![]() A few more lines produce a beautiful field.ĭraw in a pond and nearby elements like trees and rocks appear as reflections in the water. ![]() Four quick shapes and a stunning mountain range appears. The AI model then immediately fills the screen with show-stopping results. Users start by sketching simple shapes and lines with a palette of real-world materials, like grass or clouds. NVIDIA Canvas is part of NVIDIA Studio, a program that provides creators with hardware and software tools to assist in realizing their creative visions. The app displays the photographic result as people paint, so they don’t need to wait to see the form of their vision - they see it right away. The NVIDIA Canvas app, now available as a free beta, brings the real-time painting tool GauGAN to anyone with an NVIDIA RTX GPU.ĭeveloped by the NVIDIA Research team, GauGAN has wowed creative communities at trade shows around the world by using deep learning models to turn rough sketches into stunning scenes.īuilding on this work, NVIDIA Canvas lets creators paint by material rather than color, using AI to turn brushstrokes into lifelike images. Please refer to (#Usage) for using the Multi-Scale models.Turning doodles into stunning landscapes - there’s an app for that. * 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |