ltx: vae: implement chunked encoder + CPU IO chunking (Big VRAM reductions) (#13062)

* ltx: vae: add cache state to downsample block

* ltx: vae: Add time stride awareness to causal_conv_3d

* ltx: vae: Automate truncation for encoder

Other VAEs just truncate without error. Do the same.

* sd/ltx: Make chunked_io a flag in its own right

Taking this bi-direcitonal, so make it a for-purpose named flag.

* ltx: vae: implement chunked encoder + CPU IO chunking

People are doing things with big frame counts in LTX including V2V
flows. Implement the time-chunked encoder to keep the VRAM down, with
the converse of the new CPU pre-allocation technique, where the chunks
are brought from the CPU JIT.

* ltx: vae-encode: round chunk sizes more strictly

Only powers of 2 and multiple of 8 are valid due to cache slicing.

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -23,6 +23,11 @@ class CausalConv3d(nn.Module):
         self.in_channels = in_channels
         self.out_channels = out_channels
 
+        if isinstance(stride, int):
+            self.time_stride = stride
+        else:
+            self.time_stride = stride[0]
+
         kernel_size = (kernel_size, kernel_size, kernel_size)
         self.time_kernel_size = kernel_size[0]
 
@@ -58,18 +63,23 @@ class CausalConv3d(nn.Module):
         pieces = [ cached, x ]
         if is_end and not causal:
             pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))
+        input_length = sum([piece.shape[2] for piece in pieces])
+        cache_length = (self.time_kernel_size - self.time_stride) + ((input_length - self.time_kernel_size) % self.time_stride)
 
         needs_caching = not is_end
-        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
+        if needs_caching and cache_length == 0:
+            self.temporal_cache_state[tid] = (x[:, :, :0, :, :], False)
             needs_caching = False
-            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+        if needs_caching and x.shape[2] >= cache_length:
+            needs_caching = False
+            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
 
         x = torch.cat(pieces, dim=2)
         del pieces
         del cached
 
         if needs_caching:
-            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+            self.temporal_cache_state[tid] = (x[:, :, -cache_length:, :, :], False)
         elif is_end:
             self.temporal_cache_state[tid] = (None, True)