Brendan O'Donoghue (Google DeepMind) explains text diffusion models. Covers how text diffusion works (training and inference), how it differs from autoregressive generation, and its pros and cons: speed via hardware efficiency (e.g., ~24 denoising steps for 256 tokens, roughly 10x fewer memory transfers), bidirectional attention enabling self-correction by revising earlier tokens, dynamic/adaptive computation, in-place text editing, and the tradeoff of lower throughput at large batch sizes. Also discusses low-latency applications and demos, including generated-on-the-fly content and apps built quickly with fast generation. Includes a Q&A session and references the Gemini Diffusion research preview. # Channel Presentation Observations — "Text Diffusion — Brendan O'Donoghue, Google DeepMind" ## Explicit Self-Description Evidence - **Speaker affiliation stated directly:** > "My name is Brendan. I'm a research scientist at DeepMind." - **Research scope claimed:** > "I'm talking today about text diffusion, which is kind of a more forward-looking research area at DeepMind." - **Team attribution:** > "The team I'm on we had a research demo release 1 year ago now called Gemini diffusion which was a variant of a Gemini model which did text diffusion instead of autoregressive next token generation." - **Role boundary claim:** When asked about pricing: > "I don't know. We haven't got to that yet. I'm a research scientist." ## Observable Presentation Patterns ### Technical Depth and Format - The talk is a **research-level presentation** with slides, live demos, and an extended audience Q&A session ending in applause — consistent with a conference or internal tech talk format. - Deep hardware-level explanation is provided (memory bandwidth, HBM, tensor cores, flops vs. bandwidth trade-offs) to explain diffusion model latency advantages. ### Intended Audience - The speaker **directly identifies the audience as AI practitioners**: > "In your own work, you're all AI engineers." - Audience members ask technically specialized questions (distillation, RL, latent diffusion, response-length prediction, batching behavior), confirming a technical/expert audience. ### Claimed Scope and Positioning - Claims competitive results: Gemini diffusion was > "very similar quality across the board" to Gemini 2.0 Flash-Lite at > "much better latencies," with > "something like 2,000 tokens a second." - Positions the work as **research preview, not product**: > "It was a research preview that was open to about 100k people." - Forward-looking framing: > "Keep posted for new developments in that direction upcoming soon" and > "we're going to release something soon." - Honest hedging of stale results: > "This is a year ago, which is like prehistoric times in this field... I wouldn't fixate too much on these numbers." ### Demonstrated Content Themes - **Latency vs. throughput trade-off analysis** as the central framing (diffusion's low latency, but > "lower throughput for large batches... It's just too expensive to serve"). - **Novel capability showcases:** bidirectional self-correction (math example where GPT-4o and Gemini 2.5 Flash reportedly erred), adaptive computation (variable denoising steps per problem difficulty), and in-place editing. - **Application demos:** on-the-fly generated Wikipedia, Reddit, and operating-system pages, plus a third-party voice-coding demo using the Gemini diffusion web page. ## Summary of Observable Channel Identity Based solely on this material, the presentation demonstrates a channel/platform featuring **DeepMind research scientists giving technical talks to AI-engineer audiences**, covering **forward-looking LLM research (text diffusion)** with demo-heavy, evidence-backed delivery, while clearly separating research claims from production status. No private motivations beyond stated enthusiasm (> "this is why I'm excited about it") are inferred. # Delivery Observations — Text Diffusion (Brendan O'Donoghue, Google DeepMind) ## Tone & Register - **Conversational, informal research-talk style** with frequent hedges and verbal fillers: "you know," "kind of," "like," "uh," "um" appear throughout. - Self-aware humor, light and dry: - "which is like you know, prehistoric times in this field" - "So, now all the responses to your posts will be by bots. They weren't already." - "I'm not sure if there are any published ones." [laughter] "I don't think so." ## Vocabulary & Framing - Consistently uses **plain-language analogies** to explain technical concepts: "corrupt it somehow," "fill in the mistakes," "canvas of tokens," "stream over the entire neural network." - Frames trade-offs explicitly and repeatedly: - "So, it's a kind of a different way of generating text. Uh um So, that obviously has some pros and cons." - Repeated **latency vs. throughput** contrast: "it's it's just faster inference" vs. "lower throughput for large batches." ## Recurring Phrases & Verbal Habits - **"So," as a paragraph opener** — extremely frequent ("So, in in the standard vanilla Gemini…", "So, when we're serving uh an auto regressive model…"). - **Repetition for emphasis**, often stammered: "it it gets," "this this process," "that that's the real reason." - **Concessive caveats** to avoid overclaiming: "I wouldn't fixate too much on these numbers," "It's not exactly monotonic, but it is roughly monotonic," "It depends." ## Teaching Habits & Structure - **Analogy-first pedagogy**: begins from familiar image/video diffusion before moving to