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Title: Diffusion Unlocked: Advanced Techniques for Training, Inference, and Deployment S81684 | GTC San Jose 2026

URL Source: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81684/

Published Time: Tue, 11 Aug 2026 16:27:11 GMT

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00:07

Hello, everyone. Welcome to NVIDIA GTC.

00:11

My name is Yves, and I work in the AI Labs team here at NVIDIA.

00:15

Today, I'll be your host for this session.

00:17

Before we begin, a few housekeeping announcements.

00:20

Make sure you check out the GTC park, where you'll find

00:24

art installations, lounge areas, food trucks, and sponsor

00:27

activations, as well as our vibrant night market, open from 7 to 9.

00:37

The session recording will be made available to attendees

00:40

in the session catalog within 72 hours and to the public

00:43

on NVIDIA On Demand next week.

00:46

Just a note, we may have some time at the end of the session

00:49

for a few questions.

00:51

So throughout the session, think about what you want

00:53

to ask this amazing lineup.

00:57

Our moderator today is Ming-Yu Liu, VP, Generative

01:00

AI Research at NVIDIA.

01:05

and a fellow of IEEE.

01:07

Ming-Yu leads the Deep Imagination Research Group at NVIDIA,

01:10

which focuses on deep generative models and their applications

01:14

in content generation.

01:16

Please welcome Ming-Yu Liu.

01:25

Thanks for the introduction.

01:27

Glad to be here. I lead NVIDIA's COSMO effort, and COSMO is

01:31

a platform where division models are absolutely essential.

01:36

So everything we discuss today is directly relevant to where

01:40

this COSMO technology is going.

01:42

And I am thrilled to be your moderator, and even more thrilled

01:47

to have the discussion with the amazing lineup we have today.

01:51

And let me introduce our panelists.

01:54

The first one is Stefano Ermon.

01:58

Stefano Ermon is CEO and co-founder of Inception Labs and a professor

02:04

at Stanford University.

02:06

And I want to be direct about something.

02:07

Stefano is the primary reason this panel exists at all.

02:12

Before division model became the dominant paradigm for

02:16

generative AI, the field was largely stocked with gains.

02:20

Powerful but notoriously unstable and hard to scale.

02:24

Stefano's lab at Stanford changed that.

02:27

His foundational work on score-based generative modeling,

02:32

showing that you could train a model to estimate the gradient

02:37

of the data distribution and then follow the gradient to

02:40

generate stunning samples, gave the field an entirely new theoretical

02:45

and practical foundation.

02:46

That was seeded what became DDPN, DALI, Stable Division, Sora,

02:52

Mid Journey, and essentially every major image and video generation

02:57

system you can name today.

03:00

Without Stefano's pioneering research, the division revolution

03:04

simply wouldn't happen.

03:06

Today, at Inception Labs, Stefano is pushing division

03:09

even further, applying to language with Mercury, the first

03:14

commercial-scale division LON, which runs up 10 times faster than

03:19

conventional autoregressive models.

03:22

Stefano is a Sloan Fellow, NSF Career Award winner,

03:26

and IHS AI Computers and Source Award recipient.

03:30

Generally one of the most important researchers working as AI today.

03:43

The next panelist is Robin Rombach.

03:46

He's a co-founder and CEO of Black Forest Labs.

03:50

And if Stefano laid the theoretical foundation for diffusion models,

03:54

Robin is the person who figured out how to make them work

03:57

at scale for the rest of the world.

04:00

During his Ph.D.

04:01

at LMU Munich, Robin was the primary architect of latent

04:06

diffusion models.

04:08

The breakthrough insight that you could run the diffusion

04:10

process in a compressed latent space rather than pixel space,

04:11

making high-resolution image generation tractable on realistic

04:13

computer Thank you.

04:18

Thank you.

04:20

Thank you.

04:21

Level Up became Stable Diffusion, which he developed in collaboration

04:26

with Stability AI, and which transformed the entire open-source

04:30

AI ecosystem overnight.

04:32

Think about the scale of the impact.

04:34

Nine out of... Sorry.

04:41

9 out of 10 of the most downloaded tattoo image models on Hugging

04:45

Face today are derivatives of stable division.

04:48

The core architectural ideas in DALI 2, DALI 3, and Sora

04:53

trace back to his research.

04:55

After leaving stability, Robin founded Black Forest Labs,

04:59

where his team built Flux.

05:02

A model family that has already been downloaded over 400 million

05:06

times and is widely regarded as setting a new benchmark

05:10

for open image generation.

05:12

If you are generating an image in the last three years, there's

05:15

a very good chance Robin's work was involved.

05:28

Next we have Anastasis Germanidis.

05:31

He's the CTO and co-founder of Runway and the person who

05:36

has arguably done more than anyone to bring generated

05:40

video from research labs into the hands of real creators.

05:44

When Runway launched Gen 1 and Gen 2, it wasn't just

05:47

a product milestone.

05:49

It's fundamentally changed how filmmakers, editors, and

05:53

storytellers thought about what was possible.

