People are slowly recognizing entrepreneurship's downside.

Growing up, entrepreneurship wasn't common. High school class of 2012 had no entrepreneurs.

Businesses were different.

They had staff and a lengthy history of achievement.

I never wanted a business. It felt unattainable. My friends didn't care.

Weird.

People desired degrees to attain good jobs at big companies.

When graduated high school:

• 9 out of 10 people attend college

• Earn minimum wage (7%) working in a restaurant or retail establishment

• Or join the military (3%)

Later, entrepreneurship became a thing.

2014-ish

I was in the military and most of my high school friends were in college, so I didn't hear anything.

Entrepreneurship soared in 2015, according to Google Trends.

Then more individuals were interested. Entrepreneurship went from unusual to cool.

In 2015, it was easier than ever to build a website, run Facebook advertisements, and achieve organic social media reach.

There were several online business tools.

You didn't need to spend years or money figuring it out. Most entry barriers were gone.

Everyone wanted a side gig to escape the 95.

Small company applications have increased during the previous 10 years.

2011-2014 trend continues.

2015 adds 150,000 applications. 2016 adds 200,000. Plus 300,000 in 2017.

The graph makes it look little, but that's a considerable annual spike with no indications of stopping.

By 2021, new business apps had doubled.

Entrepreneurship will return to its early 2010s level.

I think we'll go backward in 5 years.

Entrepreneurship is half as popular as it was in 2015.

In the late 2020s and 30s, entrepreneurship will again be obscure.

Entrepreneurship's decade-long splendor is fading. People will cease escaping 9-5 and launch fewer companies.

That’s not a bad thing.

I think people have a rose-colored vision of entrepreneurship. It's fashionable. People feel that they're missing out if they're not entrepreneurial.

Reality is showing up.

People say on social media, "I knew starting a business would be hard, but not this hard."

More negative posts on entrepreneurship:

Luke adds:

Is being an entrepreneur ‘healthy’? I don’t really think so. Many like Gary V, are not role models for a well-balanced life. Despite what feel-good LinkedIn tells you the odds are against you as an entrepreneur. You have to work your face off. It’s a tough but rewarding lifestyle. So maybe let’s stop glorifying it because it takes a lot of (bleepin) work to survive a pandemic, mental health battles, and a competitive market.

Entrepreneurship is no longer a pipe dream.

It’s hard.

I went full-time in March 2020. I was done by April 2021. I had a good-paying job with perks.

When that fell through (on my start date), I had to continue my entrepreneurial path. I needed money by May 1 to pay rent.

Entrepreneurship isn't as great as many think.

Entrepreneurship is a serious business.

If you have a 9-5, the grass isn't greener here. Most people aren't telling the whole story when they post on social media or quote successful entrepreneurs.

People prefer to communicate their victories than their defeats.

Is this a bad thing?

I don’t think so.

Over the previous decade, entrepreneurship went from impossible to the finest thing ever.

It peaked in 2020-21 and is returning to reality.

Startups aren't for everyone.

If you like your job, don't quit.

Entrepreneurship won't amaze people if you quit your job.

It's irrelevant.

You're doomed.

And you'll probably make less money.

If you hate your job, quit. Change jobs and bosses. Changing jobs could net you a greater pay or better perks.

When you go solo, your paycheck and perks vanish. Did I mention you'll fail, sleep less, and stress more?

Nobody will stop you from pursuing entrepreneurship. You'll face several challenges.

Possibly.

Entrepreneurship may be romanticized for years.

Based on what I see from entrepreneurs on social media and trends, entrepreneurship is challenging and few will succeed.

Many people find the internet's various business models bewildering.

This article summarizes 31 startup e-books.

1. Using the freemium business model (free plus premium),

The freemium business model offers basic software, games, or services for free and charges for enhancements.

Examples include Slack, iCloud, and Google Drive

Provide a rudimentary, free version of your product or service to users.

Google Drive and Dropbox offer 15GB and 2GB of free space but charge for more.

Freemium business model details (Click here)

2. The Business Model of Subscription

Subscription business models sell a product or service for recurring monthly or yearly revenue.

Examples: Tinder, Netflix, Shopify, etc

It's the next step to Freemium if a customer wants to pay monthly for premium features.

Subscription Business Model (Click here)

3. A market-based business strategy

It's an e-commerce site or app where third-party sellers sell products or services.

Examples are Amazon and Fiverr.

• On Amazon's marketplace, a third-party vendor sells a product.

• Freelancers on Fiverr offer specialized skills like graphic design.

Marketplace's business concept is explained.

4. Business plans using aggregates

In the aggregator business model, the service is branded.

Uber, Airbnb, and other examples

Marketplace and Aggregator business models differ.

Amazon and Fiverr link merchants and customers and take a 10-20% revenue split.