text; returns to image-editing analogies for in-place editing ("take an image… cut something out of it"). - **Concrete numeric examples** to anchor hardware arguments: "if you can do, say, 24 passes to generate 256 tokens, you'll be doing 10 times fewer memory transfers." - **Live-demo walkthrough with step counting**: narrates the math example pass-by-pass ("After one forward pass… After two forward passes… After three forward passes…"). - **Signposting transitions**: "Okay, so…", "So that's that's one advantage…", "Lastly is the kind of fast in-place editing." - Anticipates audience state: opening line "Some people are still filtering into the room… they won't miss anything." ## Audience Awareness & Q&A Style - Addresses audience identity directly: "in your own work, you're all AI engineers." - **Terse, candid Q&A answers**, including frank admissions of uncertainty: - "Uh I don't know. We haven't got to that yet. I'm a research scientist." - "Um I don't know. I don't think we've tried to do that. [laughter] Probably would work." - Frequent minimal confirmations ("Yeah, yeah," "Mhm") while questioners finish. ## Uncertainty Notes - Some habits (e.g., "you know" frequency, self-correction mid-sentence) may be partially artifacts of the transcript's disfluency capture; exact rates can't be reliably quantified from this text alone. - No timestamps are available beyond bracket markers; timing/pace observations (pauses, slide dwell time) are not inferable. # Text Diffusion — Brendan O'Donoghue, Google DeepMind ## Position & Reasoning Record ### Speaker - **Brendan O'Donoghue**, research scientist at DeepMind, presenting on **text diffusion** as a forward-looking research area. --- ## Core Technical Explanation ### How text diffusion works - Start with a clean token sequence; corrupt it at multiple noise levels (continuous or **discrete** — e.g., replacing tokens with random ones). - Train a network to correct the corruption; at inference, initialize pure random tokens and iteratively refine. - Contrasts with **autoregressive** generation (one token at a time, causal attention). ### Gemini Diffusion demo (1 year prior) - Research preview, ~100k users; a Gemini variant using diffusion instead of next-token generation. - Compared against **Gemini 2.0 Flash-Lite**: "very similar quality across the board" — slight advantage in code, slight disadvantage elsewhere, at much better latencies. - **Qualification:** "a year ago, which is like prehistoric times in this field... I wouldn't fixate too much on these numbers." - Teased: new developments "upcoming soon." --- ## Claimed Advantages ### 1. Lower latency / faster inference (the "main pro") - **Hardware reasoning:** GPUs/TPUs are **memory-bound** — bandwidth from HBM to tensor cores is the bottleneck. Autoregressive generation streams all weights/KV-cache per token; diffusion generates e.g. 256 tokens in ~24 passes → ~10× fewer memory transfers → ~10× speedup if truly memory-bound. - Gemini Diffusion demo hit ~**2,000 tokens/sec** (including prefill, real browser tokens); faster for long sequences, prefill-dominated for very short ones. ### 2. Bidirectional attention & self-correction - Can attend to future tokens; can detect a wrong answer mid-reasoning and go back to fix it. - **Math demo** (Google IO): answer 39; diffusion model guessed 60 → 49 → corrected to 39 across passes. - **Comparative claim:** at the time, **GPT-4o** said 40 then corrected to 39; **Gemini 2.5 Flash** said 42 and "stuck to its guns," incorporating the error into reasoning. He frames this as "a flaw of autoregressive models that text diffusion models don't have." - **Qualification:** fixable with reasoning/thinking models, though he says that "punts the problem into something else." ### 3. Dynamic and adaptive computation - Quality roughly monotonic (not exactly) with more denoising steps; rises across six internal coding evals. - Model can self-determine when finished: first 100 digits of pi = 4 steps; FizzBuzz = 18 steps; explain quantum mechanics = 31 steps. - Harder evals (GPQA Diamond) took longer; easier (MBPP) took less — "entirely determined by the model itself." ### 4. Fast in-place editing - Fix code, add documentation, insert a story paragraph consistent with surrounding text — not token-by-token. --- ## Claimed Disadvantage ### Lower throughput for large batches (main blocker) - Multiple forward passes per request hit compute thresholds earlier; higher serving cost. - "No one's landing text diffusion into any of these big models primarily because of that disadvantage. It's just too expensive to serve." - References Claude's current throughput concerns as evidence throughput matters. --- ## Hardware Positioning (Q&A) - **On-device use cases** (robotics, within Alphabet ecosystem) favor diffusion: no batch-serving constraints, latency is valued, and "quality isn't really any [different] — they're the same quality basically." - **On quality parity:** affirms current research reaches roughly frontier-model quality; "Quality isn't the concern, it's the throughput." - **Scaling:** bigger models need fewer steps — diminishing serving cost even as flops/pass grow. ## Other Q&A Positions - **Data:** same training data as other models; algorithms change somewhat; distillation possible; RL applicable to reasoning with algorithm changes. - **Output length:** fix a window length (block-wise autoregressive) is the standard approach; can also add a length-prediction head; unlimited text still