Fix LoRA metadata hash calculation bug in svd_merge_lora.py, sdxl_merge_lora.py, and resize_lora.py closes kohya-ss#1722

Author: kohya-ss

README.md

@@ -137,6 +137,14 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ## Change History
 
+### Oct 26, 2024 / 2024-10-26: 
+
+- Fixed a bug in `svd_merge_lora.py`, `sdxl_merge_lora.py`, and `resize_lora.py` where the hash value of LoRA metadata was not correctly calculated when the `save_precision` was different from the  `precision` used in the calculation. See issue [#1722](https://github.com/kohya-ss/sd-scripts/pull/1722) for details. Thanks to JujoHotaru for raising the issue.
+- It will be included in the next release.
+
+- `svd_merge_lora.py`、`sdxl_merge_lora.py`、`resize_lora.py`で、保存時の精度が計算時の精度と異なる場合、LoRAメタデータのハッシュ値が正しく計算されない不具合を修正しました。詳細は issue [#1722](https://github.com/kohya-ss/sd-scripts/pull/1722) をご覧ください。問題提起していただいた JujoHotaru 氏に感謝します。
+- 以上は次回リリースに含まれます。
+
 ### Sep 13, 2024 / 2024-09-13: 
 
 - `sdxl_merge_lora.py` now supports OFT. Thanks to Maru-mee for the PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580). 

networks/resize_lora.py

@@ -39,12 +39,7 @@ def load_state_dict(file_name, dtype):
     return sd, metadata
 
 
-def save_to_file(file_name, state_dict, dtype, metadata):
-    if dtype is not None:
-        for key in list(state_dict.keys()):
-            if type(state_dict[key]) == torch.Tensor:
-                state_dict[key] = state_dict[key].to(dtype)
-
+def save_to_file(file_name, state_dict, metadata):
     if model_util.is_safetensors(file_name):
         save_file(state_dict, file_name, metadata)
     else:
@@ -349,12 +344,18 @@ def str_to_dtype(p):
         metadata["ss_network_dim"] = "Dynamic"
         metadata["ss_network_alpha"] = "Dynamic"
 
+    # cast to save_dtype before calculating hashes
+    for key in list(state_dict.keys()):
+        value = state_dict[key]
+        if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
+            state_dict[key] = value.to(save_dtype)
+
     model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
     metadata["sshs_model_hash"] = model_hash
     metadata["sshs_legacy_hash"] = legacy_hash
 
     logger.info(f"saving model to: {args.save_to}")
-    save_to_file(args.save_to, state_dict, save_dtype, metadata)
+    save_to_file(args.save_to, state_dict, metadata)
 
 
 def setup_parser() -> argparse.ArgumentParser:

networks/sdxl_merge_lora.py

@@ -35,12 +35,7 @@ def load_state_dict(file_name, dtype):
     return sd, metadata
 
 
-def save_to_file(file_name, model, state_dict, dtype, metadata):
-    if dtype is not None:
-        for key in list(state_dict.keys()):
-            if type(state_dict[key]) == torch.Tensor:
-                state_dict[key] = state_dict[key].to(dtype)
-
+def save_to_file(file_name, model, metadata):
     if os.path.splitext(file_name)[1] == ".safetensors":
         save_file(model, file_name, metadata=metadata)
     else:
@@ -430,6 +425,12 @@ def str_to_dtype(p):
     else:
         state_dict, metadata = merge_lora_models(args.models, args.ratios, args.lbws, merge_dtype, args.concat, args.shuffle)
 
+        # cast to save_dtype before calculating hashes
+        for key in list(state_dict.keys()):
+            value = state_dict[key]
+            if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
+                state_dict[key] = value.to(save_dtype)
+
         logger.info(f"calculating hashes and creating metadata...")
 
         model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
@@ -445,7 +446,7 @@ def str_to_dtype(p):
             metadata.update(sai_metadata)
 
         logger.info(f"saving model to: {args.save_to}")
-        save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
+        save_to_file(args.save_to, state_dict, metadata)
 
 
 def setup_parser() -> argparse.ArgumentParser:

networks/svd_merge_lora.py

@@ -216,12 +216,7 @@ def load_state_dict(file_name, dtype):
     return sd, metadata
 
 
-def save_to_file(file_name, state_dict, dtype, metadata):
-    if dtype is not None:
-        for key in list(state_dict.keys()):
-            if type(state_dict[key]) == torch.Tensor:
-                state_dict[key] = state_dict[key].to(dtype)
-
+def save_to_file(file_name, state_dict, metadata):
     if os.path.splitext(file_name)[1] == ".safetensors":
         save_file(state_dict, file_name, metadata=metadata)
     else:
@@ -430,6 +425,12 @@ def str_to_dtype(p):
         args.models, args.ratios, args.lbws, args.new_rank, new_conv_rank, args.device, merge_dtype
     )
 
+    # cast to save_dtype before calculating hashes
+    for key in list(state_dict.keys()):
+        value = state_dict[key]
+        if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
+            state_dict[key] = value.to(save_dtype)
+
     logger.info(f"calculating hashes and creating metadata...")
 
     model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
@@ -451,7 +452,7 @@ def str_to_dtype(p):
         metadata.update(sai_metadata)
 
     logger.info(f"saving model to: {args.save_to}")
-    save_to_file(args.save_to, state_dict, save_dtype, metadata)
+    save_to_file(args.save_to, state_dict, metadata)
 
 
 def setup_parser() -> argparse.ArgumentParser: