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.Qwen has cooked really deep 🔥 Multi-Layer Decomposition is here! This is so photoshop completely AI powered! QwenImage-Layered is an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers That means, you can have targeted editing with much more precision without troubling other... show more

1LittleCoder💻

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We’re finally getting layers in AI images. The new Qwen Image Layered LoRA allows you to decompose any image into layers - which means you can move, resize, or replace an object / background. This is Photoshop-grade editing, offered as an open source model 🤯

We’re finally getting layers in AI images. The new Qwen Image Layered LoRA allows you to decompose any image into layers - which means you can move, resize, or replace an object / background. This is Photoshop-grade editing, offered as an open source model 🤯

Justine Moore

50,816 görüntüleme • 8 ay önce

President Carter’s time on this earth is coming to an end according to his grandson Jason Carter. What a great man. At 99 he has accomplished so much. With an election coming up let’s remember all he has done for voting rights. Jason Carter: "My grandfather is doing okay. He has been in hospice, as you know, for almost a year and a half now, and he really is, I think, coming to the end. I’ve said before, there’s a part of this faith journey that is so important to him, and there’s a part of that faith journey that you only can live at the very end and I think he has been there in that space."

President Carter’s time on this earth is coming to an end according to his grandson Jason Carter. What a great man. At 99 he has accomplished so much. With an election coming up let’s remember all he has done for voting rights. Jason Carter: "My grandfather is doing okay. He has been in hospice, as you know, for almost a year and a half now, and he really is, I think, coming to the end. I’ve said before, there’s a part of this faith journey that is so important to him, and there’s a part of that faith journey that you only can live at the very end and I think he has been there in that space."

Brian Krassenstein

1,739,066 görüntüleme • 2 yıl önce

Wait… why did nobody tell me AI-generated posters could go back into an actual Photoshop workflow? Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I generated a poster in Dreamina first, then sent it to GPT-6 Astra. Astra used Photoshop to break the image apart into separate layers, so I could finally adjust the background, typography, and individual elements instead of being stuck with one flat image. For designers, this feels like a missing step between AI generation and professional editing. And because Dreamina keeps the creation cost competitive, I can test multiple poster ideas before deciding which one deserves a full edit. This workflow is kind of a game changer for rapid design exploration. #Dreamina #DreaminaPartner

Wait… why did nobody tell me AI-generated posters could go back into an actual Photoshop workflow? Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I generated a poster in Dreamina first, then sent it to GPT-6 Astra. Astra used Photoshop to break the image apart into separate layers, so I could finally adjust the background, typography, and individual elements instead of being stuck with one flat image. For designers, this feels like a missing step between AI generation and professional editing. And because Dreamina keeps the creation cost competitive, I can test multiple poster ideas before deciding which one deserves a full edit. This workflow is kind of a game changer for rapid design exploration. #Dreamina #DreaminaPartner

Katherine

80,449 görüntüleme • 6 gün önce

The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation

The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation

AK

41,830 görüntüleme • 3 yıl önce

I honestly didn’t expect this part of the AI design workflow to work this smoothly. Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I started by creating a poster concept in Dreamina. The image looked finished, but I wanted to edit the individual elements instead of recreating everything. So I gave the poster to GPT-6 Astra and had it call Photoshop to separate the design into editable layers — background, text, objects, and visual elements all became independent pieces. This is the part that surprised me: AI doesn’t just help make the design anymore, it can help turn the final image back into a real design file. Dreamina makes exploring different poster concepts much easier with its competitive pricing, then Astra helps take them into the editing stage. #Dreamina #DreaminaPartner

I honestly didn’t expect this part of the AI design workflow to work this smoothly. Turns out GPT-6 Astra + Dreamina Seedance 2.5 is an actual pipeline. I started by creating a poster concept in Dreamina. The image looked finished, but I wanted to edit the individual elements instead of recreating everything. So I gave the poster to GPT-6 Astra and had it call Photoshop to separate the design into editable layers — background, text, objects, and visual elements all became independent pieces. This is the part that surprised me: AI doesn’t just help make the design anymore, it can help turn the final image back into a real design file. Dreamina makes exploring different poster concepts much easier with its competitive pricing, then Astra helps take them into the editing stage. #Dreamina #DreaminaPartner

Vipin Gautam (Viipin I Gautam)

45,029 görüntüleme • 6 gün önce

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

Miko

30,108 görüntüleme • 4 ay önce

Introducing RL Environment Creator Skill Now any one can create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.

