cyclomon / diffuseit Goto Github PK
View Code? Open in Web Editor NEWOfficial repository of "Diffusion-based Image Translation using Disentangled Style and Content Representation" ( ICLR 2023 )
License: MIT License
Official repository of "Diffusion-based Image Translation using Disentangled Style and Content Representation" ( ICLR 2023 )
License: MIT License
Hi, this is a great work! could you please provide the training codes for own dataset when you are available? Thanks!
Hello,
Thanks for sharing the code.
Could you please provide some guidance on how to set up the training process?
Looking forward to your response.
Hello, thank you for sharing your awesome project :)
I have a question on the hyperparameters that you used for Figure 8 in your paper.
I guess the command would look like this:
python main.py -I "source_image" --output_path "output_path"
-tg "target_image" --use_range_restart --diff_iter 100 --timestep_respacing 200 --skip_timesteps 80
--use_colormatch --use_noise_aug_all --use_ffhq
Please correct me if I am wrong.
Thank you for reading my question!
Minor thing to note:
You may want to add information about the pretrained arc face model to the README file.
Hello,when I use the second training method(Image-guided Image translation to train). No pictures are generated in the folder generated after training.
The nomenclature I use is as follows: python main.py -i "input_example/source.jpg" --output_path "./outputs/output_reptile3" -tg "input_example/leopard.jpg" --use_range_restart --diff_iter 100 --timestep_respacing 200 --skip_timesteps 80
Why? What should I do? Thank you very much!!
Hi author, thanks for your awesome project. Also, I wonder if you could provide scripts for training this model? Thanks in advance.
/content/DiffuseIT2
Using device: cpu
Using cache found in /root/.cache/torch/hub/facebookresearch_dino_main
Start iterations 0
0% 0/120 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/content/DiffuseIT2/main.py", line 8, in
image_editor.edit_image_by_prompt()
File "/content/DiffuseIT2/optimization/image_editor.py", line 287, in edit_image_by_prompt
for j, sample in enumerate(samples):
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/gaussian_diffusion.py", line 542, in p_sample_loop_progressive
out = self.p_sample(
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/gaussian_diffusion.py", line 386, in p_sample
out = self.p_mean_variance(
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/respace.py", line 91, in p_mean_variance
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/gaussian_diffusion.py", line 240, in p_mean_variance
model_output = model(x, self._scale_timesteps(t), **model_kwargs)
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/respace.py", line 128, in call
return self.model(x, new_ts, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/unet.py", line 656, in forward
h = module(h, emb)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/content/DiffuseIT2/guided_diffusion/guided_diffusion/unet.py", line 77, in forward
x = layer(x)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
Dear @cyclomon
I am running your Text-guided Image translation script python main.py -p "Black Leopard" -s "Lion" -i "input_example/lion1.jpg" --output_path "./outputs/output_leopard" --use_range_restart --use_noise_aug_all --regularize_content
unfortunately it gives an error.
Traceback
result = func(*args)
File "/home2/coremax/anaconda3/envs/DiffuseIT/lib/python3.9/urllib/request.py", line 1389, in https_open
return self.do_open(http.client.HTTPSConnection, req,
File "/home2/coremax/anaconda3/envs/DiffuseIT/lib/python3.9/urllib/request.py", line 1349, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno 101] Network is unreachable>
hi, thank you for your work. I want to know about identity loss you wrote in github.
"For face identity loss when using FFHQ pre-trained model, download pre-trained ArcFace model LINK"
First of all, thank you for your great work!
I'd like to know the exact configurations for reproducing the figure 1. It would be great if you can provide the details.
Thanks!
Hi, I really enjoyed going through your work!!!
Could you please address the official link(github/paper) of pre-trained diffusion model used for FFHQ dataset? Is it a guided diffusion trained on FFHQ dataset?
Hey, I tried your pre-trained models. They are really good. Do you have code to train a custom model on a different dataset from the ones mentioned in the Readme file.
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