possible. - **Step limits:** step caps can be set ahead of time for latency predictability; models often finish early. - **Multi-window revision:** possible in principle, "but for us we just set it in stone and continue." - **Discrete vs. latent:** this work is discrete (tokens in/out); latent-space variants exist in literature but most text diffusion today is discrete. - **Initializing with a smaller model:** not done — a model trained on noise expects noise; training on small-model output "adds complexity." - **Combining diffusion with autoregressive models:** "Yeah, you can do that." - **Pricing:** "I don't know. We haven't got to that yet. I'm a research scientist." ## Demo Applications (latency unlocks) - Wikipedia page (HTML + text) generated on the fly. - Fake Reddit — text via Gemini Diffusion, images via Imagen ("Juno... before Nano Banana") arriving "a minute later." - Generated operating system responding to every click. - Third-party Twitter demo: live voice-driven to-do app coding ("15 seconds of work") — noted as external so the praise wasn't self-promotional. - His position: low latency "can really unlock some new experiences... it's not just the same thing faster." --- ## Preserved Tensions - Latency advantage vs. throughput/cost disadvantage remains unresolved; no big-model deployment. - Benchmark claims are one year old and explicitly flagged as possibly stale. - Self-correction advantage partly undercut by acknowledgment that reasoning models also fix errors. - Quality parity asserted in Q&A but based on year-old comparisons to Gemini 2.0 Flash-Lite. # Video Attribution Record — Text Diffusion — Brendan O’Donoghue, Google DeepMind ## Speaker Inventory | Speaker | Role | Evidence | |---|---|---| | **Brendan O'Donoghue** | **Creator / presenter**, research scientist at DeepMind | Self-identified: "My name is Brendan. I'm a research scientist at DeepMind" | | **Unnamed voice coder (demo)** | **Guest / third-party demonstrator** | A person "outside of our team" shown in a recorded demo using the Gemini diffusion web page to live-code a to-do app by voice | | **Audience members (multiple, unnamed)** | **Questioners** | Q&A session after the talk; questions and occasional laughter recorded in transcript | | **No separate narrator** | — | Talk is delivered directly by the creator | ## Role Boundaries - **Presentation (main body):** Brendan describes text diffusion research, the Gemini diffusion demo, latency analysis, and internal demos. - **Embedded third-party content:** The live-coding demo is explicitly attributed by Brendan to "someone on Twitter" outside his team; the "15 seconds of work" quotation comes from that demonstrator, not Brendan. - **Q&A:** Brendan answers audience questions; audience voices are partial and unattributed in the transcript. ## Referenced Sources & Claims ### Internal / DeepMind - **Gemini Diffusion** — research demo released ~1 year prior, open to ~100k people; ~2,000 tokens/sec reported. - **Gemini 2.0 Flash-Lite** — cited as the competitor model / architecture baseline; "very similar quality across the board." - **Gemini 2.5 Flash** — cited as making an error (answered 42) on the sqrt(81) prompt. - **Internal demos:** Wikipedia-on-the-fly, Reddit-on-the-fly (paired with an image model, "Juno," pre-"Nano Banana"), on-the-fly operating system. - **Internal coding evals** (six, unnamed) and **GPQA Diamond** / **MBPP** — referenced for adaptive-computation results. ### Third-party claims - **GPT-4o** ("new at the time") — claimed to have answered 40 to the sqrt(81) prompt, then corrected itself. - **Claude** — referenced as having "some throughput concerns" per user experience. - **Google IO demo (last year)** — square-root-of-81 prompt shown at IO. - **External literature** — Brendan notes "there's a bunch of literature out there" on discrete/latent diffusion and distillation, without naming specific papers; says he is "not sure if there are any published ones" regarding distillation. ## Promotional Segments - **Teaser / forward-looking promotion:** "keep posted for new new developments in that direction upcoming soon" and "we're going to release something soon" — flagged as promotional statements about future releases from his team. - **Invitation to audience:** Encourages audience ("you're all AI engineers") to anticipate the next diffusion model release — soft promotional framing. ## Availability & Provenance Limits - **Comments:** No information in transcript or metadata about comment availability. - **Demo access:** Gemini diffusion research preview limited to ~100k people; not via API for the Twitter demo (web page only). - **Unpublished results:** Six internal coding evals and specific training details are internal; Brendan explicitly uncertain whether related work is published. - **No curated metadata** was supplied beyond title and transcript. ## Notes on Uncertainty - Audience questioners are **unidentified**; their statements are fragmentary in the transcript. - The demonstrator's name/handle is not given, only "someone on Twitter." - Brendan's own hedged statements ("I think it was Juno," "Maybe they're selling externally") are recorded as uncertainty, not fact. \>\> Some people are still filtering into the room. But it's mostly intro stuff for the first couple of slides so they won't miss anything. Okay, welcome everybody. My name is Brendan. I'm I'm a research scientist at DeepMind. I'm talking today about text diffusion, which is kind of a more forward-looking