🎨 A text-to-image model trained entirely from scratch with a fully open recipe, closing in on top-tier model performance. Title: LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes URL: 🖼️ Overview LLaDA-Image trains a 6B-parameter Diffusion Transformer from scratch and pairs it with a dLLM-based vision-language understanding module in one unified generation-and-editing model. A TwinFlow-distilled Turbo version also ships, cutting inference down to just 2-4 sampling steps. 🧩 Problem Solved High-quality image generation has traditionally demanded massive paired image-text datasets, while inconsistent caption quality and the difficulty of unifying generation and editing in a single model remained open challenges. 🛠️ Methodology & Proposed Approach The recipe centers on image-only pretraining: over 90% of the 220M training samples are image-only, with real images making up 98% of generation-stage data and over 70% even during supervised fine-tuning — a deliberately "real-data-dominant" strategy. A parameter-free RMSNorm stabilizes long training runs, and an editing architecture conditions on reference images through both a semantic path and a pixel path. 📊 Use Cases / Experimental Results LLaDA-Image scores 53.53 on the English Qwen-Image-Bench track and 53.38 on Chinese, beating the leading open-source Z-Image Turbo (51.66 / 52.71). It also tops the long-text-rendering CVTG-2K benchmark with 0.875 word accuracy. On the flip side, GenEval's counting sub-task sits at just 0.53, a clearly flagged area for improvement. #ImageGeneration# #DiffusionModels#