More on Entrepreneurship/Creators
Aaron Dinin, PhD
3 years ago
I put my faith in a billionaire, and he destroyed my business.
How did his money blind me?
Like most fledgling entrepreneurs, I wanted a mentor. I met as many nearby folks with "entrepreneur" in their LinkedIn biographies for coffee.
These meetings taught me a lot, and I'd suggest them to any new creator. Attention! Meeting with many experienced entrepreneurs means getting contradictory advice. One entrepreneur will tell you to do X, then the next one you talk to may tell you to do Y, which are sometimes opposites. You'll have to chose which suggestion to take after the chats.
I experienced this. Same afternoon, I had two coffee meetings with experienced entrepreneurs. The first meeting was with a billionaire entrepreneur who took his company public.
I met him in a swanky hotel lobby and ordered a drink I didn't pay for. As a fledgling entrepreneur, money was scarce.
During the meeting, I demoed the software I'd built, he liked it, and we spent the hour discussing what features would make it a success. By the end of the meeting, he requested I include a killer feature we both agreed would attract buyers. The feature was complex and would require some time. The billionaire I was sipping coffee with in a beautiful hotel lobby insisted people would love it, and that got me enthusiastic.
The second meeting was with a young entrepreneur who had recently raised a small amount of investment and looked as eager to pitch me as I was to pitch him. I forgot his name. I mostly recall meeting him in a filthy coffee shop in a bad section of town and buying his pricey cappuccino. Water for me.
After his pitch, I demoed my app. When I was done, he barely noticed. He questioned my customer acquisition plan. Who was my client? What did they offer? What was my plan? Etc. No decent answers.
After our meeting, he insisted I spend more time learning my market and selling. He ignored my questions about features. Don't worry about features, he said. Customers will request features. First, find them.
Putting your faith in results over relevance
Problems plagued my afternoon. I met with two entrepreneurs who gave me differing advice about how to proceed, and I had to decide which to pursue. I couldn't decide.
Ultimately, I followed the advice of the billionaire.
Obviously.
Who wouldn’t? That was the guy who clearly knew more.
A few months later, I constructed the feature the billionaire said people would line up for.
The new feature was unpopular. I couldn't even get the billionaire to answer an email showing him what I'd done. He disappeared.
Within a few months, I shut down the company, wasting all the time and effort I'd invested into constructing the killer feature the billionaire said I required.
Would follow the struggling entrepreneur's advice have saved my company? It would have saved me time in retrospect. Potential consumers would have told me they didn't want what I was producing, and I could have shut down the company sooner or built something they did want. Both outcomes would have been better.
Now I know, but not then. I favored achievement above relevance.
Success vs. relevance
The millionaire gave me advice on building a large, successful public firm. A successful public firm is different from a startup. Priorities change in the last phase of business building, which few entrepreneurs reach. He gave wonderful advice to founders trying to double their stock values in two years, but it wasn't beneficial for me.
The other failing entrepreneur had relevant, recent experience. He'd recently been in my shoes. We still had lots of problems. He may not have achieved huge success, but he had valuable advice on how to pass the closest hurdle.
The money blinded me at the moment. Not alone So much of company success is defined by money valuations, fundraising, exits, etc., so entrepreneurs easily fall into this trap. Money chatter obscures the value of knowledge.
Don't base startup advice on a person's income. Focus on what and when the person has learned. Relevance to you and your goals is more important than a person's accomplishments when considering advice.
Pat Vieljeux
3 years ago
In 5 minutes, you can tell if a startup will succeed.
Or the “lie to me” method.
I can predict a startup's success in minutes.
Just interview its founder.
Ask "why?"
I question "why" till I sense him.
I need to feel the person I have in front of me. I need to know if he or she can deliver. Startups aren't easy. Without abilities, a brilliant idea will fail.
Good entrepreneurs have these qualities: He's a leader, determined, and resilient.
For me, they can be split in two categories.
The first entrepreneur aspires to live meaningfully. The second wants to get rich. The second is communicative. He wants to wow the crowd. He's motivated by the thought of one day sailing a boat past palm trees and sunny beaches.
What drives the first entrepreneur is evident in his speech, face, and voice. He will not speak about his product. He's (nearly) uninterested. He's not selling anything. He's not a salesman. He wants to succeed. The product is his fuel.
He'll explain his decision. He'll share his motivations. His desire. And he'll use meaningful words.
Paul Ekman has shown that face expressions aren't cultural. His study influenced the American TV series "lie to me" about body language and speech.
