Move people with tiny words.

Apple fanboy here.

• Macs are awesome.

• Their iPhones rock.

• $19 cloths are great.

• $999 stands are amazing.

I love Apple's microcopy even more.

It's like the marketing goddess bit into the Apple logo and blessed the world with microcopy.

I took on a 30-day micro-stalking mission.

Every time I caught myself wasting time on YouTube, I had to visit Apple’s website to learn the secrets of the marketing goddess herself.

We've learned. Golden apples are calling.

Cut the friction

Benefit-first, not commitment-first.

Brands lose customers through friction.

Most brands don't think like customers.

• Brands want sales.

• Brands want newsletter signups.

Here's their microcopy:

• “Buy it now.”

• “Sign up for our newsletter.”

Both are difficult. They ask for big commitments.

People are simple creatures. Want pleasure without commitment.

Apple nails this.

So, instead of highlighting the commitment, they highlight the benefit of the commitment.

Saving on the latest iPhone sounds easier than buying it. Everyone saves, but not everyone buys.

A subtle change in framing reduces friction.

Apple eliminates customer objections to reduce friction.

Less customer friction means simpler processes.

Apple's copy expertly reassures customers about shipping fees and not being home. Apple assures customers that returning faulty products is easy.

Apple knows that talking to a real person is the best way to reduce friction and improve their copy.

Always rhyme

Learn about fine rhyme.

Poets make things beautiful with rhyme.

Copywriters use rhyme to stand out.

Apple’s copywriters have mastered the art of corporate rhyme.

Two techniques are used.

1. Perfect rhyme

Here, rhymes are identical.

2. Imperfect rhyme

Here, rhyming sounds vary.

Apple prioritizes meaning over rhyme.

Apple never forces rhymes that don't fit.

It fits so well that the copy seems accidental.

Add alliteration

Alliteration always entertains.

Alliteration repeats initial sounds in nearby words.

Apple's copy uses alliteration like no other brand I've seen to create a rhyming effect or make the text more fun to read.

For example, in the sentence "Sam saw seven swans swimming," the initial "s" sound is repeated five times. This creates a pleasing rhythm.

Microcopy overuse is like pouring ketchup on a Michelin-star meal.

Alliteration creates a memorable phrase in copywriting. It's subtler than rhyme, and most people wouldn't notice; it simply resonates.

I love how Apple uses alliteration and contrast between "wonders" and "ease".

Assonance, or repeating vowels, isn't Apple's thing.

You ≠ Hero, Customer = Hero

Your brand shouldn't be the hero.

Because they'll be using your product or service, your customer should be the hero of your copywriting. With your help, they should feel like they can achieve their goals.

I love how Apple emphasizes what you can do with the machine in this microcopy.

It's divine how they position their tools as sidekicks to help below.

This one takes the cake:

Dialogue-style writing

Conversational copy engages.

Excellent copy Like sharing gum with a friend.

This helps build audience trust.

Apple does this by using natural connecting words like "so" and phrases like "But that's not all."

Snowclone-proof

The mother of all microcopy techniques.

A snowclone uses an existing phrase or sentence to create a new one. The new phrase or sentence uses the same structure but different words.

It’s usually a well know saying like:

To be or not to be.

This becomes a formula:

To _ or not to _.

Copywriters fill in the blanks with cause-related words. Example:

To click or not to click.

Apple turns "survival of the fittest" into "arrival of the fittest."

It's unexpected and surprises the reader.

So this was fun.

But my fun has just begun.

Microcopy is 21st-century poetry.

I came as an Apple fanboy.

I leave as an Apple fanatic.

Now I’m off to find an apple tree.

Cause you know how it goes.

(Apples, trees, etc.)

This post is a summary. Original post available here.

Here's how to be clear.

“I have only made this letter longer because I have not had the time to make it shorter.” — French mathematician, physicist, inventor, philosopher, and writer Blaise Pascal

Concise.

People want this. We tend to repeat ourselves and use unnecessary words.

Being vague frustrates readers. It focuses their limited attention span on figuring out what you're saying rather than your message.

Edit carefully.

“Examine every word you put on paper. You’ll find a surprising number that don’t serve any purpose.” — American writer, editor, literary critic, and teacher William Zinsser

How do you write succinctly?

Here are three ways to polish your writing.

1. Delete

Your readers will appreciate it if you delete unnecessary words. If a word or phrase is essential, keep it. Don't force it.

Many readers dislike bloated sentences. Ask yourself if cutting a word or phrase will change the meaning or dilute your message.

For example, you could say, “It’s absolutely essential that I attend this meeting today, so I know the final outcome.” It’s better to say, “It’s critical I attend the meeting today, so I know the results.”

Key takeaway

Delete actually, completely, just, full, kind of, really, and totally. Keep the necessary words, cut the rest.

2. Just Do It

Don't tell readers your plans. Your readers don't need to know your plans. Who are you?

