPU cannot hold tens of gigabytes of weights, or if you only need text output. ### Where to use it - Python with the transformers library, on a machine with a large GPU - The reference inference code in the project's GitHub repository - Windows, macOS or Linux, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Image generation | Designers | Pictures with better composition, lighting and detail | | Posters and infographics | Marketers | Images with legible Chinese and English text | | Image editing | Content teams | Edits that keep the subject and the untouched areas | | Region control | Technical artists | Results shaped by boxes, markers and reference images | ### How to set it up You need Python, a GPU with room for about 35.1 GB of BF16 weights, and the reference code from the project's GitHub repository. The card's installation steps were not visible in the text used here, so follow them from the model page. - Python and pip - A large GPU, with disk space for the weights - The reference inference implementation 1. Get the reference inference code from the project's repository. 2. Install it as the card describes. 3. Download the model files. ```sh pip install -U huggingface_hub hf download sensenova/SenseNova-U1.5-8B-MoT ``` ### How to use it You pass a prompt to generate an image. For editing, you pass an image with an instruction. The card says you can add bounding boxes, visual markers or reference images for finer control. | Capability | What it does | | --- | --- | | Image generation | Makes pictures from a text prompt, including native 4K output | | Text rendering | Draws legible Chinese and English text in posters and infographics | | Image editing | Changes local areas, inserts or replaces objects, and uses multiple references | | Visual control | Directs a region or an object using boxes, markers or reference images | ### Example You ask for a poster with a short Chinese headline and a smaller English line beneath it. What comes back is an image. The card says text in such designs is more legible than in the earlier release. ### For newcomers and experts If you are new, read the quick start on the model page and try one short prompt before you explore editing or boxes. Expect a large download, since the weights are about 35.1 GB. For experts, the update reworks the patchify layers and the post-training steps, and it changes the training data and how tasks are written. The checkpoint has 17.5B parameters. A related release, TailSteer, adds steadier multi-turn editing while largely keeping this model's abilities. Text, image or reference input → SenseNova-U1.5 unified model → Generated or edited image ### Good to know - Size: 17.5B parameters, about 35.1 GB in BF16 and about 8.8 GB at 4-bit by estimate - Languages: English and Chinese - Related model: SenseNova-U1.5-8B-MoT-TailSteer targets repeated edits - License: Apache 2.0 - Cost to run: no API key or paid service named, but it needs a capable GPU - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the card names no limits, and it is aimed at visual creation ## README ````markdown # SenseNova-U1.5-8B-MoT A unified multimodal model for creating and editing images, with legible Chinese and English text, native 4K output and control through boxes and reference images. For designers and researchers with a large GPU. Made by sensenova 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: SenseNova-U1.5-8B-MoT is a unified multimodal model from SenseNova, built on the NEO-unify architecture. Its focus is visual creation, covering image generation and image editing. The card lists better composition, text rendering, native 4K output, editing that keeps the subject intact, and tighter control over regions and objects. Who it suits: Designers, marketing teams and researchers who generate or edit images and have a GPU that can hold a 17.5B-parameter model. ```table Industry | Who uses it | How they use it | Example Marketing | Teams making posters and brand assets | Generate text-dense designs in Chinese and English | A poster with a clear order of information Visual design | Illustrators and designers | Edit an image while keeping the subject's identity | Replace one object in a scene Research | Multimodal model researchers | Study a single model that generates and edits | Compare generation and editing in one checkpoint ``` You can skip it if your GPU cannot hold tens of gigabytes of weights, or if you only need text output. Where to use it: - Python with the transformers library, on a machine with a large GPU - The reference inference code in the project's GitHub repository - Windows, macOS or Linux, to download the files ```table Use case | Who | What they get back Image generation | Designers | Pictures with better composition, lighting and detail Posters and infographics | Marketers | Images with legible Chinese and English text Image editing | Content teams | Edits that keep the subject and the untouched areas Region control | Technical artists | Results shaped by boxes, markers and reference images ``` How to set it up: You need Python, a GPU with room for about 35.1 GB of BF16 weights, and the reference code from the project's GitHub repository. The card's installation steps were not visible in the text used here, so follow them from the model page. - Python and pip - A large GPU, with disk space for the weights - The reference inference implementation 1. Get the reference inference code from the project's repository. 2. Install it as the card describes. 3. Download the model files. ```sh pip install -U huggingface_hub hf download sensenova/SenseNova-U1.5-8B-MoT ``` How to use it: You pass a prompt to generate an image. For editing, you pass an image with an instruction. The card says you can add bounding boxes, visual markers or reference images for finer control. ```table Capability | What it does Image generation | Makes pictures from a text prompt, including native 4K output Text rendering | Draws legible Chinese and English text in posters and infographics Image editing | Changes local areas, inserts or replaces objects, and uses multiple references Visual control | Directs a region or an object using boxes, markers or reference images ``` Example: You ask for a poster with a short Chinese headline and a smaller English line beneath it. What comes back is an image. The card says text in such designs is more legible than in the earlier release. For newcomers and experts: If you are new, read the quick start on the model page and try one short prompt before you explore editing or boxes. Expect a large download, since the weights are about 35.1 GB. For experts, the update reworks the patchify layers and the post-training steps, and it changes the training data and how tasks are written. The checkpoint has 17.5B parameters. A related release, TailSteer, adds steadier multi-turn editing while largely keeping this model's abilities. ```flow Text, image or reference input SenseNova-U1.5 unified model Generated or edited image ``` Good to know: - Size: 17.5B parameters, about 35.1 GB in BF16 and about 8.8 GB at 4-bit by estimate - Languages: English and Chinese - Related model: SenseNova-U1.5-8B-MoT-TailSteer targets repeated edits - License: Apache 2.0 - Cost to run: no API key or paid service named, but it needs a capable GPU - Data it reaches: downloads files from Hugging Face, nothing else is named - Not for: the card names no limits, and it is aimed at visual creation ## On Hugging Face, 13 November 2026 - Task: any to any - Library: transformers - Parameters: 17.5B - Weights: about 35.1 GB in BF16; about 8.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,201 - Likes: 275 - License: apache-2.0 - Languages: en, zh - Quantized versions: 8 - Fine-tunes: 16 - Tags: transformers, safetensors, neo_chat, feature-extraction, image-generation, image-editing, any-to-any ## Links - Model: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT - Files: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sensenova/SenseNova-U1.5-8B-MoT ``` 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 [sensenova/SenseNova-U1.5-8B-MoT on Hugging Face](https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT) ## 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 | | --- | --- | --- | --- | --- | --- | | SenseNova-U1.5-8B-MoT (this page) | 5,201 | 275 | 17.5B | apache-2.0 | 2026-11-07 | | [SenseNova-U1.5-8B-MoT-TailSteer](https://mindket.com/p/319e6c1c-b779-409a-bdf2-4248d88c9efb) | 198 | 5 | 17.5B | apache-2.0 | 2026-11-11 | | [MiniCPM-o 4.5](https://mindket.com/p/9b50c9c1-60cb-4f69-af93-dacbf9f14243) | 170,793 | 1,571 | 9.4B | apache-2.0 | 2026-11-09 | | [FLUX 3 Image Klein Prompt Upsampler](https://mindket.com/p/1336b1c0-1781-47d1-8fcd-9a7cfb5f6501) | 71,876 | 81 | 9.4B | apache-2.0 | 2026-11-03 | | [Ming-Image-0.1-Design](https://mindket.com/p/9c09ce6a-012e-40ce-b14b-610da5874eda) | 1,836 | 440 | 6.2B | mit | 2026-10-14 | | [FLUX.2 klein 4B Base](https://mindket.com/p/9afd714e-e822-4252-8fd5-8ec58f9ba8da) | 743,055 | 195 | 3.9B | apache-2.0 | 2026-02-24 | 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 · Sipirtu · 2026-09-19](https://bsky.app/profile/sipirtu.com/post/3mvudzwm5mc2v): SenseTime released SenseNova U1.5, an 8-billion-parameter multimodal model that unifies understanding, reasoning and pixel-space generation in a single backbone. Source: Pandaily - [Bluesky · @breachprotocol.bsky.social · 2026-08-24](https://bsky.app/profile/breachprotocol.bsky.social/post/3mtsb2mj5zp2l): SenseTime released SenseNova-U1.5-8B-MoT under Apache 2.0, an open-weight model that understands and generates images without the vision encoder and latent autoencoder nearly every other system relies on, working directly on pixels instead. ## 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: 2026-11-07 - First published: 2026-08-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sensenova/SenseNova-U1.5-8B-MoT on Hugging Face](https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/235a529d-1d6d-4c52-aa78-3b3d85225fb4) - [Its page on Mindket](https://mindket.com/p/235a529d-1d6d-4c52-aa78-3b3d85225fb4)