revise lora patching

backend/patcher/lora.py

@@ -68,8 +68,13 @@ def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, computation
 def merge_lora_to_weight(patches, weight, key="online_lora", computation_dtype=torch.float32):
     # Modified from https://github.com/comfyanonymous/ComfyUI/blob/39f114c44bb99d4a221e8da451d4f2a20119c674/comfy/model_patcher.py#L446
 
-    weight_original_dtype = weight.dtype
+    weight_dtype_backup = None
-    weight = weight.to(dtype=computation_dtype)
+
+    if computation_dtype == weight.dtype:
+        weight = weight.clone()
+    else:
+        weight_dtype_backup = weight.dtype
+        weight = weight.to(dtype=computation_dtype)
 
     for p in patches:
         strength = p[0]
@@ -253,7 +258,9 @@ def merge_lora_to_weight(patches, weight, key="online_lora", computation_dtype=t
         if old_weight is not None:
             weight = old_weight
 
-    weight = weight.to(dtype=weight_original_dtype)
+    if weight_dtype_backup is not None:
+        weight = weight.to(dtype=weight_dtype_backup)
+
     return weight
 
 
@@ -267,6 +274,7 @@ class LoraLoader:
         self.backup = {}
         self.online_backup = []
         self.dirty = False
+        self.online_mode = False
 
     def clear_patches(self):
         self.patches.clear()
@@ -296,6 +304,13 @@ class LoraLoader:
                 self.patches[key] = current_patches
 
         self.dirty = True
+
+        self.online_mode = dynamic_args.get('online_lora', False)
+
+        if hasattr(self.model, 'storage_dtype'):
+            if self.model.storage_dtype in [torch.float32, torch.float16, torch.bfloat16]:
+                self.online_mode = False
+
         return list(p)
 
     @torch.inference_mode()
@@ -323,7 +338,11 @@ class LoraLoader:
 
         self.backup = {}
 
-        online_mode = dynamic_args.get('online_lora', False)
+        if len(self.patches) > 0:
+            if self.online_mode:
+                print('Patching LoRA in on-the-fly.')
+            else:
+                print('Patching LoRA by precomputing model weights.')
 
         # Patch
 
@@ -337,7 +356,7 @@ class LoraLoader:
             if key not in self.backup:
                 self.backup[key] = weight.to(device=offload_device)
 
-            if online_mode:
+            if self.online_mode:
                 if not hasattr(parent_layer, 'forge_online_loras'):
                     parent_layer.forge_online_loras = {}
 

backend/sampling/sampling_function.py

@@ -376,7 +376,7 @@ def sampling_prepare(unet, x):
         additional_inference_memory += unet.controlnet_linked_list.inference_memory_requirements(unet.model_dtype())
         additional_model_patchers += unet.controlnet_linked_list.get_models()
 
-    if dynamic_args.get('online_lora', False):
+    if unet.lora_loader.online_mode:
         lora_memory = utils.nested_compute_size(unet.lora_loader.patches)
         additional_inference_memory += lora_memory
 
@@ -384,11 +384,9 @@ def sampling_prepare(unet, x):
         models=[unet] + additional_model_patchers,
         memory_required=unet_inference_memory + additional_inference_memory)
 
-    if dynamic_args.get('online_lora', False):
+    if unet.lora_loader.online_mode:
         utils.nested_move_to_device(unet.lora_loader.patches, device=unet.current_device)
 
-    unet.lora_loader.patches = {}
-
     real_model = unet.model
 
     percent_to_timestep_function = lambda p: real_model.predictor.percent_to_sigma(p)
@@ -400,6 +398,8 @@ def sampling_prepare(unet, x):
 
 
 def sampling_cleanup(unet):
+    if unet.lora_loader.online_mode:
+        utils.nested_move_to_device(unet.lora_loader.patches, device=unet.offload_device)
     for cnet in unet.list_controlnets():
         cnet.cleanup()
     cleanup_cache()