Late in 2018, I published my first Medium article, but I didn't start writing seriously until 2019. Since then, I've written more than 240 articles, earned over $50,000 through Medium's Partner Program, and had over 2.5 million page views.

Write A Lot

Most people don't have the patience and persistence for this simple writing secret:

Write + Write + Write = possible success

Writing more improves your skills.

The more articles you publish, the more likely one will go viral.

If you only publish once a month, you have no views. If you publish 10 or 20 articles a month, your success odds increase 10- or 20-fold.

Tim Denning, Ayodeji Awosika, Megan Holstein, and Zulie Rane. Medium is their jam. How are these authors alike? They're productive and consistent. They're prolific.

80% is publishable

Many writers battle perfectionism. 

To succeed as a writer, you must publish often. You'll never publish if you aim for perfection.

Adopt the 80 percent-is-good-enough mindset to publish more. It sounds terrible, but it'll boost your writing success.

Your work won't be perfect. Always improve. Waiting for perfection before publishing will take a long time.

Second, readers are your true critics, not you. What you consider "not perfect" may be life-changing for the reader. Don't let perfectionism hinder the reader.

Don't let perfectionism hinder the reader. ou don't want to publish mediocre articles. When the article is 80% done, publish it. Don't spend hours editing. Realize it. Get feedback. Only this will work.

Make Your Headline Irresistible

We all judge books by their covers, despite the saying. And headlines. Readers, including yourself, judge articles by their titles. We use it to decide if an article is worth reading.

Make your headlines irresistible. Want more article views? Then, whether you like it or not, write an attractive article title.

Many high-quality articles are collecting dust because of dull, vague headlines. It didn't make the reader click.

As a writer, you must do more than produce quality content. You must also make people click on your article. This is a writer's job. How to create irresistible headlines:

Curiosity makes readers click. Here's a tempting example...

• Example: What Women Actually Look For in a Guy, According to a Huge Study by Luba Sigaud

Use Numbers: Click-bait lists. I mean, which article would you click first? ‘Some ways to improve your productivity’ or ’17 ways to improve your productivity.’ Which would I click?

• Example: 9 Uncomfortable Truths You Should Accept Early in Life by Sinem Günel

Most headlines are dull. If you want clicks, get 'sexy'. Buzzword-ify. Invoke emotion. Trendy words.

• Example: 20 Realistic Micro-Habits To Live Better Every Day by Amardeep Parmar

Concise paragraphs

Our culture lacks focus. If your headline gets a click, keep paragraphs short to keep readers' attention.

Some writers use 6–8 lines per paragraph, but I prefer 3–4. Longer paragraphs lose readers' interest.

A writer should help the reader finish an article, in my opinion. I consider it a job requirement. You can't force readers to finish an article, but you can make it 'snackable'

Help readers finish an article with concise paragraphs, interesting subheadings, exciting images, clever formatting, or bold attention grabbers.

Work And Move On

I've learned over the years not to get too attached to my articles. Many writers report a strange phenomenon:

The articles you're most excited about usually bomb, while the ones you're not tend to do well.

This isn't always true, but I've noticed it in my own writing. My hopes for an article usually make it worse. The more objective I am, the better an article does.

Let go of a finished article. 40 or 40,000 views, whatever. Now let the article do its job. Onward. Next story. Start another project.

Disregard Haters

Online content creators will encounter haters, whether on YouTube, Instagram, or Medium. More views equal more haters. Fun, right?

As a web content creator, I learned:

Don't debate haters. Never.

It's a mistake I've made several times. It's tempting to prove haters wrong, but they'll always find a way to be 'right'. Your response is their fuel.

I smile and ignore hateful comments. I'm indifferent. I won't enter a negative environment. I have goals, money, and a life to build. "I'm not paid to argue," Drake once said.

Use Grammarly

Grammarly saves me as a non-native English speaker. You know Grammarly. It shows writing errors and makes article suggestions.

As a writer, you need Grammarly. I have a paid plan, but their free version works. It improved my writing greatly.

Put The Reader First, Not Yourself

Many writers write for themselves. They focus on themselves rather than the reader.

Ask yourself:

This article teaches what? How can they be entertained or educated?

Personal examples and experiences improve writing quality. Don't focus on yourself.

It's not about you, the content creator. Reader-focused. Putting the reader first will change things.

Extreme ownership: Stop blaming others

I remember writing a lot on Medium but not getting many views. I blamed Medium first. Poor algorithm. Poor publishing. All sucked.

Instead of looking at what I could do better, I blamed others.

When you blame others, you lose power. Owning your results gives you power.

As a content creator, you must take full responsibility. Extreme ownership means 100% responsibility for work and results.

You don’t blame others. You don't blame the economy, president, platform, founders, or audience. Instead, you look for ways to improve. Few people can do this.

Blaming is useless. Zero. Taking ownership of your work and results will help you progress. It makes you smarter, better, and stronger.

Instead of blaming others, you'll learn writing, marketing, copywriting, content creation, productivity, and other skills. Game-changer.

I started smoking in 2002 at IIT BHU. Most of us thought it was enjoyable at first. I didn't realize the cost later.

In 2005, during my final semester, I lost my father. Suddenly, I felt more accountable for my mother and myself.

I quit before starting my first job in Bangalore. I didn't see any smoking friends in my hometown for 2 months before moving to Bangalore.

For the next 5-6 years, I had no regimen and smoked only when drinking.

Due to personal concerns, I started smoking again after my 2011 marriage. Now smoking was a constant guilty pleasure.

I smoked 3-4 cigarettes a day, but never in front of my family or on weekends. I used to excuse this with pride! First office ritual: smoking. Even with guilt, I couldn't stop this time because of personal concerns.

After 8-9 years, in mid 2019, a personal development program solved all my problems. I felt complete in myself. After this, I just needed one cigarette each day.

The hardest thing was leaving this final cigarette behind, even though I didn't want it.

James Clear's Atomic Habits was published last year. I'd only read 2-3 non-tech books before reading this one in August 2021. I knew everything but couldn't use it.

In April 2022, I realized the compounding effect of a bad habit thanks to my subconscious mind. 1 cigarette per day (excluding weekends) equals 240 = 24 packs per year, which is a lot. No matter how much I did, it felt negative.

Then I applied the 2nd principle of this book, identifying the trigger. I tried to identify all the major triggers of smoking. I found social drinking is one of them & If I am able to control it during that time, I can easily control it in other situations as well. Going further whenever I drank, I was pre-determined to ignore the craving at any cost. Believe me, it was very hard initially but gradually this craving started fading away even with drinks.

I've been smoke-free for 3 months. Now I know a bad habit's effects. After realizing the power of habits, I'm developing other good habits which I ignored all my life.

Prudence is advised. Be debt-free. Be frugal. Spend less.

This advice sounds nice, but it rarely works.

Most people never learn these two money rules. Both approaches will impact how you see personal finance.

It may safeguard you from inflation or the inability to preserve money.

Let’s dive in.

#1: Making long-term debt your ally

High-interest debt hurts consumers. Many credit cards carry 25% yearly interest (or more), so always pay on time. Otherwise, you’re losing money.

Some low-interest debt is good. Especially when buying an appreciating asset with borrowed money.

Inflation helps you.

If you borrow $800,000 at 3% interest and invest it at 7%, you'll make $32,000 (4%).

As money loses value, fixed payments get cheaper. Your assets' value and cash flow rise.

The never-in-debt crowd doesn't know this. They lose money paying off mortgages and low-interest loans early when they could have bought assets instead.

#2: How To Buy Or Build Assets To Make Inflation Irrelevant

Dozens of studies demonstrate actual wage growth is static; $2.50 in 1964 was equivalent to $22.65 now.

These reports never give solutions unless they're selling gold.

But there is one.

Assets beat inflation.

$100 invested into the S&P 500 would have an inflation-adjusted return of 17,739.30%.

Likewise, you can build assets from nothing.  Doing is easy and quick. The returns can boost your income by 10% or more.

The people who obsess over inflation inadvertently make the problem worse for themselves.  They wait for The Big Crash to buy assets. Or they moan about debt clocks and spending bills instead of seeking a solution.

Conclusion

Being ultra-prudent is like playing golf with a putter to avoid hitting the ball into the water. Sure, you might not slice a drive into the pond. But, you aren’t going to play well either. Or have very much fun.

Money has rules.

Avoiding debt or investment risks will limit your rewards. Long-term, being too cautious hurts your finances.

Disclaimer: This article is for entertainment purposes only. It is not financial advice, always do your own research.

Canonicalization and canonical URLs are essential for SEO, and improper implementation can negatively impact your site's performance.

Canonical tags were introduced in 2009 to help webmasters with duplicate or similar content on multiple URLs.

To use canonical tags properly, you must understand their purpose, operation, and implementation.

Canonical URLs and Tags

Canonical tags tell search engines that a certain URL is a page's master copy. They specify a page's canonical URL. Webmasters can avoid duplicate content by linking to the "canonical" or "preferred" version of a page.

How are canonical tags and URLs different? Can these be specified differently?

Tags

Canonical tags are found in an HTML page's head></head> section.

<link rel="canonical" href="https://www.website.com/page/" />

These can be self-referencing or reference another page's URL to consolidate signals.

Canonical tags and URLs are often used interchangeably, which is incorrect.

The rel="canonical" tag is the most common way to set canonical URLs, but it's not the only way.

Canonical URLs

What's a canonical link? Canonical link is the'master' URL for duplicate pages.

In Google's own words:

A canonical URL is the page Google thinks is most representative of duplicate pages on your site.

— Google Search Console Help

You can indicate your preferred canonical URL. For various reasons, Google may choose a different page than you.

When set correctly, the canonical URL is usually your specified URL.

Canonical URLs determine which page will be shown in search results (unless a duplicate is explicitly better for a user, like a mobile version).

Canonical URLs can be on different domains.

Other ways to specify canonical URLs

Canonical tags are the most common way to specify a canonical URL.

You can also set canonicals by:

• Setting the HTTP header rel=canonical.

• All pages listed in a sitemap are suggested as canonicals, but Google decides which pages are duplicates.

• Redirects 301.

Google recommends these methods, but they aren't all appropriate for every situation, as we'll see below. Each has its own recommended uses.

Setting canonical URLs isn't required; if you don't, Google will use other signals to determine the best page version.

To control how your site appears in search engines and to avoid duplicate content issues, you should use canonicalization effectively.

Why Duplicate Content Exists

Before we discuss why you should use canonical URLs and how to specify them in popular CMSs, we must first explain why duplicate content exists. Nobody intentionally duplicates website content.

Content management systems create multiple URLs when you launch a page, have indexable versions of your site, or use dynamic URLs.

Assume the following URLs display the same content to a user:

1. https://www.website.com/category/product-a/

2. https://www.website.com/product-a/

3. https://website.com/product-a/

4. http://www.website.com/product-a/

5. http://website.com/product-a/

6. https://m.website.com/product-a/

7. https://www.website.com/product-a

8. https://www.website.com/product-A/

A search engine sees eight duplicate pages, not one.

• URLs #1 and #2: the CMS saves product URLs with and without the category name.

• #3, #4, and #5 result from the site being accessible via HTTP, HTTPS, www, and non-www.

• #6 is a subdomain mobile-friendly URL.

• URL #7 lacks URL #2's trailing slash.

• URL #8 uses a capital "A" instead of a lowercase one.

Duplicate content may also exist in URLs like:

https://www.website.com
https://www.website.com/index.php

Duplicate content is easy to create.

Canonical URLs help search engines identify different page variations as a single URL on many sites.

SEO Canonical URLs

Canonical URLs help you manage duplicate content that could affect site performance.

Canonical URLs are a technical SEO focus area for many reasons.

Specify URL for search results

When you set a canonical URL, you tell Google which page version to display.

Which would you click?

https://www.domain.com/page-1/

https://www.domain.com/index.php?id=2

First, probably.

Canonicals tell search engines which URL to rank.

Consolidate link signals on similar pages

When you have duplicate or nearly identical pages on your site, the URLs may get external links.

Canonical URLs consolidate multiple pages' link signals into a single URL.

This helps your site rank because signals from multiple URLs are consolidated into one.

Syndication management

Content is often syndicated to reach new audiences.

Canonical URLs consolidate ranking signals to prevent duplicate pages from ranking and ensure the original content ranks.

Avoid Googlebot duplicate page crawling

Canonical URLs ensure that Googlebot crawls your new pages rather than duplicated versions of the same one across mobile and desktop versions, for example.

Crawl budgets aren't an issue for most sites unless they have 100,000+ pages.

How to Correctly Implement the rel=canonical Tag

Using the header tag rel="canonical" is the most common way to specify canonical URLs.

Adding tags and HTML code may seem daunting if you're not a developer, but most CMS platforms allow canonicals out-of-the-box.

These URLs each have one product.

How to Correctly Implement a rel="canonical" HTTP Header

A rel="canonical" HTTP header can replace canonical tags.

This is how to implement a canonical URL for PDFs or non-HTML documents.

You can specify a canonical URL in your site's.htaccess file using the code below.

<Files "file-to-canonicalize.pdf"> Header add Link "< http://www.website.com/canonical-page/>; rel=\"canonical\"" </Files>

301 redirects for canonical URLs

Google says 301 redirects can specify canonical URLs.

Only the canonical URL will exist if you use 301 redirects. This will redirect duplicates.

This is the best way to fix duplicate content across:

• HTTPS and HTTP

• Non-WWW and WWW

• Trailing-Slash and Non-Trailing Slash URLs

On a single page, you should use canonical tags unless you can confidently delete and redirect the page.

Sitemaps' canonical URLs

Google assumes sitemap URLs are canonical, so don't include non-canonical URLs.

This does not guarantee canonical URLs, but is a best practice for sitemaps.

Best-practice Canonical Tag

Once you understand a few simple best practices for canonical tags, spotting and cleaning up duplicate content becomes much easier.

