The thing about textual inversions is that they create embeddings that are cross combatable with the base models. A textual inversion trained with SD 1.5 will work with all 1.5 based models, and here is the kicker, you can combine them without having to do any model merging. That is HUGE.

@lewingtonn

Жыл бұрын

yeah, the flexibility of textual inversion is a big factor, also it's really cool conceptually!!

@zyin

Жыл бұрын

The video really should have mentioned this, it's an incredible advantage for embeddings that was just left out.

@neilslater8223

Жыл бұрын

Yes, combining two, three or more Dreambooth models is possible, but it takes time and generates yet another 2GB+ model that you need to save somewhere. Whilst textual inversions can be used flexibly within the prompts in any combination, including weighting them, using as negative prompts, all on the fly with no extra file management However, textual inversion cannot learn to output things that the base model is not able to do at all. So depending on the base model, it may not be possible to train a textual inversion for a specific concept.

@infocyde2024

Жыл бұрын

@@expodemita I do not think they are compatible between 1.4/1.5 and 2.0 2.1. 2.0 and 2.1 should be compatible.

@alexandrmalafeev7182

Жыл бұрын

@@infocyde2024 2.0 and 2.1 are for sure

Much appreciated, having someone clever distil all of this dense information down and explain it succinctly and with so much enthusiasm is so refreshing!

Awesome comparison mate, great addition with the statistics, thanks a lot

Thanks for your hard work putting this together, very helpful to evolve my understanding of the different approaches. Much appreciated!

Thanks for explaining these so well, your visual diagrams are great!

This video is exactly what I needed, and you went about it in the best way possible. Thanks for this

I love it, love your promises on what we are going to get from your video at the very starting few seconds of the video itself, keep it going man, love your channel and your energy.

This was a very informative video in fact, thank you! And I like your very dramatic delivery of the content! :)

Liked and Subscribed, Thank you for all the hard work!

I greatly appreciate this video sir! It is really helpful for me to have context of how things actually work behind the scenes to make mental connections and improve how I interact with the external program.

Thanks so much for this. Very underrated channel, literally was thinking something like this would be really helpful.

Outstanding job explaining these concepts! Well done!

you deserve more subscribers, only channel I found that actually delivers what you need to know

Such an EXCELLENT video. Very very well researched and perfectly presented. Thanks for sharing all your findings and appreciate the time it took.

What a great video! Thanks for your academic sharing and empirical results❤

Superb video. I don't think I've ever seen a tutorial/explaination for anything that is this good.

I love your videos, always on point !

Wow. Thanks for this video, esp. the first part which gave just enough detail to understand the trade-offs and underlying approaches.

You explained that amazingly, very easy to understand - also things move fast because it seems like LoRA is now the most popular.

Thanks for the input, good research!!

Very nice summary- thank you 🙏

Hey Koiboi. Great video. When you made this video, as you said yourself, LoRA was still very new and the stats are probably not accurate. Now that a good amount of time has passed, I would love to watch an updated analysis video on the effectiveness of LoRA compared to Dreambooth and Textual Inversion. Either way, this is the most informative video I've watched so far comparing these fine-tuning models. Liked and subbed 👍.

You are really good at explaining this stuff. Thanks!

This is great. Thank you for sharing your knowledge, and about Excalidraw.

Really appreciate this, so many videos showing how to do this stuff, but not how it works, and specially not how it works dumbed down to a level I can understand. Very cool, thankyou

Thanks a ton for this breakdown, I've been struggling with this same question for a few weeks now. I had already come to a similar conclusion myself, but this was very validating. Dreambooth is preferred, but the models sizes make it so cumbersome and challenging to test different versions. With textual inversion, the file sizes are insignificant, and you can stack them on top of each other, making them very flexible. I haven't actually evaluated embeddedings (textual inversion) yet for quality because the animation notebook I use doesn't support them, but the developer just made it compatible, so I'm looking forward to testing it out more.

Thank you so much for this video! Amazing work

this is the best video about the topic ive ever seen, thanks so much

Thanks for making such great and informative video. Keep up the good work

Great video. Big thanks

Thank You a lot. This has been a really good explanation that I felt missing.

Great explanation! I've learned more about how AI art works from this video alone than all my previous watched videos combined. Everyone tends to say how to configure things without explaining how it works.

Absolutely incredible video, thank you!

That was a really helpful video that definitely saved me a bunch of time trying to understand these differences by myself :P

@lewingtonn

Жыл бұрын

saving people time makes me super happy, thanks!

THANK YOU THANK YOU THANK YOU!! Great video. Great insight.

Loved this breakdown. You need more followers!

That was an insanely good explanation. Thank you!

Thank you for an informative and engaging video!

This video is dope. Super clear and informative. Thank you!!!

One interesting piece of data is Lora has quite a high faves per download rating while only being out for a short period of time

@lewingtonn

Жыл бұрын

yeah, I saw that too.... good sign!

thanks for the short explanation. Loved it!

brilliant video, thank you for all the efforts!

Best video I ever seen. Best vibes! Thanks so much

Incredable explanation! Thanks a lot.

Really great video, thanks for sharing all your research!

@lewingtonn

Жыл бұрын

glad it helped!

This is so well taught man thank you so much

Great tutorial mate, thank you!

i'm a total beginner to AI, and i suck at math, but you somehow managed to clear a shit ton of confusion. I was hooked on Dreambooth tutorials and trust me, you don't want that. I literally thought i was not going to be able to get started simply because of the massive resources it required. Trust me, you are really good at explaning things :) Really appreaciate the help

@glasco_

Жыл бұрын

I’ve been trying to install dream booth for 3 days now. No success. Ready to walk in front of a bus

This video is AMAZING! Thank you SO MUCH.

