* PIPNet's precision in facial landmark detection with a record 2.6% NME on the 300W benchmark. Explore the model and its capabilities by visiting our GitHub repository.

Watch the performance showcase on YouTube. Support and star our project on GitHub for future advancements.

video: https://www.youtube.com/watch?v=cxi1WQr-HKE

source code: https://github.com/Shohruh72/PIPNet/tree/main

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Generative Models by Stability AI

source code: https://github.com/stability-ai/generative-models

paper: https://static1.squarespace.com/static/6213c340453c3f502425776e/t/655ce779b9d47d342a93c890/1700587395994/stable_video_diffusion.pdf


provided a streamlit demo for text-to-image and image-to-image sampling in scripts/demo/sampling.py. They provide file hashes for the complete file as well as for only the saved tensors in the file ( see Model Spec for a script to evaluate that).

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📢 FREE TRAINING: Master MLOps for ML deployment and operations on AWS, Azure, GCP & open-source tools! 🚀

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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

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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.

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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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