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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
join our community:
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[Paper] [Project Page] [Hugging Face Demo]
Source Code: https://github.com/harboryuan/ovsam?tab=readme-ov-file
join our community:
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π22β€5
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PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
[Paper] [Project Page] [Model Card]
[π€ Demo (Realistic)] [π€ Demo (Stylization)]
π 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]
[π€ Demo (Realistic)] [π€ Demo (Stylization)]
π 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]
join our community:
π @deeplearning_ai
Unleashing the Power of Large-Scale Unlabeled Data
[Paper] [Code] [Demo]
join our community:
π @deeplearning_ai
π24β€8
MLOps Masterclass
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Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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Schedule:
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Highlights of this Masterclass:
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Schedule:
February 24th (Sat) & 25th (Sun), 10AM to 2:30 PM
Highlights of this Masterclass:
βͺοΈMLOps Introduction
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βͺοΈMLOps and Stages
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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
join our community:
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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
join our community:
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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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ππ Magic-Me: Identity-Specific Video ππ
πhashtag#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 π
ππ’π π‘π₯π’π π‘ππ¬:
β Novel Video Custom Diffusion (VCD) framework
β High-Quality ID-specific videos generation
β Improvement in aligning IDs-images and text
β Robust 3D Gaussian Noise Prior for denoising
β Better Inter-frame correlation / video consistency
β New modules F-VCD/T-VCD for videos upscale
β 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
πChannel: @deeplearning_ai
πPaper https://arxiv.org/pdf/2402.09368.pdf
πProject https://magic-me-webpage.github.io/
πCode https://github.com/Zhen-Dong/Magic-Me
πhashtag#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 π
ππ’π π‘π₯π’π π‘ππ¬:
β Novel Video Custom Diffusion (VCD) framework
β High-Quality ID-specific videos generation
β Improvement in aligning IDs-images and text
β Robust 3D Gaussian Noise Prior for denoising
β Better Inter-frame correlation / video consistency
β New modules F-VCD/T-VCD for videos upscale
β 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
πChannel: @deeplearning_ai
πPaper https://arxiv.org/pdf/2402.09368.pdf
πProject https://magic-me-webpage.github.io/
πCode https://github.com/Zhen-Dong/Magic-Me
π23β€5
Result.gif
23.1 MB
π 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:
π @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:
π @deeplearning_ai
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data
For collaborations: @love_data
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π’ FREE TRAINING:
Navigating the Landscape of MLOps & LLMOps π
π₯ Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud π
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π What you'll gain:
1οΈβ£ ML model deployment techniques on AWS, Azure, GCP & open source.
2οΈβ£ Efficient data management insights.
3οΈβ£ Explore the latest MLOps tools.
4οΈβ£ Real-time interaction with expert instructors.
π© Limited spots available! Don't miss out!
π Enroll now:
https://bit.ly/mlops-webinar
π₯ Share with fellow ML enthusiasts! πβ¨
Navigating the Landscape of MLOps & LLMOps π
π₯ Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud π
π Reserve your seat now:
https://bit.ly/mlops-webinar
π What you'll gain:
1οΈβ£ ML model deployment techniques on AWS, Azure, GCP & open source.
2οΈβ£ Efficient data management insights.
3οΈβ£ Explore the latest MLOps tools.
4οΈβ£ Real-time interaction with expert instructors.
π© Limited spots available! Don't miss out!
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π₯ Share with fellow ML enthusiasts! πβ¨
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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:
β Revolutionary MDE approach tailored for SIDE tasks
β Enhanced semantic context via ViT embeddings
β Superior performance in zero-shot transfer tasks
β 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.
π Read the Paper
π» Get the Code
#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse
join our community:
π @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:
β Revolutionary MDE approach tailored for SIDE tasks
β Enhanced semantic context via ViT embeddings
β Superior performance in zero-shot transfer tasks
β 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.
π Read the Paper
π» Get the Code
#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse
join our community:
π @deeplearning_ai
π16β€2