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Cut MiniMax H3 render times from 10 mins to 3 mins! 🚀⚡ I stacked 3 game-changing speed nodes in ComfyUI to accelerate MiniMax H3 across T2V, I2V, and Ref2Video: 🔥 Sol-Attn (Dynamic compute focus) ⚡ SageAttention (GPU-optimized attention) 📦 EasyCache (Temporal feature reuse) Up to 3.2x faster with zero... show more
32,870 Aufrufe • vor 1 Monat •via X (Twitter)
12 Kommentare
"zero visual degradation" Making this kind of claim just discredit everything else you're saying.
EasyCache creates glitches for sure at least when i test it
Probably Sol attn alone is the same.
There is degradation lol, my egocentric Lora has its finger smash together when I turn this on. 😔 sad. So close too.
It's not zero
Im i wrong. I thought sol-att. Was for blackrock or can it be used on others like a i9
EasyCache is not needed...
Can you pls share your hardware config?
EasyCache degrades the quality do not use it.
Good workflow, but I tried the bf16 ref2va, amazing speed and quality, 14 minutes for 2k 5sec
Easycache làm hỏng card vga, và cho chất lượng video cực kì tệ hại.
Can you render native 1080p with this setup? Dramatic speed up for 720p, but above that hangs.
![Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:](https://image.24vids.com/tw-1994972575768727743/amplify_video_thumb/1994972220930621440/img/_5CCZJyz89oDfqQJ.jpg)
![😎6 Reasons to Hold $LMP 🚀 Most crypto tokens try to win attention through hype. $LMP is trying to build value through real economic activity. That difference matters. ⚙️🌍 If you are trying to understand what makes $LMP interesting, here are 6 reasons people may choose to hold it. 👇 1) 🏢 It is built around a business-driven ecosystem $LMP is not positioned as a token with no clear use case. It is designed to represent activity inside the LinkersMap ecosystem, where merchants and customers can interact in a structured way. 🤝🛒 That means the token is tied to a platform with a practical purpose, not just speculation. 📌 2) 🔥 Business transactions are linked to buying and burning One of the most notable ideas behind $LMP is the transaction-back-and-burn model. 🔄🔥 When qualifying business activity happens, the system is designed to automatically buy and burn $LMP. This creates a token model that is intended to support utility rather than pure trading demand. 📉⚡ In simple terms: more real activity can mean more token pressure over time. 📊 3) 🌍 It is designed for real-world utility Many tokens only matter on exchanges. $LMP is intended to have a role inside a growing business network built on InterLink Chain. 🌐⛓️ That gives it a different kind of purpose: → supporting business interactions 🏪 → connecting users and merchants 👥 → adding value within the ecosystem itself 🧩 A token with real use can be more meaningful than one that only moves on charts. 📈 4) 📉 The supply is fixed $LMP has a maximum supply of 100,000,000 tokens. No additional token issuance is planned. 🛑🔒 That matters because a fixed supply gives holders clarity. It removes one of the biggest worries in crypto: unexpected dilution from ongoing minting. ✅ A capped supply does not guarantee success, but it does create a more predictable token structure. 🎯 5) 💱 It is designed for on-chain trading $LMP is designed to trade against $ITL through: → its liquidity pool 💧 → the InterLink Business Token Exchange (IBTE) 🏦 This gives the token a trading structure inside its own ecosystem, rather than relying only on external speculation. 🔗 For holders, that can mean a more integrated and organised market environment. 📋 6) 🚀 Its value proposition is tied to long-term ecosystem growth The strongest case for holding $LMP is not short-term price movement. It is the idea that as more: • businesses join 🏪 • users transact 👤 • merchants adopt the platform 🛍️ •ecosystem activity grows 🌱➡️🌳 …the utility and demand around $LMP may grow as well. 📈✨ In other words, the token is designed to move with the expansion of the business network behind it. 🏗️ Final thought 🧠✨ Holding $LMP is not about chasing quick hype. It is about believing in a model where blockchain, business activity, and utility are connected in one ecosystem. 🔗🏢🌍 That said, every investor should still do their own research before making any decision. 🕵️♂️📚 A strong vision is important, but execution matters even more. ⚖️ DYOR. Understand the model. Follow the activity. Make informed decisions. ✅ Ooh, if you’re looking for the lyrics, here you go... full version below. 😁😎 6 Reasons to Hold $LMP [Lyrics] Six reasons, one chain, watch the value align, Real merchants, real customers, LinkersMap shine. Biz in the system, that’s a stronger design, Every move got purpose, every trade on time. Back and burn, let it light up the lane, Utility token, not just hype in the game. Capped at a hundred mil, that’s the frame, $LMP got a future, remember the name. Trade with $ITL, on-chain flow in the pool, IBTE on the beat, keep the market cool. More users, more business, that’s the growth rule, $LMP move fuel. Hopefully, you have enjoyed the song. 😁👍 InterLink Labs 👤 + 🌐 Dr Altcoin ✝️ LinkersMap Arif Ahmed Core Ambassador | Interlink Labs KV Reina | InterLink Labs #LinkersMap #LMP #InterLink #ITLG #ITL #Web3 #Blockchain #Commerce #BusinessToken #DigitalEconomy 🚀](https://image.24vids.com/tw-2086143794739843190/amplify_video_thumb/2086143660828303360/img/1qJGlLcCPoV4mIx8.jpg)