Uber and Airbnb-style aggregator Join these businesses and provide their products.

5. The pay-as-you-go concept of business

This is a consumption-based pricing system. Cloud companies use it.

Example: Amazon Web Service and Google Cloud Platform (GCP) (AWS)

AWS, an Amazon subsidiary, offers over 200 pay-as-you-go cloud services.

“In short, the more you use the more you pay”

When it's difficult to divide clients into pricing levels, pay-as-you is employed.

6. The business model known as fee-for-service (FFS)

FFS charges fixed and variable fees for each successful payment.

For instance, PayU, Paypal, and Stripe

Stripe charges 2.9% + 30 per payment.

These firms offer a payment gateway to take consumer payments and deposit them to a business account.

Fintech business model

7. EdTech business strategy

In edtech, you generate money by selling material or teaching as a service.

edtech business models

Freemium When course content is free but certification isn't, e.g. Coursera

FREE TRIAL SkillShare offers free trials followed by monthly or annual subscriptions.

Self-serving marketplace approach where you pick what to learn.

Ad-revenue model The company makes money by showing adverts to its huge user base.

Lock-in business strategy

Lock in prevents customers from switching to a competitor's brand or offering.

It uses switching costs or effort to transmit (soft lock-in), improved brand experience, or incentives.

Apple, SAP, and other examples

Apple offers an iPhone and then locks you in with extra hardware (Watch, Airpod) and platform services (Apple Store, Apple Music, cloud, etc.).

9. Business Model for API Licensing

APIs let third-party apps communicate with your service.

Uber and Airbnb use Google Maps APIs for app navigation.

Examples are Google Map APIs (Map), Sendgrid (Email), and Twilio (SMS).

Business models for APIs

1. Free: The simplest API-driven business model that enables unrestricted API access for app developers. Google Translate and Facebook are two examples.

2. Developer Pays: Under this arrangement, service providers such as AWS, Twilio, Github, Stripe, and others must be paid by application developers.

3. The developer receives payment: These are the compensated content producers or developers who distribute the APIs utilizing their work. For example, Amazon affiliate programs

10. Open-source enterprise

Open-source software can be inspected, modified, and improved by anybody.

For instance, use Firefox, Java, or Android.

Google paid Mozilla $435,702 million to be their primary search engine in 2018.

Open-source software profits in six ways.

1. Paid assistance The Project Manager can charge for customization because he is quite knowledgeable about the codebase.

2. A full database solution is available as a Software as a Service (MongoDB Atlas), but there is a fee for the monitoring tool.

3. Open-core design R studio is a better GUI substitute for open-source applications.

4. sponsors of GitHub Sponsorships benefit the developers in full.

5. demands for paid features Earn Money By Developing Open Source Add-Ons for Current Products

Open-source business model

11. The business model for data

If the software or algorithm collects client data to improve or monetize the system.

Open AI GPT3 gets smarter with use.

Foursquare allows users to exchange check-in locations.

Later, they compiled large datasets to enable retailers like Starbucks launch new outlets.

12. Business Model Using Blockchain

Blockchain is a distributed ledger technology that allows firms to deploy smart contracts without a central authority.

Examples include Alchemy, Solana, and Ethereum.

Business models using blockchain

1. Economy of tokens or utility When a business uses a token business model, it issues some kind of token as one of the ways to compensate token holders or miners. For instance, Solana and Ethereum

2. Bitcoin Cash P2P Business Model Peer-to-peer (P2P) blockchain technology permits direct communication between end users. as in IPFS

3. Enterprise Blockchain as a Service (Baas) BaaS focuses on offering ecosystem services similar to those offered by Amazon (AWS) and Microsoft (Azure) in the web 3 sector. Example: Ethereum Blockchain as a Service with Bitcoin (EBaaS).

4. Blockchain-Based Aggregators With AWS for blockchain, you can use that service by making an API call to your preferred blockchain. As an illustration, Alchemy offers nodes for many blockchains.

13. The free-enterprise model

In the freeterprise business model, free professional accounts are led into the funnel by the free product and later become B2B/enterprise accounts.

For instance, Slack and Zoom

Freeterprise companies flourish through collaboration.

Start with a free professional account to build an enterprise.

14. Business plan for razor blades

It's employed in hardware where one piece is sold at a loss and profits are made through refills or add-ons.

Gillet razor & blades, coffee machine & beans, HP printer & cartridge, etc.

Sony sells the Playstation console at a loss but makes up for it by selling games and charging for online services.

Advantages of the Razor-Razorblade Method

1. lowers the risk a customer will try a product. enables buyers to test the goods and services without having to pay a high initial investment.

2. The product's ongoing revenue stream has the potential to generate sales that much outweigh the original investments.

Razor blade business model

15. The business model of direct-to-consumer (D2C)

In D2C, the company sells directly to the end consumer through its website using a third-party logistic partner.

