Image Generation and Visual Intelligence with Black Forest Labs

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Welcome to another episode of the Practical AI podcast. This is Daniel White, and I am CEO at Prediction Guard, and I'm joined as always by my co-host Chris Benson, who is a principal AI and autonomy research engineer. How are you doing, Chris?

Hey, I'm doing great. I can't wait to get into today's conversation. It's going to be fun.

Yes, yes. For, for an audio podcast. We're gonna talk about a lot of interesting visual things. Maybe before we get started, just a little teaser, Practical AI is posting some videos on YouTube now. So if you do consume podcasts that way, you might go check us out on our YouTube page. But speaking of images, videos, and more specifically image generation, really excited to have with us today Dustin Podell. Who is co-founder and researcher at Black Forest Labs. Welcome, Dustin.

Yeah, yeah, thanks for having me, guys. It's really great to be here.

Yeah, and I know that Black Forest Labs does more than just kind of raw image generation. There's a lot of workflow-related things, hardware optimization, all sorts of cool stuff you're involved with. But as we get into some of that, I'm wondering if you can just help our audience with a bit of a, state of image generation methods and workflows for the industry. We've talked on the show before about diffusion models, and we'll link some of those episodes in the show notes maybe. But a lot has happened, right? There's a lot of people working on a lot of interesting things, and I'd love to kind of understand, like, over the last year, what are some of those main, main points that might be good for people to orient themselves to where things are at now?

Yeah, yeah, no, it's a good question. I mean, if I'm allowed to take it even a little bit further back, I mean, the state of— Absolutely. Yeah, yeah, the state of ImageGen, VideoGen, generative models as a whole has kind of gone crazy, so to speak, in the last like 3 or 4 years.

Yeah.

So where are we now? Where we came from about 4 years ago, where I first kind of entered into the scene, so to speak, is we were at models that were essentially just doing little blobs of color that were kind of related a little bit to where you were with the prompt, you know, okay, oh, a lighthouse on the beach or this and that. And you would get something that, okay, that vaguely looks like it. And you would show someone and it'd be, um, yeah, I could see it, I guess. And, and maybe it would interest a few nerdy people, but, and, and then now today we're at the point where, you know, if you've probably been seeing plenty of this stuff online where we're, seeing whole short films made entirely with AI generation where certain scenes are almost entirely indistinguishable from, from reality, so to speak. So I would say we've, we've come quite far, but I will also say the core of the technology hasn't actually changed that much. It's been a pretty nice, like, forward progress. I don't want to, like, dive immediately into anything technical here, but, but it's certainly, I mean, for anyone who's been paying attention, I'm sure, or anyone who really hasn't been paying attention, this probably came a bit out of nowhere.. Yeah. Yeah.

I was gonna say, I guess, you know, with the broadly attention by the general public is so much on kind of more of the LLM generative world in terms of, you know, that, that stuff and everyone's finally on apps regardless of whether they're technical or not. And so I think a lot of people kind of miss the tremendous advancements you guys are making on that side. And so like, you know, uh, could, could you, could you take a quick moment and now that you've kind of done the highest level, maybe step through some of the things and people may remember, like we talked about Stable Diffusion and things, a couple of those things that have kind of led up to what we're gonna dive into today with some more specifics. And as Daniel mentioned, we can offer some links for past conversations if people wanna dive into those specifically. But that would really be interesting to kind of hear distinct points on that timeline as we hit to this point.

Sure, sure. First, before I do that, I guess I'll ask Chris, how technical am I allowed to get here?

You're allowed, we're gonna, so we'll, we'll, like, if you dive into something, we may stop you and say, that is what, like, what is that acronym? But other than that, you can go as technical as you want and we'll make sure that everybody can stick with us.

Okay. Including me. I guess, yeah, yeah. I guess if I'm allowed, I'll do kind of at least my, the best of my knowledge overview of the last bit of time since, so obviously the field of AI and ML has been around for a while trying to, trying to structure and learn distributions of different data, mostly was used for tabular prediction, all sorts of other things for a while. And then in 2017, some researchers at Google figured out this very cool thing called the transformer, which I'm sure people have heard many, many times about right now, which I guess the best way to go down this path is to say we found a way to train much more general models, learn from much more general data. I'm trying to think how to, how to essentially take this story.

It's okay. It's a dramatic pause. It works, man.

