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> okay that’s just because you’re not good at using the models. You literally cannot respond with "you are holding it wrong" specially when I'm claiming that

> okay that’s just because you’re not good at using the models. You literally cannot respond with "you are holding it wrong" specially when I'm claiming that even for the popular _example prompts_ SD authors used they had to hand-pick the best random result over a sea of extremely shitty images. And even in the original paper they disclaim it by saying "oh, our model is just bad at limbs". No, it's not just bad at limbs. They just happened to try examples where it could particularly show how terrible it is at limbs (i.e. spider legged horses and the like). But in truth, it's just bad at everything.

kyleyeats 4y ago
This is like throwing your hammer down in frustration over each nail taking more than one swing.
- ChatGTP 4y ago
  I guess the difference is hammers are logical, simple tools to use with a known use case. They’re fairly hard to use incorrectly, although it does take some practice to use one, I’ll admit.
LewisVerstappen 4y ago
Nope. You literally just tried out one prompt and saw one good image and several bad ones and just shook your fist at the computer and gave up. I'll repeat myself. You have to play around with the models and learn how to use it (just like you have to do for everything) . > But in truth, it's just bad at everything Thousands of people (including myself) have had the complete opposite result and have gotten amazing pictures. You can play around with the finetuning with different models from civitai and get completely different art styles too. Like, this is so dumb I don't even know how to respond lol. You're like some guy who got a computer for the first time and couldn't figure out how to open the web browser, so he just dismissed it as useless.
- AshamedCaptain 4y ago
  I don't think you understand the point. Your claims that "all of this needs extensive tuning and hand-holding and picking results" do not help your argument, they help _mine_. Most egregious if you are even doing more tuning and cherry picking that the authors of the models are doing, which you definitely are.
  - SanderNL 4y ago
    It sounds magic to you because you are unskilled. Like people using the mouse for the first time. The things he talks about are very basic, very easy.
- whateveracct 4y ago
  I would rather just learn to draw than constantly write different text until it looks good Drawing isn't hard, you know
  - sebzim4500 4y ago
    I could spend 20,000 hours trying to learn to draw and I would still be far worse than what I could generate with Stable Diffusion + Control Net + etc.
    - whateveracct 4y ago
      I'm fairly sure if you can best stable diffusion after 7 years of daily 8h investment into drawing :)
      - johnnyyyy 4y ago
        I doubt you would be better than someone who would use Stable Diffusion for 7 years. and I don’t even include the technological advancements in the next 7 years.
        4y ago
cubefox 4y ago
"It's bad at everything" ... bad by what standards? Just a few years ago it would have been regarded as unbelievable science fiction that a model with such capabilities would soon be available. As soon as they are here, people stop being impressed. But the objective impressiveness of a technology is determined by how unlikely it was regarded in the past, not by how impressed people are now. People get used to things pretty quickly. Besides, there are models that are much more capable than Stable Diffusion. The best one currently seems to be Midjourney V5.
- wizzwizz4 4y ago
  > Just a few years ago it would have been regarded as unbelievable science fiction that a model with such capabilities would soon be available. No, it wouldn't have – not to people in the know. We just didn't have powerful enough computers back in the 90s. Sure, the techniques we've got now are better, but 90s algorithms (with modern supercomputers) can get you most of the way. Transformers are awesome, but they're not that much of a stretch from 90s technology. GANs are… ridiculously obvious, in hindsight, and people have been doing similar things since the dawn of AI; I imagine the people who came up with the idea were pretty confident of its capabilities even before they tested them. Both these kinds of system – and neural-net-based systems in general – are based around mimicry. Their inability to draw limbs, or to tell the truth, or count, are fundamental to how they function, and iterative improvement isn't going to fix them. Iterative improvement would be going faster, if researchers (outside of OpenAI and similar corporations) thought it was worthwhile to focus on improving these systems specifically. ChatGPT is not where transformers shine. StyleGAN3 is not where GANs shine. Midjourney is not where diffusion models shine. They're really useful lenses for visualising the way the architectures work, so they are useful test-beds for iterative algorithmic improvements…¹ but they aren't all that they're made out to be. ¹: See the 3 in StyleGAN3. Unlike the 4 in GPT-4, it actually means something more than "we made it bigger and changed the training data a bit".
  - cubefox 4y ago
    > No, it wouldn't have – not to people in the know. We just didn't have powerful enough computers back in the 90s. I'm not talking about the 90s. I'm talking about April 29, 2020.
dragonwriter 4y ago
> You literally cannot respond with "you are holding it wrong" specially when I'm claiming that even for the popular _example prompts_ SD authors used they had to hand-pick the best random result over a sea of extremely shitty images. I do a lot of my SD with a fixed seed and 1-image batches, once you know the specific model you are using getting decent pictures isn't hard, and zeroing in on a specific vision is easier with a fixed seed. Once I am happy with it, I might do multiple images without a fixed seed using the final prompt to see if I get something better If you are using a web interface that only use the base SD models and don’t allow negative prompts, yes, its harder (negative prompts and in particular good, model specific, negative embeddings are an SD superpower.)

kyleyeats 4y ago

This is like throwing your hammer down in frustration over each nail taking more than one swing.

