177 Huntington: Are you able to hear?
177 Huntington: You can unmute yourself? Kevin? Oh, yeah, okay, that's great. Kevin says, yeah, okay, all right.
177 Huntington: So I don't know. If let me, did I record? Yeah, you did. You did. Is it recorded? Okay? So somewhere, the recording is going.
177 Huntington: okay, is that my, hey?
177 Huntington: Hello, everyone! I think most of you know me. I'm a rohit. I'm a second year Phd. Student at the lab
177 Huntington: or 3, rd depending on who you ask
177 Huntington: so I'll be talking about this new line of work that I've been interested in recently, which is.
177 Huntington: can we discover novel visual concepts inside the diffusion models.
177 Huntington: okay, sounds very complicated. So we can maybe dissect it a little bit and see what I actually mean by this.
177 Huntington: So image generation has started from a very humble place.
177 Huntington: He used to be so bad.
177 Huntington: You can't figure out what those images are, because they don't make sense.
177 Huntington: But the state of the art is so good
177 Huntington: and it's so creative that you can generate almost anything that you throw at it.
177 Huntington: Thanks.
177 Huntington: So when a new diffusion model comes out.
177 Huntington: maybe some of you have played around with it.
177 Huntington: The usual way that we explore a diffusion model is we
177 Huntington: tested by pushing a bunch of prompts through it, and figure out what the model gives.
177 Huntington: And that's the usual way of exploring the model.
177 Huntington: And it's super boring right? You
177 Huntington: just throw a bunch of prompts at it. People like flood their twitter with Oh, my God! This look at this model versus that model. It's so good. This is better.
177 Huntington: But maybe there are like some fun ways to do it right. We've released concept sliders last year which tries to make this text based exploration a little fun.
177 Huntington: So
177 Huntington: the idea is, when you ask the model a person, it gives you a nice image of a person.
177 Huntington: and when you say No, I need a bearded person. It knows what a bearded person looks like.
177 Huntington: and when you ask for a clean shaven, it knows that, too.
177 Huntington: Right? If the model knows all of these concepts, maybe there are certain locations inside the model which you can tap into and figure out how to control that aspect of
177 Huntington: in this, in this example, the beard aspect of the image
177 Huntington: and concept sliders gives you these very nice slider based adapters
177 Huntington: which you can essentially attach to your model.
177 Huntington: Right? So what does it really do? Why is, why am I saying it's fun? Well, if you ask for a person, you get a person.
177 Huntington: But now, if you have a slider for beard, and you add to your model and run the same prompt again. Now your person has beard.
177 Huntington: and do you see the scale on top of the beard, Slider? You can reduce it, and it reduces the intensity of Slider the beard.
177 Huntington: Now that's a little fun. But you can also add.
177 Huntington: other sliders that you can maybe train like maybe smile, Slider, and it makes the person smile glasses.
177 Huntington: Right? This is just one example.
177 Huntington: But the general theme is that you have this complex control. Now that is enabled to you.
177 Huntington: You train 100 sliders. You have 100 dimension control that you can get on your model. You are basically steering it to have a certain behavior
177 Huntington: right? And now, maybe that's a little fun, right? Little fun, because it's more fine-grained, or you you have a better exploration. Now, right? You can compose concepts and like maybe try to come up with this new concept.
177 Huntington: So the example here is that
177 Huntington: we have open source, the code. Oh, but I think to be explicit, though I think the answer is, yes, because you're not changing the layout
177 Huntington: of the image you you can stick. You put the same image in.
177 Huntington: It is more fine grained right, or you at least you have control over how fine grain it is.
177 Huntington: But does it work as good as got it?
177 Huntington: The the model that you train those Slider on
177 Huntington: like, if you get a new model, the new model releases. Can you use the same sider and it works as good as yeah. So if there's a distilled version of that model, you don't have to train the ciders again.
177 Huntington: Okay, but if it's a totally new architecture. Then probably you have to, because there are adapters on top of the architecture itself. Yeah. So, depending on the new model, you might have to retrain them. But once you retrain it and you open source it, anyone can take it and use it
177 Huntington: irrespective of what the problems are. That's a generalized Slider that always makes everyone smile, and you can control the level of intensity
177 Huntington: of.
177 Huntington: So the story is that once we released it, people loved it, and they started open, sourcing all their sliders. There are websites just for sliders where people can go in and like
177 Huntington: publish their sliders. Show of that. Oh, see how creative I am, and people would be like. No, no, I have a better idea. Let me do this, and we have a database of like super creative sliders which we couldn't come up with in our paper.
177 Huntington: So now that you have this additional control, it's you have
177 Huntington: a better way to explore these things right?
177 Huntington: But it's a very subjective experience. If you're creative enough and like really creative, you can dig in a lot from these models. And the more you dig in the more you squeeze out
177 Huntington: the performance or the concepts that you discovered.
177 Huntington: So
177 Huntington: so to like, really get to the point. What I'm trying to make here is I'm going to take a figure which is inspired from the Bean Kim's paper
177 Huntington: right? There's a human knowledge that we all know that we have right. Some of us have
177 Huntington: a lot of it. Some of us are like really creative. But I'm collecting all of it. I'm making this huge human knowledge.
177 Huntington: And we made this pre training data for the model, which is like a really small portion of it. Right. It's just the visual portion of human knowledge.
177 Huntington: And we train a model on top. And it's not gonna be 100% accurate. It's not gonna capture everything. Maybe this much right? Can I call this the models? Knowledge?
177 Huntington: Maybe not. Because this model can
177 Huntington: can create, like so many different things which never really existed.
177 Huntington: In the pre-training data, right? It can make
177 Huntington: It can make horses ride on astronauts. It can make astronaut hatching from an egg.
177 Huntington: Any anything you throw at it? It's pretty much gonna help you build it.
177 Huntington: So it has like these new physics, or some sort of concepts inside it which we don't know that it exists.
177 Huntington: Yeah, sorry. I. In this case, I kind of want to understand how to like. I think how nuance is knowledge, because, like, I think, each word specifically like astronaut hatching an egg is probably something that was there in like past images. Yeah. And so like, yeah, I guess how compounded you are talking about the knowledge of that
177 Huntington: at this point, like, I'm not making any restrictions on the knowledge definition. Like in this specific example. What I'm trying to get at is
177 Huntington: this particular example doesn't exist, and the physics of it like, how does it know that the Astronaut is going to put hands on top of the eggshells, and it's trying to come out like these are things which you need to have like a more deeper understanding of
177 Huntington: than just if I if I show you like, you were born. And I show you like 5 billion images, you, it's really hard to come up with these
177 Huntington: compositions and like these new physics that we didn't explicitly teach it.
177 Huntington: So this is an example, because I could come up with this. That means this is my human knowledge, right? If I came up with this problem
177 Huntington: but this is still something which I wouldn't assume that model has. So what does the model have in addition that I can't relax?
177 Huntington: Look into?
177 Huntington: Is there another image where like, get a prompt and then look at the generated image, and would say, like, Oh me! As a human, I would never have come up with that image
177 Huntington: which goes a bit beyond this one. Because, like, if you say, if you give this prompt to me, I'm not a good draw. But maybe I would draw something similar. Right? Yeah, so it may, it gets to my point, which is, I don't know how to get to such an image, because like it restricts to my knowledge of what could happen with these models.
177 Huntington: So, having an example like this maybe throws you off a little. But the point is that I don't know what that new concept is, but the fact that it can draw this, even though it doesn't exist, gives me like some hope that there's probably some
177 Huntington: additional knowledge outside this gap which the model could have.
177 Huntington: And I'll show you some evidences that maybe it does.
177 Huntington: So concept Sliders is going to let you explore this space of this intersection between the models, knowledge, and human's knowledge, any prompt you throw at it. It's going to help you control that aspect.
177 Huntington: and with 50,000 sliders like, you have 50 K dimensions of control over your images.
177 Huntington: But what about this space? How do I control it like there's no I I can't even describe what the image might look like. How would I even go about controlling it?
