The past year I have been trying to get diffusion models to work for language generation, without having to retrain a LLM from scratch. And recently, we finally succeeded:
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The past year I have been trying to get diffusion models to work for language generation, without having to retrain a LLM from scratch. And recently, we finally succeeded:
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We introduce "LAD: LoRA-Adapted Denoiser", a method to convert a LLaMA model into a text diffusion model using LoRA finetuning and structured input corruption.
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We introduce "LAD: LoRA-Adapted Denoiser", a method to convert a LLaMA model into a text diffusion model using LoRA finetuning and structured input corruption.
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🎯 Try the demo and read the write-up here!
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🎯 Try the demo and read the write-up here!
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https://ruurdkuiper.github.io/tini-lad/
https://ruurdkuiper.github.io/tini-lad/
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Unlike autoregressive (word-for-word) models like ChatGPT, diffusion models iteratively refine a noised sequence. However, most current diffusion approaches rely on all-parameter retraining and repeatedly remasking tokens, which is costly and slow during both training and inference!
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Unlike autoregressive (word-for-word) models like ChatGPT, diffusion models iteratively refine a noised sequence. However, most current diffusion approaches rely on all-parameter retraining and repeatedly remasking tokens, which is costly and slow during both training and inference!
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🧠 With LAD:
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🧠 With LAD:
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- We can finetune an autoregressive model for diffusive generation in just 10 hours on a single GPU.
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- We can finetune an autoregressive model for diffusive generation in just 10 hours on a single GPU.
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- Test-time compute is fully adjustable: fewer steps means faster outputs while more steps improve output quality.
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- Test-time compute is fully adjustable: fewer steps means faster outputs while more steps improve output quality.
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- Due to our unique noising schedule, remasking is not always needed during inference. All tokens are attended to in each iteration!
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- Due to our unique noising schedule, remasking is not always needed during inference. All tokens are attended to in each iteration!
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🔍 LAD is built using:
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🔍 LAD is built using:
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 – A frozen LLaMA-8B backbone
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 – A frozen LLaMA-8B backbone
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 – Structured noising: token swaps, duplications, replacements, span shifts
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 – Structured noising: token swaps, duplications, replacements, span shifts
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 – Modified attention masks for bidirectional decoding
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 – Modified attention masks for bidirectional decoding
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💡 We show that even small, fast-trained models can perform diffusive generation — with competitive benchmark performance, perplexity and more flexible test-time behavior than traditional transformers.
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💡 We show that even small, fast-trained models can perform diffusive generation — with competitive benchmark performance, perplexity and more flexible test-time behavior than traditional transformers.
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The past year I have been trying to get diffusion models to work for language generation, without having to retrain a LLM from scratch. And recently, we finally succeeded:

We introduce "LAD: LoRA-Adapted Denoiser", a method to convert a LLaMA model into a text diffusion model using LoRA finetuning and structured input corruption.

🎯 Try the demo and read the write-up here!
https://ruurdkuiper.github.io/tini-lad/

Unlike autoregressive (word-for-word) models like ChatGPT, diffusion models iteratively refine a noised sequence. However, most current diffusion approaches rely on all-parameter retraining and repeatedly remasking tokens, which is costly and slow during both training and inference!

🧠 With LAD:
- We can finetune an autoregressive model for diffusive generation in just 10 hours on a single GPU.
- Test-time compute is fully adjustable: fewer steps means faster outputs while more steps improve output quality.
- Due to our unique noising schedule, remasking is not always needed during inference. All tokens are attended to in each iteration!

🔍 LAD is built using:
 – A frozen LLaMA-8B backbone
 – Structured noising: token swaps, duplications, replacements, span shifts
 – Modified attention masks for bidirectional decoding

💡 We show that even small, fast-trained models can perform diffusive generation — with competitive benchmark performance, perplexity and more flexible test-time behavior than traditional transformers.


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Ruurd Kuiper
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