Three options exist. The third one is the quickest and most fruitful.

You've mastered crypto trading and want to become a broker.

So you may wonder: Where to begin?

If so, keep reading.

Today I'll compare three different approaches to becoming a cryptocurrency trader.

What are cryptocurrency brokers, and how do they vary from stockbrokers?

A stockbroker implements clients' market orders (retail or institutional ones).

Brokerage firms are regulated, insured, and subject to regulatory monitoring.

Stockbrokers are required between buyers and sellers. They can't trade without a broker. To trade, a trader must open a broker account and deposit money. When a trader shops, he tells his broker what orders to place.

Crypto brokerage is trade intermediation with cryptocurrency.

In crypto trading, however, brokers are optional.

Crypto exchanges offer direct transactions. Open an exchange account (no broker needed) and make a deposit.

Question:

Since crypto allows DIY trading, why use a broker?

Let's compare cryptocurrency exchanges vs. brokers.

Broker versus cryptocurrency exchange

Most existing crypto exchanges are basically brokers.

Examine their primary services:

• connecting purchasers and suppliers

• having custody of clients' money (with the exception of decentralized cryptocurrency exchanges),

• clearance of transactions.

Brokerage is comparable, don't you think?

There are exceptions. I mean a few large crypto exchanges that follow the stock exchange paradigm. They outsource brokerage, custody, and clearing operations. Classic exchange setups are rare in today's bitcoin industry.

Back to our favorite “standard” crypto exchanges. All-in-one exchanges and brokers. And usually, they operate under a broker or a broker-dealer license, save for the exchanges registered somewhere in a free-trade offshore paradise. Those don’t bother with any licensing.

What’s the sense of having two brokers at a time?

Better liquidity and trading convenience.

The crypto business is compartmentalized.

We have CEXs, DEXs, hybrid exchanges, and semi-exchanges (those that aggregate liquidity but do not execute orders on their sides). All have unique regulations and act as sovereign states.

There are about 18k coins and hundreds of blockchain protocols, most of which are heterogeneous (i.e., different in design and not interoperable).

A trader must register many accounts on different exchanges, deposit funds, and manage them all concurrently to access global crypto liquidity.

It’s extremely inconvenient.

Crypto liquidity fragmentation is the largest obstacle and bottleneck blocking crypto from mass adoption.

Crypto brokers help clients solve this challenge by providing one-gate access to deep and diverse crypto liquidity from numerous exchanges and suppliers. Professionals and institutions need it.

Another killer feature of a brokerage may be allowing clients to trade crypto with fiat funds exclusively, without fiat/crypto conversion. It is essential for professional and institutional traders.

Who may work as a cryptocurrency broker?

Apparently, not anyone. Brokerage requires high-powered specialists because it involves other people's money.

Here's the essentials:

• excellent knowledge, skills, and years of trading experience

• high-quality, quick, and secure infrastructure

• highly developed team

• outstanding trading capital

• High-ROI network: long-standing, trustworthy connections with customers, exchanges, liquidity providers, payment gates, and similar entities

• outstanding marketing and commercial development skills.

What about a license for a cryptocurrency broker? Is it necessary?

Complex question.

If you plan to play in white-glove jurisdictions, you may need a license. For example, in the US, as a “money transmitter” or as a CASSP (crypto asset secondary services provider) in Australia.

Even in these jurisdictions, there are no clear, holistic crypto brokerage and licensing policies.

Your lawyer will help you decide if your crypto brokerage needs a license.

Getting a license isn't quick. Two years of patience are needed.

How can you turn into a cryptocurrency broker?

Finally, we got there! 🎉

Three actionable ways exist:

1. To kickstart a regulated stand-alone crypto broker

2. To get a crypto broker franchise, and

3. To become a liquidity network broker.

Let's examine each.

1. Opening a regulated cryptocurrency broker

It's difficult. Especially If you're targeting first-world users.

You must comply with many regulatory, technical, financial, HR, and reporting obligations to keep your organization running. Some are mentioned above.

The licensing process depends on the products you want to offer (spots or derivatives) and the geographic areas you plan to service. There are no general rules for that.

