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Sam Hickmann

Sam Hickmann

2 years ago

Nomad.xyz got exploited for $190M

(Edited)

More on Web3 & Crypto

Jeff Scallop

Jeff Scallop

1 year ago

The Age of Decentralized Capitalism and DeFi

DeCap is DeFi's killer app.

The Battle of the Moneybags and the Strongboxes (Pieter Bruegel the Elder and Pieter van der Heyden)

“Software is eating the world.” Marc Andreesen, venture capitalist

DeFi. Imagine a blockchain-based alternative financial system that offers the same products and services as traditional finance, but with more variety, faster, more secure, lower cost, and simpler access.

Decentralised finance (DeFi) is a marketplace without gatekeepers or central authority managing the flow of money, where customers engage directly with smart contracts running on a blockchain.

DeFi grew exponentially in 2020/21, with Total Value Locked (an inadequate estimate for market size) topping at $100 billion. After that, it crashed.

The accumulation of funds by individuals with high discretionary income during the epidemic, the novelty of crypto trading, and the high yields given (5% APY for stablecoins on established platforms to 100%+ for risky assets) are among the primary elements explaining this exponential increase.

No longer your older brothers DeFi

Since transactions are anonymous, borrowers had to overcollateralize DeFi 1.0. To borrow $100 in stablecoins, you must deposit $150 in ETH. DeFi 1.0's business strategy raises two problems.

  • Why does DeFi offer interest rates that are higher than those of the conventional financial system?;

  • Why would somebody put down more cash than they intended to borrow?

Maxed out on their own resources, investors took loans to acquire more crypto; the demand for those loans raised DeFi yields, which kept crypto prices increasing; as crypto prices rose, investors made a return on their positions, allowing them to deposit more money and borrow more crypto.

This is a bull market game. DeFi 1.0's overcollateralization speculation is dead. Cryptocrash sank it.

The “speculation by overcollateralisation” world of DeFi 1.0 is dead

At a JP Morgan digital assets conference, institutional investors were more interested in DeFi than crypto or fintech. To me, that shows DeFi 2.0's institutional future.

DeFi 2.0 protocols must handle KYC/AML, tax compliance, market abuse, and cybersecurity problems to be institutional-ready.

Stablecoins gaining market share under benign regulation and more CBDCs coming online in the next couple of years could help DeFi 2.0 separate from crypto volatility.

DeFi 2.0 will have a better footing to finally decouple from crypto volatility

Then we can transition from speculation through overcollateralization to DeFi's genuine comparative advantages: cheaper transaction costs, near-instant settlement, more efficient price discovery, faster time-to-market for financial innovation, and a superior audit trail.

Akin to Amazon for financial goods

Amazon decimated brick-and-mortar shops by offering millions of things online, warehouses by keeping just-in-time inventory, and back-offices by automating invoicing and payments. Software devoured retail. DeFi will eat banking with software.

DeFi is the Amazon for financial items that will replace fintech. Even the most advanced internet brokers offer only 100 currency pairings and limited bonds, equities, and ETFs.

Old banks settlement systems and inefficient, hard-to-upgrade outdated software harm them. For advanced gamers, it's like driving an F1 vehicle on dirt.

It is like driving a F1 car on a dirt road, for the most sophisticated players

Central bankers throughout the world know how expensive and difficult it is to handle cross-border payments using the US dollar as the reserve currency, which is vulnerable to the economic cycle and geopolitical tensions.

Decentralization is the only method to deliver 24h global financial markets. DeFi 2.0 lets you buy and sell startup shares like Google or Tesla. VC funds will trade like mutual funds. Or create a bundle coverage for your car, house, and NFTs. Defi 2.0 consumes banking and creates Global Wall Street.

Defi 2.0 is how software eats banking and delivers the global Wall Street

Decentralized Capitalism is Emerging

90% of markets are digital. 10% is hardest to digitalize. That's money creation, ID, and asset tokenization.

90% of financial markets are already digital. The only problem is that the 10% left is the hardest to digitalize

Debt helped Athens construct a powerful navy that secured trade routes. Bonds financed the Renaissance's wars and supply chains. Equity fueled industrial growth. FX drove globalization's payments system. DeFi's plans:

If the 20th century was a conflict between governments and markets over economic drivers, the 21st century will be between centralized and decentralized corporate structures.

