More on Web3 & Crypto
ANDREW SINGER
3 years ago
Crypto seen as the ‘future of money’ in inflation-mired countries
Crypto as the ‘future of money' in inflation-stricken nations
Citizens of devalued currencies “need” crypto. “Nice to have” in the developed world.
According to Gemini's 2022 Global State of Crypto report, cryptocurrencies “evolved from what many considered a niche investment into an established asset class” last year.
More than half of crypto owners in Brazil (51%), Hong Kong (51%), and India (54%), according to the report, bought cryptocurrency for the first time in 2021.
The study found that inflation and currency devaluation are powerful drivers of crypto adoption, especially in emerging market (EM) countries:
“Respondents in countries that have seen a 50% or greater devaluation of their currency against the USD over the last decade were more than 5 times as likely to plan to purchase crypto in the coming year.”
Between 2011 and 2021, the real lost 218 percent of its value against the dollar, and 45 percent of Brazilians surveyed by Gemini said they planned to buy crypto in 2019.
The rand (South Africa's currency) has fallen 103 percent in value over the last decade, second only to the Brazilian real, and 32 percent of South Africans expect to own crypto in the coming year. Mexico and India, the third and fourth highest devaluation countries, followed suit.
Compared to the US dollar, Hong Kong and the UK currencies have not devalued in the last decade. Meanwhile, only 5% and 8% of those surveyed in those countries expressed interest in buying crypto.
What can be concluded? Noah Perlman, COO of Gemini, sees various crypto use cases depending on one's location.
‘Need to have' investment in countries where the local currency has devalued against the dollar, whereas in the developed world it is still seen as a ‘nice to have'.
Crypto as money substitute
As an adjunct professor at New York University School of Law, Winston Ma distinguishes between an asset used as an inflation hedge and one used as a currency replacement.
Unlike gold, he believes Bitcoin (BTC) is not a “inflation hedge”. They acted more like growth stocks in 2022. “Bitcoin correlated more closely with the S&P 500 index — and Ether with the NASDAQ — than gold,” he told Cointelegraph. But in the developing world, things are different:
“Inflation may be a primary driver of cryptocurrency adoption in emerging markets like Brazil, India, and Mexico.”
According to Justin d'Anethan, institutional sales director at the Amber Group, a Singapore-based digital asset firm, early adoption was driven by countries where currency stability and/or access to proper banking services were issues. Simply put, he said, developing countries want alternatives to easily debased fiat currencies.
“The larger flows may still come from institutions and developed countries, but the actual users may come from places like Lebanon, Turkey, Venezuela, and Indonesia.”
“Inflation is one of the factors that has and continues to drive adoption of Bitcoin and other crypto assets globally,” said Sean Stein Smith, assistant professor of economics and business at Lehman College.
But it's only one factor, and different regions have different factors, says Stein Smith. As a “instantaneously accessible, traceable, and cost-effective transaction option,” investors and entrepreneurs increasingly recognize the benefits of crypto assets. Other places promote crypto adoption due to “potential capital gains and returns”.
According to the report, “legal uncertainty around cryptocurrency,” tax questions, and a general education deficit could hinder adoption in Asia Pacific and Latin America. In Africa, 56% of respondents said more educational resources were needed to explain cryptocurrencies.
Not only inflation, but empowering our youth to live better than their parents without fear of failure or allegiance to legacy financial markets or products, said Monica Singer, ConsenSys South Africa lead. Also, “the issue of cash and remittances is huge in Africa, as is the issue of social grants.”
Money's future?
The survey found that Brazil and Indonesia had the most cryptocurrency ownership. In each country, 41% of those polled said they owned crypto. Only 20% of Americans surveyed said they owned cryptocurrency.
These markets are more likely to see cryptocurrencies as the future of money. The survey found:
“The majority of respondents in Latin America (59%) and Africa (58%) say crypto is the future of money.”
Brazil (66%), Nigeria (63%), Indonesia (61%), and South Africa (57%). Europe and Australia had the fewest believers, with Denmark at 12%, Norway at 15%, and Australia at 17%.
Will the Ukraine conflict impact adoption?
The poll was taken before the war. Will the devastating conflict slow global crypto adoption growth?
With over $100 million in crypto donations directly requested by the Ukrainian government since the war began, Stein Smith says the war has certainly brought crypto into the mainstream conversation.
“This real-world demonstration of decentralized money's power could spur wider adoption, policy debate, and increased use of crypto as a medium of exchange.”
But the war may not affect all developing nations. “The Ukraine war has no impact on African demand for crypto,” Others loom larger. “Yes, inflation, but also a lack of trust in government in many African countries, and a young demographic very familiar with mobile phones and the internet.”
A major success story like Mpesa in Kenya has influenced the continent and may help accelerate crypto adoption. Creating a plan when everyone you trust fails you is directly related to the African spirit, she said.
