Cyber woes for SMBs are exacerbated by a lack of resources to enact advanced security measures, Microsoft Security said in a report.

Cyberattacks hit 1 in 3 SMBs last year | Cybersecurity Dive

Yet many districts and public and private schools lack the technology or staffing to stay ahead of criminals and safeguard the sensitive data entrusted to them. The K-12 education sector, like some other industries, also has no federal mandatory and uniform cybersecurity standards for identifying ...

School ransomware attacks are on the rise. What can districts do? | K-12 Dive

Welcome to the Bajaj Finance Stock Liveblog, your real-time source for the latest updates and comprehensive analysis on a prominent stock. Dive into the current details of Bajaj Finance, including: Last traded price 7026.9, Market capitalization: 436070.53, Volume: 612010, Price-to-earnings ...

Bajaj Finance Stocks Live Updates: Bajaj Finance Recent Performance - The Economic Times

The deal follows a marketwide push for vendor consolidation and a growing interest in more end-to-end offerings from a single provider.

Sophos to buy Secureworks in $859M push into XDR | Cybersecurity Dive

The cybersecurity workforce has grown each year since 2013, yet hundreds of thousands of more workers are needed to close current supply gaps.

Skills shortage persists in cybersecurity despite decade of hiring | HR Dive

The credit ratings firm will pay State Street CFO Eric Aboaf a $2.4 million signing bonus when he takes their top financial seat in February.

S&P Global swipes State Street CFO for finance chief | CFO Dive

The Cybersecurity and Infrastructure Security Agency added the vulnerability, listed as CVE-2024-23113, to its known exploited vulnerabilities catalog on Wednesday.

Critical CVE in 4 Fortinet products actively exploited | Cybersecurity Dive

Asking customers to foot the bill for data breach remediation will not prevent future data breaches or address the issues that cause costs to increase.

Where organizations invest after a data breach | Cybersecurity Dive

CFOs don’t need to code AI themselves, but they do need to try it out and learn how it can be applied to their individual situations, consultants say.

What must-have AI skills do finance execs need? First, dig in. | CFO Dive

The cybersecurity vendor finds itself operating from a vulnerable position. Efforts to earn back trust are complex and some require industrywide support.

What’s next for CrowdStrike on the road to repair its reputation? | Cybersecurity Dive

