Qwen-Image-VAE-2.0 Technical Report
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To address the reconstruction bottlenecks of high compression, we adopt an improved architecture featuring Global Skip Connections (GSC) and expanded latent channels. Moreover, we scale training to billions of images and incorporate a synthetic rendering engine to improve performance in text-rich scenarios. To tackle the convergence challenges of high-dimensional latent space, we implement an enhanced semantic alignment strategy to make the latent space highly amenable to diffusion modeling. To optimize computationa...
| Authors | Zekai Zhang, Deqing Li, Kuan Cao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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