177 Huntington: And I don't know. Like, if this is the right proportion of it like, maybe there's more to it than it actually knows like, actually, we know about it. So it's such a mysterious question which is still open.
177 Huntington: There is like a lot of work that's happening in
177 Huntington: the Alphago side of things with this sort of like that's been Kim's work
177 Huntington: they have like really excellent work in this, but visually we don't know how much diffusion models are capable of yet.
177 Huntington: So
177 Huntington: let's try to like more. Solidify what I'm trying to say here, right? You have your pretending data
177 Huntington: and the corresponding text captions. You train them for days.
177 Huntington: and then something happens. And the model has this knowledge which it's, you know, probably referring to because it doesn't have access to the training data anymore. It has learned some compact representations of it.
177 Huntington: But the sad part is that we don't have access to it right? Or at least
177 Huntington: the general public. You interpret, such as, if I throw you the diffusion model, you'll probably figure it out. But the general sense for today is that we don't really know what's the entire knowledge base of any model yet. But that's what we are all working towards.
177 Huntington: So what might be happening when you prompt a model is that it's probably tapping into, like one of its concepts, that it has learned, or like combinations of concepts, and giving you this
177 Huntington: nice image.
177 Huntington: A much cooler way to explore this model would be like if you really break it. If you break the barrier and get into the weeds of it, and just probe these individual concepts
177 Huntington: and like, try to figure out what the model is doing
177 Huntington: right like you. Maybe you could get like some of the concepts that you've seen.
177 Huntington: The most interesting thing is you're going to see concepts which you probably have never seen or like even dreamt of before. Right. There's a high chance that you're going to stumble upon something novel which I think is like the most interesting part beyond the use of generative models right?
177 Huntington: But it's not so easy to break it right. If it was, then probably we all would be doing that.
177 Huntington: So let's
177 Huntington: try to make this problem easier, so that we can tackle it from some one of the edges on this like multi edged sword. Right?
177 Huntington: The most common fact that we know about diffusion models is that if you give one prompt, you can use diffusion model to literally generate infinite samples and variations of that one prompt right.
177 Huntington: The only way to do it is that you can vary the initial input noise that you give to the diffusion model.
177 Huntington: And you have, like infinite potential to generate images.
177 Huntington: So a question is, how is the model generating so many diverse toys
177 Huntington: right there? Maybe there's like, if you look into it, maybe the model has some compact representations, or breaking a concept into like these lower level.
177 Huntington: Features, for example, Lego plusnet.
177 Huntington: and what it could be doing for like that one for each image is that it's weighing these
177 Huntington: lower dimension concepts like 90% plush. This, this image I'm going to make
177 Huntington: 90% Lego 0%, plus maybe like 2 to 3% of net.
177 Huntington: right? It has access to these.
177 Huntington: if it has access to such directions. This is such a
177 Huntington: intuitive way to make it work to have infinite samples.
177 Huntington: So how does it break a concept and encode this information is what we worked on in the adobe's project that I've done over summer.
177 Huntington: and we have maybe some understanding of how it might be doing it, or if there are such concepts that exist.
177 Huntington: So what is happening is basically the overall view is that
177 Huntington: we look at through concept sliders. We try to discover directions unsupervisedly.
177 Huntington: Just let us the model to give me.
177 Huntington: What do you think a toy is made of?
177 Huntington: And it gives me like 64 directions, or like it's a it's it's a hyperparameter, 1,000 directions.
177 Huntington: And then we we go into the each of these directions and like, look at what this direction could be
177 Huntington: right. We label these directions post discovery.
177 Huntington: and we found that some of them can be labeled by these large vision language models like Claude or Gpt-four.
177 Huntington: and most of them cannot be right. We get these so generic repeated. Not so realistic labels.
177 Huntington: And I think that's like a sign of maybe we don't know some concepts that these models are encoding.
177 Huntington: So I'm going to stop here for like a few seconds to see like, if you understood, like, what the
177 Huntington: what sliders are doing if you have any questions.
177 Huntington: Oh, I haven't read this paper yet, so I'm just wondering how like if you were able to give a quick explanation of how you discovered all the direction.
177 Huntington: No, just the motivation, or like. What we are aiming to do is if that's clear, I can go ahead.
177 Huntington: Motivation is really clear. Okay,
177 Huntington: so yeah, we want to discover directions. But we don't want to explicitly mention because that we are restating ourselves as humans. If we just say, I want Lego, I want flush, maybe. Ask the model, what do you think the toy is made of? Right? That's the
177 Huntington: short version of it.
177 Huntington: So how does it work? Well, we 1st generate a bunch of images.
177 Huntington: and there are hacks on how we do this. But the overview is simplest version of doing it right. And then we extract some semantic features in that in that image distribution that the model gives us.
177 Huntington: and what we aim to do is maybe we can get these sliders to mimic the
177 Huntington: highest variation directions in the semantic space.
177 Huntington: Okay?
177 Huntington: so what? What? What maybe like, I might not go into like the details of training it.
177 Huntington: The the simplest version is this, which is, we generate a bunch of images.
177 Huntington: Now, that's the distribution that the model like that's a proxy for okay.
177 Huntington: Model. Tell me what you know about toy. It's like, Okay, here are like 20 K images.
177 Huntington: That's not the number I'm just giving just an example. Here are 20 K images. This is what I know about toys. Can you figure out what it is, and we go into like, we need some semantic information, right? We just don't want
177 Huntington: random directions that are given by the model. So what we do is we try to encode these images into a semantic representation space. So Clip, I think most of you are aware of it, which is
177 Huntington: clip is trained on a contrastive loss. So Clip knows what an image is, and what the text caption of that could be
177 Huntington: right. So that means all the image representations have to be grounded to something meaningful.
177 Huntington: So now we transform all these images into this semantic embedding space. And then we say, we say, let's try to look at what are the top variation directions in the semantic space.
177 Huntington: like, maybe the model thinks Lego is like a really strong direction in toys.
177 Huntington: And what better space to look at than look at the semantic space itself? We have ablations doing like different semantic spaces
177 Huntington: just the diffusion space. Like no other semantic encoder. We just look at the diffusion outputs and look at the PC directions.
177 Huntington: But we found that having the semantic space as a additional tool to view these directions
177 Huntington: gives some really consistent and maybe useful fun directions for us to work with.
177 Huntington: How logistically.
177 Huntington: How large is it? Clip l, or is it? Clip? 8. I think it's the clip. L. 16
177 Huntington: layers, I mean the
177 Huntington: I mean the I don't know what people do nowadays, but the Stx or something, is still conditioned also using clip embeddings. Right? It's funny so to go like for
177 Huntington: circling some of this right? I'm going to show you like dyno features and some face, net, features, face net is basically an encoder that would help with facial recognition, or you can take whatever you want. Whatever you want. Any encoder that you have, you can just use it instead of the clip.
177 Huntington: Yes, and I assume that you get very different
177 Huntington: Pca directions based on the encoder. Right? So
177 Huntington: you don't just ask, okay, what's the diffusion models knowledge.
177 Huntington: But what's the diffusion plus semantic embeddings, knowledge, and the
177 Huntington: exactly. Yes. So it's what's like. Explain this diffusion models knowledge restricted to a semantic space, right? So whatever that semantic encodings.
177 Huntington: whatever the width of it is, we are restitting it to that right now. We tried not restitting it, and we didn't find
177 Huntington: consistent, amazing directions. You tried not restricting it. So instead of taking the Pca in the clips, feature space, we just take the in the models in the models. Representation space. Yeah.
177 Huntington: but the directions that pop out of it are usually the ones that corresponds to color texture not super interesting.
177 Huntington: but it does exist. When you take the models representation space. Do you take the output of the diffusion? Or do you take something intermediate? So we've tried both of it. The output is usually like super granular. If you take intermediate states, then
177 Huntington: we couldn't really look at like none of that could be labeled
177 Huntington: We we do have. Are you using turbo? Or actually, are you doing like multiple diffusion steps here? So this, this is multiple diffusion steps. We do. Dmd, Turbo, we do flux, the latest models.
