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We just brought flow maps to language modeling for one-step sequence generation 💥 Discrete diffusion is not necessary -- continuous flows over one-hot encodings achieve SoTA performance and ≥8.3× faster generation 🔥 We believe this is a major step forward for discrete generative modeling and language modeling alike. 🚀... show more

Nicholas Boffi

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44,062 görüntüleme • 5 ay önce •via X (Twitter)

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Seed-Music A Unified Framework for High Quality and Controlled Music Generation discuss: We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio.

Seed-Music A Unified Framework for High Quality and Controlled Music Generation discuss: We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: controlled music generation and post-production editing. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio.

AK

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We found a hack to make Whisper transcribe into *any* language (not just English)! ⚡️ This is an EXPERIMENTAL one-step feature: enter your audio as input, and get a transcription in another language. 🔥 Check it out here:

We found a hack to make Whisper transcribe into any language (not just English)! ⚡️ This is an EXPERIMENTAL one-step feature: enter your audio as input, and get a transcription in another language. 🔥 Check it out here:

Vaibhav (VB) Srivastav

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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

AK

125,319 görüntüleme • 3 yıl önce

Today we are launching creator monetization for Poe! This program lets any bot creator on Poe generate revenue. This is a major step forward for the platform and is the first program of its kind, so we are very excited to see what it lets everyone create. (thread)

Today we are launching creator monetization for Poe! This program lets any bot creator on Poe generate revenue. This is a major step forward for the platform and is the first program of its kind, so we are very excited to see what it lets everyone create. (thread)

Adam D'Angelo

4,138,549 görüntüleme • 2 yıl önce

Presto! Distilling Steps and Layers for Accelerating Music Generation Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge.

Presto! Distilling Steps and Layers for Accelerating Music Generation Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than comparable SOTA) -- the fastest high-quality TTM to our knowledge.

AK

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#WATCH | Chennai, Tamil Nadu: On her resignation from BJP over 3-language policy under NEP (National Education Policy, actor Ranjana Nachiyar says, " My reason for resignation...our children, they are not well versed even in Tamil so why do you have to bother them with another language? The burden of another language is not necessary right now...we don't have any dire necessity for another language because we have English and all over the world we can travel...I feel proud to stand for Tamil...if we stay silent now our future generation will suffer..."

#WATCH | Chennai, Tamil Nadu: On her resignation from BJP over 3-language policy under NEP (National Education Policy, actor Ranjana Nachiyar says, " My reason for resignation...our children, they are not well versed even in Tamil so why do you have to bother them with another language? The burden of another language is not necessary right now...we don't have any dire necessity for another language because we have English and all over the world we can travel...I feel proud to stand for Tamil...if we stay silent now our future generation will suffer..."

ANI

324,498 görüntüleme • 1 yıl önce

Every wondered if we can model motion as a language? can we tokenize this new language? is it useful? Turns out tremendously! 🚀 In out latest #NeurIPS2024 paper on QueST: Self-Supervised Skill Abstractions for Learning Continuous Control, we find that action tokenization matters a lot! We can learn skill encodings by representing temporal action abstractions with a discrete codebook. This enables 2 things 1. Better Behaviour Cloning: we can better assimilate multi-task data (>9%) over best paper. This is currently best in class BC method! 2. generalization of this language to represent new tasks in 5-shot transfer to longer horizon tasks! Check out the thread by Atharva Mete for more details. And check out more details at: Joint work with Atharva Mete Albert Wilcox Haotian Xue Yongxin Chen Georgia Tech School of Interactive Computing Machine Learning at Georgia Tech Robotics@GT NVIDIA Robotics

Every wondered if we can model motion as a language? can we tokenize this new language? is it useful? Turns out tremendously! 🚀 In out latest #NeurIPS2024 paper on QueST: Self-Supervised Skill Abstractions for Learning Continuous Control, we find that action tokenization matters a lot! We can learn skill encodings by representing temporal action abstractions with a discrete codebook. This enables 2 things 1. Better Behaviour Cloning: we can better assimilate multi-task data (>9%) over best paper. This is currently best in class BC method! 2. generalization of this language to represent new tasks in 5-shot transfer to longer horizon tasks! Check out the thread by Atharva Mete for more details. And check out more details at: Joint work with Atharva Mete Albert Wilcox Haotian Xue Yongxin Chen Georgia Tech School of Interactive Computing Machine Learning at Georgia Tech Robotics@GT NVIDIA Robotics

