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🥳 We present our #ICASSP2024 paper: A diffusion model that generates production-quality (bass) audio stems to any audio input. 😎🎸 According to our experience, that's more useful to artists than generating full mixes. 🙃 📜Paper: 🎶Demo: by Marco Pasini 👈💪SonyCSL(Paris)_Music Team #MusicAI

Stefan Lattner

1,614 subscribers

20,284 views • 2 years ago •via X (Twitter)

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3 Comments

Xavier Manuel Mountain2 years ago

Seriously dope! It’s about to be the digital renaissance in music real quick.

SteveRatatata2 years ago

Thanks for the great work! Are there any plans to release the code?

m҉a҉r҉k҉2 years ago

this is amazing!

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26,915 views • 1 year ago

2 years ago, Riffusion was one of the first music generating AI models, by finetuning Stable Diffusion literally on images of waveforms, and then decoding those as audio again I made a song for my gf with it called "Bunny Trouble, Trouble Bubble" about a year ago, extremely catchy and we loved it But it was capped to 12 seconds and I could only imagine how it'd be as a full song Now Suno is finally good enough and I used it as an input so I could extend it from a 12 second clip to 3 minute song, it's essentially the audio version of img2img It took a few times extending it and half the times the output was very bad but the other half it was really great The quality is still a bit low because Riffusion's quality itself was quite low, so that's not Suno's fault, it just extends the low quality into a full song, what I need is an audio upscaler to fix that The fun thing about this, every year music generating AI will be better and I can try inputting the song again to make it better and better If you wanna do the same with your short clip: 1) go to Suno 2) click Create 3) click Custom 4) Upload Audio, upload your short clip here Your existing clip now gets added to the listing on the right (bit confusing) 5) Now hover over it and tap extend 6) More confusingly it will now only play the extended part not the full song 7) To get the full song, click the 3 vertical dots ... -> Create -> Get Whole Song 8) That will stitch your original input clip and the extended clip into one Now you'll probably have a 1 minute clip, so 9) Extend that WHOLE SONG clip again and do the same stitch thing (Get Whole Song) again Depending on the clips you like and not, choose the ones you wanna continue with by stitching them By now you'll have about a 3 minute song!

2 years ago, Riffusion was one of the first music generating AI models, by finetuning Stable Diffusion literally on images of waveforms, and then decoding those as audio again I made a song for my gf with it called "Bunny Trouble, Trouble Bubble" about a year ago, extremely catchy and we loved it But it was capped to 12 seconds and I could only imagine how it'd be as a full song Now Suno is finally good enough and I used it as an input so I could extend it from a 12 second clip to 3 minute song, it's essentially the audio version of img2img It took a few times extending it and half the times the output was very bad but the other half it was really great The quality is still a bit low because Riffusion's quality itself was quite low, so that's not Suno's fault, it just extends the low quality into a full song, what I need is an audio upscaler to fix that The fun thing about this, every year music generating AI will be better and I can try inputting the song again to make it better and better If you wanna do the same with your short clip: 1) go to Suno 2) click Create 3) click Custom 4) Upload Audio, upload your short clip here Your existing clip now gets added to the listing on the right (bit confusing) 5) Now hover over it and tap extend 6) More confusingly it will now only play the extended part not the full song 7) To get the full song, click the 3 vertical dots ... -> Create -> Get Whole Song 8) That will stitch your original input clip and the extended clip into one Now you'll probably have a 1 minute clip, so 9) Extend that WHOLE SONG clip again and do the same stitch thing (Get Whole Song) again Depending on the clips you like and not, choose the ones you wanna continue with by stitching them By now you'll have about a 3 minute song!

@levelsio

55,113 views • 1 year ago

🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM

🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM

OpenBMB

559,199 views • 4 months ago

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic paper page:

AK

46,151 views • 3 years ago

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.

