A24's Google Deal, Krea 2 Goes Open Weights, LTX Trainer Arrives

Speaker A: It kind of feels like a slight open source version of Interpositive, Ben Affleck's AI company, where we're sort of like, we shoot some footage and then we kind of train on the footage and we can make more things with the footage. This feels like an open source version in that realm.

Speaker B: Or maybe Inner Positive is using LTX Trainer under the hood.

Speaker A: It's LTX the whole time.

Speaker B: It was LTX the whole time.

Speaker A: Welcome back to Denoised It. It's been a minute, Addie. Two minutes. And who this two minutes too long? Um, yeah, Addie was on a nice trip. I got busy.

Speaker B: I'm sunburned, as you can tell on YouTube. Yes. Uh, it was very hot where I went, and I'm glad to be back. And most importantly, I'm really happy to be podcasting again. I miss, uh, all of the engagement for our viewers.

Speaker A: Yeah, I miss. Yeah, I mean, it has been nice. I still run into people and they're like, I love the podcast. I'm like, we haven't posted in a few weeks, but thank you. But we're back. We're back. We had a hiatus, but, yeah, out of your God. You missed. You missed all of the Fable fun. You missed the Fable excitement. Uh, what else did you miss?

Speaker B: I was in a very analog world where there was no WI fi and certainly no AI. So, yeah, tell me what's going on.

Speaker A: All right, well. Well, the Fable thing was. That wasn't even a story we're going to talk about, but that was an aside. Anthropic release. Fable. The Fable model.

Speaker B: Oh, yes. I heard that it hacked the NSA in, like, a matter of minutes. They had to pull it back.

Speaker A: Right. I believe that was Mythos. So they released Fable, which was like a safer version of Mythos. Their super crazy model that they're like, is too dangerous, too hot to handle. Fable was released for, like, a weekend, and then the Trump administration, I forgot the specific order, issued an order that basically said it was like, it had to be restricted to, like, U.S. citizens only. And, uh, there's no way. They don't have a way to do that. So they had to pull the model. Uh, and so the model is off, but everyone got to use it for, like, a weekend. It was crazy. The thing was crazy. I could give it a prompt to build an app, and then it just went and did it with, like, no guidance. Like, it just did the thing and it worked.

Speaker B: So it's like cloud code on steroids, like agentic cloud code.

Speaker A: It was like, yeah, I Mean, I used it for a cloud code use case, but it just kind of, like, ran and did the thing unsupervised for an hour and built the thing like,

Speaker B: I'll be right back, I'm sure. And then it just goes away.

Speaker A: Crazier uses that happen. It was also very restrictive where, like, it wouldn't do anything that was related to science and some other restrictive cases. And then they pulled it.

Speaker B: I heard that it was, uh. Actually, I'm kidding. I. I knew I. I was following along while I was on vacation. I can't stop consuming AI news. But, uh, I heard that it was specifically built to exploit, um, vulnerabilities in software, which makes it very usable for hackers.

Speaker A: Oh, yeah, for sure. Yeah, yeah. Zero day exploits and stuff. And then, yeah, you also, Midjourney is now a biomedical company, so you missed that.

Speaker B: Yeah. That's crazy. And, uh, at first, you know, yeah, we were all throwing our hands up, like, oh, I guess, uh, they're kind of broke. They need the money from biomedical. Turns out, no, their technology is actually really useful for this very specific thing.

Speaker A: Diffusing. Yeah, diffusing images, diffusing scans. I mean, my first thought was, like, do you trust that if it's diffusing a scan of a human body? But apparently with the science that is far more technical than I understand, it actually could be very viable and useful.

Speaker B: Yeah. From the article that you sent me, um, the Cliff Notes, was that the way the ultrasonic sound kind of reads your body is just like. There's thousands of speakers that blast you with noise. Right. So the entire thing is super noisy. So when you get the scan, it's not much different from Gaussian noise that we denoise in, uh, image diffusion. So why not just use the same type of mechanism to denoise the, uh, acoustic noise instead of pixel noise?

Speaker A: Yeah. I'm surprised no one made a joke where it's like, we denoised your scan, and then it's just like, you as, like, an anime character. Can you.

Speaker B: Only I would laugh at that. This is the world we're in where I laugh at your jokes, you laugh at mine, and that's it.

Speaker A: Can I give you an SREF to style my scan, please? So my scan looks.

Speaker B: It's like, uh, I need more muscle

Speaker A: than modern, uh, style, um, scan in a minimalist, abstract style.

Speaker B: Look, I'm just glad that, um, AI technology is being used for something good versus slop for once. So.

Speaker A: Yeah, I mean, look, if this thing comes out and it's like, uh, you could reduce a cost of a full body scan from, like, thousands of dollars to something. Their vision was, like, this would just be in a. In a spa, like, easily accessible, and you could scan yourself much more frequently. Um, you know, what can you discover earlier and treat before it becomes an issue?

Speaker B: I just hope it doesn't fall into the shady medical practice category of, like, chiropractors and, like, people that think they're real doctors, but they're not. They wear the white lab coat and stuff.

Speaker A: I mean, hopefully indicator of, like, I see something, it's a yellow flag. Let me go talk to a real doctor about this. But, like, at least. Yeah, helps with earlier detection to then go further to a professional.

Speaker B: Exactly. Like, do you remember the time before weed was legal? You had to go to a shady doctor and he had, like, the lab coat on.

Speaker A: Oh, yeah.

Speaker B: Oh, uh, you have, uh, aches and pain. Yeah. Here's a prescription. And he wasn't a doctor. He was just, like, a guy from Home Depot.

Speaker A: All right, uh, real stories. Sorry about that. Happened this week or in the last week so. Big one Google, DeepMind and A24 announced a partnership. Google is investing $70 million into A24 in exchange for being a research partner.

Speaker B: So if we, like, take that budget into, like, what, uh, Obsession's budget was, divide that by 70 million. I mean, we're talking about tons of movies, man.

Speaker A: Yeah. I don't know if Obsession's a good model for budgeting, but, you know, the. Google already had the Darren Aronofsky thing. They were involved in the Doug Lyman Satoshi film as, like, a tech partner. So they've been involved in a lot of movie stuff for a while. This is. It's called an AI research partnership into a 24. And it's. It was clear. This is not to, like, license A24 data. This is not to, like, take any of the A24 films and train on them. This was to invest in a 24, uh, in one of their latest, uh, rounds, and then also to help develop tools and stuff in the filmmaking process. The easy go to one that everyone loves to mention and feel safer is like, Previs Prep storyboarding.

Speaker B: Sure.

Speaker A: You know, which gets people less annoyed when they focus on that also side thing. Martin Scorsese is also now involved in Black Forest Labs Flux with again, pitching storyboard and Previs.

Speaker B: He caught some heat on that one online. Right.

Speaker A: I'm sure everyone catches heat, but then it blows over. Like, I think you just have to, like, expect you're gonna get, like, annoying things for like a week and then people move on to the next thing they want to grab their pitchforks over.

Speaker B: Yeah, it's. The contrast is so stark, right? Like you have one of the greatest filmmakers of all time, Scorsese, who's now embracing AI on the other hand, the upcoming and emerging filmmakers, like I think it's E. Curry Barker or Kane Parsons, one of the two newer ones, they're like, oh yeah, I would never use AI. AI sucks.

Speaker A: Yeah, Kane Parsons.

Speaker B: It's crazy. Like it should be the other way around, right? Like the OG guys should be like, nah, I'm good. And the newer guys should be like, yeah, that sounds good.

Speaker A: I think, you know, we talk about this endlessly. I think, you know, a lot of it's also just like a big misunderstanding or mislabeling of what AI even means. Where it's like you say AI and people think, I'm gonna go on the computer. When you see dance and spit out a movie. From text to video generation. And it's like there's a huge gamut of what this means. If you're just talking about Previz and it's like, hey, I mean look, both of those guys, uh, Kane Parsons especially, grew up with Blender and figuring out those tools. And it's just like, okay, what if it's just an extension of Blender? What if you could just roughly block out your scenes in Blender and then get a much better previz by uh, video to video and getting uh, higher fidelity previz out of your thing using uh, a video to video model, um, things like that where it's like, okay, I think once you get better education and understand that it's not, there's a huge gamut of what this means. You get come around into it or get on board or they just relabel it to something else.

Speaker B: Yeah, machine learning. So let me ask you this, like what is the eventuality in your opinion of this partnership between Google and a 24? Like where do you see this few years from now?

Speaker A: I don't. I mean I'm more curious, like what's Google's interest in this, uh, like training better models. But you know, for what, and we've talked about how like the media, entertainment or film industry specifically is like pretty tiny compared to gaming and then compared to like autonomous vehicles, robotics, self driving cars.

Speaker B: I mean you went to Google IO, like what is the general theme there, right? What is, what are they? What is a trillion dollar idea for them? It's not obviously film and television, but that's a means to an end to get to something bigger. Right?

Speaker A: I mean, you know, we've talked about like, I think it just helps with the, it helps feed in and test out their world models. Um, you know, that's it. If people get Omni in their hands and mess around with it and push it and test it, um, maybe for them it's also a way to just give access to better or more unrestricted versions of the models with trusted partners to then get better data. Because like Omni still is pretty restrictive of what just the general consumer level can do. Like if I was trying to edit, I was trying to modify some footage with it and then the footage had like a face in it and then it was like, oh, we're still not doing changes with stuff with faces in it. And so it's hard for me to use it. But I would imagine if they have a studio partner that they would give them a version of Omni that is less restrictive and then you can get better learning data off that.

Speaker B: The, the other, the other thing that I'm thinking about, and I think in the article that covered this partnership, it strictly says that Google is not getting A24 data any of their movies, whatnot. I don't know about that, Joey. I think that's kind of bs. The data play is so such a, it's the elephant in the room. It's like when you partner up with a studio, they got the ip man, you want some of that?

Speaker A: I mean, I would believe that they're not going to feed in the archive or whatever Also. Does it really? I mean, uh, they probably know a lot already anyway, so it doesn't really matter.

Speaker B: It's highly stylized, highly specific, creative intent oriented content which is so far away from, you know, publicly open source data or stock photo stuff that like, that stuff is really a low quality data. This is the highest grade of data that you can get your hands on.

Speaker A: I think when they're using the tool, who knows what the training deal on that is. Where if they're giving the inputs and the feedback into Omni or the model, I would imagine that it's learning off of that and that's being used as training input, um, to improve the model and also just to learn from it. So maybe not the archive, but the future stuff they do, maybe that's in the model or maybe that's being used to train.

Speaker B: I'm going to draw a two year timeline for both companies and um, I'm probably going to regret this in two years, but I'll say it anyway. So on Google's ends getting their video models to behave better, more accurately, you know, have better control, all that stuff, it just feeds into their overall AI play of training robots, training cars, um, delivery vehicles, what have you. Like all of their um, big industrial skill stuff depends on having high quality world model, video model, image model and so that's their play. I think they could care less about being an uh, M and E powerhouse.

Speaker A: Right.

Speaker B: Like Google's not, not trying to go into the Oscars and grab a Scitech Academy Award or whatever. A24. I think they know, they see the writing on the wall that if they don't do it then Wonder Studios or somebody that's AI native will take their place. Right. They'll grow into an A24. So they're sort of uh, doubling down on this um, even though there is hesitation internally because they have really indie creators who like Kane Parsons who don't like it. I think as a survival strategy they're looking at it as a way to sort of just keep up with the times and try to find efficiency along the way.

Speaker A: Yeah, I mean I think as a studio, I mean we know most of these studios are doing stuff like this anyways whether they publicly talk about it or not. But like uh, on the very just base level of just having some AI infrastructure for all the operation stuff for all of the previs stuff for all of the other things that might not necessarily be involved in production and then maybe in some post production stuff like VFX help or cleanup or other things that would speed things up.

