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Excited to share what my team has been working on lately - Gemini diffusion! We bring diffusion to language modeling, yielding more power and blazing speeds! 🚀🚀🚀 Gemini diffusion is especially strong at coding. In this example the model generates at 2000 tokens/sec, including overheads like tokenization, prefill, safety...

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Brendan O'Donoghue

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577,889 görüntüleme • 1 yıl önce •via X (Twitter)

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With Gemini diffusion you can 'vibe code' so fast that it feels almost instant:

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Brendan O'Donoghue1 yıl önce

Lightning speeds are not the only advantage. Unlike autoregressive models that are restricted to generating one token at a time, diffusion can do non-causal reasoning within the generation. Take this example: "What is (√(81) * (2/3))^2 + (15 - 3) / (2^2)). First provide the answer and then derive the solution." This is a very hard prompt for AR models because they can't reason about the solution before generating it, but diffusion models can reason non-causally to get it right (ans: 39). GPT-4o fails this problem:

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A similar one inspired by the 'Sparks of AGI paper' by @SebastienBubeck et al: "How many primes are there between 150 and 250? The first thing you should output is the total number, then print the exact list inside [ ] brackets." (ans: 18) GPT-4o fails this one too:

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For more details, and to get access, see here:

Jeremy Howard profil fotoğrafı
Jeremy Howard1 yıl önce

Makes me so happy to see this! :D I've hearing about this project for quite some time, and was really hoping that it would see the light of day.

Antoine Moulin profil fotoğrafı
Antoine Moulin1 yıl önce

Congrats to the team!!

Brendan O'Donoghue profil fotoğrafı
Brendan O'Donoghue1 yıl önce

Thank you!

David Zhang (▲) profil fotoğrafı
David Zhang (▲)1 yıl önce

Do you feel the AGI yet?

heiner profil fotoğrafı
heiner1 yıl önce

congrats

mike profil fotoğrafı
mike1 yıl önce

This thing really justifies the mind blown emoji. Well done. Absolutely incredible preview of the future.

Benzer Videolar

🚀Meet Gemini Diffusion, our first diffusion-based and super fast language model, just announced at Google I/O!🚀 Very excited to be able to share what I've been working on for the past little while with our amazing small team Google DeepMind.
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🚀Meet Gemini Diffusion, our first diffusion-based and super fast language model, just announced at Google I/O!🚀 Very excited to be able to share what I've been working on for the past little while with our amazing small team Google DeepMind.

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Today's autoregressive models generate one token at a time. Mercury 2 generates tokens in parallel. Over 1,000 tok/sec on standard GPUs, at comparable quality to speed-optimized models. Since launch, the community has been showing what diffusion LLMs can unlock. Thanks to the team at Clyep for the breakdown.
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Today's autoregressive models generate one token at a time. Mercury 2 generates tokens in parallel. Over 1,000 tok/sec on standard GPUs, at comparable quality to speed-optimized models. Since launch, the community has been showing what diffusion LLMs can unlock. Thanks to the team at Clyep for the breakdown.

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🤔The more I studied diffusion language models, the more I came to appreciate the simplicity of autoregressive (AR) language models. AR models are trained to agree with what they generate, and their serving stacks are built to preserve that structure. DLMs often do neither: they lack introspective consistency, and high TPF does not necessarily translate into high real-world TPS. We propose Introspective Diffusion Language Model (I-DLM), which unifies introspection and generation in a single pass: 1. 🧑‍🎓I-DLM brings introspective consistency to DLMs with only 5B training tokens, achieving AR-thinking-level quality. 2. 🚀 I-DLM carefully trades compute for higher TPF while converting that advantage into real TPS under high-concurrency serving. 📖Website: ⌨️Code:
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🤔The more I studied diffusion language models, the more I came to appreciate the simplicity of autoregressive (AR) language models. AR models are trained to agree with what they generate, and their serving stacks are built to preserve that structure. DLMs often do neither: they lack introspective consistency, and high TPF does not necessarily translate into high real-world TPS. We propose Introspective Diffusion Language Model (I-DLM), which unifies introspection and generation in a single pass: 1. 🧑‍🎓I-DLM brings introspective consistency to DLMs with only 5B training tokens, achieving AR-thinking-level quality. 2. 🚀 I-DLM carefully trades compute for higher TPF while converting that advantage into real TPS under high-concurrency serving. 📖Website: ⌨️Code:

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Very excited to share the best coding model we’ve ever built! Today we’re launching Gemini 2.5 Pro Preview 'I/O edition' with massively improved coding capabilities. Ranks no.1 on LMArena in Coding and no.1 on the WebDev Arena Leaderboard. It’s especially good at building interactive web apps - this demo shows how it can be helpful for prototyping ideas. Try it in Google Gemini, Vertex AI, and AI Studio Enjoy the pre-I/O goodies !

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I'll remember this week for the rest of my life. Here's my interview with the incredible Sundar Pichai, CEO of Google. We spoke about Gemini, world models, self-improving AI, AI anxiety, and more. Full vid below: 0:00 - Intro 0:40 - Gemini as a World Model 1:30 - Gemini Diffusion 2:33 - Self-improving AI 4:09 - What AI needs to improve 5:06 - Agent Memory 6:46 - AI Form Factors 7:58 - Google Search Future 9:33 - AI Anxiety

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Also starting today, Gemini Advanced users can create with Canvas on Gemini 2.5 Pro. This model’s advanced coding abilities make it easy for anyone to collaborate, create, and share imaginative prototypes (like this example of a 3D dino world, made in under 5 minutes with just a few prompts). Here’s how to get started: 1) Go to 2) Choose the “2.5 Pro (experimental)” model 3) Select “Canvas” and start prompting We’re working hard to make Gemini 2.5 Pro available for more people to try soon–watch this space for updates.

