Inverting a clean image into its corresponding noise representation—rather than starting from random noise—enables seamless inpainting without boundary artifacts.
InverField achieves artifact-free inpainting by inverting the clean image into its specific noise representation, then combining that inverted noise with new noise in the mask region, allowing seamless harmonization without visible boundaries. ✦ AI generated
Fati Periqi · The TWIML AI Podcast · 2026-08-12 · original ↗
starts at this moment · 50:42
“so what's the core idea behind this paper”
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 process where we actually train the model. 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 thing you know very well studied understood and it's very fast like 60 mcond we can take a large image and then end up create going through the reverse 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 entire 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 field prompt text prompt.
verbatim transcript · starts at 50:42
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