SenseNova U1 8B MoT Infographic

--- license: apache-2.0 library_name: transformers pipeline_tag: any-to-any tags: - multimodal - text-to-image - image-to-text - image-editing - interleaved-generation - custom_code --- # SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture English | 简体中文 --> ## 📣 Updated News - `[2026.05.15]` Release [SenseNova-U1-8B-MoT-Infographic 📊](https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-Infographic), for improved infographic generation. See [U1 Infographic Model](docs/u1_infographic_model.md) for details, and [✨ Infographic Showcases ](docs/u1_infographic_showcases.md) for 100 generated examples. ✨ Click to expand older news - `[2026.05.10]` Release [🔥SenseNova-U1 Technical Report🔥](https://github.com/OpenSenseNova/SenseNova-U1/blob/main/docs/pdf/SenseNOVA_U1.pdf) and the weights for [SenseNova-U1-A3B-MoT-SFT](https://huggingface.co/sensenova/SenseNova-U1-A3B-MoT-SFT) & [SenseNova-U1-A3B-MoT](https://huggingface.co/sensenova/SenseNova-U1-A3B-MoT). - `[2026.05.08]` Add **GGUF quantized checkpoints** and **layer-offload VRAM modes** for low-VRAM single-GPU inference. See [Memory-efficient inference](#-memory-efficient-inference-gguf--vram-modes). GGUF weights for `SenseNova-U1-8B-MoT-Merger` are available at [🤗 smthem/SenseNova-U1-8B-MoT-Merger-gguf](https://huggingface.co/smthem/SenseNova-U1-8B-MoT-Merger-gguf) — many thanks to [@smthem](https://github.com/smthem) for contributing the quantized weights. - `[2026.05.06]` Release [SenseNova-U1-8B-MoT-LoRA-8step-V1.0](https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-LoRAs/blob/main/SenseNova-U1-8B-MoT-LoRA-8step-V1.0.safetensors). Please see the [example script](docs/base_vs_distill.md#run-base-and-distilled-model). - `[2026.04.30]` Release the preview version of the 8-step inference model [SenseNova-U1-8B-MoT-8step-preview](https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-8step-preview). In most cases, the image generation quality of this model closely matches that of the base model (see [comparison and existing issues](docs/base_vs_distill.md)). To test this model, you can use the [inference scripts](examples/README.md), but with the following parameters: ```--cfg_scale 1.0 --num_steps 8``` . - `[2026.04.27]` Initial release of the weights for [SenseNova-U1-8B-MoT-SFT](https://huggingface.co/sensenova/SenseNova-U1-8B-MoT-SFT) and [SenseNova-U1-8B-MoT](https://huggingface.co/sensenova/SenseNova-U1-8B-MoT). - `[2026.04.27]` Initial release of the [inference code](https://github.com/OpenSenseNova/SenseNova-U1/blob/main/examples/README.md) for SenseNova-U1. ## 🌟 Overview 🚀 **SenseNova U1** is a new series of native multimodal models that unifies multimodal understanding, reasoning, and generation within a monolithic architecture. It marks a fundamental paradigm shift in multimodal AI: **from modality integration to true unification**. Rather than relying on adapters to translate between modalities, SenseNova U1 models