That's good. Yeah. Essentially the story I want to tell is, is the story between the, the, the kind of, I don't want to call it the battle that's been going on the last few years, but essentially the story between diffusion and autoregression that's been going on the last few years. So, let me, let me start by trying to define a little bit what diffusion autoregression is. Autoregression. Is anyone who's been playing with language models, oh, you know, all these, you know, GPT, Claude, all the new stuff, everything that's been going on in the last few years, these are what you would call an autoregressive language model. And so what this is, is it's predicting data essentially one piece at a time. So it predicts one piece of data, and then it looks, okay, what did it make? Then it predicts the next piece of data. It looks at what it made, then it predicts the next piece, and it slowly, slowly, slowly, slowly builds out. You know, at first it was just very basic language and then it became, you know, now I mean we're having agents and a lot of very, very fancy things with them. On the other side of the coin has been something called diffusion modeling. And I mean, I don't want to get into it because now we're doing more like flow matching, but let's just go with diffusion as the base term here. And with diffusion, what you're doing is you're not trying to model one, say like in language model, it's, you know, you call it a token, but you assume like, okay, one word at a time. With diffusion, what you're trying to do is essentially trying to think how to tell, how to, how to, how to cleanly tell the story here. Yeah.

Yeah. No, it's all good. I, I, I love, yeah, I feel like I'm watching like a thriller where you're about to like reveal, uh, the next thing. It's good. Keep.

Yeah. So, so what we do with diffusion is I, I'm just gonna tell practically what we do and then we can kind of break down. You could ask me questions.

That's fine.

Um, So what we do with diffusion is we take a whole continuous medium. And what I mean by continuous medium is the most famous one is images. The world is continuous. There's not, you know, it's not that there's, you know, uh, one, you know, like, like a letter is discrete. There's a T, there's an A, a B, a C, a D, an E, and an F. But color, shape, the structure of the world, the color, you know, the, the light, the, all of this, this is, this, these are continuous natural media. And so, um, what we do is we take one of these continuous natural mediums that, that we've now encoded into digital space. So we use RGB. So, you know, okay, 256 colors we typically use, uh, for red, green, and blue. And then we encode this into an image. And then what we do is instead of trying to predict— so an autoregressive, you might try to predict like pixel by pixel by pixel. Instead, what we do is we essentially try to remove information. And the way we remove that information is by adding noise. So you imagine you like add a little bit of grain on top of an image. You can still kind of see the image. Maybe it's a little grainy, maybe it's a little blurry. Oh, you can't quite see it. And you add a little bit more. Maybe you see some of the shapes, maybe, okay, there's like, is that a dog? Yeah, you kind of see it. The color starts to fade and then you keep adding and eventually you just get, okay, this is just noise. I don't see anything in this image at all. And with the diffusion model, what we're trying to do is we're essentially adding a bit of noise and then we're saying, now try to predict what the clean image is essentially for this. So, you know, okay, so we add a little bit of noise, predict the clean. It just has to remove a little bit of noise. You add a lot of noise and now what it has to do is there's so little information, it has to actually like come up with essentially like how to infill this properly. And then what you do is then in, in what we call inference, which is when you actually run the model, you ask for an image, what you would do is then you start at a fully noisy image and you say, gimme a dog, you know, wearing a top hat on the beach. And then essentially what it does is it tries to remove a little bit of that noise to get closer to this idea of a dog in a top hat at a beach. And you might get some structure. It tries to figure it out. It's very coarse. It's very just trying to figure out the shape. And then it, it has a little bit of shape to latch onto. And then it does the next step where it removes a little bit more of that. Oh, and it starts to come into view. It starts to, you start to get a little bit more in focus. And you do this doo doo doo doo doo doo doo doo doo. And what you see is essentially you completely remove the noise of this image and oh my goodness, now you have this, this completely generated image of a dog on the beach with a top hat. For a long time, this was, uh, I don't want to say not good. I mean, all the models have gotten a lot better, but when it first started out, these looked like blobs of color. It was like these little, like, uh, you could barely see it. And, but fundamentally what I just described is still the process that's been happening over the last 4 years from these old image generator models for anyone that maybe tried like DALL-E Mini. I mean, the old Stable Diffusion models that we worked on, you know, uh, I don't know, there, there many, many of the different models are out there now to, to, to these modern video models that are producing whole 10-second sequences. Fundamentally, it's doing the exact same process of removing this information slowly with this noise and then just slowly going the other direction, essentially creating info to infill this. If this makes sense, I'll, I'll pause there for a second just to kind of like, right.

So let me tell you, I, 'cause as I'm listening, very, very good explanation, probably the best one that I've heard. So you are right on target if you just carry on what you're doing. So I'm enjoying this.

