This week's Java roundup for October 16th, 2023, features news from OpenJDK, JDK 22, BellSoft, Oracle VS Code extension, WildFly 30, Payara Platform, MicroProfile 6.1, EclipseCon and releases for GraalVM Native Build Tools,

Java News Roundup: Unnamed Variables and Patterns, WildFly 30, MicroProfile 6.1, Payara Platform

Hardware made in Christchurch will be used in the first deep-space mining mission. And it’s one that will help shape things back on Earth. Palladium, used in the manufacture of emission-reducing catalytic converters for cars,

Deep-space mining: Dawn Aerospace delivers propulsion system for AstroForge’s first asteroid mission

Alexis Ohanian is a billionaire from the United States, well-known as the co-founder of Reddit, one of the internet's most popular social media and discussion platforms. Apart from Reddit, Alexis co-founded a venture capital firm called Initialized Capital in 2011.

Understanding Alexis Ohanian's net worth: What is his valuation?

Andrew Sather, principal at Initialized Capital, on why he invested in AstroForge, a US asteroid-mining startup that will use propulsion systems supplied by Christchurch's Dawn Aerospace. Video / Astr

AstroForge's plan to mine near-Earth asteroids

Over a third of consumers said they were likely to splurge in multiple categories, as household debt sits at record highs.

Nearly 7 in 10 Shoppers Likely to Overspend This Holiday Season, Survey Finds

Gulf Cooperation Council (GCC) and Pakistan initialized a free trade agreement, the GCC said on messaging platform X on Thursday. The agreement was signed by GCC Secretary-General Jasem al-Budaiwi and Pakistani trade minister Gohar Ejaz.

Gulf Cooperation Council, Pakistan Sign Free Trade Agreement

Gulf Cooperation Council (GCC) and Pakistan "initialized" a free trade agreement, the GCC said on messaging platform X on Thursday.The agreement was signed by GCC Secretary-General Jasem al-Budaiwi and Pakistani trade minister Gohar Ejaz.

Gulf Cooperation Council says it and Pakistan 'initialize' free trade agreement

If you are looking for the best nicknames for boys and girls, this post is for you. Millions of people search the internet, but there are some websites that offer some nickname ideas for boys and girls, but in this post, I am going to share 1000+ nicknames ...

366 + Best Nicknames For boys and Girls (New & Latest) 2023

Since its launch, the MetaTrace Quest platform has accumulated over 250,000 registered users, meaning a total of at least 250,000 Trace Wallets have been initialized and created. The platform launched nearly three weeks ago and has experienced exceptional recognition since its debut.

MetaTrace Has Reached 250,000 Users on the Quest Platform

If the drive that fails to initialize is empty or contains no essential data, you should format it. Then, you can create a new partition by converting its format to GPT and assigning the new volume. It is the most recommended method for resolving the issue and has worked for many users. To accomplish that, follow these steps:

How to Fix the “Disk Unknown Not Initialized” Issue on Windows

Initialized

initialized

Ending The Crypto Winter with Garry Tan and Brett Gibson of Initialized Capital

Artificial Minds, Human Consequences: Explaining AI’s Impact on Our Education, Cognition, and More

