Technical Lead

MegaStyle trains FLUX on 1.4M styled images. Researchers from Tongji University, Tencent, and five other institutions released MegaStyle, a 1.4-million image dataset for style transfer alongside a FLUX-based model. Researchers from Tongji University, Tencent, and five other institutions released MegaStyle, a 1.4-million image dataset purpose-built for style transfer alongside a FLUX-based model that applies artistic styles to new images. The dataset provides 170,000 style prompts combined with 400,000 content prompts, creating up to 68 billion potential training pairs. What happened. MegaStyle addresses a core problem in AI style transfer: existing datasets are too small, inconsistent in style labeling, or lack diversity. The team built a scalable data curation pipeline that uses text-to-image models to generate images matching specific style descriptions, drawing source material from JourneyDB (1M images), WikiArt (80K), and LAION-Aesthetics (1M). The project ships two tools. MegaStyle-FLUX is a diffusion model trained on the full dataset that takes a reference style image and applies it to new content. MegaStyle-Encoder is a style-specialized image encoder fine-tuned with contrastive learning for measuring style similarity and retrieving matching styles. Why it matters. Style transfer has been possible for years, but quality and consistency have lagged behind other generative AI capabilities. MegaStyle's approach of building a massive, structured dataset first and then training models on it produces measurably better results. The encoder achieves 87.26 mAP@1 on the StyleRetrieval benchmark, with 97.61 Recall@10 for finding similar styles. For designers and illustrators, the FLUX-based model means applying an artistic style from one reference image to new content with higher fidelity than current alternatives. The encoder adds the ability to search large image collections by visual style rather than just by content or keywords. Key details. * Dataset: 1.4M images across 170K style categories, with intra-style consistency and inter-style diversity verified at scale * MegaStyle-FLUX: Concatenates reference style tokens with noisy image tokens and text inputs in the MM-DiT backbone for style-conditioned generation * MegaStyle-Encoder: Style-supervised contrastive learning (SSCL) produces embeddings that capture style independently from content * Contributors: Tongji University, Tencent, NTU Singapore, HKUST, Fuzhou University, HKU, NUS What to do next. The full research paper details the dataset construction pipeline and benchmark results. The project page provides visual comparisons against existing style transfer methods. Creators working with FLUX-based workflows should watch for code and model weight releases, which would enable integration into existing image generation pipelines.

China Ruyi enters strategic content and gaming agreements with Tencent. April 7, 2026 at 12:20 PM UTC - By FilingReader AI China Ruyi Holdings Limited announced that it entered into two major framework agreements with Tencent Computer on 7 April 2026. The first, the 2026 Game Cooperation Framework Agreement, facilitates collaboration in the gaming field, including exclusive distribution, joint operations, and marketing services. The second deal, the Drama Series and Movies Framework Agreement, allows the group to license broadcasting rights for original content and co-produce made-to-order series for Tencent's platforms. Both agreements are set for a term ending 31 December 2028. Financial projections for these partnerships indicate significant scaling of operations. For the gaming agreement, the proposed annual caps for payments to Tencent are set to rise from RMB 700 million in 2026 to RMB 2,100 million by 2028. Conversely, the annual caps for receivables from Tencent under the gaming deal are projected at a steady RMB 1,800 million per year. The drama and movie licensing agreement carries a fixed annual cap of RMB 800 million for each of the three years. As Tencent Holdings indirectly holds approximately 15.37% of China Ruyi, these deals are classified as continuing connected transactions. Consequently, the agreements require approval from independent shareholders at a forthcoming general meeting. To ensure fair pricing, the group will utilize a dedicated team to benchmark terms against at least three independent third parties. This report was generated by FilingReader's AI system from regulatory filings and company disclosures. To request a correction, contact [email protected]