Qwen Image โ The Latest Image Generation Model๐ฅ
Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source. [ ๐๐ ๐๐-๐ธ๐๐๐๐ : Qwen/Qwen-Image ]
Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery.
Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.
. . . To know more about it, visit the model card of the respective model. !!
XBai o4 claims to beat Claude Opus 4 and o3-mini, and they provide verifiable proof. My skepticism circuits overloaded, but my local AI FOMO module screamed louder. I've thrown this 33B monoblock LLM onto a single GPU and used Roo Code for someโฆ letโs call it โvibe testingโ. Itโs terrifyingly competent. As an architect, itโs the best open-weight model Iโve touched this side of 2025.