Examples include GymShark and Kylie Cosmetics.

D2C brands can only expand via websites, marketplaces (Amazon, eBay), etc.

D2C benefits

• Lower reliance on middlemen = greater profitability

• You now have access to more precise demographic and geographic customer data.

• Additional space for product testing

• Increased customisation throughout your entire product line-Inventory Less

16. Business model: White Label vs. Private Label

Private label/White label products are made by a contract or third-party manufacturer.

Most amazon electronics are made in china and white-labeled.

Amazon supplements and electronics.

Contract manufacturers handle everything after brands select product quantities on design labels.

17. The franchise model

The franchisee uses the franchisor's trademark, branding, and business strategy (company).

For instance, KFC, Domino's, etc.

Subway, Domino, Burger King, etc. use this business strategy.

Many people pick a franchise because opening a restaurant is risky.

18. Ad-based business model

Social media and search engine giants exploit search and interest data to deliver adverts.

Google, Meta, TikTok, and Snapchat are some examples.

Users don't pay for the service or product given, e.g. Google users don't pay for searches.

In exchange, they collected data and hyper-personalized adverts to maximize revenue.

19. Business plan for octopuses

Each business unit functions separately but is connected to the main body.

Instance: Oyo

OYO is Asia's Airbnb, operating hotels, co-working, co-living, and vacation houses.

20, Transactional business model, number

Sales to customers produce revenue.

E-commerce sites and online purchases employ SSL.

Goli is an ex-GymShark.

21. The peer-to-peer (P2P) business model

In P2P, two people buy and sell goods and services without a third party or platform.

Consider OLX.

22. P2P lending as a manner of operation

In P2P lending, one private individual (P2P Lender) lends/invests or borrows money from another (P2P Borrower).

Instance: Kabbage

Social lending lets people lend and borrow money directly from each other without an intermediary financial institution.

23. A business model for brokers

Brokerages charge a commission or fee for their services.

Examples include eBay, Coinbase, and Robinhood.

Brokerage businesses are common in Real estate, finance, and online and operate on this model.

1. Buy/sell similar models Examples include financial brokers, insurance brokers, and others who match purchase and sell transactions and charge a commission.

2. These brokers charge an advertiser a fee based on the date, place, size, or type of an advertisement. This is known as the classified-advertiser model. For instance, Craiglist

24. Drop shipping as an industry

Dropshipping allows stores to sell things without holding physical inventories.

When a customer orders, use a third-party supplier and logistic partners.

Retailer product portfolio and customer experience Fulfiller The consumer places the order.

Dropshipping advantages

• Less money is needed (Low overhead-No Inventory or warehousing)

• Simple to start (costs under $100)

• flexible work environment

• New product testing is simpler

25. Business Model for Space as a Service

It's centered on a shared economy that lets millennials live or work in communal areas without ownership or lease.

Consider WeWork and Airbnb.

WeWork helps businesses with real estate, legal compliance, maintenance, and repair.

26. The business model for third-party logistics (3PL)

In 3PL, a business outsources product delivery, warehousing, and fulfillment to an external logistics company.

Examples include Ship Bob, Amazon Fulfillment, and more.

3PL partners warehouse, fulfill, and return inbound and outbound items for a charge.

Inbound logistics involves bringing products from suppliers to your warehouse.

Outbound logistics refers to a company's production line, warehouse, and customer.

27. The last-mile delivery paradigm as a commercial strategy

Last-mile delivery is the collection of supply chain actions that reach the end client.

Examples include Rappi, Gojek, and Postmates.

Last-mile is tied to on-demand and has a nighttime peak.

28. The use of affiliate marketing

Affiliate marketing involves promoting other companies' products and charging commissions.

Examples include Hubspot, Amazon, and Skillshare.

Your favorite youtube channel probably uses these short amazon links to get 5% of sales.

Affiliate marketing's benefits

• In exchange for a success fee or commission, it enables numerous independent marketers to promote on its behalf.

• Ensure system transparency by giving the influencers a specific tracking link and an online dashboard to view their profits.

• Learn about the newest bargains and have access to promotional materials.

29. The business model for virtual goods

This is an in-app purchase for an intangible product.

Examples include PubG, Roblox, Candy Crush, etc.

Consumables are like gaming cash that runs out. Non-consumable products provide a permanent advantage without repeated purchases.

30. Business Models for Cloud Kitchens

Ghost, Dark, Black Box, etc.

Delivery-only restaurant.

These restaurants don't provide dine-in, only delivery.

For instance, NextBite and Faasos

31. Crowdsourcing as a Business Model

Crowdsourcing = Using the crowd as a platform's source.

In crowdsourcing, you get support from people around the world without hiring them.

Crowdsourcing sites

1. Open-Source Software gives access to the software's source code so that developers can edit or enhance it. Examples include Firefox browsers and Linux operating systems.

2. Crowdfunding The oculus headgear would be an example of crowdfunding in essence, with no expectations.

Don't know how to start an online business? Here's a guide. By following these recommendations, you can build a lucrative and profitable online business.

What Are Online Businesses Used For?

Most online businesses are websites. A self-created, self-managed website. You may sell things and services online.

To establish an internet business, you must locate a host and set up accounts with numerous companies. Once your accounts are set up, you may start publishing content and selling products or services.

How to Make Money from Your Online Business

Advertising and marketing are the best ways to make money online. You must develop strategies to contact new customers and generate leads. Make sure your website is search engine optimized so people can find you online.

Top 5 Online Business Tips for Startups:

1. Know your target audience's needs.

2. Make your website as appealing as possible.

3. Generate leads and sales with marketing.

4. Track your progress and learn from your mistakes to improve.

5. Be prepared to expand into new markets or regions.

How to Launch a Successful Online Business Without Putting in a Lot of Work

Build with a solid business model to start a profitable online business. By using these tips, you can start your online business without paying much.

First, develop a user-friendly website. You can use an internet marketing platform or create your own website. Once your website is live, optimize it for search engines and add relevant content.

Second, sell online. This can be done through ads or direct sales to website visitors. Finally, use social media to advertise your internet business. By accomplishing these things, you'll draw visitors to your website and make money.

When launching a business, invest long-term. This involves knowing your goals and how you'll pay for them. Volatility can have several effects on your business. If you offer things online, you may need to examine if the market is ready for them.

Invest wisely

Investing all your money in one endeavor can lead to too much risk and little ROI. Diversify your investments to take advantage of all available chances. So, your investments won't encounter unexpected price swings and you'll be immune to economic upheavals.

Financial news updates

When launching or running a thriving online business, financial news is crucial. By knowing current trends and upcoming developments, you can keep your business lucrative.

Keeping up with financial news can also help you avoid potential traps that could harm your bottom line. If you don't know about new legislation that could affect your industry, potential customers may choose another store when they learn about your business's problems.

Volatility ahead

You should expect volatility in the financial sector. Without a plan for coping with volatility, you could run into difficulty. If your organization relies on client input, you may not be able to exploit customer behavior shifts.

Your company could go bankrupt if you don't understand how fickle the stock market can be. By preparing for volatility, you can ensure your organization survives difficult times and market crashes.

Conclusion

Many internet businesses can be profitable. Start quickly with a few straightforward steps. Diversify your investments, follow financial news, and be prepared for volatility to develop a successful business.

Thanks for reading!

Peter Thiel studied philosophy as an undergraduate.

Peter Thiel has $7.36 billion.

Peter is a world-ranked chess player, has a legal degree, and has written profitable novels.

In 1999, he co-founded PayPal with Max Levchin, which merged with X.com.

Peter Thiel made $55 million after selling the company to eBay for $1.5 billion in 2002.

You may be wondering…

How did Peter turn $55 million into his now multi-billion dollar net worth?

One amazing investment?

Facebook.

Thiel was Facebook's first external investor. He bought 10% of the company for $500,000 in 2004.

This investment returned 159% annually, 200x in 8 years.

By 2012, Thiel sold almost all his Facebook shares, becoming a billionaire.

What was the investment thesis of Peter?

This investment appeared ridiculous. Facebook was an innovative startup.

Thiel's $500,000 contribution transformed Facebook.

Harvard students have access to Facebook's 8 features and 1 photo per profile.

How did Peter determine that this would be a wise investment, then?

Facebook is a mimetic desire machine.

Social media's popularity is odd. Why peek at strangers' images on a computer?

Peter Thiel studied under French thinker Rene Girard at Stanford.

Mimetic Desire explains social media's success.

Mimetic Desire is the idea that humans desire things simply because other people do.

If nobody wanted it, would you?

Would you desire a family, a luxury car, or expensive clothes if no one else did? Girard says no.

People we admire affect our aspirations because we're social animals. Every person has a role model.

Our nonreligious culture implies role models are increasingly other humans, not God.

The idea explains why social media influencers are so powerful.

Why would Andrew Tate or Kim Kardashian matter if people weren't mimetic?

Humanity is fundamentally motivated by social comparison.

Facebook takes advantage of this need for social comparison, and puts it on a global scale.

It aggregates photographs and updates from millions of individuals.

Facebook mobile allows 24/7 social comparison.

Thiel studied mimetic desire with Girard and realized Facebook exploits the urge for social comparison to gain money.

Social media is more significant and influential than ever, despite Facebook's decline.

Thiel and Girard show that applied philosophy (particularly in business) can be immensely profitable.

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.