mlsanityData to 14 Oct 2026

oboro: Text-to-Image Synthesis on Limited Data using Flow-based Diffusion Transformer with MMH Attention

arXiv 2511.08168 ↗First posted 11 Nov 2025cs.AIcited by 0

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

This project was conducted as a 2nd-term adopted project of the "Post-5G Information and Communication System Infrastructure Enhancement R&D Project Development of Competitive Generative AI Foundation Models (GENIAC)," a business of the Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO). To address challenges such as labor shortages in Japan's anime production industry, this project aims to develop an image generation model from scratch. This report details the technical specifications of the developed image generation model, "oboro:." We have developed "oboro:," a new image generation model built from scratch, using only copyright-cleared images for training. A key characteristic is its architecture, designed to generate high-quality images even from limited datasets. The foundation model weights and inference code are publicly available alongside this report. This project marks the first release of an open-source, commercially-oriented image generation AI fully developed in Japan. AiHUB originated from the OSS community; by maintaining transparency in our development process, we aim to contribute to Japan's AI researcher and engineer community and promote the domestic AI development ecosystem.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 33 of its 52 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

33 of 52 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space17 Jun
  2. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment17 Oct
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Models10 Feb

Other references · 30

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  7. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  8. 2022Scalable Diffusion Models with Transformerscited by 6,054
  9. 2022Generative Adversarial Networkscited by 5,627
  10. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  11. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  12. 2021Zero-Shot Text-to-Image Generationcited by 4,288
18 more
  1. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  2. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  3. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
  4. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  5. 2020GLU Variants Improve Transformercited by 1,804
  6. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
  7. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  8. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 879
  9. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
  10. 2023TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answeringcited by 428
  11. 2024Rotary Position Embedding for Vision Transformercited by 236
  12. 2024The Road Less Scheduledcited by 163
  13. 2023Matcha-TTS: A fast TTS architecture with conditional flow matchingcited by 145
  14. 2024Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiTcited by 132
  15. 2025Lumina-Image 2.0: A Unified and Efficient Image Generative Frameworkcited by 96
  16. 2021NormFormer: Improved Transformer Pretraining with Extra Normalizationcited by 83
  17. 2025Encoder-Decoder Gemma: Improving the Quality-Efficiency Trade-Off via Adaptationcited by 28
  18. 2025ZClip: Adaptive Spike Mitigation for LLM Pre-Trainingcited by 10

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  3. 2023The Challenges of Image Generation Models in Generating Multi-Component Images
  4. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications
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  6. 2025IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models
  7. 2025EliGen: Entity-Level Controlled Image Generation with Regional Attention
  8. 2022Implementing and Experimenting with Diffusion Models for Text-to-Image Generation