![]() With advanced machine learning algorithms, these tools can analyze the surrounding pixels of an image and generate realistic fills that blend seamlessly with the rest of the picture. It can be used to repair old and damaged photos, remove unwanted objects or people from images, and even generate new images from scratch. Are there any ethical concerns associated with AI image inpainting tools?ĪI image inpainting is a computer vision technique that involves filling in missing or damaged parts of an image using artificial intelligence algorithms.What should I do if the results of an AI image inpainting tool are unsatisfactory?.How accurate are AI image inpainting tools?.How do I get started with AI image inpainting?.Cost and Availability of AI Image Inpainting Tools.AI Image Inpainting vs Traditional Image Editing Techniques.Challenges and Limitations of AI Image Inpainting.Tips for Using AI Image Inpainting Tools.Technical Aspects of AI Image Inpainting Tools.Applications of AI Image Inpainting Tools.Benefits of Using AI Image Inpainting Tools.Please refer to (#Usage) for using the Multi-Scale models. * The weights of DAVIS's refined stages have been released and you can download from ( ). * The frames and masks of our movie demo have been put into ( ). * **Support for PyTorch>1.0:** Sorry for the late update and the pre-release verison for supporting PyTorch>1.0 has been integrated into our new ( ). * More results can be found and downloaded ( ). Python tools/frame_inpaint.py -test_img xxx.png -test_mask xxx.png -image_shape 512 512 * To use the Deepfillv1-Pytorch model for image inpainting, Python tools/infer_liteflownet.py -frame_dir xxx/video_name/frames You can just change the **th_warp** param for getting better results in your video. pretrained_models/DAVIS_model/davis_stage3.pth \ pretrained_models/DAVIS_model/davis_stage2.pth \ img_size 448 896 -DFC -LiteFlowNet -Propagation \ The following command can help you to get the result:ĬUDA_VISIBLE_DEVICES=0 python tools/video_inpaint.py -frame_dir. Please download the lady-running resources ( ) and * For fixed region inpainting, we provide the model weights of refined stages in DAVIS. Please refer to ( ) for detailed use and training settings. Weights for LiteFlowNet are hosted by ( ): ( ), ( ), ( ). We provide the original model weight used in our movie demo which use ResNet101 as backbone and other related weights pls download from ( ). demo/masks -img_size 512 832 -LiteFlowNet -DFC -ResNet101 -Propagation ![]() Python tools/video_inpaint.py -frame_dir. * To use our video inpainting tool for object removing, we recommend that the frames should be put into `xxx/video_name/frames`Īnd the mask of each frame should be put into `xxx/video_name/masks`.Īnd please download the resources of the demo and model weights from ( ).Īn example demo containing frames and masks has been put into the demo and running the following command will get the result: * Image Inpainting(reimplemented from ( )) * Extract Flow: LiteFlowNet(( ) reimplemented from ( )) There exist three components in this repo: Install it using `pip install cupy` or install one of the provided binaries (listed ( )). The correlation layer for LiteFlowNet is implemented in CUDA using CuPy. Please refer to `requirements.txt` for detailed information.Īlternatively, you can run it with the provided (docker/README.md). The code has been tested on pytorch=0.4.0 and python3.6.
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