research area at DeepMind. So you're probably familiar with image and video diffusion, which is kind of state of the art for these modalities right now where you you know, you take ground truth, say image, you add noise to it in training, and then you train a neural network to remove that noise gradually, and then at inference time you just initialize the the picture with pure noise, and then you iteratively refine out the noise to recover back to, you know, whatever image or video or audio or whatever you're looking for. And the principle is essentially the same for text for text diffusion, where you start with a clean sequence of tokens, like so a clean sentence or something like that. And then you gradually add noise, you corrupt it somehow. There's lots of different ways to do that. You can do it in continuous or discrete way, but let's just say discrete for now, which would in this case just mean adding random tokens or replacing tokens with other random tokens. And you do that for a bunch of different noise levels, and you train the neural network to try to fill in to try to correct the mistakes basically in the text. And then at inference time you initialize the sequence of tokens to just pure noise, like pure random discrete tokens from the vocabulary, and then you iteratively refine through that to fill in the information in the order that the neural network wants to do to recover back to, say, a clean sentence. And then in practice, so we, you know, I showed we I showed you some GIFs here of what it looks like for images. You get very similar looking at for text, where it kind of uh you know, starts off all noisy and then gradually like fills in the text and then you get clean relatively clean outputs at the end. Okay, so the the team I'm on we had a a research demo release 1 year ago now called Gemini diffusion which was a variant of a Gemini model which did text diffusion instead of auto aggressive next token generation and that was like a research preview that was open to about 100k people. Uh Uh \>\> \[clears throat\] \>\> And you know, we're still you know, keep keep posted for new new developments in that direction upcoming soon. Um And we did have some good numbers at the time, but again it's a year ago which is like you know, prehistoric times in this field. Our kind of main competitor model was Gemini 2.0 flashlight at the time cuz that was the architecture we were branching from the text fusion model and we basically had very similar quality across the board there. Um mostly you know, a little bit of advantage in code, a little bit of disadvantage in some other areas, but kind of relatively similar performance at much better latencies. Um but again yeah, this is a year ago so I wouldn't fixate too much on these numbers. Okay, so what's the difference between auto aggressive generation and diffusion? So in in the standard vanilla Gemini Gemma you know, GPT whatever um generation of text you do this you know, you have some context that comes in and you want to generate some response to that and the model does it one token at a time. So it generates the first token and then condition on that generates the next token and so on. Whereas in diffusion you have the you know, a context will come in whatever that is and it'll initialize like I like I mentioned like a long sequence of tokens could be hundreds could be thousands could be shorter depends on the model uh to be random noise and then it iteratively refines that canvas to remove the noise over the course of a few denoising steps. So rather than one token at a time it does the entire block together but over a couple of iterations. So it's not just one pass, it does multiple passes, but it gets to attend to the future tokens and so on. So, it's a kind of a different way of generating text. Uh um So, that obviously has some pros and cons. So, the main pro that people really like and probably is the biggest advantage that text-to-image models have is that it's it's just faster inference. It just generates faster tokens per second cuz it makes much better use of the hardware, the TPU and the GPU. And I have some slides on that to explain why. Um but some other advantages are it can do bidirectional attention within the within this canvas of tokens. So, you know, autoregressive models can only attend to the past. They have causal attention within their, you know, their transformer. Whereas the a text-to-image model is not restricted at all. It can attend to the future. And that has some interesting properties like it can do self-correcting generation based on future tokens. So, it could like, you know, do some reasoning, see that it got the answer incorrect, and then go back and fix the reasoning and do it again. I have I have a demo of that. Um because this this process is is iterative, it does a number of steps to to to respond, and that means the model can actually do adaptive computation. It it it turns out that you can train the model to spend more time on harder problems and less time on easier problems. And like like the fusion models in general, you can do things like in-place editing where you say like fix the last tokens and give me the prefix that corresponds to those tokens and stuff. Um but the main disadvantage it has and the reason why it's not kind of used everywhere right now is lower throughput for large batches. So, autoregressive models, they're slow, but you can have a big batch of queries together, and then if you push that through the neural network through on the GPU, each individual user is slow, but that you make good use of the TPU by doing that. And so, you can serve a lot of queries. And so, you keep your costs down. You can serve a lot. Whereas since text-to-image does multiple forward passes on the same data, it, you know, it hits a compute threshold basically earlier, and it's it's even though it's lower latency for any one user, it tends to be, you know, lower throughput overall. So, higher cost to serve. And right now, you know, if you have if you've played with Claude recently, uh you'll know that they have some throughput concerns. So, uh people really care about throughput right now. And so, no one's landing text diffusion into any of these big models primarily because of that disadvantage. It's just too expensive to serve, even if it is much lower latency. Okay, so just leading into why does it have lower latency in case you're not familiar with kind of the architecture of how GPUs and TPUs run today. So, in a in a GPU, there's like a a tensor core, which does these like big matrix multiplies. It's very efficient, has a lot of flops or hops or, you know, whatever. Uh and then the memory that sits on the TPU GPU, this this HBM, that's where the weights and the activations and everything are stored, and that has to transfer over from the memory, all the weights and the activations and the KV cache, into the tensor core in order to do the computation. And so, it has to flow through this bandwidth channel. And that bandwidth channel is very tight. It turns out that the the both GPUs and TPUs have a lot of flops and not that much bandwidth. It's quite hard to It's expensive to put bandwidth onto these chips, and it's easy to put flops. So, because of that ratio, you can uh you you know, you can If you do the more flops you do for each streaming amount of data you put through, the better. All right, so So, when we're serving uh an auto regressive model, these these chips are memory bound. They're basically bottlenecked by this bandwidth. Um so, when you do auto regressive next token generation, for each token, you're doing one token at a time. Let's say batch size one. You have to stream over the entire neural network and all the KV cache and everything to get one token, and then you do it again for the next token and so on. Whereas for text diffusion, you're generating, say, 256 tokens, you still will over everything, but if you can do that less times than the number of tokens, this iterative refinement process, then you'll get a speed up. So, if you can do, say, 24 passes to generate 256 tokens, you'll be doing 10 times fewer memory transfers than an autoregressive model. And if you are truly memory bound, then you'll be 10 times faster, something like that. So, that's that's the real reason, that's the hardware reason why uh text-to-image models are much lower latency than autoregressive models. Okay. So, you know, we had this Gemini diffusion demo. Maybe some of you got access to it uh last year. And, you know, that was able to hit, you know, something like 2,000 tokens a second um pretty consistently, depending on the length of the query. Obviously, it depends. The longer sequence it's generating, the less it's prefill dominated. And so, you can really lean into these very long sequences of very fast tokens. But, if you're only generating one token, for instance, you'll be just dominated by the cost of the prefill. And the tokens per second number that was reported on this webpage was incorporated prefill and everything like that. So, this was 2,000 tokens a second. Those were genuine raw tokens that you would receive in your web browser. Okay, so that was the kind of a whirlwind tour of text-to-image and its main advantage, which is latency. But, I want to dig in a little bit into some of the other advantages that text-to-image has, which are kind of a little bit less talked about in the literature. Um but, I think are pretty cool, and this is why I'm excited about it. So, at at the at IO last year, Google IO last year, they showed this demo for the text-to-image model, which is you know, this is really easy prompt. But, uh you know, lots of models actually make mistakes on it. So, the prompt is, do you go to next slide? What is the square root of 81 \* 2/3 squared plus, you know, blah blah blah. And I think the answer is 39 to this problem. And so, you know, you pass that into the model, and you ask the you ask the Gemini diffusion to respond to that. After one forward pass, these are the tokens it's generated. So, one forward pass through the model, it's starting to respond. Now, since it's an iterative refine the process, a one forward pass is is not all it's going to do, but after one forward pass it has this. So, it has answer equals and then it said 60. It's not correct, but that's what it's guessing for now. And then it starts to do the reasoning. So, a solution calculate square root of 81 and so on. After two forward passes, it's changed 60 to 49. And it's gotten a little bit further into the reasoning. So, it's gotten like, you know, five steps into the reasoning. 2 squared equals 4 and some of the blue tokens are kind of still going to change. And then after three forward passes, it's actually gotten all the way through the reasoning. So, it gets it gets and it gets the answer correct at the end. 36 + 3 is equal to 39. And it's gone back and fixed the original response to say 39. So, it's it had a mistake twice, 60 and then 49, but once it finished the reasoning, it was able to return back and fix the mistake that it made at the start. Now, it's going to do a couple more forward passes in order to fix, you know, some of these tokens that aren't quite right in the text, but overall that's basically the structure of the output that it'll return. And this is, you know, this is a a property that text division models have. This ability to do bidirectional reasoning. So, to to not only see the past, but also see the future that it's going to utter. It's going to respond. And also to use that information to do self-correction. So, it made a mistake, but it was able to, you know, had another forward pass, it was able to go through and fix that mistake. At the time, you know, much, much bigger models than the one we were serving made a mistake for this problem. So, both Chat GPT 4o, which was new at the time, and Gemini 2.5 Flash, which was brand new at the time, both made an error on this on this exact problem. So, you give them the exact same prompt and then they would say, you know, remember the answer is 39. GPT 4o said 40. That's the best guess it can do at that one token. It went through the reasoning and then it said 30 It did manage to figure out it was 39 and said I made a mistake. It's 39, not 40. Uh Gemini 2.5 Flash also made a mistake, said 42 and then it it actually just stuck to its guns and never changed it and said 36 + 3 is 42. So it like incorporated the error into its reasoning later. Uh and these are way bigger models than the Gemini diffusion models. So it's it's really is a property a flaw of auto aggressive models that the text diffusion models don't have. Um and you know, you can fix this with modern reasoning thinking models. Uh but you know, then you're just kind of punting the the problem into something else, but Anyway, okay. So that's that's that's one advantage, which is bidirectional reasoning self-correction. Another one is what I I hinted at before, which is dynamic computation. So you can you can give the model more time at inference, more forward passes, you get a bigger budget, and it can just do better. It's not exactly monotonic, but it is roughly monotonic that the quality across every eval basically just continues to go up because it can it gets even if the even if the solution is almost entirely clean and correct, it gets to look at it and see that it made a mistake and then fix it. So you get this you get this nice kind of curve where you always see as the number of denoising steps such as the forward passes go increases, you get you can overall the the quality gets higher. And these are just six coding evals that we monitor internally. On top of that, a slightly different concept is it is the model can do adaptive computation, which is that you can allow the model, you train it in a way to determine itself when it is finished. And then for easy responses, it can use a little bit of compute and for harder responses, it can take longer. So here's just three examples from from the Gemini diffusion model. Which is what are the first 100 digits of pi? This is actually 100 tokens. It looks like a short response. This is actually 100 tokens. And it only takes four steps to do that. Because the model it's an easy prompt. It's an easy response because you've just memorized the 100 digits of pi. You can just output it. Whereas you know, for an auto aggressive model in the same time would have only done four tokens. Um so that's a very easy one. Slightly more challenging is to write a little bit of code, so that takes you know, 18 forward passes uh to generate FizzBuzz. And then some something more complicated is explained quantum mechanics in a single paragraph, and that took 31 denoising steps. So, it just took its time just for whatever reason decided to spend longer on those ones. And so, the model naturally gets to decide I guess to determine when it's going to finish and return the response. And And typically, we see that harder evals take more time. So, this is This is a year ago now, so the evals are kind of old school, but um the you know, on the higher on the harder end is GPQA Diamond, which for this the model size we were targeting was quite a hard eval, and that took a long time for it to respond to those ones. Whereas on the other end are like MBPP, which is mostly basic Python programs, it was very it was very easy to you know, took a very little time for it to respond to these ones. And this is entirely determined by the model itself. Just easier problems, easier prompts, it could respond to quickly, and harder ones it decided itself to spend more time reasoning. Okay, so that's that's another property, which is the a dynamic and adaptive computation. Lastly is the kind of fast in-place editing. So, diffusion models in general have this very nice property where you can so, take an image like in this example, you know, cut something out of it or whatever or give it a little prompt, and it'll fill it in, and you can use the context uh that that you haven't cut out to fill in the the piece you've cut out correctly. And so, you you can use that for like clever image editing, things like that. Um And then because the reason it can do that is because uh you know, it's not autoregressive. There are autoregressive image generators, right, which go left to right, top to bottom like raster order, generating pixels, you know, but diffusion doesn't work like that. It'll just see the entire image and then start to denoise it. And because of that, because it gets to see every pix- every pixel gets to see every pixel, it can fill in the missing information and and do it in a way that's kind of consistent with whatever prompt you're giving. Uh Uh we can do something similar. So, I have a couple of demos here. Uh Can you see that? Yeah, so this is just some code and you say, "There is a bug in this code. Can you fix it?" And it just It'll just make the edit in the correct place. Like, it won't you You can barely see that, but it's just doing a little fix here of the indices. Uh and you can say things like, "Can you add documentation?" And it'll go in. It's not It's not just one by one generating all the tokens. It's doing a clever editing procedure to to actually fill in the uh the correct edits here. You can do that with, you know, more general text. Like, you can take a story and then say, "Add a middle paragraph." And because it can see that the first and the and this third paragraph, it can fill in the paragraph in a way that's consistent with the the rest of the story, for instance. And this is just in-place editing, basically. Um okay. So, that's uh Those are some of the advantages. Uh don't have a lot of time. Uh the biggest advantage, like I mentioned, is this low latency. And you know, we we really lean into that. And uh I just want to show you a couple of demos of some of the things that people internally have built to kind of show what the advantage of low latency can give you. So, it's not just the same thing faster. It can really unlock some new some really new applications. So, in your own work, you're all AI engineers. Uh it'd be interesting to see when when the next diffusion model comes out from our team what the low latency could unlock. Uh and kind of what new applications can be built. So, here are just some demos. So, this is Wikipedia. Let me just pause it, actually. This is Wikipedia where everything is generated on the fly, even the HTML. So, this This is actually being Oops. This is actually being generated by the by the model on the fly. So, that It's a web page with the HTML and the text and everything being generated on the fly. So, it looks like regular Wikipedia. And when you click on it, it's it's the the latency is low enough that it can just fill in the page as if it was a real Wikipedia page. Um so, that's kind of uh Wikipedia generated on the fly by a this is a very low latency model. We have a similar thing where we did it for Reddit. So, now all the responses to your posts will be by bots. They weren't already. Um and it's generating fake comments. Not the So, uh the Gemini diffusion model was not an image generating model. So, this demo links in the which was our startup state-of-the-art image generator model at the time, which I think was Juno. Was it was before Nano Banana. So, it is the two of these models working together to fill in the web page. So, it's the image generation model is a little slower. Um but you can see that it's like, you know, you can you can invent any Reddit you want, sharks in this case, and it'll generate the page with the text. The images follow a minute a minute later, and then you can interact with this website as if it was a real website with, you know, real users and so on. Just being entire All the comments, all the images, all the HTML, everything is being generated entirely on the fly here. This is being generated by the model. Um I love this one. This is my favorite one. This is an operating system also being entirely generated on the fly. So, every click here is generating the next page of the operating system. So, it looks like a real operating system, but it's all being generated by the model on the fly responding to every click. So, every time you enter like the readme, it generates the text, but it also generates, you know, the the web pages you can do, you know, go back to the desktop and and so on. I think this Yeah, okay. Um and then this is a this is a demo from someone on Twitter who used the who used the Gemini diffusion API uh or sorry, not the API, the web page to do some live coding with his voice. So, I I really like this one. \>\> Create a to-do app. Add 10 random to-dos. Allow to-dos to have a completed state. Mark four random to-dos as completed. Allow me to sort to-do's by name and by state. All right, let's see if this works. Sort by name, sort by state. Testing, enter, was added to the bottom. We'll try deleting a few. Add one. Everything's working. Please convert this to dark mode. And this was literally 15 seconds of work. \>\> Okay, so that was uh yeah, that was someone outside of our team so he he could say that. Uh yeah, but five coding by voice but but just in general I think that, you know, low latency models can really unlock some new experiences uh for users and new products. And so we're excited to see what people will do when the next generation comes out. Okay, and on that note, thank you very much. \>\> \[applause\] \>\> Questions? Yeah? \>\> Yeah, yeah. So yeah, we use all the same data. Yeah. I mean, the algorithms have to change a bit but we use all the same data. Yeah. You can distill them, yeah. Yeah, they're I'm not sure if there are any published ones. \>\> \[laughter\] \>\> I don't think so. Maybe they're selling externally but There's a Yeah, so we're going to release something soon. Yeah. Yeah. Yeah. Uh yeah, so there's a the bigger models tend to require less