In an overgeneralized way, here are the boxes you will have to check:

• capital availability (usually a large amount of capital c is required)

• You will have to move some of your team members to the nation providing the license in order to establish an office presence there.

• the core team with the necessary professional training (especially applies to CEO, Head of Trading, Assistant to Head of Trading, etc.)

• insurance

• infrastructure that is trustworthy and secure

• adopted proper AML/KYC/financial monitoring policies, etc.

Assuming you passed, what's next?

I bet it won’t be mind-blowing for you that the license is just a part of the deal. It won't attract clients or revenue.

To bring in high-dollar clientele, you must be a killer marketer and seller. It's not easy to convince people to give you money.

You'll need to be a great business developer to form successful, long-term agreements with exchanges (ideally for no fees), liquidity providers, banks, payment gates, etc. Persuade clients.

It's a tough job, isn't it?

I expect a Quora-type question here:

Can I start an unlicensed crypto broker?

Well, there is always a workaround with crypto!

You can register your broker in a free-trade zone like Seychelles to avoid US and other markets with strong watchdogs.

This is neither wise nor sustainable.

First, such experiments are illegal.

Second, you'll have trouble attracting clients and strategic partners.

A license equals trust. That’s it.

Even a pseudo-license from Mauritius matters.

Here are this method's benefits and downsides.

Cons first.

• As you navigate this difficult and expensive legal process, you run the risk of missing out on business prospects. It's quite simple to become excellent compliance yet unable to work. Because your competitors are already courting potential customers while you are focusing all of your effort on paperwork.

• Only God knows how long it will take you to pass the break-even point when everything with the license has been completed.

• It is a money-burning business, especially in the beginning when the majority of your expenses will go toward marketing, sales, and maintaining license requirements. Make sure you have the fortitude and resources necessary to face such a difficult challenge.

Pros

• It may eventually develop into a tool for making money. Because big guys who are professionals at trading require a white-glove regulated brokerage. You have every possibility if you work hard in the areas of sales, marketing, business development, and wealth. Simply put, everything must align.

Launching a regulated crypto broker is analogous to launching a crypto exchange. It's ROUGH. Sure you can take it?

2. Franchise for Crypto Broker (Crypto Sub-Brokerage)

A broker franchise is easier and faster than becoming a regulated crypto broker. Not a traditional brokerage.

A broker franchisee, often termed a sub-broker, joins with a broker (a franchisor) to bring them new clients. Sub-brokers market a broker's products and services to clients.

Sub-brokers are the middlemen between a broker and an investor.

Why is sub-brokering easier?

• less demanding qualifications and legal complexity. All you need to do is keep a few certificates on hand (each time depends on the jurisdiction).

• No significant investment is required

• there is no demand that you be a trading member of an exchange, etc.

As a sub-broker, you can do identical duties without as many rights and certifications.

What about the crypto broker franchise?

Sub-brokers aren't common in crypto.

In most existing examples (PayBito, PCEX, etc.), franchises are offered by crypto exchanges, not brokers. Though we remember that crypto exchanges are, in fact, brokers, do we?

Similarly:

• For a commission, a franchiser crypto broker receives new leads from a crypto sub-broker.

See above for why enrolling is easy.

Finding clients is difficult. Most crypto traders prefer to buy-sell on their own or through brokers over sub-broker franchises.

3. Broker of the Crypto Trading Network (or a Network Broker)

It's the greatest approach to execute crypto brokerage, based on effort/return.

Network broker isn't an established word. I wrote it for clarity.

Remember how we called crypto liquidity fragmentation the current crypto finance paradigm's main bottleneck?

Where there's a challenge, there's progress.

Several well-funded projects are aiming to fix crypto liquidity fragmentation. Instead of launching another crypto exchange with siloed trading, the greatest minds create trading networks that aggregate crypto liquidity from desynchronized sources and enable quick, safe, and affordable cross-blockchain transactions. Each project offers a distinct option for users.