Introducing RL Environment Creator Skill Now any one can create RL environments $ npx skills add adithya-s-k/RL_Envs_101 > You can create environments across multiple frameworks like OpenEnv, OpenReward, Verifiers, NemoGym ... > the repo has live working examples of environments that your coding agent can reference > The skill is design to first understand what type of model you are training and create an environment while keeping that in mind ps. There’s a lot more to building RL environments that can be used for training. One major aspect is the data, which this skill can’t directly solve. However, the skill will help with implementing tools, rewards, and other components of an RL environment, making it easier to go from idea to implementation quickly across different frameworks. Let me know if you’d be interested in a detailed, end-to-end blog/tutorial on building an environment and actually training a model for a useful use case.

Adithya S K

46,948 görüntüleme • 4 ay önce

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

Michael Black

22,182 görüntüleme • 9 ay önce

HOW TO DODGE EVERY SKILLSHOT IN LEAGUE OF LEGENDS SO YOU GET ACCUSED OF SCRIPTING - Script in your mind - Draw out how far, wide, fast an ability is relative to your character thats all the easy stuff that I have been preaching already you can find in my free discord for improvement however one thing that League coaches fail to explain is the human aspect of it every game you play in League of Legends, every single person in the game is constantly building their profile in a game on how they operate both sides are constantly trying to mind f*ck each other to land and dodge skillshots. I have broken it down into layers the three layers to dodging are layer 0 - no dodge (unconscious) layer 1 - dodge (conscious) layer 2 - no dodge (conscious) Notice how in the clip in a challenger game below Olaf shoots a layer 0 skillshot, but because I am playing at a layer 1, I dodge his axe. Now the Thresh hook gets a little deeper bare with me, because I built the profile that I will dodge an ability in that moment, he thinks that I won't dodge and is shooting a hook at a layer 2 thinking that I will dodge at a layer 2 also. However I know that he knows I will likely not juke and walk straight so I make the conscious choice to dodge AGAIN playing at a layer 1 resulting in me dodging the hook, of course he could be accounting for my tumble but the point still stands. There are many deeper things to consider like zoning abilities, environment etc but you generally want to always play at a layer 1 until you gain more data in a game to adapt. However one thing that always stays true throughout my 13 years of playing League is in teamfights that have gone on for awhile, human beings tend to panic and default to layer 0 of shooting abilities, so if your able to operate at layer 1 as a teamfight progresses, you will likely dodge that one final skillshot that wins you the game. study the saskio way

Tony Chau

186,203 görüntüleme • 10 ay önce

I think the future of AI-assisted anime is not video models, but image continuity models like GPT Image 2.5. Predicting the next frame conditioned on the previous frame gives you much better control than predicting the next few seconds of video. You also don't need to predict all 24 frames every second. Since anime is basically layered animation, you can keep the same image for 5seconds while only the hand is waving or the eyes are blinking. Frame by frame prediction is also the nature of 2D anime. I've never really get the idea with turning everything into ultra-smooth 60 FPS animation. The problem with this model right now is that the 'invisible watermark' is too visible, which is really annoying. When you do chained generations, the watermark keeps stacking up to the point where it actually makes the project worse. It also has very poor color consistency.

I think the future of AI-assisted anime is not video models, but image continuity models like GPT Image 2.5. Predicting the next frame conditioned on the previous frame gives you much better control than predicting the next few seconds of video. You also don't need to predict all 24 frames every second. Since anime is basically layered animation, you can keep the same image for 5seconds while only the hand is waving or the eyes are blinking. Frame by frame prediction is also the nature of 2D anime. I've never really get the idea with turning everything into ultra-smooth 60 FPS animation. The problem with this model right now is that the 'invisible watermark' is too visible, which is really annoying. When you do chained generations, the watermark keeps stacking up to the point where it actually makes the project worse. It also has very poor color consistency.

