Layered designs let people move elements before regenerating pixels

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 11:01

   Yeah, that's a great question. So the answer is both. So we train them individually, but then there's always a time where we're trying to look at the end-to-end. But I would say that the steps are actually three now. First, we take all the input and the prompt or what the context may be, and we try to amplify and make it as powerful as possible. Then there's a process of taking that and converting into basically a structured form of data, more like a scene graph, if you want.

2. 11:36

   And then the last step is generation of the pixels where our model training from scratch has shown really, really good performance. And by breaking down into these steps, you're also allowing for a better integration with agents, with LLMs, because there could be some components of that pipeline. They could be useful for some tasks. If I say, hey, can you just make me 10 different layouts of this single page, right?

3. 12:07

   I don't need the LN to generate pixels. You can use, you know, the craft design planning, can create different plans, can have a human in the loop to confirm and then can do all the generations, for example. Right, so there's actually, we are seeing new workflows coming out from breaking down into these steps. It seems like that would also help with editability um yeah historically you have a scene and you say oh make the person a little bit bigger and the thing you get back is totally different like we're a lot better now uh like the gpt 2.5 image models are really really good at consistency between uh revisions but it does strike me that if you could change the scene graph and move the thing over or make the thing bigger and not have to re-render, that that would help with consistency as you're trying to move things around.

4. 13:05

   Like 100%. We believe on this so strongly that we release magic layers at the beginning of the year, which is basically taking a flat image decomposing in layers and elements so that people can really have that fine control. I mean, how many times have you tried to say to any image model generation Oh, can you move that thing a little bit to the right or like two feet? But in that image, there is no information to know what is two feet, you know, or two inches or centimeters.

5. 13:37

   And so it's a lot easier for a designer to just move that element to the right and then regenerate. And so our beliefs is with craft, basically, the humans and the designers can edit elements, can make them bigger, smaller, move them to the left or the right and then regenerate the pixels later. And we also are extending that capability to images that are generated outside Canva using magic layers.

6. 14:05

   So actually the majority of images that we process with magic layers come from outside Canva and people generate them somewhere else and then they come into Canva and they want to edit. And so there we can decompose into layers and elements and people can start moving things around and have that fine level of control. They always want, I mean, people want consistency and control, right? Those are the two things that any creative has asked AI for the last, I would say, two years.

7. 14:35

   And we came quite far along, but like, it's still, you know, we don't want to provide slot machines, you know, where like, hey, if you generate 20 times, you might get lucky, right? We want to actually make sure the time investment that people are putting into it is really controlled and the result is predictable. You kind of referenced earlier two dimensions that were important, quality and efficiency.