Offices vs. telecommuting. China vs. onshoring/friendshoring. Oil & gas vs. diverse energy matrix. National vs. multilateral policymaking. DAOs vs. corporations Fiat vs. crypto. TradFi vs.

An age where the network effects of the sharing economy will overtake the gains of scale of the monopolistic competition economy

This is the dawn of Decentralized Capitalism (or DeCap), an age where the network effects of the sharing economy will reach a tipping point and surpass the scale gains of the monopolistic competition economy, further eliminating inefficiencies and creating a more robust economy through better data and automation. DeFi 2.0 enables this.

DeFi needs to pay the piper now.

DeCap won't be Web3.0's Shangri-La, though. That's too much for an ailing Atlas. When push comes to shove, DeFi folks want to survive and fight another day for the revolution. If feasible, make a tidy profit.

Decentralization wasn't meant to circumvent regulation. It circumvents censorship. On-ramp, off-ramp measures (control DeFi's entry and exit points, not what happens in between) sound like a good compromise for DeFi 2.0.

The sooner authorities realize that DeFi regulation is made ex-ante by writing code and constructing smart contracts with rules, the faster DeFi 2.0 will become the more efficient and safe financial marketplace.

More crucially, we must boost system liquidity. DeFi's financial stability risks are downplayed. DeFi must improve its liquidity management if it's to become mainstream, just as banks rely on capital constraints.

This reveals the complex and, frankly, inadequate governance arrangements for DeFi protocols. They redistribute control from tokenholders to developers, which is bad governance regardless of the economic model.

But crypto can only ride the existing banking system for so long before forming its own economy. DeFi will upgrade web2.0's financial rails till then.

Alex Bentley

Alex Bentley

2 years ago

Why Bill Gates thinks Bitcoin, crypto, and NFTs are foolish

Microsoft co-founder Bill Gates assesses digital assets while the bull is caged.

Bill Gates is well-respected.

Reasonably. He co-founded and led Microsoft during its 1980s and 1990s revolution.

After leaving Microsoft, Bill Gates pursued other interests. He and his wife founded one of the world's largest philanthropic organizations, Bill & Melinda Gates Foundation. He also supports immunizations, population control, and other global health programs.

When Gates criticized Bitcoin, cryptocurrencies, and NFTs, it made news.

Bill Gates said at the 58th Munich Security Conference...

“You have an asset class that’s 100% based on some sort of greater fool theory that somebody’s going to pay more for it than I do.”

Gates means digital assets. Like many bitcoin critics, he says digital coins and tokens are speculative.

And he's not alone. Financial experts have dubbed Bitcoin and other digital assets a "bubble" for a decade.

Gates also made fun of Bored Ape Yacht Club and NFTs, saying, "Obviously pricey digital photographs of monkeys will help the world."

Why does Bill Gates dislike digital assets?

According to Gates' latest comments, Bitcoin, cryptos, and NFTs aren't good ways to hold value.

Bill Gates is a better investor than Elon Musk.

“I’m used to asset classes, like a farm where they have output, or like a company where they make products,” Gates said.

The Guardian claimed in April 2021 that Bill and Melinda Gates owned the most U.S. farms. Over 242,000 acres of farmland.

The Gates couple has enough farmland to cover Hong Kong.

Bill Gates is a classic investor. He wants companies with an excellent track record, strong fundamentals, and good management. Or tangible assets like land and property.

Gates prefers the "old economy" over the "new economy"

Gates' criticism of Bitcoin and cryptocurrency ventures isn't surprising. These digital assets lack all of Gates's investing criteria.

Volatile digital assets include Bitcoin. Their costs might change dramatically in a day. Volatility scares risk-averse investors like Gates.

Gates has a stake in the old financial system. As Microsoft's co-founder, Gates helped develop a dominant tech company.

Because of his business, he's one of the world's richest men.

Bill Gates is invested in protecting the current paradigm.

He won't invest in anything that could destroy the global economy.