![I made an ad for GPT Image 2.5, full prompt below. It's yours now. Put your own character in @[image1], your own poster in @[image2], change the city, change what she turns into. Quote this with your version, I want to see what you all make of it. Seedance 2.5 on Higgsfield AI 🧩. Seedance 2.5 Prompt: [GLOBAL] 30-second commercial. Photoreal live-action cinema texture, digital cinema camera, 35mm and 24mm lenses, shallow depth of field, real overcast rain-light, low-contrast cool grade, light film grain. 16:9. The spine of the film is one continuous over-the-shoulder tracking shot; all transitions are hidden inside motion blur and the crystallization effect. [CHARACTER ANCHOR] @[image1] = the protagonist, a Japanese woman in her early twenties with a Tokyo street-fashion look: bleached pale-blonde wolf-cut shag with dark roots and a single mint-green streak in the under-layer; clean Japanese makeup — thin brows, pearlescent eyeshadow, nude lip; an oversized charcoal coach jacket with an original graphic print on the back, a long white tee underneath, baggy black cargo pants, chunky platform sneakers, layered silver jewelry, wired over-ear headphones around her neck, a small crossbody bag. Her face, hair and wardrobe stay 100% identical from 0s to 28s — never altered, never touched by any "generation" effect. She is the only constant in the film until the final beat. Emotional arc: oblivious → noticing → confused → stunned → awestruck → transformed. [ARTWORK REFERENCE] @[image2] = the billboard artwork reference. Used ONLY for the flat graphic displayed on the bus-shelter digital billboard at 11–15s — including its complete typographic lockup ("Higgsfield × OpenAI" in small caps, "GPT IMAGE" in heavy white, the lime-green gradient "2.5", and the wide-tracked "SUNBURST" beneath), along with its layout, palette and composition. @[image2] must appear as a complete flat advertising image inside the billboard screen, carrying correct perspective, screen reflection and rain refraction across the glass surface. The woman pictured in @[image2] must NEVER be used for the protagonist's design, must NEVER leave the billboard screen, and must NEVER appear in the physical space of the scene. @[image1] and the woman in @[image2] are two entirely different people — never conflate them, never let one influence the other. [CORE VISUAL MOTIF — THE ONLY TRANSFORMATION LANGUAGE IN THE FILM] Anything being "regenerated" must follow the exact same three-stage process: ① the original object disintegrates from its edges into a cloud of tens of thousands of small translucent white crystalline beads — like bubble-film or shattered glass pearls, with real volume, specular highlights and real weight; ② the bead cloud hangs briefly and rapidly rearranges; ③ the new form grows outward from inside the cloud, the final layer of beads absorbing into its surface and vanishing. Do NOT use flash cuts, dissolves, particle-dispersal effects, or digital-glitch styling for any transformation. [SETTING] A Midtown Manhattan intersection after rain. Wet asphalt reflections, crosswalk stripes, yellow cabs, dense glass curtain walls and older stone buildings, neon and storefront signage, a fire hydrant, a newsstand, a bus-shelter digital billboard. Overcast diffused light throughout, no direct sun. 0–3s: Over-the-shoulder medium shot, following behind @[image1]. Her back is to camera as she stands on the crosswalk. To frame right, a 1970s green sedan in motion crystallizes entirely and reassembles as a black muscle car; a few frames later it crystallizes again and reassembles as a white wheel-less hover car. She hasn't registered it yet. 3–7s: The camera arcs around to her side. She starts looking around, the ends of her wolf cut swinging with the head turn. A businessman with a briefcase crystallizes mid-stride — the human-shaped bead cloud holds the walking pose for two full steps — and reassembles as a bare-chested barbarian warrior with feathered ornaments, continuing the same stride. Two or three more pedestrians crystallize in the background simultaneously, without pulling focus from her. 7–11s: Handheld tracking accelerates. More and more people on the street are replaced: a purple-skinned alien warrior woman, white-armored robot troopers, an original gold-suited superhero character (all original designs — no real-world recognizable characters, logos or trademarks). She walks faster, keeps turning to look back, her breathing quickening, one hand instinctively gripping her crossbody strap. The replacement frequency keeps rising, but the street's spatial relationships, lighting and wet reflections remain continuous throughout — no jump cuts. 11–15s: She stops in front of the bus shelter. The digital billboard inside cycles rapidly: a photoreal movie poster → a hand-painted concept illustration → a fashion editorial — each refresh executed with the same crystalline bead reassembly. It finally settles on the complete @[image2] artwork filling the entire screen, its typography and palette exactly as in @[image2]. She raises a hand and points at the screen, mouth open, unable to speak. 15–18s: The camera tilts down to her feet. The sidewalk paving crystallizes outward from where she stands in a perfect circle, white beads spreading three meters along the joints between slabs, the manhole cover and the standing water turning crystalline with it. She looks down. The ground gives out beneath her platform sneakers. 18–21s: Wide 24mm. The ground collapses and she falls straight down. The camera falls with her looking up — the entire city contracts above her into a shrinking square opening, the buildings on either side peeling away, tumbling and receding upward like building blocks, the sky turning to deep blue-black void. She throws her arms out, her hair, jacket hem and bag strap lifted by the updraft. 21–24s: During the fall the visual style jumps repeatedly, each jump bridged by one full-frame sweep of crystalline beads. In order: ① rich 1980s film stock — high saturation, heavy halation, heavy grain; ② black-and-white halftone manga panel — the character rendered in ink linework and dot screens against dense radial speed lines; ③ a neon purple-blue digital vortex with long light streaks stretching backward at speed; ④ near-total darkness with extreme motion blur. Her facial structure, hair and wardrobe must remain recognizably the same person in every one of these styles. 24–27s: She lands flat on her back on a smooth white floor. A wide lens pushes in slowly and tilts up to reveal the environment: an infinitely extending pure-white showroom/hangar with a gridded softbox ceiling. Hundreds of black circular pedestals cover the floor, each holding a character statue or a parked vehicle — original-design warriors, mechs, robots, sci-fi troopers, plus large military aircraft and helicopters. A small line of floating white label text sits in front of each pedestal: "FANTASY CHARACTER", "VEHICLE", "MECH" — set at a small point size, present purely as environmental information. 27–28s: She sits up and pushes herself to her feet, turning slowly to take in the hall, eyes widening in awe. The camera settles into a full-body medium-wide, centered, the statue array receding out of focus behind her. 28–30s [THE FINAL TRANSFORMATION]: The instant she stands fully upright, the crystalline beads rise from the floor around her feet and sweep up her body in one continuous wave — and for the first time in the film, SHE is the thing being regenerated. Her streetwear disintegrates into the bead cloud and a magical-girl battle uniform grows outward in its place, following the exact same three-stage motif, generating in a readable order from the torso outward: a white high-collar fitted bodice → a mint-green sailor collar with three silver stripes → a deep mint pleated skirt with silver trim, its pleats opening one by one → long white gloves to the upper arm with mint piping → white knee-high boots with a mid heel and mint cuffs → layered silver jewelry reforming as a silver star brooch at her chest → a large mint-green bow at her back with long ribbons falling to the backs of her knees → a fine silver tiara condensing across her forehead. Her face, hairstyle, hair color, the mint streak and the headphones around her neck remain completely unchanged — only the clothing is regenerated. The last beads absorb into the fabric. She looks down at her own hands, then slowly raises her head, the ribbons still settling behind her, and looks straight past camera with a small, dawning smile. The film cuts on that frame. No end card, no title, no tagline, no subtitle of any kind. [SOUND] Rain and wet-road traffic, urban ambient bed; high-frequency glass/ice shatter-and-rearrange sounds on every crystallization; a short bright locking tone as each reassembly completes; electronic refresh tones from the billboard; low-frequency collapse as the ground gives way; wind through the fall, with a distinct timbre shift for each of the four style jumps (film hiss / paper-and-ink texture / synth sweep / silence); one muffled impact on landing; near-total quiet inside the hall with only faint HVAC room tone; then, over the final transformation, an ascending shimmer of silk and crystal, each garment piece seating with a crisp click, the bow tying with a single sweep of ribbon, and one clear bell tone as the tiara locks in. One escalating electronic music bed ties it together, landing its final hit on her raised head. [HARD RULES] @[image1]'s face, hairstyle, hair color and headphones are absolutely unchanged across all 30 seconds. Her wardrobe is unchanged from 0s to 28s and may be regenerated ONLY in the 28–30s final beat — the transformation affects clothing only and must never alter her face, hair or identity. All other replacements happen only to the people, vehicles, ground and environment around her. The film may use only the single "crystalline bead disintegration → rearrangement → growth" transformation language, including for the final costume change; no other transition effect may be mixed in. From 0–18s the film must hold the logic of one spatially continuous tracking shot — the street's architecture, lighting and wet reflections must never jump. Every character and vehicle in the hall must be an original design; no real-world recognizable characters, brand marks, trademarks or identifiable film properties may appear, and the final magical-girl uniform must be an original design, not any existing anime character's costume. The only brand copy permitted anywhere in the film is the @[image2] artwork on the billboard screen at 11–15s, existing solely as a flat image inside that screen and never repeated elsewhere. No end card, no title card, no subtitles, no watermark, no overlaid text anywhere in the film. Avoid: cartoon rendering, game-CG feel, plastic materials, full-frame bloom, digital-glitch transitions, weightless floating, incorrect finger structure, garbled text, revealing or suggestive posing or camera angles.](https://image.24vids.com/tw-2098420840073236536/amplify_video_thumb/2098419846346113033/img/ntZLO-Woz5NhUKJy.jpg)