open_clip
lucide
open_clip | lucide | |
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30 | 36 | |
8,850 | 9,192 | |
4.5% | 6.2% | |
8.1 | 9.6 | |
4 days ago | 4 days ago | |
Jupyter Notebook | JavaScript | |
GNU General Public License v3.0 or later | ISC License |
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open_clip
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Binarize Clip for Multimodal Applications
The part of CLIP[1] that you need to know to understand this is that it embeds text and images into the same space. ie: the word "dog" is close to images of dogs. Normally this space is a high dimensional real space. Think 512-dimensional or 512 floating point numbers. When you want to measure "closeness" between vectors in this space cosine similarity[2] is a natural choice.
Why would you want to quantize values? Well, instead of using a 32-bit float for each dimension, what if you could get away with 1-bit? You would save you 31x the space. Often you'll want to embed millions or billions of pieces of text or images, so the savings represent a huge speed & cost savings and if accuracy isn't impacted too much then it could be worth it.
If you naively clip the floats of an existing model, it severely impacts accuracy. However, if you train a model from scratch that produces binary outputs, then it appears to perform better.
There is one twist. Deep learning models rely on gradient descent to train and binary output doesn't produce useful gradients. We use cosine similarity on floating point vectors and hamming distance on bit vectors. Is there a function that behaves like hamming distance but is nicely differentiable? We can then use this function during training and then vanilla hamming distance during inference. It seems like they've done that.
I'd suggest playing around with OpenCLIP[3]. My background is in data science but all my CLIP knowledge comes from doing a side project over the course of a couple weekends.
1. https://huggingface.co/docs/transformers/model_doc/clip
2. https://en.wikipedia.org/wiki/Cosine_similarity
3. https://github.com/mlfoundations/open_clip
- FLaNK-AIM: 20 May 2024 Weekly
- FLaNK AI Weekly for 29 April 2024
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A History of CLIP Model Training Data Advances
While OpenAI’s CLIP model has garnered a lot of attention, it is far from the only game in town—and far from the best! On the OpenCLIP leaderboard, for instance, the largest and most capable CLIP model from OpenAI ranks just 41st(!) in its average zero-shot accuracy across 38 datasets.
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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Database of 16,000 Artists Used to Train Midjourney AI Goes Viral
It is a misconception that Adobe's models have not been trained on copyrighted work. Nobody should be repeating their marketing claims.
Adobe has not shown how they train the text encoders in Firefly, or what images were used for the text-based conditioning (i.e. "text to image") part of their image generation model. They are almost certainly using CLIP or T5, which are trained on LAION2b, an image dataset with the very problems they are trying to address, C4 (a text dataset similarly encumbered) and similar.
I welcome anyone who works at Adobe to simply answer this question of how they trained the text encoders for text conditioning and put it to rest. There is absolutely nothing sensitive about the issue, unless it exposes them in a lie.
So no chance. I think it's a big fat lie. They'd have to have made some other scientific breakthrough, which they didn't.
Using information from https://openai.com/research/clip and https://github.com/mlfoundations/open_clip, it's possible to investigate the likelihood that using just their stock image dataset, can they make a working text encoder?
It's certainly not impossible, but it's impracticable. On 248m images (roughly the size of Adobe Stock), CLIP gets 37% on ImageNet, and on the 2000m from LAION, it performs 71-80%. And even with 2000m images, CLIP is substantially worse performing than the approach that Imagen uses for "text comprehension," which relies on essentially many billions more images and text tokens.
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MetaCLIP – Meta AI Research
https://github.com/mlfoundations/open_clip/blob/main/docs/op...
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Is Nicholas Renotte a good guide for a person who knows nothing about ML?
also, if you describe your task a bit more, we might be able to direct you to a fairly out-of-the-box solution, e.g. you might be able to use one of the pretrained models supported by https://github.com/mlfoundations/open_clip without any additional training
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Generate Image from Vector Embedding
It says on the Stable Diffusion Github repo that it uses the “OpenCLIP-ViT/H” https://github.com/mlfoundations/open_clip model as a text encoder, and from my prior experience with CLIP, I have found that it is very easy to generate image and text embeddings (because CLIP is a multimodal model).
lucide
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10+ Best Open Source Icon Libraries in 2024
Website: https://lucide.dev/
- Lucide: Beautiful and Consistent Icons
- Lucide
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The one thing that makes me miss Notion is how pretty and functional it can be. I'd love to try to make a more visual home page like this in Obsidian but I don't think Tables or Dataviews or whatever can really replicate this level of form and function.
Here's my result, thanks again! Turns out yes you can put emoji in the file name. Haven't figured out a way to use SVG icons which would have been nice but I'll keep at it, and this is stellar anyway.
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Which types of quote blocks I can use? (e.g: >[!tip]...)
Where --callout-color is the RGB value and --callout-icon is anything you pick from https://lucide.dev/
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Introducing Shadcn UI: A reusable UI component collection
The theme editor’s interface allows you to configure themes for properties such as color, border radius, and mode (light or dark). You can also choose between two styles: default and new-york. The styles have unique components, animations, icons, and more. The default style has larger input fields and uses lucide-react icons and tailwindcss-animate for animations. The new-york style has smaller buttons, cards with shadows, and uses Radix Icons.
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Add Lucide Icons to Astro
There's no first-class Lucide integration for Astro. But making a custom one isn't too hard.
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Svelte Libraries / feature completeness
for icons i'm using iconify (https://iconify.design/docs/icon-components/svelte/), but I'm looking forward svelte-icons (https://svelte-icons.vercel.app/) and lucide.dev looks as well
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Creating an Astro Site: Beginners’ Tutorial
import type { FC } from "react"; import { useState } from "react"; const Menu: FC = function Menu() { const [open, setOpen] = useState(false); const handleClick = () => { setOpen(!open); }; // icons by Lucide: https://lucide.dev/ return (
{open ? ( path> svg> ) : ( path> svg> )} button>- Homea> li>
- Abouta> li> ul> nav> div> ); }; export default Menu;
- [Obsidian Md] Les icônes de callout ne fonctionnent pas?
What are some alternatives?
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
icons - Effortless Icon Packs & Components for Svelte, React, Vue and more..
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
react-icons - svg react icons of popular icon packs
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
tabler - Tabler is free and open-source HTML Dashboard UI Kit built on Bootstrap
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
iconify - Universal icon framework. One syntax for FontAwesome, Material Design Icons, DashIcons, Feather Icons, EmojiOne, Noto Emoji and many other open source icon sets (over 150 icon sets and 200k icons). SVG framework, React, Vue and Svelte components!
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
heroicons - A set of free MIT-licensed high-quality SVG icons for UI development.
clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them
homepage - The homepage of Phosphor Icons, a flexible icon family for everyone