ok, thank you
i have a problem now,
i follow the guide to train 512 model
first i prepare 6W pics
and i run python scripts/crop_align_vggface2_FFHQalign.py --input_dir $DATAPATH$/VGGface2/train --output_dir_ffhqalign $ALIGN_OUTDIR$ --mode ffhq --crop_size 256
and python scripts/inference_gfpgan_forvggface2.py --input_path $ALIGN_OUTDIR$ --batchSize 8 --save_dir $HQ_OUTDIR$
to make my 512*512 dataset
then i run python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True
to train my model
i use python test_video_swapsingle.py --crop_size 512 --use_mask --name simswap512 --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results to test
but finallly error comes error appears []Pretrained network G has fewer layers; The following are not initialized: ['down0', 'first_layer', 'last_layer', 'up0']
I don't know where I went wrong I really look forward to your help
ok, thank you
i have a problem now,
i follow the guide to train 512 model
first i prepare 6W pics
and i run `python scripts/crop_align_vggface2_FFHQalign.py --input_dir $DATAPATH$/VGGface2/train --output_dir_ffhqalign $ALIGN_OUTDIR$ --mode ffhq --crop_size 256`
and `python scripts/inference_gfpgan_forvggface2.py --input_path $ALIGN_OUTDIR$ --batchSize 8 --save_dir $HQ_OUTDIR$`
to make my 512*512 dataset
then i run `python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True`
to train my model
i use `python test_video_swapsingle.py --crop_size 512 --use_mask --name simswap512 --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results ` to test
but finallly error comes
`error appears []Pretrained network G has fewer layers; The following are not initialized:
['down0', 'first_layer', 'last_layer', 'up0']`
I don't know where I went wrong I really look forward to your help
Finally I found the problem
I have been following simswap continuously, which led me to try training for the first time
I'm using the April 20th version train.py # Last Modified: Wednesday, 20th April 2022 6:33:30 pm
but trian.py updated # Last Modified: Friday, 22nd April 2022 10:49:26 am
remove line 182,183,184 if opt.train_simswap: src_image1 = F.interpolate(src_image1,size=(256,256), mode='bicubic') src_image2 = F.interpolate(src_image2,size=(256,256), mode='bicubic')
this might be my problem
I hope you pay attention to the training version
Finally I found the problem
I have been following simswap continuously, which led me to try training for the first time
I'm using the April 20th version train.py
`# Last Modified: Wednesday, 20th April 2022 6:33:30 pm`
but trian.py updated
`# Last Modified: Friday, 22nd April 2022 10:49:26 am`
remove line 182,183,184
`if opt.train_simswap:
src_image1 = F.interpolate(src_image1,size=(256,256), mode='bicubic')
src_image2 = F.interpolate(src_image2,size=(256,256), mode='bicubic')`
this might be my problem
I hope you pay attention to the training version
Hey, Im glad you found the issue! Good luck, will definitely be paying attention to see how your guy's model turns out. Remember to not undertrain but also, dont over train 🙂
Hey, Im glad you found the issue! Good luck, will definitely be paying attention to see how your guy's model turns out. Remember to not undertrain but also, dont over train 🙂
ok, thank you i have a problem now, i follow the guide to train 512 model first i prepare 6W pics and i run python scripts/crop_align_vggface2_FFHQalign.py --input_dir $DATAPATH$/VGGface2/train --output_dir_ffhqalign $ALIGN_OUTDIR$ --mode ffhq --crop_size 256 and python scripts/inference_gfpgan_forvggface2.py --input_path $ALIGN_OUTDIR$ --batchSize 8 --save_dir $HQ_OUTDIR$
to make my 512*512 dataset
then i run python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True to train my model
i use python test_video_swapsingle.py --crop_size 512 --use_mask --name simswap512 --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results to test but finallly error comes error appears []Pretrained network G has fewer layers; The following are not initialized: ['down0', 'first_layer', 'last_layer', 'up0'] I don't know where I went wrong I really look forward to your help
If I'm not mistaken, the dataset published in the VGGFace2-HQ repository is already aligned, croped and processed with GFPGAN to 512
> ok, thank you i have a problem now, i follow the guide to train 512 model first i prepare 6W pics and i run `python scripts/crop_align_vggface2_FFHQalign.py --input_dir $DATAPATH$/VGGface2/train --output_dir_ffhqalign $ALIGN_OUTDIR$ --mode ffhq --crop_size 256` and `python scripts/inference_gfpgan_forvggface2.py --input_path $ALIGN_OUTDIR$ --batchSize 8 --save_dir $HQ_OUTDIR$`
>
> to make my 512*512 dataset
>
> then i run `python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep True` to train my model
>
> i use `python test_video_swapsingle.py --crop_size 512 --use_mask --name simswap512 --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_results ` to test but finallly error comes `error appears []Pretrained network G has fewer layers; The following are not initialized: ['down0', 'first_layer', 'last_layer', 'up0']` I don't know where I went wrong I really look forward to your help
If I'm not mistaken, the [dataset](https://drive.google.com/drive/folders/1ZHy7jrd6cGb2lUa4qYugXe41G_Ef9Ibw) published in the [VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ) repository is already aligned, croped and processed with GFPGAN to 512
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Please refrain from spamming issues, reread the Updated preparation guide and if you have a specific problem, im sure one of us can help you. Thanks 🙏
ok, thank you
i have a problem now,
i follow the guide to train 512 model
first i prepare 6W pics
and i run
python scripts/crop_align_vggface2_FFHQalign.py --input_dir $DATAPATH$/VGGface2/train --output_dir_ffhqalign $ALIGN_OUTDIR$ --mode ffhq --crop_size 256and
python scripts/inference_gfpgan_forvggface2.py --input_path $ALIGN_OUTDIR$ --batchSize 8 --save_dir $HQ_OUTDIR$to make my 512*512 dataset
then i run
python train.py --name simswap512_test --batchSize 16 --gpu_ids 0 --dataset /path/to/VGGFace2HQ --Gdeep Trueto train my model
i use
python test_video_swapsingle.py --crop_size 512 --use_mask --name simswap512 --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path ./demo_file/Iron_man.jpg --video_path ./demo_file/multi_people_1080p.mp4 --output_path ./output/multi_test_swapsingle.mp4 --temp_path ./temp_resultsto testbut finallly error comes
error appears []Pretrained network G has fewer layers; The following are not initialized: ['down0', 'first_layer', 'last_layer', 'up0']I don't know where I went wrong I really look forward to your help
Finally I found the problem
I have been following simswap continuously, which led me to try training for the first time
I'm using the April 20th version train.py
# Last Modified: Wednesday, 20th April 2022 6:33:30 pmbut trian.py updated
# Last Modified: Friday, 22nd April 2022 10:49:26 amremove line 182,183,184
if opt.train_simswap: src_image1 = F.interpolate(src_image1,size=(256,256), mode='bicubic') src_image2 = F.interpolate(src_image2,size=(256,256), mode='bicubic')this might be my problem
I hope you pay attention to the training version
Hey, Im glad you found the issue! Good luck, will definitely be paying attention to see how your guy's model turns out. Remember to not undertrain but also, dont over train 🙂
If I'm not mistaken, the dataset published in the VGGFace2-HQ repository is already aligned, croped and processed with GFPGAN to 512