diff --git a/README.md b/README.md
index 666da50..440d24f 100644
--- a/README.md
+++ b/README.md
@@ -252,6 +252,9 @@ docker run -it --gpus all -v $HOME/.cache/huggingface:/root/.cache/huggingface -
 
 ## ChangeLog
 
+**7.6.0**
+- feature: ability to load safetensors
+
 **7.5.0**
 - feature:  🎉 outpainting. Examples: `--outpaint up10,down300,left50,right50` or `--outpaint all100` or `--outpaint u100,d200,l300,r400`
 
diff --git a/imaginairy/model_manager.py b/imaginairy/model_manager.py
index 1a02057..d1e6a44 100644
--- a/imaginairy/model_manager.py
+++ b/imaginairy/model_manager.py
@@ -11,6 +11,7 @@ import torch
 from huggingface_hub import hf_hub_download as _hf_hub_download
 from huggingface_hub import try_to_load_from_cache
 from omegaconf import OmegaConf
+from safetensors.torch import load_file
 from transformers.utils.hub import HfFolder
 
 from imaginairy import config as iconfig
@@ -81,6 +82,14 @@ class MemoryAwareModel:
         gc.collect()
 
 
+def load_tensors(tensorfile, map_location=None):
+    if tensorfile.endswith(".ckpt"):
+        return torch.load(tensorfile, map_location=map_location)
+    if tensorfile.endswith(".safetensors"):
+        return load_file(tensorfile, device=map_location)
+    raise ValueError(f"Unknown tensorfile type: {tensorfile}")
+
+
 def load_model_from_config(config, weights_location):
     if weights_location.startswith("http"):
         ckpt_path = get_cached_url_path(weights_location, category="weights")
@@ -89,7 +98,7 @@ def load_model_from_config(config, weights_location):
     logger.info(f"Loading model {ckpt_path} onto {get_device()} backend...")
     pl_sd = None
     try:
-        pl_sd = torch.load(ckpt_path, map_location="cpu")
+        pl_sd = load_tensors(ckpt_path, map_location="cpu")
     except FileNotFoundError as e:
         if e.errno == 2:
             logger.error(
@@ -103,12 +112,15 @@ def load_model_from_config(config, weights_location):
                 logger.warning("Corrupt checkpoint. deleting and re-downloading...")
                 os.remove(ckpt_path)
                 ckpt_path = get_cached_url_path(weights_location, category="weights")
-                pl_sd = torch.load(ckpt_path, map_location="cpu")
+                pl_sd = load_tensors(ckpt_path, map_location="cpu")
         if pl_sd is None:
             raise e
     if "global_step" in pl_sd:
         logger.debug(f"Global Step: {pl_sd['global_step']}")
-    state_dict = pl_sd["state_dict"]
+    if "state_dict" in pl_sd:
+        state_dict = pl_sd["state_dict"]
+    else:
+        state_dict = pl_sd
     model = instantiate_from_config(config.model)
     missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)