diff --git a/environment.yaml b/environment.yaml
index f41c3cada..c369cefae 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -10,6 +10,7 @@ dependencies:
   - torchvision=0.12.0
   - numpy=1.20.3
   - pip:
+    - safetensors==0.3.1
     - albumentations==0.4.3
     - opencv-python==4.1.2.30
     - pudb==2019.2
diff --git a/optimizedSD/optimized_txt2img.py b/optimizedSD/optimized_txt2img.py
index c82918240..3fef33370 100644
--- a/optimizedSD/optimized_txt2img.py
+++ b/optimizedSD/optimized_txt2img.py
@@ -15,21 +15,58 @@
 from ldm.util import instantiate_from_config
 from optimUtils import split_weighted_subprompts, logger
 from transformers import logging
+import os
+import safetensors.torch
 # from samplers import CompVisDenoiser
 logging.set_verbosity_error()
 
 
+checkpoint_dict_replacements = {
+    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
 def chunk(it, size):
     it = iter(it)
     return iter(lambda: tuple(islice(it, size)), ())
+    
+def transform_checkpoint_dict_key(k):
+    for text, replacement in checkpoint_dict_replacements.items():
+        if k.startswith(text):
+            k = replacement + k[len(text):]
+
+    return k
+
+def get_state_dict_from_checkpoint(pl_sd):
+    pl_sd = pl_sd.pop("state_dict", pl_sd)
+    pl_sd.pop("state_dict", None)
+
+    sd = {}
+    for k, v in pl_sd.items():
+        new_key = transform_checkpoint_dict_key(k)
 
+        if new_key is not None:
+            sd[new_key] = v
 
+    pl_sd.clear()
+    pl_sd.update(sd)
+
+    return pl_sd
+
+# The code for loading model with safetensors was taken from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/sd_models.py
 def load_model_from_config(ckpt, verbose=False):
+
     print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
+    _, extension = os.path.splitext(ckpt)
+    if extension.lower() == ".safetensors":
+    	pl_sd = safetensors.torch.load_file(ckpt)
+    else:
+    	pl_sd = torch.load(ckpt, map_location="cpu")
+    	if "global_step" in pl_sd:
+    		print(f"Global Step: {pl_sd['global_step']}")
+    #sd = pl_sd["state_dict"]
+    sd = get_state_dict_from_checkpoint(pl_sd)
     return sd