InfiNet

InfiNet

WIP, tests are to be done!

InfiNet — ControlNet-like structure for video diffusion (Unet3D-based) models allowing them to train on arbitrary long videos and as result produce extremely long videos on consumer PCs via the DiffusionOverDiffusion architecture proposed by Microsoft in https://arxiv.org/abs/2303.12346 for their NUWA-XL model.

Thanks to it utilizing so-called zero-convolutions, it’s possible to add the InfiNet model on top of an already pretrained U-net to save resources.

This repo contains the code of ModelScope’s text2video model with InfiNet being injected into it by appending it as a submodule and hijacking the forward function.

The InfiNet module itself is located here https://github.com/kabachuha/InfiNet/blob/master/t2v_modules/dod_unet.py

The key difference with ControlNet is that this model has to control both Upsample and Downsample blocks, whereas ControlNet controls only the Upsample blocks, so it couldn’t be just another fine-tuned CN.

References

1. StabilityAI’s Stable Diffusion https://github.com/CompVis/stable-diffusion
2. Microsoft’s NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation https://arxiv.org/abs/2303.12346
3. lllyasviel’s ControlNet https://github.com/lllyasviel/ControlNet

Training it yourself

Hyperparameters

Due to the exponential frame count growth at deeper levels, the number of parameters giving the target length is limited.

For Microsoft’s claimed 11 minutes long Flintstones episodes the most plausible combination is L=6 subdivisions of 12 fps sampled videos, resulting in 5 depth levels. It gives 10.8 minutes long videos with both a natural framerate and unsurprisingly close to their value.

Making Dataset for DiffusionOverDiffusion

1. Chop the large video into smaller subdivisions by launching python video_chop.py your_vide.mp4 --L sample_frames where sample_frames is the number of divisions on each level. Defaults to 12.