dome272 / vqgan-pytorch Goto Github PK

I just want to say this is a great repo! I spent two days tuning the environment of the original taming-transformer repo but fails with incompatibility of different packages. But this one is so neat and clean with very basic dependencies. I successfully run it the first time I try. Thank you the repo owner!!!

Best,
Dayang

Hello, your tutorial is great! I have a question I would like to ask, how do I add conditions in the form of pictures when training the second stage transformer

I have d question that is there anywhere i can donwload the vqgan_last_ckpt.pt?

in the train_vqgan.py, the code real_fake_images = torch.cat((imgs[:4], decoded_images.add(1).mul(0.5)[:4])) should be revised real_fake_images = torch.cat((imgs.add(1).mul(0.5)[:4], decoded_images.add(1).mul(0.5)[:4]))

Thanks for the clean and nice implementation of VQGAN.
Could you please provide a requirements.txt or environment.yaml file to set up the environment?
Thanks.

Hi, my discriminator loss curve looks like this:

I don't think that's right because it means most of the times the discriminator knows the generated image is fake but the generator failed to generate better images...
Does anyone have similar issue?

Hi, when I run this code I got vertical and horizontal lines on the image:

I came to this repo from the youtube video, and it seems the OP has diagonal lines instead...
https://youtu.be/_Br5WRwUz_U?t=1637
Can anyone tell me why, and how to prevent it?

Hello, your tutorial is great! I have a question I would like to ask, how do I add conditions in the form of pictures when training the second stage transformer

Hi, thank you very much for sharing your clean implementation. I would like to ask you some questions:

1. How much data we need to produce a good generation result.
2. Does the data need to be of the same category (e.g. flowers).
3. Does transformer require pre-training.

Looking forward for your reply, thanks in advance.

I didn't find how to predict the whole sequence of z_q in the training step. It's not matched with the $p(s) = \prod_ip(s_i|s_{< i})$ in the paper

I try to train the Transformers, and I get this errors: RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

Hello, thank you very much for your code and videos!
I'm using this code repository to train on the flowers dataset with a batch size of 32 for 200 epochs, but the reconstructed images still only have rough outlines without specific details.
Is there something wrong somewhere?

Hi! Thank you for sharing this project and for the video tutorial on writing it! I have a question: why do the generated images all have a black border? Can this be fixed? I tried to add "reflect" to all Conv2d layers but that didn't fix it.

Hello, your tutorial is great! I have a question I would like to ask: when I want to add label conditions, such as gender or age, or both, how should the transformer be adjusted, and could you provide an example?

Thanks for your video.
Since the transformer take in the quantified image token generated by VQGAN, which codebook has indices (0~n_embed-1), and transformer’s sos token is also set to zero defaultly. Could you tell me why we don't distinguish codebook vector0 and the sos token when training transformer?

In training_vqgan.py, there is no valid step. Is the model trained with this setup usable?