Dive

dive

Bitcoin. Rushing to $72,000. The name evokes decentralized revolution and… frustratingly slow, expensive transactions. While Bitcoin's core technology is undeniably revolutionary, its scalability has long been a major hurdle. Enter the Lightning Network: a game-changer poised to catapult Bitcoin into the mainstream and redefine finance. Forget the technical mumbo-jumbo. The Lightning Network isn't just another layer-2 solution; it's Bitcoin's secret weapon, a high-speed express lane built directly on the existing highway. It uses clever off-chain payment channels and smart contracts to enable near-instantaneous, incredibly cheap transactions, all while maintaining Bitcoin's core principles of decentralization and security. Why is this a Big Deal? Imagine using Bitcoin to buy coffee. Transaction fees could easily exceed the cost of the drink itself. This isn't hyperbole; it's the reality of Bitcoin's current limitations. The Lightning Network obliterates this. We're talking micropayments – transactions measured in satoshis (1 BTC = 100 million satoshis) – becoming not just possible, but practical. This isn't just about convenience; it unlocks Bitcoin's full potential. Consider: Global Microtransactions: Cross-border remittances as seamless as texting. The Lightning Network makes this a reality.
Decentralized Apps (dApps) on Steroids: Lightning's speed and low fees create fertile ground for a new generation of dApps, far surpassing current blockchain limitations.
The Death of Payment Gateways: Peer-to-peer payments, bypassing centralized intermediaries, empower individuals and businesses. The Numbers Don't Lie Bitcoin's main blockchain processes roughly seven transactions per second. Visa handles thousands. The Lightning Network? With 48,620 active payment channels (as per 1ML's data), its theoretical throughput reaches a staggering 24.31 million transactions per second. And that number can increase as more nodes and channels join the network. Source: https://glossary.blockstream.com/lightning-network/ The graphic's LN TPS (500 TPS x 85,000 channels) uses outdated channel data (current: 48,620). This isn't just theoretical. The average Bitcoin transaction fee hovers around $1. During peak periods, it soared to $125! With Lightning, a $100 transaction might cost less than a cent. Beyond Speed and Cost: The P2P Revolution The Lightning Network isn't just faster transactions; it's the cornerstone of a truly decentralized, peer-to-peer economy. It's the infrastructure for a world where individuals and businesses transact directly, cutting out the middlemen and fostering a more equitable, efficient financial system. Furthermore, transactions on the Lightning Network don't directly record on the blockchain, enhancing user privacy. This is further improved by innovations like Nervos CKB's Fiber Network, which plans to implement PTLC (Point Time Locked Contracts) building on HTLC (Hash Time Locked Contracts) to enhance privacy even further by using different secret values at each hop in the payment path. The Road Ahead: Stablecoins and Mass Adoption While impressive, the Lightning Network's full potential remains untapped. Integrating stablecoins – cryptocurrencies pegged to stable assets like the US dollar – is crucial for mass adoption. This will mitigate Bitcoin's price volatility, making the Lightning Network far more attractive for everyday users and businesses. Initiatives like Lightning Labs' work on LND support for Taproot Assets-based stablecoins and Nervos CKB's Fiber Network's inherent support for custom assets, including stablecoins like Stable++, are paving the way for this integration and will unlock the full potential of the Lightning Network. Source: https://mempool.space/graphs/lightning/capacity#all The Verdict? The Lightning Network isn't just a technological upgrade; it's a paradigm shift. It's the catalyst for Bitcoin's evolution from a volatile store of value to a ubiquitous, everyday payment system. While challenges remain, the potential is undeniable. This isn't just about Bitcoin's future; it's about the future of finance itself. Buckle up. The ride is about to get a whole lot faster. Bitcoin. Rushing to $72,000. The name evokes decentralized revolution and… frustratingly slow, expensive transactions. While Bitcoin's core technology is undeniably revolutionary, its scalability has long been a major hurdle. Enter the Lightning Network: a game-changer poised to catapult Bitcoin into the mainstream and redefine finance. Forget the technical mumbo-jumbo. The Lightning Network isn't just another layer-2 solution; it's Bitcoin's secret weapon, a high-speed express lane built directly on the existing highway. It uses clever off-chain payment channels and smart contracts to enable near-instantaneous, incredibly cheap transactions, all while maintaining Bitcoin's core principles of decentralization and security. Why is this a Big Deal? Imagine using Bitcoin to buy coffee. Transaction fees could easily exceed the cost of the drink itself. This isn't hyperbole; it's the reality of Bitcoin's current limitations. The Lightning Network obliterates this. We're talking micropayments – transactions measured in satoshis (1 BTC = 100 million satoshis) – becoming not just possible, but practical. This isn't just about convenience; it unlocks Bitcoin's full potential. Consider: Global Microtransactions: Cross-border remittances as seamless as texting. The Lightning Network makes this a reality. Decentralized Apps (dApps) on Steroids: Lightning's speed and low fees create fertile ground for a new generation of dApps, far surpassing current blockchain limitations. The Death of Payment Gateways: Peer-to-peer payments, bypassing centralized intermediaries, empower individuals and businesses. Global Microtransactions : Cross-border remittances as seamless as texting. The Lightning Network makes this a reality. Global Microtransactions Decentralized Apps (dApps) on Steroids : Lightning's speed and low fees create fertile ground for a new generation of dApps, far surpassing current blockchain limitations. Decentralized Apps (dApps) on Steroids The Death of Payment Gateways : Peer-to-peer payments, bypassing centralized intermediaries, empower individuals and businesses. The Death of Payment Gateways The Numbers Don't Lie The Numbers Don't Lie Bitcoin's main blockchain processes roughly seven transactions per second. Visa handles thousands. The Lightning Network? With 48,620 active payment channels (as per 1ML's data), its theoretical throughput reaches a staggering 24.31 million transactions per second. And that number can increase as more nodes and channels join the network. Source: https://glossary.blockstream.com/lightning-network/ Source: Source: https://glossary.blockstream.com/lightning-network/ https://glossary.blockstream.com/lightning-network/ https://glossary.blockstream.com/lightning-network/ The graphic's LN TPS (500 TPS x 85,000 channels) uses outdated channel data (current: 48,620). The graphic's LN TPS (500 TPS x 85,000 channels) uses outdated channel data (current: 48,620). The graphic's LN TPS (500 TPS x 85,000 channels) uses outdated channel data (current: 48,620). This isn't just theoretical. The average Bitcoin transaction fee hovers around $1. During peak periods, it soared to $125! With Lightning, a $100 transaction might cost less than a cent. Beyond Speed and Cost: The P2P Revolution The Lightning Network isn't just faster transactions; it's the cornerstone of a truly decentralized, peer-to-peer economy. It's the infrastructure for a world where individuals and businesses transact directly, cutting out the middlemen and fostering a more equitable, efficient financial system. Furthermore, transactions on the Lightning Network don't directly record on the blockchain, enhancing user privacy. This is further improved by innovations like Nervos CKB's Fiber Network, which plans to implement PTLC (Point Time Locked Contracts) building on HTLC (Hash Time Locked Contracts) to enhance privacy even further by using different secret values at each hop in the payment path. The Road Ahead: Stablecoins and Mass Adoption While impressive, the Lightning Network's full potential remains untapped. Integrating stablecoins – cryptocurrencies pegged to stable assets like the US dollar – is crucial for mass adoption. This will mitigate Bitcoin's price volatility, making the Lightning Network far more attractive for everyday users and businesses. Initiatives like Lightning Labs' work on LND support for Taproot Assets-based stablecoins and Nervos CKB's Fiber Network's inherent support for custom assets, including stablecoins like Stable++, are paving the way for this integration and will unlock the full potential of the Lightning Network. Source: https://mempool.space/graphs/lightning/capacity#all Source: https://mempool.space/graphs/lightning/capacity#all https://mempool.space/graphs/lightning/capacity#all The Verdict? The Lightning Network isn't just a technological upgrade; it's a paradigm shift. It's the catalyst for Bitcoin's evolution from a volatile store of value to a ubiquitous, everyday payment system. While challenges remain, the potential is undeniable. This isn't just about Bitcoin's future; it's about the future of finance itself. Buckle up. The ride is about to get a whole lot faster.

Deep Dive into Bitcoin's Lightning Network Technology

Ponořte se do technologie Lightning Network společnosti Bitcoin

Análisis en profundidad de la tecnología de la red Lightning de Bitcoin

Sukella syvälle Bitcoinin Lightning Network -teknologiaan

Plonje byen fon nan teknoloji rezo zeklè Bitcoin a

Merüljön el mélyen a Bitcoin Lightning Network technológiájában

צלול עמוק לתוך טכנולוגיית Lightning Network של ביטקוין

ビットコインのライトニングネットワーク技術を詳しく見る

Ponorte sa do technológie Lightning Network spoločnosti Bitcoin

Zhytje e thellë në teknologjinë e rrjetit Lightning të Bitcoin

Дубоко зароните у Битцоин-ову Лигхтнинг Нетворк технологију

“Bitcoin” -iň ýyldyrym tor tehnologiýasyna çuňňur çümmek

Ukuntywila ngokunzulu kwiTekhnoloji yeTekhnoloji yoMbane weBitcoin

 A Deep Dive Into GitHub Actions From Software Development to Data Engineering

Hello Supporters, TSJ Diversified Group has been nominated in HackerNoon's annual Startups of The Year awards in Dar es Salaam, Tanzania. Please vote for us here. Read more about us below to understand why we deserve your vote. Meet TSJ Diversified Group Founded in 2021 by a team of innovative and forward-thinking individuals (Samuel Byalugaba and Sylvester Mayaya), TSJ Diversified Group is a dynamic company that operates across various industries, including digital marketing, IT solutions, and technology. Our mission is to empower businesses with cutting-edge solutions that drive growth and efficiency. Since our inception, we’ve strived to build a diversified portfolio that aligns with our core values: excellence, innovation, and a commitment to creating positive impacts within the communities we serve. How TSJ Diversified Group achieved product-market fit From the beginning, TSJ Diversified Group focused on deeply understanding the needs of our clients and the markets we serve. By continuously gathering feedback and analyzing market trends, we’ve been able to refine our offerings and develop solutions that resonate with our customers. Our success in achieving product-market fit comes from listening to our clients and creating tailored strategies that address their specific challenges, whether in digital marketing, IT solutions, or beyond. The evolution of TSJ Diversified Group TSJ Diversified Group began with a vision to leverage digital transformation for businesses of all sizes. Initially focused on digital marketing, we quickly recognized the potential to diversify into other sectors such as IT solutions, agriculture, real estate, and tourism. Over time, we’ve expanded our services, continuously evolving to stay ahead of market trends and technological advancements. This adaptability has enabled us to meet the growing needs of our clients while maintaining our core identity as an innovative and solution-driven company. Milestones and achievements Since our founding, TSJ Diversified Group has celebrated several key milestones. Among them: Expanding our service portfolio to include a wide range of industries, offering tailored solutions for both small and large businesses.
Building a strong client base across multiple sectors, which has solidified our reputation as a trusted partner.
Being nominated for the 2024 Startups of the Year award, a testament to our hard work, innovation, and commitment to delivering exceptional results. These achievements reflect the dedication of our team and the trust of our clients, both of which are instrumental to our continued success. Lessons learned As with any startup, our journey has come with its challenges, and we’ve learned valuable lessons along the way. One key lesson is the importance of agility, being able to adapt quickly to changing market conditions and customer needs. Another is the significance of customer feedback, which has been critical in helping us refine our products and services to better serve the market. Finally, we’ve learned that building strong relationships, whether with clients, partners, or team members is the foundation of long-term success. What Startups of The Year means to us Being nominated for Startups of the Year 2024 is not just an honor, but a validation of the hard work and dedication our team has put into building TSJ Diversified Group. This recognition means that our efforts to innovate and diversify have resonated within the industry, and it motivates us to continue pushing the boundaries of what we can achieve. More than just a milestone, it’s an opportunity to reflect on our growth and look forward to what’s next. Conclusion Being nominated for Startups of the Year 2024 is not just an honor, but a validation of the hard work and dedication our team has put into building TSJ Diversified Group. This recognition means that our efforts to innovate and diversify have resonated within the industry, and it motivates us to continue pushing the boundaries of what we can achieve. More than just a milestone, it’s an opportunity to reflect on our growth and look forward to what’s next. Vote for us today here! Hello Supporters, TSJ Diversified Group has been nominated in HackerNoon's annual Startups of The Year awards in Dar es Salaam, Tanzania. Please vote for us here. Please vote for us here . here Read more about us below to understand why we deserve your vote. Meet TSJ Diversified Group Founded in 2021 by a team of innovative and forward-thinking individuals (Samuel Byalugaba and Sylvester Mayaya), TSJ Diversified Group is a dynamic company that operates across various industries, including digital marketing, IT solutions, and technology. Our mission is to empower businesses with cutting-edge solutions that drive growth and efficiency. Since our inception, we’ve strived to build a diversified portfolio that aligns with our core values: excellence, innovation, and a commitment to creating positive impacts within the communities we serve. TSJ Diversified Group How TSJ Diversified Group achieved product-market fit From the beginning, TSJ Diversified Group focused on deeply understanding the needs of our clients and the markets we serve. By continuously gathering feedback and analyzing market trends, we’ve been able to refine our offerings and develop solutions that resonate with our customers. Our success in achieving product-market fit comes from listening to our clients and creating tailored strategies that address their specific challenges, whether in digital marketing, IT solutions, or beyond. product-market fit The evolution of TSJ Diversified Group TSJ Diversified Group began with a vision to leverage digital transformation for businesses of all sizes. Initially focused on digital marketing, we quickly recognized the potential to diversify into other sectors such as IT solutions, agriculture, real estate, and tourism. Over time, we’ve expanded our services, continuously evolving to stay ahead of market trends and technological advancements. This adaptability has enabled us to meet the growing needs of our clients while maintaining our core identity as an innovative and solution-driven company. Milestones and achievements Since our founding, TSJ Diversified Group has celebrated several key milestones. Among them: Expanding our service portfolio to include a wide range of industries, offering tailored solutions for both small and large businesses. Building a strong client base across multiple sectors, which has solidified our reputation as a trusted partner. Being nominated for the 2024 Startups of the Year award, a testament to our hard work, innovation, and commitment to delivering exceptional results. These achievements reflect the dedication of our team and the trust of our clients, both of which are instrumental to our continued success. Expanding our service portfolio to include a wide range of industries, offering tailored solutions for both small and large businesses. Building a strong client base across multiple sectors, which has solidified our reputation as a trusted partner. Being nominated for the 2024 Startups of the Year award, a testament to our hard work, innovation, and commitment to delivering exceptional results. These achievements reflect the dedication of our team and the trust of our clients, both of which are instrumental to our continued success. 2024 Startups of the Year Lessons learned As with any startup, our journey has come with its challenges, and we’ve learned valuable lessons along the way. One key lesson is the importance of agility , being able to adapt quickly to changing market conditions and customer needs. Another is the significance of customer feedback , which has been critical in helping us refine our products and services to better serve the market. Finally, we’ve learned that building strong relationships, whether with clients, partners, or team members is the foundation of long-term success. agility customer feedback What Startups of The Year means to us Being nominated for Startups of the Year 2024 is not just an honor, but a validation of the hard work and dedication our team has put into building TSJ Diversified Group. This recognition means that our efforts to innovate and diversify have resonated within the industry, and it motivates us to continue pushing the boundaries of what we can achieve. More than just a milestone, it’s an opportunity to reflect on our growth and look forward to what’s next. Startups of the Year 2024 Conclusion Being nominated for Startups of the Year 2024 is not just an honor, but a validation of the hard work and dedication our team has put into building TSJ Diversified Group. This recognition means that our efforts to innovate and diversify have resonated within the industry, and it motivates us to continue pushing the boundaries of what we can achieve. More than just a milestone, it’s an opportunity to reflect on our growth and look forward to what’s next. Startups of the Year 2024 Vote for us today here! Vote for us today here ! here

Meet TSJ Diversified Group, Startups of the Year 2024 Nominee

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

Vara vs Aptos, Monad, Sui, and Sei: A Technical Deep Dive into Data Parallelism

Authors:
(1) Dustin Podell, Stability AI, Applied Research;
(2) Zion English, Stability AI, Applied Research;
(3) Kyle Lacey, Stability AI, Applied Research;
(4) Andreas Blattmann, Stability AI, Applied Research;
(5) Tim Dockhorn, Stability AI, Applied Research;
(6) Jonas Müller, Stability AI, Applied Research;
(7) Joe Penna, Stability AI, Applied Research;
(8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work Appendix A Acknowledgements B Limitations C Diffusion Models D Comparison to the State of the Art E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline References 2.3 Multi-Aspect Training Real-world datasets include images of widely varying sizes and aspect-ratios (c.f. fig. 2) While the common output resolutions for text-to-image models are square images of 512 × 512 or 1024 × 1024 pixels, we argue that this is a rather unnatural choice, given the widespread distribution and use of landscape (e.g., 16:9) or portrait format screens. In practice, we apply multi-aspect training as a finetuning stage after pretraining the model at a fixed aspect-ratio and resolution and combine it with the conditioning techniques introduced in Sec. 2.2 via concatenation along the channel axis. Fig. 16 in App. J provides python-code for this operation. Note that crop-conditioning and multi-aspect training are complementary operations, and crop-conditioning then only works within the bucket boundaries (usually 64 pixels). For ease of implementation, however, we opt to keep this control parameter for multi-aspect models. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Authors: Authors: (1) Dustin Podell, Stability AI, Applied Research; (2) Zion English, Stability AI, Applied Research; (3) Kyle Lacey, Stability AI, Applied Research; (4) Andreas Blattmann, Stability AI, Applied Research; (5) Tim Dockhorn, Stability AI, Applied Research; (6) Jonas Müller, Stability AI, Applied Research; (7) Joe Penna, Stability AI, Applied Research; (8) Robin Rombach, Stability AI, Applied Research. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Improving Stable Diffusion 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work 3 Future Work Appendix Appendix A Acknowledgements A Acknowledgements B Limitations B Limitations C Diffusion Models C Diffusion Models D Comparison to the State of the Art D Comparison to the State of the Art E Comparison to Midjourney v5.1 E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline G Additional Comparison between Single- and Two-Stage SDXL pipeline References References 2.3 Multi-Aspect Training Real-world datasets include images of widely varying sizes and aspect-ratios (c.f. fig. 2) While the common output resolutions for text-to-image models are square images of 512 × 512 or 1024 × 1024 pixels, we argue that this is a rather unnatural choice, given the widespread distribution and use of landscape (e.g., 16:9) or portrait format screens. In practice, we apply multi-aspect training as a finetuning stage after pretraining the model at a fixed aspect-ratio and resolution and combine it with the conditioning techniques introduced in Sec. 2.2 via concatenation along the channel axis. Fig. 16 in App. J provides python-code for this operation. Note that crop-conditioning and multi-aspect training are complementary operations, and crop-conditioning then only works within the bucket boundaries (usually 64 pixels). For ease of implementation, however, we opt to keep this control parameter for multi-aspect models. 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

Stories, News & Mentions about DIVE

Dive’s Stories on HackerNoon

Dive's News & Mentions