Don't say, "I want to highlight our marketing's problems." Our marketing issues are A, B, and C. This cuts 5–7 words per sentence.

Keep your reader's attention on the essentials, not the fluff. What are you doing? You won't lose readers because you get to the point quickly and don't build up.

Key takeaway

Delete words that don't add to your message. Do something, don't tell readers you will.

3. Cut Overlap

You probably repeat yourself unintentionally. You may add redundant sentences when brainstorming. Read aloud to detect overlap.

Remove repetition from your writing. It's important to edit our writing and thinking to avoid repetition.

Key Takeaway

If you're repeating yourself, combine sentences to avoid overlap.

4. Simplify

Write as you would to family or friends. Communicate clearly. Don't use jargon. These words confuse readers.

Readers want specifics, not jargon. Write simply. Done.

Most adults read at 8th-grade level. Jargon and buzzwords make speech fluffy. This confuses readers who want simple language.

Key takeaway

Ensure all audiences can understand you. USA Today's 5th-grade reading level is intentional. They want everyone to understand.

5. Active voice

Subjects perform actions in active voice. When you write in passive voice, the subject receives the action.

For example, “the board of directors decided to vote on the topic” is an active voice, while “a decision to vote on the topic was made by the board of directors” is a passive voice.

Key takeaway

Active voice clarifies sentences. Active voice is simple and concise.

Bringing It All Together

Five tips help you write clearly. Delete, just do it, cut overlap, use simple language, and write in an active voice.

Clear writing is effective. It's okay to occasionally use unnecessary words or phrases. Realizing it is key. Check your writing.

Adding words costs.

Write more concisely. People will appreciate it and read your future articles, emails, and messages. Spending extra time will increase trust and influence.

“Not that the story need be long, but it will take a long while to make it short.” — Naturalist, essayist, poet, and philosopher Henry David Thoreau

Start now.

When it comes to building my business, I’m passionate about a lot of things. I love creating user experiences that simplify branding essentials. I love creating new typefaces and color combinations to inspire logo designers. I love fixing problems to improve my product.

Business marketing isn't my thing.

This is shared by many. Many solopreneurs, like me, struggle to advertise their business and drive themselves to work on it.

Without a lot of promotion, no company will succeed. Marketing is 80% of developing a firm, and when you're starting out, it's even more. Some believe that you shouldn't build anything until you've begun marketing your idea and found enough buyers.

Marketing your business without marketing experience is difficult. There are various outlets and techniques to learn. Instead of figuring out where to start, it's easier to return to your area of expertise, whether that's writing, designing product features, or improving your site's back end. Right?

First, realize that your role as a founder is to market your firm. Being a founder focused on product, I rarely work on it.

Secondly, use these basic methods that have helped me dedicate adequate time and focus to marketing. They're all simple to apply, and they've increased my business's visibility and success.

1. Establish buckets for every task.

You've probably heard to schedule tasks you don't like. As simple as it sounds, blocking a substantial piece of my workday for marketing duties like LinkedIn or Twitter outreach, AppSumo customer support, or SEO has forced me to spend time on them.

Giving me lots of room to focus on product development has helped even more. Sure, this means scheduling time to work on product enhancements after my four-hour marketing sprint.

It also involves making space to store product inspiration and ideas throughout the day so I don't get distracted. This is like the advice to keep a notebook beside your bed to write down your insomniac ideas. I keep fonts, color palettes, and product ideas in folders on my desktop. Knowing these concepts won't be lost lets me focus on marketing in the moment. When I have limited time to work on something, I don't have to conduct the research I've been collecting, so I can get more done faster.

2. Look for various accountability systems

Accountability is essential for self-discipline. To keep focused on my marketing tasks, I've needed various streams of accountability, big and little.

Accountability groups are great for bigger things. SaaS Camp, a sales outreach coaching program, is mine. We discuss marketing duties and results every week. This motivates me to do enough each week to be proud of my accomplishments. Yet hearing what works (or doesn't) for others gives me benchmarks for my own marketing outcomes and plenty of fresh techniques to attempt.

… say, I want to DM 50 people on Twitter about my product — I get that many Q-tips and place them in one pen holder on my desk.

The best accountability group can't watch you 24/7. I use a friend's simple method that shouldn't work (but it does). When I have a lot of marketing chores, like DMing 50 Twitter users about my product, That many Q-tips go in my desk pen holder. After each task, I relocate one Q-tip to an empty pen holder. When you have a lot of minor jobs to perform, it helps to see your progress. You might use toothpicks, M&Ms, or anything else you have a lot of.

3. Continue to monitor your feedback loops

Knowing which marketing methods work best requires monitoring results. As an entrepreneur with little go-to-market expertise, every tactic I pursue is an experiment. I need to know how each trial is doing to maximize my time.