05:57

Runway's tool has been used in Hollywood production,

06:00

music videos, and advertising campaigns worldwide.

06:04

Gen2 has particularly captured the public imagination in

06:07

a way that few AI models, few AI demos ever have, showing

06:12

that high-quality text-to-video generation was no longer a distant

06:16

research ambition, but a usable creative tool available today.

06:20

Runway was named one of the Time Magazine's 100 most influential

06:25

companies in 2023, and under Anastasis' technical leadership,

06:29

the company has continued to push the frontier, more

06:32

recently with the Gen4, which sets a new bar on consistency,

06:36

controllability, and cinematic quality in AI video generation.

06:41

Anastasis has built Runway into one of the world's most important

06:44

applied AI research organizations,

06:47

Sitting at the intersection of cutting-edge research and

06:49

real-world creative impact.

06:51

Fantastic.

06:52

Please come.

07:00

And finally, Cynthia Lu.

07:02

She's a Senior Director of Engineering at Adobe, where

07:06

she leads the Applied Research team behind Adobe Firefly.

07:10

Firefly is Adobe's central base on the future of creative

07:14

AI, and Cynthia has been the engine behind it from day one.

07:19

Her team built Adobe's first large-scale generated foundation

07:23

model entirely from scratch.

07:25

No small fits inside a company with the complexity and scale of Adobe.

07:30

And today, Firefly is deeply woven into the fabrics of Photoshop,

07:35

Illustrator, Premium Pro, and Adobe Sprites, used by tens of

07:40

millions of creative professionals around the world every single day.

07:44

The ratio of that word is staggering.

07:46

When a graphic designer uses generative field in Photoshop,

07:49

when a video editor uses generative intent in Premiere, when a

07:53

marketer spins up a campaign asset at Adobe Express, then

07:58

Cynthia's team works in place.

08:01

Firefly has become one of the fastest-adopted generative

08:04

AI platform in the creative industry, and Adobe has reported

08:08

billions of Firefly power generation since launch.

08:11

Beyond images, Cynthia has driven Firefly expansion to

08:15

video and audio generation, pushing Adobe toward a future where every

08:19

creative workflow has generative AI deeply embedded in it.

08:23

Her work represents one of the most successful examples

08:26

in the industry of

08:27

Taking frontier research to translate it to tools that

08:31

generally change how people create for living.

08:35

Let's welcome Cynthia.

08:41

Okay, so let's start our session.

08:48

Okay, so we're gonna start the first question on data

08:53

quality versus data quantity.

08:57

What do you think about data quality versus data quantity?

09:00

Is there a point where more data start helping?

09:03

And how do you know when you have hit it?

09:07

Maybe we can start with Cynthia.

09:09

Sure, that's a really great question.

09:12

Data, I think, is front and center for our foundation

09:15

model development, and that's what we think about almost every day.

09:19

And I would say that both are very important.

09:22

But maybe the importance shifts around different

09:27

stages of the training.

09:28

In early stages of the development, the data quantity really matters

09:33

a lot because earlier on, the model is trying to learn

09:36

the really rich visual world from data like text-image pairs and

09:41

text-video pairs, things like that.

09:44

And at some point, I think once you have trained it for

09:49

a while and then we start to notice that by doing more careful

09:53

filtering and the duplications of these data, we find that

09:59

data quality starts to matter more.

10:03

Towards the post-training, really the quality actually

10:05

trumped the raw volume.

10:08

And in our five versions of image release and two versions of video

10:12

release, we actually gradually found that in the last few

10:15

iterations, the quality improvement brought by the post-training

10:20

stage, like supervised fine-tuning and reinforcement learning,

10:25

actually is more significant compared to the volume of the

10:31

pre-training data, as well as some minor architecture improvement.

10:37

In terms of the question when do we see that happen usually

10:41

we notice it's probably a good time to start focusing

10:45

on quality when the failure mode is always very consistent you

10:50

always see the same mistake being made by the model and you don't

10:55

see as big metric improvements such as problem alignment

10:59

visual quality as much as before where really the where you're

11:04

seeing diminution return when you're adding more data into it.

11:08

And so yeah, usually that's the time when we start to really think

11:12

about okay, what are the corner cases the model is struggling

11:16

and What are corner cases that are actually important to our users

11:19

because we really focus on serving our creative professionals, right?

11:23

So at that time we we find that very important to start collecting

11:29

specific data targeting corner case, targeting solving specific

11:33

problems, rather than just introducing more volume of the same

11:37

data with the same distribution.

11:41

Tasia, do you have something to add up?

11:45

I would say I guess data quality versus data quantity it's

11:48

kind of like is water more important is food more important

11:52

like it's both I think the there is probably there's systematic

11:57

ways of measuring it where you know at the larger model scales

12:00

you might it's fine to introduce more noisy data like there is

12:04

kind of there is basically scaling laws of data filtering that you can

12:08

you can use to determine like how

12:11

How to think about that trade-off and how to think

12:14

about data repetition.

12:16

But ultimately, I think you need to... Data is one of

12:20

the most important... It's not the most important aspect

12:21

of building great models.

12:23

And you need to invest in both the quantity and the quality and being

12:29

very obsessive about the quality through manual inspection as well.

12:34

I think it's...

12:35

Looking at the data is the kind of thing that is probably

12:40

a law of even if you think it's important, you don't

12:43

think it's important enough.

12:46

You can never be obsessive enough about data quality.

12:53

Yeah, 100%.

12:56

You have to be obsessive. There's so much trash in large-scale

12:58

data sets, which you have to filter out, I think.

13:00

Especially, like, deduplication and stuff like that is

13:02

extremely important.

13:04

And I would say it depends on what is it that you want to build.

13:07

If you want to build, like,

13:09

If it's just a high quality image to video model, then

13:11

you have to put even more attention on the quality of the data,

13:14

but then if you're more interested in making this pre-trained formable

13:18

substance, which a multimodal model can be, then getting

13:23

a lot of task diversity into the data is maybe more important.

13:28

Stefano?

13:29

I mean, I can comment from the perspective of training

13:32

diffusion models on text instead of images and video.

13:35

And yeah, we're seeing similar kind of patterns.

13:37

I think there is growing evidence that diffusion models tend

13:40

to be more data efficient compared to autoregressive models.

13:43

I think the intuition is just that there is essentially

13:45

data augmentation going on under the hood, like you're perturbing

13:48

the data in many different ways.

13:50

And so you're kind of like learning from multiple views

13:52

of the same data points.

13:53

It's like the old idea that always kind of works in machine learning.

13:57

But yeah, it's still, you know, they may be more efficient

14:00

than autoregressive models, but it's still really, really

14:02

important to focus on having enough data and high quality data.

14:08

And I would say we see similar patterns in the sense that

14:10

during pre-training, you know, you can kind of like get away

14:13

with lower quality data, but then as you move further,

14:17

In the pipeline, it becomes more important to be very careful about

14:21

selecting high-quality datasets for SFT and RL and all the other kind

14:26

of post-training of the models.

14:30

I think we have consensus on these questions.

14:34

The next question is, why Diffusion LON?

14:37

Although request model has had an extraordinary run,

14:42

GPT, Cloud, Gemini, what are the fundamental arguments

14:47

for why Diffusion is a better foundation for language modeling?

14:52

Maybe Stefano? STEFANO TORRES- Yeah.

14:54

Yeah, I mean, it's a pretty interesting stage we are at

14:57

right now in generative AI.

14:58

Like, there is two main types of models.

15:00

There's diffusion models and autoregressive models.

15:03

And if you think about continuous modalities, images, video,

15:05

you know, diffusion models or flow matching, like, variants

15:09

of diffusion models tend to be the best approach we have today.

15:12

But if you think about discrete data, text, code,

15:16

DNA, these kind of models,

15:19

A lot of the frontier labs are betting on autoregressive

15:22

type models, where you kind of like generate left to right

15:25

one token at a time.

15:27

And in my opinion, you know, diffusion models are a superior

15:34

type of generative model and there are different ways to

15:37

look at the problem.

15:39

Fundamentally, if you think about an autoregressive model,

15:43

the kind of computation that you're doing is very sequential.

15:47

Like, there is a structural bottleneck where you cannot

15:49

generate a token until you've generated everything

15:51

that comes before it.

15:54

And that is leading to some pretty big inefficiencies

15:57

like that kind of computational pattern does not map well to GPUs.

16:02

That's the fundamental reason we're seeing like big, all

16:05

the inference workloads we see today are bottlenecked by memory.

16:09

On the other hand, if you think about a diffusion model

16:12

where you generate, you know, in a course-defined way, you

16:16

start with a rough guess of what the answer should be, and

16:18

then you gradually refine it using a neural network, either removing

16:21

noise or fixing mistakes, and there's different flavors of it.

16:25

But the key difference is that a diffusion model is

16:27

inherently parallel.

16:28

A diffusion model can modify many tokens, many pixels

16:31

at the same time.

16:32

And so you still need big neural networks, but each neural network

16:35

evaluation is not just giving you a single token, it's able to modify

16:39

many tokens at the same time.

16:40

And so to the extent that you're able to train these

16:42

models to be efficient in terms of like the number of

16:45

denoising steps that you need.

16:47

And we know that there are many different ways of kind of

16:49

like achieving that sort of result.

16:52

And, you know, Robin has done a lot of great work in that space then.

16:57

You know, that diffusion model can be much more efficient.

16:59

And that's what we're seeing at inception.

17:01

Once you figure out the right recipe for training diffusion

17:04

language models, you can match the quality of autoregressive models

17:09

while being significantly faster and cheaper to serve the models.

17:14

Our latest Mercury 2 model is basically matching the quality

17:18

of speed optimized models from Frontier Labs, Haiku models, Mini

17:21

models, Flash models from Google, while being about 10x faster.

17:27

And this is running on just regular NVIDIA GPUs.

17:30

So that's very exciting because we're starting to get to the

17:33

speeds that you would get if you were to serve out aggressive models

17:36

on specialized AI inference chips.

17:39

But we can get those kind of speeds on just NVIDIA GPUs.

17:42

And so I feel like that's it's basically an architecture

17:45

that is built to to be very, very efficient on GPUs.

17:48

It's built to on top of matrix, matrix multiplies.

17:52

And so that's why we're able to get really, really good efficiencies.

17:54

And that's why I think it's going to be the

17:56

architecture of the future.

17:58

I'm sold.

18:00

Any... No.

18:03

I think, like, maybe one thing to consider is that currently

18:06

a lot of, like, the large-scale workloads are optimized for

18:10

autoregressive models.

18:11

Everyone in the industry is doing that.

18:14

So maybe there's a little bit of, like, oh, we see that,

18:17

right, like, there's a head start, but then... Of course.

18:20

Right now I think what's interesting is combining these

18:22

two approaches, like transfusion and these types of work where you

18:25

combine continuous flow matching for image or video generation

18:30

with an autoregressive part.

18:31

I think the key benefit for multimodal models is that you

18:34

want to extract as much, have as much compounding effect as possible

18:40

by training on large-scale text, large-scale image, large-scale

18:44

video at the same time.

18:46

Now, I think your arguments probably still hold that like

18:49

if this is like in the future like a diffusion approach for language

18:54

modeling would be the best and you can just plug that in there, right?

18:58

But currently, I think like these like mixed approaches

19:01

are very interesting.

19:05

Yeah, similar to Robin's point, I think another way of combining

19:08

the two is kind of this block-wise autoregressive approach, which

19:11

I think has been, it seems really promising both on the

19:15

language side and on the image and video side as well.

19:20

That essentially means predicting a few a few tokens at a time

19:25

and we did some work, a bit of work on language.

19:29

We're primarily focused on imagine video but we did a project kind

19:32

of taking an autoregressive visual language model and then turning it

19:37

into a blockwise diffusion language model and there is a nice kind of

19:44

There's a trade-off that then you can have where you can,

19:48

if you increase the block size all the way to the full sequence,

19:52

then it's a diffusion model.

19:53

If you decrease it to one token at a time, then it's

19:55

an autoregressive model.

19:57

And you can trade off kind of latency and performance

20:00

in a nice way there.

20:01

And we found that it doesn't take too much compute to actually

20:05

turn an autoregressive model into a kind of block-wise diffusion

20:09

model, and you can maintain a lot of the performance as well.

20:12

But we're particularly excited about block-wise diffusion on video

20:19

models and how it can enable those real-time interactive experiences

20:23

where you can generate one or a few frames at a time and then provide

20:27

actions every step of the way.

20:30

This approach, we basically inherit all the painful parts

20:35

of training diffusion models and the painful parts of training

20:37

autoregressive models, but it basically allows you to, I think it

20:43

really opens up the possibilities of kind of interactive generation

20:46

and the kind of experience that you can build on top of that.

20:52

It's hard to add more points on top of all the smart answers.

20:56

I would just say that I think probably a problem that can

21:03

be solved even better is how to handle the differences

21:06

between the discrete signal in language versus the continuous

21:10

signal in image and video.

21:11

I think the hybrid approach does show a lot of promising

21:15

results in academia and apparently in industry as well.

21:19

That's something that we're looking into as well.

21:23

So hopefully, next year, we'll see more diffusion-based IOM work.

21:28

Yeah, I think we are biased, all the people here.

21:32

OK, so next question.

21:36

Low latency inference and deployments, right?

21:38

What does it actually take to serve diffusion model at

21:44

low latency and scale?

21:46

And where are the body's spirit that nobody talk about Publicity.

21:55

So I guess I'll build up kind of from my previous answer as well.

22:00

I think we spend a lot of time, especially over the past year, into

22:04

thinking about real-time generation and interactive generation

22:07

and thinking of how do we take the full-sequence diffusion models

22:11

that we have, which can take, on the video side, a minute or two to

22:15

generate a video and turn it into a real-time interactive video model.

22:20

And I think some of the modeling challenges are well studied

22:23

of how to do post-training and step distillation and

22:26

do kind of this and train for like autoregressive objective.

22:32

I think some of the points that are a bit less obvious are the kind of

22:39

overcoming the causal bias of the failure and causality of like the

22:45

diffusion models regularly show.

22:48

So if you

22:51

If you see how a lot of the videos are generated by full-sequence

22:55

diffusion models, you often have this success bias or issues in

23:00

causality where the effect precedes the cause, like you might have.

23:05

Or you don't accurately model how a specific interaction

23:12

causes an effect, so you might I have a door opening before

23:17

a person actually taps the handle.

23:19

So one of the benefits of training for causality is that you're able

23:28

to model that kind of interaction and cause and effect a lot better.

23:34

I think in practice, making the models work in real-time

23:37

involves little trade-offs and involves thinking through

23:40

very carefully about what your use case is, and in some cases making

23:45

sure that you're, you know, like in some cases you might want this kind

23:50

of long-term consistency when you can kind of move around the space

23:53

and then go back and have kind of the environment be preserved.