Linaqruf

12,591 görüntüleme • 2 gün önce

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

Brett Adcock

93,986 görüntüleme • 1 yıl önce

From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

Teodora P L

28,523 görüntüleme • 2 yıl önce

you are making video games all wrong for decades almost all game art time went into reverse-engineering the end result you start with concept art of what you WANT it to look like. then for YEARS you walk the computer backwards into something it can process: a rendering engine, 3d assets that fit that engine, and a gamer’s local hardware that has to turn all of that back into a high fidelity image incredibly slow. incredibly expensive. but it was the best we had ai makes that pipeline faster as everyone is figuring out today. but they’re missing the real unlock: YOU DON’T NEED THAT PIPELINE AT ALL if you have a reference for the end look, you don’t model it in 3d. you don’t reverse-engineer into an engine you keep a very basic greybox model of the world as the source of truth, and track every entity in real time. it doesn’t need to look good at all… the player never sees it! a fast video model turns that into the “game” in real time. it doesn’t have to look like a game. it can look like a 2d cartoon or cinematic live action fal’s h3 max is very fast. astra 6 low-effort too. neither is fully realtime yet but together they can generate continuous, coherent, player-controlled scenes, faster than playback early demo:

you are making video games all wrong for decades almost all game art time went into reverse-engineering the end result you start with concept art of what you WANT it to look like. then for YEARS you walk the computer backwards into something it can process: a rendering engine, 3d assets that fit that engine, and a gamer’s local hardware that has to turn all of that back into a high fidelity image incredibly slow. incredibly expensive. but it was the best we had ai makes that pipeline faster as everyone is figuring out today. but they’re missing the real unlock: YOU DON’T NEED THAT PIPELINE AT ALL if you have a reference for the end look, you don’t model it in 3d. you don’t reverse-engineer into an engine you keep a very basic greybox model of the world as the source of truth, and track every entity in real time. it doesn’t need to look good at all… the player never sees it! a fast video model turns that into the “game” in real time. it doesn’t have to look like a game. it can look like a 2d cartoon or cinematic live action fal’s h3 max is very fast. astra 6 low-effort too. neither is fully realtime yet but together they can generate continuous, coherent, player-controlled scenes, faster than playback early demo:

Udi Wertheimer

124,378 görüntüleme • 12 gün önce

🚨 So the newest slop on X is "THE RING DISAPPEARS" 🤦‍♂️😂 So, the ring does not... if you keep playing the video, you can see the camera angle, color, lighting, and filters makes it seem this way. As someone who has seen this in my own videos I have edited (particularly outside in sunlight) - specifically with a Bokeh filter - I promise it isn't "AI" doing it. This is simply a filter, or (as you see with high end cameras that focus center) a focus and compression of the uploaded video. As you can see at the end of the video, the ring is quite thin and similar to his skin color. The reason people are noticing this vs other videos is because they're putting a weird, hyper-focus on his newest videos. Does anyone even know where the original "proof/evidence" that Netanyahu was dead came from? You first have to answer that before we determine what is true or debunked. People just took that for granted without explanation lol

🚨 So the newest slop on X is "THE RING DISAPPEARS" 🤦‍♂️😂 So, the ring does not... if you keep playing the video, you can see the camera angle, color, lighting, and filters makes it seem this way. As someone who has seen this in my own videos I have edited (particularly outside in sunlight) - specifically with a Bokeh filter - I promise it isn't "AI" doing it. This is simply a filter, or (as you see with high end cameras that focus center) a focus and compression of the uploaded video. As you can see at the end of the video, the ring is quite thin and similar to his skin color. The reason people are noticing this vs other videos is because they're putting a weird, hyper-focus on his newest videos. Does anyone even know where the original "proof/evidence" that Netanyahu was dead came from? You first have to answer that before we determine what is true or debunked. People just took that for granted without explanation lol

Nick Matau

23,761 görüntüleme • 6 ay önce

EXCLUSIVE: I recently spoke with Rep. Al Greene who was just escorted from Donald Trump’s speech. We’ll have a chance to ask him more questions this evening. Here’s what he told me: “We have a president who has indicated that Russia is not an aggressor. He is sending a message that he really does believe that he's above the law. I think we have to do as much as we can as fast as we can to prevent him from becoming a dictator.” Thank you for showing courage this evening, Rep. Greene! 💙