Speaker B: Oh, speaking of Wonder Studios, I mean did you see Kayvon, the kids new release last recall?

Speaker A: I have not, I have not shout

Speaker B: out to Kayvon man that he's Chef's Kiss like but that's what I'm saying is like okay, maybe that's not you know a 24 grade cinema yet, but they have the capability and they can grow into uh, like a professional cinema powerhouse. Right. If given the funding or whatever, let's say Wonder Studios gets acquired, I don't know, by Google or somebody and now they're bringing on studio executives and distribution folks and da da da da da. And then they figure out the portion of the studio that A24 has and then they have the AIPs covered. Right. Like that could well happen in our uh, in the next few years.

Speaker A: Yeah, I mean look, also you know, so we're talking about this like Google's interest in having better video models. Uh, you know, maybe it's not obviously the Film industry is not that big, but Google has a massive ad network with videos and they need tools for people to be able to create ads quickly and fast that are videos and YouTube and whatever, the AI tools that keep popping up in YouTube and enabling people to make videos where they don't have to use a camera, whatever that ends up shaking out to be. So you know, it's, it's is um, useful for Google to have really good video models.

Speaker B: Yeah. Oh, it's interesting on YouTube side, uh, businesses. I thought I saw something like 20% of YouTube content is already AI slob. Like it's, it's a, it's a problem for them and I don't think they want the platform to turn into like a 90% AI content platform. So they're curbing that early and I think some of their strategies are already in place. Like they still want it to be a human driven platform. AI should just assist. But what's happening is uh, people are automating a lot of content and just putting it like faceless videos and things like that.

Speaker A: Yeah, you could automate a agent right now to just write scripts, generate videos and upload them endlessly.

Speaker B: Like one a day, one an hour, whatever.

Speaker A: Yeah, you could just keep churning this out. Yeah, I know that's been an issue for them too because I mean on one hand they have encouraged AI tools and some other AI tools to help create stuff. And then I've seen complaints from creators like having their account shut down by either being accused by Google of using AI too much in their channel and it's just like where's the line between what Google considers too much AI versus what they're okay with? Especially when they're building tools to help you do this. But then if you post it and the channels get shut down, what are they? It's a little confusing of what they're looking for. Like where the line is between uh, okay and too much. I've also heard of other creators that are still fully like Blender animators being shut down or accused of, of AI and then having issues with their channel. And it's like I did all this in Blender.

Speaker B: Like it's like I'm struggling out here.

Speaker A: Yeah, it was all 3D animated. Yeah, yeah. Obviously we're curious to see how this goes. But yeah, I mean I feel like if you're a studio you need some AI partner integration to help with. Definitely just help with operations and like previs and stuff. You know, if it's like, you know, nothing about this says part of the plan is like to make a generative backrooms world like that you could explore. This is all filmmaker tools that they've talked about so far. All right, models, Open models.

Speaker B: Let's do it. I love me some open models.

Speaker A: All right. Creator has been dropping some bangers lately. So uh, they dropped the. Well first they dropped the creator model like a few weeks ago and I messed around with was like the closest to kind of mid journey that I've felt in a while of just very cool aesthetics and very like a uh, fun model to just mess uh, around

Speaker B: with and explore different styles like an RT model.

Speaker A: So that was closed and then this week they dropped a uh, Creator two

Speaker B: open weight and I think they dropped it in two pieces. Right.

Speaker A: So there's a bigger raw model that with the intent, with that is sort of to train uh, and build out your own Loras and then a turbo model for like executing for just inference. Yeah, Loras are back.

Speaker B: Should we do another episode?

Speaker A: Maybe we could. Maybe we could revisit this with um.

Speaker B: Yeah.

Speaker A: Uh, uh, Korea two Loras versus um, reference images or style prompts. Like we.

