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.

Four times the energy density and a fraction of lithium-cost ion's

As the globe abandons fossil fuels, batteries become more important. EVs, solar, wind, tidal, wave, and even local energy grids will use them. We need a battery revolution since our present batteries are big, expensive, and detrimental to the environment. A recent publication describes a battery that solves these problems. But will it be enough?

Sodium-sulfur molten salt battery. It has existed for a long time and uses molten salt as an electrolyte (read more about molten salt batteries here). These batteries are cheaper, safer, and more environmentally friendly because they use less eco-damaging materials, are non-toxic, and are non-flammable.

Previous molten salt batteries used aluminium-sulphur chemistries, which had a low energy density and required high temperatures to keep the salt liquid. This one uses a revolutionary sodium-sulphur chemistry and a room-temperature-melting salt, making it more useful, affordable, and eco-friendly. To investigate this, researchers constructed a button-cell prototype and tested it.

First, the battery was 1,017 mAh/g. This battery is four times as energy dense as high-density lithium-ion batteries (250 mAh/g).

No one knows how much this battery would cost. A more expensive molten-salt battery costs $15 per kWh. Current lithium-ion batteries cost $132/kWh. If this new molten salt battery costs the same as present cells, it will be 90% cheaper.

This room-temperature molten salt battery could be utilized in an EV. Cold-weather heaters just need a modest backup battery.

The ultimate EV battery? If used in a Tesla Model S, you could install four times the capacity with no weight gain, offering a 1,620-mile range. This huge battery pack would cost less than Tesla's. This battery would nearly perfect EVs.

Or would it?

The battery's capacity declined by 50% after 1,000 charge cycles. This means that our hypothetical Model S would suffer this decline after 1.6 million miles, but for more cheap vehicles that use smaller packs, this would be too short. This test cell wasn't supposed to last long, so this is shocking. Future versions of this cell could be modified to live longer.

This affordable and eco-friendly cell is best employed as a grid-storage battery for renewable energy. Its safety and affordable price outweigh its short lifespan. Because this battery is made of easily accessible materials, it may be utilized to boost grid-storage capacity without causing supply chain concerns or EV battery prices to skyrocket.

Researchers are designing a bigger pouch cell (like those in phones and laptops) for this purpose. The battery revolution we need could be near. Let’s just hope it isn’t too late.

Unleash the power of the Option key ⌥

#1 Open a link in the Private tab first.

Previously, if I needed to open a Safari link in a private window, I would:

• copied the URL with the right click command,

• choose File > New Private Window to open a private window, and

• clicked return after pasting the URL.

I've found a more straightforward way.

Right-clicking a link shows this, right?

Hold option (⌥) for:

Click Open Link in New Private Window while holding.

Finished!

#2. Instead of searching for specific characters, try this

You may use unicode for business or school. Most people Google them when they need them.

That is lengthy!

You can type some special characters just by pressing ⌥ and a key.

For instance

• ⌥+2 -> ™ (Trademark)
• ⌥+0 -> ° (Degree)
• ⌥+G -> © (Copyright)
• ⌥+= -> ≠ (Not equal to)
• ⌥+< -> ≤ (Less than or equal to)
• ⌥+> -> ≥ (Greater then or equal to)
• ⌥+/ -> ÷ (Different symbol for division)

#3 Activate Do Not Disturb silently.

Do Not Disturb when sharing my screen is awkward for me (because people may think Im trying to hide some secret notifications).

Here's another method.

Hold ⌥ and click on Time (at the extreme right on the menu-bar).

Now, DND is activated (secretly!). To turn it off, do it again.

Note: This works only for DND focus.

#4. Resize a window starting from its center

Although this is rarely useful, it is still a hidden trick.

When you resize a window, the opposite edge or corner is used as the pivot, right?

However, if you want to resize it with its center as the pivot, hold while doing so.

#5. Yes, Cut-Paste is available on Macs as well (though it is slightly different).

I call it copy-move rather than cut-paste. This is how it works.

Carry it out.

Choose a file (by clicking on it), then copy it (⌘+C).

Go to a new location on your Mac. Do you use ⌘+V to paste it? However, to move it, press ⌘+⌥+V.

This removes the file from its original location and copies it here. And it works exactly like cut-and-paste on Windows.

#6. Instantly expand all folders

Set your Mac's folders to List view.

Assume you have one folder with multiple subfolders, each of which contains multiple files. And you wanted to look at every single file that was over there.

How would you do?

You're used to clicking the ⌄ glyph near the folder and each subfolder to expand them all, right? Instead, hold down ⌥ while clicking ⌄ on the parent folder.

This is what happens next.

Everything expands.

View/Copy a file's path as an added bonus

If you want to see the path of a file in Finder, select it and hold ⌥, and you'll see it at the bottom for a moment.

To copy its path, right-click on the folder and hold down ⌥ to see this

Click on Copy <"folder name"> as Pathname to do it.

#7 "Save As"

I was irritated by the lack of "Save As" in Pages when I first got a Mac (after 15 years of being a Windows guy).

