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.

Is anyone here experimenting seriously with Diffusion for text generation? I’d love to learn about your experiences!

I'm no expert (just a monkey... ;) ), but isn't Diffusion supposed to generate ALL of the output at once? From their diagram, it looks like their I-LDM model seems to use previously generated context to generate the next tokens (or blocks).

Does this mean I should switch to sglang? How hard is it to add the capability for these type of models to vLLM? Or does it already handle them?

> 2025-04-12: Initial code release with training and inference support.

> 2025-04-12: Released I-DLM-8B, I-DLM-32B, and I-DLM-8B-LoRA on HuggingFace.

Is this old already? Not saying that's a bad thing, since it seems very sophisticated. Just curious if there's an update

3倍向上したとこのとですが、ボトルネックはMemory BandwidthからComputeに移行したの？ それともMemory Bandwidthが支配的ですか？

Can diffusion models have reasoning steps where they generate a block, introspect and then generate another until the output is satisfactory?

So can you just use this and have a faster Qwen32b?

https://huggingface.co/yifanyu/I-DLM-32B/tree/main

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