Speaker B: Yeah, I think we were using Z Image before but yeah, look, Krea 2 is obviously going to get you a really specific look and feel and I think that's the market that they're going after is like we're never going to compete with nana banana or GPT image 2 as far as universal capability like across, across the board, you know, generating whatever. But if we can get really specific down to like photography styles or you know, Instagram styles or what have you, I think that could be a really interesting market. And if you pair the fact that this is all very comfy UI oriented where somebody would need to go into comfy because it's all open source stuff and then put the building block together that goes into then artist driven, human in the loop territory. So it doesn't feel as inauthentic and agentic.

Speaker A: Yeah. I mean the styles I've seen out of this and just the aesthetics, it's been really fun to see and look at. It's been very um, inspiring. We get to see some of the images and styles here. Just all sorts of stuff and. Yeah. So you were able to train your own Laura as to have these more consistent.

Speaker B: Uh, these are all fun. Yeah. I mean like I could just take any one of these. Yeah. And just put, put it on a piece of like a frame. You know, it's just like a painting.

Speaker A: Yeah. And it's open. You run it locally. You can modify it you can jailbreak, uh, it if you search around the Internet.

Speaker B: Yeah. And CREA is known to be real time or close to real time. So talk about the um, distilled model.

Speaker A: The other thing that was interesting, I haven't fully dug into it, but they released this massive technical report that basically detailed how they trained the entire model and it goes into a lot of detail. Uh, I haven't, I haven't finished my NotebookLM podcast explaining this to me yet. But one of the headlines was um, that they trained. That was sort of like part of the issue with AI images where they all look too clean and perfect is because they're all trained on stock images and they all are trained on really good looking stuff. Kreia trained on good stuff but also crappy images and bad stuff or quote bad stuff, which gave it more of this knowledge to create these kind of funkier styles and different um, visual aesthetics by training on a variety of images, including blurry, crappier, not technical.

Speaker B: Where did they get the crappier stuff? That's my question.

Speaker A: I have no idea.

Speaker B: That is the big question. And honestly that question is what prevents KRIA from adoption in our industry? If the training data is a question mark, then I don't know if this is.

Speaker A: Yeah, I mean I think it's a good mood. Boarding style thing, reference. Um, also, I mean it's just uh, I believe it's just text to image. I don't think there's a image image

Speaker B: to image or edit image. Yeah, it doesn't seem like it.

Speaker A: So it's like much more for like still photography or just like still imagery or design or mockups.

Speaker B: Like yeah, this, this feels to me

Speaker A: like I'm not going to be making character. I'm not going to be making character sheets or reframing um, stuff or making first frames in this. It's like more of a, more of

Speaker B: a, like a visual explanation one and done kind of thing. Yeah, it's.

Speaker A: I mean what I guess I should. There's gotta be an image to image with a Lora if you wanted to give it an input image and change it. Right.

Speaker B: I think you can build that in comfy, like give it structure from a previous image. But yeah, this feels like SDXL or Z image where you know, sky's the limit to as much as you want to modify because this is all open source and it plays well within comfy.

Speaker A: So yeah, Crave 2 has been cool and now I know I was like not so bullish on Loras for a while, but this Seems like maybe ah,

Speaker B: Joey's coming back around to Laura's making

Speaker A: a comeback also because I don't know if they're technically called a Lora in the next one, but the next update, another open model, ltx, which you know we talked about, one of the best

Speaker B: LTX is amazing video models.

Speaker A: Um, they released LTX Trainer, which is kind of like a video lora. You can train LTX to kind of just do a specific task in your video and basically to give it an input video and modify it in a very specific way. Um, sort of basically creating these like video.

Speaker B: Yeah. And just to kind of clarify here, what we're looking at is some um, style transfer stuff which is great. Like you know, if you want to do a color lut or like a certain look and feel, maybe apply a lens effect.

Speaker A: But this is a better.

Speaker B: Yeah, I, I saw something really interesting online. I think it was uh, Greg Teargarden who we covered in the past and like, like they're using LTX's trainer to do HDR up sampling, which is nuts. Yeah, like that's, that's thinking outside the box for sure.

