Qwen-Image-Edit is the editing version of the 20B Qwen-Image model. It offers precise, model-native editing, including bilingual text modification and both high-level semantic and low-level appearance changes.
Hi everyone!
The most productive part of an image generation model isn't just the quality of the initial render, it's the ability to precisely edit key parts of the image.
We've seen a lot of innovation at the interaction layer, but the best editing has to come from native support within the model itself. With Qwen-Image-Edit, the Qwen team has delivered their answer.
It supports both high-level semantic changes (like style transfers) and low-level appearance edits (like adding or removing objects), and its ability to edit text directly in an image is especially powerful!
Is Qwen3 designed to handle both reasoning and multimodality (like text and images) or is the current version focused solely on text with plans for multimodal features in the future?
I believe Qwen3 has incredible potential but I think the key challenge will be around documentation and getting developers on board. If the community struggles to easily test, deploy or compare benchmarks it might not get the usage it deserves even if it’s technically top-notch.
Will Qwen3 be seamlessly integrated into Alibaba Cloud services like Elastic Compute, data pipelines or BI tools or is it mainly going to be a standalone open-source offering?
What an impressive release. Does Qwen3 offer dedicated optimizations for both Chinese and English or is it designed to perform well across a broad range of languages right from the start?
Replies
It’s fantastic to see such strong LLM innovation coming from Asia. Will Qwen3 support multimodality right out of the box?
Can Qwen3 work with existing open-source frameworks such as LangChain or LlamaIndex?
Is Qwen3 designed to handle both reasoning and multimodality (like text and images) or is the current version focused solely on text with plans for multimodal features in the future?
I love how Qwen3 is enhancing the global AI ecosystem.
I believe Qwen3 has incredible potential but I think the key challenge will be around documentation and getting developers on board. If the community struggles to easily test, deploy or compare benchmarks it might not get the usage it deserves even if it’s technically top-notch.
With growing concerns about data provenance and copyright in LLM training could you share how Qwen3 ensures dataset transparency?
Will Qwen3 be seamlessly integrated into Alibaba Cloud services like Elastic Compute, data pipelines or BI tools or is it mainly going to be a standalone open-source offering?
This is fantastic. Does Qwen3 come with built-in support for fine-tuning or adapters or is it mainly geared towards zero/few-shot performance?
What an impressive release. Does Qwen3 offer dedicated optimizations for both Chinese and English or is it designed to perform well across a broad range of languages right from the start?
What’s the ideal hardware setup for running Qwen3 locally?
Is it as GPU-intensive as other LLMs of its size or does it include lighter models that can run smoothly on standard cloud instances?
What an exciting release. What’s the most significant upgrade from Qwen2 to Qwen3?
Impressive work from Alibaba Cloud how does Qwen3 stack up against GPT-4o in terms of speed and reasoning?
Amazing progress are there any benchmarks for Qwen3 compared to LLaMA 3 or Claude?
Pocketlink
super cool work, congrats on launching :) is there a free tier for beginners?