think-and-act across language and vision natively. ✨ Click to expand architecture details Unifying visual understanding and generation in an end-to-end architecture from pixel to word opens tremendous possibilities, enabling highly efficient and strong understanding, generation, and interleaved reasoning in a natively multimodal manner. #### 🏗️ *Key Pillars:* At the core of SenseNova U1 is **[NEO-unify](https://huggingface.co/blog/sensenova/neo-unify)**, a novel architecture designed from the first principles for multimodal AI: *It eliminates both Visual Encoder (VE) and Variational Auto-Encoder (VAE) where pixel-word information are inherently and deeply correlated.* Several important features are as follows: - 🔗 Model language and visual information end-to-end as a unified compound. - 🖼️ Preserve semantic richness while maintaining pixel-level visual fidelity. - 🧠 Reason across modalities with high efficiency & minimal conflict via native MoTs. Powered by this new core architecture, **SenseNova U1-8B-MoT-Infographic** (infographic-specifically enhanced version of SenseNova U1-8B-MoT) delivers exceptional efficiency and state-of-the-art infographic performance: Generation Latency vs. Averaging Performance on Infographic Benchmarks (BizGenEval, IGenBench). Generation Latency vs. Averaging Performance on general benchmarks (OneIG, LongText, CVTG). - **Benchmark Performance:** Compared with the base **SenseNova-U1-8B-MoT** model, BizGenEval hard/easy increased from **39.8 / 61.1** to **46.6 / 65.4** (**+6.8 / +4.3 points**), and IGenBench Q-ACC/I-ACC increased from **51.3 / 4.2** to **69.5 / 17.0** (**+18.2 / +12.8 points**), while maintaining robust visual understanding capabilities without substantial degradation. - **Generation Quality:** The model produces complex infographics across 100+ styles and layouts, with improved visual aesthetics and text rendering — including dense small text such as arXiv-style pages. ✨ Click to expand Benchmark Details | Model | BizGenEval Avg. (hard / easy) ↑ | IGenBench Q-ACC↑ | IGenBench I-ACC ↑ | OneIG(EN) ↑ | OneIG(ZH) ↑ | | --- | ---: | ---: | ---: | ---: | ---: | | ***Commercial Models*** | | | Nano-Banana-Pro | 76.7 / 93.7 | 90.6 | 48.8 | 58.1 | 56.8 | | Nano-Banana-2.0 | 68.5 / 92.5 | 85.6 | 34.4 | 54.0 | 54.9 | | GPT-Image-1.5 | 35.9 / 81.6 | 55.0 | 12.0 | - | - | | Qwen-Image-2.0 | 45.5 / 65.8 | 50.0 | 3.0 | 54.1 | 50.9 | | Seedream-4.5 | 30.1 / 66.2 | 61.0 | 6.0 | 56.4 | 55.0 | | ***Open-source Models*** | | | **SenseNova-U1-8B-MoT-Infographic** | **46.6 / 65.4** | **69.5** | **17.0** | **55.6** | **53.3** | | **SenseNova-U1-8B-MoT** | 39.8 / 61.1 | 51.3 | 4.2 | 54.5 | 53.8 | | Z-Image | 8.2 / 43.8 | 30.0 | 1.0 | 54.6 | 53.5 | | Qwen-Image-2512 | 6.3 / 41.0 | 32.2 | 1.0 | 53.0 | 51.5 | | Qwen-Image | 2.8 / 23.8 | 36.0 | 0.0 | 53.9 | 54.8 | | Bagel | 2.0 / 3.7 | 4.9 | 0.0 | 36.1 | 37.0 | IGenBench scores are reported as percentages. Models are ordered by the arithmetic mean of BizGenEval