Speaker A: Right. Well this one, what we're seeing is this uh, is a model trained on, on just water. And so it's basically giving, it's a water simulation model. So you give it shots without water and then it adds water whether it's like rain, reflections in a street, river, uh, babbling brooks in the background.

Speaker B: So cool.

Speaker A: So this one's kind of.

Speaker B: That is cool. Yeah. So it's like a very specific physics tool or a simulation tool, whatever you

Speaker A: want it to be. But it's like you want it to do. You want to modify your video with one specific thing. A look is an easy one. But that one where you're like make my footage wet or add water is, is uh, is very cool and specific.

Speaker B: Yeah. Um, but these are, I think six months from now we're going to see something cool like it just hit. Right. And people are going to get creative, they're going to experiment, trial and error. These things take time to figure out those golden use cases out of.

Speaker A: Yeah. Uh, but I mean I think the coolest thing is where it's like this is working in a video to video space and you're able to train it on whatever specific use you're trying to do and then you could just feed it. Yeah. So probably the funniest version of this was a cross eyed model where you give it the input video and it just makes everyone cross eyed which was on display Here in this clip from Comfy. Yeah, that.

Speaker B: That goes into Scary Movie territory, doesn't it? Did you end up going to the theater to see that?

Speaker A: Oh, Scary Movie.

Speaker B: Yeah, I did.

Speaker A: It's great.

Speaker B: It was great.

Speaker A: Uh, yeah, Scary Movie was hilarious. And I remember, yes. In the last episode I was like, I want Scary Movie to do well.

Speaker B: Because White Chicks too.

Speaker A: Because I want white chicks too. Um, there was a. There was a White Chicks 2 cameo.

Speaker B: Yeah. the end I saw somebody put their cell phone and posted it on YouTube. I saw that, like, at the end. Right.

Speaker A: Um, it was like. No, it was like more in the middle. It was like. Or like it was. It was in the middle of the video. Okay.

Speaker B: Yeah.

Speaker A: But, um, that definitely got the biggest applause in the audience. Oh, you know.

Speaker B: Oh, Decompression. Wow, that's interesting. Yeah.

Speaker A: Wow, this is interesting. So, yeah, giving it some pretty crappy web footage and then it's super, uh, sharp.

Speaker B: But that's, that's along the lines of making the image and the frame better. You know, like HDR up sampling or up resing and then denoising and de blocking. That's all really useful stuff. And it doesn't impact the image in a stylistic way, but technically it helps you a lot. Right. Like as you go along your platform.

Speaker A: Yeah. Other interesting thing is, uh, you could train audio with the video too.

Speaker B: Interesting.

Speaker A: I haven't seen examples of that yet, but, um, it is possible. All right, I'm going to dig in this more. I'm very curious, but this has been another very cool case. It also feels like. It kind of feels like a slight open source version of what they're doing or talking about at, um, uh, Interpositive, Ben Affleck's AI company, where we're sort of like, we shoot some footage and then we kind of train on the footage and we can make more things with the footage. This feels like an open source version in that realm.

Speaker B: Or maybe Inner Positive is using LTX Trainer under the hood.

Speaker A: It's LTX the whole time.

Speaker B: It was LTX the whole time. Yeah.

Speaker A: I have wondered with a lot of the companies where they spent a lot in R and D for like the last two years. And then it's like we have this cool thing that we could do and it's proprietary. And then either, uh, one of the open source models or just one of the big models comes out with a thing that's just like, we do it in one shot and like, it's done.

Speaker B: Yeah. If I, if I was in the AI, uh, Startup space. Now, I would highly prevent myself from building anything from scratch, training any new model from scratch. Just like, go out there, see what.

Speaker A: How are you going to compete with, with Google, uh, or ByteDance or any of these massive companies that have huge server.

Speaker B: Yeah, you. You don't compete with them head on. You figure your niche and your target use case and your demographic, and you go after that way harder than they can. Like, I think that's.

