Image inpainting models produce visible boundary artifacts because they initialize only the masked region with random noise, losing coherence with the surrounding image context.

49:20we want to remove. The challenge is well I can still see you know kind of you create the texture sometimes okay it is meaningful but then >> there's those artifacts that you see in the background like the >> sand on the beach where you remove the the person who what didn't need to be in the picture is kind of funky looking >> and maybe the texture is not really

49:43compliant with the rest of the image or I see literally the artifacts around the boundary so car now yeah it is like a toy you know yeah you can do that but I'm not going to really use it but we are saying that hey you don't need to be there you can do much better object removal or image in painting image editing and that's what this invert

50:05field paper is talking about and so what's the core idea behind this paper I'm imagining that you're using diffusion um you know maybe multi-step or or onestep diffusion somewhere So in inver field what we do okay we have this input image and um we allow generating noise in the background and then we have also this um noise in the mask like the bird that we want to remove. But what I

50:42mean by like this noise in the background the rest of the image. So in den noising we started with uh when during training with a noisy image and then we progressively removed noise and end up with a clean image. In image generation we also do the same thing right we start with a noise and then end up in with a clean image. But think about other process like the

51:11way that we actually train those models. Those models when we train we start with the clean image and then add noise noise and at the end it becomes like noisy image. So think about the reverse pro process where we actually train the model. Um so we can take this input image and map into noise. So we are progressively inverting a real let's say clean image into noisy versions. So kind of this

51:43thing you know very well studied understood and it's very fast like 60 mcond we can take a large image and then end up uh create going through the reverse uh den noising and a noisy version of it. So this noise is not random noise anymore. It is specific to the input image. So if I change the input image the noise is going to be different. So this is about

52:13entire image and then we have this mask of the bird. Now I added noise to there for the bird because I allow I want to allow algorithm to generate a new bird compliant with my uh field prompt text prompt. So now I changed the way that we kind of create this image. uh but I don't need to change the original model. I can still you know I change the way that I

52:40initialize the diffusion in this uh painting. And when we do that when we start with you know like this inverted noise plus the you know mask and new noise within the mask. Now uh first of all uh we can retain background but we also allow background to slightly uh uh impact the foreground like the mask itself. allows seamless harmonization uh and it generates you know high quality images but most importantly