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Amelia Winger-Bearskin

Amelia Winger-Bearskin

1 year ago

Hate NFTs? I must break some awful news to you...

More on NFTs & Art

Jake Prins

Jake Prins

1 year ago

What are NFTs 2.0 and what issues are they meant to address?

New standards help NFTs reach their full potential.

NFTs 2.0

NFTs lack interoperability and functionality. They have great potential but are mostly speculative. To maximize NFTs, we need flexible smart contracts.

Current requirements are too restrictive.

Most NFTs are based on ERC-721, which makes exchanging them easy. CryptoKitties, a popular online game, used the 2017 standard to demonstrate NFTs' potential.

This simple standard includes a base URI and incremental IDs for tokens. Add the tokenID to the base URI to get the token's metadata.

This let creators collect NFTs. Many NFT projects store metadata on IPFS, a distributed storage network, but others use Google Drive. NFT buyers often don't realize that if the creators delete or move the files, their NFT is just a pointer.

This isn't the standard's biggest issue. There's no way to validate NFT projects.

Creators are one of the most important aspects of art, but nothing is stored on-chain.

ERC-721 contracts only have a name and symbol.

Most of the data on OpenSea's collection pages isn't from the NFT's smart contract. It was added through a platform input field, so it's in the marketplace's database. Other websites may have different NFT information.

In five years, your NFT will be just a name, symbol, and ID.

Your NFT doesn't mention its creators. Although the smart contract has a public key, it doesn't reveal who created it.

The NFT's creators and their reputation are crucial to its value. Think digital fashion and big brands working with well-known designers when more professionals use NFTs. Don't you want them in your NFT?

Would paintings be as valuable if their artists were unknown? Would you believe it's real?

Buying directly from an on-chain artist would reduce scams. Current standards don't allow this data.

Most creator profiles live on centralized marketplaces and could disappear. Current platforms have outpaced underlying standards. The industry's standards are lagging.

For NFTs to grow beyond pointers to a monkey picture file, we may need to use new Web3-based standards.

Introducing NFTs 2.0

Fabian Vogelsteller, creator of ERC-20, developed new web3 standards. He proposed LSP7 Digital Asset and LSP8 Identifiable Digital Asset, also called NFT 2.0.

NFT and token metadata inputs are extendable. Changes to on-chain metadata inputs allow NFTs to evolve. Instead of public keys, the contract can have Universal Profile addresses attached. These profiles show creators' faces and reputations. NFTs can notify asset receivers, automating smart contracts.

LSP7 and LSP8 use ERC725Y. Using a generic data key-value store gives contracts much-needed features:

  • The asset can be customized and made to stand out more by allowing for unlimited data attachment.

  • Recognizing changes to the metadata

  • using a hash reference for metadata rather than a URL reference

This base will allow more metadata customization and upgradeability. These guidelines are:

  • Genuine and Verifiable Now, the creation of an NFT by a specific Universal Profile can be confirmed by smart contracts.

  • Dynamic NFTs can update Flexible & Updatable Metadata, allowing certain things to evolve over time.

  • Protected metadata Now, secure metadata that is readable by smart contracts can be added indefinitely.

  • Better NFTS prevent the locking of NFTs by only being sent to Universal Profiles or a smart contract that can interact with them.

Summary

NFTS standards lack standardization and powering features, limiting the industry.

ERC-721 is the most popular NFT standard, but it only represents incremental tokenIDs without metadata or asset representation. No standard sender-receiver interaction or security measures ensure safe asset transfers.

NFT 2.0 refers to the new LSP7-DigitalAsset and LSP8-IdentifiableDigitalAsset standards.

They have new standards for flexible metadata, secure transfers, asset representation, and interactive transfer.

With NFTs 2.0 and Universal Profiles, creators could build on-chain reputations.

NFTs 2.0 could bring the industry's needed innovation if it wants to move beyond trading profile pictures for speculation.

Protos

Protos

2 years ago

Plagiarism on OpenSea: humans and computers

OpenSea, a non-fungible token (NFT) marketplace, is fighting plagiarism. A new “two-pronged” approach will aim to root out and remove copies of authentic NFTs and changes to its blue tick verified badge system will seek to enhance customer confidence.

According to a blog post, the anti-plagiarism system will use algorithmic detection of “copymints” with human reviewers to keep it in check.

Last year, NFT collectors were duped into buying flipped images of the popular BAYC collection, according to The Verge. The largest NFT marketplace had to remove its delay pay minting service due to an influx of copymints.

80% of NFTs removed by the platform were minted using its lazy minting service, which kept the digital asset off-chain until the first purchase.

NFTs copied from popular collections are opportunistic money-grabs. Right-click, save, and mint the jacked JPEGs that are then flogged as an authentic NFT.

The anti-plagiarism system will scour OpenSea's collections for flipped and rotated images, as well as other undescribed permutations. The lack of detail here may be a deterrent to scammers, or it may reflect the new system's current rudimentary nature.

Thus, human detectors will be needed to verify images flagged by the detection system and help train it to work independently.

“Our long-term goal with this system is two-fold: first, to eliminate all existing copymints on OpenSea, and second, to help prevent new copymints from appearing,” it said.

“We've already started delisting identified copymint collections, and we'll continue to do so over the coming weeks.”

It works for Twitter, why not OpenSea

OpenSea is also changing account verification. Early adopters will be invited to apply for verification if their NFT stack is worth $100 or more. OpenSea plans to give the blue checkmark to people who are active on Twitter and Discord.

This is just the beginning. We are committed to a future where authentic creators can be verified, keeping scammers out.

Also, collections with a lot of hype and sales will get a blue checkmark. For example, a new NFT collection sold by the verified BAYC account will have a blue badge to verify its legitimacy.

New requests will be responded to within seven days, according to OpenSea.

These programs and products help protect creators and collectors while ensuring our community can confidently navigate the world of NFTs.

By elevating authentic content and removing plagiarism, these changes improve trust in the NFT ecosystem, according to OpenSea.

OpenSea is indeed catching up with the digital art economy. Last August, DevianArt upgraded its AI image recognition system to find stolen tokenized art on marketplaces like OpenSea.

It scans all uploaded art and compares it to “public blockchain events” like Ethereum NFTs to detect stolen art.

nft now

nft now

2 years ago

Instagram NFTs Are Here… How does this affect artists?

Instagram (IG) is officially joining NFT. With the debut of new in-app NFT functionalities, influential producers can interact with blockchain tech on the social media platform.

Meta unveiled intentions for an Instagram NFT marketplace in March, but these latest capabilities focus more on content sharing than commerce. And why shouldn’t they? IG's entry into the NFT market is overdue, given that Twitter and Discord are NFT hotspots.

The NFT marketplace/Web3 social media race has continued to expand, with the expected Coinbase NFT Beta now live and blazing a trail through the NFT ecosystem.

IG's focus is on visual art. It's unlike any NFT marketplace or platform. IG NFTs and artists: what's the deal? Let’s take a look.

What are Instagram’s NFT features anyways?

As said, not everyone has Instagram's new features. 16 artists, NFT makers, and collectors can now post NFTs on IG by integrating third-party digital wallets (like Rainbow or MetaMask) in-app. IG doesn't charge to publish or share digital collectibles.

NFTs displayed on the app have a "shimmer" aesthetic effect. NFT posts also have a "digital collectable" badge that lists metadata such as the creator and/or owner, the platform it was created on, a brief description, and a blockchain identification.

Meta's social media NFTs have launched on Instagram, but the company is also preparing to roll out digital collectibles on Facebook, with more on the way for IG. Currently, only Ethereum and Polygon are supported, but Flow and Solana will be added soon.

How will artists use these new features?

Artists are publishing NFTs they developed or own on IG by linking third-party digital wallets. These features have no NFT trading aspects built-in, but are aimed to let authors share NFTs with IG audiences.

Creators, like IG-native aerial/street photographer Natalie Amrossi (@misshattan), are discovering novel uses for IG NFTs.

Amrossi chose to not only upload his own NFTs but also encourage other artists in the field. "That's the beauty of connecting your wallet and sharing NFTs. It's not just what you make, but also what you accumulate."

Amrossi has been producing and posting Instagram art for years. With IG's NFT features, she can understand Instagram's importance in supporting artists.

Web2 offered Amrossi the tools to become an artist and make a life. "Before 'influencer' existed, I was just making art. Instagram helped me reach so many individuals and brands, giving me a living.

Even artists without millions of viewers are encouraged to share NFTs on IG. Wilson, a relatively new name in the NFT space, seems to have already gone above and beyond the scope of these new IG features. By releasing "Losing My Mind" via IG NFT posts, she has evaded the lack of IG NFT commerce by using her network to market her multi-piece collection.

"'Losing My Mind' is a long-running photo series. Wilson was preparing to release it as NFTs before IG approached him, so it was a perfect match.

Wilson says the series is about Black feminine figures and media depiction. Respectable effort, given POC artists have been underrepresented in NFT so far.

“Over the past year, I've had mental health concerns that made my emotions so severe it was impossible to function in daily life, therefore that prompted this photo series. Every Wednesday and Friday for three weeks, I'll release a new Meta photo for sale.

Wilson hopes these new IG capabilities will help develop a connection between the NFT community and other internet subcultures that thrive on Instagram.

“NFTs can look scary as an outsider, but seeing them on your daily IG feed makes it less foreign,” adds Wilson. I think Instagram might become a hub for NFT aficionados, making them more accessible to artists and collectors.

What does it all mean for the NFT space?

Meta's NFT and metaverse activities will continue to impact Instagram's NFT ecosystem. Many think it will be for the better, as IG NFT frauds are another problem hurting the NFT industry.

IG's new NFT features seem similar to Twitter's PFP NFT verifications, but Instagram's tools should help cut down on scams as users can now verify the creation and ownership of whole NFT collections included in IG posts.

Given the number of visual artists and NFT creators on IG, it might become another hub for NFT fans, as Wilson noted. If this happens, it raises questions about Instagram success. Will artists be incentivized to distribute NFTs? Or will those with a large fanbase dominate?

Elise Swopes (@swopes) believes these new features should benefit smaller artists. Swopes was one of the first profiles placed to Instagram's original suggested user list in 2012.

Swopes says she wants IG to be a magnet for discovery and understands the value of NFT artists and producers.

"I'd love to see IG become a focus of discovery for everyone, not just the Beeples and Apes and PFPs. That's terrific for them, but [IG NFT features] are more about using new technology to promote emerging artists, Swopes added.

“Especially music artists. It's everywhere. Dancers, writers, painters, sculptors, musicians. My element isn't just for digital artists; it can be anything. I'm delighted to witness people's creativity."

Swopes, Wilson, and Amrossi all believe IG's new features can help smaller artists. It remains to be seen how these new features will effect the NFT ecosystem once unlocked for the rest of the IG NFT community, but we will likely see more social media NFT integrations in the months and years ahead.

Read the full article here

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Will Lockett

Will Lockett

1 year ago

Tesla recently disclosed its greatest secret.

Photo by Taun Stewart on Unsplash

The VP has revealed a secret that should frighten the rest of the EV world.

Tesla led the EV revolution. Elon Musk's invention offers a viable alternative to gas-guzzlers. Tesla has lost ground in recent years. VW, BMW, Mercedes, and Ford offer EVs with similar ranges, charging speeds, performance, and cost. Tesla's next-generation 4680 battery pack, Roadster, Cybertruck, and Semi were all delayed. CATL offers superior batteries than the 4680. Martin Viecha, Tesla's Vice President, recently told Business Insider something that startled the EV world and will establish Tesla as the EV king.

Viecha mentioned that Tesla's production costs have dropped 57% since 2017. This isn't due to cheaper batteries or devices like Model 3. No, this is due to amazing factory efficiency gains.

Musk wasn't crazy to want a nearly 100% automated production line, and Tesla's strategy of sticking with one model and improving it has paid off. Others change models every several years. This implies they must spend on new R&D, set up factories, and modernize service and parts systems. All of this costs a ton of money and prevents them from refining production to cut expenses.

Meanwhile, Tesla updates its vehicles progressively. Everything from the backseats to the screen has been enhanced in a 2022 Model 3. Tesla can refine, standardize, and cheaply produce every part without changing the production line.

In 2017, Tesla's automobile production averaged $84,000. In 2022, it'll be $36,000.

Mr. Viecha also claimed that new factories in Shanghai and Berlin will be significantly cheaper to operate once fully operating.

Tesla's hand is visible. Tesla selling $36,000 cars for $60,000 This barely beats the competition. Model Y long-range costs just over $60,000. Tesla makes $24,000+ every sale, giving it a 40% profit margin, one of the best in the auto business.

VW I.D4 costs about the same but makes no profit. Tesla's rivals face similar challenges. Their EVs make little or no profit.

Tesla costs the same as other EVs, but they're in a different league.

But don't forget that the battery pack accounts for 40% of an EV's cost. Tesla may soon fully utilize its 4680 battery pack.

The 4680 battery pack has larger cells and a unique internal design. This means fewer cells are needed for a car, making it cheaper to assemble and produce (per kWh). Energy density and charge speeds increase slightly.

Tesla underestimated the difficulty of making this revolutionary new cell. Each time they try to scale up production, quality drops and rejected cells rise.

Tesla recently installed this battery pack in Model Ys and is scaling production. If they succeed, Tesla battery prices will plummet.

Tesla's Model Ys 2170 battery costs $11,000. The same size pack with 4680 cells costs $3,400 less. Once scaled, it could be $5,500 (50%) less. The 4680 battery pack could reduce Tesla production costs by 20%.

With these cost savings, Tesla could sell Model Ys for $40,000 while still making a profit. They could offer a $25,000 car.

Even with new battery technology, it seems like other manufacturers will struggle to make EVs profitable.

Teslas cost about the same as competitors, so don't be fooled. Behind the scenes, they're still years ahead, and the 4680 battery pack and new factories will only increase that lead. Musk faces a first. He could sell Teslas at current prices and make billions while other manufacturers struggle. Or, he could massively undercut everyone and crush the competition once and for all. Tesla and Elon win.

Michael Le

Michael Le

2 years ago

Union LA x Air Jordan 2 “Future Is Now” PREVIEW

With the help of Virgil Abloh and Union LA‘s Chris Gibbs, it's now clear that Jordan Brand intended to bring the Air Jordan 2 back in 2022.
The “Future Is Now” collection includes two colorways of MJ's second signature as well as an extensive range of apparel and accessories.

“We wanted to juxtapose what some futuristic gear might look like after being worn and patina'd,”
Union stated on the collaboration's landing page.

“You often see people's future visions that are crisp and sterile. We thought it would be cool to wear it in and make it organic...”

The classic co-branding appears on short-sleeve tees, hoodies, and sweat shorts/sweat pants, all lightly distressed at the hems and seams.
Also, a filtered black-and-white photo of MJ graces the adjacent long sleeves, labels stitch into the socks, and the Jumpman logo adorns the four caps.
Liner jackets and flight pants will also be available, adding reimagined militaria to a civilian ensemble.
The Union LA x Air Jordan 2 (Grey Fog and Rattan) shares many of the same beats. Vintage suedes show age, while perforations and detailing reimagine Bruce Kilgore's design for the future.
The “UN/LA” tag across the modified eye stays, the leather patch across the tongue, and the label that wraps over the lateral side of the collar complete the look.
The footwear will also include a Crater Slide in the “Grey Fog” color scheme.

BUYING

On 4/9 and 4/10 from 9am-3pm, Union LA will be giving away a pair of Air Jordan 2s at their La Brea storefront (110 S. LA BREA AVE. LA, CA 90036). The raffle is only open to LA County residents with a valid CA ID. You must enter by 11:59pm on 4/10 to win. Winners will be notified via email.



Dmitrii Eliuseev

Dmitrii Eliuseev

1 year ago

Creating Images on Your Local PC Using Stable Diffusion AI

Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Image generated by Stable Diffusion 2.1

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:

Model architecture, Source © https://arxiv.org/pdf/2112.10752.pdf

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:

The SD V1.4 first example, Image by the author

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:

The SD V1.4 second example, Image by the author

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):

An image sketch, Image by the author

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:

The SD V1.4 third example, Image by the author

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:

Stable Diffusion UI © Image by author

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:

A Stable Diffusion 2.1 example

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 4X upscaler running on CPU © Image by author

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:

“Modern art painting” © Google’s Image search result

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.