If I’m reading this right, this is pretty wild. They turned a Qwen autoregressor into a diffuser by using a bunch of really clever techniques, and they vastly outperform any “native diffuser,” actually being competitive with the base model they were trained from. The obvious upside here is the massive speedup in generation.

And then through a LoRA adapter, you can ground the diffuser on the base model’s distribution (essentially have it “compare” its proposals against what the base model would’ve generated), which effectively means: exact same byte-for-byte output for the same seed, just roughly twice as fast (which should improve even more for batched tasks).

I’m not an expert, more of a “practicing enthusiast,” so I might be missing something, but at first glance, this reads super exciting to me.