Welcome to a new edition of "This Week in JavaScript"! Today, we’ve got some HUGE new updates for the JavaScript language from TC39, Deno 2’s Official Release, TypeScript 5.7 Beta and a lot more, so let’s get right into it! TC39 Updates: What's Coming to JavaScript? The recent TC39 meeting in Tokyo brought some great updates that will make working with JavaScript even better. JavaScript features go through six different "stages" before becoming available, and here are some highlights: Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data.
Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility.
Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code.
Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Deno 2 Official Release Deno 2just dropped, and it's packed with new features! Built to simplify JavaScript and TypeScript development, Deno 2 is now backward compatible with Node.js, which means you can run your existing Node projects smoothly. Some standout features include: 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs.
Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node.
JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. TypeScript 5.7 Beta TypeScript 5.7 Beta has landed, bringing with it some helpful updates: New check for never-initialized variables: A handy feature to catch bugs early.
Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. TypeScript 5.7 is nearly feature-stable, so expect bug fixes and performance improvements in the final release. New Tools and Releases Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times.
Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time.
DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript.
TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Welcome to a new edition of "This Week in JavaScript"! Today, we’ve got some HUGE new updates for the JavaScript language from TC39, Deno 2’s Official Release, TypeScript 5.7 Beta and a lot more, so let’s get right into it! TC39 Updates: What's Coming to JavaScript? TC39 Updates: What's Coming to JavaScript? The recent TC39 meeting in Tokyo brought some great updates that will make working with JavaScript even better. JavaScript features go through six different "stages" before becoming available, and here are some highlights: TC39 Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data. Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility. Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code. Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Iterator Helpers: Just reached stage 4! These will make handling large data sets a breeze, adding more tools to process data. Iterator Helpers: Import Attributes & JSON Modules: This update will make importing JSON files smoother, improving performance and flexibility. Import Attributes & JSON Modules: Promise.try: Now in stage 4, it simplifies error handling in promises, reducing the need for extra code. Promise.try: Other cool updates include Math.sumPrecise and Shared Structs, both aimed at enhancing JavaScript's efficiency. Other cool updates Deno 2 Official Release Deno 2 Official Release Deno 2 just dropped, and it's packed with new features! Deno 2 Built to simplify JavaScript and TypeScript development, Deno 2 is now backward compatible with Node.js, which means you can run your existing Node projects smoothly. Some standout features include: Some standout features include: 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs. Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node. JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. 90% faster package manager: With a hot cache, Deno’s package manager is much quicker than npm, so no more waiting around for installs. 90% faster package manager: Workspaces and Monorepos: Managing large projects is now easier, especially when working with both Deno and Node. Workspaces and Monorepos: JSR (JavaScript Registry): Makes sharing TypeScript modules across environments seamless. JSR (JavaScript Registry): TypeScript 5.7 Beta TypeScript 5.7 Beta TypeScript 5.7 Beta has landed, bringing with it some helpful updates: TypeScript 5.7 Beta New check for never-initialized variables: A handy feature to catch bugs early. Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. New check for never-initialized variables: A handy feature to catch bugs early. New check for never-initialized variables: Path rewrites for relative imports: Simplifies building and running code across Deno, Bun, and Node.js. Path rewrites for relative imports: TypeScript 5.7 is nearly feature-stable , so expect bug fixes and performance improvements in the final release. feature-stable New Tools and Releases New Tools and Releases Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times. Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time. DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript. TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. Bun v1.1.30: Now supports bundling multiple CSS files, introduces a new bun publish command, and offers faster startup times. Bun v1.1.30: Node.js v20.18.0: Features experimental network inspection, helping you monitor network activity in real time. Node.js v20.18.0: DOCX 9.0: Allows easy generation and editing of Word files via JavaScript or TypeScript. DOCX 9.0: TinyJS: A lightweight library for DOM manipulation, perfect for simplifying HTML elements in your projects. TinyJS: If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

TC39 JavaScript Updates, TypeScript 5.7 Beta, Node.js V20.18.0 and More

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How to Find the “Routes” of All-Pairs Shortest Paths With the Floyd-Warshall Algorithm in C#

Cómo encontrar las “rutas” de los caminos más cortos de todos los pares con el algoritmo Floyd-Warshall en C#

Nola aurkitu bikote guztietako bide laburrenen "ibilbideak" Floyd-Warshall algoritmoarekin C#-n

چگونه با الگوریتم فلوید-وارشال در سی شارپ «مسیرها» کوتاه‌ترین مسیرهای تمام جفت را پیدا کنیم

כיצד למצוא את ה"מסלולים" של כל הזוגות הקצרים ביותר עם אלגוריתם פלויד-ורשל ב-C#

C# でフロイド・ワーシャル アルゴリズムを使用して全ペア最短経路の「ルート」を見つける方法

C# тилинде Флойд-Уоршалл алгоритми менен бардык жуптардын эң кыска жолдорунун "маршруттарын" кантип тапса болот

ວິທີການຊອກຫາ "ເສັ້ນທາງ" ຂອງທຸກຄູ່ເສັ້ນທາງທີ່ສັ້ນທີ່ສຸດດ້ວຍ Floyd-Warshall Algorithm ໃນ C#

Jak znaleźć „trasy” najkrótszych ścieżek dla wszystkich par za pomocą algorytmu Floyda-Warshalla w języku C#

Mawaniro e "Nzira" dzeVese-Pairi Pfupi Nzira Neiyo Floyd-Warshall Algorithm muC#.

C#'da Floyd-Warshall Algoritması ile Tüm Çiftlerin En Kısa Yollarının "Rotaları" Nasıl Bulunur

Як знайти «маршрути» найкоротших шляхів усіх пар за допомогою алгоритму Флойда-Воршалла в C#

Cách tìm "Tuyến đường" của tất cả các đường dẫn ngắn nhất của cặp với thuật toán Floyd-Warshall trong C#

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure D Model details Stable Diffusion {v1-4, v1-5, v2-base, v2-1}. Stable Diffusion (v1-4, v1-5, v2-base, v2-1) is a family of 1B-parameter text-to-image models based on latent diffusion [4] trained on LAION [40], a large-scale paired text-image dataset. Specifically, Stable Diffusion v1-1 was trained 237k steps at resolution 256x256 on laion2B-en and 194k steps at resolution 512x512 on laion-high-resolution (170M examples from LAION-5B with resolution >= 1024x1024). Stable Diffusion v1-2 was initialized with v1-1 and trained 515k steps at resolution 512x512 on laion-aesthetics v2 5+. Stable Diffusion v1-4 is initialized with v1-2 and trained 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Similarly, Stable Diffusion v1-5 is initialized with v1-2 and trained 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning. Stable Diffusion v2-base is trained from scratch 550k steps at resolution 256x256 on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier with punsafe = 0.1 and an aesthetic score >= 4.5. Then it is further trained for 850k steps at resolution 512x512 on the same dataset on images with resolution >= 512x512. Stable Diffusion v2-1 is resumed from Stable diffusion v2-base and finetuned using a v-objective [82] on a filtered subset of the LAION dataset. Lexica Search (Stable Diffusion v1-5). Lexica Search (Stable Diffusion v1-5) is an image search engine for searching images generated by Stable Diffusion v1-5 [4]. DALL-E 2. DALL-E 2 [3] is a 3.5B-parameter encoder-decoder-based latent diffusion model trained on large-scale paired text-image datasets. The model is available via the OpenAI API. Dreamlike Diffusion 1.0. Dreamlike Diffusion 1.0 [45] is a Stable Diffusion v1-5 model fine-tuned on high-quality art images. Dreamlike Photoreal 2.0. Dreamlike Photoreal 2.0 [46] is a photorealistic model fine-tuned from Stable Diffusion 1.5. While the original Stable Diffusion generates resolutions of 512×512 by default, Dreamlike Photoreal 2.0 generates 768×768 by default. Openjourney {v1, v4}. Openjourney [47] is a Stable Diffusion model fine-tuned on Midjourney images. Openjourney v4 [48] was further fine-tuned using +124000 images, 12400 steps, 4 epochs +32 training hours. Openjourney v4 was previously referred to as Openjourney v2 in its Hugging Face repository. Redshift Diffusion. Redshift Diffusion [49] is a Stable Diffusion model fine-tuned on high-resolution 3D artworks. Vintedois (22h) Diffusion. Vintedois (22h) Diffusion [50] is a Stable Diffusion v1-5 model finetuned on a large number of high-quality images with simple prompts to generate beautiful images without a lot of prompt engineering. SafeStableDiffusion-{Weak, Medium, Strong, Max}. Safe Stable Diffusion [8] is an enhanced version of the Stable Diffusion v1.5 model. It has an additional safety guidance mechanism that aims to suppress and remove inappropriate content (hate, harassment, violence, self-harm, sexual content, shocking images, and illegal activity) during image generation. The strength levels for inappropriate content removal are categorized as: {Weak, Medium, Strong, Max}. Promptist + Stable Diffusion v1-4. Promptist [28] is a prompt engineering model, initialized by a 1.5 billion parameter GPT-2 model [83], specifically designed to refine user input into prompts that are favored by image generation models. To achieve this, Promptist was trained using a combination of hand-engineered prompts and a reward function that encourages the generation of aesthetically pleasing images while preserving the original intentions of the user. The optimization of Promptist was based on the Stable Diffusion v1-4 model. DALL-E {mini, mega}. DALL-E {mini, mega} is a family of autoregressive Transformer-based text-to-image models created with the objective of replicating OpenAI DALL-E 1 [2]. The mini and mega variants have 0.4B and 2.6B parameters, respectively. minDALL-E. minDALL-E [53], named after minGPT, is a 1.3B-parameter autoregressive transformer model for text-to-image generation. It was trained using 14 million image-text pairs. CogView2. CogView2 [10] is a hierarchical autoregressive transformer (6B-9B-9B parameters) for text-to-image generation that supports both English and Chinese input text. MultiFusion. MultiFusion (13B) [54] is a multimodal, multilingual diffusion model that extends the capabilities of Stable Diffusion v1.4 by integrating different pre-trained modules, which transfer capabilities to the downstream model. This combination results in novel decoder embeddings, which enable prompting of the image generation model with interleaved multimodal, multilingual inputs, despite being trained solely on monomodal data in a single language. DeepFloyd-IF { M, L, XL } v1.0. DeepFloyd-IF [55] is a pixel-based text-to-image triple-cascaded diffusion model with state-of-the-art photorealism and language understanding. Each cascaded diffusion module is designed to generate images of increasing resolution: 64×64, 256×256, and 1024×1024. All stages utilize a frozen T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling. The model is available in three different sizes: M, L, and XL. M has 0.4B parameters, L has 0.9B parameters, and XL has 4.3B parameters. GigaGAN. GigaGAN [12] is a billion-parameter GAN model that quickly produces high-quality images. The model was trained on text and image pairs from LAION2B-en [40] and COYO-700M [84]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure D Model details Stable Diffusion {v1-4, v1-5, v2-base, v2-1}. Stable Diffusion (v1-4, v1-5, v2-base, v2-1) is a family of 1B-parameter text-to-image models based on latent diffusion [4] trained on LAION [40], a large-scale paired text-image dataset. Stable Diffusion {v1-4, v1-5, v2-base, v2-1}. Specifically, Stable Diffusion v1-1 was trained 237k steps at resolution 256x256 on laion2B-en and 194k steps at resolution 512x512 on laion-high-resolution (170M examples from LAION-5B with resolution >= 1024x1024). Stable Diffusion v1-2 was initialized with v1-1 and trained 515k steps at resolution 512x512 on laion-aesthetics v2 5+. Stable Diffusion v1-4 is initialized with v1-2 and trained 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Similarly, Stable Diffusion v1-5 is initialized with v1-2 and trained 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning. Stable Diffusion v1-4 Stable Diffusion v1-5 Stable Diffusion v2-base is trained from scratch 550k steps at resolution 256x256 on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier with punsafe = 0.1 and an aesthetic score >= 4.5. Then it is further trained for 850k steps at resolution 512x512 on the same dataset on images with resolution >= 512x512. Stable Diffusion v2-1 is resumed from Stable diffusion v2-base and finetuned using a v-objective [82] on a filtered subset of the LAION dataset. Stable Diffusion v2-base Stable Diffusion v2-1 Lexica Search (Stable Diffusion v1-5). Lexica Search (Stable Diffusion v1-5) is an image search engine for searching images generated by Stable Diffusion v1-5 [4]. Lexica Search (Stable Diffusion v1-5). DALL-E 2. DALL-E 2 [3] is a 3.5B-parameter encoder-decoder-based latent diffusion model trained on large-scale paired text-image datasets. The model is available via the OpenAI API. DALL-E 2. Dreamlike Diffusion 1.0. Dreamlike Diffusion 1.0 [45] is a Stable Diffusion v1-5 model fine-tuned on high-quality art images. Dreamlike Diffusion 1.0. Dreamlike Photoreal 2.0. Dreamlike Photoreal 2.0 [46] is a photorealistic model fine-tuned from Stable Diffusion 1.5. While the original Stable Diffusion generates resolutions of 512×512 by default, Dreamlike Photoreal 2.0 generates 768×768 by default. Dreamlike Photoreal 2.0. Openjourney {v1, v4}. Openjourney [47] is a Stable Diffusion model fine-tuned on Midjourney images. Openjourney v4 [48] was further fine-tuned using +124000 images, 12400 steps, 4 epochs +32 training hours. Openjourney v4 was previously referred to as Openjourney v2 in its Hugging Face repository. Openjourney {v1, v4}. Redshift Diffusion. Redshift Diffusion [49] is a Stable Diffusion model fine-tuned on high-resolution 3D artworks. Redshift Diffusion. Vintedois (22h) Diffusion . Vintedois (22h) Diffusion [50] is a Stable Diffusion v1-5 model finetuned on a large number of high-quality images with simple prompts to generate beautiful images without a lot of prompt engineering. Vintedois (22h) Diffusion SafeStableDiffusion-{Weak, Medium, Strong, Max}. Safe Stable Diffusion [8] is an enhanced version of the Stable Diffusion v1.5 model. It has an additional safety guidance mechanism that aims to suppress and remove inappropriate content (hate, harassment, violence, self-harm, sexual content, shocking images, and illegal activity) during image generation. The strength levels for inappropriate content removal are categorized as: {Weak, Medium, Strong, Max}. SafeStableDiffusion-{Weak, Medium, Strong, Max}. Promptist + Stable Diffusion v1-4. Promptist [28] is a prompt engineering model, initialized by a 1.5 billion parameter GPT-2 model [83], specifically designed to refine user input into prompts that are favored by image generation models. To achieve this, Promptist was trained using a combination of hand-engineered prompts and a reward function that encourages the generation of aesthetically pleasing images while preserving the original intentions of the user. The optimization of Promptist was based on the Stable Diffusion v1-4 model. Promptist + Stable Diffusion v1-4. DALL-E {mini, mega}. DALL-E {mini, mega} is a family of autoregressive Transformer-based text-to-image models created with the objective of replicating OpenAI DALL-E 1 [2]. The mini and mega variants have 0.4B and 2.6B parameters, respectively. DALL-E {mini, mega}. minDALL-E. minDALL-E [53], named after minGPT, is a 1.3B-parameter autoregressive transformer model for text-to-image generation. It was trained using 14 million image-text pairs. minDALL-E. CogView2 . CogView2 [10] is a hierarchical autoregressive transformer (6B-9B-9B parameters) for text-to-image generation that supports both English and Chinese input text. CogView2 MultiFusion . MultiFusion (13B) [54] is a multimodal, multilingual diffusion model that extends the capabilities of Stable Diffusion v1.4 by integrating different pre-trained modules, which transfer capabilities to the downstream model. This combination results in novel decoder embeddings, which enable prompting of the image generation model with interleaved multimodal, multilingual inputs, despite being trained solely on monomodal data in a single language. MultiFusion DeepFloyd-IF { M, L, XL } v1.0. DeepFloyd-IF [55] is a pixel-based text-to-image triple-cascaded diffusion model with state-of-the-art photorealism and language understanding. Each cascaded diffusion module is designed to generate images of increasing resolution: 64×64, 256×256, and 1024×1024. All stages utilize a frozen T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention-pooling. The model is available in three different sizes: M, L, and XL. M has 0.4B parameters, L has 0.9B parameters, and XL has 4.3B parameters. DeepFloyd-IF { M, L, XL } v1.0. GigaGAN. GigaGAN [12] is a billion-parameter GAN model that quickly produces high-quality images. The model was trained on text and image pairs from LAION2B-en [40] and COYO-700M [84]. GigaGAN. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Deep Dive Into Stable Diffusion and Other Leading Text-to-Image Models

Hello! My name is Artem and I am the author of the Android Insights YouTube channel Today we will enter the world of delegates and delegated properties in Kotlin. This topic might seem complex at first glance, but I'll do my best to explain it as clearly as possible. Let's get started! What Are Delegates? Before going into the details, let's clarify some fundamental concepts. A delegate is an object that another object, well, delegates certain tasks to. In Kotlin, delegation is a powerful tool that allows you to reuse code and implement complex behavior without the need for inheritance. Example of Delegation Let's consider a simple example: interface Base {
    fun print()
}

class BaseImpl(private val x: Int) : Base {
    override fun print() {
        println(x)
    }
}

class Derived(b: Base) : Base by b

fun main() {
    val b = BaseImpl(10)
    Derived(b).print() // Outputs: 10
} In this example: We define an interface Base with a method print().
The class BaseImpl implements this interface and holds a value x.
The class Derived delegates the implementation of Base to the object b. When we call print() on an instance of Derived, it actually executes the print() method of the BaseImpl object. This allows us to reuse code and add flexibility to the application's architecture without excessive inheritance. Delegated Properties Now, let's discuss delegated properties. A delegated property is a property that delegates its getter and setter to another object. The syntax for declaring a delegated property looks like this: class Example {
    var p: String by Delegate()
} Here, p is a delegated property. All accesses to p will be redirected to the Delegate() object. Built-in Delegates in Kotlin Kotlin provides several built-in delegates that simplify common tasks. lazy — Lazy Initialization The lazy delegate is used for lazy initialization of a property. The value is computed only upon the first access. val lazyValue: String by lazy {
    println("Computing the value...")
    "Hello"
}

fun main() {
    println(lazyValue) // Outputs: Computing the value... Hello
    println(lazyValue) // Outputs: Hello
} In this example, when we first access lazyValue, the code block inside lazy is executed, and the value is stored for subsequent uses. observable — Observable Property The observable delegate allows you to track changes to a property and react accordingly. import kotlin.properties.Delegates

var name: String by Delegates.observable("Initial value") { prop, old, new ->
    println("$old -> $new")
}

fun main() {
    name = "First"   // Outputs: Initial value -> First
    name = "Second"  // Outputs: First -> Second
} Here, every time name changes, the code block is executed, printing the old and new values. Creating Custom Delegates In addition to built-in delegates, we can create custom delegates to implement specific behavior. This can be done using the ReadOnlyProperty and ReadWriteProperty interfaces, or by directly implementing the getValue and setValue operators. Using ReadOnlyProperty Let's create a delegate that always returns the same value and logs each access. import kotlin.properties.ReadOnlyProperty
import kotlin.reflect.KProperty

class ConstantValue<T>(private val value: T) : ReadOnlyProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}'")
        return value
    }
}

class Example {
    val constant: String by ConstantValue("Hello, World!")
}

fun main() {
    val example = Example()
    println(example.constant)
    // Outputs:
    // Getting value of property 'constant'
    // Hello, World!
} Using ReadWriteProperty Now let's create a delegate that logs read and write operations of a property. import kotlin.properties.ReadWriteProperty
import kotlin.reflect.KProperty

class LoggingProperty<T>(private var value: T) : ReadWriteProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}': $value")
        return value
    }

    override fun setValue(thisRef: Any?, property: KProperty<*>, newValue: T) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var logged: String by LoggingProperty("Initial")
}

fun main() {
    val example = Example()
    println(example.logged)
    example.logged = "New Value"
    println(example.logged)
    // Outputs:
    // Getting value of property 'logged': Initial
    // Initial
    // Setting value of property 'logged' to New Value
    // Getting value of property 'logged': New Value
    // New Value
} Using ReadOnlyProperty and ReadWriteProperty allows us to explicitly indicate which operations the delegate supports, making the code more readable and understandable. Direct Implementation of getValue and setValue Alternatively, we can directly implement the getValue and setValue operators. import kotlin.reflect.KProperty

class StringDelegate {
    private var value: String = ""

    operator fun getValue(thisRef: Any?, property: KProperty<*>): String {
        println("Getting value of property '${property.name}'")
        return value
    }

    operator fun setValue(thisRef: Any?, property: KProperty<*>, newValue: String) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var str: String by StringDelegate()
}

fun main() {
    val example = Example()
    example.str = "Hello"
    println(example.str)
    // Outputs:
    // Setting value of property 'str' to Hello
    // Getting value of property 'str'
    // Hello
} This approach offers more flexibility, allowing us to implement only the necessary methods for our purposes. Practical Applications of Delegates Let's explore some practical scenarios where delegates can be particularly useful. Lazy Initialization of Resource-Intensive Objects The lazy delegate is perfect for deferred initialization of objects whose creation requires significant resources. class ResourceManager {
    val database by lazy {
        println("Connecting to the database...")
        Database.connect()
    }
}

fun main() {
    val manager = ResourceManager()
    println("ResourceManager created")
    // The database is not initialized yet
    manager.database.query("SELECT * FROM users")
    // The database is already initialized
    manager.database.query("SELECT * FROM products")
} In this example, the connection to the database occurs only upon the first access to database, conserving resources until they are actually needed. Implementing the Observer Pattern With the observable delegate, we can easily implement the Observer pattern by tracking property changes. import kotlin.properties.Delegates

class User {
    var name: String by Delegates.observable("") { prop, old, new ->
        println("User's name changed from '$old' to '$new'")
    }
}

fun main() {
    val user = User()
    user.name = "Alice"  // Outputs: User's name changed from '' to 'Alice'
    user.name = "Bob"    // Outputs: User's name changed from 'Alice' to 'Bob'
} This allows us to perform specific actions when properties change, such as updating the UI, validating data, or sending notifications. Conclusion Delegates are a powerful tool in the Kotlin developer's arsenal. They enable you to write cleaner, more modular, and flexible code, opening up new possibilities for implementing complex logic and design patterns. I hope this guide has helped you better understand delegates and delegated properties in Kotlin. Feel free to experiment and apply these concepts in your own projects! Thank you for your attention, and happy coding! Hello! My name is Artem and I am the author of the Android Insights YouTube channel Android Insights Today we will enter the world of delegates and delegated properties in Kotlin . This topic might seem complex at first glance, but I'll do my best to explain it as clearly as possible. Let's get started! Kotlin What Are Delegates? Before going into the details, let's clarify some fundamental concepts. A delegate is an object that another object, well, delegates certain tasks to. In Kotlin , delegation is a powerful tool that allows you to reuse code and implement complex behavior without the need for inheritance. Kotlin Example of Delegation Let's consider a simple example: Let's consider a simple example: interface Base {
    fun print()
}

class BaseImpl(private val x: Int) : Base {
    override fun print() {
        println(x)
    }
}

class Derived(b: Base) : Base by b

fun main() {
    val b = BaseImpl(10)
    Derived(b).print() // Outputs: 10
} interface Base {
    fun print()
}

class BaseImpl(private val x: Int) : Base {
    override fun print() {
        println(x)
    }
}

class Derived(b: Base) : Base by b

fun main() {
    val b = BaseImpl(10)
    Derived(b).print() // Outputs: 10
} In this example: We define an interface Base with a method print(). The class BaseImpl implements this interface and holds a value x. The class Derived delegates the implementation of Base to the object b. We define an interface Base with a method print() . Base print() The class BaseImpl implements this interface and holds a value x . BaseImpl x The class Derived delegates the implementation of Base to the object b . Derived Base b When we call print() on an instance of Derived , it actually executes the print() method of the BaseImpl object. This allows us to reuse code and add flexibility to the application's architecture without excessive inheritance. print() Derived print() BaseImpl Delegated Properties Now, let's discuss delegated properties. A delegated property is a property that delegates its getter and setter to another object. The syntax for declaring a delegated property looks like this: class Example {
    var p: String by Delegate()
} class Example {
    var p: String by Delegate()
} Here, p is a delegated property. All accesses to p will be redirected to the Delegate() object. p p Delegate() Built-in Delegates in Kotlin Kotlin provides several built-in delegates that simplify common tasks. lazy — Lazy Initialization lazy The lazy delegate is used for lazy initialization of a property. The value is computed only upon the first access. lazy val lazyValue: String by lazy {
    println("Computing the value...")
    "Hello"
}

fun main() {
    println(lazyValue) // Outputs: Computing the value... Hello
    println(lazyValue) // Outputs: Hello
} val lazyValue: String by lazy {
    println("Computing the value...")
    "Hello"
}

fun main() {
    println(lazyValue) // Outputs: Computing the value... Hello
    println(lazyValue) // Outputs: Hello
} In this example, when we first access lazyValue , the code block inside lazy is executed, and the value is stored for subsequent uses. lazyValue lazy observable — Observable Property observable The observable delegate allows you to track changes to a property and react accordingly. observable import kotlin.properties.Delegates

var name: String by Delegates.observable("Initial value") { prop, old, new ->
    println("$old -> $new")
}

fun main() {
    name = "First"   // Outputs: Initial value -> First
    name = "Second"  // Outputs: First -> Second
} import kotlin.properties.Delegates

var name: String by Delegates.observable("Initial value") { prop, old, new ->
    println("$old -> $new")
}

fun main() {
    name = "First"   // Outputs: Initial value -> First
    name = "Second"  // Outputs: First -> Second
} Here, every time name changes, the code block is executed, printing the old and new values. name Creating Custom Delegates In addition to built-in delegates, we can create custom delegates to implement specific behavior. This can be done using the ReadOnlyProperty and ReadWriteProperty interfaces, or by directly implementing the getValue and setValue operators. ReadOnlyProperty ReadWriteProperty getValue setValue Using ReadOnlyProperty ReadOnlyProperty Let's create a delegate that always returns the same value and logs each access. import kotlin.properties.ReadOnlyProperty
import kotlin.reflect.KProperty

class ConstantValue<T>(private val value: T) : ReadOnlyProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}'")
        return value
    }
}

class Example {
    val constant: String by ConstantValue("Hello, World!")
}

fun main() {
    val example = Example()
    println(example.constant)
    // Outputs:
    // Getting value of property 'constant'
    // Hello, World!
} import kotlin.properties.ReadOnlyProperty
import kotlin.reflect.KProperty

class ConstantValue<T>(private val value: T) : ReadOnlyProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}'")
        return value
    }
}

class Example {
    val constant: String by ConstantValue("Hello, World!")
}

fun main() {
    val example = Example()
    println(example.constant)
    // Outputs:
    // Getting value of property 'constant'
    // Hello, World!
} Using ReadWriteProperty ReadWriteProperty Now let's create a delegate that logs read and write operations of a property. import kotlin.properties.ReadWriteProperty
import kotlin.reflect.KProperty

class LoggingProperty<T>(private var value: T) : ReadWriteProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}': $value")
        return value
    }

    override fun setValue(thisRef: Any?, property: KProperty<*>, newValue: T) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var logged: String by LoggingProperty("Initial")
}

fun main() {
    val example = Example()
    println(example.logged)
    example.logged = "New Value"
    println(example.logged)
    // Outputs:
    // Getting value of property 'logged': Initial
    // Initial
    // Setting value of property 'logged' to New Value
    // Getting value of property 'logged': New Value
    // New Value
} import kotlin.properties.ReadWriteProperty
import kotlin.reflect.KProperty

class LoggingProperty<T>(private var value: T) : ReadWriteProperty<Any?, T> {
    override fun getValue(thisRef: Any?, property: KProperty<*>): T {
        println("Getting value of property '${property.name}': $value")
        return value
    }

    override fun setValue(thisRef: Any?, property: KProperty<*>, newValue: T) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var logged: String by LoggingProperty("Initial")
}

fun main() {
    val example = Example()
    println(example.logged)
    example.logged = "New Value"
    println(example.logged)
    // Outputs:
    // Getting value of property 'logged': Initial
    // Initial
    // Setting value of property 'logged' to New Value
    // Getting value of property 'logged': New Value
    // New Value
} Using ReadOnlyProperty and ReadWriteProperty allows us to explicitly indicate which operations the delegate supports, making the code more readable and understandable. ReadOnlyProperty ReadWriteProperty Direct Implementation of getValue and setValue getValue setValue Alternatively, we can directly implement the getValue and setValue operators. getValue setValue import kotlin.reflect.KProperty

class StringDelegate {
    private var value: String = ""

    operator fun getValue(thisRef: Any?, property: KProperty<*>): String {
        println("Getting value of property '${property.name}'")
        return value
    }

    operator fun setValue(thisRef: Any?, property: KProperty<*>, newValue: String) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var str: String by StringDelegate()
}

fun main() {
    val example = Example()
    example.str = "Hello"
    println(example.str)
    // Outputs:
    // Setting value of property 'str' to Hello
    // Getting value of property 'str'
    // Hello
} import kotlin.reflect.KProperty

class StringDelegate {
    private var value: String = ""

    operator fun getValue(thisRef: Any?, property: KProperty<*>): String {
        println("Getting value of property '${property.name}'")
        return value
    }

    operator fun setValue(thisRef: Any?, property: KProperty<*>, newValue: String) {
        println("Setting value of property '${property.name}' to $newValue")
        value = newValue
    }
}

class Example {
    var str: String by StringDelegate()
}

fun main() {
    val example = Example()
    example.str = "Hello"
    println(example.str)
    // Outputs:
    // Setting value of property 'str' to Hello
    // Getting value of property 'str'
    // Hello
} This approach offers more flexibility, allowing us to implement only the necessary methods for our purposes. Practical Applications of Delegates Let's explore some practical scenarios where delegates can be particularly useful. Lazy Initialization of Resource-Intensive Objects The lazy delegate is perfect for deferred initialization of objects whose creation requires significant resources. lazy class ResourceManager {
    val database by lazy {
        println("Connecting to the database...")
        Database.connect()
    }
}

fun main() {
    val manager = ResourceManager()
    println("ResourceManager created")
    // The database is not initialized yet
    manager.database.query("SELECT * FROM users")
    // The database is already initialized
    manager.database.query("SELECT * FROM products")
} class ResourceManager {
    val database by lazy {
        println("Connecting to the database...")
        Database.connect()
    }
}

fun main() {
    val manager = ResourceManager()
    println("ResourceManager created")
    // The database is not initialized yet
    manager.database.query("SELECT * FROM users")
    // The database is already initialized
    manager.database.query("SELECT * FROM products")
} In this example, the connection to the database occurs only upon the first access to database , conserving resources until they are actually needed. database Implementing the Observer Pattern With the observable delegate, we can easily implement the Observer pattern by tracking property changes. observable import kotlin.properties.Delegates

class User {
    var name: String by Delegates.observable("") { prop, old, new ->
        println("User's name changed from '$old' to '$new'")
    }
}

fun main() {
    val user = User()
    user.name = "Alice"  // Outputs: User's name changed from '' to 'Alice'
    user.name = "Bob"    // Outputs: User's name changed from 'Alice' to 'Bob'
} import kotlin.properties.Delegates

class User {
    var name: String by Delegates.observable("") { prop, old, new ->
        println("User's name changed from '$old' to '$new'")
    }
}

fun main() {
    val user = User()
    user.name = "Alice"  // Outputs: User's name changed from '' to 'Alice'
    user.name = "Bob"    // Outputs: User's name changed from 'Alice' to 'Bob'
} This allows us to perform specific actions when properties change, such as updating the UI, validating data, or sending notifications. Conclusion Delegates are a powerful tool in the Kotlin developer's arsenal. They enable you to write cleaner, more modular, and flexible code, opening up new possibilities for implementing complex logic and design patterns. I hope this guide has helped you better understand delegates and delegated properties in Kotlin. Feel free to experiment and apply these concepts in your own projects! Thank you for your attention, and happy coding! Thank you for your attention, and happy coding!

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