add disable_mmap to args

library/sdxl_model_util.py

@@ -1,4 +1,5 @@
 import torch
+import safetensors
 from accelerate import init_empty_weights
 from accelerate.utils.modeling import set_module_tensor_to_device
 from safetensors.torch import load_file, save_file
@@ -163,17 +164,20 @@ def _load_state_dict_on_device(model, state_dict, device, dtype=None):
     raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs)))
 
 
-def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None):
+def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None, disable_mmap=False):
     # model_version is reserved for future use
     # dtype is used for full_fp16/bf16 integration. Text Encoder will remain fp32, because it runs on CPU when caching
 
     # Load the state dict
     if model_util.is_safetensors(ckpt_path):
         checkpoint = None
-        try:
-            state_dict = load_file(ckpt_path, device=map_location)
-        except:
-            state_dict = load_file(ckpt_path)  # prevent device invalid Error
+        if(disable_mmap):
+            state_dict = safetensors.torch.load(open(ckpt_path, 'rb').read())
+        else:
+            try:
+                state_dict = load_file(ckpt_path, device=map_location)
+            except:
+                state_dict = load_file(ckpt_path)  # prevent device invalid Error
         epoch = None
         global_step = None
     else:

library/sdxl_train_util.py

@@ -44,6 +44,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
                 weight_dtype,
                 accelerator.device if args.lowram else "cpu",
                 model_dtype,
+                args.disable_mmap_load_safetensors
             )
 
             # work on low-ram device
@@ -60,7 +61,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
 
 
 def _load_target_model(
-    name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None
+    name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None, disable_mmap=False
 ):
     # model_dtype only work with full fp16/bf16
     name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
@@ -75,7 +76,7 @@ def _load_target_model(
             unet,
             logit_scale,
             ckpt_info,
-        ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, model_dtype)
+        ) = sdxl_model_util.load_models_from_sdxl_checkpoint(model_version, name_or_path, device, model_dtype, disable_mmap)
     else:
         # Diffusers model is loaded to CPU
         from diffusers import StableDiffusionXLPipeline
@@ -332,6 +333,10 @@ def add_sdxl_training_arguments(parser: argparse.ArgumentParser):
         action="store_true",
         help="cache text encoder outputs to disk / text encoderの出力をディスクにキャッシュする",
     )
+    parser.add_argument(
+        "--disable_mmap_load_safetensors",
+        action="store_true",
+    )
 
 
 def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCaching: bool = True):