diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 3eac77275..df2d8e827 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -704,7 +704,7 @@ class ModelPatcher:
 
                 lowvram_weight = False
 
-                potential_offload = max(offload_buffer, module_offload_mem * (comfy.model_management.NUM_STREAMS + 1))
+                potential_offload = max(offload_buffer, module_offload_mem + (comfy.model_management.NUM_STREAMS * module_mem))
                 lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory
 
                 weight_key = "{}.weight".format(n)
@@ -883,7 +883,7 @@ class ModelPatcher:
                     break
                 module_offload_mem, module_mem, n, m, params = unload
 
-                potential_offload = (comfy.model_management.NUM_STREAMS + 1) * module_offload_mem
+                potential_offload = module_offload_mem + (comfy.model_management.NUM_STREAMS * module_mem)
 
                 lowvram_possible = hasattr(m, "comfy_cast_weights")
                 if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
diff --git a/comfy/ops.py b/comfy/ops.py
index 61a2f0754..eae434e68 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -111,22 +111,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
     if s.bias is not None:
         bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
 
-        if bias_has_function:
-            with wf_context:
-                for f in s.bias_function:
-                    bias = f(bias)
+    comfy.model_management.sync_stream(device, offload_stream)
+
+    bias_a = bias
+    weight_a = weight
+
+    if s.bias is not None:
+        for f in s.bias_function:
+            bias = f(bias)
 
     if weight_has_function or weight.dtype != dtype:
-        with wf_context:
-            weight = weight.to(dtype=dtype)
-            if isinstance(weight, QuantizedTensor):
-                weight = weight.dequantize()
-            for f in s.weight_function:
-                weight = f(weight)
+        weight = weight.to(dtype=dtype)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        for f in s.weight_function:
+            weight = f(weight)
 
-    comfy.model_management.sync_stream(device, offload_stream)
     if offloadable:
-        return weight, bias, offload_stream
+        return weight, bias, (offload_stream, weight_a, bias_a)
     else:
         #Legacy function signature
         return weight, bias
@@ -135,13 +137,16 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
 def uncast_bias_weight(s, weight, bias, offload_stream):
     if offload_stream is None:
         return
-    if weight is not None:
-        device = weight.device
+    os, weight_a, bias_a = offload_stream
+    if os is None:
+        return
+    if weight_a is not None:
+        device = weight_a.device
     else:
-        if bias is None:
+        if bias_a is None:
             return
-        device = bias.device
-    offload_stream.wait_stream(comfy.model_management.current_stream(device))
+        device = bias_a.device
+    os.wait_stream(comfy.model_management.current_stream(device))
 
 
 class CastWeightBiasOp: