Record them anyway. There’ll be more ways to de-anonymize them in the future.

This is definitely the type that grants wishes.

But the people making money off of all of that are mad now, hence this article.

You can’t be sued over or copyright styles. Studio Ponoc is made up of ex-Ghibli staff, and they have been releasing moves for a while. Stop spreading misinformation.

https://www.imdb.com/title/tt16369708/

https://www.imdb.com/title/tt15054592/

https://www.imdb.com/title/tt8223844/

https://www.imdb.com/title/tt6336356/

So you don’t interact with AI stuff outside of that? Have you seen any cool research papers or messed with any local models recently? Getting a bit of experience with the stuff can help you better inform people and see through the more bogus headlines.

It definitely seems that way depending on what media you choose to consume. You should try to balance the doomer scroll with actual research and open source news.

Ok, but is training an AI so it can plagiarize, often verbatim or with extreme visual accuracy, fair use? I see the 2 first articles argue that it is, but they don’t mention the many cases where the crawlers and scrappers ignored rules set up to tell them to piss off. That would certainly invalidate several cases of fair use

You can plagiarize with a computer with copy & paste too. That doesn’t change the fact that computers have legitimate non-infringing use cases.

Instead of charging for everything they scrap, law should force them to release all their data and training sets for free.

I agree

I’d wager 99.9% of the art and content created by AI could go straight to the trashcan and nobody would miss it. Comparing AI to the internet is like comparing writing to doing drugs.

But 99.9% of the internet is stuff that no one would miss. Things don’t have to have value to you to be worth having around. That trash could serve as inspiration for your 0.1% of people or garner feedback for people to improve.

But the law is largely the reverse. It only denies use of copyright works in certain ways. Using things “without permission” forms the bedrock on which artistic expression and free speech are built upon.

AI training isn’t only for mega-corporations. Setting up barriers like these only benefit the ultra-wealthy and will end with corporations gaining a monopoly of a public technology by making it prohibitively expensive and cumbersome for regular folks. What the people writing this article want would mean the end of open access to competitive, corporate-independent tools and would jeopardize research, reviews, reverse engineering, and even indexing information. They want you to believe that analyzing things without permission somehow goes against copyright, when in reality, fair use is a part of copyright law, and the reason our discourse isn’t wholly controlled by mega-corporations and the rich.

I recommend reading this article by Kit Walsh, and this one by Tory Noble staff attorneys at the EFF, this one by Katherine Klosek, the director of information policy and federal relations at the Association of Research Libraries, and these two by Cory Doctorow.

Is Miyazaki going to go in on his son again?

Fuck 'em. I don’t care. I hope no one uses them.

He and I had this talk two years ago.

He’s not trying to get copyright for something he generated, he’s trying to have the court award copyright to his AI system “DABUS”, but copyright is for humans. Humans using Gen AI are eligible for copyright according to the latest guidance by the United States Copyright Office.

We’re saying the same thing here. It’s just your characterization of gen AI as a “tech-enabled copying device” isn’t accurate. You should read this which breaks down how all this works.

The fair use doctrine allows you to do just that. The alternative would be someone being able to publish a book and then shutting anyone else out of publishing, discussing, or building on their ideas without them getting a kick-back.

I just want to mess with this one too. I had a hard time finding an abliterated one before that didn’t fail the Tiananmen Square question regularly.

Can’t wait to try a distillation. The full model is huge.

Do you get them by mail?

Swarm UI is pretty beginner-friendly. This tutorial will get you started.

This seems like a good place for discussion so if you’ll humor me, I’d like to explain some things you might find in a prompt, maybe some things you weren’t aware you could do. Web services don’t allow for a lot of freedom to keep users from generating things outside their terms of use, but with open source tools you can get a lot more involved.