And you mentioned that when this started, right, you sort of ended with these blobs, et cetera. I think there's a lot of people that have seen very impressive things like the, like you were saying, like whole parts of maybe movies or commercials or something being generated in this way. What, what is the current, I guess, state of the art in terms of whether it be your, your models or others? And I know there's more to talk through as we get in, like there's more than just raw image generation, there's how this fits into workflows and, and doing certain tasks. But what is kind of the state of the art in terms of what we're able to generate? Where are the, where the bounds currently and in terms of quality and that sort of thing?

Sure. So I'll tell you kind of like In the last year, I'll, I'll split it up into, let's call it 3 different categories, but they kind of blend a little bit, which is these continuous mediums I talked about. So I gave the example of a continuous medium with image. Video is obviously an extension of that, adding the, the time dimension. But then there's also audio as well, which now a lot of these video models generate audio. But then there's also things like, I don't know, maybe you've heard the, the, like Suno or these music models. Some of these are also using this, uh, exact same technique of removing the information with noise, bringing it back in. It's just a different. Medium that they're essentially applying to. I'm happy to talk about our models all day. I love talking about our models. But I also want to be fair to just anyone listening and honest to kind of the state of the world on if you want to, you know, go out and try some nice things out there as well. I will say that before I kind of like dive a little bit into what I think is, you know, maybe the best one to recommend, I will make like one statement of that, like best quote unquote here is a little bit hard to define sometimes. And this is something we're always like wrestling with is like what makes a best model for someone. Because, you know, obviously you, when someone uses these models, they're typically prompting or putting in now you can reference images or videos or other sorts of things and kind of treat it a little bit like, uh, more like a, like a creative companion. Um, and then like what you expect out of this can be very different for different types of people. That said, I would say that if you want to see kind of the quote unquote, uh, state of the art right now, um, for what I would say is like text to video at the moment, um, it would be SeedDance. So SeedDance has done some extremely impressive work with the SeedDance 2 model over the, over the last year. So they're doing, uh, I think it's now 4K generations up to 15 seconds. Um, which is, I mean, it's very nice quality. It's, it's focused very much on like cinematics. I will throw a shout out to anyone that, you know, if anyone from the Sora team ever listens to this, I think Sora 2 was quite a nice model. It didn't rank so high on some of these like leaderboards that we, in the AI community, there's a, I'm sure you guys talk about this plenty, like leaderboards, rankings, where models place. I think in the LLM side of things, this is much better covered where there's very clear metrics of how well does it program? How well does it do math? On the more creative side of things with video models, image models, audio models, we typically end up falling down to this single preference type benchmark, which is just like a general preference. Everyone in the world goes and can vote on, you know, on these leaderboards. There's a couple different nice companies and sites that do this. But I feel like this kind of removes some of the specificity that the— or the granularity, that's the word I was looking for.

Listen, I've been to an incredible amount of AI events, many of which are good but many of which are not practical. You know I love practicality. We're on Practical AI. And some are just hype-focused, some are sales-focused. That's why I'm always eager to share about an event that I truly think is practical and useful for people. That's what I discovered last year at the Midwest AI Summit, and they're going to have another Midwest AI Summit, October 15th in Indianapolis this year, 2026. One of the reasons why I love this event was there was an actual AI engineering lounge where you could sit down and talk through your use cases with actual experienced AI engineers and practitioners to really brainstorm and come back from the event with actual solutions and practicality rather than just a bunch of content and slides. But there were also amazing keynote speakers, speakers from even that had been on the podcast before, like Rajiv Shah, uh, was at last year's event. I would really recommend that you go to MidwestAIsummit.com, and, uh, for our listeners, you can actually get 20% off with the code PracticalAI20. So go to Midwest ai-summit.com. Don't miss your chance to attend this event and get 20% off with the code PracticalAI20.

I was going to ask you, are the models that you're describing, are they kind of the traditional diffusion models? Or you had mentioned in passing a few minutes ago about flow matching. And so I was just in my head, I'm trying to kind of categorize how do the ones that you're talking about right now, how do they fit in And, and like, and how, what is it to go back and pull that up as you were talking about diffusion and you made the reference to flow matching. How do those, what is that, what is that transition and how do those models that you're talking about now fit into that?

I'll, I'll, I'll first keep it very simple and say all of these models are doing the same process of what I described earlier of adding noise and training the model to then remove this noise. Yeah. The difference kind of, this is where it gets a bit more technical in that, in how we actually approach this. So we used to do more of this process called diffusion, which I don't honestly know if I want to get into how deep and technical this is, but essentially what we've done is we've cleaned this process up to what we call flow matching. And flow matching is essentially this very simple process of still doing the same thing, still training the model to remove this noise. But fundamentally what it's learning under the surface is anyone out there who has ever seen like a velocity map or like a flow map, what this essentially looks like is if you can envision like a— I almost want to mark the whiteboard behind you. You're going to have to—

for the audio folks only. They're not going to see it.

It's probably about to turn to the whiteboard.

Yeah, yeah, it was great. Dustin, if you're on video, you saw it, but Dustin was literally about to turn back to the whiteboard behind him, which I love. Like, if I was in the room, I'd be like, go, man, take me there. Unfortunately, a good bit of the audience won't be able to see it, so you're going to have to describe it.

Yeah, yeah, no worries. I'll, I'll, let's, let's keep it purely in the audio space. So I'm going to, I'll draw a picture with my words as best as I can. I spent enough time prompting anyway, so hopefully I can, I can do this.

All good.

But yeah, essentially the way I like to think about it is, if you imagine like a landscape, like, I don't know, you can imagine your town or the region you're in. And imagine you're looking at it from like the sky and you would see like your house, you know, maybe right in the center of this map and what you might want to do. Let now, now imagine, okay, there's wind going all over, all over the area. And, and the thing that you want to do is you want to be able to throw up like a paper airplane from anywhere in this, in this landscape, your city, and you want the wind to carry it so it lands on your house. And functionally what we're trying to train here is essentially that in a much, much grander hyperdimensional space, um, where instead of it being your house and instead of it being a wind and a paper airplane, what it is is your house in the scenario is what we would call the manifold of real images. It's essentially the place in, I'm trying not to use too many, stop me if I use too many words here. I want to—

No, no, I'll ask, I'll ask you, but you're doing fine. Keep going. We're fine on technical. We're just going to explain it as we go.

Yeah, it's, it's the place in latent space that would be where the actual real images, images are. So, um, another way I'll, I'll try to, try to describe in simple terms. Okay. With your, your town here, you're in 2D space. You're where you are above an XY and the paper airplane has to fly in this XY coordinates. So you land and then you have the XY. In this space, instead of it just being 2 coordinates, it's enough to have all of the colors of the entire image. So that's why I say it's hyperdimensional. It's the center is where literally real images are. And so what we're doing is instead of it being wind and the rest of your town, imagine the rest of your town here is every image that isn't real. And what that means is noise. If you think about noise as a real image, you can go generate a bunch of noise and save it to your computer. But that noise would exist somewhere in this space of all possible images. They're not real images, they're, they're noise, but they, you can, you can Save them, you know, they're, they're, they're RGB values. You have them. And so what we're doing with this, this essentially this flow matching is we're training the model. Like when we say we're, we're training it to remove noise, what's really happening under the surface is we're training this flow map so that we can land anywhere in this field of noise. And then these flows, these winds in our scenario, when you take a step, will take you closer to your house or to the manifold of real images. And that's what, like, as you get closer, it starts to look more and more like a real image. It's blurry, it has this. And then when you actually finally land on this, this manifold, boom, you have hopefully a real image, but you'll have some image, you know? And then the better the model is trained, the better you have mapped this flow to actually take you to where you want to end up.

And I'm assuming, yeah, no, and I'm assuming because you are kind of mapping this, uh, from, from where you start to where you end up with this real image, maybe in a way that's, uh, just to be crude, I guess, less, less random. I mean, at the end of the day, all AI and machine learning, like it's sort of that training process is very much trial and error, but we have a lot of optimizations around it. Right. So I'm, I'm assuming that this then allows you maybe to, uh, I guess advantage-wise shorten the, the training or make that more efficient or Am I misconstruing that in some way?

Yeah. Yeah. I mean, definitely like one of the things that's taken us a lot further over the last 3 to 4 years is just figuring out a lot of optimizations, both in the actual training itself and then also in just, you know, architectures of the actual models have improved. But I will say the like fundamental underlying process of just removing this information and finding a path back to it is still the same process. It's, it's just like the car, you know, you've been having cars go down the road since the 1950s and we've made better engines and better safety and all this, but you're still driving down the road like that. That's yeah.

Yeah. That, that makes sense. And now I definitely, I think that's a good setting in the sense that we've got to a point where, you know, your, the models coming out of Black Forest Labs, the models coming out from other places, the models that are even like now in my text messaging, right? I get an image, I can immediately remix it with an image model, right? In some way. So these things are becoming more embedded in our lives, but I don't know if everyone in the audience, some, some might have been along for this ride where it was like, oh cool, I can generate an image of an astronaut riding a horse on a, you know, you know, wherever. And that doesn't seem that practical to folks. So I'm wondering if you could now kind of, given that, that foundation that we have and we know sort of where we're oriented in the state of, I love how you put it on your website, visual intelligence, which I think is, I love that statement because it gets to more like language, although it, it's being used a lot in terms of agents and intelligence, like you mentioned, it is only, it is very much a subset of the information that we process as as humans, right? There's this visual element, there's the audio, et cetera. So, um, now that we have that foundation, could you help the audience understand some of, I guess, the practicalities and the outworkings of the, like, okay, we can do this now, so what? So how, how does that, how does that help people in the real world other in ways other than maybe just pure creativity? There's certainly like the, the, cinema, like you were saying, that side of things, not everyone's going to be generating movies, maybe, or maybe more people will, I guess. But, but yeah, I think you're understanding what I'm saying. Like, where is this going to impact? Where is it impacting me now? Where is it practically going in terms of the application?