Speaker A: Yeah, yeah, I think that. Yeah, I think you're right. Like, you can't train the model, but you can figure out, like, what's the workflow that gets the results and looks really good. How do you plug all these pieces together in a unique way that other

Speaker B: people haven't quite figured out yet and then attach some creatives to it and then make them make some cool shit?

Speaker A: Also, one other thing that we just saw break this morning. Uh, Adobe is acquiring Topaz Labs.

Speaker B: Yeah. You're gonna ask me about it?

Speaker A: I mean, I know you don't know anything besides.

Speaker B: Yeah, I just. I just found it. So. Yeah. So if some of our viewers don't know, nor could you disclose, I guess I haven't officially said it on the podcast, but I do work for Adobe as my day job. And, um, the views expressed on this show are that of my own and not of my employer. I just want to say that Joey's too. Joey and I are highly opinionated people. Look, uh, we've been fan of Topaz for a long time. Joey's covered it on VP land, right. He interviewed Manji and like, the team. And, uh, they're the closest thing to a universal, uh, upscaler service that we have in the AI industry today.

Speaker A: Yeah. And they really, I mean, it's crazy because Topaz has been around for like

Speaker B: 20, 30 years as a, as a non AI upscaler.

Speaker A: As a non AI upscaler, they used GAN models, uh, which I don't understand the math behind that, but. Yeah, I mean, I used Topaz for years because I worked in documentaries and that was the go to. If you had old crappy footage and you needed to try to like, get something else out of it, Topaz was the only answer. And then they really did an amazing job pivoting and building out these, um, generative upscalers, uh, with Starlight, um, Astra, um, and then have become this sort of secret sauce end workflow piece for every AI, uh, pipeline where it's like, oh, you're done with your shots. Okay, well, you still want to give it a little extra sharpness or a Little extra finish. And you're going to use Topaz with that. And so they're really like that end node in everyone's AI pipeline. Uh, no matter what models and everything else you use, the Topaz usually still ends up at the end of the line, 100%. Adobe's had integration with Topaz for a while as one of their.

Speaker B: Yeah, like a plugin type thing providers.

Speaker A: Yeah. And Photoshop for uprising images and Firefly, uh, as one of the M partner models to upscale stuff. So, you know, I mean, it makes sense that they would want to acquire them and put them in their AI pipeline.

Speaker B: Yeah, I mean, I'll just say this like, without giving away any secret sauce. Uh, I don't want to get in trouble, but, you know, some of the AI stuff that we have seen out of Adobe, like Firefly boards, um, Premiere Pro, features like Morph Cut, you know, Nano Banana, integration in Photoshop, uh, harmonize all those things. Right. I think it's nothing compared to the next 24 months of stuff coming out. There's like a whole rethinking of what an AI workflow looks like and what tools are associated with it. So absolutely, Topaz will play a key part in that. But like, if you think of the Adobe offering in the ecosystem, um, that is today's ecosystem, that necessarily won't be tomorrow's. Like, it'll be much more embellished with AI in a really useful, creative focused way. So I'm really excited for that future. I'm, um, ready to meet these guys and see where we are on the org chart and how we can work together.

Speaker A: So.

Speaker B: Yeah, yeah, that'll be cool.

Speaker A: And um, I mean, it's cool. It seems like they're keeping the whole team intact and kind of keeping it as a separate entity or not separate entity, but keeping Topaz as like a thing. So, like, Eric Yang, who's the CEO of Topaz, will still lead the team. I mean, yeah, congrats to them and I think, uh, it's a smart play. Uh, Topaz is great. They've been around forever and, uh, excited to see where this, where this goes.

Speaker B: Same, same. We have some other episodes coming for, for the viewers. I think that you guys will dig. You know, we want to, like, we want to dig into a specific topic more than just cover AI developments. So, yeah, stay tuned for that, uh,

Speaker A: links for everything Talked about@denoisedpodcast.com yeah, energize.

Speaker B: And wake up our YouTube, Spotify, Apple podcast channels again by hitting a Like or subscribe that would be amazingly beneficial.

Speaker A: Thumbs up. Five stars. You got it. All right, thanks, everyone. We'll catch you in the next episode.