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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

This channels is for Programmers, Coders, Software Engineers.

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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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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.

Depth Anything
Unleashing the Power of Large-Scale Unlabeled Data

[Paper] [Code] [Demo]

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MLOps Masterclass

🔥Closing registration soon!

Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy

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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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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

🆔🆔 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

🌟 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]

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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 🚀

👉 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.

🚩 Limited spots available! Don't miss out!

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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

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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

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