Crypto liquidity implies:

• One-account access to cryptocurrency liquidity pooled from network participants' exchanges and other liquidity sources

• compiled price feeds

• Cross-chain transactions that are quick and inexpensive, even for HFTs

• link between participants of all kinds, and

• interoperability among diverse blockchains

Fast, diversified, and cheap global crypto trading from one account.

How does a trading network help cryptocurrency brokers?

I’ll explain it, taking Yellow Network as an example.

Yellow provides decentralized Layer-3 peer-to-peer trading.

• trade across chains globally with real-time settlement and

• Between cryptocurrency exchanges, brokers, trading companies, and other sorts of network members, there is communication and the exchange of financial information.

Have you ever heard about ECN (electronic communication network)? If not, it's an automated system that automatically matches buy and sell orders. Yellow is a decentralized digital asset ECN.

Brokers can:

• Start trading right now without having to meet stringent requirements; all you need to do is integrate with Yellow Protocol and successfully complete some KYC verification.

• Access global aggregated crypto liquidity through a single point.

• B2B (Broker to Broker) liquidity channels that provide peer liquidity from other brokers. Orders from the other broker will appear in the order book of a broker who is peering with another broker on the market. It will enable a broker to broaden his offer and raise the total amount of liquidity that is available to his clients.

• Select a custodian or use non-custodial practices.

Comparing network crypto brokerage to other types:

• A licensed stand-alone brokerage business is much more difficult and time-consuming to launch than network brokerage, and

• Network brokerage, in contrast to crypto sub-brokerage, is scalable, independent, and offers limitless possibilities for revenue generation.

Yellow Network Whitepaper. has more details on how to start a brokerage business and what rewards you'll obtain.

Final thoughts

There are three ways to become a cryptocurrency broker, including the non-conventional liquidity network brokerage. The last option appears time/cost-effective.

Crypto brokerage isn't crowded yet. Act quickly to find your right place in this market.

Choose the way that works for you best and see you in crypto trading.

Discover Web3 & DeFi with Yellow Network!

Yellow, powered by Openware, is developing a cross-chain P2P liquidity aggregator to unite the crypto sector and provide global remittance services that aid people.

Join the Yellow Community and plunge into this decade's biggest product-oriented crypto project.

• Observe Yellow Twitter

• Enroll in Yellow Telegram

• Visit Yellow Discord.

• On Hacker Noon, look us up.

Yellow Network will expose development, technology, developer tools, crypto brokerage nodes software, and community liquidity mining.

I read 1k+ page 3AC liquidation documentation so you don't have to. Also sharing revised Celsius recovery plans.

3AC's liquidation documents:

Someone disclosed 3AC liquidation records in the BVI courts recently. I'll discuss the leak's timeline and other highlights.

Three Arrows Capital began trading traditional currencies in emerging markets in 2012. They switched to equities and crypto, then purely crypto in 2018.

By 2020, the firm had $703mm in net assets and $1.8bn in loans (these guys really like debt).

The firm's net assets under control reached $3bn in April 2022, according to the filings. 3AC had $600mm of LUNA/UST exposure before May 9th 2022, which put them over.

LUNA and UST go to zero quickly (I wrote about the mechanics of the blowup here). Kyle Davies, 3AC co-founder, told Blockchain.com on May 13 that they have $2.4bn in assets and $2.3bn NAV vs. $2bn in borrowings. As BTC and ETH plunged 33% and 50%, the company became insolvent by mid-2022.

3AC sent $32mm to Tai Ping Shen, a Cayman Islands business owned by Su Zhu and Davies' partner, Kelly Kaili Chen (who knows what is going on here).

3AC had borrowed over $3.5bn in notional principle, with Genesis ($2.4bn) and Voyager ($650mm) having the most exposure.

Genesis demanded $355mm in further collateral in June.

Deribit (another 3AC investment) called for $80 million in mid-June.

Even in mid-June, the corporation was trying to borrow more money to stay afloat. They approached Genesis for another $125mm loan (to pay another lender) and HODLnauts for BTC & ETH loans.

Pretty crazy. 3AC founders used borrowed money to buy a $50 million boat, according to the leak.

