Dynamic VRAM fixes - Ace 1.5 performance + a VRAM leak (#12368)

* revert threaded model loader change

This change was only needed to get around the pytorch 2.7 mempool bugs,
and should have been reverted along with #12260. This fixes a different
memory leak where pytorch gets confused about cache emptying.

* load non comfy weights

* MPDynamic: Pre-generate the tensors for vbars

Apparently this is an expensive operation that slows down things.

* bump to aimdo 1.8

New features:
watermark limit feature
logging enhancements
-O2 build on linux

execution.py

@@ -13,8 +13,11 @@ from contextlib import nullcontext
 
 import torch
 
+from comfy.cli_args import args
 import comfy.memory_management
 import comfy.model_management
+import comfy_aimdo.model_vbar
+
 from latent_preview import set_preview_method
 import nodes
 from comfy_execution.caching import (
@@ -527,8 +530,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                 finally:
                     if allocator is not None:
+                        if args.verbose == "DEBUG":
+                            comfy_aimdo.model_vbar.vbars_analyze()
                         comfy.model_management.reset_cast_buffers()
-                        torch.cuda.synchronize()
+                        comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
 
             if has_pending_tasks:
                 pending_async_nodes[unique_id] = output_data