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StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
paper: https://arxiv.org/pdf/2312.12491v1.pdf
source code: https://github.com/cumulo-autumn/streamdiffusion?tab=readme-ov-file
paper: https://arxiv.org/pdf/2312.12491v1.pdf
source code: https://github.com/cumulo-autumn/streamdiffusion?tab=readme-ov-file
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Open-Vocabulary SAM
[Paper] [Project Page] [Hugging Face Demo]
Source Code: https://github.com/harboryuan/ovsam?tab=readme-ov-file
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[Paper] [Project Page] [Hugging Face Demo]
Source Code: https://github.com/harboryuan/ovsam?tab=readme-ov-file
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PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
[Paper] [Project Page] [Model Card]
[🤗 Demo (Realistic)] [🤗 Demo (Stylization)]
🌠 Key Features:
1. Rapid customization within seconds, with no additional LoRA training.
2. Ensures impressive ID fidelity, offering diversity, promising text controllability, and high-quality generation.
3. Can serve as an Adapter to collaborate with other Base Models alongside LoRA modules in community.
[Paper] [Project Page] [Model Card]
[🤗 Demo (Realistic)] [🤗 Demo (Stylization)]
🌠 Key Features:
1. Rapid customization within seconds, with no additional LoRA training.
2. Ensures impressive ID fidelity, offering diversity, promising text controllability, and high-quality generation.
3. Can serve as an Adapter to collaborate with other Base Models alongside LoRA modules in community.
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Depth Anything
Unleashing the Power of Large-Scale Unlabeled Data
[Paper] [Code] [Demo]
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Unleashing the Power of Large-Scale Unlabeled Data
[Paper] [Code] [Demo]
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MLOps Masterclass
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Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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Schedule:
February 24th (Sat) & 25th (Sun), 10AM to 2:30 PM
Highlights of this Masterclass:
▪️MLOps Introduction
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▪️MLOps and Stages
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Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
Register Now👇
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Schedule:
February 24th (Sat) & 25th (Sun), 10AM to 2:30 PM
Highlights of this Masterclass:
▪️MLOps Introduction
▪️MLOps for LLM's (LLMOps)
▪️MLOps and Stages
▪️AWS SageMaker
▪️CI/CD for MLOps
▪️AWS MLOps - Build, Train & deploy ML Model
🔥 Limited Seats Available!
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Awesome-AIGC-3D
A curated list of awesome AIGC 3D papers, inspired by awesome-NeRF.
Source code: https://github.com/hitcslj/awesome-aigc-3d?tab=readme-ov-file
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A curated list of awesome AIGC 3D papers, inspired by awesome-NeRF.
Source code: https://github.com/hitcslj/awesome-aigc-3d?tab=readme-ov-file
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EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction
1. Channel: @deeplearning_ai
2.Source Code: https://github.com/mit-han-lab/efficientvit
3. Paper: https://arxiv.org/abs/2402.05008
1. Channel: @deeplearning_ai
2.Source Code: https://github.com/mit-han-lab/efficientvit
3. Paper: https://arxiv.org/abs/2402.05008
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🆔🆔 Magic-Me: Identity-Specific Video 🆔🆔
👉hashtag#ByteDance (+UC Berkeley) unveils VCD for video-gen: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt. Impressive results, source code under Apache 2.0 💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel Video Custom Diffusion (VCD) framework
✅High-Quality ID-specific videos generation
✅Improvement in aligning IDs-images and text
✅Robust 3D Gaussian Noise Prior for denoising
✅Better Inter-frame correlation / video consistency
✅New modules F-VCD/T-VCD for videos upscale
✅New train with masked loss by prompt-to-segmentation
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse
👉Channel: @deeplearning_ai
👉Paper https://arxiv.org/pdf/2402.09368.pdf
👉Project https://magic-me-webpage.github.io/
👉Code https://github.com/Zhen-Dong/Magic-Me
👉hashtag#ByteDance (+UC Berkeley) unveils VCD for video-gen: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt. Impressive results, source code under Apache 2.0 💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel Video Custom Diffusion (VCD) framework
✅High-Quality ID-specific videos generation
✅Improvement in aligning IDs-images and text
✅Robust 3D Gaussian Noise Prior for denoising
✅Better Inter-frame correlation / video consistency
✅New modules F-VCD/T-VCD for videos upscale
✅New train with masked loss by prompt-to-segmentation
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse
👉Channel: @deeplearning_ai
👉Paper https://arxiv.org/pdf/2402.09368.pdf
👉Project https://magic-me-webpage.github.io/
👉Code https://github.com/Zhen-Dong/Magic-Me
Result.gif
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
👉 @deeplearning_ai
Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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Data Science, Machine Learning & Artificial Intelligence Certification for FREE in 2024
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Amazing new year gifts for my subscribers
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🎁 Build Chatbots using LLM
🎁 Learn Generative AI
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📢 FREE TRAINING:
Navigating the Landscape of MLOps & LLMOps 🚀
🔥 Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud 🚀
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🚩 Limited spots available! Don't miss out!