hard, BizGenEval easy, IGenBench Q-ACC, and IGenBench I-ACC within the commercial and open-source groups separately. OneIG is included as a general generation reference. Full per-category results are intended for the Hugging Face model card. - 📰 **High-density information rendering (Specialized)**: This specific model demonstrates strong capabilities in dense visual communication, generating richly structured layouts for knowledge illustrations, posters, presentations, comics, resumes, and other information-rich formats. - 🏆 **Open-source SoTA**: SenseNova U1 sets a new standard for unified multimodal understanding and generation, achieving state-of-the-art infographic performance among open-source models. ## 🎨 Infographic Showcases > 📸 **More generation samples:** see [✨ Infographic Showcases](docs/u1_infographic_showcases.md). ✨ Click to collapse infographic showcases ## Qualitative Comparison We present a qualitative comparison between the base **SenseNova-U1-8B-MoT** and the fine-tuned **SenseNova-U1-8B-MoT-Infographic** model across five key dimensions: background stability, chart accuracy, text Rendering Accuracy and size appropriateness, arXiv paper rendering quality, and overall layout and content understanding. For the full comparison, please refer to [✨ Comparation Infographic Cases](docs/u1_infographic_model.md). ✨ Click to collapse qualitative comparison ### Background Stability U1-8B-MoT 8B-MoT-Infographic U1-8B-MoT 8B-MoT-Infographic Prompt 该信息图题为“版权视觉概览”,整体采用横向分栏布局,分为上下两个主要部分。上半部分为视觉化概览区,由四个彩色矩形区块并列组成,每个区块通过图标和简短标题传达一个核心概念;下半部分为“【版权基础常识】”详细解释区,包含四个编号条目,对应上半部分的四个主题,提供更详尽的文字说明。 **上半部分:版权视觉概览** 此区域由四个水平排列的彩色方块构成,从左至右依次为浅蓝色、浅黄色、浅绿色和浅紫色,每个方块内含一组图标和下方的中文标题。 1. **第一块(浅蓝色):创作即产生** * **图标**:左侧是一个发光的灯泡,中间是一个带有笔的文档图标,右侧是一个锁头图标,三者之间用箭头连接,表示“创意 → 创作 → 保护”的流程。 * **文字**: * 图标下方有小字“自动保护”。 * 方块底部有大字标题“创作即产生”。 2. **第二块(浅黄色):核心权利** * **图标**:中心是一只手掌向上托举,上方有多个元素围绕:一个带©符号的圆圈、一个喇叭、一堆金币和美元符号、以及多个指向不同方向的箭头,象征权利的多种表现形式和收益。 * **文字**: * 图标下方无额外小字。 * 方块底部有大字标题“核心权利”。 3. **第三块(浅绿色):特定条件平衡** * **图标**:一个天平,左侧托盘上有打开的书本和标有“NEWS”的麦克风,代表“合理使用”;右侧托盘上有一个带锁的文件夹,代表“受控作品”。天平向右侧倾斜。 * **文字**: * 左侧托盘下方标注“合理使用”。 * 右侧托盘下方标注“受控作品”。 * 方块底部有大字标题“特定条件平衡”。 4. **第四块(浅紫色):保护期限** * **图标**:左侧是一个沙漏,中间是一个向右的粗箭头,右侧是一个墓碑(顶部有十字架)。沙漏下方还有一个时钟图标。 * **文字**: * 墓碑旁标注“作者有生之年 + X年”。 * 方块底部有大字标题“保护期限”。 **下半部分:【版权基础常识】** 此区域位于上半部分下方,背景为白色,包含四个独立的文本框,每个文本框都有一个彩色标题栏和下方的详细说明文字,颜色与上半部分对应。 1. **1. 自动获得保护** * **标题栏**:蓝色背景,白色文字“1. 自动获得保护”。 * **正文**:“作品创作完成之时起,即自动享有版权,无需登记(登记主要是举证)。” 2. **2. 核心权利** * **标题栏**:橙黄色背景,白色文字“2. 核心权利”。 * **正文**:“包括人身权(如署名权、修改权)和财产权(如复制权、发行权、信息网络传播权,可许可或转让获利)。” 3. **3. 合理使用** * **标题栏**:绿色背景,白色文字“3. 合理使用”。 * **正文**:“在特定条件下(如教学、新闻报道、个人学习等),可以不经许可、不支付报酬使用,但需指明作者和出处,且不得侵犯其他权利。” 4. **4. 保护期限** * **标题栏**:紫色背景,白色文字“4. 保护期限”。 * **正文**:“一般为作者有生之年加死后50年(中国大陆等多数地区),期限届满后进入公有领域。” **整体风格与数据编码**: 该信息图采用扁平化设计风格,色彩鲜明且分区清晰。通过颜色编码(蓝、黄、绿、紫)将四个主题进行视觉区分,并在上下两部分保持一致。图标作为主要的数据可视化手段,直观地表达了抽象概念。所有文字均为简体中文,内容结构严谨,逻辑清晰,旨在以图文结合的方式普及版权基础知识。 Prompt 该信息图以中文为主要语言,采用横向四格布局,清晰呈现一个品牌从衰落到复兴的四个关键阶段。