advimman/lama
Lama
Lama is an image restoration framework and deep learning model designed for image inpainting and object removal. It provides the tools necessary to train and evaluate neural networks that fill masked areas and repair corrupted visual data.
The system utilizes a Fourier convolution neural network to maintain global image structure and reconstruct periodic patterns. This architecture allows for resolution-independent inference, enabling the processing of high-resolution images without increasing memory or computational requirements.
The project includes a synthetic dataset generator that creates randomized training masks over raw images. It also features quantitative evaluation tools that compare predicted restoration results against ground truth data to calculate accuracy metrics.
Features
- Image Inpainting - Provides a deep learning framework specifically designed for filling missing regions in static images.
- Fourier Convolutional Networks - Implements a Fourier convolution neural network to maintain global image structure and periodic patterns.
- Custom Model Training - Provides a framework for developing and optimizing image restoration models using custom training masks.
- Deep Learning Training Pipelines - Implements an end-to-end workflow for training neural networks to map masked images to restored versions.
- Image Region Reconstruction - Reconstructs missing image regions and complex structures using fast Fourier convolutions to maintain a global perspective.
- Image Restoration Models - Provides a comprehensive toolset for training and evaluating deep learning models for image reconstruction and artifact removal.
- Image Restoration Model Training - Develops deep learning models on custom or standard datasets using various architectures to optimize image restoration.
- Resolution-Independent Inference - Enables processing of high-resolution images without increasing memory or computational requirements during inference.
- Fast Fourier Convolutions - Utilizes fast Fourier convolutions to maintain global structural consistency and periodic patterns across large image areas.
- Object Removal - Eliminates unwanted elements from high-resolution photos while preserving structural consistency and repeating patterns.
- Synthetic Dataset Generators - Includes a generator that creates randomized training masks over raw images to simulate missing data.
- Training Mask Generation - Creates random masks of varying thickness over raw images to build synthetic datasets for model training.
- Model Evaluation Metrics - Calculates quantitative performance metrics by comparing predicted results against ground truth images.
- Frequency-Domain - Processes image data in the frequency domain to efficiently reconstruct repeating patterns and periodic textures.
- High-Performance Image Pipelines - Processes large image files for element removal using memory-efficient pipelines.
- Resolution-Independent Inference - Implements resolution-independent inference to process high-resolution images without increasing memory or computational requirements.
- Model Evaluation - Provides tools for measuring restoration accuracy by comparing predicted results against original ground truth images.
- Image Inpainting Models - Resolution-robust inpainting using fast Fourier convolutions.
- Image Restoration and Enhancement - Resolution-robust inpainting using Fourier convolutions.
- Image inpainting - Listed in the “Image inpainting” section of the Ailia Models awesome list.