Take a look at these generation parameters: sarasf, 1girl, solo, robe, long sleeves, white footwear, smile, wide sleeves, closed mouth, blush, looking at viewer, sitting, tree stump, forest, tree, sky, traditional media, 1990s \(style\), <lora:sarasf_V2-10:0.7>

Negative prompt: (worst quality, low quality:1.4), FastNegativeV2

Steps: 21, VAE: kl-f8-anime2.ckpt, Size: 512x768, Seed: 2303584416, Model: Based64mix-V3-Pruned, Version: v1.6.0, Sampler: DPM++ 2M Karras, VAE hash: df3c506e51, CFG scale: 6, Clip skip: 2, Model hash: 98a1428d4c, Hires steps: 16, "sarasf_V2-10: 1ca692d73fb1", Hires upscale: 2, Hires upscaler: 4x_foolhardy_Remacri, "FastNegativeV2: a7465e7cc2a2",

ADetailer model: face_yolov8n.pt, ADetailer version: 23.11.1, Denoising strength: 0.38, ADetailer mask blur: 4, ADetailer model 2nd: Eyes.pt, ADetailer confidence: 0.3, ADetailer dilate erode: 4, ADetailer mask blur 2nd: 4, ADetailer confidence 2nd: 0.3, ADetailer inpaint padding: 32, ADetailer dilate erode 2nd: 4, ADetailer denoising strength: 0.42, ADetailer inpaint only masked: True, ADetailer inpaint padding 2nd: 32, ADetailer denoising strength 2nd: 0.43, ADetailer inpaint only masked 2nd: True

To break down a bit of what’s going on here, I’d like to explain some of the elements found here. sarasf is the token for the LoRA of the character in this image, and <lora:sarasf_V2-10:0.7> is the character LoRA for Sarah from Shining Force II. LoRA are like supplementary models you use on top of a base model to capture a style or concept, like a patch. Some LoRA don’t have activation tokens, and some with them can be used without their token to get different results.

The 0.7 in <lora:sarasf_V2-10:0.7> refers to the strength at which the weights from the LoRA are applied to the output. Lowering the number causes the concept to manifest weaker in the output. You can blend styles and concepts this way with just the base model or multiple LoRA at the same time at different strengths. You can even take a monochrome LoRA and take the weight into the negative to get some crazy colors.

The Negative Prompt is where you include things you don’t want in your image. (worst quality, low quality:1.4), here have their attention set to 1.4, attention is sort of like weight, but for tokens. LoRA bring their own weights to add onto the model, whereas attention on tokens works completely inside the weights they’re given. In this negative prompt FastNegativeV2 is an embedding known as a Textual Inversion. It’s sort of like a crystallized collection of tokens that tell the model something precise you want without having to enter the tokens yourself or mess around with the attention manually. Embeddings you put in the negative prompt are known as Negative Embeddings.

In the next part, Steps stands for how many steps you want the model to take to solve the starting noise into an image. More steps take longer. VAE is the name of the Variational Autoencoder used in this generation. The VAE is responsible for working with the weights to make each image unique. A mismatch of VAE and model can yield blurry and desaturated images, so some models opt to have their VAE baked in, Size are the dimensions in pixels the image will be generated at. Seed is the number representation of the starting noise for the image. You need this to be able to reproduce a specific image.

Model is the name of the model used, and Sampler is the name of the algorithm that solves the noise into an image. There are a few different samplers, also known as schedulers, each with their own trade-offs for speed, quality, and memory usage. CFG is basically how close you want the model to follow your prompt. Some models can’t handle high CFG values and flip out, giving over-exposed or nonsense output. Hires steps represents the amount of steps you want to take on the second pass to upscale the output. This is necessary to get higher resolution images without visual artifacts. Hires upscaler is the name of the model that was used during the upscaling step, and again there are a ton of those with their own trade-offs and use cases.

After ADetailer are the parameters for Adetailer, an extension that does a post-process pass to fix things like broken anatomy, faces, and hands. We’ll just leave it at that because I don’t feel like explaining all the different settings found there.

I could continue if you want to hear more.

I remember you. It was my thread you commented in. You’re mad because a moderator removed your comment after you showed up and started antagonizing no one in particular?

I’d like to ask you a question. How much experience do you have with any Stable Diffusion tools?