steps for the same output. So, they kind of you know, even if the model is getting bigger and the flops per forward pass are getting bigger, they tend to reduce the the forward passes they need. So, it's kind of a you kind of have some sort of a diminishing cost of serving even the biggest models. Yeah. \>\> So, the next question, do you how do you price the price of the structure doesn't matter for the diffusion \>\> Uh I don't know. We haven't got to that yet. I'm a research scientist. Uh Yeah. \>\> How do you define the size of the answer because for \>\> Yeah. \>\> you expect that I have this image and I have the output \>\> Yeah. \>\> frame. How do you do that in \>\> Mhm. \>\> coding or text? \>\> So, there's a there's a there's a few different ways. The easiest way is to just fix some some window length and then just iterate on that. So, it's like auto aggressive, block-wise auto aggressive. It's kind of the standard way to do it. But, you can have like a a head that will predict the length of the response and stuff like that if you wanted. Yeah. \>\> Yeah, but you have to fix the outcome in order to let the diffusion model \>\> No, it still can generate unlimited texts texts, but it's just if you fix a window length then it just does that window length auto aggressively if it needs to generate like many many windows of text. Yeah. \>\> I might have already mentioned this, but when you gave that example of the denoising steps being different ranges with different, you know, uh \>\> Yeah. \>\> Could you ahead of time set like a a limit on the denoising steps that you that you require for a problem so you can have \>\> Yeah. \>\> almost just understand what your latency is going to be ahead of time? \>\> Mhm. Yeah, yeah, these are all with a limit, but they just finish as with earlier than the limit. Yeah. \>\> If you if you had multiple windows like you just said going through, can can they then go back and attempt to previous windows? Is that kind of as the window goes is it \>\> It's Yeah, it's it's I mean that that you could potentially, but for for us we just set it in stone and continue. Yeah. Yeah. Other questions here? Yeah, yeah, that's how it works. Yeah. Oh no, so it's prefill is the same. It's just so you've got some context and you pre you prefill. Uh and then after that the generation step is typically in blocks of some fixed size, like 512 or 1,000 or 32 or whatever you want. And then that's auto aggressive. Yeah. So you can do that. Um the easiest way to do it is to just have like a logits at the top. Just vanilla prediction head at the top. Well, it So this is like this is all like discrete diffusion, right? So it's always tokens in tokens out. Like if I show you the For every step, yeah. So if I go back here, it's always like a discrete corruption process. And then you fill in a discrete token back in. So it's always So you're always in a discrete space. But you can do it in latent spaces, but and people have done that, but most of the most of the text diffusion models in literature is discrete diffusion today. Yeah. There might be one day. Yeah. \>\> I think for now they have different use cases. So, you know, if you think about what a low latency model provides, that's like that's worse throughput. So, what's that trade-off is on-device applications. So, we are in a couple of on-device applications already. Um and within like the Alphabet ecosystem. So, robotics, things like that, where you want to run a a model on the device itself. So, your phone or a robot or whatever. And then you but you want it to be low latency and you're not batching with thousands of other queries like Gemini being served in the service side. So, you want the lowest latency model. Quality isn't really any They're the same quality basically. So, then you may as well pick the low latency one cuz you don't have the throughput concerns. \>\> What do you mean like in the in the future like can we get quality up to the bar with with the current black box models? \>\> I Quality isn't the concern, it's the throughput uh for serving in a big batch setting. Yeah, yeah. Yeah. \>\> So, currently you are getting more or less the same quality of frontier models in your research? \>\> Yeah, yeah. \>\> So, what about reasoning tasks? Because we we can really verify what we want. \>\> Sorry? \>\> What what about reasoning tasks? Because we we can really verify what we want. Something like this. \>\> Yeah, yeah. Yeah, you can do RL. Yeah. You just need to change the algorithm, you know, but you can still do it. Yeah. Yeah. \>\> Is there much scope for combining diffusion with autoregressive text models? \>\> Yeah, you can do that. Yeah. \>\> \[laughter\] \>\> Yeah. Yeah. \>\> Can you like initialize with a smaller model so you don't have to start from noise so you can have to do less steps? \>\> Um the problem with that is if you train it with noise, it expects noise. You'd have to train it with the small model. As you like the output and that just adds complexity. So, we don't usually do that. Yeah. Um Well, not from not from our team, but there are there's a bunch of literature out there, yeah. Yeah. Yeah. You get the idea, I think. Yeah. Any other questions? There's a lot of questions. Gone through them all. That's good. Oh, one more. Okay. Um I don't know. I don't think we've tried to do that. \>\> \[laughter\] \>\> Probably would work. It would probably do something. Yeah. Cool. Okay. Thanks, everybody. \>\> \[applause\]