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🚀 Create stunning, character-consistent videos with Hailuo's Subject Reference (S2V-01) Model 🚀: ✨ Single-Image Simplicity: Unlock precise facial details from just one reference image. ⚡ Ultra-Low Cost: Achieve results at less than 1% of traditional computational demands. 🌟 High Flexibility: Enjoy creative freedom with simple text prompts. ⏳ Faster... show more

MiniMax Design (H3)

81,462 subscribers

632,852 просмотров • 1 год назад •via X (Twitter)

Образование Искусство Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля cyberyogi

cyberyogi1 год назад

It’s incredible how easy this feature is to use compared to other AI models that require tons of tokens and multiple inputs for similar functionality. Amazing work! 🙌

Фото профиля Freepik

Freepik1 год назад

🚀 Introducing Freepik AI video generator. Everything you need to create high-quality, physically accurate videos in one place. 🤩

Фото профиля イッチ@AI術士

イッチ@AI術士1 год назад

This feature is very wonderful 👍

Фото профиля Shine by Nous ✨

Shine by Nous ✨1 год назад

Great update 🔥

Фото профиля Zerocarbon

Zerocarbon1 год назад

tried it results are amazing, but if the prompt contains any text generation in the video, it does not generate correctly..

Фото профиля OscarAI

OscarAI1 год назад

This new feature is awesome 👌

Фото профиля Maskai

Maskai1 год назад

This is amazing

Фото профиля AI ArtProdigy

AI ArtProdigy1 год назад

Being able to keep characters consistent with just one image is a game changer. I'm really excited to try this out and see what kind of videos I can whip up. Thanks for this awesome feature! 🎥✨

Фото профиля B.M. Cifer

B.M. Cifer1 год назад

Awesome update. Enticing feature.

Фото профиля Özge Döner

Özge Döner1 год назад

❤️❤️

Фото профиля ysf.ai

ysf.ai1 год назад

👍👍👍👍👍

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🎥 Introducing Hailuo's Subject Reference: Revolutionizing Character Consistency in Video Creation 🔥 We’re excited to present Hailuo's S2V-01 model, a groundbreaking innovation in AI video generation that tackles one of the industry’s biggest challenges: maintaining consistent, realistic facial features and identity across dynamic video content, regardless of camera angles or movements. 💡 Why It’s a Game Changer: - Pioneering Technology: The first-of-its-kind to ensure character consistency in dynamic video generation, surpassing even fine-tuned models in performance. - Minimal Input, Maximum Impact: Generate character-consistent videos from just one reference image. Every frame remains true to the original identity with unmatched accuracy and reliability. - Enhanced Flexibility: Adjust more than just facial features—modify posture, expressions, lighting, and more, all with simple text-based prompts. 🌟While the new model enhances subject consistency, it may occasionally follow prompts less precisely than T2V or I2V, with some environmental morphing. Despite these early-stage challenges, Hailuo Subject Reference marks a significant leap in AI video generation. We’re committed to continual improvements including multi-subject references, objects references, and complex, multi-layered scenes. Explore the future of creative, consistent video production with Hailuo S2V-01 today. 🔥We believe the possibilities are endless.

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AI COMMERCIAL BREAKTHROUGH: CHARACTER CONSISTENCY IS LIVE! 🚀 Lovart & Veo 3.1 just solved the biggest time-sink in modern ad production. 👋 Create high-aesthetic, continuous commercials across ANY style with: ✅ Seamless Character Identity: Your brand spokesperson, the same actor, the same outfit, in every frame—no matter the style or scene. 🖼️ Single Reference Image Power: Lock in your lead character from just one image. 🎬 Professional Consistency: Maintain narrative continuity that delivers stunning, agency-ready results. Don't let style shifts break your brand story. Upgrade your commercial directing with Veo 3.1 today! 👉 Get Started Now: Log in to choose Veo 3.1 #Lovart #CreateWithLovart #Veo3_1 #AICommercials #AdTech #ConsistencyWins

