# GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting arXiv: https://arxiv.org/abs/2205.07162 ยท HTML: https://mlsanity.com/paper/2205.07162/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 43 references that are in the map. - First posted on arXiv 15 May 2022. ## Builds on (most cited first, 28 of 28) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016) - [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021) - [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018) - [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018) - [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018) - [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014) - [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021) - [Focal Frequency Loss for Image Reconstruction and Synthesis](https://mlsanity.com/paper/2012.12821/) (2020) - [Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature](https://mlsanity.com/paper/1505.00855/) (2015) - [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021) - [Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations](https://mlsanity.com/paper/2007.06929/) (2020) - [Bringing Old Photos Back to Life](https://mlsanity.com/paper/2004.09484/) (2020) - [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021) - [Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding](https://mlsanity.com/paper/2203.00867/) (2022) - [Diverse Image Inpainting with Bidirectional and Autoregressive Transformers](https://mlsanity.com/paper/2104.12335/) (2021) - [Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving](https://mlsanity.com/paper/2011.13611/) (2020) - [Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness](https://mlsanity.com/paper/2104.13743/) (2021) - [Spectral Regularization for Combating Mode Collapse in GANs](https://mlsanity.com/paper/1908.10999/) (2019) - [Spectral Distribution Aware Image Generation](https://mlsanity.com/paper/2012.03110/) (2020) - [SWAGAN: A Style-based Wavelet-driven Generative Model](https://mlsanity.com/paper/2102.06108/) (2021) ## Built on by (most cited first, 15 of 15) - [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024) - [RAD: Region-Aware Diffusion Models for Image Inpainting](https://mlsanity.com/paper/2412.09191/) (2024) - [FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model](https://mlsanity.com/paper/2410.13925/) (2024) - [VTAE: Variational Transformer Autoencoder with Manifolds Learning](https://mlsanity.com/paper/2304.00948/) (2023) - [Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt](https://mlsanity.com/paper/2305.04430/) (2023) - [Sketch-guided Image Inpainting with Partial Discrete Diffusion Process](https://mlsanity.com/paper/2404.11949/) (2024) - [From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion](https://mlsanity.com/paper/2504.14294/) (2025) - [Photorealistic Facial Wrinkles Removal](https://mlsanity.com/paper/2211.01930/) (2022) - [Compensation Sampling for Improved Convergence in Diffusion Models](https://mlsanity.com/paper/2312.06285/) (2023) - [Image Completion via Dual-path Cooperative Filtering](https://mlsanity.com/paper/2305.00379/) (2023) - [Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting](https://mlsanity.com/paper/2307.07678/) (2023) - [Distance Weighted Trans Network for Image Completion](https://mlsanity.com/paper/2310.07440/) (2023) - [Windowed-FourierMixer: Enhancing Clutter-Free Room Modeling with Fourier Transform](https://mlsanity.com/paper/2402.18287/) (2024) - [RETHINED: A New Benchmark and Baseline for Real-Time High-Resolution Image Inpainting On Edge Devices](https://mlsanity.com/paper/2503.14757/) (2025) - [Time-variant Image Inpainting via Interactive Distribution Transition Estimation](https://mlsanity.com/paper/2506.23461/) (2025) ## Abstract (arXiv) The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional neural networks (CNNs), image inpainting task has made great breakthroughs. However, most of the work consider insufficient types of mask, and their performance will drop dramatically when encountering unseen masks. To combat these challenges, we propose a simple yet general method to solve this problem based on the LaMa image inpainting framework, dubbed GLaMa. Our proposed GLaMa can better capture different types of missing information by using more types of masks. By incorporating more degraded images in the training phase, we can expect to enhance the robustness of the model with respect to various masks. In order to yield more reasonable results, we further introduce a frequency-based loss in addition to the traditional spatial reconstruction loss and adversarial loss. In particular, we introduce an effective reconstruction loss both in the spatial and frequency domain to reduce the chessboard effect and ripples in the reconstructed image. Extensive experiments demonstrate that our method can boost the performance over the original LaMa method for each type of mask on FFHQ, ImageNet, Places2 and WikiArt dataset. The proposed GLaMa was ranked first in terms of PSNR, LPIPS and SSIM in the NTIRE 2022 Image Inpainting Challenge Track 1 Unsupervised.