Su requesting for $5m + Chen Kaili Kelly asserting they loaned $65m unsecured to 3AC are identified as creditors.

Celsius:

This bankruptcy presentation shows the Celsius breakdown from March to July 14, 2022. From $22bn to $4bn, crypto assets plummeted from $14.6bn to $1.8bn (ouch). $16.5bn in user liabilities dropped to $4.72bn.

In my recent post, I examined if "forced selling" is over, with Celsius' crypto assets being a major overhang. In this presentation, it looks that Chapter 11 will provide clients the opportunity to accept cash at a discount or remain long crypto. Provided that a fresh source of money is unlikely to enter the Celsius situation, cash at a discount or crypto given to customers will likely remain a near-term market risk - cash at a discount will likely come from selling crypto assets, while customers who receive crypto could sell at any time. I'll share any Celsius updates I find.

Conclusion

Only Celsius and the Mt Gox BTC unlock remain as forced selling catalysts. While everything went through a "relief" pump, with ETH up 75% from the bottom and numerous alts multiples higher, there are still macro dangers to equities + risk assets. There's a lot of wealth waiting to be deployed in crypto ($153bn in stables), but fund managers are risk apprehensive (lower than 2008 levels).

We're hopefully over crypto's "bottom," with peak anxiety and forced selling behind us, but we may chop around.

To see the full article, click here.

This is how to build a car while driving.

Building a high-growth startup is compared to building a car while it's speeding down the highway.

How to plan without going crazy? Or, without losing team, board, and investor buy-in?

I just delivered our company's product roadmap for the rest of the year. Complete. Thorough. Page-long. I'm optimistic about its chances of surviving as everything around us changes, from internal priorities to the global economy.

It's tricky. This isn't the first time I've created a startup roadmap. I didn't invent a document. It took time to deliver a document that will be relevant for months.

Goals matter.

Although they never change, goals are rarely understood.

This is the third in a series about a startup's unique roadmapping needs. Velocity is the intensity at which a startup must produce to survive.

A high-growth startup moves at breakneck speed, which I alluded to when I said priorities and economic factors can change daily or weekly.

At that speed, a startup's roadmap must be flexible, bend but not break, and be brief and to the point. I can't tell you how many startups and large companies develop a product roadmap every quarter and then tuck it away.

Big, wealthy companies can do this. It's suicide for a startup.

The drawer thing happens because startup product roadmaps are often valid for a short time. The roadmap is a random list of features prioritized by different company factions and unrelated to company goals.

It's not because the goals changed that a roadmap is shelved or ignored. Because the company's goals were never communicated or documented in the context of its product.

In the previous post, I discussed how to turn company goals into a product roadmap. In this post, I'll show you how to make a one-page startup roadmap.

In a future post, I'll show you how to follow this roadmap. This roadmap helps you track company goals, something a roadmap must do.

Be vague for growth, but direct for execution.

Here's my plan. The real one has more entries and more content in each.

Let's discuss smaller boxes.

Product developers and engineers know that the further out they predict, the more wrong they'll be. When developing the product roadmap, this rule is ignored. Then it bites us three, six, or nine months later when we haven't even started.

Why do we put everything in a product roadmap like a project plan?

Yes, I know. We use it when the product roadmap isn't goal-based.

A goal-based roadmap begins with a document that outlines each goal's idea, execution, growth, and refinement.

Once the goals are broken down into epics, initiatives, projects, and programs, only the idea and execution phases should be modeled. Any goal growth or refinement items should be vague and loosely mapped.

Why? First, any idea or execution-phase goal will result in growth initiatives that are unimaginable today. Second, internal priorities and external factors will change, but the goals won't. Locking items into calendar slots reduces flexibility and forces deviation from the single source of truth.

No soothsayers. Predicting the future is pointless; just prepare.

A map is useless if you don't know where you're going.

As we speed down the road, the car and the road will change. Goals define the destination.

This quarter and next quarter's roadmap should be set. After that, you should track destination milestones, not how to get there.

When you do that, even the most critical investors will understand the roadmap and buy in. When you track progress at the end of the quarter and revise your roadmap, the destination won't change.

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.