23:57

In some other cases that might be less important, so we've been

24:00

deploying real-time models for Or

24:03

You know, conversational interaction where that long-term

24:09

consistency is less important.

24:10

And then we've also been working on models for navigational

24:14

kind of world models where that becomes more important.

24:18

So the more clarity you have about the use case, the better you

24:23

can kind of think about the design and the modeling choices that

24:26

you do so that you're basically able to kind of Diffusion models.

24:28

Diffusion models. Diffusion models. Diffusion models.

24:31

Satisfy that use case very well.

24:33

And we found that thinking through that kind of the end experience

24:36

is even more important for real-time deployment than it is for

24:41

kind of more traditional kind of video and image generation models.

24:48

Robin, maybe you also serve in our skill.

24:51

Yeah, I think real-time generation is interesting because it

24:56

forces you to think through all of the aspects.

24:59

It's like model design.

25:01

It starts from finding an efficient representation,

25:05

especially for video, which enables you to serve like X seconds

25:10

of generation time in like X seconds, like X seconds of content

25:15

in X seconds of generation time.

25:18

And you can go ahead and try to find an architecture for,

25:23

let's say, video or encoder or something like that, that

25:25

has a very efficient representation that enables that.

25:28

But then you have to have very good reconstruction quality,

25:30

learnability of that latent space to actually train the model.

25:35

Then in addition to that, you want to consider stuff like parallelism.

25:38

When you serve the model,

25:41

you have to think about what's like the target for

25:43

like the step distillation that you do in the end.

25:46

And then, yeah, like how much context do you want

25:49

to feed into the model?

25:50

Like a lot of the architectures right now, they are like very like

25:55

All operating on the same kind of sequence, diffusion

25:58

transformer style, but maybe it is worth thinking about, can I feed

26:02

context to some other mechanism?

26:05

Maybe even some non-intentional architectures, like Mumber

26:09

or something like that.

26:10

But I think there's a lot of different approaches that

26:13

really get combined in the real-time scenarios.

26:16

That's why it's just a super interesting perspective, which

26:20

forces you to think a lot about different challenges

26:22

that you have in modeling.

26:26

Yeah, I think what's exciting about diffusion models is that training

26:30

and inference are decoupled.

26:31

Actually, that was the main reason I started working on

26:34

diffusion models was this idea of can we have a really, really

26:37

deep computation graph at inference time without having to actually

26:40

unroll it all the way during training time, like something that

26:43

you can still train efficiently, but then it allows you to kind of

26:46

like scale compute, which I guess is what we would now Now called

26:50

test time inference, test time compute, but this idea of being

26:53

Having access to a way of throwing more compute and improving

26:58

the quality of the results.

26:59

And that's a very, very unique property of Diffusion models,

27:02

which kind of goes against the deep learning wisdom that

27:04

training and inference have to be coupled together.

27:07

But this idea of test time inference, this idea of being

27:10

able to control that inference time, how much compute you

27:12

want to use, and then trade off quality for speed and cost,

27:15

I think is really, really powerful.

27:17

And it's actually the right way to think about test time inference

27:19

and test time compute, as opposed to just generating a super long

27:22

Think and trace, which I think is just not the right

27:24

way to do things.

27:25

And if you start thinking about that perspective, and there is all

27:28

kinds of interesting algorithmic opportunities for accelerating

27:32

inference in diffusion models.

27:34

There are some beautiful connections with like numerical

27:36

methods at ODE and SDE solving.

27:38

You know, I was on some of the early papers on DDIM and

27:41

kind of like thinking about fast solvers for ODEs and how those

27:45

translate into efficient sampling techniques for diffusion models.

27:50

At the same time, you always need to be very careful and

27:52

think about the MLCs aspects.

27:55

Like it's one thing to come up with fast inference algorithms,

27:58

but you always need to think about, okay, how does that map onto

28:02

GPUs, onto the kind of operations that we can do efficiently today?

28:06

And so that's something that I've also been thinking

28:08

about for a long time.

28:09

I was on the early Flash Attention paper with TRII, and so I've

28:13

always been very, very keen to think about, OK, how do

28:16

we co-design the kind of like algorithm and the implementation

28:19

so that we have something that actually works well in practice?

28:22

And even at Inception, we spend a lot of time thinking about how

28:25

to serve these models efficiently.

28:27

If you have a traditional autoregressive LLM, there

28:29

is a bunch of serving engines that have been developed, some

28:32

open source, some closed source, VLLM, SGLang, TensorRT from NVIDIA.

28:37

There is pretty good software to serve autoregressive models

28:39

at scale and handle continuous batching and all the kinds

28:43

of things you need to do to serve workloads in production.

28:46

At inception, we had to build our own serving engine.

28:49

So we have a serving engine that is very specialized for

28:52

serving Diffusion language models.

28:54

And again, that is opening up.

28:55

Interesting opportunities for optimizations that are

28:58

just not possible if you have like a very sequential kind

29:01

of computation on ultra-aggressive models where there is only so many

29:04

things you can do and you know you have to use the model at inference

29:06

time the same way you use it at training, predict the next token.

29:10

And so I feel like that's another really really exciting

29:12

opportunity for coming up with more efficient better ways

29:15

of serving LLMs which is the main workload in generative AI today.

29:23

Xinxia? Yeah, so let me add some other perspectives.

29:27

I think people touched on a lot about algorithms.

29:32

I think there are other things that usually come as a surprise

29:37

that will impact the quality, it will impact the overall

29:41

latency and cost when you are deploying these models at scale.

29:45

So I think most Diffusion model developer would

29:49

They will apply step distillation, will apply various kind of

29:53

distillation and replacing full attention with more efficient

29:59

attention mechanisms, right?

30:00

And they sometimes will bring quality impact in ways that

30:05

you might not predict.

30:07

And sometimes you will discover that in skilled deployment.

30:12

And we also find that at Adobe, because we care a lot about

30:17

the society impact of these models, so we are very, very careful

30:22

having pre-generation guardrails and post-generation guardrails.

30:26

And we found that, especially in the imaging model space,

30:28

where the model is already optimized to be very efficient

30:31

with the size of the capacity of the model and the number of steps

30:36

of the model, You can approach close to few-step inference.

30:43

And then other aspects of the pipeline start to become

30:46

the bottleneck, right?

30:48

The guardrail, the prompt rewrite, leveraging language model,

30:51

VLMs, the post-generation guardrail checking for harm and bias, IP

30:57

infringement, and things like that.

30:59

And those become other aspects that we need to pay more

31:04

attention to optimize.

31:06

So I would say, you know, quality sometimes these

31:09

optimizations will bring.

31:11

Surprising impact and as we're going more into really making

31:17

these models available to everybody in the society, we have to pay

31:21

more attention to the surrounding models, the surrounding algorithms

31:26

beyond just the diffusion model.

31:29

Thank you.

31:30

Okay, next question.

31:33

Lesson learned from Shipping Diffusion Models.

31:36

We have all deployed division model in production.

31:39

What is one thing you got completely wrong before you

31:43

actually ship it?

31:47

Yeah, I think probably everything.

31:49

Like one thing that we got wrong at one point was we

31:52

shipped the wrong checkpoint.

31:54

It can happen.

31:56

I think actually the whole operations around building a good

32:02

backend to serve these models is

32:06

Something that I maybe underestimated in the

32:08

beginning, but it is like all of what you said, right?

32:10

Like the pre-processing, the post-processing, the coordination

32:13

of all these different kind of layers that run on top

32:17

of the generation.

32:18

I think that's just like a huge engineering challenge.

32:24

Yeah, something that we continuously iterate on

32:27

and try to improve.

32:28

And then you have to improve the efficiency, not only of

32:31

the diffusion model, although that is the most exciting

32:33

one from a modeling perspective.

32:35

But then you have so many other parts typically in the pipeline

32:39

that you also need to optimize.

32:40

That's something that maybe sometimes gets a little

32:44

bit less attention.

32:46

Stefano, do you have any experience like this?

32:49

Yeah, we also got a lot of things wrong.

32:51

I think, and I agree, I think that the bells and whistles that you

32:54

have to put around the checkpoint and the basic kind of like serving

32:58

engine, that they matter a lot and it's easy to get things wrong.

33:01

You know, it's a very complex stack and you're dealing with,

33:07

there's a lot of non-determinism, which I think makes

33:11

Debugging, very, very challenging.

33:13

And there are subtle bugs that it just makes it hard

33:17

to to to to figure out something is going wrong because, you know, even

33:21

quality, it's it's hard to measure.

33:22

Yeah, you can use benchmarks.

33:24

You can kind of see, but then there is variance and you never

33:27

know if things are going, you know, it's doing the right thing or not.

33:30

Is there a bug or not?

33:32

These are just very complex systems and it takes a lot

33:36

of effort to get things right, build the right tests, build

33:39

the right observability on top to make sure that things

33:43

are working as expected, especially as you have a large number

33:47

of GPUs and things can go wrong at every stage and you really

33:51

need to have a good infrastructure set up to detect all the things

33:56

Things that could go wrong.

34:01

Yeah, I mean, it's hard to admit mistakes that we

34:05

have made after the amazing introduction we have done.

34:09

I would say that people might be aware of the GenField,

34:13

GenExpand features that we released in Photoshop and actually in the

34:18

second release that we found that sometimes when we train the model,

34:23

collect data to optimize what we think the users need might not

34:28

be what they They actually need.

34:30

Right?

34:32

So, for example, we actually optimize our model to work

34:36

better to handle a large whole area when we in-paint.

34:42

But then we actually found out users in Photoshop

34:45

tend to use these

34:47

These very powerful diffusion models to impane very tiny

34:51

holes, remove skin blemishes, and inspect perfect high-resolution

34:58

texture details within the hole, which is not what we anticipated,

35:04

because there are many other traditional tools in Photoshop

35:06

that can be used for that purpose.

35:09

So I think that was one of the mistakes we have made, and since

35:11

then we needed to really Diffusion models may contain following words.

35:13

Diffusion models may contain following words.

35:14

Diffusion models may contain following words.

35:16

More attention towards what the user respects from the

35:20

model and build our data set and evolve our approach towards

35:26

solving the real user gap.

35:32

Yeah, so I think we had, you know, the benefit and the

35:36

curse of being very early in deploying video models.

35:40

So the curse being kind of there is no examples of how

35:43

to do that effectively, how to build great infrastructure

35:46

for model serving, for specifically kind of video diffusion models.

35:50

The benefit is that we basically

35:53

We had the time to do all the mistakes early on before a lot

35:58

of people were using our models.

36:00

And then we built a pretty robust infrastructure and

36:02

processes around how we think about auto scaling on demand, how

36:06

we think about capacity planning, model releases to make sure that

36:11

People don't experience very long waiting times when we

36:14

actually release.

36:16

But I think the most important piece that we improved on

36:21

over the past years has been how we do offline evolves

36:24

and check for regressions when we release new versions of our models.

36:27

So we've become very intentional around making sure that we

36:32

have really great eval sets around and capture the actual

36:36

use case and the kinds of things that people will try to do

36:39

with our models and making sure we have great instrumentation around

36:42

how people are using our model so that we can understand that kind

36:47

of drift between how we thought our models would be used and how they

36:51

They're actually being used.

36:53

So catching regressions, I think, it's an underlooked

36:56

point, but it actually enables you to push more incremental updates

37:00

to your models and not wait for the big kind of splashy releases.

37:05

And that, I think, builds also the user expectation that the tool and

37:10

the model kind of improves all the time and they don't have to wait

37:15

half a year to get the next model.

37:19

Thank you.

37:21

Last question.

37:22

Most impactful problem to solve.

37:25

If you could pick one unsolved research problem in diffusion

37:28

models that you think will define the next five years

37:32

of the field, one thing that is solved would unlock everything

37:35

else, what would it be?

37:37

Do you know?

37:39

Yeah, I think there is two, and they are related to each other.

37:43

I think the fundamental open question in the whole

37:46

space of generative models and generative AI is like,

37:48

why do these models even work?

37:50

They are not supposed to work, like there is some pretty

37:52

good reasons for, you know, there's cursive dimensionality,

37:55

we know that unbiased density estimation is provably impossible,

37:59

yet these models work and they work really, really well

38:03

and nobody understands why, nobody understands how they generalize.

38:08

And what this means is that we don't even understand

38:10

like the basic principles for building these models.

38:13

Like right now, everything is extremely empirical.

38:16

And so I think we lack a lot of the fundamental scientific insights

38:21

into how these models work, how they generalize, what's the

38:23

best way of building these models.

38:25

So building on that, I think the next, the follow-up is like,

38:29

what is the optimal architecture?

38:30

Like, what is the optimal way of building generative models?

38:33

Is it diffusion? Is it autoregressive?

38:35

Is it something else?

38:37

I think nobody knows and nobody has, the only way to kind

38:40

of provide an answer right now is just to show an artifact,

38:42

you know, Robin comes up with a better checkpoint and I come

38:45

up with a better diffusion language model and we kind of compete.

38:49

There is not really a good way of figuring out, like,

38:52

you know, what is the best way of building these models.

38:56

And so I'd be very interested to know, like, what is going to

38:58

be the architecture of the future?

39:00

Is it possible to have a single architecture that can handle

39:03

all modalities like right now?

39:05

Diffusion for continuous data.

39:07

We have autoregressive models mostly for text.

39:10

Is there a way to have a single architecture that

39:12

can handle all modalities?

39:14

I think that's pretty exciting and that would probably be the most

39:17

impactful problem anyone can solve.

39:19

I think it's very hard, but I think that would be my vote.

39:25

Yeah, 100% like on the empirical side, I think multimodality,

39:29

real-time combining these into a single architecture,

39:33

especially combining like representation learning and

39:35

generation is something that is extremely interesting.

39:38

We just published a paper on combining these two things and

39:41

like a single multimodal approach.

39:42

I think it's promising because it's like right now there's

39:46

a lot of like combination of I I take some pre-trained visual model.

39:51

um that was trained on some other objective and then use that

39:55

to guide the diffusion learning process but it's kind of junky uh

39:58

janky and um and yeah i think like unifying there is is interesting

40:05

while maintaining real-time generation capabilities and then

40:08

i think like especially in like the thing that's called world modeling

40:12

having like consistency and like

40:15

a latent world state that is independent of observations is

40:20

something that is very interesting.

40:21

So if you think about, you know, you render like from

40:24

two different perspectives at the same time from like a, I don't

40:28

know, like a video model or like world model, and then currently

40:31

that's just like not consistent and having like a representation

40:35

inside the network that enables that and that, I don't know,

40:37

propagates the state of the world while you observe from different

40:40

perspectives and then maintain consistency, I think that's

40:42

an interesting problem to solve.

40:45

Cool.

40:47

Plus one to everything that Robin said, I think.

