🚀 Meet Qwen-Image-2.1-Turbo — create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation architecture. Fewer steps does not mean lower quality: it still generates strong 2K images from text, and supports continued creation through natural-language edits, from adding accessories to changing a scene. Start directly with Diffusers: load QwenImage21Pipeline and the checkpoint’s recommended 8-step sampling schedule is ready to go. 🌐 Prefer an API? Qwen-Image-2.1 Pro and Turbo APIs are now officially live. Choose a hosted API for your application, or use the Turbo weights in your own workflow. 🤖 ModelScope: 🤗 Hugging Face: 🔗 Pro API: 🔗 Turbo API: What will you create with 8 steps? Share your results with us!

⚡ Meet Qwen-Audio-3.1! ASR, TTS & Realtime are fully upgraded, joined by two new models: TTS-Next for audio creation and ASR-Next for audio understanding. Five models, one complete audio stack: understanding, generation, interaction & creation. Plus big price cuts across the lineup: TTS ~70% off, Realtime ~85% off, and ASR up to 95% off. Highlights: 🥳 - ASR: stronger multilingual & dialect recognition, plus native polishing that auto-removes fillers & repetitions for cleaner, more logical transcripts. - ASR-Next: supports multi-speaker ASR with speaker labels, timestamps & aligned transcripts, and understands emotions, ambient & machine sounds for sound captioning, event localization, audio QA & reasoning. - TTS: multilingual & dialect synthesis with natural cross-lingual voice transfer; control emotion, speed & style via simple instructions. - TTS-Next: unified LM + diffusion framework generating voice, sound effects & background audio in one pass for audiobooks, podcasts, games & ads. - Realtime: speak & listen at once with anytime interruption, just like a real call; it even slows down and responds empathetically when it senses a low mood. Unlock the full potential of Qwen-Audio-3.1! 👇 - Blog: - Qwen-Audio-3.1-ASR: - Qwen-Audio-3.1-Realtime: - More APIs: coming soon @qwen_cloud

Qwen-Image-2.1 by @Alibaba_Qwen just landed as the #1# open source model in the Image Edit Arena and Text-to-Image Arena! With 1367 pts in the Image Edit Arena, Qwen-Image-2.1 took the #1# spot among open. It landed #16# overall, just 3 pts from GPT-Image-1.5-high-fidelity at #15#. See the leaderboard for the Text-to-Image arena below. Congrats to the @Alibaba_Qwen team on this contribution to the open source ecosystem!

@ArchSenex Hi there, thanks for your question! 👋 Just a quick clarification: Outputs are not part of the licensed Materials. Users retain the rights to images and other content they generate using the model.

Excellent text writing by the new @Alibaba_Qwen image 2.1 - qwen research license - vllm omni / diffuseres ready - both text 2 image and image edit in the same model Give it a try at:

Qwen-Image-2.1 from @Alibaba_Qwen has day-0 support in vLLM-Omni. 🎉 Generate, edit, and create transparent images with one model: a 7.1B DiT paired with Qwen3-VL-8B. Setup and supported combinations 👇

Qwen-Image-2.1 is now supported in ComfyUI! Open weights. One 7B checkpoint that generates and edits. → Image generation at native 2K → Instruction editing from up to 10 reference images in a single pass → RGBA output, alpha included

Qwen-Image-2.1 experiments on Apple Silicon (M5 Max, MLX, bf16) with my custom MFLUX 1600×672 (2.39:1), fixed seed: 10 steps → ~26s 20 steps → ~44s 30 steps → ~61s 40 steps → ~79s 50 steps → ~97s ~1.78s per step + ~8s fixed (load, prompt encode, VAE decode) Peak memory: 31 GB Prompt: A city street bends upward at the horizon: the ground and its buildings curve up from street level and arc into the sky like a giant wave, one single continuous landscape where skyscrapers grow progressively more vertical until they tower overhead, the road itself rising and wrapping up into the clouds. Dream physics like a folded dream city: no mirror duplication, just one seamless city rolling from horizontal to vertical. Wet reflective asphalt, cool blue-grey palette with warm window lights, glass and steel towers, low clouds threading between the rising districts, slight lens distortion, IMAX 70mm film look, cinematic wide-angle, photorealistic, volumetric haze, epic impossible scale.

As a Dev ambassador I got access to a preview of @Alibaba_Qwen image 2.1 Same prompt: first image is Qwen 2.1, Second is @higgsfield Qwen image 2.1s lighting is really impressive!

I got early access to @Alibaba_Qwen's new image model, Qwen-Image 2.1. I ran 5 prompts (one-shot generation) covering a dense infographic, a multi-panel comic, a macro photo, a chalkboard menu, and a movie poster. Thread below 🧵

Meet Qwen3.8-LiveTranslate, Qwen's next-generation real-time simultaneous interpretation model! 📢 Built on an Interleave architecture, it improves faithfulness, fluency, and conciseness while reducing average lagging (LAAL) from 2.8s to 2.3s across 60 languages. New capabilities: 🙌 - Real-time speaker diarization — distinguishes speakers in multi-party speech and preserves each speaker's voice through more stable voice cloning. - Synchronized bilingual display — source and translation on screen together. - Long-context disambiguation — leverages conversation history to clarify names and terminology for consistent translations. Let's try Qwen3.8-LiveTranslate! 🥳 - Blog: - QwenCloud:

🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result. Highlights: 🥳 - Audio-video intelligence that gets things done: jointly reason over what's seen and heard, and orchestrate tools across long workflows to auto-edit vlogs, translate short videos, and turn movies into recaps. - A major leap: approaching Gemini 3.8 Flash in audio-video capabilities; +19.5 points on average in agent performance across WildClawBench-MM & UniClawBench. - 1M-token context with agentic perception: actively explore long videos and locate key moments with higher accuracy, using 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are reduced by about 89% compared with Qwen3.5-Omni-Plus, making long-form audio-video understanding and agentic workflows more affordable than ever. To help you build apps around Omni, we're also open-sourcing Qwen-MM-Plugins and Qwen-Live Harness! 🛠️ We can't wait to see what you build with Qwen3.8-Omni-Flash! 👀 - Blog: - Qwencloud: - Qwen Studio: - API: - Qwen-MM-Plugins: - Qwen-Live Harness: coming soon