add comment about scaling

networks/extract_lora_from_dylora.py

@@ -3,6 +3,7 @@
 # Thanks to cloneofsimo
 
 import argparse
+import math
 import os
 import torch
 from safetensors.torch import load_file, save_file, safe_open
@@ -43,6 +44,7 @@ def split_lora_model(lora_sd, unit):
 
     rank = unit
     splitted_models = []
+    new_alpha = None
     while rank < max_rank:
         print(f"Splitting rank {rank}")
         new_sd = {}
@@ -52,9 +54,15 @@ def split_lora_model(lora_sd, unit):
             elif "lora_up" in key:
                 new_sd[key] = value[:, :rank].contiguous()
             else:
-                new_sd[key] = value  # alpha and other parameters
+                # なぜかscaleするとおかしくなる……
+                # this_rank = lora_sd[key.replace("alpha", "lora_down.weight")].size()[0]
+                # scale = math.sqrt(this_rank / rank)  # rank is > unit
+                # print(key, value.size(), this_rank, rank, value, scale)
+                # new_alpha = value * scale  # always same
+                # new_sd[key] = new_alpha
+                new_sd[key] = value
 
-        splitted_models.append((new_sd, rank))
+        splitted_models.append((new_sd, rank, new_alpha))
         rank += unit
 
     return max_rank, splitted_models
@@ -68,7 +76,7 @@ def split(args):
     original_rank, splitted_models = split_lora_model(lora_sd, args.unit)
 
     comment = metadata.get("ss_training_comment", "")
-    for state_dict, new_rank in splitted_models:
+    for state_dict, new_rank, new_alpha in splitted_models:
         # update metadata
         if metadata is None:
             new_metadata = {}
@@ -77,6 +85,7 @@ def split(args):
 
         new_metadata["ss_training_comment"] = f"split from DyLoRA, rank {original_rank} to {new_rank}; {comment}"
         new_metadata["ss_network_dim"] = str(new_rank)
+        # new_metadata["ss_network_alpha"] = str(new_alpha.float().numpy())
 
         model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
         metadata["sshs_model_hash"] = model_hash