Passionate entrepreneurs are obvious. It's palpable. Faking passion is tough. Someone who wants your favor and money will expose his actual motives through his expressions and language.
The good liar will be able to fool you for a while, but not for long if you pay attention to his body language and how he expresses himself.
And also, if you look at his business plan.
His business plan reveals his goals. Read between the lines.
Entrepreneur 1 will focus on his "why", whereas Entrepreneur 2 will focus on the "how".
Entrepreneur 1 will develop a vision-driven culture.
The second, on the other hand, will focus on his EBITDA.
Why is the culture so critical? Because it will allow entrepreneur 1 to develop a solid team that can tackle his problems and trials. His team's "why" will keep them together in tough times.
"Give me a terrific start-up team with a mediocre idea over a weak one any day." Because a great team knows when to pivot and trusts each other. Weak teams fail.” — Bernhard Schroeder
Closings thoughts
Every VC must ask Why. Entrepreneur's motivations. This "why" will create the team's culture. This culture will help the team adjust to any setback.
Bradley Vangelder
3 years ago
How we started and then quickly sold our startup
From a simple landing where we tested our MVP to a platform that distributes 20,000 codes per month, we learned a lot.
Starting point
Kwotet was my first startup. Everyone might post book quotes online.
I wanted a change.
Kwotet lacked attention, thus I felt stuck. After experiencing the trials of starting Kwotet, I thought of developing a waitlist service, but I required a strong co-founder.
I knew Dries from school, but we weren't close. He was an entrepreneurial programmer who worked a lot outside school. I needed this.
We brainstormed throughout school hours. We developed features to put us first. We worked until 3 am to launch this product.
Putting in the hours is KEY when building a startup
The instant that we lost our spark
In Belgium, college seniors do their internship in their last semester.
As we both made the decision to pick a quite challenging company, little time was left for Lancero.
Eventually, we lost interest. We lost the spark…
The only logical choice was to find someone with the same spark we started with to acquire Lancero.
And we did @ MicroAcquire.
Sell before your product dies. Make sure to profit from all the gains.
What did we do following the sale?
Not far from selling Lancero I lost my dad. I was about to start a new company. It was focused on positivity. I got none left at the time.
We still didn’t let go of the dream of becoming full-time entrepreneurs. As Dries launched the amazing company Plunk, and I’m still in the discovering stages of my next journey!
Dream!
You’re an entrepreneur if:
You're imaginative.
You enjoy disassembling and reassembling things.
You're adept at making new friends.
YOU HAVE DREAMS.
You don’t need to believe me if I tell you “everything is possible”… I wouldn't believe it myself if anyone told me this 2 years ago.
Until I started doing, living my dreams.
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Monroe Mayfield
2 years ago
CES 2023: A Third Look At Upcoming Trends
Las Vegas hosted CES 2023. This third and last look at CES 2023 previews upcoming consumer electronics trends that will be crucial for market share.
Definitely start with ICT. Qualcomm CEO Cristiano Amon spoke to CNBC from Las Vegas on China's crackdown and the company's automated driving systems for electric vehicles (EV). The business showed a concept car and its latest Snapdragon processor designs, which offer expanded digital interactions through SalesForce-partnered CRM platforms.
Electrification is reviving Michigan's automobile industry. Michigan Local News reports that $14 billion in EV and battery manufacturing investments will benefit the state. The report also revealed that the Strategic Outreach and Attraction Reserve (SOAR) fund had generated roughly $1 billion for the state's automotive sector.
Ars Technica is great for technology, society, and the future. After CES 2023, Jonathan M. Gitlin published How many electric car chargers are enough? Read about EV charging network issues and infrastructure spending. Politics aside, rapid technological advances enable EV charging network expansion in American cities and abroad.
Finally, the UNEP's The Future of Electric Vehicles and Material Resources: A Foresight Brief. Understanding how lithium-ion batteries will affect EV sales is crucial. Climate change affects EVs in various ways, but electrification and mining trends stand out because more EVs demand more energy-intensive metals and rare earths. Areas & Producers has been publishing my electrification and mining trends articles. Follow me if you wish to write for the publication.
The Weekend Brief (TWB) will routinely cover tech, industrials, and global commodities in global markets, including stock markets. Read more about the future of key areas and critical producers of the global economy in Areas & Producers.
Jared A. Brock
3 years ago
Here is the actual reason why Russia invaded Ukraine
Democracy's demise
Our Ukrainian brothers and sisters are being attacked by a far superior force.
It's the biggest invasion since WWII.