It was necessary for me to save the file as a new file, in a different location, with a different name, or both.

Unfortunately, I couldn't do it on a Mac.

However, I recently discovered that it appears when you hold ⌥ when in the File menu.

Yay!

America should avoid BRICS

Vladimir Putin has watched videos of Muammar Gaddafi's CIA-backed demise.

Gaddafi...

Thief.

Did you know Gaddafi wanted a gold-backed dinar for Africa? Because he considered our global financial system was a Ponzi scheme, he wanted to discontinue trading oil in US dollars.

Or, Gaddafi's Libya enjoyed Africa's highest quality of living before becoming freed. Pictured:

Vladimir Putin is a nasty guy, but he had his reasons for not mentioning NATO assisting Ukraine in resisting US imperialism. Nobody tells you. Sure.

The US dollar's corruption post-2008, debasement by quantitative easing, and lack of value are key factors. BRICS will replace the dollar.

BRICS aren't bricks.

Economy-related.

Brazil, Russia, India, China, and South Africa have cooperated for 14 years to fight U.S. hegemony with a new international currency: BRICS.

BRICS is mostly comical. Now. Saudi Arabia, the second-largest oil hegemon, wants to join.

So what?

The New World Currency is BRICS

Russia was kicked out of G8 for its aggressiveness in Crimea in 2014.

It's now G7.

No biggie, said Putin, he said, and I quote, “Bon appetite.”

He was prepared. China, India, and Brazil lead the New World Order.

Together, they constitute 40% of the world's population and, according to the IMF, 50% of the world's GDP by 2030.

Here’s what the BRICS president Marcos Prado Troyjo had to say earlier this year about no longer needing the US dollar: “We have implemented the mechanism of mutual settlements in rubles and rupees, and there is no need for our countries to use the dollar in mutual settlements. And today a similar mechanism of mutual settlements in rubles and yuan is being developed by China.”

Ick. That's D.C. and NYC warmongers licking their chops for WW3 nasty.

Here's a lovely picture of BRICS to relax you:

If Saudi Arabia joins BRICS, as President Mohammed Bin Salman has expressed interest, a majority of the Middle East will have joined forces to construct a new world order not based on the US currency.

I'm not sure of the new acronym.

SBRICSS? CIRBSS? CRIBSS?

The Reason America Is Harvesting What It Sowed

BRICS began 14 years ago.

14 years ago, what occurred? Concentrate. It involved CDOs, bad subprime mortgages, and Wall Street quants crunching numbers.

2008 recession

When two nations trade, they do so in US dollars, not Euros or gold.

What happened when 2008, an avoidable crisis caused by US banks' cupidity and ignorance, what happened?

Everyone WORLDWIDE felt the pain.

Mostly due to corporate America's avarice.

This should have been a warning that China and Russia had enough of our bs. Like when France sent a battleship to America after Nixon scrapped the gold standard. The US was warned to shape up or be dethroned (or at least try).

Nixon improved in 1971. Kinda. Invented PetroDollar.

Another BS system that unfairly favors America and possibly pushed Russia, China, and Saudi Arabia into BRICS.

The PetroDollar forces oil-exporting nations to trade in US dollars and invest in US Treasury bonds. Brilliant. Genius evil.

Our misdeeds are:

• In conflicts that are not its concern, the USA uses the global reserve currency as a weapon.

• Targeted nations abandon the dollar, and rightfully so, as do nations that depend on them for trade in vital resources.

• The dollar's position as the world's reserve currency is in jeopardy, which could have disastrous economic effects.

• Although we have actually sown our own doom, we appear astonished. According to the Bible, whomever sows to appease his sinful nature will reap destruction from that nature whereas whoever sows to appease the Spirit will reap eternal life from the Spirit.

Americans, even our leaders, lack caution and delayed pleasure. When our unsustainable systems fail, we double down. Bailouts of the banks in 2008 were myopic, puerile, and another nail in America's hegemony.

America has screwed everyone.

We're unpopular.

The BRICS's future

It's happened before.

Saddam Hussein sold oil in Euros in 2000, and the US invaded Iraq a month later. The media has devalued the word conspiracy. The Iraq conspiracy.

There were no WMDs, but NYT journalists like Judy Miller drove Americans into a warmongering frenzy because Saddam would ruin the PetroDollar. Does anyone recall that this war spawned ISIS?

I think America has done good for the world. You can make a convincing case that we're many people's villain.

Learn more in Confessions of an Economic Hitman, The Devil's Chessboard, or Tyranny of the Federal Reserve. Or ignore it. That's easier.

We, America, should extend an olive branch, ask for forgiveness, and learn from our faults, as the Tao Te Ching advises. Unlikely. Our population is apathetic and stupid, and our government is corrupt.

Argentina, Iran, Egypt, and Turkey have also indicated interest in joining BRICS. They're also considering making it gold-backed, making it a new world reserve currency.

You should pay attention.

Thanks for reading!