Always include:

One canonical URL per page

If you specify multiple canonical URLs per page, they will likely be ignored.

Correct Domain Protocol

If your site uses HTTPS, use this as the canonical URL. It's easy to reference the wrong protocol, so check for it to catch it early.

Trailing slash or non-trailing slash URLs

Be sure to include trailing slashes in your canonical URL if your site uses them.

Specify URLs other than WWW

Search engines see non-WWW and WWW URLs as duplicate pages, so use the correct one.

Absolute URLs

To ensure proper interpretation, canonical tags should use absolute URLs.

So use:

<link rel="canonical" href="https://www.website.com/page-a/" />

And not:

<link rel="canonical" href="/page-a/" />

If not canonicalizing, use self-referential canonical URLs.

When a page isn't canonicalizing to another URL, use self-referencing canonical URLs.

Canonical tags refer to themselves here.

Common Canonical Tags Mistakes

Here are some common canonical tag mistakes.

301 Canonicalization

Set the canonical URL as the redirect target, not a redirected URL.

Incorrect Domain Canonicalization

If your site uses HTTPS, don't set canonical URLs to HTTP.

Irrelevant Canonicalization

Canonicalize URLs to duplicate or near-identical content only.

SEOs sometimes try to pass link signals via canonical tags from unrelated content to increase rank. This isn't how canonicalization should be used and should be avoided.

Multiple Canonical URLs

Only use one canonical tag or URL per page; otherwise, they may all be ignored.

When overriding defaults in some CMSs, you may accidentally include two canonical tags in your page's <head>.

Pagination vs. Canonicalization

Incorrect pagination can cause duplicate content. Canonicalizing URLs to the first page isn't always the best solution.

Canonicalize to a 'view all' page.

How to Audit Canonical Tags (and Fix Issues)

Audit your site's canonical tags to find canonicalization issues.

SEMrush Site Audit can help. You'll find canonical tag checks in your website's site audit report.

Let's examine these issues and their solutions.

No Canonical Tag on AMP

Site Audit will flag AMP pages without canonical tags.

Canonicalization between AMP and non-AMP pages is important.

Add a rel="canonical" tag to each AMP page's head>.

No HTTPS redirect or canonical from HTTP homepage

Duplicate content issues will be flagged in the Site Audit if your site is accessible via HTTPS and HTTP.

You can fix this by 301 redirecting or adding a canonical tag to HTTP pages that references HTTPS.

Broken canonical links

Broken canonical links won't be considered canonical URLs.

This error could mean your canonical links point to non-existent pages, complicating crawling and indexing.

Update broken canonical links to the correct URLs.

Multiple canonical URLs

This error occurs when a page has multiple canonical URLs.

Remove duplicate tags and leave one.

Canonicalization is a key SEO concept, and using it incorrectly can hurt your site's performance.

Once you understand how it works, what it does, and how to find and fix issues, you can use it effectively to remove duplicate content from your site.

Canonicalization SEO Myths

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.

This is for young entrepreneurs, especially in tech.

People give useless success advice on TikTok and Reddit. Early risers, bookworms, etc. Entrepreneurship courses. Work hard and hustle.

False. These aren't successful traits.

I mean, organization is good. As someone who founded several businesses and now works at a VC firm, I find these tips to be clichés.

Based on founding four successful businesses and working with other successful firms, here's my best actionable advice:

1. Choose a sector or a niche and become an expert in it.

This is more generic than my next tip, but it's a must-do that's often overlooked. Become an expert in the industry or niche you want to enter. Discover everything.

Buy (future) competitors' products. Understand consumers' pain points. Market-test. Target keyword combos. Learn technical details.

The most successful businesses I've worked with were all formed by 9-5 employees. They knew the industry's pain points. They started a business targeting these pain points.

2. Choose a niche or industry crossroads to target.

How do you choose an industry or niche? What if your industry is too competitive?

List your skills and hobbies. Randomness is fine. Find an intersection between two interests or skills.

Say you build websites well. You like cars.

Web design is a *very* competitive industry. Cars and web design?

Instead of web design, target car dealers and mechanics. Build a few fake demo auto mechanic websites, then cold call shops with poor websites. Verticalize.

I've noticed a pattern:

• Person works in a particular industry for a corporation.

• Person gains expertise in the relevant industry.

• Person quits their job and launches a small business to address a problem that their former employer was unwilling to address.

I originally posted this on Reddit and it seemed to have taken off so I decided to share it with you all.

Focus on the product. When someone buys from you, you convince them the product's value exceeds the price. It's not fair and favors the buyer.

Creating a superior product or service will win. Narrowing this helps you outcompete others.

You may be their only (lucky) option.