Comments (3)
@basamelatex no one has shown that this can work with a straight encoder yet afaik, but people have discretized the pixel space and then used a decoder to generate the image as with iGPT and Image Transformer
from vit-pytorch.
Thanks a lot for your answer, I checked out the papers you mentioned above. I noticed that they were able to generate only quite small images such as 64x64 and used relatively small datasets like CIFAR10. On the other hand, in the Vit paper they were suggesting that the model doesn't work well on small datasets. Do you think this would be the case in image generation as well? Do we really need a huge dataset for Vit to work on image generation? I would like to give it a try, but I feel a bit skeptical after seeing 300M dataset they use..
from vit-pytorch.
I would like to know how to use this model for spatial-temporal state forecast, such as nowcasting using radar echo, like ConvLSTM.
from vit-pytorch.
Related Issues (20)
- structural 3D ViT HOT 4
- Saving and loading model seems to be regressing to lower performance HOT 1
- vit_pytorch -> cross_vit.py(mistake) HOT 1
- Using vision transformers for different image resolutions HOT 1
- Potential regression with PT 2.0 and CUDA 12.2/CuDNN 8.9.4 HOT 1
- Not correctly understanding the Multi Head Attention part of the ViT implementation... HOT 3
- CvT with 1 channel input data HOT 2
- Layernorm in Cross attention HOT 4
- how to train HOT 2
- Questions about distill_loss HOT 1
- Trouble loading ViT - Dino structure for channels>3?
- Question regarding 1d fft use HOT 1
- Masking attention with batches
- can we use CvT model for segmentation?
- Multi-target Regression Question
- Problems regarding training 3D Vision transformer : model does not converge
- Add implementation of LongVit HOT 4
- A question with ViT 3d
- Cuda memory for 3D VIT HOT 2
- PyPi page markdown render HOT 1
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from vit-pytorch.