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Andy ττ

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FELIX

97,682 просмотров • 3 месяцев назад

Let this chocolate brown Countach heal your algorithm! This commercial started as nothing more than an idea. I wanted to create something that felt timeless a chocolate brown Lamborghini Countach, an old-money atmosphere, a powerful woman, a luxury mansion party, natural daylight, authentic human interactions, and a commercial so realistic it wouldn't feel AI-generated at all. So I sat down with ChatGPT and explained the vision in detail. First, we built the story. I described the world, the mood, the characters, the setting, and the feeling I wanted viewers to experience. From there, ChatGPT helped transform that vision into a complete narrative, turning scattered ideas into a cinematic concept. Next came the planning stage. The story was broken down into a professional commercial timeline, scene by scene, shot by shot, exactly how a luxury automotive campaign would be storyboarded by a creative director and videographer. Then we went even deeper. Every scene was translated into detailed prompts, camera directions, movement instructions, lighting setups, slow-motion sequences, environmental details, character positioning, and luxury automotive close-ups. Once the sequence felt perfect, those ideas were converted into structured JSON prompts to maintain consistency throughout the project. After that, I generated individual storyboard images for every scene. Each image became a visual reference frame representing a specific moment of the commercial the mansion, the audience, the old-money styling, the Countach hero shots, the interior reveals, the scissor doors, and the final cinematic departure. Those reference images were then brought into Yapper where I used Seedance 2.0 to generate the actual video sequences. Multiple generations. Multiple revisions. Multiple refinements. Frame by frame, the vision started becoming reality. Finally, I edited both video sequences together, refined the pacing, synchronized the transitions, selected the music, and shaped everything into one seamless luxury automotive commercial. What you're watching isn't just an AI video. It's the result of storytelling, creative direction, prompt engineering, shot planning, storyboard creation, image generation, video generation, editing, and countless creative decisions. A chocolate brown Lamborghini Countach. An old-money lifestyle. A cinematic luxury fantasy brought to life from a single idea. Built through imagination. Designed through collaboration. Created frame by frame.

Let this chocolate brown Countach heal your algorithm! This commercial started as nothing more than an idea. I wanted to create something that felt timeless a chocolate brown Lamborghini Countach, an old-money atmosphere, a powerful woman, a luxury mansion party, natural daylight, authentic human interactions, and a commercial so realistic it wouldn't feel AI-generated at all. So I sat down with ChatGPT and explained the vision in detail. First, we built the story. I described the world, the mood, the characters, the setting, and the feeling I wanted viewers to experience. From there, ChatGPT helped transform that vision into a complete narrative, turning scattered ideas into a cinematic concept. Next came the planning stage. The story was broken down into a professional commercial timeline, scene by scene, shot by shot, exactly how a luxury automotive campaign would be storyboarded by a creative director and videographer. Then we went even deeper. Every scene was translated into detailed prompts, camera directions, movement instructions, lighting setups, slow-motion sequences, environmental details, character positioning, and luxury automotive close-ups. Once the sequence felt perfect, those ideas were converted into structured JSON prompts to maintain consistency throughout the project. After that, I generated individual storyboard images for every scene. Each image became a visual reference frame representing a specific moment of the commercial the mansion, the audience, the old-money styling, the Countach hero shots, the interior reveals, the scissor doors, and the final cinematic departure. Those reference images were then brought into Yapper where I used Seedance 2.0 to generate the actual video sequences. Multiple generations. Multiple revisions. Multiple refinements. Frame by frame, the vision started becoming reality. Finally, I edited both video sequences together, refined the pacing, synchronized the transitions, selected the music, and shaped everything into one seamless luxury automotive commercial. What you're watching isn't just an AI video. It's the result of storytelling, creative direction, prompt engineering, shot planning, storyboard creation, image generation, video generation, editing, and countless creative decisions. A chocolate brown Lamborghini Countach. An old-money lifestyle. A cinematic luxury fantasy brought to life from a single idea. Built through imagination. Designed through collaboration. Created frame by frame.

