Each category contains 1000 masks with and without border constraints. NVIDIA Irregular Mask Dataset: Training Set Testing Set For our training, we use threshold 0.6 to binarize the masks first and then use from 9 to 49 pixels dilation to randomly dilate the holes, followed by random translation, rotation and cropping. To train the network, please use random augmentation tricks including random translation, rotation, dilation and cropping to augment the dataset. Data (NVIDIA Irregular Mask Dataset) Training Set Video Media Coverage (Selected)įortune, Forbes, Fast Company, Engadget, SlashGear, Digital Trends, TNW, eTeknix, Game Debate, Alphr, Gizbot, Fossbytes Techradar, Beeborn, Bit-tech, Hexus, HotHardWare, BleepingComputer, hardocp, boingboing, PetaPixel, 搜狐, 新浪, 量子位(知乎) Online Demo We show qualitative and quantitative comparisons with other methods to validate our approach. Our model outperforms other methods for irregular masks. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. This often leads to artifacts such as color discrepancy and blurriness. Įxisting deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro, Image Inpainting for Irregular Holes Using Partial Convolutions, Proceedings of the European Conference on Computer Vision (ECCV) 2018. Recommended citation: Guilin Liu, Fitsum A. Tl dr OpenAI is just a brand name now and not a literal goal or mission statement at the moment.Image Inpainting for Irregular Holes Using Partial Convolutions Whereas Stability.AI (StableDiffusion) actually made specific decisions for 'less accuracy' and certain compromises specifically so that it would be able to be run on consumer grade hardware since they want to empower the world (reach 1+ billion people) and you can't do that with really high hardware requirements. ![]() So if everyday people can't run them, why not just charge the big companies that can and do that. So most people wouldn't realistically be able to run them even if they released them. ![]() I believe many of them require like at least 100GB of videoram or something insane. One of the other differences is OpenAI and their models can't actually be reasonable run on consumer everyday hardware. ![]() Stable Diffusion which is made by Stability.AI has the same goals that OpenAI originally had, and is just in a better place and more appropriate backing to do accomplish it. I'm not sure if they have any aspirations to try and become open again. They still have certain financial obligations to meet, thus they aren't that "open" at the moment. However due to funding shortage or financial pressures, they were about to be heading to becoming bankrupt or something so they decided to pivot to stay 'afloat' and took some private investors money who required some things change to pull out of the hole they were in. They were originally actually OpenAI as the name implies and wanted to be more open. I don't know the whole story but this is what I think it is.
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