177 Huntington: So this is not restricted to any time steps, or like the model distillation.
177 Huntington: So you briefly go into some level of technical detail of like, how a slider is implemented. I mean, I imagine it is not the same thing as a se,
177 Huntington: you know, just adding a vector to some.
177 Huntington: yeah, I don't have how I implement a slider I could go into it like, maybe I'll just switch the slide to sliders a little bit and give you just how it in, how it's implemented.
177 Huntington: In a bit like just after I finish. There are some questions, and then I'll just do no questions. Okay?
177 Huntington: Right? So to close off this discussion, which is like, I think, very important.
177 Huntington: There, you could do this without estlating it to an encoder.
177 Huntington: but the outcome of it is so vast and so indescribable that it was hard for us to put it in the main paper
177 Huntington: but we have most of it in appendix, and some of it in the main paper as well.
177 Huntington: It's just that none of it was describable because you're not restricting, restricting it to the semantic space.
177 Huntington: So, for the sake of presentation, I am going to show you most of the results from clipboard, I know, or facenet.
177 Huntington: because, like we get the top.
177 Huntington: 4 or 5 features are going to be describable, and the rest are going to be like super crazy and wacky
177 Huntington: but yeah, just so that you feel. Oh, I know that it's a Lego, but I don't know what the other one is because I have no idea just to give the theatric effect. Is this open source? This is going to be open sourced this week next week.
177 Huntington: Yeah.
177 Huntington: Whoops. How do I get out of it?
177 Huntington: Okay.
177 Huntington: I have some clip models to train them like the
177 Huntington: latent space of the Stxi turbo vae.
177 Huntington: So I could do this cheaper
177 Huntington: by not having to decode the images. And I would like to see what my clip sees
177 Huntington: oh, so you're using you use your diffusion model for training data for your clip?
177 Huntington: Yeah, yeah, I I I via encoded a bunch of Lionel.
177 Huntington: Oh, so it's actually it's not trained on generated images, trained on, effective on generated images. Nice? Nice, that's great.
177 Huntington: So a Slider is basically a low rank adapter on top of
177 Huntington: whatever layer you have like, it's linear layers, convolution layers. You can have low rank adapters on top.
177 Huntington: So it's as simple as that implementation wise.
177 Huntington: We just attach a low rank adapter. The way we train it is different for concept sliders and different for Slider space
177 Huntington: but I think most of you know, concept
177 Huntington: in principle, could have Laura's in the whole, in the whole network. Yes.
177 Huntington: okay. But we just restricted to
177 Huntington: non cross attentions, because we've we believe that's where the visual information is.
177 Huntington: because cross attentions are going to be in this work. Now in concept sliders and slider space
177 Huntington: in both the works.
177 Huntington: So the reason is it dates back to. Are you raising
177 Huntington: concepts? Work where we found that actually, Jaden found that? If you restrict all your edits to the cross attentions.
177 Huntington: then you are going to also restrict it only to the text understanding of the model.
177 Huntington: But if you go beyond these layers, because cross attentions are probably point 4% of all your models, weights.
177 Huntington: the remaining convolution layers all the linear layers, Mlps resnets are going to be encoding the visual understanding of the model, which probably, and we did discover that with concept sliders you can discover things that cannot be described in words.
177 Huntington: but can be described with image pairs. Most of the artists have done that. So I can show like an example of what I mean.
177 Huntington: So there's this wavy eyebrow thing. I don't know if you can see it. It's
177 Huntington: Maybe I'll try to oof.
177 Huntington: So this is like a 1 of the art by this open source artist who always makes
177 Huntington: these photographs with people, and then he edits these eyebrows to be wavy in shape.
177 Huntington: So we collected his data set, and then we were able to encode it only when you train sliders on
177 Huntington: self attentions and the remaining layers. But when you restituted to cross attentions, it has a really hard time
177 Huntington: to figure out what this is.
177 Huntington: Vvi brose. Yeah, it doesn't do it. You can try stable diffusion, excel. It doesn't do it very well
177 Huntington: even like this gigantic eyes that it has. It goes to certain level. But beyond the animation it doesn't go after a certain point.
177 Huntington: So so yeah, that's the sliders, part of it. It's
177 Huntington: it's really like as simple as just having a low rank adapter. But the training is pretty different from
177 Huntington: regular low hangar address.
177 Huntington: Yeah, do you have any questions other questions about the method?
177 Huntington: Do you think it's just like a fundamental language thing. Or you think it's more that the model, being trained with image and text, learns to defer to images for things that are more image, like like wavy eyebrows, could be very metaphorical from like a language standpoint. And but in the clear context here it makes more sense for it to be like an image specific thing rather than like a yeah.
177 Huntington: yeah, that's, I imagine, like a really strong like language model, like text only would be able to.
177 Huntington: And somehow, like, understand conceptually what that is. But I guess, like here it doesn't. And I'm just wondering whether you think that there's like a trade off like a a certain like minimal amount language learned on the flip side versus the
177 Huntington: okay, that's I think that's an interesting question.
177 Huntington: there are models that use these powerful language models like Llama 8 B. Was integrated with sdxl.
177 Huntington: I wonder maybe if you look at the cross attentions there. Maybe it already knows all of this information. It could be grounded to the the clip text encoder.
177 Huntington: That's used to condition these diffusion models.
177 Huntington: But that's an interesting question. I don't have an answer for it. But it's probably interesting to look at if
177 Huntington: the text understanding of the model has increased because you use the powerful language model?
177 Huntington: So yeah, we decompose some concepts we tried decomposing monster.
177 Huntington: The 1st few are pretty decent, like spiky tentacles we have. So it's something that we would think of when you think of monsters.
177 Huntington: And by the time, like somewhere around the the middle layers like the Middle Pca. Directions. Under like the end Pca. Directions.
177 Huntington: we find things that are hard to describe, but the model gets to like some point close to it.
177 Huntington: Blue bestial is not the right description for it, but
177 Huntington: It's the closest that the model could get to.
177 Huntington: and we don't have a name for what that is yet.
177 Huntington: Unless, like, there are
177 Huntington: linguists who can like get to it and tell what that is. I don't know what that is.
177 Huntington: For cars. I'm just showing like some.
177 Huntington: Oh, some of these directions we found again.
177 Huntington: So yeah, that's the the steel tinted vintage. It's probably not that, but that's the best label that the model could give.
177 Huntington: There's something very nuanced that the Slider is doing. Which really, we don't understand what it is.
177 Huntington: So here are like some sliders. Since these are sliders, you can like slide the effect of these directions.
177 Huntington: And with the spaceships we found Ufos, and, like Star Wars and Battlestar Galactica related spaceships
177 Huntington: with wizards. Also we have those names. I don't know how much I trust the labels. But it's Claude given. So I'm just gonna put it the slide.
177 Huntington: We have more of it on our website so many more. So if you want, you could like, just go
177 Huntington: and visit and have fun with it.
177 Huntington: So
177 Huntington: that that's like, that's just the fun part of things. I'll talk more application oriented business side of things.
177 Huntington: Art style. Exploration is
177 Huntington: usually done through manual efforts. When a new model comes out. There's this team called Pirate Zone, which they're like, really
177 Huntington: passionate about listing out all the art styles that a new model can mimic.
177 Huntington: And so far, we have around 8,000 artists that were manually explored. Inside this table diffusion.
177 Huntington: The reality part of it is that it's a 7 month effort done by 2 50 artists. And it's a very labor, intensive work.
