Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

• URL: http://arxiv.org/abs/2607.13125v2
• Date: Sat, 18 Jul 2026 17:28:17 GMT
• Title: Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
• Abstract summary: We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family.<n>It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Chinese-English) text rendering.
• Score: 110.65684175617822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Chinese-English) text rendering. Closed-source multimodal systems like Nano-Banana-Pro and GPT-Image-2 achieve strong performance through system-level integration rather than a single model, yet their internal practices remain largely undisclosed. In this work, we demonstrate that strengthening the understanding capability of the system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation and editing performance even under highly constrained compute budgets. Comprehensive evaluations show that Boogu-Image-0.1 consistently matches or surpasses other open-source models across standard benchmarks, and achieves results approaching leading closed-source systems. Notably, this is accomplished with only 208.62 million unique images. The base model's theoretical training cost is only approximately \$400K. We share practical discussions that we believe are valuable to the broader research community, and release weights, code, and recipes under Apache 2.0 to advance the open ecosystem for unified multimodal understanding and generation. Our code is available here: https://github.com/Boogu-Project/Boogu-Image.

Related papers

• DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing [67.77471070868852]
  DeepGen 1.0 is a lightweight 5B unified model for image generation and editing.<n>It is trained on only 50M samples, surpassing the 80B HunyuanImage by 28% on WISE and the 27B Qwen-Image-Edit by 37% on UniREditBench.<n>By open-sourcing our training code, weights, and datasets, we provide an efficient, high-performance alternative to democratize unified multimodal research.
  arXiv  Detail & Related papers  (2026-02-12T17:44:24Z)
• DuoGen: Towards General Purpose Interleaved Multimodal Generation [65.13479486098419]
  DuoGen is a general-purpose interleaved generation framework that addresses data curation, architecture design, and evaluation.<n>We build a large-scale, high-quality instruction-tuning dataset by combining multimodal conversations rewritten from curated raw websites.<n>A two-stage decoupled strategy first instruction-tunes the MLLM, then aligns DiT with it using curated interleaved image-text sequences.
  arXiv  Detail & Related papers  (2026-01-31T04:35:15Z)
• LightBagel: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation [48.02842078521973]
  We show that competitive performance can be obtained far more efficiently by strategically fusing publicly available models specialized for either generation or understanding.<n>Our key design is to retain the original blocks while additionally interleaving multimodal self-attention blocks throughout the networks.<n>By training with only 35B tokens, this approach achieves strong results across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-10-27T02:59:57Z)
• Query-Kontext: An Unified Multimodal Model for Image Generation and Editing [53.765351127477224]
  Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I)<n>We introduce Query-Kontext, a novel approach that bridges the VLM and diffusion model via a multimodal kontext'' composed of semantic cues and coarse-grained image conditions encoded from multimodal inputs.<n> Experiments show that our approach matches strong unified baselines and even outperforms task-specific state-of-the-art methods in several cases.
  arXiv  Detail & Related papers  (2025-09-30T17:59:46Z)
• MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models [30.494968865008513]
  Recent text-to-image models struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex image generation.<n>We propose MENTOR, a novel framework for efficient Multimodal-conditioned Tuning for Autoregressive multimodal image generation.<n>Our method delivers superior image reconstruction fidelity, broad task adaptability, and improved training efficiency compared to diffusion-based methods.
  arXiv  Detail & Related papers  (2025-07-13T10:52:59Z)
• OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation [44.34131932968147]
  We present OpenUni, a lightweight, open-source baseline for unifying multimodal understanding and generation.<n>Inspired by prevailing practices in unified model learning, we adopt an efficient training strategy that minimizes the training complexity and overhead.<n>We demonstrate that OpenUni can: 1) generate high-quality and instruction-aligned images, and 2) achieve exceptional performance on standard benchmarks such as GenEval, DPG- Bench, and WISE, with only 1.1B and 3.1B activated parameters.
  arXiv  Detail & Related papers  (2025-05-29T17:09:44Z)
• Emerging Properties in Unified Multimodal Pretraining [32.856334401494145]
  We introduce BAGEL, an open-source foundational model that supports multimodal understanding and generation.<n>BAGEL is a unified, decoder-only model pretrained on trillions of tokens curated from large-scale interleaved text, image, video, and web data.<n>It significantly outperforms open-source unified models in both multimodal generation and understanding across standard benchmarks.
  arXiv  Detail & Related papers  (2025-05-20T17:59:30Z)
• Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation [54.588082888166504]
  We present Mogao, a unified framework that enables interleaved multi-modal generation through a causal approach.<n>Mogoo integrates a set of key technical improvements in architecture design, including a deep-fusion design, dual vision encoders, interleaved rotary position embeddings, and multi-modal classifier-free guidance.<n>Experiments show that Mogao achieves state-of-the-art performance in multi-modal understanding and text-to-image generation, but also excels in producing high-quality, coherent interleaved outputs.
  arXiv  Detail & Related papers  (2025-05-08T17:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.