Port back unload_model_clones

lib_layerdiffusion/attention_sharing.py

@@ -324,9 +324,42 @@ def __init__(self, layer_list):
         self.layers = torch.nn.ModuleList(layer_list)
 
 
+def unload_model_clones(model, unload_weights_only=True, force_unload=True):
+    current_loaded_models = model_management.current_loaded_models
+
+    to_unload = []
+    for i, m in enumerate(current_loaded_models):
+        if model.is_clone(m.model):
+            to_unload = [i] + to_unload
+
+    if len(to_unload) == 0:
+        return True
+
+    same_weights = 0
+    for i in to_unload:
+        if model.clone_has_same_weights(current_loaded_models[i].model):
+            same_weights += 1
+
+    if same_weights == len(to_unload):
+        unload_weight = False
+    else:
+        unload_weight = True
+
+    if not force_unload:
+        if unload_weights_only and unload_weight == False:
+            return None
+    else:
+        unload_weight = True
+
+    for i in to_unload:
+        current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
+
+    return unload_weight
+
 class AttentionSharingPatcher(torch.nn.Module):
     def __init__(self, unet, frames=2, use_control=True, rank=256):
         super().__init__()
+        unload_model_clones(unet)
 
         units = []
         for i in range(32):