177 Huntington: So the natural question we had was, oh, there were some questions in the chat.
177 Huntington: Oh, okay, hold on.
177 Huntington: Oh, okay, is it all concept? Oh, yeah, I don't think.
177 Huntington: Okay. So the natural question we asked was.
177 Huntington: if I use the prompt art style.
177 Huntington: Oh, maybe I didn't.
177 Huntington: I should have mentioned this so Slider space to train Slider space.
177 Huntington: All you need is one text prompt
177 Huntington: of the concept that you want to break, and the rest of it is taken care of by the method and at the output of the Slider space is going to be. How many of our sliders you need like hundreds, thousands of sliders
177 Huntington: would just pop out of the model's knowledge. So to explore style, we just use this text from art style.
177 Huntington: And then Slider space gave out some directions.
177 Huntington: and the interesting part is none of these directions exist in real life.
177 Huntington: So we passed on all the slide the artistic directions we have to parrot zone team.
177 Huntington: and they don't recognize any of it.
177 Huntington: But they believe that this could be like some inspirations that the model is taking and building its own art styles like, for instance, the 25th direction sort of looks like Van Gogh inspiration. But it's not really that. It's just an inspired
177 Huntington: version of it is what the team feels.
177 Huntington: So
177 Huntington: one takeaway which is not application. Takeaways, which is like just a very interpret takeaway is that
177 Huntington: when the model is learning art, it's not really learning to mimic art styles.
177 Huntington: It's actually dissecting the concept of art into these intuitive directions. And it's combining multiple directions to make the mimic that
177 Huntington: so
177 Huntington: this is probably more ethical way of exploring art and diffusion models. You don't. You don't have to say, Van Gogh, art and like, have the artist offended. It's just a slider space Direction 25, which looks sort of like Van Gogh. But it's not
177 Huntington: tape.
177 Huntington: So getting back to the motivation side of things, it's almost like, if you go to an art class, and the outcome of it is just you able to mimic art styles. It's probably not the
177 Huntington: the best outcome.
177 Huntington: But probably you would want to create your own art styles which you are inspired from all this art that you see in nature, or like in the class that you've seen.
177 Huntington: and maybe come up with your own.
177 Huntington: But that doesn't mean that you can't mimic them. You still can. Except you have your own knowledge of what artists, and I think that's
177 Huntington: very interesting that we found with the Slider space.
177 Huntington: So I'm going to show you some experiments, especially now it comes to expo like the evaluation side of things
177 Huntington: some natural questions, or like question as a person who doesn't believe in slider space that I can ask
177 Huntington: is, wait a minute. Why should I trust in Slider space more? Why should I even train Slider space? Why can I not do some alternatives to extract all this information from the model.
177 Huntington: Right? So let's look at some baselines that we came up with. And I'm like really happy to hear your thoughts on. Are there better baselines, or are we really underselling it? It's something that I would want to hear from you.
177 Huntington: So one baseline, is that. Oh, wait. So there's this real artist. What this means is that this is our gold standard data set right. We have all the manually curated artist names from this huge study that people have made.
177 Huntington: So we generate images using the artist names and collect a huge data set of gold standard models, entire knowledge of art, right?
177 Huntington: or in other ways. That's the art that it can mimic that it has seen in its pre-training data. Right? That's the good way to put it.
177 Huntington: So I'm going to call this my true distribution.
177 Huntington: and my 1st baseline is going to be generic, prompt. I'm going to just pass the word art style, in the, in artwork, in the style of famous artist.
177 Huntington: and sample different images from the model.
177 Huntington: What I would expect is that the model would give me different images because it's supposed to be diverse. Right? Different art styles different scenarios.
177 Huntington: What ends up happening is that the model is collapsed to one art size.
177 Huntington: and it always gives the same art style. Again and again, it's super boring, right? So for instance.
177 Huntington: Fid is basically a distance that measures. How close is my distribution to the true distribution. In this case it's the real artist. The higher the fid, the more far away. You are
177 Huntington: right. So 76 is like, you are very, very far away from your true distribution. So it's probably that you're not covering most of it
177 Huntington: now. A good baseline is. Why can I not use Llm. Generated prompts. Right? I asked Claude to give me. Oh, give me like diverse art styles, so that I can prompt my vision model.
177 Huntington: and it gives me, like so many
177 Huntington: different prompts, in fact, for each sample, for each image that we sample for this distribution. We use a new generated Llm. Prompt.
177 Huntington: And it's context that means we are not letting the Llm. Duplicate the art style
177 Huntington: right? All these 8,000 prompts are generated in one. Go, and we ask the diffusion language model to check its list, so that there are no duplications.
177 Huntington: and we also run like a duplication test. That there are similar art styles are not repeated in this 8,000 prompts.
177 Huntington: So that means this is the full potential that I could get out of a language model if I were to say, Do not repeat any art style, be as diverse as possible.
177 Huntington: and we get like much closer than the generic prompts. Right? 38 fid like that's almost half
177 Huntington: more than half close to how it was compared to generic prompts.
177 Huntington: The final one is, we train concept sliders.
177 Huntington: And how do we train? It is we use language models again to give us training prompts
177 Huntington: to train a bunch of sliders.
177 Huntington: And now there's an additional added bonus. Is that we sample random number of sliders and compose all these art styles
177 Huntington: right? So for each image
177 Huntington: you are sampling 3 different random concept sliders, and you are composing them in different variations, like 10% Renaissance. And like 20% Van Gogh, 50% Picasso.
177 Huntington: and each image has like. Now, there's an added level of variation.
177 Huntington: and it doesn't really close the gap it gets to like 32.
177 Huntington: That's yes. How many concept sodas can you combine the maps?
177 Huntington: so it's a funny story in our paper. We've combined 50, because that's the most number of sliders we could come up with for a 1 single concept. Now that we have this huge dictionary of sliders, people have shown up to 100 200 that you can combine. So in this experiment, we combine around. We combine 64 sliders
177 Huntington: just to keep it consistent with our Slider space, which I'm going to show next.
177 Huntington: Right? So we we train 64 concept sliders
177 Huntington: for 64 different art styles.
177 Huntington: and then we compose them in a very random way, like we, we pick out 3 sliders for each sample 3 random sliders
177 Huntington: and have them varying in different scales.
177 Huntington: Bit of an outdoor idea, but thought about doing rally Jeff with the sliders.
177 Huntington: So like, get someone, maybe yourself and then have a Rl. Agent that like can control the sliders. And then at certain points present something, and then you give feedback, and then you just keep doing it and see like where it goes. I think that's a new new method on its own sliders. I like the idea, though. Yeah, it's pretty good.
177 Huntington: So is the real artist like there is that a photograph in the left like, does it have photography? And like, maybe that's why it's so, because, like, it looks like the ones that are being generated are all like paintings and stuff. But the real artist has more like digital art. And like, I don't know what the gray one is, but it looks.
177 Huntington: yeah, yeah, there are. There are photographies, there are comics renditions, and there are like so many different art styles.
177 Huntington: But we that's the hope that we maybe would get to it. With all these different methods
177 Huntington: I will close.
177 Huntington: Would you get to the real
177 Huntington: images if you just took the real images and noised them.
177 Huntington: and then denoised the partially noised pretty much.
177 Huntington: Yeah, I haven't. That's maybe a good baseline to have. We could test that
177 Huntington: on the top of my head. Maybe not as much as 0. Right you. It wouldn't be very close.
177 Huntington: especially because do you look at the generate prompts. They're all like, very more collapsed.
177 Huntington: I suspect, little bit that maybe you could be pushing these denoised images back to being this more collapsed.
177 Huntington: But since you are denoising noising them only till certain extent some of that would still exist. So it wouldn't be as close, definitely closer than concept status, maybe because you have the true distribution, and then you are using that to generate it.
177 Huntington: But I wouldn't assume it would be exact match the model would definitely destroyed at some point. Yeah.
177 Huntington: Oh, but sorry. By the way, this real artist are generated by the model.
177 Huntington: These are not. Yeah, I should have explained it. These are. This is not directly the training data set that we collected, since we know these are the artist names that can be
177 Huntington: oh.
177 Huntington: generated by the model. We condition the model on those artist names and get these. The reason we did that is because
177 Huntington: diffusion model itself is not very good, as natural as a real image. So there's already a gap between the true true distribution and the model generated distribution, and we didn't want any such noise in our experiments.
177 Huntington: So we let the model generate its own
177 Huntington: whole standard by having this list, which we usually don't have when you have a new model. But since we have it. We just used it on
177 Huntington: good information.
177 Huntington: When you in your in your 4th baseline, you provide 3 concept sliders
177 Huntington: and get a specific fid score. How does the Fid score change with the number of sliders that you provide?
177 Huntington: The final is a slider space. So with slider space.
177 Huntington: Again, to just hit to the home. The point is that
177 Huntington: it's the model that's giving us the directions
177 Huntington: and it is the closest that we could get to the true distribution without ever having to name the artists again.
177 Huntington: With slider space we again generate 64 sliders.
177 Huntington: sliders like 64 sliders from Slider space.
177 Huntington: and then do the same sampling that we did for concept sliders randomly pick 3 sliders from the Slider space.
177 Huntington: vary them with different intensity scales, and then generate the images.
177 Huntington: And if you notice, except for the Llm. Generated, prompts
177 Huntington: rest, all of the Baselines are going to use the same generic, prompt, which is art in the style of a famous artist. Except concept. Sliders and slider space are really tapping into the models. Knowledge
177 Huntington: concept sliders is tapping into the human non-knowledge, whereas Slider space is looking at
177 Huntington: the model's entire knowledge that it has so yes, also. Also.
177 Huntington: So yeah. So again, with the baseline, with real artists. Where was it all generated with art and art in the style of artist name?
177 Huntington: So like the same prompt. That's like that prompt yeah. Art in the style of Van Gogh all the 8 8,000 names we have.
177 Huntington: And what if you take like the real artist split
177 Huntington: like you take the real artist to assent stuff I do with half just to see like what the lowest possible. That's a good question. Yeah.
177 Huntington: yeah, I'll I'll take a note of that.
177 Huntington: This Latt is basically. So we don't have the training set, that, do we?
177 Huntington: Oh, no, no, I think, she said. Split the this data set into 2.
177 Huntington: Oh, that's what you're asking self. Id, is that a thing I don't know? Oh, yeah, it's a good question. Yeah, yeah, it should be low. I you know I did. Yeah.
177 Huntington: yeah, I mean, nobody has done it before. So I don't know what the outcome of it would be
177 Huntington: because maybe it maybe it's like 19. And then you you did it. Yeah, the natural question is, with the real data set, we don't have the real data set. So what is? Oh, the one that was 8,000 art images or artists that was from models. Yeah, that's on the model. We know 8,000 artist names. So we prompted the model to give me the
177 Huntington: generated up.
177 Huntington: So that's the generated R. 2, except it's the standard gold standard.
177 Huntington: Exhaustive list of all art.
177 Huntington: The status base score in the in the end is with concepts derived from clip. Right?
177 Huntington: Yes.
177 Huntington: How does the fad score change with different embedding models at oh.
177 Huntington: I don't have it for art.
177 Huntington: I have for concept decompositions.
177 Huntington: I I should do it for R. 2. Yeah.
177 Huntington: just to be consistent. I haven't done it.
177 Huntington: This is such an expensive experiment, and like we didn't have time to do it for rebuttal. We did have the question from the reviewer, though
177 Huntington: I could do it. Now, that's Id and Aunt.
177 Huntington: Okay?
177 Huntington: So
177 Huntington: so yes, turns out Slider space represents more of the true distribution of the modest knowledge compared to the baselines that we have.
177 Huntington: So here's 1 of the questions or related questions that John was asking, which is how many directions or like, do I really need to cover most of this dis like the distribution?
177 Huntington: Like, if you see this.
177 Huntington: if you have 64 concept sliders that are generated by language models
177 Huntington: turns out with 10 slider space
177 Huntington: slide directions. You can cover the distribution that concept sliders is covering.
177 Huntington: So it tells us that how much restrictions that we are putting on ourselves and on the model when we explore it with human
177 Huntington: described knowledge, or like language described, Llm described. Knowledge.
177 Huntington: So how many concept sliders? Again, concept sliders are 64. So okay, Slider space, we varies like different number. And we found, like with 10, you could cover what 64 concept sliders are covering. So like, I didn't catch that earlier like these 10,
177 Huntington: they computed from just one prompt with a bunch of different noise.
177 Huntington: Yes, one prompt bunch of different random noises.
177 Huntington: And then we look at what is a model really varying so that but the that same one prompt that got really a horrible Fi score. You just look at the outputs right? Oh, yeah, there's a good point there.
177 Huntington: and then you you filter all of these outputs with Clip.
177 Huntington: Yes, that's a really good question. I'm going to explain how we did it. But yes, I, I would keep that in mind. That's a very good question, which is
177 Huntington: when we looked at this generic prompt right?
177 Huntington: Literally, that's the problem we are using to create slider space. That's wild and
177 Huntington: that's supposed like, technically, that's supposed to goes only one direction, right? Which is this.
177 Huntington: washy looking art style.
177 Huntington: But we got like much closer to the distribution. And I have a nice visualization to maybe try to give the intuition of why that happened.
177 Huntington: But yeah, it it is wild, though. Yeah, it's pretty wild.
177 Huntington: So here is the
177 Huntington: But concept sliders is not bad, right? I have to also define concept Sliders. Who wrote that one? I don't know who wrote that one, but it's such a good paper.
177 Huntington: So what that we what we really found is that
177 Huntington: P. The Fid is really capturing. How much of a distribution did I cover compared to the true distribution? Right? There's a second question I could ask, which is
177 Huntington: the samples that I'm generating? How diverse are there inside the distribution
177 Huntington: right in one single distribution? How is the distance between each image that I have.
177 Huntington: So if I do the same metric on the original model, that means I'm looking at the same art style.
177 Huntington: and the distance is probably not too high across the bunch of 10,000 images that I generated with the generic prompt that I have.
177 Huntington: So when we look at how much
177 Huntington: the gray bar is how much the
177 Huntington: original model has. And it's not very high. It's like point 3
177 Huntington: and with slider space, you actually improve that diversity inter sample distance. It goes pretty high. Now, you have, like much diverse representations.
177 Huntington: turns out concept. Sliders also has the same, or like similar amount of diversity internally, like inter sample distance.
177 Huntington: So the
177 Huntington: outcome of this, or like the takeaway from both of these plots together, would be concept sliders and slider space are going to give you the same amount of diversity into sample.
177 Huntington: But what slider space does is that it covers the true distribution of the models knowledge, whereas Slider space is just covering
177 Huntington: the human, describable knowledge, part of it, but still giving you like the same amount of diversity.
177 Huntington: Right? If it makes sense.
177 Huntington: Oh, no, the brush.
177 Huntington: Okay, so I think I'm doing good on Taiwa.
177 Huntington: So should I train this on every concept that there is
177 Huntington: maybe not right. We've trained these sliders on facenet embeddings. So that means this is a different encoder, not clip anymore. Because Clip doesn't have really rich representation of a human face which is interesting. I didn't expect that to happen
177 Huntington: but with face net when we train we got directions like long hair wrinkled.
177 Huntington: The cheekbone is probably not cheekbone. It's something else. It's like some ghost looking thing.
177 Huntington: Yeah, these are the best labels that Claude could find.
177 Huntington: But these are some of the labeled labelable directions that I have, that I'm showing here. There are more directions which we couldn't label, and it's honestly like hard to show what they are.
177 Huntington: But yeah, some nice directions. You can use the same sliders
177 Huntington: on a different concept like police during inference, and it generalizes to that concept right?
177 Huntington: Like also to athlete, it goes to that direction. It just generalizes well to that direction.
177 Huntington: You can ask. But it's very boring right? Because police and athlete are also people.
177 Huntington: And this generalization probably something you expect.
177 Huntington: We found that it also generalizes to out of distribution, sampling right like dogs with glasses
177 Huntington: dog with a hoodie. Apparently that's like the closest it could get with a hoodie.
177 Huntington: They just have a nice mane on top. There's a wrinkly old dog there.
177 Huntington: So the original label for the long hair was Jesus. I didn't put it there because I thought maybe I'll offend some people. But yeah, apparently the the Claude described that dog as Jesus dog. So it's fun.
177 Huntington: so yeah, it generalizes pretty well to other concepts. So the directions that we are finding
177 Huntington: I don't know what it tells about the model itself. Maybe the model has.
177 Huntington: pretty clever representations that it's encoding efficiently, which could be used across different concepts, even the ones that we would think
177 Huntington: are not in distribution.
177 Huntington: Alright! This is the final thing that I'll talk about is
177 Huntington: now that it generalizes so well.
177 Huntington: Maybe I can have universal sliders that would help with improving the diversity. Right? So what that means is that there are base models of diffusions that are usually the ones the most popular ones. Right stable diffusion. Excel.
177 Huntington: Sd. 1.4. They're called the base models
177 Huntington: they have. They generate beautiful images, but they are very slow, right? You need so many time steps to generate them.
177 Huntington: So what we what we didn't do it. But what the community did is that they tried to distill these models.
177 Huntington: but not in terms of the parameters, but to reduce the time steps that the model requires
177 Huntington: to generate an image.
177 Huntington: Right? That's the distillation that they do so. If the original model requires 100 time steps. The distilled model would require one to 4 time steps
177 Huntington: the. And now we fix the problem of slow.
177 Huntington: The a new problem appears, which is, the images are not diverse anymore. There's a mode collapse.
177 Huntington: And that's 1 of the examples I showed with art. That's actually on a distilled model.
177 Huntington: And it is very more collapsed on the art styles that it has.
177 Huntington: So what's the general thing that we want to do is that, okay, let's take this distilled model.
177 Huntington: and let's train the Slider space on all the concepts. What does that mean? Well, do you remember the image which I was showing that we generate a bunch of training images and then do Pca
177 Huntington: Without using just one single prompt. We use diverse prompts from a district like a pre training data set captions like coco data set.
177 Huntington: So these are like some of the training pre-training captions that it has seen.
177 Huntington: And we take a bunch of pre training captions and generate all sorts of diverse images and try to capture what's the most variation
177 Huntington: variating directions across this huge distribution? Right? That's the Slider space on all concepts.
177 Huntington: And what we see is that it's probably explained in one or 2 easier directions, most of the variation and rest. It just steepsly. It drops very steeply because they're all mode collapsed.
177 Huntington: So there's not a lot of variation there, right? For example, if I were to talk about this. I'll take the same graph.
177 Huntington: So the top 2 directions are probably the washy looking art styles and rest of the art styles that the model knew when it was a base model.
177 Huntington: probably got suppressed in the time of the distillation.
177 Huntington: but they still do exist because
177 Huntington: we generated the real data set using the same model right?
177 Huntington: Like. So
177 Huntington: except, the model doesn't activate them very often anymore. And what slider space does is that it captures all of these directions, irrespective of how much variations that they are explaining.
177 Huntington: and we can artificially boost the variations by just sampling them more often than the ones the other ones right? The usual common ones.
177 Huntington: That's what we do with Slider space
177 Huntington: in art styles. That's the reason we were able to get so close a distribution compared to the original model.
177 Huntington: So now we do that so artificially, we are increasing all of the variations because we have the capacity to sample the directions of slider space
177 Huntington: that would capture the least variation directions
177 Huntington: and just increase their scale, or have this weighted sampling where you sample them more often.
177 Huntington: So with that. We saw that we, we have a like, we have this fast model that is also almost as diverse as the base model
177 Huntington: and the fact that these are generic sliders, if once open, sourced, can be just used.
177 Huntington: With these distillation models, as a universal standard just to have this improved variations.
177 Huntington: So
177 Huntington: this is pretty neat. Except like, I still believe there's room for improvement. We did get much closer compared to the original Sdxl, Dmd. Is the distilled model.
177 Huntington: You can see how the fid is far away from HD. Excel is the base of the same model.
177 Huntington: But with Slider space on Dmdv, so Dmd, Slider, space is Stx area. Dmd, Slider space.
177 Huntington: So Sdxl, Dmd, model with Slider space attached.
177 Huntington: So yeah, that's that's the project we have so far.
177 Huntington: there are some directions which we are thinking
177 Huntington: for, like, what could be next? Or is there something that we didn't cover?
177 Huntington: Well, we think that Slider space is really capturing the visual variations with these sliders.
177 Huntington: But
177 Huntington: like there are other abstractions that we could find in these models is, does the model know that a face has eyes, nose, and smile?
177 Huntington: Right now, when you ask for face. It gives us like all these long hair and sort of things. But does it know that it's made of ice nose
177 Huntington: a mouth is something that I don't know like how to describe that part of things.
177 Huntington: It's a different abstraction.
177 Huntington: Can we do this with minimal or no data at all like, how much can we learn from weights? Is a
177 Huntington: potentially another direction with one single image when the model is generating? And we if we capture these activations, how much of
177 Huntington: the knowledge variation can be captured with that model is also home potential direction in the future.
177 Huntington: But yeah, that's pretty much what I have for the talk.
177 Huntington: If, instead of Pca, you would do special drink for her, I don't know.
177 Huntington: On the Clip Embeddings.
177 Huntington: You're not.
177 Huntington: It's fun.
177 Huntington: Like you. You turn your data, you turn whatever
177 Huntington: you see it, the directions with a bunch of clip embeddings
177 Huntington: instead of Pca. In them, you could say, decompose them.
177 Huntington: Hmm, yeah, I haven't thought about it. I mean, I need to think more about it.
177 Huntington: but that would also require like the same thing, right? Like, you need to probably label it post discovery. And oh, yeah, yeah, it would be just different directions again. And we could steer the model with the Sims, too.
177 Huntington: Oh, it's you! Just get more vectors. It's just a different way of getting vectors so you could use them for painting the sliders. I guess maybe they wouldn't be as naturally ordered
177 Huntington: like you wouldn't know where at which you should look like.
177 Huntington: So for your question about does the model know that a face is made of eyes, nose, and smile?
177 Huntington: What do you think of this direction? So like
177 Huntington: one step, one you asked to generate eyes, nose, smile respectively so, and see if by default like these are like in faces, and then you use your erasing concept model and like, remove those concepts and then ask to draw face.
177 Huntington: and then see if like, for like, if it actually removes the eyes, nose, or smell like, does that like.
177 Huntington: because I know it is just like one direction, but not like both sides. But is that like, yeah, that's actually one of the directions we're trying. We have some results, but maybe it's for the next talk that I have during Iccv but I can give you like spoilers.
177 Huntington: we found that when you erase such
177 Huntington: hard encoded concepts like sky, car or nose, the model has a very hard time to raise it. And it feels like they're so hardly encoded in the model that
177 Huntington: of if we really want to erase it, we eventually end up breaking the model
177 Huntington: but we did try it on these
177 Huntington: slightly less aggressive concepts, like horn and unicorn like. If you erase horn from unicorn, it does draw unicorns without horns or or just a horse.
177 Huntington: and then we try to look at which layers do this.
177 Huntington: and they seem to be pretty causal to look at it.
177 Huntington: Yeah. But yeah, we'll we'll like Jaden has, like so many nice visualizations in this to show.
177 Huntington: probably for Icc. I'll just keep you in the suspense.
177 Huntington: I had a follow up question. But I'll no, no, you can ask. Follow up questions. Okay, okay, do you think like these concepts like, since they are like, as you said, hardly encoded. That means that
177 Huntington: maybe you might find matching activations to say that these were like for eyes, and, like those activations of eyes show up when you're trying to
177 Huntington: like.
177 Huntington: make a face or something. Are you sneaking into our meetings without knowing us? We literally talked about this last meeting, we should probably look at these patches of ice and look for similarity. Okay, cool. But yes, we don't know the answer. We that's a
177 Huntington: that's that's the thing that Jayden is looking into now. He's going to. He's going to look at.
177 Huntington: if if you draw, if you ask for an eye. Sorry. Wait. If you maybe I'll ask an easy question if you ask for parking lot. The image. Naturally the model naturally draws car in it, and if you ask a car, it draws a car. So if you look at only the patches that has car. In both of these images, we should be able to find some common representations in some subspace, some layer, and if we find it is that
177 Huntington: like, it's a question for us, too. Is that a question? Is that evidence that
177 Huntington: probably there's an association across parking lot and car. And if so, can we break it? Can we like manipulate? It is
177 Huntington: potential direction that we are looking
177 Huntington: sounds good. But yeah, you read our minds.
177 Huntington: How would you get the hierarchy for that? I feel like parking lot is like lower than car. Right? Yeah, there'll be some crawling like some, some search. Oh, yeah. So they it could be like different. So
177 Huntington: that that's a good question right? Like, for instance, if I ask, how do you break the concept of a horse?
177 Huntington: You could go as low as sorry you could say, Yeah, it's made of like a head, it has a main legs. You can also go like more, deeper, and say, like the skin color, how many hoop hoops, hoops, or whatever oops
177 Huntington: you just take the representation and see like try to decompose into other representation that you already know.
177 Huntington: Maybe, or like.
177 Huntington: No, it's how would you distinguish parking lot from car? Right? Like
177 Huntington: it's probably not a lower level feature, because if it has car in it, it's
177 Huntington: it's like somewhere sharing the representation, or like taking some of the representations of car.
177 Huntington: But then is car a low level representation? Right? It isn't right. Right? The car also has wheels. Yeah. So maybe in parking lot we should also break it into wheels. So wheels a low level representation. I know we also have cars. So in the sae they have, like a meta sae thing, where they also like.
177 Huntington: do some decomposition, and then take at the individual features that they find. Maybe Einstein breaks up into science and physics in Germany, or whatever they take the
177 Huntington: Germany feature and break it up, and in the end they will land up. There's a bit of Einstein in Germany as well. And then.
177 Huntington: yeah, once he's still doing that.
177 Huntington: I thought he moved to. I thought he moved to New Jersey.
177 Huntington: Yeah, yeah, it's it's like having this graph. Right? It's like, probably the circular connections in there. You probably get to the same thing. Right? Yeah, it's just graph crawl who's doing a graph crawl
177 Huntington: this week. So so you know you.
177 Huntington: I hadn't thought about this for the whole development of this paper. But
177 Huntington: but you know, distillation is such a big deal just
177 Huntington: you know. A little bit of distillation seems to have shaken up the Llm. World a few weeks ago.
177 Huntington: right with with the deep seat folks.
177 Huntington: Do you? Do you think that this kind of method could be used to
177 Huntington: recover some of the lost capabilities when you do, when you still language wise?
177 Huntington: But there's a different distillation still here you distill into the same
177 Huntington: architecture. No, no, even if the distillation is weights, distillation, you could use the same method to capture all of those directions. Right? It has nothing to do with one-to-one correspondence of architectures.
177 Huntington: It has to do with the sampling method.
177 Huntington: so you can sample it, even though if it's a smaller model, you just sample a bunch of things and try to recover. These, not so well represented represented concepts again. But okay, so I mean, you could. You could also imagine, if you want, if you're worried that this is not good at recovering
177 Huntington: parameter diversity to reduce the parameters.
177 Huntington: there's still there's gonna be this future question, because reasoning models
177 Huntington: are are gonna be so darn slow to do inference on. They are horribly slow, horribly slow. Oh, my gosh! Wait! Wait! Wait!
177 Huntington: Let me think about this again. Hold on for a second. Wait! I'm not sure about this. Let me wait. Let's think about this again.
177 Huntington: Right? They're they're just like a diffusion model.
177 Huntington: right? And so we're we're definitely gonna get abbreviated versions of these. Oh, yeah, right and and then I think it raises this question, oh, wow! This is kind of
177 Huntington: interesting right? Once you make them dumb again by
177 Huntington: just stealing away all the thinking.
177 Huntington: Is there some way to say, oh, we we know where the smarts are, can we bring that back?
177 Huntington: Right?
177 Huntington: So one question I have, which I imagine would be hard to capture. But
177 Huntington: have you explored trying to like encapsulate physical.
177 Huntington: like concepts in poke, like the idea of like gravity, like the idea of like exploration or something.
177 Huntington: you know, we tried out something slightly different.
177 Huntington: So with the with concept sliders.
177 Huntington: we captured the direction of repair
177 Huntington: which is using, like some text prompts like natural physics driven.
177 Huntington: the model was able to fix its own
177 Huntington: mistakes. All using text forms. We didn't use any additional training. It's just leave that@firstst We try to break that concept and see what happens.
177 Huntington: I don't have labels for it, but it does something very funky. I could show you more recent. I don't have a slide or an image for it, but I could show you just the results.
177 Huntington: But I I was thinking about the figure where you have like 10 directions for from Slider space versus 64 from
177 Huntington: your concept sliders. But like,
177 Huntington: so that what was on of id? Yeah. Yeah.
177 Huntington: Do you feel like this tells you anything about like the the
177 Huntington: cardinality or dimensionality of what the model knows? Or is it just that
177 Huntington: the stuff that we know is limited.
177 Huntington: I think the
177 Huntington: yeah. Maybe it's a bit of both. I I think we did rest it like with 64 sliders. We couldn't cover like 10
177 Huntington: at us from what we discovered from the model. So it feels like, maybe we don't know all of it. That. The model knows like that's the comparison right? This fids across what the model knows it
177 Huntington: so compared to the model. Maybe we don't cover a lot of distribution.
177 Huntington: but that also says that after 30 or 40 directions the model is also pretty much
177 Huntington: going down like it's flattening at a point.
177 Huntington: So it feels like like there are these 40 directions which would cover most of what the model knows.
177 Huntington: But 10 of them are like covering. What we know is, I feel like it's a bit of both of what you said.
177 Huntington: Yeah, I guess I guess it depends on the reference distribution, too. But but yeah, it's a good point. Thanks.
177 Huntington: Now, the shape of this
177 Huntington: curve might might have been different. If you audit a different one for each different subject or something. Is that right like, if you said for just people.
177 Huntington: then you might see like more what there's more directions, and if you say.
177 Huntington: mountains, let's have a few directions, yeah, right
177 Huntington: right this is for just for the concept, art, yeah, and yeah, we do have those plots somewhere, I don't know where, like for other, just for other distributions.
177 Huntington: And yeah, it just may, it's May. It's probably an appendix. If not, I could show after the presentation.
177 Huntington: I need to find it.
177 Huntington: But yeah, like we found that
177 Huntington: Some of these concepts, like person, is represented across a larger number of dimensions compared to art styles like Van Gogh.
177 Huntington: That's the least. It's probably represented in 3 to 4 directions. Whereas person was represented in 40.
177 Huntington: Oh, that's pretty interesting, did your analysis on? Yes, most of it.
177 Huntington: The the the discovery of this method happened in adobe's cluster.
177 Huntington: The final experiments happened in in our service most of our service.
177 Huntington: and you did most of it. No, no, so art the art one ran on the adobe's cluster.
177 Huntington: Rest all happened in our all of your machines.
177 Huntington: Thank you. So many of you like. Message me in the middle of the night. Go ahead. Leave it to it. Sorry, my bad.
177 Huntington: you you had some other questions.
177 Huntington: No, okay.
177 Huntington: Oh, we have a zoom
177 Huntington: Notice that most your examples are about images with like
177 Huntington: a single object. Therefore, just one person there, it's so. So the Slider. If you apply some slider to it, it just adjusted the
177 Huntington: like the features of the one object I'm thinking of like, if there are like multiple objects. For example, there are 2 different people, if you want, like, like one person to be older, one person to be younger. It's a good. That's a very good question. I'll show you one. Follow up work on concepts like this.
177 Huntington: It's called check sliders. Maybe.
177 Huntington: Some of the concept about like relationships, or like you have to bind something to some entity.
177 Huntington: Yes, so they, these people, have no so oh, awesome!
177 Huntington: Oh, what is it going control? Prompt sliders.
177 Huntington: Do you know what's the what is the issue? What's the professor's name? Who did stable decision? OMME. R. Omar?