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Diffusion has shown great promise for generating robot **actions**, can it act as a **world model** to generate the future conditioned on actions? In our work led by han qi Haocheng Yin and in collaboration with Yilun Du, we show a **controllable** action-conditioned video diffusion model can produce photorealistic and (near) physics-accurate future predictions. This ability strengthens the policy via: - ranking different action proposals and selecting the best, or - **visual** trajectory optimization by optimizing the action proposals using gradient ascent. Learn more about Generative Predictive Control (GPC) at:

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New episode of the Information Bottleneck! We talked with Stefano Ermon about why he thinks diffusion LLMs will replace autoregressive ones. Stefano co-invented DDIM, FlashAttention, DPO, and score-based diffusion models. He's a Stanford professor and now runs Inception AI, where they built Mercury II. We go deep but also cover the bigger picture - the startup journey, PhD vs industry, and where AI is heading. A few things that stuck with me: - He thinks of autoregressive models as typewriters and diffusion models as editors. One goes left to right. The other starts messy and refines. - Mercury II (their text difussion model) already beats the fastest autoregressive models on latency-critical stuff as voice agents, code suggestions, anything where you have a tight time budget. And it does it because diffusion generates tokens in parallel instead of one at a time. - We also got into whether AI will actually replace software engineers (his answer: no), PhD vs industry advice, and what it was like going from an ICML best paper to raising money.

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GLM 4.7 is one of the top open-source models on LM Arena—and it's going toe-to-toe with Claude Opus 4.5 and Gemini Pro. We sat down with Anastasios Nikolas Angelopoulos, co-founder and CEO of Arena.ai, to break down 8,000+ developer votes: → Within 30 points of Gemini Pro in math & coding → Frontier-level multi-turn & instruction following → The open-source model devs are actually switching to The best part? You can run it at 1,500+ tokens/sec on Cerebras—for free.

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28,515 görüntüleme • 6 ay önce

Wow 🤩 the response to Project Starlight, the first-ever diffusion model for AI video restoration, has been incredible🚀 Many of you couldn't wait and sent us your videos to restore with Starlight So let’s make it a thing: Send us your videos, and we’ll restore them for free using Starlight 🌟 Read instructions in 🧵👇 Like & comment Starlight to get early access, if you haven’t done so already #ProjectStarlight
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Wow 🤩 the response to Project Starlight, the first-ever diffusion model for AI video restoration, has been incredible🚀 Many of you couldn't wait and sent us your videos to restore with Starlight So let’s make it a thing: Send us your videos, and we’ll restore them for free using Starlight 🌟 Read instructions in 🧵👇 Like & comment Starlight to get early access, if you haven’t done so already #ProjectStarlight

Topaz Labs

329,624 görüntüleme • 1 yıl önce

Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.
0:31

Auto regressive LLMs are officially on notice. run Gemma 4 26B diffusion gguf with llama.cpp Google just dropped DiffusionGemma-26B, and it completely flips how we generate text. instead of predicting words one by one, it generates 256 tokens in parallel using bi-directional attention. its like stable diffusion, but for language. the model starts with random text "noise" and iteratively refines and self-corrects the entire block in real-time to fix formatting and reasoning errors on the fly. since it’s a Mixture of Experts (MoE) that only activates 3.8B parameters during inference, it fits perfectly on consumer hardware. You can run the Q4_K_M quant with an 18GB VRAM budget on a single RTX 3090 or RTX 4090 with exceptional throughput. Tested on Ubuntu 22 with CUDA 13.1 using the cutting edge experimental llama.cpp branch. Here is how to compile and run it with the live terminal denoising visualizer: # 1. Clone & check out the experimental PR (#24423) - 1) git clone && cd llama.cpp -git fetch origin 2) pull/24423/head:diffusiongemma && --git checkout diffusiongemma # 2. Build with CUDA support 1) cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native 2) cmake --build build -j $(nproc) --config Release --target llama-diffusion-cli # 3. Run with live visual denoising (llama.cpp flags) ./build/bin/llama-diffusion-cli \ -m /path/to/diffusiongemma-26B-A4B-it-Q4_K_M.gguf \ -ngl 99 -cnv -n 2048 --diffusion-visual Watch the video below to see the live --diffusion-visual canvas iteratively de noising the prompt output in real time. guide and unsloth's hugging face GGUF model links are in the comments below! Is auto regressive generation officially legacy tech? Let me know what you think.

Alok

52,656 görüntüleme • 2 ay önce

We just launched the new Figma app (focused on FigJam workflows) in ChatGPT at OpenAI's DevDay! Going from chat to diagram is now easier than ever. I'm really excited about where we can take this. More in video below — including a tech tree to the year 2100 🧬🚀🦾🧠
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We just launched the new Figma app (focused on FigJam workflows) in ChatGPT at OpenAI's DevDay! Going from chat to diagram is now easier than ever. I'm really excited about where we can take this. More in video below — including a tech tree to the year 2100 🧬🚀🦾🧠

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