Patchification research to condition the model

patchify_technique.py

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+import torch
+
+# Creating a Multi Channel Single Video
+a = torch.ones(1,2,4,4) # video
+b = torch.randn(1,2,4,4) # conditioning
+c = torch.cat([a,b],dim=1)
+
+# Final shape.
+print(c.shape)
+print(c)
+
+# ImageOrVideoDataset Shape result 
+# _preprocess_video [F, C, H, W] video tensor
+# _preprocess_image [1, C, H, W] (1-frame video)
+
+# VAE Shape Results Dims
+
+# VideoProcessor latent output shape preprocess_video
+
+# Video Tensor Shape 
+
+# Latent Tensor Shape 
+
+# VAE Patchification Result Shape
+
+# encode and then decode and decouple the videos
+
+#
+# check transformer hidden state, should still be same sequence or slightly larger sequence.
+
+def patchify_multichannel_latent(video_latent:torch.tensor,patch_size:int):# B, C, F, W, H 
+    pass 
+
+def unpatchify_multichannel_laten(video_latent:torch.tensor,patch_size:int): # B, C ,F, W, H
+    pass
+
+def mask_multichannel_latent(mask_shape:torch.tensor):
+    pass