43.3 million peaceful Ukrainians awoke this morning to tanks, mortars, and missiles. Russia is already 15 miles away.
America and the West will not deploy troops.
They're sanctioning. Except railways. And luxuries. And energy. Diamonds. Their dependence on Russian energy exports means they won't even cut Russia off from SWIFT.
Ukraine is desperate enough to hand out guns on the street.
France, Austria, Turkey, and the EU are considering military aid, but Ukraine will fall without America or NATO.
The Russian goal is likely to encircle Kyiv and topple Zelenskyy's government. A proxy power will be reinstated once Russia has total control.
“Western security services believe Putin intends to overthrow the government and install a puppet regime,” says Financial Times foreign affairs commentator Gideon Rachman. This “decapitation” strategy includes municipalities. Ukrainian officials are being targeted for arrest or death.”
Also, Putin has never lost a war.
Why is Russia attacking Ukraine?
Putin, like a snowflake college student, “feels unsafe.”
Why?
Because Ukraine is full of “Nazi ideas.”
Putin claims he has felt threatened by Ukraine since the country's pro-Putin leader was ousted and replaced by a popular Jewish comedian.
Hee hee
He fears a full-scale enemy on his doorstep if Ukraine joins NATO. But he refuses to see it both ways. NATO has never invaded Russia, but Russia has always stolen land from its neighbors. Can you blame them for joining a mutual defense alliance when a real threat exists?
Nations that feel threatened can join NATO. That doesn't justify an attack by Russia. It allows them to defend themselves. But NATO isn't attacking Moscow. They aren't.
Russian President Putin's "special operation" aims to de-Nazify the Jewish-led nation.
To keep Crimea and the other two regions he has already stolen, he wants Ukraine undefended by NATO.
(Warlords have fought for control of the strategically important Crimea for over 2,000 years.)
Putin wants to own all of Ukraine.
Why?
The Black Sea is his goal.
Ports bring money and power, and Ukraine pipelines transport Russian energy products.
Putin wants their wheat, too — with 70% crop coverage, Ukraine would be their southern breadbasket, and Russia has no qualms about starving millions of Ukrainians to death to feed its people.
In the end, it's all about greed and power.
Putin wants to own everything Russia has ever owned. This year he turns 70, and he wants to be remembered like his hero Peter the Great.
In order to get it, he's willing to kill thousands of Ukrainians
Art imitates life
This story began when a Jewish TV comedian portrayed a teacher elected President after ranting about corruption.
Servant of the People, the hit sitcom, is now the leading centrist political party.
Right, President Zelenskyy won the hearts and minds of Ukrainians by imagining a fairer world.
A fair fight is something dictators, corporatists, monopolists, and warlords despise.
Now Zelenskyy and his people will die, allowing one of history's most corrupt leaders to amass even more power.
The poor always lose
Meanwhile, the West will impose economic sanctions on Russia.
China is likely to step in to help Russia — or at least the wealthy.
The poor and working class in Russia will suffer greatly if there is a hard crash or long-term depression.
Putin's friends will continue to drink champagne and eat caviar.
Russia cutting off oil, gas, and fertilizer could cause more inflation and possibly a recession if it cuts off supplies to the West. This causes more suffering and hardship for the Western poor and working class.
Why? a billionaire sociopath gets his dirt.
Yes, Russia is simply copying America. Some of us think all war is morally wrong, regardless of who does it.
But let's not kid ourselves right now.
The markets rallied after the biggest invasion in Europe since WWII.
Investors hope Ukraine collapses and Russian oil flows.
Unbridled capitalists value lifeless.
What we can do about Ukraine
When the Russian army invaded eastern Finland, my wife's grandmother fled as a child. 80 years later, Russia still has Karelia.
Russia invaded Ukraine today to retake two eastern provinces.
History has taught us nothing.
Past mistakes won't fix the future.
Instead, we should try:
- Pray and/or meditate on our actions with our families.
- Stop buying Russian products (vodka, obviously, but also pay more for hydro/solar/geothermal/etc.)
- Stop wasting money on frivolous items and donate it to Ukrainian charities.
Here are 35+ places to donate.
- To protest, gather a few friends, contact the media, and shake signs in front of the Russian embassy.
- Prepare to welcome refugees.
More war won't save the planet or change hearts.
Only love can work.
Dmitrii Eliuseev
2 years ago
Creating Images on Your Local PC Using Stable Diffusion AI
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 condaInstall 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 --upgradeDownload 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 1Almost. 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 1Stable 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 1The 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 0.8It 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 ldmHugging 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.ckptThis 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.