🎨 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#

Image Generation Quality Mode is now available on the xAI API. This model has already powered the generation of over 300 million images on Grok. It brings higher realism, stronger text rendering, and better creative control for business professionals.

Image generation just landed on the xAI API. Pretty cool stuff—developers can now build some wild visuals. Have at it! - API Console: - Developer Docs:

Grok Imagine Image 2.0 just dropped, and it’s a massive quality upgrade for image generation and editing It comes with sharper text, better layouts, precise regional edits, multi-reference editing, background removal, smart resize, and templates Built for real creative work

Grok Imagine Image 2.0 just released and it's a MASSIVE upgrade Image 2.0 brings: • Much better instruction following • Sharper text + significantly better typography • Complex layouts that actually hold together • Precise region editing with the Magic Wand • Segmentation to edit specific parts of an image • One-click background removal • Multi-reference editing with up to 5 images at once • Smart Resize that expands an image into almost any aspect ratio • Much stronger consistency across generations and edits • New ready-made templates for product shots, headshots, e-commerce, game assets, icons, merch and more And the performance is already at the top: Grok Image 2.0 now ranks #2# in the world for BOTH text-to-image generation and image editing on Arena

🔔 Qwen-Image-3.0 is now live on Qwen Cloud! Ranked #1# among Chinese models and #2# among mainstream models in Arena. ai's Text-to-Image Arena @arena, now one API call away. - Up to 4.5k-token prompts: newspapers, storyboards, menus, exam papers in one pass - Text legible down to 10px, near-photographic detail - 12 languages, 100+ art styles, realistic UI simulation - Generation + editing in one model 💰Price: Qwen-Image-3.0 Pro — is best suited for complex layouts, accurate text rendering, and commercial-grade visuals, starting at $0.04 per image. Qwen-Image-3.0 Standard — is ideal for bulk, everyday image generation, starting at just $0.03 per image. Qwen-Image-3.0-Pro & Standard. From prompt to production-ready image. 👉 Try it and get your API key on Qwen Cloud: - Mainstream models = model families with 100K+ average daily MaaS API calls #QwenCloud# #Qwen#

GEAR: 10× faster autoregressive image generation Tencent Hunyuan's new method jointly trains VQ tokenizers and AR generators end-to-end, beating LlamaGen-REPA with a novel dual read-out. All tokenizers are on Hugging Face.

Meet Boogu-Image-0.1 from @BooguTeam , an open-source unified image generation and editing model series from Boogu team. Apache 2.0. 🚀 🤖 🖼️ Photorealistic generation with natural lighting and consistent spatial relationships 📝 Bilingual text rendering (Chinese/English) across posters, UI, brand guidelines, handwriting boards 🎨 Stylized generation: miniature 3D scenes, anime portraits, fantasy visuals, mythological art ✏️ Fine-grained text editing: replace, add, or delete characters with font/color/layout control Research preview. Trained on roughly 10x less data than comparable closed-source systems. Three variants: • Base (dense text rendering, posters, documents) • Turbo (fast generation + photorealism) • Edit (object insertion, replacement, removal, style transfer)