Used here inpaint algorithms can be combined with various neural networks models, especially in the cases when inpaint involves complicated features and big resolution, speed can be also significantly improved.Fooocus 2.1.822 added inpaint and outpaint similar to Midjourney’s “left/right/top/bottom” arrows: Use AI and deep learning to speed up algorithms and optimal inpaint radius selectionĪssist inpaint algorithms with AI for classification, automation of algorithms type and optimal radius size selection. MagicInpainter 3.0 is currently limited to single photos. One other limitation is that inpaint radius can not exceed 64 pixels. Optimization algorithms used in MagicInpainter 3.0 become sometimes too slow and unstable for images with high resolution (in these cases Zoom In/Out buttons can be used). GPU performance - several optimizations are possible for the GPU to work even faster, also search algorithms can be greately improved In comming releases there would be several improvements: For very large image resolutions above 4K and large noise area inpaint can be too slow.Curently the library is avaliable and tested only for Windows and Python versions Python 3.6 - Python 3.12.InpaintGPUfast - this is the recomended GPU method for real-life photos, results can be very little worse but perfomance can rise more than 500 percents InpaintGPU - this is the most precise GPU method, however for high resolution is also too slow Results can be worse and this method would be too slow so is not recommended for images with high resolution and large noise area. InpaintCPU - this is the default inpaint method if GPU with CUDA is missing. Inpaint methodsįollowing MagicInpaint methods are available: OpenCV inpaint methods are faster but works well only for small scratches and line defects. imread ( img_path ) if img is None : print ( 'can not open image:', img_path ) exit () mask = cv2. Inpaint with cv2.INPAINT_TELEA: import numpy as np import cv2 img_path = 'test/cloth1_red1.png' mask_path = 'test/cloth1_mask.bmp' out_path = 'results_cv2/cloth1_inpaint_cv2_Telea_r30.png' img = cv2. Inpaint with cv2.INPAINT_NS: import numpy as np import cv2 img_path = 'test/08_beach1.png' mask_path = 'test/08_beach1_mask.png' out_path = 'results_cv2/08_beach1_inpaint_cv2_NS_r30.png' img = cv2. verbose - print details, by default is Falseįunction outputs True if sucessful, otherwise False.out_path - path to output image file, if None result is saved in image file with appended string "onpaint".mask_file - path to mask black-white image file, 0-valid pixels, 255- noise pixels to remove.image_file - path to color image file, supported formats JPG,BMP,PNG.inpaint_file ( img_path, mask_path, out_path, 30, mi. version ()) img_path = 'test/cloth1_red1.png' mask_path = 'test/cloth1_mask.png' out_path = 'results_mi/cloth1_inpaint_mi_GPUfast_r10.png' mi. There is also a method that allows to process image files directly (supported image extensions are JPG,BMP,PNG): import magicinpaint as mi # check magicinpaint version print ( mi. IMREAD_GRAYSCALE ) if mask is None : print ( 'Can not open mask:', mask_path ) exit () mi. imread ( img_path ) if img is None : print ( 'Can not open image:', img_path ) exit () mask = cv2. version ()) img_path = '08_beach1.jpg' mask_path = '08_beach1_mask.png' out_path = '08_beach1_inpaint.png' img = cv2. To use MagicInpaint for image files with cv2: import numpy as np import magicinpaint as mi import os, sys import cv2 # check magicinpaint version print ( mi. To install MagicInpaint for CPU: pip install magicinpaint-cpuįor the GPU version to work you need to have NVIDIA GPU card and correctly installed CUDA driver (GTX or RTX)! Inpaint with MagicInpaint Normally, this should print the version and the application type: MagicInpaint 1.01 (gpu) To test if MagicInpaint is installed: import magicinpaint as mi mi. To install MagicInpaint for GPU: pip install magicinpaint-gpu There is also and image processing tool using the library available here. MagicInpaint is image processing Python library for high quality image reconstruction and inpaint.
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