Offices without books are like bodies without souls.

After 10 years, my CEO sold his company for $30 million. I've shared many of his lessons on medium. You could ask him anything at his always-open office. He also said we could use his office for meetings while he was away. When I used his office for work, I was always struck by how many books he had.

Books are useful in almost every aspect of learning. Building a business, improving family relationships, learning a new language, a new skill... Books teach, guide, and structure. Whether fiction or nonfiction, books inspire, give ideas, and develop critical thinking skills.

My CEO prefers non-fiction and attends a Friday book club. This article discusses 5 books I found in his office that impacted my life/business. My CEO sold his company for $30 million, but I've built a steady business through blogging and video making.

I recall events and lessons I learned from my CEO and how they relate to each book, and I explain how I applied the book's lessons to my business and life.

Note: This post has no affiliate links.

1. The One Thing — Gary Keller

Gary Keller, a real estate agent, wanted more customers. So he and his team brainstormed ways to get more customers. They decided to write a bestseller about work and productivity. The more people who saw the book, the more customers they'd get.

Gary Keller focused on writing the best book on productivity, work, and efficiency for months. His business experience. Keller's business grew after the book's release.

The author summarizes the book in one question.

"What's the one thing that will make everything else easier or unnecessary?"

When I started my blog and business alongside my 9–5, I quickly identified my one thing: writing. My business relied on it, so it had to be great. Without writing, there was no content, traffic, or business.

My CEO focused on funding when he started his business. Even in his final years, he spent a lot of time on the phone with investors, either to get more money or to explain what he was doing with it. My CEO's top concern was money, and the other super important factors were handled by separate teams.

• Product tech and design

• Incredible customer support team

• Excellent promotion team

• Profitable sales team

My CEO didn't always focus on one thing and ignore the rest. He was on all of those teams when I started my job. He'd start his day in tech, have lunch with marketing, and then work in sales. He was in his office on the phone at night.

He eventually realized his errors. Investors told him he couldn't do everything for the company. If needed, he had to change internally. He learned to let go, mind his own business, and focus for the next four years. Then he sold for $30 million.

The bigger your project/company/idea, the more you'll need to delegate to stay laser-focused. I started something new every few months for 10 years before realizing this. So much to do makes it easy to avoid progress. Once you identify the most important aspect of your project and enlist others' help, you'll be successful.

2. Eat That Frog — Brian Tracy

The author quote sums up book's essence:

Mark Twain said that if you eat a live frog in the morning, it's probably the worst thing that will happen to you all day. Your "frog" is the biggest, most important task you're most likely to procrastinate on.

"Frog" and "One Thing" are both about focusing on what's most important. Eat That Frog recommends doing the most important task first thing in the morning.

I shared my CEO's calendar in an article 10 months ago. Like this:

CEO's average week (some information crossed out for confidentiality)

Notice anything about 8am-8:45am? Almost every day is the same (except Friday). My CEO started his day with a management check-in for 2 reasons:

• Checking in with all managers is cognitively demanding, and my CEO is a morning person.

• In a young startup where everyone is busy, the morning management check-in was crucial. After 10 am, you couldn't gather all managers.

When I started my blog, writing was my passion. I'm a morning person, so I woke up at 6 am and started writing by 6:30 am every day for a year. This allowed me to publish 3 articles a week for 52 weeks to build my blog and audience. After 2 years, I'm not stopping.

3. Deep Work — Cal Newport

Deep work is focusing on a cognitively demanding task without distractions (like a morning management meeting). It helps you master complex information quickly and produce better results faster. In a competitive world 10 or 20 years ago, focus wasn't a huge advantage. Smartphones, emails, and social media made focus a rare, valuable skill.

Most people can't focus anymore. Screens light up, notifications buzz, emails arrive, Instagram feeds... Many people don't realize they're interrupted because it's become part of their normal workflow.

Cal Newport mentions Bill Gates' "Think Weeks" in Deep Work.

Microsoft CEO Bill Gates would isolate himself (often in a lakeside cottage) twice a year to read and think big thoughts.