ChatGTP 4y ago
I guess the difference is hammers are logical, simple tools to use with a known use case. They’re fairly hard to use incorrectly, although it does take some practice to use one, I’ll admit.

LewisVerstappen 4y ago

Nope. You literally just tried out one prompt and saw one good image and several bad ones and just shook your fist at the computer and gave up. I'll repeat myself. You have to play around with the models and learn how to use it (just like you have to do for everything) . > But in truth, it's just bad at everything Thousands of people (including myself) have had the complete opposite result and have gotten amazing pictures. You can play around with the finetuning with different models from civitai and get completely different art styles too. Like, this is so dumb I don't even know how to respond lol. You're like some guy who got a computer for the first time and couldn't figure out how to open the web browser, so he just dismissed it as useless.

AshamedCaptain 4y ago
I don't think you understand the point. Your claims that "all of this needs extensive tuning and hand-holding and picking results" do not help your argument, they help _mine_. Most egregious if you are even doing more tuning and cherry picking that the authors of the models are doing, which you definitely are.
- SanderNL 4y ago
  It sounds magic to you because you are unskilled. Like people using the mouse for the first time. The things he talks about are very basic, very easy.
whateveracct 4y ago
I would rather just learn to draw than constantly write different text until it looks good Drawing isn't hard, you know
- sebzim4500 4y ago
  I could spend 20,000 hours trying to learn to draw and I would still be far worse than what I could generate with Stable Diffusion + Control Net + etc.
  - whateveracct 4y ago
    I'm fairly sure if you can best stable diffusion after 7 years of daily 8h investment into drawing :)
    - johnnyyyy 4y ago
      I doubt you would be better than someone who would use Stable Diffusion for 7 years. and I don’t even include the technological advancements in the next 7 years.
      - 4y ago

cubefox 4y ago

"It's bad at everything" ... bad by what standards? Just a few years ago it would have been regarded as unbelievable science fiction that a model with such capabilities would soon be available. As soon as they are here, people stop being impressed. But the objective impressiveness of a technology is determined by how unlikely it was regarded in the past, not by how impressed people are now. People get used to things pretty quickly. Besides, there are models that are much more capable than Stable Diffusion. The best one currently seems to be Midjourney V5.

wizzwizz4 4y ago
> Just a few years ago it would have been regarded as unbelievable science fiction that a model with such capabilities would soon be available. No, it wouldn't have – not to people in the know. We just didn't have powerful enough computers back in the 90s. Sure, the techniques we've got now are better, but 90s algorithms (with modern supercomputers) can get you most of the way. Transformers are awesome, but they're not that much of a stretch from 90s technology. GANs are… ridiculously obvious, in hindsight, and people have been doing similar things since the dawn of AI; I imagine the people who came up with the idea were pretty confident of its capabilities even before they tested them. Both these kinds of system – and neural-net-based systems in general – are based around mimicry. Their inability to draw limbs, or to tell the truth, or count, are fundamental to how they function, and iterative improvement isn't going to fix them. Iterative improvement would be going faster, if researchers (outside of OpenAI and similar corporations) thought it was worthwhile to focus on improving these systems specifically. ChatGPT is not where transformers shine. StyleGAN3 is not where GANs shine. Midjourney is not where diffusion models shine. They're really useful lenses for visualising the way the architectures work, so they are useful test-beds for iterative algorithmic improvements…¹ but they aren't all that they're made out to be. ¹: See the 3 in StyleGAN3. Unlike the 4 in GPT-4, it actually means something more than "we made it bigger and changed the training data a bit".
- cubefox 4y ago
  > No, it wouldn't have – not to people in the know. We just didn't have powerful enough computers back in the 90s. I'm not talking about the 90s. I'm talking about April 29, 2020.

dragonwriter 4y ago

> You literally cannot respond with "you are holding it wrong" specially when I'm claiming that even for the popular _example prompts_ SD authors used they had to hand-pick the best random result over a sea of extremely shitty images. I do a lot of my SD with a fixed seed and 1-image batches, once you know the specific model you are using getting decent pictures isn't hard, and zeroing in on a specific vision is easier with a fixed seed. Once I am happy with it, I might do multiple images without a fixed seed using the final prompt to see if I get something better If you are using a web interface that only use the base SD models and don’t allow negative prompts, yes, its harder (negative prompts and in particular good, model specific, negative embeddings are an SD superpower.)