EXCLUSIVE: I recently spoke with Rep. Al Greene who was just escorted from Donald Trump’s speech. We’ll have a chance to ask him more questions this evening. Here’s what he told me: “We have a president who has indicated that Russia is not an aggressor. He is sending a message that he really does believe that he's above the law. I think we have to do as much as we can as fast as we can to prevent him from becoming a dictator.” Thank you for showing courage this evening, Rep. Greene! 💙

CALL TO ACTIVISM

1,628,052 görüntüleme • 1 yıl önce

- In the 4th Clip Ribbit Capital asks: "How do these companies all connect?" - Then this clip shows up at the end 👇 Companies are connected with the rails and THE FROG IS BRINGING THE POWER - AS A GRID - Did you recognize this exact frog ? Let's check the Ribbit Capital Token Letter. EXACTLY 🎯🐸 That was the Infrastructure Layer. I believe more videos about to come. Intent and Execution layers... Then $TIBBIR - RIBBIT = POWER

- In the 4th Clip Ribbit Capital asks: "How do these companies all connect?" - Then this clip shows up at the end 👇 Companies are connected with the rails and THE FROG IS BRINGING THE POWER - AS A GRID - Did you recognize this exact frog ? Let's check the Ribbit Capital Token Letter. EXACTLY 🎯🐸 That was the Infrastructure Layer. I believe more videos about to come. Intent and Execution layers... Then $TIBBIR - RIBBIT = POWER

NEO I ЯEBEL

13,621 görüntüleme • 9 ay önce

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.

HyperGPT

99,258 görüntüleme • 5 ay önce

JUST IN: Meta AI introduces Voicebox, an all-in-one generative speech model. Voicebox is an impressive breakthrough! It could do for speech what other models like GPT-3 and Stable Diffusion have done for text and images. Some key details: - Voicebox can synthesize speech across 6 languages - It's a general-purpose model that can perform tasks it wasn't trained on. It can perform noise removal, content editing, style conversion, and more - Supports in-context text-to-speech synthesis and cross-lingual style transfer - It's 20x faster than current models and outperforms single-purpose models through in-context learning paper: blog:

JUST IN: Meta AI introduces Voicebox, an all-in-one generative speech model. Voicebox is an impressive breakthrough! It could do for speech what other models like GPT-3 and Stable Diffusion have done for text and images. Some key details: - Voicebox can synthesize speech across 6 languages - It's a general-purpose model that can perform tasks it wasn't trained on. It can perform noise removal, content editing, style conversion, and more - Supports in-context text-to-speech synthesis and cross-lingual style transfer - It's 20x faster than current models and outperforms single-purpose models through in-context learning paper: blog:

elvis

88,518 görüntüleme • 3 yıl önce

GPT Image 2.0 is now available on Akool 🚀 This isn’t just an upgrade—it’s a major leap in AI image generation. With GPT Image 2.0, you get: • Sharper, high-resolution outputs with improved detail and realism • Much stronger text rendering (finally, text in images that actually looks right) • Better prompt understanding for more accurate and controllable results • Improved consistency across variations and multi-image generations • More precise editing & refinement for iterative creative workflows Whether you're creating marketing visuals, product images, or storytelling assets, GPT Image 2.0 delivers cleaner, more reliable, and production-ready results. Now live on Akool, try the latest in AI image generation today.

GPT Image 2.0 is now available on Akool 🚀 This isn’t just an upgrade—it’s a major leap in AI image generation. With GPT Image 2.0, you get: • Sharper, high-resolution outputs with improved detail and realism • Much stronger text rendering (finally, text in images that actually looks right) • Better prompt understanding for more accurate and controllable results • Improved consistency across variations and multi-image generations • More precise editing & refinement for iterative creative workflows Whether you're creating marketing visuals, product images, or storytelling assets, GPT Image 2.0 delivers cleaner, more reliable, and production-ready results. Now live on Akool, try the latest in AI image generation today.

Akool Inc

1,541,129 görüntüleme • 4 ay önce