I relocated my notes database from Roam Research to Obsidian earlier this year expecting to stay there for a long. Obsidian is a terrific tool, and I explained my move in that post.

Moving everything to Tana faster than intended. Tana? Why?

Tana is just another note-taking app, but it does it differently. Three note-taking apps existed before Tana:

1. simple note-taking programs like Apple Notes and Google Keep.

2. Roam Research and Obsidian are two graph-style applications that assisted connect your notes.

3. You can create effective tables and charts with data-focused tools like Notion and Airtable.

Tana is the first great software I've encountered that combines graph and data notes. Google Keep will certainly remain my rapid notes app of preference. This Shu Omi video gives a good overview:

Tana handles everything I did in Obsidian with books, people, and blog entries, plus more. I can find book quotes, log my workouts, and connect my thoughts more easily. It should make writing blog entries notes easier, so we'll see.

Tana is now invite-only, but if you're interested, visit their site and sign up. As Shu noted in the video above, the product hasn't been published yet but seems quite polished.

Whether I stay with Tana or not, I'm excited to see where these apps are going and how they can benefit us all.

Many can't concentrate. The first 20% of the day isn't optimized.

Elon Musk, Tony Robbins, and Bill Gates share something:

Morning Routines.

A repeatable morning ritual saves time.

The result?

Time for hobbies.

I'll discuss 5 easy morning routines you can use.

1. Stop pressing snooze

Waking up starts the day. You disrupt your routine by hitting snooze.

One sleep becomes three. Your morning routine gets derailed.

Fix it:

Hide your phone. This disables snooze and wakes you up.

Once awake, staying awake is 10x easier. Simple trick, big results.

2. Drink water

Chronic dehydration is common. Mostly urban, air-conditioned workers/residents.

2% cerebral dehydration causes short-term memory loss.

Dehydration shrinks brain cells.

Drink 3-4 liters of water daily to avoid this.

3. Improve your focus

How to focus better?

Meditation.

• Improve your mood

• Enhance your memory

• increase mental clarity

• Reduce blood pressure and stress

Headspace helps with the habit.

Here's a meditation guide.

1. Sit comfortably

2. Shut your eyes.

3. Concentrate on your breathing

4. Breathe in through your nose

5. Breathe out your mouth.

5 in, 5 out.

Repeat for 1 to 20 minutes.

Here's a beginner's video:

4. Workout

Exercise raises:

• Mental Health

• Effort levels

• focus and memory

15-60 minutes of fun:

• Exercise Lifting

• Running

• Walking

• Stretching and yoga

This helps you now and later.

5. Keep a journal

You have countless thoughts daily. Many quietly steal your focus.

Here’s how to clear these:

Write for 5-10 minutes.

You'll gain 2x more mental clarity.

Recap

5 morning practices for 5x more productivity:

1. Say no to snoozing

2. Hydrate

3. Improve your focus

4. Exercise

5. Journaling

Conclusion

One step starts a thousand-mile journey. Try these easy yet effective behaviors if you have trouble concentrating or have too many thoughts.

Start with one of these behaviors, then add the others. Its astonishing results are instant.

Smarter work yields better results.

17-year-old me worked and studied 20 hours a day. During school breaks, I did coursework and ran a nonprofit at night. Long hours earned me national campaigns, A-list opportunities, and a great career. As I aged, my thoughts changed. Working harder isn't necessarily the key to success.

In some cases, doing less work might lead to better outcomes.

Consider a hard-working small business owner. He can't beat his corporate rivals by working hard. Time's limited. An entrepreneur can work 24 hours a day, 7 days a week, but a rival can invest more money, create a staff, and put in more man hours. Why have small startups done what larger companies couldn't? Facebook paid $1 billion for 13-person Instagram. Snapchat, a 30-person startup, rejected Facebook and Google bids. Luck and efficiency each contributed to their achievement.

The key to success is not working hard. It’s working smart.

Being busy and productive are different. Busy doesn't always equal productive. Productivity is less about time management and more about energy management. Life's work. It's using less energy to obtain more rewards. I cut my work week from 80 to 40 hours and got more done. I value simplicity.

Here are seven activities I gave up in order to be more productive.

1. Give up working extra hours and boost productivity instead.

When did the five-day, 40-hour work week start? Henry Ford, Ford Motor Company founder, experimented with his workers in 1926.

He decreased their daily hours from 10 to 8, and shortened the work week from 6 days to 5. As a result, he saw his workers’ productivity increase.

According to a 1980 Business Roundtable report, Scheduled Overtime Effect on Construction Projects, the more you work, the less effective and productive you become.

“Where a work schedule of 60 or more hours per week is continued longer than about two months, the cumulative effect of decreased productivity will cause a delay in the completion date beyond that which could have been realized with the same crew size on a 40-hour week.” Source: Calculating Loss of Productivity Due to Overtime Using Published Charts — Fact or Fiction

AlterNet editor Sara Robinson cited US military research showing that losing one hour of sleep per night for a week causes cognitive impairment equivalent to a.10 blood alcohol level. You can get fired for showing up drunk, but an all-nighter is fine.

Irrespective of how well you were able to get on with your day after that most recent night without sleep, it is unlikely that you felt especially upbeat and joyous about the world. Your more-negative-than-usual perspective will have resulted from a generalized low mood, which is a normal consequence of being overtired. More important than just the mood, this mind-set is often accompanied by decreases in willingness to think and act proactively, control impulses, feel positive about yourself, empathize with others, and generally use emotional intelligence. Source: The Secret World of Sleep: The Surprising Science of the Mind at Rest

To be productive, don't overwork and get enough sleep. If you're not productive, lack of sleep may be to blame. James Maas, a sleep researcher and expert, said 7/10 Americans don't get enough sleep.

Did you know?

• Leonardo da Vinci slept little at night and frequently took naps.

• Napoleon, the French emperor, had no qualms about napping. He splurged every day.

• Even though Thomas Edison felt self-conscious about his napping behavior, he regularly engaged in this ritual.

• President Franklin D. Roosevelt's wife Eleanor used to take naps before speeches to increase her energy.

• The Singing Cowboy, Gene Autry, was known for taking regular naps in his dressing area in between shows.

• Every day, President John F. Kennedy took a siesta after eating his lunch in bed.

• Every afternoon, oil businessman and philanthropist John D. Rockefeller took a nap in his office.

• It was unavoidable for Winston Churchill to take an afternoon snooze. He thought it enabled him to accomplish twice as much each day.

• Every afternoon around 3:30, President Lyndon B. Johnson took a nap to divide his day into two segments.

• Ronald Reagan, the 40th president, was well known for taking naps as well.

Source: 5 Reasons Why You Should Take a Nap Every Day — Michael Hyatt

Since I started getting 7 to 8 hours of sleep a night, I've been more productive and completed more work than when I worked 16 hours a day. Who knew marketers could use sleep?

2. Refrain from accepting too frequently

Pareto's principle states that 20% of effort produces 80% of results, but 20% of results takes 80% of effort. Instead of working harder, we should prioritize the initiatives that produce the most outcomes. So we can focus on crucial tasks. Stop accepting unproductive tasks.

“The difference between successful people and very successful people is that very successful people say “no” to almost everything.” — Warren Buffett

What should you accept? Why say no? Consider doing a split test to determine if anything is worth your attention. Track what you do, how long it takes, and the consequences. Then, evaluate your list to discover what worked (or didn't) to optimize future chores.

Most of us say yes more often than we should, out of guilt, overextension, and because it's simpler than no. Nobody likes being awful.

Researchers separated 120 students into two groups for a 2012 Journal of Consumer Research study. One group was educated to say “I can't” while discussing choices, while the other used “I don't”.

The students who told themselves “I can’t eat X” chose to eat the chocolate candy bar 61% of the time. Meanwhile, the students who told themselves “I don’t eat X” chose to eat the chocolate candy bars only 36% of the time. This simple change in terminology significantly improved the odds that each person would make a more healthy food choice.

Next time you need to say no, utilize I don't to encourage saying no to unimportant things.

The 20-second rule is another wonderful way to avoid pursuits with little value. Add a 20-second roadblock to things you shouldn't do or bad habits you want to break. Delete social media apps from your phone so it takes you 20 seconds to find your laptop to access them. You'll be less likely to engage in a draining hobby or habit if you add an inconvenience.

Lower the activation energy for habits you want to adopt and raise it for habits you want to avoid. The more we can lower or even eliminate the activation energy for our desired actions, the more we enhance our ability to jump-start positive change. Source: The Happiness Advantage: The Seven Principles of Positive Psychology That Fuel Success and Performance at Work

3. Stop doing everything yourself and start letting people help you

I once managed a large community and couldn't do it alone. The community took over once I burned out. Members did better than I could have alone. I learned about community and user-generated content.

Consumers know what they want better than marketers. Octoly says user-generated videos on YouTube are viewed 10 times more than brand-generated videos. 51% of Americans trust user-generated material more than a brand's official website (16%) or media coverage (22%). (14 percent). Marketers should seek help from the brand community.

Being a successful content marketer isn't about generating the best content, but cultivating a wonderful community.

We should seek aid when needed. We can't do everything. It's best to delegate work so you may focus on the most critical things. Instead of overworking or doing things alone, let others help.

Having friends or coworkers around can boost your productivity even if they can't help.

Just having friends nearby can push you toward productivity. “There’s a concept in ADHD treatment called the ‘body double,’ ” says David Nowell, Ph.D., a clinical neuropsychologist from Worcester, Massachusetts. “Distractable people get more done when there is someone else there, even if he isn’t coaching or assisting them.” If you’re facing a task that is dull or difficult, such as cleaning out your closets or pulling together your receipts for tax time, get a friend to be your body double. Source: Friendfluence: The Surprising Ways Friends Make Us Who We Are

4. Give up striving for perfection

Perfectionism hinders professors' research output. Dr. Simon Sherry, a psychology professor at Dalhousie University, did a study on perfectionism and productivity. Dr. Sherry established a link between perfectionism and productivity.

Perfectionism has its drawbacks.

• They work on a task longer than necessary.

• They delay and wait for the ideal opportunity. If the time is right in business, you are already past the point.

• They pay too much attention to the details and miss the big picture.

Marketers await the right time. They miss out.

The perfect moment is NOW.

5. Automate monotonous chores instead of continuing to do them.

A team of five workers who spent 3%, 20%, 25%, 30%, and 70% of their time on repetitive tasks reduced their time spent to 3%, 10%, 15%, 15%, and 10% after two months of working to improve their productivity.

Last week, I wrote a 15-minute Python program. I wanted to generate content utilizing Twitter API data and Hootsuite to bulk schedule it. Automation has cut this task from a day to five minutes. Whenever I do something more than five times, I try to automate it.

Automate monotonous chores without coding. Skills and resources are nice, but not required.  If you cannot build it, buy it.

People forget time equals money. Manual work is easy and requires little investigation. You can moderate 30 Instagram photographs for your UGC campaign. You need digital asset management software to manage 30,000 photographs and movies from five platforms. Filemobile helps individuals develop more user-generated content. You may buy software to manage rich media and address most internet difficulties.

Hire an expert if you can't find a solution. Spend money to make money, and time is your most precious asset.

Visit GitHub or Google Apps Script library, marketers. You may often find free, easy-to-use open source code.

6. Stop relying on intuition and start supporting your choices with data.

You may optimize your life by optimizing webpages for search engines.

Numerous studies might help you boost your productivity. Did you know individuals are most distracted from midday to 4 p.m.? This is what a Penn State psychology professor found. Even if you can't find data on a particular question, it's easy to run a split test and review your own results.

7. Stop working and spend some time doing absolutely nothing.

Most people don't know that being too focused can be destructive to our work or achievements. The Boston Globe's The Power of Lonely says solo time is excellent for the brain and spirit.

One ongoing Harvard study indicates that people form more lasting and accurate memories if they believe they’re experiencing something alone. Another indicates that a certain amount of solitude can make a person more capable of empathy towards others. And while no one would dispute that too much isolation early in life can be unhealthy, a certain amount of solitude has been shown to help teenagers improve their moods and earn good grades in school. Source: The Power of Lonely

Reflection is vital. We find solutions when we're not looking.

We don't become more productive overnight. It demands effort and practice. Waiting for change doesn't work. Instead, learn about your body and identify ways to optimize your energy and time for a happy existence.

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.

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 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:

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 0.8

It 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 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:

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 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.

Wasn't that a get-rich-quick scheme?

OpenSea processed $2.7 billion in NFT transactions in May 2021.

Fueled by a crypto bull run, rumors of unfathomable riches, and FOMO, Bored Apes, Crypto Punks, and other JPEG-format trash projects flew off the virtual shelves, snatched up by retail investors and celebrities alike.

Over a year later, those shelves are overflowing and warehouses are backlogged. Since March, I've been writing less. In May and June, the bubble was close to bursting.

Apparently, the boom has finally peaked.

This bubble has punctured, and deflation has begun. On Aug. 28, OpenSea processed $9.34 million.

From that euphoric high of $2.7 billion, $9.34 million represents a spectacular decline of 99%.

OpenSea contradicts the data. A trading platform spokeswoman stated the comparison is unfair because it compares the site's highest and lowest trading days. They're the perfect two data points to assess the drop. OpenSea chooses to use ETH volume measures, which ignore crypto's shifting price. Since January 2022, monthly ETH volume has dropped 140%, according to Dune.

Unconvincing counterargument.

Further OpenSea indicators point to declining NFT demand:

• Since January 2022, daily user visits have decreased by 50%.

• Daily transactions have decreased by 50% since the beginning of the year in the same manner.

Off-platform, the floor price of Bored Apes has dropped from 145 ETH to 77 ETH. (At $4,800, a reduction from $700,000 to $370,000). Google search data shows waning popular interest.

It is a trend that will soon vanish, just like laser eyes.

NFTs haven't moved since the new year. Eminem and Snoop Dogg can utilize their apes in music videos or as 3D visuals to perform at the VMAs, but the reality is that NFTs have lost their public appeal and the market is trying to regain its footing.

They've lost popularity because?

Breaking records. The technology still lacks genuine use cases a year and a half after being popular.

They're pricey prestige symbols that have made a few people rich through cunning timing or less-than-savory scams or rug pulling. Over $10.5 billion has been taken through frauds, most of which are NFT enterprises promising to be the next Bored Apes, according to Web3 is going wonderfully. As the market falls, many ordinary investors realize they purchased into a self-fulfilling ecosystem that's halted. Many NFTs are sold between owner-held accounts to boost their price, data suggests. Most projects rely on social media excitement to debut with a high price before the first owners sell and chuckle to the bank. When they don't, the initiative fails, leaving investors high and dry.

NFTs are fading like laser eyes. Most people pushing the technology don't believe in it or the future it may bring. No, they just need a Kool-Aid-drunk buyer.

Everybody wins. When your JPEGs are worth 99% less than when you bought them, you've lost.

When demand reaches zero, many will lose.