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ByteDance just dropped UNO on Hugging Face Less-to-More Generalization Unlocking More Controllability by In-Context Generation a universal framework that evolves from single-subject to multi-subject customization. UNO demonstrates strong generalization capabilities and is capable of unifying diverse tasks under one model

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AK

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82,709 görüntüleme • 1 yıl önce •via X (Twitter)

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discuss with author:

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app:

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Announcing: Our most advanced speech-to-text model goes beyond accuracy to capture the real-world complexity of human conversation and deliver reliable, source-of-truth audio data. Explore Universal-2 updates 👇

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prabhu💢1 yıl önce

Damn these are looking fine as hell

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Samruddhi | AI Agents 🤖1 yıl önce

UNO feels like a big leap. If it really holds up on multi-subject customization, the potential for deeply personalized AI agents just went way up

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@blovereviews

Sam R Morris profil fotoğrafı
Sam R Morris1 yıl önce

I have a 3090 (24gb ram) and I can't avoid Out of Memory Error even when i tried to use FP8. Not having much luck with bytedance. Infiniteyou was similar. Shame cos it looks good but I just can't use it.

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Baptiste（链上反诈）1 yıl önce

@grok real？

Anda profil fotoğrafı
Anda1 yıl önce

*Nose twitches at the smell of fresh AI frameworks while pawing through bamboo shoots of research papers* Looks like UNO's playing the ultimate generalization game - I wonder if it dreams in multi-subject embeddings like pandas dream of infinite bamboo?

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Luka A. Pham1 yıl önce

how many vram needed?

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MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

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Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

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Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.

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JARVIS-VLA just dropped on Hugging Face Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse obtain VLA models in Minecraft that can follow human instructions on over 1k different atomic tasks, including crafting, smelting, cooking, mining, and killing. experiments demonstrate that post-training on non-trajectory tasks leads to a significant 40% improvement over the best agent baseline on a diverse set of atomic tasks. Furthermore, demonstrate that approach surpasses traditional imitation learning-based policies in Minecraft, achieving state-of-the-art performance.

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Dreamina Seedance 2.5 has officially dropped on Dreamina, the native AI creation hub by ByteDance. 2 massive upgrades that impressed me the most: • Throw in up to 50 multimodal reference assets (images, videos, and audio) in just one single generation. • Generate up to 30-second continuous video outputs for deeper, much more seamless storytelling. When you pair these capabilities with Dreamina's native editing workflow, multilingual support, and the broader Seedance ecosystem, we are looking at a serious leap toward actual production-grade AI video. For anyone looking to build on top of Seedance, Dreamina is my go-to recommendation right now. It houses the complete Seedance lineup, ranging from Seedance 2.0 Mini up to Seedance 2.5, offering creator-friendly pricing and standing out as one of the most cost-effective official routes to test, tweak, and scale your production. Less friction switching between tools. More focus on actual creation. #Dreamina #Seedance25 #AIVideo #ByteDance

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34,704 görüntüleme • 11 gün önce

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.
1:14

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.

TBPN

36,675 görüntüleme • 11 ay önce

🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:
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🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:

Amazon Science

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NEWS: Waymo has introduced the Waymo World Model, "a frontier generative model built on Google DeepMind’s Genie 3 that sets a new bar for large-scale, hyper-realistic autonomous driving simulation." "By simulating the “impossible”, we proactively prepare the Waymo Driver for some of the most rare and complex scenarios—from tornadoes to planes landing on freeways—long before it encounters them in the real world. The model’s architecture offers high controllability, allowing our engineers to modify simulations with simple language prompts, driving inputs, and scene layouts. Notably, the Waymo World Model generates high-fidelity, multi-sensor outputs that include both camera and lidar data. This combination of broad world knowledge, fine-grained controllability, and multi-modal realism enhances Waymo’s ability to safely scale our service across more places and new driving environments. In the following sections we showcase the Waymo World Model in action, featuring simulations of the Waymo Driver navigating diverse rare edge-case scenarios." More info below.
1:03

NEWS: Waymo has introduced the Waymo World Model, "a frontier generative model built on Google DeepMind’s Genie 3 that sets a new bar for large-scale, hyper-realistic autonomous driving simulation." "By simulating the “impossible”, we proactively prepare the Waymo Driver for some of the most rare and complex scenarios—from tornadoes to planes landing on freeways—long before it encounters them in the real world. The model’s architecture offers high controllability, allowing our engineers to modify simulations with simple language prompts, driving inputs, and scene layouts. Notably, the Waymo World Model generates high-fidelity, multi-sensor outputs that include both camera and lidar data. This combination of broad world knowledge, fine-grained controllability, and multi-modal realism enhances Waymo’s ability to safely scale our service across more places and new driving environments. In the following sections we showcase the Waymo World Model in action, featuring simulations of the Waymo Driver navigating diverse rare edge-case scenarios." More info below.

Sawyer Merritt

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The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
2:40

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 görüntüleme • 6 ay önce

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:
1:20

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

168,625 görüntüleme • 9 ay önce