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.

It's free now.

Most content writers—including me—write about side hustles because they do well.

I wrote several articles on a profitable programming side hustle.

Later, I learnt about AI content-generating platforms like Jasper.ai and Copy.ai, which I thought were terrible because they required subscriptions and had bad content. Even I dislike automatic online posting.

Since ChatGPT is becoming more popular, I tried it. It's free now.

Note: I wasn’t compensated to write this piece by this tool. In other words, I don’t receive any payment from their founders. There is no affiliate link, and right now it is free to use. Also, ChatGPT can do a lot more than what I ever did.

Begin.

OpenAI account registration requires visiting the website (if you don’t have one). Just write anything into the chatbot and get a response.

First, I wrote about the top 10 2023 side businesses.

This came in seconds.

2023 is shaping up to be a year of growth and opportunity for entrepreneurs and side hustlers. With the rise of remote work and the growing gig economy, there are many income streams to consider launching in the coming year. Here are the top 10 income streams to launch in 2023:

1. Virtual Assistant — The demand for virtual assistants is on the rise as more businesses move online. As a virtual assistant, you can offer administrative, technical, or creative services to clients remotely.

2. Online Tutoring — If you have expertise in a particular subject, consider becoming an online tutor. There are many platforms that connect tutors with students looking for help with their studies.

3. Freelance Writing — If you have a knack for writing, consider becoming a freelance writer. You can offer your services to businesses, bloggers, and individuals looking for content for their websites or social media platforms.

4. Graphic Design — If you have a talent for design, consider starting a graphic design side hustle. You can create logos, banners, and other visual content for businesses and individuals.

5. Social Media Management — Many businesses are looking for help with managing their social media accounts. As a social media manager, you can help businesses create and execute social media strategies to reach their target audience.

6. Dropshipping — Dropshipping is a business model where you sell products without actually holding any inventory. You can partner with suppliers and ship the products directly to the customer on behalf of the business.

7. Pet Sitting — If you love animals, consider starting a pet-sitting side hustle. You can offer your services to pet owners who need someone to take care of their pets while they are away.

8. Home Cleaning — With busy schedules, many people are looking for help with cleaning their homes. As a home cleaning service provider, you can offer your services to help keep homes tidy and organized.

9. Event Planning — If you have a talent for organization and planning, consider starting an event planning side hustle. You can offer your services to help plan and execute events for businesses and individuals.

10. Online Teaching — With the rise of online education, there is a growing demand for online teachers. If you have expertise in a particular subject, consider becoming an online teacher and offer your services to students looking for personalized instruction.

In conclusion, 2023 is shaping up to be a great year for launching new income streams. Whether you are looking to start a virtual assistant business, become an online tutor, or offer home cleaning services, there are many opportunities to turn your skills and expertise into a profitable side hustle.

Better than most online side hustle posts, right?

Write about the top 10 programming side hustles to start in 2023.

I got this.

If you are a programmer looking to start a side hustle, 2023 is the perfect time to launch your own business. With the growing demand for software and app development, there are many opportunities for programmers to turn their skills into a profitable side hustle. Here are the top 10 programming side hustles to start in 2023:

1. App Development — With the rise of smartphones and tablets, there is a growing demand for app development. You can offer your services to businesses and individuals looking to create custom apps for their needs.

2. Website Development — Many businesses and individuals are looking for help with creating and maintaining their websites. As a website developer, you can offer your services to help design, build, and manage websites for your clients.

3. Software Development — If you have expertise in a particular programming language, consider starting a software development side hustle. You can offer your services to businesses and individuals looking to create custom software solutions for their needs.

4. Game Development — The gaming industry is thriving, and there is a growing demand for game development. If you have experience in game development, you can offer your services to businesses and individuals looking to create custom games.

5. Data Analysis — With the rise of big data, there is a growing demand for data analysts. As a data analyst, you can offer your services to businesses and individuals looking to make sense of their data and gain insights from it.

6. Machine Learning — Machine learning is a rapidly growing field, and there is a growing demand for machine learning experts. If you have experience in machine learning, you can offer your services to businesses and individuals looking to develop custom machine learning solutions.

7. Cloud Computing — With the rise of cloud computing, there is a growing demand for cloud experts. If you have experience in cloud computing, you can offer your services to businesses and individuals looking to migrate their applications and data to the cloud.

8. Blockchain Development — Blockchain technology is gaining popularity, and there is a growing demand for blockchain developers. If you have experience in blockchain development, you can offer your services to businesses and individuals looking to develop custom blockchain solutions.

9. Artificial Intelligence — Artificial intelligence is a rapidly growing field, and there is a growing demand for AI experts. If you have experience in artificial intelligence, you can offer your services to businesses and individuals looking to develop custom AI solutions.

10. Virtual Reality — Virtual reality is an exciting and emerging technology, and there is a growing demand for VR developers. If you have experience in VR development, you can offer your services to businesses and individuals looking to create custom VR experiences.

In conclusion, 2023 is a great time to start a programming side hustle. Whether you are interested in app development, website development, or machine learning, there are many opportunities for programmers to turn their skills into a profitable business.

