luping-liu / pndm Goto Github PK

After training a model, how do I create a .ckpt file to use with the sample runner? The training loop writes out .pth files that seem almost correct, but they are slightly mismatched. After a bit of research, it appears that the model is saved with the nn.DataParallel module, but is loaded without it. Reference: https://discuss.pytorch.org/t/solved-keyerror-unexpected-key-module-encoder-embedding-weight-in-state-dict/1686/2

Thanks for your inspiring work.

I found the implementation of FON is confusing, which may not what claimed in the paper.

Paper claims

However,

def gen_fon(img, t, t_next, model, alphas_cump, ets):
    t_list = [t, (t + t_next) / 2.0, t_next]
    if len(ets) > 2:
        noise = model(img, t)
        img_next = transfer(img, t, t-1, noise, alphas_cump)
        delta = img_next - img
        ets.append(delta)
    else:
        noise = model(img, t_list[0])
        img_ = transfer(img, t, t - 1, noise, alphas_cump)
        delta_1 = img_ - img
        ets.append(delta_1)

        img_2 = img + delta_1 * (t - t_next).view(-1, 1, 1, 1) / 2.0
        noise = model(img_2, t_list[1])
        img_ = transfer(img, t, t - 1, noise, alphas_cump)
        delta_2 = img_ - img

        img_3 = img + delta_2 * (t - t_next).view(-1, 1, 1, 1) / 2.0
        noise = model(img_3, t_list[1])
        img_ = transfer(img, t, t - 1, noise, alphas_cump)
        delta_3 = img_ - img

        img_4 = img + delta_3 * (t - t_next).view(-1, 1, 1, 1)
        noise = model(img_4, t_list[2])
        img_ = transfer(img, t, t - 1, noise, alphas_cump)
        delta_4 = img_ - img
        delta = (1 / 6.0) * (delta_1 + 2*delta_2 + 2*delta_3 + delta_4)

    img_next = img + delta * (t - t_next).view(-1, 1, 1, 1)
    return img_next

After len(ets) > 2, delta seems only use first order info?

I am writing a paper and need to use your code, but the checkpoint and the fid is empty in the aliyundirve.

Hi, thanks for the great work!
I'm wondering is it possible to get a predicted x_0 during the denoising process like DDIM? If yes, could you maybe expalin a bit?
Thanks a lot!

Hi @luping-liu! Thank you for your job. It´s really interesting!

Could you help me to know how could I train a model with new data? I have Image in 256x256 size and I have divided it into validation and train because it is medical data and must not be mixed because the train and the validation data will be very correlated.

Regards.

Hi, I wanted to use PNDM for conditional image generation and would like to know how to integrate that. Thanks

Hi, thanks for your interesting work.

What is your training time on each dataset?

Hey I am back, so i moved the program over to a google colab. Ran this command:

!python main.py --runner sample --method F-PNDM --sample_speed 50 --device cuda --config ddim_cifar10.yml --image_path results --model_path models/ddim_cifar10.ckpt
Everything was going fine and then ->

any advice?

Hi, thank you for your interesting work.

I have a question. Is the "pseudo numerical method" one of the methods of classical numerical? Or is it just named specifically for your research? As I searched for the name in the numerical methods domain, but I didn't find it. So I guess it is a specific term you named specifically for your work?

Looking forward to your reply. Thanks!