I want to change hair color on FFHQ data, however hairstyle of some of results are change. Did I do wrong? The following is my command

python scripts/inference.py
--exp_dir=./experiment
--checkpoint_path=../pretrained_models/hairclip.pt
--latents_test_path=./latents.pt
--editing_type=color
--input_type=text
--color_description=red

Hi. 🤗 This is an awesome work. 👍 Thanks for all of you, the contributors. 🌹 I am wondering if you could tell me if you have any plan to make one demo public on huggingface/spaces, etc. 🤔 ？

Hello, can I use my own image for the resend test, I found that the input was test_face.pt (test data set ?) file, and I did not find the input image content in the code, The only thing that feels like an input image is w(w=torch.Size([1, 18, 512])), But it's not the size of a picture

I tested the effect and found that the hair style of the generated image is quite different from that of the reference image. Here is my test script. The reference image is selected from CelebAMask-HQ dataset. Is there a problem in my test process？

python scripts/inference.py \ --exp_dir=../outputs/0321/ \ --checkpoint_path=../pretrained_models/hairclip.pt \ --latents_test_path=../pretrained_models/test_faces.pt \ --editing_type=both \ --input_type=image_image \ --color_ref_img_test_path=../input/16 \ --hairstyle_ref_img_test_path=../input/16 --num_of_ref_img 1

mask_512 = (torch.unsqueeze(torch.max(labels_predict, 1)[1], 1)==13).float() 1.why hair equal 13, bg not equal 13? 2.unet infer results that have 19 channels, what did they means?

Thank you for your great project!

In this paper, you said “We train and evaluate our hair mapper on the CelebA-HQ dataset. Since we use e4e [43] as our inversion encoder, we follow its division of the training set and test set.” However, I found that e4e used the FFHQ dataset for training and the CelebA-HQ test dataset for evaluation. Hence, I feel confused. My question is that how to split the training and test datasets on the CelebA-HQ dataset?

Hi, Great work! But I have a question about the modulation module of mapper network. I assume the dimension of x and e should be 1x1xC. If so, what is the mean and std of x? channel-wise average? And how about the output dimensions of fr(e) and fb(e)?

Thanks.

输入命令： E:\Linux\XSpace\papers\HairCLIP\mapper>python scripts/inference.py --exp_dir=E:\Linux\XSpace\pap ers\HairCLIP\data\exp --checkpoint_path=F:\Dataset\CelebA\Data\hairclip.pt --latents_test_path=F:\Dataset\CelebA\Data\test_faces.pt --editin g_type=color --input_type=image --hairstyle_description="hairstyle_list.txt" --color_ref_img_test_path=E:\Linux\XSpace\papers\HairCLIP\data
ref

在 latent_mappers.py 中的 x = clip_model.encode_image(masked_generated_renormed) 报错了，错误信息如下：

*** RuntimeError: The following operation failed in the TorchScript interpreter. Traceback of TorchScript, serialized code (most recent call last): File "code/torch/multimodal/model/multimodal_transformer/___torch_mangle_9591.py", line 19, in encode_image _0 = self.visual input = torch.to(image, torch.device("cuda:0"), 5, False, False, None) return (_0).forward(input, ) ~~~~~~~~~~~ <--- HERE def encode_text(self: torch.multimodal.model.multimodal_transformer.___torch_mangle_9591.Multimodal, input: Tensor) -> Tensor: File "code/torch/multimodal/model/multimodal_transformer.py", line 34, in forward x2 = torch.add(x1, torch.to(_4, 5, False, False, None), alpha=1) x3 = torch.permute((_3).forward(x2, ), [1, 0, 2]) x4 = torch.permute((_2).forward(x3, ), [1, 0, 2]) ~~~~~~~~~~~ <--- HERE _15 = torch.slice(x4, 0, 0, 9223372036854775807, 1) x5 = torch.slice(torch.select(_15, 1, 0), 1, 0, 9223372036854775807, 1) File "code/torch/multimodal/model/multimodal_transformer/___torch_mangle_9477.py", line 8, in forward def forward(self: torch.multimodal.model.multimodal_transformer.___torch_mangle_9477.Transformer, x: Tensor) -> Tensor: return (self.resblocks).forward(x, ) ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE def forward1(self: torch.multimodal.model.multimodal_transformer.___torch_mangle_9477.Transformer, x: Tensor) -> Tensor: File "code/torch/torch/nn/modules/container/___torch_mangle_9476.py", line 29, in forward _8 = getattr(self, "3") _9 = getattr(self, "2") _10 = (getattr(self, "1")).forward((getattr(self, "0")).forward(x, ), ) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE _11 = (_7).forward((_8).forward((_9).forward(_10, ), ), ) _12 = (_4).forward((_5).forward((_6).forward(_11, ), ), ) File "code/torch/multimodal/model/multimodal_transformer/___torch_mangle_9376.py", line 13, in forward _0 = self.mlp _1 = self.ln_2 _2 = (self.attn).forward((self.ln_1).forward(x, ), ) ~~~~~~~~~~~~~~~~~~ <--- HERE x0 = torch.add(x, _2, alpha=1) x1 = torch.add(x0, (_0).forward((_1).forward(x0, ), ), alpha=1) File "code/torch/torch/nn/modules/activation/___torch_mangle_9369.py", line 38, in forward _16 = [-1, int(torch.mul(bsz, CONSTANTS.c0)), _8] v0 = torch.transpose(torch.view(_15, _16), 0, 1) attn_output_weights = torch.bmm(q2, torch.transpose(k0, 1, 2)) ~~~~~~~~~ <--- HERE input = torch.softmax(attn_output_weights, -1, None) attn_output_weights0 = torch.dropout(input, 0., True)