Yeah, yeah, absolutely. I'm very happy to talk about this. This is a, I think we're going through like a very nice transition period right now where we finally get to leverage these for some very useful things. And if I'm allowed to take a little bit of a tangent and kind of lead up to it again.

Wherever you want to go is good. We're all good. Yeah.

Yeah. So, I mean, I guess I'll take it back to like, um, a little bit of a timeline of things. So, uh, okay. So, so early on we had these nice models where most people recognize them for, you put in a prompt, you get an image out, you put in a prompt, you get a video, maybe you get a song. It's just this one way. Okay. You make something, this appeals to maybe creators or ad agencies or movie, you know, now cinematographers and, and I mean, we love this stuff. We love the creative side of this and, and, and what it allows. 'Cause it fundamentally to me, this is like a nice potential communication tool. Um, but then I, I feel like something, uh, changed. I don't wanna say changed, but, uh, there, there was definitely a, a, a, a timeline moment when we started to move into, um, editing. So, uh, was it 2 years ago now?

A year ago?

I don't know, around a year and give or take, uh, we released our first, um, in-context editing model called FluxContext. And, uh, on the surface you would look at this and go, okay, well, this is, this is an image editing model. This is something you could take a photo, you can clean it up, you can add a hat, you can do silly things, you know, you can, uh, whatever you want to do with it. It's a general editing model that's supposed to, supposed to do all these interrelational things. And on the surface, that's very cool. It's, it seems like another creative thing. But if you think about like what's actually going on under the surface, for the model to be able to do this, it has to understand a significant amount of relationships in the world and what it means for these, like, like how these relationships actually interact together. So if I say, take a picture of a, like a, I don't know, we have like a water glass here on the table. Um, and I, I, I take a picture of this water glass and I say to the model, knock the water glass over and shows, you know, show me what happens. The model has to understand some part of the actual world, like it has to actually model the world in some way to know, okay, it spills over, maybe something gets wet, maybe XYZ happens. And essentially what we're, we were trying to do is train a model that could do all of these types of relationships. So, so the model is learning not just like this one, one thing, but just how the world works fundamentally. So you can do this editing. Now we can take this a step further and we can look at all the video models that are, that are coming out right now. And you can do a very similar thing. You can take, um, you know, in a NIT image and say, you know, okay, this person now grabs a fire extinguisher and puts out a fire. And it has to actually understand these relationships to do that. So while maybe this wasn't our original goal way back in the day, you know, we were trying to make very cool models and figure out how to— I mean, I think in some sense we wanted to model the world, but maybe weren't thinking this far ahead. Inherently through this whole process, we've learned to build these models that are developing this. And I'm trying, really trying to avoid the use of the term world model here.

'Cause I'm gonna go there if you don't, I'm just telling you.

Yeah, you might notice me trying to like skirt this 'cause I think it's a little overused these days, but we can definitely talk about it. But fundamentally this is—

Too many people, which is where I was going, so.

Yeah, yeah, fundamentally this is what these models are doing when you train them at scale and you really train them to be general and robust is they need to be able to simulate parts of the world to get an output. And on one end you can take that and go, okay, Well, this is nice for creativity 'cause you can make a film scene and it looks nice. But on the other side, this is why we're starting to move in this, in this era, you know, this, this area where we're calling visual intelligence. And now we're starting to see how we can actually leverage what we're calling, not just us calling it, this is, this is a general field term, but the representation inside of this model of the world to actually go do practical things. Now, this isn't to say we're gonna drop the creativity side of things. We're still definitely pushing on the side. This is, uh, you know, something that, that we're all still very fond of, but Looking forward, like if our models are understanding these relationships and the physics of the world like this, well, this is a great place to put it in, say, uh, robotics and actually, okay, now, now a robot has this, this understanding, this model of the world inside of it to go act and, and take actions with confidence in the world. So I'll leave it there as kind of a, kind of trying to build up to it.

