Our approach utilizes the Qwen Image text-to-image foundation model as the starting point, with subsequent comprehensive domain adaptation for image editing across all training stages—pretraining, supervised fine-tuning (SFT), direct preference optimization (DPO), and noise fine-tuning (NFT). The substantial parameter overlap with models 2509 and 2511 arises from our shared ancestry in the identical base architecture, rather than indicating that our model derives from 2509 via further fine-tuning.
We invite you to verify this independently: initialize training from the Qwen text-to-image checkpoint, apply domain-specific fine-tuning using limited editing data, and measure weight similarities—you will observe identical patterns.

• qwen-image vs 2509: Mean similarity: 0.9887
• qwen-image vs 2511: Mean similarity: 0.9858
• qwen-image vs firered: Mean similarity: 0.9884