5 New implementations of Diffusion Models
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5 New implementations of Diffusion Models
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Diffusion models are widely used for image and video generation but remain underexplored in text generation, where autoregressive models (ARMs) dominate. Unlike ARMs, which produce tokens sequentially, diffusion models iteratively refine noise through denoising steps, offering greater flexibility and speed.
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Diffusion models are widely used for image and video generation but remain underexplored in text generation, where autoregressive models (ARMs) dominate. Unlike ARMs, which produce tokens sequentially, diffusion models iteratively refine noise through denoising steps, offering greater flexibility and speed.
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Recent advancements show a shift toward using diffusion models in place of, or alongside, ARMs. Researchers also combine strengths from both methods and integrate autoregressive concepts into diffusion.
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Recent advancements show a shift toward using diffusion models in place of, or alongside, ARMs. Researchers also combine strengths from both methods and integrate autoregressive concepts into diffusion.
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Here are 5 new implementations of diffusion models:
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Here are 5 new implementations of diffusion models:
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1. Mercury family of diffusion LLMs (dLLMs) by Inception Labs -> 
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1. Mercury family of diffusion LLMs (dLLMs) by Inception Labs -> 
https://www.inceptionlabs.ai/news
https://www.inceptionlabs.ai/news
link
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It applies diffusion to text and code data, enabling sequence generation 10x faster than today's top LLMs. Now available Mercury Coder can run at over 1,000 tokens/sec on NVIDIA H100s.
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It applies diffusion to text and code data, enabling sequence generation 10x faster than today's top LLMs. Now available Mercury Coder can run at over 1,000 tokens/sec on NVIDIA H100s.
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2. Diffusion of Thoughts (DoT) -> 
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2. Diffusion of Thoughts (DoT) -> 
Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language
  Models (2402.07754)
https://huggingface.co/papers/2402.07754
2402.07754
paper
resource
https://huggingface.co/papers/2402.07754
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Integrates diffusion models with Chain-of-Thought. DoT allows reasoning steps to diffuse gradually over time. This flexibility enables balancing between reasoning quality and computational cost.
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Integrates diffusion models with Chain-of-Thought. DoT allows reasoning steps to diffuse gradually over time. This flexibility enables balancing between reasoning quality and computational cost.
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3. LLaDA -> 
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3. LLaDA -> 
Large Language Diffusion Models (2502.09992)
https://huggingface.co/papers/2502.09992
2502.09992
paper
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https://huggingface.co/papers/2502.09992
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Shows diffusion models' potential in replacing ARMs. Trained with pre-training and SFT, LLaDA masks tokens, predicts them via a Transformer, and optimizes a likelihood bound. LLaDA matches key LLM skills, and surpasses GPT-4o in reversal poetry.
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Shows diffusion models' potential in replacing ARMs. Trained with pre-training and SFT, LLaDA masks tokens, predicts them via a Transformer, and optimizes a likelihood bound. LLaDA matches key LLM skills, and surpasses GPT-4o in reversal poetry.
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4. LanDiff -> 
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4. LanDiff -> 
The Best of Both Worlds: Integrating Language Models and Diffusion
  Models for Video Generation (2503.04606)
https://huggingface.co/papers/2503.04606
2503.04606
paper
resource
https://huggingface.co/papers/2503.04606
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This hybrid text-to-video model combines autoregressive and diffusion paradigms, introducing a semantic tokenizer, an LM for token generation, and a streaming diffusion model. LanDiff outperforms models like Sora.
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This hybrid text-to-video model combines autoregressive and diffusion paradigms, introducing a semantic tokenizer, an LM for token generation, and a streaming diffusion model. LanDiff outperforms models like Sora.
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5. General Interpolating Discrete Diffusion (GIDD) -> 
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5. General Interpolating Discrete Diffusion (GIDD) -> 
Generalized Interpolating Discrete Diffusion (2503.04482)
https://huggingface.co/papers/2503.04482
2503.04482
paper
resource
https://huggingface.co/papers/2503.04482
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A flexible noising process with a novel diffusion ELBO enables combining masking and uniform noise, allowing diffusion models to correct mistakes, where ARMs struggle.
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A flexible noising process with a novel diffusion ELBO enables combining masking and uniform noise, allowing diffusion models to correct mistakes, where ARMs struggle.
5 New implementations of Diffusion Models

Diffusion models are widely used for image and video generation but remain underexplored in text generation, where autoregressive models (ARMs) dominate. Unlike ARMs, which produce tokens sequentially, diffusion models iteratively refine noise through denoising steps, offering greater flexibility and speed.
Recent advancements show a shift toward using diffusion models in place of, or alongside, ARMs. Researchers also combine strengths from both methods and integrate autoregressive concepts into diffusion.

Here are 5 new implementations of diffusion models:

1. Mercury family of diffusion LLMs (dLLMs) by Inception Labs -> https://www.inceptionlabs.ai/news
It applies diffusion to text and code data, enabling sequence generation 10x faster than today's top LLMs. Now available Mercury Coder can run at over 1,000 tokens/sec on NVIDIA H100s.

2. Diffusion of Thoughts (DoT) -> https://huggingface.co/papers/2402.07754
Integrates diffusion models with Chain-of-Thought. DoT allows reasoning steps to diffuse gradually over time. This flexibility enables balancing between reasoning quality and computational cost.

3. LLaDA -> https://huggingface.co/papers/2502.09992
Shows diffusion models' potential in replacing ARMs. Trained with pre-training and SFT, LLaDA masks tokens, predicts them via a Transformer, and optimizes a likelihood bound. LLaDA matches key LLM skills, and surpasses GPT-4o in reversal poetry.

4. LanDiff -> https://huggingface.co/papers/2503.04606
This hybrid text-to-video model combines autoregressive and diffusion paradigms, introducing a semantic tokenizer, an LM for token generation, and a streaming diffusion model. LanDiff outperforms models like Sora.

5. General Interpolating Discrete Diffusion (GIDD) -> https://huggingface.co/papers/2503.04482
A flexible noising process with a novel diffusion ELBO enables combining masking and uniform noise, allowing diffusion models to correct mistakes, where ARMs struggle.
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