But, um, let me, let me ask it. I'm just going to go there because that's kind of, you're, you're in the area that I spend all my time, which is, you know, embodied intelligence, robotics. UXVs and stuff like that. That's my world. And so even within our space, the notion of world models has— there's a lot of interpretation. If you get into a meeting with 20 people, there's 20 different definitions and we have to start off by sorting all that out in terms of how we're communicating. As you add in this notion there and of this kind of contextual understanding that it has a representation, I am curious before we move on, do you, how do you, do you, and you've mentioned now robotics, do you see it as the same thing or is it kind of yet another variation of a world model, you know, using the word and stuff? Like how closely would you believe those two to be related, you know, as you're talking about that, just because both are big topics, you know, and—

Sorry, sorry, can I just ask for clarification? You asked me like how close do I believe like our models are to like a world model or a—

Well, like when you say world model, and I'm just kind of trying to clarify the same thing I did when there's 20 people in the room and you're asking what they mean by it. And as you mentioned, robotics and stuff, we all need this representation of the world out there so that we can do better about acknowledging the context of what we're working on, whether it's robotics or in visual intelligence, presumably. Are they, I'm just curious, in your mind, do you think that they are very closely related or are they kind of distinct ideas of what a world model is? What is your take on that?

I would say, I would say that they're pretty related. I mean, I, I, to, to me it's like fundamentally under the surface what we're doing is we're, we're trying to teach the model as much as we can about the world that we exist in and then asking for it to utilize that in some way. And up to this point it's basically been mostly through creative mediums, but like that representation that we're talking about that, you know, and again, I'm, I'm trying to avoid this term 'cause we, I don't know, it's a little overused that the term world model, but, but it is, I mean, it, this is, this is what it is. It's a, It is modeling, it's creating a representation of the world we live in, and then it is using essentially that intelligence it has to be able to act in it. So the idea is that if it understands enough of these relationships and, and the actual like physical properties of it, it's, it's a really good foundation to, you know, build and train robotics on top of.

Gotcha.

And I hope that, hope that answers that. I don't know.

That did. No, that was good.

Yeah.

Thank you for, thank you for putting up with, with that. I was just curious.

No, not at all. Not at all.

It's not unusual to, to navigate that. So go ahead, Daniel. Sorry about that.

Yeah, I, I, from the, from the non-robotics person in, in the room, um, that, that was really interesting. I appreciate you going into that. And I'm, and I'm wondering, there's that kind of outworking of some of this where you are now kind of understanding the context that's in this model, um, maybe how it's representing the world. There's also things that I've seen just crossing my paths, whether it be kind of an e-commerce or like I say, my, my phone, my text messaging. So on, on the web where these models are being more and more integrated into workflows, whether that be kind of like try on these clothes or glasses or whatever, or it's creative tools in like visual editing platforms. Um, are you seeing that with, with your all's models in terms of kind of what's the state of, and I know, uh, I want to get into your model families here in a second and they do different things, right?, and many of them, uh, I, I know there's a big, uh, uh, some that are, that are open weights, so you might not know all the ways that they're being used, right? But from, from at least those partners that you're working with, in terms of, of today, what are some of those creative and maybe more most practical uses of these models that you see out there beyond just kind of the, uh, fun image generation? Kind of side of things.

Yeah, yeah, no, absolutely. Um, I would say again, I'll come back to the, the moment we started to get context into the model that wasn't just text made a huge, like it was, it was a fundamental change in what these models could do and how people actually worked with them. Now our first model, FlexContext, um, only could take one image reference. It was mostly used as an editing model, but you could reference a, you know, a picture of a product and then tell it to generate like a, you know, a nice product photography set. And this was very nice. Uh, then moving on, we, we introduced like the Flux2 family and then, and then the Klein speedier, smaller variant that now could take many references. And then now we're looking at, I mean, people, and this kind of come back, comes back again to like what I was just saying of having this like representation of how things relate and that the better we actually build this like representation, the more interesting ways you can essentially like tie in all of these different things. So, I mean, throw it out, one of the most commonly used, or I don't wanna say commonly used, but like kind of obvious cases was, okay, like clothing try-on. Like this is a very, here's a picture of me, here's a picture of some clothes. Could you please show me what this looks like? I've seen people, like I actually did some home decoration earlier this year, just trying to see like what different couches and furniture look like in my place. One of the most interesting uses I'll say that kind of stood out to me that I saw at a hackathon, was, and, and I, I don't know how much this could be used for like, uh, real planning purposes, but I just thought it was interesting is someone was taking pictures of fire exits in a building and then generating what it would look like if a crowd was trying to leave through this fire exit in an emergency so that they could, they could actually like—

Brilliant.

