Qwen Image 2.1 🤩Viggle-turbo V0.2.1 -5× faster than the 40-step base model end to end, and very competitive with it in quality . -The clearest gap is small, dense text -It does both text-to-image and instruction-driven editing with 1–3 reference images

Qwen-Image-2.1-viggle-turbo v0.2 is out on Hugging Face Text-to-image and image editing in 6 steps about 5× faster than the 40-step Qwen-Image-2.1 model:

Qwen Image 2.1 💡🙂☀️Natural-Exposure-LoRA Designed to transform images with balanced, neutral exposure while preserving natural colors, details, lighting, and overall image consistency.

Qwen3.7-Max and Qwen3.8-Flash from @Alibaba_Qwen are now 40% off on serverless through September 30. Use Max for long-horizon agent work and Flash for high-volume, cost-sensitive workloads. Get started today!

Qwen3.8-2.4T on vLLM: a Pareto frontier spanning 5K total tokens/s/GPU at high throughput and 180 output tokens/s/user at low latency, across tuned PD configurations on @nvidia GB300 NVL72. Workload: 8K input / 1K output. Drawing on lessons from trial and error, we walk through the tuning decisions step by step: budget KV cache, benchmark prefill and decode separately, then choose topologies and MTP settings for each serving target. Deployment configs are included so you can reproduce the results. Great work from the @NVIDIAAI contributors and the vLLM community! Explore the frontier:

Qwen introduces RecreationBench, a benchmark for Hybrid Computer-Use Agents with 250 application-recreation tasks across Ubuntu, macOS, Windows, Android, and Web, spanning domains such as productivity, development, graphics, multimedia, and science. Unlike GUI-only or terminal-only benchmarks, agents must explore a running reference app, recreate it in code, and pass both programmatic tests and VLM-based visual evaluation. The playground is ready. Let’s build! 🚀Dataset:

Qwen’s Image 2.1 released but it come with lots of improvements but just as many caveats. The old Qwen-Image-2512 was a 20B model with a massive: 💾 40.9GB BF16 image transformer But the new Qwen-Image-2.1 comes with … 🧠 7B visual generator 💾 14.2GB BF16 🔥 7.26GB INT8 already available for ComfyUI 👈👀 (Day-1) This little thing can … 🎨 generate AND edit images 🖼️ generate native 2K 🫥 create real transparent RGBA images 👥 use up to 10 reference images ✏️ preserve people/products while editing 🔤 render text 🎯 do masked/local edits Qwen basically took the huge local image model and reduced with a catch … ⚠️ The tiny 7.26GB image model comes with an entire pipeline. It uses a Qwen3-VL 8B encoder + VAE. ComfyUI already has a 6.31GB W4A8 encoder, and Qwen supports CPU offloading for smaller GPUs. Also… 🚫 Qwen-Image-2.1 is NON-COMMERCIAL under the new Qwen Research License. That’s especially notable because Qwen-Image-2512 was Apache 2.0.

Qwen3.8-27B at up to 144 tok/s on an M5 Max MacBook Pro?! You have to try this Splash Engine! It's a new open-source inference engine called Splash. Instead of being a universal runtime like llama.cpp or Ollama, Splash optimizes the whole stack around the exact model 👇 ⚙️ model-specific kernels 🧠 hardware-aware memory planning 🚀 DFlash2 speculative decoding 💾 prompt-cache reuse 👥 continuous batching On the same 48GB M5 Pro running Qwen3.8-27B: 🚀 Splash: 74 tok/s ⚡ oMLX: 38 tok/s 🐌 Ollama: 24 tok/s At 32K context: 🚀 Splash: 54 tok/s And with 4 concurrent requests: 🔥 170 aggregate tok/s vs 43 tok/s for oMLX Now Inco + LM Studio are showing up to ~144 tok/s on M5 Max. And LM Studio Bionic 1.1.5 already added Splash as an experimental runtime. Requirements 🍎 M3 or newer 💾 36GB minimum 👍 48GB+ recommended Completely different performance because the software stack is optimized around what it is running. ⚠️ 144 tok/s is an Inco/LM Studio result. The detailed numbers above are from Inco's 48GB M5 Pro test. 🔗