AK

327,105 views • 2 years ago

We are thrilled to see that our recent Nature Biotechnology paper just became the most accessed publication in the journal within only three months (>225K accesses 🤩)—a fantastic achievement reflecting the power of teamwork! This paper has 7 first authors: two computer scientists, two biologists, and three chemists working together. It perfectly illustrates something I’ve learned deeply over the past 10 years leading my group: great science is mostly the synergy of diverse minds and skills. Building a successful team is like assembling Lego bricks—each person complements the others, fits precisely, and collectively forms something much greater than any single piece. Over the years, we’ve made mistakes and learned valuable lessons, particularly in finding team members whose values and work styles align with our team culture. Now, we’re more intentional about ensuring a mutual fit, making the collaboration enjoyable, reciprocal, and highly productive. This paper is a perfect example: by combining high-resolution imaging and AI, our interdisciplinary team enabled unprecedented visualization and therapeutic development at the single-cell level throughout entire organisms. Proud of the team effort behind this impactful research and deeply grateful to each member who made it possible! What are your experiences working in a highly collaborative team vs. better focusing on your own project? The latter is totally fine and works perfectly for many academic labs.

We are thrilled to see that our recent Nature Biotechnology paper just became the most accessed publication in the journal within only three months (>225K accesses 🤩)—a fantastic achievement reflecting the power of teamwork! This paper has 7 first authors: two computer scientists, two biologists, and three chemists working together. It perfectly illustrates something I’ve learned deeply over the past 10 years leading my group: great science is mostly the synergy of diverse minds and skills. Building a successful team is like assembling Lego bricks—each person complements the others, fits precisely, and collectively forms something much greater than any single piece. Over the years, we’ve made mistakes and learned valuable lessons, particularly in finding team members whose values and work styles align with our team culture. Now, we’re more intentional about ensuring a mutual fit, making the collaboration enjoyable, reciprocal, and highly productive. This paper is a perfect example: by combining high-resolution imaging and AI, our interdisciplinary team enabled unprecedented visualization and therapeutic development at the single-cell level throughout entire organisms. Proud of the team effort behind this impactful research and deeply grateful to each member who made it possible! What are your experiences working in a highly collaborative team vs. better focusing on your own project? The latter is totally fine and works perfectly for many academic labs.

Ali Max Erturk

19,678 views • 1 year ago

We knew from day one that unlocking constellation-scale manufacturing would be more than just a hardware challenge. That's why we built Octopus — our proprietary operating system, developed in-house to handle the many complexities of rapidly scaling satellite manufacturing. Octopus provides an end-to-end source of truth, from first customer conversation to orbit, enabling streamlined traceability that ensures speed never comes at the expense of quality. Octopus is built on Palantir's Foundry and AIP platforms, which provide the underlying infrastructure to rapidly prototype and deploy custom applications across the entire company. By building Octopus on Palantir, we have unified scheduling, procurement, receiving, inspection, inventory management, and production into a single integrated workflow — enabling predictable and secure, constellation-scale production. Hear from Apex CEO, Ian Cinnamon, and the rest of the team on how Apex's partnership with Palantir is unlocking rate production at Factory One, and laying the foundation for the next generation of satellite manufacturing.

We knew from day one that unlocking constellation-scale manufacturing would be more than just a hardware challenge. That's why we built Octopus — our proprietary operating system, developed in-house to handle the many complexities of rapidly scaling satellite manufacturing. Octopus provides an end-to-end source of truth, from first customer conversation to orbit, enabling streamlined traceability that ensures speed never comes at the expense of quality. Octopus is built on Palantir's Foundry and AIP platforms, which provide the underlying infrastructure to rapidly prototype and deploy custom applications across the entire company. By building Octopus on Palantir, we have unified scheduling, procurement, receiving, inspection, inventory management, and production into a single integrated workflow — enabling predictable and secure, constellation-scale production. Hear from Apex CEO, Ian Cinnamon, and the rest of the team on how Apex's partnership with Palantir is unlocking rate production at Factory One, and laying the foundation for the next generation of satellite manufacturing.

Apex - Satellite Platforms 🛰

33,843 views • 5 months ago