kailashahirwar

4 ms·

1.
by kailashahirwar 1y ago
On my Medium blog, I explore topics such as Generative AI, Machine learning, Deep Learning, Computer Vision, LLMs, Artificial Intelligence in general, and groundbreaking advancements in image generation, editing, and virtual try-on technolo
2.
by kailashahirwar 1y ago
‘Black Forest Labs’ recently unveiled their technical report on ‘FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.’ FLUX.1-Kontext-dev is a groundbreaking open-source model that has quickly gained tr
3.
by kailashahirwar 1y ago
In this article, we’ll explore and decode the research paper titled “MV-VTON: Multi-View Virtual Try-On with Diffusion Models.” This paper offers interesting insights into multi-view virtual try-on technology, and we’ll simplify its concept
4.
Decoding DreamO: A Unified Framework for Image Customization(kailashahirwar.medium.com)
2 points by kailashahirwar 1y ago|1 comments
5.
by kailashahirwar 1y ago
A unified image customization framework designed to support a wide range of tasks while enabling seamless integration of multiple conditions. DreamO accomplishes complex multi-condition customization by training a small set of additional pa
6.
Decoding SANA: Efficient High-Resolution Image Synthesis with Linear DiT(kailashahirwar.medium.com)
1 points by kailashahirwar 1y ago|1 comments
7.
by kailashahirwar 1y ago
Recently, I read “SANA: Efficient High-Resolution Image Synthesis With Linear Diffusion Transformer” by NVIDIA Labs in collaboration with MIT and Tsinghua University. In this blog post, I want to share research notes that explain SANA, an e
8.
FLUX.1-Dev LoRA Outfit Generator by TryOn Labs(huggingface.co)
2 points by kailashahirwar 2y ago|0 comments
9.
Demystifying SSR-Encoder: Encoding Selective Subject Representation(kailashahirwar.medium.com)
1 points by kailashahirwar 2y ago|1 comments
10.
by kailashahirwar 2y ago
Subject-driven image generation is challenging. It aims to capture the most informative representation of a subject. Some approaches require very costly fine-tuning, and others require encoding the reference image into an image embedding to
11.
OpenAI Swarm: A Lightweight Multi-Agent Orchestration Framework(kailashahirwar.medium.com)
2 points by kailashahirwar 2y ago|0 comments
12.
A Short Introduction to Large Action Models(LAMs)(kailashahirwar.medium.com)
1 points by kailashahirwar 2y ago|1 comments
13.
by kailashahirwar 2y ago
In recent years, Large Language Models(LLMs) have taken Artificial intelligence on a roller coaster ride. Indeed, it was a good ride. These models are primarily for language processing and generation and can generate detailed steps to follo
14.
Harnessing LLMs and LlamaIndex for Natural Language Database Queries (Part 1)(kailashahirwar.medium.com)
1 points by kailashahirwar 2y ago|1 comments
15.
by kailashahirwar 2y ago
Large Language Models(LLMs) come pre-trained on huge amounts of publicly available data. One disadvantage of LLMs is that they are not trained on your data. LlamaIndex is an open-source framework for creating large language models(LLM) appl
16.
Essential Virtual Try-On Research Papers for Machine Learning Engineers(medium.com)
1 points by kailashahirwar 3y ago|1 comments
17.
by kailashahirwar 3y ago
Virtual Try-On is revolutionizing the way customers are shopping online. It allows customers to experience products virtually before making a purchase, enhancing their confidence and satisfaction with their online shopping choices. In this
18.
Aigen, an open-source library to create AINFTs effortlessly(github.com)
1 points by kailashahirwar 3y ago|1 comments
19.
by kailashahirwar 3y ago
What is an AINFT? AINFT is a Fractional NFT of an AI model that allows AI owners to register the ownership of their model on Blockchain to protect their AI assets and generate new revenue streams through fractional ownership sharing. Why Ai
20.
Snapy Collect – Snap Images, Share Images and Earn Rewards(play.google.com)
1 points by kailashahirwar 7y ago|0 comments
21.
AutoOut: Automated Outlier Detection and Treatment Tool(github.com)
1 points by kailashahirwar 7y ago|0 comments
22.
The Rise of Generative Adversarial Networks(blog.usejournal.com)
4 points by kailashahirwar 8y ago|0 comments
23.
Essential Cheat Sheets for Machine Learning Engineers-Product Hunt(producthunt.com)
2 points by kailashahirwar 9y ago|0 comments
24.
by kailashahirwar 9y ago
These cheat sheets are not to replace official documentation, these are to help. And we are not having a competition here, if these are useful for you use them otherwise don't.
25.
Essential Cheat Sheets for Machine Learning and Deep Learning Engineers(medium.com)
2 points by kailashahirwar 9y ago|0 comments