SenseNova-U1-MoT-Infographic

📣 Updated News

• [2026.07.16] Release SenseNova-U1-8B-MoT-Infographic-V3 📊, designed for integrated infographic generation and editing. It retains strong text-to-image (T2I) capabilities while significantly enhancing infographic editing, supporting localized text and content editing, global style editing, and global layout editing. See ✨ U1 Infographic Model Series for model details and benchmark results.

• [2026.06.29] Release SenseNova-U1-8B-MoT-Infographic-V2 📊, an upgraded infographic model with improved dense small-text rendering with sharper text edges, stronger complex dense-layout generation, and better overall visual aesthetics and harmony, plus a fix for the black-background issue. Model details and visual examples are available in ✨ U1 Infographic Model Series.

• [2026.06.12] Release SenseNova-U1-8B-MoT-Infographic-LoRA-8step-V1.0 for faster infographic generation. Please see the example script.

• [2026.06.11] Release SenseNova-U1-8B-MoT-Interleaved 📖, specially optimized for interleaved image-text generation, with notably improved narrative coherence, character and style consistency, and text-image alignment in multi-page content.

• [2026.05.21] Release the full-parameter fine-tuning training code for SenseNova-U1.

• [2026.05.15] Release SenseNova-U1-8B-MoT-Infographic 📊 model for improved infographic generation. See U1 Infographic Model Series for details, and ✨ Infographic Showcases for 100 generated examples.

• [2026.05.10] Release 🔥SenseNova-U1 Technical Report🔥 and the weights for SenseNova-U1-A3B-MoT-SFT & 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. GGUF weights for SenseNova-U1-8B-MoT-Merger are available at 🤗 smthem/SenseNova-U1-8B-MoT-Merger-gguf — many thanks to @smthem for contributing the quantized weights.

• [2026.05.06] Release SenseNova-U1-8B-MoT-LoRA-8step-V1.0. Please see the example script.

• [2026.04.30] Release the preview version of the 8-step inference model 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). To test this model, you can use the inference scripts, 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 and SenseNova-U1-8B-MoT.

• [2026.04.27] Initial release of the inference code 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.

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, 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-V3 is the recommended release for an integrated infographic generation-and-editing workflow. It retains strong text-to-image (T2I) capability while adding image editing (IT2I) within the same unified model.

• Benchmark Performance: V3 reaches an Overall Total of 50.23 on Qwen-Image-Bench, improving by 2.23 over V2's 48.00. It also achieves an Average of 5.89 on WeEdit and an overall score of 7.89 on GEdit-Bench, ranking first among the open-source models included in both editing evaluations.
• Generation and Editing Quality: V3 supports b