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Bitcoin has recently rallied over $30,000 in just one month, leaving no doubt that the BULL MARKET is FULLY BACK! 🚀 So is this rally just getting started, or do we need to take a step back and make sure we’re not getting ahead of ourselves? 🤔 Full video here! 👇

Bitcoin Magazine Pro

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An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

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.SnowflakeDB is thrilled to announce #SnowflakeArctic: A state-of-the-art large language model uniquely designed to be the most open, enterprise-grade LLM on the market. This is a big step forward for open source LLMs. And it’s a big moment for Snowflake in our #AI journey as we continue to build best-in-class enterprise-grade products for our customers. The era of enterprise AI is here. 🚀

.SnowflakeDB is thrilled to announce #SnowflakeArctic: A state-of-the-art large language model uniquely designed to be the most open, enterprise-grade LLM on the market. This is a big step forward for open source LLMs. And it’s a big moment for Snowflake in our #AI journey as we continue to build best-in-class enterprise-grade products for our customers. The era of enterprise AI is here. 🚀

sridhar

243,719 görüntüleme • 2 yıl önce

🚨 BOMBSHELL: The CHILLING Epstein Files Clip That Shows How the GROOMING Operation Worked —He Used The Lure Of "Modeling" to TRAP His Victims!🤯 This isn't just about abuse; this is about the method. This clip is the smoking gun that proves this was a sophisticated, manipulative grooming operation disguised as a "modeling" shoot. The clip starts with Jeffrey Epstein coaching a young woman with a European accent to pose for a photo. She's trying, but it's not enough for him. He says, "Is that the best you can do?" She replies, "Yes." Epstein pushes back: "No, come on, do like a modeling photo." The girl is perplexed. She answers back: "A modeling photo?" And then... REDACTED. The audio goes silent. The government cuts the tape right at the most critical moment. When the audio returns, we hear the aftermath of whatever a "modeling photo" entailed. A male voice says, "That's not... Oh no. Your boob is sticking out." The woman denies it. This is the playbook. Epstein is using the alluring promise of "modeling" to lure this young woman into a specific, exploitative pose. The fact that she seems confused or unsure proves she's not a willing participant in the way they'd have you believe. She's being manipulated. And the government's redaction is the second crime here. What happened in those silent seconds? What instructions were given? What was said that they don't want the public to hear? They are actively protecting the methodology of the grooming operation. This is a pattern i keep seeing in all of these videos in the files. This clip is the key. It shows the premeditation, the coaching, the bait-and-switch, and the system of exploitation. It proves this wasn't random; it was a well-oiled machine, using the dream of modeling as a trap. They can redact the audio, but they can't redact the truth. We know what this "modeling" led to. We know what this operation was all about. The redaction is the confession. They are hiding how the operation worked. Share this everywhere. The world needs to see the manipulation for themselves.

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🚨 BOMBSHELL: The CHILLING Epstein Files Clip That Shows How the GROOMING Operation Worked —He Used The Lure Of "Modeling" to TRAP His Victims!🤯 This isn't just about abuse; this is about the method. This clip is the smoking gun that proves this was a sophisticated, manipulative grooming operation disguised as a "modeling" shoot. The clip starts with Jeffrey Epstein coaching a young woman with a European accent to pose for a photo. She's trying, but it's not enough for him. He says, "Is that the best you can do?" She replies, "Yes." Epstein pushes back: "No, come on, do like a modeling photo." The girl is perplexed. She answers back: "A modeling photo?" And then... REDACTED. The audio goes silent. The government cuts the tape right at the most critical moment. When the audio returns, we hear the aftermath of whatever a "modeling photo" entailed. A male voice says, "That's not... Oh no. Your boob is sticking out." The woman denies it. This is the playbook. Epstein is using the alluring promise of "modeling" to lure this young woman into a specific, exploitative pose. The fact that she seems confused or unsure proves she's not a willing participant in the way they'd have you believe. She's being manipulated. And the government's redaction is the second crime here. What happened in those silent seconds? What instructions were given? What was said that they don't want the public to hear? They are actively protecting the methodology of the grooming operation. This is a pattern i keep seeing in all of these videos in the files. This clip is the key. It shows the premeditation, the coaching, the bait-and-switch, and the system of exploitation. It proves this wasn't random; it was a well-oiled machine, using the dream of modeling as a trap. They can redact the audio, but they can't redact the truth. We know what this "modeling" led to. We know what this operation was all about. The redaction is the confession. They are hiding how the operation worked. Share this everywhere. The world needs to see the manipulation for themselves.

