Tejas Kulkarni

been planning to but came across a big issue in the stack trace before it. I am unable to get really good temporally coherence in the basic prediction task (gradient...

@lucidrains still training (moving mnist) and it might reach temporal coherence after more training but this is where it's at after 40k steps.  

@lucidrains let me start a run now and see

much more stable but there is a temporal coherence issue. actually the problem might be deeper than inference sampling -- we would expect the predictions in just the training snippets...

@lucidrains tried this https://github.com/lucidrains/video-diffusion-pytorch/blob/main/video_diffusion_pytorch/video_diffusion_pytorch.py#L284. Do we mean the same thing or are you referring to something else?

@lucidrains it looks like they are mixing random frames at the end of a video seq -- "To implement this joint training, we concatenate random independent image frames to the...

@lucidrains one interesting thing I noticed is that without `focus_on_the_present` turned out, it has a hard time even memorizing a single video frame. It does a good job with this...

@lucidrains yes but it is a overfitting test but I suspect it will work (smooth motions won't happen or there might also be mixing of digits). I also ran a...

@lucidrains ok sounds good. will wait for your ping to do some more debugging/testing once you take a stab. btw before 0.3.1 I got errors on forward due to `SpatialLinearAttention`...

@lucidrains awesome! the samples, even at the beginning look qualitatively different. loss seems to be steadily going down. wonder why its blobby