Yeah. So they could actually gauge like, what would this look under like emergency? Like where is the crowd? Like, I don't, and obviously there, there's, you know, there's a generated component, so you have to take it with a little bit of grain of salt, but you still could get a general idea. Of this is what this would look like under this scenario. And I thought that was extremely—

Yeah, without derailing us, you just sparked a whole bunch of ideas on that, uh, in my head. So like, I'm, I'm just like, oh, this is great stuff. Keep going. Sorry.

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Yeah, yeah. I mean, we're— I would say fundamentally, like, as a core, we're still like a I'll say we're still like a research lab that wants to push the boundaries. So with each one of our model releases, we want to try to like level up some capability of the model, not just, you know, make a general improvement, but like really see, see some, some new versioning with it. So, I mean, I can take you back through, there's not that many models. So you take back through a little bit of a history, you know, it was a little, what was it around 2 years ago now? We released the first, our first family models, which was the Flux series, and this was the first set of models that we created, um, after we formed the company, after a very fun but intense, uh, I think it was around 4 or 5 month sprint to, uh, to really like build our chops here and get something out. And with that, we came up with this, uh, at the time, this, this essentially distinction between, uh, the models where we released the Flux one. So we had the Flux one pro model, which was on our API. We had the FluxOne Dev model, which was this commercially licensable model, but the weights were open for people out in the world to use. And then we had the FluxOne Schnell model, which was a high-speed, like, step-distilled model that was totally— I don't know if it was MIT or Apache, but it was open to use for whatever purposes you want. Then from there, we worked on our Flux 2, Flux Tools series of models where we realized like we needed a lot more control with the models. And this is where alongside this work started the context project to, okay, these are people who want a lot more control with the models. These models are learning a lot of relationships. How can we leverage this? Which led up to the Flux context release, which I talked about. This was our big edit release. Everything I've mentioned up to this point was still kind of our like historical models. Now we're getting into our more up-to-date models, although They're getting a little bit older now, but we have a, I don't know, something I'm excited coming. I don't wanna give dates soon. Hopefully later this summer. That'll, uh, be very exciting to talk about when it comes out. But, um, looking forward to it. Yeah. Yeah.

But we'll have to get you back on.

Definitely. Yeah. Yeah. Happy to, happy to come back. But then we got to our Flux 2 series of models and Flux 2 was a very big upgrade for us, um, where we really pushed the capabilities, not just on T2i, but also on editing, not just on single image, but this is where we also do introduce like the multi-image, the kind of OmniEdit where you could put many people, many different items. Have all sorts of relationships. This is where we started to see, you know, not just these kind of more standard advertising use cases, which still were like the most common and we really want to support this, but like some more, you know, interesting how people kind of build, you know, like things like this fire exit thing or other types of stuff. Um, and then, uh, right after that, we, we came to our Klein series of models, which was essentially like a size distillation where we really wanted to pack as much performance as we could into a small model. For this, you know, um, for both use on our API, but also just for open weight release. We know that there's many people out in the world who use our models locally, and we really wanted to make sure they had something powerful they could use. So it was a text image and an editing model. It was a, it was a very fast model and it was, it was pretty small in comparison to, uh, you know, it was even smaller than our Flux-1 series. And then we've done further, a further speed up on that with our Klein KV model, which introduced, um, I believe for the first time, KV caching, which is this, uh, I'll just say it's an optimization technique that's very common with the language model world that we were able to bring into the, the editing world to get a very, very big speed up on, um, local editing. So people who wanted to actually like use these models locally, but also, you know, we also serve this as well. You know, and now, now, um, since then we've done a couple blog releases, one fairly fun, uh, research research blog on something called Cellflow, which I'm repping partially because I am on there. I'm not the lead author. Our lead author is incredible, Hila. She's amazing. But, um, and then now we're, we're kind of all pushing forward to our next big release, which is hopefully going to be, uh, I, I'm quite excited about it.

That's awesome. And just to follow up on that, I know some people out there, our listeners are always trying things on, you know, on their laptop or wherever they're, they're pulling down things. I know for quite some time when I tried to access some of these models and run them myself, it was either very difficult to find, you know, with my limited resources, the right kind of configuration to run this, but also it was sometimes incredibly slow. So what, to just give a sense of some of those, you know, more hardware-optimized models that you mentioned, I think the the client and other models. Um, how, uh, maybe my question is, can I reasonably run one of these models on my laptop now and create a great image? Like, what's, what's required here? Certainly I'm not going to serve my production web app in an enterprise environment off of my laptop, but just maybe give us a little bit of a sense, because that has changed on the LLM side, right, where that has continually updated. And obviously the smaller models are not up to the same output quality as the larger models, but you can run a small model, you know, even on a CPU now, at least for some tasks that, uh, is, is pretty reasonable. So how has that progression happened on the, uh, visual intelligence or image generation side?