On the other hand, Ma views the Ukraine conflict as a sort of crisis check for cryptocurrencies. For those in emerging markets, the Ukraine-Russia war has served as a “stress test” for the cryptocurrency payment rail, he told Cointelegraph.
“These emerging markets may see the greatest future gains in crypto adoption.”
Inflation and currency devaluation are persistent global concerns. In such places, Bitcoin and other cryptocurrencies are now seen as the “future of money.” Not in the developed world, but that could change with better regulation and education. Inflation and its impact on cash holdings are waking up even Western nations.
Read original post here.
Ann
2 years ago
These new DeFi protocols are just amazing.
I've never seen this before.
Focus on native crypto development, not price activity or turmoil.
CT is boring now. Either folks are still angry about FTX or they're distracted by AI. Plus, it's year-end, and people rest for the holidays. 2022 was rough.
So DeFi fans can get inspired by something fresh. Who's building? As I read the Defillama daily roundup, many updates are still on FTX and its contagion.
I've used the same method on their Raises page. Not much happened :(. Maybe my high standards are to fault, but the business may be resting. OK.
The handful I locate might last us till the end of the year. (If another big blowup occurs.)
Hashflow
An on-chain monitor account I follow reported a huge transfer of $HFT from Binance to Jump Tradings.
I was intrigued. Stacking? So I checked and discovered out the project was launched through Binance Launchpad, which has introduced many 100x tokens (although momentarily) in the past, such as GALA and STEPN.
Hashflow appears to be pumpable. Binance launchpad, VC backers, CEX listing immediately. What's the protocol?
Hasflow is intriguing and timely, I discovered. After the FTX collapse, people looked more at DEXs.
Hashflow is a decentralized exchange that connects traders with professional market makers, according to its Binance launchpad description. Post-FTX, market makers lost their MM-ing chance with the collapse of the world's third-largest exchange. Jump and Wintermute back them?
Why is that the case? Hashflow doesn't use bonding curves like standard AMM. On AMMs, you pay more for the following trade because the prior trade reduces liquidity (supply and demand). With market maker quotations, you get a CEX-like experience (fewer coins in the pool, higher price). Stable prices, no MEV frontrunning.
Hashflow is innovative because...
DEXs gained from the FTX crash, but let's be honest: DEXs aren't as good as CEXs. Hashflow will change this.
Hashflow offers MEV protection, which major dealers seek in DEXs. You can trade large amounts without front running and sandwich assaults.
Hasflow offers a user-friendly swapping platform besides MEV. Any chain can be traded smoothly. This is a benefit because DEXs lag CEXs in UX.
Status, timeline:
Wintermute wrote in August that prominent market makers will work on Hashflow. Binance launched a month-long farming session in December. Jump probably participated in this initial sell, therefore we witnessed a significant transfer after the introduction.
Binance began trading HFT token on November 11 (the day FTX imploded). coincidence?)
Tokens are used for community rewards. Perhaps they'd copy dYdX. (Airdrop?). Read their documents about their future plans. Tokenomics doesn't impress me. Governance, rewards, and NFT.
Their stat page details their activity. First came Ethereum, then Arbitrum. For a new protocol in a bear market, they handled a lot of unique users daily.
It’s interesting to see their future. Will they be thriving? Not only against DEXs, but also among the CEXs too.
STFX
I forget how I found STFX. Possibly a Twitter thread concerning Arbitrum applications. STFX was the only new protocol I found interesting.
STFX is a new concept and trader problem-solver. I've never seen this protocol.
STFX allows you copy trades. You give someone your money to trade for you.
It's a marketplace. Traders are everywhere. You put your entry, exit, liquidation point, and trading theory. Twitter has a verification system for socials. Leaderboards display your trading skill.
This service could be popular. Staying disciplined is the hardest part of trading. Sometimes you take-profit too early or too late, or sell at a loss when an asset dumps, then it soon recovers (often happens in crypto.) It's hard to stick to entry-exit and liquidation plans.
What if you could hire someone to run your trade for a little commission? Set-and-forget.
Trading money isn't easy. Trust how? How do you know they won't steal your money?
Smart contracts.
STFX's trader is a vault maker/manager. One trade=one vault. User sets long/short, entrance, exit, and liquidation point. Anyone who agrees can exchange instantly. The smart contract will keep the fund during the trade and limit the manager's actions.
Here's STFX's transaction flow.
Managers and the treasury receive fees. It's a sustainable business strategy that benefits everyone.
I'm impressed by $STFX's planned use. Brilliant priority access. A crypto dealer opens a vault here. Many would join. STFX tokens offer VIP access over those without tokens.
STFX provides short-term trading, which is mind-blowing to me. I agree with their platform's purpose. Crypto market pricing actions foster short-termism. When you trade, the turnover could be larger than long-term holding or trading. 2017 BTC buyers waited 5 years to complete their holdings.
STFX teams simply adapted. Volatility aids trading.
All things about STFX scream Degen. The protocol fully embraces the degen nature of some, if not most, crypto natives.
An enjoyable dApp. Leaderboards are fun for reputation-building. FLEXING COMPETITIONS. You can join for as low as $10. STFX uses Arbitrum, therefore gas costs are low. Alpha procedure completes the degen feeling.
Despite looking like they don't take themselves seriously, I sense a strong business plan below. There is a real demand for the solution STFX offers.
Amelie Carver
3 years ago
Web3 Needs More Writers to Educate Us About It
WRITE FOR THE WEB3
Why web3’s messaging is lost and how crypto winter is growing growth seeds
People interested in crypto, blockchain, and web3 typically read Bitcoin and Ethereum's white papers. It's a good idea. Documents produced for developers and academia aren't always the ideal resource for beginners.
Given the surge of extremely technical material and the number of fly-by-nights, rug pulls, and other scams, it's little wonder mainstream audiences regard the blockchain sector as an expensive sideshow act.
What's the solution?
Web3 needs more than just builders.
After joining TikTok, I followed Amy Suto of SutoScience. Amy switched from TV scriptwriting to IT copywriting years ago. She concentrates on web3 now. Decentralized autonomous organizations (DAOs) are seeking skilled copywriters for web3.
Amy has found that web3's basics are easy to grasp; you don't need technical knowledge. There's a paradigm shift in knowing the basics; be persistent and patient.
Apple is positioning itself as a data privacy advocate, leveraging web3's zero-trust ethos on data ownership.
Finn Lobsien, who writes about web3 copywriting for the Mirror and Twitter, agrees: acronyms and abstractions won't do.
Web3 preached to the choir. Curious newcomers have only found whitepapers and scams when trying to learn why the community loves it. No wonder people resist education and buy-in.
Due to the gender gap in crypto (Crypto Bro is not just a stereotype), it attracts people singing to the choir or trying to cash in on the next big thing.
Last year, the industry was booming, so writing wasn't necessary. Now that the bear market has returned (for everyone, but especially web3), holding readers' attention is a valuable skill.
White papers and the Web3
Why does web3 rely so much on non-growth content?
Businesses must polish and improve their messaging moving into the 2022 recession. The 2021 tech boom provided such a sense of affluence and (unsustainable) growth that no one needed great marketing material. The market found them.
This was especially true for web3 and the first-time crypto believers. Obviously. If they knew which was good.
White papers help. White papers are highly technical texts that walk a reader through a product's details. How Does a White Paper Help Your Business and That White Paper Guy discuss them.
They're meant for knowledgeable readers. Investors and the technical (academic/developer) community read web3 white papers. White papers are used when a product is extremely technical or difficult to assist an informed reader to a conclusion. Web3 uses them most often for ICOs (initial coin offerings).
White papers for web3 education help newcomers learn about the web3 industry's components. It's like sending a first-grader to the Annotated Oxford English Dictionary to learn to read. It's a reference, not a learning tool, for words.
Newcomers can use platforms that teach the basics. These included Coinbase's Crypto Basics tutorials or Cryptochicks Academy, founded by the mother of Ethereum's inventor to get more women utilizing and working in crypto.
Discord and Web3 communities
Discord communities are web3's opposite. Discord communities involve personal communications and group involvement.
Online audience growth begins with community building. User personas prefer 1000 dedicated admirers over 1 million lukewarm followers, and the language is much more easygoing. Discord groups are renowned for phishing scams, compromised wallets, and incorrect information, especially since the crypto crisis.
White papers and Discord increase industry insularity. White papers are complicated, and Discord has a high risk threshold.
Web3 and writing ads
Copywriting is emotional, but white papers are logical. It uses the brain's quick-decision centers. It's meant to make the reader invest immediately.
Not bad. People think sales are sleazy, but they can spot the poor things.
Ethical copywriting helps you reach the correct audience. People who gain a following on Medium are likely to have copywriting training and a readership (or three) in mind when they publish. Tim Denning and Sinem Günel know how to identify a target audience and make them want to learn more.
In a fast-moving market, copywriting is less about long-form content like sales pages or blogs, but many organizations do. Instead, the copy is concise, individualized, and high-value. Tweets, email marketing, and IM apps (Discord, Telegram, Slack to a lesser extent) keep engagement high.
What does web3's messaging lack? As DAOs add stricter copyrighting, narrative and connecting tales seem to be missing.
Web3 is passionate about constructing the next internet. Now, they can connect their passion to a specific audience so newcomers understand why.
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Stephen Rivers
3 years ago
Because of regulations, the $3 million Mercedes-AMG ONE will not (officially) be available in the United States or Canada.
We asked Mercedes to clarify whether "customers" refers to people who have expressed interest in buying the AMG ONE but haven't made a down payment or paid in full for a production slot, and a company spokesperson told that it's the latter – "Actual customers for AMG ONE in the United States and Canada."
The Mercedes-AMG ONE has finally arrived in manufacturing form after numerous delays. This may be the most complicated and magnificent hypercar ever created, but according to Mercedes, those roads will not be found in the United States or Canada.
Despite all of the well-deserved excitement around the gorgeous AMG ONE, there was no word on when US customers could expect their cars. Our Editor-in-Chief became aware of this and contacted Mercedes to clarify the matter. Mercedes-hypercar AMG's with the F1-derived 1,049 HP 1.6-liter V6 engine will not be homologated for the US market, they've confirmed.
Mercedes has informed its customers in the United States and Canada that the ONE will not be arriving to North America after all, as of today, June 1, 2022. The whole text of the letter is included below, so sit back and wait for Mercedes to explain why we (or they) won't be getting (or seeing) the hypercar. Mercedes claims that all 275 cars it wants to produce have already been reserved, with net pricing in Europe starting at €2.75 million (about US$2.93 million at today's exchange rates), before country-specific taxes.
"The AMG-ONE was created with one purpose in mind: to provide a straight technology transfer of the World Championship-winning Mercedes-AMG Petronas Formula 1 E PERFORMANCE drive unit to the road." It's the first time a complete Formula 1 drive unit has been integrated into a road car.
Every component of the AMG ONE has been engineered to redefine high performance, with 1,000+ horsepower, four electric motors, and a blazing top speed of more than 217 mph. While the engine's beginnings are in competition, continuous research and refinement has left us with a difficult choice for the US market.
We determined that following US road requirements would considerably damage its performance and overall driving character in order to preserve the distinctive nature of its F1 powerplant. We've made the strategic choice to make the automobile available for road use in Europe, where it complies with all necessary rules."
If this is the first time US customers have heard about it, which it shouldn't be, we understand if it's a bit off-putting. The AMG ONE could very probably be Mercedes' final internal combustion hypercar of this type.
Nonetheless, we wouldn't be surprised if a few make their way to the United States via the federal government's "Show and Display" exemption provision. This legislation permits the importation of automobiles such as the AMG ONE, but only for a total of 2,500 miles per year.
The McLaren Speedtail, the Koenigsegg One:1, and the Bugatti EB110 are among the automobiles that have been imported under this special rule. We just hope we don't have to wait too long to see the ONE in the United States.
Hasan AboulHasan
2 years ago
High attachment products can help you earn money automatically.
Affiliate marketing is a popular online moneymaker. You promote others' products and get commissions. Affiliate marketing requires constant product promotion.
Affiliate marketing can be profitable even without much promotion. Yes, this is Autopilot Money.
How to Pick an Affiliate Program to Generate Income Autonomously
Autopilot moneymaking requires a recurring affiliate marketing program.
Finding the best product and testing it takes a lot of time and effort.
Here are three ways to choose the best service or product to promote:
Find a good attachment-rate product or service.
When choosing a product, ask if you can easily switch to another service. Attachment rate is how much people like a product.
Higher attachment rates mean better Autopilot products.
Consider promoting GetResponse. It's a 33% recurring commission email marketing tool. This means you get 33% of the customer's plan as long as he pays.
GetResponse has a high attachment rate because it's hard to leave and start over with another tool.
2. Pick a good or service with a lot of affiliate assets.
Check if a program has affiliate assets or creatives before joining.
Images and banners to promote the product in your business.
They save time; I look for promotional creatives. Creatives or affiliate assets are website banners or images. This reduces design time.
3. Select a service or item that consumers already adore.
New products are hard to sell. Choosing a trusted company's popular product or service is helpful.
As a beginner, let people buy a product they already love.
Online entrepreneurs and digital marketers love Systeme.io. It offers tools for creating pages, email marketing, funnels, and more. This product guarantees a high ROI.
Make the product known!
Affiliate marketers struggle to get traffic. Using affiliate marketing to make money is easier than you think if you have a solid marketing strategy.
Your plan should include:
1- Publish affiliate-related blog posts and SEO-optimize them
2- Sending new visitors product-related emails
3- Create a product resource page.
4-Review products
5-Make YouTube videos with links in the description.
6- Answering FAQs about your products and services on your blog and Quora.
7- Create an eCourse on how to use this product.
8- Adding Affiliate Banners to Your Website.
With these tips, you can promote your products and make money on autopilot.
Dmitrii Eliuseev
2 years 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.
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:
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 condaInstall 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 --upgradeDownload 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 1Almost. 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 1Stable 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 1The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:
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:
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):
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 0.8It was far better than my initial drawing:
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:
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 ldmHugging 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:
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.ckptThis 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 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:
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.