Inside Bill's Brain on Netflix shows Newport's lakeside cottage. I've always wanted a lakeside cabin to work in. My CEO bought a lakehouse after selling his company, but now he's retired.

As a company grows, you can focus less on it. In a previous section, I said investors told my CEO to get back to basics and stop micromanaging. My CEO's commitment and ability to get work done helped save the company. His deep work and new frameworks helped us survive the corona crisis (more on this later).

The ability to deep work will be a huge competitive advantage in the next century. Those who learn to work deeply will likely be successful while everyone else is glued to their screens, Bluetooth-synced to their watches, and playing Candy Crush on their tablets.

4. The 7 Habits of Highly Effective People — Stephen R. Covey

It took me a while to start reading this book because it seemed like another shallow self-help bible. I kept finding this book when researching self-improvement. I tried it because it was everywhere.

Stephen Covey taught me 2 years ago to have a personal mission statement.

A 7 Habits mission statement describes the life you want to lead, the character traits you want to embody, and the impact you want to have on others. shortform.com

I've had many lunches with my CEO and talked about Vipassana meditation and Sunday forest runs, but I've never seen his mission statement. I'm sure his family is important, though. In the above calendar screenshot, you can see he always included family events (in green) so we could all see those time slots. We couldn't book him then. Although he never spent as much time with his family as he wanted, he always made sure to be on time for his kid's birthday rather than a conference call.

My CEO emphasized his company's mission. Your mission statement should answer 3 questions.

• What does your company do?

• How does it do it?

• Why does your company do it?

As a graphic designer, I had to create mission-statement posters. My CEO hung posters in each office.

5. Measure What Matters — John Doerr

This book is about Andrew Grove's OKR strategy, developed in 1968. When he joined Google's early investors board, he introduced it to Larry Page and Sergey Brin. Google still uses OKR.

Objective Key Results

• Objective: It explains your goals and desired outcome. When one goal is reached, another replaces it. OKR objectives aren't technical, measured, or numerical. They must be clear.

• Key Result should be precise, technical, and measurable, unlike the Objective. It shows if the Goal is being worked on. Time-bound results are quarterly or yearly.

Our company almost sank several times. Sales goals were missed, management failed, and bad decisions were made. On a Monday, our CEO announced we'd implement OKR to revamp our processes.

This was a year before the pandemic, and I'm certain we wouldn't have sold millions or survived without this change. This book impacted the company the most, not just management but all levels. Organization and transparency improved. We reached realistic goals. Happy investors. We used the online tool Gtmhub to implement OKR across the organization.

My CEO's company went from near bankruptcy to being acquired for $30 million in 2 years after implementing OKR.

I hope you enjoyed this booklist. Here's a recap of the 5 books and the lessons I learned from each.

1. The 7 Habits of Highly Effective People — Stephen R. Covey

Have a mission statement that outlines your goals, character traits, and impact on others.

1. Deep Work — Cal Newport

Focus is a rare skill; master it. Deep workers will succeed in our hyper-connected, distracted world.

1. The One Thing — Gary Keller

What can you do that will make everything else easier or unnecessary? Once you've identified it, focus on it.

1. Eat That Frog — Brian Tracy

Identify your most important task the night before and do it first thing in the morning. You'll have a lighter day.

1. Measure What Matters — John Doerr

On a timeline, divide each long-term goal into chunks. Divide those slices into daily tasks (your goals). Time-bound results are quarterly or yearly. Objectives aren't measured or numbered.

Thanks for reading. Enjoy the ride!

Product management wizardry.

Early adopters buy a product even if it hasn't hit the market or has flaws.

Who are the early adopters?

Early adopters try a new technology or product first. Early adopters are interested in trying or buying new technologies and products before others. They're risk-tolerant and can provide initial cash flow and product reviews. They help a company's new product or technology gain social proof.

Early adopters are most common in the technology industry, but they're in every industry. They don't follow the crowd. They seek innovation and report product flaws before mass production. If the product works well, the first users become loyal customers, and colleagues value their opinion.

What to do with early adopters?

They can be used to collect feedback and initial product promotion, first sales, and product value validation.

How to find early followers?

Start with your immediate environment and target audience. Communicate with them to see if they're interested in your value proposition.

1) Innovators (2.5% of the population) are risk-takers seeking novelty. These people are the first to buy new and trendy items and drive social innovation. However, these people are usually elite;

Early adopters (13.5%) are inclined to accept innovations but are more cautious than innovators; they start using novelties when innovators or famous people do;

3) The early majority (34%) is conservative; they start using new products when many people have mastered them. When the early majority accepted the innovation, it became ingrained in people's minds.

4) Attracting 34% of the population later means the novelty has become a mass-market product. Innovators are using newer products;

5) Laggards (16%) are the most conservative, usually elderly people who use the same products.

Stages of new information acceptance

1. The information is strange and rejected by most. Accepted only by innovators;

2. When early adopters join, more people believe it's not so bad; when a critical mass is reached, the novelty becomes fashionable and most people use it.

3. Fascination with a novelty peaks, then declines; the majority and laggards start using it later; novelty becomes obsolete; innovators master something new.

Problems with early implementation

Early adopter sales have disadvantages.

Higher risk of defects

Selling to first-time users increases the risk of defects. Early adopters are often influential, so this can affect the brand's and its products' long-term perception.

Not what was expected

First-time buyers may be disappointed by the product. Marketing messages can mislead consumers, and if the first users believe the company misrepresented the product, this will affect future sales.

Compatibility issues

Some technological advances cause compatibility issues. Consumers may be disappointed if new technology is incompatible with their electronics.

Method 5 WHY

Let's talk about 5 why, a good tool for finding project problems' root causes. This method is also known as the five why rule, method, or questions.

The 5 why technique came from Toyota's lean manufacturing and helps quickly determine a problem's root cause.

On one, two, and three, you simply do this:

1. We identify and frame the issue for which a solution is sought.

2. We frequently ponder this question. The first 2-3 responses are frequently very dull, making you want to give up on this pointless exercise. However, after that, things get interesting. And occasionally it's so fascinating that you question whether you really needed to know.

3. We consider the final response, ponder it, and choose a course of action.

Always do the 5 whys with the customer or team to have a reasonable discussion and better understand what's happening.

And the “five whys” is a wonderful and simplest tool for introspection. With the accumulated practice, it is used almost automatically in any situation like “I can’t force myself to work, the mood is bad in the morning” or “why did I decide that I have no life without this food processor for 20,000 rubles, which will take half of my rather big kitchen.”

An illustration of the five whys

A simple, but real example from my work practice that I think is very indicative, given the participants' low IT skills.  Anonymized, of course.

Users spend too long looking for tender documents.

Why? Because they must search through many company tender documents.

Why? Because the system can't filter department-specific bids.

Why? Because our contract management system requirements didn't include a department-tender link. That's it, right? We'll add a filter and be happy. but still…

why? Because we based the system's requirements on regulations for working with paper tender documents (when they still had envelopes and autopsies), not electronic ones, and there was no search mechanism.

Why? We didn't consider how our work would change when switching from paper to electronic tenders when drafting the requirements.

Now I know what to do in the future. We add a filter, enter department data, and teach users to use it. This is tactical, but strategically we review the same forgotten requirements to make all the necessary changes in a package, plus we include it in the checklist for the acceptance of final requirements for the future.

Errors when using 5 why

Five whys seems simple, but it can be misused.

Popular ones:

1. The accusation of everyone and everything is then introduced. After all, the 5 why method focuses on identifying the underlying causes rather than criticizing others. As a result, at the third step, it is not a good idea to conclude that the system is ineffective because users are stupid and that we can therefore do nothing about it.

2. to fight with all my might so that the outcome would be exactly 5 reasons, neither more nor less. 5 questions is a typical number (it sounds nice, yes), but there could be 3 or 7 in actuality.

3. Do not capture in-between responses. It is difficult to overestimate the power of the written or printed word, so the result is so-so when the focus is lost. That's it, I suppose. Simple, quick, and brilliant, like other project management tools.

Conclusion

Today we analyzed important study elements:

Early adopters and 5 WHY We've analyzed cases and live examples of how these methods help with product research and growth point identification. Next, consider the HADI cycle.

Let's see this once-dominant IT corporation crumble.

What's the capital of Kazakhstan? If you don't know the answer, you can probably find it by Googling. Google Search returned results for Nur-Sultan in 0.66 seconds.

Google is the best search engine I've ever used. Did you know another search engine ruled the Internet? I'm sure you guessed Yahoo!

Google's friendly UI and wide selection of services make it my top choice. Let's explore Yahoo's decline.

Yahoo!

YAHOO stands for Yet Another Hierarchically Organized Oracle. Jerry Yang and David Filo established Yahoo.

Yahoo is primarily a search engine and email provider. It offers News and an advertising platform. It was a popular website in 1995 that let people search the Internet directly. Yahoo began offering free email in 1997 by acquiring RocketMail.

According to a study, Yahoo used Google Search Engine technology until 2000 and then developed its own in 2004.

Yahoo! rejected buying Google for $1 billion

Larry Page and Sergey Brin, Google's founders, approached Yahoo in 1998 to sell Google for $1 billion so they could focus on their studies. Yahoo denied the offer, thinking it was overvalued at the time.

Yahoo realized its error and offered Google $3 billion in 2002, but Google demanded $5 billion since it was more valuable. Yahoo thought $5 billion was overpriced for the existing market.

In 2022, Google is worth $1.56 Trillion.

What happened to Yahoo!

Yahoo refused to buy Google, and Google's valuation rose, making a purchase unfeasible.

Yahoo started losing users when Google launched Gmail. Google's UI was far cleaner than Yahoo's.

Yahoo offered $1 billion to buy Facebook in July 2006, but Zuckerberg and the board sought $1.1 billion. Yahoo rejected, and Facebook's valuation rose, making it difficult to buy.

Yahoo was losing users daily while Google and Facebook gained many. Google and Facebook's popularity soared. Yahoo lost value daily.

Microsoft offered $45 billion to buy Yahoo in February 2008, but Yahoo declined. Microsoft increased its bid to $47 billion after Yahoo said it was too low, but Yahoo rejected it. Then Microsoft rejected Yahoo’s 10% bid increase in May 2008.

In 2015, Verizon bought Yahoo for $4.5 billion, and Apollo Global Management bought 90% of Yahoo's shares for $5 billion in May 2021. Verizon kept 10%.

Yahoo's opportunity to acquire Google and Facebook could have been a turning moment. It declined Microsoft's $45 billion deal in 2008 and was sold to Verizon for $4.5 billion in 2015. Poor decisions and lack of vision caused its downfall. Yahoo's aim wasn't obvious and it didn't stick to a single domain.

Hence, a corporation needs a clear vision and a leader who can see its future.

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My growth-killing mistake

When you initially start developing your audience, you need guidance.

What does my audience like? What do they not like? How can I grow more?

When I started writing two years ago, I inquired daily. Taking cues from your audience to develop more valuable content is a good concept, but it's simple to let them destroy your growth.

A small audience doesn't represent the full picture.

When I had fewer than 100 YouTube subscribers, I tried several video styles and topics. I looked to my audience for what to preserve and what to change.

If my views, click-through rate, or average view % dropped, that topic or style was awful. Avoiding that style helped me grow.

Vlogs, talking head videos on writing, and long-form tutorials didn't fare well.

Since I was small, I've limited the types of films I make. I have decided to make my own videos.

Surprisingly, the videos I avoided making meet or exceed my views, CTR, and audience retention.

A limited audience can't tell you what your tribe wants. Therefore, limiting your innovation will prohibit you from reaching the right audience. Finding them may take longer.

Large Creators Experience The Same Issue

In the last two years, I've heard Vanessa Lau and Cathrin Manning say they felt pigeonholed into generating videos they didn't want to do.

Why does this happen over and over again?

Once you have a popular piece of content, your audience will grow. So when you publish inconsistent material, fewer of your new audience will view it. You interpret the drop in views as a sign that your audience doesn't want the content, so you stop making it.

Repeat this procedure a few times, and you'll create stuff you're not passionate about because you're frightened to publish it.

How to Manage Your Creativity and Audience Development

I'm not recommending you generate random content.