40:50

But I think all those problems around building world models

40:53

and maintaining consistency over long periods of time,

40:58

making sure you can get the best possible performance

41:01

while deploying in real time.

41:03

I think one piece that I think is very critical, if I think

41:09

about five years from now and like where the models will be is.

41:14

Training models that jointly are able to generate a lot

41:17

of modalities or that can handle a lot of modalities and that's

41:21

from first principles It sounds something that should be that

41:23

should work like if you're training a model on Massive volumes of video

41:29

and massing kind of corpus of text like there should be a lot of like

41:32

positive transfer from from that

41:35

but in practice it's actually very difficult and there is

41:37

a lot of ways in which it can go wrong and i think people

41:40

have not really figured out how to do it well um obviously

41:44

there is models deployed out there that kind of can jointly uh can

41:48

can generate multiple modalities and like even you know as we look

41:52

at physical ai and robotics like

41:54

you know models that can do kind of visual vision language

41:57

action models that can combine language model backbones and

42:01

diffusion backbone to predict actions but even even there

42:06

there is a lot of challenges in actually training on jointly

42:09

to generate multiple modalities but

42:12

In my mind, like the ultimate goal is to train a single model on every

42:16

possible modality of the universe.

42:19

And if and in order to do that, we need a more systematic

42:23

and principled approach of tackling this problem.

42:28

Yeah, I really agree with everything being said.

42:31

I think in the context of creativity for creative

42:36

professionals, one thing that is very important to them

42:40

is really the last mile precision, the last mile quality, right?

42:45

A lot of us might think that, hey, image generation and

42:49

editing is a solved problem.

42:51

To our customers, it is not.

42:54

Because creation is an iterated process.

42:57

You don't just write a prompt, generate once, and be done.

43:02

Usually, the user will want to change a little bit in the

43:07

prompt, change a little bit here and there, but they would expect...

43:11

A near-perfect consistency of character, identity, of

43:16

layout, and lighting, environment, and only expect regions that they

43:21

want to change to actually change.

43:22

And everything else, pixel-perfect preserved.

43:26

So I think that ties into what has been said.

43:28

I think really stateful world models where

43:32

It has some memory and has consistency of the physical

43:36

world to some extent.

43:38

We'll make sure that the results come out more

43:41

predictable and controllable to professionals' need.

43:45

And I think some other aspect that would really change the world,

43:49

change how creative professionals interact with these models,

43:53

is really the interactivity piece.

43:57

Right now, there are still fairly

44:01

I would say expensive, right, and on the cloud.

44:05

But imagine a world where really the generation just

44:08

happen on the fly, wherever you go, right, based on your contacts.

44:13

It generates on the fly, adapt to your own needs, adapt to your

44:17

multi-modality input and generate multi-modality output based

44:21

on your personalized preference.

44:23

I think that would be a really amazing and more creative world.

44:28

And interactivity, handling of multi-modality will be

44:31

critically important.

44:32

And hyper-personalization of these models to our people's taste.

44:36

Thank you.

44:37

I think there's a lot of inspiration, a lot of insights

44:42

on what it takes to build another

44:47

A huge pressure for our field, right?

44:49

So we know about, we actually don't know why it works.

44:54

And we spend so much people, resources, GPU, money, on

45:00

something that we don't really know why it works.

45:03

And we also have a...

45:05

I want to push for the real-time aspect, interactive aspect,

45:09

multimodality aspect.

45:10

I think this is still a very exciting field, and hopefully

45:14

we can see more interesting work from everybody here.

45:18

And I think it ties up.

45:20

I have a fun time chatting with our panelists, and I

45:24

hope you all have fun.

45:25

And let's send the panelists.

# Diffusion Unlocked: Advanced Techniques for Training, Inference, and Deployment

Cynthia Lu,Senior Director of Engineering,Adobe

Anastasis Germanidis,CTO and Co-Founder,Runway

Ming-Yu Liu,VP Generative AI Research,NVIDIA

Stefano Ermon,CEO,Inception Labs

Robin Rombach,Co-Founder and CEO,Black Forest Labs

AI-Generated Summary of this Video

Rate Now

Diffusion models have exploded from research labs into the mainstream, powering everything from photorealistic image generation to creative co-pilots. But moving these models from a notebook to a scalable, low-latency, production-ready service is one of the greatest engineering challenges in modern AI. The "magic" of generation hides a complex stack of optimization and infrastructure hurdles.

We will bypass high-level concepts to debate the concrete architectural trade-offs required for low latency and high throughput, covering unique training challenges, up-and-coming model architectures, inference and runtime optimization, low-precision and NVFP4, and scaling topologies.

### Learn More About This Topic

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Events & Trainings:GTC San Jose

Date:March 2026

Topic:Agentic AI / Generative AI - Image Generation

Industry:All Industries

Level:Technical – Advanced

Language:English

NVIDIA technology:Cloud / Data Center GPU,RTX GPU,DGX Platform,CUDA,TensorRT, Infiniband Networking,cuDNN,Interconnect Networking,NVLink / NVSwitch,Triton

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