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👥 Share with fellow ML enthusiasts! 🚀✨
Navigating the Landscape of MLOps & LLMOps 🚀
🔥 Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud 🚀
👉 Reserve your seat now:
https://bit.ly/mlops-webinar
🌟 What you'll gain:
1️⃣ ML model deployment techniques on AWS, Azure, GCP & open source.
2️⃣ Efficient data management insights.
3️⃣ Explore the latest MLOps tools.
4️⃣ Real-time interaction with expert instructors.
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👥 Share with fellow ML enthusiasts! 🚀✨
Forwarded from SHOHRUH
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Introducing ECoDepth: The New Benchmark in Diffusive Mono-Depth
From the labs of IITD, we unveil ECoDepth - our groundbreaking SIDE model powered by a diffusion backbone and enriched with ViT embeddings. This innovation sets a new standard in single image depth estimation (SIDE), offering unprecedented accuracy and semantic understanding.
Key Features:
✅Revolutionary MDE approach tailored for SIDE tasks
✅Enhanced semantic context via ViT embeddings
✅Superior performance in zero-shot transfer tasks
✅Surpasses previous SOTA models by up to 14%
Dive into the future of depth estimation with ECoDepth. Access our source code and explore the full potential of our model.
📖 Read the Paper
💻 Get the Code
#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse
join our community:
👉 @deeplearning_ai
From the labs of IITD, we unveil ECoDepth - our groundbreaking SIDE model powered by a diffusion backbone and enriched with ViT embeddings. This innovation sets a new standard in single image depth estimation (SIDE), offering unprecedented accuracy and semantic understanding.
Key Features:
✅Revolutionary MDE approach tailored for SIDE tasks
✅Enhanced semantic context via ViT embeddings
✅Superior performance in zero-shot transfer tasks
✅Surpasses previous SOTA models by up to 14%
Dive into the future of depth estimation with ECoDepth. Access our source code and explore the full potential of our model.
📖 Read the Paper
💻 Get the Code
#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse
join our community:
👉 @deeplearning_ai
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🕷️🕷️ GenN2N: Generative NeRF2NeRF Translation.🕷️🕷️
Key Features:
* Collaborative Excellence.
* Advanced 3D VAE-GAN Architecture
* Universal NeRF Editing
* Contrastive Learning
* Optimized Performance
[Paper]
[Source Code]
[Project Page]
Join our community: @deeplearning_ai
Key Features:
* Collaborative Excellence.
* Advanced 3D VAE-GAN Architecture
* Universal NeRF Editing
* Contrastive Learning
* Optimized Performance
[Paper]
[Source Code]
[Project Page]
Join our community: @deeplearning_ai
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Neural Bodies with Clothes: Overview
Introduction: Neural-ABC, a cutting-edge parametric model developed by the University of Science & Technology of China, innovatively represents clothed human bodies.
Key Features:
✅Novel approach for modeling clothed human figures.
✅Unified framework accommodating various clothing types.
✅Consistent representation of both body and clothing.
✅Enables seamless modification of identity, shape, clothing, and pose.
✅Extensive dataset with detailed clothing information.
Explore More:
💻Project Details: Discover More
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
Relevance: #artificialintelligence #machinelearning #AI #deeplearning #computervision
join our community:
👉 @deeplearning_ai
Introduction: Neural-ABC, a cutting-edge parametric model developed by the University of Science & Technology of China, innovatively represents clothed human bodies.
Key Features:
✅Novel approach for modeling clothed human figures.
✅Unified framework accommodating various clothing types.
✅Consistent representation of both body and clothing.
✅Enables seamless modification of identity, shape, clothing, and pose.
✅Extensive dataset with detailed clothing information.
Explore More:
💻Project Details: Discover More
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
Relevance: #artificialintelligence #machinelearning #AI #deeplearning #computervision
join our community:
👉 @deeplearning_ai
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🛞 6Img-to-3D driving scenarios 🛞
👮♀️ EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
🥺 Review: https://shorturl.at/dZ018
🤨 Paper: arxiv.org/pdf/2404.12378.pdf
👉 Project: 6img-to-3d.github.io/
👉 Code: github.com/continental/6Img-to-3D
✅ https://www.tg-me.com/deeplearning_ai
👮♀️ EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
🥺 Review: https://shorturl.at/dZ018
🤨 Paper: arxiv.org/pdf/2404.12378.pdf
👉 Project: 6img-to-3d.github.io/
👉 Code: github.com/continental/6Img-to-3D
✅ https://www.tg-me.com/deeplearning_ai