Project Constitution

54,678 görüntüleme • 3 ay önce

THIS TOOL LETS ANYONE BUILD A 3D WORLD FROM ONE PHOTO - AND THE MONEY IS ALREADY THERE FOR WHOEVER MOVES FIRST World Labs raised $230 million to build exactly this - upload one image from your phone, get a full explorable 3D world you can walk through no team, no 3D modeling skills, no development budget - just one photo and a few minutes architects, game developers and film studios pay hundreds of millions and years of work to build what this does in minutes anyone can now sell 3D world generation as a service - virtual tours, game environments, film sets, architectural visualization - clients who used to pay $10,000-50,000 for this still need it done the barrier just dropped from millions of dollars to one photo the people making money from this in 12 months are the ones who started today

THIS TOOL LETS ANYONE BUILD A 3D WORLD FROM ONE PHOTO - AND THE MONEY IS ALREADY THERE FOR WHOEVER MOVES FIRST World Labs raised $230 million to build exactly this - upload one image from your phone, get a full explorable 3D world you can walk through no team, no 3D modeling skills, no development budget - just one photo and a few minutes architects, game developers and film studios pay hundreds of millions and years of work to build what this does in minutes anyone can now sell 3D world generation as a service - virtual tours, game environments, film sets, architectural visualization - clients who used to pay $10,000-50,000 for this still need it done the barrier just dropped from millions of dollars to one photo the people making money from this in 12 months are the ones who started today

Cortex

12,123 görüntüleme • 1 ay önce

Soldiers go to the battlefield not just to fight… They put their heart and soul in every step 💥 This song is like a war anthem full fire, full emotion, full fighter spirit. For every jawan who stands strong till the last breath… This one is their song..🔥 #Ikkis #MaddockFilms

Soldiers go to the battlefield not just to fight… They put their heart and soul in every step 💥 This song is like a war anthem full fire, full emotion, full fighter spirit. For every jawan who stands strong till the last breath… This one is their song..🔥 #Ikkis #MaddockFilms

Vishwajit Patil

33,234 görüntüleme • 7 ay önce

One of the most popular uses for NotebookLM is language learning. From audio overviews to our most recent flashcards and quizzes launch— just upload a vocabulary list, and you'll be multilingual in no time. Check out this public notebook we made for learning Spanish ⬇️

One of the most popular uses for NotebookLM is language learning. From audio overviews to our most recent flashcards and quizzes launch— just upload a vocabulary list, and you'll be multilingual in no time. Check out this public notebook we made for learning Spanish ⬇️

NotebookLM

191,655 görüntüleme • 10 ay önce

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

AK

16,062 görüntüleme • 1 yıl önce

This is how you bring young people over to the nationalist side. Describe the England that was stolen from us and we so narrowly missed out on. How the children of the Blairite-One Nation project had their inheritance ripped from underneath us and how we will restore it for our own children and grandchildren. Because our generation will either achieve historical greatness or we will be the last generation in over a thousand years of Englishmen. What a magical opportunity we have been blessed to have bestowed on us. We were born into an era thought to be “the end of history”. Turns out history is very much still with us and we hold the pen.

This is how you bring young people over to the nationalist side. Describe the England that was stolen from us and we so narrowly missed out on. How the children of the Blairite-One Nation project had their inheritance ripped from underneath us and how we will restore it for our own children and grandchildren. Because our generation will either achieve historical greatness or we will be the last generation in over a thousand years of Englishmen. What a magical opportunity we have been blessed to have bestowed on us. We were born into an era thought to be “the end of history”. Turns out history is very much still with us and we hold the pen.

Wolf 🐺

21,368 görüntüleme • 2 ay önce

Agile Continuous Jumping in Discontinuous Terrains discuss: We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.

Agile Continuous Jumping in Discontinuous Terrains discuss: We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.

AK

35,794 görüntüleme • 1 yıl önce

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

Genesis AI

2,715,548 görüntüleme • 2 ay önce

🚀 Project Zero – Release #5 Our biggest upgrade so far is live, a major step forward in usability, security, and readiness for scale. ✨ Full Frontend Redesign for a faster, cleaner, more intuitive experience 📊 New My Dashboard to track activity, progress, and key info in one place 👤 Updated Profile & Settings pages with clearer layouts and smoother management 🛡️ New Authentication Flow + Whitelist Removal for stronger, scalable security This release makes Project Zero feel more polished, more reliable, and better prepared for the next phase: welcoming more users, partners, and enterprise integrations. 👉 Try it: 👉 Full release note:

Project Zero

25,192 görüntüleme • 8 ay önce