Julia Clark

26,529 просмотров • 3 месяцев назад

As I promised yesterday, I'll briefly explain LoRA training and share a workflow I made so you can do it quickly. First, let me answer a very common question: 'Why train LoRAs when we have such advanced models?' Even though we have incredibly advanced models now (like NBP), we still can't always get them to do specific things we want. Simplest example: the spritesheet LoRA I made the other day. I generated 1000 images with Nano Banana and only 100 were what I wanted. The LoRA I trained using those 100 images gives me nearly 100% consistent results. Second point is cost and speed. With LoRA, we can cut costs by 4-5x. And while doing that, we're generating 4-5x faster. How many images do you need for a good LoRA? This depends on your LoRA's complexity. For example, when I training the spritesheet LoRA, even though I used 100 images, I didn't include buildings in the training data, so this LoRA doesn't work for buildings. So think about your LoRA's use cases and add examples for as many use cases as possible to improve quality. What are paired images and how to train LoRAs for image-editing? When training LoRAs for image editing on fal, we call each edit example paired images - one with _start suffix, one with _end suffix. For example, if you're training a background remove LoRA, the unedited original photo will be your '_start' image. The image with background removed will be the '_end' image. Simply put: images we want to edit or use as reference get _start, target images we want to achieve get '_end'. Important: save both images with the same name. Like image332_start.jpg and image332_end.jpg. This way the system knows which images pair together. What about training LoRAs for models with multiple image inputs? Same logic. We still use _start and _end suffixes, but with one difference. Since there are multiple input images, we can number them: _start, _start1, _start2. Example: start images, 1st image = Woman portrait (image35_start.jpg) 2nd image = Glasses photo (image35_start1.jpg) 3rd image = Hat photo (image35_start2.jpg) Output image = portrait of woman wearing glasses and hat (image35_end.jpg) Can we do more detailed captioning? Yes. Similarly, you can improve training quality by creating a txt file for each set with the caption inside. Example: create image35.txt and write: 'Recreate the image by putting the glasses from the second image and the hat from the third image on the woman in the first image.' What are Steps? How many should I use? What's Learning Rate? Steps determines how many times the model sees and processes your training data (your images). Each step, the model learns a bit more. But as steps increase, so does the risk of overfitting. So there's no real default. But for a simpler LoRA with 20 paired images, 1000 steps is ideal. Here's a metaphor for the Steps and Learning Rate relationship: Imagine you have a balloon. Our goal is to inflate it to the optimal size. Steps = How many times we blow into the balloon Learning rate = How hard we blow each time If we blow too softly, we need to blow many more times. If we blow too hard, we risk popping it quickly and can't reach optimal size. Of course training won't explode, but it won't work as intended because it wasn't trained optimally. Training's done, now what? Once training's complete, you'll have a safetensors file. Every model you train on fal has a LoRA inference endpoint. In that inference, add your safetensors file link to the LoRA url input, and you can use your LoRA. Thanks for the read! The workflow in the video: If I forgot anything, let me know in the replies.

As I promised yesterday, I'll briefly explain LoRA training and share a workflow I made so you can do it quickly. First, let me answer a very common question: 'Why train LoRAs when we have such advanced models?' Even though we have incredibly advanced models now (like NBP), we still can't always get them to do specific things we want. Simplest example: the spritesheet LoRA I made the other day. I generated 1000 images with Nano Banana and only 100 were what I wanted. The LoRA I trained using those 100 images gives me nearly 100% consistent results. Second point is cost and speed. With LoRA, we can cut costs by 4-5x. And while doing that, we're generating 4-5x faster. How many images do you need for a good LoRA? This depends on your LoRA's complexity. For example, when I training the spritesheet LoRA, even though I used 100 images, I didn't include buildings in the training data, so this LoRA doesn't work for buildings. So think about your LoRA's use cases and add examples for as many use cases as possible to improve quality. What are paired images and how to train LoRAs for image-editing? When training LoRAs for image editing on fal, we call each edit example paired images - one with _start suffix, one with _end suffix. For example, if you're training a background remove LoRA, the unedited original photo will be your '_start' image. The image with background removed will be the '_end' image. Simply put: images we want to edit or use as reference get _start, target images we want to achieve get '_end'. Important: save both images with the same name. Like image332_start.jpg and image332_end.jpg. This way the system knows which images pair together. What about training LoRAs for models with multiple image inputs? Same logic. We still use _start and _end suffixes, but with one difference. Since there are multiple input images, we can number them: _start, _start1, _start2. Example: start images, 1st image = Woman portrait (image35_start.jpg) 2nd image = Glasses photo (image35_start1.jpg) 3rd image = Hat photo (image35_start2.jpg) Output image = portrait of woman wearing glasses and hat (image35_end.jpg) Can we do more detailed captioning? Yes. Similarly, you can improve training quality by creating a txt file for each set with the caption inside. Example: create image35.txt and write: 'Recreate the image by putting the glasses from the second image and the hat from the third image on the woman in the first image.' What are Steps? How many should I use? What's Learning Rate? Steps determines how many times the model sees and processes your training data (your images). Each step, the model learns a bit more. But as steps increase, so does the risk of overfitting. So there's no real default. But for a simpler LoRA with 20 paired images, 1000 steps is ideal. Here's a metaphor for the Steps and Learning Rate relationship: Imagine you have a balloon. Our goal is to inflate it to the optimal size. Steps = How many times we blow into the balloon Learning rate = How hard we blow each time If we blow too softly, we need to blow many more times. If we blow too hard, we risk popping it quickly and can't reach optimal size. Of course training won't explode, but it won't work as intended because it wasn't trained optimally. Training's done, now what? Once training's complete, you'll have a safetensors file. Every model you train on fal has a LoRA inference endpoint. In that inference, add your safetensors file link to the LoRA url input, and you can use your LoRA. Thanks for the read! The workflow in the video: If I forgot anything, let me know in the replies.

ilker

15,192 просмотров • 7 месяцев назад

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,438 просмотров • 9 месяцев назад

save this post to get the most out of unlimited Seedance 2.5 for up to 33 days on Higgsfield i'm going to show you how to use loops to produce ANY video format: ads, cinema, vlogs, UGC, music videos... with one system idea > vault > agent > references > images > script > video > montage > upscaling Seedance 2.5 one-shots a full 30 second video, audio generated in the same pass, carrying up to 30 image, 10 video and 10 audio references into a single generation here's a full breakdown of the setup: > idea: steal taste from work that already worked: - frameset․app and shotdeck․com for film stills - savee․com and cosmos․so for boards - eyecannndy․com for transitions then have a vision model name the lens, light, palette and grain of your picks in one locked paragraph you paste into every prompt > vault: an obsidian folder as your reference bible, one page per asset (idea, locked style, character sheets, reference images, the exact prompts that worked) plus one index page, reviewed after every session so it never rots into dead files > agent: three commands make every model callable from Claude Code: - npm install -g @ higgsfield/cli - higgsfield auth login - npx skills add higgsfield-ai/skills and your agent now submits, polls, retries and logs every job > references: build reference images by hand first, midjourney for cinema and stylized shots, nanobanana pro or gpt images 2 for realism use one locked style across the whole project, recurring characters turned into full sheets (front, side, back, blank background), and once locked you never regenerate them, you fix the motion prompt instead > images: frames before motion, always, a frame costs seconds and a clip costs minutes, so exploration happens at the cheap layer and only winners get animated > script: every shot gets the same six details, subject, action, place, camera, style, rules, and the 30 seconds splits into four timed beats inside one prompt, 0-6 set the scene, 6-14 build it out, 14-24 the turn, 24-30 the end > video: every reference gets a job and a boundary, "Video 1 defines motion and pacing" is half the instruction, "do not use the person's identity, clothing or scene" is the half that stops one reference leaking into shots it was never meant to touch > montage: the cut is a text file, one line per clip with its duration and an audio flag, ffmpeg renders the film from it, so the whole edit reruns in seconds > upscaling: once, at the end, on the finished cut, 720p while exploring, 1080p for keepers, 4K only for the master (use Topaz) for UGC ads, the same loop with two changes render the hook clip alone first, approve the face and the voice before anything else inherits them, then anchor every later clip with the approved hook's audio so one voice carries the whole ad and the script math is fixed, about 3.5 words per second, a 30 second ad is roughly 105 words, counted before anything renders unlimited means every loop above costs nothing to run... start one tonight

save this post to get the most out of unlimited Seedance 2.5 for up to 33 days on Higgsfield i'm going to show you how to use loops to produce ANY video format: ads, cinema, vlogs, UGC, music videos... with one system idea > vault > agent > references > images > script > video > montage > upscaling Seedance 2.5 one-shots a full 30 second video, audio generated in the same pass, carrying up to 30 image, 10 video and 10 audio references into a single generation here's a full breakdown of the setup: > idea: steal taste from work that already worked: - frameset․app and shotdeck․com for film stills - savee․com and cosmos․so for boards - eyecannndy․com for transitions then have a vision model name the lens, light, palette and grain of your picks in one locked paragraph you paste into every prompt > vault: an obsidian folder as your reference bible, one page per asset (idea, locked style, character sheets, reference images, the exact prompts that worked) plus one index page, reviewed after every session so it never rots into dead files > agent: three commands make every model callable from Claude Code: - npm install -g @ higgsfield/cli - higgsfield auth login - npx skills add higgsfield-ai/skills and your agent now submits, polls, retries and logs every job > references: build reference images by hand first, midjourney for cinema and stylized shots, nanobanana pro or gpt images 2 for realism use one locked style across the whole project, recurring characters turned into full sheets (front, side, back, blank background), and once locked you never regenerate them, you fix the motion prompt instead > images: frames before motion, always, a frame costs seconds and a clip costs minutes, so exploration happens at the cheap layer and only winners get animated > script: every shot gets the same six details, subject, action, place, camera, style, rules, and the 30 seconds splits into four timed beats inside one prompt, 0-6 set the scene, 6-14 build it out, 14-24 the turn, 24-30 the end > video: every reference gets a job and a boundary, "Video 1 defines motion and pacing" is half the instruction, "do not use the person's identity, clothing or scene" is the half that stops one reference leaking into shots it was never meant to touch > montage: the cut is a text file, one line per clip with its duration and an audio flag, ffmpeg renders the film from it, so the whole edit reruns in seconds > upscaling: once, at the end, on the finished cut, 720p while exploring, 1080p for keepers, 4K only for the master (use Topaz) for UGC ads, the same loop with two changes render the hook clip alone first, approve the face and the voice before anything else inherits them, then anchor every later clip with the approved hook's audio so one voice carries the whole ad and the script math is fixed, about 3.5 words per second, a 30 second ad is roughly 105 words, counted before anything renders unlimited means every loop above costs nothing to run... start one tonight

Machina

39,015 просмотров • 28 дней назад

🚨 The Next Evolution of AI Music is Here 🚨 We haven’t been standing still. We’ve been building at an incredible pace, with laser-sharp focus, pushing the boundaries of AI-powered music creation like never before. Our latest upgrade isn’t just more powerful—it’s more versatile, precise, and deeply creative than anything before. 🎶 Proof is in the sound: This song was generated from a simple prompt—“Blues with slight Arabian influence about a man lost in the desert searching for his bride.” Listen to the end and hear how $SUEDE AI captures emotion, style, and storytelling like never before. But this is just the beginning. Our new features are built for artists who want total creative control. Get extremely granular with how you craft and shape your sound: 🎛️ Full Production Control – Download an entire pack of every isolated instrument. 🎤 Use Your Own Voice – Or someone else’s. 📝 Exact Lyrics, Your Way – Have your words set to music seamlessly. 🎶 Reference Songs – Upload one for style analysis, extraction, and modeling—or simply note a publicly available track. 🔊 Text-to-Speech & AI Vocalists – Shape voices like never before. 🎼 Melody Collaboration – Upload a melody idea and let others build around it—or vice versa. However, due to cost considerations, we’ve capped it at 3 free songs per trial until subscription payments roll out in the next day or two. We’ll be launching a new payment gateway soon, so stay tuned for more details. And remember, all of this is powered by the $SUEDE token. It fuels the entire ecosystem, allowing artists to generate, own, and monetize their work like never before. We’re still working out a few kinks—like image generation—but prepare to be impressed. A major post is coming soon, breaking down these game-changing features and the revenue model behind them. Thread dropping soon. Turn notifications on. $SUEDE powers the future of culture. #SuedeAI #Web3Music

