Comments (9)
What is the size of your dataset ? If I remember correctly, it takes like 10 epochs of training on imagenet for the output to be good. So that is about 12M iterations
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Oh sorry I mis-calculate a bit. 10 epochs on ImageNet with batch size of 128 is about 100K iterations. But the dataset size is also matters, it does not work well with small dataset
from enhancing-transformers.
from enhancing-transformers.
Actually, only the stage1 code is good. The code for stage2 is not good. Most of the time, it produces bad results but sometimes good so I recommend using another code until I can find what wrong
from enhancing-transformers.
from enhancing-transformers.
from enhancing-transformers.
from enhancing-transformers.
- Yeah you can do simple thing like zero latent code for unconditional
- If you want to re-use the provided pretrained weight then you must use VQ-GAN code, if you want to use RQ-VAE code then you must train a compatible stage1 model
- Training ViT-VQGAN takes a really long time. In the paper they use like 128 TPUs which I certainly do not have access to so I cannot match their numbers unfortunately
from enhancing-transformers.
from enhancing-transformers.
Related Issues (20)
- Pre-norm and Post-norm HOT 6
- An inplace operation in the forward process HOT 1
- Reconstruction Visualization HOT 2
- stage2 transformer HOT 2
- stage1 pretraining HOT 7
- Model license HOT 4
- about training loss HOT 1
- The question is done. HOT 1
- ImageNet version.
- Reconstruction results HOT 4
- Learning rate and scheduler for stage 1 training. HOT 1
- why not used pretrained discriminator
- Incomplete implementation of RQ-VAE
- raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['where', 'cl']' returned non-zero exit status 1. HOT 5
- Results HOT 5
- Smaller images HOT 10
- OOM for imagenet_gpt_vitvq_base and a 100M params GPT on A100 40G HOT 3
- Pretrained Stage 2 Transformer for ViT-VQGAN HOT 8
- Training time and number of GPUS HOT 6
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