Generalize SD torch load/save to implement safetensor merging compat

modules/sd_models.py

@@ -4,7 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
-from safetensors.torch import load_file
+from safetensors.torch import load_file, save_file
 import re
 from omegaconf import OmegaConf
 
@@ -143,6 +143,22 @@ def transform_checkpoint_dict_key(k):
 
     return k
 
+def torch_load(model_filename, model_info, map_override=None):
+    map_override=shared.weight_load_location if not map_override else map_override
+    if(checkpoint_types[model_info.exttype] == 'safetensors'):
+        # safely load weights
+        # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+        return load_file(model_filename, device=map_override)
+    else:
+        return torch.load(model_filename, map_location=map_override)
+
+def torch_save(model, output_filename):
+    basename, exttype = os.path.splitext(output_filename)
+    if(checkpoint_types[exttype] == 'safetensors'):
+        # [=====  >] Reticulating brines...
+        save_file(model, output_filename, metadata={"format": "pt"})
+    else:
+        torch.save(model, output_filename)
 
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
@@ -175,12 +191,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
-            # safely load weights
-            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
-            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
-        else:
-            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        pl_sd = torch_load(checkpoint_file, checkpoint_info)
 
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")