Instead of feeling trapped by your audience, you can cultivate a diverse audience.

Create quality material on a range of topics and styles as you improve. Be creative until you get 100 followers. Look for comments on how to improve your article.

If you observe trends in the types of content that expand your audience, focus 50-75% of your material on those trends. Allow yourself to develop 25% non-performing material.

This method can help you expand your audience faster with your primary trends and like all your stuff. Slowly, people will find 25% of your material, which will boost its performance.

How to Expand Your Audience Without Having More Limited Content

Follow these techniques to build your audience without feeling confined.

• Don't think that you need restrict yourself to what your limited audience prefers.

• Don't let the poor performance of your desired material demotivate you.

• You shouldn't restrict the type of content you publish or the themes you cover when you have less than 100 followers.

• When your audience expands, save 25% of your content for your personal interests, regardless of how well it does.

I've spent over 50 hours researching one-person firms, which interest me. I've found countless one-person enterprises that made millions on the founder's determination and perseverance.

Throughout my investigation, I found three of the most outstanding one-person enterprises. These enterprises show that people who work hard and dedicate themselves to their ideas may succeed.

Eric Barone (@ConcernedApe) created Stardew Valley in 2011 to better his job prospects. Eric loved making the game, in which players inherit a farm, grow crops, raise livestock, make friends with the villagers, and form a family.

Eric handled complete game production, including 3D graphics, animations, and music, to maintain creative control. He stopped job hunting and worked 8-15 hours a day on the game.

Eric developed a Stardew Valley website and subreddit to engage with gamers and get feedback. Eric's devoted community helped him meet Steam's minimum vote requirement for single creators.

Stardew Valley sold 1 million copies in two months after Eric launched it for $15 in 2016. The game has sold 20 million copies and made $300 million.

The game's inexpensive price, outsourcing of PR, marketing, and publication, and loyal player base helped it succeed. Eric has turned down million-dollar proposals from Sony and Nintendo to sell the game and instead updates and improves it. Haunted Chocolatier is Eric's new game.

Is farming not profitable? Ask Stardew Valley creator Eric Barone.

Gary Brewer established BuiltWith to assist users find website technologies and services. BuiltWith boasts 3000 paying customers and $14 million in yearly revenue, making it a significant resource for businesses wishing to generate leads, do customer analytics, obtain business insight, compare websites, or search websites by keyword.

BuiltWith has one full-time employee, Gary, and one or two part-time contractors that help with the blog. Gary handles sales, customer service, and other company functions alone.

BuiltWith acquired popularity through blog promotions and a top Digg ranking. About Us, a domain directory, connected to BuiltWith on every domain page, boosting it. Gary introduced $295–$995 monthly subscriptions to search technology, keywords, and potential consumers in response to customer demand.

Gary uses numerous methods to manage a firm without staff. He spends one to two hours every day answering user queries, most of which are handled quickly by linking to BuiltWiths knowledge store. Gary creates step-by-step essays or videos for complex problems. Gary can focus on providing new features based on customer comments and requests since he makes it easy to unsubscribe.

BuiltWith is entirely automated and successful due to its unique approach and useful offerings. It works for Google, Meta, Amazon, and Twitter.

Digital Inspiration develops Google Documents, Sheets, and Slides plugins. Digital Inspiration, founded by Amit Agarwal, receives 5 million monthly visits and earns $10 million. 40 million individuals have downloaded Digital Inspirations plugins.

Amit started Digital Inspiration by advertising his blog at tech events and getting Indian filter blogs and other newspapers to promote his articles. Amit built plugins and promoted them on the blog once the blog acquired popularity, using ideas from comments, friends, and Reddit. Digital Inspiration has over 20 free and premium plugins.

Mail Merge, Notifications for Google Forms, YouTube Uploader, and Document Studio are some of Digital Inspiration's most popular plugins. Mail Merge allows users to send personalized emails in bulk and track email opens and clicks.

Since Amits manages Digital Inspiration alone, his success is astounding. Amit developed a successful company via hard work and creativity, despite platform dependence. His tale inspires entrepreneurs.

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.