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.

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!

Software can change any company.

Software is becoming essential. Everyone should consider how software affects their lives and others'.

Software on your phone, tablet, or computer offers many new options. We're experts in enough ways now.

Software Business Ideas will be popular by 2023.

ERP Programs

ERP software meets rising demand.

ERP solutions automate and monitor tasks that large organizations, businesses, and even schools would struggle to do manually.

ERP software could reach $49 billion by 2024.

CRM Program

CRM software is a must-have for any customer-focused business.

Having an open mind about your business services and products allows you to change platforms.

Another company may only want your CRM service.

Medical software

Healthcare facilities need reliable, easy-to-use software.

EHRs, MDDBs, E-Prescribing, and more are software options.

The global medical software market could reach $11 billion by 2025, and mobile medical apps may follow.

Presentation Software in the Cloud

SaaS presentation tools are great.

They're easy to use, comprehensive, and full of traditional Software features.

In today's cloud-based world, these solutions make life easier for people. We don't know about you, but we like it.

Software for Project Management

People began working remotely without signs or warnings before the 2020 COVID-19 pandemic.

Many organizations found it difficult to track projects and set deadlines.

With PMP software tools, teams can manage remote units and collaborate effectively.

App for Blockchain-Based Invoicing

This advanced billing and invoicing solution is for businesses and freelancers.

These blockchain-based apps can calculate taxes, manage debts, and manage transactions.

Intelligent contracts help blockchain track transactions more efficiently. It speeds up and improves invoice generation.

Software for Business Communications

Internal business messaging is tricky.

Top business software tools for communication can share files, collaborate on documents, host video conferences, and more.

Payroll Automation System

Software development also includes developing an automated payroll system.

These software systems reduce manual tasks for timely employee payments.

These tools help enterprise clients calculate total wages quickly, simplify tax calculations, improve record-keeping, and support better financial planning.

System for Detecting Data Leaks

Both businesses and individuals value data highly. Yahoo's data breach is dangerous because of this.

This area of software development can help people protect their data.

You can design an advanced data loss prevention system.

AI-based Retail System

AI-powered shopping systems are popular. The systems analyze customers' search and purchase patterns and store history and are equipped with a keyword database.

These systems offer many customers pre-loaded products.

AI-based shopping algorithms also help users make purchases.

Software for Detecting Plagiarism

Software can help ensure your projects are original and not plagiarized.

These tools detect plagiarized content that Google, media, and educational institutions don't like.

Software for Converting Audio to Text

Machine Learning converts speech to text automatically.

These programs can quickly transcribe cloud-based files.

Software for daily horoscopes

Daily and monthly horoscopes will continue to be popular.

Software platforms that can predict forecasts, calculate birth charts, and other astrology resources are good business ideas.

E-learning Programs

Traditional study methods are losing popularity as virtual schools proliferate and physical space shrinks.

Khan Academy online courses are the best way to keep learning.

Online education portals can boost your learning. If you want to start a tech startup, consider creating an e-learning program.

Conclusion

Software is booming. There's never been a better time to start a software development business, with so many people using computers and smartphones. This article lists eight business ideas for 2023. Consider these ideas if you're just starting out or looking to expand.

Calendars are great unpaid employees.

After using Notion to organize my next three months' goals, my days were a mess.

I grew very chaotic afterward. I was overwhelmed, unsure of what to do, and wasting time attempting to plan the day after it had started.

Imagine if our skeletons were on the outside. Doesn’t work.

The goals were too big; I needed to break them into smaller chunks. But how?

Enters Google Calendar

RescueTime’s recommendations took me seven hours to make a daily planner. This epic narrative begins with a sheet of paper and concludes with a daily calendar that helps me focus and achieve more goals. Ain’t nobody got time for “what’s next?” all day.

Onward!

Return to the Paleolithic Era

Plan in writing.

Not on the list, but it helped me plan my day. Physical writing boosts creativity and recall.

Find My Heart

i.e. prioritize

RescueTime suggested I prioritize before planning. Personal and business goals were proposed.

My top priorities are to exercise, eat healthily, spend time in nature, and avoid stress.

Priorities include writing and publishing Medium articles, conducting more freelance editing and Medium outreach, and writing/editing sci-fi books.

These eight things will help me feel accomplished every day.

Make a baby calendar.

Create daily calendar templates.

Make family, pleasure, etc. calendars.

Google Calendar instructions:

• Other calendars

• Press the “+” button

• Create a new calendar

• Create recurring events for each day

My calendar, without the template:

Empty, so I can fill it with vital tasks.

With the template:

My daily skeleton corresponds with my priorities. I've been overwhelmed for years because I lack daily, weekly, monthly, and yearly structure.

Google Calendars helps me reach my goals and focus my energy.

Get your colored pencils ready

Time-block color-coding.

Color labeling lets me quickly see what's happening. Maybe you are too.

Google Calendar instructions:

• Determine which colors correspond to each time block.

• When establishing new events, select a color.

• Save

My calendar is color-coded as follows:

• Yellow — passive income or other future-related activities

• Red — important activities, like my monthly breast exam

• Flamingo — shallow work, like emails, Twitter, etc.

• Blue — all my favorite activities, like walking, watching comedy, napping, and sleeping. Oh, and eating.

• Green — money-related events required for this adulting thing

• Purple — writing-related stuff

Associating a time block with a color helps me stay focused. Less distractions mean faster work.

Open My Email

aka receive a daily email from Google Calendar.

Google Calendar sends a daily email feed of your calendars. I sent myself the template calendar in this email.

Google Calendar instructions:

• Access settings

• Select the calendar that you want to send (left side)

• Go down the page to see more alerts

• Under the daily agenda area, click Email.

Get in Touch With Your Red Bull Wings — Naturally

aka audit your energy levels.

My daily planner has arrows. These indicate how much energy each activity requires or how much I have.

Rightward arrow denotes medium energy.

I do my Medium and professional editing in the morning because it's energy-intensive.

Niharikaa Sodhi recommends morning Medium editing.

I’m a morning person. As long as I go to bed at a reasonable time, 5 a.m. is super wild GO-TIME. It’s like the world was just born, and I marvel at its wonderfulness.

Freelance editing lets me do what I want. An afternoon snooze will help me finish on time.

Ditch Schedule View

aka focus on the weekly view.

RescueTime advocated utilizing the weekly view of Google Calendar, so I switched.

When you launch the phone app or desktop calendar, a red line shows where you are in the day.

I'll follow the red line's instructions. My digital supervisor is easy to follow.

In the image above, it's almost 3 p.m., therefore the red line implies it's time to snooze.

I won't forget this block ;).

Reduce the Lighting

aka dim previous days.

This is another Google Calendar feature I didn't know about. Once the allotted time passes, the time block dims. This keeps me present.

Google Calendar instructions:

• Access settings

• remaining general

• To view choices, click.

• Check Diminish the glare of the past.

Bonus

Two additional RescueTimes hacks:

Maintain a space between tasks

I left 15 minutes between each time block to transition smoothly. This relates to my goal of less stress. If I set strict start and end times, I'll be stressed.

With a buffer, I can breathe, stroll around, and start the following time block fresh.

Find a time is related to the buffer.

This option allows you conclude small meetings five minutes early and longer ones ten. Before the next meeting, relax or go wild.

Decide on a backup day.

This productivity technique is amazing.

Spend this excess day catching up on work. It helps reduce tension and clutter.

That's all I can say about Google Calendar's functionality.