🚨 The Next Evolution of AI Music is Here 🚨 We haven’t been standing still. We’ve been building at an incredible pace, with laser-sharp focus, pushing the boundaries of AI-powered music creation like never before. Our latest upgrade isn’t just more powerful—it’s more versatile, precise, and deeply creative than anything before. 🎶 Proof is in the sound: This song was generated from a simple prompt—“Blues with slight Arabian influence about a man lost in the desert searching for his bride.” Listen to the end and hear how $SUEDE AI captures emotion, style, and storytelling like never before. But this is just the beginning. Our new features are built for artists who want total creative control. Get extremely granular with how you craft and shape your sound: 🎛️ Full Production Control – Download an entire pack of every isolated instrument. 🎤 Use Your Own Voice – Or someone else’s. 📝 Exact Lyrics, Your Way – Have your words set to music seamlessly. 🎶 Reference Songs – Upload one for style analysis, extraction, and modeling—or simply note a publicly available track. 🔊 Text-to-Speech & AI Vocalists – Shape voices like never before. 🎼 Melody Collaboration – Upload a melody idea and let others build around it—or vice versa. However, due to cost considerations, we’ve capped it at 3 free songs per trial until subscription payments roll out in the next day or two. We’ll be launching a new payment gateway soon, so stay tuned for more details. And remember, all of this is powered by the $SUEDE token. It fuels the entire ecosystem, allowing artists to generate, own, and monetize their work like never before. We’re still working out a few kinks—like image generation—but prepare to be impressed. A major post is coming soon, breaking down these game-changing features and the revenue model behind them. Thread dropping soon. Turn notifications on. $SUEDE powers the future of culture. #SuedeAI #Web3Music

Suede Labs

17,425 просмотров • 1 год назад

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,524 просмотров • 8 месяцев назад

📖SEEDANCE 2.0 JUST MADE EVERY FILM SCHOOL IRRELEVANT FOR SOLO CREATORS Solo creators with the right workflow are closing clients that used to require a full production studio. Seedance 2.0 inside Dreamina holds character consistency across scenes in a way no other tool at this price point comes close to. Same face, same costume, same lighting logic — frame after frame after frame. That's the feature that turns a single prompt session into a short film. The lava demon materializing inside a gothic cathedral. The girl in black holding her ground while everything burns around her. Two characters with completely different visual languages sharing the same atmospheric world — and Seedance holds both of them consistent across every cut. That's not a generation. That's a production. Here's the 7-step workflow that produced this: • Step 1 — Define the character before you define the scene. Write a complete physical description — face structure, hair, clothing, skin, posture. This becomes the anchor every future generation references. • Step 2 — Build the world separately from the character. Gothic cathedral, candlelight, fog, cracked stone, scattered bodies. Define the atmosphere as its own entity before you place anyone inside it. • Step 3 — Generate the reference frame. One image that establishes the visual language, the color grade, the lighting temperature. Lock this as your style reference before generating any video. • Step 4 — Feed the reference into Seedance's image-to-video pipeline with a motion prompt. Camera behavior only — slow push, hold, circle. The image handles the subject. The prompt handles the direction. • Step 5 — Generate four variations per scene. Delete the two that look generated. Keep the one where the character's face holds and the atmosphere feels physical rather than rendered. • Step 6 — Edit in CapCut or Premiere Pro. Add music that matches the emotional temperature of the grade — the visual already tells you what the sound should feel like. Dark orchestral, slow tempo, single instrument carrying the melody. • Step 7 — Save the character description and reference frame as a template. The next episode starts from the same character in the same world. Series content becomes a system, not a restart. How a freelancer sells this: Dark fantasy content for game studios, music artists, and fantasy brands is a real market with real budgets. A musician dropping an album needs a visual world. A game studio needs promotional cinematics. A fantasy brand needs a story. Where to find clients: • Music artists on SoundCloud and Spotify releasing dark, gothic, or cinematic albums — search by genre, find artists with 1k–50k listeners who have no visual content. They have the audience and the need but no production budget for traditional video. • Indie game studios on Itch and Steam launching fantasy or horror titles — they need promotional cinematics and trailers but can't afford a production company. A single free scene built from their game's character art opens every conversation. • Dark fantasy and gothic brands on Instagram and TikTok with strong photo content but zero video presence — jewelry brands, clothing labels, occult lifestyle brands. They have the aesthetic already built. You just add motion to it. • Fantasy and horror fiction authors on Instagram and Substack launching new books — they need visual teasers, trailers, and world-building content to build pre-launch audiences. Most have no idea this kind of production is accessible at this price point. • Tabletop RPG creators and Dungeon Masters on Patreon and Kickstarter — they build entire fantasy universes and need cinematic content for campaigns, promotional videos, and subscriber rewards. The niche is underserved and the creators inside it spend consistently on content tools and services. 📥Tomorrow I'll show you what's sitting right next to this opportunity. 🔖Save this if you are looking for practical AI methods that actually pay.

Zentrix⌚️

50,098 просмотров • 2 месяцев назад

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.

Bilawal Sidhu

34,440 просмотров • 1 год назад