Reduce LTX VAE VRAM usage and save use cases from OOMs/Tiler (#13013)

* ltx: vae: scale the chunk size with the users VRAM

Scale this linearly down for users with low VRAM.

* ltx: vae: free non-chunking recursive intermediates

* ltx: vae: cleanup some intermediates

The conv layer can be the VRAM peak and it does a torch.cat. So cleanup
the pieces of the cat. Also clear our the cache ASAP as each layer detect
its end as this VAE surges in VRAM at the end due to the ended padding
increasing the size of the final frame convolutions off-the-books to
the chunker. So if all the earlier layers free up their cache it can
offset that surge.

Its a fragmentation nightmare, and the chance of it having to recache the
pyt allocator is very high, but you wont OOM.

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -65,9 +65,13 @@ class CausalConv3d(nn.Module):
             self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], 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)
+        elif is_end:
+            self.temporal_cache_state[tid] = (None, True)
 
         return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]