Comments (2)
Hello,
You are right, I forgot to mention this in the report. Actually, the warm-up on the CFG comes from the MUSE paper (Section 2.7): "This allows the early tokens to be sampled more freely, with little or no guidance, but increases the influence of the conditioning prompt on the later tokens".
I'll try to do some experiments on the influence of this warm-up when I have time, and update the report accordingly. But I cannot give you an exact release date.
Thanks for the correction of the weight calculation, I will also update the code :)
Best,
Victor
from maskgit-pytorch.
Thank you for the reply! I've also read the MUSE paper, but I might miss that part. I'll check it out!
I'll also investigate how each technique affects sampling results by myself.
Let me close this thread.
Best regard,
Yukara
from maskgit-pytorch.
Related Issues (11)
- Regarding training a mask model with my own data, could you please provide guidance on the steps involved HOT 1
- train vqgan HOT 3
- Sampling with CFG = 0 HOT 2
- About the training intermediate result. HOT 1
- Target tokens for loss computation HOT 2
- Unneccesary Dropout layer in FeedForward network HOT 2
- Has anyone successfully run the code HOT 2
- reproducibility HOT 2
- How can I use my own dataset to train maskgit? HOT 2
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