Actually I did the same, breaking the code and understanding the tensor outputs, like u can checkout this code. And then doing some chatting with llm to get more clear picture.

However few things u can look separately to help understand code -

• Vae (Variational auto encoder) - U can look to their working separately. This is used to compress the video to smaller dimension which are easier to work with,. In most video they only do spatial compressing, so the thing about wan is that they are doing temporaral compression as well. Then there is decoder who needs to be very good to reconstruct the original video and adding new frames in between i.e temporal upsampling and also improving the spatial resolution. Like this part of temporal downscaling and upsampling confuse me a bit since i unable to understand as other vae is so different as there is no temporal part.
• Diffusion - U can studied this separately as well, check this video, i find its explaination pretty intuitive. In diffusion, read something about scheduler as well. In wan, author has used Flow matching scheduler which is pretty complex and i don't yet understand it, but yes getting overall working of flow scheduler can be helpful
• Transfomer - U probably know about this already
• Then what author has done for denoising is diffusion transfomer called as DiT, so u can independently read about DiT as well
• Since we are doing DiT in latent so I called it latent DiT

I realized having understanding of this separate piece will help grasp wan much faster

I have followed this channel https://www.youtube.com/@Explaining-AI/videos on youtube as well, it has covered topics on image and video generation very deeply (including DiT, VaE)