Traceback of TorchScript, original code (most recent call last): /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py(4294): multi_head_attention_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py(985): forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl /root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(45): attention /root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(48): forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl /opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py(117): forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl /root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(63): forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl /root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(93): forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl /root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(221): visual_forward /opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py(940): trace_module (36): export_torchscript_models (3): /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3418): run_code /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3338): run_ast_nodes /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3147): run_cell_async /opt/conda/lib/python3.7/site-packages/IPython/core/async_helpers.py(68): _pseudo_sync_runner /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2923): _run_cell /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2878): run_cell /opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(555): interact /opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(564): mainloop /opt/conda/lib/python3.7/site-packages/IPython/terminal/ipapp.py(356): start /opt/conda/lib/python3.7/site-packages/traitlets/config/application.py(845): launch_instance /opt/conda/lib/python3.7/site-packages/IPython/init.py(126): start_ipython /opt/conda/bin/ipython(8): RuntimeError: cublas runtime error : unknown error at C:/cb/pytorch_1000000000000/work/aten/src/THC/THCBlas.cu:225 (Pdb) img_tensor.shape torch.Size([1, 3, 1024, 1024])

请问是输入的tensor大小不对吗

Hello, I want to ask if the speed of run the inferrence.py for testing is normal. This is my executive code: cd mapper python scripts/inference.py
--exp_dir=/home/ps/HairCLIP/mapper/path/to/experiment
--checkpoint_path=/home/ps/HairCLIP/pretrained_models/hairclip.pt
--latents_test_path=/home/ps/HairCLIP/mapper/path/to/test_faces.pt
--editing_type=hairstyle
--input_type=text
--hairstyle_description="/home/ps/HairCLIP/mapper/hairstyle_list.txt" \

First of all , thanks for you excellent work! There are many hairstyles in hairstyle.txt, but actually I found only a few styles in result images after trying all styles. More or less repeat the following images.

• cornrows cut hairstyle

• crew cut hairstyle (the points on left glasses in right image is mouse)

the following is my command:

python scripts/inference.py 
--exp_dir=../result/test_1/
--checkpoint_path=../pretrained_models/hairclip.pt
--latents_test_path=../inference_data/test_1/latent.pt
--editing_type=hairstyle
--input_type=text
--hairstyle_description="hairstyle_list.txt"

What's the problem? Should I train with my own dataset?

I list some hairstyles which have the same effect:

• 1. the same as cornrows: crown braid hairstyle, dreadlocks hairstyle, finger waves hairstyle, french braid hairstyle and so on.
• 2. the same as crew cut hairstyle: caesar cut hairstyle, dido flip hairstyle, extensions hairstyle, fade hairstyle, fauxhawk hairstyle, frosted tops hairstyle ,full crown hairstyle, harvard clip hairstyle, high and tigh hairstyle, hime cut hairstyle, hi-top fade hairstyle and so son.

Hi!

First off, thank you for your work!

I'm trying to create a Colab Notebook to play with your model, but since the weights and stuff are hosted inside google drive, the download limits seems to restrict me from simply downloading it with gdown or wget.

Could I download it and move it to another hosting service (i.e archive.org) to avoid this issue? Of course, I would add all the references to all the authors and parties involved.

Again, thanks for your work!

Hello, boss. I noticed that the neural network structure diagram may be incorrectly drawn in the paper. F should be fine, meaning high-level semantic information; C should be coarse, meaning low-level semantic information.

@wty-ustc Thank you for the amazing work! I try to split the CelebA-HQ by official list_eval_partition.txt. Eventually, I got 24183/2993/2824 images for training/validation/testing split. but i found the len of train.pt is 24176 ...so... I'm very confused about what data you're used?

Thanks you for you great works! Do you think video hair editing based on HairCLIP is achievable？ I have a little try, but the region of hairstyle still hard to control. Consistency in hair styles is quite difficult to maintain. Can you give me some insights about video-hairstyle-editing?