Merge branch 'master' into fix/ltx2-prompt-empty-output

Author: kijai

comfy/model_prefetch.py

@@ -1,4 +1,5 @@
 import torch
+import warnings
 import weakref
 
 import comfy_aimdo.model_vbar
@@ -28,6 +29,18 @@ def cleanup_prefetched_modules(module, comfy_modules):
         comfy_aimdo.model_vbar.vbar_unpin(module._v_block)
         del module._v_block_faulted
 
+def _drop_graph(module):
+    graph = getattr(module, "_comfy_graph", None)
+    if graph is None:
+        return
+    # reset() through the bound method surfaces the allocator's benign
+    # "uncaptured free of a captured allocation" as catchable Python warnings;
+    # a plain del frees from the C++ dealloc path and spams stderr instead
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        graph["graph"].reset()
+    del module._comfy_graph
+
 def cleanup_prefetch_queues():
     global PREFETCH_QUEUES, GRAPH_CAPTURE_STREAMS
 
@@ -41,7 +54,7 @@ def cleanup_prefetch_queues():
                 cleanup_prefetched_modules(prefetched_module, comfy_modules)
     PREFETCH_QUEUES = []
     for module in GRAPH_MODULES:
-        del module._comfy_graph
+        _drop_graph(module)
     GRAPH_MODULES.clear()
     GRAPH_WARMED_MODULES.clear()
     GRAPH_CAPTURE_STREAMS = {}
@@ -117,6 +130,7 @@ def prefetch_queue_pop(queue, device, module, dtype=None, core=None, enable_grap
                 if signature is not None:
                     module._v_block_faulted = True
             if signature is not None:
+                _drop_graph(module)
                 graph = torch.cuda.CUDAGraph()
                 if generator is not None:
                     graph.register_generator_state(generator)

comfy/ops.py

@@ -1639,7 +1639,7 @@ def forward_comfy_cast_weights(self, input, out_dtype=None):
                             self.norm_type, self.scale_grad_by_freq, self.sparse)
                     target_dtype = out_dtype if out_dtype is not None else weight._params.orig_dtype
                     x = x.to(dtype=target_dtype)
-                    if scale is not None and scale != 1.0:
+                    if scale is not None:
                         x = x * scale.to(dtype=target_dtype)
                     return x