I placed Google and Facebook advertisements on hold since they took too much time and money to obtain Return. LinkedIn outreach has been invaluable to me. I feel that talking to potential consumers one-on-one is the fastest method to grasp their problem areas, figure out my messaging, and find product market fit.

Data proximity offers another benefit. Seeing positive results makes it simpler to maintain doing a work you don't like. Why every fitness program tracks progress.

Marketing's goal is to increase customers and revenues, therefore I've found it helpful to track those metrics and celebrate monthly advances. I provide these updates for extra accountability.

Finding faster feedback loops is also motivating. Marketing brings more clients and feedback, in my opinion. Product-focused founders love that feedback. Positive reviews make me proud that my product is benefitting others, while negative ones provide me with suggestions for product changes that can improve my business.

The best advice I can give a lone creator who's afraid of marketing is to just start. Start early to learn by doing and reduce marketing stress. Start early to develop habits and successes that will keep you going. The sooner you start, the sooner you'll have enough consumers to return to your favorite work.

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.

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.

Only two of the following three options can be achieved: consistency, availability, and partition tolerance

Someone told me that growing from 30 to 60 is the biggest adjustment for a team or business.

I remember thinking, That's random. Each company is unique. I've seen teams of all types confront the same issues during development periods. With new enterprises starting every year, we should be better at navigating growing difficulties.

As a team grows, its processes and systems break down, requiring reorganization or declining results. Why always? Why isn't there a perfect scaling model? Why hasn't that been found?

The Three Things Productive Organizations Must Have

Any company should be efficient and productive. Three items are needed:

First, it must verify that no two team members have conflicting information about the roadmap, strategy, or any input that could affect execution. Teamwork is required.

Second, it must ensure that everyone can receive the information they need from everyone else quickly, especially as teams become more specialized (an inevitability in a developing organization). It requires everyone's accessibility.

Third, it must ensure that the organization can operate efficiently even if a piece is unavailable. It's partition-tolerant.

From my experience with the many teams I've been on, invested in, or advised, achieving all three is nearly impossible. Why a perfect organization model cannot exist is clear after analysis.

The CAP Theorem: What is it?

Eric Brewer of Berkeley discovered the CAP Theorem, which argues that a distributed data storage should have three benefits. One can only have two at once.

The three benefits are consistency, availability, and partition tolerance, which implies that even if part of the system is offline, the remainder continues to work.

This notion is usually applied to computer science, but I've realized it's also true for human organizations. In a post-COVID world, many organizations are hiring non-co-located staff as they grow. CAP Theorem is more important than ever. Growing teams sometimes think they can develop ways to bypass this law, dooming themselves to a less-than-optimal team dynamic. They should adopt CAP to maximize productivity.

Path 1: Consistency and availability equal no tolerance for partitions

Let's imagine you want your team to always be in sync (i.e., for someone to be the source of truth for the latest information) and to be able to share information with each other. Only division into domains will do.

Numerous developing organizations do this, especially after the early stage (say, 30 people) when everyone may wear many hats and be aware of all the moving elements. After a certain point, it's tougher to keep generalists aligned than to divide them into specialized tasks.

In a specialized, segmented team, leaders optimize consistency and availability (i.e. every function is up-to-speed on the latest strategy, no one is out of sync, and everyone is able to unblock and inform everyone else).

Partition tolerance suffers. If any component of the organization breaks down (someone goes on vacation, quits, underperforms, or Gmail or Slack goes down), productivity stops. There's no way to give the team stability, availability, and smooth operation during a hiccup.

Path 2: Partition Tolerance and Availability = No Consistency

Some businesses avoid relying too heavily on any one person or sub-team by maximizing availability and partition tolerance (the organization continues to function as a whole even if particular components fail). Only redundancy can do that. Instead of specializing each member, the team spreads expertise so people can work in parallel. I switched from Path 1 to Path 2 because I realized too much reliance on one person is risky.

What happens after redundancy? Unreliable. The more people may run independently and in parallel, the less anyone can be the truth. Lack of alignment or updated information can lead to people executing slightly different strategies. So, resources are squandered on the wrong work.

Path 3: Partition and Consistency "Tolerance" equates to "absence"

The third, least-used path stresses partition tolerance and consistency (meaning answers are always correct and up-to-date). In this organizational style, it's most critical to maintain the system operating and keep everyone aligned. No one is allowed to read anything without an assurance that it's up-to-date (i.e. there’s no availability).

Always short-lived. In my experience, a business that prioritizes quality and scalability over speedy information transmission can get bogged down in heavy processes that hinder production. Large-scale, this is unsustainable.

Accepting CAP

When two puzzle pieces fit, the third won't. I've watched developing teams try to tackle these difficulties, only to find, as their ancestors did, that they can never be entirely solved. Idealized solutions fail in reality, causing lost effort, confusion, and lower production.

As teams develop and change, they should embrace CAP, acknowledge there is a limit to productivity in a scaling business, and choose the best two-out-of-three path.