177 Huntington: Yeah, yeah.
177 Huntington: wow. So many papers I know. Yes, he's got a Patrick Esser. And yeah, going nuts.
177 Huntington: Okay, I'll try to find it. But
177 Huntington: okay, I'll try to find that for you. But they, they have tried to do it for this multi
177 Huntington: concept. Decomposition for sliders.
177 Huntington: There's a second. There's there was a 4th application for Slider space which we didn't really go through
177 Huntington: is
177 Huntington: You could dissect a customized concept. For instance.
177 Huntington: if you train both of us together as one concept to the model, like there is customization which you can. You can add our faces to the model.
177 Huntington: We can add it together both of our faces.
177 Huntington: And when you train a slider space on those customizations, sometimes you get sliders which are specific to me, and sometimes you get sliders that are very specific to you.
177 Huntington: like 2 different subjects.
177 Huntington: But that's very rare case. Most of that happens across all the subjects. But it's not like a consistent. It's not consistent where you have separate, and I have separate.
177 Huntington: There are one or 2 sliders that come out that way. So we thought it wasn't maybe as interesting.
177 Huntington: Yeah, you guys have a split.
177 Huntington: Why, I I thought of the project about like
177 Huntington: the building system to like help artists to have better control on like generated image. But
177 Huntington: but we don't have a much good idea about how how to do this with one control. Yeah, maybe with some like
177 Huntington: mechanistic methods like, Oh, yeah, I could talk to you. There are like so many ways to do it. I could probably talk to you after the yeah meeting.
177 Huntington: Oh, yes, it's good
177 Huntington: kind of theoretical, but it's more of like a in your intuitions. Do you think that, like any 2 pair of like human.
177 Huntington: semantically meaningful images have are like connected meaning, like, there's some set of sliders with human semantic concepts that you can essentially slide from one image to the other. Or do you think that there's like a conceptual like gap between?
177 Huntington: So.
177 Huntington: keeping semantics aside, any 2 images that are generated by diffusion models can be interpolated across each other.
177 Huntington: The question of, Can I find this right sliders to interpolate? It is
177 Huntington: very interesting. You should be able to do it.
177 Huntington: Maybe you can. There is this. I'll go back to Concept Slide for the other question.
177 Huntington: The I found the paper after you're done? Which one? Okay, which one? Yeah, it's it's called the continuous subject specific. That guy. Oh, yeah. Yeah. Oh, thanks. So this is the paper I was talking about.
177 Huntington: So so they train sliders for that one specific man token.
177 Huntington: And then if you just slide that, only the
177 Huntington: yeah, only the the man gets older, whereas with concept riders you're just training for like
177 Huntington: old in general. So everything in the image gets older. So now you're back in the cross attention. Right?
177 Huntington: With this, yeah, yeah, with this. Yeah. So it's like, you apply the Slider on a specific token. Yeah, in the in the like text problem, right? This is. Yeah.
177 Huntington: There are like other ways to do it. You can mask a portion of the image and only let sliders go there. We in our in our code base. We have a way to do it
177 Huntington: where you can select the portions on which you want your status to be applied.
177 Huntington: So that's just an alternative for this. Especially like, if you just have the prompt a close up of a couple.
177 Huntington: then it gets hard to do it based on this method.
177 Huntington: right? Because you didn't really specify both these subjects, which is usually the case, you you are explaining to me.
177 Huntington: So in such cases you can have like a mask.
177 Huntington: and only sometimes mask, sometimes maybe like, associate it with some concept like or tokens. Exactly. Yeah, if you have tokens best way to do it. If you don't mask it and use concepts or users at the same time
177 Huntington: feel like masking is the most natural thing for
177 Huntington: so like, if I mentioned, this is a Photoshop Plugin. Now.
177 Huntington: something it is. It is a Photoshop plug right
177 Huntington: after the meeting is stopped recording. Then I'll probably tell you some stuff
177 Huntington: if adobe is listening. I don't know anything. I think students Northeastern students get adobe creative cloud. Oh, yeah, I think, but I don't know what post get.
177 Huntington: I don't know if AI.
177 Huntington: Is there some like additional plug, then I think we have access to it.
177 Huntington: and they? They have a free version of it. It's called Firefly. I don't know if it's free anymore or not. Firefly also has this Plugin. So your concept. Sliders are in Photoshop. Pro
177 Huntington: ye? Yeah, we open source that. So I didn't really go and apply anything. They just took our method
177 Huntington: solid.
177 Huntington: But yeah, Slider space probably won't be because they are mostly concerned about the speed of discovering it
177 Huntington: right now. It takes 1 h to discover 64 sliders. But then you could just upload them to Cvti, and then they could download them. And that's what is happening now. There are unofficial code implementations of it. There are already slider space in Cvti.
177 Huntington: But yeah, even though the code implementation was not right, they work so well.
177 Huntington: I wonder if it's a different method of its own. Which, yeah, I could, I could reach out to the
177 Huntington: that's a crazy impact.
177 Huntington: Honestly, yeah. So there's a nice story about this concept sliders was actually
177 Huntington: inspired from this author here Ingri zu!
177 Huntington: He is a high school student from Japan and
177 Huntington: just happen to look at. We we raise erasing language, diffusion, concepts from diffusion models.
177 Huntington: He took that work, implemented it on low rank directions, sliders, sorry, low rank adapters, and said that you can control the erasure effect
177 Huntington: on how much you want to it as a concept. And he had this open source repository of it.
177 Huntington: But it was all in Japanese. And we didn't know how to. So we just asked them to join our team. And then
177 Huntington: he just helped us. It's a very cool story. Yeah. Yeah.
177 Huntington: So without, if the Laura came with this guy, what did you do before
177 Huntington: to delete the concepts? No, we we fine tune the entire layer.
177 Huntington: I think Laura was just implemented, or maybe was like wasn't as popular in the vision community.
177 Huntington: You did. Sure we're all rank 1. 0, the rank. One thing right? But we didn't have the full name. Yeah. Oh, but Uc was rank one. So yeah.
177 Huntington: we should have thought of. We should have thought of it. See, that's right. As soon as you mentioned, he's he did this. It's like, Oh, that that should be our paper
177 Huntington: that's groom. And I said, find this find this guy?
177 Huntington: Yeah, yeah, we had like these Google translator apps to talk to each other.
177 Huntington: Yeah, he would write all his code in Japanese on discord.
177 Huntington: on which discord he has his own discord like. So I emailed him on the Github, how do you find a random guys? Discord?
177 Huntington: No, no, he has a Github repository repository.
177 Huntington: Like just going through open source. He was making sliders. He was making sliders and open source community was like trying to get into it. They were trying to figure out the Japanese, but most of them contacted me because the the repository had this is based on erasing. Oh, yeah, it was a fork of your report. It was a fork of my report.
177 Huntington: so he was like, he didn't speak English, so people reached out to me. And I was like, Okay, we need to close this barrier like, let's just bring him in and make this method more stronger. So the the Leco had its. It's called Liko. That's what he named it. Low, rank erasure of concepts.
177 Huntington: And then it had its like our eras and concepts had its own issues like, it changes the images a lot.
177 Huntington: So we worked on. Okay, how to make it more precise, more continuous.
177 Huntington: So yeah, he just helped us do all of this as part of it.
177 Huntington: Yeah, pretty cool story. Yeah.
177 Huntington: And now, yeah, he has, like his own follower base, and he's like he retweeted my tweet, and it got like so much attention. I was like, Wow! We help each other now.
177 Huntington: So there is stability in Japan as well.
177 Huntington: He's hired by, you know. File AI
177 Huntington: Fal. Oh, I know I followed the CEO or something. Yeah, yeah. He was hired by them. Now he works for them. He makes sliders. He's like, did he ever finish? I spoke. I didn't talk to him. He probably didn't, or like. Maybe he did, and then he stopped.
177 Huntington: I'm glad you took them into your project.
177 Huntington: That's right. That's the only problem, I think.
177 Huntington: Well, thank you, Rohit. Thank you very much for that.