Inspired by someone I never met

Inspiration is good and bad.

Paul Jarvis inspires me. He's a web person and writer who created his own category by being himself.

Paul said no thank you when everyone else was developing, building, and assuming greater responsibilities. This isn't success. He rewrote the rules. Working for himself, expanding at his own speed, and doing what he loves were his definitions of success.

Play with a problem that you have

The biggest problem can be not recognizing a problem.

Acceptance without question is deception. When you don't push limits, you forget how. You start thinking everything must be as it is.

For example: working. Paul worked a 9-5 agency work with little autonomy. He questioned whether the 9-5 was a way to live, not the way.

Another option existed. So he chipped away at how to live in this new environment.

Don't simply jump

Internet writers tell people considering quitting 9-5 to just quit. To throw in the towel. To do what you like.

The advice is harmful, despite the good intentions. People think quitting is hard. Like courage is the issue. Like handing your boss a resignation letter.

Nope. The tough part comes after. It’s easy to jump. Landing is difficult.

The landing

Paul didn't quit. Intelligent individuals don't. Smart folks focus on landing. They imagine life after 9-5.

Paul had been a web developer for a long time, had solid clients, and was respected. Hence if he pushed the limits and discovered another route, he had the potential to execute.

Working on the side

Society loves polarization. It’s left or right. Either way. Or chaos. It's 9-5 or entrepreneurship.

But like Paul, you can stretch polarization's limits. In-between exists.

You can work a 9-5 and side jobs (as I do). A mix of your favorites. The 9-5's stability and creativity. Fire and routine.

Remember you can't have everything but anything. You can create and work part-time.

My hybrid lifestyle

Not selling books doesn't destroy my world. My globe keeps spinning if my new business fails or if people don't like my Tweets. Unhappy algorithm? Cool. I'm not bothered (okay maybe a little).

The mix gives me the best of both worlds. To create, hone my skill, and grasp big-business basics. I like routine, but I also appreciate spending 4 hours on Saturdays writing.

Some days I adore leaving work at 5 pm and disconnecting. Other days, I adore having a place to write if inspiration strikes during a run or a discussion.

I’m a part-time creator

I’m a part-time creator. No, I'm not trying to quit. I don't work 5 pm - 2 am on the side. No, I'm not at $10,000 MRR.

I work part-time but enjoy my 9-5. My 9-5 has goodies. My side job as well.

It combines both to meet my lifestyle. I'm satisfied.

Join the Part-time Creators Club for free here. I’ll send you tips to enhance your creative game.

I pitched 10 brands per workday for a total of 200.

How did I do?

It was difficult.

I've never pitched so much.

What did this challenge teach me?

• the superiority of quality over quantity

• When you need help, outsource

• Don't disregard burnout in order to complete a challenge because it exists.

First, pitching brands for brand deals requires quality. Find firms that align with your brand to expose to your audience.

If you associate with any company, you'll lose audience loyalty. I didn't lose sight of that, but I couldn't resist finishing the task.

Outsourcing.

Delegating work to teammates is effective.

I wish I'd done it.

Three people can pitch 200 companies a month significantly faster than one.

One person does research, one to two do outreach, and one to two do follow-up and negotiating.

Simple.

In 2022, I'll outsource everything.

Burnout.

I felt this, so I slowed down at the end of the month.

Thanksgiving week in November was slow.

I was buying and decorating for Christmas. First time putting up outdoor holiday lights was fun.

Much was happening.

I'm not perfect.

I'm being honest.

The Outcomes

Less than 50 brands pitched.

Result: A deal with 3 brands.

I hoped for 4 brands with reaching out to 200 companies, so three with under 50 is wonderful.

That’s a 6% conversion rate!

Whoo-hoo!

I needed 2%.

Here's a screenshot from one of the deals I booked.

These companies fit my company well. Each campaign is different, but I've booked $2,450 in brand work with a couple of pending transactions for December and January.

$2,450 in brand work booked!

How did I do? You tell me.

Is this something you’d try yourself?

My nameless, faceless channel case study

The Numbers

I anonymize this YouTube channel.

It's in a trendy, crowded niche. Sharing it publicly will likely enhance competition.

I'll still share my dashboard numbers:

A year ago, the video was released.

What I earned

I'll stop stalling. Here's a screenshot of my YouTube statistics page displaying Adsense profits.

YouTube Adsense made me ZERO dollars.

OMG!

How is this possible?

YouTube Adsense can't monetize my niche. This is typical in faceless niches like TikTok's rain videos. If they were started a while ago, I'm sure certain rain accounts are monetized, but not today.

I actually started a soothing sounds faceless YouTube channel. This was another account of mine.

I looped Pexels films for hours. No background music, just wind, rain, etc.

People could watch these videos to relax or get ready for bed. They're ideal for background noise and relaxation.

They're long-lasting, too. It's easy to make a lot from YouTube Adsense if you insert ads.

Anyway, I tried to monetize it and couldn’t. This was about a year ago. That’s why I doubt new accounts in this genre would be able to get approved for ads.

Back to my faceless channel with 68 million views.

I received nothing from YouTube Adsense, but I made money elsewhere.

Getting paid by the gods of affiliate marketing

Place links in the video and other videos on the channel to get money. Visitors that buy through your affiliate link earn you a commission.

This video earned many clicks on my affiliate links.

I linked to a couple of Amazon products, a YouTube creator tool, my kofi link, and my subscribe link.

Sponsorships

Brands pay you to include ads in your videos.

This video led to many sponsorships.

I've done dozens of sponsorship campaigns that paid $40 to $50 for an end screen to $450 for a preroll ad.

Last word

Overall, I made less than $3,000.

If I had time, I'd be more proactive with sponsorships. You can pitch brand sponsorships. This actually works.

I'd do that if I could rewind time.

I still can, but I think the reaction rate would be higher closer to the viral video's premiere date.

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.

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.