Yeah, I would say, um, I'll first state that the LLM world definitely has a lot more people working on it. So they, they definitely have a lot more of this up and down. Said, um, with our Klein series, I, I haven't run this personally. Maybe I'm not the best person in the world to speak on this, but I, I'm 99% certain you could run this on say like a modern M series Mac, like a MacBook Pro. Um, as for speed, I don't, I don't have any numbers I can quote because I haven't tested this myself. But, um, um, but, but this is always like a big kind of trade-off in this world of, of how, you know, even on the language side, you know, you see this scaling of a, you know, like a lot of the new big open releases that people are excited about are getting into the hundreds of billions of parameters. And this is why we, you know, this is why we did like the Klein series, for example, because FLUX 2 was a, it was a 32B model. It was, it was quite chunky. You could run it on like a, you know, higher end local computer, but it was slower to run locally. You needed some more powerful, you know, computing. This is where like the Klein series popped in, but it's, it's always a trade-off because we want to push the bat, you know, we want to push performance and and make something that we're really proud of. And then we want to try to bring that again back into, um, into a smaller, faster scale. And this is something we've continually done, um, you know, with our Fluxon series, we had Schnell, with Flux 2, we had Klein. I don't want to promise anything going forward, but we definitely want to like keep bringing this like bigger power that we generate into these, uh, into these smaller models that, uh, people can hopefully run locally.

Absolutely. Uh, super cool. I'm excited to try some of those with like, as we are starting to wind up And you guys have done such some really, really cool work in this space. One of the things we always like to ask as we're closing out these episodes are kind of like we want to get a glimpse into your head, into your thinking about what's to come in and kind of to frame it a little bit as you're thinking about like, you know, you're out of the day's work and you're, you know, enjoying your evening and your mind wanders and you're kind of thinking about like down the road, what would you know, what you want, where your passion is, is taking you and what you'd like to see. Can you share, can you share a little bit about what the future that you would like to, to create with Visual Intelligence in terms of what's next that you guys haven't addressed? I'm not asking for a product release so much because I know those, those need to stay in, but kind of just like what's, what, what, you know, where, what is your aspiration? What's your dream in terms of where these kind of capabilities might ultimately lead and love to get your kind of your dream context, if you will, as we finish up.

Sure. If I'm allowed to give two split answers here. Absolutely. Yeah, there's totally fine. There's kind of two areas that constantly sit on the front of my mind. At some point they'll hopefully become one, but I imagine the next— and this is no glimpse into saying what we're doing, but I'm just— this is stuff I'm personally excited about.

Fair enough.

Um, I would say the two areas that I'm the most excited about is one is long context, truly multimodal models. So now, nowadays, obviously we're seeing lots of people work with agents constantly. Um, and we're, you know, and on, on our front where we're having these, these more like continuous generative models, um, we're starting to introduce more and more, you know, contextual usage with these like references and Um, and I'm looking for, I'm looking forward to when this kind of bridges a little bit more and we have models that not just can like, okay, you have, say the agent calls the generative model, but when this kind of becomes more or less a model that not just can, you know, do work in the text and language and agent, but actually maybe can think visually as well and generate audio for you and has the full context of say all the stuff you've done over the last few weeks. You know, you don't need to say, give it the reference, give it the right prompt. It just like already has this context and can reference this as needed in this like continuous space. This excites me a lot. The other side I'll say is the real-time stuff that, you know, we're seeing a little bit of it now out there. I think this is like very early and I think it's going to be very exciting for real-time video, audio duplex interactions. And, you know, being able to— I don't know, you see a little bit of these like interactive, you know, stuff with like Genie where you play the game, but also on the side of, you know, you can bridge this back into like robotics where it needs to take in the real world in real time and make decisions.

Yeah, I was going to say that sounds really familiar in terms of interest on that side of things. So yeah, really cool. Great conversation. And your lead-in to what Black Forest is Forest Labs is doing was also really good contextually in terms of kind of explaining. So hope our audience got a lot out of that. Dustin, thank you very, very much for coming on the show. Great conversation. And as you've hinted, there are things to come, and looking forward to having our next conversation as things move forward a little bit.

Absolutely. Thank you so much for having me, guys.

All right, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Bluesky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner PredictionGuard for providing operational support for the show. Check them out at predictionguard.com. Also, thanks to BrakeMaster Cylinder for the beats, and to you for listening. That's all for now, but you'll hear from us again next week.