整体风格为手绘卡通插画,色彩柔和,线条简洁,具有亲和力和叙事性。每个阶段由上方的标题、中间的插图和下方的文字说明三部分构成,通过虚线分隔,结构分明。 第一阶段标题为“1. 曾经的辉煌与没落”,插图描绘了一座破败的城堡,城堡上挂着悲伤的表情,周围散落着皇冠,象征昔日荣耀的消逝;旁边立有标牌“OLD BRAND”,背景中可见大本钟,暗示传统或历史品牌。下方文字说明:“曾经是市场领导者,但未能跟上时代步伐,逐渐被遗忘,面临生存危机。” 第二阶段标题为“2. 创新与重塑”,插图展示四人团队围坐讨论,其中一人指向白板上的绿色叶子标志设计,周围环绕齿轮、灯泡(代表创意)和标牌“NEW IDEAS”。下方文字说明:“进行深度市场调研,重新定位品牌,引入创新设计和数字化策略,重塑核心价值。” 第三阶段标题为“3. 成功翻盘”,插图包含一只浴火重生的凤凰,象征涅槃;右侧是上升趋势的柱状图,下方是一个带有爱心的包裹,代表产品交付;一群欢呼的人群表达喜悦。下方文字说明:“凭借新产品和新形象重获消费者信任,业绩逆势上扬,重新赢得市场份额。” 第四阶段标题为“4. 未来展望”,插图描绘一枚火箭从地球轨道发射升空,周围有星星、云朵和一片绿叶,象征可持续发展;下方横幅写着“FUTURE READY”。下方文字说明:“持续创新,关注可持续发展和用户连接,立志成为更具影响力的未来品牌。” 整个信息图通过视觉隐喻(如城堡、凤凰、火箭)和数据图表(柱状图)结合,生动讲述了一个品牌从危机到复兴的完整故事,强调创新、用户信任和可持续发展的重要性。所有文本均为简体中文,无英文以外的其他语言。 Prompt The infographic titled "College Entrance Pathway Reforce Comparison" presents a structured comparison of key aspects for prospective students in Guangdong, China, aiming to enter college through a specialized entrance examination. The layout is organized as a multi-column table with four main columns: "Content Item / Evaluation Criteria", "Statistics", "Quotes", and "Key Terms". Each row corresponds to a distinct evaluation criterion or step in the preparation process, with visual icons, text, and data points enhancing clarity. The infographic uses a clean, minimalist design with black line art icons on a light beige background. Text is primarily in bold sans-serif font, with headings emphasized for readability. Data is encoded using icons (e.g., graduation cap, calendar, books, target, rocket) to visually represent concepts, while numerical values are explicitly labeled for precision. The first row addresses **Eligibility Criteria**: - In the "Statistics" column, it features an icon of a person checking a map of Guangdong with the text: "Official Eligibility Requirements Confirm if you qualify to register". - The "Quotes" column lists three eligible groups with corresponding icons: "Final-Year Guangdong Junior College Student", "Guangdong Resident Prompt The infographic titled "12-Month Market Performance: US vs. Asia" presents a structured, puzzle-piece-based visual analysis comparing the performance of US and Asian equity markets over a 12-month period. The layout is organized into three main steps, arranged in a central vertical flow with interconnected puzzle pieces, emphasizing a modular, analytical approach to market comparison. The design uses clean black-and-white line art with light blue accents for key sections, icons for visual representation, and clear typography for readability. **Step 1** (top center) introduces the scope of the analysis. It features an illustration of four people examining charts, symbolizing data analysis. To the right, it defines the market indices being compared: - **US Markets**: S&P 500, NASDAQ - **Asian Markets**: Nikkei 225, Hang Seng, KOSPI, CSI 300 It also lists the types of data analyzed: - Trailing Return (represented by a rising bar chart icon) - Average Daily Volume (represented by a stacked bar chart icon) - Top...