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ByteDance Seed is back with SeedVR2, now on Hugging Face! This one-step video restoration model leverages diffusion adversarial post-training for impressive results, even on high-resolution videos.

DailyPapers

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54,539 次观看 • 1 年前 •via X (Twitter)

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NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

Lior Alexander

158,565 次观看 • 3 年前

INSANE...this is an Open Source Video model available for free on Hugging Face. LongCat just dropped an amazing video avatar model that can do crazy things. And it's 100% Opensource.

Shubham Saboo

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IBM’s watsonx․ai geospatial foundation model is now openly available on Hugging Face. Built with NASA Earth, it’s the first-ever open-source #AI foundation model built with NASA & largest geospatial model on HuggingFace. Explore more:

IBM’s watsonx․ai geospatial foundation model is now openly available on Hugging Face. Built with NASA Earth, it’s the first-ever open-source #AI foundation model built with NASA & largest geospatial model on HuggingFace. Explore more:

IBM News

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We are building “Open Source Nano Banana for Video” - here is open source demo v0.1 We are open sourcing Lucy Edit, the first foundation model for text-guided video editing! Get the model on Hugging Face 🤗, API on @FAL, and nodes on ComfyUI 🧵

We are building “Open Source Nano Banana for Video” - here is open source demo v0.1 We are open sourcing Lucy Edit, the first foundation model for text-guided video editing! Get the model on Hugging Face 🤗, API on @FAL, and nodes on ComfyUI 🧵

Decart

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🔬 Built with an entirely new model architecture, our diffusion-based approach uses 6B+ parameters and leverages the latest NVIDIA hardware. This is the most dynamic and wide-ranging video enhancing method we’ve ever created, setting a new standard for AI video restoration. Videos degrade due to compression artifacts, blurring, aliasing, noise, atmospheric distortion, missing pixels, etc. Each frame suffers from unique types of corruption, making AI video restoration a highly challenging task. Our technology solves this complexity by analyzing hundreds of frames to accurately restore details, delivering unmatched detail recovery combined with unparalleled temporal consistency.

🔬 Built with an entirely new model architecture, our diffusion-based approach uses 6B+ parameters and leverages the latest NVIDIA hardware. This is the most dynamic and wide-ranging video enhancing method we’ve ever created, setting a new standard for AI video restoration. Videos degrade due to compression artifacts, blurring, aliasing, noise, atmospheric distortion, missing pixels, etc. Each frame suffers from unique types of corruption, making AI video restoration a highly challenging task. Our technology solves this complexity by analyzing hundreds of frames to accurately restore details, delivering unmatched detail recovery combined with unparalleled temporal consistency.

Topaz Labs

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This is RICULOUSLY good, TRELLIS 3D Generation model by Microsoft! 🔥 Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!

This is RICULOUSLY good, TRELLIS 3D Generation model by Microsoft! 🔥 Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!

Vaibhav (VB) Srivastav

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seedance 2.0 + my v2 AI UGC prompting system is giving insane results i spent the last 24 hours generating over 200 seedance 2.0 videos to figure out the best prompting framework system for AI UGC this video was made with 1 prompt and 1 tool, no editing was done to the video this was just a prompt to a video this is by far the best model i've ever used and the craziest part is that it can be fully automated this is the first time we can actually automate high quality ai ugc at this level bytedance owns tiktok so this model is trained on millions of high quality ugc videos. you just need to know how to extract that and call it in your prompt. we are so early... it's insane

seedance 2.0 + my v2 AI UGC prompting system is giving insane results i spent the last 24 hours generating over 200 seedance 2.0 videos to figure out the best prompting framework system for AI UGC this video was made with 1 prompt and 1 tool, no editing was done to the video this was just a prompt to a video this is by far the best model i've ever used and the craziest part is that it can be fully automated this is the first time we can actually automate high quality ai ugc at this level bytedance owns tiktok so this model is trained on millions of high quality ugc videos. you just need to know how to extract that and call it in your prompt. we are so early... it's insane

Miko

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introducing Seedance 2.0. the world's most powerful video model, supporting text, image, video, and even audio as input, and capable of producing long multi-shot videos with high quality. now available for everyone.

introducing Seedance 2.0. the world's most powerful video model, supporting text, image, video, and even audio as input, and capable of producing long multi-shot videos with high quality. now available for everyone.

Krea

44,876 次观看 • 4 个月前

Seedance 2.0 is now live in ComfyUI for everyone. This state-of-the-art model brings us one step closer to production-quality video generations.

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This massive ship model with the size of a forearm is printed in one piece on Jupiter 2, and the 16K resolution ensures sharp details on the ship body and deck🧐🚢 Printed with: 3D model: Flying Dutchman from Gambody

This massive ship model with the size of a forearm is printed in one piece on Jupiter 2, and the 16K resolution ensures sharp details on the ship body and deck🧐🚢 Printed with: 3D model: Flying Dutchman from Gambody

ELEGOO

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It's high time we bring back Durban music now, Gom. Vele, and Amapiano are slowly fading away now 3 Step is already irritating with the same sound on all songs

It's high time we bring back Durban music now, Gom. Vele, and Amapiano are slowly fading away now 3 Step is already irritating with the same sound on all songs

SA🇿🇦 FIRST BEFORE AFRICA🌍

77,084 次观看 • 6 个月前

🚀 LongCat-Video Now Open-Source: Text/Image-to-Video + Video Continuation in One Model 🏆 Text/Image-to-Video Performance Hits Open-Source SOTA 🎬 Minutes-Long High-Quality Videos: No Color Drift/Quality Loss (Industry-Standout) ⚙ 13.6B Params | Strong Open-Source DiT-Based Unified Multitask Video Base Model ⚡ C2F Pipeline + Block Sparse Attention: 720p/30fps Video in Minutes 🤗 Open-Source Links: GitHub： Hugging Face： Project Page：

🚀 LongCat-Video Now Open-Source: Text/Image-to-Video + Video Continuation in One Model 🏆 Text/Image-to-Video Performance Hits Open-Source SOTA 🎬 Minutes-Long High-Quality Videos: No Color Drift/Quality Loss (Industry-Standout) ⚙ 13.6B Params | Strong Open-Source DiT-Based Unified Multitask Video Base Model ⚡ C2F Pipeline + Block Sparse Attention: 720p/30fps Video in Minutes 🤗 Open-Source Links: GitHub： Hugging Face： Project Page：

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You can now route each request to the "fastest" ⚡ or "cheapest" 💸 provider with Hugging Face Inference Providers in Copilot Chat on Visual Studio Code!🤗 For each model, you can explicitly choose "cheapest" when price matters - or "fastest" when throughput is the priority. thanks to Simon Brandeis for shipping this!

You can now route each request to the "fastest" ⚡ or "cheapest" 💸 provider with Hugging Face Inference Providers in Copilot Chat on Visual Studio Code!🤗 For each model, you can explicitly choose "cheapest" when price matters - or "fastest" when throughput is the priority. thanks to Simon Brandeis for shipping this!

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This #CVPR2026 paper from our research team is trending #1 on Hugging Face 🤗 Meet LocateAnything: a vision-language detection model that rethinks bounding box prediction. For AI agents and robots, “seeing” is only useful if a model can pinpoint where something is fast enough to act. Trained on 138M high-quality samples, LocateAnything decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy while dramatically increasing throughput for visual grounding and detection. Project page:

NVIDIA AI

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🚀 Self-speculation brings 6.75x real speedup for LLM generation with SGLang inference! Same model drafts future tokens in Diffusion mode → then verifies them in AR (causal) mode. One model and one KV cache. Just different attention masks. Thanks to perfect alignment, we get 2× longer acceptance lengths than MTP techniques (Eagle-3, MTP, dFlash). We run 2 forward passes… but the 2× higher acceptance means we break even - and with zero overhead from extra drafter, KV cache, or LM head that comes with MTP - those are not free. Last week we released Nemotron-Labs-Diffusion + Tri-mode LLMs! We did continued pre-training on Ministral-3 models by switching attention patterns (block causal bidirectional). Result: one model that runs AR mode, Diffusion mode, and Self-Speculation. Diffusion mode already shows high benchmark accuracy - excited to see what happens when someone beats left-to-right acceptance! 🔥 Github: Paper: SGLang inference: Try the models on HF:

Pavlo Molchanov

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Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

AK

375,160 次观看 • 3 年前

😶‍🌫️ Late night experiment: high-resolution, volumetric cloud rendering in Unity URP. Doing R&D for my fog, too. Lighting is rather basic, ~I just started on this one.

😶‍🌫️ Late night experiment: high-resolution, volumetric cloud rendering in Unity URP. Doing R&D for my fog, too. Lighting is rather basic, ~I just started on this one.

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A recent Reddit post in /brokers, with the caption "we're doomed." One comment is asking what's going on here. The response: "This is a dispatch training session at a dispatch training school in Punjab(India), they are doing the practical training right now by calling a broker and booking a load for a carrier that the trainer works for"

A recent Reddit post in /brokers, with the caption "we're doomed." One comment is asking what's going on here. The response: "This is a dispatch training session at a dispatch training school in Punjab(India), they are doing the practical training right now by calling a broker and booking a load for a carrier that the trainer works for"

maybe danielle 💻🚛🇺🇸

38,146 次观看 • 2 个月前

This video style is getting BILLIONS of views across social media right now We built an AI workflow that prints this style of videos on demand That’s how we made this ad Honestly it’s too much sauce So I’ll record a step by step breakdown if this post gets 500 comments

This video style is getting BILLIONS of views across social media right now We built an AI workflow that prints this style of videos on demand That’s how we made this ad Honestly it’s too much sauce So I’ll record a step by step breakdown if this post gets 500 comments

Karlo

69,793 次观看 • 9 个月前