From bd3b65a142a71c5c7dd7c6601f4e506bc3fb3bca Mon Sep 17 00:00:00 2001 From: Bo Zhang Date: Mon, 12 Jul 2021 20:02:03 +0800 Subject: [PATCH] Update README.md --- README.md | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 4713ab7a..cfc4622b 100755 --- a/README.md +++ b/README.md @@ -17,9 +17,8 @@ [Fang Wen](https://www.microsoft.com/en-us/research/people/fangwen/)2
1City University of Hong Kong, 2Microsoft Research Asia, 3Microsoft Cloud AI, 4USTC -## Notes of this project -The code originates from our research project and the aim is to demonstrate the research idea, so we have not optimized it from a product perspective. And we will spend time to address some common issues, such as out of memory issue, limited resolution, but will not involve too much in engineering problems, such as speedup of the inference, fastapi deployment and so on. **We welcome volunteers to contribute to this project to make it more usable for practical application.** -~~We are improving the algorithm so as to process high resolution photos. It takes time and please stay tuned.~~ + ## :sparkles: News The framework now supports the restoration of high-resolution input. @@ -104,7 +103,7 @@ python run.py --input_folder [test_image_folder_path] \ --with_scratch ``` -For high-resolution images with scratches: +**For high-resolution images with scratches**: ``` python run.py --input_folder [test_image_folder_path] \ @@ -221,13 +220,6 @@ Traing the mapping with scraches (Multi-Scale Patch Attention for HR input): python train_mapping.py --no_TTUR --NL_res --random_hole --use_SN --correlation_renormalize --training_dataset mapping --NL_use_mask --NL_fusion_method combine --non_local Setting_42 --use_v2_degradation --use_vae_which_epoch 200 --continue_train --name mapping_Pathc_Attention --label_nc 0 --loadSize 256 --fineSize 256 --dataroot [your_data_folder] --no_instance --resize_or_crop crop_only --batchSize 36 --no_html --gpu_ids 0,1,2,3 --nThreads 8 --load_pretrainA [ckpt_of_domainA_SR_old_photos] --load_pretrainB [ckpt_of_domainB_old_photos] --l2_feat 60 --n_downsample_global 3 --mc 64 --k_size 4 --start_r 1 --mapping_n_block 6 --map_mc 512 --use_l1_feat --niter 150 --niter_decay 100 --outputs_dir [your_output_folder] --checkpoints_dir [your_ckpt_folder] --irregular_mask [absolute_path_of_mask_file] --mapping_exp 1 ``` -## To Do -- [x] Clean testing code -- [x] Release pretrained model -- [x] Collab demo -- [x] Release training code -- [x] Processing of high-resolution input - ## Citation