These two blogs show that AI can simplify blog authoring.

I asked some tough programming questions, and most were suitable.

The content may occasionally not be what we want, but it will almost always be very helpful to you.

Enjoy.

TOP APPS

Who survives spring cleaning?

Relax. Notion is off-limits. This topic is popular.

(I wrote about it 2 years ago, before everyone else did.) So).

These apps are probably new to you. I hope you find a new phone app after reading this.

Outdooractive

ViewRanger is Google Maps for outdoor enthusiasts.

This app has been so important to me as a freedom-loving long-distance walker and hiker.

This app shows nearby trails and right-of-ways on top of an Open Street Map.

Helpful detail and data. Any route's distance,

You can download and follow tons of routes planned by app users.

This has helped me find new routes and places a fellow explorer has tried.

Free with non-intrusive ads. Years passed before I subscribed. Pro costs £2.23/month.

This app is for outdoor lovers.

Google Files

New phones come with bloatware. These rushed apps are frustrating.

We must replace these apps. 2017 was Google's year.

Files is a file manager. It's quick, innovative, and clean. They've given people what they want.

It's easy to organize files, clear space, and clear cache.

I recommend Gallery by Google as a gallery app alternative. It's quick and easy.

Trainline

App for trains, buses, and coaches.

I've used this app for years. It did the basics well when I first used it.

Since then, it's improved. It's constantly adding features to make traveling easier and less stressful.

Split-ticketing helps me save hundreds a year on train fares. This app is only available in the UK and Europe.

This service doesn't link to a third-party site. Their app handles everything.

Not all train and coach companies use this app. All the big names are there, though.

Here's more on the app.

Battlefield: Mobile

Play Store has 478,000 games. Few can turn my phone into a console.

Call of Duty Mobile and Asphalt 8/9 are examples.

Asphalt's loot boxes and ads make it unplayable. Call of Duty opens with a few ads. Close them to play without hassle.

This game uses all your phone's features to provide a high-quality, seamless experience. If my internet connection is good, I never experience lag or glitches.

The gameplay is energizing and intense, just like on consoles. Sometimes I'm too involved. I've thrown my phone in anger. I'm totally absorbed.

Customizability is my favorite. Since phones have limited screen space, we should only have the buttons we need, placed conveniently.

Size, opacity, and position are modifiable. Adjust audio, graphics, and textures. It's customizable.

This game has been on my phone for three years. It began well and has gotten better. When I think the creators can't do more, they do.

If you play, read my tips for winning a Battle Royale.

Lightroom

As a photographer, I believe your best camera is on you. The phone.

2017 was a big year for this app. I've tried many photo-editing apps since then. This always wins.

The app is dull. I've never seen better photo editing on a phone.

Adjusting settings and sliders doesn't damage or compress photos. It's detailed.

This is important for phone photos, which are lower quality than professional ones.

Some tools are behind a £4.49/month paywall. Adobe must charge a subscription fee instead of selling licenses. (I'm still bitter about Creative Cloud's price)

Snapseed is my pick. Lightroom is where I do basic editing before moving to Snapseed. Snapseed review:

These apps are great. They cover basic and complex editing needs while traveling.

Final Reflections

I hope you downloaded one of these. Share your favorite apps. These apps are scarce.

UST depegs and LUNA crashes 99.999% in a fraction of the time it takes the Moon to orbit the Earth.

Fat Man, a Terra whistle-blower, promises to expose Do Kwon's dirty secrets and shady deals.

The Terra community has voted to relaunch Terra LUNA on a new blockchain. The Terra 2.0 Pheonix-1 blockchain went live on May 28, 2022, and people were airdropped the new LUNA, now called LUNA, while the old LUNA became LUNA Classic.

Does LUNA deserve another chance? To answer this, or at least start a conversation about the Terra 2.0 chain's advantages and limitations, we must assess its fundamentals, ideology, and long-term vision.

Whatever the result, our analysis must be thorough and ruthless. A failure of this magnitude cannot happen again, so we must magnify every potential breaking point by 10.

Will UST and LUNA holders be compensated in full?

The obvious. First, and arguably most important, is to restore previous UST and LUNA holders' bags.

Terra 2.0 has 1,000,000,000,000 tokens to distribute.

• 25% of a community pool

• Holders of pre-attack LUNA: 35%

• 10% of aUST holders prior to attack

• Holders of LUNA after an attack: 10%

• UST holders as of the attack: 20%

Every LUNA and UST holder has been compensated according to the above proposal.

According to self-reported data, the new chain has 210.000.000 tokens and a $1.3bn marketcap. LUNC and UST alone lost $40bn. The new token must fill this gap. Since launch:

LUNA holders collectively own $1b worth of LUNA if we subtract the 25% community pool airdrop from the current market cap and assume airdropped LUNA was never sold.

At the current supply, the chain must grow 40 times to compensate holders. At the current supply, LUNA must reach $240.

LUNA needs a full-on Bull Market to make LUNC and UST holders whole.

Who knows if you'll be whole? From the time you bought to the amount and price, there are too many variables to determine if Terra can cover individual losses.

The above distribution doesn't consider individual cases. Terra didn't solve individual cases. It would have been huge.

What does LUNA offer in terms of value?

UST's marketcap peaked at $18bn, while LUNC's was $41bn. LUNC and UST drove the Terra chain's value.

After it was confirmed (again) that algorithmic stablecoins are bad, Terra 2.0 will no longer support them.

Algorithmic stablecoins contributed greatly to Terra's growth and value proposition. Terra 2.0 has no product without algorithmic stablecoins.

Terra 2.0 has an identity crisis because it has no actual product. It's like Volkswagen faking carbon emission results and then stopping car production.

A project that has already lost the trust of its users and nearly all of its value cannot survive without a clear and in-demand use case.

Do Kwon, how about him?

Oh, the Twitter-caller-poor? Who challenges crypto billionaires to break his LUNA chain? Who dissolved Terra Labs South Korea before depeg? Arrogant guy?

That's not a good image for LUNA, especially when making amends. I think he should step down and let a nicer person be Terra 2.0's frontman.

The verdict

Terra has a terrific community with an arrogant, unlikeable leader. The new LUNA chain must grow 40 times before it can start making up its losses, and even then, not everyone's losses will be covered.

I won't invest in Terra 2.0 or other algorithmic stablecoins in the near future. I won't be near any Do Kwon-related project within 100 miles. My opinion.

Can Terra 2.0 be saved? Comment below.

What works and what’s just more toxic positivity

Just keep at it; you’ll get it.

I closed the Zoom call and immediately dropped my head. Open tabs included material on inspiration, burnout, and recovery.

I searched everywhere for ways to avoid burnout.

It wasn't that I needed to keep going, change my routine, employ 8D audio playlists, or come up with fresh ideas. I had several ideas and a schedule. I knew what to do.

I wasn't interested. I kept reading, changing my self-care and mental health routines, and writing even though it was tiring.

Since burnout became a psychiatric illness in 2019, thousands have shared their experiences. It's spreading rapidly among writers.

What is the actual key to recovering from burnout?

Every A-list burnout story emphasizes prevention. Other lists provide repackaged self-care tips. More discuss mental health.

It's like the mid-2000s, when pink quotes about bubble baths saturated social media.

The self-care mania cost us all. Self-care is crucial, but utilizing it to address everything didn't work then or now.

How can you recover from burnout?

Time

Are extended breaks actually good for you? Most people need a break every 62 days or so to avoid burnout.

Real-life burnout victims all took breaks. Perhaps not a long hiatus, but breaks nonetheless.

Burnout is slow and gradual. It takes little bits of your motivation and passion at a time. Sometimes it’s so slow that you barely notice or blame it on other things like stress and poor sleep.

Burnout doesn't come overnight; neither will recovery.

I don’t care what anyone else says the cure for burnout is. It has to be time because time is what gave us all burnout in the first place.

Will Tesla be able to maintain its lead in the EV market for very long?

This is one of the most pressing issues in the American auto sector today. One positive aspect of Tesla is the company's devoted customer base and recognizable name recognition (similar to Apple). It also invests more in research and development per vehicle than its rivals and has a head start in EV production.

Conversely, established automakers like Volkswagen are actively plotting their strategy to surpass Tesla. As the current market leaders, they have decades of experience in the auto industry and are spending billions to catch up.

We've visualized data from the EPA's 2022 Automotive Trends Report to bring you up to speed on this developing story.

Info for the Model Year of 2021

The full production data used in this infographic is for the 2021 model year, but it comes from a report for 2022.

Combined EV and PHEV output is shown in the table below (plug-in hybrid electric vehicle).

It is important to note that Toyota and Stellantis, the two largest legacy automakers in this dataset, only produced PHEVs. Toyota's first electric vehicle, the bZ4X, won't hit the market until 2023.

Stellantis seems to be falling even further behind, despite having enormous unrealized potential in its Jeep and Ram brands. Stellantis CEO Carlos Tavares said in a recent interview that the firm has budgeted $36 billion for electrification and software.

Legacy Brands with the Most Momentum

In the race to develop electric vehicles, some long-standing manufacturers have gotten the jump on their rivals.

Volkswagen, one of these storied manufacturers, has made a significant investment in electric vehicles (EVs) in the wake of the Dieselgate scandal. The company plans to roll out multiple EV models, including the ID.3 hatchback, ID.4 SUV, and ID. Buzz, with the goal of producing 22 million EVs by 2028. (an electric revival of the classic Microbus).

Even Ford is keeping up, having just announced an EV investment of $22 billion between 2021 and 2025. In November of 2022, the company manufactured their 150,000th Mustang Mach-E, and by the end of 2023, they hoped to have 270,000 of them in circulation.

Additionally, over 200,000 F-150 Lightnings have been reserved since Ford announced the truck. The Lightning is scheduled to have a production run of 15,000 in 2022, 55,000 in 2023, and 80,000 in 2024. Ford's main competitor in the electric pickup truck segment, Rivian, is on track to sell 25,000 vehicles by 2022.