When Gates criticizes Bitcoin, cryptocurrencies, and NFTs, he's suggesting they're a hoax. These soapbox speeches are one way he protects his interests.

Digital assets aren't a bad investment, though. Many think they're the future.

Changpeng Zhao and Brian Armstrong are two digital asset billionaires. Two crypto exchange CEOs. Binance/Coinbase.

Digital asset revolution won't end soon.

If you disagree with Bill Gates and plan to invest in Bitcoin, cryptocurrencies, or NFTs, do your own research and understand the risks.

But don’t take Bill Gates’ word for it.

He’s just an old rich guy with a lot of farmland.

He has a lot to lose if Bitcoin and other digital assets gain global popularity.


This post is a summary. Read the full article here.

Trent Lapinski

Trent Lapinski

2 years ago

What The Hell Is A Crypto Punk?

We are Crypto Punks, and we are changing your world.

A “Crypto Punk” is a new generation of entrepreneurs who value individual liberty and collective value creation and co-creation through decentralization. While many Crypto Punks were born and raised in a digital world, some of the early pioneers in the crypto space are from the Oregon Trail generation. They were born to an analog world, but grew up simultaneously alongside the birth of home computing, the Internet, and mobile computing.

A Crypto Punk’s world view is not the same as previous generations. By the time most Crypto Punks were born everything from fiat currency, the stock market, pharmaceuticals, the Internet, to advanced operating systems and microprocessing were already present or emerging. Crypto Punks were born into pre-existing conditions and systems of control, not governed by logic or reason but by greed, corporatism, subversion, bureaucracy, censorship, and inefficiency.

All Systems Are Human Made

Crypto Punks understand that all systems were created by people and that previous generations did not have access to information technologies that we have today. This is why Crypto Punks have different values than their parents, and value liberty, decentralization, equality, social justice, and freedom over wealth, money, and power. They understand that the only path forward is to work together to build new and better systems that make the old world order obsolete.

Unlike the original cypher punks and cyber punks, Crypto Punks are a new iteration or evolution of these previous cultures influenced by cryptography, blockchain technology, crypto economics, libertarianism, holographics, democratic socialism, and artificial intelligence. They are tasked with not only undoing the mistakes of previous generations, but also innovating and creating new ways of solving complex problems with advanced technology and solutions.

Where Crypto Punks truly differ is in their understanding that computer systems can exist for more than just engagement and entertainment, but actually improve the human condition by automating bureaucracy and inefficiency by creating more efficient economic incentives and systems.

Crypto Punks Value Transparency and Do Not Trust Flawed, Unequal, and Corrupt Systems

Crypto Punks have a strong distrust for inherently flawed and corrupt systems. This why Crypto Punks value transparency, free speech, privacy, and decentralization. As well as arguably computer systems over human powered systems.

Crypto Punks are the children of the Great Recession, and will never forget the economic corruption that still enslaves younger generations.

Crypto Punks were born to think different, and raised by computers to view reality through an LED looking glass. They will not surrender to the flawed systems of economic wage slavery, inequality, censorship, and subjection. They will literally engineer their own unstoppable financial systems and trade in cryptography over fiat currency merely to prove that belief systems are more powerful than corruption.

Crypto Punks are here to help achieve freedom from world governments, corporations and bankers who monetizine our data to control our lives.

Crypto Punks Decentralize

Despite all the evils of the world today, Crypto Punks know they have the power to create change. This is why Crypto Punks are optimistic about the future despite all the indicators that humanity is destined for failure.

Crypto Punks believe in systems that prioritize people and the planet above profit. Even so, Crypto Punks still believe in capitalistic systems, but only capitalistic systems that incentivize good behaviors that do not violate the common good for the sake of profit.

Cyber Punks Are Co-Creators

We are Crypto Punks, and we will build a better world for all of us. For the true price of creation is not in US dollars, but through working together as equals to replace the unequal and corrupt greedy systems of previous generations.

Where they have failed, Crypto Punks will succeed. Not because we want to, but because we have to. The world we were born into is so corrupt and its systems so flawed and unequal we were never given a choice.

We have to be the change we seek.

We are Crypto Punks.

Either help us, or get out of our way.

Are you a Crypto Punk?

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Hannah Elliott

2 years ago

Pebble Beach Auto Auctions Set $469M Record

The world's most prestigious vintage vehicle show included amazing autos and record-breaking sums.

This 1932 Duesenberg J Figoni Sports Torpedo earned Best of Show in 2022.

This 1932 Duesenberg J Figoni Sports Torpedo earned Best of Show in 2022.

David Paul Morris (DPM)/Bloomberg

2022 Pebble Beach Concours d'Elegance winner was a pre-war roadster.

Lee Anderson's 1932 Duesenberg J Figoni Sports Torpedo won Best of Show at Pebble Beach Golf Links near Carmel, Calif., on Sunday. First American win since 2013.

Sandra Button, chairperson of the annual concours, said the car, whose chassis and body had been separated for years, "marries American force with European style." "Its resurrection story is passionate."

Pebble Beach Concours d'Elegance Auction

Pebble Beach Concours d'Elegance Auction

Since 1950, the Pebble Beach Concours d'Elegance has welcomed the world's most costly collectable vehicles for a week of parties, auctions, rallies, and high-roller meetings. The cold, dreary weather highlighted the automobiles' stunning lines and hues.

DPM/Bloomberg

A visitor photographs a 1948 Ferrari 166 MM Touring Barchetta

A visitor photographs a 1948 Ferrari 166 MM Touring Barchetta. This is one of 25 Ferraris manufactured in the years after World War II. First shown at the 1948 Turin Salon. Others finished Mille Miglia and Le Mans, which set the tone for Ferrari racing for years.

DPM/Bloomberg

This year's frontrunners were ultra-rare pre-war and post-war automobiles with long and difficult titles, such a 1937 Talbot-Lago T150C-SS Figoni & Falaschi Teardrop Coupe and a 1951 Talbot-Lago T26 Grand Sport Stabilimenti Farina Cabriolet.

The hefty, enormous coaches inspire visions of golden pasts when mysterious saloons swept over the road with otherworldly style, speed, and grace. Only the richest and most powerful people, like Indian maharaja and Hollywood stars, owned such vehicles.

Antonio Chopitea, a Peruvian sugar tycoon, ordered a new Duesenberg in Paris. Hemmings says the two-tone blue beauty was moved to the US and dismantled in the 1960s. Body and chassis were sold separately and rejoined decades later in a three-year, prize-winning restoration.

The concours is the highlight of Monterey Car Week, a five-day Super Bowl for car enthusiasts. Early events included Porsche and Ferrari displays, antique automobile races, and new-vehicle debuts. Many auto executives call Monterey Car Week the "new auto show."

Many visitors were drawn to the record-breaking auctions.

A 1969 Porsche 908/02 auctioned for $4.185 million.

A 1969 Porsche 908/02 auctioned for $4.185 million. Flat-eight air-cooled engine, 90.6-inch wheelbase, 1,320-pound weight. Vic Elford, Richard Attwood, Rudi Lins, Gérard Larrousse, Kurt Ahrens Jr., Masten Gregory, and Pedro Rodriguez drove it, according to Gooding.

DPM/Bloomberg

The 1931 Bentley Eight Liter Sports Tourer doesn't meet its reserve

The 1931 Bentley Eight Liter Sports Tourer doesn't meet its reserve. Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. This powerful open-top tourer is one of W.O. Bentley's 100 automobiles. Only 80 remain.

DPM/Bloomberg

The final auction on Aug. 21 brought in $456.1 million, breaking the previous high of $394.48 million established in 2015 in Monterey. “The week put an exclamation point on what has been an exceptional year for the collector automobile market,” Hagerty analyst John Wiley said.

Many cars that go unsold at public auction are sold privately in the days after. After-sales pushed the week's haul to $469 million on Aug. 22, up 18.9% from 2015's record.

In today's currencies, 2015's record sales amount to $490 million, Wiley noted. The dollar is degrading faster than old autos.

Still, 113 million-dollar automobiles sold. The average car sale price was $583,211, up from $446,042 last year, while multimillion-dollar hammer prices made up around 75% of total sales.

Industry insiders and market gurus expected that stock market volatility, the crisis in Ukraine, and the dollar-euro exchange rate wouldn't influence the world's biggest spenders.

Classic.com's CEO said there's no hint of a recession in an e-mail. Big sales and crowds.

Ticket-holders wore huge hats, flowery skirts, and other Kentucky Derby-esque attire.

Ticket-holders wore huge hats, flowery skirts, and other Kentucky Derby-esque attire. Coffee, beverages, and food are extra.

DPM/Bloomberg

Mercedes-Benz 300 SL Gullwing, 1955. Mercedes produced the two-seat gullwing coupe from 1954–1957 and the roadster from 1957–1963

Mercedes-Benz 300 SL Gullwing, 1955. Mercedes produced the two-seat gullwing coupe from 1954–1957 and the roadster from 1957–1963. It was once West Germany's fastest and most powerful automobile. You'd be hard-pressed to locate one for less $1 million.

DPM/Bloomberg

1955 Ferrari 410 Sport sold for $22 million at RM Sotheby's. It sold a 1937 Mercedes-Benz 540K Sindelfingen Roadster for $9.9 million and a 1924 Hispano-Suiza H6C Transformable Torpedo for $9.245 million. The family-run mansion sold $221.7 million with a 90% sell-through rate, up from $147 million in 2021. This year, RM Sotheby's cars averaged $1.3 million.

Not everyone saw such great benefits.

Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. 1937 Bugatti Type 57SC Atalante, 1990 Ferrari F40, and 1994 Bugatti EB110 Super Sport were top sellers.

The 1969 Autobianchi A112 Bertone.

The 1969 Autobianchi A112 Bertone. This idea two-seater became a Hot Wheels toy but was never produced. It has a four-speed manual drive and an inline-four mid-engine arrangement like the Lamborghini Miura.

DPM/Bloomberg

1956 Porsche 356 A Speedster at Gooding & Co. The Porsche 356 is a lightweight,

1956 Porsche 356 A Speedster at Gooding & Co. The Porsche 356 is a lightweight, rear-engine, rear-wheel drive vehicle that lacks driving power but is loved for its rounded, Beetle-like hardtop coupé and open-top versions.

DPM/Bloomberg

Mecum sold $50.8 million with a 64% sell-through rate, down from $53.8 million and 77% in 2021. Its top lot, a 1958 Ferrari 250 GT 'Tour de France' Alloy Coupe, sold for $2.86 million, but its average price was $174,016.

Bonhams had $27.8 million in sales with an 88% sell-through rate. The same sell-through generated $35.9 million in 2021.

Gooding & Co. and RM Sotheby's posted all 10 top sales, leaving Bonhams, Mecum, and Hagerty-owned Broad Arrow fighting for leftovers. Six of the top 10 sellers were Ferraris, which remain the gold standard for collectable automobiles. Their prices have grown over decades.

Classic.com's Calle claimed RM Sotheby's "stole the show," but "BroadArrow will be a force to reckon with."

Although pre-war cars were hot, '80s and '90s cars showed the most appreciation and attention. Generational transition and new buyer profile."

2022 Pebble Beach Concours d'Elegance judges inspect 1953 Siata 208

2022 Pebble Beach Concours d'Elegance judges inspect 1953 Siata 208. The rounded coupe was introduced at the 1952 Turin Auto Show in Italy and is one of 18 ever produced. It sports a 120hp Fiat engine, five-speed manual transmission, and alloy drum brakes. Owners liked their style, but not their reliability.

DPM/Bloomberg

The Czinger 21 CV Max at Pebble Beach

The Czinger 21 CV Max at Pebble Beach. Monterey Car Week concentrates on historic and classic automobiles, but modern versions like this Czinger hypercar also showed.

DPM/Bloomberg

The 1932 Duesenberg J Figoni Sports Torpedo won Best in Show in 2022

The 1932 Duesenberg J Figoni Sports Torpedo won Best in Show in 2022. Lee and Penny Anderson of Naples, Fla., own the once-separate-chassis-from-body automobile.

DPM/Bloomberg

Dmitrii Eliuseev

Dmitrii Eliuseev

1 year ago

Creating Images on Your Local PC Using Stable Diffusion AI

Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Image generated by Stable Diffusion 2.1

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

  • A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

  • An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

  • A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

Model architecture, Source © https://arxiv.org/pdf/2112.10752.pdf

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

  • Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

  • Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

  • Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

  • Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

The SD V1.4 first example, Image by the author

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

The SD V1.4 second example, Image by the author

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

An image sketch, Image by the author

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

The SD V1.4 third example, Image by the author

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

  • Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

  • Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

Stable Diffusion UI © Image by author

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

  • alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

  • a new depth model that may be used to the output of image-to-image generation.

  • a revolutionary upscaling technique that can quadruple the resolution of an image.

  • Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

A Stable Diffusion 2.1 example

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion 4X upscaler running on CPU © Image by author

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

“Modern art painting” © Google’s Image search result

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

  • I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

  • Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

  • It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

  • When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

James White

James White

2 years ago

I read three of Elon Musk's suggested books (And His Taste Is Incredible)

A reading list for successful people

Daniel Oberhaus via Flickr

Elon Musk reads and talks. So, one learns. Many brilliant individuals & amazing literature.

This article recommends 3 Elon Musk novels. All of them helped me succeed. Hope they'll help you.

Douglas Adams's The Hitchhiker's Guide to the Galaxy

Page Count: 193
Rating on Goodreads: 4.23

Arthur Dent is pulled off Earth by a buddy seconds before it's razed for a cosmic motorway. The trio hitchhikes through space and gets into problems.

I initially read Hitchhiker's as a child. To evade my mum, I'd read with a flashlight under the covers. She'd scold at me for not sleeping on school nights when she found out. Oops.

The Hitchhiker's Guide to the Galaxy is lighthearted science fiction.

Goodreads

My favorite book quotes are:

  • “Space is big. You won’t believe how vastly, hugely, mind-bogglingly big it is. I mean, you may think it’s a long way down the road to the chemist’s, but that’s just peanuts to space.”

  • “Far out in the uncharted backwaters of the unfashionable end of the western spiral arm of the Galaxy lies a small unregarded yellow sun. Orbiting this at a distance of roughly ninety-two million miles is an utterly insignificant little blue-green planet whose ape-descended life forms are so amazingly primitive that they still think digital watches are a pretty neat idea.”

  • “On planet Earth, man had always assumed that he was more intelligent than dolphins because he had achieved so much — the wheel, New York, wars, and so on — whilst all the dolphins had ever done was muck about in the water having a good time. But conversely, the dolphins had always believed that they were far more intelligent than man — for precisely the same reasons.”

the Sun Tzu book The Art Of War

Page Count: 273
Rating on Goodreads: 3.97

It's a classic. You may apply The Art of War's ideas to (nearly) every facet of life. Ex:

  • Pick your fights.

  • Keep in mind that timing is crucial.

  • Create a backup plan in case something goes wrong.

  • Obstacles provide us a chance to adapt and change.

This book was my first. Since then, I'm a more strategic entrepreneur. Excellent book. And read it ASAP!

Goodreads

My favorite book quotes are:

  • “Victorious warriors win first and then go to war, while defeated warriors go to war first and then seek to win.”

  • “Engage people with what they expect; it is what they are able to discern and confirms their projections. It settles them into predictable patterns of response, occupying their minds while you wait for the extraordinary moment — that which they cannot anticipate.”

  • “If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained, you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

Peter Thiel's book Zero to One

Page Count: 195
Rating on Goodreads: 4.18

Peter argues the best money-making strategies are typically unproven. Entrepreneurship should never have a defined path to success. Whoever says differently is lying.

Zero to One explores technology and society. Peter is a philosophy major and law school graduate, which informs the work.

Peters' ideas, depth, and intellect stood out in Zero to One. It's a top business book.

Goodreads

My favorite book quotes are:

  • “The most valuable businesses of coming decades will be built by entrepreneurs who seek to empower people rather than try to make them obsolete.”

  • “The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. And the next Mark Zuckerberg won’t create a social network. If you are copying these guys, you aren’t learning from them.”

  • “If your goal is to never make a mistake in your life, you shouldn’t look for secrets. The prospect of being lonely but right — dedicating your life to something that no one else believes in — is already hard. The prospect of being lonely and wrong can be unbearable.”