A browser extension enables local image generation using SDXL models via WebGPU, running on the user's GPU without external setups. The tool supports two models: SDXL-Lighting fp16 (7 GB) and a 4-bit version (3.6 GB), with requirements including at least 8 GB VRAM for the full model and a browser with WebGPU support (Chrome/Edge 122+ or latest Firefox).
SDXL Running Locally in Browser on WebGPU, Open-Source
Microsoft releases Mage-Flow, a 4B native-resolution model for image generation and editing
Microsoft has released Mage-Flow, a compact 4B-scale generative stack designed for efficient text-to-image generation and instruction-based image editing. The system achieves state-of-the-art-competitive quality through the co-design of a lightweight tokenizer, Mage-VAE, and a Native-Resolution Multimodal Diffusion Transformer (NR-MMDiT).
SenseNova releases open source SenseNova-U1-8b-MoT models for infographic design
SenseNova has released the SenseNova-U1-8b-MoT series, a set of Mixture of Transformers models designed for generating and editing dense infographics. The latest release, Infographic V2, serves as a 50-step base model that is licensed under Apache 2.
PP-OCRv6 Released on Hugging Face with 50-Language Support
PP-OCRv6, a new optical character recognition model, is now available on Hugging Face. It supports 50 languages and scales from 1.5 million to 34.5 million parameters, offering improved accuracy and efficiency across diverse languages.
Best open-source vision model runnable on RTX 6000 Pro
The user is seeking the current best open-source vision model that can run on an RTX 6000 Pro for OCR and classification of historical scanned documents. They note Gemma 4 31B performs well and is better than Qwen 3.6's vision encoder, asking for recommendations beyond this model.
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning
DreamReasoner-8B is an open-source block diffusion model that demonstrates strong long-chain-of-thought reasoning. A systematic study shows that small training block sizes preserve reasoning effectiveness, while large sizes degrade performance. Block-size curriculum learning gradually transitions training from fine to coarse blocks, enabling robust and generalizable reasoning across inference settings, with results competitive to Qwen3-8B on mathematical and code benchmarks.