That's so much work! Thank you man

Very clear explanation, thank you!

This is awesome! thank you!

Textual inversion is far better on 2.1 than 1.5, and i think that's why they don't get the same love dreambooth receives. You can also speed up textual inversion training if you spend a few minutes getting the initializing text right so the vectors start in relatively close proximity to their final resting place. The best part imo, is you can combine many embeddings together, something which dreamtbooth doesn't really allow.

@leonardom862

Жыл бұрын

How can you get the initializing text right before the training?

@alefratat4018

Жыл бұрын

@@leonardom862 By running image to text I suppose ?

@nathanbollman

Жыл бұрын

Ironically I haven't been able to run dreambooth yet,I switched to linux for AI... something broken with PyTorch2.0 and Cuda11.7 only thing affected is dreambooth training. Turn on gradient checkpoint and it cant train, turn it off and I cant make it to the first epoch without running out of 24GB of vram? I hope this gets fixed soon.

@sub-jec-tiv

Жыл бұрын

Totally agree. Suuper crucial to be able to call multiple embeddings in a prompt!

Thank you so much for this video! 🙏

Very great job! Thank you!🥰

Awesome explanation, thank you!

thanks for making those complex concepts easy to understand!

That is the type of quality content that I'm digging for. I want to understand Stable Diffusion and everything related. But my attention span/knowledge about programming is not enough that I can just read papers about it. So I need videos, with visuals, and easy explainations. And your video was Perfect. Liked + Subscribed :)

Very nice introduction! Thank you!

Great info! And coincides with what I learned on Computerphile's channel. Slowly but surely my mind is able to wrap around with what we're dealing with.

really great video! finally understand the differences. just the conclusion is already out of date, since we're moving so incredibly fast. lora, is the most popular format on civitai now. understandable, since training is the quickest, even though ti's end-result is much smaller.

clearly explained, much appreciated!

Great video, really helped me understand this

Great Video! Thank you very much!

thank you for the great explanation!

You're an absolute legend. Great video

Why am I only now seeing this? Great video and thanks for the feature ❤

Fantastic video!

wow that was great, thank you so much!

Amazing work thank you. New sub.

Great work!

Great video!!

What I was looking for in that video was the comparison of usability in different scenarios. Which model is good for faces which one for style transfer etc. I'm missing that, other than that quite comprehensive comparison. Good job!

amazing content, insta subbed !

Amazing educational video!

All different now! LoRA is by far the best all round method now and hugely gaining popularity... Great video by the way, excellent explanations!

Great video! Thanks!

Great video and explanation! I really want TI to be the future but I agree, the quality of dreambooth training is usually better.

@lewingtonn

Жыл бұрын

thank's for the data point!

man you're a hero

Thank you, sir, for this explanation!

insane work and attention here

Awesome explanantion for everyone!

Muy buen Video! Gracias

Amazing, thank you

You explained this so well. My smooth brain couldn't understand these different methods for the longest time \uwu/

thanks a lot for your effort, great job

what a great list of checkpoints you have, a man of culture 🤣

Nice video, even though I watched it 6 months later, lots of things happened, your video is still extremely helpful (except the LoRA part 😊)

Much appreciated! Having this nodes of wisdom to operate with AI models is a huge contribution to society! Props to you.

Thanks you for really nice explonation

I've been messing with Loras and they seem to work really well. You can also do a good amount of mix and matching with loras whereas a full model checkpoint only allows you to use that one model at a time. if I had a fruits lora and a vegetables lora, then I could just turn them both on to get fruits and vegies in my random prompt that doesn't ask for fruits or vegies. If I later just want fruit then I could just remove the vegies lora. I think loras are going to be big going forward, most people just don't know about them yet.

@treyslider6954

Жыл бұрын

I get the feeling that Textual Inversion is the go-to for when you have a new idea you want to teach the model (like a specific character or subject), and Lora is great for when you have a concept you don't want to stop and explain to the model, or may have difficulty doing so. They're very similar things, but not quite the same. For example; loras are great for mimicking a specific art style, because instead of having to describe "I want a painted animation style like this specific style, but with eyes drawn just so", you can train a lora and then just say "" at the end of your prompt, and since it isn't actually part of the prompt, this clears up tokens for describing the actual thing you want depicted in that style.

@ArbJunkAgeG

Жыл бұрын

This is exactly how i feel about lora. It’s disappointing that people don’t seem to gasp the same values of how beneficial loras can be.

@tbuk8350

Жыл бұрын

@@treyslider6954 And also, as described in the Automatic1111 docs, Textual Inversion can't teach COMPLETELY new concepts. The example they gave is that if you trained a model that only knew how to make apples on images of bananas, it wouldn't learn what a banana is, it would just make long yellow apples (in the best-case scenario). Because it's not actually changing model weights, it's better for teaching a style than a new subject, because unless the subject is very similar to something it's seen, it can't learn it. LoRAs can teach a model something it's never seen before, because they are directly inserting weights into the model, meaning it's actually modifying the model and not the input going into it. Basically, Textual Inversion for simple styles, LoRA for anything complicated.

Thanks koiboi! I absolutely love these in depth videos. Any plans to give ControlNet this sort of treatment?

Sir, thanks for your effort and detailed explanation 🫡🫡🫡learned so much🙇‍♂🙇‍♂🙇‍♂

Great video thanks

Sooo usefull video, thx!

Amazing explanation

muy buena clase. Aprendi mucho.

Bro, this video really helpful!

ok you had me @00:27 , would be cool to see a video on civitai

This video is incredibly helpful. I'm probably going to use either LoRA or Dreambooth, as Textual Inversion can't teach brand new subjects as well as you can by directly inserting or modifying weights in the model.