Even_Adder , 5 months ago

Yes, using existing works as reference is obviously something that real human artists do all the time, there’s no arguing that is the case. That’s how people learn to create art to begin with.

But, the fact is, generative AI is not creative, nor does it understand what creativity is, nor will it ever. Because all it is doing is performing complex data statistical analysis algorithms to generate a matrix of pixels or a string of words.

Im sorry, but the person entering in the prompt to instruct the algorithm is also not doing anything creative either. Do you think it is art to go through a fast food drive through and place an order? That’s what people are objecting to - people calling themselves artists because they put some nonsense word salad together and then think what they get out of it is some unique thing that they feel they created and take ownership of. If not for the AI model they are using and the creative works it was trained on, they could not have created it or likely even imagined it without it.

I’d like to ask you what experience you have with generative art, because I’d like to explain a bit of what I know,

There’s also a spectrum of involvement depending on what tool you’re using. I know with web based interfaces don’t allow for a lot of freedom due to wanting to keep users from generating things outside their terms of use, but with open source models based on Stable Diffusion you can get a lot more involved and get a lot more freedom. We’re in a completely different world from March 2023 as far as generative tools go. Take a quick look at things work.

Let’s take these generation parameters for instance: 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 this way with just the base model or multiple LoRA at the same time at different strengths. Furthurmore you can adjust the UNet and Text Encoder by adding another colon like so : <lora:sarasf_V2-10:1:0.7> for even more varied results. Doing this allows you to separate the “idea” from the “look” of the LoRA. You can even use 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 are quality tags and 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. https://i.imgur.com/uAMbOUv.pnghttps://i.imgur.com/yjF1p0j.pnghttps://i.imgur.com/4uQfgR4.png

youtu.be/-JQDtzSaAuA?t=97

youtu.be/1d_jns4W1cM

www.youtube.com/watch?v=HtbEuERXSqk

Not all selfies are art, but you can make art with cameras. I think the same applies here.

People are actively losing their livelihoods because AI tech is being oversold and overhyped as something that it’s not. Execs are all jumping on the bandwagon and because they see AI as something that will save them a bunch of money, they are laying off people they think aren’t needed anymore. So, just try to incorporate that sentiment into your understanding of why people are also upset about AI. You may not be personally affected, but there are countless that are. In fact, over the next two years, as many as 203,000 entertainment workers in the US alone could be affected

This EFF article by Katharine Trendacosta and Cory Doctorow touches on this. I think it’s worth a read.

You want to have fun creating fancy kitbashed images based off of other people’s work, go right ahead.

This is misinformation, and not how the technology works. Here’s a quick video explanation,

Just don’t call it art and call yourself an artist, unless you could actually make it yourself using practical skills.

This is just snobbery that people have always used to devalue the efforts of others. Punching down and gatekeeping won’t solve your problems, the people you’re really mad at are above you.

https://i.imgur.com/0qP5dZ1.png https://i.imgur.com/0YC6cSH.png https://i.imgur.com/JrACQYc.png

Art is about bringing your ideas into the world, anything beyond that is fetish. Spending hundreds of hours learning a skill isn’t art, it’s work. While I believe the effort invested in a work can contribute to its depth and meaning, that doesn’t make them better than works without as much effort.

cont.